WO2006002240A2 - Computer systems and methods for constructing biological classifiers and uses thereof - Google Patents

Computer systems and methods for constructing biological classifiers and uses thereof Download PDF

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Publication number
WO2006002240A2
WO2006002240A2 PCT/US2005/022071 US2005022071W WO2006002240A2 WO 2006002240 A2 WO2006002240 A2 WO 2006002240A2 US 2005022071 W US2005022071 W US 2005022071W WO 2006002240 A2 WO2006002240 A2 WO 2006002240A2
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Prior art keywords
molecular markers
classifiers
trait
subgroup
trait subgroup
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PCT/US2005/022071
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French (fr)
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WO2006002240A3 (en
WO2006002240A8 (en
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Choong-Chin Liew
Tom Yager
Adam Dempsey
Samuel Chao
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Chondrogene, Inc.
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Priority to EP05766673A priority Critical patent/EP1766043A4/en
Priority to CA002571180A priority patent/CA2571180A1/en
Priority to US10/568,264 priority patent/US7991557B2/en
Publication of WO2006002240A2 publication Critical patent/WO2006002240A2/en
Publication of WO2006002240A3 publication Critical patent/WO2006002240A3/en
Publication of WO2006002240A8 publication Critical patent/WO2006002240A8/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • Control 2005 (Affy) E Sequence Related Table 198KB June 17, TABLE4E.TXT regarding OA moderate v. 2005 Control (ChondroChip) F Sequence Related Table 576KB June 17, TABLE4F.TXT regarding OA moderate v. 2005 Control (Affy) G Sequence Related Table 203KB June 17, TABLE4G.TXT regarding OA marked v. 2005 Control (ChondroChip) H Sequence Related Table 679KB June 17, TABLE4H.TXT regarding OA marked v. 2005 Control (Affy) 1 Sequence Related Table 291KB June 17, TABLE4I.TXT regarding OA severe v.
  • Embodiments of the present invention address many of the shortcomings and drawbacks found in the prior art by the novel approach of using molecular marker measurement data from blood and methods of processing such data to screen the large numbers of candidate molecular markers in blood so as to identify useful combinations of these molecular markers.
  • Embodiments of the present invention involve the construction of classifiers and use of these classifiers.
  • embodiments of the invention involve the use of the molecular markers identified by these classifiers to diagnose or otherwise determine whether a test subject has a specific trait of interest.
  • Blood offers a surprisingly informative alternative to tissues as a source of information.
  • Blood includes numerous cell types including monocytes, leukocytes, lymphocytes, erythrocytes, platelets, as well as possibly many other cell types.
  • monocytes including monocytes, leukocytes, lymphocytes, erythrocytes, platelets, as well as possibly many other cell types.
  • the turnover of cells in the human circulatory system is rapid.
  • it has been hypothesized that the changes that occur within the cells or tissues of the body will trigger specific changes in gene expression within blood. See, for example, United States patent application serial No. 10/601,518, filed June 20, 2003 , United States patent application serial No.10/802,875, filed March 12, 2004, United States patent application serial No. 10/809,675, filed March 25, 2004, United States patent application serial No.
  • Fig. 1 illustrates a computer system for determining and selecting useful biological classifiers.
  • Fig. 2 illustrates a method for deriving biological classifiers in accordance with an embodiment of the present invention.
  • FIG. 3 is a flowchart of a method of applying the classifiers to a patient.
  • Fig. 4 illustrates a data structure for storing high throughput information for a plurality of molecular markers in accordance with one embodiment of the present invention.
  • Fig. 5 illustrates a data structure for storing a plurality of classifiers in accordance with one embodiment of the present invention.
  • Fig. 6 illustrates a patient database for storing data for molecular markers for a plurality of patients in accordance with an embodiment of the present invention.
  • Fig. 7 illustrates a Receiver Operating Characteristic (ROC) curve that is used to assess the discriminating ability of a molecular marker or a classifier in accordance with one embodiment of the present invention.
  • Fig. ROC Receiver Operating Characteristic
  • Table 8 illustrates ROC curves corresponding to two candidate classifiers for osteoarthritis computed in accordance with one embodiment of the present invention.
  • Table 1 as a group of tables identifies the molecular markers that are differentially expressed in blood samples from patients with a disease or patients who are co-morbid as compared to blood samples from healthy patients or patients without said disease, or with only one of said co-morbid diseases and also shows the sequences of selected products of the identified molecular markers.
  • Molecular marker data from the molecular markers listed in each table or a subset of these molecular markers can be used can be used in steps 214- 218 as outlined in Figure 2B so as to identify classifiers and the combinations of molecular markers which form the classifiers useful in diagnosis.
  • Table IA identifies the molecular markers which are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and hypertension as compared with a second trait subgroup wherein each member of the second trait subgroup has neither osteoarthritis nor hypertension using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IB shows the identity of those molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and obesity as compared with a second trait subgroup wherein each member of the second trait subgroup has neither osteoarthritis nor obesity using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 1C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and allergies as compared with a second trait subgroup wherein each member of the second trait subgroup has neither osteoarthritis nor allergies using the ChondroChipTM platform.
  • Table ID shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and subject to systemic steroids as compared with normal patients using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IE shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has hypertension as compared to a second trait subgroup wherein each member of the second trait subgroup did not have hypertension using the ChondroChipTM platform.
  • Table IF shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has obesity as compared to a second trait subgroup wherein each member of the second trait subgroup did not have obesity using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IG shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has hypertension and OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only wherein molecular markers identified in Table IA have been removed so as to identify molecular markers which are unique to hypertension.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IH shows the molecular markers which were identified in Table IA which are shared with those molecular markers differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both hypertension and OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table II shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both obesity and have OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only and wherein molecular markers identified in Table IB have been removed so as to identify molecular markers which are unique to obesity.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IJ shows the molecular markers identified in Table IB which are shared with those molecular markers differentially expressed in blood samples from patients who are obese and have OA when compared with patients who have OA.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IK shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both allergies and OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only wherein molecular markers identified in Table 1C have been removed so as to identify molecular markers which are unique to allergies.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IL shows the identify of those molecular markers identified in Table 3 C which are shared with those molecular markers differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both allergies and OA when compared with a second trait subgroup wherein each member of the second trait subgroup having OA only.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IM shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking systemic steroids and has OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only wherein molecular markers identified in Table ID have been removed so as to identify molecular markers which are unique to patients on systemic steroids.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IN shows the identify of those molecular markers identified in Table ID which are shared with those molecular markers differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup who are on systemic steroids and have OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IO shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup are either taking birth control, on prednisone or on hormone replacement therapy and presenting with OA using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IP shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both type II diabetes as compared to a second trait subgroup wherein each member of the second trait subgroup does not have type II diabetes using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IQ shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Hyperlipidemia as compared to a second trait subgroup wherein each member of the second trait subgroup does not have Hyperlipidemia using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IR shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has lung disease as compared to a second trait subgroup wherein each member of the second trait subgroup does not have lung disease using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IS shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has bladder cancer as compared to a second trait subgroup wherein each member of the second trait subgroup does not have bladder cancer using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IT shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has early stage bladder cancer, late stage bladder cancer with a second trait subgroup wherein each member of the second trait subgroup does not have bladder cancer using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IU shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has coronary artery disease (CAD) as compared to a second trait subgroup wherein each member of the second trait subgroup does not have not having CAD using the ChondroChipTM platform.
  • CAD coronary artery disease
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IV shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has rheumatoid arthritis as compared to a second trait subgroup wherein each member of the second trait subgroup does not have rheumatoid arthritis using the ChondroChipTM platform .
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IW shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has rheumatoid arthritis as compared to a second trait subgroup wherein each member of the second trait subgroup does not have rheumatoid arthritis using the Affymetrix® platform .
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IX shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has depression as compared with a second trait subgroup wherein each member of the second trait subgroup does not having depression using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IY shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has one of various stages of osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IZ shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have liver cancer using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IZ(B) shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have liver cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAA shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has schizophrenia as compared with a second trait subgroup wherein each member of the second trait subgroup does not have schizophrenia using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAB shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Chagas disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chagas disease using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAC shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both asthma and osteoarthritis as compared a second trait subgroup wherein each member of the second trait subgroup has only osteoarthritis using the ChondroChipTM.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAD shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has asthma as compared with a second trait subgroup wherein each member of the second trait subgroup does not have asthma using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAE shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has lung cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have lung cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAG shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has hypertension as compared with a second trait subgroup wherein each member of the second trait subgroup does not have hypertension using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAH shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has obesity as compared with a second trait subgroup wherein each member of the second trait subgroup does not have obesity using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table IAI shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has ankylosing spondylitis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have ankylosing spondylitis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 2 shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has either mild or severe OA, but for which molecular markers relevant to asthma, obesity, hypertension, systemic steroids and allergies have been removed.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3 is a group of tables wherein each table shows those molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a first disease as compared to blood samples from a second trait subgroup wherein each member of the second trait subgroup has a second disease so as to allow differential diagnosis as between said first and second disease.
  • Table 3A shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has schizophrenia as compared with a second trait subgroup wherein each member of the second trait subgroup has manic depression syndrome (MDS) using the Affymetrix® platform.
  • MDS manic depression syndrome
  • Table 3B shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has hepatitis as compared with a second trait subgroup wherein each member of the second trait subgroup has liver cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3C shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has bladder cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has liver cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3D shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has bladder cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has testicular cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3E shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has testicular cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has kidney cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3F shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has stomach cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3G shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has colon cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3H shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has stomach cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has colon cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 31 shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Rheumatoid Arthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3K shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Chagas Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Heart Failure using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3L shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Chagas Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Coronary Artery Disease using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3N shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Coronary Artery Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Heart Failure using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3P shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Asymptomatic Chagas Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Symptomatic Chagas Disease using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3Q shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Alzheimer's' as compared with a second trait subgroup wherein each member of the second trait subgroup has Schizophrenia using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 3R shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Alzheimer's' as compared with a second trait subgroup wherein each member of the second trait subgroup has Manic Depression Syndrome using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4 tables are those which shows molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a stage of Osteoarthritis as compared to blood samples from a second trait subgroup wherein each member of the second trait subgroup has a second stage of Osteoarthritis so as to allow monitoring of progression and/or regression of disease.
  • Each table also shows the sequences of selected products of the identified molecular markers.
  • Table 4A shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4B shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4C shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without mild Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4D shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4E shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with patients without Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4F shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4G shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4H shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 41 shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has severe Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4J shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has severe Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4K shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has moderate Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4L shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has moderate Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4M shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4N shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4O shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4P shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers .
  • Table 4Q shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4R shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4S shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with patients a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4T shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4U shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 4V shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5 tables are those which identify molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a disease or condition of interest as compared to blood samples from a second trait subgroup wherein each member of the second trait subgroup is without said disease or condition.
  • the tables also shows the sequences of selected products of the identified molecular markers.
  • Table 5A shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has psoriasis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have psoriasis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5B shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has thyroid disorder as compared with a second trait subgroup wherein each member of the second trait subgroup does not have thyroid disorder using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has irritable bowel syndrome as compared with a second trait subgroup wherein each member of the second trait subgroup does not have irritable bowel syndrome using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5D shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has osteoporosis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have osteoporosis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5E shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has migraine headaches as compared with a second trait subgroup wherein each member of the second trait subgroup does not have migraine headaches using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5F shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has eczema as compared with a second trait subgroup wherein each member of the second trait subgroup does not have eczema using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5G shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has NASH as compared with a second trait subgroup wherein each member of the second trait subgroup does not have NASH using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5H shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Alzheimers' disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Alzheimer's disease using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 51 shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Manic Depression Syndrome as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Manic Depression Syndrome using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5J shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Crohn's Colitis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Crohn's Colitis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5K shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Chronic Cholecystis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chronic Cholecystis using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5L shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Heart Failure as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Heart Failure using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5M shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Cervical Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Cervical Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5N shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Stomach Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Stomach Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5O shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Kidney Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Kidney Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5P shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Testicular Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Testicular Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5Q shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Colon Cancer as compared a second trait subgroup wherein each member of the second trait subgroup does not have Colon Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5R shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Hepatitis B as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Hepatitis B using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5S shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Pancreatic Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Pancreatic Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5T shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Asymptomatic Chagas as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chagas using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5U shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Symptomatic Chagas as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chagas using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 5V shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Bladder Cancer as compared with patients not having Bladder Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 6 tables are those tables which show those molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has any one of a series of related conditions as compared to blood samples a second trait subgroup wherein each member of the second trait subgroup does not have said related conditions.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 6A shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Cancer using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 6B shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup with Cardiovascular Disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have a Cardiovascular Disease using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 6C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a Neurological Disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have a Neurological Disease using the Affymetrix® platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7 tables are those tables which show molecular markers that are differentially expressed in blood samples from with a condition wherein said condition is a treatment as compared to blood samples from patients without said treatment or with a different said treatment.
  • Table 7 A shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Celebrex® as compared a second trait subgroup wherein each member of the second trait subgroup is taking a Cox Inhibitor which was not Celebrex® using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7B shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Celebrex® as compared with a second trait subgroup wherein each member of the second trait subgroup is not taking Celebrex® using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Vioxx® as compared a second trait subgroup wherein each member of the second trait subgroup is not taking Vioxx® using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7D shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Vioxx® as compared with a second trait subgroup wherein each member of the second trait subgroup on a Cox inhibitor but not on Vioxx® using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7E shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking NSAIDS as compared with patients not on NSAIDS using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7F shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Cortisone as compared with a second trait subgroup wherein each member of the second trait subgroup on not on Cortisone using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7G shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Visco Supplement as compared with a second trait subgroup wherein each member of the second trait subgroup not on Visco Supplement using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 7H shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Lipitor® as compared with a second trait subgroup wherein each member of the second trait subgroup not on Lipitor® using the ChondroChipTM platform.
  • the table also shows the sequences of selected products of the identified molecular markers.
  • Table 71 shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is who are smokers as compared with a second trait subgroup wherein each member of the second trait subgroup who are not smokers using the ChondroChipTM platform.
  • Tables IAA; IAB; IAD; IAE; IAG; IAH; IAT; IS; IT; IU; IW; lZ(b); 3A; 3B; 3C; 3D; 3E; 3F; 3G; 3H; 31; 3K; 3L; 3P; 3Q; 3R; 4B; 4D; 4F; 4H; 4J; 4L; 4N; 4P; 4R; 4T; 4V; 5A; 5B; 5C; 5D; 5EE; 5F; 5G; 5H; 51; 5J; 5K; 5L; 5M; 5N; 50; 5P 5Q; 5R; 5S; 5T; 5U; 5V; 6A; 6B; 6C; 7F; and 7G each identify the molecular markers identified using the Affymetrix® genechip to screen the products of the majority of molecular markers of the human genome in accordance with step 202.
  • Tables IA; 1 AC; IB; 1C; ID; IE; IF; IG; IH; II; U; IK; IL; IM; IN; 10; IP; IQ; IR; IV; IX; IY; IZ; 2; 4A; 4C; 4E; 4G; 41; 4K; 4M; 40; 4Q; 4S; 4V; 7A; 7B; 7C; 7D; 7E; 7H; and 71 each identify molecular markers identified using our own ChondroChipTM genechip to screen the products of the majority of the molecular markers of the human genome.
  • Like reference numerals refer to corresponding parts throughout the several views of the drawings. 5.
  • the embodiments of the present invention use novel approaches to screen and select molecular markers and develop classifiers that can be used to harness the use of molecular marker data from blood.
  • the present invention thus provides systems and methods for constructing biological classifiers using molecular marker data from blood by providing a method to screen and select from a large variety of potential molecular markers so as to identify a small subset of molecular markers.
  • a "condition” includes a mode or state of being including a physical, emotional, psychological or pathological state.
  • a condition can be as a result of both "genetic” (ie genetically inherited) and/or “environmental” factors (ie the result of exposure to internal or external influences).
  • a condition is a disease.
  • a condition is a stage of a disease.
  • a condition is a mode or state of being which is not a disease.
  • a condition which is not a disease is a condition resulting from the progression of time.
  • a condition resulting from progression of time can include, but is not limited to: memory loss, loss of skin elasticity, loss of muscle tone, and loss of sexual desire.
  • a condition which is not a disease is the response to treatment.
  • a treatment can include, but is not limited to disease modifying treatments as well as treatments useful in mitigating the symptoms of disease.
  • treatments can include drugs specific for a disease of the invention.
  • diagnosis includes the ability to determine that an individual has or does not have a specific condition or conditions. Diagnosis also refers to the ability to determine that an individual has one condition or conditions as compared with one or more other condition or conditions. In some embodiments, diagnosis refers to the ability to demonstrate an increased likelihood that an individual has a specific condition. “diagnosis” refers to the ability to demonstrate an increased likelihood that an individual has one condition as compared to a second condition.
  • diagnosis refers to a process whereby there is an increased likelihood that an individual is properly characterized as having a condition ("true positive”) or is properly characterized as not having a condition (or is properly characterized as having the second condition where the diagnosis is as between two conditions) ("true negative") while minimizing the likelihood that the individual is improperly characterized with said condition ("false positive”) or improperly characterized as not being afflicted with said condition (or improperly characterized as having the second condition)("false negative”).
  • the term “differential expression” refers to a difference in the level of expression of the RNA and/or protein products of a molecular marker of the invention, as measured by the amount or level of RNA or protein.
  • RNA can include difference in the level of expression of mRNA, and/or one or more spliced variants of mRNA of the biomarker in one sample as compared with the level of expression of the same one or more biomarkers of the invention as measured by the amount or level of RNA, including mRNA and/or one or more spliced variants of mRNA in a second sample.
  • "Differentially expressed” or “differential expression” can also include a measurement of the protein, or one or more protein variants encoded by a molecular marker of the invention in a sample or population of samples as compared with the amount or level of protein expression, including one or more protein variants of a molecular marker of the invention.
  • Differential expression can be determined as described herein and as would be understood by a person skilled in the art.
  • the term “differentially expressed” or “changes in the level of expression” refers to an increase or decrease in the measurable expression level of a given product of a molecular marker as measured by the amount of RNA and/or the amount of protein in a sample as compared with the measurable expression level of a given product of the molecular marker in a second sample.
  • the first sample and second sample need not be from different patients, but can be samples from the same patient taken at different time points.
  • differentiated can also refer to an increase or decrease in the measurable expression level of a given molecular marker in a population of samples as compared with the measurable expression level of the molecular marker in a second population of samples.
  • “differentially expressed” when referring to a single sample can be measured using the ratio of the level of expression of a given molecular marker in said sample as compared with the mean expression level of the given molecular marker of a control population wherein the ratio is not equal to 1.0.
  • Differentially expressed can also be used to include comparing a first population of samples as compared with a second population of samples or a single sample to a population of samples using either a ratio of the level of expression or using p-value.
  • p-value a nucleic acid transcript including hnRNA and mRNA is identified as being differentially expressed as between a first and second population when the p-value is less than 0.1, less than 0.05, less than 0.01, less than 0.005, less than 0.001 etc.
  • RNA or protein product of the molecular marker When determining differential expression on the basis of the ratio of the level of molecular marker product — expression of an RNA or protein product of the molecular marker is differentially expressed if the ratio of the level of the RNA or protein product in a first sample as compared with that in a second sample is greater than or less than 1.0. For instance, a ratio of greater than 1, for example 1.2, 1.5, 1.7, 2, 3, 4, 10, 20, or a ratio of less than 1, for example 0.8, 0.6, 0.4, 0.2, 0.1. 0.05, of RNA or protein product of a molecular marker would be indicative of differential expression.
  • a molecular marker is differentially expressed if the mean level of expression of a nucleic acid transcript including the hnRNA and/or mRNA transcript in a first population as compared with its mean level of expression of the transcript in a second population is greater than or less than 1.0. For instance, a ratio of greater than 1, for example 1.2, 1.5, 1.7, 2, 3, 4, 10, 20, or a ratio less than 1, for example 0.8, 0.6, 0.4, 0.2, 0.1. 0.05 would be indicative of differential expression.
  • a molecular marker is differentially expressed if the ratio of the level of the hnRNA and/or mRNA transcript in a first sample as compared with the mean level of the transcript of the second population is greater than or less than 1.0 and includes for example, a ratio of greater than 1, for instance 1.2, 1.5, 1.7, 2, 3, 4, 10, 20, or a ratio less than 1, for example 0.8, 0.6, 0.4, 0.2, 0.1. 0.05.
  • "Differentially increased expression” refers to 1.1 fold, 1.2 fold, 1.4 fold, 1.6 fold, 1.8 fold, or more, relative to a standard, such as the mean of the expression level of the second population.
  • “Differentially decreased expression” refers to less than 1.0 fold, 0.8 fold, 0.6 fold, 0.4 fold, 0.2 fold, 0.1 fold or less, relative to a standard, such as the mean of the expression level of the second population.
  • the term "molecular marker” (or sometimes referred to as a “biomarker”) refers to a gene or a genetic element. In some embodiments, the molecular marker of interest is identified by the Gene ID (formerly Locus Link ID) as is published by the National Center for Biotechnology Information (NCBI) Database as would be understood by a person skilled in the art.
  • oligonucleotide is defined as a molecule comprised of two or more deoxyribonucleotides and/ or ribonucleotides, and preferably more than three. Its exact size will depend upon many factors which, in turn, depend upon the ultimate function and use of the oligonucleotide.
  • the oligonucleotides may be from about 8 to about 1,000 nucleotides long.
  • oliognucleotides of 8 to 100 nucleotides are useful in the invention, preferred oligonucleotides range from about 8 to about 15 bases in length, from about 8 to about 20 bases in length, from about 8 to about 25 bases in length, from about 8 to about 30 bases in length, from about 8 to about 40 bases in length or from about 8 to about 50 bases in length.
  • primer refers to an oligonucleotide, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand, is induced, i.e., in the presence of nucleotides and an inducing agent such as a DNA polymerase and at a suitable temperature and pH.
  • the primer may be either single-stranded or double- stranded and must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer will depend upon many factors, including temperature, source of primer and the method used.
  • the oligonucleotide primer typically contains 15-25 or more nucleotides, although it may contain fewer nucleotides.
  • the factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art.
  • the design and selection of primers embodied by the instant invention is according to methods that are standard and well known in the art, see Dieffenbach, C.W., Lowe, T.M.J., Dveksler, G.S. (1995) General Concepts for PCR Primer Design. In: PCR Primer, A Laboratory Manual (Eds.
  • probe means oligonucleotides and analogs thereof and refers to a range of chemical species that recognise polynucleotide target sequences through hydrogen bonding interactions with the nucleotide bases of the target sequences.
  • the probe or the target sequences may be single- or double-stranded RNA or single- or double-stranded DNA or a combination of DNA and RNA bases.
  • a probe is at least 8 nucleotides in length and less than the length of a complete gene.
  • a probe may be 10, 20, 30, 50, 75, 100, 150, 200, 250, 400, 500 and up to 2000 nucleotides in length.
  • Probes can include oligonucleotides modified so as to have a tag which is detectable by fluorescence, chemiluminescence and the like.
  • the probe can also be modified so as to have both a detectable tag and a quencher molecule, for example Taqman® and Molecular Beacon® probes.
  • product of the molecular marker or “molecular marker product” refers to the RNA or protein found in blood which corresponds to the molecular marker (ie is transcribed from the gene or genetic element or is translated from RNA which is transcribed from the gene or genetic element).
  • RNA resulting from the molecular marker can include one or more of the following species; hnRNA, niRNA, and/or one or more spliced variants of mRNA.
  • proteins resulting from the molecular marker can include any proteins found in blood which correspond to the RNA resulting from the molecular marker.
  • selective amplified or “selective amplification” refers to a process whereby one or more copies of a particular target nucleic acid sequence is selectively generated from a template nucleic acid.
  • Selective amplification or selectively amplified is to be compared with amplification in general which can be used as a method in combination with, for example, random primers and an oligodT primer to amplify a population of nucleic acid sequences (e.g. mRNA).
  • Selective amplification is preferably done by the method of polymerase chain reaction (Mullis and Faloona, 1987, Methods Enzymol. 155:335).
  • the term "selectively binds" in the context of proteins encompassed by the invention refers to the specific interaction of any two of a peptide, a protein, a polypeptide, and an antibody, wherein the interaction preferentially occurs as between any two of a peptide, protein, polypeptide and antibody preferentially as compared with any other peptide, protein, polypeptide and antibody.
  • the two molecules are protein molecules, a structure on the first molecule recognises and binds to a structure on the second molecule, rather than to other proteins.
  • Selective binding means that a molecule binds its specific binding partner with at least 2-fold greater affinity, and preferably at least 10-fold, 20-fold, 50-fold, 100-fold or higher affinity than it binds a non-specific molecule.
  • selective hybridization in the context of this invention refers to a hybridization which occurs as between a polynucleotide encompassed by the invention and an RNA , and its complement thereof (ie a cDNA copy), of the molecular marker of the invention, wherein the hybridization is such that the polynucleotide preferentially binds to the RNA products of the molecular marker of the invention relative to the RNA products of other molecular markers or other genes in the genome in question.
  • a polynucleotide which "selectively hybridizes" is one which hybridizes with a selectivity of greater than 70%, greater than 80%, greater than 90% and most preferably of 100% (i.e.
  • RNA cross hybridization with other RNA species preferably occurs at less than 30%, less than 20%, less than 10%).
  • a polynucleotide which "selectively hybridizes" to the RNA product of a biomarker of the invention can be determined taking into account the length and composition.
  • “specifically hybridizes”, “specific hybridization” refers to hybridization which occurs when two nucleic acid sequences are substantially complementary (at least about 65% complementary over a stretch of at least 14 to 25 nucleotides, preferably at least about 75% complementary, more preferably at least about 90% complementary). See Kanehisa, M., 1984, Nucleic acids Res., 12:203, incorporated herein by reference.
  • mismatch may be small, such as a mono-, di- or tri-nucleotide.
  • a region of mismatch can encompass loops, which are defined as regions in which there exists a mismatch in an uninterrupted series of four or more nucleotides.
  • Numerous factors influence the efficiency and selectivity of hybridization of two nucleic acids, for example, the hybridization of a nucleic acid member on an array to a target nucleic acid sequence. These factors include nucleic acid member length, nucleotide sequence and/or composition, hybridization temperature, buffer composition and potential for steric hindrance in the region to which the nucleic acid member is required to hybridize.
  • longer sequences have a higher melting temperature (TM) than do shorter ones, and are less likely to be repeated within a given target sequence, thereby minimizing non ⁇ specific hybridization.
  • TM melting temperature
  • Hybridization temperature varies inversely with nucleic acid member annealing efficiency.
  • concentration of organic solvents, e.g., formamide in varies inversely with annealing efficiency, while increases in salt concentration in the hybridization mixture facilitate annealing. Under stringent annealing conditions, longer nucleic acids, hybridize more efficiently than do shorter ones, which are sufficient under more permissive conditions.
  • the term “specifically binds” refers to the interaction of two molecules, e.g., a ligand and a protein or peptide, or an antibody and a protein or peptide wherein the interaction is dependent upon the presence of particular structures on the respective molecules.
  • two molecules are protein molecules
  • a structure on the first molecule recognises and binds to a structure on the second molecule, rather than to proteins in general.
  • Specific binding means that a molecule binds its specific binding partner with at least 2-fold greater affinity, and preferably at least 10-fold, 20-fold, 50-fold, 100-fold or higher affinity than it binds a non ⁇ specific molecule.
  • standard stringent conditions and “stringent conditions” means hybridization will occur only if there is at least 95% and preferably, at least 97% identity between the sequences, wherein the region of identity comprises at least 10 nucleotides.
  • the sequences hybridize under stringent conditions following incubation of the sequences overnight at 42DC, followed by stringent washes (0.2X SSC at 65 D C).
  • the degree of stringency of washing can be varied by changing the temperature, pH, ionic strength, divalent cation concentration, volume and duration of the washing.
  • the stringency of hybridization may be varied by conducting the hybridization at varying temperatures below the melting temperatures of the probes.
  • the hybridization temperature may be decreased in increments of 5 0 C from 68 0 C to 42 0 C in a hybridization buffer having a Na+ concentration of approximately IM.
  • the filter may be washed with 2X SSC, 0.5% SDS at the temperature of hybridization.
  • the hybridization may be carried out in buffers, such as 6X SSC, containing formamide at a temperature of 42 0 C.
  • concentration of formamide in the hybridization buffer may be reduced in 5% increments from 50% to 0% to identify clones having decreasing levels of homology to the probe.
  • the filter may be washed with 6X SSC, 0.5% SDS at 50 DC.
  • Hybridization conditions are considered to be "moderate stringency" conditions when hybridization fluids are comprised of above 25% formamide and “low stringency" conditions when hybridization fluids are comprised of below 25% formamide.
  • a specific example of "moderate stringency" hybridization conditions is when the above hybridization is conducted at 30% formamide.
  • the term “responder” is used to mean an individual who responds to treatment.
  • the use of the term “responds to treatment” depends upon the context of the treatment and the disease or condition, but in some embodiments indicate a sufficiently effective and safe response by an individual to the administration of treatment.
  • the term “non-responder” is used to mean an individual who does not respond positively to treatment.
  • the use of the phrase “does not respond to treatment” also depends upon the context of the treatment and the disease or condition, but in some embodiments indicates an ineffective or unsafe response by an individual to the administration of treatment.
  • trait is a mode or state of being including a physical, emotional, psychological or pathological state.
  • a trait can include both “genetic” and/or “environmentally” influenced factors.
  • the term “genetic factors” means genetically inherited elements which affect one or more traits as a result of the genetic makeup of the individual.
  • the term “environmental factors” includes exposure to internal or external influences including but not limited to medical treatments, non-medical drugs, pollution, environmental toxins, lead poisoning, mercury poisoning, exposure to genetically modified organisms, radioactivity, pesticides, insecticides, cigarette smoke, alcohol, or exercise and can affect abundance of RNA or affect gene expression as a result of epigenetic mutations and/or non genetic mutations.
  • a physiological or pathological trait can include the status with regards to a condition including having a condition including a disease, having risk factors of a disease having a certain stage of disease or having a certain response to treatment or a risk of a certain response to treatment.
  • a displayed trait can actually be the result of one or more underlying traits.
  • a trait also includes clinically measurable parameters including those clinically measurable parameters which are indicators of state of health or disease.
  • a clinically measurable parameter includes blood pressure, lung capacity, electrolyte level, enzyme levels (e.g.
  • Serum Glutamic Oxaloacetic Transaminase, alkaline phosphatase, Gamma-Glutamyltransferase or Gamma-Glutamyl Transpeptidase, Lactic dehydrogenase) hormone levels e.g. thyroid stimulating hormone
  • protein levels e.g. Prostate specific antigen PSA
  • Clinically measurable parameters can include disease specific clinical indicators, for example prostate specific antigen as an indicator of prostate cancer; insulin levels as an indicator of diabetes; thyroid stimulating hormone levels as an indicator of thyroid disorder and the like.
  • the term "trait subgroup” is used to define a group of subjects where each subject has at least one trait or group of traits in common, for example, each subject has a disease, a specific stage of disease, same response to treatment, taking the same drug, etc.
  • treatment includes administration of one or more compounds, combination of one or more compounds, application of a non-compound based therapeutic regimen, or any combination thereof where administration includes application of a single treatment, a regiment or course of treatment etc. to reduce or amelioration of the progression, severity and/or duration of a disease or condition and/or the reduction or amelioration of the symptoms of a disease or condition resulting from the use of a treatment and/or treatment regime.
  • Fig. 1 shows an exemplary system according to an embodiment of the invention that supports the functionality described herein.
  • the system is preferably a computer system 10 having: • one or more central processors 22; • a main non- volatile storage unit 14, for example a hard disk drive, for storing software and data, the storage unit 14 controlled by storage controller 12; • a system memory 36, preferably high speed random-access memory (RAM), for storing system control programs, data, and application programs, comprising programs and data loaded from non- volatile storage unit 14; system memory 36 may also include read-only memory (ROM); • an optional user interface 32, comprising one or more input devices (e.g., keyboard 28) and a display 26 or other output device; • an optional network interface card 20 for connecting to any wired or wireless communication network 34 (e.g., a wide area network such as the Internet); • an internal bus 30 for interconnecting the aforementioned elements of the system; and • a power source 24 to power the aforementioned elements.
  • ROM read-only memory
  • Operating system 40 can be stored in system memory 36.
  • system memory 36 includes various components described below. Those of skill in the art will appreciate that such components can be wholly resident in RAM 36 or non- volatile storage unit 14. Furthermore, at any given time, such components can partially reside both in RAM 36 and non- volatile storage unit 14. Further still, some of the components shown in Fig. 1 as being resident in RAM 36 can be resident in another computer (a remote computer) that is addressable by computer 10 over wide area network 34. It will be appreciated that such a remote computer may physically be resident in the same room as computer 10 or in another physical location. As illustrated in Fig.
  • RAM 36 comprises programs and data to interact with components in the computer system 10 for configuring: • file system 42 for controlling access to the various files and data structures used by embodiments of the present invention; • a training population 44 for use in construction of one or more classifiers ; • a molecular marker data processing module I- 54 for processing molecular marker data representative of a genome or a portion thereof for members of training population 44; • a molecular marker screening module A (56A) for identifying molecular markers whose molecular marker data individually discriminates between two or more trait subgroups of the training population using molecular marker data of module 54; • a molecular marker screening module B (56B) for identifying molecular markers whose molecular marker data do not individually discriminate, but demonstrate ability to differentiate between two or more trait subgroups of the training population when used in combination using molecular marker data of module 54; • a candidate molecular marker data structure 58 for storing information about candidate molecular markers identified by molecular marker candidate
  • computer 10 comprises software program modules and data structures.
  • the data structures either stored in computer 10 or accessible to computer 10 include a training population 44, candidate molecular marker data structures 58, patient database 68, and classifier database 70.
  • Each of these data structures can comprise any form of data storage system including, but not limited to, a flat ASCII or binary file, an Excel spreadsheet, a relational database (e.g. SQL), or an on-line analytical processing (OLAP) database (MDX and/or variants thereof).
  • OLAP on-line analytical processing
  • such data structures are each in the form of one or more databases that include hierarchical structure (e.g., a star schema).
  • such data structures are each in the form of databases that do not have explicit hierarchy (e.g., dimension tables that are not hierarchically arranged).
  • each of the data structures stored or accessible to system 10 are single data structures.
  • such data structures in fact comprise a plurality of data structures (e.g., databases, files, archives) that may or may not all be hosted by the same computer 10.
  • training population 44 comprises a plurality of Excel spreadsheets that are stored either on computer 10 and/or on computers that are addressable by computer 10 across wide area network 34.
  • patient database 68 comprises a database that is either stored on computer 10 or is distributed across one or more computers that are addressable by computer 10 across wide area network 34.
  • Section 5.9 describes exemplary architectures for training population 44, candidate molecular marker data structure 58, patient database 68, and/or classifier database 70.
  • modules and data structures illustrated in Fig. 1 can be located on one or more remote computers.
  • classifier polling and reporting module 66 and other modules can be used by a physician to treat a patient and can reside on a client computer that is in communication with computer 10 via network 34.
  • classifier polling and reporting module 66 can be an interactive web page.
  • training population 44, candidate molecular marker data structure 58, patient database 68 and/or classifier database 70 and modules e.g.
  • modules 54, 56A, 56B, 57, 61, 61-5, 62, 64, and 66) illustrated in Fig. 1 are on a single computer (computer 10) and in other embodiments one or more of such data structures and module are hosted by one or more remote computers (not shown). Any arrangement of the data structures and software modules illustrated in Fig. 1 on one or more computers is within the scope of the present invention so long as these data structures and software modules are addressable with respect to each other across network 34 or by other electronic means. Thus, the present invention fully encompasses a broad array of computer systems. Now that an overview of a system in accordance with one embodiment of the present invention has been described, various advantageous methods in accordance with embodiments of the present invention will now be disclosed in conjunction with Figs. 2 through 5.
  • FIG. 2 is a flowchart showing a method of selecting molecular markers and developing one or more classifiers or groups of classifiers according to an embodiment of the invention.
  • Step 202 molecular marker data reflective of the abundance of each of a plurality of RNA and/or proteins found in the blood for members of training population 44 is obtained using one or more of the techniques as described in Section 5.3 and/or 5.4.
  • the data is reflective of the abundance of RNA products of the molecular marker.
  • the RNA products are those expressed in blood.
  • the RNA products are those which are found in blood, but may not necessarily be expressed in blood (e.g.
  • the data is reflective of the abundance of protein products of the molecular markers. In some embodiments, the data is reflective of the level of proteins expressed in blood. In other embodiments, the data is reflective of proteins found in blood in sufficient quantity to be detected. Measuring of molecular marker data (ie data reflective of the level of the product of the molecular marker) can be done using those techniques known to persons skilled in the art. Note that in some embodiments, data may be obtained using public sources or other sources of data rather than performing one or more of the techniques described. For example, it is anticipated that databases of microarray data collected from blood may be available in future.
  • the molecular marker data for each molecular marker is obtained using the same technique to allow greater comparability.
  • a priori information is known about all or a portion of such genes and in some embodiments a priori information about such genes is either not known or not considered in step 202.
  • the molecular markers resulting from step are those molecular markers identified in Tables IA through to 71.
  • Measurement of molecular marker data of a plurality of the molecular markers in the blood of each member of training population 44 can be done using any known technique and preferably is done using large scale techniques which allow for the ability to obtain data for a large number of molecular markers and/or for a large number of individuals quickly and efficiently and at a relatively low cost.
  • training population 44 includes a population of individuals made up of one or more trait subgroups with each individual in such trait subgroups having one or more traits.
  • each trait subgroup represented in training population 44 includes molecular marker data from at least 3-4 different subjects.
  • each trait subgroup represented in training population 44 includes molecular marker data for at least ten different subjects. Still more preferably each trait subgroup represented in the training population 44 includes molecular marker data for at least 30, 40, 50, 100, 200, 500, 1000 or more subjects.
  • Each training population is selected to include two or more trait subgroups, each subgroup comprising trait subgroup members. Each of these two or more trait subgroups differs with respect to a trait of interest and/or an aspect of a trait of interest.
  • the members of each of the trait subgroups have been diagnosed as having or not having the trait of interest by one or more known techniques.
  • the members of each trait subgroup are diagnosed for having or not having the trait of interest using a well accepted methodology for diagnosing of said trait.
  • each member of a first trait subgroup of the training population has liver cancer, whereas each member of a second trait subgroup of the training population does not have liver cancer.
  • each member of a first trait subgroup of the training population has Alzheimer's, whereas each member of a second trait subgroup of the training population has manic depressive disorder, and each member of a third trait subgroup of the training population has schizophrenia, and each member of a fourth trait subgroup does not have any of the above conditions.
  • the trait of interest is a disease such as prostate cancer, and the aspect of interest is the degree of advancement of the prostate cancer.
  • each member of the first trait subgroup can be those subjects that have early stage prostate cancer
  • each member of a second trait subgroup can be those subjects that have later stage prostate cancer
  • each member of a third trait subgroup can be those subjects that do not have prostate cancer.
  • the trait of interest is responsiveness of individuals having musculoskeletal disorders to a Cox 2 inhibitor.
  • a first trait subgroup is comprised of individuals who are responsive to a treatment
  • a second trait subgroup is comprised of individuals who are responsive to treatment but demonstrate a toxic side-effect
  • a third trait subgroup is comprised of individuals who are nonresponsive to treatment.
  • one trait subgroup can include those subjects that have not yet undergone treatment but who are later identified as being responders to treatment and a second trait subgroup can include those subjects that have not yet undergone treatment but who are later identified as non responders (e.g. demonstrates a toxic side-effect, demonstrates no improvement in condition, demonstrates a worsening of condition, etc.).
  • members of each trait subgroup of the training population 44 are preferably selected such that each trait subgroup of the training population 44 has a similar distribution with respect to at least one, two, three, four, five, six, one or more, two or more, three or more, four or more, five or more, six or more, between one and 1000 other traits.
  • a treatment can include, but is not limited to, disease modifying treatments as well as treatments useful in mitigating the symptoms of disease and includes administration of one or more compounds, combinations of one or more compounds, application of a non- compound based therapeutic regimen, or any combination thereof where administration includes application of a single treatment, a regimen or course of treatment and the like.
  • treatments can include drugs specific for a disease such as drugs specific for Alzheimer's, cardiovascular disease, manic depression syndrome, schizophrenia, diabetes cancers including liver cancer, testicular cancer, bladder cancer, prostate cancer, kidney cancer, breast cancer, colon cancer, osteoarthritis, rheumatoid arthritis, osteoporosis, ankylosing spondylitis, or any other disease including those listed herein. .
  • drugs specific for a disease such as drugs specific for Alzheimer's, cardiovascular disease, manic depression syndrome, schizophrenia, diabetes cancers including liver cancer, testicular cancer, bladder cancer, prostate cancer, kidney cancer, breast cancer, colon cancer, osteoarthritis, rheumatoid arthritis, osteoporosis, ankylosing spondylitis, or any other disease including those listed herein. .
  • treatments can include but are not limited to administration of VIOXX®, Celebrex®, non- steroidal anti-inflammatory drugs (NSAIDS), cortisone, visco supplement, Lipitor®, Adriamycin®, Cytoxan®, Herceptin®, Nolvadex®, Avastin®, Erbitux®, Fluorouracil®, Largactil®, Sparine®, Vesprin®, Stelazine®, Fentazine®, Prolixin®, Compazine®, Tindal®, Modecate®, Moditen®, Mellarin, Serentil, Norvane, ®, Fluanxol®, Clopixol®, Taractan®, Depixol®, Clopixol®, Haldol®, Haldol®, Decanoate, Orap®, Inapsine®, Imap®, Semap®, Loxitane®, Daxol®, lithium, anticonvulsants (e.g., carba
  • a treatment can include any treatment or drug described in the Compendium of Pharmaceuticals and Specialties, Canadian Pharmaceutical Association; 26th edition, June, 1991; Krogh, Compendium of Pharmaceuticals and Specialties, Canadian Pharmaceutical Association; 27th edition, April, 1992.
  • a treatment can include administration of any compound described in the United States Food and Drug Administration list of approved drug products (the "Orange Book") that is found at http://www.mco.edu/research/fda.html.
  • molecular marker data is not obtainable from each member of a training population or each member of a trait subgroup (for example, using microarray technology there may be an insufficient signal for one or more molecular markers for any particular member of the training population).
  • candidate molecular markers can still be selected on the basis of the molecular marker data so long as data is obtainable for a sufficient number of molecular markers from a sufficient number of members of the training population. For example, for each molecular marker, it is sufficient if data is available for at least 75%, 80%, 85%, 90% or 95% of the each trait subgroup of the training population.
  • Section 5.2 provides details on the types of blood samples from subjects in the training population that can be used to obtain data for molecular markers. Section 5.2 further provides details on how such blood samples can be obtained. Section 5.2 also provides details on the types of subjects that can be used to form training population 44 and the types of subpopulations that can be used in the training population.
  • Fig. 1 illustrates the data structure of a training population 44 in accordance with one embodiment of the present invention.
  • Each record 46 includes an optional subject identifier 48 for uniquely identifying the subject.
  • Each record 46 includes a molecular marker data file 50 for storage of the molecular marker data measured in step 202.
  • Fig. 4 provides more details on a molecular profile 50 resulting from Molecular Marker data processing module I in accordance with one embodiment of the present invention.
  • the molecular profile 50 of Fig. 4 includes an identifier 302 for each molecular marker 302 tracked by profile 50. Then, for each respective molecular marker 302 in profile 50, there exists one or more measurements of molecular marker data 304.
  • more than one data point is measured for molecular markers 302. If more than one data point is measured for a molecular marker, then a statistical measure of central tendency (e.g. mean, median, average etc.) can be computed. Accordingly, such measurements for molecular marker data 304 can be stored in data structure 50.
  • a trait characterization field 52 for each subject in training population 44.
  • Documented traits include known condition or clinically measurable parameters; genetic likelihood of disease or condition; medications both past and current; environmental exposures, ethnicity, age, sex and the like.
  • training population 44 includes only two trait subgroups and trait characterization 52 is a binary choice between two values, where one value indicates that the corresponding subject belongs in a first trait subgroup and a second value indicates that the subject belongs in a second trait subgroup.
  • training population 44 is divided into a plurality of lists, where each list in the plurality of lists represents a different trait subgroup. In such embodiments, there is no need for a phenotypic characterization field 52.
  • scoring population in addition to the training population. The scoring population is used to evaluate each of the classifiers derived from the training population.
  • the scoring population is made up of one or more individuals that have at least two trait subgroups in common with the training population.
  • multiple scoring populations are generated from the training population using one or more resampling or cross validation procedures including: bootstrapping; leave one out; leave n out; percent split and the like so as to evaluate the classifiers derived from the training population.
  • the members of the scoring population are not the members used in the training population.
  • some aspects of step 202 are performed by first data molecular processing module I 54.
  • first data molecular processing module 54 can be a known software program, such as commercially available and/or academically available data processing programs. Step 204.
  • step 204 using the data measured in step 202, individual candidate molecular markers are identified, where the molecular marker data allows the differentiation as between two of the trait subgroups of the training population.
  • step 204 represents a series of pairwise comparisons, where each pairwise comparison is between molecular marker data for subjects from two different trait subgroups.
  • data associated with molecular markers from a population of samples having one aspect of a trait of interest are compared with a population of samples having a second aspect of a trait of interest (a second trait subgroup) so as to identify molecular markers that are able to differentiate between the two trait subgroups (ie the molecular marker data enables the ability to differentiate between the two trait subgroups.
  • more than two pairwise comparisons can be performed on the molecular marker data to identify lists of candidate molecular markers for each of the possible pairwise comparison.
  • a number of statistical techniques can be used to perform the pairwise comparisons of step 204.
  • standard statistical techniques such as a t-test are used. Methods based on conventional t-tests provide the probability (P) that a difference in measured values for the data of a molecular marker between two different trait subgroups occurs by chance. See, for example, Baldi et al, 2001, Bioinformatics 17, pp. 509-519, 2001, which is hereby incorporated herein by reference in its entirety.
  • the t-test compares the actual difference between two means in relation to the variation in the data (expressed as the standard deviation of the difference between the means). For instance, to determine whether a particular molecular marker discriminates between a first trait subgroup and a second trait subgroup, the mean of the data for the molecular marker in the first trait subgroup is compared to the mean of the data for the molecular marker in the second subgroup in accordance with the t-test.
  • the t-test is applied with a Bonferroni correction or similar form of correction, hi some embodiments, rather than using a t-test, nonparametric equivalents such as the WaId- Wolfowitz runs test, the Mann- Whitney U test, the Kolmogorov-Smirnov two-sample test or ROC are used.
  • training population molecular marker data is obtained using a microarray and the Significant Analysis of Microarrays technique of Tusher et al. is used to identify molecular markers whose data discriminates between trait subgroups.
  • Manduchis' algorithms for assigning confidence to differentially expressed genes is used to identify molecular markers whose data discriminates trait subgroups. See, for example, Manduchi et al, 2000, Bioinformatics 16, 685-598. In some embodiments, there are a number of different trait subgroups represented within the training population.
  • ANOVA is described, for example, in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 155-187, which is hereby incorporated herein by reference in its entirety.
  • nonparametric equivalents to ANOVA such as the Kruskal-Wallis analysis of ranks test, the Median test, Friedman's two-way analysis of variance, or the Cochran Q test, are used to identify molecular markers that have statistically different means as between two of the rait subgroups.
  • a means comparisons test such as Duncan's, Student-Newman-Keuls (SNK), Tukey-Kramer, Tukey's HSD, or Least Significant Difference (LSD) are run to determine which molecular markers have data that statistically differentiates as between one or more of the trait subgroups tested (e.g. has statistically different values in one or more of the trait subgroups tested).
  • such tests are performed instead of ANOVA or pairwise t-tests to identify molecular markers that discriminate two or more trait subgroups.
  • a parametric test and a nonparametric test are run in step 204. If there are only two trait subgroups being compared (e.g., non-disease versus disease) then a Welch t-test (parametric test) and a Mann- Whitney test are used without multiple test corrections at decreasing p-values ⁇ e.g., p ⁇ 0.05, p ⁇ 0.01, p ⁇ 0.005, p ⁇ 0.001, etc.) until the number of candidate molecular markers is less than 50, less than 100, less than 150, less than 200, less than 250 etc..
  • the Kruskal-Wallis (nonparametric) and Welch ANOVA (parametric) tests are used. Following the same procedure for instances where only two trait subgroups are being compared, additional molecular marker sets are produced in conjunction with multiple test corrections.
  • An example of a test correction is the Benjamin-Hochberg false discovery rate.
  • the post-hoc statistical tests for groups of three or more provided in GeneSpring version 6.1+ is used. The post-hoc test provides a means for determining which molecular markers are different between particular trait subgroups. Such post-hoc tests include the Student Newman Keuls test and the Tukey test.
  • ANOVA can be used to identify molecular markers that statistically discriminate between one of the possible quadruplets in the training population.
  • processing step 204 to select candidate molecular markers is done by looks for differential data abundances (e.g., differential expression). Such methods differ from those described above in the sense that variance as between the level of expression as between members of the same trait subgroup are not necessarily considered.
  • a statistical measure of central tendency e.g. mean, median, average etc.
  • the measure of differential data abundance (or differential expression) for a molecular marker product as between a first trait subgroup and a second trait subgroup is determined by measuring the fold change, for example a fold change of greater than 1.5, 2.0, 2.5, 3.0, 4,0 or higher can be selected.
  • the measure of differential data abundance need not be quantified, but molecular markers products which on visual inspection display a clear difference in expression (ie abundance levels) as between two trait subgroups can be selected. To illustrate, consider a population P' that comprises trait subgroups "A" and "B". That is, each member of P ' is classified into either subgroup "A" or "B” based on whether or not they exhibit or have a particular trait.
  • molecular marker list A might include molecular markers whose measured data (including normalized data etc.) discriminates between a first and second trait subgroup as determined by a first pairwise t-test.
  • molecular marker list B might include molecular markers whose data discriminates between a first and third trait subgroup as determined by a second pairwise t-test.
  • Molecular marker list C might include molecular markers whose data discriminates between a first, second and third trait subgroup as determined by ANOVA.
  • each candidate molecular marker list 60 is preserved as an independent list in candidate molecular marker data structure 58 (Fig. 1).
  • processing step 204 identifies a total of between 10 and 4000 candidate molecular markers for any particular training population. In other embodiments, step 204 identifies a total of between 500 and 2000 candidate molecular markers. In yet another embodiments, processing step 204 identified a total of between 100 and 1000 candidate molecular markers. In some embodiments, some aspects of step 204 are performed by molecular marker candidate identification module 56A. Additionally, there exist known programs that can perform some of the functionality described in step 204. Such programs include those formerly sold by Silicon Genetics (e.g. GeneSpringTM); now Agilent Technologies.
  • candidate molecular markers identified using the above- described statistical or nonstatistical tests are clustered in order to visualize relationships between the genes.
  • GeneSpringTM is used to perform hierarchical clustering using the Spearman correlation statistic.
  • QT clustering is used to identify genes that have a similar pattern of expression across the specimens in training population 44. Clustering that can be employed in step 204 is described generally in Section 5.12. Further, Section 5.12 gives examples of some clustering techniques that can be used in step 204.
  • Molecular markers for use in subsequent sections can be ranked and selected by one or more criteria, including, but not limited to, fold change differences in molecular marker data between two or more trait subgroups, standard deviations of molecular marker data as between two or more trait subgroups using the above-described statistical tests, coefficient of variation, statistical significance (e.g., p- value from ANOVA and/or Mest, or other tests described above), level of expression as determined using molecular marker data, gene function, reproducibility of molecular marker data (includes intra- and inter-experimental), elucidated pathways / networks of the molecular markers as would be understood by a person skilled in the art (e.g.
  • molecular markers for use in subsequent sections are chosen on the basis of the p value identified as a result of step 204 as a measure of the likelihood that the molecular marker data can distinguish as between the two trait subgroups and more particularly molecular markers are chosen wherein the p value is less than 0.5; less than 0.1, less than 0.05, less than 0.01, less than 0.005, less than 0.001, less than 0.0005, less than 0.0001, less than 0.00005, less than 0.00001, less than 0.000005, less than 0.000001 etc.
  • molecular markers for subsequent steps are chosen on the basis of the level of differential expression displayed by the molecular marker products as between the two or more trait subgroups. Note that in measuring differential fold change in blood, the fold change differences can be quite small, thus in some embodiments, selection of molecular markers is based on a differential fold change where the fold change is greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2.0, greater than 2.1, greater than 2.2, greater than 2.3, greater than 2.4, greater than 2.5, greater than 2.6, greater than 2.7, greater than 2.8, greater than 2.9, greater than 3.0, greater than 3.1, greater than 3.2.
  • molecular markers are first selected as outlined above on the basis of the p value resulting from the molecular marker data and then a subselection of said molecular markers is chosen on the basis of the differential fold change determined from the molecular marker data.
  • molecular markers are first selected on the basis of differential fold change, and then subselection is made on the basis of p value, hi some embodiments, the use of one or more of the selection criteria and subsequent ranking permits the selection of the top 2.5%, 5%, 7.5%, 10%, 12.5%, 15%, 17.5%, 20%, 30%, 40%, 50% or more of the ranked molecular markers for use in subsequent steps.
  • the selection criteria noted above can be set on the basis of the desired number of selected molecular markers for use in steps 206 and or other steps leading to the selection of the available set of markers for step 214.
  • a selection criteria based on the desired number of selected molecular markers will depend upon the resources available for obtaining the molecular marker data for step 206 and/or the computer resources available for calculating and evaluating classifiers of all or a portion of possible combinations of the selected molecular markers.
  • the desired number of selected molecular markers for use in step 214 can be 4,000; 3,000; 2,000; 1,000; 900; 800; 700; 600; 500; 400; 300; 200; 190; 180; 170; 160; 150; 140; 130; 120; 110; 100; 90; 80; 70; 60; 50; 40; 30; 20; 10.
  • a neural network is used to identify such outliers.
  • a neural network has a layered structure that includes, at a minimum, a layer of input units (and the bias) connected by a layer of weights to a layer of output units. Such units are also referred to as neurons. For output along a single dimension, the layer of output units includes just one output unit.
  • neural networks can handle multiple quantitative responses (outputs along multiple dimensions) in a seamless fashion by providing multiple units in the layer of output units.
  • multilayer neural networks there are input units (input layer), hidden units (hidden layer), and output units (output layer). There is, furthermore, a single bias unit that is connected to each unit other than the input units.
  • Neural networks are described in Duda et al, 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et al, 2001, The Elements of Statistical Learning, Springer- Verlag, New York.
  • the basic approach to the use of neural networks to identify outliers is to start with an untrained network. A training pattern is then presented to the untrained network.
  • This training pattern comprises a training population and, for each respective member of the training population, an association of the respective member with a specific trait subgroup.
  • the training pattern specifies measured molecular marker data from blood for molecular markers for each member of a training population as well as an indication as to which trait subgroup each member of the training population belongs.
  • training of the neural network is best achieved when the training population includes members from more than one trait subgroup.
  • individual weights in the neural network are seeded with arbitrary weights and then the molecular marker data for each member of the training population is applied to the input layer. Signals are passed through the neural network and the output determined. The output is used to adjust individual weights.
  • a neural network trained in this fashion classifies each individual of the training population with respect to one of the input trait subgroups.
  • the initial neural network does not correctly classify each member of the training population.
  • Those individuals in the training population that are misclassified identify and determine an error or criterion function for the initial neural network.
  • This error or criterion function is some scalar function of the trained neural network weights and is minimized when the network outputs match the desired outputs. In other words, the error or criterion function is minimized when the network correctly classifies each member of the training population into the correct trait subgroup.
  • the neural network weights are adjusted to reduce this measure of error. For regression, this error can be sum-of-squared errors.
  • this error can be either squared error or cross-entropy (deviation). See, e.g. Hastie et al., 2001, The Elements of Statistical Learning, Springer- Verlag, New York. Those individuals of the training population which are still incorrectly classified by the trained neural network, once training of the network has been completed, are identified as outliers and can be removed prior to proceeding.
  • an ensemble of neural networks can be used on the training population and individuals ranked on the basis of the number of times an individual is misclassified by each neural network. In order to create the ensemble, each neural network can differ with respect to the initial seeded weighting.
  • each neural network can differ on the basis of randomly generated noise added to the molecular marker data for one or more molecular markers of each individual of the training population added to the input layer.
  • this randomly generated noise can be applied by changing the amount of each of the measured molecular marker data of each member of the training population by a scaled random amount. When larger amounts of noise are required, the magnitude of the scaled random amount is increased.
  • the number of layers and the number of units in each layer can be adjusted in order to provide optimal results for any given set of conditions. In this manner, one can identify outliers which are misclassified in as many asl ⁇ %, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more of the neural networks used. Step 205.
  • Step 205 is optional.
  • certain molecular markers whose molecular marker data fails to discriminate individually as between two trait subgroups in step 204 are still more than incrementally useful when utilized in combinations with other candidate molecular markers selected in step 204.
  • step 205 is performed.
  • step 205 all or a portion of the molecular markers for which data has been or can be obtained in at least two of the trait subgroups of the training population are utilized ("putative combination molecular markers").
  • the data is obtained using a technique which allows for fast and efficient data generation for all of the molecular markers of the genome of interest chosen.
  • the data is obtained using one or more of the techniques as described in Section 5.3 and/or 5.4.
  • the data is obtained using microarray technology.
  • the data used is the data obtained for step 202
  • combinations of molecular markers are chosen and a mathematical model applied to the molecular marker data for each molecular marker of the combination resulting in a classifier for each combination.
  • the mathematical model applied can be selected from those defined in Section 5.14. In some embodiments, each possible combination of 2, and/or 3, and/or 4, and/or 5, and/or 6, and/or 7, and/or 8, and/or 9 and/or 10 or more of the putative combination friendly molecular markers are tested.
  • each possible combination of 8 or less molecular markers can be written as follows: 8.000!/((8,OQO - 8)1 (8V) ach classifier resulting from each combination is scored as described more fully in step 220.
  • the classifiers are scored using the training population so as to permit time and cost savings.
  • the classifiers are scored using a scoring population.
  • the classifiers are scored using other resampling or cross validation procedures so as to generate multiple scoring populations. Having scored the classifiers, a subset of classifiers are then selected based on the score.
  • the subset of classifiers is any number less than the total number of combinations evaluated.
  • the top 10%, top 20%, top 30%, top 40%, or top 50% of the classifiers generated are chosen.
  • those classifiers with an ROC area under the curve of 0.5, 0.6, 0.7, 0.8, 0.9 or 1.0 are selected. Having selected a number of classifiers, each representing a combination of molecular markers, the number of occurrences of each putative combination friendly molecular marker in the combinations of the selected classifiers are determined.
  • Putative combination friendly molecular markers can then be selected as combination friendly molecular markers so as to used as candidate molecular markers based on the number of reoccurrences of said molecular marker in the selection of combinations evaluated.
  • 5, 10, 15, 20, 30, 50, 100, 150, 200 or more additional candidate molecular markers not previously selected in step 204 are chosen to proceed to step 206.
  • the top 10%, top 20%, top 30%, top 40%, or top 50% of combination friendly molecular markers as determined by reoccurrence statistics are selected to be included in the selected set of candidate molecular markers for purposes of choosing combinations to create classifiers in accordance with steps 214 to 218. Step 206.
  • second molecular marker data is obtained for the candidate molecular markers identified in step 204.
  • the second molecular marker data is measured using any technique described in Section 5.3 or 5.4 or equivalents thereof.
  • the same technique used to measure the first molecular marker data in step 202 is used to obtain the second molecular marker data.
  • an alternative technique is used to measure the second molecular marker data.
  • the first molecular marker data is obtained using one of the following techniques: microarray, and/or RT-PCR and the second molecular marker data is obtained using any technique but microarray.
  • the first molecular marker data is obtained using microarray and the second molecular marker data is obtained using any technique except for microarray.
  • the use of second molecular marker data is preferred because the changes in differential expression or abundance of the product of the molecular marker in blood as between trait subgroups can be as low as a 1.1 fold, 1.2 fold, 1.3 fold, 1.4 fold, 1.5 fold etc. which makes less sensitive and reproducible techniques less reliable.
  • techniques which are preferable for the data collection to allow large scale screening in step 202 such as microarray have been shown to have significant inherent reproducibility issues with high standard deviation as between experiments. As such it is necessary to obtain second molecular marker data so as to ensure the accuracy of the ultimate classifiers identified.
  • Techniques utilized to obtaining second molecular marker data for the plurality of molecular marker products are those techniques known to measure abundance of RNA and/or protein including the techniques described in Section 5.3 and 5.4.
  • a third series of molecular marker data ie third molecular marker data, fourth molecular marker data etc.
  • step 206 is performed by molecular marker data processing module 11 - 61.
  • molecular processing module 61 will depend on the type of measurement assay used in step 206. However, it is contemplated that module 61 will be used to record measurement values for molecular marker data in a profile similar to molecular marker data processing module I - 50, perform any necessary error correction techniques, normalization techniques (e.g., techniques described in Bevington and Robinson, Data Reduction and Error Analysis for the Physical Sciences, Second Edition, WCB/ McGraw-Hill, 1992, etc.) and/or perform any measurement techniques that can be coded in a digital computer . Step 208.
  • normalization techniques e.g., techniques described in Bevington and Robinson, Data Reduction and Error Analysis for the Physical Sciences, Second Edition, WCB/ McGraw-Hill, 1992, etc.
  • Step 208 is optional and allows for the selection of individual candidate molecular markers of step 206 which can be removed prior to the process of selecting and evaluating combinations of molecular markers in steps 214/216.
  • one or more candidate molecular markers in data structure 58 (Fig. 1) are eliminated.
  • the same types of tests that were performed in step 204 can be performed. The main difference is that in step 208, the quantitative data measured in step 206 using low throughput methods is used whereas in step 204, the high throughput data measured in step 202 is used. Data measured in step 206 is used in step 208 to validate the candidate molecular markers.
  • training population 44 consists of a first trait subgroup and a second trait subgroup and step 208 comprises performing a t-test or a nonparametric equivalent of the t-test on each candidate molecular marker using the molecular marker data measured in step 206 to verify for each candidate molecular marker that the molecular marker data differentiates between the first trait subgroup and the second trait subgroup with some measure of statistical confidence.
  • Candidate molecular markers whose molecular marker data are less effective in differentiating between the two trait subgroups e.g. have a p value that is greater than .05
  • training population 44 consists of a first trait subgroup, a second trait subgroup, and a third trait subgroup.
  • ANOVA or a nonparametric equivalent is performed independently on each candidate molecular marker in data structure 58 to verify that each molecular marker differentiates between the three subgroups using the molecular marker data.
  • Candidate molecular markers whose molecular marker data are less effective in differentiating between the three trait subgroups e.g. have a p value that is greater than .05
  • each molecular marker is validated by using the data for each molecular marker to generate a Receiver Operating Characteristic (ROC) curve.
  • ROC Receiver Operating Characteristic
  • ROC curves are generally discussed in Park et al., Korean J. Radiol. 5, p. 11, which is hereby incorporated herein by reference in its entirety.
  • an ROC curve is computed for each candidate molecular marker in training population 44 using the molecular marker data measured in step 206.
  • training population 44 includes, for each specimen in the training population, an indication 52 as to whether or not the specimen has a particular trait under study.
  • Each respective ROC curve graphs the True Positive Fraction (TPF) as compared with 1/ the False Positive Fraction (FPF).
  • Table B provides hypothetical values for the abundance of A across training population 44.
  • Table B Values for molecular marker data of A across training set 44.
  • each line represents a different specimen in the training population. If the relationship between [A] (data of cellular constituent A) and the presence of disease in subjects in the training population is statistically very significant, all positive results (where specimens have the disease) would be at the top of Table B and all negative results (where biological specimens do not have the disease) at the bottom of the Table B.
  • the table is divided into a number of cutoff levels. Then, the sensitivity (TPF) and specificity (TNF) of each cutoff level is computed. Sensitivity and specificity are defined with reference to the decision matrix of Table C. Table C: Decision matrix.
  • TP means the number of true positives
  • FP means the number of false positives
  • FN means the number of false negatives
  • TN means the number of true negatives.
  • Sensitivity is the proportion of subject with a trait ⁇ e.g., a disease or particular biological phenotype) who test positive for the feature.
  • P(T + ID + ) TP / (TP+FN).
  • Specificity is the proportion of patients without the trait who test negative for the feature.
  • D " ) TN / (TN + FP).
  • the ROC curve is defined as a plot of the sensitivity as the y-coordinate versus 1- specificity (false positive rate) as the x-coordinate.
  • Table B where each line of the Table B represents an independent cutoff level, the following ROC data points are derived.
  • Table D ROC data points for Table B.
  • Ratio Cutoff Level Sensitivity 1 -Specificity _ No row 0 First row 0.2 0 First two rows 0.4 0 First three rows 0.6 0 First four rows 0.8 0 Ratio Cutoff Level Sensitivity 1 -Specificity First five rows 08 025 First six rows 1 0.25 First seven rows 1 0.5 First eight rows 1 0.75 First nine rows 1 1
  • the number of TP, FP, FN, and TN are counted in Table B when the condition is imposed that the classifier predicts that no specimen in Table B is positive for presence of the trait (e.g., disease or a particular biological phenotype).
  • This is not an accurate classifier as reflected in the respective sensitivity and specificity values of 0 and 1.
  • Plotting sensitivity by 1 -specificity yields the coordinate (0,0) as illustrated in the last row of Table D.
  • Figure 7 illustrates the ROC curve based upon the data points illustrated in Table D. As illustrated in Fig. 7, a ROC curve begins at coordinate (0,0) and ends at coordinate (1,1).
  • an area under the ROC curve can be quantified.
  • an area of 1.0 represents a molecular marker that is a perfect diagnostic indicator of the presence of absence of the trait.
  • an area of greater than 0.5 is desired for a diagnostic indicator, but it will depend upon the trait of interest. For example measurement of protein PSA levels currently used to diagnose prostate cancer has an ROC of 0.47.
  • the number of candidate molecular markers that remain can be set to any desired number by raising or lowering the criteria for eliminating molecular markers.
  • Steps 210 and 212 are optional and allows the additional removal of individual candidate molecular markers of step 206 by evaluating how each individual candidate molecular marker performs within a model which evaluates a combination of molecular markers prior to performing the evaluation of combinations in step 214/216.
  • step 210 all or a portion of the remaining candidate molecular markers in data structure 58 are used to generate a regression classifier.
  • measured data from step 206 for the molecular markers in two different trait subgroups in training population 44 are used.
  • the two different trait subgroups respectively represent a diseased and nondiseased state. In some embodiments, the two different trait subgroups respectively represent a first diseased state (e.g. cancer) and a second unrelated diseased state (e.g., Alzheimer's disease). In some embodiments, the two different trait subgroups represent those subjects that are responsive to drug therapy and those subjects that are not responsive to drug therapy. In still other embodiments, the two different trait subgroups from which molecular marker data is obtained represent data from subjects obtained a priori to treatment, but that have been classified into different trait subgroups on the basis of the ultimate response to treatment.
  • a first diseased state e.g. cancer
  • a second unrelated diseased state e.g., Alzheimer's disease
  • the two different trait subgroups represent those subjects that are responsive to drug therapy and those subjects that are not responsive to drug therapy.
  • the two different trait subgroups from which molecular marker data is obtained represent data from subjects obtained a priori to treatment, but that have been classified into
  • the two different trait subgroups respectfully represent two different stages of a disease (e.g., moderate versus advanced).
  • data for between ten and thirty candidate molecular markers in the two select trait subgroups is used in the logistic regression.
  • between twenty and one hundred candidate molecular makers in the two select trait subgroups is used in the logistic regression.
  • all the candidate molecular markers in the two select trait subgroups are used in the logistic regression.
  • logistic regression can be used because one of the dependent variables is binary - absence or presence of a particular phenotype.
  • step 210 For example, consider the case in which molecular marker data from a first trait subgroup and molecular marker data from a second trait subgroup is used in step 210.
  • the first trait subgroup is characterized by a first disease and the second trait subgroup is characterized by a second disease.
  • what can be considered by logistic regression is absence or presence of the first disease in subjects.
  • what can be considered by logistic regression is absence or presence of the second disease in subjects.
  • the multiple regression equation of interest can be written
  • Y the dependent variable
  • the dependent variable is presence (when Y is positive) or absence (when Y is negative) of the trait (e.g., phenotype, condition) associated with the first trait subgroup considered in step 204.
  • This classifier says that the dependent variable Y depends on k explanatory variables (the measured data values for the k candidate molecular markers from subjects in the first and second trait subgroups in training population 44), plus an error term that encompasses various unspecified omitted factors.
  • the parameter P 1 gauges the effect of the first explanatory variable X ⁇ on the dependent variable Y, holding the other explanatory variables constant.
  • ⁇ 2 gives the effect of the explanatory variable X 2 on Y, holding the remaining explanatory variables constant.
  • estimates for P 1 are obtained by taking into account how uncontrolled changes in other variables influence Y.
  • regression is used to eliminate at least some of the candidate molecular markers rather than relying entirely on the tests described in step 208 because the regression takes into account patterns in which multiple molecular markers influence the dependent variable (absence or presence of a trait) in a concerted fashion.
  • the dependent variable data is binary, logistic regression can be used.
  • the logistic regression classifier is a non-linear transformation of the linear regression.
  • the logistic regression classifier is termed the "logit" classifier and can be expressed as
  • the "logistic" distribution is an S-shaped distribution function.
  • the logit distribution constrains the estimated probabilities (p) to lie between 0 and 1.
  • the logistic regression classifier is fit by maximum likelihood estimation (MLE).
  • MLE maximum likelihood estimation
  • the coefficients ⁇ e.g., a, ⁇ i, ⁇ 2 , ...) are determined by maximum likelihood.
  • a likelihood is a conditional probability (e.g., P(Y
  • the likelihood function (L) measures the probability of observing the particular set of dependent variable values (Y 1 , Y 2 , ..., Y n ) that occur in the sample data set.
  • MLE involves finding the coefficients ( ⁇ , ⁇ i, ⁇ 2 , ...) that makes the log of the likelihood function (LL ⁇ 0) as large as possible or -2 times the log of the likelihood function (-2LL) as small as possible, hi MLE, some initial estimates of the parameters ⁇ , ⁇ i, ⁇ 2 , • • • are made. Then the likelihood of the data given these parameter estimates is computed. The parameter estimates are improved the likelihood of the data is recalculated. This process is repeated until the parameter estimates do not change much (for example, a change of less than .01 or .001 in the probability).
  • Step 212 In specific embodiments, all or a portion of the candidate molecular markers are used and the molecular marker data fit using logistic regression. Then, in a stepwise fashion, some of the molecular markers are eliminated from the classifier using backward stepwise regression. Backward stepwise regression begins with a full or saturated classifier and variables are eliminated from the classifier in an iterative process. The fit of the classifier is tested after the elimination of each variable (molecular marker) to ensure that the classifier still adequately fits the molecular marker data.
  • the regression applied in step 210 is used to refine the candidate molecular marker list to less than 25, less than 24, less than 23, less than 22, less than 21, or less than 20 molecular markers.
  • a logistic regression classifier is computed using all or a portion of the available candidate molecular markers in data structure 58. Then, coefficients are tested for significance for inclusion or elimination from the classifier using a WaId test, a likelihood-ratio test (chi-squared statistic), a Hosmer-Lemshow Goodness of Fit Test, or the like.
  • the likelihood-ratio test uses the ratio of the maximized value of the likelihood function for the full classifier (L 1 ) over the maximized value of the likelihood function for the simpler classifier (L 0 ) in which one or more molecular markers have been removed.
  • the likelihood-ratio test statistic equals:
  • Step 213 is optional. We have found that performing optional step 213 provides a significant improvement in identifying classifiers which are particularly useful in diagnosis of a disease or condition of interest.
  • clinically measurable parameters are identified which are thought to be relevant to the trait of interest for which a classifier is desired.
  • clinically measurable parameters chosen are those that are known or are shown to be relevant to the trait of interest.
  • clinically measurable parameters relevant to prostate cancer can include age of subject, level of prostate specific antigen (PSA); and volume of prostate.
  • PSA prostate specific antigen
  • some relevant clinically measurable parameters are age and body mass index (BMI).
  • BMI body mass index
  • the selected clinically measurable parameters are then included as part of the "selected set of candidate molecular markers" and are treated as molecular markers for the purpose of selecting combinations and developing classifiers in step 214 through 218 as described below.
  • the clinically measurable parameter In order to treat the clinically measurable parameter as a molecular marker for purposes of step 214 through 218, the clinically measurable parameter must have associated data.
  • the clinically measurable parameter is one which has an associated value - for example age, blood glucose level, PSA level, blood pressure, body mass index, etc.
  • the value can be treated as the molecular marker data for purposes of steps 214 through 218.
  • a value can be assigned to represent each aspect of the clinically measurable parameter. For example where the sex of a person is the clinically measurable parameter, a value of 1 can be assigned to represent that the person is male, and a value of 0 can be assigned to represent that the person is female.
  • the relevant clinically measurable parameter is ethnicity
  • a different value can be assigned to each ethnicity (e.g. 1 Caucasian, 2 asian, 3ashkanazi jew etc. and said value can be used as the molecular marker data associated with ethnicity for purposes of step 214 through 218.
  • Step 214 Steps 214 through 218 provide an approach in which all or a portion of the possible combinations of the selected set of candidate molecular markers resulting from steps 202- 213 are chosen.
  • Molecular marker data from each candidate molecular marker in a elected combination is applied to a mathematical model as described more fully in Section 5.14. If there are N selected molecular markers at this stage then, in some embodiments, as many as 2N- 1 different combinations can be selected and classifiers can be computed for each of these combinations. For example, consider the case in which three molecular markers are selected after any combination of steps 201 through 212 have been performed and logistic regression is used in step 216. In this case, the following 23-1 mathematical models can be used to form 23-1 corresponding classifiers:
  • ⁇ n[p/(l -p)] a+j3 1 X l + ⁇ 2 X 2 + ⁇ 3 X 3 + ⁇
  • ⁇ , ⁇ i, ⁇ 2 , ..., ⁇ N represent coefficients that are regressed against molecular marker data whereas X 1 , X 2 , ..., X N each represent a different RNA or protein (or more generally, a molecular marker) for which molecular marker data is available.
  • any one of elements X 1 , X 2 , ..., X N can represent a clinically measurable parameters.
  • at least one of the molecular markers of the series OfX 1 , X 2 , ..., X N does not represent a clinically measurable parameter.
  • additional interaction terms are also considered, producing non linear behaviour and resulting in greater than or less than as 2 N 1 different combinations.
  • additional interaction terms are also considered, producing non linear behaviour.
  • more than 2 N -1 "combinations" and thus more than 2 N -1 classifiers are considered.
  • nonlinear variables include variables that are squared, squared rooted, or in fact, taken to any power.
  • a logarithmic or exponential function is applied to one or more of the variables.
  • ratios of molecular marker data can be used as a mathematical model. For example, consider the case in which regression is used to apply molecular marker data to the following equation in order to develop a classifier:
  • X 1 , X 2 , ..., Z N each represent the product of a different gene (or more generally, a molecular marker or molecular marker like element) for which molecular marker data is available.
  • each X 1 , X 2 , ..., X N can in fact represent a ratio of abundance and/or expression levels for two different molecular marker products e.g. RNA or proteins, or any other type of molecular marker.
  • X 1 can represent the ratio between molecular marker data measured in step 206 representative of gene (molecular marker) A and data for gene B in training population 44. Mixed forms of mathematical models are also possible.
  • variables X can represent ratios between the molecular marker data of two molecular markers whereas other variables X can represent molecular marker data of discrete molecular markers as opposed to ratios of molecular marker data.
  • a "ratio" of the RNA products of two molecular markers can be used and all or some of the possible combinations of said "ratios" can be utilized.
  • the molecular marker data is a measure of abundance of RNA is determined using quantitative RT-PCR
  • the measure of the expression level of gene (molecular marker) A and the measure of the expression level of gene B can be used as a single term.
  • ⁇ Ct Ctgene A - Ct ⁇ actin
  • Ctgene A is the threshold cycle of amplification of GeneA
  • Ct ⁇ actin is the threshold cycle of amplification of the internal control ⁇ -actin.
  • each term represents the "ratio" of two molecular markers.
  • the Ct scores are compared directly rather than compared with an internal control. For example, consider the case in which the desire is to form a classifier that discriminates between a first trait subgroup and a second trait subgroup wherein each term of the classifier represents molecular marker data which is derived from a combination of molecular markers using ratios as outlined above (e.g.
  • each variable actually represents two molecular markers - the ratio of the molecular marker data for two molecular markers. Therefore in instances, for example, where 10 molecular markers have been identified by the funneling process of steps 202-210, 45 possible combinations of "ratios" of molecular markers can be formed e.g. n! (n-2)!2! where n is the number of possible molecular markers (e.g. 10).
  • molecular markers whose molecular marker data can be combined as ratios are first identified.
  • the use of molecular marker data as variables in which the variable representing the product of the molecular marker is in the form of ratios or raised to some arbitrary power ⁇ e.g., ⁇ , 2, N, etc.) is not limited to mathematical models based on regression. Such variables can be used in any of the mathematical models described herein (e.g., neural networks).
  • a first set of molecular markers and a second set of molecular markers are selected to create ratios such that the first set of molecular marker data demonstrates the molecular marker is upregulated in the first trait subgroup (relative to the second trait subgroup) in training population 44 and a second set of molecular marker data demonstrates that the molecular marker is downregulated in the first trait subgroup in training population 44 (relative to the second trait subgroup).
  • the term upregulated or downregulated generally means that such up or down regulation is observed in the training population with some measure of statistical confidence, for example a Mest having ap value of 0.05, less than 0.01, less than 0.005, less than 0.001, less than 0.0005, less than 0.0001, less than 0.00005, less than 0.00001, or less.
  • ratios of molecular markers can be formed using one molecular marker from the first set and a second molecular marker from the second set.
  • a ratio for use in a selected combination is one in which the numerator represents the molecular marker data that demonstrates the molecular marker is upregulated in a first trait subgroup (as compared with a second trait subgroup) and the denominator represents the molecular marker data that demonstrates the molecular marker is upregulated in a second trait subgroup (as compared with a first trait subgroup).
  • a value greater than "1 " indicates that the organism from which the molecular marker data was measured is a member of the first trait subgroup whereas a value less than "1" indicates that this organism is a member of the second trait subgroup.
  • step 214 comprises obtaining combinations of ratios of molecular marker data.
  • step 214 comprises obtaining some multiple of molecular markers and forming a plurality of ratios of the molecular marker data so as to generate a plurality of combinations of molecular markers.
  • a combination of molecular markers is selected.
  • this combination of molecular markers consists of a single molecular marker.
  • this combination of molecular markers comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, as many as 30, as many as 40, as many as 50 or more molecular markers.
  • this combination of molecular markers consists of a combination of ratios of molecular markers wherein the combination comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, as many as 30, as many as 40, as many as 50 or more molecular markers.
  • the number of possible combinations grows exponentially. The limitation to the number of combinations selected for evaluation is dependent upon the capacity of the computer, network of computers or supercomputers utilized.
  • all possible combinations of molecular markers resulting from steps 202-213 or resulting from some subset of steps 202-213) are chosen, hi another embodiment, all possible combinations of ratios of molecular markers resulting from steps 202-213 (or resulting from some subset of steps 202-213) are chosen.
  • all possible combinations of two sets of ratios are chosen, in another embodiment, all possible combinations of three sets of ratios are chosen, in another embodiment, all possible combinations of four sets of ratios are chosen, in another embodiment, all possible combinations of five sets of ratios are chosen.
  • Each of the combinations of molecular markers is evaluated in subsequent processing steps. Step 216.
  • a classifier is computed using each combination of molecular markers chosen in the last instance of step 214 and by applying the classifier to the molecular marker data measured for each molecular marker of this combination of molecular markers to a mathematical model, such as the mathematical models defined in Section 5.14 resulting in one or more classifiers for each combination.
  • a mathematical model such as the mathematical models defined in Section 5.14 resulting in one or more classifiers for each combination.
  • one or more subjects of the training population are identified as outliers and are removed prior to computing classifiers for each combination of molecular markers chosen.
  • the mathematical model is a regression model, a neural network, a clustering model, principal component analysis, nearest neighbor classifier analysis, linear discriminant analysis, quadratic discriminant analysis, a support vector machine, a decision tree, a genetic algorithm, classifier optimization using bagging, classifier optimization using boosting, classifier optimization using the Random Subspace Method, Bayesian Networks (see F. V. Jensen. "Bayesian Networks and Decision Graphs". Springer. 2001, which is incorporated herein by reference in its entirety), a projection pursuit, weighted voting, a ratio or combination of ratios, or any combination of the above.
  • Representative mathematical models that can be used in the present invention are described in Section 5.14.
  • steps 214 and 216 involve using the low throughput molecular marker data from training population 44 that was measured in step 206 to determine which molecular markers should be in the numerator of the ratios and which molecular markers should be in the denominator of the ratios.
  • the mathematical model used comprises a plurality of ratios of the molecular marker data.
  • the molecular marker data used in the numerators of the plurality of ratios can be the same or different than the molecular marker data used in the denominators of the plurality of ratios.
  • a given molecular marker can be represented in the numerator of more than one ratio in the plurality of ratios or represented in the denominator of more than one ratio in the plurality of ratios.
  • Step 218 a determination is made as to whether all of the possible desired combinations of molecular markers to be tested have been considered. As discussed above in step 214, all or a portion of possible combinations may be tested. If not (218-No) process control returns to step 214 where another combination of molecular markers is selected and, at step 216, this new combination of molecular markers is evaluated using a mathematical model applied to the molecular marker data of the new combination.
  • the candidate molecular marker list comprises less than 25, less than 24, less than 23, less than 22, less than 21, or less than 20 molecular markers at step 214.
  • step 218 requires that a classifier be computed for all possible combinations of molecular markers. In other embodiments, step 218 requires that classifiers for only a portion of the possible combinations of molecular markers be considered.
  • some aspects of steps 214-218 are performed by molecular marker classifier evaluation module 62. In fact, in some embodiments, several different software programs, such as Microsoft (Redmond, Washington) Excel, are used in steps 214-218. Step 220.
  • the classifiers are evaluated to determine which of the classifiers are most effective.
  • the resulting classifiers of loop 214-218 are scored.
  • scoring is done using the training population 44.
  • scoring is done using a "scoring population" wherein the scoring population includes at least some members not present in the training population.
  • the scoring population includes members of the training population in addition to one or more members not used in the training population. In some embodiments, five percent or less, ten percent or less, twenty percent or less, thirty percent or less, fifty percent or less, or ninety percent or less of the members of the training population are common to the scoring population.
  • the Percent Correct Predictions statistic is used to score each classifier.
  • the "Percent Correct Predictions" statistic assumes that if the estimated p is greater than or equal to 0.5, then the event is expected to occur and to not occur otherwise. By assigning these probabilities zeros and ones, a comparison can be made to the values of the samples in the training population to determine what percentage of the training population was sampled correctly.
  • ROC analysis is performed and is used to score the classifiers.
  • the area under the ROC curve is used to judge the quality of the classifier. As would be understood by those of skill in the relevant arts, area under the curve converts the two dimensional information contained in the ROC curve into one dimensional information.
  • information from the two dimensional aspect of the ROC curve is utilized directly.
  • the ROC curve also provides information with respect to the sensitivity and specificity of the classifier.
  • classifiers are selected on the basis of either sensitivity or specificity. This can be an important scoring indicator. For example, a diagnostic classifier with high sensitivity (ie high true positive rate and low false negative rate may be important in situations where it is safer to misdiagnosis an individual as having disease rather than misdiagnosing a disease bearing person as normal. Therefore in some embodiments, a cutoff can be set for either sensitivity or specificity and the classifier ranked or scored on the basis of the remaining variable.
  • ROC curves are generated for each model computed in an instance of step 216 using data obtained in step 206 for members of either the training population or scoring population or both.
  • the classifier resulting from the application of the mathematical itself results in a score as to the accuracy of the model. In this embodiment, the score is based on the accuracy of the model within the training population only.
  • a classifier is a weighted logistic regression model characterized by a multicategory logit model. For example, in some embodiments, a classifier discriminates between two different trait groups. In other embodiments, a classifier discriminates between more than two different trait groups.
  • Logit models including multicategory logit models are described in Agresti, An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc., 1996, New York, Chapters 7 and 8, which is hereby incorporated by reference.
  • Table E illustrates the data that is used to form an ROC curve based on expression data applied to a mathematical model that uses the logit:
  • Table E Each row in Table E corresponds to a different specimen in the scoring population.
  • the left column represents the results of the logit for the classifier being sampled.
  • the specimens in Table E are ranked by the logit score listed in the left hand column.
  • the right hand column details the presence or absence of the trait that is being considered by the regression equation.
  • Table E can be used to compute a ROC curve using the same techniques disclosed in step 208 (in which each row in Table E is considered a threshold cutoff value in order to compute ROC curve datapoints). Then, the area under the ROC curve can be computed in order to assess the predictive quality of the classifier.
  • each classifier is scored using any of the techniques disclosed here or that are known in the art. The classifiers can then be ranked based on their score.
  • step 220 is performed by molecular marker evaluation module 64.
  • Step 222 is optional.
  • Optional step 222 provides additional filtering to eliminate some of the candidate classifiers computed in loop 214-218. In one such filter, limited to the case in which the classifiers computed in steps 214-218 are based on application of data to regression based mathematical models, classifiers that have at least one coefficient that is large are eliminated. Such classifiers have the potential to magnify small errors in the data.
  • determination as to whether or not a coefficient is large can require multiple computation steps, hi instances where a coefficient uniquely represents a molecular marker, the maximum value (MAX) for the data measured for the molecular marker in a trait subgroup associated with the classifier is identified. For example, consider the case in which a given coefficient uniquely represents the expression of gene A in blood. Further suppose that low throughput data for gene A from 10 individuals of a particular trait subgroup was measured in step 206. The value MAX would be the largest expression value observed for gene A in the ten individuals from the subject trait subgroup. For example, if individual #7 in the set often individuals exhibited the highest expression level for gene A as determined by the methods of step 206, then the expression value measured for gene A in individual #7 will represent MAX.
  • the minimum value (MIN) for the data measured for the molecular marker in the subject trait subgroup is identified.
  • MIN is the expression level of gene A in the subject having the lowest expression level for gene A in the set often subjects as determined by the low throughput measurement methods of step 206.
  • the coefficient derived in the regression for the unique molecular marker e.g., the coefficient for gene A
  • TEST the test value
  • a classifier is used to determine whether or not a subject has a particular cancer.
  • one of the trait subgroups in training population 44 will represent patients that have this cancer.
  • the low value and high value for the measured data of the molecular marker i in the trait subgroup is obtained and the difference between these two values is multiplied against the coefficient value in order to obtain the value of TEST,.
  • a coefficient / is considered large and the classifier that includes the coefficient is discarded when the value is greater than 5, greater than 10, greater than 100 or greater than 1000.
  • a classifier will determine whether a subject falls into one of at least two different trait subgroups.
  • the classifier will discriminate between at least two different trait subgroups.
  • a test has been presented above for determining whether a coefficient in a regression derived classifier is too large. This test used one of the trait subgroups that the classifier discriminates. In some embodiments, the test is repeated for each of the trait subgroups that the classifier can discriminate. For example, in the case of a classifier that can discriminate between the cancerous trait subgroup and the non-cancerous trait subgroup, the test is independently run using data from each trait subgroup. That is, the test is first run using only data from the cancerous trait subgroup and then the test is run a second time using only data from the non ⁇ cancerous trait subgroup.
  • the classifier is eliminated from further consideration.
  • the test is run against only one of the possible trait subgroups that the subject classifer can discriminate.
  • some classifiers are eliminated on the basis of the score. For example, where the scoring system used is receiver operating characteristic (ROC) curve score determined by an area under the ROC curve, in some embodiments, those classifiers with scores of less than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0.45 or less can be eliminated.
  • ROC receiver operating characteristic
  • a sensitivity threshold can be set and classifiers ranked on the basis of the specificity, for example with a cutoff for specificity of less than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0.45 or less can be eliminated.
  • the specificity threshold can be set and classifiers ranked on the basis of sensitivity for example with a cutoff for sensitivity of less than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0.45 or less can be eliminated.
  • the top 10 ranking classifiers, the top 20 ranking classifiers, or the top 100 ranking classifiers are selected and the remaining classifiers eliminated.
  • Step 224 After classifiers have been scored and ranked and some classifiers optionally eliminated; one or more classifiers can be combined to create a classifier group. For instance, in some embodiments, the top 10 ranking classifiers, the top 20 ranking classifiers, or the top 100 ranking classifiers are selected. In some embodiments, any of the top 1 to top 500 ranking classifiers is selected, hi instances where more than one classifier is selected, in one embodiment, each classifier contributes one vote to the diagnosis of the test subject such that diagnosis of the test subject is determined as a result of a combination of classifiers.
  • weighting schema can include weighting on the basis of factors such as the original score the classifier, the logs odd ratio ("logit"), the size of the coefficients for each classifier, some combination thereof and the like.
  • Step 226 is optional.
  • Optional step 226 is useful if training population 44 is comprised of more than two trait subgroups. In cases where there are more than two trait subgroups, multiple binary classifiers (or groups of said classifiers) can be developed wherein each binary classifier (or group of said classifiers) is directed towards differentiating as between two traits. In one embodiment, each round of the funnel (e.g.
  • steps 202, 204, 206, 214, 216, 220 and 224) produces a set of binary classifiers.
  • multiple lists of binary candidate molecular markers are developed by performing step 202, and then binary classifiers (or groups of binary classifiers) are developed by proceeding with multiple rounds of the remainder of the funnel (e.g. steps 204, 206, 214, 216, 220 and step 224). Because each classifier represents only a binary test e.g. the absence or presence of a single trait, in step 226 a determination is made as to whether all classifiers have been developed for training population 44. If not (226-No), process control returns to step 210 and work is initiated to develop a classifier for a different trait represented by training population 44.
  • steps 210-224 can optionally be repeated until one or more classifiers or groups of classifiers have been selected for each of the trait subgroups represented by training population 44.
  • each classifier or group of classifiers developed in accordance with embodiments of the present invention is stored in classifier database 70.
  • Fig. 5 illustrates an exemplary classifier database 70 in accordance with one embodiment of the present invention.
  • Database 70 includes an entry 400 for each classifier 400.
  • Each classifier 400 is optionally given a classifier name 402.
  • Each classifier 400 is part of classifier.
  • a given classifier can consist of only a single classifier. In other embodiments, a given classifier can consist of one or a plurality of classifiers.
  • each classifier 402 includes an indicator 403 to indicate which classifier the classifier is in.
  • each classifier 400 has an optional indicator 404 to indicate the trait that the classifier can discriminate.
  • optional indicator indicates the trait subgroups that the classifier can discriminate.
  • such information can be inferred from the classifier identifier field 403 since each classifier represents the absence or presence of a particular trait (e.g., absence or presence of cancer).
  • each classifier includes the identity 406 of one or more molecular markers and the respective coefficients 408 for each of the molecular markers.
  • Step 3 is a flowchart of a method of applying the classifiers to a patient.
  • Step 328 can be performed after the previously described steps, or can be used in conjunction with a classifier or classifier groups derived using the methods disclosed herein.
  • steps 328-334 represent a completely independent method of the present invention and can be performed at any time once suitable classifiers have been developed using, for example, steps 301 through 326.
  • Step 328 is used in conjunction with step 330 to diagnose a trait of interest of an individual not represented in either the training population or the scoring population (a "test individual").
  • Each classifier or classifier group identified previously can be used to determine whether a test individual has a trait of interest.
  • molecular marker data for each molecular marker of the classifier or classifier group of interest is required.
  • a sample of blood from the subject is obtained using any of the techniques described in Section 5.2.
  • the sample is used to measure molecular marker data for each molecular marker in the sample using any of the techniques described in Sections 5.3 or 5.4.
  • molecular marker data for use with the classifier or classifier groups can be obtained using either high throughput or low throughput techniques.
  • the molecular marker data obtained in step 328 can be stored in patient database 68 for later use.
  • the data is obtained from a subject in patient database 68.
  • the molecular marker data was previously loaded into patient database 68.
  • Fig. 6 illustrates a patient database 68 in accordance with one embodiment of the present invention.
  • Each patient record 500 optionally includes a patient identifier 502 to uniquely identify the patient. In some embodiments such unique identifiers can be inferred from the patient record value 500.
  • Each patient record 500 includes a molecular profile 504 comprising molecular marker data collected for a plurality of molecular marker products from a sample defined in Section 5.2 using any one of the techniques described in Sections 5.3 and 5.4.
  • a molecular profile 504 includes a plurality of molecular marker identities 506 and the corresponding measured molecular marker data values 508 for such molecular markers.
  • each patient record 502 can include one or more traits 510. Such trait characterizations can be assigned by observation of the subject and/or by testing the patient's molecular profile using the classifiers constructed in accordance with the methods of the present invention. Section 5.10, below, provides more details on exemplary patient databases 68. Step 330.
  • step 330 the classifier created using some or more of the previous steps is used to diagnose a test individual.
  • diagnosis can be performed using a classifier group from step 224.
  • a test subject can be diagnosed by using the results of all or some of these classifiers in the form of a classifier group as described in step 224.
  • diagnosis means the results of a single classifier or group of classifiers resulting from the application of the funneling method described in steps 202-224.
  • the resulting classifier or group of classifiers will enable the ability to determine whether a test individual belongs to one of two possible trait subgroups.
  • diagnosis is meant the results of multiple classifiers or multiple groups of classifiers (ie classifiers resulting from the application of more than one round of the funneling method described in steps 202 — 224).
  • the resulting classifiers or groups of classifiers used in series can allow a diagnosis as to whether an individual belongs to one of three or more possible trait subgroups (e.g. results of first classifier distinguish as to whether person has schizophrenia or does not have schizophrenia - If not schizophrenia apply a second classifier or group of classifiers to determine whether individual has bipolar disorder or does not have bipolar disorder etc.)
  • the use of the classifiers to diagnose depends upon the trait subgroups used to develop the classifier.
  • the classifier can be used to diagnose a test subject as being either of the first trait subgroup or the second trait subgroup.
  • a quantitative technique such as quantitative RT-PCR is utilized to obtain molecular marker data measured in step 328 is used.
  • the exemplary classifier group consisting of two classifiers.
  • the data e.g. abundance level, activity level, etc.
  • these data measurement values are placed into the classifier equations and the equations are processed and the output is used to predict outcome
  • classifier equation values that approach a value of one indicate that the sample has the trait associated with the classifier whereas classifier equation values that approach zero indicate that the sample does not have the trait associated with the classifier.
  • the equations are regressed so that the opposite relationship hold (e.g., equation values approach one indicate absence of an associated trait).
  • each equation is assigned a "+1" vote if the equation approaches one or a "-1" vote if the equation approaches zero. Equation votes are summed. If the net summation is positive, then the subject is deemed to have the trait associated with the classifier. If the net summation is negative, then the subject is deemed not to have the trait associated with the classifier.
  • step 330 is performed by model polling and reporting module 66. Step 332.
  • step 332 a determination is made as to whether the patient has been tested for each pair of traits for which a determination is required. If additional determinations are required (332-No), process control is returned to step 330 and the measured molecular marker data from the patient is used to help determine the likelihood as to whether a subject has other traits represented by a trait subgroup in training population 44.
  • Step 334 When the patient sample has been used to determine if the subject has any of a plurality of different traits as determined by one or more classifiers or classifier groups, a report is generated. In some embodiments, this report includes the results of each classification test. In other words, the report provides an indication as to whether it is likely the tested subject has any one of a plurality of different traits.
  • step 334 is performed by classifier polling and reporting module 66. Section 5.5 provides a summary of some of the applications for classifiers constructed using the methods of the present invention. 5.2 SOURCE OF MOLECULAR MARKER DATA
  • the present invention provides methods for identifying molecular markers by obtaining molecular marker data which represents the products of molecular markers found in a blood sample. Molecular markers are thus identified that correlate with, are associated with, or indicate a trait.
  • the present invention also provides methods for detecting, diagnosing, monitoring, prognosing or predicting a trait or reoccurrence of a trait based upon data corresponding to the expression of molecular markers in a blood sample.
  • the terms "subject” and “patient” and “individual” are used interchangeably to refer to an animal (e.g., a mammal, a fish, an amphibian, a reptile, a bird, and an insect).
  • a subject is a mammal (e.g., a non-human mammal and a human).
  • a subject is a pet (e.g., a dog, a cat, a guinea pig, a monkey and a bird), a farm animal (e.g., a horse, a cow, a pig, a goat and a chicken) or a laboratory animal (e.g., a mouse and rat).
  • the subject is a primate (e.g., a chimpanzee and a human).
  • the subject is a human. 5.2.1 SOURCEOFABLOODSAMPLE
  • a blood sample obtained from any subject may be used in accordance with the methods of the invention.
  • subjects from which a blood sample can be obtained and utilized in accordance with the methods of the invention include, but are not limited to, asymptomatic subjects, subjects manifesting or exhibiting 1, 2, 3, 4 or more traits or symptoms of a trait, subjects clinically diagnosed as having a trait, subjects predisposed to a trait (e.g., subjects with a family history of a trait, subjects with a genetic predisposition to a trait, and subjects that lead a lifestyle that predisposes them to a trait or increases the likelihood of contracting a trait), subjects suspected of having a trait, subjects undergoing therapy for a trait, subjects non-responsive to a therapy, subjects responsive to a therapy, subjects with more than one trait (e.g., subjects with 2, 3, 4, 5 or more traits), subjects not undergoing therapy for a trait, subjects determined by a medical practitioner (e.g., a physician) to be healthy or disease-free, subjects that are in remission, subjects cured
  • condition is a disease.
  • a condition is any state that is codified in the International Classification of Diseases, 9 th Revision, Department of Health and Human Services (ICD-9 codes) and/or SNOMED Clinical Terms (SNOMED CT®) which is hereby incorporated by reference, or equivalent treatise.
  • Non-limiting examples of disease include, but are not limited to, blood disorder, blood lipid disease, autoimmune disease, arthritis (e.g., osteoarthritis, rheumatoid arthritis, juvenile rheumatoid arthritis and the like), bone or joint disorder, lupus, an allergy, a cardiovascular disorder (e.g., heart failure, congenital heart disease, rheumatic fever, valvular heart disease, pumonale, cardiomyopathy, myocarditis, pericardial disease, vascular diseases such as atherosclerosis, acute myocardial infarction, ischemic heart disease and the like), obesity, respiratory disease (e.g., asthma, pneumonitis, pneumonia, pulmonary infections, lung disease, bronchiectasis, tuberculosis, cystic fibrosis, interstitial lung disease, chronic bronchitis emphysema, pulmonary hypertension, pulmonary thromboembolism, acute respiratory distress syndrome and the like), hyperlipidemias,
  • the disease is an immune disorder, such as those associated with overexpression of a gene or expression of a mutant gene (e.g., autoimmune diseases, such as diabetes mellitus, arthritis (including rheumatoid arthritis, juvenile rheumatoid arthritis, osteoarthritis, and psoriatic arthritis), multiple sclerosis, encephalomyelitis, myasthenia gravis, systemic lupus erythematosis, automimmune thyroiditis, dermatitis (including atopic dermatitis and eczematous dermatitis), psoriasis, Sjogren's Syndrome, Crohn's disease, aphthous ulcer, ulceris, conjunctivitis, keratoconjunctivitis, ulcerative colitis, asthma, allergic asthma, cutaneous lupus erythematosus, scleroderma, vaginitis, proctitis, drug eruptions, lepros
  • autoimmune diseases such
  • a disease of the invention is a cellular proliferative and/or differentiative disorder that includes, but is not limited to, cancer, e.g., carcinoma, sarcoma or other metastatic disorders and the like.
  • cancer refers to cells having the capacity for autonomous growth, i.e., an abnormal state of condition characterized by rapidly proliferating cell growth.
  • cancer is meant to include all types of cancerous growths, pre-cancerous growths or lesions, oncogenic processes, metastatic tissues or malignantly transformed cells, tissues, or organs, irrespective of histopathologic type or stage of invasiveness.
  • cancers include, but are not limited to, solid tumors, tissue specific tumors, benign cancer, metastatic cancers, early stage cancer, late stage cancer and leukemias, including: apudoma, choristoma, branchioma, malignant carcinoid syndrome, carcinoid heart disease, carcinoma (e.g., Walker, basal cell, basosquamous, Brown-Pearce, ductal, Ehrlich tumour, in situ, Krebs 2, Merkel cell, mucinous, non-small cell lung, oat cell, papillary, scirrhous, bronchiolar, bronchogenic, squamous cell, and transitional cell), histiocytic disorders, leukaemia (e.g., B cell, mixed cell, null cell, T cell, T-cell chronic, HTLV-II-associated, lymphocytic acute, lymphocytic chronic, mast cell, and myeloid), histiocytosis malignant, Hodgkin disease, immunoprolifer
  • a sample of blood is obtained from a subject according to methods well known in the art.
  • the present invention can use whole blood, but can also use blood in which the serum or plasma has been removed and the RNA or mRNA isolated from the remaining sample in accordance with methods known in the art (for example, using preferably gentle centrifugation at 300-800xg for five to ten minutes).
  • a drop of blood is collected from a simple pin prick made in the skin of a subject. In such embodiments, this drop of blood collected from a pin prick is all that is needed.
  • a drop of blood can include volumes of anywhere from lOul through to 10OuI.
  • Blood may be drawn from a subject from any part of the body (e.g., a finger, a hand, a wrist, an arm, a leg, a foot, an ankle, a stomach, and a neck) using techniques known to one of skill in the art, in particular methods of phlebotomy known in the art.
  • venous blood is obtained from a subject and utilized in accordance with the methods of the invention.
  • arterial blood is obtained and utilized in accordance with the methods of the invention.
  • the composition of venous blood varies according to the metabolic needs of the area of the body it is servicing. In contrast, the composition of arterial blood is consistent throughout the body. For routine blood tests, venous blood is generally used.
  • Venous blood can be obtained from any source including the basilic vein, cephalic vein, or median vein.
  • Arterial blood can be obtained from the radial artery, brachial artery or femoral artery.
  • a vacuum tube, a syringe or a butterfly may be used to draw the blood.
  • the puncture site is cleaned, a tourniquet is applied approximately 3-4 inches above the puncture site, a needle is inserted at about a 15-45 degree angle, and if using a vacuum tube, the tube is pushed into the needle holder as soon as the needle penetrates the wall of the vein.
  • the needle is removed and pressure is maintained on the puncture site.
  • heparin or another type of anticoagulant is in the tube or vial that the blood is collected in so that the blood does not clot.
  • anesthetics can be administered prior to collection.
  • the amount of blood collected will vary depending upon the site of collection, the amount required for a method of the invention, and the comfort of the subject.
  • an advantage of one embodiment of the present invention is that the amount of blood required to implement the methods of the present invention can be so small that more invasive procedures are not required to obtain the sample.
  • all that is required is a drop of blood. This drop of blood can be obtained, for example, from a simple pinprick.
  • any amount of blood is collected that is sufficient to measure molecular marker data.
  • the amount of blood that is collected is 1 ⁇ l or less, 0.5 ⁇ l or less, 0.1 ⁇ l or less, or 0.01 ⁇ l or less.
  • the present invention is not limited to such embodiments. In some embodiments more blood is available and in some embodiments, more blood can be used to effect the methods of the present invention. As such, a broad range of blood volumes is contemplated and can be used to obtain the molecular marker data measurement data used in the present invention.
  • 0.001 ml, 0.005 ml, 0.01 ml, 0.05 ml, 0.1 ml, 0.15 ml, 0.2 ml, 0.25 ml, 0.5 ml, 0.75 ml, 1 ml, 1.5 ml, 2 ml, 3 ml, 4 ml, 5 ml, 10 ml, 15 ml or more of blood is collected from a subject.
  • 0.001 ml to 15ml, 0.01 ml to 10 ml, 0.1 ml to 10 ml, 0.1 ml to 5 ml, 1 to 5 ml of blood is collected from a subject.
  • blood is stored within a K3/EDTA tube.
  • tubes for storing blood which contain stabilizing agents such as disclosed in United States patent No. 6,617,170.
  • the PAXgeneTM blood RNA system provided by PreAnalytiX, a Qiagen/BD company, can be used to collect blood.
  • the TempusTM blood RNA collection tubes offered by Applied Biosystems, can be used. TempusTM collection tubes provide a closed evacuated plastic tube containing RNA stabilizing reagent for whole blood collection.
  • the collected blood is optionally but preferably stored at refrigerated temperatures, such as 4 °C, prior to molecular marker data measurement.
  • a portion of the blood sample is used for molecular measurement at a first instance of time whereas one or more remaining portions of the blood sample is stored for a period of time for later use.
  • This period of time can be an hour or more, a day or more, a week or more, a month or more, a year or more, or indefinitely.
  • storage methods well known in the art such as storage at cryo temperatures (e.g. below -60 °C) can be used, hi some embodiments, in addition to storage of the blood (or instead of storage oft he blood), isolated molecular markers (e.g., nucleic acid, protein, carbohydrates, lipids, metabolites, etc.) are stored for a period of time for later use. Storage of such molecular markers can be for an hour or more, a day or more, a week or more, a month or more, a year or more, or indefinitely. 5.2.3 METHOD OF ISOLATING BLOOD CELLS
  • fractionated blood is meant blood in which the blood cells are separated prior to isolation of the molecular markers using techniques known in the art.
  • fractionated blood includes blood wherein the blood cells are fractionated using Ficoll-Hypaque (Pharmacia) gradient centrifugation. Such centrifugation separates erythrocytes (red blood cells) from various types of nucleated cells and from plasma.
  • a blood sample of the invention is fractionated blood.
  • peripheral blood leukocytes are utilized ("PBLs").
  • PBLs are separated from the remainder of the blood using a Ficoll® gradient.
  • macrophages can be obtained as follows. Mononuclear cells are isolated from peripheral blood of a subject, by syringe removal of blood followed by Ficoll-Hypaque gradient centrifugation. Tissue culture dishes are pre-coated with the subject's own serum or with AB+ human serum and incubated at 37°C for one hour. Non-adherent cells are removed by pipetting. Cold (4°C) ImM EDTA in phosphate-buffered saline is added to the adherent cells left in the dish and the dishes are left at room temperature for fifteen minutes.
  • the cells are harvested, washed with RPMI buffer and suspended in RPMI buffer. Increased numbers of macrophages can be obtained by incubating at 37 0 C with macrophage-colony stimulating factor (M-CSF).
  • M-CSF macrophage-colony stimulating factor
  • Antibodies against macrophage specific surface markers, such as Mac- 1 5 can be labeled by conjugation of an affinity compound to such molecules to facilitate detection and separation of macrophages.
  • Affinity compounds that can be used include but are not limited to biotin, photobiotin, fluorescein isothiocyante (FITC) 5 or phycoerythrin (PE), or other compounds known in the art.
  • FACS Fluorescence activated cell sorting
  • An antibody or ligand used to detect a blood cell antigenic determinant present on the cell surface of particular blood cells is labeled with a fluorochrome, such as FITC or phycoerythrin.
  • the cells are incubated with the fluorescently labeled antibody or ligand for a time period sufficient to allow the labeled antibody or ligand to bind to cells.
  • the cells are processed through the cell sorter, allowing separation of the cells of interest from other cells.
  • FACS sorted particles can be directly deposited into individual wells of microtiter plates to facilitate separation. Magnetic beads can be also used to separate blood cells in some embodiments of the present invention.
  • blood cells can be sorted using a magnetic activated cell sorting (MACS) technique, a method for separating particles based on their ability to bind magnetic beads (0.5 100 Dm diameter).
  • MCS magnetic activated cell sorting
  • a variety of useful modifications can be performed on the magnetic microspheres, including covalent addition of an antibody which specifically recognizes a cell solid phase surface molecule or hapten.
  • a magnetic field is then applied, to physically manipulate the selected beads.
  • antibodies to a blood cell surface marker are coupled to magnetic beads.
  • the beads are then mixed with the blood cell culture to allow binding.
  • Cells are then passed through a magnetic field to separate out cells having the blood cell surface markers of interest. These cells can then be isolated.
  • the surface of a culture dish may be coated with antibodies, and used to separate blood cells by a method called panning. Separate dishes can be coated with antibody specific to particular blood cells. Cells can be added first to a dish coated with blood cell specific antibodies of interest. After thorough rinsing, the cells left bound to the dish will be cells that express the blood cell markers of interest.
  • cell surface antigenic determinants or markers include, but are not limited to, CD2 for T lymphocytes and natural killer cells, CD3 for T lymphocytes, CDl Ia for leukocytes, CD28 for T lymphocytes, CD 19 for B lymphocytes, CD20 for B lymphocytes, CD21 for B lymphocytes, CD22 for B lymphocytes, CD23 for B lymphocytes, CD29 for leukocytes, CD 14 for monocytes, CD41 for platelets, CD61 for platelets, CD66 for granulocytes, CD67 for granulocytes and CD68 for monocytes and macrophages.
  • Whole blood can also be fractioned into cells types such as leukocytes, platelets, erythrocytes, etc.
  • Leukocytes can be further separated into granulocytes and agranulocytes using standard techniques.
  • Granulocytes can be separated into cell types such as neutrophils, eosinophils, and basophils using standard techniques.
  • Agranulocytes can be separated into lymphocytes (e.g., T lymphocytes and B lymphocytes) and monocytes using standard techniques.
  • T lymphocytes can be separated from B lymphocytes and helper T cells separated from cytotoxic T cells using standard techniques.
  • Separated blood cells e.g., leukocytes
  • the term "blood sample” can include any of the samples discussed in section 5.2.3. In some embodiments, this includes whole blood, fractionated blood, a sample of subsets of fractionated blood, and a sample of specific types of blood cells. In a specific embodiment, the whole blood sample can have the plasma or serum removed by centrifugation, using preferably gentle centrifugation at 30O-800xg for five to ten minutes.
  • a blood sample of the invention is a sample of peripheral blood leukocytes (PBLs). In another embodiment, a blood sample of the invention is a sample of granulocytes.
  • a blood sample of the invention is a sample of neutrophils, eosinophils, basophils or any combination thereof.
  • a blood sample of the invention is a sample of agranulocytes.
  • a blood sample of the invention is a sample of lymphocytes, monocytes or a combination thereof.
  • a blood sample of the invention is a sample of T lymphocytes, B lymphocytes or a combination thereof. See, e.g., Section 5.4.3 supra for methods of isolating blood cells.
  • a blood sample that is useful according to the invention is in an amount that is sufficient for the detection of one or more molecular markers according to the invention.
  • a blood sample useful according to the invention is in an amount ranging from 1 ⁇ l to 100 ml, preferably 10 ⁇ l to 50 ml, more preferably 10 ⁇ l to 25 ml and most preferably 10 ⁇ l to 1 ml.
  • whole blood, or serum free whole blood is taken and the redblood cells are lysed with lysing buffer.
  • the Lysis Buffer (IL) consists of 0.6g EDTA 1.Og KHCO 2 , and 8.2g NH 4 Cl adjusted to pH 7.4 (using NaOH).
  • lysed whole blood RNA or mRNA extracted in accordance with methods known in the art
  • lysed whole blood RNA or mRNA extracted in accordance with methods known in the art
  • the use of whole blood or lysed whole blood is preferred since it avoids the costly and time-consuming need to separate out the cell types within the blood (Liew et al. U.S. Patent Application No.US 2004/0014059).
  • RNA is isolated from whole blood which has been isolated using PAXgene® and additional globin reduction protocols followed. 5.3 METHODS FOR MEASURING MOLECULAR MARKER DATA
  • the techniques described in this section are particularly useful for obtaining molecular marker data for step 202 wherein the data is reflective of the products of the molecular marker -e.g. measurement of the abundance of RNA or protein products in blood corresponding to all of the molecular markers of the genome or "a portion thereof.
  • the techniques useful for step 202 are those techniques which allow the ability to comprehensively screen for candidate molecular markers quickly and effectively. These techniques preferably provide molecular marker data for a large number of molecular markers concurrently, thereby allowing greater ability to screen molecular markers corresponding to the entire genome, or a portion thereof in a short period of time.
  • RNA or protein corresponding to the entire genome or "a portion thereof can be used to measure such data.
  • a portion thereof is data corresponding to the amount of RNA or protein expressed from more than 1,000, more than 2,000, more than 5,000, more than 10,000, more than 20,000, more than 30,000 molecular markers.
  • the "a portion thereof refers to at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at leat 95% of the genome.
  • RNA MEASUREMENT TECHNIQUES See, e.g., Sambrook, Fritsch & Maniatis, 1982, Molecular Cloning: A Laboratory Manual; DNA Cloning: A Practical Approach, volumes I and II (D.N. Glover ed. 1985); Oligonucleotide Synthesis Gait ed., 1984; Nucleic Acid Hybridization, Hames & Higgins eds., 1985; Transcription and Translation, Hames & Higgins eds., 1984, Animal Cell Culture, Freshney ed., 1986, Immobilized Cells And Enzymes, IRL Press, 1986, Perbal, 1984, A Practical Guide To Molecular Cloning, each of which is hereby incorporated by reference in its entirety. In one embodiment more than technique can be used to measure data for each molecular marker to perform step 202. 5.3.1 RNA MEASUREMENT TECHNIQUES
  • RNA in blood corresponding to the molecular marker is measured.
  • corresponding to a molecular marker is meant RNA transcribed from a molecular marker (or proteins translated from RNA which is transcribed from a molecular marker when referring to protein products of a molecular marker).
  • RNA or protein which corresponds to a molecular marker are also considered the product of the molecular marker.
  • the level of an RNA product is measured using a echnique which permits generation of data for a large number of molecular markers.
  • nucleic acid arrays are employed for analyzing the level of RNA product of each molecular marker of the genome in a blood sample.
  • molecular marker data is obtained by hybridizing detectably labeled polynucleotides representing the nucleic acid sequences in mRNA transcripts present in a cell (e.g., fluorescently labeled cDNA synthesized from total cell mRNA) to a microarray.
  • expressed transcripts that may or may not represent genes expressed in the blood sample are analyzed.
  • a microarray is an array of positionally-addressable binding (e.g., hybridization) sites on a support for representing many of the nucleic acid sequences in the genome of a cell or organism.
  • a microarray represents most or almost all of the genes in a species.
  • each microarray binding site consists of polynucleotide probes bound to a predetermined region on the support.
  • Microarrays are described in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 15-16, which is hereby incorporated by reference in its entirety. Microarrays can be made in a number of ways.
  • microarrays are reproducible, allowing multiple copies of a given array to be produced and results from the microarrays compared with each other.
  • the microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions.
  • Microarrays are preferably small, e.g., between 1 cm 2 and 25 cm 2 , preferably 1 to 3 cm 2 .
  • a given binding site or unique set of binding sites in the microarray will specifically bind (e.g., hybridize) to a nucleotide sequence in a single gene from a cell or organism (e.g., to exon of a specific mRNA or a specific cDNA derived therefrom).
  • Microarrays used in the present invention can include one or more test probes. In some embodiments each such test probe comprises a polynucleotide sequence that is complementary to a subsequence of RNA or DNA to be detected.
  • Each probe typically has a different nucleic acid sequence, and the position of each probe on the solid surface of the array is usually known or can be determined.
  • Microarrays useful in accordance with the invention can include oligonucleotide microarrays, cDNA based arrays, SNP arrays, spliced variant arrays and any other array able to provide a quantitative or semi quantitative data of the invention.
  • Some types of microarrays are addressable arrays. More specifically, some microarrays are positionally addressable arrays.
  • each probe of the array is located at a known, predetermined position on the solid support so that the identity (e.g., the sequence) of each probe can be determined from its position on the array (e.g., on the support or surface).
  • the arrays are ordered arrays.
  • the density of probes on a microarray or a set of microarrays is 100 different (e.g., non-identical) probes per 1 cm 2 or higher. More preferably, a microarray used in the methods of the invention will have at least 550 probes per 1 cm 2 , at least 1,000 probes per 1 cm 2 , at least 1,500 probes per 1 cm 2 or at least 2,000 probes per 1 cm .
  • the microarray is a high density array, preferably having a density of at least 2,500 different probes per 1 cm 2 .
  • the microarrays used in the invention therefore preferably contain at least 2,500, at least 5,000, at least 10,000, at least 15,000, at least 20,000, at least 25,000, at least 50,000 or at least 55,000 different (i.e., non-identical) probes.
  • the microarray is an array (e.g., a matrix) in which each position represents a discrete binding site for a nucleotide sequence of a transcript encoded by a gene (e.g., for an exon of an mRNA or a cDNA derived therefrom).
  • the collection of binding sites on a microarray contains sets of binding sites for a plurality of genes and thus can be used, in some embodiments to measure molecular marker data.
  • the microarrays of the invention can comprise binding sites for products encoded by fewer than 50% of the genes in the genome of an organism.
  • the microarrays of the invention can have binding sites for the products encoded by at least 50%, at least 75%, at least 85%, at least 90%, at least 95%, at least 99% or 100% of the molecular markersof an organism.
  • the microarrays of the invention can having binding sites for products encoded by fewer than 50%, by at least 50%, by at least 75%, by at least 85%, by at least 90%, by at least 95%, by at least 99% or by 100% of the genes expressed by a cell of an organism.
  • the binding site can be a DNA or DNA analog to which a particular RNA can specifically hybridize.
  • the DNA or DNA analog can be, e.g., a synthetic oligomer or a gene fragment, e.g. corresponding to an exon.
  • an expressed transcript is represented in the nucleic acid arrays.
  • a set of binding sites can include probes with different polynucleotides that are complementary to different sequence segments of the expressed transcript. Exemplary polynucleotides that fall within this class can be of length of 15 to 200 bases, 20 to 100 bases, 40-60 bases or some other range of bases.
  • Each probe sequence can also comprise linker sequences in addition to the sequence that is complementary to its target sequence.
  • a linker sequence is a sequence between the sequence that is complementary to its target sequence and the surface of support.
  • the nucleic acid arrays of the invention comprise one probe specific to each target gene or exon.
  • the nucleic acid arrays can contain at least 2, 5, 10, 100, or 1000 or more probes specific to some expressed transcript.
  • the array may contain probes tiled across the sequence of the longest mRNA isoform of a gene at single base steps.
  • a set of polynucleotide probes of successive overlapping sequences, i.e., tiled sequences, across the genomic region containing the longest variant of an exon can be included in the exon nucleic acid arrays.
  • the set of polynucleotide probes can comprise successive overlapping sequences at steps of a predetermined base intervals, e.g. at steps of 1, 5, or 10 base intervals, span, or are tiled across, the mRNA containing the longest variant.
  • Such sets of probes therefore can be used to scan the genomic region containing all variants of an exon to determine the expressed variant or variants of the exon to determine the expressed variant or variants of the exon.
  • a set of polynucleotide probes comprising exon specific probes and/or variant junction probes can be included in the exon profiling array.
  • a variant junction probe refers to a probe specific to the junction region of the particular exon variant and the neighboring exon.
  • the probe set contains variant junction probes specifically hybridizable to each of all different splice junction sequences of the exon.
  • the probe set contains exon specific probes specifically hybridizable to the common sequences in all different variants of the exon, and/or variant junction probes specifically hybridizable to the different splice junction sequences of the exon.
  • an exon is represented in the exon nucleic acid arrays by a probe comprising a polynucleotide that is complementary to the full length exon.
  • an exon is represented by a single binding site on the profiling arrays.
  • an exon is represented by one or more binding sites on the profiling arrays, each of the binding sites comprising a probe with a polynucleotide sequence that is complementary to an RNA fragment that is a substantial portion of the target exon.
  • the lengths of such probes are normally between 15-600 bases, preferably between 20-200 bases, more preferably between 30-100 bases, and most preferably between 40-80 bases.
  • the average length of an exon is 200 bases (see, e.g., Lewin, Genes V, Oxford University Press, Oxford, 1994).
  • a probe of length of 40-80 allows more specific binding of the exon than a probe of shorter length, thereby increasing the specificity of the probe to the target exon.
  • one or more targeted exons may have sequence lengths less than 40-80 bases. In such cases, if probes with sequences longer than the target exons are to be used, it may be desirable to design probes comprising sequences that include the entire target exon flanked by sequences from the adjacent constitutively splice exon or exons such that the probe sequences are complementary to the corresponding sequence segments in the mRNAs.
  • flanking sequence from adjacent constitutively spliced exon or exons rather than the genomic flanking sequences, i.e., intron sequences, permits comparable hybridization stringency with other probes of the same length.
  • the flanking sequence used are from the adjacent constitutively spliced exon or exons that are not involved in any alternative pathways. More preferably the flanking sequences used do not comprise a significant portion of the sequence of the adjacent exon or exons so that cross-hybridization can be minimized.
  • probes comprising flanking sequences in different alternatively spliced mRNAs are designed so that expression level of the exon expressed in different alternatively spliced mRNAs can be measured.
  • the DNA array or set of arrays can also comprise probes that are complementary to sequences spanning the junction regions of two adjacent exons.
  • such probes comprise sequences from the two exons which are not substantially overlapped with probes for each individual exons so that cross hybridization can be minimized.
  • Probes that comprise sequences from more than one exons are useful in distinguishing alternative splicing pathways and/or expression of duplicated exons in separate genes if the exons occurs in one or more alternative spliced rnRNAs and/or one or more separated genes that contain the duplicated exons but not in other alternatively spliced mRNAs and/or other genes that contain the duplicated exons.
  • any of the probe schemes, supra can be combined on the same nucleic acid array and/or on different arrays within the same set of nucleic acid arrays so that a more accurate determination of the expression profile for a plurality of molecular marker products can be accomplished.
  • the different probe schemes can also be used for different levels of accuracies in profiling. For example, a nucleic acid array or array set comprising a small set of probes for each expressed transcript or each region thereof may be used to identify molecular markers under certain specific conditions. An array or array set comprising larger sets of probes for the exons that are of interest is then used to more accurately determine the specific molecular marker products under such specific conditions.
  • the microarrays used in the invention can include binding sites (e.g., probes) for sets of exons for one or more genes relevant to the condition of interest.
  • the number of genes in a genome can be estimated from the number of mRNAs expressed by the cell or organism, or by extrapolation of a well characterized portion of the genome.
  • the number of ORFs can be determined and mRNA coding regions identified by analysis of the DNA sequence. For example, the human genome is now known. Genome sequences for other organisms are also completed or nearly completed.
  • an array set comprising the total probes for all known or predicted exons in the genome of an organism.
  • the present invention provides an array set comprising one or two probes for each known or predicted exon of a molecular marker of the human genome. It will be appreciated that when cDNA complementary to the RNA of a cell is made and hybridized to a microarray under suitable hybridization conditions, the level of hybridization to the site in the array corresponding to an exon of any particular molecular marker will reflect the prevalence in the cell of mRNA or mRNAs containing the exon transcribed from that molecular marker.
  • the site on the array corresponding to an exon of a gene i. e. , capable of specifically binding the product or products of the gene expressing
  • a signal e.g., fluorescent signal
  • an exon of a gene for which the encoded mRNA expressing the exon is prevalent will have a relatively strong signal.
  • the relative abundance of different mRNAs produced from the same gene by alternative splicing is then determined by the signal strength pattern across the whole set of exons monitored for the gene.
  • cDNA from a single cell and compare, for example, the absolute amount of a particular exon in, e.g., a. drug-treated or pathway-perturbed cell and an untreated cell.
  • labeling with more than two colors is also contemplated in the present invention. In some embodiments of the invention, at least 5, 10, 20, or 100 dyes of different colors can be used for labeling. Such labeling permits simultaneous hybridizing of the distinguishably labeled cDNA populations to the same array, and thus measuring, and optionally comparing the expression levels of, mRNA molecules derived from more than two samples.
  • Dyes that can be used include, but are not limited to, fluorescein and its derivatives, rhodamine and its derivatives, texas red, 5'carboxy- fluorescein (“FMA”), 2',7'-dimethoxy-4',5'-dichloro-6-carboxy-fluorescein (“JOE”), N,N,N',N'-tetramethyl-6-carboxy-rhodamine (“TAMRA”), 6'carboxy-X-rhodamine (“ROX”), HEX, TET, IRD40, and IRD41, cyamine dyes, including but are not limited to Cy3, Cy3.5 and Cy5; BODIPY dyes including but are not limited to BODIPY-FL, BODIPY-TR, BODIPY-TMR, BODIPY-630/650, and BODIPY-650/670; and ALEXA dyes, including but are not limited to ALEXA-488, ALEXA
  • hybridization levels at different hybridization times are measured separately on different, identical microarrays.
  • the microarray is washed briefly, preferably in room temperature in an aqueous solution of high to moderate salt concentration (e.g., 0.5 to 3 M salt concentration) under conditions which retain all bound or hybridized polynucleotides while removing all unbound polynucleotides.
  • the detectable label on the remaining, hybridized polynucleotide molecules on each probe is then measured by a method which is appropriate to the particular labeling method used.
  • the resulted hybridization levels are then combined to form a hybridization curve.
  • hybridization levels are measured in real time using a single microarray.
  • the microarray is allowed to hybridize to the sample without interruption and the microarray is interrogated at each hybridization time in a non ⁇ invasive manner.
  • one can use one array hybridize for a short time, wash and measure the hybridization level, put back to the same sample, hybridize for another period of time, wash and measure again to get the hybridization time curve.
  • the Affymetrix® Human Genome Ul 33 (HG-Ul 33) Set consisting of two GeneChip® arrays, is used in accordance with known methods.
  • the Human Genome Ul 33 (HG-U133) Set contains almost 45,000 probe sets representing more than 39,000 transcripts derived from approximately 33,000 well-substantiated human genes.
  • the HG-Ul 33 A array is used in accordance with the methods of the invention.
  • the HG-U133A array includes representation of the RefSeq database sequences and probe sets related to sequences previously represented on the Human Genome U95Av2 array.
  • the HG-Ul 33B array contains primarily probe sets representing EST clusters.
  • the Ul 33 Plus 2.0 GeneChip® is used in the invention.
  • the U133 Plus 2.0 GeneChip® represents over 47,000 transcripts.
  • a cDNA based microarray is used.
  • the ChondroChipTM is used in accordance with the methods of the invention.
  • the ChondroChipTM is a cDNA based microarray.
  • One version of the ChondroChipTM includes 14,976 distinct elements: 10,382 known genes (69%), 4,112 EST/genomic DNA matches (28%), 328 clones with no significant match (2.2%), and 154 control spots (1.0%). Most if not all of the elements on the ChondroChipTM are complementary to ESTs identified as expressed in human chondrocytes.
  • the BloodChipTM is used in accordance with the methods of the invention.
  • the BloodChip is a cDNA microarray slide with 10,368 PCR products derived from peripheral blood cell cDNA libraries.
  • the creation of the BloodChipTM microarray is described in Ma and Liew, 2003, Journal of Molecular and Cellular Cardiology 8, 993-998, which is hereby incorporated by reference in its entirety.
  • Target polynucleotides that can be analyzed by the methods and compositions of the invention include RNA molecules such as, but by no means limited to, expressed RNA molecules which includes messenger RNA (mRNA) molecules, mRNA spliced variants as well as other regulatory RNA, cRNA molecules (e.g., RNA molecules prepared from cDNA molecules that are transcribed in vivo) and fragments thereof.
  • mRNA messenger RNA
  • cRNA molecules e.g., RNA molecules prepared from cDNA molecules that are transcribed in vivo
  • Target polynucleotides which may also be analyzed by the methods and compositions of the present invention include, but are not limited to DNA molecules such as genomic DNA molecules, cDNA molecules, and fragments thereof including oligonucleotides, ESTs, STSs, etc
  • the target polynucleotide molecules may be naturally occurring nucleic acid molecules such as genomic or extragenomic DNA molecules isolated from a blood sample, or RNA molecules, such as mRNA molecules, isolated from a blood sample.
  • the sample of target polynucleotides can comprise, e.g., molecules of DNA, RNA, or copolymers of DNA and RNA.
  • the target polynucleotides of the invention will correspond to particular genes or to particular gene transcripts (e.g., to particular mRNA sequences expressed in specific cell types or to particular cDNA sequences derived from such mRNA sequences).
  • the target polynucleotides may correspond to different exons of the same gene, e.g., so that different splice variants of that gene may be detected and/or analyzed.
  • the target polynucleotides to be analyzed are prepared in vitro from nucleic acids extracted from a blood sample.
  • RNA is extracted from a blood sample (e.g., total cellular RNA, poly(A)+ messenger RNA, fraction thereof) and messenger RNA is purified from the total extracted RNA.
  • a blood sample e.g., total cellular RNA, poly(A)+ messenger RNA, fraction thereof
  • messenger RNA is purified from the total extracted RNA.
  • RNA is extracted from a blood sample using guanidinium thiocyanate lysis followed by CsCl centrifugation and an oligo dT purification (Chirgwin et al., 1979, Biochemistry 18:5294-5299).
  • RNA is extracted from a blood sample using guanidinium thiocyanate lysis followed by purification on RNeasy columns (Qiagen).
  • cDNA is then synthesized from the purified mRNA using, e.g., oligo-dT or random primers.
  • the target polynucleotides are cRNA prepared from purified messenger RNA extracted from a blood sample.
  • cRNA is defined here as RNA complementary to the source RNA.
  • the extracted RNAs can be amplified using a process in which doubled-stranded cDNAs are synthesized from the RNAs using a primer linked to an RNA polymerase promoter in a direction capable of directing transcription of anti-sense RNA.
  • Anti-sense RNAs or cRNAs are then transcribed from the second strand of the double-stranded cDNAs using an RNA polymerase (see, e.g., U.S. Patent Nos. 5,891,636, 5,716,785; 5,545,522 and 6,132,997; see also, U.S. Patent No. 6,271,002, and U.S.
  • oligo-dT primers U.S. Patent Nos. 5,545,522 and 6,132,997
  • random primers U.S. Provisional Patent Application Serial No. 60/253,641, filed on November 28, 2000, by Ziman et al.
  • the target polynucleotides are short and/or fragmented polynucleotide molecules which are representative of the original nucleic acid population of the blood sample.
  • the target polynucleotides to be analyzed by the methods and compositions of the invention can be detectably labeled.
  • cDNA can be labeled directly, e.g., with nucleotide analogs, or indirectly, e.g., by making a second, labeled cDNA strand using the first strand as a template.
  • the double-stranded cDNA can be transcribed into cRNA and labeled.
  • the detectable label is a fluorescent label, e.g., by incorporation of nucleotide analogs.
  • radioactive isotopes include 32 P, 35 S, 14 C, 15 N and 125 I.
  • Fluorescent molecules suitable for the present invention include, but are not limited to, fluorescein and its derivatives, rhodamine and its derivatives, texas red, 5'carboxy-fluorescein (“FMA”), 2',7'-dimethoxy-4',5'-dichloro-6-carboxy-fluorescein (“JOE”), N,N,N',N'- tetramethyl-6- carboxy-rhodamine (“TAMRA”), 6'carboxy-X-rhodamine (“ROX”), HEX, TET, IRD40, and IRD41.
  • FMA fluorescein and its derivatives
  • rhodamine and its derivatives texas red
  • FMA 5'carboxy-fluorescein
  • JE 2',7'-dimethoxy-4',5'-dichloro-6-carboxy-fluorescein
  • TAMRA N,N,N',N'- tetra
  • Fluroescent molecules that are suitable for the invention further include: cyamine dyes, including by not limited to Cy3, Cy3.5 and Cy5; BODIPY dyes including but not limited to BODIPY-FL, BODIPY-TR, BODIPY-TMR, BODIPY-630/650, and BODIPY-650/670; and ALEXA dyes, including but not limited to ALEXA-488, ALEXA- 532, ALEXA-546, ALEXA-568, and ALEXA-594; as well as other fluorescent dyes which will be known to those who are skilled in the art.
  • Electron rich indicator molecules suitable for the present invention include, but are not limited to, ferritin, hemocyanin, and colloidal gold.
  • the target polynucleotides may be labeled by specifically complexing a first group to the polynucleotide.
  • a second group covalently linked to an indicator molecules and which has an affinity for the first group, can be used to indirectly detect the target polynucleotide.
  • compounds suitable for use as a first group include, but are not limited to, biotin and iminobiotin.
  • Compounds suitable for use as a second group include, but are not limited to, avidin and streptavidin.
  • the target polynucleotides are prepared as follows: 2 ⁇ g Oligo-dT primers are annealed to 2 ⁇ g of mRNA isolated from a blood sample of a patient in a total volume of 15 ⁇ l, by heating to 70°C for 10 min, and cooled on ice.
  • RNA is reverse transcribed by incubating the sample at 42°C for 1.5-2 hours in a 100 ⁇ l volume containing a final concentration of 5OmM Tris-HCl (pH 8.3), 75mM KCl, 3mM MgCl 2 , 25mM DTT, 25mM unlabeled dNTPs, 400 units of Superscript II (200U/ ⁇ L, Gibco BRL), and 15mM of Cy 3 or Cy5 (Amersham). RNA is then degraded by addition of 15 ⁇ l of 0.1N NaOH, and incubation at 70DC for 10 min.
  • the reaction mixture is neutralized by addition of 15 ⁇ l of 0.1N HCl, and the volume is brought to 500 ⁇ l with TE (1OmM Tris, 1 mM EDTA), and 20 D g of Cotl human DNA (Gibco-BRL) is added.
  • the labeled target polynucleotide molecules are purified by centrifugation in a Centricon-30 micro-concentrator (Amicon). If two different target polynucleotide samples (e.g., two samples derived from a healthy patient vs. patient with a disease) are being analyzed and compared by hybridization to the same array, each target nucleic acid sample is labeled with a different fluorescent label (e.g., Cy3 and Cy5) and separately concentrated.
  • a different fluorescent label e.g., Cy3 and Cy5
  • the separately concentrated target nucleic acid samples (Cy3 and Cy5 labeled) are combined into a fresh centricon, washed with 500 ⁇ l TE, and concentrated again to a volume of less than 7 ⁇ l.
  • l ⁇ L of lO ⁇ g/ ⁇ l polyA RNA (Sigma, #P9403) and l ⁇ l of lO ⁇ g/ ⁇ l tRNA (Gibco-BRL, #15401-011) is added and the volume is adjusted to 9.5 ⁇ l with distilled water.
  • For final target polynucleotide preparation 2.1 ⁇ l 20XSSC (1.5M NaCl, 150mM NaCitrate (pH8.0)) and 0.35 ⁇ l 10%SDS is added.
  • 5.3.1.3 HYBRIDIZATION TO MICROARRAYS
  • nucleic acid hybridization and wash conditions are chosen so that the polynucleotide molecules to be analyzed by the invention (e.g., "target polynucleotide molecules) specifically bind or specifically hybridize to the complementary polynucleotide sequences of the array, typically to a specific array site, where its complementary DNA is located.
  • Arrays containing double-stranded probe DNA situated thereon can be subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target polynucleotide molecules.
  • Arrays containing single-stranded probe DNA may need to be denatured prior to contacting with the target polynucleotide molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences.
  • Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids.
  • hybridization conditions are also provided in, e.g., Tijessen, 1993, Hybridization With Nucleic Acid Probes, Elsevier Science Publishers B. V. and Kricka, 1992, Nonisotopic DNA Probe Techniques, Academic Press, San Diego, CA.
  • Representative hybridization conditions for use with the screening and/or signaling chips in accordance with some embodiments of the present invention include hybridization at a temperature at or near the mean melting temperature of the probes (e.g. , within 5 °C, more typically within 2 0 C) in 1 M NaCl, 50 niM MES buffer (pH 6.5), 0.5% sodium Sarcosine and 30% formamide.
  • a labeled target polynucleotide molecules are denatured by heating for two minutes at 100 0 C, and incubated at 37°C for 20-30 min before being placed on a nucleic acid array under a 22mm x 22mm glass cover slip.
  • Hybridization is carried out at 65°C for fourteen to eighteen hours in a custom slide chamber with humidity maintained by a small reservoir of 3XSSC.
  • the array is washed by submersion and agitation for between two and five minutes in 2X SSC with 0.1%SDS, followed by IX SSC, and 0.1X SSC.
  • cDNA complementary to the total cellular mRNA when detectably labeled (e.g., with a fluorophore) cDNA complementary to the total cellular mRNA is hybridized to a microarray, the site on the array corresponding to an exon of a gene (i.e., capable of specifically binding the product or products of the gene expressing) that is not transcribed or is removed during RNA splicing in the cell will have little or no signal (e.g., fluorescent signal), and an exon of a gene for which the encoded mRNA expressing the exon is prevalent will have a relatively strong signal.
  • the relative abundance of different mRNAs produced from the same gene by alternative splicing is then determined by the signal strength pattern across the whole set of exons monitored for the gene.
  • any form of image processing may be used to digitize the microarrays and thereby obtain high throughput data for molecular markers in the present invention.
  • any of the image processing techniques described or referenced in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 33-58, which is hereby incorporated by reference in its entirety, can be used.
  • two-color fluorescence is used.
  • the labeled probes are scanned using a GMS Scanner 418 and Scananlzyer software (Michael Eisen, Stamford University), followed by GeneSpring software (Silcon Genetics, CA) analysis.
  • GMS Scanner 428 and Jaguar software are used followed by GeneSpring software analysis.
  • a normalization routine such as any of the normalization routines described in Section 5.7, is used. 5.3.2 RT-PCR AND QUANTITATIVE RT-PCR
  • the abundance or level of expression of an RNA product of a molecular marker can be measured performing reverse transcription on the RNA from blood and subsequently amplifying the resulting product ("RT-PCR").
  • the abundance or level of expression of RNA can be measured from a blood sample by using quantitative RT-PCR or real time PCR ("QRT-PCR") on cDNA copy of RNA.
  • Total RNA, or mRNA from a blood sample can be used as a template and a primer specific to the transcribed portion of a gene of the invention is used to initiate reverse transcription.
  • Methods of reverse transcribing RNA into cDNA are well known and described in, for example, Sambrook et al., 1989, supra.
  • Primer design can be accomplished utilizing commercially available software (e.g., Primer Designer 1.0, academic software, etc.).
  • the product of the reverse transcription is subsequently used as a template for PCR.
  • a one step process can be used for either the RT- PCR and/or the QRT-PCR(combining the reverse transcription and PCR in a single reaction).
  • a two step process can be used for either the RT-PCR and/or the QRT-PCR (first doing the reverse transcription step and subsequently performing the PCR) .
  • oligo(dT)-primed first strand cDNA synthesis is performed so as to specifically target the mRNA population (e.g.
  • the reportable value is the Ct- value, which is the threshold cycle at which PCR is in the logarithmic phase.
  • Quantitative RT-PCR can be done using probes including Taqman® probes (Perkin Elmer, Foster City, California), The probe is specific for the PCR product and has both a quencher and fluorescent reporter attached to the probe. Different fluorescent markers can be utilized.
  • multiple probes can be used in the quantitative RT-PCR process to allow for multiplexing reactions (e.g. allow for measurement of two molecular markers in one reaction well or container).
  • TaqMan® probes Taq DNA polymerase is used which has 5'-to-3' exonuclease activity and thus will cleave of the fluorescent reporter of the probe, freeing the fluorescent molecular from the quencher molecule.
  • the emission of fluorescence is is used to measure the amount of PCR product being made.
  • Other probes are also useful for quantitative RT-PCR including Molecular Beacons®.
  • Other known techniques for quantitative Rt-PCR is to use an intercolating dye such as the commercially available QuantiTectTM S YBR® Green PCR (Qiagen, Valencia California).
  • Other systems to quantitatively measure mRNA expression products are known including Scorpions® (Zeneca Limited) or Fluorescent Polarization Probes (see e.g. Zeneca Limited, 6,007,984) etc. 5.3.3 NUCLEASE PROTECTION ASSAYS
  • Nuclease protection assays can be used to detect and quantify specific products of molecular markers.
  • an antisense probe (labeled with, e.g., radiolabeled or nonisotopic) hybridizes in solution to an RNA sample. Following hybridization, single- stranded, unhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments.
  • solution hybridization is more efficient than membrane-based hybridization, and it can accommodate up to 100 ⁇ g of sample RNA, compared with the 20-30 ⁇ g maximum of blot hybridizations.
  • the ribonuclease protection assay which is the most common type of nuclease protection assay, requires the use of RNA probes. Oligonucleotides and other single- stranded DNA probes can only be used in assays containing Sl nuclease. The single- stranded, antisense probe must typically be completely homologous to target RNA to prevent cleavage of the probeitarget hybrid by nuclease. 5.3.4 MASS SPECTROMETRY
  • Mass spectrometry e.g., electrospray ionization "ESI”, matrix-assisted laser desorption-ionization “MALDI”, and Fourier-transform ion cyclotron resonance "FT- ICR" can be used to measure data (e.g., mass, charge) of molecular markers in blood samples.
  • molecular markers that can be characterized by mass spectrometry include but are not limited to, proteins, nucleic acids, carbohydrates, and other biological macromolecules. This section provides brief and non-limiting examples of mass spectrometry techniques that can be used to quantitatively characterize molecular markers.
  • MALDI uses a pulsed laser for desorption of the ions and a time-of-flight analyzer and has been used for the detection of noncovalent tRNA:amino-acyl-tRNA synthetase complexes. See, for example, Gruic-Sovulj et al., 1997, J. Biol. Chem. 272:32084.
  • ESI mass spectrometry (“ESI-MS”) has been used for studying non-covalent molecular interactions. ESI-MS generates molecular ions with little to no fragmentation. See, for example, Xavier et al., 2000, Trends Biotechnol. 18:349.
  • FT-ICR Fourier-transform ion cyclotron resonance
  • the proteins can first be digested into peptides using an enzyme such as trypsin and then subjected to liquid chromatography tandem mass spectrometry (MS/MS).
  • MS/MS liquid chromatography tandem mass spectrometry
  • Liquid chromatography provides an initial separation of the peptides, which are then ionized directly into a mass spectrometer.
  • the mass spectrometer selects a parent ion, fragments it and obtains the mass spectrum of the generated fragments.
  • These fragmentation patterns are called tandem mass spectra or MS/MS spectra.
  • the level of expression of a molecular marker in blood can be measured by amplifying RNA from a blood sample using transcription based amplification systems (TAS), including nucleic acid sequence amplification (NASBA) and 3SR.
  • TAS transcription based amplification systems
  • NASBA nucleic acid sequence amplification
  • 3SR 3SR
  • nucleic acids can be prepared for amplification using conventional phenol/chloroform extraction, heat denaturation, treatment with lysis buffer and minispin columns for isolation of DNA and RNA or guanidinium chloride extraction of RNA.
  • amplification techniques involve annealing a primer that has target specific sequences.
  • DNA/RNA hybrids are digested with RNase H while double stranded DNA molecules are heat denatured again. In either case, the single stranded DNA is made fully double stranded by addition of second target specific primer, followed by polymerization.
  • the double-stranded DNA molecules are then multiply transcribed by a polymerase such as T7 or SP6.
  • RNA's are reverse transcribed into double stranded DNA, and transcribed once with a polymerase such as T7 or SP6.
  • a polymerase such as T7 or SP6.
  • the resulting products whether truncated or complete, indicate target specific sequences. 5.3.7 ADDITIONAL TECHNIQUES FOR DETECTING AND QUANTIFYING RNA
  • Non-limiting examples of such techniques include Northern blotting, nuclease protection assays, RNA fingerprinting, polymerase chain reaction, ligase chain reaction, Qbeta replicase, isothermal amplification method, strand displacement amplification, transcription based amplification systems, nuclease protection (SI nuclease or RNAse protection assays) as well as methods disclosed in International Publication Nos. WO 88/10315 and WO 89/06700, and International Applications Nos. PCT/US87/00880 and PCT/US 89/01025.
  • a standard Northern blot assay can be used to ascertain an RNA transcript size, identify alternatively spliced RNA transcripts, and the relative amounts of mRNA in a blood sample, in accordance with conventional Northern hybridization techniques known to those persons of ordinary skill in the art.
  • Northern blots RNA samples are first separated by size via electrophoresis in an agarose gel under denaturing conditions. The RNA is then transferred to a membrane, crosslinked and hybridized with a labeled probe.
  • Nonisotopic or high specific activity radiolabeled probes can be used including random- primed, nick-translated, or PCR-generated DNA probes, in vitro transcribed RNA probes, and oligonucleotides.
  • sequences with only partial homology may be used as probes.
  • the labeled probe e.g., a radiolabeled cDNA, either containing the full- length, single stranded DNA or a fragment of that DNA sequence may be at least 20, at least 30, at least 50, or at least 100 consecutive nucleotides in length.
  • the probe can be labeled by any of the many different methods known to those skilled in this art.
  • the labels most commonly employed for these studies are radioactive elements, enzymes, chemicals that fluoresce when exposed to ultraviolet light, and others. A number of fluorescent materials are known and can be utilized as labels.
  • a articular detecting material is anti-rabbit antibody prepared in goats and conjugated with fluorescein through an isothiocyanate. Proteins can also be labeled with a radioactive element or with an enzyme. The radioactive label can be detected by any of the currently available counting procedures. Non-limiting examples of isotopes include 3H, 14C, 32P, 35S, 36Cl, 51Cr, 57Co, 58Co, 59Fe, 9OY, 1251, 1311, and 186Re.
  • Enzyme labels are likewise useful, and can be detected by any of the presently utilized colorimetric, pectrophotometric, fluorospectrophotometric, amperometric or gasometric techniques.
  • the enzyme is conjugated to the selected particle by reaction with bridging molecules such as carbodiimides, diisocyanates, glutaraldehyde and the like. Any enzymes known to one of skill in the art can be utilized. Examples of such enzymes include, but are not limited to, peroxidase, beta-D-galactosidase, urease, glucose oxidase plus peroxidase and alkaline phosphatase. U.S. Patent Nos.
  • Nuclease protection assays can be used to detect and quantitate specific mRNAs.
  • an antisense probe (labeled with, e.g., radiolabeled or nonisotopic) hybridizes in solution to an RNA sample. Following hybridization, single-stranded, unhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments.
  • solution hybridization is more efficient than membrane-based hybridization, and it can accommodate up to 100 ⁇ g of sample RNA, compared with the 20-30 ⁇ g maximum of blot hybridizations.
  • the ribonuclease protection assay which is the most common type of nuclease protection assay, requires the use of RNA probes. Oligonucleotides and other single- stranded DNA probes can only be used in assays containing Sl nuclease. The single- stranded, antisense probe must typically be completely homologous to target RNA to prevent cleavage of the probe:target hybrid by nuclease.
  • RNA expression includes, but are not limited to, ligase chain reaction, Qbeta replicase (see, e.g., International Application No. PCT/US87/00880), isothermal amplification method (see, e.g., Walker et al.,1992, PNAS 89:382-396), strand displacement amplification (SDA), repair chain reaction, Asymmetric Quantitative PCR (see, e.g., U.S. Publication No. US200330134307A1) and the multiplex microsphere bead assay described in Fuja et al., 2004, Journal of Biotechnology 108:193-205. 5.3.7.1 SEPARATION OF AMPLIFICATION PRODUCTS
  • amplification products can be separated by agarose, agarose-acrylamide or polyacrylamide gel electrophoresis using conventional methods.
  • Several techniques for detecting PCR products quantitatively without electrophoresis can also be used according to the invention. See, for example, CR Protocols, A Guide to Methods and Applications, Innis et aL, Academic Press, Inc. N. Y., 1990).
  • chromatographic techniques can be employed to effect separation.
  • chromatography There are many kinds of chromatography that can be used in the present invention: adsorption, partition, ion- exchange and molecular sieve, HPLC, and many specialized techniques for using them including column, paper, thin-layer and gas chromatography (Freifelder, Physical Biochemistry Applications to Biochemistry and Molecular Biology, 2nd ed., Wm. Freeman and Co., New York, N. Y., 1982).
  • Another example of a separation methodology is done by covalently labeling the oligonucleotide primers used in a PCR reaction with various types of small molecule ligands. In one such separation, a different ligand is present on each oligonucleotide.
  • a molecule, perhaps an antibody or avidin if the ligand is biotin, that specifically binds to one of the ligands is used to coat the surface of a plate such as a 96 well ELISA plate.
  • a plate such as a 96 well ELISA plate.
  • the PCR products are bound with specificity to the surface.
  • a solution containing a second molecule that binds to the first ligand is added.
  • This second molecule is linked to some kind of reporter system. The second molecule only binds to the plate if a PCR product has been produced whereby both oligonucleotide primers are incorporated into the final PCR products.
  • Amplification products are visualized, for example, in order to confirm amplification of the marker sequences.
  • One typical visualization method involves staining of a gel with ethidium bromide and visualization under UV light.
  • the amplification products may then be exposed to x-ray film or visualized under the appropriate stimulating spectra, following separation.
  • visualization is achieved indirectly. Following separation of amplification products, a labeled, nucleic acid probe is brought into contact with the amplified marker sequence.
  • the probe preferably is conjugated to a chromophore but may be radiolabeled.
  • the probe is conjugated to a binding partner, such as an antibody or biotin, where the other member of the binding pair carries a detectable moiety.
  • detection is by Southern blotting and hybridization with a labeled probe. The techniques involved in Southern blotting are well known to those of skill in the art and may be found in many standard books on molecular protocols. See Sambrook et al., 1989. Briefly, amplification products are separated by gel electrophoresis. The gel is then contacted with a membrane, such as nitrocellulose, permitting transfer of the nucleic acid and non-covalent binding.
  • the membrane is incubated with a chromophore-conjugated probe that is capable of hybridizing with a target amplification product. Detection is by exposure of the membrane to x-ray film or ion-emitting detection devices.
  • a chromophore-conjugated probe that is capable of hybridizing with a target amplification product.
  • Detection is by exposure of the membrane to x-ray film or ion-emitting detection devices.
  • U.S. Pat. No. 5,279,721 discloses an apparatus and method for the automated electrophoresis and transfer of nucleic acids.
  • the apparatus permits electrophoresis and blotting without external manipulation of the gel and is ideally suited to carrying out methods according to the present invention.
  • Measurement of the abundance of protein products of molecular markers in blood may be performed using a number of separation techniques combined with a monitoring system.
  • whole genome monitoring of protein e.g., the "proteome," can be carried out using commercial systems such as a SELDI® Chip by Ciphergen.
  • protein microarrays comprised of immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome can be used, (e.g., The ProteinChip® Biomarker System, Ciphergen, Fremont, California).
  • antibodies can be used to measure protein products of the candidate molecular markers.
  • monoclonal antibodies are raised against synthetic peptide fragments designed based on genomic sequence of the cell.
  • proteins from the cell are contacted to the array and their binding is assayed with assays known in the art.
  • Immunoassays known to one of skill in the art can be used to detect and quantify protein levels.
  • ELISAs can be used to detect and quantify protein levels.
  • ELISAs comprise preparing antigen, coating the well of a 96 well microtiter plate with the antigen, adding the antibody of interest conjugated to a detectable compound such as an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase) to the well and incubating for a period of time, and detecting the presence of the antigen.
  • a detectable compound such as an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase)
  • a detectable compound such as an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase)
  • an ELISA may be performed by coating a high binding 96-well microtiter plate (Costar) with 2 ⁇ g/ml of rhu-IL-9 in PBS overnight. Following three washes with PBS, the plate is incubated with three-fold serial dilutions of Fab at 25 0 C for 1 hour.
  • Immunoprecipitation protocols generally comprise lysing a population of cells in a lysis buffer such as RIPA buffer (1% NP-40 or Triton X- 100, 1% sodium deoxycholate, 0.1% SDS, 0.15 M NaCl, 0.01 M sodium phosphate at pH 7.2, 1% Trasylol) supplemented with protein phosphatase and/or protease inhibitors (e.g., EDTA, PMSF, aprotinin, sodium vanadate), adding the antibody of interest to the cell lysate, incubating for a period of time (e.g., 1 to 4 hours) at 40° C, adding protein A and/or protein G sepharose beads to the cell lysate, incubating for about an hour or more at 40° C, washing the beads in lysis buffer and resuspending the beads in SDS/sample buffer.
  • a lysis buffer such as RIPA buffer (1% NP-40 or Triton X- 100, 1% sodium de
  • the ability of the antibody of interest to immunoprecipitate a particular antigen can be assessed by, e.g., western blot analysis.
  • One of skill in the art would be knowledgeable as to the parameters that can be modified to increase the binding of the antibody to an antigen and decrease the background (e.g., pre-clearing the cell lysate with sepharose beads).
  • immunoprecipitation protocols see, e.g., Ausubel et al, eds, 1994, Current Protocols in Molecular Biology, Vol. 1, John Wiley & Sons, Inc., New York at 10.16.1.
  • Western blot analysis generally comprises preparing protein samples, electrophoresis of the protein samples in a polyacrylamide gel (e.g., 8%- 20% SDS-PAGE depending on the molecular weight of the antigen), transferring the protein sample from the polyacrylamide gel to a membrane such as nitrocellulose, PVDF or nylon, incubating the membrane in blocking solution (e.g., PBS with 3% BSA or non-fat milk), washing the membrane in washing buffer (e.g., PBS-T ween 20), incubating the membrane with primary antibody (the antibody of interest) diluted in blocking buffer, washing the membrane in washing buffer, incubating the membrane with a secondary antibody (which recognizes the primary antibody, e.g., an anti-human antibody) conjugated to an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase) or radioactive molecule (e.g., 32p or 125j) diluted in blocking buffer, washing the membrane in wash buffer, and detecting the presence
  • Protein expression levels can also be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well-known in the art and typically involves isoelectric focusing along a first dimension followed by SDS-PAGE electrophoresis along a second dimension.
  • the classifiers constructed in accordance with the present invention can be used to detect, diagnose, prognose and/or monitor a trait in a test individual.
  • a classifier or classifier group constructed in accordance with Section 5.1 is used to detect, diagnose, prognose and/or monitor a disease in said test individual.
  • a classifier or classifier group constructed in accordance with Section 5.1 is used to detect, diagnose, prognose and/or monitor a reoccurrence of disease in said test individual.
  • a classifyer or classifier group constructed in accordance with the invention is used to evaluate or predict the efficacy of treatment in a subject.
  • a classifier or classifier group constructed in accordance with the invention is used to predict whether a subject will be responsive to treatment and/or treatment outcomes. In another embodiment, a classifier or classifier group constructed in accordance with the invention is used to monitor and/or predict treatment compliance or non-compliance. In another embodiment, a classifier or classifier group constructed in accordance with the methods of the invention is indicative of the responsiveness of a subject to a stimulus (whether external or internal, e.g., smoke, pollution, sunlight, heat, and mutations) and is used to evaluate or predict the response of a subject to such stimulus.
  • a stimulus whether external or internal, e.g., smoke, pollution, sunlight, heat, and mutations
  • the molecular markers identified by the classifiers of the invention can be used independently of the classifier to detect, diagnose, prognose, predict and/or monitor a trait.
  • said detection, diagnosis, prognosis, prediction and/or monitoring of a test individual can be accomplished by monitoring the gene expression pattern or profile of the molecular markers identified by the classifier or classifier group of the invention of a test individual and comparing said pattern or profile to a gene expression pattern or profile of a control individual or group of individuals who have said trait.
  • the gene expression pattern or profile of the test individual can be compared with a control individual or group of individuals who do not have said trait.
  • the gene expression pattern or profile of the test individual is compared as between individuals or group of individuals who have said trait and who do not have said trait. In yet another embodiment, the gene expression pattern or profile of the test individual is compared with the individuals used for the training population. In yet another embodiment, the gene expression pattern or profile of the test individual is compared with the individuals of the scoring population.
  • a "gene expression pattern” or “gene expression profile” indicates the combined pattern of the results of the analysis of the level of expression of two or more biomarkers of the invention including 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or all of the biomarkers of the classifier or classifier groups.
  • a gene expression pattern or gene expression profile can result from the measurement of expression of the products of the biomarkers of the invention and can be done by measuring either the RNA or the proteins corresponding to said molecular marker using any any of the techniques described herein.
  • techniques to measure expression of the RNA products of the biomarkers of the invention includes, PCR based methods (including RT-PCR and quantitative RT-PCR) and non PCR based method % as well as microarray analysis.
  • techniques include western blotting and ELISA analysis. 5.6 KITS
  • kits for measuring molecular marker data by providing the materials necessary to measure the abundance of one or more of the products of one or more molecular markers of the classifier or classifier groups identified.
  • kits may comprise materials and reagents required for measuring molecular marker data where the product of the molecular marker is RNA or protein.
  • such kits include microarrays wherein the microarray is comprised of oligonucleotides and/or DNA and/or RNA fragments which hybridize to one or more of the products of one or more of the molecular markers of a classifier or classifier group.
  • kits may include primers for PCR of either the RNA product or the cDNA copy of the RNA product of the molecular marker or both.
  • such kits may include primers for PCR as well as probes for Quantitative PCR.
  • such kits may include multiple primers and multiple probes wherein some of said probes have different flourophores so as to permit multiplexing of multiple products of a single molecular marker or multiple products wherein each product results from a single molecular marker.
  • such kits may further include materials and reagents for creating cDNA from RNA.
  • such kits may include antibodies specific for the protein products of a molecular marker.
  • kits may additionally comprise materials and reagents for isolating RNA and/or proteins from a blood sample.
  • Such kits may additionally comprise materials and reagents for isolating RNA and/or proteins from a non-blood tissue sample.
  • such kits may include materials and reagents for synthesizing cDNA from RNA isolated from a blood sample.
  • such kits may include, a computer program product embedded on computer readable media for determining whether a subject has a trait of interest.
  • the kits of the invention may include a computer program product embedded on a computer readable media along with instructions.
  • the invention provides kits for measuring the expression of one or more nucleic acid sequences of one or more molecular markers.
  • kits measure the expression of one or more nucleic acid sequences associated with a molecular marker which has been determined according to the method of the invention as being indicative of a trait of interest
  • the kits may comprise materials and reagents that are necessary for measuring the expression of particular nucleic acid sequence products of molecular markers identified by a classifier or classifier group of the invention.
  • a microarray or RT-PCR kit may be produced for a specific condition and contain only those reagents and materials necessary for measuring the levels of specific RNA transcript products of the molecular markers associated with the classifier or classifier groups selected in accordance with one embodiment of the invention.
  • kits can comprise materials and reagents that are not limited to those required to measure the expression of particular nucleic acid sequences of any particular molecular marker.
  • the kits comprise materials and reagents necessary for measuring the levels of expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more of the molecular markers of the invention, in addition to reagents and materials necessary for measuring the levels of the expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more genes other than the molecular markers of the invention.
  • kits contain reagents and materials necessary for measuring the levels of expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more of the molecular markers of the invention, and 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, or more genes that are not molecular markers of the invention, or 1-10, 1-100, 1-150, 1-200, 1-300, 1-400, 1-500, 1-1000, 25-100, 25-200, 25-300, 25-400, 25- 500, 25-1000, 100-150, 100-200, 100-300, 100-400, 100-500, 100-1000 or 500-1000 genes that are not molecular markers of the invention.
  • the kits generally comprise probes attached to a solid support surface.
  • probes can be either oligonucleotides or longer length probes including probes ranging from 150 nucleotides in length to 800 nucleotides in length.
  • the probes may be labeled with a detectable label.
  • the probes are specific for one or more of the products of a specific molecular marker identified following the methods of section 5.1.
  • the microarray kits may comprise instructions for performing the assay and methods for interpreting and analyzing the data resulting from the performance of the assay.
  • the kits comprise instructions for diagnosing a trait of interest.
  • kits may also comprise hybridization reagents and/or reagents necessary for detecting a signal produced when a probe hybridizes to a target nucleic acid sequence.
  • the materials and reagents for the microarray kits are in one or more containers. Each component of the kit is generally in its own a suitable container.
  • a nucleic acid microarray kit comprises materials and reagents necessary for measuring the levels of expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more of the molecular markers of the invention, in addition to reagents and materials necessary for measuring the levels of the expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more genes other than the molecular markers of the invention.
  • a nucleic acid microarray kit contains reagents and materials necessary for measuring the levels of expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more of the molecular markers of the invention, and 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, or more genes that are not molecular markers of the invention, or 1-10, 1-100, 1-150, 1-200, 1-300, 1-400, 1-500, 1-1000, 25-100, 25- 200, 25-300, 25-400, 25-500, 25-1000, 100-150, 100-200, 100-300, 100-400, 100-500, 100-1000 or 500-1000 genes that are not
  • the kits generally comprise pre-selected primers specific for particular nucleic acid sequences.
  • the Quantitative PCR kits may also comprise enzymes suitable for amplifying nucleic acids (e.g., polymerases such as Taq), and deoxynucleotides and buffers needed for the reaction mixture for amplification.
  • the Quantitative PCR kits may also comprise probes specific for the nucleic acid sequences associated with or indicative of a condition.
  • the probes may or may not be labeled with a flourophore.
  • the probes may or may not be labeled with a quencher molecule.
  • the Quantitative PCR kits also comprise components suitable for reverse- transcribing RNA including enzymes (e.g.
  • kits for performing the assay and methods for interpreting and analyzing the data resulting from the performance of the assay.
  • the kits contain instructions for diagnosing a trait of interest.
  • the kit can comprise, for example: (1) a first antibody (which may or may not be attached to a solid support) which binds to a peptide, polypeptide or protein of interest; and, optionally, (2) a second, different antibody which binds to either the peptide, polypeptide or protein, or the first antibody and is conjugated to a detectable label (e.g., a fluorescent label, radioactive isotope or enzyme).
  • a detectable label e.g., a fluorescent label, radioactive isotope or enzyme
  • the peptide, polypeptide or protein of interest is associated with or indicative of a condition (e.g., a disease).
  • the antibody-based kits may also comprise beads for conducting an immunoprecipitation. Each component of the antibody-based kits is generally in its own suitable container.
  • kits generally comprise distinct containers suitable for each antibody.
  • the antibody-based kits may comprise instructions for performing the assay and methods for interpreting and analyzing the data resulting from the performance of the assay.
  • the kits contain instructions for diagnosing a trait of interest.
  • a number of different normalization protocols can be used to normalize molecular marker data obtained using microarrays. Some such normalization protocols are described in this section. Typically, the normalization comprises normalizing the expression level measurement of each gene in a plurality of genes that is expressed by a subject. Many of the normalization protocols described in this section are used to normalize microarray data. It will be appreciated that there are many other suitable normalization protocols that may be used in accordance with the present invention. All such protocols are within the scope of the present invention. Many of the normalization protocols found in this section are found in publicly available software, such as Microarray Explorer (Image Processing Section, Laboratory of Experimental and Computational Biology, National Cancer Institute, Frederick, MD 21702, USA). One normalization protocol is Z-score of intensity.
  • raw expression intensities are normalized by the (mean intensity)/(standard deviation) of raw intensities for all spots in a sample.
  • the Z-score of intensity method normalizes each hybridized sample by the mean and standard deviation of the raw intensities for all of the spots in that sample.
  • the mean intensity mnlj and the standard deviation sdlj are computed for the raw intensity of control genes. It is useful for standardizing the mean (to 0.0) and the range of data between hybridized samples to about -3.0 to +3.0.
  • the Z differences (Z d i ff ) are computed rather than ratios.
  • Another normalization protocol is the median intensity normalization protocol in which the raw intensities for all spots in each sample are normalized by the median of the raw intensities.
  • the median intensity normalization method normalizes each hybridized sample by the median of the raw intensities of control genes (median ⁇ ) for all of the spots in that sample.
  • the raw intensity Iy for probe i and spot j has the value liny
  • Another normalization protocol is the log median intensity protocol.
  • raw expression intensities are normalized by the log of the median scaled raw intensities of representative spots for all spots in the sample.
  • the log median intensity method normalizes each hybridized sample by the log of median scaled raw intensities of control genes (medianlj) for all of the spots in that sample.
  • control genes are a set of genes that have reproducible accurately measured expression values. The value 1.0 is added to the intensity value to avoid taking the log(O.O) when intensity has zero value.
  • Z-score standard deviation log of intensity protocol Yet another normalization protocol is the Z-score standard deviation log of intensity protocol.
  • raw expression intensities are normalized by the mean log intensity (rnnLIj) and standard deviation log intensity (sdLIj).
  • rnnLIj mean log intensity
  • sdLIj standard deviation log intensity
  • the mean log intensity and the standard deviation log intensity is computed for the log of raw intensity of control genes.
  • Still another normalization protocol is the Z-score mean absolute deviation of log intensity protocol.
  • raw expression intensities are normalized by the Z-score of the log intensity using the equation (log(intensity)-mean logarithm) / standard deviation logarithm.
  • the Z-score mean absolute deviation of log intensity protocol normalizes each bound sample by the mean and mean absolute deviation of the logs of the raw intensities for all of the spots in the sample.
  • the mean log intensity mnLIj and the mean absolute deviation log intensity madLI are computed for the log of raw intensity of control genes.
  • Another normalization protocol is the user normalization gene set protocol.
  • raw expression intensities are normalized by the sum of the genes in a user defined gene set in each sample. This method is useful if a subset of genes has been determined to have relatively constant expression across a set of samples.
  • Yet another normalization protocol is the calibration DNA gene set protocol in which each sample is normalized by the sum of calibration DNA genes.
  • calibration DNA genes are genes that produce reproducible expression values that are accurately measured. Such genes tend to have the same expression values on each of several different microarray s.
  • the algorithm is the same as user normalization gene set protocol described above, but the set is predefined as the genes flagged as calibration DNA.
  • Yet another normalization protocol is the ratio median intensity correction protocol.
  • This protocol is useful in embodiments in which a two-color fluorescence labeling and detection scheme is used.
  • the two fluors in a two-color fluorescence labeling and detection scheme are Cy 3 and Cy 5
  • measurements are normalized by multiplying the ratio (Cy3/Cy5) by medianCy5/medianCy3 intensities.
  • background correction is enabled, measurements are normalized by multiplying the ratio (Cy3/Cy5) by (medianCy5-medianBkgdCy5) / (medianCy3-medianBkgdCy3) where medianBkgd means median background levels.
  • intensity background correction is used to normalize measurements.
  • the background intensity data from a spot quantification programs may be used to correct spot intensity. Background may be specified as either a global value or on a per-spot basis. If the array images have low background, then intensity background correction may not be necessary. 5.8 EXEMPLARY DISEASES
  • the present invention provides methods for developing classifiers that can be used to determine whether a patient has a certain trait including a disease.
  • diseases that can be identified include asthma, cancers, common late- onset Alzheimer's disease, diabetes, heart disease, hereditary early-onset Alzheimer's disease (George-Hyslop etal, 1990, Nature 347: 194), hereditary nonpolyposis colon cancer, hypertension, infection, maturity-onset diabetes of the young (Barbosa et al., 1976, Diabete Metab.
  • NAFL nonalcoholic fatty liver
  • NASH nonalcoholic steatohepatitis
  • Blood disorder also includes, blood disorder, blood lipid disease, autoimmune disease, arthritis (including osteoarthritis, rheumatoid arthritis, lupus, allergies, juvenile rheumatoid arthritis and the like), bone or joint disorder, a cardiovascular disorder (including heart failure, congenital heart disease; rheumatic fever, valvular heart disease; cor pulmonale, cardiomyopathy, myocarditis, pericardial disease; vascular diseases such as atherosclerosis, acute myocardial infarction, ischemic heart disease and the like), obesity, respiratory disease (including asthma, pneumonitis, pneumonia, pulmonary infections, lung disease, bronchiectasis, tuburculosis, cystic fibrosis, interstitial lung disease, chronic bronchitis emphysema, pulmonary hypertension, pulmonary thromboembolism, acute respiratory distress syndrome and the like), hyperlipidemias, endocrine disorder, immune disorder, infectious disease, muscle wasting and whole body wasting
  • Cancers that can be identified using the inventive techniques of the present invention include, but are not limited to, human sarcomas and carcinomas, e.g., fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, pancreatic cancer, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma,
  • training population 44, candidate molecular marker data structure 58, patient database 68, and/or classifier database 70 comprise or are stored in one or more data warehouses.
  • Data warehouses are typically structured as either relational databases or multidimensional data cubes. This section describes relational databases and multidimensional data cube architectures that can be used to store training data, candidate molecular marker lists, patient molecular marker data and/or classifiers of the present invention.
  • the "data warehouse” can be described as a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management decisions.
  • Data warehouses are built using both relational databases and specialized multidimensional structures called data cubes.
  • a database stored in computer 10 or stored in a computer addressable by computer 10 across wide area network 34 is a relational database or a datacube. 5.9.2 RELATIONAL DATABASES
  • Relational databases organize data into tables where each row corresponds to a basic entity or fact and each column represents a property of that entity.
  • a table can represent transactions in a bank, where each row corresponds to a single transaction, and each transaction has multiple attributes, such as the transaction amount, the account balance, the bank branch, and the customer.
  • the relational table is referred to as a relation, a row as a tuple, and a column as an attribute or field.
  • the attributes within a relation can be partitioned into two types: dimensions and measures. Dimensions and measures are similar to independent and dependent variables in traditional analysis. For example, the bank branch and the customer would be dimensions, while the account balance would be a measure.
  • a single relational database will often describe many heterogeneous but interrelated entities. For example, a database designed for a restaurant chain might maintain information about employees, products, and sales.
  • the database schema defines the relations in a database, the relationships between those relations, and how the relations classify the entities of interest. 5.9.3 DATA CUB
  • a data warehouse can be constructed as a relational database using either a star or snowflake schema and will provide a conceptual classifier of a multidimensional data set.
  • Each axis in the corresponding data cube represents a dimension in a relational schema and consists of every possible value for that dimension. For example, an axis corresponding to states would have fifty values, one for each state.
  • Each cell in the data cube corresponds to a unique combination of values for the dimensions. For instance, if there are two imensions, "state” and "product”, then there would be a cell for every unique combination of the two, e.g., one cell each for (California, Tea), (California, Coffee), (Florida, Tea), (Florida, Coffee), etc.
  • Each cell contains one value per measure of the data cube. So if product production and consumption information is needed, then each cell would contain two values, one for the number of products of each type consumed in that state, and one for the number of products of each type produced in that state. Dimensions within a data warehouse are often augmented with a hierarchical structure. If each dimension has a hierarchical structure, then the data warehouse is not a single data cube but rather a lattice of data cubes. 5.10 EXEMPLARY PATIENT DATABASE
  • an exemplary patient database 68 includes a plurality of patient records 500 (Fig. 6). There is no limit on the number of patient records 500 that can be held in patient database 68. Database 68 can hold as few as one patient record 500. More typically, database 68 holds between 1 and 100 patient records, more than 100 patient records, more than a thousand patient records, more than ten thousand patient records, more than 100 thousand patient records, between 1 patient record and one million patient records, or more. Each patient record 500 preferably includes a patient identifier 502. As those skilled in database arts will appreciate, a patient identifier 502 need not be explicitly enumerated in certain database systems.
  • a patient identifier 502 can simply be a patient record 500 identifier. However, in some embodiments, a patient identifier 502 can be a number that uniquely identifies a patient within a health care program or clinical trial.
  • An advantage of database 68 is that it has the capability of tracking molecular marker data profile 504 and trait characterization information 510 for each patient registered in database 68.
  • a molecular profile 504 is the abundance levels of a plurality of molecular marker products in blood specimens obtained from a patient in accordance with Section 5.2. hi some embodiments, such abundance levels are normalized using any of the techniques disclosed in Section 5.7.
  • a molecular profile 504 comprises the processed microarray image data from the biological specimen obtained from the patient.
  • molecular profile data 504 comprises molecular marker abundance information for all or a portion of the cellular constituents represented in a microarray, optional background signal information, and optional associated annotation information describing the probes used for the respective molecular marker.
  • Molecular markers include, but are not limited to RNA (e.g., mRNA) and protein.
  • a molecular profile 504 represents the transcriptional state of cellular constituents in a biological specimen. However, in other embodiments, a molecular profile 504 can track aspects of the biological state other than or in addition to transcriptional state.
  • molecular profile 504 data is, in fact, protein levels for various proteins in the blood taken from the patient.
  • molecular profiles 504 comprise amounts or concentrations of the molecular markers in biological specimens obtained in accordance with Section 5.2.
  • the amount of at least one molecular marker that is tracked in a molecular profile 504 comprises abundances of at least one RNA species present in one or more cells in the blood obtained from the patient.
  • Such abundances can be measured by a method comprising contacting a gene transcript array with RNA derived from one or more cells of the biological specimen, or with cDNA derived therefrom.
  • a gene transcript array comprises a surface with attached nucleic acids or nucleic acid mimics. The nucleic acids or nucleic acid mimics are capable of hybridizing with the RNA species or with cDNA derived from the RNA species.
  • the abundance of the RNA is measured by contacting a gene transcript array with the RNA from one or more cells of the biological specimen, or with nucleic acid derived from the RNA, such that the gene transcript array comprises a positionally addressable surface with attached nucleic acids or nucleic acid mimics, where the nucleic acids or nucleic acid mimics are capable of hybridizing with the RNA species, or with nucleic acid derived from the RNA species.
  • a molecular profile 504 can include abundance information or activity information about ten or more molecular markers (e.g., genes or proteins), between ten and one thousand molecular markers, between one thousand and twenty thousand molecular markers, or more than twenty thousand molecular markers.
  • a molecular profile 504 tracks polymorphism information.
  • polymorphism information includes, but is not limited to, single nucleotide polymorphisms (SNPs), SNP haplotypes, microsatellite markers, restriction fragment length polymorphisms (RFLPs), short tandem repeats, sequence length polymorphisms, DNA methylation, random amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLP), and "simple sequence repeats.”
  • SNPs occur approximately once every 600 base pairs in the genome. See, for example, Kruglyak and Nickerson, 2001, Nature Genetics 27, 235. Alleles making up blocks of such SNPs in close physical proximity are often correlated, resulting in reduced genetic variability and defining a limited number of "SNP haplotypes" each of which reflects descent from a single ancient ancestral chromosome. See Fullerton et ah, 2000, Am. J. Hum. Genet. 67, 881. Such haplotype structure is used in some embodiments of the present invention. Patil et al.
  • RFLPs are the product of allelic differences between DNA restriction fragments caused by nucleotide sequence variability. As is well known to those of skill in the art, RFLPs are typically detected by extraction of genomic DNA and digestion with a restriction endonuclease. Generally, the resulting fragments are separated according to size and hybridized with a probe; single copy probes are preferred. As a result, restriction fragments from homologous chromosomes are revealed.
  • RAPD random amplified polymorphic DNA
  • AFLP technology refers to a process that is designed to generate large numbers of randomly distributed molecular markers (see, for example, European Patent Application No. 0534858 Al).
  • "Simple sequence repeats" or “SSRs” are di-, tri- or tetra-nucleotide tandem repeats within a genome. The repeat region can vary in length between genotypes while the DNA flanking the repeat is conserved such that the same primers will work in a plurality of genotypes.
  • a polymorphism between two genotypes represents repeats of different lengths between the two flanking conserved DNA sequences (see, for example, Akagi et al, 1996, Theor. Appl. Genet.
  • patient records 500 include trait characterizations 510.
  • a trait characterization 510 comprises observations made by a patient's physician.
  • the observations made by a physician include a code from the International Classification of Diseases, 9 th Revision, prepared by the Department of Health and Human Services (ICD-9 codes), or an equivalent, and dates such observations were made.
  • ICD-9 codes International Classification of Diseases
  • 5 codes EXEMPLARY GENES AS CANDIDATE MOLECULAR MARKERS
  • genes useful as molecular markers for use in the invention can include, but are not limited to, genes specific for or involved in a particular biological process, such as apoptosis, differentiation, stress response, aging, proliferation, etc.; cellular mechanism genes, e.g., cell-cycle, signal transduction, metabolism of toxic compounds, and the like; disease associated genes, e.g., genes involved in cancer, schizophrenia, diabetes, high blood pressure, atherosclerosis, viral-host interaction and infection and the like. Exemplary genes can also include immune responsive genes. Further examples of genes can include, but are not limited to, oncogenes whose expression within a cell induces that cell to become converted from a normal cell into a tumor cell.
  • genes can include, but are not limited to cytokine genes. See, for example, Rubinstein et al, 1998, Cytokine Growth Factor Rev. 9:175-81.
  • genes can include idiotype protein genes (e.g., Benezra., et al, 2001 Oncogene 20:8334-41; Norton, 2000, J. Cell Sci. 113:3897), prion genes (e.g., Prusiner et al, 1998, Cell 93:337-48; Safar & Prusiner, 1998, Prog. Brain Res.
  • genes that express molecules that induce angiogenesis e.g., Gould & Wagner, 2002, Hum. Pathol. 33:1061
  • genes encoding adhesion molecules e.g., Chothia, & Jones, 1997, Annu. Rev. Biochem. 66:823; Parise et al, 2000, Semin. Cancer Biol. 10:407-14
  • genes encoding cell surface receptors e.g., Deller and Jones, 2000, Curr. Opin. Struct. Biol. 10:213
  • genes of proteins that are involved in metastasizing and/or invasive processes e.g., Boyd, 1996, Cancer Metastasis Rev.
  • a gene is an immune response gene or a non-immune response gene such as cytokines (e.g., interleukins and interferons such as TNF-alpha, IL-10, IL- 12, IL-2, IL-4, IL-10, IL- 12, IL- 13, TGF-Beta, IFN-gamma; immunoglobulins, complement and the like).
  • cytokines e.g., interleukins and interferons such as TNF-alpha, IL-10, IL- 12, IL-2, IL-4, IL-10, IL- 12, IL- 13, TGF-Beta, IFN-gamma; immunoglobulins, complement and the like.
  • clustering is used. For instance, clustering can be used in step 204 to visualize the relationship between the data measured for a plurality of molecular markers in step 202.
  • any of the clustering techniques described in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 263-297, which is hereby incorporated by reference in its entirety, are used in the present invnetion. Clustering is also described on pages 211-256 of Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York, which is hereby incorporated by reference.
  • the clustering problem is described as one of finding natural groupings in a dataset.
  • two issues are addressed.
  • a way to measure similarity (or dissimilarity) between two samples is determined. This metric (similarity measure) is used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters.
  • a mechanism for partitioning the data into clusters using the similarity measure is determined. Similarity measures are discussed in Section 6.7 of Duda, where it is stated that one way to begin a clustering investigation is to define a distance function and to compute the matrix of distances between all pairs of samples in a dataset.
  • clustering does not require the use of a distance metric.
  • a nonmetric similarity function s(x, x') can be used to compare two vectors x and x'.
  • s(x, x') is a symmetric function whose value is large when x and x' are somehow "similar".
  • An example of a nonmetric similarity function s(x, x') is provided on page 216 of Duda.
  • clustering requires a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function are used to cluster the data. See page 217 of Duda. Criterion functions are discussed in Section 6.8 of Duda. More recently, Duda et al., Pattern Classification, 2 nd edition, John Wiley & Sons, Inc. New York, has been published. Pages 537-563 describe clustering in detail.
  • Hierarchical cluster analysis is a statistical method for finding relatively homogenous clusters of elements based on measured data.
  • n samples into c clusters. The first of these is a partition into n clusters, each cluster containing exactly one sample. The next is a partition into n 1 clusters, the next is partition into n-2, and so on until the n*, in which all the samples form one cluster.
  • level one corresponds to n clusters and level n corresponds to one cluster.
  • sequence has the property that whenever two samples are in the same cluster at level k they remain together at all higher levels, then the sequence is said to be a hierarchical clustering.
  • hierarchical clustering includes agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm. See, for example WO03100557. 5.12.1.1 CLUSTERING WITH PEARSON CORRELATION COEFFICIENTS
  • molecular marker data data is clustered using agglomerative hierarchical clustering with Pearson correlation coefficients.
  • similarity is determined using Pearson correlation coefficients between sets of molecular marker data measurements.
  • Other metrics that can be used, in addition to the Pearson correlation coefficient include but are not limited to, a Euclidean distance, a squared Euclidean distance, a Euclidean sum of squares, a Manhattan metric, and a squared Pearson correlation coefficient.
  • Such metrics can be computed using SAS (Statistics Analysis Systems Institute, Gary, North Carolina) or S-Plus (Statistical Sciences, Inc., Seattle, Washington). 5.12.1.2 DIVISIVE CLUSTERING
  • the hierarchical clustering technique used to cluster molecular marker data measurements is a divisive clustering procedure.
  • Divisive (top- down clustering) procedures start with all of the samples in one cluster and form the sequence by successfully splitting clusters.
  • Divisive clustering techniques are classified as either a polymeric or a monothetic method.
  • a polythetic approach divides clusters into arbitrary subsets. 5.12.2 K-MEANS CLUSTERING
  • fuzzy k-means clustering algorithm which is also known as the fuzzy c-means algorithm
  • the fuzzy k-means clustering algorithm the assumption that every set of molecular marker data measurements is in exactly one cluster at any given time is relaxed so that every set has some graded or "fuzzy" membership in a cluster. See Duda et ah, 2001, Pattern Classification, John Wiley & Sons, New York, NY, pp. 528-530. 5.12.3 JARVIS-PATRICK CLUSTERING
  • Jarvis-Patrick clustering is a nearest-neighbor non-hierarchical clustering method in which a set of objects is partitioned into clusters on the basis of the number of shared nearest-neighbors.
  • a preprocessing stage identifies the K nearest-neighbors of each object in the dataset.
  • Molecular marker is used herein to mean a gene or genetic element. All genes and genetic elements are considered molecular markers, but the invention teaches how to identify molecular markers useful for diagnosing a trait of interest. 5.14 REPRESENTATIVE MATHEMATICAL MODELS THAT CAN BE USED TO BUILD CLASSIFIERS
  • This section describes various mathematical models that can be used to build classifier in accordance with the methods of the present invention.
  • the classifier constructed in step 216 is a regression classifier, preferably a logistic regression classifier.
  • a regression classifier includes a coefficient for each of the molecular markers selected in the last instance of step 214.
  • the coefficients for the regression classifier are computed using, for example, a maximum likelihood approach.
  • the data measured for the molecular markers in step 206 e.g., RT-PCR data
  • molecular marker data from only two trait subgroups is used and the dependent variable is absence or presence of a particular trait in the subjects for which molecular marker data is available.
  • the two different trait subgroups can, for example, respectively represent a diseased and nondiseased state, a first diseased state (e.g. liver cancer) and a second phenotypically similar (e.g. hepatitis B) or unrelated diseased state (e.g., Alzheimer's disease), those subjects that are responsive to drug therapy and those subjects that are not responsive to drug therapy, or subjects that have been subjected to a perturbation (e.g., drug treatment) versus those subjects that have not been subjected to a perturbation.
  • training population 44 consists of a plurality of trait subgroups (e.g., three or more trait subgroups, four or more specific trait subgroups, etc.).
  • a generalization of the logistic regression model that handles multicategory responses can be used in step 216 to develop a classifier that discriminates between the various trait subgroups found in the training population.
  • measured data for selected molecular markers can be applied to any of the mulitcategory logit models described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., New York, Chapter 8, which is hereby incorporated herein by reference in its entirety, in order to develop a classifier capable of discriminating between any of a plurality of trait subgroups represented in a training population.
  • the present invention is not limited to the use of logistic regression.
  • the data measured for the molecular markers in step 206 can be used to train a neural network.
  • a neural network is derived in each successive instance of step 216 of Fig. 2 A using the combination of molecular markers selected in the corresponding instance of step 214 of Fig. 2 A.
  • a neural network is a two-stage regression or classification classifier.
  • a neural network has a layered structure that includes a layer of input units (and the bias) connected by a layer of weights to a layer of output units. For regression, the layer of output units typically includes just one output unit.
  • neural networks can handle multiple quantitative responses in a seamless fashion.
  • multilayer neural networks there are input units (input layer), hidden units (hidden layer), and output units (output layer). There is, furthermore, a single bias unit that is connected to each unit other than the input units.
  • Neural networks are described in Duda et ah, 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et ah, 2001, The Elements of Statistical Learning, Springer-Verlag, New York.
  • the basic approach to the use of neural networks is to start with an untrained network, present a training pattern to the input layer, and to pass signals through the net and determine the output at the output layer. These outputs are then compared to the target values; any difference corresponds to an error.
  • This error or criterion function is some scalar function of the weights and is minimized when the network outputs match the desired outputs. Thus, the weights are adjusted to reduce this measure of error.
  • this error can be sum-of-squared errors.
  • this error can be either squared error or cross-entropy (deviation). See, e.g., Hastie et al, 2001, The Elements of Statistical Learning, Springer-Verlag, New York.
  • Three commonly used training protocols are stochastic, batch, and on-line. In stochastic training, patterns are chosen randomly from the training set and the network weights are updated for each pattern presentation.
  • Multilayer nonlinear networks trained by gradient descent methods perform a maximum- likelihood estimation of the weight values in the classifier defined by the network topology.
  • batch training all patterns are presented to the network before learning takes place. Typically, in batch training, several passes are made through the training data. In online training, each pattern is presented once and only once to the net. In some embodiments, consideration is given to starting values for weights. If the weights are near zero, then the operative part of the sigmoid commonly used in the hidden layer of a neural network (see, e.g., Hastie et ah, 2001, The Elements of Statistical Learning, Springer- Verlag, New York) is roughly linear, and hence the neural network collapses into an approximately linear classifier.
  • starting values for weights are chosen to be random values near zero.
  • the classifier starts out nearly linear, and becomes nonlinear as the weights increase. Individual units localize to directions and introduce nonlinearities where needed. Use of exact zero weights leads to zero derivatives and perfect symmetry, and the algorithm never moves. Alternatively, starting with large weights often leads to poor solutions. Since the scaling of inputs determines the effective scaling of weights in the bottom layer, it can have a large effect on the quality of the final solution.
  • all expression values are standardized to have mean zero and a standard deviation of one. This ensures all inputs are treated equally in the regularization process, and allows one to choose a meaningful range for the random starting weights.
  • a recurrent problem in the use of three-layer networks is the optimal number of hidden units to use in the network.
  • the number of inputs and outputs of a three-layer network are determined by the problem to be solved.
  • the number of inputs for a given neural network can, in some embodiments, equal the number of molecular markers selected in the corresponding instance of step 214.
  • two or more molecular markers will be selected (for example wherein ratios of genes (A/B) are utilized.
  • the number of outputs for the neural network will typically be just one (ie wherein the ouput neuron is one dimensional e.g.
  • new additional output neurons may be created.
  • more than one output is used so that more than just two states can be defined by the network. If too many hidden units are used in a neural network, the network will have too many degrees of freedom and is trained too long, there is a danger that the network will overfit the data. If there are too few hidden units, the training set cannot be learned. Generally speaking, however, it is better to have too many hidden units than too few. With too few hidden units, the classifier might not have enough flexibility to capture the nonlinearities in the data; with too many hidden units, the extra weight can be shrunk towards zero if appropriate regularization or pruning, as described below, is used.
  • the number of hidden units in somewhere in the range of 5 to 100, with the number increasing with the number of inputs and number of training cases.
  • One general approach to determining the number of hidden units to use is to apply a regularization approach.
  • La the regularization approach a new criterion function is constructed that depends not only on the classical training error, but also on classifier complexity. Specifically, the new criterion function penalizes highly complex classifiers; searching for the minimum in this criterion is to balance error on the training set with error on the training set plus a regularization term, which expresses constraints or desirable properties of solutions: J J pat " ⁇ " Adreg- The parameter ⁇ is adjusted to impose the regularization more or less strongly.
  • WaId statistics are computed.
  • the fundamental idea in WaId Statistics is that they can be used to estimate the importance of a hidden unit (weight) in a classifier. Then, hidden units having the least importance are eliminated (by setting their input and output weights to zero).
  • Two algorithms in this regard are the Optimal Brain Damage (OBD) and the Optimal Brain Surgeon (OBS) algorithms that use second-order approximation to predict how the training error depends upon a weight, and eliminate the weight that leads to the smallest increase in training error.
  • OBD Optimal Brain Damage
  • OBS Optimal Brain Surgeon
  • Optimal Brain Damage and Optimal Brain Surgeon share the same basic approach of training a network to local minimum error at weight w, and then pruning a weight that leads to the smallest increase in the training error.
  • the predicted functional increase in the error for a change in full weight vector ⁇ w is:
  • u q is the unit vector along the qth direction in weight space and L q is approximation to the saliency of the weight q - the increase in training error if weight q is pruned and the other weights updated ⁇ w.
  • H 0 "1 Of 1 I, where ⁇ is a small parameter - effectively a weight constant.
  • the matrix is updated with each pattern according to
  • the Optimal Brain Surgeon method is: 1 begin initialize ⁇ H , W, ⁇ train a reasonably large network to minimum error do compute H "1 by Eqn. 1
  • the Optimal Brain Damage method is computationally simpler because the calculation of the inverse Hessian matrix in line 3 is particularly simple for a diagonal matrix.
  • the above algorithm terminates when the error is greater than a criterion initialized to be ⁇ .
  • Another approach is to change line 6 to terminate when the change in J(w) due to elimination of a weight is greater than some criterion value.
  • the back-propagation neural network see, for example Abdi, 1994, "A neural network primer", J. Biol System. 2, 247-283 containing a single bidden layer often neurons (ten hidden units) found in EasyNN-Plus version 4.Og software package (Neural Planner Software Inc.) is used.
  • "outlier" samples are identified by performing twenty independently-seeded trials involving 20,000 learning cycles each.
  • the expression values for select genes are used to cluster a training set. For example, consider the case in which ten genes are used. Each member m of the training population will have expression values for each of the ten genes. Such values from a member m in the training population define the vector:
  • Xj m is the expression level of the i* gene in organism m. If there are m organisms in the training set, selection of i genes will define m vectors. Note that the methods of the present invention do not require that each expression value of every single gene used in the vectors be represented in every single vector m. In other words, data from a subject in which one of the i th genes is not found can still be used for clustering. In such instances, the missing expression value is assigned either a "zero" or some other normalized value. In some embodiments, prior to clustering, the gene expression values are normalized to have a mean value of zero and unit variance. Those members of the training population that exhibit similar expression patterns across the training group will tend to cluster together.
  • a particular combination of genes of the present invention is considered to be a good classifier in this aspect of the invention when the vectors cluster into the trait groups found in the training population. For instance, if the training population includes patients with no osteoarthritis, mild osteoarthritis, moderate osteoarthritis, marked osteoarthritis, and severe osteoarthritis an ideal clustering classifier will cluster the population into five groups, with each group uniquely representing either absence or one of the four stages of osteoarthritis. In some embodiments, the clustering classifier simply clusters the population into a first subgroup (a first cluster) that does not have osteoarthritis and a second subgroup (a second cluster) that has osteoarthritis.
  • the classifier clusters the data into a first subgroup that has a particular stage of osteoarthritis (e.g., mild) and two or more subgroups that do not include subjects having the particular stage of osteoarthritis represented in the first subgroup.
  • Clustering is described on pages 211 -256 of Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York.
  • the clustering problem is described as one of finding natural groupings in a dataset. To identify natural groupings, two issues are addressed. First, a way to measure similarity (or dissimilarity) between two samples is determined.
  • This metric is used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters.
  • a nonmetric similarity function s(x, x') can be used to compare two vectors x and x'.
  • s(x, x') is a symmetric function whose value is large when x and x' are somehow "similar".
  • An example of a nonmetric similarity function s(x, x') is provided on page 216 of Duda.
  • Particular exemplary clustering techniques that can be used in the present invention include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering.
  • hierarchical clustering agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm
  • k-means clustering fuzzy k-means clustering algorithm
  • Jarvis-Patrick clustering 5.14.4
  • PRINCIPAL COMPONENT ANALYSIS Principal component analysis (PCA) has been proposed to analyze gene expression data. Principal component analysis is a classical technique to reduce the dimensionality of a data set by transforming the data to a new set of variable (principal components) that summarize the features of the data.
  • PCs Principal components
  • the & th PC can be interpreted as the direction that maximizes the variation of the projections of the data points such that it is orthogonal to the first k - 1 PCs.
  • the first few PCs capture most of the variation in the data set.
  • the last few PCs are often assumed to capture only the residual 'noise' in the data.
  • PCA can also be used to create a classifier in accordance with the present invention. In such an approach, vectors for the select genes described in the present invention can be constructed in the same manner described for clustering above.
  • the set of vectors where each vector represents the expression values for the select genes from a particular member of the training population, can be considered a matrix.
  • this matrix is represented in a Free-Wilson method of qualitative binary description of monomers (Kubinyi, 1990, 3D QSAR in drug design theory methods and applications, Pergamon Press, Oxford, pp 589-638), and distributed in a maximally compressed space using PCA so that the first principal component (PC) captures the largest amount of variance information possible, the second principal component (PC) captures the second largest amount of all variance information, and so forth until all variance information in the matrix has been accounted for. Then, each of the vectors (where each vector represents a member of the training population) is plotted.
  • a one-dimensional plot is made.
  • the value for the first principal component from each of the members of the training population is plotted.
  • the expectation is that members of a first subgroup (e.g. those subjects that do not have osteoarthritis) will cluster in one range of first principal component values and members of a second subgroup (e.g., those subjects that have osteoarthritis) will cluster in a second range of first principal component values.
  • the training population comprises two subgroups: "control" and "patients with osteoarthritis.”
  • the first principal component is computed using the molecular marker expression values for the select genes of the present invention across the entire training population data set.
  • each member of the training set is plotted as a function of the value for the first principal component.
  • those members of the training population in which the first principal component is positive are the "responders" and those members of the training population in which the first principal component is negative are "patients with osteoarthritis.”
  • the members of the training population are plotted against more than one principal component.
  • the members of the training population are plotted on a two-dimensional plot in which the first dimension is the first principal component and the second dimension is the second principal component. In such a two-dimensional plot, the expectation is that members of each subgroup represented in the training population will cluster into discrete groups.
  • a first cluster of members in the two-dimensional plot will represent subjects with mild osteoarthritis
  • a second cluster of members in the two-dimensional plot will represent subjects with moderate osteoarthritis
  • the members of the training population are plotted against more than two principal components and a determination is made as to whether the members of the training population are clustering into groups that each uniquely represents a subgroup found in the training population.
  • principal component analysis is performed by using the R mva package (Anderson, 1973, Cluster Analysis for applications, Academic Press, New York 1973; Gordon, Classification, Second Edition, Chapman and Hall, CRC, 1999.). Principal component analysis is further described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc. 5.14.5 NEAREST NEIGHBOR CLASSIFIER ANALYSIS
  • Nearest neighbor classifiers are memory-based and require no classifier to be fit. Given a query point XQ, the k training points JC (r) , r, ..., k closest in distance to XQ are identified and then the point X 0 is classified using the k nearest neighbors. Ties can be broken at random. In some embodiments, Euclidean distance in feature space is used to determine distance as: "(O
  • the expression data used to compute the linear discriminant is standardized to have mean zero and variance 1.
  • the members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set.
  • a select combination of genes described in the present invention represents the feature space into which members of the test set are plotted.
  • the ability of the training set to correctly characterize the members of the test set is computed.
  • nearest neighbor computation is performed several times for a given combination of genes of the present invention.
  • the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of genes is taken as the average of each such iteration of the nearest neighbor computation.
  • the nearest neighbor rule can be refined to deal with issues of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting for the neighbors. For more information on nearest neighbor analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York. 5.14.6 LINEAR DISCRIMINANT ANALYSIS
  • LDA Linear discriminant analysis attempts to classify a subject into one of two categories based on certain object properties. In other words, LDA tests whether object attributes measured in an experiment predict categorization of the objects. LDA typically requires continuous independent variables and a dichotomous categorical dependent variable. In the present invention, the expression values for the select combinations of genes described in the present invention across a subset of the training population serve as the requisite continuous independent variables. The trait subgroup classification of each of the members of the training population serves as the dichotomous categorical dependent variable. LDA seeks the linear combination of variables that maximizes the ratio of between-group variance and within-group variance by using the grouping information.
  • the linear weights used by LDA depend on how the expression of a molecular marker across the training set separates in the two groups (e.g., a group that has osteoarthritis and a group that does not have osteoarthritis) and how this gene expression correlates with the expression of other genes.
  • LDA is applied to the data matrix of the N members in the training sample by K genes in a combination of genes described in the present invention. Then, the linear discriminant of each member of the training population is plotted. Ideally, those members of the training population representing a first subgroup (e.g.
  • Quadratic discriminant analysis takes the same input parameters and returns the same results as LDA.
  • QDA uses quadratic equations, rather than linear equations, to produce results.
  • LDA and QDA are interchangeable, and which to use is a matter of preference and/or availability of software to support the analysis.
  • Logistic regression takes the same input parameters and returns the same results as LDA and QDA.
  • support vector machines are used to classify subjects using genesor genetic information.
  • SVMs are a relatively new type of learning algorithm. See, for example, Cristianini and Shawe-Taylor, 2000, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, Boser et al, 1992, "A training algorithm for optimal margin classifiers, in Proceedings of the 5 th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, PA, pp. 142-152; Vapnik, 1998, Statistical Learning Theory, Wiley, New York.
  • SVMs separate a given set of binary labeled training data with a hyper- plane that is [a maximal distance from each point using a fitting algorithm.
  • SVMs can work in combination with the technique of 'kernels', which automatically realizes a non-linear mapping to a feature space.
  • the hyper-plane found by the SVM in feature space corresponds to a non-linear decision boundary in the input space.
  • the gene expression data is standardized to have mean zero and unit variance and the members of a training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set.
  • the expression values for a combination of genes is used to train the SVM.
  • this computation is performed several times for a given combination of molecular markers.
  • the members of the training population are randomly assigned to the training set and the test set.
  • the quality of the combination of molecular markers is taken as the average of each such iteration of the SVM computation.
  • decision trees are used to classify subjects using expression data for combinations of genes.
  • Decision tree algorithms belong to the class of supervised learning algorithms.
  • the aim of a decision tree is to induce a classifier (a tree) from real-world example data. This tree can be used to classify unseen examples which have not been used to derive the decision tree.
  • a decision tree is derived from training data. An example contains values for the different attributes and what class the example belongs.
  • the training data is expression data for a combination of genes across the training population.
  • the I- value shows how much information we need in order to be able to describe the outcome of a classification for the specific dataset used. Supposing that the dataset contains p positive (e.g. has osteoarthritis) and n negative (e.g. healthy) examples (e.g. individuals), the information contained in a correct answer is:
  • log 2 is the logarithm using base two.
  • v is the number of unique attribute values for attribute A in a certain dataset
  • i is a certain attribute value
  • pi is the number of examples for attribute A where the classification is positive (e.g. cancer)
  • n is the number of examples for attribute A where the classification is negative (e.g. healthy).
  • the information gain of a specific attribute A is calculated as the difference between the information content for the classes and the remainder of attribute A:
  • GainLA /(— ⁇ - * — ⁇ - ) -temaind ⁇ ri ⁇ ) p + n ' p + n
  • the information gain is used to evaluate how important the different attributes are for the classification (how well they split up the examples), and the attribute with the highest information.
  • decision tree algorithms many of which are described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc.
  • Decision tree algorithms often require consideration of feature processing, impurity measure, stopping criterion, and pruning.
  • Specific decision tree algorithms include, cut are not limited to classification and regression trees (CART), multivariate decision trees, ID3, and C4.5.
  • CART classification and regression trees
  • ID3, and C4.5 the gene expression data for a select combination of genes described in the present invention across a training population is standardized to have mean zero and unit variance.
  • the members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set.
  • the expression values for a select combination of genes described in the present invention is used to construct the decision tree. Then, the ability for the decision tree to correctly classify members in the test set is determined. In some embodiments, this computation is performed several times for a given combination of molecular markers. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of molecular markers is taken as the average of each such iteration of the decision tree computation. 5.14.10 EVOLUTIONARY METHODS
  • classifier design employs a stochastic search for an optimal classifier.
  • such methods create several classifiers - a population - from a combination of genes described in the present invention. Each classifier varies somewhat from the other.
  • the classifiers are scored on expression data across the training population. In keeping with the analogy with biological evolution, the resulting (scalar) score is sometimes called the fitness.
  • the classifiers are ranked according to their score and the best classifiers are retained (some portion of the total population of classifiers). Again, in keeping with biological terminology, this is called survival of the fittest.
  • the classifiers are stochastically altered in the next generation - the children or offspring.
  • Bagging, boosting and the random subspace method are combining techniques that can be used to improve weak classifiers. These techniques are designed for, and usually applied to, decision trees. In addition, Skurichina and Duin provide evidence to suggest that such techniques can also be useful in linear discriminant analysis.
  • bagging one samples the training set, generating random independent bootstrap replicates, constructs the classifier on each of these, and aggregates them by a simple majority vote in the final decision rule. See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron & Tibshirani, An Introduction to Boostrap, Chapman & Hall, New York, 1993.
  • classifiers are constructed on weighted versions of the training set, which are dependent on previous classification results.
  • extreme phenotype 1 e.g., severe osteoarthritis
  • extreme phenotype 2 e.g., no osteoarthritis
  • a classifier G(X) produces a prediction taking one of the type values in the two value set: ⁇ extreme phenotype 1, extreme phenotype 2 ⁇ .
  • the error rate on the training sample is
  • N is the number of subjects in the training set (the sum total of the subjects that have either extreme phenotype 1 or extreme phenotype 2). For example, if there are 49 organisms that have severe osteoarthritis and 72 organisms that have no osteoarthritis under study, N is 121.
  • ⁇ 1? ⁇ 2j ... , a ⁇ are computed by the boosting algorithm and their purpose is to weigh the contribution of each respective G m (x). Their effect is to give higher influence to the more accurate classifiers in the sequence.
  • the current classifier G m (x) is induced on the weighted observations at line 2a.
  • the resulting weighted error rate is computed at line 2b.
  • Line 2c calculates the weight ⁇ m given to G m (x) in producing the final classifier G(x) (line 3).
  • the individual weights of each of the observations are updated for the next iteration at line 2d.
  • Observations misclassified by G m (x) have their weights scaled by a factor exp( ⁇ m ), increasing their relative influence for inducing the next classifier G m+ ⁇ x) in the sequence.
  • modifications of the Freund and Schapire, 1997, Journal of Computer and System Sciences 55, pp. 119-139, boosting method are used.
  • boosting or adaptive boosting methods are used.
  • modifications of Freund and Schapire, 1997, Journal of Computer and System Sciences 55, pp. 119-139 are used.
  • feature preselection is performed using a technique such as the nonparametric scoring methods of Park et al., 2002, Pac. Symp. Biocomput. 6, 52-63. Feature preselection is a form of dimensionality reduction in which the genes that discriminate between classifications the best are selected for use in the classifier.
  • the LogitBoost procedure introduced by Friedman et al., 2000, Ann Stat 28, 337-407 is used rather than the boosting procedure of Freund and Schapire.
  • the boosting and other classification methods of Ben-Dor et al., 2000, Journal of Computational Biology 7, 559-583 are used in the present invention.
  • the boosting and other classification methods of Freund and Schapire, 1997, Journal of Computer and System Sciences 55, 119-139 are used.
  • classifiers are constructed in random subspaces of the data feature space. These classifiers are usually combined by simple majority voting in the final decision rule. See, for example, Ho, "The Random subspace method for constructing decision forests," IEEE Trans Pattern Analysis and Machine Intelligence, 1998; 20(8): 832-844. 5.14.12 OTHER MATHEMATICAL MODELS
  • the pattern classification and statistical techniques described above are merely examples of the types of classifiers that can be used to construct a classifier. Moreover, combinations of the techniques described above can be used. Some combinations, such as the use of the combination of decision trees and boosting, have been described. However, many other combinations are possible. In addition, other techniques in the art such as Projection Pursuit and Weighted Voting can be used to construct classifiers in instances of step 216. 5.15 IMPLEMENTING THE INVENTION
  • the present invention provides methods and systems for screening molecular markers to identify classifiers and/or for identifying classifiers for a trait and allows for the configuration of classifiers based on combinations of a large number of molecular markers.
  • the invention provides a selection process for reducing the potential large number of candidate molecular markers and/or combinations thereof down to a manageable number which can be evaluated in one or more mathematical models to derive one or more classifiers.
  • Some embodiments of the invention are preferably implemented on a computer system having a processor and a memory unit.
  • the embodiments may be implemented as one or more software programs operating on a general purpose computer, such as a personal computer or workstation, or as dedicated special purpose hardware components.
  • the invention allows for an identification of classifiers while reducing the system requirements.
  • Some embodiments of the invention provide a data-driven selection of a subset of the candidate molecular markers based on their discrimination ability. Thus, it becomes possible to start out with a large group of potentially interesting molecular markers and to automatically prune the set of candidate molecular markers so that the computer system can handle the classifier identification process more efficiently, i.e. within less processing time and less memory space.
  • the invention can be implemented on a computer having less computational power and memory while the quality of the derived classifiers is maintained.
  • the invention allows for the consideration of more molecular markers, which are potentially interesting for a given application (trait), with the same available system resources resulting in a possible higher classification accuracy for the derived classifiers.
  • the invention further provides a data-driven selection of the derived classifiers to remove classifiers and/or molecular markers which do not significantly contribute to the trait discrimination in the later application phase.
  • This automatic pruning step evaluates the discrimination power of the individual classifiers to reduce the necessary system requirements of a diagnostic system applying the selected classifiers for the wide variety of possible medical applications.
  • a diagnostic system configured with the identified classifiers needs less computational power and memory and may be implemented on a smaller, less expensive device. This becomes more important when the applied mathematical models are more complex and powerful. Examples of complex classifiers are described in section 5.14 of this description and include, e.g. neural networks, nearest neighbor classifiers, decision tress, etc.
  • the invention enables the operation of optimized combinations of complex classifiers on diagnostic devises with limited resources. 5.16 EMBODIMENTS OF THE INVENTION
  • Osteoarthritis is a form of degenerative joint disease that involves the deterioration of and changes to the cartilage and bone. In response to inflammation in and about the joint, the body responds with bony recalcification around the joint structure. This process can be slow and gradual with minimal outward symptoms, or more rapidly progressive with significant pain and discomfort. Arthritic changes can occur in response to infection and injury of the joint as well. Step 202 - generation of a training population. Blood samples were taken from 44 test individuals not having any symptoms of osteoarthritis and 50 individuals having mild osteoarthritis using the methods described in Section 5.2.
  • a molecular marker profile resulting in data for molecular marker products of the entire human genome was measured from each of these samples.
  • This gene expression profile data together with knowledge of which subjects have osteoarthritis and which do not constitutes the training population 44.
  • the 44 test individuals that do not have any symptoms of osteoarthritis constitute one trait subgroup within the training population and the 50 individuals having mild osteoarthritis constitute another trait subgroup within the training population.
  • the training population collected in step 202 was used in order to identify combinations of genes that can serve as a classifier to differentiate mild osteoarthritis from non-osteoarthritis.
  • classifiers developed in this example are designed to yield a positive score when they predict that a subject has mild osteoarthritis and a negative score when they predict that the subject is in the control population.
  • two specific classifiers were developed: 100000252 and 100000511.
  • EGRl, G2AN, HSPCA, IKBKAP, IL13RA1, LAMCl, MAFB, and TNFAIP6 are genes that were identified in step 204 and validated in step 208 (Section 5.1) for their ability to discriminate between subjects that have mild osteoarthritis and subjects that do not have osteoarthritis.
  • Step 220 To judge which classifier is more suitable as a classifier for mild osteoarthritis, a ROC curve was computed for both classifiers using the gene expression data from the 44 test individuals not having any symptoms of osteoarthritis and the 50 individuals having mild osteoarthritis. The results of the ROC computation are illustrated in Fig. 8. The area under each ROC was computed. From this computation, it was determined that the area under the ROC curve corresponding to classifier 100000252 was 0.863 whereas the area under the ROC curve corresponding to classifier 100000511 was 0.8169. Step 224.
  • a classifier can be constructed that includes both classifiers 100000252 and 100000511 using the voting methods described in Section 5.1.
  • classifier 100000252 is selected to serve as a classifier for mild osteoarthritis because it generated a larger area under the ROC curve corresponding to the classifier when tested against the training population.
  • manic depression syndrome refers to a mood disorder characterized by alternating mania and depression.
  • Step 202 Blood samples were taken from patients who were diagnosed with manic depression as defined herein, hi each case, the diagnosis of manic depression was corroborated by a skilled Board certified physician. Molecular marker data was measured for each of the molecular markers of the entire human genome using blood samples from individuals who were identified as having manic depression as described herein and ndividuals not having manic depression.
  • Molecular markers were selected from those identified with p value of ⁇ 0.05 and the ability to discriminate between the control and diseased trait subgroups were confirmed using quantitative RT-PCR. Steps 214-218. Eight candidate molecular markers were chosen and an exhaustive analysis of all possible combinations of said molecular markers were considered. Molecular marker data for each of the eight candidate molecular markers was obtained for each member of the training population and logistic regression applied to the molecular marker data so as to develop multiple classifiers. Each classifier was ranked on the basis of area under the curve and those classifiers with an ROC of greater than 0.9 chosen. 6.4 CONSTRUCTION OF CLASSIFIERS FOR PREDICTING RESPONSE TO TREATMENT
  • Blood samples are taken from patients (for example patients with a disease) who are going to enter into treatment (for the disease), or who are already undergoing treatment, but at a timepoint before being able to determine how the patients will respond to treatment.
  • blood samples are taken from patientswho are about to enter into a clinical trial for a new treatment, or who are in the early stages of a clinical trial.
  • blood samples are processed so as to preserve the RNA and/or the protein products of the molecular markers.
  • the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients.
  • patients continue to be monitored for response to treatment using traditional diagnostic methods and grouped into trait subgroups on the basis of the response to treatment.
  • trait subgroups can include patients with a positive response and no negative side effects, patients with a positive response and mild side effects, patients with a negative response, patients with a toxic response, and the like.
  • the evaluation of response to treatment can take days, weeks, months or years.
  • data as described in step 202 of Section 5.1 and Section 5.3 is obtained upon processing of the blood sample.
  • data of step 202 is obtained only after a determination of response to treatment has been made.
  • molecular marker data is obtained from a blood sample taken at a timepoint prior to being able to determine response to treatment.
  • gene expression profiles of blood samples of each trait subgroup are compared using GeneSpringTM software analysis. Hybridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U133A and U133 Plus 2.0) platforms (U133A and U133 Plus 2.0) as described herein. Samples from patients are clustered into two trait subgroups on the basis of the response to treatment.
  • one trait subgroup demonstrates a positive response to treatment whereas the second trait subgroup demonstrates a toxic response to treatment.
  • Step 204. The Wilcox Mann Whitney rank sum test is used to identify candidate molecular markers by identifying molecular marker data that discriminates between the response and non-response trait subgroups with a p value of ⁇ 0.05 to obtain candidate molecular markers.
  • Step 206. Additional molecular marker data for the candidate molecular markers are obtained using quantitative RT-PCR. Some candidate molecular markers are removed at this point should the quantitative RT-PCR data not confirm the ability of each candidate molecular marker to discriminate as between the response trait subgroups. Steps 214 - 218.
  • Candidate molecular marker combinations are chosen and an exhaustive analysis of all possible combinations of molecular markers are tested.
  • logistic regression is applied to the molecular marker data so as to develop multiple classifiers.
  • Each classifier is ranked on the basis of area under the curve using the training population.
  • Those classifiers ranking with an ROC area under curve of greater than 0.9 are further evaluated using a scoring population which is not the training population.
  • the blood samples used for the scoring population are obtained at the same time point as the blood samples used for the training population (e.g., at a time prior to being able to determine response to treatment).
  • blood samples are taken from patients at a time when said patients are disease free.
  • blood samples are processed so as to preserve the RNA and/or the protein products of all molecular markers of the entire genome of said individual. More preferably the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients.
  • patients continue to be monitored for development of said disease using traditional diagnostic methods.
  • two trait subgroups are identified, namely individuals who develop said disease of interest and individuals who do not develop said disease of interest.
  • the timepoint at which trait subgroups are identified can take days, weeks, months or years.
  • data as described in step 202 of Section 5.1 and Section 5.3 is obtained upon processing of the blood sample.
  • data of step 202 is obtained only after a determination of trait subgroups has been made. In all cases, data is obtained from a blood sample taken at a timepoint prior to being able to determine disease.
  • Hybridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U133A and U133 Plus 2.0) platforms (U133A and U133 Plus 2.0) as described herein and candidate molecular markers are identified where the molecular marker data is able to differentiate as between said two trait subgroups with a p value of ⁇ 0.05.
  • Said candidate molecular markers are subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of predicting disease. 6.5.2
  • This example demonstrates the selection of the composition of the training population and the trait subgroups of the training population so as to result in classifiers which are useful to determine treatment compliance. Step 202.
  • blood samples are taken from patients who are complying with said treatment of interest and patients who are not complying with said treatment.
  • blood samples are processed so as to preserve the RNA and/or the protein corresponding to molecular markers of the entire genome of said individual. More preferably the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients.
  • Molecular marker data is obtained as described in step 202 of Section 5.1 and Section 5.3 upon processing of the blood sample.
  • Hybridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U 133 A and Ul 33 Plus 2.0) platforms (U133A and U133 Plus 2.0) as described herein and candidate molecular markers are identified which differentiate as between patients who comply with said treatment of interest as compared with patients who do not comply with said treatment of interest with a p value of 0.05. Said candidate molecular markers are subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of determining treatment compliance. 6.5.3 This example demonstrates the selection of the composition of the training population and the trait subgroups of the training population so as to result in classifiers which are useful to predict reoccurrence of disease. Step 202.
  • blood samples are taken from patients, all of whom have had a disease of interest, at a time when all of said patients are disease free.
  • blood samples are processed so as to preserve the RNA and/or the protein corresponding to molecular markers of the entire genome of said individual. More preferably the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients. Subsequent to when the blood samples are taken, patients continue to be monitored for reoccurence of said disease using traditional diagnostic methods.
  • two trait subgroups are identified, namely individuals who develop reoccurrence of said disease of interest and individuals who do not develop said disease of interest.
  • the timepoint at which trait subgroups are identified can take days, weeks, months or years.
  • data as described in step 202 of Section 5.1 and Section 5.3 is obtained upon processing of the blood sample.
  • data of step 202 is obtained only after a determination of trait subgroups has been made. In all cases, data is obtained from a blood sample taken at a timepoint prior to being able to determine reoccurrence of disease.
  • Hy bridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U133A ana U133 FMs 2 ! ⁇ ) platforms (U133A and U133 Plus 2.0) as described herein and candidate molecular markers are identified whose molecular marker data differentiates as between said two trait subgroups with a p value of ⁇ 0.05. Said candidate molecular markers are subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of predicting reoccurrence of disease.
  • This example demonstrates the use of classifiers used in series so as to diagnose a patient with a stage of disease, for example a specific stage of osteoarthritis.
  • a stage of disease for example a specific stage of osteoarthritis.
  • Classifiers of the invention are able to differentiate as between two subgroups. As such — multiple classifiers are required to specifically stage an individual.
  • a first classifier is developed which differentiates as between osteoarthritis and non-osteoarthritis.
  • a training population is selected comprised of two trait subgroups where said first trait subgroup is comprised of individuals having osteoarthritis and the second trait subgroup comprised of individuals not having osteoarthritis.
  • Identification of candidate molecular markers which differentiate as between these two trait subgroups are identified as per step 202 and subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of differentiating between osteoarthritis and non-osteoarthritis.
  • classifiers are identified which are capable of differentiating between (a) mild osteoarthritis and moderate osteoarthritis (b) moderate osteoarthritis and marked osteoarthritis (c) marked osteoarthritis and severe osteoarthritis.
  • a series of tests are applied in order to diagnose an individual as having marked osteoarthritis and not having mild osteoarthritis or severe osteoarthritis.
  • a series of tests are applied in order to diagnose an individual as having marked osteoarthritis and not having mild osteoarthritis or severe osteoarthritis.
  • a classifier which determines whether said patient has osteoarthritis or not is applied. Assuming said patient has osteoarthritis, a classifier is applied which determines whether said patient has either mild osteoarthritis or marked osteoarthritis. Assuming said patient has marked osteoarthritis,"!
  • Tables IY, 4C and 4D For example, combining Tables IY, 4C and 4D and selecting molecular markers where the molecular marker data demonstrates an ability to differentiate as between the two trait subgroups with a p value of less than 0.0001 results in 212 molecular markers.
  • p values resulting from the molecular marker data for each molecular marker identified in any of Tables IA to 71 can be found in Tables 8 A or Tables 8B below.
  • Table 8 A identifies molecular markers via the Clone ID of the probe used to hybridize to the molecular marker products.
  • the Clone ID corresponds to the Clone ID found in tables IA; 1 AC; IB; 1C; ID; IE; IF; IG; IH; II; U; IK; IL; IM; IN; 10; IP; IQ; IR; IV; IX; IY; IZ; 2; 4A; 4C; 4E; 4G; 41; 4K; 4M; 40; 4Q; 4S; 4V; 7A; 7B; 7C; 7D; 7E; 7H; and 71 (ie those Tables generated using the ChondroChipTM as outlined herein).
  • Table 8A then identifies the corresponding Table in which the molecular market- is identified via said Clone ID. Finally the p value of the molecular marker data obtained using the ChondroChipTM is listed. Note that Table 8A is sorted first by Table number and then by p value.
  • Table 8B identifies molecular markers via the Affymetrix® Spot ID of the probe pair used to hybridize to the molecular marker products.
  • the Affy Spot ID corresponds to the Affy Spot ID found in tables Tables IAA; IAB; IAD; IAE; IAG; IAH; IAT; IS; IT; IU; IW; lZ(b); 3A; 3B; 3C; 3D; 3E; 3F; 3G; 3H; 31; 3K; 3L; 3P; 3Q; 3R; 4B; 4D; 4F; 4H; 4J; 4L; 4N; 4P; 4R; 4T; 4V; 5 A; 5B; 5C; 5D; 5EE; 5F; 5G; 5H; 51; 5 J; 5K; 5L; 5M; 5N; 50; 5P 5Q; 5R; 5S; 5T; 5U; 5V; 6A; 6B; 6C; 7F; and 7G (ie those Tables generated
  • Table 8B then identifies the corresponding Table in which the molecular marker is identified via said Affymetrix® Spot ID. Finally the p value of the molecular marker data obtained using the ChondroChipTM is listed. Note that Table 8B is sorted first by Table number and then by p value. Step 206
  • a training population is chosen having two trait subgroups where the two trait subgroups are outlined in Table F above as corresponding to the Tables used to select the molecular markers, thus in this example, the first trait subgroup is members having mild osteoarthritis and the second trait subgroup is members not having osteoarthritis.
  • a blood sample from each member of the training population is obtained and processed using techniques as described herein and mRNA isolated.
  • the resulting mRNA is reverse transcribed using ABFs High Capacity cDNA Archive Kit and the cDNA is then used for quantitative RT-PCR so as to collect molecular marker data for application to a logistic regression model.
  • Amplification primers are designed for each of the 212 molecular markers.
  • primers are chosen which amplify across an intron junction.
  • Quantitative Real Time PCR is performed using Qiagen's QuantiTectTMSybr Green RT-PCR kit and data corresponding to the level of RNA for each of the molecular markers Steps 214 - 218. From the 212 candidate molecular markers selected, an exhaustive analysis of all possible combinations of molecular markers using the molecular marker data obtained using quantitative RT-PCR data is tested using logistic regression so as to develop multiple classifiers. Each classifier is ranked on the basis of area under the curve using the training population. Those classifiers ranking with an ROC area under curve of greater than 0.8 are further evaluated using a scoring population which is not the training population.
  • Those classifiers resulting in an ROC area under the curve of greater than 0.7 as determined using the scoring population are selected.
  • Each of The selected ciassif ⁇ eia isc-omprjsed of a combination of molecular markers from one of Tables IY, 4C and 4D. USE OF THE SELECTED CLASSIFIER TO DIAGNOSE AN INDIVIDUAL AS HAVING MILD OSTEOARTHRITIS. Any of the selected classifiers can be used to diagnose an individual as having mild osteoarthritis.
  • a blood sample from a test individual is processed using techniques as described herein to isolate mRNA. mRNA is reverse transcribed using ABFs High Capacity cDNA Archive Kit and the cDNA is then used for quantitative RT-PCR.
  • Amplification primers are designed for each of the molecular markers in the classifier selected which amplify across an intron junction.
  • Quantitative Real Time PCR is performed using Qiagen's QuantiTectTMSybr Green RT-PCR kit and data corresponding to the level of RNA for each of the molecular markers of the selected classifier obtained. The data is then used in conjunction with the logistic regression classifier so as to convert the data resulting from the Quantitative RT-PCR into a single number. If the number is greater than 0 - the test individual is diagnosed as having mild osteoarthritis is the number is less than 0 the test individual is diagnosed as not having osteoarthritis. 7.
  • the present invention can be implemented as a computer program product that comprises a computer program mechanism embedded in a computer readable storage medium.
  • the computer program product could contain the program modules shown in Fig. 1. These program modules may be stored on a CD-ROM, DVD, magnetic disk storage product, or any other computer readable data or program storage product.
  • the software modules in the computer program product can also be distributed electronically, via the Internet or otherwise, by transmission of a computer data signal (in which the software modules are embedded) on a carrier wave.
  • ncr9779 " ⁇ R' 0702 fcrc5850 IR 0.03 ncrbO367 IR 0.02 mioa6585 IR 0.03 seob0949 IR 0.02 mioc2961 IR 0.03 seob9871 IR 0.02 miodl885 IR 0.03 fcrl608 IR 0.02 ncrc5232 IR 0.03 fcr3181 IR 0.02 seoa7366 IR 0.03 seocl957 IR 0.02 seoa8609 IR 0.03 ncrc3526 IR 0.02 seob3545 IR 0.03 ncr6811 IR 0.02 seob6020 IR 0.03 fcrb6410 IR 0.02 seob6680 IR 0.03 fcrbl924 IR 0.02 seoa5525 IR 0.03 seoa6387 IR 0.02 mioc7296 IR 0.03 seoa0221 IR 0.02 seob6132
  • ncrc2443 IR 0.04 seoc2030 IR 0.04 ncr6637 IR 0.04 seoal644 IV 2.4e-07 fcrb5679 IR 0.04 fcrb4985 IV 7.42e-07 seob6028 IR 0.04 seob3307 IV 8.48e-06 seoa0238 IR 0.04 seob2775 IV 1.28e-05 fcrcO559 IR 0.04 fcrb5214 IV 2.5e-05 ncrc0803 IR 0.04 fcrb8489 IV 2.68e-05 mioc6925 IR 0.04 mioc6211 IV 3.64e-05 hfcr3224 IR 0.04 miod6234 IV 3.66e-05 ncrcO863 IR 0.04 hfcr3180 IV 4.19e-05 miob4760 IR 0.04 fcrb2256 IV 4.65e-05 seob5954 IR 0.04 nc
  • ncr5651 IV 0.01 seoa0823 IV 0.01 fcrc2429 IV 0.01 seob9435 IV 0.01 ncrc7085 IV 0.01 mioc4280 IV 0.01 mioa3963 IV 0.01 seoc4436 IV 0.01 seocl906 IV 0.01 mioa5059 IV 0.01 r ⁇ ioa7976 IV 0.01 ncrcl606 IV 0.01 seoa9656 IV 0.01 miocl379 IV 0.01 fcrb3476 IV 0.01 miob0939 IV 0.01 ncrc5091 IV 0.01 fcr2582 IV 0.01 mioc0206 IV 0.01 fcrbO146 IV 0.01 ncr5709 IV 0.01 fcrbl689 IV 0.01 seoa5787 IV 0.01 fcrb2017 IV 0.01 seob6492 IV 0.01 fcrb3576 IV 0.01 seob8194 IV 0.01 fcrb3725 lv 0.
  • ncrO775 40 0.03 ncrl352 40 0.03 ncrc6861 40 0.03 fcrcO775 40 0.03 seob3468 40 0.03 fcr3599 40 0.03 mioa0245 40 0.03 miod5591 40 0.03 mioa8984 40 0.03 seoal460 40 0.03 fcrO986 40 0.03 seob8099 40 0.03 seoc4720 40 0.03 mioc3574 40 0.03 ncrc2675 40 0.03 fcrb2090 40 0.03 fcrb5416 40 0.03 fcrb4248 40 0.03 fcrb2198 40 0.03 fcrb8740 40 0.03 miod5030 40 0.03 seob8489 40 0.03 ncrc4047 40 0.03 fcrb9602 40 0.03 seoc0778 40 0.03 fcrb2472 40 0.03 ncr7967 40 0.03 seob5081 40 0.03

Abstract

The present invention provides systems and method for constructing classifiers that distinguish between trait subgroups using molecular marker data from blood samples. The invention further encompasses the use of the classifiers and combinations of molecular markers identified by the classifiers in a wide variety of applications including: diagnosis; prognosis; prediction of disease, stage of disease or disease risk; monitoring disease progression and/or regression; monitoring disease reoccurrence and identifying risk of disease reoccurrence; determining and/or predicting response to treatment and/or treatment outcomes; monitoring and/or predicting treatment compliance or non compliance and the like. The invention further provides a variety of selected molecular markers and a means to identify combinations of the selected molecular markers useful for diagnosing particular traits of interest.

Description

COMPUTER SYSTEMS AND METHODS FOR CONSTRUCTING BIOLOGICAL CLASSIFIERS AND USES THEREOF
This application is entitled to and claims priority benefit under 35 U.S. C. Section
119(e) to U.S. provisional application Number 60/581,312, filed June 19, 2004, U.S.
provisional application Number 60/581,977, filed June 21, 2004, U.S. provisional
application Number 60/643,475, filed January 12, 2005, and U.S. provisional application
Number 60/663,722, filed March 22, 2005, each of which is incorporated herein by
reference in its entirety.
1. FIELD OF THE INVENTION
The field of this invention relates to computer systems and methods to identify
classifiers using data obtained from blood. The invention further encompasses the use of
the classifiers and combinations of molecular markers identified by the classifiers in a
wide variety of applications including: diagnosis; prognosis; prediction of disease, stage of
disease or disease risk; monitoring disease progression and/or regression; monitoring
disease reoccurrence and identifying risk of disease reoccurrence; determining and/or
predicting response to treatment and/or treatment outcomes; monitoring and/or predicting
treatment compliance or non compliance and the like.
Table DESCRIPTION SIZE Date Text File Name Recorded IA Sequence Related Table 96KB June 17, TABLE1A.TXT regarding Comorbid 2005 Hypertension IB Sequence Related Table 102KB June 17, TABLE1B.TXT regarding Comorbid Obesity 2005 1C Sequence Related Table 49KB June 17, TABLE1C.TXT regarding Comorbid Allergies 2005 ID Sequence Related Table 59KB June 17, TABLE10.TXT regarding Comorbid Systemic 2005 Steroids IE Sequence Related Table 204KB June 17, TABLE1E.TXT regarding Hypertension 2005 (Chondro) IF Sequence Related Table 251KB June 17, TABLE1F.TXT regarding Obesity (Chondro) 2005 IG Sequence Related Table 57KB June 17, TABLE1G.TXT regarding CoMorbid 2005 Hypertension Only IH Sequence Related Table 23KB June 17, TABLE1H.TXT regarding Hypertension OA 2005 Shared II Sequence Related Table 60KB June 17, TABLE1I.TXT regarding Comorbid Obesity 2005 Only IJ Sequence "Related Table 18KB June 17, TABLE1J.TXT regarding Obesity OA Shared 2005 IK Sequence Related Table 28KB June 17, TABLE1K.TXT regarding Comorbid Allergy 2005 Only IL Sequence Related Table 19KB June 17, TABLE1L.TXT regarding Allergy OA Shared 2005 IM Sequence Related Table 40KB June 17, TABLE1M.TXT regarding Comorbid Steroid 2005 Shared IN Sequence Related Table 23KB June 17, TABLE1N.TXT regarding Steroid OA Shared 2005 10 Sequence Related Table 49KB June 17, TABLE1O.TXT regarding Differentiating 2005 Systemic Steroids ( IP Sequence Related Table 118KB June 17, TABLE1P.TXT regarding Diabetes 2005 IQ Sequence Related Table 165KB June 17, TABLE1Q.TXT regarding Hyperlipidemia 2005 IR Sequence Related Table 102KB June 17, TABLE1R.TXT regarding Lung Disease 2005 IS Sequence Related Table 830KB June 17, TABLE1S.TXT regarding Bladder Cancer 2005 IT Sequence Related Table 483KB June 17, TABLE1T.TXT regarding Bladder Cancer 2005 Staging IU Sequence Related Table 657KB June 17, TABLE1U.TXT regarding Coronary Artery 2005 Disease IV Sequence Related Table 380KB June 17, TABLE1V.TXT regarding Rheumatoid 2005 Arthritis IW Sequence Related Table 183KB June 17, TABLE1W.TXT regarding Rheumatoid 2005 Arthritis IX Sequence Related Table 165KB June 17, TABLE1X.TXT regarding Depression 2005 IY Sequence Related Table 32KB June 17, TABLE1Y.TXT regarding OAStaging 2005 IZ Sequence Related Table 19KB June 17, TABLE1Z.TXT regarding Liver Cancer 2005 IZb Seqeuence Related Table 430KB June 17, TABLE1Z.TXT regarding Liver Cancer 2005 IAA Sequence Related Table 592KB June 17, TABLE1AA.TXT regarding Schizophrenia 2005 IAB Sequence Related Table 142KB June 17, TABLE1AB.TXT regarding Chagas Disease 2005 IAC Sequence Related Table 64KB June 17, TABLE1AC.TXT regarding Asthma (Chondro) 2005 IAD Sequence Related Table 57KB June 17, TABLE1AD.TXT regarding Asthma (Affy) 2005 IAE Sequence Related Table 118KB June 17, TABLE 1AE.TXT regarding Lung Cancer 2005 IAG Sequence Related Table 157KB June 17, TABLE1AG.TXT regarding Hypertension 2005 (Afrymetrix) IAH Sequence Related Table 203KB June 17, TABLE1AH.TXT regarding Obesity 2005 (Affymetrix) IAI Sequence Related Table 267KB June 17, TABLE1AI.TXT regarding Ankylosing 2005 Spondylitis (Afify) 2 Sequence Related Table 19KB June 17, TABLE2.TXT regarding OA Only 2005 Subtraction 3A Sequence Related Table 228KB June 17, TABLE3A.TXT regarding Schizophrenia v. 2005 MDS 3B Sequence Related Table 347KB June 17, TABLE3B.TXT regarding Hepatitis v. Liver 2005 Cancer 3C Sequence Related Table 470KB June 17, TABLE3C.TXT regarding Bladder Cancer v. 2005 Kidney Cancer 3D Sequence Related Table 556KB June 17, TABLE3D.TXT regarding Bladder Cancer v. 2005 Testicular Cancer 3E Sequence Related Table 588KB June 17, TABLE3E.TXT regarding Testicular Cancer v. 2005 Kidney Cancer 3F Sequence Related Table 84KB June 17, TABLE3F.TXT regarding Liver Cancer v. 2005 Stomach Cancer 3G Sequence Related Table 149KB June 17, TABLE3G.TXT regarding Liver Cancer v. 2005 Colon Cancer 3H Sequence Related Table 166KB June 17, TABLE3H.TXT regarding Stomach Cancer v. 2005 Colon Cancer 31 Sequence Related Table 214KB June 17, TABLE3I.TXT regarding OA v. RA 2005 3K Sequence Related Table 16KB June 17, TABLE3K.TXT regarding Chagas Disease 2005 v.Heart Failure 3L Sequence Related Table 19KB June 17, TABLE3L.TXT regarding Chagas Disease v. 2005 CAD 3N Sequence Related Table 13KB June 17, TABLE3N.TXT regarding CAD v. Heart 2005 Failure 3P Sequence Related Table 68KB June 17, TABLE3P.TXT regarding Asymptomatic 2005 Chagas v. Symptomatic Chagas
Q Sequence Related Table 56KB June 17, TABLE3Q.TXT regarding Alzheimers' v. 2005 Schizophrenia R Sequence Related Table 51KB June 17, TABLE3R.TXT regarding Alzheimers' v. 2005 Manic Depression A Sequence Related Table 538KB June 17, TABLE4A.TXT regarding OA v. Control 2005 (ChondroChip) B Sequence Related Table 550KB June 17, TABLE4B.TXT regarding OA v. Control 2005 (Affy) C Sequence Related Table 321KB June 17, TABLE4C.TXT regarding OA mild v. Control 2005 (ChondroChip) D Sequence Related Table 587KB June 17, TABLE 4D.TXT regarding OA mild v. Control 2005 (Affy) E Sequence Related Table 198KB June 17, TABLE4E.TXT regarding OA moderate v. 2005 Control (ChondroChip) F Sequence Related Table 576KB June 17, TABLE4F.TXT regarding OA moderate v. 2005 Control (Affy) G Sequence Related Table 203KB June 17, TABLE4G.TXT regarding OA marked v. 2005 Control (ChondroChip) H Sequence Related Table 679KB June 17, TABLE4H.TXT regarding OA marked v. 2005 Control (Affy) 1 Sequence Related Table 291KB June 17, TABLE4I.TXT regarding OA severe v. 2005 Control (ChondroChip) J Sequence Related Table 607KB June 17, TABLE4J.TXT regarding OA severe v. 2005 Control (Affy) K Sequence Related Table 113KB June 17, TABLE4K.TXT regarding OA mild v. 2005 moderate (ChondroChip) L Sequence Related Table 488KB June 17, TABLE4L.TXT regarding OA mild v. 2005 moderate (Affy) M Sequence Related Table 93KB June 17, TABLE4M.TXT regarding OA mild v. marked 2005 (ChondroChip) N Sequence Related Table 373KB June 17, TABLE4N.TXT regarding OA mild v. marked 2005 (Affy) 0 Sequence Related Table 177KB June 17, TABLE4O.TXT regarding OA mild v. severe 2005 (ChondroChip) P Sequence Related Table 687KB June 17, TABLE4P.TXT regarding OA mild v. severe 2005 (Affy) Q Sequence Related Table 103KB June 17, TABLE4Q.TXT regarding OA moderate v. 2005 marked (ChondroChip) R Sequence Related Table 450KB June 17, TABLE4R.TXT regarding OA moderate v. 2005 marked (Affy) S Sequence Related Table 79KB June 17, TABLE4S.TXT regarding OA moderate v. 2005 severe (ChondroChip) T Sequence Related Table 627KB June 17, TABLE4T.TXT regarding OA moderate v. 2005 severe (Affy) U Sequence Related Table 66KB June 17, TABLE4U.TXT regarding OA marked v. 2005 severe (ChondroChip) V Sequence Related Table 758KB June 17, TABLE4V.TXT regarding OA marked v. 2005 severe (Affy) A Sequence Related Table 80KB June 17, TABLE5A.TXT regarding Psoriasis v. Control 2005 B Sequence Related Table 373KB June 17, TABLE5B.TXT regarding Thyroid Disorder v. 2005 Control C Sequence Related Table 87KB June 17, TABLE5C.TXT regarding Irritable Bowel 2005 Syndrome v. Control D Sequence Related Table 79KB June 17, TABLE5D.TXT regarding Osteoporosis v. 2005 Control E Sequence Related Table 231KB June 17, TABLE5E.TXT regarding Migraine 2005 Headaches v. Control F Sequence Related Table 56KB June 17, TABLE5F.TXT regarding Eczema v. Control 2005 G Sequence Related Table 349KB June 17, TABLE5G.TXT regarding NASH v. Control 2005 H Sequence Related Table 268KB June 17, TABLE5H.TXT regarding Alzheimers' v. 2005 Control 1 Sequence Related Table 298KB June 17, TABLE5I.TXT regarding Manic Depression 2005 v. Control J Sequence Related Table 45KB June 17, TABLE5J.TXT regarding Crohns' Colitis v. 2005 Control K Sequence Related Table 53KB June 17, TABLE5K.TXT regarding Chronic Cholecystis 2005 v. Control 5L Sequence Related Table 160KB June 17, TABLE5L.TXT regarding Heart Failure v. 2005 Control 5M Sequence Related Table 304KB June 17, TABLE5M.TXT regarding Cervical Cancer v. 2005 Control 5N Sequence Related Table 185KB June 17, TABLE5N.TXT regarding Stomach Cancer v. 2005 Control 50 Sequence Related Table 404KB June 17, TABLE5O.TXT regarding Kidney Cancer v. 2005 Control 5P Sequence Related Table 486KB June 17, TABLE5P.TXT regarding Testicular Cancer v. 2005 Control 5Q Sequence Related Table 380KB June 17, TABLE5Q.TXT regarding Colon Cancer v. 2005 Control 5R Sequence Related Table 140KB June 17, TABLE5R.TXT regarding Hepatitis B v. 2005 Control 5S Sequence Related Table 177KB June 17, TABLE5S.TXT regarding Pancreatic Cancer 2005 v. Control 5T Sequence Related Table 63KB June 17, TABLE5T.TXT regarding Asymptomatic 2005 Chagas v. Control 5U Sequence Related Table 77KB June 17, TABLE5U.TXT regarding Symptomatic 2005 Chagas v. Control 5V Sequence Related Table 383KB June 17, TABLE5V.TXT regarding Bladder Cancer v. 2005 Control 6A Sequence Related Table 163KB June 17, TABLE6A.TXT regarding Cancer (all types) v. 2005 Control 6B Sequence Related Table 73KB June 17, TABLE6B.TXT regarding Cardiovascular 2005 Disease v. Control βc Sequence Related Table 337KB June 17, TABLE6C.TXT regarding Neurological 2005 Diseases v. Control 7A Sequence Related Table 55KB June 17, TABLE7A.TXT regarding Celebrex® v. all 2005 Cox inhibitors except Celebrex 7B Sequence Related Table 57KB June 17, TABLE7B.TXT regarding Celebrex® v. 2005 Control 7C Sequence Related Table 53KB June 17, TABLE7C.TXT regarding Vioxx® v. Control 2005 7D Sequence Related Table 49KB June 17, TABLE7D.TXT regarding Vioxx® v. All Cox 2005 Inhibitors except Vioxx® 7E Sequence Related Table 72KB June 17, TABLE7E.TXT regarding NSAIDS v. Control 2005 7F Sequence Related Table 208KB June 17, TABLE7F.TXT regarding Cortisone v. Control 2005 7G Sequence Related Table 316KB June 17, TABLE7G.TXT regarding Visco Supplement 2005 v. Control 7H Sequence Related Table 131KB June 17, TABLE7H.TXT regarding Lipitor® v. Control 2005 71 Sequence Related Table 23KB June 17, TABLE7I.TXT regarding Smoker v. Non- 2005 Smoker
2. BACKGROUND OF THE INVENTION
The prior art is deficient in simple, non-invasive and effective methods of
identifying molecular markers and the use of said molecular markers for purposes of:
diagnosis; prognosis; prediction of disease, stage of disease or disease risk; monitor
disease progression and/or regression; monitor disease reoccurrence and identifying risk of
disease reoccurrence or the like. The prior art is also deficient in simple non-invasive
methods of identifying molecular markers and use of said molecular markers to determine
and/or predict response to treatment and/or treatment outcomes, monitor and/or predict
treatment compliance or non-compliance, etc. Although progress has been made in
identifying molecular markers by detecting the products of putative molecular markers
using expression arrays in a variety of diagnostic areas and therapeutic areas, such
progress has been primarily limited to studying non-blood tissue samples, such as primary
tumors, that are difficult to obtain and thus have limited potential as a diagnostic. What is
even more unsatisfactory is that retrieval of such tissue samples often requires invasive
medical procedures such as surgery. Prediction of response to treatment is also a
significant problem. It is well understood that, for many currently recognized treatments,
only a small percentage of the population (for example approximately 20-30%) will
respond positively. Amongst the remainder of the population, there are those who do not
improve, and others who display a negative or toxic response to the treatment. As a result
of these detrimental effects to some, many effective treatments do not get to market. The
prior art is thus deficient in simple, non-invasive methods to analyze and predict treatment
and response to treatment. Such drawbacks have made identification of molecular markers unsatisfactorily difficult. See, for example, Alon et al, 1999, Proc. Natl. Acad. Sci. USA 96, pp. 6745- 6750; Schummer et al, 1999, Gene 238, pp. 375-385; and van't Veer, 2002, Nature 415, pp. 530-536. Even where progress has been made in identifying molecular markers by monitoring molecular marker products using expression arrays - whether in blood or using tissue - the techniques utilized merely identify large number of molecular markers two or more of which may be required so as to permit categorizing an unknown sample for diagnosis. It is not clear, however, which of these molecular markers are most useful to accurately diagnose an unknown sample. In addition, techniques currently available in the art are not sufficiently robust (ie high levels of reproducibility) in accordance with scientific and regulatory standards so as to be used reliably to diagnose a test individual. Thus what is required in the art is a means to select smaller subsets of useful molecular markers which when used in combination permit the accurate and reproducible diagnosis of an unknown sample for a particular trait of interest. Further what is required in the art is a means of translating the molecular marker data from these selected combinations so as to convert these into a diagnosis. Discussion or citation of a reference herein will not be construed as an admission that such reference is prior art to the present invention. 3. SUMMARY OF THE INVENTION
Thus what is needed in the art is a method to identify useful combinations of molecular markers and a means of using said combinations of molecular markers (or more accurately measurement of the products of said molecular markers) so as to permit diagnosis of a test sample. Embodiments of the present invention address many of the shortcomings and drawbacks found in the prior art by the novel approach of using molecular marker measurement data from blood and methods of processing such data to screen the large numbers of candidate molecular markers in blood so as to identify useful combinations of these molecular markers. Embodiments of the present invention involve the construction of classifiers and use of these classifiers. In addition, embodiments of the invention involve the use of the molecular markers identified by these classifiers to diagnose or otherwise determine whether a test subject has a specific trait of interest. Blood offers a surprisingly informative alternative to tissues as a source of information. Blood includes numerous cell types including monocytes, leukocytes, lymphocytes, erythrocytes, platelets, as well as possibly many other cell types. The turnover of cells in the human circulatory system is rapid. As a consequence of continuous interactions between the blood and the body, it has been hypothesized that the changes that occur within the cells or tissues of the body will trigger specific changes in gene expression within blood. See, for example, United States patent application serial No. 10/601,518, filed June 20, 2003 , United States patent application serial No.10/802,875, filed March 12, 2004, United States patent application serial No. 10/809,675, filed March 25, 2004, United States patent application serial No. 10/268,730, filed October 9, 2002, United States patent application serial No. 09/477,148, filed January 4, 2000, and United States patent application serial No. 60/115,125, filed Jan. 6, 1999, which are hereby incorporated herein by reference in their entirety. Thus, blood has the potential to provide a powerful indicator of what is happening in the human body at any given time, but provides unique challenges to harness this vast amount of potential information available. Embodiments of the current invention help address this challenge. 4. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 illustrates a computer system for determining and selecting useful biological classifiers. Fig. 2 illustrates a method for deriving biological classifiers in accordance with an embodiment of the present invention. Fig. 3 is a flowchart of a method of applying the classifiers to a patient. Fig. 4 illustrates a data structure for storing high throughput information for a plurality of molecular markers in accordance with one embodiment of the present invention. Fig. 5 illustrates a data structure for storing a plurality of classifiers in accordance with one embodiment of the present invention. Fig. 6 illustrates a patient database for storing data for molecular markers for a plurality of patients in accordance with an embodiment of the present invention. Fig. 7 illustrates a Receiver Operating Characteristic (ROC) curve that is used to assess the discriminating ability of a molecular marker or a classifier in accordance with one embodiment of the present invention. Fig. 8 illustrates ROC curves corresponding to two candidate classifiers for osteoarthritis computed in accordance with one embodiment of the present invention. Description of Tables; Table 1 as a group of tables identifies the molecular markers that are differentially expressed in blood samples from patients with a disease or patients who are co-morbid as compared to blood samples from healthy patients or patients without said disease, or with only one of said co-morbid diseases and also shows the sequences of selected products of the identified molecular markers. Molecular marker data from the molecular markers listed in each table or a subset of these molecular markers can be used can be used in steps 214- 218 as outlined in Figure 2B so as to identify classifiers and the combinations of molecular markers which form the classifiers useful in diagnosis. Table IA identifies the molecular markers which are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and hypertension as compared with a second trait subgroup wherein each member of the second trait subgroup has neither osteoarthritis nor hypertension using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IB shows the identity of those molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and obesity as compared with a second trait subgroup wherein each member of the second trait subgroup has neither osteoarthritis nor obesity using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 1C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and allergies as compared with a second trait subgroup wherein each member of the second trait subgroup has neither osteoarthritis nor allergies using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table ID shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both osteoarthritis and subject to systemic steroids as compared with normal patients using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IE shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has hypertension as compared to a second trait subgroup wherein each member of the second trait subgroup did not have hypertension using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IF shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has obesity as compared to a second trait subgroup wherein each member of the second trait subgroup did not have obesity using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IG shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has hypertension and OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only wherein molecular markers identified in Table IA have been removed so as to identify molecular markers which are unique to hypertension. The table also shows the sequences of selected products of the identified molecular markers. Table IH shows the molecular markers which were identified in Table IA which are shared with those molecular markers differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both hypertension and OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only. The table also shows the sequences of selected products of the identified molecular markers. Table II shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both obesity and have OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only and wherein molecular markers identified in Table IB have been removed so as to identify molecular markers which are unique to obesity. The table also shows the sequences of selected products of the identified molecular markers. Table IJ shows the molecular markers identified in Table IB which are shared with those molecular markers differentially expressed in blood samples from patients who are obese and have OA when compared with patients who have OA. The table also shows the sequences of selected products of the identified molecular markers. Table IK shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both allergies and OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only wherein molecular markers identified in Table 1C have been removed so as to identify molecular markers which are unique to allergies. The table also shows the sequences of selected products of the identified molecular markers. Table IL shows the identify of those molecular markers identified in Table 3 C which are shared with those molecular markers differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both allergies and OA when compared with a second trait subgroup wherein each member of the second trait subgroup having OA only. The table also shows the sequences of selected products of the identified molecular markers. Table IM shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking systemic steroids and has OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only wherein molecular markers identified in Table ID have been removed so as to identify molecular markers which are unique to patients on systemic steroids. The table also shows the sequences of selected products of the identified molecular markers. Table IN shows the identify of those molecular markers identified in Table ID which are shared with those molecular markers differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup who are on systemic steroids and have OA when compared with a second trait subgroup wherein each member of the second trait subgroup have OA only. The table also shows the sequences of selected products of the identified molecular markers. Table IO shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup are either taking birth control, on prednisone or on hormone replacement therapy and presenting with OA using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IP shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both type II diabetes as compared to a second trait subgroup wherein each member of the second trait subgroup does not have type II diabetes using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IQ shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Hyperlipidemia as compared to a second trait subgroup wherein each member of the second trait subgroup does not have Hyperlipidemia using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IR shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has lung disease as compared to a second trait subgroup wherein each member of the second trait subgroup does not have lung disease using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IS shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has bladder cancer as compared to a second trait subgroup wherein each member of the second trait subgroup does not have bladder cancer using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IT shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has early stage bladder cancer, late stage bladder cancer with a second trait subgroup wherein each member of the second trait subgroup does not have bladder cancer using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IU shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has coronary artery disease (CAD) as compared to a second trait subgroup wherein each member of the second trait subgroup does not have not having CAD using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IV shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has rheumatoid arthritis as compared to a second trait subgroup wherein each member of the second trait subgroup does not have rheumatoid arthritis using the ChondroChip™ platform . The table also shows the sequences of selected products of the identified molecular markers. Table IW shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has rheumatoid arthritis as compared to a second trait subgroup wherein each member of the second trait subgroup does not have rheumatoid arthritis using the Affymetrix® platform . The table also shows the sequences of selected products of the identified molecular markers. Table IX shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has depression as compared with a second trait subgroup wherein each member of the second trait subgroup does not having depression using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IY shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has one of various stages of osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IZ shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have liver cancer using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table IZ(B) shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have liver cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table IAA shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has schizophrenia as compared with a second trait subgroup wherein each member of the second trait subgroup does not have schizophrenia using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table IAB shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Chagas disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chagas disease using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table IAC shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has both asthma and osteoarthritis as compared a second trait subgroup wherein each member of the second trait subgroup has only osteoarthritis using the ChondroChip™. The table also shows the sequences of selected products of the identified molecular markers. Table IAD shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has asthma as compared with a second trait subgroup wherein each member of the second trait subgroup does not have asthma using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table IAE shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has lung cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have lung cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table IAG shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has hypertension as compared with a second trait subgroup wherein each member of the second trait subgroup does not have hypertension using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table IAH shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has obesity as compared with a second trait subgroup wherein each member of the second trait subgroup does not have obesity using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table IAI shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has ankylosing spondylitis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have ankylosing spondylitis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 2 shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has either mild or severe OA, but for which molecular markers relevant to asthma, obesity, hypertension, systemic steroids and allergies have been removed. The table also shows the sequences of selected products of the identified molecular markers. Table 3 is a group of tables wherein each table shows those molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a first disease as compared to blood samples from a second trait subgroup wherein each member of the second trait subgroup has a second disease so as to allow differential diagnosis as between said first and second disease. Table 3A shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has schizophrenia as compared with a second trait subgroup wherein each member of the second trait subgroup has manic depression syndrome (MDS) using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3B shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has hepatitis as compared with a second trait subgroup wherein each member of the second trait subgroup has liver cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3C shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has bladder cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has liver cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3D shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has bladder cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has testicular cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3E shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has testicular cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has kidney cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3F shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has stomach cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3G shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has liver cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has colon cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3H shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has stomach cancer as compared with a second trait subgroup wherein each member of the second trait subgroup has colon cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 31 shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Rheumatoid Arthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3K shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Chagas Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Heart Failure using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3L shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Chagas Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Coronary Artery Disease using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3N shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Coronary Artery Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Heart Failure using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3P shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Asymptomatic Chagas Disease as compared with a second trait subgroup wherein each member of the second trait subgroup has Symptomatic Chagas Disease using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3Q shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Alzheimer's' as compared with a second trait subgroup wherein each member of the second trait subgroup has Schizophrenia using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 3R shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Alzheimer's' as compared with a second trait subgroup wherein each member of the second trait subgroup has Manic Depression Syndrome using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4 tables are those which shows molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a stage of Osteoarthritis as compared to blood samples from a second trait subgroup wherein each member of the second trait subgroup has a second stage of Osteoarthritis so as to allow monitoring of progression and/or regression of disease. Each table also shows the sequences of selected products of the identified molecular markers. Table 4A shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4B shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4C shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without mild Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4D shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4E shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with patients without Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4F shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4G shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4H shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 41 shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has severe Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4J shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has severe Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup is without Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4K shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has moderate Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4L shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has moderate Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4M shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4N shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4O shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4P shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has mild Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers . Table 4Q shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4R shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has marked Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4S shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with patients a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4T shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has moderate Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4U shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 4V shows the molecular markers that are differentially expressed in blood from a training population comprised of a first trait subgroup where each member of the subgroup has marked Osteoarthritis as compared with a second trait subgroup wherein each member of the second trait subgroup has severe Osteoarthritis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5 tables are those which identify molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a disease or condition of interest as compared to blood samples from a second trait subgroup wherein each member of the second trait subgroup is without said disease or condition. The tables also shows the sequences of selected products of the identified molecular markers. Table 5A shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has psoriasis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have psoriasis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5B shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has thyroid disorder as compared with a second trait subgroup wherein each member of the second trait subgroup does not have thyroid disorder using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has irritable bowel syndrome as compared with a second trait subgroup wherein each member of the second trait subgroup does not have irritable bowel syndrome using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5D shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has osteoporosis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have osteoporosis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5E shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has migraine headaches as compared with a second trait subgroup wherein each member of the second trait subgroup does not have migraine headaches using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5F shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has eczema as compared with a second trait subgroup wherein each member of the second trait subgroup does not have eczema using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5G shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has NASH as compared with a second trait subgroup wherein each member of the second trait subgroup does not have NASH using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5H shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Alzheimers' disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Alzheimer's disease using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 51 shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Manic Depression Syndrome as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Manic Depression Syndrome using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5J shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Crohn's Colitis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Crohn's Colitis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5K shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Chronic Cholecystis as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chronic Cholecystis using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5L shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Heart Failure as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Heart Failure using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5M shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Cervical Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Cervical Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5N shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Stomach Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Stomach Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5O shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Kidney Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Kidney Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5P shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Testicular Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Testicular Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5Q shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Colon Cancer as compared a second trait subgroup wherein each member of the second trait subgroup does not have Colon Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5R shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Hepatitis B as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Hepatitis B using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5S shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Pancreatic Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Pancreatic Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5T shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Asymptomatic Chagas as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chagas using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5U shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Symptomatic Chagas as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Chagas using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 5V shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Bladder Cancer as compared with patients not having Bladder Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 6 tables are those tables which show those molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has any one of a series of related conditions as compared to blood samples a second trait subgroup wherein each member of the second trait subgroup does not have said related conditions. The table also shows the sequences of selected products of the identified molecular markers. Table 6A shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has Cancer as compared with a second trait subgroup wherein each member of the second trait subgroup does not have Cancer using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 6B shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup with Cardiovascular Disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have a Cardiovascular Disease using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 6C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup has a Neurological Disease as compared with a second trait subgroup wherein each member of the second trait subgroup does not have a Neurological Disease using the Affymetrix® platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7 tables are those tables which show molecular markers that are differentially expressed in blood samples from with a condition wherein said condition is a treatment as compared to blood samples from patients without said treatment or with a different said treatment. Table 7 A shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Celebrex® as compared a second trait subgroup wherein each member of the second trait subgroup is taking a Cox Inhibitor which was not Celebrex® using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7B shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Celebrex® as compared with a second trait subgroup wherein each member of the second trait subgroup is not taking Celebrex® using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7C shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Vioxx® as compared a second trait subgroup wherein each member of the second trait subgroup is not taking Vioxx® using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7D shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Vioxx® as compared with a second trait subgroup wherein each member of the second trait subgroup on a Cox inhibitor but not on Vioxx® using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7E shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking NSAIDS as compared with patients not on NSAIDS using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7F shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Cortisone as compared with a second trait subgroup wherein each member of the second trait subgroup on not on Cortisone using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7G shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Visco Supplement as compared with a second trait subgroup wherein each member of the second trait subgroup not on Visco Supplement using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 7H shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is taking Lipitor® as compared with a second trait subgroup wherein each member of the second trait subgroup not on Lipitor® using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. Table 71 shows the molecular markers that are differentially expressed in blood samples from a training population comprised of a first trait subgroup where each member of the subgroup is who are smokers as compared with a second trait subgroup wherein each member of the second trait subgroup who are not smokers using the ChondroChip™ platform. The table also shows the sequences of selected products of the identified molecular markers. To further clarify, Tables IAA; IAB; IAD; IAE; IAG; IAH; IAT; IS; IT; IU; IW; lZ(b); 3A; 3B; 3C; 3D; 3E; 3F; 3G; 3H; 31; 3K; 3L; 3P; 3Q; 3R; 4B; 4D; 4F; 4H; 4J; 4L; 4N; 4P; 4R; 4T; 4V; 5A; 5B; 5C; 5D; 5EE; 5F; 5G; 5H; 51; 5J; 5K; 5L; 5M; 5N; 50; 5P 5Q; 5R; 5S; 5T; 5U; 5V; 6A; 6B; 6C; 7F; and 7G each identify the molecular markers identified using the Affymetrix® genechip to screen the products of the majority of molecular markers of the human genome in accordance with step 202. Tables IA; 1 AC; IB; 1C; ID; IE; IF; IG; IH; II; U; IK; IL; IM; IN; 10; IP; IQ; IR; IV; IX; IY; IZ; 2; 4A; 4C; 4E; 4G; 41; 4K; 4M; 40; 4Q; 4S; 4V; 7A; 7B; 7C; 7D; 7E; 7H; and 71 each identify molecular markers identified using our own ChondroChip™ genechip to screen the products of the majority of the molecular markers of the human genome. Like reference numerals refer to corresponding parts throughout the several views of the drawings. 5. DETAILED DESCRIPTION
The embodiments of the present invention use novel approaches to screen and select molecular markers and develop classifiers that can be used to harness the use of molecular marker data from blood. The present invention thus provides systems and methods for constructing biological classifiers using molecular marker data from blood by providing a method to screen and select from a large variety of potential molecular markers so as to identify a small subset of molecular markers. The classifiers and the combinations of molecular markers identified using aspects of the current invention are useful for a wide variety of purposes including: diagnosis; prognosis; prediction of disease, stage of disease or disease risk; monitoring disease progression and/or regression; monitoring disease reoccurrence and identifying risk of disease reoccurrence; determining and/or predicting response to treatment and/or treatment outcomes; monitoring and/or predicting treatment compliance or non-compliance and the like. As used herein, a "condition" includes a mode or state of being including a physical, emotional, psychological or pathological state. A condition can be as a result of both "genetic" (ie genetically inherited) and/or "environmental" factors (ie the result of exposure to internal or external influences). In one embodiment of the invention, a condition is a disease. In another embodiment of the invention, a condition is a stage of a disease. In yet another embodiment of the invention, a condition is a mode or state of being which is not a disease. For example in one embodiment, a condition which is not a disease is a condition resulting from the progression of time. A condition resulting from progression of time can include, but is not limited to: memory loss, loss of skin elasticity, loss of muscle tone, and loss of sexual desire. In a further embodiment of the invention a condition which is not a disease is the response to treatment. A treatment can include, but is not limited to disease modifying treatments as well as treatments useful in mitigating the symptoms of disease. For example treatments can include drugs specific for a disease of the invention. As used herein, the term "data" or "molecular marker data" generally refers to data reflective of the abundance of a product of a molecular marker in blood including either or both of RNA and protein. As used herein, "diagnosis" includes the ability to determine that an individual has or does not have a specific condition or conditions. Diagnosis also refers to the ability to determine that an individual has one condition or conditions as compared with one or more other condition or conditions. In some embodiments, diagnosis refers to the ability to demonstrate an increased likelihood that an individual has a specific condition. "diagnosis" refers to the ability to demonstrate an increased likelihood that an individual has one condition as compared to a second condition. More particularly "diagnosis" refers to a process whereby there is an increased likelihood that an individual is properly characterized as having a condition ("true positive") or is properly characterized as not having a condition (or is properly characterized as having the second condition where the diagnosis is as between two conditions) ("true negative") while minimizing the likelihood that the individual is improperly characterized with said condition ("false positive") or improperly characterized as not being afflicted with said condition (or improperly characterized as having the second condition)("false negative"). As used herein, the term "differential expression" refers to a difference in the level of expression of the RNA and/or protein products of a molecular marker of the invention, as measured by the amount or level of RNA or protein. In reference to RNA, it can include difference in the level of expression of mRNA, and/or one or more spliced variants of mRNA of the biomarker in one sample as compared with the level of expression of the same one or more biomarkers of the invention as measured by the amount or level of RNA, including mRNA and/or one or more spliced variants of mRNA in a second sample. "Differentially expressed" or "differential expression" can also include a measurement of the protein, or one or more protein variants encoded by a molecular marker of the invention in a sample or population of samples as compared with the amount or level of protein expression, including one or more protein variants of a molecular marker of the invention. Differential expression can be determined as described herein and as would be understood by a person skilled in the art. The term "differentially expressed" or "changes in the level of expression" refers to an increase or decrease in the measurable expression level of a given product of a molecular marker as measured by the amount of RNA and/or the amount of protein in a sample as compared with the measurable expression level of a given product of the molecular marker in a second sample. The first sample and second sample need not be from different patients, but can be samples from the same patient taken at different time points. The term "differentially expressed" or "changes in the level of expression" can also refer to an increase or decrease in the measurable expression level of a given molecular marker in a population of samples as compared with the measurable expression level of the molecular marker in a second population of samples. As used herein, "differentially expressed" when referring to a single sample can be measured using the ratio of the level of expression of a given molecular marker in said sample as compared with the mean expression level of the given molecular marker of a control population wherein the ratio is not equal to 1.0. Differentially expressed can also be used to include comparing a first population of samples as compared with a second population of samples or a single sample to a population of samples using either a ratio of the level of expression or using p-value. When using p-value, a nucleic acid transcript including hnRNA and mRNA is identified as being differentially expressed as between a first and second population when the p-value is less than 0.1, less than 0.05, less than 0.01, less than 0.005, less than 0.001 etc. When determining differential expression on the basis of the ratio of the level of molecular marker product — expression of an RNA or protein product of the molecular marker is differentially expressed if the ratio of the level of the RNA or protein product in a first sample as compared with that in a second sample is greater than or less than 1.0. For instance, a ratio of greater than 1, for example 1.2, 1.5, 1.7, 2, 3, 4, 10, 20, or a ratio of less than 1, for example 0.8, 0.6, 0.4, 0.2, 0.1. 0.05, of RNA or protein product of a molecular marker would be indicative of differential expression. In another embodiment of the invention, a molecular marker is differentially expressed if the mean level of expression of a nucleic acid transcript including the hnRNA and/or mRNA transcript in a first population as compared with its mean level of expression of the transcript in a second population is greater than or less than 1.0. For instance, a ratio of greater than 1, for example 1.2, 1.5, 1.7, 2, 3, 4, 10, 20, or a ratio less than 1, for example 0.8, 0.6, 0.4, 0.2, 0.1. 0.05 would be indicative of differential expression. In another embodiment of the invention a molecular marker is differentially expressed if the ratio of the level of the hnRNA and/or mRNA transcript in a first sample as compared with the mean level of the transcript of the second population is greater than or less than 1.0 and includes for example, a ratio of greater than 1, for instance 1.2, 1.5, 1.7, 2, 3, 4, 10, 20, or a ratio less than 1, for example 0.8, 0.6, 0.4, 0.2, 0.1. 0.05. "Differentially increased expression" refers to 1.1 fold, 1.2 fold, 1.4 fold, 1.6 fold, 1.8 fold, or more, relative to a standard, such as the mean of the expression level of the second population. "Differentially decreased expression" refers to less than 1.0 fold, 0.8 fold, 0.6 fold, 0.4 fold, 0.2 fold, 0.1 fold or less, relative to a standard, such as the mean of the expression level of the second population. As used herein, the term "molecular marker" (or sometimes referred to as a "biomarker") refers to a gene or a genetic element. In some embodiments, the molecular marker of interest is identified by the Gene ID (formerly Locus Link ID) as is published by the National Center for Biotechnology Information (NCBI) Database as would be understood by a person skilled in the art. As used herein, the term "oligonucleotide" is defined as a molecule comprised of two or more deoxyribonucleotides and/ or ribonucleotides, and preferably more than three. Its exact size will depend upon many factors which, in turn, depend upon the ultimate function and use of the oligonucleotide. The oligonucleotides may be from about 8 to about 1,000 nucleotides long. Although oliognucleotides of 8 to 100 nucleotides are useful in the invention, preferred oligonucleotides range from about 8 to about 15 bases in length, from about 8 to about 20 bases in length, from about 8 to about 25 bases in length, from about 8 to about 30 bases in length, from about 8 to about 40 bases in length or from about 8 to about 50 bases in length. The term, "primer", as used herein refers to an oligonucleotide, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand, is induced, i.e., in the presence of nucleotides and an inducing agent such as a DNA polymerase and at a suitable temperature and pH. The primer may be either single-stranded or double- stranded and must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer will depend upon many factors, including temperature, source of primer and the method used. For example, for diagnostic applications, depending on the complexity of the target sequence, the oligonucleotide primer typically contains 15-25 or more nucleotides, although it may contain fewer nucleotides. The factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art. In general, the design and selection of primers embodied by the instant invention is according to methods that are standard and well known in the art, see Dieffenbach, C.W., Lowe, T.M.J., Dveksler, G.S. (1995) General Concepts for PCR Primer Design. In: PCR Primer, A Laboratory Manual (Eds. Dieffenbach, CW, and Dveksler, G.S.) Cold Spring Harbor Laboratory Press, New York, 133-155; Innis, M.A., and Gelfand, D.H. (1990) Optimization of PCRs. In: PCR protocols, A Guide to Methods and Applications (Eds. Innis, M.A., Gelfand, D.H., Sninsky, J.J., and White, TJ.) Academic Press, San Diego, 3-12; Sharrocks, A.D. (1994) The design of primers for PCR. In: PCR Technology, Current Innovations (Eds. Griffin, H.G., and Griffin, A.M, Ed.) CRC Press, London, 5-11. As used herein, the term "probe" means oligonucleotides and analogs thereof and refers to a range of chemical species that recognise polynucleotide target sequences through hydrogen bonding interactions with the nucleotide bases of the target sequences. The probe or the target sequences may be single- or double-stranded RNA or single- or double-stranded DNA or a combination of DNA and RNA bases. A probe is at least 8 nucleotides in length and less than the length of a complete gene. A probe may be 10, 20, 30, 50, 75, 100, 150, 200, 250, 400, 500 and up to 2000 nucleotides in length. Probes can include oligonucleotides modified so as to have a tag which is detectable by fluorescence, chemiluminescence and the like. The probe can also be modified so as to have both a detectable tag and a quencher molecule, for example Taqman® and Molecular Beacon® probes. As used herein, the term "product of the molecular marker" or "molecular marker product" refers to the RNA or protein found in blood which corresponds to the molecular marker (ie is transcribed from the gene or genetic element or is translated from RNA which is transcribed from the gene or genetic element). For example, in some embodiments RNA resulting from the molecular marker can include one or more of the following species; hnRNA, niRNA, and/or one or more spliced variants of mRNA. In some embodiments, proteins resulting from the molecular marker can include any proteins found in blood which correspond to the RNA resulting from the molecular marker. As used herein, the term "selectively amplified" or "selective amplification", refers to a process whereby one or more copies of a particular target nucleic acid sequence is selectively generated from a template nucleic acid. Selective amplification or selectively amplified is to be compared with amplification in general which can be used as a method in combination with, for example, random primers and an oligodT primer to amplify a population of nucleic acid sequences (e.g. mRNA). Selective amplification is preferably done by the method of polymerase chain reaction (Mullis and Faloona, 1987, Methods Enzymol. 155:335). As used herein, the term "selectively binds" in the context of proteins encompassed by the invention refers to the specific interaction of any two of a peptide, a protein, a polypeptide, and an antibody, wherein the interaction preferentially occurs as between any two of a peptide, protein, polypeptide and antibody preferentially as compared with any other peptide, protein, polypeptide and antibody. For example, when the two molecules are protein molecules, a structure on the first molecule recognises and binds to a structure on the second molecule, rather than to other proteins. "Selective binding", "Selective binding", as the term is used herein, means that a molecule binds its specific binding partner with at least 2-fold greater affinity, and preferably at least 10-fold, 20-fold, 50-fold, 100-fold or higher affinity than it binds a non-specific molecule. As used herein "selective hybridization" in the context of this invention refers to a hybridization which occurs as between a polynucleotide encompassed by the invention and an RNA , and its complement thereof (ie a cDNA copy), of the molecular marker of the invention, wherein the hybridization is such that the polynucleotide preferentially binds to the RNA products of the molecular marker of the invention relative to the RNA products of other molecular markers or other genes in the genome in question. In a preferred embodiment a polynucleotide which "selectively hybridizes" is one which hybridizes with a selectivity of greater than 70%, greater than 80%, greater than 90% and most preferably of 100% (i.e. cross hybridization with other RNA species preferably occurs at less than 30%, less than 20%, less than 10%). As would be understood to a person skilled in the art, a polynucleotide which "selectively hybridizes" to the RNA product of a biomarker of the invention can be determined taking into account the length and composition. As used herein, "specifically hybridizes", "specific hybridization" refers to hybridization which occurs when two nucleic acid sequences are substantially complementary (at least about 65% complementary over a stretch of at least 14 to 25 nucleotides, preferably at least about 75% complementary, more preferably at least about 90% complementary). See Kanehisa, M., 1984, Nucleic acids Res., 12:203, incorporated herein by reference. As a result, it is expected that a certain degree of mismatch is tolerated. Such mismatch may be small, such as a mono-, di- or tri-nucleotide. Alternatively, a region of mismatch can encompass loops, which are defined as regions in which there exists a mismatch in an uninterrupted series of four or more nucleotides. Numerous factors influence the efficiency and selectivity of hybridization of two nucleic acids, for example, the hybridization of a nucleic acid member on an array to a target nucleic acid sequence. These factors include nucleic acid member length, nucleotide sequence and/or composition, hybridization temperature, buffer composition and potential for steric hindrance in the region to which the nucleic acid member is required to hybridize. A positive correlation exists between the nucleic acid length and both the efficiency and accuracy with which a nucleic acid will anneal to a target sequence. In particular, longer sequences have a higher melting temperature (TM) than do shorter ones, and are less likely to be repeated within a given target sequence, thereby minimizing non¬ specific hybridization. Hybridization temperature varies inversely with nucleic acid member annealing efficiency. Similarly the concentration of organic solvents, e.g., formamide, in a hybridization mixture varies inversely with annealing efficiency, while increases in salt concentration in the hybridization mixture facilitate annealing. Under stringent annealing conditions, longer nucleic acids, hybridize more efficiently than do shorter ones, which are sufficient under more permissive conditions. As used herein, the term "specifically binds" refers to the interaction of two molecules, e.g., a ligand and a protein or peptide, or an antibody and a protein or peptide wherein the interaction is dependent upon the presence of particular structures on the respective molecules. For example, when the two molecules are protein molecules, a structure on the first molecule recognises and binds to a structure on the second molecule, rather than to proteins in general. "Specific binding", as the term is used herein, means that a molecule binds its specific binding partner with at least 2-fold greater affinity, and preferably at least 10-fold, 20-fold, 50-fold, 100-fold or higher affinity than it binds a non¬ specific molecule. As herein used, the term "standard stringent conditions" and "stringent conditions" means hybridization will occur only if there is at least 95% and preferably, at least 97% identity between the sequences, wherein the region of identity comprises at least 10 nucleotides. In one embodiment, the sequences hybridize under stringent conditions following incubation of the sequences overnight at 42DC, followed by stringent washes (0.2X SSC at 65 D C). The degree of stringency of washing can be varied by changing the temperature, pH, ionic strength, divalent cation concentration, volume and duration of the washing. For example, the stringency of hybridization may be varied by conducting the hybridization at varying temperatures below the melting temperatures of the probes. The melting temperature of the probe may be calculated using the following formulas: For oligonucleotide probes, between 14 and 70 nucleotides in length, the melting temperature (Tm) in degrees Celcius may be calculated using the formula: Tm=81.5+16.6(log [Na+]) + 0.41 (fraction G+C)-(600/N) where N is the length of the oligonucleotide.
For example, the hybridization temperature may be decreased in increments of 50C from 680C to 420C in a hybridization buffer having a Na+ concentration of approximately IM. Following hybridization, the filter may be washed with 2X SSC, 0.5% SDS at the temperature of hybridization. These conditions are considered to be "moderate stringency" conditions above 5O0C and "low stringency" conditions below 5O0C. A specific example of "moderate stringency" hybridization conditions is when the above hybridization is conducted at 550C. A specific example of "low stringency" hybridization conditions is when the above hybridization is conducted at 450C. If the hybridization is carried out in a solution containing formamide, the melting temperature of the annealing nucleic acid strands may be calculated using the equation Tm=81.5+16.6(log [Na +]) + 0.41(fraction G + C)-(0.63% formamide)-(600/N), where N is the length of the probe. If the hybridization is carried out in a solution containing formamide, the melting temperature of the annealing nucleic acid strands may be calculated using the equation Tm=81.5+16.6(log [Na+]) + 0.41(fraction G + C)-(0.63% formamide)-(600/N), where N is the length of the probe. For example, the hybridization may be carried out in buffers, such as 6X SSC, containing formamide at a temperature of 420C. In this case, the concentration of formamide in the hybridization buffer may be reduced in 5% increments from 50% to 0% to identify clones having decreasing levels of homology to the probe. Following hybridization, the filter may be washed with 6X SSC, 0.5% SDS at 50 DC. Hybridization conditions are considered to be "moderate stringency" conditions when hybridization fluids are comprised of above 25% formamide and "low stringency" conditions when hybridization fluids are comprised of below 25% formamide. A specific example of "moderate stringency" hybridization conditions is when the above hybridization is conducted at 30% formamide. A specific example of "low stringency" hybridization conditions is when the above hybridization is conducted at 10% formamide. As used herein, the term "responder" is used to mean an individual who responds to treatment. The use of the term "responds to treatment" depends upon the context of the treatment and the disease or condition, but in some embodiments indicate a sufficiently effective and safe response by an individual to the administration of treatment. As used herein, the term "non-responder" is used to mean an individual who does not respond positively to treatment. The use of the phrase "does not respond to treatment" also depends upon the context of the treatment and the disease or condition, but in some embodiments indicates an ineffective or unsafe response by an individual to the administration of treatment. As used herein, the term "trait" is a mode or state of being including a physical, emotional, psychological or pathological state. A trait can include both "genetic" and/or "environmentally" influenced factors. The term "genetic factors" means genetically inherited elements which affect one or more traits as a result of the genetic makeup of the individual. The term "environmental factors" includes exposure to internal or external influences including but not limited to medical treatments, non-medical drugs, pollution, environmental toxins, lead poisoning, mercury poisoning, exposure to genetically modified organisms, radioactivity, pesticides, insecticides, cigarette smoke, alcohol, or exercise and can affect abundance of RNA or affect gene expression as a result of epigenetic mutations and/or non genetic mutations. A physiological or pathological trait can include the status with regards to a condition including having a condition including a disease, having risk factors of a disease having a certain stage of disease or having a certain response to treatment or a risk of a certain response to treatment. In some cases a displayed trait can actually be the result of one or more underlying traits. A trait also includes clinically measurable parameters including those clinically measurable parameters which are indicators of state of health or disease. For example, a clinically measurable parameter includes blood pressure, lung capacity, electrolyte level, enzyme levels (e.g. Serum Glutamic Oxaloacetic Transaminase, alkaline phosphatase, Gamma-Glutamyltransferase or Gamma-Glutamyl Transpeptidase, Lactic dehydrogenase) hormone levels (e.g. thyroid stimulating hormone); protein levels (e.g. Prostate specific antigen PSA) and the like. Clinically measurable parameters can include disease specific clinical indicators, for example prostate specific antigen as an indicator of prostate cancer; insulin levels as an indicator of diabetes; thyroid stimulating hormone levels as an indicator of thyroid disorder and the like. As used herein, the term "trait subgroup" is used to define a group of subjects where each subject has at least one trait or group of traits in common, for example, each subject has a disease, a specific stage of disease, same response to treatment, taking the same drug, etc. As used herein, the terms "treatment", "treat", and "treating" includes administration of one or more compounds, combination of one or more compounds, application of a non-compound based therapeutic regimen, or any combination thereof where administration includes application of a single treatment, a regiment or course of treatment etc. to reduce or amelioration of the progression, severity and/or duration of a disease or condition and/or the reduction or amelioration of the symptoms of a disease or condition resulting from the use of a treatment and/or treatment regime. 5.1 INVENTIVE SYSTEMS AND ALGORITHMS
Fig. 1 shows an exemplary system according to an embodiment of the invention that supports the functionality described herein. The system is preferably a computer system 10 having: • one or more central processors 22; • a main non- volatile storage unit 14, for example a hard disk drive, for storing software and data, the storage unit 14 controlled by storage controller 12; • a system memory 36, preferably high speed random-access memory (RAM), for storing system control programs, data, and application programs, comprising programs and data loaded from non- volatile storage unit 14; system memory 36 may also include read-only memory (ROM); • an optional user interface 32, comprising one or more input devices (e.g., keyboard 28) and a display 26 or other output device; • an optional network interface card 20 for connecting to any wired or wireless communication network 34 (e.g., a wide area network such as the Internet); • an internal bus 30 for interconnecting the aforementioned elements of the system; and • a power source 24 to power the aforementioned elements. Operation of computer 10 is controlled primarily by operating system 40, which is executed by central processing unit 22. Operating system 40 can be stored in system memory 36. In addition to operating system 40, in a typical implementation, system memory 36 includes various components described below. Those of skill in the art will appreciate that such components can be wholly resident in RAM 36 or non- volatile storage unit 14. Furthermore, at any given time, such components can partially reside both in RAM 36 and non- volatile storage unit 14. Further still, some of the components shown in Fig. 1 as being resident in RAM 36 can be resident in another computer (a remote computer) that is addressable by computer 10 over wide area network 34. It will be appreciated that such a remote computer may physically be resident in the same room as computer 10 or in another physical location. As illustrated in Fig. 1, in one exemplary embodiment of the invention, RAM 36 comprises programs and data to interact with components in the computer system 10 for configuring: • file system 42 for controlling access to the various files and data structures used by embodiments of the present invention; • a training population 44 for use in construction of one or more classifiers ; • a molecular marker data processing module I- 54 for processing molecular marker data representative of a genome or a portion thereof for members of training population 44; • a molecular marker screening module A (56A) for identifying molecular markers whose molecular marker data individually discriminates between two or more trait subgroups of the training population using molecular marker data of module 54; • a molecular marker screening module B (56B) for identifying molecular markers whose molecular marker data do not individually discriminate, but demonstrate ability to differentiate between two or more trait subgroups of the training population when used in combination using molecular marker data of module 54; • a candidate molecular marker data structure 58 for storing information about candidate molecular markers identified by molecular marker candidate screening module 56A and optionally molecular marker candidate screening module 56B; • a second molecular marker data processing module II 61 for processing additional molecular marker data for a selection of the candidate molecular markers identified in screening module 56A and, optionally, screening module 56B for members of a training population; • an outlier selection module 57 for evaluating molecular marker data identified in either module 56A and/or 56B or module 61 so as to remove one or more individuals from the training population as outliers; • a combination module 61-5 which selects combinations of molecular markers from candidate molecular markers identified in module 56 A and optionally module 56B • a molecular marker classifier construction module 62 for constructing candidate classifiers from combinations of molecular markers identified by molecular marker combination module 61-5; • a molecular marker classifier evaluation module 64 for evaluating and selecting candidate classifiers constructed by molecular marker construction module 62; • a classifier polling and reporting module 66 for receiving patient or subject molecular marker data and polling one or more classifiers selected by evaluation module 64 in order to determine whether a patient or subject has the disease or trait associated with each of the respective classifiers; • a patient database 68 for storage of molecular marker data for diagnostic, prognostic or predictive use; and • a classifier database 70 for storage of one or more classifiers selected by molecular marker classifier evaluation module 64. As illustrated in Fig. 1, computer 10 comprises software program modules and data structures. The data structures either stored in computer 10 or accessible to computer 10 include a training population 44, candidate molecular marker data structures 58, patient database 68, and classifier database 70. Each of these data structures can comprise any form of data storage system including, but not limited to, a flat ASCII or binary file, an Excel spreadsheet, a relational database (e.g. SQL), or an on-line analytical processing (OLAP) database (MDX and/or variants thereof). In some specific embodiments, such data structures are each in the form of one or more databases that include hierarchical structure (e.g., a star schema). In some embodiments, such data structures are each in the form of databases that do not have explicit hierarchy (e.g., dimension tables that are not hierarchically arranged). In some embodiments, each of the data structures stored or accessible to system 10 are single data structures. In other embodiments, such data structures in fact comprise a plurality of data structures (e.g., databases, files, archives) that may or may not all be hosted by the same computer 10. For example, in some embodiments, training population 44 comprises a plurality of Excel spreadsheets that are stored either on computer 10 and/or on computers that are addressable by computer 10 across wide area network 34. In another example, patient database 68 comprises a database that is either stored on computer 10 or is distributed across one or more computers that are addressable by computer 10 across wide area network 34. Section 5.9 describes exemplary architectures for training population 44, candidate molecular marker data structure 58, patient database 68, and/or classifier database 70. It will be appreciated that many of the modules and data structures illustrated in Fig. 1 can be located on one or more remote computers. For example, some embodiments of the present application are web service-type implementations. In such embodiments, classifier polling and reporting module 66 and other modules can be used by a physician to treat a patient and can reside on a client computer that is in communication with computer 10 via network 34. In some embodiments, for example, classifier polling and reporting module 66 can be an interactive web page. In some embodiments, training population 44, candidate molecular marker data structure 58, patient database 68 and/or classifier database 70 and modules (e.g. modules 54, 56A, 56B, 57, 61, 61-5, 62, 64, and 66) illustrated in Fig. 1 are on a single computer (computer 10) and in other embodiments one or more of such data structures and module are hosted by one or more remote computers (not shown). Any arrangement of the data structures and software modules illustrated in Fig. 1 on one or more computers is within the scope of the present invention so long as these data structures and software modules are addressable with respect to each other across network 34 or by other electronic means. Thus, the present invention fully encompasses a broad array of computer systems. Now that an overview of a system in accordance with one embodiment of the present invention has been described, various advantageous methods in accordance with embodiments of the present invention will now be disclosed in conjunction with Figs. 2 through 5. Figure 2 is a flowchart showing a method of selecting molecular markers and developing one or more classifiers or groups of classifiers according to an embodiment of the invention. Step 202. Referring to Fig. 2 A, in step 202, molecular marker data reflective of the abundance of each of a plurality of RNA and/or proteins found in the blood for members of training population 44 is obtained using one or more of the techniques as described in Section 5.3 and/or 5.4. In some embodiments, the data is reflective of the abundance of RNA products of the molecular marker. In some embodiments, the RNA products are those expressed in blood. In other embodiments, the RNA products are those which are found in blood, but may not necessarily be expressed in blood (e.g. in instances where sufficient mRNA is transported into the blood to be detected. In some embodiments, the data is reflective of the abundance of protein products of the molecular markers. In some embodiments, the data is reflective of the level of proteins expressed in blood. In other embodiments, the data is reflective of proteins found in blood in sufficient quantity to be detected. Measuring of molecular marker data (ie data reflective of the level of the product of the molecular marker) can be done using those techniques known to persons skilled in the art. Note that in some embodiments, data may be obtained using public sources or other sources of data rather than performing one or more of the techniques described. For example, it is anticipated that databases of microarray data collected from blood may be available in future. In some embodiments, the molecular marker data for each molecular marker is obtained using the same technique to allow greater comparability. In some instances a priori information is known about all or a portion of such genes and in some embodiments a priori information about such genes is either not known or not considered in step 202. In some embodiments the molecular markers resulting from step are those molecular markers identified in Tables IA through to 71. Measurement of molecular marker data of a plurality of the molecular markers in the blood of each member of training population 44 can be done using any known technique and preferably is done using large scale techniques which allow for the ability to obtain data for a large number of molecular markers and/or for a large number of individuals quickly and efficiently and at a relatively low cost. For example, microarray techniques and RT-PCR and/or Quantitative RT-PCR can be useful large scale techniques. For example, a high throughput or large scale technique is a technique which allows one to obtain data for large numbers of genes concurrently e.g.1,000 genes, 5,000 genes, 10,000 genes, 15,000 genes 30,000 genes; or all of the genes of the genome of interest. It is expected that additional techniques are being developed and are also useful in embodiments of the invention to screen large numbers of genes quickly and efficiently. Training population 44 includes a population of individuals made up of one or more trait subgroups with each individual in such trait subgroups having one or more traits. In some embodiments, each trait subgroup represented in training population 44 includes molecular marker data from at least 3-4 different subjects. More preferably, each trait subgroup represented in training population 44 includes molecular marker data for at least ten different subjects. Still more preferably each trait subgroup represented in the training population 44 includes molecular marker data for at least 30, 40, 50, 100, 200, 500, 1000 or more subjects. Each training population is selected to include two or more trait subgroups, each subgroup comprising trait subgroup members. Each of these two or more trait subgroups differs with respect to a trait of interest and/or an aspect of a trait of interest. In one embodiment, the members of each of the trait subgroups have been diagnosed as having or not having the trait of interest by one or more known techniques. In another embodiment, the members of each trait subgroup are diagnosed for having or not having the trait of interest using a well accepted methodology for diagnosing of said trait. For example, each member of a first trait subgroup of the training population has liver cancer, whereas each member of a second trait subgroup of the training population does not have liver cancer. In another embodiment, each member of a first trait subgroup of the training population has Alzheimer's, whereas each member of a second trait subgroup of the training population has manic depressive disorder, and each member of a third trait subgroup of the training population has schizophrenia, and each member of a fourth trait subgroup does not have any of the above conditions. In another example, the trait of interest is a disease such as prostate cancer, and the aspect of interest is the degree of advancement of the prostate cancer. Thus, each member of the first trait subgroup can be those subjects that have early stage prostate cancer, each member of a second trait subgroup can be those subjects that have later stage prostate cancer and each member of a third trait subgroup can be those subjects that do not have prostate cancer. In another example, the trait of interest is responsiveness of individuals having musculoskeletal disorders to a Cox 2 inhibitor. A first trait subgroup is comprised of individuals who are responsive to a treatment, a second trait subgroup is comprised of individuals who are responsive to treatment but demonstrate a toxic side-effect, and a third trait subgroup is comprised of individuals who are nonresponsive to treatment. In another embodiment, one trait subgroup can include those subjects that have not yet undergone treatment but who are later identified as being responders to treatment and a second trait subgroup can include those subjects that have not yet undergone treatment but who are later identified as non responders (e.g. demonstrates a toxic side-effect, demonstrates no improvement in condition, demonstrates a worsening of condition, etc.). In some embodiments, members of each trait subgroup of the training population 44 are preferably selected such that each trait subgroup of the training population 44 has a similar distribution with respect to at least one, two, three, four, five, six, one or more, two or more, three or more, four or more, five or more, six or more, between one and 1000 other traits. For example, age, sex, body mass index (BMI), genetic variation information (e.g., gene SNP mutations, restriction fragment length polymorphisms, microsatellite markers, restriction fragment length polymorphisms, and presence, absence or characterization of short tandem repeats.), treatment regimens; co-morbidities; concentrations of metabolites, blood chemistry levels, and/or other indicators of health and/or wellness. A treatment can include, but is not limited to, disease modifying treatments as well as treatments useful in mitigating the symptoms of disease and includes administration of one or more compounds, combinations of one or more compounds, application of a non- compound based therapeutic regimen, or any combination thereof where administration includes application of a single treatment, a regimen or course of treatment and the like. For example, treatments can include drugs specific for a disease such as drugs specific for Alzheimer's, cardiovascular disease, manic depression syndrome, schizophrenia, diabetes cancers including liver cancer, testicular cancer, bladder cancer, prostate cancer, kidney cancer, breast cancer, colon cancer, osteoarthritis, rheumatoid arthritis, osteoporosis, ankylosing spondylitis, or any other disease including those listed herein. . For example, treatments can include but are not limited to administration of VIOXX®, Celebrex®, non- steroidal anti-inflammatory drugs (NSAIDS), cortisone, visco supplement, Lipitor®, Adriamycin®, Cytoxan®, Herceptin®, Nolvadex®, Avastin®, Erbitux®, Fluorouracil®, Largactil®, Sparine®, Vesprin®, Stelazine®, Fentazine®, Prolixin®, Compazine®, Tindal®, Modecate®, Moditen®, Mellarin, Serentil, Norvane, ®, Fluanxol®, Clopixol®, Taractan®, Depixol®, Clopixol®, Haldol®, Haldol®, Decanoate, Orap®, Inapsine®, Imap®, Semap®, Loxitane®, Daxol®, lithium, anticonvulsants (e.g., carbamazepine), antidepressants, and/or Moban®. More generally, a treatment can include any treatment or drug described in the Compendium of Pharmaceuticals and Specialties, Canadian Pharmaceutical Association; 26th edition, June, 1991; Krogh, Compendium of Pharmaceuticals and Specialties, Canadian Pharmaceutical Association; 27th edition, April, 1992. In another embodiment, a treatment can include administration of any compound described in the United States Food and Drug Administration list of approved drug products (the "Orange Book") that is found at http://www.mco.edu/research/fda.html. In some embodiments, molecular marker data is not obtainable from each member of a training population or each member of a trait subgroup (for example, using microarray technology there may be an insufficient signal for one or more molecular markers for any particular member of the training population). Nevertheless, as would be understood by a person skilled in the art, candidate molecular markers can still be selected on the basis of the molecular marker data so long as data is obtainable for a sufficient number of molecular markers from a sufficient number of members of the training population. For example, for each molecular marker, it is sufficient if data is available for at least 75%, 80%, 85%, 90% or 95% of the each trait subgroup of the training population. Section 5.2 provides details on the types of blood samples from subjects in the training population that can be used to obtain data for molecular markers. Section 5.2 further provides details on how such blood samples can be obtained. Section 5.2 also provides details on the types of subjects that can be used to form training population 44 and the types of subpopulations that can be used in the training population. Fig. 1 illustrates the data structure of a training population 44 in accordance with one embodiment of the present invention. There is a record 46 for each subject in training population 44. Each record 46 includes an optional subject identifier 48 for uniquely identifying the subject. Each record 46 includes a molecular marker data file 50 for storage of the molecular marker data measured in step 202. Fig. 4 provides more details on a molecular profile 50 resulting from Molecular Marker data processing module I in accordance with one embodiment of the present invention. The molecular profile 50 of Fig. 4 includes an identifier 302 for each molecular marker 302 tracked by profile 50. Then, for each respective molecular marker 302 in profile 50, there exists one or more measurements of molecular marker data 304. In some embodiments, more than one data point is measured for molecular markers 302. If more than one data point is measured for a molecular marker, then a statistical measure of central tendency (e.g. mean, median, average etc.) can be computed. Accordingly, such measurements for molecular marker data 304 can be stored in data structure 50. There exists a trait characterization field 52 for each subject in training population 44. Preferably as many as possible traits of each member of training population 44 are documented in a trait characterization record (52). Documented traits include known condition or clinically measurable parameters; genetic likelihood of disease or condition; medications both past and current; environmental exposures, ethnicity, age, sex and the like. In some embodiments, training population 44 includes only two trait subgroups and trait characterization 52 is a binary choice between two values, where one value indicates that the corresponding subject belongs in a first trait subgroup and a second value indicates that the subject belongs in a second trait subgroup. In some embodiments, training population 44 is divided into a plurality of lists, where each list in the plurality of lists represents a different trait subgroup. In such embodiments, there is no need for a phenotypic characterization field 52. Although not illustrated in Fig. 1, in some embodiments, there exists a scoring population in addition to the training population. The scoring population is used to evaluate each of the classifiers derived from the training population. The scoring population is made up of one or more individuals that have at least two trait subgroups in common with the training population. In some embodiments, multiple scoring populations are generated from the training population using one or more resampling or cross validation procedures including: bootstrapping; leave one out; leave n out; percent split and the like so as to evaluate the classifiers derived from the training population. In preferred embodiments, the members of the scoring population are not the members used in the training population. In some embodiments, some aspects of step 202 are performed by first data molecular processing module I 54. As such, first data molecular processing module 54 can be a known software program, such as commercially available and/or academically available data processing programs. Step 204. In step 204, using the data measured in step 202, individual candidate molecular markers are identified, where the molecular marker data allows the differentiation as between two of the trait subgroups of the training population. In some embodiments, step 204 represents a series of pairwise comparisons, where each pairwise comparison is between molecular marker data for subjects from two different trait subgroups. In other words, data associated with molecular markers from a population of samples having one aspect of a trait of interest (a first trait subgroup) are compared with a population of samples having a second aspect of a trait of interest (a second trait subgroup) so as to identify molecular markers that are able to differentiate between the two trait subgroups (ie the molecular marker data enables the ability to differentiate between the two trait subgroups. In instances where more than two trait subgroups are represented by a training population 44, more than two pairwise comparisons can be performed on the molecular marker data to identify lists of candidate molecular markers for each of the possible pairwise comparison. A number of statistical techniques can be used to perform the pairwise comparisons of step 204. In some embodiments, standard statistical techniques such as a t-test are used. Methods based on conventional t-tests provide the probability (P) that a difference in measured values for the data of a molecular marker between two different trait subgroups occurs by chance. See, for example, Baldi et al, 2001, Bioinformatics 17, pp. 509-519, 2001, which is hereby incorporated herein by reference in its entirety. The t-test compares the actual difference between two means in relation to the variation in the data (expressed as the standard deviation of the difference between the means). For instance, to determine whether a particular molecular marker discriminates between a first trait subgroup and a second trait subgroup, the mean of the data for the molecular marker in the first trait subgroup is compared to the mean of the data for the molecular marker in the second subgroup in accordance with the t-test. In some embodiments, the molecular marker data is such that it is deemed to discriminate between two trait subgroups when the t-test yields a score that matches or exceeds the p = 0.05 level (95% confidence, "significant confidence"), the/? = 0.01 level (99% confidence, "highly significant confidence") orp = 0.001 (99.9% confidence, "very highly significant confidence"). In some embodiments, the t-test is applied with a Bonferroni correction or similar form of correction, hi some embodiments, rather than using a t-test, nonparametric equivalents such as the WaId- Wolfowitz runs test, the Mann- Whitney U test, the Kolmogorov-Smirnov two-sample test or ROC are used. In some embodiments, training population molecular marker data is obtained using a microarray and the Significant Analysis of Microarrays technique of Tusher et al. is used to identify molecular markers whose data discriminates between trait subgroups. See, for example, Tusher et al , 2001 , Proc. Natl. Acad. Sci. USA 98, 5116-5121. In some embodiments, Manduchis' algorithms for assigning confidence to differentially expressed genes is used to identify molecular markers whose data discriminates trait subgroups. See, for example, Manduchi et al, 2000, Bioinformatics 16, 685-598. In some embodiments, there are a number of different trait subgroups represented within the training population. Thus, in such embodiments, application of a series of pairwise t-test can become computationally intensive and prone to underinclusiveness in the identification of discriminating molecular markers resulting for each binary comparison since the number of pairwise comparisons that must be performed grows quickly as a function of the number of trait subgroups present in training population 44. For example, if there are seven trait subgroups present in training population 44, a total of 21 pairwise (t-test class) test can be performed. In such embodiments, analysis of variance (ANOVA) can be used to simultaneously consider whether the data for a molecular marker produces statistically different means for each of the phenotypic data structures. ANOVA considers the data for a given molecular marker from each of the trait subgroups present in training population 44 and produces a single number (the F-statistic) that can be evaluated for significance at any desired confidence value (e.g., the/? = 0.05 level, the/? = 0.01 level, the/?=0.001 level, etc). ANOVA is described, for example, in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 155-187, which is hereby incorporated herein by reference in its entirety. In some embodiments, nonparametric equivalents to ANOVA, such as the Kruskal-Wallis analysis of ranks test, the Median test, Friedman's two-way analysis of variance, or the Cochran Q test, are used to identify molecular markers that have statistically different means as between two of the rait subgroups. In some embodiments, after running ANOVA, a means comparisons test such as Duncan's, Student-Newman-Keuls (SNK), Tukey-Kramer, Tukey's HSD, or Least Significant Difference (LSD) are run to determine which molecular markers have data that statistically differentiates as between one or more of the trait subgroups tested (e.g. has statistically different values in one or more of the trait subgroups tested). In some embodiments, such tests are performed instead of ANOVA or pairwise t-tests to identify molecular markers that discriminate two or more trait subgroups. In some embodiments, a parametric test and a nonparametric test are run in step 204. If there are only two trait subgroups being compared (e.g., non-disease versus disease) then a Welch t-test (parametric test) and a Mann- Whitney test are used without multiple test corrections at decreasing p-values {e.g., p< 0.05, p < 0.01, p < 0.005, p < 0.001, etc.) until the number of candidate molecular markers is less than 50, less than 100, less than 150, less than 200, less than 250 etc.. If three or more trait subgroups are being compared, the Kruskal-Wallis (nonparametric) and Welch ANOVA (parametric) tests are used. Following the same procedure for instances where only two trait subgroups are being compared, additional molecular marker sets are produced in conjunction with multiple test corrections. An example of a test correction is the Benjamin-Hochberg false discovery rate. In some embodiments the post-hoc statistical tests for groups of three or more provided in GeneSpring version 6.1+ is used. The post-hoc test provides a means for determining which molecular markers are different between particular trait subgroups. Such post-hoc tests include the Student Newman Keuls test and the Tukey test. In some embodiments, data for a test molecular marker from all possible trait subgroups in training population 44 is not used. Rather, similar to the case where pairwise t-tests are used, ANOVA is used to determine whether the data for a molecular marker can discriminate between some or all of the trait subgroups. For example, consider the case in which training population 44 includes five trait subgroups. In one approach, a pairwise t- test can be used to identify molecular markers that statistically discriminate (e.g. p = 0.05) between one of the possible pairs of trait subgroups in the training population. Similarly, ANOVA can be used to identify molecular markers that statistically discriminate between one of the possible triplets in the training population. Alternatively, ANOVA can be used to identify molecular markers that statistically discriminate between one of the possible quadruplets in the training population. In some embodiments, either in addition to using statistical methods, or independently of statistical methods such as described herein, processing step 204 to select candidate molecular markers is done by looks for differential data abundances (e.g., differential expression). Such methods differ from those described above in the sense that variance as between the level of expression as between members of the same trait subgroup are not necessarily considered. In order to compare differential expression as between two or more trait subgroups, a statistical measure of central tendency (e.g. mean, median, average etc.) can be computed for each molecular marker within any trait subgroup. In some embodiments, the measure of differential data abundance (or differential expression) for a molecular marker product as between a first trait subgroup and a second trait subgroup is determined by measuring the fold change, for example a fold change of greater than 1.5, 2.0, 2.5, 3.0, 4,0 or higher can be selected.. In another embodiment, the measure of differential data abundance need not be quantified, but molecular markers products which on visual inspection display a clear difference in expression (ie abundance levels) as between two trait subgroups can be selected. To illustrate, consider a population P' that comprises trait subgroups "A" and "B". That is, each member of P ' is classified into either subgroup "A" or "B" based on whether or not they exhibit or have a particular trait. In this situation, products of molecular markers that are present in large quantities/abundance in one group ("A" or "B") but not the other group are identified. For instance, molecular markers that strongly express in subgroup "A" but not in subgroup "B" can be identified from the measurements taken for each member of each subgroup in step 202. Likewise, molecular markers that express strongly in subgroup "B" but not subgroup "A" can be identified. These patterns of differential abundance of the products of the molecular markers can be used to identify candidate molecular markers. For an illustration of this approach, see Glob et al. 1999, Science 286: 531. In embodiments where molecular markers are identified in select pairwise tests, such as pairwise t-tests or ANOVA tests of subsets of the total number of trait subgroups in training population 44, there might be several different lists of molecular markers. For example, molecular marker list A might include molecular markers whose measured data (including normalized data etc.) discriminates between a first and second trait subgroup as determined by a first pairwise t-test. Molecular marker list B might include molecular markers whose data discriminates between a first and third trait subgroup as determined by a second pairwise t-test. Molecular marker list C might include molecular markers whose data discriminates between a first, second and third trait subgroup as determined by ANOVA. In some embodiments, each candidate molecular marker list 60 is preserved as an independent list in candidate molecular marker data structure 58 (Fig. 1). In some embodiments, processing step 204 identifies a total of between 10 and 4000 candidate molecular markers for any particular training population. In other embodiments, step 204 identifies a total of between 500 and 2000 candidate molecular markers. In yet another embodiments, processing step 204 identified a total of between 100 and 1000 candidate molecular markers. In some embodiments, some aspects of step 204 are performed by molecular marker candidate identification module 56A. Additionally, there exist known programs that can perform some of the functionality described in step 204. Such programs include those formerly sold by Silicon Genetics (e.g. GeneSpring™); now Agilent Technologies. In some embodiments, candidate molecular markers identified using the above- described statistical or nonstatistical tests are clustered in order to visualize relationships between the genes. For example, in some embodiments GeneSpring™ is used to perform hierarchical clustering using the Spearman correlation statistic. In some embodiments, QT clustering is used to identify genes that have a similar pattern of expression across the specimens in training population 44. Clustering that can be employed in step 204 is described generally in Section 5.12. Further, Section 5.12 gives examples of some clustering techniques that can be used in step 204. Molecular markers for use in subsequent sections (e.g., for quantitative measurement using methods as described in step 206 below) can be ranked and selected by one or more criteria, including, but not limited to, fold change differences in molecular marker data between two or more trait subgroups, standard deviations of molecular marker data as between two or more trait subgroups using the above-described statistical tests, coefficient of variation, statistical significance (e.g., p- value from ANOVA and/or Mest, or other tests described above), level of expression as determined using molecular marker data, gene function, reproducibility of molecular marker data (includes intra- and inter-experimental), elucidated pathways / networks of the molecular markers as would be understood by a person skilled in the art (e.g. selecting a molecular marker on the basis of an understanding of how said gene is known to function in the body) and the like. Thus in some embodiments, molecular markers for use in subsequent sections are chosen on the basis of the p value identified as a result of step 204 as a measure of the likelihood that the molecular marker data can distinguish as between the two trait subgroups and more particularly molecular markers are chosen wherein the p value is less than 0.5; less than 0.1, less than 0.05, less than 0.01, less than 0.005, less than 0.001, less than 0.0005, less than 0.0001, less than 0.00005, less than 0.00001, less than 0.000005, less than 0.000001 etc. In some embodiments, molecular markers for subsequent steps are chosen on the basis of the level of differential expression displayed by the molecular marker products as between the two or more trait subgroups. Note that in measuring differential fold change in blood, the fold change differences can be quite small, thus in some embodiments, selection of molecular markers is based on a differential fold change where the fold change is greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2.0, greater than 2.1, greater than 2.2, greater than 2.3, greater than 2.4, greater than 2.5, greater than 2.6, greater than 2.7, greater than 2.8, greater than 2.9, greater than 3.0, greater than 3.1, greater than 3.2. greater than 3.3, greater than 3.4 greater than 3.5, greater than 4.0 and the like. In some embodiments, it is helpful to select molecular markers on a basis of the combination of both p value and fold change as would be understood by a person skilled in the art. Thus in some embodiments, molecular markers are first selected as outlined above on the basis of the p value resulting from the molecular marker data and then a subselection of said molecular markers is chosen on the basis of the differential fold change determined from the molecular marker data. In other embodiments, molecular markers are first selected on the basis of differential fold change, and then subselection is made on the basis of p value, hi some embodiments, the use of one or more of the selection criteria and subsequent ranking permits the selection of the top 2.5%, 5%, 7.5%, 10%, 12.5%, 15%, 17.5%, 20%, 30%, 40%, 50% or more of the ranked molecular markers for use in subsequent steps. In other embodiments, the selection criteria noted above can be set on the basis of the desired number of selected molecular markers for use in steps 206 and or other steps leading to the selection of the available set of markers for step 214. As would be understood, a selection criteria based on the desired number of selected molecular markers will depend upon the resources available for obtaining the molecular marker data for step 206 and/or the computer resources available for calculating and evaluating classifiers of all or a portion of possible combinations of the selected molecular markers. In some embodiments, the desired number of selected molecular markers for use in step 214 can be 4,000; 3,000; 2,000; 1,000; 900; 800; 700; 600; 500; 400; 300; 200; 190; 180; 170; 160; 150; 140; 130; 120; 110; 100; 90; 80; 70; 60; 50; 40; 30; 20; 10. The more molecular markers which can be selected for use in step 214; the greater the likelihood of identifying classifier or classifiers which are particularly useful for diagnosis. In some embodiments, one or more subjects of the training population are identified as outliers and are removed prior to identifying individual candidate molecular markers as described herein. These outlier members can then be removed from the training population prior to proceeding to later steps. As described herein, in one embodiment a neural network is used to identify such outliers. A neural network has a layered structure that includes, at a minimum, a layer of input units (and the bias) connected by a layer of weights to a layer of output units. Such units are also referred to as neurons. For output along a single dimension, the layer of output units includes just one output unit. However, neural networks can handle multiple quantitative responses (outputs along multiple dimensions) in a seamless fashion by providing multiple units in the layer of output units. In multilayer neural networks, there are input units (input layer), hidden units (hidden layer), and output units (output layer). There is, furthermore, a single bias unit that is connected to each unit other than the input units. Neural networks are described in Duda et al, 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et al, 2001, The Elements of Statistical Learning, Springer- Verlag, New York. The basic approach to the use of neural networks to identify outliers is to start with an untrained network. A training pattern is then presented to the untrained network. This training pattern comprises a training population and, for each respective member of the training population, an association of the respective member with a specific trait subgroup. Thus, the training pattern specifies measured molecular marker data from blood for molecular markers for each member of a training population as well as an indication as to which trait subgroup each member of the training population belongs. In preferred embodiments, training of the neural network is best achieved when the training population includes members from more than one trait subgroup. In the training process, individual weights in the neural network are seeded with arbitrary weights and then the molecular marker data for each member of the training population is applied to the input layer. Signals are passed through the neural network and the output determined. The output is used to adjust individual weights. A neural network trained in this fashion classifies each individual of the training population with respect to one of the input trait subgroups. In typical instances, the initial neural network does not correctly classify each member of the training population. Those individuals in the training population that are misclassified identify and determine an error or criterion function for the initial neural network. This error or criterion function is some scalar function of the trained neural network weights and is minimized when the network outputs match the desired outputs. In other words, the error or criterion function is minimized when the network correctly classifies each member of the training population into the correct trait subgroup. Thus, as part of the training process, the neural network weights are adjusted to reduce this measure of error. For regression, this error can be sum-of-squared errors. For classification, this error can be either squared error or cross-entropy (deviation). See, e.g. Hastie et al., 2001, The Elements of Statistical Learning, Springer- Verlag, New York. Those individuals of the training population which are still incorrectly classified by the trained neural network, once training of the network has been completed, are identified as outliers and can be removed prior to proceeding. In some embodiments, an ensemble of neural networks can be used on the training population and individuals ranked on the basis of the number of times an individual is misclassified by each neural network. In order to create the ensemble, each neural network can differ with respect to the initial seeded weighting. In another embodiment, each neural network can differ on the basis of randomly generated noise added to the molecular marker data for one or more molecular markers of each individual of the training population added to the input layer. In such an embodiment, this randomly generated noise can be applied by changing the amount of each of the measured molecular marker data of each member of the training population by a scaled random amount. When larger amounts of noise are required, the magnitude of the scaled random amount is increased. In any of these embodiments, the number of layers and the number of units in each layer can be adjusted in order to provide optimal results for any given set of conditions. In this manner, one can identify outliers which are misclassified in as many aslθ%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more of the neural networks used. Step 205. Step 205 is optional. We have surprisingly found that certain molecular markers whose molecular marker data fails to discriminate individually as between two trait subgroups in step 204, are still more than incrementally useful when utilized in combinations with other candidate molecular markers selected in step 204. In particular we have been able to identify molecular markers whose data fails to individually discriminate as between two trait subgroups in the pairwise comparison of step 204 but contribute to a classifier identified in step 216 from a combination which includes one or more candidate molecular markers identified in step 204 ("combination friendly molecular markers"). Thus, in order to ensure that molecular markers which may be useful in combinations are not removed prematurely, optional step 205 is performed. In step 205, all or a portion of the molecular markers for which data has been or can be obtained in at least two of the trait subgroups of the training population are utilized ("putative combination molecular markers"). For purposes of step 205, in one embodiment, the data is obtained using a technique which allows for fast and efficient data generation for all of the molecular markers of the genome of interest chosen. In another embodiment the data is obtained using one or more of the techniques as described in Section 5.3 and/or 5.4. In another embodiment, the data is obtained using microarray technology. In a preferred embodiment, the data used is the data obtained for step 202 In order to identify additional candidate molecular markers, combinations of molecular markers are chosen and a mathematical model applied to the molecular marker data for each molecular marker of the combination resulting in a classifier for each combination. The mathematical model applied can be selected from those defined in Section 5.14. In some embodiments, each possible combination of 2, and/or 3, and/or 4, and/or 5, and/or 6, and/or 7, and/or 8, and/or 9 and/or 10 or more of the putative combination friendly molecular markers are tested. For example, if there are 8,000 putative combination friendly molecular markers, each possible combination of 8 or less molecular markers can be written as follows: 8.000!/((8,OQO - 8)1 (8V) ach classifier resulting from each combination is scored as described more fully in step 220. In some embodiments, the classifiers are scored using the training population so as to permit time and cost savings. In other embodiments, the classifiers are scored using a scoring population. In yet other embodiments, the classifiers are scored using other resampling or cross validation procedures so as to generate multiple scoring populations. Having scored the classifiers, a subset of classifiers are then selected based on the score. In some embodiments, the subset of classifiers is any number less than the total number of combinations evaluated. In some embodiments, the top 10%, top 20%, top 30%, top 40%, or top 50% of the classifiers generated are chosen. In some embodiments, wherein scoring is done using ROC area under the curve, those classifiers with an ROC area under the curve of 0.5, 0.6, 0.7, 0.8, 0.9 or 1.0 are selected. Having selected a number of classifiers, each representing a combination of molecular markers, the number of occurrences of each putative combination friendly molecular marker in the combinations of the selected classifiers are determined. Putative combination friendly molecular markers can then be selected as combination friendly molecular markers so as to used as candidate molecular markers based on the number of reoccurrences of said molecular marker in the selection of combinations evaluated. In one embodiment, 5, 10, 15, 20, 30, 50, 100, 150, 200 or more additional candidate molecular markers not previously selected in step 204 are chosen to proceed to step 206. In other embodiments, the top 10%, top 20%, top 30%, top 40%, or top 50% of combination friendly molecular markers as determined by reoccurrence statistics are selected to be included in the selected set of candidate molecular markers for purposes of choosing combinations to create classifiers in accordance with steps 214 to 218. Step 206. In step 206, second molecular marker data is obtained for the candidate molecular markers identified in step 204. In one embodiment, the second molecular marker data is measured using any technique described in Section 5.3 or 5.4 or equivalents thereof. In another embodiment, the same technique used to measure the first molecular marker data in step 202 is used to obtain the second molecular marker data. In other embodiments, an alternative technique is used to measure the second molecular marker data. In other embodiments, the first molecular marker data is obtained using one of the following techniques: microarray, and/or RT-PCR and the second molecular marker data is obtained using any technique but microarray. In other embodiments, the first molecular marker data is obtained using microarray and the second molecular marker data is obtained using any technique except for microarray. The use of second molecular marker data is preferred because the changes in differential expression or abundance of the product of the molecular marker in blood as between trait subgroups can be as low as a 1.1 fold, 1.2 fold, 1.3 fold, 1.4 fold, 1.5 fold etc. which makes less sensitive and reproducible techniques less reliable. In addition, techniques which are preferable for the data collection to allow large scale screening in step 202 such as microarray have been shown to have significant inherent reproducibility issues with high standard deviation as between experiments. As such it is necessary to obtain second molecular marker data so as to ensure the accuracy of the ultimate classifiers identified. Techniques utilized to obtaining second molecular marker data for the plurality of molecular marker products are those techniques known to measure abundance of RNA and/or protein including the techniques described in Section 5.3 and 5.4. In some embodiments, it is helpful to obtain a third series of molecular marker data (ie third molecular marker data, fourth molecular marker data etc.) which can be molecular marker data of the training population used in steps 202 or of a different training or scoring population using any known technique including those techniques described in sections 5.3 and 5.4. Preferably more expensive and/or time consuming techniques are used once smaller numbers of candidate molecular markers have been identified In some embodiments, some aspects of step 206 are performed by molecular marker data processing module 11 - 61. The exact nature of the functionality of molecular processing module 61 will depend on the type of measurement assay used in step 206. However, it is contemplated that module 61 will be used to record measurement values for molecular marker data in a profile similar to molecular marker data processing module I - 50, perform any necessary error correction techniques, normalization techniques (e.g., techniques described in Bevington and Robinson, Data Reduction and Error Analysis for the Physical Sciences, Second Edition, WCB/ McGraw-Hill, 1992, etc.) and/or perform any measurement techniques that can be coded in a digital computer . Step 208. Step 208 is optional and allows for the selection of individual candidate molecular markers of step 206 which can be removed prior to the process of selecting and evaluating combinations of molecular markers in steps 214/216. In optional step 208, one or more candidate molecular markers in data structure 58 (Fig. 1) are eliminated. In optional step 208, the same types of tests that were performed in step 204 can be performed. The main difference is that in step 208, the quantitative data measured in step 206 using low throughput methods is used whereas in step 204, the high throughput data measured in step 202 is used. Data measured in step 206 is used in step 208 to validate the candidate molecular markers. In one specific embodiment, training population 44 consists of a first trait subgroup and a second trait subgroup and step 208 comprises performing a t-test or a nonparametric equivalent of the t-test on each candidate molecular marker using the molecular marker data measured in step 206 to verify for each candidate molecular marker that the molecular marker data differentiates between the first trait subgroup and the second trait subgroup with some measure of statistical confidence. Candidate molecular markers whose molecular marker data are less effective in differentiating between the two trait subgroups (e.g. have a p value that is greater than .05) are removed from data structure 58 and are no longer considered as candidate molecular markers. In another specific embodiment, training population 44 consists of a first trait subgroup, a second trait subgroup, and a third trait subgroup. In this specific embodiment, ANOVA or a nonparametric equivalent is performed independently on each candidate molecular marker in data structure 58 to verify that each molecular marker differentiates between the three subgroups using the molecular marker data. Candidate molecular markers whose molecular marker data are less effective in differentiating between the three trait subgroups (e.g. have a p value that is greater than .05) are removed from data structure 58 and are no longer considered as candidate molecular markers. In some embodiments, each molecular marker is validated by using the data for each molecular marker to generate a Receiver Operating Characteristic (ROC) curve. ROC curves are generally discussed in Park et al., Korean J. Radiol. 5, p. 11, which is hereby incorporated herein by reference in its entirety. In one embodiment of the present invention, an ROC curve is computed for each candidate molecular marker in training population 44 using the molecular marker data measured in step 206. As noted in step 202, training population 44 includes, for each specimen in the training population, an indication 52 as to whether or not the specimen has a particular trait under study. Each respective ROC curve graphs the True Positive Fraction (TPF) as compared with 1/ the False Positive Fraction (FPF). For example, consider the case in which molecular marker A is being validated and the data for molecular marker A that was measured in step 206 is expression level of the molecular marker A in blood samples from subjects. Table B provides hypothetical values for the abundance of A across training population 44. Table B: Values for molecular marker data of A across training set 44.
[A] Presence / Absence of Disease 453 Y 437 Y 424 Y 374 Y 202 N 158 Y 102 N 37 N 0.54 N In Table B, each line represents a different specimen in the training population. If the relationship between [A] (data of cellular constituent A) and the presence of disease in subjects in the training population is statistically very significant, all positive results (where specimens have the disease) would be at the top of Table B and all negative results (where biological specimens do not have the disease) at the bottom of the Table B. To plot the ROC curve corresponding to the test illustrated in Table B, the table is divided into a number of cutoff levels. Then, the sensitivity (TPF) and specificity (TNF) of each cutoff level is computed. Sensitivity and specificity are defined with reference to the decision matrix of Table C. Table C: Decision matrix.
True Feature Status Test result Positive Negative Total Positive TP FP T+ Negative FN TN T- Total D+ D-
In Table C, TP means the number of true positives, FP means the number of false positives, FN means the number of false negatives, and TN means the number of true negatives. Sensitivity is the proportion of subject with a trait {e.g., a disease or particular biological phenotype) who test positive for the feature. In probability notation sensitivity is P(T+ID+) = TP / (TP+FN). Specificity is the proportion of patients without the trait who test negative for the feature. In probability notation specificity is P(T'|D") = TN / (TN + FP). The ROC curve is defined as a plot of the sensitivity as the y-coordinate versus 1- specificity (false positive rate) as the x-coordinate. Thus, for Table B, where each line of the Table B represents an independent cutoff level, the following ROC data points are derived. Table D: ROC data points for Table B.
Ratio Cutoff Level Sensitivity 1 -Specificity _ No row 0 First row 0.2 0 First two rows 0.4 0 First three rows 0.6 0 First four rows 0.8 0 Ratio Cutoff Level Sensitivity 1 -Specificity First five rows 08 025 First six rows 1 0.25 First seven rows 1 0.5 First eight rows 1 0.75 First nine rows 1 1
To compute the last row of Table D, the number of TP, FP, FN, and TN are counted in Table B when the condition is imposed that the classifier predicts that no specimen in Table B is positive for presence of the trait (e.g., disease or a particular biological phenotype). This, of course, is not an accurate classifier as reflected in the respective sensitivity and specificity values of 0 and 1. Plotting sensitivity by 1 -specificity yields the coordinate (0,0) as illustrated in the last row of Table D. Figure 7 illustrates the ROC curve based upon the data points illustrated in Table D. As illustrated in Fig. 7, a ROC curve begins at coordinate (0,0) and ends at coordinate (1,1). Once an ROC curve has been computed for a molecular marker, in one embodiment, the area under the ROC curve can be quantified. Generally, an area of 1.0 represents a molecular marker that is a perfect diagnostic indicator of the presence of absence of the trait. Preferably an area of greater than 0.5 is desired for a diagnostic indicator, but it will depend upon the trait of interest. For example measurement of protein PSA levels currently used to diagnose prostate cancer has an ROC of 0.47. In some embodiments there are as many as fifty candidate molecular markers in data structure 58 at this stage of the inventive method. In some embodiments there are more than fifty candidate molecular markers. In practice, the number of candidate molecular markers that remain can be set to any desired number by raising or lowering the criteria for eliminating molecular markers. For example, smaller p values from ANOVA or t-tests, or larger ROC curve areas can be required if the total number of molecular markers is too large. Steps 210 and 212. Step 210 is optional and allows the additional removal of individual candidate molecular markers of step 206 by evaluating how each individual candidate molecular marker performs within a model which evaluates a combination of molecular markers prior to performing the evaluation of combinations in step 214/216. In optional step 210 all or a portion of the remaining candidate molecular markers in data structure 58 are used to generate a regression classifier. To compute the regression classifier, measured data from step 206 for the molecular markers in two different trait subgroups in training population 44 are used. In some embodiments, the two different trait subgroups respectively represent a diseased and nondiseased state. In some embodiments, the two different trait subgroups respectively represent a first diseased state (e.g. cancer) and a second unrelated diseased state (e.g., Alzheimer's disease). In some embodiments, the two different trait subgroups represent those subjects that are responsive to drug therapy and those subjects that are not responsive to drug therapy. In still other embodiments, the two different trait subgroups from which molecular marker data is obtained represent data from subjects obtained a priori to treatment, but that have been classified into different trait subgroups on the basis of the ultimate response to treatment. In some embodiments, the two different trait subgroups respectfully represent two different stages of a disease (e.g., moderate versus advanced). In some embodiments, data for between ten and thirty candidate molecular markers in the two select trait subgroups is used in the logistic regression. In some embodiments, between twenty and one hundred candidate molecular makers in the two select trait subgroups is used in the logistic regression. In still other embodiments, all the candidate molecular markers in the two select trait subgroups are used in the logistic regression. In step 210 logistic regression can be used because one of the dependent variables is binary - absence or presence of a particular phenotype. For example, consider the case in which molecular marker data from a first trait subgroup and molecular marker data from a second trait subgroup is used in step 210. The first trait subgroup is characterized by a first disease and the second trait subgroup is characterized by a second disease. In such instances, what can be considered by logistic regression is absence or presence of the first disease in subjects. Alternatively, what can be considered by logistic regression is absence or presence of the second disease in subjects. In general, the multiple regression equation of interest can be written
Y =a+βιXι +β2X2 + - +βkXk +ε
where Y, the dependent variable, is presence (when Y is positive) or absence (when Y is negative) of the trait (e.g., phenotype, condition) associated with the first trait subgroup considered in step 204. This classifier says that the dependent variable Y depends on k explanatory variables (the measured data values for the k candidate molecular markers from subjects in the first and second trait subgroups in training population 44), plus an error term that encompasses various unspecified omitted factors. In the above-identified classifier, the parameter P1 gauges the effect of the first explanatory variable X\ on the dependent variable Y, holding the other explanatory variables constant. Similarly, β2 gives the effect of the explanatory variable X2 on Y, holding the remaining explanatory variables constant. In general, in the multiple regression procedure, estimates for P1 are obtained by taking into account how uncontrolled changes in other variables influence Y. Thus, in specific embodiments of the present invention, regression is used to eliminate at least some of the candidate molecular markers rather than relying entirely on the tests described in step 208 because the regression takes into account patterns in which multiple molecular markers influence the dependent variable (absence or presence of a trait) in a concerted fashion. Because the dependent variable data is binary, logistic regression can be used. The logistic regression classifier is a non-linear transformation of the linear regression. The logistic regression classifier is termed the "logit" classifier and can be expressed as
lnlp/(l -p)] =cc+βιXι +β2X2 + - +βkXk +ε Qr
[p/(l - p)] = exp a expA r' expA'V2 x • • • xexpA'Y* expff
where, In is the natural logarithm, loge, where e=2.71828..., p is the probability that the event Y occurs, p(Y=l), (l-p)j the probability that the event Y does not occur, p (Y≠)0, p/(l-p) is the "odds ratio", ln[p/(l-p)] is the log odds ratio, or "logit", and all other components of the classifier are the same as the general regression equation described above. It will be appreciated by those of skill in the art that the term for α and ε can be folded into a single constant. Indeed, in preferred embodiments, a single term is used to represent α and ε . The "logistic" distribution is an S-shaped distribution function. The logit distribution constrains the estimated probabilities (p) to lie between 0 and 1. In some embodiments of the present invention, the logistic regression classifier is fit by maximum likelihood estimation (MLE). In other words, the coefficients {e.g., a, βi, β 2, ...) are determined by maximum likelihood. A likelihood is a conditional probability (e.g., P(Y|X), the probability of Y given X). The likelihood function (L) measures the probability of observing the particular set of dependent variable values (Y1, Y2, ..., Yn) that occur in the sample data set. It is written as the probability of the product of the dependent variables:
Figure imgf000065_0001
The higher the likelihood function, the higher the probability of observing the Ys in the sample. MLE involves finding the coefficients (α, βi, β 2, ...) that makes the log of the likelihood function (LL < 0) as large as possible or -2 times the log of the likelihood function (-2LL) as small as possible, hi MLE, some initial estimates of the parameters α, βi, β 2, • • • are made. Then the likelihood of the data given these parameter estimates is computed. The parameter estimates are improved the likelihood of the data is recalculated. This process is repeated until the parameter estimates do not change much (for example, a change of less than .01 or .001 in the probability). Examples of logistic regression and fitting logistic regression classifiers are found in Hastie, The Elements of Statistical Learning, Springer, New York, 2001, pp. 95-100, which is hereby incorporated by reference in its entirety. Step 212. In specific embodiments, all or a portion of the candidate molecular markers are used and the molecular marker data fit using logistic regression. Then, in a stepwise fashion, some of the molecular markers are eliminated from the classifier using backward stepwise regression. Backward stepwise regression begins with a full or saturated classifier and variables are eliminated from the classifier in an iterative process. The fit of the classifier is tested after the elimination of each variable (molecular marker) to ensure that the classifier still adequately fits the molecular marker data. When no more variables can be eliminated from the classifier or a desired number of molecular markers remain in the classifier, the analysis has been completed. In specific embodiments, the regression applied in step 210 is used to refine the candidate molecular marker list to less than 25, less than 24, less than 23, less than 22, less than 21, or less than 20 molecular markers. In one embodiment, a logistic regression classifier is computed using all or a portion of the available candidate molecular markers in data structure 58. Then, coefficients are tested for significance for inclusion or elimination from the classifier using a WaId test, a likelihood-ratio test (chi-squared statistic), a Hosmer-Lemshow Goodness of Fit Test, or the like. For example, the likelihood-ratio test uses the ratio of the maximized value of the likelihood function for the full classifier (L1) over the maximized value of the likelihood function for the simpler classifier (L0) in which one or more molecular markers have been removed. The likelihood-ratio test statistic equals:
Figure imgf000066_0001
This log transformation of the likelihood functions yields a chi -squared statistic. Step 213. Step 213 is optional. We have found that performing optional step 213 provides a significant improvement in identifying classifiers which are particularly useful in diagnosis of a disease or condition of interest. In optional step 213, clinically measurable parameters are identified which are thought to be relevant to the trait of interest for which a classifier is desired. For example, where the trait of interest is prostate cancer, clinically measurable parameters chosen are those that are known or are shown to be relevant to the trait of interest. For example in one embodiment, clinically measurable parameters relevant to prostate cancer can include age of subject, level of prostate specific antigen (PSA); and volume of prostate. In yet another embodiment, where the trait of interest is osteoarthritis, some relevant clinically measurable parameters are age and body mass index (BMI). The selected clinically measurable parameters are then included as part of the "selected set of candidate molecular markers" and are treated as molecular markers for the purpose of selecting combinations and developing classifiers in step 214 through 218 as described below. In order to treat the clinically measurable parameter as a molecular marker for purposes of step 214 through 218, the clinically measurable parameter must have associated data. In some embodiments, where the clinically measurable parameter is one which has an associated value - for example age, blood glucose level, PSA level, blood pressure, body mass index, etc., the value can be treated as the molecular marker data for purposes of steps 214 through 218. In some embodiments there is no value associated with the clinically measurable parameter, for example where the relevant clinical parameter is determinable but does not provide a value. In those cases, a value can be assigned to represent each aspect of the clinically measurable parameter. For example where the sex of a person is the clinically measurable parameter, a value of 1 can be assigned to represent that the person is male, and a value of 0 can be assigned to represent that the person is female. As yet another example, where the relevant clinically measurable parameter is ethnicity, a different value can be assigned to each ethnicity (e.g. 1 Caucasian, 2 asian, 3ashkanazi jew etc. and said value can be used as the molecular marker data associated with ethnicity for purposes of step 214 through 218. Step 214. Steps 214 through 218 provide an approach in which all or a portion of the possible combinations of the selected set of candidate molecular markers resulting from steps 202- 213 are chosen. Molecular marker data from each candidate molecular marker in a elected combination is applied to a mathematical model as described more fully in Section 5.14. If there are N selected molecular markers at this stage then, in some embodiments, as many as 2N- 1 different combinations can be selected and classifiers can be computed for each of these combinations. For example, consider the case in which three molecular markers are selected after any combination of steps 201 through 212 have been performed and logistic regression is used in step 216. In this case, the following 23-1 mathematical models can be used to form 23-1 corresponding classifiers:
\n[p/(l -p)] =a+j31Xl +β2X2 +β3X3 +ε
ln[p/(l - p)] ^+/J1X1 +β2X2 +ε
ln[p/(l - p)] = a + βxXx + JS3X3 + ε
Figure imgf000067_0001
ln[ρ/(l -ρ)] =α+^1X1 +f 5
ln[p/(l -p)] =tf +/?2X2 +£ 5 and
ln\p/(l -p)] =a+β3X3 +ε
In these mathematical models, α, βi, β2, ..., βN represent coefficients that are regressed against molecular marker data whereas X1, X2, ..., XN each represent a different RNA or protein (or more generally, a molecular marker) for which molecular marker data is available. In some embodiments any one of elements X1, X2, ..., XN can represent a clinically measurable parameters. In a preferred embodiment for each combination chosen, at least one of the molecular markers of the series OfX1, X2, ..., XN does not represent a clinically measurable parameter. In some embodiments, additional interaction terms are also considered, producing non linear behaviour and resulting in greater than or less than as 2N 1 different combinations. In some embodiments, additional interaction terms are also considered, producing non linear behaviour. For instance, in the example above, another mathematical model to which molecular marker data can be applied in order to form a classifier is: ln[p/(l-p)] = α + β2X2 + β3X3 + P4XaX3 + ε where the coefficient /?4 represents the interaction between molecular marker X2 and X3. In such embodiments, more than 2N-1 "combinations" and thus more than 2N-1 classifiers are considered. In addition to the possibility of interaction terms, the present invention encompasses nonlinear variables. Examples of nonlinear variables include variables that are squared, squared rooted, or in fact, taken to any power. For instance, additional examples of mathematical models to which molecular marker data can be applied include: ln[p/(l-p)] = a + β2(X2)2 + β3X3 + P4X2X3 + ε ln[p/(l-p)] = α + β2 (X2)1/2 + β3X3 + β^X, + ε In some embodiments, a logarithmic or exponential function is applied to one or more of the variables. In some embodiments, ratios of molecular marker data can be used as a mathematical model. For example, consider the case in which regression is used to apply molecular marker data to the following equation in order to develop a classifier:
ln[p/(l- p)] =a+βιX1 +β2X2 +β3X3 +ε
Above, it was noted that X1, X2, ..., ZN each represent the product of a different gene (or more generally, a molecular marker or molecular marker like element) for which molecular marker data is available. However, in the case where ratios are selected for use in mathematical models which are subsequently scored, each X1, X2, ..., XN can in fact represent a ratio of abundance and/or expression levels for two different molecular marker products e.g. RNA or proteins, or any other type of molecular marker. For example, X1 can represent the ratio between molecular marker data measured in step 206 representative of gene (molecular marker) A and data for gene B in training population 44. Mixed forms of mathematical models are also possible. For example, some variables X can represent ratios between the molecular marker data of two molecular markers whereas other variables X can represent molecular marker data of discrete molecular markers as opposed to ratios of molecular marker data. In one specific embodiment a "ratio" of the RNA products of two molecular markers can be used and all or some of the possible combinations of said "ratios" can be utilized. For example, where the molecular marker data is a measure of abundance of RNA is determined using quantitative RT-PCR, the measure of the expression level of gene (molecular marker) A and the measure of the expression level of gene B can be used as a single term. In some embodiments this is done by first determining the level of expression of each gene individually as compared with an internal housekeeping control such as β-actin: e.g. ΔCt = Ctgene A - Ctβactin where Ctgene A is the threshold cycle of amplification of GeneA and Ctβactin is the threshold cycle of amplification of the internal control β-actin. Similarly the level of expression of gene B is also determined in comparison with an internal housekeeping control e.g. ΔCt = Ctgene B " Ctβactin In order to combine the terms into a single term for purposes of creating a classifier using "ratios" of the two terms - the terms are combined as follows to form a single variable (e.g. X). ΔCt = Ctgene A — Ctgene B This is commonly described as the use of ratios given the logarithmic nature of the measure of Ct. Thus in some embodiments, the X\, X2, ... , X^ of a classifier, each term represents the "ratio" of two molecular markers. In other embodiments the Ct scores are compared directly rather than compared with an internal control. For example, consider the case in which the desire is to form a classifier that discriminates between a first trait subgroup and a second trait subgroup wherein each term of the classifier represents molecular marker data which is derived from a combination of molecular markers using ratios as outlined above (e.g. ΔCt = Ctgene A - Ctgene B)- In such a case, each variable actually represents two molecular markers - the ratio of the molecular marker data for two molecular markers. Therefore in instances, for example, where 10 molecular markers have been identified by the funneling process of steps 202-210, 45 possible combinations of "ratios" of molecular markers can be formed e.g. n! (n-2)!2! where n is the number of possible molecular markers (e.g. 10). In some embodiments it is particularly useful to select molecular markers where the molecular marker data will work well in the form of a "ratio" thus, as part of step 214, in one embodiment, prior to selecting combinations of molecular markers, molecular markers whose molecular marker data can be combined as ratios are first identified. The use of molecular marker data as variables in which the variable representing the product of the molecular marker is in the form of ratios or raised to some arbitrary power {e.g., α, 2, N, etc.) is not limited to mathematical models based on regression. Such variables can be used in any of the mathematical models described herein (e.g., neural networks). For example, consider the case in which the desire is to form a classifier that discriminates between a first trait subgroup and a second trait subgroup wherein each term of the classifier is a combination of molecular markers evaluated as a ratio. In step 214, in one embodiment, prior to selecting ratios of molecular markers, molecular markers which can be combined as ratios are first identified. Molecular markers which can be combined as ratios are those molecular markers wherein the ratio as between said molecular markers is a value which is not equal 1 1.0 (or in one embodiment wherein ΔCt = Ctgene A - Ctgene B does not equal zero. In one embodiment, a first set of molecular markers and a second set of molecular markers are selected to create ratios such that the first set of molecular marker data demonstrates the molecular marker is upregulated in the first trait subgroup (relative to the second trait subgroup) in training population 44 and a second set of molecular marker data demonstrates that the molecular marker is downregulated in the first trait subgroup in training population 44 (relative to the second trait subgroup). Thus, for example an upregulated gene is one in which ΔCt = Ctgene B - Ctpactin >0 and a downregulated gene is one in which ΔCt = Ctgene A - Ctpactin <0, Here, the term upregulated or downregulated generally means that such up or down regulation is observed in the training population with some measure of statistical confidence, for example a Mest having ap value of 0.05, less than 0.01, less than 0.005, less than 0.001, less than 0.0005, less than 0.0001, less than 0.00005, less than 0.00001, or less. Then, ratios of molecular markers can be formed using one molecular marker from the first set and a second molecular marker from the second set. In another embodiment, a ratio for use in a selected combination is one in which the numerator represents the molecular marker data that demonstrates the molecular marker is upregulated in a first trait subgroup (as compared with a second trait subgroup) and the denominator represents the molecular marker data that demonstrates the molecular marker is upregulated in a second trait subgroup (as compared with a first trait subgroup). With such a ratio, a value greater than "1 " indicates that the organism from which the molecular marker data was measured is a member of the first trait subgroup whereas a value less than "1" indicates that this organism is a member of the second trait subgroup. Thus, in some embodiments, step 214 comprises obtaining combinations of ratios of molecular marker data. In some embodiments, step 214 comprises obtaining some multiple of molecular markers and forming a plurality of ratios of the molecular marker data so as to generate a plurality of combinations of molecular markers. In step 214, a combination of molecular markers is selected. In some embodiments, this combination of molecular markers consists of a single molecular marker. In some embodiments, this combination of molecular markers comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, as many as 30, as many as 40, as many as 50 or more molecular markers. In some embodiments, this combination of molecular markers consists of a combination of ratios of molecular markers wherein the combination comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, as many as 30, as many as 40, as many as 50 or more molecular markers. For each candidate molecular marker added, the number of possible combinations grows exponentially. The limitation to the number of combinations selected for evaluation is dependent upon the capacity of the computer, network of computers or supercomputers utilized. In one embodiment, all possible combinations of molecular markers resulting from steps 202-213 (or resulting from some subset of steps 202-213) are chosen, hi another embodiment, all possible combinations of ratios of molecular markers resulting from steps 202-213 (or resulting from some subset of steps 202-213) are chosen. In another embodiment, one can subject all possible pairs of candidate molecular markers; all possible combinations of three molecular markers, all possible combinations of four molecular markers; all possible combinations of five molecular markers, all possible combinations of six molecular markers, all possible combinations of seven molecular markers etc. In another embodiment, all possible combinations of two sets of ratios are chosen, in another embodiment, all possible combinations of three sets of ratios are chosen, in another embodiment, all possible combinations of four sets of ratios are chosen, in another embodiment, all possible combinations of five sets of ratios are chosen. Each of the combinations of molecular markers is evaluated in subsequent processing steps. Step 216. In step 216, a classifier is computed using each combination of molecular markers chosen in the last instance of step 214 and by applying the classifier to the molecular marker data measured for each molecular marker of this combination of molecular markers to a mathematical model, such as the mathematical models defined in Section 5.14 resulting in one or more classifiers for each combination. As described more thoroughly in Step 204, in some embodiments, one or more subjects of the training population are identified as outliers and are removed prior to computing classifiers for each combination of molecular markers chosen. In order to compute a classifier, in some embodiments, the mathematical model is a regression model, a neural network, a clustering model, principal component analysis, nearest neighbor classifier analysis, linear discriminant analysis, quadratic discriminant analysis, a support vector machine, a decision tree, a genetic algorithm, classifier optimization using bagging, classifier optimization using boosting, classifier optimization using the Random Subspace Method, Bayesian Networks (see F. V. Jensen. "Bayesian Networks and Decision Graphs". Springer. 2001, which is incorporated herein by reference in its entirety), a projection pursuit, weighted voting, a ratio or combination of ratios, or any combination of the above. Representative mathematical models that can be used in the present invention are described in Section 5.14. In the case where the mathematical model is a ratio or combination of ratios, steps 214 and 216 involve using the low throughput molecular marker data from training population 44 that was measured in step 206 to determine which molecular markers should be in the numerator of the ratios and which molecular markers should be in the denominator of the ratios. In some embodiments, the mathematical model used comprises a plurality of ratios of the molecular marker data. In such embodiments, the molecular marker data used in the numerators of the plurality of ratios can be the same or different than the molecular marker data used in the denominators of the plurality of ratios. In other words, a given molecular marker can be represented in the numerator of more than one ratio in the plurality of ratios or represented in the denominator of more than one ratio in the plurality of ratios. Step 218. In optional step 218 a determination is made as to whether all of the possible desired combinations of molecular markers to be tested have been considered. As discussed above in step 214, all or a portion of possible combinations may be tested. If not (218-No) process control returns to step 214 where another combination of molecular markers is selected and, at step 216, this new combination of molecular markers is evaluated using a mathematical model applied to the molecular marker data of the new combination. In some embodiments, the candidate molecular marker list comprises less than 25, less than 24, less than 23, less than 22, less than 21, or less than 20 molecular markers at step 214. In some embodiments, step 218 requires that a classifier be computed for all possible combinations of molecular markers. In other embodiments, step 218 requires that classifiers for only a portion of the possible combinations of molecular markers be considered. In some embodiments, some aspects of steps 214-218 are performed by molecular marker classifier evaluation module 62. In fact, in some embodiments, several different software programs, such as Microsoft (Redmond, Washington) Excel, are used in steps 214-218. Step 220. Once all the desired classifiers have been computed by loop 214-218, the classifiers are evaluated to determine which of the classifiers are most effective. In one embodiment the resulting classifiers of loop 214-218 are scored. In some embodiments, scoring is done using the training population 44. In other embodiments, scoring is done using a "scoring population" wherein the scoring population includes at least some members not present in the training population. In one embodiment, the scoring population includes members of the training population in addition to one or more members not used in the training population. In some embodiments, five percent or less, ten percent or less, twenty percent or less, thirty percent or less, fifty percent or less, or ninety percent or less of the members of the training population are common to the scoring population. In some embodiments, the Percent Correct Predictions statistic is used to score each classifier. The "Percent Correct Predictions" statistic assumes that if the estimated p is greater than or equal to 0.5, then the event is expected to occur and to not occur otherwise. By assigning these probabilities zeros and ones, a comparison can be made to the values of the samples in the training population to determine what percentage of the training population was sampled correctly. In some embodiments, ROC analysis is performed and is used to score the classifiers. In one embodiment, the area under the ROC curve is used to judge the quality of the classifier. As would be understood by those of skill in the relevant arts, area under the curve converts the two dimensional information contained in the ROC curve into one dimensional information. In other embodiments, information from the two dimensional aspect of the ROC curve is utilized directly. For example, the ROC curve also provides information with respect to the sensitivity and specificity of the classifier. In some embodiments, classifiers are selected on the basis of either sensitivity or specificity. This can be an important scoring indicator. For example, a diagnostic classifier with high sensitivity (ie high true positive rate and low false negative rate may be important in situations where it is safer to misdiagnosis an individual as having disease rather than misdiagnosing a disease bearing person as normal. Therefore in some embodiments, a cutoff can be set for either sensitivity or specificity and the classifier ranked or scored on the basis of the remaining variable. In some embodiments, ROC curves are generated for each model computed in an instance of step 216 using data obtained in step 206 for members of either the training population or scoring population or both. In some embodiments, the classifier resulting from the application of the mathematical itself results in a score as to the accuracy of the model. In this embodiment, the score is based on the accuracy of the model within the training population only. In some embodiments, a classifier is a weighted logistic regression model characterized by a multicategory logit model. For example, in some embodiments, a classifier discriminates between two different trait groups. In other embodiments, a classifier discriminates between more than two different trait groups. Logit models, including multicategory logit models are described in Agresti, An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc., 1996, New York, Chapters 7 and 8, which is hereby incorporated by reference. Table E illustrates the data that is used to form an ROC curve based on expression data applied to a mathematical model that uses the logit:
ln[p/(l -p)] =α+^1JSr1 +β2X2 +ε
Table E: Values for the logit ln[p/(l-p)] =a+βιXι +β2X2 +ε using hypothetical values for training population 44
ln[p/( 1 - p)] Presence / Absence of a Trait 0.98 Y 0.97 Y 0.95 Y 0.93 Y 0.91 N 0.11 Y 0.07 N 0.03 N
Each row in Table E corresponds to a different specimen in the scoring population. The left column represents the results of the logit for the classifier being sampled. The specimens in Table E are ranked by the logit score listed in the left hand column. The right hand column details the presence or absence of the trait that is being considered by the regression equation. Table E can be used to compute a ROC curve using the same techniques disclosed in step 208 (in which each row in Table E is considered a threshold cutoff value in order to compute ROC curve datapoints). Then, the area under the ROC curve can be computed in order to assess the predictive quality of the classifier. In step 220, each classifier is scored using any of the techniques disclosed here or that are known in the art. The classifiers can then be ranked based on their score. For example, they can be ranked based on the percent correct predictions, area under the ROC curve, sensitivity or specificity or some weighted or unweighted combination of the two scoring techniques. In some embodiments, step 220 is performed by molecular marker evaluation module 64. Step 222. Step 222 is optional. Optional step 222 provides additional filtering to eliminate some of the candidate classifiers computed in loop 214-218. In one such filter, limited to the case in which the classifiers computed in steps 214-218 are based on application of data to regression based mathematical models, classifiers that have at least one coefficient that is large are eliminated. Such classifiers have the potential to magnify small errors in the data. In some embodiments, determination as to whether or not a coefficient is large can require multiple computation steps, hi instances where a coefficient uniquely represents a molecular marker, the maximum value (MAX) for the data measured for the molecular marker in a trait subgroup associated with the classifier is identified. For example, consider the case in which a given coefficient uniquely represents the expression of gene A in blood. Further suppose that low throughput data for gene A from 10 individuals of a particular trait subgroup was measured in step 206. The value MAX would be the largest expression value observed for gene A in the ten individuals from the subject trait subgroup. For example, if individual #7 in the set often individuals exhibited the highest expression level for gene A as determined by the methods of step 206, then the expression value measured for gene A in individual #7 will represent MAX. Next, the minimum value (MIN) for the data measured for the molecular marker in the subject trait subgroup is identified. In the example presented above, MIN is the expression level of gene A in the subject having the lowest expression level for gene A in the set often subjects as determined by the low throughput measurement methods of step 206. Next, the coefficient derived in the regression for the unique molecular marker (e.g., the coefficient for gene A) is multiplied by the difference between (MAX) and (MIN) in order to obtain the test value (TEST). In other words, for each coefficient / in a classifier, the following equation is computed: TEST/= coefficient, * [MAX - MIN]. As an example, consider the case in which a classifier is used to determine whether or not a subject has a particular cancer. In this case, one of the trait subgroups in training population 44 will represent patients that have this cancer. To evaluate a coefficient of a classifier in this case, the low value and high value for the measured data of the molecular marker i in the trait subgroup is obtained and the difference between these two values is multiplied against the coefficient value in order to obtain the value of TEST,. In some embodiments, a coefficient / is considered large and the classifier that includes the coefficient is discarded when the value is greater than 5, greater than 10, greater than 100 or greater than 1000. In typical embodiments, a classifier will determine whether a subject falls into one of at least two different trait subgroups. In other words, the classifier will discriminate between at least two different trait subgroups. A test has been presented above for determining whether a coefficient in a regression derived classifier is too large. This test used one of the trait subgroups that the classifier discriminates. In some embodiments, the test is repeated for each of the trait subgroups that the classifier can discriminate. For example, in the case of a classifier that can discriminate between the cancerous trait subgroup and the non-cancerous trait subgroup, the test is independently run using data from each trait subgroup. That is, the test is first run using only data from the cancerous trait subgroup and then the test is run a second time using only data from the non¬ cancerous trait subgroup. If a coefficient is too large in any such independent test, the classifier is eliminated from further consideration. In some embodiments, the test is run against only one of the possible trait subgroups that the subject classifer can discriminate. In another embodiment, some classifiers are eliminated on the basis of the score. For example, where the scoring system used is receiver operating characteristic (ROC) curve score determined by an area under the ROC curve, in some embodiments, those classifiers with scores of less than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0.45 or less can be eliminated. In other embodiments, where specificity is important to the use of the classifier, a sensitivity threshold can be set and classifiers ranked on the basis of the specificity, for example with a cutoff for specificity of less than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0.45 or less can be eliminated. Similarly, the specificity threshold can be set and classifiers ranked on the basis of sensitivity for example with a cutoff for sensitivity of less than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0.45 or less can be eliminated. Thus in some embodiments, only the top 10 ranking classifiers, the top 20 ranking classifiers, or the top 100 ranking classifiers are selected and the remaining classifiers eliminated. Step 224. After classifiers have been scored and ranked and some classifiers optionally eliminated; one or more classifiers can be combined to create a classifier group. For instance, in some embodiments, the top 10 ranking classifiers, the top 20 ranking classifiers, or the top 100 ranking classifiers are selected. In some embodiments, any of the top 1 to top 500 ranking classifiers is selected, hi instances where more than one classifier is selected, in one embodiment, each classifier contributes one vote to the diagnosis of the test subject such that diagnosis of the test subject is determined as a result of a combination of classifiers. In other embodiments, multiple classifiers can be used and different weighting schema applied to each classifier. For example, weighting schema can include weighting on the basis of factors such as the original score the classifier, the logs odd ratio ("logit"), the size of the coefficients for each classifier, some combination thereof and the like. Step 226. Step 226 is optional. Optional step 226 is useful if training population 44 is comprised of more than two trait subgroups. In cases where there are more than two trait subgroups, multiple binary classifiers (or groups of said classifiers) can be developed wherein each binary classifier (or group of said classifiers) is directed towards differentiating as between two traits. In one embodiment, each round of the funnel (e.g. steps 202, 204, 206, 214, 216, 220 and 224) produces a set of binary classifiers. In another embodiment, multiple lists of binary candidate molecular markers are developed by performing step 202, and then binary classifiers (or groups of binary classifiers) are developed by proceeding with multiple rounds of the remainder of the funnel (e.g. steps 204, 206, 214, 216, 220 and step 224). Because each classifier represents only a binary test e.g. the absence or presence of a single trait, in step 226 a determination is made as to whether all classifiers have been developed for training population 44. If not (226-No), process control returns to step 210 and work is initiated to develop a classifier for a different trait represented by training population 44. Therefore, steps 210-224 can optionally be repeated until one or more classifiers or groups of classifiers have been selected for each of the trait subgroups represented by training population 44. In some embodiments, each classifier or group of classifiers developed in accordance with embodiments of the present invention is stored in classifier database 70. Fig. 5 illustrates an exemplary classifier database 70 in accordance with one embodiment of the present invention. Database 70 includes an entry 400 for each classifier 400. Each classifier 400 is optionally given a classifier name 402. Each classifier 400 is part of classifier. For example, a given classifier can consist of only a single classifier. In other embodiments, a given classifier can consist of one or a plurality of classifiers. Therefore, each classifier 402 includes an indicator 403 to indicate which classifier the classifier is in. Further, each classifier 400 has an optional indicator 404 to indicate the trait that the classifier can discriminate. In some embodiments, optional indicator indicates the trait subgroups that the classifier can discriminate. In some embodiments such information can be inferred from the classifier identifier field 403 since each classifier represents the absence or presence of a particular trait (e.g., absence or presence of cancer). In addition to this header information, each classifier includes the identity 406 of one or more molecular markers and the respective coefficients 408 for each of the molecular markers. Once classifiers or classifier groups have been developed, the classifiers can be used to diagnose a patient that has presented as possibly having a disease that can be differentiated by the classifiers. Figure 3 is a flowchart of a method of applying the classifiers to a patient. Step 328. Step 328 can be performed after the previously described steps, or can be used in conjunction with a classifier or classifier groups derived using the methods disclosed herein. As such, steps 328-334 represent a completely independent method of the present invention and can be performed at any time once suitable classifiers have been developed using, for example, steps 301 through 326. Step 328 is used in conjunction with step 330 to diagnose a trait of interest of an individual not represented in either the training population or the scoring population (a "test individual"). Each classifier or classifier group identified previously can be used to determine whether a test individual has a trait of interest. In order to perform such tests, molecular marker data for each molecular marker of the classifier or classifier group of interest is required. To obtain such data, a sample of blood from the subject is obtained using any of the techniques described in Section 5.2. The sample is used to measure molecular marker data for each molecular marker in the sample using any of the techniques described in Sections 5.3 or 5.4. Thus in step 328, once classifiers have been identified, molecular marker data for use with the classifier or classifier groups can be obtained using either high throughput or low throughput techniques. Advantageously, the molecular marker data obtained in step 328 can be stored in patient database 68 for later use. In fact, in some embodiments, rather than obtaining molecular marker data from a patient sample in step 328, the data is obtained from a subject in patient database 68. In such embodiments, the molecular marker data was previously loaded into patient database 68. Fig. 6 illustrates a patient database 68 in accordance with one embodiment of the present invention. There is a record 500 for each patient (subject) tracked by patient database 68. Each patient record 500 optionally includes a patient identifier 502 to uniquely identify the patient. In some embodiments such unique identifiers can be inferred from the patient record value 500. Each patient record 500 includes a molecular profile 504 comprising molecular marker data collected for a plurality of molecular marker products from a sample defined in Section 5.2 using any one of the techniques described in Sections 5.3 and 5.4. In typical embodiments, a molecular profile 504 includes a plurality of molecular marker identities 506 and the corresponding measured molecular marker data values 508 for such molecular markers. In addition to the molecular profile, each patient record 502 can include one or more traits 510. Such trait characterizations can be assigned by observation of the subject and/or by testing the patient's molecular profile using the classifiers constructed in accordance with the methods of the present invention. Section 5.10, below, provides more details on exemplary patient databases 68. Step 330. In step 330 the classifier created using some or more of the previous steps is used to diagnose a test individual. In some embodiments diagnosis can be performed using a classifier group from step 224. For example, in one embodiment, where there are numerous classifiers after step 222 that provide satisfactory scores (given the purpose for use), a test subject can be diagnosed by using the results of all or some of these classifiers in the form of a classifier group as described in step 224. In one embodiment, the term diagnosis means the results of a single classifier or group of classifiers resulting from the application of the funneling method described in steps 202-224. For example, the resulting classifier or group of classifiers will enable the ability to determine whether a test individual belongs to one of two possible trait subgroups. In another embodiment, by the term diagnosis is meant the results of multiple classifiers or multiple groups of classifiers (ie classifiers resulting from the application of more than one round of the funneling method described in steps 202 — 224). For example, the resulting classifiers or groups of classifiers used in series can allow a diagnosis as to whether an individual belongs to one of three or more possible trait subgroups (e.g. results of first classifier distinguish as to whether person has schizophrenia or does not have schizophrenia - If not schizophrenia apply a second classifier or group of classifiers to determine whether individual has bipolar disorder or does not have bipolar disorder etc.) The use of the classifiers to diagnose depends upon the trait subgroups used to develop the classifier. For example, if the classifier was developed to differentiate as between two trait subgroups, the classifier can be used to diagnose a test subject as being either of the first trait subgroup or the second trait subgroup. To diagnose a test subject, preferably a quantitative technique such as quantitative RT-PCR is utilized to obtain molecular marker data measured in step 328 is used. To illustrate the use of a classifier to diagnose, consider the case in which the classifier comprises the classifier group:
ln[p/(l - p)] =0.34 +0.24JT1 +0.74X3 +0.03
ln[p/(l - p)] =0.54- OAST2 +83X3 +0.01
That is, the exemplary classifier group consisting of two classifiers. To poll the classifier, the data (e.g. abundance level, activity level, etc.) for molecular markers X1, X2 and X3 is measured using any of the techniques described in Section 5.3 or 5.4. Then, these data measurement values are placed into the classifier equations and the equations are processed and the output is used to predict outcome In one embodiment, classifier equation values that approach a value of one indicate that the sample has the trait associated with the classifier whereas classifier equation values that approach zero indicate that the sample does not have the trait associated with the classifier. In other embodiments, the equations are regressed so that the opposite relationship hold (e.g., equation values approach one indicate absence of an associated trait). In one embodiment, each equation is assigned a "+1" vote if the equation approaches one or a "-1" vote if the equation approaches zero. Equation votes are summed. If the net summation is positive, then the subject is deemed to have the trait associated with the classifier. If the net summation is negative, then the subject is deemed not to have the trait associated with the classifier. In some embodiments, step 330 is performed by model polling and reporting module 66. Step 332. One of the advantages of the present invention is that a single sample collected in accordance with Section 5.2 can be used to test the patient for one or more of a plurality of molecular markers which may be useful for one or more traits. Accordingly, in step 332, a determination is made as to whether the patient has been tested for each pair of traits for which a determination is required. If additional determinations are required (332-No), process control is returned to step 330 and the measured molecular marker data from the patient is used to help determine the likelihood as to whether a subject has other traits represented by a trait subgroup in training population 44. Step 334. When the patient sample has been used to determine if the subject has any of a plurality of different traits as determined by one or more classifiers or classifier groups, a report is generated. In some embodiments, this report includes the results of each classification test. In other words, the report provides an indication as to whether it is likely the tested subject has any one of a plurality of different traits. In some embodiments, step 334 is performed by classifier polling and reporting module 66. Section 5.5 provides a summary of some of the applications for classifiers constructed using the methods of the present invention. 5.2 SOURCE OF MOLECULAR MARKER DATA
The present invention provides methods for identifying molecular markers by obtaining molecular marker data which represents the products of molecular markers found in a blood sample. Molecular markers are thus identified that correlate with, are associated with, or indicate a trait. The present invention also provides methods for detecting, diagnosing, monitoring, prognosing or predicting a trait or reoccurrence of a trait based upon data corresponding to the expression of molecular markers in a blood sample. As used herein, the terms "subject" and "patient" and "individual" are used interchangeably to refer to an animal (e.g., a mammal, a fish, an amphibian, a reptile, a bird, and an insect). In a specific embodiment, a subject is a mammal (e.g., a non-human mammal and a human). In another embodiment, a subject is a pet (e.g., a dog, a cat, a guinea pig, a monkey and a bird), a farm animal (e.g., a horse, a cow, a pig, a goat and a chicken) or a laboratory animal (e.g., a mouse and rat). In another embodiment, the subject is a primate (e.g., a chimpanzee and a human). In a preferred embodiment, the subject is a human. 5.2.1 SOURCEOFABLOODSAMPLE
A blood sample obtained from any subject may be used in accordance with the methods of the invention. Examples of subjects from which a blood sample can be obtained and utilized in accordance with the methods of the invention include, but are not limited to, asymptomatic subjects, subjects manifesting or exhibiting 1, 2, 3, 4 or more traits or symptoms of a trait, subjects clinically diagnosed as having a trait, subjects predisposed to a trait (e.g., subjects with a family history of a trait, subjects with a genetic predisposition to a trait, and subjects that lead a lifestyle that predisposes them to a trait or increases the likelihood of contracting a trait), subjects suspected of having a trait, subjects undergoing therapy for a trait, subjects non-responsive to a therapy, subjects responsive to a therapy, subjects with more than one trait (e.g., subjects with 2, 3, 4, 5 or more traits), subjects not undergoing therapy for a trait, subjects determined by a medical practitioner (e.g., a physician) to be healthy or disease-free, subjects that are in remission, subjects cured of trait, and subjects that have not been diagnosed with a condition. In specific embodiment, the condition is a disease. In another specific embodiment, a condition is any state that is codified in the International Classification of Diseases, 9th Revision, Department of Health and Human Services (ICD-9 codes) and/or SNOMED Clinical Terms (SNOMED CT®) which is hereby incorporated by reference, or equivalent treatise. Non-limiting examples of disease include, but are not limited to, blood disorder, blood lipid disease, autoimmune disease, arthritis (e.g., osteoarthritis, rheumatoid arthritis, juvenile rheumatoid arthritis and the like), bone or joint disorder, lupus, an allergy, a cardiovascular disorder (e.g., heart failure, congenital heart disease, rheumatic fever, valvular heart disease, corpulmonale, cardiomyopathy, myocarditis, pericardial disease, vascular diseases such as atherosclerosis, acute myocardial infarction, ischemic heart disease and the like), obesity, respiratory disease (e.g., asthma, pneumonitis, pneumonia, pulmonary infections, lung disease, bronchiectasis, tuberculosis, cystic fibrosis, interstitial lung disease, chronic bronchitis emphysema, pulmonary hypertension, pulmonary thromboembolism, acute respiratory distress syndrome and the like), hyperlipidemias, endocrine disorder, immune disorder, infectious disease, muscle wasting and whole body wasting disorder, neurological disorder (e.g., migraines, seizures, epilepsy, cerebrovascular disease, Parkinson's, ataxic disorders, motor neuron diseases, cranial nerve disorders, spinal cord disorders, meningitis and the like), neurodegenerative disease (e.g., alzheimers, dementia and the like), neuropsychiatric disease (e.g., schizophrenia, anxiety and the like), mood disorders (e.g., bipolar disorder; manic depression and the like), skin disorder, kidney disease, scleroderma, stroke, hereditary hemorrhage telangiectasia, diabetes, disorders associated with diabetes (e.g., PVD), hypertension, Gaucher's disease, cystic fibrosis, sickle cell anemia, liver disease, stomach disease, pancreatic disease, eye disease, ear disease, nose disease, throat disease, diseases affecting the reproductive organs, gastrointestinal diseases (including diseases of the colon, diseases of the spleen, appendix, gall bladder, and others) and the like. For further discussion of human diseases, see Mendelian Inheritance in Man: A Catalog of Human Genes and Genetic Disorders by Victor A. McKusick (12th Edition, 3 volume set, June 1998, Johns Hopkins University Press, ISBN: 0801857422) and Harrison's Principles of Internal Medicine by Braunwald, Fauci, Kasper, Hauser, Longo, & Jameson (15th Edition 2001), the entirety of each of which is incorporated herein. Additional examples of disease are disclosed in Section 5.8, below. In one embodiment of the invention, the disease is an immune disorder, such as those associated with overexpression of a gene or expression of a mutant gene (e.g., autoimmune diseases, such as diabetes mellitus, arthritis (including rheumatoid arthritis, juvenile rheumatoid arthritis, osteoarthritis, and psoriatic arthritis), multiple sclerosis, encephalomyelitis, myasthenia gravis, systemic lupus erythematosis, automimmune thyroiditis, dermatitis (including atopic dermatitis and eczematous dermatitis), psoriasis, Sjogren's Syndrome, Crohn's disease, aphthous ulcer, iritis, conjunctivitis, keratoconjunctivitis, ulcerative colitis, asthma, allergic asthma, cutaneous lupus erythematosus, scleroderma, vaginitis, proctitis, drug eruptions, leprosy reversal reactions, erythema nodosum leprosum, autoimmune uveitis, allergic encephalomyelitis, acute necrotizing hemorrhagic encephalopathy, idiopathic bilateral progressive sensorineural hearing, loss, aplastic anemia, pure red cell anemia, idiopathic thrombocytopenia, polychondritis, Wegener's granulomatosis, chronic active hepatitis, Stevens- Johnson syndrome, idiopathic sprue, lichen planus, Graves' disease, sarcoidosis, primary biliary cirrhosis, uveitis posterior, and interstitial lung fibrosis), graft-versus-host disease, cases of transplantation, and allergy. In another embodiment, a disease of the invention is a cellular proliferative and/or differentiative disorder that includes, but is not limited to, cancer, e.g., carcinoma, sarcoma or other metastatic disorders and the like. As used herein, the term "cancer" refers to cells having the capacity for autonomous growth, i.e., an abnormal state of condition characterized by rapidly proliferating cell growth. "Cancer" is meant to include all types of cancerous growths, pre-cancerous growths or lesions, oncogenic processes, metastatic tissues or malignantly transformed cells, tissues, or organs, irrespective of histopathologic type or stage of invasiveness. Examples of cancers include, but are not limited to, solid tumors, tissue specific tumors, benign cancer, metastatic cancers, early stage cancer, late stage cancer and leukemias, including: apudoma, choristoma, branchioma, malignant carcinoid syndrome, carcinoid heart disease, carcinoma (e.g., Walker, basal cell, basosquamous, Brown-Pearce, ductal, Ehrlich tumour, in situ, Krebs 2, Merkel cell, mucinous, non-small cell lung, oat cell, papillary, scirrhous, bronchiolar, bronchogenic, squamous cell, and transitional cell), histiocytic disorders, leukaemia (e.g., B cell, mixed cell, null cell, T cell, T-cell chronic, HTLV-II-associated, lymphocytic acute, lymphocytic chronic, mast cell, and myeloid), histiocytosis malignant, Hodgkin disease, immunoproliferative small, non-Hodgkin lymphoma, plasmacytoma, reticuloendotheliosis, melanoma, chondroblastoma, chondroma, chondrosarcoma, fibroma, fibrosarcoma, giant cell tumors, histiocytoma, lipoma, liposarcoma, mesothelioma, myxoma, myxosarcoma, osteoma, osteosarcoma, Ewing sarcoma, synovioma, adenofibroma, adenolymphoma, carcinosarcoma, chordoma, craniopharyngioma, dysgerminoma, hamartoma, mesenchymoma, mesonephroma, myosarcoma, ameloblastoma, cementoma, odontoma, teratoma, thymoma, trophoblastic tumour, adeno-carcinoma, adenoma, cholangioma, cholesteatoma, cylindroma, cystadenocarcinoma, cystadenoma, granulosa cell tumour, gynandroblastoma, hepatoma, hidradenoma, islet cell tumour, Leydig cell tumour, papilloma, Sertoli cell tumour, theca cell tumour, leiomyoma, leiomyosarcoma, myoblastoma, mymoma, myosarcoma, rhabdomyoma, rhabdomyosarcoma, ependymoma, ganglioneuroma, glioma, medulloblastoma, meningioma, neurilemmoma, neuroblastoma, neuroepithelioma, neurofibroma, neuroma, paraganglioma, paraganglioma nonchromaffin, angiokeratoma, angiolymphoid hyperplasia with eosinophilia, angioma sclerosing, angiomatosis, glomangioma, hemangioendothelioma, hemangioma, hemangiopericytoma, hemangiosarcoma, lymphangioma, lymphangiomyoma, lymphangiosarcoma, pinealoma, carcinosarcoma, chondrosarcoma, cystosarcoma, phyllodes, fibrosarcoma, hemangiosarcoma, leimyosarcoma, leukosarcoma, liposarcoma, lymphangiosarcoma, myosarcoma, myxosarcoma, ovarian carcinoma, rhabdomyosarcoma, sarcoma (e.g., Ewing, experimental, Kaposi, and mast cell), neoplasms (e.g., bone, breast, digestive system, colorectal, liver, pancreatic, pituitary, testicular, orbital, head and neck, central nervous system, acoustic, pelvic respiratory tract, and urogenital), neurofibromatosis, and cervical dysplasia, and other conditions in which cells have become immortalized or transformed. 5.2.2 METHODS FOR COLLECTING BLOOD
In one aspect, a sample of blood is obtained from a subject according to methods well known in the art. The present invention can use whole blood, but can also use blood in which the serum or plasma has been removed and the RNA or mRNA isolated from the remaining sample in accordance with methods known in the art (for example, using preferably gentle centrifugation at 300-800xg for five to ten minutes). In some embodiments a drop of blood is collected from a simple pin prick made in the skin of a subject. In such embodiments, this drop of blood collected from a pin prick is all that is needed. A drop of blood can include volumes of anywhere from lOul through to 10OuI. Blood may be drawn from a subject from any part of the body (e.g., a finger, a hand, a wrist, an arm, a leg, a foot, an ankle, a stomach, and a neck) using techniques known to one of skill in the art, in particular methods of phlebotomy known in the art. In a specific embodiment, venous blood is obtained from a subject and utilized in accordance with the methods of the invention. In another embodiment, arterial blood is obtained and utilized in accordance with the methods of the invention. The composition of venous blood varies according to the metabolic needs of the area of the body it is servicing. In contrast, the composition of arterial blood is consistent throughout the body. For routine blood tests, venous blood is generally used. Venous blood can be obtained from any source including the basilic vein, cephalic vein, or median vein. Arterial blood can be obtained from the radial artery, brachial artery or femoral artery. A vacuum tube, a syringe or a butterfly may be used to draw the blood. Typically, the puncture site is cleaned, a tourniquet is applied approximately 3-4 inches above the puncture site, a needle is inserted at about a 15-45 degree angle, and if using a vacuum tube, the tube is pushed into the needle holder as soon as the needle penetrates the wall of the vein. When finished collecting the blood, the needle is removed and pressure is maintained on the puncture site. Usually, heparin or another type of anticoagulant is in the tube or vial that the blood is collected in so that the blood does not clot. When collecting arterial blood, anesthetics can be administered prior to collection. The amount of blood collected will vary depending upon the site of collection, the amount required for a method of the invention, and the comfort of the subject. However, an advantage of one embodiment of the present invention is that the amount of blood required to implement the methods of the present invention can be so small that more invasive procedures are not required to obtain the sample. For example, in some embodiments, all that is required is a drop of blood. This drop of blood can be obtained, for example, from a simple pinprick. In some embodiments, any amount of blood is collected that is sufficient to measure molecular marker data. As such, in some embodiments, the amount of blood that is collected is 1 μl or less, 0.5 μl or less, 0.1 μl or less, or 0.01 μl or less. However, the present invention is not limited to such embodiments. In some embodiments more blood is available and in some embodiments, more blood can be used to effect the methods of the present invention. As such, a broad range of blood volumes is contemplated and can be used to obtain the molecular marker data measurement data used in the present invention. In various specific embodiments, 0.001 ml, 0.005 ml, 0.01 ml, 0.05 ml, 0.1 ml, 0.15 ml, 0.2 ml, 0.25 ml, 0.5 ml, 0.75 ml, 1 ml, 1.5 ml, 2 ml, 3 ml, 4 ml, 5 ml, 10 ml, 15 ml or more of blood is collected from a subject. In other specific embodiments, 0.001 ml to 15ml, 0.01 ml to 10 ml, 0.1 ml to 10 ml, 0.1 ml to 5 ml, 1 to 5 ml of blood is collected from a subject. In some embodiments of the present invention, blood is stored within a K3/EDTA tube. In another embodiment, one can utilize tubes for storing blood which contain stabilizing agents such as disclosed in United States patent No. 6,617,170. In another embodiment, the PAXgene™ blood RNA system provided by PreAnalytiX, a Qiagen/BD company, can be used to collect blood. In yet another embodiment, the Tempus™ blood RNA collection tubes, offered by Applied Biosystems, can be used. Tempus™ collection tubes provide a closed evacuated plastic tube containing RNA stabilizing reagent for whole blood collection. The collected blood is optionally but preferably stored at refrigerated temperatures, such as 4 °C, prior to molecular marker data measurement. In some embodiments, a portion of the blood sample is used for molecular measurement at a first instance of time whereas one or more remaining portions of the blood sample is stored for a period of time for later use. This period of time can be an hour or more, a day or more, a week or more, a month or more, a year or more, or indefinitely. For long term storage, storage methods well known in the art, such as storage at cryo temperatures (e.g. below -60 °C) can be used, hi some embodiments, in addition to storage of the blood (or instead of storage oft he blood), isolated molecular markers (e.g., nucleic acid, protein, carbohydrates, lipids, metabolites, etc.) are stored for a period of time for later use. Storage of such molecular markers can be for an hour or more, a day or more, a week or more, a month or more, a year or more, or indefinitely. 5.2.3 METHOD OF ISOLATING BLOOD CELLS
In some embodiments of the present invention, whole blood is used directly to isolate and analyze the products of one or more molecular markers so as to obtain molecular marker data. In other embodiments of the invention fractionated blood can be used. By fractionated blood is meant blood in which the blood cells are separated prior to isolation of the molecular markers using techniques known in the art. For example, fractionated blood includes blood wherein the blood cells are fractionated using Ficoll-Hypaque (Pharmacia) gradient centrifugation. Such centrifugation separates erythrocytes (red blood cells) from various types of nucleated cells and from plasma. As such, in some embodiments of the present invention, a blood sample of the invention is fractionated blood. In one embodiment, peripheral blood leukocytes are utilized ("PBLs"). PBLs are separated from the remainder of the blood using a Ficoll® gradient. By way of example but not limitation, macrophages can be obtained as follows. Mononuclear cells are isolated from peripheral blood of a subject, by syringe removal of blood followed by Ficoll-Hypaque gradient centrifugation. Tissue culture dishes are pre-coated with the subject's own serum or with AB+ human serum and incubated at 37°C for one hour. Non-adherent cells are removed by pipetting. Cold (4°C) ImM EDTA in phosphate-buffered saline is added to the adherent cells left in the dish and the dishes are left at room temperature for fifteen minutes. The cells are harvested, washed with RPMI buffer and suspended in RPMI buffer. Increased numbers of macrophages can be obtained by incubating at 370C with macrophage-colony stimulating factor (M-CSF). Antibodies against macrophage specific surface markers, such as Mac- 15 can be labeled by conjugation of an affinity compound to such molecules to facilitate detection and separation of macrophages. Affinity compounds that can be used include but are not limited to biotin, photobiotin, fluorescein isothiocyante (FITC)5 or phycoerythrin (PE), or other compounds known in the art. Cells retaining labeled antibodies are then separated from cells that do not bind such antibodies by techniques known in the art such as, but not limited to, various cell sorting methods, affinity chromatography, and panning. Blood cells can be fractionated using a fluorescence activated cell sorter (FACS). Fluorescence activated cell sorting (FACS) is a known method for separating particles, ncluding cells, based on the fluorescent properties of the particles. See, for example, Kamarch, 1987, Methods Enzymol 151:150 165. Laser excitation of fluorescent moieties in the individual particles results in a small electrical charge allowing electromagnetic separation of positive and negative particles from a mixture. An antibody or ligand used to detect a blood cell antigenic determinant present on the cell surface of particular blood cells is labeled with a fluorochrome, such as FITC or phycoerythrin. The cells are incubated with the fluorescently labeled antibody or ligand for a time period sufficient to allow the labeled antibody or ligand to bind to cells. The cells are processed through the cell sorter, allowing separation of the cells of interest from other cells. FACS sorted particles can be directly deposited into individual wells of microtiter plates to facilitate separation. Magnetic beads can be also used to separate blood cells in some embodiments of the present invention. For example, blood cells can be sorted using a magnetic activated cell sorting (MACS) technique, a method for separating particles based on their ability to bind magnetic beads (0.5 100 Dm diameter). A variety of useful modifications can be performed on the magnetic microspheres, including covalent addition of an antibody which specifically recognizes a cell solid phase surface molecule or hapten. A magnetic field is then applied, to physically manipulate the selected beads. In a specific embodiment, antibodies to a blood cell surface marker are coupled to magnetic beads. The beads are then mixed with the blood cell culture to allow binding. Cells are then passed through a magnetic field to separate out cells having the blood cell surface markers of interest. These cells can then be isolated. In some embodiments, the surface of a culture dish may be coated with antibodies, and used to separate blood cells by a method called panning. Separate dishes can be coated with antibody specific to particular blood cells. Cells can be added first to a dish coated with blood cell specific antibodies of interest. After thorough rinsing, the cells left bound to the dish will be cells that express the blood cell markers of interest. Examples of cell surface antigenic determinants or markers include, but are not limited to, CD2 for T lymphocytes and natural killer cells, CD3 for T lymphocytes, CDl Ia for leukocytes, CD28 for T lymphocytes, CD 19 for B lymphocytes, CD20 for B lymphocytes, CD21 for B lymphocytes, CD22 for B lymphocytes, CD23 for B lymphocytes, CD29 for leukocytes, CD 14 for monocytes, CD41 for platelets, CD61 for platelets, CD66 for granulocytes, CD67 for granulocytes and CD68 for monocytes and macrophages. Whole blood can also be fractioned into cells types such as leukocytes, platelets, erythrocytes, etc. Leukocytes can be further separated into granulocytes and agranulocytes using standard techniques. Granulocytes can be separated into cell types such as neutrophils, eosinophils, and basophils using standard techniques. Agranulocytes can be separated into lymphocytes (e.g., T lymphocytes and B lymphocytes) and monocytes using standard techniques. T lymphocytes can be separated from B lymphocytes and helper T cells separated from cytotoxic T cells using standard techniques. Separated blood cells (e.g., leukocytes) can be frozen by standard techniques prior to use in the present methods. 5.2.4 BLOOD SAMPLES USED IN METHODS OF THE INVENTION
In accordance with the methods of the invention, the term "blood sample"can include any of the samples discussed in section 5.2.3. In some embodiments, this includes whole blood, fractionated blood, a sample of subsets of fractionated blood, and a sample of specific types of blood cells. In a specific embodiment, the whole blood sample can have the plasma or serum removed by centrifugation, using preferably gentle centrifugation at 30O-800xg for five to ten minutes. In another embodiment, a blood sample of the invention is a sample of peripheral blood leukocytes (PBLs). In another embodiment, a blood sample of the invention is a sample of granulocytes. In another embodiment, a blood sample of the invention is a sample of neutrophils, eosinophils, basophils or any combination thereof. In another embodiment, a blood sample of the invention is a sample of agranulocytes. In another embodiment, a blood sample of the invention is a sample of lymphocytes, monocytes or a combination thereof. In yet another embodiment, a blood sample of the invention is a sample of T lymphocytes, B lymphocytes or a combination thereof. See, e.g., Section 5.4.3 supra for methods of isolating blood cells. A blood sample that is useful according to the invention is in an amount that is sufficient for the detection of one or more molecular markers according to the invention. In a specific embodiment, a blood sample useful according to the invention is in an amount ranging from 1 μl to 100 ml, preferably 10 μl to 50 ml, more preferably 10 μl to 25 ml and most preferably 10 μl to 1 ml. In one embodiment whole blood, or serum free whole blood is taken and the redblood cells are lysed with lysing buffer. In a specific embodiment, the Lysis Buffer (IL) consists of 0.6g EDTA 1.Og KHCO2, and 8.2g NH4Cl adjusted to pH 7.4 (using NaOH). Once mixed with lysing buffer, the sample is centrifuged and the cell pellet retained and RNA or mRNA extracted in accordance with methods known in the art ("lysed whole blood") (see, for example, Sambrook, Fritsch & Maniatis, "Molecular Cloning: A Laboratory Manual (1982); "DNA Cloning: A Practical Approach," Volumes I and II (D.N. Glover ed. 1985). The use of whole blood or lysed whole blood is preferred since it avoids the costly and time-consuming need to separate out the cell types within the blood (Liew et al. U.S. Patent Application No.US 2004/0014059). In another embodiment, whole blood is stored and stabilized using PAXgene® tubes and RNA can be isolated using the PAXgene® RNA Isolation system. In yet another embodiment, RNA is isolated from whole blood which has been isolated using PAXgene® and additional globin reduction protocols followed. 5.3 METHODS FOR MEASURING MOLECULAR MARKER DATA
The techniques described in this section are particularly useful for obtaining molecular marker data for step 202 wherein the data is reflective of the products of the molecular marker -e.g. measurement of the abundance of RNA or protein products in blood corresponding to all of the molecular markers of the genome or "a portion thereof. In particular, the techniques useful for step 202 are those techniques which allow the ability to comprehensively screen for candidate molecular markers quickly and effectively. These techniques preferably provide molecular marker data for a large number of molecular markers concurrently, thereby allowing greater ability to screen molecular markers corresponding to the entire genome, or a portion thereof in a short period of time. In addition to the techniques described in this Section 5.3, any technique known to one of skill in the art to measure the abundance of RNA or protein corresponding to the entire genome or "a portion thereof can be used to measure such data. In one embodiment,"a portion thereof is data corresponding to the amount of RNA or protein expressed from more than 1,000, more than 2,000, more than 5,000, more than 10,000, more than 20,000, more than 30,000 molecular markers. In another embodiment, the "a portion thereof refers to at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at leat 95% of the genome. See, e.g., Sambrook, Fritsch & Maniatis, 1982, Molecular Cloning: A Laboratory Manual; DNA Cloning: A Practical Approach, volumes I and II (D.N. Glover ed. 1985); Oligonucleotide Synthesis Gait ed., 1984; Nucleic Acid Hybridization, Hames & Higgins eds., 1985; Transcription and Translation, Hames & Higgins eds., 1984, Animal Cell Culture, Freshney ed., 1986, Immobilized Cells And Enzymes, IRL Press, 1986, Perbal, 1984, A Practical Guide To Molecular Cloning, each of which is hereby incorporated by reference in its entirety. In one embodiment more than technique can be used to measure data for each molecular marker to perform step 202. 5.3.1 RNA MEASUREMENT TECHNIQUES
Any technique known to one of skill in the art may be used to measure the level of expression of a molecular marker by measuring the amount of the product of the molecular marker. In one embodiment the RNA in blood corresponding to the molecular marker is measured. By "corresponding to a molecular marker" is meant RNA transcribed from a molecular marker (or proteins translated from RNA which is transcribed from a molecular marker when referring to protein products of a molecular marker). RNA or protein which corresponds to a molecular marker are also considered the product of the molecular marker. In a specific embodiment, the level of an RNA product is measured using a echnique which permits generation of data for a large number of molecular markers. However measured, the result is either the absolute or relative amounts of abundance of nucleic acids corresponding to the molecular markers, including but not limited to values representing abundances or abundance ratios. 5.3.1.1 MICROARRAYS n one embodiment, nucleic acid arrays are employed for analyzing the level of RNA product of each molecular marker of the genome in a blood sample. In a specific embodiment, molecular marker data is obtained by hybridizing detectably labeled polynucleotides representing the nucleic acid sequences in mRNA transcripts present in a cell (e.g., fluorescently labeled cDNA synthesized from total cell mRNA) to a microarray. In some embodiments expressed transcripts that may or may not represent genes expressed in the blood sample are analyzed. In some embodiments, a microarray is an array of positionally-addressable binding (e.g., hybridization) sites on a support for representing many of the nucleic acid sequences in the genome of a cell or organism. Is some embodiments, a microarray represents most or almost all of the genes in a species. In some embodiments, each microarray binding site consists of polynucleotide probes bound to a predetermined region on the support. Microarrays are described in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 15-16, which is hereby incorporated by reference in its entirety. Microarrays can be made in a number of ways. See, for example, Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 16-22, which is hereby incorporated by reference in its entirety. Preferably microarrays are reproducible, allowing multiple copies of a given array to be produced and results from the microarrays compared with each other. Preferably, the microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. Microarrays are preferably small, e.g., between 1 cm2 and 25 cm2, preferably 1 to 3 cm2. However, both larger and smaller arrays are also contemplated and may be preferable, e.g., for simultaneously evaluating a very large number or very small number of different probes. In some embodiments, a given binding site or unique set of binding sites in the microarray will specifically bind (e.g., hybridize) to a nucleotide sequence in a single gene from a cell or organism (e.g., to exon of a specific mRNA or a specific cDNA derived therefrom). Microarrays used in the present invention can include one or more test probes. In some embodiments each such test probe comprises a polynucleotide sequence that is complementary to a subsequence of RNA or DNA to be detected. Each probe typically has a different nucleic acid sequence, and the position of each probe on the solid surface of the array is usually known or can be determined. Microarrays useful in accordance with the invention can include oligonucleotide microarrays, cDNA based arrays, SNP arrays, spliced variant arrays and any other array able to provide a quantitative or semi quantitative data of the invention. Some types of microarrays are addressable arrays. More specifically, some microarrays are positionally addressable arrays. In some embodiments, each probe of the array is located at a known, predetermined position on the solid support so that the identity (e.g., the sequence) of each probe can be determined from its position on the array (e.g., on the support or surface). In some embodiments, the arrays are ordered arrays. In some embodiments, the density of probes on a microarray or a set of microarrays is 100 different (e.g., non-identical) probes per 1 cm2 or higher. More preferably, a microarray used in the methods of the invention will have at least 550 probes per 1 cm2, at least 1,000 probes per 1 cm2, at least 1,500 probes per 1 cm2 or at least 2,000 probes per 1 cm . In a particularly preferred embodiment, the microarray is a high density array, preferably having a density of at least 2,500 different probes per 1 cm2. The microarrays used in the invention therefore preferably contain at least 2,500, at least 5,000, at least 10,000, at least 15,000, at least 20,000, at least 25,000, at least 50,000 or at least 55,000 different (i.e., non-identical) probes. In one embodiment, the microarray is an array (e.g., a matrix) in which each position represents a discrete binding site for a nucleotide sequence of a transcript encoded by a gene (e.g., for an exon of an mRNA or a cDNA derived therefrom). The collection of binding sites on a microarray contains sets of binding sites for a plurality of genes and thus can be used, in some embodiments to measure molecular marker data. For example, in various embodiments, the microarrays of the invention can comprise binding sites for products encoded by fewer than 50% of the genes in the genome of an organism. Alternatively, the microarrays of the invention can have binding sites for the products encoded by at least 50%, at least 75%, at least 85%, at least 90%, at least 95%, at least 99% or 100% of the molecular markersof an organism. In other embodiments, the microarrays of the invention can having binding sites for products encoded by fewer than 50%, by at least 50%, by at least 75%, by at least 85%, by at least 90%, by at least 95%, by at least 99% or by 100% of the genes expressed by a cell of an organism. The binding site can be a DNA or DNA analog to which a particular RNA can specifically hybridize. The DNA or DNA analog can be, e.g., a synthetic oligomer or a gene fragment, e.g. corresponding to an exon. In some embodiments of the present invention, an expressed transcript is represented in the nucleic acid arrays. In such embodiments, a set of binding sites can include probes with different polynucleotides that are complementary to different sequence segments of the expressed transcript. Exemplary polynucleotides that fall within this class can be of length of 15 to 200 bases, 20 to 100 bases, 40-60 bases or some other range of bases. Each probe sequence can also comprise linker sequences in addition to the sequence that is complementary to its target sequence. As used herein, a linker sequence is a sequence between the sequence that is complementary to its target sequence and the surface of support. For example, in some embodiments, the nucleic acid arrays of the invention comprise one probe specific to each target gene or exon. However, if desired, the nucleic acid arrays can contain at least 2, 5, 10, 100, or 1000 or more probes specific to some expressed transcript. For example, the array may contain probes tiled across the sequence of the longest mRNA isoform of a gene at single base steps. In specific embodiments of the invention, when an exon has alternative spliced variants, a set of polynucleotide probes of successive overlapping sequences, i.e., tiled sequences, across the genomic region containing the longest variant of an exon can be included in the exon nucleic acid arrays. The set of polynucleotide probes can comprise successive overlapping sequences at steps of a predetermined base intervals, e.g. at steps of 1, 5, or 10 base intervals, span, or are tiled across, the mRNA containing the longest variant. Such sets of probes therefore can be used to scan the genomic region containing all variants of an exon to determine the expressed variant or variants of the exon to determine the expressed variant or variants of the exon. Alternatively or additionally, a set of polynucleotide probes comprising exon specific probes and/or variant junction probes can be included in the exon profiling array. As used herein, a variant junction probe refers to a probe specific to the junction region of the particular exon variant and the neighboring exon. In some cases, the probe set contains variant junction probes specifically hybridizable to each of all different splice junction sequences of the exon. In other cases, the probe set contains exon specific probes specifically hybridizable to the common sequences in all different variants of the exon, and/or variant junction probes specifically hybridizable to the different splice junction sequences of the exon. In some cases, an exon is represented in the exon nucleic acid arrays by a probe comprising a polynucleotide that is complementary to the full length exon. In such instances, an exon is represented by a single binding site on the profiling arrays. In some preferred cases, an exon is represented by one or more binding sites on the profiling arrays, each of the binding sites comprising a probe with a polynucleotide sequence that is complementary to an RNA fragment that is a substantial portion of the target exon. The lengths of such probes are normally between 15-600 bases, preferably between 20-200 bases, more preferably between 30-100 bases, and most preferably between 40-80 bases. The average length of an exon is 200 bases (see, e.g., Lewin, Genes V, Oxford University Press, Oxford, 1994). A probe of length of 40-80 allows more specific binding of the exon than a probe of shorter length, thereby increasing the specificity of the probe to the target exon. For certain genes, one or more targeted exons may have sequence lengths less than 40-80 bases. In such cases, if probes with sequences longer than the target exons are to be used, it may be desirable to design probes comprising sequences that include the entire target exon flanked by sequences from the adjacent constitutively splice exon or exons such that the probe sequences are complementary to the corresponding sequence segments in the mRNAs. Using flanking sequence from adjacent constitutively spliced exon or exons rather than the genomic flanking sequences, i.e., intron sequences, permits comparable hybridization stringency with other probes of the same length. Preferably the flanking sequence used are from the adjacent constitutively spliced exon or exons that are not involved in any alternative pathways. More preferably the flanking sequences used do not comprise a significant portion of the sequence of the adjacent exon or exons so that cross-hybridization can be minimized. In some embodiments, when a target exon that is shorter than the desired probe length is involved in alternative splicing, probes comprising flanking sequences in different alternatively spliced mRNAs are designed so that expression level of the exon expressed in different alternatively spliced mRNAs can be measured. In some instances, when alternative splicing pathways and/or exon duplication in separate genes are to be distinguished, the DNA array or set of arrays can also comprise probes that are complementary to sequences spanning the junction regions of two adjacent exons. Preferably, such probes comprise sequences from the two exons which are not substantially overlapped with probes for each individual exons so that cross hybridization can be minimized. Probes that comprise sequences from more than one exons are useful in distinguishing alternative splicing pathways and/or expression of duplicated exons in separate genes if the exons occurs in one or more alternative spliced rnRNAs and/or one or more separated genes that contain the duplicated exons but not in other alternatively spliced mRNAs and/or other genes that contain the duplicated exons. Alternatively, for duplicate exons in separate genes, if the exons from different genes show substantial difference in sequence homology, it is preferable to include probes that are different so that the exons from different genes can be distinguished. It will be apparent to one skilled in the art that any of the probe schemes, supra, can be combined on the same nucleic acid array and/or on different arrays within the same set of nucleic acid arrays so that a more accurate determination of the expression profile for a plurality of molecular marker products can be accomplished. It will also be apparent to one skilled in the art that the different probe schemes can also be used for different levels of accuracies in profiling. For example, a nucleic acid array or array set comprising a small set of probes for each expressed transcript or each region thereof may be used to identify molecular markers under certain specific conditions. An array or array set comprising larger sets of probes for the exons that are of interest is then used to more accurately determine the specific molecular marker products under such specific conditions. Other DNA array strategies that allow more advantageous use of different probe schemes are also encompassed. In some embodiments, the microarrays used in the invention can include binding sites (e.g., probes) for sets of exons for one or more genes relevant to the condition of interest. The number of genes in a genome can be estimated from the number of mRNAs expressed by the cell or organism, or by extrapolation of a well characterized portion of the genome. When the genome of the organism of interest has been sequenced, the number of ORFs can be determined and mRNA coding regions identified by analysis of the DNA sequence. For example, the human genome is now known. Genome sequences for other organisms are also completed or nearly completed. Thus, in some embodiments of the invention, an array set comprising the total probes for all known or predicted exons in the genome of an organism is provided. As a non-limiting example, the present invention provides an array set comprising one or two probes for each known or predicted exon of a molecular marker of the human genome. It will be appreciated that when cDNA complementary to the RNA of a cell is made and hybridized to a microarray under suitable hybridization conditions, the level of hybridization to the site in the array corresponding to an exon of any particular molecular marker will reflect the prevalence in the cell of mRNA or mRNAs containing the exon transcribed from that molecular marker. For example, when detectably labeled (e.g., with a fluorophore) cDNA complementary to the total cellular mRNA is hybridized to a microarray, the site on the array corresponding to an exon of a gene (i. e. , capable of specifically binding the product or products of the gene expressing) that is not transcribed or is removed during RNA splicing in the cell will have little or no signal (e.g., fluorescent signal), and an exon of a gene for which the encoded mRNA expressing the exon is prevalent will have a relatively strong signal. The relative abundance of different mRNAs produced from the same gene by alternative splicing is then determined by the signal strength pattern across the whole set of exons monitored for the gene. The use of a two-color fluorescence labeling and detection scheme to define alterations in gene expression has been described in connection with detection of mRNAs, e.g., in Shena et al, 1995, Science 270:467-470, which is incorporated by reference in its entirety for all purposes. In some embodiments, such schemes are used to measure molecular marker data. Such schemes are equally applicable to labeling and detection of expressed transcripts. An advantage of using cDNA labeled with two different fluorophores is that a direct and internally controlled comparison of the mRNA or exon expression levels corresponding to each arrayed gene in two blood samples can be made, and variations due to minor differences in experimental conditions (e.g., hybridization conditions) will not affect subsequent analyses. However, it will be recognized that it is also possible to use cDNA from a single cell, and compare, for example, the absolute amount of a particular exon in, e.g., a. drug-treated or pathway-perturbed cell and an untreated cell. Furthermore, labeling with more than two colors is also contemplated in the present invention. In some embodiments of the invention, at least 5, 10, 20, or 100 dyes of different colors can be used for labeling. Such labeling permits simultaneous hybridizing of the distinguishably labeled cDNA populations to the same array, and thus measuring, and optionally comparing the expression levels of, mRNA molecules derived from more than two samples. Dyes that can be used include, but are not limited to, fluorescein and its derivatives, rhodamine and its derivatives, texas red, 5'carboxy- fluorescein ("FMA"), 2',7'-dimethoxy-4',5'-dichloro-6-carboxy-fluorescein ("JOE"), N,N,N',N'-tetramethyl-6-carboxy-rhodamine ("TAMRA"), 6'carboxy-X-rhodamine ("ROX"), HEX, TET, IRD40, and IRD41, cyamine dyes, including but are not limited to Cy3, Cy3.5 and Cy5; BODIPY dyes including but are not limited to BODIPY-FL, BODIPY-TR, BODIPY-TMR, BODIPY-630/650, and BODIPY-650/670; and ALEXA dyes, including but are not limited to ALEXA-488, ALEXA-532, ALEXA-546, ALEXA- 568, and ALEXA-594; as well as other fluorescent dyes which will be known to those who are skilled in the art. In one embodiment, hybridization levels at different hybridization times are measured separately on different, identical microarrays. For each such measurement, at hybridization time when hybridization level is measured, the microarray is washed briefly, preferably in room temperature in an aqueous solution of high to moderate salt concentration (e.g., 0.5 to 3 M salt concentration) under conditions which retain all bound or hybridized polynucleotides while removing all unbound polynucleotides. The detectable label on the remaining, hybridized polynucleotide molecules on each probe is then measured by a method which is appropriate to the particular labeling method used. The resulted hybridization levels are then combined to form a hybridization curve. In another embodiment, hybridization levels are measured in real time using a single microarray. In this embodiment, the microarray is allowed to hybridize to the sample without interruption and the microarray is interrogated at each hybridization time in a non¬ invasive manner. In still another embodiment, one can use one array, hybridize for a short time, wash and measure the hybridization level, put back to the same sample, hybridize for another period of time, wash and measure again to get the hybridization time curve. In a specific embodiment, the Affymetrix® Human Genome Ul 33 (HG-Ul 33) Set, consisting of two GeneChip® arrays, is used in accordance with known methods. The Human Genome Ul 33 (HG-U133) Set contains almost 45,000 probe sets representing more than 39,000 transcripts derived from approximately 33,000 well-substantiated human genes. This set design uses sequences selected from GenBank®, dbEST, and RefSeq. The sequence clusters were created from the UniGene database (Build 133, April 20, 2001). They were then refined by analysis and comparison with a number of other publicly available databases including the Washington University EST trace repository and the University of California, Santa Cruz Golden Path human genome database (April 2001 release). In another embodiment, the HG-Ul 33 A array is used in accordance with the methods of the invention. The HG-U133A array includes representation of the RefSeq database sequences and probe sets related to sequences previously represented on the Human Genome U95Av2 array. The HG-Ul 33B array contains primarily probe sets representing EST clusters. In another embodiment, the Ul 33 Plus 2.0 GeneChip® is used in the invention. The U133 Plus 2.0 GeneChip® represents over 47,000 transcripts. In another embodiment, a cDNA based microarray is used. In one embodiment the ChondroChip™ is used in accordance with the methods of the invention. The ChondroChip™ is a cDNA based microarray. One version of the ChondroChip™ includes 14,976 distinct elements: 10,382 known genes (69%), 4,112 EST/genomic DNA matches (28%), 328 clones with no significant match (2.2%), and 154 control spots (1.0%). Most if not all of the elements on the ChondroChip™ are complementary to ESTs identified as expressed in human chondrocytes. An article that describes the creation of a version of the ChondroChip™ is Zhang et ah, 2002, Osteoarthritis and Cartilage 10, 950-960, which is hereby incorporated by reference in its entirety. In another embodiment, the BloodChip™ is used in accordance with the methods of the invention. The BloodChip is a cDNA microarray slide with 10,368 PCR products derived from peripheral blood cell cDNA libraries. The creation of the BloodChip™ microarray is described in Ma and Liew, 2003, Journal of Molecular and Cellular Cardiology 8, 993-998, which is hereby incorporated by reference in its entirety. 5.3.1.2 TARGET POLYNUCLEOTIDE MOLECULES
Target polynucleotides that can be analyzed by the methods and compositions of the invention include RNA molecules such as, but by no means limited to, expressed RNA molecules which includes messenger RNA (mRNA) molecules, mRNA spliced variants as well as other regulatory RNA, cRNA molecules (e.g., RNA molecules prepared from cDNA molecules that are transcribed in vivo) and fragments thereof. Target polynucleotides which may also be analyzed by the methods and compositions of the present invention include, but are not limited to DNA molecules such as genomic DNA molecules, cDNA molecules, and fragments thereof including oligonucleotides, ESTs, STSs, etc The target polynucleotide molecules may be naturally occurring nucleic acid molecules such as genomic or extragenomic DNA molecules isolated from a blood sample, or RNA molecules, such as mRNA molecules, isolated from a blood sample. The sample of target polynucleotides can comprise, e.g., molecules of DNA, RNA, or copolymers of DNA and RNA. In specific embodiments, the target polynucleotides of the invention will correspond to particular genes or to particular gene transcripts (e.g., to particular mRNA sequences expressed in specific cell types or to particular cDNA sequences derived from such mRNA sequences). The target polynucleotides may correspond to different exons of the same gene, e.g., so that different splice variants of that gene may be detected and/or analyzed. In specific embodiments, the target polynucleotides to be analyzed are prepared in vitro from nucleic acids extracted from a blood sample. For example, in one embodiment, RNA is extracted from a blood sample (e.g., total cellular RNA, poly(A)+ messenger RNA, fraction thereof) and messenger RNA is purified from the total extracted RNA. Methods for preparing total and poly(A)+ RNA are well known in the art, and are described generally, e.g., in Sambrook, Fritsch & Maniatis, "Molecular Cloning: A Laboratory Manual (1982); "DNA Cloning: A Practical Approach," Volumes I and II (D.N. Glover ed. 1985). In one embodiment, RNA is extracted from a blood sample using guanidinium thiocyanate lysis followed by CsCl centrifugation and an oligo dT purification (Chirgwin et al., 1979, Biochemistry 18:5294-5299). In another embodiment, RNA is extracted from a blood sample using guanidinium thiocyanate lysis followed by purification on RNeasy columns (Qiagen). cDNA is then synthesized from the purified mRNA using, e.g., oligo-dT or random primers. In specific embodiments, the target polynucleotides are cRNA prepared from purified messenger RNA extracted from a blood sample. As used herein, cRNA is defined here as RNA complementary to the source RNA. The extracted RNAs can be amplified using a process in which doubled-stranded cDNAs are synthesized from the RNAs using a primer linked to an RNA polymerase promoter in a direction capable of directing transcription of anti-sense RNA. Anti-sense RNAs or cRNAs are then transcribed from the second strand of the double-stranded cDNAs using an RNA polymerase (see, e.g., U.S. Patent Nos. 5,891,636, 5,716,785; 5,545,522 and 6,132,997; see also, U.S. Patent No. 6,271,002, and U.S. Provisional Patent Application Serial No. 60/253,641, filed on November 28, 2000, by Ziman et al.). Both oligo-dT primers (U.S. Patent Nos. 5,545,522 and 6,132,997) or random primers (U.S. Provisional Patent Application Serial No. 60/253,641, filed on November 28, 2000, by Ziman et al.) that contain an RNA polymerase promoter or complement thereof can be used. In some embodiments the target polynucleotides are short and/or fragmented polynucleotide molecules which are representative of the original nucleic acid population of the blood sample. The target polynucleotides to be analyzed by the methods and compositions of the invention can be detectably labeled. For example, cDNA can be labeled directly, e.g., with nucleotide analogs, or indirectly, e.g., by making a second, labeled cDNA strand using the first strand as a template. Alternatively, the double-stranded cDNA can be transcribed into cRNA and labeled. In some embodiments the detectable label is a fluorescent label, e.g., by incorporation of nucleotide analogs. Other labels suitable for use in the present invention include, but are not limited to, biotin, imminobiotin, antigens, cofactors, dinitrophenol, lipoic acid, olefinic compounds, detectable polypeptides, electron rich molecules, enzymes capable of generating a detectable signal by action upon a substrate, and radioactive isotopes. Suitable radioactive isotopes include 32P, 35S, 14C, 15N and 125I. Fluorescent molecules suitable for the present invention include, but are not limited to, fluorescein and its derivatives, rhodamine and its derivatives, texas red, 5'carboxy-fluorescein ("FMA"), 2',7'-dimethoxy-4',5'-dichloro-6-carboxy-fluorescein ("JOE"), N,N,N',N'- tetramethyl-6- carboxy-rhodamine ("TAMRA"), 6'carboxy-X-rhodamine ("ROX"), HEX, TET, IRD40, and IRD41. Fluroescent molecules that are suitable for the invention further include: cyamine dyes, including by not limited to Cy3, Cy3.5 and Cy5; BODIPY dyes including but not limited to BODIPY-FL, BODIPY-TR, BODIPY-TMR, BODIPY-630/650, and BODIPY-650/670; and ALEXA dyes, including but not limited to ALEXA-488, ALEXA- 532, ALEXA-546, ALEXA-568, and ALEXA-594; as well as other fluorescent dyes which will be known to those who are skilled in the art. Electron rich indicator molecules suitable for the present invention include, but are not limited to, ferritin, hemocyanin, and colloidal gold. Alternatively, in less some embodiments the target polynucleotides may be labeled by specifically complexing a first group to the polynucleotide. A second group, covalently linked to an indicator molecules and which has an affinity for the first group, can be used to indirectly detect the target polynucleotide. In such an embodiment, compounds suitable for use as a first group include, but are not limited to, biotin and iminobiotin. Compounds suitable for use as a second group include, but are not limited to, avidin and streptavidin. In a specific embodiment, the target polynucleotides are prepared as follows: 2μg Oligo-dT primers are annealed to 2μg of mRNA isolated from a blood sample of a patient in a total volume of 15μl, by heating to 70°C for 10 min, and cooled on ice. The mRNA is reverse transcribed by incubating the sample at 42°C for 1.5-2 hours in a 100 μl volume containing a final concentration of 5OmM Tris-HCl (pH 8.3), 75mM KCl, 3mM MgCl2, 25mM DTT, 25mM unlabeled dNTPs, 400 units of Superscript II (200U/μL, Gibco BRL), and 15mM of Cy 3 or Cy5 (Amersham). RNA is then degraded by addition of 15μl of 0.1N NaOH, and incubation at 70DC for 10 min. The reaction mixture is neutralized by addition of 15μl of 0.1N HCl, and the volume is brought to 500μl with TE (1OmM Tris, 1 mM EDTA), and 20 D g of Cotl human DNA (Gibco-BRL) is added. The labeled target polynucleotide molecules are purified by centrifugation in a Centricon-30 micro-concentrator (Amicon). If two different target polynucleotide samples (e.g., two samples derived from a healthy patient vs. patient with a disease) are being analyzed and compared by hybridization to the same array, each target nucleic acid sample is labeled with a different fluorescent label (e.g., Cy3 and Cy5) and separately concentrated. The separately concentrated target nucleic acid samples (Cy3 and Cy5 labeled) are combined into a fresh centricon, washed with 500μl TE, and concentrated again to a volume of less than 7μl. lμL of lOμg/μl polyA RNA (Sigma, #P9403) and lμl of lOμg/μl tRNA (Gibco-BRL, #15401-011) is added and the volume is adjusted to 9.5μl with distilled water. For final target polynucleotide preparation 2.1 μl 20XSSC (1.5M NaCl, 150mM NaCitrate (pH8.0)) and 0.35μl 10%SDS is added. 5.3.1.3 HYBRIDIZATION TO MICROARRAYS
In some embodiments, nucleic acid hybridization and wash conditions are chosen so that the polynucleotide molecules to be analyzed by the invention (e.g., "target polynucleotide molecules) specifically bind or specifically hybridize to the complementary polynucleotide sequences of the array, typically to a specific array site, where its complementary DNA is located. Arrays containing double-stranded probe DNA situated thereon can be subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target polynucleotide molecules. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured prior to contacting with the target polynucleotide molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences. Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambfook et ah, (supra), and in Ausubel et ah, 1987, Current Protocols in Molecular Biology, Greene Publishing and Wiley-Interscience, New York. When the cDNA microarrays of Schena et al. are used, typical hybridization conditions are hybridization in 5 X SSC plus 0.2% SDS at 65 0C for four hours, followed by washes at 25°C in low stringency wash buffer (1 X SSC plus 0.2% SDS), followed by 10 minutes at 25°C in higher stringency wash buffer (0.1 X SSC plus 0.2% SDS) (Shena et al, 1996, Proc. Natl. Acad. Sci. U.S.A. P3.10614). Useful hybridization conditions are also provided in, e.g., Tijessen, 1993, Hybridization With Nucleic Acid Probes, Elsevier Science Publishers B. V. and Kricka, 1992, Nonisotopic DNA Probe Techniques, Academic Press, San Diego, CA. Representative hybridization conditions for use with the screening and/or signaling chips in accordance with some embodiments of the present invention include hybridization at a temperature at or near the mean melting temperature of the probes (e.g. , within 5 °C, more typically within 2 0C) in 1 M NaCl, 50 niM MES buffer (pH 6.5), 0.5% sodium Sarcosine and 30% formamide. In a specific embodiment, a labeled target polynucleotide molecules are denatured by heating for two minutes at 1000C, and incubated at 37°C for 20-30 min before being placed on a nucleic acid array under a 22mm x 22mm glass cover slip. Hybridization is carried out at 65°C for fourteen to eighteen hours in a custom slide chamber with humidity maintained by a small reservoir of 3XSSC. The array is washed by submersion and agitation for between two and five minutes in 2X SSC with 0.1%SDS, followed by IX SSC, and 0.1X SSC. Finally, the array is dried by centrifugation for 2 min in a slide rack in a Beckman GS-6 tabletop centrifuge in Microplus carriers at 650 RPM for two minutes. 5.3.1.4 SIGNAL DETECTION AND DATA ANALYSIS It will be appreciated that when target sequences, e.g., cDNA or cRNA, complementary to the RNA of a blood sample is made and hybridized to a microarray under suitable hybridization conditions, the level of hybridization to the site in the array corresponding to an exon of any particular gene will reflect the prevalence in the cell of mRNA or mRNAs containing the exon transcribed from that gene. For example, when detectably labeled (e.g., with a fluorophore) cDNA complementary to the total cellular mRNA is hybridized to a microarray, the site on the array corresponding to an exon of a gene (i.e., capable of specifically binding the product or products of the gene expressing) that is not transcribed or is removed during RNA splicing in the cell will have little or no signal (e.g., fluorescent signal), and an exon of a gene for which the encoded mRNA expressing the exon is prevalent will have a relatively strong signal. The relative abundance of different mRNAs produced from the same gene by alternative splicing is then determined by the signal strength pattern across the whole set of exons monitored for the gene. Generally, any form of image processing may be used to digitize the microarrays and thereby obtain high throughput data for molecular markers in the present invention. For example, any of the image processing techniques described or referenced in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 33-58, which is hereby incorporated by reference in its entirety, can be used. In some embodiments, two-color fluorescence is used. The use of a two-color fluorescence labeling and detection scheme to define alterations in gene expression has been described in connection with detection of mRNAs, e.g., in Shena et al, 1995, Science 270:467-470, which is hereby incorporated by reference in its entirety for all purposes. The scheme is equally applicable to labeling and detection of exons. An advantage of using target sequences, e.g., cDNAs or cRNAs, labeled with two different fluorophores is that a direct and internally controlled comparison of the mRNA or exon expression levels corresponding to each arrayed gene in two states can be made, and variations due to minor differences in experimental conditions {e.g., hybridization conditions) will not affect subsequent analyses. In a specific embodiment, the labeled probes are scanned using a GMS Scanner 418 and Scananlzyer software (Michael Eisen, Stamford University), followed by GeneSpring software (Silcon Genetics, CA) analysis. In another embodiment, a GMS Scanner 428 and Jaguar software are used followed by GeneSpring software analysis. In some embodiments a normalization routine, such as any of the normalization routines described in Section 5.7, is used. 5.3.2 RT-PCR AND QUANTITATIVE RT-PCR
In one aspect of the invention, the abundance or level of expression of an RNA product of a molecular marker can be measured performing reverse transcription on the RNA from blood and subsequently amplifying the resulting product ("RT-PCR"). In another embodiment, the abundance or level of expression of RNA can be measured from a blood sample by using quantitative RT-PCR or real time PCR ("QRT-PCR") on cDNA copy of RNA. Total RNA, or mRNA from a blood sample can be used as a template and a primer specific to the transcribed portion of a gene of the invention is used to initiate reverse transcription. Methods of reverse transcribing RNA into cDNA are well known and described in, for example, Sambrook et al., 1989, supra. Primer design can be accomplished utilizing commercially available software (e.g., Primer Designer 1.0, academic software, etc.). The product of the reverse transcription is subsequently used as a template for PCR. In one embodiment, a one step process can be used for either the RT- PCR and/or the QRT-PCR(combining the reverse transcription and PCR in a single reaction). In another embodiment, a two step process can be used for either the RT-PCR and/or the QRT-PCR (first doing the reverse transcription step and subsequently performing the PCR) . In some embodiments, oligo(dT)-primed first strand cDNA synthesis is performed so as to specifically target the mRNA population (e.g. using the Applied Biosystems High Capacity cDNA Archive Kit (cat # 4322171), on a Perkin-Elmer DNA Thermal Cycler. For quantitative RT-PCR, in some embodiments the reportable value is the Ct- value, which is the threshold cycle at which PCR is in the logarithmic phase. For each gene of interest in each RNA sample, a ΔCt value can be calculated by the formula: ΔCt = (Ct, target gene) - (Ct,β-actin). The ΔCt values from different groups of RNA samples can then be compared by the Mann- Whitney Rank Sum test. In some embodiments, Quantitative RT-PCR can be done using probes including Taqman® probes (Perkin Elmer, Foster City, California), The probe is specific for the PCR product and has both a quencher and fluorescent reporter attached to the probe. Different fluorescent markers can be utilized. In some embodiments, multiple probes can be used in the quantitative RT-PCR process to allow for multiplexing reactions (e.g. allow for measurement of two molecular markers in one reaction well or container). When using TaqMan® probes, Taq DNA polymerase is used which has 5'-to-3' exonuclease activity and thus will cleave of the fluorescent reporter of the probe, freeing the fluorescent molecular from the quencher molecule. Thus the emission of fluorescence is is used to measure the amount of PCR product being made. Other probes are also useful for quantitative RT-PCR including Molecular Beacons®. Other known techniques for quantitative Rt-PCR is to use an intercolating dye such as the commercially available QuantiTect™ S YBR® Green PCR (Qiagen, Valencia California). Additionally, other systems to quantitatively measure mRNA expression products are known including Scorpions® (Zeneca Limited) or Fluorescent Polarization Probes (see e.g. Zeneca Limited, 6,007,984) etc. 5.3.3 NUCLEASE PROTECTION ASSAYS
Nuclease protection assays (including both ribonuclease protection assays and Sl nuclease assays) can be used to detect and quantify specific products of molecular markers. In nuclease protection assays, an antisense probe (labeled with, e.g., radiolabeled or nonisotopic) hybridizes in solution to an RNA sample. Following hybridization, single- stranded, unhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments. Typically, solution hybridization is more efficient than membrane-based hybridization, and it can accommodate up to 100 μg of sample RNA, compared with the 20-30 μg maximum of blot hybridizations. The ribonuclease protection assay, which is the most common type of nuclease protection assay, requires the use of RNA probes. Oligonucleotides and other single- stranded DNA probes can only be used in assays containing Sl nuclease. The single- stranded, antisense probe must typically be completely homologous to target RNA to prevent cleavage of the probeitarget hybrid by nuclease. 5.3.4 MASS SPECTROMETRY
Mass spectrometry (e.g., electrospray ionization "ESI", matrix-assisted laser desorption-ionization "MALDI ", and Fourier-transform ion cyclotron resonance "FT- ICR") can be used to measure data (e.g., mass, charge) of molecular markers in blood samples. Such molecular markers that can be characterized by mass spectrometry include but are not limited to, proteins, nucleic acids, carbohydrates, and other biological macromolecules. This section provides brief and non-limiting examples of mass spectrometry techniques that can be used to quantitatively characterize molecular markers. MALDI uses a pulsed laser for desorption of the ions and a time-of-flight analyzer and has been used for the detection of noncovalent tRNA:amino-acyl-tRNA synthetase complexes. See, for example, Gruic-Sovulj et al., 1997, J. Biol. Chem. 272:32084. ESI mass spectrometry ("ESI-MS") has been used for studying non-covalent molecular interactions. ESI-MS generates molecular ions with little to no fragmentation. See, for example, Xavier et al., 2000, Trends Biotechnol. 18:349. Fourier-transform ion cyclotron resonance ("FT-ICR") mass spectrometry provides high-resolution spectra, isotope- resolved precursor ion selection, and accurate mass assignments. See, for example, Xavier et al., 2000, Trends Biotechnol. 18:349. Tandem mass spectrometry is described in Link et al., 1999, Nat. Biotechnol. 17, 676-682; Washburn et al. 2001, Nat. Biotechnol. 19, 242; Gaven et al., 2002, Nature 415, 141; and Ho et al., 2002, Nature 418, 180). In the case of proteins from a blood sample, the proteins can first be digested into peptides using an enzyme such as trypsin and then subjected to liquid chromatography tandem mass spectrometry (MS/MS). Liquid chromatography provides an initial separation of the peptides, which are then ionized directly into a mass spectrometer. Following an initial scan in which the mass/charge ratio of all intact (parent) ions from the peptides are measured, the mass spectrometer selects a parent ion, fragments it and obtains the mass spectrum of the generated fragments. These fragmentation patterns are called tandem mass spectra or MS/MS spectra. This process of ion selection and fragmentation is repeated throughout the liquid chromatography separation, thus generating a set of time resolved MS/MS spectra, with each spectrum representing a species eluting at a particular time from the LC separation. The resolving power of the liquid chromatography step, combined with the high mass resolution of modern mass spectrometers typically assures that each MS/MS spectrum represents the fragmentation pattern of a unique peptide in the digest. 5.3.5 COMPARATIVE GENE EXPRESSION PROFILING
In some embodiments of the present invention quantitative measurement of molecular marker data is performed using comparative gene-expression profiling. An example of such technology is the multiplex microsphere bead assay used by Fuja et al., 2004, Journal of Biotechnology 108, 193,. 5.3.6 TRANSCRIPTION BASED AMPLIFICATION SYSTEMS In another aspect of the invention, the level of expression of a molecular marker in blood can be measured by amplifying RNA from a blood sample using transcription based amplification systems (TAS), including nucleic acid sequence amplification (NASBA) and 3SR. See, e.g., Kwoh et al., 1989, PNAS USA 86:1173; International Publication No. WO 88/10315; and U.S. Patent No. 6,329,179. In NASBA, the nucleic acids can be prepared for amplification using conventional phenol/chloroform extraction, heat denaturation, treatment with lysis buffer and minispin columns for isolation of DNA and RNA or guanidinium chloride extraction of RNA. These amplification techniques involve annealing a primer that has target specific sequences. Following polymerization, DNA/RNA hybrids are digested with RNase H while double stranded DNA molecules are heat denatured again. In either case, the single stranded DNA is made fully double stranded by addition of second target specific primer, followed by polymerization. The double-stranded DNA molecules are then multiply transcribed by a polymerase such as T7 or SP6. In an isothermal cyclic reaction, the RNA's are reverse transcribed into double stranded DNA, and transcribed once with a polymerase such as T7 or SP6. The resulting products, whether truncated or complete, indicate target specific sequences. 5.3.7 ADDITIONAL TECHNIQUES FOR DETECTING AND QUANTIFYING RNA
Many other techniques are known to one of skill for detecting and measuring RNA and can be used in accordance with the methods of the invention. Non-limiting examples of such techniques include Northern blotting, nuclease protection assays, RNA fingerprinting, polymerase chain reaction, ligase chain reaction, Qbeta replicase, isothermal amplification method, strand displacement amplification, transcription based amplification systems, nuclease protection (SI nuclease or RNAse protection assays) as well as methods disclosed in International Publication Nos. WO 88/10315 and WO 89/06700, and International Applications Nos. PCT/US87/00880 and PCT/US 89/01025. A standard Northern blot assay can be used to ascertain an RNA transcript size, identify alternatively spliced RNA transcripts, and the relative amounts of mRNA in a blood sample, in accordance with conventional Northern hybridization techniques known to those persons of ordinary skill in the art. In Northern blots, RNA samples are first separated by size via electrophoresis in an agarose gel under denaturing conditions. The RNA is then transferred to a membrane, crosslinked and hybridized with a labeled probe. Nonisotopic or high specific activity radiolabeled probes can be used including random- primed, nick-translated, or PCR-generated DNA probes, in vitro transcribed RNA probes, and oligonucleotides. Additionally, sequences with only partial homology (e.g., cDNA from a different species or genomic DNA fragments that might contain an exon) may be used as probes. The labeled probe, e.g., a radiolabeled cDNA, either containing the full- length, single stranded DNA or a fragment of that DNA sequence may be at least 20, at least 30, at least 50, or at least 100 consecutive nucleotides in length. The probe can be labeled by any of the many different methods known to those skilled in this art. The labels most commonly employed for these studies are radioactive elements, enzymes, chemicals that fluoresce when exposed to ultraviolet light, and others. A number of fluorescent materials are known and can be utilized as labels. These include, but are not limited to, fluorescein, rhodamine, auramine, Texas Red, AMCA blue and Lucifer Yellow. A articular detecting material is anti-rabbit antibody prepared in goats and conjugated with fluorescein through an isothiocyanate. Proteins can also be labeled with a radioactive element or with an enzyme. The radioactive label can be detected by any of the currently available counting procedures. Non-limiting examples of isotopes include 3H, 14C, 32P, 35S, 36Cl, 51Cr, 57Co, 58Co, 59Fe, 9OY, 1251, 1311, and 186Re. Enzyme labels are likewise useful, and can be detected by any of the presently utilized colorimetric, pectrophotometric, fluorospectrophotometric, amperometric or gasometric techniques. The enzyme is conjugated to the selected particle by reaction with bridging molecules such as carbodiimides, diisocyanates, glutaraldehyde and the like. Any enzymes known to one of skill in the art can be utilized. Examples of such enzymes include, but are not limited to, peroxidase, beta-D-galactosidase, urease, glucose oxidase plus peroxidase and alkaline phosphatase. U.S. Patent Nos. 3,654,090, 3,850,752, and 4,016,043 are referred to by way of example for their disclosure of alternate labeling material and methods. Nuclease protection assays (including both ribonuclease protection assays and Sl nuclease assays) can be used to detect and quantitate specific mRNAs. In nuclease protection assays, an antisense probe (labeled with, e.g., radiolabeled or nonisotopic) hybridizes in solution to an RNA sample. Following hybridization, single-stranded, unhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments. Typically, solution hybridization is more efficient than membrane-based hybridization, and it can accommodate up to 100 μg of sample RNA, compared with the 20-30 μg maximum of blot hybridizations. The ribonuclease protection assay, which is the most common type of nuclease protection assay, requires the use of RNA probes. Oligonucleotides and other single- stranded DNA probes can only be used in assays containing Sl nuclease. The single- stranded, antisense probe must typically be completely homologous to target RNA to prevent cleavage of the probe:target hybrid by nuclease. Additional techniques to quantitatively measure RNA expression include, but are not limited to, ligase chain reaction, Qbeta replicase (see, e.g., International Application No. PCT/US87/00880), isothermal amplification method (see, e.g., Walker et al.,1992, PNAS 89:382-396), strand displacement amplification (SDA), repair chain reaction, Asymmetric Quantitative PCR (see, e.g., U.S. Publication No. US200330134307A1) and the multiplex microsphere bead assay described in Fuja et al., 2004, Journal of Biotechnology 108:193-205. 5.3.7.1 SEPARATION OF AMPLIFICATION PRODUCTS
Some of the quantitative measurement techniques described above may require separation of amplification products. Several techniques can be used to separate such amplification products. For example, amplification products can be separated by agarose, agarose-acrylamide or polyacrylamide gel electrophoresis using conventional methods. Several techniques for detecting PCR products quantitatively without electrophoresis can also be used according to the invention. See, for example, CR Protocols, A Guide to Methods and Applications, Innis et aL, Academic Press, Inc. N. Y., 1990). For example, chromatographic techniques can be employed to effect separation. There are many kinds of chromatography that can be used in the present invention: adsorption, partition, ion- exchange and molecular sieve, HPLC, and many specialized techniques for using them including column, paper, thin-layer and gas chromatography (Freifelder, Physical Biochemistry Applications to Biochemistry and Molecular Biology, 2nd ed., Wm. Freeman and Co., New York, N. Y., 1982). Another example of a separation methodology is done by covalently labeling the oligonucleotide primers used in a PCR reaction with various types of small molecule ligands. In one such separation, a different ligand is present on each oligonucleotide. A molecule, perhaps an antibody or avidin if the ligand is biotin, that specifically binds to one of the ligands is used to coat the surface of a plate such as a 96 well ELISA plate. Upon application of the PCR reactions to the surface of such a prepared plate, the PCR products are bound with specificity to the surface. After washing the plate to remove unbound reagents, a solution containing a second molecule that binds to the first ligand is added. This second molecule is linked to some kind of reporter system. The second molecule only binds to the plate if a PCR product has been produced whereby both oligonucleotide primers are incorporated into the final PCR products. The amount of the PCR product is then detected and quantified in a commercial plate reader much as ELISA reactions are detected and quantified. An ELISA-like system such as the one described here has been developed by the Raggio Italgene company under the C-Track trade name. 5.3.7.2 VISUALIZATION OF AMPLIFICATION PRODUCTS
Some of the quantitative measurement techniques described above may require visualization of amplification products. Amplification products are visualized, for example, in order to confirm amplification of the marker sequences. One typical visualization method involves staining of a gel with ethidium bromide and visualization under UV light. Alternatively, if the amplification products are integrally labeled with radio- or fluorometrically-labeled nucleotides, the amplification products may then be exposed to x-ray film or visualized under the appropriate stimulating spectra, following separation. In one embodiment, visualization is achieved indirectly. Following separation of amplification products, a labeled, nucleic acid probe is brought into contact with the amplified marker sequence. The probe preferably is conjugated to a chromophore but may be radiolabeled. In another embodiment, the probe is conjugated to a binding partner, such as an antibody or biotin, where the other member of the binding pair carries a detectable moiety. In another embodiment, detection is by Southern blotting and hybridization with a labeled probe. The techniques involved in Southern blotting are well known to those of skill in the art and may be found in many standard books on molecular protocols. See Sambrook et al., 1989. Briefly, amplification products are separated by gel electrophoresis. The gel is then contacted with a membrane, such as nitrocellulose, permitting transfer of the nucleic acid and non-covalent binding. Subsequently, the membrane is incubated with a chromophore-conjugated probe that is capable of hybridizing with a target amplification product. Detection is by exposure of the membrane to x-ray film or ion-emitting detection devices. One example of the foregoing is described in U.S. Pat. No. 5,279,721, incorporated by reference herein, which discloses an apparatus and method for the automated electrophoresis and transfer of nucleic acids. The apparatus permits electrophoresis and blotting without external manipulation of the gel and is ideally suited to carrying out methods according to the present invention. 5.4 METHODS FOR MEASURING MOLECULAR MARKER DATA REFLECTIVE OF ABUNDANCE OF PROTEIN PRODUCTS OF MOLECULAR MARKERS Measurement of the abundance of protein products of molecular markers in blood may be performed using a number of separation techniques combined with a monitoring system. For example, whole genome monitoring of protein (e.g., the "proteome,") can be carried out using commercial systems such as a SELDI® Chip by Ciphergen. In addition, protein microarrays comprised of immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome can be used, (e.g., The ProteinChip® Biomarker System, Ciphergen, Fremont, California). See also, for example, Lin, 2004, Modern Pathology, 1-9; Li, 2004, Journal of Urology 171, 1782-1787; Wadsworth, 2004, Clinical Cancer Research, 10, 1625-1632; Prieto, 2003, Journal of Liquid Chromatography & Related Technologies 26, 2315-2328; Coombes, 2003, Clinical Chemistry 49, 1615-1623; Mian, 2003, Proteomics 3, 1725-1737; Lehre et al, 2003, BJU International 92, 223-225; and Diamond, 2003, Journal of the American Society for Mass Spectrometry 14, 760-765, which are hereby incorporated by reference in their entireties. In one embodiment, antibodies can be used to measure protein products of the candidate molecular markers. Methods for making monoclonal antibodies are well known (see, e.g., Harlow and Lane, 1988, Antibodies: A Laboratory Manual, Cold Spring Harbor, New York, which is incorporated in its entirety for all purposes). In one embodiment, monoclonal antibodies are raised against synthetic peptide fragments designed based on genomic sequence of the cell. With such an antibody array, proteins from the cell are contacted to the array and their binding is assayed with assays known in the art. Immunoassays known to one of skill in the art can be used to detect and quantify protein levels. For example, ELISAs can be used to detect and quantify protein levels. ELISAs comprise preparing antigen, coating the well of a 96 well microtiter plate with the antigen, adding the antibody of interest conjugated to a detectable compound such as an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase) to the well and incubating for a period of time, and detecting the presence of the antigen. In ELISAs the antibody of interest does not have to be conjugated to a detectable compound; instead, a second antibody (which recognizes the antibody of interest) conjugated to a detectable compound may be added to the well. Further, instead of coating the well with the antigen, the antibody may be coated to the well. In this case, a second antibody conjugated to a detectable compound may be added following the addition of the antigen of interest to the coated well. One of skill in the art would be knowledgeable as to the parameters that can be modified to increase the signal detected as well as other variations of ELISAs known in the art. In a preferred embodiment, an ELISA may be performed by coating a high binding 96-well microtiter plate (Costar) with 2μg/ml of rhu-IL-9 in PBS overnight. Following three washes with PBS, the plate is incubated with three-fold serial dilutions of Fab at 250C for 1 hour. Following another three washes of PBS, lμg/ml anti-human kappa-alkaline phosphatase-conjugate is added and the plate is incubated for 1 hour at 250C. Following three washes with PBST, the alkaline phosphatase activity is determined in 50μl/AMP/PPMP substrate. The reactions are stopped and the absorbance at 560 nm is determined with a VMAX microplate reader. For further discussion regarding ELISAs see, e.g., Ausubel et al, eds, 1994, Current Protocols in Molecular Biology, Vol. 1, John Wiley & Sons, Inc., New York at 11.2.1. Protein levels may be determined by Western blot analysis. Further, protein levels as well as the phosphorylation of proteins can be determined by immunoprecitation followed by Western blot analysis. Immunoprecipitation protocols generally comprise lysing a population of cells in a lysis buffer such as RIPA buffer (1% NP-40 or Triton X- 100, 1% sodium deoxycholate, 0.1% SDS, 0.15 M NaCl, 0.01 M sodium phosphate at pH 7.2, 1% Trasylol) supplemented with protein phosphatase and/or protease inhibitors (e.g., EDTA, PMSF, aprotinin, sodium vanadate), adding the antibody of interest to the cell lysate, incubating for a period of time (e.g., 1 to 4 hours) at 40° C, adding protein A and/or protein G sepharose beads to the cell lysate, incubating for about an hour or more at 40° C, washing the beads in lysis buffer and resuspending the beads in SDS/sample buffer. The ability of the antibody of interest to immunoprecipitate a particular antigen can be assessed by, e.g., western blot analysis. One of skill in the art would be knowledgeable as to the parameters that can be modified to increase the binding of the antibody to an antigen and decrease the background (e.g., pre-clearing the cell lysate with sepharose beads). For further discussion regarding immunoprecipitation protocols see, e.g., Ausubel et al, eds, 1994, Current Protocols in Molecular Biology, Vol. 1, John Wiley & Sons, Inc., New York at 10.16.1. Western blot analysis generally comprises preparing protein samples, electrophoresis of the protein samples in a polyacrylamide gel (e.g., 8%- 20% SDS-PAGE depending on the molecular weight of the antigen), transferring the protein sample from the polyacrylamide gel to a membrane such as nitrocellulose, PVDF or nylon, incubating the membrane in blocking solution (e.g., PBS with 3% BSA or non-fat milk), washing the membrane in washing buffer (e.g., PBS-T ween 20), incubating the membrane with primary antibody (the antibody of interest) diluted in blocking buffer, washing the membrane in washing buffer, incubating the membrane with a secondary antibody (which recognizes the primary antibody, e.g., an anti-human antibody) conjugated to an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase) or radioactive molecule (e.g., 32p or 125j) diluted in blocking buffer, washing the membrane in wash buffer, and detecting the presence of the antigen. One of skill in the art would be knowledgeable as to the parameters that can be modified to increase the signal detected and to reduce the background noise. For further discussion regarding western blot protocols see, e.g., Ausubel et al, eds, 1994, Current Protocols in Molecular Biology, Vol. 1, John Wiley & Sons, Inc., New York at 10.8.1. Protein expression levels can also be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well-known in the art and typically involves isoelectric focusing along a first dimension followed by SDS-PAGE electrophoresis along a second dimension. See, e.g., Hames et al, 1990, Gel Electrophoresis of Proteins: A Practical Approach, IRL Press, New York; Shevchenko et al., 1996, Proc. Natl. Acad. ScL USA 93:1440-1445; Sagliocco et al., 1996, Yeast 12:1519- 1533; Lander, 1996, Science 274:536-539. The resulting electropherograms can be analyzed by numerous techniques, including mass spectrometric techniques, Western blotting and immunoblot analysis using polyclonal and monoclonal antibodies, and internal and N-terminal micro-sequencing. 5.5 USES OF CLASSIFIERS IDENTIFIED
In exemplary embodiments, the classifiers constructed in accordance with the present invention can be used to detect, diagnose, prognose and/or monitor a trait in a test individual. In a specific embodiment, a classifier or classifier group constructed in accordance with Section 5.1 is used to detect, diagnose, prognose and/or monitor a disease in said test individual. In another embodiment, a classifier or classifier group constructed in accordance with Section 5.1 is used to detect, diagnose, prognose and/or monitor a reoccurrence of disease in said test individual. In another embodiment, a classifyer or classifier group constructed in accordance with the invention is used to evaluate or predict the efficacy of treatment in a subject. In another embodiment, a classifier or classifier group constructed in accordance with the invention is used to predict whether a subject will be responsive to treatment and/or treatment outcomes. In another embodiment, a classifier or classifier group constructed in accordance with the invention is used to monitor and/or predict treatment compliance or non-compliance. In another embodiment, a classifier or classifier group constructed in accordance with the methods of the invention is indicative of the responsiveness of a subject to a stimulus (whether external or internal, e.g., smoke, pollution, sunlight, heat, and mutations) and is used to evaluate or predict the response of a subject to such stimulus. In yet another embodiment, the molecular markers identified by the classifiers of the invention can be used independently of the classifier to detect, diagnose, prognose, predict and/or monitor a trait. In such embodiments, said detection, diagnosis, prognosis, prediction and/or monitoring of a test individual can be accomplished by monitoring the gene expression pattern or profile of the molecular markers identified by the classifier or classifier group of the invention of a test individual and comparing said pattern or profile to a gene expression pattern or profile of a control individual or group of individuals who have said trait. In another embodiment, the gene expression pattern or profile of the test individual can be compared with a control individual or group of individuals who do not have said trait. In another embodiment, the gene expression pattern or profile of the test individual is compared as between individuals or group of individuals who have said trait and who do not have said trait. In yet another embodiment, the gene expression pattern or profile of the test individual is compared with the individuals used for the training population. In yet another embodiment, the gene expression pattern or profile of the test individual is compared with the individuals of the scoring population. As used herein, a "gene expression pattern" or "gene expression profile" indicates the combined pattern of the results of the analysis of the level of expression of two or more biomarkers of the invention including 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or all of the biomarkers of the classifier or classifier groups. A gene expression pattern or gene expression profile can result from the measurement of expression of the products of the biomarkers of the invention and can be done by measuring either the RNA or the proteins corresponding to said molecular marker using any any of the techniques described herein. For example techniques to measure expression of the RNA products of the biomarkers of the invention includes, PCR based methods (including RT-PCR and quantitative RT-PCR) and non PCR based method % as well as microarray analysis. To measure protein products of the biomarkers of the invention, techniques include western blotting and ELISA analysis. 5.6 KITS
One embodiment of the present invention comprises kits for measuring molecular marker data by providing the materials necessary to measure the abundance of one or more of the products of one or more molecular markers of the classifier or classifier groups identified. Such kits may comprise materials and reagents required for measuring molecular marker data where the product of the molecular marker is RNA or protein. In some embodiments, such kits include microarrays wherein the microarray is comprised of oligonucleotides and/or DNA and/or RNA fragments which hybridize to one or more of the products of one or more of the molecular markers of a classifier or classifier group. In some embodiments, such kits may include primers for PCR of either the RNA product or the cDNA copy of the RNA product of the molecular marker or both. In some embodiments, such kits may include primers for PCR as well as probes for Quantitative PCR. In some embodiments, such kits may include multiple primers and multiple probes wherein some of said probes have different flourophores so as to permit multiplexing of multiple products of a single molecular marker or multiple products wherein each product results from a single molecular marker. In some embodiments, such kits may further include materials and reagents for creating cDNA from RNA. In some embodiments, such kits may include antibodies specific for the protein products of a molecular marker. Such kits may additionally comprise materials and reagents for isolating RNA and/or proteins from a blood sample. Such kits may additionally comprise materials and reagents for isolating RNA and/or proteins from a non-blood tissue sample. In addition such kits may include materials and reagents for synthesizing cDNA from RNA isolated from a blood sample. In some embodiments of the present invention such kits may include, a computer program product embedded on computer readable media for determining whether a subject has a trait of interest. In some embodiments of the present invention, the kits of the invention may include a computer program product embedded on a computer readable media along with instructions. In some embodiments, the invention provides kits for measuring the expression of one or more nucleic acid sequences of one or more molecular markers. In a specific embodiment, such kits measure the expression of one or more nucleic acid sequences associated with a molecular marker which has been determined according to the method of the invention as being indicative of a trait of interest, hi accordance with this embodiment, the kits may comprise materials and reagents that are necessary for measuring the expression of particular nucleic acid sequence products of molecular markers identified by a classifier or classifier group of the invention. For example, a microarray or RT-PCR kit may be produced for a specific condition and contain only those reagents and materials necessary for measuring the levels of specific RNA transcript products of the molecular markers associated with the classifier or classifier groups selected in accordance with one embodiment of the invention. Alternatively, in some embodiments, the kits can comprise materials and reagents that are not limited to those required to measure the expression of particular nucleic acid sequences of any particular molecular marker. For example, in certain embodiments, the kits comprise materials and reagents necessary for measuring the levels of expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more of the molecular markers of the invention, in addition to reagents and materials necessary for measuring the levels of the expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more genes other than the molecular markers of the invention. In other embodiments, the kits contain reagents and materials necessary for measuring the levels of expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more of the molecular markers of the invention, and 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, or more genes that are not molecular markers of the invention, or 1-10, 1-100, 1-150, 1-200, 1-300, 1-400, 1-500, 1-1000, 25-100, 25-200, 25-300, 25-400, 25- 500, 25-1000, 100-150, 100-200, 100-300, 100-400, 100-500, 100-1000 or 500-1000 genes that are not molecular markers of the invention. For nucleic acid micoarray kits, the kits generally comprise probes attached to a solid support surface. In one such embodiment, probes can be either oligonucleotides or longer length probes including probes ranging from 150 nucleotides in length to 800 nucleotides in length. The probes may be labeled with a detectable label. In a specific embodiment, the probes are specific for one or more of the products of a specific molecular marker identified following the methods of section 5.1. The microarray kits may comprise instructions for performing the assay and methods for interpreting and analyzing the data resulting from the performance of the assay. In a specific embodiment, the kits comprise instructions for diagnosing a trait of interest. The kits may also comprise hybridization reagents and/or reagents necessary for detecting a signal produced when a probe hybridizes to a target nucleic acid sequence. Generally, the materials and reagents for the microarray kits are in one or more containers. Each component of the kit is generally in its own a suitable container. In certain embodiments, a nucleic acid microarray kit comprises materials and reagents necessary for measuring the levels of expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more of the molecular markers of the invention, in addition to reagents and materials necessary for measuring the levels of the expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more genes other than the molecular markers of the invention. In other embodiments, a nucleic acid microarray kit contains reagents and materials necessary for measuring the levels of expression of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 or more of the molecular markers of the invention, and 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, or more genes that are not molecular markers of the invention, or 1-10, 1-100, 1-150, 1-200, 1-300, 1-400, 1-500, 1-1000, 25-100, 25- 200, 25-300, 25-400, 25-500, 25-1000, 100-150, 100-200, 100-300, 100-400, 100-500, 100-1000 or 500-1000 genes that are not molecular markers of the invention. For Quantitative PCR, the kits generally comprise pre-selected primers specific for particular nucleic acid sequences. The Quantitative PCR kits may also comprise enzymes suitable for amplifying nucleic acids (e.g., polymerases such as Taq), and deoxynucleotides and buffers needed for the reaction mixture for amplification. The Quantitative PCR kits may also comprise probes specific for the nucleic acid sequences associated with or indicative of a condition. The probes may or may not be labeled with a flourophore. The probes may or may not be labeled with a quencher molecule. In some embodiments the Quantitative PCR kits also comprise components suitable for reverse- transcribing RNA including enzymes (e.g. reverse transcriptases such as AMV, MMLV and the like) and primers for reverse transcription along with deoxynucleotides and buffers needed for the reverse transcription reaction. Each component of the quantitative PCR kit is generally in its own suitable container. Thus, these kits generally comprise distinct containers suitable for each individual reagent, enzyme, primer and probe. Further, the quantitative PCR kits may comprise instructions for performing the assay and methods for interpreting and analyzing the data resulting from the performance of the assay. In a specific embodiment, the kits contain instructions for diagnosing a trait of interest. For antibody based kits, the kit can comprise, for example: (1) a first antibody (which may or may not be attached to a solid support) which binds to a peptide, polypeptide or protein of interest; and, optionally, (2) a second, different antibody which binds to either the peptide, polypeptide or protein, or the first antibody and is conjugated to a detectable label (e.g., a fluorescent label, radioactive isotope or enzyme). In a specific embodiment, the peptide, polypeptide or protein of interest is associated with or indicative of a condition (e.g., a disease). The antibody-based kits may also comprise beads for conducting an immunoprecipitation. Each component of the antibody-based kits is generally in its own suitable container. Thus, these kits generally comprise distinct containers suitable for each antibody. Further, the antibody-based kits may comprise instructions for performing the assay and methods for interpreting and analyzing the data resulting from the performance of the assay. In a specific embodiment, the kits contain instructions for diagnosing a trait of interest. 5.7 EXEMPLARY NORMALIZATION ROUTINES
A number of different normalization protocols can be used to normalize molecular marker data obtained using microarrays. Some such normalization protocols are described in this section. Typically, the normalization comprises normalizing the expression level measurement of each gene in a plurality of genes that is expressed by a subject. Many of the normalization protocols described in this section are used to normalize microarray data. It will be appreciated that there are many other suitable normalization protocols that may be used in accordance with the present invention. All such protocols are within the scope of the present invention. Many of the normalization protocols found in this section are found in publicly available software, such as Microarray Explorer (Image Processing Section, Laboratory of Experimental and Computational Biology, National Cancer Institute, Frederick, MD 21702, USA). One normalization protocol is Z-score of intensity. In this protocol, raw expression intensities are normalized by the (mean intensity)/(standard deviation) of raw intensities for all spots in a sample. For microarray data, the Z-score of intensity method normalizes each hybridized sample by the mean and standard deviation of the raw intensities for all of the spots in that sample. The mean intensity mnlj and the standard deviation sdlj are computed for the raw intensity of control genes. It is useful for standardizing the mean (to 0.0) and the range of data between hybridized samples to about -3.0 to +3.0. When using the Z-score, the Z differences (Z diff) are computed rather than ratios. The Z-score intensity (Z-scoreij) for intensity Iy for probe i (hybridization probe, protein, or other binding entity) and spot j is computed as: Z-scoreij = (Iy - rrmli) / sdlj, and Zdiffj(x,y) = Z-scoreXj - Z-scoreyJ- where x represents the x channel and y represents the y channel. Another normalization protocol is the median intensity normalization protocol in which the raw intensities for all spots in each sample are normalized by the median of the raw intensities. For microarray data, the median intensity normalization method normalizes each hybridized sample by the median of the raw intensities of control genes (medianπ) for all of the spots in that sample. Thus, upon normalization by the median intensity normalization method, the raw intensity Iy for probe i and spot j, has the value liny where,
Imy = (Iy/ medianli).
Another normalization protocol is the log median intensity protocol. In this protocol, raw expression intensities are normalized by the log of the median scaled raw intensities of representative spots for all spots in the sample. For microarray data, the log median intensity method normalizes each hybridized sample by the log of median scaled raw intensities of control genes (medianlj) for all of the spots in that sample. As used herein, control genes are a set of genes that have reproducible accurately measured expression values. The value 1.0 is added to the intensity value to avoid taking the log(O.O) when intensity has zero value. Upon normalization by the median intensity normalization method, the raw intensity Iy for probe i and spot j, has the value Imy where, Imjj = log(1.0 + (Ijj/ medianli)).
Yet another normalization protocol is the Z-score standard deviation log of intensity protocol. In this protocol, raw expression intensities are normalized by the mean log intensity (rnnLIj) and standard deviation log intensity (sdLIj). For microarray data, the mean log intensity and the standard deviation log intensity is computed for the log of raw intensity of control genes. Then, the Z-score intensity ZlogSy for probe i and spot j is: ZlogSjj = (log(Iij) - mnLIi)/sdLIi. Still another normalization protocol is the Z-score mean absolute deviation of log intensity protocol. In this protocol, raw expression intensities are normalized by the Z-score of the log intensity using the equation (log(intensity)-mean logarithm) / standard deviation logarithm. For microarray data, the Z-score mean absolute deviation of log intensity protocol normalizes each bound sample by the mean and mean absolute deviation of the logs of the raw intensities for all of the spots in the sample. The mean log intensity mnLIj and the mean absolute deviation log intensity madLI, are computed for the log of raw intensity of control genes. Then, the Z-score intensity ZlogAy for probe i and spot j is: ZlogAjj = (log(Ijj) - mnLIj)/madLIi. Another normalization protocol is the user normalization gene set protocol. In this protocol, raw expression intensities are normalized by the sum of the genes in a user defined gene set in each sample. This method is useful if a subset of genes has been determined to have relatively constant expression across a set of samples. Yet another normalization protocol is the calibration DNA gene set protocol in which each sample is normalized by the sum of calibration DNA genes. As used herein, calibration DNA genes are genes that produce reproducible expression values that are accurately measured. Such genes tend to have the same expression values on each of several different microarray s. The algorithm is the same as user normalization gene set protocol described above, but the set is predefined as the genes flagged as calibration DNA. Yet another normalization protocol is the ratio median intensity correction protocol. This protocol is useful in embodiments in which a two-color fluorescence labeling and detection scheme is used. In the case where the two fluors in a two-color fluorescence labeling and detection scheme are Cy 3 and Cy 5, measurements are normalized by multiplying the ratio (Cy3/Cy5) by medianCy5/medianCy3 intensities. If background correction is enabled, measurements are normalized by multiplying the ratio (Cy3/Cy5) by (medianCy5-medianBkgdCy5) / (medianCy3-medianBkgdCy3) where medianBkgd means median background levels. In some embodiments, intensity background correction is used to normalize measurements. The background intensity data from a spot quantification programs may be used to correct spot intensity. Background may be specified as either a global value or on a per-spot basis. If the array images have low background, then intensity background correction may not be necessary. 5.8 EXEMPLARY DISEASES
As discussed supra, the present invention provides methods for developing classifiers that can be used to determine whether a patient has a certain trait including a disease. Exemplary diseases that can be identified include asthma, cancers, common late- onset Alzheimer's disease, diabetes, heart disease, hereditary early-onset Alzheimer's disease (George-Hyslop etal, 1990, Nature 347: 194), hereditary nonpolyposis colon cancer, hypertension, infection, maturity-onset diabetes of the young (Barbosa et al., 1976, Diabete Metab. 2: 160), mellitus, nonalcoholic fatty liver (NAFL) (Younossi, et al, 2002, Hepatology 35, 746-752), nonalcoholic steatohepatitis (NASH) (James & Day, 1998, J. Hepatol. 29: 495-501), non-insulin-dependent diabetes mellitus, andpolycystic kidney disease (Reeders et al, 1987, Human Genetics 76: 348). Disease also includes, blood disorder, blood lipid disease, autoimmune disease, arthritis (including osteoarthritis, rheumatoid arthritis, lupus, allergies, juvenile rheumatoid arthritis and the like), bone or joint disorder, a cardiovascular disorder (including heart failure, congenital heart disease; rheumatic fever, valvular heart disease; cor pulmonale, cardiomyopathy, myocarditis, pericardial disease; vascular diseases such as atherosclerosis, acute myocardial infarction, ischemic heart disease and the like), obesity, respiratory disease (including asthma, pneumonitis, pneumonia, pulmonary infections, lung disease, bronchiectasis, tuburculosis, cystic fibrosis, interstitial lung disease, chronic bronchitis emphysema, pulmonary hypertension, pulmonary thromboembolism, acute respiratory distress syndrome and the like), hyperlipidemias, endocrine disorder, immune disorder, infectious disease, muscle wasting and whole body wasting disorder, neurological disorders (including migraines, seizures, epilepsy, cerebrovascular diseases, alzheimers, dementia, parkinsons, ataxic disorders, motor neuron diseases, cranial nerve disorders, spinal cord disorders, meningitis and the like) including neurodegenerative and/or neuropsychiatric diseases and mood disorders (including schizophrenia, anxiety, bipolar disorder; manic depression and the like, skin disorder, kidney disease, scleroderma, stroke, hereditary hemorrhage telangiectasia, diabetes, disorders associated with diabetes (e.g., PVD), hypertension, Gaucher's disease, cystic fibrosis, sickle cell anemia, liver disease, pancreatic disease, eye, ear, nose and/or throat disease, diseases affecting the reproductive organs, gastrointestinal diseases (including diseases of the colon, diseases of the spleen, appendix, gall bladder, and others) and the like. For further discussion of human diseases, see Mendelian Inheritance in Man: A Catalog of Human Genes and Genetic Disorders by Victor A. McKusick (12th Edition (3 volume set) June 1998, Johns Hopkins University Press, ISBN: 0801857422) and Harrison's Principles of Internal Medicine by Braunwald, Fauci, Kasper, Hauser, Longo, & Jameson (15th Edition 2001), the entirety of which is incorporated herein. Cancers that can be identified using the inventive techniques of the present invention include, but are not limited to, human sarcomas and carcinomas, e.g., fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, pancreatic cancer, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, testicular tumor, lung carcinoma, small cell lung carcinoma, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, melanoma, neuroblastoma, retinoblastoma; leukemias, e.g., acute lymphocytic leukemia and acute myelocytic leukemia (myeloblastic, promyelocyte, myelomonocytic, monocytic and erythroleukemia); chronic leukemia (chronic myelocytic (granulocytic) leukemia and chronic lymphocytic leukemia); and polycythemia vera, lymphoma (Hodgkin's disease and non-Hodgkin's disease), multiple myeloma, Waldenstrom's macroglobulinemia, and heavy chain disease. 5.9 EXEMPLARY DATABASE ARCHITECTURES
In some embodiments, training population 44, candidate molecular marker data structure 58, patient database 68, and/or classifier database 70 comprise or are stored in one or more data warehouses. Data warehouses are typically structured as either relational databases or multidimensional data cubes. This section describes relational databases and multidimensional data cube architectures that can be used to store training data, candidate molecular marker lists, patient molecular marker data and/or classifiers of the present invention. More information on relational databases and multidimensional data cubes is found in Berson and Smith, 1997, Data Warehousing, Data Mining and OLAP, McGraw- Hill, New York; Freeze, 2000, Unlocking OLAP with Microsoft SQL Server and Excel 2000, IDG Books Worldwide, Inc., Foster City, California; and Thomson, 1997, OLAP Solutions: Building Multidimensional Information Systems, Wiley Computer Publishing, New York. 5.9.1 DATA ORGANIZATION Databases have typically been used for operational purposes, such as order entry, accounting and inventory control. More recently, corporations and scientific projects have been building databases, called data warehouses or large on-line analytical processing (OLAP) databases, explicitly for the purposes of exploration and analysis. The "data warehouse" can be described as a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management decisions. Data warehouses are built using both relational databases and specialized multidimensional structures called data cubes. In some embodiments a database stored in computer 10 or stored in a computer addressable by computer 10 across wide area network 34 is a relational database or a datacube. 5.9.2 RELATIONAL DATABASES
Relational databases organize data into tables where each row corresponds to a basic entity or fact and each column represents a property of that entity. For example, a table can represent transactions in a bank, where each row corresponds to a single transaction, and each transaction has multiple attributes, such as the transaction amount, the account balance, the bank branch, and the customer. The relational table is referred to as a relation, a row as a tuple, and a column as an attribute or field. The attributes within a relation can be partitioned into two types: dimensions and measures. Dimensions and measures are similar to independent and dependent variables in traditional analysis. For example, the bank branch and the customer would be dimensions, while the account balance would be a measure. A single relational database will often describe many heterogeneous but interrelated entities. For example, a database designed for a restaurant chain might maintain information about employees, products, and sales. The database schema defines the relations in a database, the relationships between those relations, and how the relations classify the entities of interest. 5.9.3 DATA CUBES
A data warehouse can be constructed as a relational database using either a star or snowflake schema and will provide a conceptual classifier of a multidimensional data set. Each axis in the corresponding data cube represents a dimension in a relational schema and consists of every possible value for that dimension. For example, an axis corresponding to states would have fifty values, one for each state. Each cell in the data cube corresponds to a unique combination of values for the dimensions. For instance, if there are two imensions, "state" and "product", then there would be a cell for every unique combination of the two, e.g., one cell each for (California, Tea), (California, Coffee), (Florida, Tea), (Florida, Coffee), etc. Each cell contains one value per measure of the data cube. So if product production and consumption information is needed, then each cell would contain two values, one for the number of products of each type consumed in that state, and one for the number of products of each type produced in that state. Dimensions within a data warehouse are often augmented with a hierarchical structure. If each dimension has a hierarchical structure, then the data warehouse is not a single data cube but rather a lattice of data cubes. 5.10 EXEMPLARY PATIENT DATABASE
This section provides a more detailed description of a patient database 68 in accordance with one aspect of the invention. As described in Section 5.1, an exemplary patient database 68 includes a plurality of patient records 500 (Fig. 6). There is no limit on the number of patient records 500 that can be held in patient database 68. Database 68 can hold as few as one patient record 500. More typically, database 68 holds between 1 and 100 patient records, more than 100 patient records, more than a thousand patient records, more than ten thousand patient records, more than 100 thousand patient records, between 1 patient record and one million patient records, or more. Each patient record 500 preferably includes a patient identifier 502. As those skilled in database arts will appreciate, a patient identifier 502 need not be explicitly enumerated in certain database systems. For instance, in some systems, a patient identifier 502 can simply be a patient record 500 identifier. However, in some embodiments, a patient identifier 502 can be a number that uniquely identifies a patient within a health care program or clinical trial. An advantage of database 68 is that it has the capability of tracking molecular marker data profile 504 and trait characterization information 510 for each patient registered in database 68. In some embodiments, a molecular profile 504 is the abundance levels of a plurality of molecular marker products in blood specimens obtained from a patient in accordance with Section 5.2. hi some embodiments, such abundance levels are normalized using any of the techniques disclosed in Section 5.7. In some embodiments, a molecular profile 504 comprises the processed microarray image data from the biological specimen obtained from the patient. In one example, molecular profile data 504 comprises molecular marker abundance information for all or a portion of the cellular constituents represented in a microarray, optional background signal information, and optional associated annotation information describing the probes used for the respective molecular marker. Molecular markers include, but are not limited to RNA (e.g., mRNA) and protein. In some embodiments, a molecular profile 504 represents the transcriptional state of cellular constituents in a biological specimen. However, in other embodiments, a molecular profile 504 can track aspects of the biological state other than or in addition to transcriptional state. Such other aspects of the biological state include, but are not limited to, the translational state, the activity state of cellular constituents in a biological sample. In some embodiments, for example, molecular profile 504 data is, in fact, protein levels for various proteins in the blood taken from the patient. Thus, in some embodiments, molecular profiles 504 comprise amounts or concentrations of the molecular markers in biological specimens obtained in accordance with Section 5.2. In one embodiment, the amount of at least one molecular marker that is tracked in a molecular profile 504 comprises abundances of at least one RNA species present in one or more cells in the blood obtained from the patient. Such abundances can be measured by a method comprising contacting a gene transcript array with RNA derived from one or more cells of the biological specimen, or with cDNA derived therefrom. A gene transcript array comprises a surface with attached nucleic acids or nucleic acid mimics. The nucleic acids or nucleic acid mimics are capable of hybridizing with the RNA species or with cDNA derived from the RNA species. In one particular embodiment, the abundance of the RNA is measured by contacting a gene transcript array with the RNA from one or more cells of the biological specimen, or with nucleic acid derived from the RNA, such that the gene transcript array comprises a positionally addressable surface with attached nucleic acids or nucleic acid mimics, where the nucleic acids or nucleic acid mimics are capable of hybridizing with the RNA species, or with nucleic acid derived from the RNA species. In some embodiments, a molecular profile 504 can include abundance information or activity information about ten or more molecular markers (e.g., genes or proteins), between ten and one thousand molecular markers, between one thousand and twenty thousand molecular markers, or more than twenty thousand molecular markers. In some embodiments, in addition to or rather than providing abundance information or activity information for molecular markers, a molecular profile 504 tracks polymorphism information. Such polymorphism information includes, but is not limited to, single nucleotide polymorphisms (SNPs), SNP haplotypes, microsatellite markers, restriction fragment length polymorphisms (RFLPs), short tandem repeats, sequence length polymorphisms, DNA methylation, random amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLP), and "simple sequence repeats." For more information on such polymorphisms, see generally, The DNA Revolution by Andrew H. Paterson 1996 (Chapter 2) in: Genome Mapping in Plants (ed. Andrew H. Paterson) by Academic Press/R. G. Landis Company, Austin, Tex., 7-21, which is hereby incorporated herein by reference in its entirety SNPs occur approximately once every 600 base pairs in the genome. See, for example, Kruglyak and Nickerson, 2001, Nature Genetics 27, 235. Alleles making up blocks of such SNPs in close physical proximity are often correlated, resulting in reduced genetic variability and defining a limited number of "SNP haplotypes" each of which reflects descent from a single ancient ancestral chromosome. See Fullerton et ah, 2000, Am. J. Hum. Genet. 67, 881. Such haplotype structure is used in some embodiments of the present invention. Patil et al. found that a very dense set of SNPs is required to capture all the common haplotype information. See Patil et al, 2001, Science 294, 1719-1723. DNA methylation is described in Grunau et al., 2003, Nucleic Acids Res. 31, pp. 75-77. RFLPs are the product of allelic differences between DNA restriction fragments caused by nucleotide sequence variability. As is well known to those of skill in the art, RFLPs are typically detected by extraction of genomic DNA and digestion with a restriction endonuclease. Generally, the resulting fragments are separated according to size and hybridized with a probe; single copy probes are preferred. As a result, restriction fragments from homologous chromosomes are revealed. Differences in fragment size among alleles represent an RFLP (see, for example, Helentjaris et ah, 1985, Plant MoL Bio. 5:109-118, and U.S. Pat. No. 5,324,631). The phrase "random amplified polymorphic DNA" or "RAPD" refers to the amplification product of the distance between DNA sequences homologous to a single oligonucleotide primer appearing on different sites on opposite strands of DNA. Mutations or rearrangements at or between binding sites will result in polymorphisms as detected by the presence or absence of amplification product (see, for example, Welsh and McClelland, 1990, Nucleic Acids Res. 18:7213-7218; Hu and Quiros, 1991, Plant Cell Rep. 10:505-511). AFLP technology refers to a process that is designed to generate large numbers of randomly distributed molecular markers (see, for example, European Patent Application No. 0534858 Al). "Simple sequence repeats" or "SSRs" are di-, tri- or tetra-nucleotide tandem repeats within a genome. The repeat region can vary in length between genotypes while the DNA flanking the repeat is conserved such that the same primers will work in a plurality of genotypes. A polymorphism between two genotypes represents repeats of different lengths between the two flanking conserved DNA sequences (see, for example, Akagi et al, 1996, Theor. Appl. Genet. 93, 1071-1077; Bligh et al, 1995, Euphytica 86:83-85; Struss et al, 1998, Theor. Appl. Genet. 97, 308-315; Wu et al, 1993, MoL Gen. Genet. 241, 225-235; and U.S. Pat. No. 5,075,217). SSR are also known as satellites or microsatellites. In addition to molecular profiles 50, patient records 500 include trait characterizations 510. In some embodiments, a trait characterization 510 comprises observations made by a patient's physician. In some instances, the observations made by a physician include a code from the International Classification of Diseases, 9th Revision, prepared by the Department of Health and Human Services (ICD-9 codes), or an equivalent, and dates such observations were made. 5.11 EXEMPLARY GENES AS CANDIDATE MOLECULAR MARKERS
Non-limiting examples of genes useful as molecular markers for use in the invention can include, but are not limited to, genes specific for or involved in a particular biological process, such as apoptosis, differentiation, stress response, aging, proliferation, etc.; cellular mechanism genes, e.g., cell-cycle, signal transduction, metabolism of toxic compounds, and the like; disease associated genes, e.g., genes involved in cancer, schizophrenia, diabetes, high blood pressure, atherosclerosis, viral-host interaction and infection and the like. Exemplary genes can also include immune responsive genes. Further examples of genes can include, but are not limited to, oncogenes whose expression within a cell induces that cell to become converted from a normal cell into a tumor cell. See for example Hanahan & Weinberg, 2000, Cell 100:57; Yokota., 2000, Carcinogenesis 21 :497. Further examples of genes can include, but are not limited to cytokine genes. See, for example, Rubinstein et al, 1998, Cytokine Growth Factor Rev. 9:175-81. Other examples of genes can include idiotype protein genes (e.g., Benezra., et al, 2001 Oncogene 20:8334-41; Norton, 2000, J. Cell Sci. 113:3897), prion genes (e.g., Prusiner et al, 1998, Cell 93:337-48; Safar & Prusiner, 1998, Prog. Brain Res. 117:421); genes that express molecules that induce angiogenesis (e.g., Gould & Wagner, 2002, Hum. Pathol. 33:1061); genes encoding adhesion molecules (e.g., Chothia, & Jones, 1997, Annu. Rev. Biochem. 66:823; Parise et al, 2000, Semin. Cancer Biol. 10:407-14); genes encoding cell surface receptors (e.g., Deller and Jones, 2000, Curr. Opin. Struct. Biol. 10:213); genes of proteins that are involved in metastasizing and/or invasive processes (e.g., Boyd, 1996, Cancer Metastasis Rev. 15:77; Yokota, 2000, Carcinogenesis 21:497); genes of proteases as well as of molecules that regulate apoptosis and the cell cycle (e.g., Matrisian, 1999, Curr. Biol. 9:R776; Krepela, 2001, Neoplasma 48:332; Basbaum and Werb, 1996, Curr. Opin. Cell Biol. 8:731; Birkedal-Hansen et al, 1993, Crit. Rev. Oral Biol. Med. 4:197- 250; Mignatti and Rifkin, 1993, Physiol. Rev. 73:161; Stetler-Stevenson et al., 1993, Annu. Rev. Cell Biol. 9:541; Brinkerhoff and Matrisan, 2002, Nature Reviews 3:207; Strasser. et al, 2000, Annu. Rev. Biochem. 69:217; Chao and Korsmeyer, 1998, Annu. Rev. Immunol. 16:395; Mullauer et al, 2001, Mutat. Res. 488:211 ; Fotedar et al, 1996, Prog. Cell Cycle Res. 2:147; Reed., 2000, Am. J. Pathol. 157:1415; D'Ari, 2001, Bioassays 23:563); or multi-drug resistance genes, such as the MDRl gene. In one embodiment, a gene is an immune response gene or a non-immune response gene such as cytokines (e.g., interleukins and interferons such as TNF-alpha, IL-10, IL- 12, IL-2, IL-4, IL-10, IL- 12, IL- 13, TGF-Beta, IFN-gamma; immunoglobulins, complement and the like). See, for example, Bellardelli, 1995, Role of interferons and other cytokines in the regulation of the immune response APMIS 103: 161. 5.12 CLUSTERING TECHNIQUES
In some embodiments, clustering is used. For instance, clustering can be used in step 204 to visualize the relationship between the data measured for a plurality of molecular markers in step 202. In some embodiments, any of the clustering techniques described in Draghici, Data Analysis Tools For DNA Microarrays, 2003, Chapman & Hall, CRC Press, New York, pp. 263-297, which is hereby incorporated by reference in its entirety, are used in the present invnetion. Clustering is also described on pages 211-256 of Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York, which is hereby incorporated by reference. As described in Section 6.7 of Duda, the clustering problem is described as one of finding natural groupings in a dataset. To identify natural groupings, two issues are addressed. First, a way to measure similarity (or dissimilarity) between two samples is determined. This metric (similarity measure) is used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters. Second, a mechanism for partitioning the data into clusters using the similarity measure is determined. Similarity measures are discussed in Section 6.7 of Duda, where it is stated that one way to begin a clustering investigation is to define a distance function and to compute the matrix of distances between all pairs of samples in a dataset. If distance is a good measure of similarity, then the distance between samples in the same cluster will be significantly less than the distance between samples in different clusters. However, as stated on page 215 of Duda, clustering does not require the use of a distance metric. For example, a nonmetric similarity function s(x, x') can be used to compare two vectors x and x'. Conventionally, s(x, x') is a symmetric function whose value is large when x and x' are somehow "similar". An example of a nonmetric similarity function s(x, x') is provided on page 216 of Duda. Once a method for measuring "similarity" or "disimilarity" between points in a dataset has been selected, clustering requires a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function are used to cluster the data. See page 217 of Duda. Criterion functions are discussed in Section 6.8 of Duda. More recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York, has been published. Pages 537-563 describe clustering in detail. More information on clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, NY; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York, NY; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, New Jersey. Now that an overview of clustering techniques has been given, more specific examples of clustering that can be performed in the methods described in Section 5.1 is presented. 5.12.1 HIERARCHICAL CLUSTERING TECHNIQUES
Hierarchical cluster analysis is a statistical method for finding relatively homogenous clusters of elements based on measured data. Consider a sequence of partitions of n samples into c clusters. The first of these is a partition into n clusters, each cluster containing exactly one sample. The next is a partition into n 1 clusters, the next is partition into n-2, and so on until the n*, in which all the samples form one cluster. Level k in the sequence of partitions occurs when c = n - k + 1. Thus, level one corresponds to n clusters and level n corresponds to one cluster. Given any two samples x and x*, at some level they will be grouped together in the same cluster. If the sequence has the property that whenever two samples are in the same cluster at level k they remain together at all higher levels, then the sequence is said to be a hierarchical clustering. Duda et al., 2001, Pattern Classification, 2nd edition, John Wiley & Sons, New York, 2001: 551. Examples of hierarchical clustering includes agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm. See, for example WO03100557. 5.12.1.1 CLUSTERING WITH PEARSON CORRELATION COEFFICIENTS
In some embodiments of the present invention, molecular marker data data is clustered using agglomerative hierarchical clustering with Pearson correlation coefficients. In this form of clustering, similarity is determined using Pearson correlation coefficients between sets of molecular marker data measurements. Other metrics that can be used, in addition to the Pearson correlation coefficient, include but are not limited to, a Euclidean distance, a squared Euclidean distance, a Euclidean sum of squares, a Manhattan metric, and a squared Pearson correlation coefficient. Such metrics can be computed using SAS (Statistics Analysis Systems Institute, Gary, North Carolina) or S-Plus (Statistical Sciences, Inc., Seattle, Washington). 5.12.1.2 DIVISIVE CLUSTERING
In some embodiments, the hierarchical clustering technique used to cluster molecular marker data measurements is a divisive clustering procedure. Divisive (top- down clustering) procedures start with all of the samples in one cluster and form the sequence by successfully splitting clusters. Divisive clustering techniques are classified as either a polymeric or a monothetic method. A polythetic approach divides clusters into arbitrary subsets. 5.12.2 K-MEANS CLUSTERING
In k-means clustering, sets of molecular marker data measurements are randomly assigned to K user specified clusters. The centroid of each cluster is computed by averaging the value of the vectors in each cluster. Then, for each i= 1, ..., N, the distance between vector xj and each of the cluster centroids is computed. Each vector x; is then reassigned to the cluster with the closest centroid. Next, the centroid of each affected cluster is recalculated. The process iterates until no more reassignments are made. See, for example, Duda et ah, 2001, Pattern Classification, John Wiley & Sons, New York, NY, pp. 526-528. A related approach is the fuzzy k-means clustering algorithm, which is also known as the fuzzy c-means algorithm, hi the fuzzy k-means clustering algorithm, the assumption that every set of molecular marker data measurements is in exactly one cluster at any given time is relaxed so that every set has some graded or "fuzzy" membership in a cluster. See Duda et ah, 2001, Pattern Classification, John Wiley & Sons, New York, NY, pp. 528-530. 5.12.3 JARVIS-PATRICK CLUSTERING
Jarvis-Patrick clustering is a nearest-neighbor non-hierarchical clustering method in which a set of objects is partitioned into clusters on the basis of the number of shared nearest-neighbors. In the standard implementation advocated by Jarvis and Patrick, 1973, IEEE Trans. Comput, C-22: 1025-1034, a preprocessing stage identifies the K nearest-neighbors of each object in the dataset. In the subsequent clustering stage, two objects i and j join the same cluster if (i) i is one of the K nearest-neighbors of j, (ii) j is one of the K nearest-neighbors of i, and (iii) i and j have at least kmjn of their K nearest-neighbors in common, where K and kmin are user-defined parameters. The method has been widely applied to clustering chemical structures on the basis of fragment descriptors and has the advantage of being much less computationally demanding than hierarchical methods, and thus more suitable for large databases. Jarvis-Patrick clustering can be performed using the Jarvis-Patrick Clustering Package 3.0 (Barnard Chemical Information, Ltd., Sheffield, United Kingdom). 5.13 MOLECULAR MARKERS
Molecular marker is used herein to mean a gene or genetic element. All genes and genetic elements are considered molecular markers, but the invention teaches how to identify molecular markers useful for diagnosing a trait of interest. 5.14 REPRESENTATIVE MATHEMATICAL MODELS THAT CAN BE USED TO BUILD CLASSIFIERS
This section describes various mathematical models that can be used to build classifier in accordance with the methods of the present invention.
5.14.1 REGRESSION CLASSIFIERS
In some embodiments, the classifier constructed in step 216 is a regression classifier, preferably a logistic regression classifier. Such a regression classifier includes a coefficient for each of the molecular markers selected in the last instance of step 214. In such embodiments, the coefficients for the regression classifier are computed using, for example, a maximum likelihood approach. In such a computation, the data measured for the molecular markers in step 206 (e.g., RT-PCR data) is used. In particular embodiments, molecular marker data from only two trait subgroups is used and the dependent variable is absence or presence of a particular trait in the subjects for which molecular marker data is available. As in the case of step 210, the two different trait subgroups can, for example, respectively represent a diseased and nondiseased state, a first diseased state (e.g. liver cancer) and a second phenotypically similar (e.g. hepatitis B) or unrelated diseased state (e.g., Alzheimer's disease), those subjects that are responsive to drug therapy and those subjects that are not responsive to drug therapy, or subjects that have been subjected to a perturbation (e.g., drug treatment) versus those subjects that have not been subjected to a perturbation. In another specific embodiment, training population 44 consists of a plurality of trait subgroups (e.g., three or more trait subgroups, four or more specific trait subgroups, etc.). In this specific embodiment, a generalization of the logistic regression model that handles multicategory responses can be used in step 216 to develop a classifier that discriminates between the various trait subgroups found in the training population. For example, measured data for selected molecular markers can be applied to any of the mulitcategory logit models described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., New York, Chapter 8, which is hereby incorporated herein by reference in its entirety, in order to develop a classifier capable of discriminating between any of a plurality of trait subgroups represented in a training population. 5.14.2 NEURAL NETWORKS
The present invention is not limited to the use of logistic regression. In some embodiments, the data measured for the molecular markers in step 206 (e.g., RT-PCR data) can be used to train a neural network. In some embodiments, a neural network is derived in each successive instance of step 216 of Fig. 2 A using the combination of molecular markers selected in the corresponding instance of step 214 of Fig. 2 A. A neural network is a two-stage regression or classification classifier. A neural network has a layered structure that includes a layer of input units (and the bias) connected by a layer of weights to a layer of output units. For regression, the layer of output units typically includes just one output unit. However, neural networks can handle multiple quantitative responses in a seamless fashion. In multilayer neural networks, there are input units (input layer), hidden units (hidden layer), and output units (output layer). There is, furthermore, a single bias unit that is connected to each unit other than the input units. Neural networks are described in Duda et ah, 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et ah, 2001, The Elements of Statistical Learning, Springer-Verlag, New York. The basic approach to the use of neural networks is to start with an untrained network, present a training pattern to the input layer, and to pass signals through the net and determine the output at the output layer. These outputs are then compared to the target values; any difference corresponds to an error. This error or criterion function is some scalar function of the weights and is minimized when the network outputs match the desired outputs. Thus, the weights are adjusted to reduce this measure of error. For regression, this error can be sum-of-squared errors. For classification, this error can be either squared error or cross-entropy (deviation). See, e.g., Hastie et al, 2001, The Elements of Statistical Learning, Springer-Verlag, New York. Three commonly used training protocols are stochastic, batch, and on-line. In stochastic training, patterns are chosen randomly from the training set and the network weights are updated for each pattern presentation. Multilayer nonlinear networks trained by gradient descent methods such as stochastic back-propagation perform a maximum- likelihood estimation of the weight values in the classifier defined by the network topology. In batch training, all patterns are presented to the network before learning takes place. Typically, in batch training, several passes are made through the training data. In online training, each pattern is presented once and only once to the net. In some embodiments, consideration is given to starting values for weights. If the weights are near zero, then the operative part of the sigmoid commonly used in the hidden layer of a neural network (see, e.g., Hastie et ah, 2001, The Elements of Statistical Learning, Springer- Verlag, New York) is roughly linear, and hence the neural network collapses into an approximately linear classifier. In some embodiments, starting values for weights are chosen to be random values near zero. Hence the classifier starts out nearly linear, and becomes nonlinear as the weights increase. Individual units localize to directions and introduce nonlinearities where needed. Use of exact zero weights leads to zero derivatives and perfect symmetry, and the algorithm never moves. Alternatively, starting with large weights often leads to poor solutions. Since the scaling of inputs determines the effective scaling of weights in the bottom layer, it can have a large effect on the quality of the final solution. Thus, in some embodiments, at the outset all expression values are standardized to have mean zero and a standard deviation of one. This ensures all inputs are treated equally in the regularization process, and allows one to choose a meaningful range for the random starting weights. With standardization inputs, it is typical to take random uniform weights over the range [- 0.7, +0.7]. A recurrent problem in the use of three-layer networks is the optimal number of hidden units to use in the network. The number of inputs and outputs of a three-layer network are determined by the problem to be solved. In embodiments of the present invention, the number of inputs for a given neural network can, in some embodiments, equal the number of molecular markers selected in the corresponding instance of step 214. In other embodiments, for each input, two or more molecular markers will be selected (for example wherein ratios of genes (A/B) are utilized. The number of outputs for the neural network will typically be just one (ie wherein the ouput neuron is one dimensional e.g. health vs. disease). If there are additional input dimensions, new additional output neurons may be created. In some embodiments more than one output is used so that more than just two states can be defined by the network. If too many hidden units are used in a neural network, the network will have too many degrees of freedom and is trained too long, there is a danger that the network will overfit the data. If there are too few hidden units, the training set cannot be learned. Generally speaking, however, it is better to have too many hidden units than too few. With too few hidden units, the classifier might not have enough flexibility to capture the nonlinearities in the data; with too many hidden units, the extra weight can be shrunk towards zero if appropriate regularization or pruning, as described below, is used. In typical embodiments, the number of hidden units in somewhere in the range of 5 to 100, with the number increasing with the number of inputs and number of training cases. One general approach to determining the number of hidden units to use is to apply a regularization approach. La the regularization approach, a new criterion function is constructed that depends not only on the classical training error, but also on classifier complexity. Specifically, the new criterion function penalizes highly complex classifiers; searching for the minimum in this criterion is to balance error on the training set with error on the training set plus a regularization term, which expresses constraints or desirable properties of solutions: J J pat " ■" Adreg- The parameter λ is adjusted to impose the regularization more or less strongly. In other words, larger values for λ will tend to shrink weights towards zero: typically cross- validation with a validation set is used to estimate λ. This validation set can be obtained by setting aside a random subset of the population measured in step 202 of Fig. 2 A. Other forms of penalty have been proposed, for example the weight elimination penalty (see, e.g., Hastie et al, 2001, The Elements of Statistical Learning, Springer- Verlag, New York). Another approach to determine the number of hidden units to use is to eliminate - prune - weights that are least needed. In one approach, the weights with the smallest magnitude are eliminated (set to zero). Such magnitude-based pruning can work, but is nonoptimal; sometimes weights with small magnitudes are important for learning and training data. In some embodiments, rather than using a magnitude-based pruning approach, WaId statistics are computed. The fundamental idea in WaId Statistics is that they can be used to estimate the importance of a hidden unit (weight) in a classifier. Then, hidden units having the least importance are eliminated (by setting their input and output weights to zero). Two algorithms in this regard are the Optimal Brain Damage (OBD) and the Optimal Brain Surgeon (OBS) algorithms that use second-order approximation to predict how the training error depends upon a weight, and eliminate the weight that leads to the smallest increase in training error. Optimal Brain Damage and Optimal Brain Surgeon share the same basic approach of training a network to local minimum error at weight w, and then pruning a weight that leads to the smallest increase in the training error. The predicted functional increase in the error for a change in full weight vector δw is:
Figure imgf000135_0001
where — - is the Hessian matrix. The first term vanishes because we are at a local dw minimum in error; third and higher order terms are ignored. The general solution for minimizing this function given the constraint of deleting one weight is:
Figure imgf000135_0002
Here, uq is the unit vector along the qth direction in weight space and Lq is approximation to the saliency of the weight q - the increase in training error if weight q is pruned and the other weights updated δw. These equations require the inverse of H. One method to calculate this inverse matrix is to start with a small value, H0 "1 = Of1I, where α is a small parameter - effectively a weight constant. Next the matrix is updated with each pattern according to
Figure imgf000135_0003
where the subscripts correspond to the pattern being presented and am decreases with m. After the full training set has been presented, the inverse Hessian matrix is given by H"1 = H~l . In algorithmic form, the Optimal Brain Surgeon method is: 1 begin initialize ΠH, W, θ train a reasonably large network to minimum error do compute H"1 by Eqn. 1
'♦
Figure imgf000135_0004
(saliency Lq)
w *~ w- (saliency Lq)
Figure imgf000135_0005
until J(w) > θ return w end The Optimal Brain Damage method is computationally simpler because the calculation of the inverse Hessian matrix in line 3 is particularly simple for a diagonal matrix. The above algorithm terminates when the error is greater than a criterion initialized to be θ. Another approach is to change line 6 to terminate when the change in J(w) due to elimination of a weight is greater than some criterion value. In some embodiments, the back-propagation neural network (see, for example Abdi, 1994, "A neural network primer", J. Biol System. 2, 247-283) containing a single bidden layer often neurons (ten hidden units) found in EasyNN-Plus version 4.Og software package (Neural Planner Software Inc.) is used. In one specific example, parameter values within the EasyNN-Plus program were set as follows: learning parameter = 0.6, and momentum parameter = 0.8. In some embodiments in which the EasyNN-Plus version 4.Og software package is used, "outlier" samples are identified by performing twenty independently-seeded trials involving 20,000 learning cycles each. 5.14.3 CLUSTERING
In some embodiments, the expression values for select genes are used to cluster a training set. For example, consider the case in which ten genes are used. Each member m of the training population will have expression values for each of the ten genes. Such values from a member m in the training population define the vector:
Figure imgf000136_0001
where Xjm is the expression level of the i* gene in organism m. If there are m organisms in the training set, selection of i genes will define m vectors. Note that the methods of the present invention do not require that each expression value of every single gene used in the vectors be represented in every single vector m. In other words, data from a subject in which one of the ith genes is not found can still be used for clustering. In such instances, the missing expression value is assigned either a "zero" or some other normalized value. In some embodiments, prior to clustering, the gene expression values are normalized to have a mean value of zero and unit variance. Those members of the training population that exhibit similar expression patterns across the training group will tend to cluster together. A particular combination of genes of the present invention is considered to be a good classifier in this aspect of the invention when the vectors cluster into the trait groups found in the training population. For instance, if the training population includes patients with no osteoarthritis, mild osteoarthritis, moderate osteoarthritis, marked osteoarthritis, and severe osteoarthritis an ideal clustering classifier will cluster the population into five groups, with each group uniquely representing either absence or one of the four stages of osteoarthritis. In some embodiments, the clustering classifier simply clusters the population into a first subgroup (a first cluster) that does not have osteoarthritis and a second subgroup (a second cluster) that has osteoarthritis. In some embodiments, the classifier clusters the data into a first subgroup that has a particular stage of osteoarthritis (e.g., mild) and two or more subgroups that do not include subjects having the particular stage of osteoarthritis represented in the first subgroup. Clustering is described on pages 211 -256 of Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York. As described in Section 6.7 of Duda, the clustering problem is described as one of finding natural groupings in a dataset. To identify natural groupings, two issues are addressed. First, a way to measure similarity (or dissimilarity) between two samples is determined. This metric (similarity measure) is used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters. Second, a mechanism for partitioning the data into clusters using the similarity measure is determined. Similarity measures are discussed in Section 6.7 of Duda, where it is stated that one way to begin a clustering investigation is to define a distance function and to compute the matrix of distances between all pairs of samples in a dataset. If distance is a good measure of similarity, then the distance between samples in the same cluster will be significantly less than the distance between samples in different clusters. However, as stated on page 215 of Duda, clustering does not require the use of a distance metric. For example, a nonmetric similarity function s(x, x') can be used to compare two vectors x and x'. Conventionally, s(x, x') is a symmetric function whose value is large when x and x' are somehow "similar". An example of a nonmetric similarity function s(x, x') is provided on page 216 of Duda. Once a method for measuring "similarity" or "dissimilarity" between points in a dataset has been selected, clustering requires a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function are used to cluster the data. See page 217 of Duda. Criterion functions are discussed in Section 6.8 of Duda. More recently, Duda et ah, Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York, has been published. Pages 537-563 describe clustering in detail. More information on clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, NY; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York, NY; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, New Jersey. Particular exemplary clustering techniques that can be used in the present invention include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. 5.14.4 PRINCIPAL COMPONENT ANALYSIS Principal component analysis (PCA) has been proposed to analyze gene expression data. Principal component analysis is a classical technique to reduce the dimensionality of a data set by transforming the data to a new set of variable (principal components) that summarize the features of the data. See, for example, Jolliffe, 1986, Principal Component Analysis, Springer, New York. Principal components (PCs) are uncorrelate and are ordered such that the &th PC has the Mi largest variance among PCs. The &th PC can be interpreted as the direction that maximizes the variation of the projections of the data points such that it is orthogonal to the first k - 1 PCs. The first few PCs capture most of the variation in the data set. In contrast, the last few PCs are often assumed to capture only the residual 'noise' in the data. PCA can also be used to create a classifier in accordance with the present invention. In such an approach, vectors for the select genes described in the present invention can be constructed in the same manner described for clustering above. In fact, the set of vectors, where each vector represents the expression values for the select genes from a particular member of the training population, can be considered a matrix. In some embodiments, this matrix is represented in a Free-Wilson method of qualitative binary description of monomers (Kubinyi, 1990, 3D QSAR in drug design theory methods and applications, Pergamon Press, Oxford, pp 589-638), and distributed in a maximally compressed space using PCA so that the first principal component (PC) captures the largest amount of variance information possible, the second principal component (PC) captures the second largest amount of all variance information, and so forth until all variance information in the matrix has been accounted for. Then, each of the vectors (where each vector represents a member of the training population) is plotted. Many different types of plots are possible. In some embodiments, a one-dimensional plot is made. In this one-dimensional plot, the value for the first principal component from each of the members of the training population is plotted. In this form of plot, the expectation is that members of a first subgroup (e.g. those subjects that do not have osteoarthritis) will cluster in one range of first principal component values and members of a second subgroup (e.g., those subjects that have osteoarthritis) will cluster in a second range of first principal component values. In one ideal example, the training population comprises two subgroups: "control" and "patients with osteoarthritis." The first principal component is computed using the molecular marker expression values for the select genes of the present invention across the entire training population data set. Then, each member of the training set is plotted as a function of the value for the first principal component. In this ideal example, those members of the training population in which the first principal component is positive are the "responders" and those members of the training population in which the first principal component is negative are "patients with osteoarthritis." In some embodiments, the members of the training population are plotted against more than one principal component. For example, in some embodiments, the members of the training population are plotted on a two-dimensional plot in which the first dimension is the first principal component and the second dimension is the second principal component. In such a two-dimensional plot, the expectation is that members of each subgroup represented in the training population will cluster into discrete groups. For example, a first cluster of members in the two-dimensional plot will represent subjects with mild osteoarthritis, a second cluster of members in the two-dimensional plot will represent subjects with moderate osteoarthritis, and so forth. In some embodiments, the members of the training population are plotted against more than two principal components and a determination is made as to whether the members of the training population are clustering into groups that each uniquely represents a subgroup found in the training population. In some embodiments, principal component analysis is performed by using the R mva package (Anderson, 1973, Cluster Analysis for applications, Academic Press, New York 1973; Gordon, Classification, Second Edition, Chapman and Hall, CRC, 1999.). Principal component analysis is further described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc. 5.14.5 NEAREST NEIGHBOR CLASSIFIER ANALYSIS
Nearest neighbor classifiers are memory-based and require no classifier to be fit. Given a query point XQ, the k training points JC(r), r, ..., k closest in distance to XQ are identified and then the point X0 is classified using the k nearest neighbors. Ties can be broken at random. In some embodiments, Euclidean distance in feature space is used to determine distance as: "(O ||Λ(0 Λo ||
Typically, when the nearest neighbor algorithm is used, the expression data used to compute the linear discriminant is standardized to have mean zero and variance 1. In the present invention, the members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set. A select combination of genes described in the present invention represents the feature space into which members of the test set are plotted. Next, the ability of the training set to correctly characterize the members of the test set is computed. In some embodiments, nearest neighbor computation is performed several times for a given combination of genes of the present invention. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of genes is taken as the average of each such iteration of the nearest neighbor computation. The nearest neighbor rule can be refined to deal with issues of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting for the neighbors. For more information on nearest neighbor analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York. 5.14.6 LINEAR DISCRIMINANT ANALYSIS
Linear discriminant analysis (LDA) attempts to classify a subject into one of two categories based on certain object properties. In other words, LDA tests whether object attributes measured in an experiment predict categorization of the objects. LDA typically requires continuous independent variables and a dichotomous categorical dependent variable. In the present invention, the expression values for the select combinations of genes described in the present invention across a subset of the training population serve as the requisite continuous independent variables. The trait subgroup classification of each of the members of the training population serves as the dichotomous categorical dependent variable. LDA seeks the linear combination of variables that maximizes the ratio of between-group variance and within-group variance by using the grouping information. Implicitly, the linear weights used by LDA depend on how the expression of a molecular marker across the training set separates in the two groups (e.g., a group that has osteoarthritis and a group that does not have osteoarthritis) and how this gene expression correlates with the expression of other genes. In some embodiments, LDA is applied to the data matrix of the N members in the training sample by K genes in a combination of genes described in the present invention. Then, the linear discriminant of each member of the training population is plotted. Ideally, those members of the training population representing a first subgroup (e.g. those subjects that do not have osteoarthritis) will cluster into one range of linear discriminant values (e.g., negative) and those member of the training population representing a second subgroup (e.g. those subjects that have osteoarthritis) will cluster into a second range of linear discriminant values (e.g., positive). The LDA is considered more successful when the separation between the clusters of discriminant values is larger. For more information on linear discriminant analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; Venables & Ripley, 1997, Modern Applied Statistics with s-plus, Springer, New York. 5.14.7 QUADRATIC DISCRIMINANT ANALYSIS
Quadratic discriminant analysis (QDA) takes the same input parameters and returns the same results as LDA. QDA uses quadratic equations, rather than linear equations, to produce results. LDA and QDA are interchangeable, and which to use is a matter of preference and/or availability of software to support the analysis. Logistic regression takes the same input parameters and returns the same results as LDA and QDA. 5.14.8 SUPPORT VECTOR MACHINES
In some embodiments of the present invention, support vector machines (SVMs) are used to classify subjects using genesor genetic information. SVMs are a relatively new type of learning algorithm. See, for example, Cristianini and Shawe-Taylor, 2000, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, Boser et al, 1992, "A training algorithm for optimal margin classifiers, in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, PA, pp. 142-152; Vapnik, 1998, Statistical Learning Theory, Wiley, New York. When used for classification, SVMs separate a given set of binary labeled training data with a hyper- plane that is [a maximal distance from each point using a fitting algorithm. For cases in which no linear separation is possible, SVMs can work in combination with the technique of 'kernels', which automatically realizes a non-linear mapping to a feature space. The hyper-plane found by the SVM in feature space corresponds to a non-linear decision boundary in the input space. In one approach, when a SVM is used, the gene expression data is standardized to have mean zero and unit variance and the members of a training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set. The expression values for a combination of genes is used to train the SVM. Then the ability for the trained SVM to correctly classify members in the test set is determined. In some embodiments, this computation is performed several times for a given combination of molecular markers. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of molecular markers is taken as the average of each such iteration of the SVM computation. For more information on SVMs, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc.; Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Furey et al, 2000, Bioinformatics 16, 906-914. 5.14.9 DECISION TREES In some embodiments of the present invention, decision trees are used to classify subjects using expression data for combinations of genes. Decision tree algorithms belong to the class of supervised learning algorithms. The aim of a decision tree is to induce a classifier (a tree) from real-world example data. This tree can be used to classify unseen examples which have not been used to derive the decision tree. A decision tree is derived from training data. An example contains values for the different attributes and what class the example belongs. In the present invention, the training data is expression data for a combination of genes across the training population. The following algorithm describes a decision tree derivation: Tree(Examples,Class, Attributes) Create a root node If all Examples have the same Class value, give the root this label Else if Attributes is empty label the root according to the most common value Else begin Calculate the information gain for each attribute Select the attribute A with highest information gain and make this the root attribute For each possible value, v, of this attribute Add a new branch below the root, corresponding to A = v Let Examples(v) be those examples with A = V If Examples(v) is empty, make the new branch a leaf node labeled with the most common value among Examples Else let the new branch be the tree created by Tree(Examples(v),Class,Attributes - {A}) end
A more detailed description of the calculation of information gain is shown in the following. If the possible classes Vj of the examples have probabilities P(vj) then the information content I of the actual answer is given by:
Figure imgf000143_0001
The I- value shows how much information we need in order to be able to describe the outcome of a classification for the specific dataset used. Supposing that the dataset contains p positive (e.g. has osteoarthritis) and n negative (e.g. healthy) examples (e.g. individuals), the information contained in a correct answer is:
rt P » N P 1no. _F UL-. W _-L_ p + n p + n p + n p + n p + n p + n
where log2 is the logarithm using base two. By testing single attributes the amount of information needed to make a correct classification can be reduced. The remainder for a specific attribute A (e.g. a gene) shows how much the information that is needed can be reduced.
Figure imgf000143_0002
"v" is the number of unique attribute values for attribute A in a certain dataset, "i" is a certain attribute value, "pi" is the number of examples for attribute A where the classification is positive (e.g. cancer), "n " is the number of examples for attribute A where the classification is negative (e.g. healthy). The information gain of a specific attribute A is calculated as the difference between the information content for the classes and the remainder of attribute A:
GainLA) = /(— ^- *— ^- ) -temaindβriΛ) p + n ' p + n
The information gain is used to evaluate how important the different attributes are for the classification (how well they split up the examples), and the attribute with the highest information. In general there are a number of different decision tree algorithms, many of which are described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc. Decision tree algorithms often require consideration of feature processing, impurity measure, stopping criterion, and pruning. Specific decision tree algorithms include, cut are not limited to classification and regression trees (CART), multivariate decision trees, ID3, and C4.5. In one approach, when a decision tree is used, the gene expression data for a select combination of genes described in the present invention across a training population is standardized to have mean zero and unit variance. The members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set. The expression values for a select combination of genes described in the present invention is used to construct the decision tree. Then, the ability for the decision tree to correctly classify members in the test set is determined. In some embodiments, this computation is performed several times for a given combination of molecular markers. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of molecular markers is taken as the average of each such iteration of the decision tree computation. 5.14.10 EVOLUTIONARY METHODS
Inspired by the process of biological evolution, evolutionary methods of classifier design employ a stochastic search for an optimal classifier. In broad overview, such methods create several classifiers - a population - from a combination of genes described in the present invention. Each classifier varies somewhat from the other. Next, the classifiers are scored on expression data across the training population. In keeping with the analogy with biological evolution, the resulting (scalar) score is sometimes called the fitness. The classifiers are ranked according to their score and the best classifiers are retained (some portion of the total population of classifiers). Again, in keeping with biological terminology, this is called survival of the fittest. The classifiers are stochastically altered in the next generation - the children or offspring. Some offspring classifiers will have higher scores than their parent in the previous generation, some will have lower scores. The overall process is then repeated for the subsequent generation: The classifiers are scored and the best ones are retained, randomly altered to give yet another generation, and so on. In part, because of the ranking, each generation has, on average, a slightly higher score than the previous one. The process is halted when the single best classifier in a generation has a score that exceeds a desired criterion value. More information on evolutionary methods is found in, for example, Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc.
5.14.11 BAGGING, BOOSTING AND THE RANDOM SUBSPACE METHOD
Bagging, boosting and the random subspace method are combining techniques that can be used to improve weak classifiers. These techniques are designed for, and usually applied to, decision trees. In addition, Skurichina and Duin provide evidence to suggest that such techniques can also be useful in linear discriminant analysis. In bagging, one samples the training set, generating random independent bootstrap replicates, constructs the classifier on each of these, and aggregates them by a simple majority vote in the final decision rule. See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron & Tibshirani, An Introduction to Boostrap, Chapman & Hall, New York, 1993. In boosting, classifiers are constructed on weighted versions of the training set, which are dependent on previous classification results. Initially, all objects have equal weights, and the first classifier is constructed on this data set. Then, weights are changed according to the performance of the classifier. Erroneously classified objects (molecular markers in the data set) get larger weights, and the next classifier is boosted on the reweighted training set. In this way, a sequence of training sets and classifiers is obtained, which is then combined by simple majority voting or by weighted majority voting in the final decision. See, for example, Freund & Schapire, "Experiments with a new boosting algorithm," Proceedings 13th International Conference on Machine Learning, 1996, 148- 156. To illustrate boosting, consider the case where there are two trait extremes exhibited by the population under study, extreme phenotype 1 (e.g., severe osteoarthritis), and extreme phenotype 2 (e.g., no osteoarthritis). Given a vector of predictor molecular marker X selected in step 214, a classifier G(X) produces a prediction taking one of the type values in the two value set: {extreme phenotype 1, extreme phenotype 2}. The error rate on the training sample is
Figure imgf000146_0001
where N is the number of subjects in the training set (the sum total of the subjects that have either extreme phenotype 1 or extreme phenotype 2). For example, if there are 49 organisms that have severe osteoarthritis and 72 organisms that have no osteoarthritis under study, N is 121. A weak classifier is one whose error rate is only slightly better than random guessing. In the boosting algorithm, the weak classification algorithm is repeatedly applied to modified versions of the data, thereby producing a sequence of weak classifiers Gm(x), m, = 1, 2, ..., M. The predictions from all of the classifiers in this sequence are then combined through a weighted majority vote to produce the final prediction:
Figure imgf000146_0002
Here α1? α2j ..., a^ are computed by the boosting algorithm and their purpose is to weigh the contribution of each respective Gm(x). Their effect is to give higher influence to the more accurate classifiers in the sequence.
The data modifications at each boosting step consist of applying weights W1, W2, ..., wn to each of the training observations (XJ, yj), i = 1, 2, ..., N. Initially all the weights are set to Wj = 1/N, so that the first step simply trains the classifier on the data in the usual manner. For each successive iteration m = 2, 3, ..., M the observation weights are individually modified and the classification algorithm is reapplied to the weighted observations. At stem m, those observations that were misclassifϊed by the classifier Gm- i(x) induced at the previous step have their weights increased, whereas the weights are decreased for those that were classified correctly. Thus as iterations proceed, observations that are difficult to correctly classify receive ever-increasing influence. Each successive classifier is thereby forced to concentrate on those training observations that are missed by previous ones in the sequence. The exemplary boosting algorithm is summarized as follows: 1. Initialize the observation weights Wj = 1/N, i = 1, 2, ..., N. 2. For m = 1 to M: (a) Fit a classifier Gm(x) to the training set using weights w,. (b) Compute
Figure imgf000147_0001
(c) Compute αm=log((l-errm)/errm). (d) Setw, < w, -expK -^y1 ≠ Gw(x, ))],* = 1,2,...,N.
3. Output G(x)
Figure imgf000147_0002
In the algorithm, the current classifier Gm(x) is induced on the weighted observations at line 2a. The resulting weighted error rate is computed at line 2b. Line 2c calculates the weight αm given to Gm(x) in producing the final classifier G(x) (line 3). The individual weights of each of the observations are updated for the next iteration at line 2d. Observations misclassified by Gm(x) have their weights scaled by a factor exp(αm), increasing their relative influence for inducing the next classifier Gm+^x) in the sequence. In some embodiments, modifications of the Freund and Schapire, 1997, Journal of Computer and System Sciences 55, pp. 119-139, boosting method are used. See, for example, Hasti et al., The Elements of Statistical Learning, 2001, Springer, New York, Chapter 10. In some embodiments, boosting or adaptive boosting methods are used. In some embodiments, modifications of Freund and Schapire, 1997, Journal of Computer and System Sciences 55, pp. 119-139, are used. For example, in some embodiments, feature preselection is performed using a technique such as the nonparametric scoring methods of Park et al., 2002, Pac. Symp. Biocomput. 6, 52-63. Feature preselection is a form of dimensionality reduction in which the genes that discriminate between classifications the best are selected for use in the classifier. Then, the LogitBoost procedure introduced by Friedman et al., 2000, Ann Stat 28, 337-407 is used rather than the boosting procedure of Freund and Schapire. In some embodiments, the boosting and other classification methods of Ben-Dor et al., 2000, Journal of Computational Biology 7, 559-583 are used in the present invention. In some embodiments, the boosting and other classification methods of Freund and Schapire, 1997, Journal of Computer and System Sciences 55, 119-139, are used. In the random subspace method, classifiers are constructed in random subspaces of the data feature space. These classifiers are usually combined by simple majority voting in the final decision rule. See, for example, Ho, "The Random subspace method for constructing decision forests," IEEE Trans Pattern Analysis and Machine Intelligence, 1998; 20(8): 832-844. 5.14.12 OTHER MATHEMATICAL MODELS
The pattern classification and statistical techniques described above are merely examples of the types of classifiers that can be used to construct a classifier. Moreover, combinations of the techniques described above can be used. Some combinations, such as the use of the combination of decision trees and boosting, have been described. However, many other combinations are possible. In addition, other techniques in the art such as Projection Pursuit and Weighted Voting can be used to construct classifiers in instances of step 216. 5.15 IMPLEMENTING THE INVENTION
The present invention provides methods and systems for screening molecular markers to identify classifiers and/or for identifying classifiers for a trait and allows for the configuration of classifiers based on combinations of a large number of molecular markers. The invention provides a selection process for reducing the potential large number of candidate molecular markers and/or combinations thereof down to a manageable number which can be evaluated in one or more mathematical models to derive one or more classifiers. Some embodiments of the invention are preferably implemented on a computer system having a processor and a memory unit. The embodiments may be implemented as one or more software programs operating on a general purpose computer, such as a personal computer or workstation, or as dedicated special purpose hardware components. The invention allows for an identification of classifiers while reducing the system requirements. It provides techniques for consideration of a large number of potential molecular markers in the classifier identification process with limited memory and computational requirements. Some embodiments of the invention provide a data-driven selection of a subset of the candidate molecular markers based on their discrimination ability. Thus, it becomes possible to start out with a large group of potentially interesting molecular markers and to automatically prune the set of candidate molecular markers so that the computer system can handle the classifier identification process more efficiently, i.e. within less processing time and less memory space. This becomes more important as combinations of the molecular markers are generated in the classifier identification process and mathematical models are applied to each combination to derive classifiers, which may be a computationally expensive process, in particular when iterative techniques are applied, such as clustering, decision trees, neural networks, or evolutionary methods. Since the possible number of combinations grows almost exponentially with the number of candidate molecular markers, the processing time for the classifier evaluation become a serious problem. The pruning of candidate molecular markers allows for the consideration of two, three, four and more combinations of candidate molecular markers as basis for the classifiers. It enables the evaluation of a large number of classifiers based on molecular markers showing a promising discrimination ability and supersedes a computer resource consuming evaluation of classifiers based on molecular markers which are likely not contributing to the final trait discrimination. Thus, the invention can be implemented on a computer having less computational power and memory while the quality of the derived classifiers is maintained. On the other hand, the invention allows for the consideration of more molecular markers, which are potentially interesting for a given application (trait), with the same available system resources resulting in a possible higher classification accuracy for the derived classifiers. The invention further provides a data-driven selection of the derived classifiers to remove classifiers and/or molecular markers which do not significantly contribute to the trait discrimination in the later application phase. This automatic pruning step evaluates the discrimination power of the individual classifiers to reduce the necessary system requirements of a diagnostic system applying the selected classifiers for the wide variety of possible medical applications. Thus, a diagnostic system configured with the identified classifiers needs less computational power and memory and may be implemented on a smaller, less expensive device. This becomes more important when the applied mathematical models are more complex and powerful. Examples of complex classifiers are described in section 5.14 of this description and include, e.g. neural networks, nearest neighbor classifiers, decision tress, etc. The invention enables the operation of optimized combinations of complex classifiers on diagnostic devises with limited resources. 5.16 EMBODIMENTS OF THE INVENTION
6. EXAMPLES
Computer systems, computer program products, methods, and kits for providing health care have been disclosed. What follows are select examples that illustrate the utility and value of the present invention. 6.1 EVALUATING OSTEOARTHRITIS CLASSIFIERS USING ROC CURVES
This example demonstrates the use of an embodiment of the invention to identify individuals with mild osteoarthritis. Osteoarthritis is a form of degenerative joint disease that involves the deterioration of and changes to the cartilage and bone. In response to inflammation in and about the joint, the body responds with bony recalcification around the joint structure. This process can be slow and gradual with minimal outward symptoms, or more rapidly progressive with significant pain and discomfort. Arthritic changes can occur in response to infection and injury of the joint as well. Step 202 - generation of a training population. Blood samples were taken from 44 test individuals not having any symptoms of osteoarthritis and 50 individuals having mild osteoarthritis using the methods described in Section 5.2. A molecular marker profile resulting in data for molecular marker products of the entire human genome was measured from each of these samples. This gene expression profile data together with knowledge of which subjects have osteoarthritis and which do not constitutes the training population 44. The 44 test individuals that do not have any symptoms of osteoarthritis constitute one trait subgroup within the training population and the 50 individuals having mild osteoarthritis constitute another trait subgroup within the training population. Steps 204-218. The training population collected in step 202 was used in order to identify combinations of genes that can serve as a classifier to differentiate mild osteoarthritis from non-osteoarthritis. Thus, the classifiers developed in this example are designed to yield a positive score when they predict that a subject has mild osteoarthritis and a negative score when they predict that the subject is in the control population. Using the approach described in Section 5.1, two specific classifiers were developed: 100000252 and 100000511. Classifier 100000252 comprises six genes and has the format: SCORE = -1.839 + 0.8*HSPCA - 1.5525*IKBKAP + 1.1O184*IL13RA1 + O.78923*LAMC1 - 1.3974*MAFB + 1.0602*PF4. Classifier 100000511 comprises nine genes and has the format: SCORE = -4.3754 + 0.10276*EGRl -1.1697*G2AN + 0.88767*HSPCA - 0.55785*IKBKAP + 0.94015*IL13RAl + O.67515*LAMC1 - 1.5068*MAFB + 1.0798*PF4 + 0.4007*TNFAIP6. Here, EGRl, G2AN, HSPCA, IKBKAP, IL13RA1, LAMCl, MAFB, and TNFAIP6 are genes that were identified in step 204 and validated in step 208 (Section 5.1) for their ability to discriminate between subjects that have mild osteoarthritis and subjects that do not have osteoarthritis. Step 220. To judge which classifier is more suitable as a classifier for mild osteoarthritis, a ROC curve was computed for both classifiers using the gene expression data from the 44 test individuals not having any symptoms of osteoarthritis and the 50 individuals having mild osteoarthritis. The results of the ROC computation are illustrated in Fig. 8. The area under each ROC was computed. From this computation, it was determined that the area under the ROC curve corresponding to classifier 100000252 was 0.863 whereas the area under the ROC curve corresponding to classifier 100000511 was 0.8169. Step 224. In some embodiments, a classifier can be constructed that includes both classifiers 100000252 and 100000511 using the voting methods described in Section 5.1. In alternative embodiments, classifier 100000252 is selected to serve as a classifier for mild osteoarthritis because it generated a larger area under the ROC curve corresponding to the classifier when tested against the training population. 6.2 IDENTIFIED MOLECULAR MARKERS AND MOLECULAR MARKER DATA MEASUREMENT TECHNIQUES
Molecular markers useful for input into one or more steps of the invention and techniques for measuring data values of such molecular markers, can be found in United States patent application serial No. 10/601,518, filed June 20, 2003, United States patent application serial No.10/802,875, filed March 12, 2004, United States patent application serial No. 10/809,675, filed March 25, 2004, United States patent application serial No. 10/268,730, filed October 9, 2002, United States patent application serial No. 09/477,148, filed January 4, 2000, United States patent application serial No. 60/115,125, filed Jan. 6, 1999; and United States patent application serial No. 60/581,977, filed June 21, 2004 each of which is hereby incorporated herein by reference in its entirety. 6.3 CONSTRUCTION OF CLASSIFIERS FOR MANIC DEPRESSION SYNDROME
This example demonstrates the use of the claimed invention to identify biomarkers to differentiate manic depression syndrome from non manic depression syndrome and use of same. As used herein, "manic depression syndrome" (MDS) refers to a mood disorder characterized by alternating mania and depression. Step 202. Blood samples were taken from patients who were diagnosed with manic depression as defined herein, hi each case, the diagnosis of manic depression was corroborated by a skilled Board certified physician. Molecular marker data was measured for each of the molecular markers of the entire human genome using blood samples from individuals who were identified as having manic depression as described herein and ndividuals not having manic depression. Molecular marker data for both trait subgroups were compared and gene expression profiles for each trait subpopulation compared using commercially available GeneSpring™ softwares. Hybridizations to create the gene expression profiles were done using Affymetrix® GeneChip® platforms (Ul 33 A and U133 Plus 2.0) as described herein (data not shown). Samples from patients were clustered into two trait subgroups. The first trait subgroup included patients who have manic depression and the second trait subgroup included patients who do not have manic depression (i.e., control individuals). Step 204. The Wilcox Mann Whitney rank sum test was used to identify molecular marker data that could discriminate between the control and diseased trait subgroups with a p value of < 0.05. Step 206. Molecular markers were selected from those identified with p value of < 0.05 and the ability to discriminate between the control and diseased trait subgroups were confirmed using quantitative RT-PCR. Steps 214-218. Eight candidate molecular markers were chosen and an exhaustive analysis of all possible combinations of said molecular markers were considered. Molecular marker data for each of the eight candidate molecular markers was obtained for each member of the training population and logistic regression applied to the molecular marker data so as to develop multiple classifiers. Each classifier was ranked on the basis of area under the curve and those classifiers with an ROC of greater than 0.9 chosen. 6.4 CONSTRUCTION OF CLASSIFIERS FOR PREDICTING RESPONSE TO TREATMENT
This example demonstrates the use of an embodiment of the invention to identify a classifier for predicting the response of a subject to treatment. Step 202. Blood samples are taken from patients (for example patients with a disease) who are going to enter into treatment (for the disease), or who are already undergoing treatment, but at a timepoint before being able to determine how the patients will respond to treatment. In one embodiment, blood samples are taken from patientswho are about to enter into a clinical trial for a new treatment, or who are in the early stages of a clinical trial. Preferably blood samples are processed so as to preserve the RNA and/or the protein products of the molecular markers. More preferably the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients. Subsequent to when the blood samples are taken, patients continue to be monitored for response to treatment using traditional diagnostic methods and grouped into trait subgroups on the basis of the response to treatment. For example, trait subgroups can include patients with a positive response and no negative side effects, patients with a positive response and mild side effects, patients with a negative response, patients with a toxic response, and the like. In some embodiments, the evaluation of response to treatment can take days, weeks, months or years. In some embodiments, data as described in step 202 of Section 5.1 and Section 5.3 is obtained upon processing of the blood sample. In other embodiments, data of step 202 is obtained only after a determination of response to treatment has been made. In all cases, molecular marker data is obtained from a blood sample taken at a timepoint prior to being able to determine response to treatment. Once trait subgroups are identified on the basis of response to treatment, gene expression profiles of blood samples of each trait subgroup are compared using GeneSpring™ software analysis. Hybridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U133A and U133 Plus 2.0) platforms (U133A and U133 Plus 2.0) as described herein. Samples from patients are clustered into two trait subgroups on the basis of the response to treatment. For example, one trait subgroup demonstrates a positive response to treatment whereas the second trait subgroup demonstrates a toxic response to treatment. Step 204. The Wilcox Mann Whitney rank sum test is used to identify candidate molecular markers by identifying molecular marker data that discriminates between the response and non-response trait subgroups with a p value of < 0.05 to obtain candidate molecular markers. Step 206. Additional molecular marker data for the candidate molecular markers are obtained using quantitative RT-PCR. Some candidate molecular markers are removed at this point should the quantitative RT-PCR data not confirm the ability of each candidate molecular marker to discriminate as between the response trait subgroups. Steps 214 - 218. Candidate molecular marker combinations are chosen and an exhaustive analysis of all possible combinations of molecular markers are tested. To test all possible combinations of molecular markers, logistic regression is applied to the molecular marker data so as to develop multiple classifiers. Each classifier is ranked on the basis of area under the curve using the training population. Those classifiers ranking with an ROC area under curve of greater than 0.9 are further evaluated using a scoring population which is not the training population. Note that the blood samples used for the scoring population are obtained at the same time point as the blood samples used for the training population (e.g., at a time prior to being able to determine response to treatment). 6.5 CONSTRUCTION OF CLASSIFIERS FOR DETERMINING A TRAIT OF INTEREST
6.5.1 This example demonstrates the selection of the composition of the training population and the trait subgroups of the training population so as to result in classifiers which are useful to predict disease.
Step 202.
In order to predict disease, blood samples are taken from patients at a time when said patients are disease free. Preferably blood samples are processed so as to preserve the RNA and/or the protein products of all molecular markers of the entire genome of said individual. More preferably the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients. Subsequent to when the blood samples are taken, patients continue to be monitored for development of said disease using traditional diagnostic methods. At a given time point, two trait subgroups are identified, namely individuals who develop said disease of interest and individuals who do not develop said disease of interest. In some embodiments, the timepoint at which trait subgroups are identified can take days, weeks, months or years. In some embodiments, data as described in step 202 of Section 5.1 and Section 5.3 is obtained upon processing of the blood sample. In other embodiments, data of step 202 is obtained only after a determination of trait subgroups has been made. In all cases, data is obtained from a blood sample taken at a timepoint prior to being able to determine disease. Once trait subgroups are identified gene expression profiles of the molecular marker data of the molecular marker products from the blood samples of each trait subgroup are compared using GeneSpring™ software analysis. Hybridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U133A and U133 Plus 2.0) platforms (U133A and U133 Plus 2.0) as described herein and candidate molecular markers are identified where the molecular marker data is able to differentiate as between said two trait subgroups with a p value of <0.05. Said candidate molecular markers are subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of predicting disease. 6.5.2 This example demonstrates the selection of the composition of the training population and the trait subgroups of the training population so as to result in classifiers which are useful to determine treatment compliance. Step 202. In order to determine treatment compliance, blood samples are taken from patients who are complying with said treatment of interest and patients who are not complying with said treatment. Preferably blood samples are processed so as to preserve the RNA and/or the protein corresponding to molecular markers of the entire genome of said individual. More preferably the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients. Molecular marker data is obtained as described in step 202 of Section 5.1 and Section 5.3 upon processing of the blood sample. Hybridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U 133 A and Ul 33 Plus 2.0) platforms (U133A and U133 Plus 2.0) as described herein and candidate molecular markers are identified which differentiate as between patients who comply with said treatment of interest as compared with patients who do not comply with said treatment of interest with a p value of 0.05. Said candidate molecular markers are subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of determining treatment compliance. 6.5.3 This example demonstrates the selection of the composition of the training population and the trait subgroups of the training population so as to result in classifiers which are useful to predict reoccurrence of disease. Step 202. In order to predict reoccurrence of disease, blood samples are taken from patients, all of whom have had a disease of interest, at a time when all of said patients are disease free. Preferably blood samples are processed so as to preserve the RNA and/or the protein corresponding to molecular markers of the entire genome of said individual. More preferably the blood samples are processed immediately, within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 10 hours, 12, hours, 18 hours or 24 hours from having taken the blood samples from the patients. Subsequent to when the blood samples are taken, patients continue to be monitored for reoccurence of said disease using traditional diagnostic methods. At a given time point, two trait subgroups are identified, namely individuals who develop reoccurrence of said disease of interest and individuals who do not develop said disease of interest. In some embodiments, the timepoint at which trait subgroups are identified can take days, weeks, months or years. In some embodiments, data as described in step 202 of Section 5.1 and Section 5.3 is obtained upon processing of the blood sample. In other embodiments, data of step 202 is obtained only after a determination of trait subgroups has been made. In all cases, data is obtained from a blood sample taken at a timepoint prior to being able to determine reoccurrence of disease. Once trait subgroups are identified molecular marker data of the molecular marker products from the blood samples of each trait subgroup are compared using GeneSpring™ software analysis. Hy bridizations to create the gene expression profiles are done using Affymetrix® GeneChip® platforms (U133A ana U133 FMs 2 !ϋ) platforms (U133A and U133 Plus 2.0) as described herein and candidate molecular markers are identified whose molecular marker data differentiates as between said two trait subgroups with a p value of <0.05. Said candidate molecular markers are subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of predicting reoccurrence of disease.
6.5.4 This example demonstrates the use of classifiers used in series so as to diagnose a patient with a stage of disease, for example a specific stage of osteoarthritis. We have identified previously four different stages of osteoarthritis; namely mild osteoarthritis, moderate osteoarthritis, marked osteoarthritis and severe osteoarthritis (see for example PCT patent application WO02070737 Entitled ""Compositions and Methods relating to osteoarthritis" In some instances it is useful to determine which stage of osteoarthritis an individual has, and more importantly to confirm that said patient does not have any other stage of osteoarthritis. For example if an individual has changed their lifestyle and lost weight -to determine whether the osteoarthritis has regressed. Classifiers of the invention are able to differentiate as between two subgroups. As such — multiple classifiers are required to specifically stage an individual. A first classifier is developed which differentiates as between osteoarthritis and non-osteoarthritis. As described, a training population is selected comprised of two trait subgroups where said first trait subgroup is comprised of individuals having osteoarthritis and the second trait subgroup comprised of individuals not having osteoarthritis. Identification of candidate molecular markers which differentiate as between these two trait subgroups are identified as per step 202 and subsequently processed as described in steps 206 to 226 to identify classifiers and molecular markers capable of differentiating between osteoarthritis and non-osteoarthritis. Similarly classifiers are identified which are capable of differentiating between (a) mild osteoarthritis and moderate osteoarthritis (b) moderate osteoarthritis and marked osteoarthritis (c) marked osteoarthritis and severe osteoarthritis. In order to diagnose an individual as having marked osteoarthritis and not having mild osteoarthritis or severe osteoarthritis, a series of tests are applied. First a classifier which determines whether said patient has osteoarthritis or not is applied. Assuming said patient has osteoarthritis, a classifier is applied which determines whether said patient has either mild osteoarthritis or marked osteoarthritis. Assuming said patient has marked osteoarthritis,"! cl'assϊKeFis 'applied to determine whether said patient has marked osteoarthritis or severe osteoarthritis. The result of these series of classifiers can determine that said patient has marked osteoarthritis and does not have any other stage of osteoarthritis.
6.6 CONSTRUCTION OF CLASSIFIERS FOR DETERMINING A TRAIT OF INTEREST (OR DIFFERENTIATING BETWEEN TWO TRAITS OF INTEREST) USING THE MOLECULAR MARKERS IDENTIFIED IN ONE OF THE DISCLOSED TABLES
While the examples described below suggest selection of molecular markers prior to generating all combinations of classifiers from the disclosed Tables can be based on a specific measure of statistical significance (p value) as disclosed for each molecular marker (see Table G and Table H) it must be appreciated that the selection of molecular markers may also be based on any other method disclosed in this application, such as differential fold change or even a combination of selection of p value and differential fold change. A skilled person in the art will recognize these other methods can also be used so as to permit the selection of subsets of the molecular markers to derive lists for which a reasonable number of combinations can be tested within the limits of the computer processing capacity. The skilled person will be able to transfer the details given in this section for examples based on a p value evaluation to carry out the selection based on other disclosed selection methods or selection methods known to him.
TABLE F
Figure imgf000158_0001
Figure imgf000159_0001
Figure imgf000160_0001
Figure imgf000161_0001
Figure imgf000162_0001
Figure imgf000163_0001
Figure imgf000164_0001
CONSTRUCTION OF CLASSIFIERS FOR DETERMINING A TRAIT OF INTEREST Steps 202-204 In order to identify useful classifiers for a trait of interest, for example mild osteoarthritis, one or more of the tables listed in Table F above which have the same recommended training population can be used. Thus for example, for mild osteoarthritis, one can select one or more of Tables IY; 4C; 4D. These molecular markers listed resulted from application of Steps 202-204 as outlined in Figure 2 A as more fully described for each Table herein. Once one or more Tables have been selected, it is helpful to select a subset of the molecular markers in the Table or Tables before proceeding to step 206. For example, combining Tables IY, 4C and 4D and selecting molecular markers where the molecular marker data demonstrates an ability to differentiate as between the two trait subgroups with a p value of less than 0.0001 results in 212 molecular markers. Note that p values resulting from the molecular marker data for each molecular marker identified in any of Tables IA to 71 can be found in Tables 8 A or Tables 8B below. Table 8 A identifies molecular markers via the Clone ID of the probe used to hybridize to the molecular marker products. The Clone ID corresponds to the Clone ID found in tables IA; 1 AC; IB; 1C; ID; IE; IF; IG; IH; II; U; IK; IL; IM; IN; 10; IP; IQ; IR; IV; IX; IY; IZ; 2; 4A; 4C; 4E; 4G; 41; 4K; 4M; 40; 4Q; 4S; 4V; 7A; 7B; 7C; 7D; 7E; 7H; and 71 (ie those Tables generated using the ChondroChip™ as outlined herein). Table 8A then identifies the corresponding Table in which the molecular market- is identified via said Clone ID. Finally the p value of the molecular marker data obtained using the ChondroChip™ is listed. Note that Table 8A is sorted first by Table number and then by p value.
Table 8B identifies molecular markers via the Affymetrix® Spot ID of the probe pair used to hybridize to the molecular marker products. The Affy Spot ID corresponds to the Affy Spot ID found in tables Tables IAA; IAB; IAD; IAE; IAG; IAH; IAT; IS; IT; IU; IW; lZ(b); 3A; 3B; 3C; 3D; 3E; 3F; 3G; 3H; 31; 3K; 3L; 3P; 3Q; 3R; 4B; 4D; 4F; 4H; 4J; 4L; 4N; 4P; 4R; 4T; 4V; 5 A; 5B; 5C; 5D; 5EE; 5F; 5G; 5H; 51; 5 J; 5K; 5L; 5M; 5N; 50; 5P 5Q; 5R; 5S; 5T; 5U; 5V; 6A; 6B; 6C; 7F; and 7G (ie those Tables generated using the Affymetrix™ Gene Chip as outlined herein). Table 8B then identifies the corresponding Table in which the molecular marker is identified via said Affymetrix® Spot ID. Finally the p value of the molecular marker data obtained using the ChondroChip™ is listed. Note that Table 8B is sorted first by Table number and then by p value. Step 206
For the 212 selected molecular markers - a training population is chosen having two trait subgroups where the two trait subgroups are outlined in Table F above as corresponding to the Tables used to select the molecular markers, thus in this example, the first trait subgroup is members having mild osteoarthritis and the second trait subgroup is members not having osteoarthritis. A blood sample from each member of the training population is obtained and processed using techniques as described herein and mRNA isolated. The resulting mRNA is reverse transcribed using ABFs High Capacity cDNA Archive Kit and the cDNA is then used for quantitative RT-PCR so as to collect molecular marker data for application to a logistic regression model. Amplification primers are designed for each of the 212 molecular markers. Preferably primers are chosen which amplify across an intron junction. Quantitative Real Time PCR is performed using Qiagen's QuantiTect™Sybr Green RT-PCR kit and data corresponding to the level of RNA for each of the molecular markers Steps 214 - 218. From the 212 candidate molecular markers selected, an exhaustive analysis of all possible combinations of molecular markers using the molecular marker data obtained using quantitative RT-PCR data is tested using logistic regression so as to develop multiple classifiers. Each classifier is ranked on the basis of area under the curve using the training population. Those classifiers ranking with an ROC area under curve of greater than 0.8 are further evaluated using a scoring population which is not the training population. Those classifiers resulting in an ROC area under the curve of greater than 0.7 as determined using the scoring population are selected. Each of The selected ciassifϊeia isc-omprjsed of a combination of molecular markers from one of Tables IY, 4C and 4D. USE OF THE SELECTED CLASSIFIER TO DIAGNOSE AN INDIVIDUAL AS HAVING MILD OSTEOARTHRITIS. Any of the selected classifiers can be used to diagnose an individual as having mild osteoarthritis. A blood sample from a test individual is processed using techniques as described herein to isolate mRNA. mRNA is reverse transcribed using ABFs High Capacity cDNA Archive Kit and the cDNA is then used for quantitative RT-PCR. Amplification primers are designed for each of the molecular markers in the classifier selected which amplify across an intron junction. Quantitative Real Time PCR is performed using Qiagen's QuantiTect™Sybr Green RT-PCR kit and data corresponding to the level of RNA for each of the molecular markers of the selected classifier obtained. The data is then used in conjunction with the logistic regression classifier so as to convert the data resulting from the Quantitative RT-PCR into a single number. If the number is greater than 0 - the test individual is diagnosed as having mild osteoarthritis is the number is less than 0 the test individual is diagnosed as not having osteoarthritis. 7. REFERENCES CITED All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes. The present invention can be implemented as a computer program product that comprises a computer program mechanism embedded in a computer readable storage medium. For instance, the computer program product could contain the program modules shown in Fig. 1. These program modules may be stored on a CD-ROM, DVD, magnetic disk storage product, or any other computer readable data or program storage product. The software modules in the computer program product can also be distributed electronically, via the Internet or otherwise, by transmission of a computer data signal (in which the software modules are embedded) on a carrier wave. Many modifications and variations of this invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only, and the invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. Table BA
Clone ID TableNo. p-value Clone ID TableNO. P Value
miob8143 IA 2.14e-05 fcrb4231 IA 3.784194e-03 ncrc5844 IA 1.135βe-04 seob0755 IA 3.784194e-03 hfcr3149 IA 1.30609e-04 seocl023 IA 3.784194e-03 fcrb3330 IA 1.71826e-04 fcrb4995 IA 4.164516e-03 ncrc9772 IA 1.71826e-04 fcrb6031 IA 4.164516e-03 fcrb2162 IA 2.24449e-04 fcrb6896 IA 4.164516e-03 ncrb8343 IA 2.55864e-04 fcrcl689 IA 4.164516e-03 fcrb4226 IA 2.91185e-04 fcrc6228 IA 4.164516e-03 seoc6182 IA 2.91185e-04 ncr7672 IA 4.164516e-03 seoa3408 IA 3.75265e-04 ncrcl885 IA 4.164516e-03 fcrb6191 IA 4.80528e-04 ncrc3544 IA 4.164516e-03 miocl028 IA 4.80528e-04 seoa2381 IA 4.164516e-03 seob2966 IA 5.4249e-04 seob9872 IA 4.164516e-03 seoc0775 IA 5.4249e-04 fcrb2090 IA 4.577725e-03 fcrcl834 IA 7.73507e-04 hfcrO489 IA 4.577725e-03 ncrc3257 IA 7.88975e-04 miodl448 IA 4.577725e-03 ncrc2472 IA 8.68041e-04 ncr7813 IA 4.577725e-03 mioc3930 IA 1.216349e-03 ncrc9712 IA 4.577725e-03 ncrO847 IA 1.216349e-03 ncrc9855 IA 4.577725e-03 miob8947 IA 1.253946e-03 seoa0256 IA 4.577725e-03 fcrb5214 IA 1.357347e-03 seoa3555 IA 4.577725e-03 miob4037 IA 1.512638e-03 mioa0601 IA 4.9929e-03 miob8932 IA 1.512638e-03 fcrb2713 IA 5.026153e-03 fcrcO166 IA 1.683445e-03 mioal704 IA 5.026153e-03 miob8249 IA 1.683445e-03 miob6419 IA 5.026153e-03 seob3485 IA 1.683445e-03 raioc2997 IA 5.026153e-03 seob7929 IA 1.683445e-03 mioc3127 IA 5.026153e-03 seob0154 IA 1.871074e-03 ncr7904 IA 5.026153e-03 fcrc6345 IA 2.076917e-03 ncrc5088 IA 5.026153e-03 mioa5059 IA 2.076917e-03 seoa2641 IA 5.026153e-03 fcrb4378 IA 2.302455e-03 seoa3359 IA 5.026153e-03 miod4066 IA 2.302455e-03 seob5069 IA 5.026153e-03 ncrc5091 IA 2.302455e-03 seob5213 IA 5.026153e-03 mioc0162 IA 2.452971e-03 ncrl948 IA 5.341175e-03 fcrc5516 IA 2.549264e-03 fcrb9324 IA 5.512248e-03 miob4308 IA 2.549264e-03 fcrc6990 IA 5.512248e-03 seobl319 IA 2.549264e-03 miocl598 IA 5.512248e-03 fcrl997 IA 2.819017e-03 seob4752 IA 5.512248e-03 fcr4471 IA 2.819017e-03 mioa0528 IA 6.038584e-03 ncrO615 IA 2.819017e-03 mioc2074 IA 6.038584e-03 ncr9549 IA 2.819017e-03 miod5703 IA 6.038584e-03 ncrcO729 IA 2.819017e-03 ncrc3391 IA 6.038584e-03 fcrb4572 IA 3.113486e-03 seob0168 IA 6.038584e-03 fcrb9655 IA 3.113486e-03 fcrb4345 IA 6.607863e-03 ncrb6320 IA 3.113486e-03 mioa2377 IA 6.607863e-03 seoa9709 IA 3.113486e-03 miocll22 IA 6.607863e-03 seoa9870 IA 3.113486e-03 ncrc9637 IA 6.607863e-03 seob2959 IA 3.113486e-03 seob9152 IA 6.607863e-03 seocl025 IA 3.113486e-03 fcrbl691 IA 7.222914e-03 fcr4214 IA 3.43455e-03 fcrb5928 IA 7.222914e-03 fcrb3897 IA 3.43455e-03 fcrcβOlO IA 7.222914e-03 fcrb6508 IA 3.43455e-03 fcrc6566 IA 7.222914e-03 fcrb7944 IA 3.43455e-03 hfcr4477 IA 7.222914e-03 fcrc2807 IA 3.43455e-03 miob8691 IA 7.222914e-03 ncrc5780 IA 3.43455e-03 miod2330 IA 7.222914e-03 seoa0429 IA 3.43455e-03 ncr3037 IA 7.222914e-03 seoal977 IA 3.43455e-03 ncrc9700 IA 7.222914e-03 fcr3559 IA 3.784194e-03 seoa9889 IA 7.222914e-03 se6b8"3ΪΪ IA' r."2"22"9"l4e-03 miod7421 IA 0.01 fcrb7584 IA 7.288639e-03 ncrOOlβ IA 0.01 fcrb5204 IA 7.886697e-03 ncrb8385 IA 0.01 fcrc5614 IA 7.886697e-03 ncrc5949 IA 0.01 mioc7331 IA 7.886697e-03 seoa2805 IA 0.01 mioc8682 IA 7.886697e-03 seoa6620 IA 0.01 ncrO212 IA 7.886697e-03 seob3141 IA 0.01 ncr7292 IA 7.886697e-03 fcr4639 IA 0.01 ncrb4331 IA 7.886697e-03 fcrb3237 IA 0.01 ncrb5595 IA 7.886697e-03 fcrb3244 IA 0.01 seoa6654 IA 7.886697e-03 fcrb4981 IA 0.01 seob4545 IA 7.886697e-03 fcrb5850 IA 0.01 seob8301 IA 7.886697e-03 fcrb9686 IA 0.01 seocl664 IA 7.886697e-03 fcrb9959 IA 0.01 fcrl312 IA 8.602308e-03 hfcrlδll IA 0.01 fcrb2041 IA 8.602308e-03 miob3953 IA 0.01 mioa5085 IA 8.602308e-03 mioc0669 IA 0.01 ncrc8892 IA 8.602308e-03 ncrc2119 IA 0.01 seoal584 IA 8.602308e-03 ncrc4575 IA 0.01 seoa5577 IA 8.602308e-03 ncrc6127 IA 0.01 seob5319 IA 8.602308e-03 ncrc6382 IA 0.01 seob6386 IA 8.602308e-03 seoc0778 IA 0.01 seob8741 IA 8.602308e-03 seoc0924 IA 0.01 fcrl879 IA 9.372976e-03 ncrcl892 IA 0.01 fcrbl457 IA 9.372976e-03 fcrO593 IA 0.01 fcrb3644 IA 9.372976e-03 fcrb3715 IA 0.01 fcrb4388 IA 9.372976e-03 mioc4420 IA 0.01 mioa8851 IA 9.372976e-03 miod3920 IA 0.01 mioa8852 IA 9.372976e-03 ncr6343 IA 0.01 miocl060 IA 9.372976e-03 seob5209 IA 0.01 miocl910 IA 9.372976e-03 ncrl876 IA 0.01 miod6437 IA 9.372976e-03 miod3417 IA 0.01 ncr6072 IA 9.372976e-03 fcr4212 IA 0.01 seoa8399 IA 9.372976e-03 fcrb4413 IA 0.01 seob4197 IA 9.372976e-03 fcrb5305 IA 0.01 seob9092 IA 9.372976e-03 fcrb9253 IA 0.01 fcrbl689 IA 0.01 hfcrll89 IA 0.01 fcrbl990 IA 0.01 miod4464 IA 0.01 fcrb8542 IA 0.01 ncr3614 IA 0.01 seoa9705 IA 0.01 ncrc3536 IA 0.01 seob0688 IA 0.01 ncrc9562 IA 0.01 seob6279 IA 0.01 seoa3105 IA 0.01 seoc3965 IA 0.01 seob0085 IA 0.01 fcr3664 IA 0.01 seob3513 IA 0.01 fcr3717 IA 0.01 seob5044 IA 0.01 fcrb3686 IA 0.01 seob5336 IA 0.01 fcrb5813 IA 0.01 fcrb4985 IA 0.01 fcrb6012 IA 0.01 hfcr6634 IA 0.01 fcrb7803 IA 0.01 mioa5586 IA 0.01 fcrb8989 IA 0.01 mioa9831 IA 0.01 mioc6937 IA 0.01 miob6536 IA 0.01 ncrb8437 IA 0.01 miob7554 IA 0.01 ncrc5631 IA 0.01 mioc0728 IA 0.01 seoa2448 IA 0.01 mioc0904 IA 0.01 seoa8543 IA 0.01 mioc3040 IA 0.01 seob6131 IA 0.01 miod5627 IA 0.01 seob6272 IA 0.01 miod6731 IA 0.01 seob9406 IA 0.01 ncr2472 IA 0.01 seob9851 IA 0.01 ncrc3799 IA 0.01 fcr7042 IA 0.01 seob0879 IA 0.01 fcrb4799 IA 0.01 seob2797 IA 0.01 fcrc6877 IA 0.01 seob3112 IA 0.01 mioa9649 IA 0.01 seocll89 IA 0.01 miocll25 IA 0.01 fcr5625 IA 0.01 fcfb-ftfeϊ""•""•■■'ΪΆ ■*-'••-■oTbi mioa6913 IA 0.02 hfcr6501 IA 0.01 miob5646 IA 0.02 miob6702 IA 0.01 mioc0528 IA 0.02 ncr3815 IA 0.01 miocl768 IA 0.02 seoa9777 IA 0.01 miod3592 IA 0.02 seob4925 IA 0.01 ncrl780 IA 0.02 seob8321 IA 0.01 ncrc6795 IA 0.02 mioc7084 IA 0.01 seoa6393 IA 0.02 fcrb4400 IA 0.01 seoa7126 IA 0.02 fcrb5550 IA 0.01 seob0497 IA 0.02 fcrb6968 IA 0.01 seob5004 IA 0.02 fcrcO241 IA 0.01 seoc2191 IA 0.02 fcrcl781 IA 0.01 hfcr3436 IA 0.02 mioa6135 IA 0.01 fcrb7240 IA 0.02 ncr8413 IA 0.01 fcrb3704 IA 0.02 seob8065 IA 0.01 fcrb3848 IA 0.02 seob9898 IA 0.01 fcrb5467 IA 0.02 seoc0499 IA 0.01 fcrb6734 IA 0.02 ncrO258 IA 0.02 fcrc4848 IA 0.02 ncrO179 IA 0.02 fcrc7228 IA 0.02 fcr4902 IA 0.02 hfcr4741 IA 0.02 fcrbl529 IA 0.02 miob3845 IA 0.02 fcrb2044 IA 0.02 mioc0301 IA 0.02 fcrb3896 IA 0.02 miocl354 IA 0.02 fcrb4890 IA 0.02 mioc5695 IA 0.02 fcrb6033 IA 0.02 miod0592 IA 0.02 fcrb6432 IA 0.02 ncr8156 IA 0.02 fcrc5402 IA 0.02 ncrc3377 IA 0.02 hfcrll41 IA 0.02 seoa5977 IA 0.02 mioa8778 IA 0.02 seoa7212 IA 0.02 miob7373 IA 0.02 seobl526 IA 0.02 miob8572 IA 0.02 seoc0945 IA 0.02 miob9130 IA 0.02 mioc0899 IA 0.02 miob9788 IA 0.02 ncr9003 IA 0.02 miocl203 IA 0.02 seoa7530 IA 0.02 ncr0808 IA 0.02 fcrb2207 IA 0.02 ncrc9633 IA 0.02 fcrb6102 IA 0.02 seoa6172 IA 0.02 hfcr0501 IA 0.02 seoa9724 IA 0.02 rαioa5447 IA 0.02 seob0253 IA 0.02 miob3809 IA 0.02 seob0288 IA 0.02 miob8531 IA 0.02 seob5478 IA 0.02 miob8802 IA 0.02 hfcr3160 IA 0.02 miob9671 IA 0.02 fcr3323 IA 0.02 mioc6156 IA 0.02 fcr5425 IA 0.02 ncrb4339 IA 0.02 fcr7295 IA 0.02 ncrb8451 IA 0.02 fcrbl428 IA 0.02 ncrcO217 IA 0.02 fcrb5389 IA 0.02 seoa0469 IA 0.02 fcrc2099 IA 0.02 seoallOO IA 0.02 miob3131 IA 0.02 seoa4066 IA 0.02 mioc2592 IA 0.02 seoa9389 IA 0.02 mioc8153 IA 0.02 fcrb9671 IA 0.03 miod4938 IA 0.02 fcrc5577 IA 0.03 miod5122 IA 0.02 hfcrO285 IA 0.03 miod5785 IA 0.02 mioa2073 IA 0.03 seoa6364 IA 0.02 miob9020 IA 0.03 seoa9150 IA 0.02 mioc2546 IA 0.03 seob4192 IA 0.02 mioc3139 IA 0.03 seob6758 IA 0.02 mioc7986 IA 0.03 mioc0852 IA 0.02 miodl316 IA 0.03 fcrb2592 IA 0.02 miod7337 IA 0.03 fcrb9680 IA 0.02 ncr3811 IA 0.03 hfcr4007 IA 0.02 ncr9975 IA 0.03 mioa5131 IA 0.02 ncrb4474 IA 0.03 ricrc5'653 IA ■<rtf3 fcrb2350 IA 0.03 ncrc6264 IA 0.03 fcrb5202 IA 0.03 seoa4802 IA 0.03 fcrb9636 IA 0.03 seoa5554 IA 0.03 fcrc4916 IA 0.03 seoa6497 IA 0.03 hfcrO237 IA 0.03 seoa8851 IA 0.03 mioa4552 IA 0.03 seob0047 IA 0.03 mioa6442 IA 0.03 seob0885 IA 0.03 miob5675 IA 0.03 fcrb3288 IA 0.03 miob6372 IA 0.03 fcrb4270 IA 0.03 miob8532 IA 0.03 fcrb4415 IA 0.03 miob8657 IA 0.03 hfcr5905 IA 0.03 mioc3208 IA 0.03 mioa4229 IA 0.03 miod4083 IA 0.03 miob9065 IA 0.03 miod6029 IA 0.03 miocl940 IA 0.03 ncr0097 IA 0.03 miod6685 IA 0.03 ncr2812 IA 0.03 ncrO836 IA 0.03 ncr3960 IA 0.03 ncr3112 IA 0.03 ncr5651 IA 0.03 ncr4647 IA 0.03 ncr9779 IA 0.03 ncr6408 IA 0.03 ncrcl871 IA 0.03 ncrb8105 IA 0.03 ncrcl889 IA 0.03 ncrc9642 IA 0.03 seoa5685 IA 0.03 ncrc9739 IA 0.03 seoa7223 IA 0.03 seoal540 IA 0.03 seoa9959 IA 0.03 seoa3429 IA 0.03 seob7649 IA 0.03 seoa3515 IA 0.03 fcrb6359 IA 0.03 seoa4452 IA 0.03 fcrb7237 IA 0.03 seoa5787 IA 0.03 fcrb8485 IA 0.03 seoa6598 IA 0.03 fcrcl849 IA 0.03 seobl844 IA 0.03 fcrc2536 IA 0.03 seob2195 IA 0.03 fcrc3993 IA 0.03 seob3533 IA 0.03 hfcr2616 IA 0.03 seoc2246 IA 0.03 hfcr4423 IA 0.03 ncr8153 IA 0.03 mioal388 IA 0.03 fcrb2133 IA 0.03 mioa9555 IA 0.03 fcrb2849 IA 0.03 mioa9581 IA 0.03 fcrb5929 IA 0.03 miob0931 IA 0.03 fcrb6201 IA 0.03 miob9495 IA 0.03 fcrcll81 IA 0.03 miocll35 IA 0.03 fcrcl858 IA 0.03 miocl438 IA 0.03 fcrc2670 IA 0.03 mioc2451 IA 0.03 fcrc4033 IA 0.03 mioc3671 IA 0.03 mioa8747 IA 0.03 mioc4119 IA 0.03 miob8773 IA 0.03 miod5258 IA 0.03 miob9128 IA 0.03 ncr2995 IA 0.03 mioc0315 IA 0.03 ncrb3957 IA 0.03 mioc0472 IA 0.03 ncrc3735 IA 0.03 mioc2662 IA 0.03 seoa2385 IA 0.03 mioc4994 IA 0.03 seoa3670 IA 0.03 ncr6142 IA 0.03 seoa3811 IA 0.03 ncrc5508 IA 0.03 seobl572 IA 0.03 ncrc6423 IA 0.03 seob7946 IA 0.03 ncrc6888 ' IA 0.03 seob9334 IA 0.03 seoa5911 IA 0.03 fcrb4515 IA 0.04 seob3517 IA 0.03 fcrb6028 IA 0.04 seob6206 IA 0.03 fcrc4380 IA 0.04 seob7907 IA 0.03 fcrc5007 IA 0.04 seoc0149 IA 0.03 fcrc5850 IA 0.04 seoc0617 IA 0.03 fcrc5898 IA 0.04 fcrbl890 IA 0.03 mioa3331 IA 0.04 seobl956 IA 0.03 mioa3392 IA 0.04 miob2756 IA 0.03 mioa4076 IA 0.04 fcrb2198 IA 0.03 mioa4484 IA 0.04 fcrb2256 IA 0.03 miob3042 IA 0.04 .,... ,((;r;tr4 miόb^β'Sβ"'" ΪA' seoc4316 IA 0.04 miob7209 IA 0.04 fcrc5506 IAC 6.56e-05 mioc4731 IA 0.04 fcrb7072 IAC 1.6e-04 ncr2363 IA 0.04 fcrb9202 IAC 2.5e-04 ncr8628 IA 0.04 fcrb2252 IAC 4.03e-04 ncrb2247 IA 0.04 miob2601 IAC 4.21e-04 ncrb4182 IA 0.04 ncrc7085 IAC 4.84e-04 ncrc3416 IA 0.04 miob9073 IAC 5.8e-04 seoal575 IA 0.04 seoc2248 IAC 6.37499e-04 seob0752 IA 0.04 seoc2682 IAC 8.42e-04 seobl423 IA 0.04 miob5885 IAC 8.48e-04 seob5099 IA 0.04 ncrbll86 IAC 1.13586e-03 seob7346 IA 0.04 fcrb9147 IAC 1.49537e-03 fcrl984 IA 0.04 seob0846 IAC 1.62274e-03 fcrb2334 IA 0.04 seoal812 IAC 1.82148e-03 fcrb8697 IA 0.04 mioa5955 IAC 2.06855e-03 fcrb9202 IA 0.04 ncrcO672 IAC 2.23855e-03 fcrc6305 IA 0.04 fcrb3040 IAC 2.56056e-03 hfcr2250 IA 0.04 fcrb6188 IAC 2.56177e-03 hfcr2314 IA 0.04 fcrc4948 IAC 2.61225e-03 hfcr3660 IA 0.04 seocl631 IAC 2.71926e-03 mioa5404 IA 0.04 ncrc6994 IAC 2.82137e-03 miob2833 IA 0.04 mioal701 IAC 2.82574e-03 miod2832 IA 0.04 ncrc9343 IAC 2.83011e-03 ncr5621 IA 0.04 hfcr2930 IAC 3.28473e-03 ncrb4248 IA 0.04 seoa9740 IAC 3.80488e-03 ncrc3598 IA 0.04 ncrc5919 IAC 3.81434e-03 seoa4327 IA 0.04 fcrc7051 IAC 3.82487e-03 seoa7094 IA 0.04 seoa3322 IAC 3.82543e-03 seoa9916 IA 0.04 seob4216 IAC 4.4181e-03 seob0700 IA 0.04 mioc5664 IAC 4.42431e-03 seobl345 IA 0.04 hfcrO521 IAC 4.76360e-03 seob4191 IA 0.04 mioa2475 IAC 4.76360e-03 seoc4720 IA 0.04 seobl660 IAC 4.76360e-03 ncrO291 IA 0.04 fcrb2054 IAC 4.90887e-03 fcr3936 IA 0.04 miob4441 IAC 5.11853e-03 fcr5190 IA 0.04 seob5778 IAC 5.14747e-03 fcrbl399 IA 0.04 miod0195 IAC 5.19208e-03 fcrbl523 IA 0.04 miocl910 IAC 5.43168e-03 fcrb5702 IA 0.04 mioa8622 IAC 5.48183e-03 fcrb7051 IA 0.04 seob2085 IAC 5.48183e-03 fcrb9430 IA 0.04 miod3579 IAC 5.84188e-03 fcrcO597 IA 0.04 fcrbl697 IAC 5.88330e-03 fcrcl879 IA 0.04 mioa6585 IAC 5.89493e-03 fcrc4658 IA 0.04 seoa6495 IAC 5.89880e-03 hfcr3134 IA 0.04 mioa6991 IAC 5.9049e-03 miob0939 IA 0.04 ncrb5737 IAC 6.09961e-03 miob2656 IA 0.04 ncrc5663 IAC 6.16618e-03 miob3690 IA 0.04 ncrcl495 IAC 6.31851e-03 mioc8635 IA 0.04 seoa5838 IAC 6.31851e-03 ncr3834 IA 0.04 mioa9604 IAC 6.43571e-03 ncrb0602 IA 0.04 hfcr2963 IAC 6.54225e-03 ncrb2282 IA 0.04 fcrb3595 IAC 6.65596e-03 ncrc9557 IA 0.04 seoc3511 IAC 6.77289e-03 seoal644 IA 0.04 miod5310 IAC 6.78152e-03 seoa3761 IA 0.04 mioal417 IAC 7.24653e-03 seoa5214 IA 0.04 ncr3763 IAC 7.46504e-03 seoa5637 IA 0.04 seoc5209 IAC 7.50804e-03 seoa6175 IA 0.04 fcrc6989 IAC 7.55113e-03 seob0133 IA 0.04 seob7755 IAC 8.04089e-03 seobl908 IA 0.04 seob3163 IAC 8.19449e-03 seob3367 IA 0.04 miobll26 IAC 8.32195e-03 seob8742 IA 0.04 fcrb3012 IAC 8.87793e-03 seoc0284 IA 0.04 ncrc2919 IAC 8.87793e-03 miδa'1337""" """1IXt ""•"*"■tr:899ϊ0e-03 seob8873 IAC 0.01 seob4333 IAC 8.89910e-03 fcrl853 IAC 0.01 ncr2930 IAC 9.25216e-03 seoa5994 IAC 0.01 fcr3277 IAC 9.43613e-03 seoa5833 IAC 0.01 mioa4077 IAC 9.48849e-03 seoa5766 IAC 0.01 fcrb2457 IAC 9.49961e-03 seoa0418 IAC 0.01 seob6572 IAC 9.54268e-03 mioa0132 IAC 0.01 fcrb2754 IAC 9.56817e-03 fcrb9289 IAC 0.01 fcrbl767 IAC 9.66528e-03 seob2974 IAC 0.01 fcrb6698 IAC 0.01 ncrb3424 IAC 0.01 seoa8684 IAC 0.01 fcrc4005 IAC 0.01 fcrb6460 IAC 0.01 miob3174 IAC 0.01 ncrb6282 IAC 0.01 seob0815 IAC 0.01 ncrc5039 IAC 0.01 fcrb5393 IAC 0.01 mioa3997 IAC 0.01 ncrc3036 IAC 0.01 mioc2602 IAC 0.01 ncrc3129 IAC 0.01 seoa3002 IAC 0.01 mioa6240 IAC 0.01 fcrcO456 IAC 0.01 ncrc6480 IAC 0.01 fcrb7063 IAC 0.01 ncr3727 IAC 0.02 fcrb9909 IAC 0.01 fcrb7650 IAC 0.02 seob8333 IAC 0.01 ncrb5837 IAC 0.02 miod3501 IAC 0.01 fcrb9280 IAC 0.02 seoa0931 IAC 0.01 seob3105 IAC 0.02 fcrcO471 IAC 0.01 ncr4535 IAC 0.02 miocl590 IAC 0.01 miocl963 IAC 0.02 mioa9258 IAC 0.01 fcrb6776 IAC 0.02 seob3317 IAC 0.01 mioal015 IAC 0.02 fcrb3476 IAC 0.01 seob3887 IAC 0.02 mioa3963 IAC 0.01 seoa7647 IAC 0.02 seob9300 IAC 0.01 seoa6598 IAC 0.02 fcr2842 IAC 0.01 mioa3514 IAC 0.02 seoa2633 IAC 0.01 seob0228 IAC 0.02 ncr0570 IAC 0.01 seoblδlδ IAC 0.02 miob7836 IAC 0.01 miod.1323 IAC 0.02 ncrb6073 IAC 0.01 mioc3603 IAC 0.02 seob6395 IAC 0.01 miob8578 IAC 0.02 miob2700 IAC 0.01 mioa8740 IAC 0.02 fcr7656 IAC 0.01 fcrcO921 IAC 0.02 ncrc4089 IAC 0.01 miob9124 IAC 0.02 fcrb8505 IAC 0.01 seoa0420 IAC 0.02 seoa6654 IAC 0.01 fcr4357 IAC 0.02 seob4835 IAC 0.01 fcrb8664 IAC 0.02 fcrb4241 IAC 0.01 seob3191 IAC 0.02 fcrcl563 IAC 0.01 fcrb6779 IAC 0.02 fcrc4051 IAC 0.01 fcrb8940 IAC 0.02 fcrc7219 IAC 0.01 hfcr2984 IAC 0.02 fcrb2321 IAC 0.01 seoa5933 IAC 0.02 seoc4470 IAC 0.01 fcr3539 IAC 0.02 ncr2869 IAC 0.01 miob8454 IAC 0.02 fcrb5202 IAC 0.01 mioc7904 IAC 0.02 ncrb8820 IAC 0.01 ncr3397 IAC 0.02 seob3158 IAC 0.01 seocl311 IAC 0.02 seoa5992 IAC 0.01 seob4793 IAC 0.02 fcrb2198 IAC 0.01 miob4055 IAC 0.02 fcr4746 IAC 0.01 mioa4285 IAC 0.02 ncrδδll IAC 0.01 ncrc2796 IAC 0.02 mioa3282 IAC 0.01 hfcrO786 IAC 0.02 ncrb5060 IAC 0.01 miob2645 IAC 0.02 seob2185 IAC 0.01 miob2968 IAC 0.02 fcrbl930 IAC 0.01 fcr2218 IAC 0.02 fcrb7215 IAC 0.01 mioc0940 IAC 0.02 fcrcO892 IAC 0.01 fcrcO355 IAC 0.02 fcrc4307 IAC 0.01 seocll80 IAC 0.02 mioa5452 IAC 0.01 fcrb8383 IAC 0.02 Ϋcrc2576 '-rkc ■h-r'-"tr'.ό2 fcrc5007 IAC 0.03 ncrl967 IAC 0.02 seoa6151 IAC 0.03 ncr8357 IAC 0.02 mioc6902 IAC 0.03 ncrc5417 IAC 0.02 seob3415 IAC 0.03 seob5493 IAC 0.02 seob4150 IAC 0.03 ncr3483 IAC 0.02 fcrb8700 IAC 0.03 ncrc9727 IAC 0.02 seoc0098 IAC 0.03 mioa4738 IAC 0.02 miob2858 IAC 0.03 seob2555 IAC 0.02 mioc2735 IAC 0.03 miocl357 IAC 0.02 ncrOOlβ IAC 0.03 seob7424 IAC 0.02 seoa2300 IAC 0.03 miob6598 IAC 0.02 seob8425 IAC 0.03 ncrc5724 IAC 0.02 seoa7917 IAC 0.03 fcrcO472 IAC 0.02 mioa8987 IAC 0.03 fcrb9659 IAC 0.02 miob8199 IAC 0.03 mioc9881 IAC 0.02 miod4686 IAC 0.03 ncrcl393 IAC 0.02 miob0795 IAC 0.03 mioal427 IAC 0.02 miob8249 IAC 0.03 ncrcO744 IAC 0.02 mioc6055 IAC 0.03 fcr3559 IAC 0.02 seocll59 IAC 0.03 hfcr3486 IAC 0.02 fcrb4721 IAC 0.03 mioc0090 IAC 0.02 mioc7370 IAC 0.03 seob9970 IAC 0.02 seoa4647 IAC 0.03 miobll39 IAC 0.02 miob8274 IAC 0.03 miob3725 IAC 0.02 hfcr2653 IAC 0.03 seoa4783 IAC 0.02 mioc2750 IAC 0.03 ncrc8865 IAC 0.03 seoa9656 IAC 0.03 ncr3793 IAC 0.03 mioa6621 IAC 0.03 ncrcllO2 IAC 0.03 ncrcl643 IAC 0.03 seoc0698 IAC 0.03 seoc2518 IAC 0.03 ncrc6778 IAC 0.03 fcrb8504 IAC 0.03 fcrb6185 IAC 0.03 ncrcO964 IAC 0.03 fcrc6084 IAC 0.03 ncr5046 IAC 0.03 miobl833 IAC 0.03 miob7550 IAC 0.03 fcrb8236 IAC 0.03 fcrb2301 IAC 0.03 miob6430 IAC 0.03 fcrc2099 IAC 0.03 fcrc2222 IAC 0.03 mioa2261 IAC 0.03 fcrb8465 IAC 0.03 miod6068 IAC 0.03 miod4142 IAC 0.03 fcrb8891 IAC 0.03 seob3090 IAC 0.03 fcr2999 IAC 0.03 fcrb4480 IAC 0.03 seoc2614 IAC 0.04 seob4002 IAC 0.03 seob6696 IAC 0.04 fcrb2806 IAC 0.03 seoa0065 IAC 0.04 ncrb7329 IAC 0.03 fcrb8080 IAC 0.04 seoa6078 IAC 0.03 mioa5681 IAC 0.04 fcrcO529 IAC 0.03 seob5147 IAC 0.04 ncrcl531 IAC 0.03 seob3520 IAC 0.04 fcrb9528 IAC 0.03 ncr3465 IAC 0.04 ncrbOδll IAC 0.03 fcrbl724 IAC 0.04 seoa6144 IAC 0.03 fcrb2784 IAC 0.04 fcrc2536 IAC 0.03 fcrb8495 IAC 0.04 miob3320 IAC 0.03 mioa7976 IAC 0.04 ncrcO583 IAC 0.03 ncrc9166 IAC 0.04 miob8989 IAC 0.03 seob8388 IAC 0.04 miob2918 IAC 0.03 ncrc6981 IAC 0.04 mioc6075 IAC 0.03 miob9495 IAC 0.04 ncrl428 IAC 0.03 mioc7952 IAC 0.04 seob4076 IAC 0.03 miod6090 IAC 0.04 seob4001 IAC 0.03 ncr9039 IAC 0.04 ncrc6127 IAC 0.03 fcrbl503 IAC 0.04 miob9463 IAC 0.03 ncrclO31 IAC 0.04 fcrb8432 IAC 0.03 seoc3836 IAC 0.04 ncrl876 IAC 0.03 mioc4190 IAC 0.04 seob0344 IAC 0.03 ncrc2128 IAC 0.04 m"iodθ"Ϊ8'7"'""' ""o".oT ncrc9772 IB 1.92179e-04 miob7308 IAC 0.04 fcrb9655 IB 2.35144e-04 ncrb0060 IAC 0.04 seob0992 IB 3.16052e-04 seoc4888 IAC 0.04 miocll22 IB 3.83188e-04 seoa4518 IAC 0.04 fcrb8542 IB 4.62953e-04 fcrO187 IAC 0.04 miod0592 IB 4.62953e-04 fcrb6211 IAC 0.04 fcrb4890 IB 5.57395e-04 fcrc2008 IAC 0.04 fcrcl834 IB 5.57395e-04 ncr3568 IAC 0.04 fcrb4378 IB 6.10836e-04 ncrcl367 IAC 0.04 fcrb6896 IB 6.68842e-04 mioa0647 IAC 0.04 seob6379 IB 7.31751e-04 ncrc9355 IAC 0.04 ncrc6888 IB 8.73736e-04 fcrc2007 IAC 0.04 ncr8975 IB 9.53596e-04 seoc0924 IAC 0.04 seoa3408 IB 9.53596e-04 mioc2596 IAC 0.04 ncrcl567 IB 1.039929e-03 fcrb5662 IAC 0.04 seob8301 IB 1.039929e-03 miob6188 IAC 0.04 seobl319 IB 1.133188e-03 fcrb2376 IAC 0.04 fcrbl689 IB 1.23385e-03 mioa9154 IAC 0.04 fcrb3654 IB 1.23385e-03 miob7638 IAC 0.04 fcrc5516 IB 1.23385e-03 fcr7114 IAC 0.04 ncrc9739 IB 1.342418e-03 fcr0206 IAC 0.04 fcrl337 IB 1.459426e-03 fcr5571 IAC 0.04 mioa9935 IB 1.459426e-03 ncr6108 IAC 0.04 ncrc5091 IB 1.459426e-03 ncr6893 IAC 0.04 seoa3639 IB 1.459426e-03 ncr9779 IAC 0.04 seoc0778 IB 1.459426e-03 seob0572 IAC 0.04 ncr8588 IB 1.585433e-03 hfcr4497 IAC 0.04 seoc0775 IB 1.585433e-03 ncr6415 IAC 0.04 ncrc4135 IB 1.661059e-03 ncrcO424 IAC 0.04 hfcr4485 IB 1.721032e-03 fcrb6236 IAC 0.04 ncrc9855 IB 1.721032e-03 seoa6152 IAC 0.04 ncrc9899 IB 1.721032e-03 ncrc3068 IAC 0.04 ncrcl892 IB 1.809776e-03 seob0999 IAC 0.04 miob8143 IB 1.866843e-03 miob9710 IAC 0.04 seocl025 IB 1.866843e-03 ncrb8518 IAC 0.04 mioc2074 IB 2.023522e-03 mioa5202 IAC 0.04 miob8932 IB 2.191755e-03 mioc2348 IAC 0.04 ncr3811 IB 2.191755e-03 ncrc9739 IAC 0.04 seoa3422 IB 2.191755e-03 seoa5552 IAC 0.04 mioal388 IB 2.317067e-03 seoa6510 IAC 0.04 ncrl780 IB 2.372265e-03 seoa7373 IAC 0.04 ncrc9700 IB 2.372265e-03 seob3313 IAC 0.04 seoa8399 IB 2.372265e-03 fcrb7981 IAC 0.04 seoa8556 IB 2.372265e-03 miod0340 IAC 0.04 fcrbO193 IB 2.565809e-03 seobl906 IAC 0.04 fcrb5850 IB 2.565809e-03 mioc3300 IAC 0.04 fcrcl298 IB 2.773181e-03 seoc2272 IAC 0.04 miob2877 IB 2.773181e-03 fcr4012 IAC 0.04 ncr7813 IB 2.773181e-03 seob3493 IAC 0.04 seoal653 IB 2.773181e-03 seoc6182 IAC 0.04 seoa4485 IB 2.773181e-03 fcrcO493 IAC 0.04 fcrb2437 IB 2.950074e-03 fcrc4848 IAC 0.04 fcrb9959 IB 2.995213e-03 seob0755 IAC 0.04 seoa3429 IB 2.995213e-03 mioc2443 IAC 0.04 mioa5059 IB 3.192597e-03 ncrc9704 IAC 0.04 ncrc9637 IB 3.232776e-03 mioa4064 IB 2.04e-05 seoa9870 IB 3.232776e-03 ncrc5844 IB 2.6e-05 seob0879 IB 3.232776e-03 mioc0899 IB 6.45e-05 seocl023 IB 3.232776e-03 fcrc6228 IB 9.2e-05 fcrb8187 IB 3.48678e-03 seob4752 IB 9.2e-05 miob9614 IB 3.48678e-03 fcrl997 IB 1.26897e-04 ncrc3936 IB 3.48678e-03 ncrc9712 IB 1.56467e-04 ncrc4780 IB 3.48678e-03 ncr7904 IB 1.63741e-04 seoa9777 IB 3.48678e-03 "seόc22"49"" IB " 3".5'9413e-03 seoa3516 IB 7.669588e-03 mioc0206 IB 3.758178e-03 seoa9709 IB 7.669588e-03 seob2169 IB 3.758178e-03 seobl411 IB 7.669588e-03 miod4066 IB 4.047962e-03 fcrlOβO IB 8.207892e-03 ncrb8203 IB 4.35717e-03 fcrb3518 IB 8.207892e-03 seoa9389 IB 4.35717e-03 miob9671 IB 8.207892e-03 fcrcl745 IB 4.686884e-03 seoa7212 IB 8.207892e-03 mioa8970 IB 4.686884e-03 seoa9889 IB 8.207892e-03 miob8249 IB 4.686884e-03 mioa8946 IB 8.355812e-03 mioc3906 IB 4.686884e-03 miob3696 IB 8.778576e-03 ncrc9491 IB 4.686884e-03 ncrc9877 IB 8.954837e-03 ncr4647 IB 5.038229e-03 fcr4214 IB 9.383215e-03 seob0089 IB 5.038229e-03 fcrb4543 IB 9.383215e-03 seob0154 IB 5.038229e-03 fcrb6028 IB 9.383215e-03 seob7946 IB 5.038229e-03 fcrb6432 IB 9.383215e-03 seob9092 IB 5.038229e-03 mioc6973 IB 9.383215e-03 ncrO212 IB 5.050847e-03 seoa0536 IB 9.383215e-03 ncrc3344 IB 5.050847e-03 seoa4452 IB 9.383215e-03 fcrbl503 IB 5.412379e-03 seoa5662 IB 9.383215e-03 fcrcO351 IB 5.412379e-03 seob0047 IB 9.383215e-03 miocl910 IB 5.412379e-03 miocl205 IB 9.969032e-03 ncrcO585 IB 5.412379e-03 fcrc6566 IB 0.01 ncrc3735 IB 5.412379e-03 mioa4229 IB 0.01 ncrc5653 IB 5.412379e-03 miob2466 IB 0.01 ncrc9704 IB 5.412379e-03 miocll25 IB 0.01 seob2797 IB 5.438596e-03 ncr7532 IB 0.01 miob8947 IB 5.658293e-03 ncrc9401 IB 0.01 ncr7672 IB 5.810553e-03 seob0755 IB 0.01 seoa3359 IB 5.810553e-03 seob4270 IB 0.01 mioa9831 IB 5.852052e-03 miod6437 IB 0.01 ncr0075 IB 5.89206e-03 ncr5488 IB 0.01 fcrb8877 IB 6.234019e-03 ncrc5072 IB 0.01 mioa2377 IB 6.234019e-03 ncrO836 IB 0.01 miob3953 IB 6.234019e-03 fcrb2704 IB 0.01 miob8657 IB 6.234019e-03 fcrb4727 IB 0.01 ncrc2080 IB 6.234019e-03 fcrc2651 IB 0.01 ncrc3520 IB 6.234019e-03 fcrc4161 IB 0.01 seoallOO IB 6.234019e-03 hfcrO285 IB 0.01 ncrcO174 IB 6.292611e-03 mioa9555 IB 0.01 fcr3559 IB 6.684094e-03 miocl028 IB 0.01 mioa0528 IB 6.684094e-03 ncrc3799 IB 0.01 ncrc3908 IB 6.684094e-03 ncrc5088 IB 0.01 ncrc9633 IB 6.684094e-03 seoa6314 IB 0.01 seoa0740 IB 6.684094e-03 seoa6393 IB 0.01 seoa2381 IB 6.684094e-03 seoc3876 IB 0.01 mioa8778 IB 6.761728e-03 fcrcO654 IB 0.01 fcrc5690 IB 7.162145e-03 fcr4471 IB 0.01 mioa2185 IB 7.162145e-03 fcrb4345 IB 0.01 ncrcO667 IB 7.162145e-03 fcrb5214 IB 0.01 ncrc5949 IB 7.162145e-03 fcrb7593 IB 0.01 ncrc8881 IB 7.162145e-03 fcrc2745 IB 0.01 seoa0469 IB 7.162145e-03 rαioal626 IB 0.01 seoa7917 IB 7.162145e-03 mioa9891 IB 0.01 miob3234 IB 7.260913e-03 ncrO615 IB 0.01 fcrbl990 IB 7.669588e-03 ncrc0807 IB 0.01 fcrcO637 IB 7.669588e-03 seoa5157 IB 0.01 fcrc4333 IB 7.669588e-03 seoa5685 IB 0.01 fcrc4360 IB 7.669588e-03 seoa5911 IB 0.01 mioa8851 IB 7.669588e-03 seoa9566 IB 0.01 miob9121 IB 7.669588e-03 seob7500 IB 0.01 miocl438 IB 7.669588e-03 seoal540 IB 0.01 ncrcO936 IB 7.669588e-03 fcr4743 IB 0.01 ncrc9867 IB 7.669588e-03 fcrb2334 IB 0.01 seoa0256 IB 7.669588e-03 fcrc6877 IB 0.01 miob9185 IB 0.01 seoa6620 IB 0.01 mioc3930 IB 0.01 seob2987 IB 0.01 ncrb8821 IB 0.01 seob3485 IB 0.01 ncrc3953 IB 0.01 seob4197 IB 0.01 ncrc5760 IB 0.01 seob7729 IB 0.01 seoa0045 IB 0.01 seob9485 IB 0.01 seoa6654 IB 0.01 seoc4316 IB 0.01 seobl399 IB 0.01 fcrc2014 IB 0.01 ncrc2685 IB 0.01 hfcrll89 IB 0.01 seobl345 IB 0.01 hfcr2275 IB 0.01 fcrb6359 IB 0.01 ncr4975 IB 0.01 hfcr4063 IB 0.01 ncrc5631 IB 0.01 mioa5586 IB 0.01 ncrc9944 IB 0.01 mioa6999 IB 0.01 seoa0429 IB 0.01 mioa9581 IB 0.01 seoa3555 IB 0.01 ncr4946 IB 0.01 seobl748 IB 0.01 seoa3717 IB 0.01 seoc3792 IB 0.01 seoa5554 IB 0.01 fcrb2380 IB 0.01 seoa5577 IB 0.01 fcrb5267 IB 0.01 seob0201 IB 0.01 fcrb7510 IB 0.01 seob9152 IB 0.01 fcrcllδl IB 0.01 ncr8153 IB 0.01 hfcr3209 IB 0.01 fcrb2849 IB 0.01 mioa3331 IB 0.01 mioa7239 IB 0.01 mioa6738 IB 0.01 miod3920 IB 0.01 miob7373 IB 0.01 ncrbl880 IB 0.01 miob9065 IB 0.01 ncrb8343 IB 0.01 ncr3034 IB 0.01 ncrc9557 IB 0.01 ncrc9159 IB 0.01 seob4192 IB 0.01 seoal644 IB 0.01 seob5726 IB 0.01 seoa3105 IB 0.01 seob6206 IB 0.01 seoa3662 IB 0.01 fcr3664 IB 0.01 seoa3811 IB 0.01 fcrb2198 IB 0.01 seoa9916 IB 0.01 fcrb2713 IB 0.01 seob0497 IB 0.01 fcrc4848 IB 0.01 seob2959 IB 0.01 mioa9649 IB 0.01 seob9241 IB 0.01 mioc2173 IB 0.01 seoc3443 IB 0.01 ncrO847 IB 0.01 ncr2182 IB 0.02 ncr3614 IB 0.01 fcrb2162 IB 0.02 ncrb8385 IB 0.01 mioa6832 IB 0.02 ncrc9428 IB 0.01 miod5703 IB 0.02 seoblOβl IB 0.01 mioc7084 IB 0.02 seobl808 IB 0.01 fcrbl898 IB 0.02 seoc5039 IB 0.01 fcrb2044 IB 0.02 fcrbl529 IB 0.01 fcrb4226 IB 0.02 fcrb5702 IB 0.01 fcrb6868 IB 0.02 mioal097 IB 0.01 mioc6417 IB 0.02 miod6731 IB 0.01 ncrc2119 IB 0.02 seob4108 IB 0.01 ncrc4444 IB 0.02 seoc0924 IB 0.01 seoa5234 IB 0.02 mioa4552 IB 0.01 seobl322 IB 0.02 ncrO291 IB 0.01 seob4165 IB 0.02 fcrb4985 IB 0.01 seob6856 IB 0.02 fcrb6033 IB 0.01 seob7346 IB 0.02 fcrb9963 IB 0.01 seob9145 IB 0.02 miob8391 IB 0.01 seocl406 IB 0.02 mioc0090 IB 0.01 hfcr3494 IB 0.02 miocll26 IB 0.01 fcrb4067 IB 0.02 ncr3112 IB 0.01 fcrb5928 IB 0.02 ncrb3980 IB 0.01 fcrclll5 IB 0.02 ncrc3713 IB 0.01 fcrc5402 IB 0.02 ncrc4089 IB 0.01 hfcrO521 IB 0.02 ncrc6996 IB 0.01 miob2341 IB 0.02 seoa4040 IB 0.01 mioc2039 IB 0.02 miόc5740 """-IB*"1'■'""■6.02 ncrc9044 IB 0.02 mioc5751 IB 0.02 seoa3556 IB 0.02 miod3306 IB 0.02 seoa4460 IB 0.02 ncr9140 IB 0.02 seobl538 IB 0.02 ncrc0640 IB 0.02 seob3493 IB 0.02 ncrc5762 IB 0.02 seob6386 IB 0.02 seobl844 IB 0.02 ncrc2463 IB 0.03 seob8065 IB 0.02 seob3317 IB 0.03 seob8129 IB 0.02 fcr5625 IB 0.03 fcrb7471 IB 0.02 fcrb3330 IB 0.03 fcrb2350 IB 0.02 fcrb6734 IB 0.03 fcrb3704 IB 0.02 fcrcl965 IB 0.03 fcrb4238 IB 0.02 fcrc2808 IB 0.03 hfcr5905 IB 0.02 fcrc6305 IB 0.03 mioa3367 IB 0.02 miob3308 IB 0.03 mioa9821 IB 0.02 miob6438 IB 0.03 miob4308 IB 0.02 ncrbO262 IB 0.03 mioc3962 IB 0.02 ncrc8873 IB 0.03 mioc7381 IB 0.02 ncrc9438 IB 0.03 ncrb8689 IB 0.02 seoa4324 IB 0.03 ncrc6871 IB 0.02 seobl268 IB 0.03 seob2195 IB 0.02 seob4075 IB 0.03 seob8311 IB 0.02 seob6131 IB 0.03 seocll75 IB 0.02 ncrb0060 IB 0.03 miod0625 IB 0.02 fcrO997 IB 0.03 ncrc3257 IB 0.02 fcrb6031 IB 0.03 seoa2819 IB 0.02 fcrb6102 IB 0.03 fcrb8119 IB 0.02 fcrb7742 IB 0.03 miob6372 IB 0.02 fcrb8133 IB 0.03 mioc0371 IB 0.02 fcrc6382 IB 0.03 ncrcO383 IB 0.02 fcrc6997 IB 0.03 ncrc3855 IB 0.02 mioa6428 IB 0.03 ncrc6423 IB 0.02 miob2227 IB 0.03 ncrc9464 IB 0.02 miob3938 IB 0.03 seoa9705 IB 0.02 miob8802 IB 0.03 seob0085 IB 0.02 mioc0181 IB 0.03 seob3513 IB 0.02 mioc4603 IB 0.03 seob4191 IB 0.02 miod4083 IB 0.03 seob5209 IB 0.02 ncr3435 IB 0.03 seob6670 IB 0.02 ncrc3856 IB 0.03 fcrb6012 IB 0.02 ncrc4371 IB 0.03 fcrc5898 IB 0.02 seoa8543 IB 0.03 hfcr2616 IB 0.02 seob0265 IB 0.03 hfcr3660 IB 0.02 seob3367 IB 0.03 mioa6102 IB 0.02 seob5319 IB 0.03 mioa6913 IB 0.02 fcrl464 IB 0.03 mioc4318 IB 0.02 ncrl550 IB 0.03 miod7081 IB 0.02 fcrO135 IB 0.03 ncr3037 IB 0.02 fcrb3074 IB 0.03 ncrc2959 IB 0.02 fcrb8485 IB 0.03 seoa4436 IB 0.02 fcrcO166 IB 0.03 seoa5977 IB 0.02 fcrc4669 IB 0.03 seob3697 IB 0.02 fcrc5504 IB 0.03 seob8839 IB 0.02 fcrc6345 IB 0.03 miod3417 IB 0.02 hfcrO734 IB 0.03 fcr3620 IB 0.02 miob0496 IB 0.03 fcr5190 IB 0.02 miobl774 IB 0.03 fcrb3897 IB 0.02 miob3690 IB 0.03 fcrb4429 IB 0.02 miocl763 IB 0.03 fcrb7495 IB 0.02 mioc3127 IB 0.03 hfcr0045 IB 0.02 mioc4731 IB 0.03 hfcrO489 IB 0.02 ncr0097 IB 0.03 mioa3945 IB 0.02 ncr5149 IB 0.03 ncrc4575 IB 0.02 ncr6343 IB 0.03 ncrb8332"' IB """1O".03 fcrb6279 IB 0.04 ncrcllll IB 0.03 hfcr6634 IB 0.04 ncrc2763 IB 0.03 mioa0294 IB 0.04 ncrc3435 IB 0.03 mioa9630 IB 0.04 ncrc4323 IB 0.03 miob8259 IB 0.04 ncrc9793 IB 0.03 miob9020 IB 0.04 seoa4587 IB 0.03 ncrl428 IB 0.04 seoa9724 IB 0.03 ncrb2247 IB 0.04 seob0288 IB 0.03 ncrc2472 IB 0.04 seoc0866 IB 0.03 ncrc3391 IB 0.04 ncrc4287 IB 0.03 seoa2936 IB 0.04 ncr2273 IB 0.03 seob6272 IB 0.04 hfcrO225 IB 0.03 seoc0651 IB 0.04 fcrb2452 IB 0.03 fcr0018 IB 0.04 fcrb4515 IB 0.03 seoa7408 IB 0.04 fcrb6808 IB 0.03 miodl846 IB 0.04 fcrb7803 IB 0.03 fcrb3298 IB 0.04 fcrc6084 IB 0.03 fcrb4799 IB 0.04 miob3042 IB 0.03 fcrb5305 IB 0.04 rαioc0301 IB 0.03 fcrb5709 IB 0.04 mioc4280 IB 0.03 hfcr2536 IB 0.04 mioc7331 IB 0.03 miob2432 IB 0.04 mioc7910 IB 0.03 miob2634 IB 0.04 ncr3834 IB 0.03 miob4037 IB 0.04 ncrb8752 IB 0.03 miod3592 IB 0.04 ncrcO423 IB 0.03 ncr9108 IB 0.04 ncrc3596 IB 0.03 ncrb6640 IB 0.04 ncrc3777 IB 0.03 ncrb8437 IB 0.04 ncrc3895 IB 0.03 ncrc4597 IB 0.04 seob0044 IB 0.03 seoa2448 IB 0.04 seob3204 IB 0.03 seob0938 IB 0.04 seob3699 IB 0.03 seob3517 IB 0.04 seob7929 IB 0.03 seob7419 IB 0.04 seoc7281 IB 0.03 seocl593 IB 0.04 fcrb5867 IB 0.03 ncrb0602 IB 0.04 miob5675 IB 0.03 ncrc4994 IB 0.04 seob7575 IB 0.03 fcrb2592 IB 0.04 fcrb6436 IB 0.03 fcrb5198 IB 0.04 fcrb7440 IB 0.03 fcrb6191 IB 0.04 fcrc2670 IB 0.03 fcrb9659 IB 0.04 fcrc5771 IB 0.03 hfcr4741 IB 0.04 hfcrll41 IB 0.03 mioa4542 IB 0.04 hfcr6043 IB 0.03 mioa6093 IB 0.04 miobl506 IB 0.03 mioa9294 IB 0.04 miob8531 IB 0.03 miob2833 IB 0.04 miob9130 IB 0.03 miod4686 IB 0.04 miocl783 IB 0.03 miod5114 IB 0.04 miocβOlβ IB 0.03 miod7440 IB 0.04 miod2977 IB 0.03 ncr0420 IB 0.04 ncr2160 IB 0.03 ncrb4182 IB 0.04 ncr2288 IB 0.03 ncrc3598 IB 0.04 ncr3960 IB 0.03 ncrc4757 IB 0.04 ncrb8605 IB 0.03 ncrc5232 IB 0.04 ncrc3171 IB 0.03 seoa2765 IB 0.04 ncrc5230 IB 0.03 seoa6377 IB 0.04 seoa0040 IB 0.03 seoa7383 IB 0.04 seoa2641 IB 0.03 seob8194 IB 0.04 seob0168 IB 0.03 seob8333 IB 0.04 seob2283 IB 0.03 seoc0843 IB 0.04 seoc2131 IB 0.03 fcrb3868 IB 0.04 fcrb5204 IB 0.03 fcrb0044 IB 0.04 seob5213 IB 0.03 miodl532 IB 0.04 ncr6072 IB 0.03 miod5301 IB 0.04 fcrbl562 IB 0.04 fcrb5202 IB 0.04 fcrb9324 'ΪB T)':"o'4 fcrc5418 1C 0.01 fcrb9636 IB 0.04 rαioa4674 1C 0.01 mioa8952 IB 0.04 mioc3906 1C 0.01 miob5646 IB 0.04 miod3421 1C 0.01 miob9163 IB 0.04 ncr7904 1C 0.01 miob9393 IB 0.04 ncrbl438 1C 0.01 mioclOβO IB 0.04 ncrc6697 1C 0.01 miocl940 IB 0.04 seob8212 1C 0.01 mioc4888 IB 0.04 ncrbl677 1C 0.01 mioc4978 IB 0.04 hfcrO338 1C 0.01 ncr3598 IB 0.04 fcrb5926 1C 0.01 ncrc2675 IB 0.04 fcrcO456 1C 0.01 ncrc3624 IB 0.04 miob0167 1C 0.01 ncrc4654 IB 0.04 miob4570 1C 0.01 seoa0135 IB 0.04 miob8515 1C 0.01 seoa4802 IB 0.04 ncr4946 1C 0.01 seoa7126 IB 0.04 ncr5613 1C 0.01 seobl362 IB 0.04 ncrc6778 1C 0.01 seob5069 IB 0.04 seobl808 1C 0.01 seob7557 IB 0.04 fcrb8202 1C 0.01 ncr2995 IB 0.04 rniob9901 1C 0.01 miod2323 IB 0.04 miod0777 1C 0.01 fcrb7324 1C 1.44645e-04 ncrcO421 1C 0.01 mioc7444 1C 1.162439e-03 ncrc7127 1C 0.01 miod3417 1C 1.503841e-03 seoa6364 1C 0.01 seoc4288 1C 1.503841e-03 seob2938 1C 0.01 ncrb3329 1C 1.928847e-03 seob5032 1C 0.01 rαiob3072 1C 2.453741e-03 seocl508 1C 0.01 ncrc9557 1C 2.453741e-03 fcr4444 1C 0.01 seob9552 1C 2.453741e-03 fcrb2200 1C 0.01 seoa5577 1C 3.097092e-03 fcrb3074 1C 0.01 fcr5369 1C 3.879912e-03 fcrb7036 1C 0.01 fcrcO775 1C 3.879912e-03 mioal354 1C 0.01 hfcrO285 1C 3.879912e-03 mioa5059 1C 0.01 mioc0238 1C 3.879912e-03 mioa6034 1C 0.01 mioc7471 1C 3.879912e-03 mioa7957 1C 0.01 seoa2819 1C 3.879912e-03 miob5675 1C 0.01 seob2937 1C 3.879912e-03 miocl425 1C 0.01 fcr2293 1C 4.825803e-03 ncr6072 1C 0.01 rαioc2596 1C 4.825803e-03 ncrc6242 1C 0.01 mioc3040 1C 4.825803e-03 seoa8195 1C 0.01 ncr3869 1C 4.825803e-03 seobl757 1C 0.01 ncrc6756 1C 4.825803e-03 miod6947 1C 0.01 seob5458 1C 4.825803e-03 ncrb6327 1C 0.01 seob5658 1C 4.825803e-03 fcr4328 1C 0.02 fcrb5016 1C 5.961096e-03 fcrb2252 1C 0.02 mioa9792 1C 5.961096e-03 fcrb3476 1C 0.02 fcr2276 1C 7.314953e-03 fcrb7340 1C 0.02 fcrb5723 1C 7.314953e-03 hfcr2367 1C 0.02 miod5349 1C 7.314953e-03 mioa6476 1C 0.02 ncrc5780 1C 7.314953e-03 mioa6734 1C 0.02 seob0133 1C 7.314953e-03 mioa9630 1C 0.02 seoc0394 1C 7.314953e-03 miob0764 1C 0.02 miod5301 1C 7.453402e-03 miob3320 1C 0.02 seob9871 1C 7.453402e-03 miocl524 1C 0.02 miob2448 1C 8.919456e-03 mioc3962 1C 0.02 miob6087 1C 8.919456e-03 miod0456 1C 0.02 miocl440 1C 8.919456e-03 miod5682 1C 0.02 ncrl387 1C 8.919456e-03 ncrO212 1C 0.02 seoa7178 1C 8.919456e-03 ncr8588 1C 0.02 seoa9997 1C 8.919456e-03 ncrb2092 1C 0.02 seob0564 1C 9.170707e-03 ncrb4339 1C 0.02 fcrb6211 1C 0.01 ncrb5704 1C 0.02 fcrb6464 1C 0.01 ncrcl665 1C 0.02 ncrc6871 1C 0.02 fcrb9680 1C 0.03 seoa3863 1C 0.02 fcrc0180 1C 0.03 seoa4327 1C 0.02 fcrc2082 1C 0.03 seoa7647 1C 0.02 fcrc4734 1C 0.03 seob3182 1C 0.02 fcrc6228 1C 0.03 seob7747 1C 0.02 fcrc6888 1C 0.03 seoc6169 1C 0.02 fcrc6970 1C 0.03 fcrbl420 1C 0.02 mioa4196 1C 0.03 fcrb8236 1C 0.02 mioa6832 1C 0.03 fcrb9450 1C 0.02 mioa9033 1C 0.03 rαioa4552 1C 0.02 mioa9891 1C 0.03 mioa8912 1C 0.02 miob2918 1C 0.03 miob3358 1C 0.02 miob3953 1C 0.03 mioc0276 1C 0.02 miob9336 1C 0.03 mioc2561 1C 0.02 miod6938 1C 0.03 mioc2592 1C 0.02 ncr2581 1C 0.03 mioc5633 1C 0.02 ncr7292 1C 0.03 miod0625 1C 0.02 ncr8234 1C 0.03 miod2570 1C 0.02 ncrb6394 1C 0.03 ncr3163 1C 0.02 ncrcO413 1C 0.03 ncr4656 1C 0.02 ncrc5959 1C 0.03 ncrb2400 1C 0.02 ncrc6795 1C 0.03 ncrb8385 1C 0.02 ncrc9642 1C 0.03 ncrc9166 1C 0.02 seoal615 1C 0.03 seoa6106 1C 0.02 seoa2042 1C 0.03 seobll44 1C 0.02 seoa4802 1C 0.03 seob3904 1C 0.02 seoa6497 1C 0.03 seob4363 1C 0.02 seoa8501 1C 0.03 seob7886 1C 0.02 seob0321 1C 0.03 fcrlO98 1C 0.03 seob4160 1C 0.03 fcrl855 1C 0.03 seob4782 1C 0.03 fcr2088 1C 0.03 seob5711 1C 0.03 fcrb2380 1C 0.03 seob6836 1C 0.03 fcrb2754 1C 0.03 seob9241 1C 0.03 fcrb7593 1C 0.03 fcrb5087 1C 0.03 fcrc4722 1C 0.03 fcr4503 1C 0.04 fcrc4816 1C 0.03 fcrb3024 1C 0.04 fcrc4841 1C 0.03 fcrb3217 1C 0.04 hfcr2895 1C 0.03 fcrb3920 1C 0.04 hfcr3486 1C 0.03 fcrc4968 1C 0.04 hfcr3514 1C 0.03 fcrc5160 1C 0.04 miob0202 1C 0.03 fcrc6609 1C 0.04 miob2227 1C 0.03 hfcr2314 1C 0.04 miob9284 1C 0.03 mioa2475 1C 0.04 rtιiob9714 1C 0.03 mioa3290 1C 0.04 miocll07 1C 0.03 miob8711 1C 0.04 mάoc3492 1C 0.03 miob9052 1C 0.04 miodl358 1C 0.03 mioc3413 1C 0.04 miod2232 1C 0.03 ncr0808 1C 0.04 miod6988 1C 0.03 ncr2370 1C 0.04 ncr0075 1C 0.03 ncr2994 1C 0.04 ncrcl653 1C 0.03 ncr3718 1C 0.04 ncrc2377 1C 0.03 ncr8481 1C 0.04 ncrc6920 1C 0.03 ncrbO328 1C 0.04 seoa6070 1C 0.03 ncrb6557 1C 0.04 seobl399 1C 0.03 ncrcO393 1C 0.04 seob2661 1C 0.03 ncrcO442 1C 0.04 seob2950 1C 0.03 ncrcO576 1C 0.04 seob6020 1C 0.03 ncrcll93 1C 0.04 seob9001 1C 0.03 ncrc6407 1C 0.04 fcrb8762 1C 0.03 seoa0913 1C 0.04 fcr456β 1C 0.03 seoal856 1C 0.04 fcrb4275 1C 0.03 seoa2178 1C 0.04 fcrb4287 1C 0.03 seoa3007 1C 0.04 seoa3106 1C 0.04 seoc6182 ID 1.703326e-03 seoa3359 1C 0.04 fcr3559 ID 2.051133e-03 seoa8399 1C 0.04 fcrc2014 ID 2.051133e-03 seob3151 1C 0.04 ncr7904 ID 2.051133e-03 seob3503 1C 0.04 ncrclO49 ID 2.459013e-03 seob5574 1C 0.04 seoa3847 ID 2.459013e-03 seoc2504 1C 0.04 seob4270 ID 2.459013e-03 seoc4416 1C 0.04 seoc0775 ID 2.459013e-03 ncrl876 1C 0.04 mioc7444 ID 2.935338e-03 ncr6401 1C 0.04 ncrb8665 ID 2.935338e-03 fcrb3016 1C 0.04 seoa4056 ID 2.935338e-03 fcrb8121 1C 0.04 seoa6620 ID 2.935338e-03 fcrb9583 1C 0.04 fcrb2633 ID 3.489334e-03 fcrc4360 1C 0.04 hfcr6370 ID 3.489334e-03 hfcr2686 1C 0.04 mioa6913 ID 3.489334e-03 mioa0601 1C 0.04 ncrcO544 ID 3.489334e-03 mioa4077 1C 0.04 seob0755 ID 3.489334e-03 mioa4484 1C 0.04 seob9871 ID 4.12035e-03 mioa4753 1C 0.04 ncrbl420 ID 4.131123e-03 mioa8952 1C 0.04 ncrc8909 ID 4.131123e-03 mioa9067 1C 0.04 seoa3639 ID 4.131123e-03 miob3257 1C 0.04 seoa5444 ID 4.131123e-03 miob3552 1C 0.04 seoa9817 ID 4.131123e-03 miob4126 1C 0.04 ncr!387 ID 4.414279e-03 miob5016 1C 0.04 fcrb6005 ID 4.871761e-03 miob9441 1C 0.04 hfcr6501 ID 4.871761e-03 miocOlδl 1C 0.04 miocl783 ID 4.871761e-03 mioc3523 1C 0.04 ncr3843 ID 4.871761e-03 mioc4331 1C 0.04 ncrcl003 ID 4.871761e-03 mioc8750 1C 0.04 ncrc6623 ID 4.871761e-03 ncrO335 1C 0.04 fcrb3169 ID 5.723282e-03 ncr2408 1C 0.04 fcrb3618 ID 5.723282e-03 ncr3141 1C 0.04 hfcr2314 ID 5.723282e-03 ncr5027 1C 0.04 iuioa4845 ID 5.723282e-03 ncr5065 1C 0.04 miob9905 ID 5.723282e-03 ncr8975 1C 0.04 miocl940 ID 5.723282e-03 ncr9378 1C 0.04 mioc4190 ID 5.723282e-03 ncrcO445 1C 0.04 mioc6320 ID 5.723282e-03 ncrcl361 1C 0.04 ncr6141 ID 5.723282e-03 ncrcl567 1C 0.04 ncrc6811 ID 5.723282e-03 ncrc5150 1C 0.04 fcrl997 ID 6.698726e-03 seoa0501 1C 0.04 fcrbl420 ID 6.698726e-03 seoal460 1C 0.04 seoa2641 ID 6.698726e-03 seoa5742 1C 0.04 seoa7669 ID 6.698726e-03 seoa7530 1C 0.04 seoa8543 ID 6.698726e-03 seob0038 1C 0.04 seob0154 ID 6.698726e-03 seob0200 1C 0.04 fcrb9655 ID 7.812173e-03 seob4127 1C 0.04 hfcrO285 ID 7.812173e-03 seob6017 1C 0.04 mioa4552 ID 7.812173e-03 seob9152 1C 0.04 mioa9821 ID 7.812173e-03 seocl628 1C 0.04 mioc5270 ID 7.812173e-03 hfcrO521 ID 1.21e-05 ncrb4319 ID 7.812173e-03 miod4464 ID 3.22085e-04 fcrb2452 ID 9.078758e-03 seoc5039 ID 4.04275e-04 fcrb6939 ID 9.078758e-03 fcrb4275 ID 6.25978e-04 hfcr3209 ID 9.078758e-03 seob0089 ID 9.48395e-04 rαiod0777 ID 9.078758e-03 ncrc0807 ID 1.158413e-03 ncr4648 ID 9.078758e-03 seoa6152 ID 1.158413e-03 seoa2428 ID 9.078758e-03 fcrb5550 ID 1.408023e-03 seoa3852 ID 9.078758e-03 ncrb8385 ID 1.408023e-03 fcrl879 ID 0.01 ncrc9637 ID 1.408023e-03 fcrb6896 ID 0.01 ncrc9910 ID 1.408023e-03 mioa0252 ID 0.01 seoa5577 ID 1.703326e-03 m±oa6476 ID 0.01 seob4363 ID 1.703326e-03 miod3591 ID 0.01 ncrb8605 ID' 'DTT(TI ncrc9286 ID 0.01 ncrc2080 ID 0.01 seoa3863 ID 0.01 seoa9870 ID 0.01 seoa4053 ID 0.01 ncr3237 ID 0.01 seob0442 ID 0.01 fcr3861 ID 0.01 seob0650 ID 0.01 fcrl562 ID 0.01 seob6279 ID 0.01 fcrc6470 ID 0.01 seoc0924 ID 0.01 mioa0820 ID 0.01 seoc4161 ID 0.01 mioa4318 ID 0.01 seoa8993 ID 0.01 mioc0302 ID 0.01 ncrO527 ID 0.02 miod7429 ID 0.01 fcrb7780 ID 0.02 ncrcO262 ID 0.01 mioa0494 ID 0.02 ncrc3045 ID 0.01 mioa3945 ID 0.02 seoa4040 ID 0.01 mioa5511 ID 0.02 seoa7546 ID 0.01 mioc2750 ID 0.02 seob0168 ID 0.01 mioc3066 ID 0.02 seobl526 ID 0.01 seoa6377 ID 0.02 seob6379 ID 0.01 seoa7897 ID 0.02 miodl909 ID 0.01 seoa8556 ID 0.02 fcrbl503 ID 0.01 seobl285 ID 0.02 fcrb5726 ID 0.01 seob5219 ID 0.02 fcrcl043 ID 0.01 seob7765 ID 0.02 fcrc5516 ID 0.01 seoc0149 ID 0.02 mioa3963 ID 0.01 fcrb3592 ID 0.02 miocl590 ID 0.01 fcrb5070 ID 0.02 mioc2997 ID 0.01 fcrc4360 ID 0.02 mioc5203 ID 0.01 hfcr2629 ID 0.02 miod4129 ID 0.01 rαioc0999 ID 0.02 ncrb8721 ID 0.01 miod4629 ID 0.02 ncrcO539 ID 0.01 miod6467 ID 0.02 ncrc2394 ID 0.01 ncr3189 ID 0.02 seoa3102 ID 0.01 seoal439 ID 0.02 seoa4802 ID 0.01 seoa4518 ID 0.02 seoa6118 ID 0.01 seoa5253 ID 0.02 seob9872 ID 0.01 seoa8401 ID 0.02 seoc2723 ID 0.01 seob6272 ID 0.02 fcrb4321 ID 0.01 ncrO451 ID 0.02 fcrbl731 ID 0.01 fcrb3192 ID 0.02 fcrb4788 ID 0.01 fcrb9856 ID 0.02 fcrb6012 ID 0.01 hfcr2890 ID 0.02 hfcrl811 ID 0.01 mioa3471 ID 0.02 mioa2185 ID 0.01 mioa8852 ID 0.02 mioc7331 ID 0.01 miob0764 ID 0.02 ncrO213 ID 0.01 miob9130 ID 0.02 ncrl523 ID 0.01 miod6324 ID 0.02 ncr4140 ID 0.01 ncr0335 ID 0.02 ncr4522 ID 0.01 ncr4946 ID 0.02 ncr6072 ID 0.01 ncrb3314 ID 0.02 ncr7532 ID 0.01 ncrb6394 ID 0.02 seoa3105 ID 0.01 ncrc4597 ID 0.02 seoa8300 ID 0.01 seoa8486 ID 0.02 seob0752 ID 0.01 seob0085 ID 0.02 seob5319 ID 0.01 seobl319 ID 0.02 seob6206 ID 0.01 seobl345 ID 0.02 seob8301 ID 0.01 seob5528 ID 0.02 fcrO997 ID 0.01 seob6703 ID 0.02 fcr2195 ID 0.01 mioc0899 ID 0.02 fcrbl580 ID 0.01 ncrc2529 ID 0.02 fcrb5796 ID 0.01 fcr5836 ID 0.03 mioa4674 ID 0.01 fcrb6715 ID 0.03 miod6938 ID 0.01 fcrb6890 ID 0.03 ncr4647 ID 0.01 fcrb9401 ID 0.03 ncrcO863 ID 0.01 fcrcO597 ID 0.03 ncrc3777 ID 0.01 fcrc2096 ID 0.03 fcrc6108 ID 0.03 ncrcO576 ID 0.03 hfcr3375 ID 0.03 ncrc6846 ID 0.03 hfcr6634 ID 0.03 ncrc9232 ID 0.03 mioa3620 ID 0.03 seoa5691 ID 0.03 πuoa4064 ID 0.03 seob0265 ID 0.03 miob3308 ID 0.03 seob5213 ID 0.03 miob8702 ID 0.03 seocl484 ID 0.03 mioc3042 ID 0.03 seocl664 ID 0.03 mioc3746 ID 0.03 miod7351 ID 0.04 mioc4064 ID 0.03 ncr6401 ID 0.04 mxod0708 ID 0.03 fcrl346 ID 0.04 mxod4066 ID 0.03 fcr2218 ID 0.04 ncrl476 ID 0.03 fcrb2208 ID 0.04 ncr3713 ID 0.03 fcrb3134 ID 0.04 ncrbl224 ID 0.03 fcrb4271 ID 0.04 ncrc2110 ID 0.03 fcrb5219 ID 0.04 ncrc3344 ID 0.03 fcrcO637 ID 0.04 seob2797 ID 0.03 fcrcl974 ID 0.04 seob2953 ID 0.03 hfcr0400 ID 0.04 seob4333 ID 0.03 mioa3528 ID 0.04 seob6853 ID 0.03 mioa4196 ID 0.04 seoc0780 ID 0.03 mioa8773 ID 0.04 seoc2589 ID 0.03 mxoa8851 ID 0.04 seoc4288 ID 0.03 mxoa9492 ID 0.04 seoc5010 ID 0.03 mioa9604 ID 0.04 mioc3296 ID 0.03 miob5646 ID 0.04 seoc3487 ID 0.03 mioc0090 ID 0.04 fcrb2350 ID 0.03 miocl028 ID 0.04 fcrb3629 ID 0.03 miocl088 ID 0.04 fcrb5181 ID 0.03 mioc3671 ID 0.04 fcrb9796 ID 0.03 miod4083 ID 0.04 miob3234 ID 0.03 ncr8588 ID 0.04 mioc4318 ID 0.03 ncrb6581 ID 0.04 mioc4888 ID 0.03 ncrcO413 ID 0.04 mioc5740 ID 0.03 ncrcl765 ID 0.04 mioc6374 ID 0.03 seoa3230 ID 0.04 mioc7421 ID 0.03 seoa7373 ID 0.04 miod0592 ID 0.03 seob0782 ID 0.04 miod4895 ID 0.03 seob3493 ID 0.04 ncr3297 ID 0.03 seob5478 ID 0.04 ncr3316 ID 0.03 seob6131 ID 0.04 ncrb2131 ID 0.03 seoc2131 ID 0.04 ncrcO646 ID 0.03 seoc2510 ID 0.04 ncrc5207 ID 0.03 seoc7281 ID 0.04 ncrc6242 ID 0.03 miocl205 ID 0.04 ncrc9557 ID 0.03 ncrl526 ID 0.04 seoalll7 ID 0.03 seoc2249 ID 0.04 seoa2744 ID 0.03 fcr3664 ID 0.04 seoa3701 ID 0.03 fcr5350 ID 0.04 seoa4132 ID 0.03 fcrbl399 ID 0.04 seob6584 ID 0.03 fcrb5850 ID 0.04 seocl480 ID 0.03 fcrcO795 ID 0.04 fcrb3857 ID 0.03 fcrc2346 ID 0.04 fcrb6639 ID 0.03 hfcrββll ID 0.04 fcrb7237 ID 0.03 mioal882 ID 0.04 fcrb7321 ID 0.03 miob2668 ID 0.04 mioa5085 ID 0.03 miob3531 ID 0.04 mioa6731 ID 0.03 miob5016 ID 0.04 miob8711 ID 0.03 miob8418 ID 0.04 mioc4994 ID 0.03 mioc0902 ID 0.04 ncr0509 ID 0.03 mioc2662 ID 0.04 ncrb2092 ID 0.03 mioc3663 ID 0.04 ncrb4182 ID 0.03 mioc8063 ID 0.04 ncrb7516 ID 0.03 miod3327 ID 0.04 itiiod6731 ID 0.04 miod7461 IE 3.68556e-04 ncrO212 ID 0.04 fcr4699 IE 4.06485e-04 ncrO673 ID 0.04 fcrb3237 IE 4.06485e-04 ncrb8332 ID 0.04 fcrb6431 IE 4.06485e-04 ncrcO849 ID 0.04 fcrc5142 IE 4.06485e-04 ncrc6697 ID 0.04 ncr4545 IE 4.9315e-04 ncrc7040 ID 0.04 seoa5253 IE 4.9315e-04 ncrc8949 ID 0.04 fcrb3244 IE 5.42475e-04 seoa3245 ID 0.04 fcrb4345 IE 5.42475e-04 seoa5911 ID 0.04 fcrcO529 IE 5.42475e-04 seob8386 ID 0.04 hfcr3149 IE 5.42475e-04 seob8639 ID 0.04 ncr3527 IE 5.96221e-04 seocl009 ID 0.04 ncrc2273 IE 5.96221e-04 seoc2518 ID 0.04 seob8311 IE 5.96221e-04 seoc5209 ID 0.04 seob9872 IE 5.96221e-04 fcr5509 IE 5.52e-06 fcrl328 IE 6.54736e-04 raiob9748 IE 6.23e-06 fcrb2993 IE 6.54736e-04 ncr4140 IE 8.29e-06 fcrc6976 IE 6.54736e-04 miob2492 IE 1.23e-05 mioa6091 IE 6.54736e-04 miodl675 IE 1.81e-05 seoa5234 IE 6.54736e-04 miocll07 IE 2.05e-05 mioa4564 IE 7.18387e-04 fcrcO672 IE 2.63e-05 miod7414 IE 7.18387e-04 ncrb2091 IE 2.97e-05 seob2195 IE 7.18387e-04 seob5379 IE 3.35e-05 fcrb3870 IE 7.8757e-04 miod5651 IE 5.38e-05 hfcr3922 IE 7.8757e-04 fcrb4799 IE 6.03e-05 miob1115 IE 7.8757e-04 fcrc0839 IE 6.03e-05 ncrc2670 IE 7.8757e-04 seobl766 IE 6.03e-05 fcrb3135 IE 8.62701e-04 fcr4084 IE 7.58e-05 fcrb6225 IE 8.62701e-04 fcrb9569 IE 7.58e-05 fcrb6808 IE 8.62701e-04 mioc2872 IE 8.48e-05 mioc2152 IE 8.62701e-04 miod7440 IE 8.48e-05 mioc2667 IE 8.62701e-04 seoa4802 IE 8.48e-05 miod3347 IE 8.62701e-04 ncr5168 IE 1.31557e-04 seob6446 IE 8.62701e-04 miob3982 IE 1.46458e-04 fcrb6502 IE 9.44225e-04 miod6947 IE 1.55187e-04 miod3854 IE 9.44225e-04 miob4668 IE 1.62887e-04 ncr3306 IE 9.44225e-04 ncr5055 IE 1.62887e-04 fcr4128 IE 1.032616e-03 seob7929 IE 1.62887e-04 fcrb3868 IE 1.032616e-03 ncrc3895 IE 1.80984e-04 fcrb6181 IE 1.032616e-03 seoc0780 IE 1.84e-04 mioc0824 IE 1.032616e-03 miodll95 IE 2.00899e-04 ncr8177 IE 1.032616e-03 ncrbl956 IE 2.00899e-04 fcrb4003 IE 1.128372e-03 fcrb7113 IE 2.05387e-04 ncrbO364 IE 1.128372e-03 fcrcl080 IE 2.22795e-04 fcrb3461 IE 1.232026e-03 miod7270 IE 2.22795e-04 miob6438 IE 1.232026e-03 ncrbO384 IE 2.22795e-04 mioc3492 IE 1.232026e-03 ncrc4531 IE 2.22795e-04 ncrc3842 IE 1.232026e-03 fcr5618 IE 2.35e-04 ncrc9428 IE 1.232026e-03 fcrb3217 IE 2.46844e-04 seoa4066 IE 1.232026e-03 fcrb4656 IE 2.46844e-04 seoa7249 IE 1.232026e-03 fcrc7057 IE 2.46844e-04 seoa7295 IE 1.232026e-03 miocl440 IE 2.46844e-04 seob9282 IE 1.232026e-03 fcrb5339 IE 2.73236e-04 fcrbl691 IE 1.34414e-03 ncrc3520 IE 2.73236e-04 miocl438 IE 1.34414e-03 ncrc4885 IE 2.73236e-04 ncrc3690 IE 1.34414e-03 ncrc9736 IE 2.73236e-04 seobll53 IE 1.34414e-03 ncrcO728 IE 3.02172e-04 fcrl748 IE 1.465309e-03 ncrc2507 IE 3.02172e-04 fcrb4988 IE 1.465309e-03 seoc0416 IE 3.02172e-04 hfcr2616 IE 1.465309e-03 fcrcO487 IE 3.33867e-04 seoa2641 IE 1.465309e-03 seob4117 IE 3.33867e-04 seob3088 IE 1.465309e-03 seoa6930 IE 3.59091e-04 seob6882 IE 1.465309e-03 fcrb4391 IE 3.68556e-04 fcrb2704 IE 1.596162e-03 fcrb4077 IE " ϊ.596162e-03 fcrb3704 IE 3.079469e-03 fcrcO775 IE 1.596162e-03 mioa2791 IE 3.079469e-03 fcrc5846 IE 1.596162e-03 mioa5586 IE 3.079469e-03 ncr4946 IE 1.596162e-03 mioc7620 IE 3.079469e-03 seoallOO IE 1.596162e-03 ncrO615 IE 3.079469e-03 fcrb3134 IE 1.737364e-03 ncr5613 IE 3.079469e-03 fcrb6187 IE 1.737364e-03 ncrb7102 IE 3.079469e-03 mioa8679 IE 1.737364e-03 ncrc2387 IE 3.079469e-03 ncrc3391 IE 1.737364e-03 ncrc3434 IE 3.079469e-03 seoa9421 IE 1.737364e-03 seoa8268 IE 3.079469e-03 fcrc4658 IE 1.889615e-03 seob6217 IE 3.132386e-03 hfcr3921 IE 1.889615e-03 fcrb5087 IE 3.151829e-03 mioc4089 IE 1.889615e-03 fcrb6432 IE 3.332111e-03 ncrc6981 IE 1.889615e-03 fcrb9655 IE 3.332111e-03 seoa9814 IE 1.889615e-03 fcrc5290 IE 3.332111e-03 fcrb2851 IE 2.053657e-03 fcrc6888 IE 3.332111e-03 fcrc6282 IE 2.053657e-03 miob3938 IE 3.332111e-03 mioa2073 IE 2.053657e-03 miob6099 IE 3.332111e-03 mioa7617 IE 2.053657e-03 ncrc3377 IE 3.332111e-03 ncr3034 IE 2.053657e-03 ncrc6417 IE 3.332111e-03 ncrb8383 IE 2.053657e-03 seoa0302 IE 3.332111e-03 seoa3989 IE 2.053657e-03 mioc7170 IE 3.415744e-03 seob7649 IE 2.053657e-03 seob3090 IE 3.433659e-03 seob7941 IE 2.053657e-03 ncrb0060 IE 3.505099e-03 mioc0899 IE 2.084443e-03 fcrb5164 IE 3.602913e-03 fcrl312 IE 2.230269e-03 hfcr2895 IE 3.602913e-03 fcr4803 IE 2.230269e-03 miob8143 IE 3.602913e-03 fcrb5305 IE 2.230269e-03 ncrO847 IE 3.602913e-03 fcrcO112 IE 2.230269e-03 ncrb6903 IE 3.602913e-03 mioa8484 IE 2.230269e-03 ncrc6459 IE 3.602913e-03 πiiob7922 IE 2.230269e-03 ncrc6953 IE 3.602913e-03 ncr4673 IE 2.230269e-03 ncrc9159 IE 3.602913e-03 ncrb3498 IE 2.230269e-03 ncrc9709 IE 3.602913e-03 ncrc2888 IE 2.230269e-03 seoal567 IE 3.602913e-03 seob0047 IE 2.230269e-03 seob7505 IE 3.602913e-03 miob8096 IE 2.267647e-03 fcr3936 IE 3.892973e-03 fcrb5253 IE 2.36428e-03 fcrb5928 IE 3.892973e-03 mioa4241 IE 2.42027e-03 fcrb9856 IE 3.892973e-03 mioa6731 IE 2.42027e-03 hfcr4349 IE 3.892973e-03 seoal540 IE 2.42027e-03 mioa0891 IE 3.892973e-03 fcrc6916 IE 2.465032e-03 ncr3112 IE 3.892973e-03 fcrb7951 IE 2.474444e-03 ncr8827 IE 3.892973e-03 fcrb7584 IE 2.624525e-03 ncrc2701 IE 3.892973e-03 fcrcOlββ IE 2.624525e-03 ncrc3436 IE 3.892973e-03 mioa3080 IE 2.624525e-03 seoa0464 IE 3.892973e-03 rαiob9714 IE 2.624525e-03 seoa5094 IE 3.892973e-03 miodl574 IE 2.624525e-03 seoa7250 IE 3.892973e-03 miod5369 IE 2.624525e-03 seob3462 IE 3.892973e-03 ncr5568 IE 2.624525e-03 seob7978 IE 3.892973e-03 ncrb0054 IE 2.624525e-03 fcrβ748 IE 4.203441e-03 ncrcO249 IE 2.624525e-03 hfcr0501 IE 4.203441e-03 ncrc3799 IE 2.624525e-03 hfcr4423 IE 4.203441e-03 ncrc6756 IE 2.624525e-03 ncr2288 IE 4.203441e-03 ncrc9855 IE 2.624525e-03 ncr8594 IE 4.203441e-03 fcrc3998 IE 2.701854e-03 ncrb8392 IE 4.203441e-03 fcrb7700 IE 2.796352e-03 ncrcl595 IE 4.203441e-03 fcrb3342 IE 2.843941e-03 ncrc9228 IE 4.203441e-03 fcrb8080 IE 2.843941e-03 seoal559 IE 4.203441e-03 miob8146 IE 2.843941e-03 seoa2734 IE 4.203441e-03 mioc2019 IE 2.843941e-03 seoa4167 IE 4.203441e-03 miod5184 IE 2.843941e-03 seob3517 IE 4.203441e-03 ncrc5744 IE 2.843941e-03 fcrbl890 IE 4.328333e-03 seoa9959 IE 2.843941e-03 hfcr2890 IE 4.366058e-03 fcrb5091 IE 2.938134e-03 seob3076 IE 4.392091e-03 seobl844 IE 4.448804e-03 seoa3415 IE 5.674225e-03 seob4090 IE 4.507008e-03 seoa4107 IE 5.674225e-03 fcrb4721 IE 4.535517e-03 seob0639 IE 5.674225e-03 fcrb6667 IE 4.535517e-03 seob7229 IE 5.674225e-03 fcrb7829 IE 4.535517e-03 seoa4608 IE 5.687664e-03 fcrcl971 IE 4.535517e-03 fcrbl801 IE 6.105844e-03 miob2634 IE 4.535517e-03 fcrb2080 IE 6.105844e~03 mioc0337 IE 4.535517e-03 fcrb2866 IE 6.105844e-03 ncrb8693 IE 4.535517e-03 fcrb3782 IE 6.105844e-03 ncrc3777 IE 4.535517e-03 fcrb5841 IE 6.105844e-03 ncrc4985 IE 4.535517e-03 fcrb8877 IE 6.105844e-03 ncrc9004 IE 4.535517e-03 fcrc2429 IE 6.105844e-03 ncrc9642 IE 4.535517e-03 fcrc5233 IE 6.105844e-03 ncrc9729 IE 4.535517e-03 hfcrO489 IE 6.105844e-03 seob7346 IE 4.535517e-03 inioa7140 IE 6.105844e-03 seoc4093 IE 4.535517e-03 miob3042 IE 6.105844e-03 seob4766 IE 4.631477e-03 miob6518 IE 6.105844e-03 ncrc4920 IE 4.676963e-03 miob9671 IE 6.105844e-03 seob7747 IE 4.676963e-03 mioc4319 IE 6.105844e-03 fcrb2113 IE 4.890457e-03 miod4784 IE 6.105844e-03 fcrb5118 IE 4.890457e-03 ncr3960 IE 6.105844e-03 fcrb5867 IE 4.890457e-03 ncrc2600 IE 6.105844e-03 fcrb6484 IE 4.890457e-03 ncrc5844 IE 6.105844e~03 fcrb8949 IE 4.890457e-03 ncrc9712 IE 6.105844e-03 fcrclO14 IE 4.890457e-03 seob4752 IE 6.105844e-03 miob2869 IE 4.890457e-03 seob8999 IE 6.105844e-03 miob5708 IE 4.890457e-03 seoc0705 IE 6.373843e-03 miod4518 IE 4.890457e-03 fcrb9096 IE 6.543015e-03 ncr4485 IE 4.890457e-03 fcrbl769 IE 6.565913e-03 ncr6755 IE 4.890457e-03 fcrb2256 IE 6.565913e~03 ncrc2080 IE 4.890457e-03 fcrb2713 IE 6.565913e-03 seoa8276 IE 4.890457e-03 fcrb3330 IE 6.565913e~03 seobl319 IE 4.890457e-03 fcrb6785 IE 6.565913e-03 seob4676 IE 4.890457e-03 fcrb8910 IE 6.565913e-03 ncr9108 IE 4.911008e-03 mioa2261 IE 6.565913e-03 fcrb2859 IE 5.26957e-03 mioa7957 IE 6.565913e-03 fcrb6635 IE 5.26957e-03 miob8572 IE 6.565913e-03 fcrcO481 IE 5.26957e-03 ncr3435 IE 6.565913e-03 fcrcl740 IE 5.26957e-03 ncrcl349 IE β.565913e-03 fcrc6345 IE 5.26957e-03 ncrc2472 IE 6.565913e-03 fcrc6868 IE 5.26957e-03 ncrc5363 IE 6.565913e-03 hfcr0560 IE 5.26957e-03 ncrc9412 IE 6.565913e-03 mioc2561 IE 5.26957e-03 seoa9389 IE 6.565913e-03 ncrb4474 IE 5.26957e-03 seob6015 IE 6.565913e-03 ncrc5553 IE 5.26957e-03 fcrlO68 IE 6.596445e-03 ncrc8949 IE 5.26957e-03 seoc0068 IE 6.651586e-03 seoa4202 IE 5.26957e-03 mioa7169 IE 6.962202e-03 seoa9094 IE 5.26957e-03 fcrbl539 IE 7.055974e-03 seob2077 IE 5.26957e-03 fcrb3314 IE 7.055974e~03 seob8300 IE 5.26957e-03 hfcr3134 IE 7.055974e-03 fcrl633 IE 5.674225e-03 ncr3811 IE 7.055974e~03 fcr4469 IE 5.674225e-03 ncr4975 IE 7.055974e~03 fcr4965 IE 5.674225e-03 ncrc3887 IE 7.055974e~03 fcrbl411 IE 5.674225e-03 seoa5157 IE 7.055974e~03 fcrb2197 IE 5.674225e-03 seoa9873 IE 7.055974e~03 fcrb6171 IE 5.674225e-03 seob0817 IE 7.055974e-03 fcrc4971 IE 5.674225e-03 seob2966 IE 7.055974e-03 mioc0162 IE 5.674225e-03 seob9485 IE 7.055974e-03 ncr8337 IE 5.674225e-03 seocl023 IE 7.055974e-03 ncr8413 IE 5.674225e-03 fcrb3227 IE 7.08625e-03 ncrc3526 IE 5.674225e-03 fcrc2775 IE 7.131424e-03 ncrc3735 IE 5.674225e-03 ncrc2227 IE 7.298626e-03 ncrc5072 IE 5.674225e-03 fcrb7812 IE 7.483981e-03 seoa0256 IE 5.674225e-03 seob0783 IE 7.547751e-03 fcrl388 IE 7.577631e-03 mxoc3045 IE 9.349172e-03 fcr7419 IE 7.577631e-03 mioc5633 IE 9.349172e-03 fcrbl492 IE 7.577631e-03 ncr0420 IE 9.349172e-03 fcrb2196 IE 7.577631e-03 ncr3163 IE 9.349172e-03 fcrb2633 IE 7.577631e-03 ncrb8352 IE 9.349172e-03 fcrb4400 IE 7.577631e-03 ncrc3598 IE 9.349172e-03 fcrb5389 IE 7.577631e-03 seob3594 IE 9.349172e-03 fcrb7944 IE 7.577631e-03 seoc0018 IE 9.349172e-03 fcrc2573 IE 7.577631e-03 fcrc0430 IE 9.763205e-03 mioal570 IE 7.577631e-03 hfcr3494 IE 9.763205e-03 miob7276 IE 7.577631e-03 fcrc6138 IE 9.895207e-03 miob8583 IE 7.577631e-03 miod2837 IE 9.96009e-03 ncr9378 IE 7.577631e-03 fcrl337 IE 0.01 ncrb4025 IE 7.577631e-03 fcr2303 IE 0.01 ncrc3596 IE 7.577631e-03 fcr4212 IE 0.01 seoal575 IE 7.577631e-03 fcr4214 IE 0.01 seob3513 IE 7.577631e-03 fcr6708 IE 0.01 seob5556 IE 7.577631e-03 fcrc4151 IE 0.01 seob6758 IE 7.577631e-03 mioa2374 IE 0.01 seob7419 IE 7.577631e-03 miocl524 IE 0.01 hfcr6406 IE 7.634442e-03 miod7442 IE 0.01 ncrc3840 IE 7.795327e-03 ncr3963 IE 0.01 fcrb4868 IE 7.885744e-03 ncrc3544 IE 0.01 mioal532 IE 7.885744e-03 ncrc4384 IE 0.01 fcrbl202 IE 8.132553e-03 ncrc6332 IE 0.01 fcrb2208 IE 8.132553e-03 miob2533 IE 0.01 fcrb6351 IE 8.132553e-03 miob9559 IE 0.01 fcrcO637 IE 8.132553e-03 fcrb7240 IE 0.01 fcrc5850 IE 8.132553e-03 seob5751 IE 0.01 ncrc0457 IE 8.132553e-03 seoa0536 IE 0.01 ncrc3257 IE 8.132553e-03 hfcrO676 IE 0.01 ncrc3802 IE 8.132553e-03 fcrb6481 IE 0.01 ncrc8988 IE 8.132553e-03 ncrO927 IE 0.01 ncrc9469 IE 8.132553e-03 seob6835 IE 0.01 seobl213 IE 8.132553e-03 fcrb4238 IE 0.01 seob8029 IE 8.132553e-03 fcrclll5 IE 0.01 seob9333 IE 8.132553e-03 fcrc6877 IE 0.01 miocl995 IE 8.415065e-03 mioa0252 IE 0.01 miod5218 IE 8.473284e-03 mioal388 IE 0.01 ncrb2085 IE 8.473284e-03 miob3427 IE 0.01 fcrl305 IE 8.722468e-03 miob8609 IE 0.01 fcrb2784 IE 8.722468e-03 miob8830 IE 0.01 fcrb3615 IE 8.722468e-03 miocll25 IE 0.01 fcrb4926 IE 8.722468e-03 mioc5226 IE 0.01 fcrb6031 IE 8.722468e-03 miodl236 IE 0.01 fcrb7127 IE 8.722468e-03 ncrc2196 IE 0.01 fcrb9222 IE 8.722468e-03 ncrc2868 IE 0.01 fcrc2471 IE 8.722468e-03 ncrc5631 IE 0.01 hfcr5737 IE 8.722468e-03 seoa3408 IE 0.01 mioc3930 IE 8.722468e-03 seoa7126 IE 0.01 mioc4270 IE 8.722468e-03 seob4584 IE 0.01 ncr2995 IE 8.722468e-03 seoc0220 IE 0.01 seoa0469 IE 8.722468e-03 seocl876 IE 0.01 seoal419 IE 8.722468e-03 miodl358 IE 0.01 seoa2765 IE 8.722468e-03 fcrOlll IE 0.01 seoa9930 IE 8.722468e-03 fcr3528 IE 0.01 seob5478 IE 8.722468e-03 fcrbl720 IE 0.01 seoc2447 IE 8.722468e-03 fcrb2639 IE 0.01 miod6364 IE 9.069698e-03 fcrb3896 IE 0.01 fcr6630 IE 9.098454e-03 fcrb4985 IE 0.01 fcrbl578 IE 9.349172e-03 fcrb9611 IE 0.01 fcrb3702 IE 9.349172e-03 fcrc2082 IE 0.01 mioa4245 IE 9.349172e-03 fcrc6419 IE 0.01 miobβ597 IE 9.349172e-03 mioa6428 IE 0.01 miob3411 IE 0.01 fcrc4307 IE 0.01 miob8707 IE 0.01 mioa7239 IE 0.01 ncrb2558 IE 0.01 miob7550 IE 0.01 ncrcO383 IE 0.01 mioc4366 IE 0.01 ncrc6941 IE 0.01 ncr3404 IE 0.01 ncrc9612 IE 0.01 ncr7924 IE 0.01 seoa3633 IE 0.01 ncrc2730 IE 0.01 seobl891 IE 0.01 ncrc3171 IE 0.01 seobl981 IE 0.01 ncrc4135 IE 0.01 miob9248 IE 0.01 ncrc5123 IE 0.01 fcrb8762 IE 0.01 seoa7542 IE 0.01 ncrb0164 IE 0.01 seoa7647 IE 0.01 fcrb3933 IE 0.01 seob0409 IE 0.01 fcrb4281 IE 0.01 seob0928 IE 0.01 fcrb4413 IE 0.01 mioc2726 IE 0.01 fcrb4712 IE 0.01 fcrb3001 IE 0.01 fcrb4721 IE 0.01 fcrb5202 IE 0.01 fcrb5440 IE 0.01 fcrcl689 IE 0.01 fcrb6062 IE 0.01 fcrc5086 IE 0.01 fcrb6236 IE 0.01 fcrc6990 IE 0.01 fcrb6750 IE 0.01 hfcr6033 IE 0.01 fcrb9963 IE 0.01 ncr4118 IE 0.01 fcrc4876 IE 0.01 ncrb5244 IE 0.01 mioal891 IE 0.01 ncrc9401 IE 0.01 miob8825 IE 0.01 seoa4485 IE 0.01 mioc2204 IE 0.01 seob0918 IE 0.01 mioc8682 IE 0.01 seob2959 IE 0.01 ncr2996 IE 0.01 seob4165 IE 0.01 ncr3843 IE 0.01 miod5793 IE 0.01 ncrb7482 IE 0.01 seoc2029 IE 0.01 ncrc5025 IE 0.01 seob2953 IE 0.01 ncrc9519 IE 0.01 fcrc6382 IE 0.01 ncrc9739 IE 0.01 mioc4667 IE 0.01 seob0063 IE 0.01 fcr0027 IE 0.01 seobl009 IE 0.01 fcrbl856 IE 0.01 seob8911 IE 0.01 fcrb2254 IE 0.01 seocl025 IE 0.01 fcrb4729 IE 0.01 seoc2136 IE 0.01 fcrb6033 IE 0.01 seoc5125 IE 0.01 fcrb6084 IE 0.01 ncrcO663 IE 0.01 fcrb7036 IE 0.01 ncrb8239 IE 0.01 fcrb7380 IE 0.01 fcrb2318 IE 0.01 fcrc4180 IE 0.01 miob8947 IE 0.01 fcrc6010 IE 0.01 seoa4464 IE 0.01 fcrc6228 IE 0.01 mioc7561 IE 0.01 inioa4229 IE 0.01 fcrbl684 IE 0.01 miob2386 IE 0.01 fcrbl969 IE 0.01 miob4370 IE 0.01 fcrb7703 IE 0.01 miob5119 IE 0.01 fcrc4360 IE 0.01 miob8704 IE 0.01 mioa4975 IE 0.01 miod5672 IE 0.01 mioa5326 IE 0.01 ncr3339 IE 0.01 mioa9025 IE 0.01 ncr5713 IE 0.01 miob2341 IE 0.01 ncrcl615 IE 0.01 ncrl768 IE 0.01 ncrc2928 IE 0.01 ncr4545 IE 0.01 ncrc5559 IE 0.01 ncrcβ697 IE 0.01 ncrc5653 IE 0.01 seob0201 IE 0.01 seoa2381 IE 0.01 seob9851 IE 0.01 seoa4460 IE 0.01 seoa9129 IE 0.01 seobll96 IE 0.01 seob2938 IE 0.01 seob5645 IE 0.01 fcrb9364 IE 0.01 seob6415 IE 0.01 miocl357 IE 0.01 seocll75 IE 0.01 seoc2904 IE 0.01 seoa5433 IE 0.01 fcrbl573 IE 0.01 ncr3118 IE 0.01 ncrb7600 IE CTTffl hfcr2821 IE 0.01 miobl059 IE 0.01 hfcr3834 IE 0.01 fcrO253 IE 0.01 mioa7955 IE 0.01 fcr3717 IE 0.01 miod3241 IE 0.01 fcrb3165 IE 0.01 ncrc9947 IE 0.01 fcrb5527 IE 0.01 seoal041 IE 0.01 fcrb6717 IE 0.01 seoal653 IE 0.01 fcrc2254 IE 0.01 seoa5741 IE 0.01 fcrc4277 IE 0.01 seob2156 IE 0.01 hfcrl210 IE 0.01 seob8839 IE 0.01 hfcr5620 IE 0.01 seob9617 IE 0.01 mioc2891 IE 0.01 seoc2264 IE 0.01 ncr5557 IE 0.01 seoc6182 IE 0.01 ncr9469 IE 0.01 fcrb9017 IE 0.01 ncrc4373 IE 0.01 miob4333 IE 0.01 ncrc4863 IE 0.01 mioc7986 IE 0.02 ncrc5492 IE 0.01 fcr3282 IE 0.02 ncrc8841 IE 0.01 fcrb3808 IE 0.02 seoa0054 IE 0.01 fcrb5675 IE 0.02 seoal672 IE 0.01 fcrc6460 IE 0.02 seob6084 IE 0.01 hfcr5959 IE 0.02 seocl009 IE 0.01 miob7443 IE 0.02 seoc3519 IE 0.01 mioc3042 IE 0.02 fcrc5690 IE 0.01 mioc7509 IE 0.02 mioc6212 IE 0.01 miodl333 IE 0.02 seoa4587 IE 0.01 miod6437 IE 0.02 fcrb2765 IE 0.01 ncr3412 IE 0.02 hfcr3160 IE 0.01 ncrb5972 IE 0.02 fcr4357 IE 0.01 ncrc277β IE 0.02 miocl203 IE 0.01 ncrc4539 IE 0.02 ncrb8201 IE 0.01 seoa0044 IE 0.02 fcrc6043 IE 0.01 seoa0221 IE 0.02 fcrbll90 IE 0.01 seoa4457 IE 0.02 fcrbl690 IE 0.01 seoa9445 IE 0.02 fcrbl877 IE 0.01 seoa9690 IE 0.02 fcrbl992 IE 0.01 seobl423 IE 0.02 fcrb2424 IE 0.01 seobl947 IE 0.02 fcrb3897 IE 0.01 seob6781 IE 0.02 fcrb8236 IE 0.01 seoc0651 IE 0.02 fcrb8422 IE 0.01 miod3160 IE 0.02 fcrc6970 IE 0.01 seoc4900 IE 0.02 fcrc6989 IE 0.01 seob8065 IE 0.02 mioa5097 IE 0.01 ncrc4772 IE 0.02 miod0894 IE 0.01 fcrO748 IE 0.02 miod5349 IE 0.01 fcrbl689 IE 0.02 ncr3975 IE 0.01 fcrb2592 IE 0.02 ncrcO288 IE 0.01 fcrb2719 IE 0.02 ncrc3453 IE 0.01 fcrb2749 IE 0.02 ncrc3953 IE 0.01 fcrb3288 IE 0.02 ncrc5423 IE 0.01 fcrb4572 IE 0.02 ncrc9793 IE 0.01 fcrb6870 IE 0.02 seoa2219 IE 0.01 miob4570 IE 0.02 seoa4783 IE 0.01 miod4521 IE 0.02 seoa5580 IE 0.01 ncr7151 IE 0.02 seoa6453 IE 0.01 ncr7960 IE 0.02 seob4192 IE 0.01 ncrcl643 IE 0.02 seocl642 IE 0.01 ncrc5039 IE 0.02 seoal736 IE 0.01 seoal410 IE 0.02 fcr2106 IE 0.01 seoal615 IE 0.02 fcrb3573 IE 0.01 seoa2775 IE 0.02 fcrb5892 IE 0.01 seob0787 IE 0.02 fcrb6202 IE 0.01 seob0857 IE 0.02 fcrb6643 IE 0.01 seocllδO IE 0.02 fcrc5060 IE 0.01 mioc0019 IE 0.02 ncrb7111 IE 0.02 seob9970 IE 0.02 fcrb2051 IE 0.02 fcrb2166 IE 0.02 fcrb2745 IE 0.02 fcrb2813 IE 0.02 fcrb3181 IE 0.02 fcrb4760 IE 0.02 fcrb3654 IE 0.02 fcrb5092 IE 0.02 fcrb4365 IE 0.02 fcrb9481 IE 0.02 fcrb5912 IE 0.02 mioc4204 IE 0.02 fcrb6549 IE 0.02 seoa4436 IE 0.02 fcrb7616 IE 0.02 seobl860 IE 0.02 fcrb9338 IE 0.02 seob5558 IE 0.02 fcrc5471 IE 0.02 seob7981 IE 0.02 mioa2156 IE 0.02 seocl495 IE 0.02 miob2664 IE 0.02 ncr8357 IE 0.02 mioc2074 IE 0.02 mioc4100 IE 0.02 mioc3111 IE 0.02 seob5551 IE 0.02 ncrb8343 IE 0.02 seob2336 IE 0.02 ncrcl631 IE 0.02 seobl052 IE 0.02 ncrc5775 IE 0.02 ncrc9354 IE 0.02 ncrc9438 IE 0.02 fcrbl337 IE 0.02 ncrc9772 IE 0.02 fcrbl380 IE 0.02 seoa5577 IE 0.02 fcrbl930 IE 0.02 seoa7077 IE 0.02 fcrb2094 IE 0.02 seoa9817 IE 0.02 fcrb4781 IE 0.02 seob0815 IE 0.02 fcrb5037 IE 0.02 seob0971 IE 0.02 fcrb5181 IE 0.02 seob3112 IE 0.02 fcrb5326 IE 0.02 seob5624 IE 0.02 fcrb6699 IE 0.02 hfcr2547 IE 0.02 fcrc0810 IE 0.02 seoa4132 IE 0.02 fcrc7219 IE 0.02 seoc4585 IE 0.02 miob0636 IE 0.02 fcrb2334 IE 0.02 miob9652 IE 0.02 miob9340 IE 0.02 mioc3105 IE 0.02 rnioa3367 IE 0.02 mioc3994 IE 0.02 fcr4695 IE 0.02 ncr2363 IE 0.02 fcrb2059 IE 0.02 ncrc0640 IE 0.02 fcrb4542 IE 0.02 ncrc3435 IE 0.02 fcrb5214 IE 0.02 ncrc3464 IE 0.02 fcrb6322 IE 0.02 ncrc4798 IE 0.02 fcrb6738 IE 0.02 ncrc5724 IE 0.02 fcrb8614 IE 0.02 seoal263 IE 0.02 fcrc4067 IE 0.02 seoal910 IE 0.02 hfcr304β IE 0.02 seob0228 IE 0.02 mioal687 IE 0.02 seoc0616 IE 0.02 mioa2423 IE 0.02 seoc2682 IE 0.02 miob7531 IE 0.02 seoa3895 IE 0.02 mioc4022 IE 0.02 seoc3469 IE 0.02 mioc4504 IE 0.02 fcrb5926 IE 0.02 miod4998 IE 0.02 ncrc4555 IE 0.02 miod7486 IE 0.02 hfcrO366 IE 0.02 ncr4700 IE 0.02 miob9010 IE 0.02 ncrb8821 IE 0.02 fcrbl296 IE 0.02 ncrc9766 IE 0.02 fcrbl809 IE 0.02 seoa3164 IE 0.02 fcrb4570 IE 0.02 seoa5843 IE 0.02 fcrb5123 IE 0.02 seobl273 IE 0.02 fcrb5723 IE 0.02 seob3485 IE 0.02 fcrcO363 IE 0.02 seob5213 IE 0.02 fcrc5379 IE 0.02 ncrbO299 IE 0.02 fcrc7087 IE 0.02 seoc2722 IE 0.02 mioa3428 IE 0.02 ncrO721 IE 0.02 mioa3629 IE 0.02 miod3776 IE 0.02 miob0986 IE 0.02 fcrb4271 IE 0.02 miod6234 IE 0.02 hfcr3010 IE 0.02 ncr0808 IE 0.02 ncrbO265 IE 0.02 ncrc7049 IE 0.02 seoa4324 TE -"■ σ.02 ncrc3593 IE 0.03 seoa5494 IE 0.02 ncrc4323 IE 0.03 seob9756 IE 0.02 ncrc5079 IE 0.03 seob9764 IE 0.02 ncrc5230 IE 0.03 seob9898 IE 0.02 seoa2179 IE 0.03 ncrb7557 IE 0.02 seoa6621 IE 0.03 seob6017 IE 0.03 seoa7543 IE 0.03 fcrl844 IE 0.03 miob8320 IE 0.03 fcrb3483 IE 0.03 miob9185 IE 0.03 fcrb5467 IE 0.03 seoc4132 IE 0.03 fcrb9634 IE 0.03 fcrbl369 IE 0.03 fcrcllβl IE 0.03 fcrcO232 IE 0.03 fcrc4722 IE 0.03 seob7030 IE 0.03 mioa8032 IE 0.03 fcr2442 IE 0.03 miob9820 IE 0.03 fcrbl876 IE 0.03 mioc0301 IE 0.03 fcrbl990 IE 0.03 miocl768 IE 0.03 fcrb3717 IE 0.03 miocl910 IE 0.03 fcrb4067 IE 0.03 mioc2097 IE 0.03 fcrb5564 IE 0.03 mioc4929 IE 0.03 fcrc0148 IE 0.03 miod4974 IE 0.03 fcrc2040 IE 0.03 ncr2994 IE 0.03 hfcr5691 IE 0.03 ncr3189 IE 0.03 mioa5836 IE 0.03 ncr8075 IE 0.03 miob6598 IE 0.03 ncrcl259 IE 0.03 miod4540 IE 0.03 ncrc2182 IE 0.03 ncr0097 IE 0.03 ncrc3624 IE 0.03 ncr3357 IE 0.03 ncrc4728 IE 0.03 ncr3402 IE 0.03 ncrc8932 IE 0.03 ncrb2027 IE 0.03 ncrc9867 IE 0.03 ncrcO284 IE 0.03 seoal739 IE 0.03 ncrc2613 IE 0.03 seoa4452 IE 0.03 ncrc2859 IE 0.03 seoa4640 IE 0.03 ncrc9284 IE 0.03 seoa6654 IE 0.03 seoal747 IE 0.03 seoa6697 IE 0.03 seoa3761 IE 0.03 seob0046 IE 0.03 seoa8822 IE 0.03 seob0219 IE 0.03 seob2717 IE 0.03 seobll55 IE 0.03 fcrcO559 IE 0.03 seob2139 IE 0.03 fcrc7222 IE 0.03 seob3189 IE 0.03 ncrb3980 IE 0.03 seocl631 IE 0.03 seoal582 IE 0.03 seoc2131 IE 0.03 mioc7952 IE 0.03 mioa5951 IE 0.03 seoc4038 IE 0.03 seob5064 IE 0.03 miobl774 IE 0.03 ncrc9055 IE 0.03 miocl030 IE 0.03 seoc8115 IE 0.03 seob7039 IE 0.03 seob0182 IE 0.03 ncr8314 IE 0.03 mioc2336 IE 0.03 fcr2079 IE 0.03 seocl307 IE 0.03 fcr2798 IE 0.03 fcr4471 IE 0.03 fcr3269 IE 0.03 fcrbl381 IE 0.03 fcr4846 IE 0.03 fcrb3120 IE 0.03 fcrb4470 IE 0.03 fcrb4782 IE 0.03 fcrb7852 IE 0.03 fcrcO376 IE 0.03 fcrb8133 IE 0.03 fcrc3993 IE 0.03 fcrc5705 IE 0.03 fcrc5134 IE 0.03 hfcrO412 IE 0.03 mioa0192 IE 0.03 hfcr2629 IE 0.03 mioal660 IE 0.03 hfcr6043 IE 0.03 mioa9154 IE 0.03 mioal380 IE 0.03 rtιioa9831 IE 0.03 mioa6913 IE 0.03 miocl560 IE 0.03 miob3072 IE 0.03 miod2369 IE 0.03 miod4066 IE 0.03 miod3254 IE 0.03 ncr3141 IE 0.03 ncrcO529 IE 0.03 ncr7267 IE 0.03 ncrc3713 -ΓE ""UTUi m±ob4673 IE 0.04 seoal552 IE 0.03 mioc3066 IE 0.04 seoal661 IE 0.03 mioc7421 IE 0.04 seoal720 IE 0.03 ncrOllO IE 0.04 seoa5911 IE 0.03 ncrlO55 IE 0.04 seoa9777 IE 0.03 ncr7904 IE 0.04 seob4925 IE 0.03 ncr9934 IE 0.04 seob8817 IE 0.03 ncrb8569 IE 0.04 fcrb5051 IE 0.03 ncrcl374 IE 0.04 mioc0910 IE 0.03 ncrc2290 IE 0.04 miod0773 IE 0.03 seoal770 IE 0.04 seob5562 IE 0.03 seoa7587 IE 0.04 hfcr2524 IE 0.03 seob0065 IE 0.04 fcr4468 IE 0.03 seob5882 IE 0.04 fcrb5725 IE 0.03 seoa9042 IE 0.04 fcrb8981 IE 0.03 seocl957 IE 0.04 fcrb9324 IE 0.03 mioaOβOl IE 0.04 fcrc5614 IE 0.03 miod5771 IE 0.04 fcrcβOll IE 0.03 seobl839 IE 0.04 fcrc6551 IE 0.03 fcrb2591 IE 0.04 fcrc7014 IE 0.03 miobl830 IE 0.04 mioa3392 IE 0.03 fcrb4981 IE 0.04 mioa3514 IE 0.03 hfcr5514 IE 0.04 mioa5447 IE 0.03 fcrb2715 IE 0.04 mioa8946 IE 0.03 fcrb2792 IE 0.04 miob7117 IE 0.03 fcrb2796 IE 0.04 miob9679 IE 0.03 fcrb2979 IE 0.04 mioc4766 IE 0.03 fcrb6817 IE 0.04 ncrO836 IE 0.03 fcrb9007 IE 0.04 ncr7286 IE 0.03 fcrb9225 IE 0.04 ncrb4319 IE 0.03 mioal944 IE 0.04 ncrc.0393 IE 0.03 mioa5202 IE 0.04 ncrcO849 IE 0.03 miob2656 IE 0.04 ncrc3537 IE 0.03 mioc5736 IE 0.04 ncrc5019 IE 0.03 mioc6878 IE 0.04 ncrc6587 IE 0.03 ncr6326 IE 0.04 seoa0135 IE 0.03 ncr7341 IE 0.04 seoa2528 IE 0.03 ncr9371 IE 0.04 seoa8351 IE 0.03 ncrb8641 IE 0.04 seoa9482 IE 0.03 ncrc3417 IE 0.04 seob0200 IE 0.03 ncrc7131 IE 0.04 seob5054 IE 0.03 seoa4327 IE 0.04 seob6133 IE 0.03 seob2735 IE 0.04 miod7212 IE 0.03 seob7729 IE 0.04 ncr2182 IE 0.03 ncrO612 IE 0.04 seoa6466 IE 0.03 ncr3834 IE 0.04 fcrb2554 IE 0.03 mioc8766 IE 0.04 seoa9997 IE 0.04 ncrO251 IE 0.04 hfcr2148 IE 0.04 ncrc2463 IE 0.04 mioa7069 IE 0.04 fcrb575β IE 0.04 miob2709 IE 0.04 miob2116 IE 0.04 fcr4494 IE 0.04 ncr3718 IE 0.04 fcrbl580 IE 0.04 fcrb8449 IE 0.04 fcrbl633 IE 0.04 ncrc5804 IE 0.04 fcrb2499 IE 0.04 fcr2276 IE 0.04 fcrb2849 IE 0.04 miod2334 IE 0.04 fcrb3219 IE 0.04 seob3139 IE 0.04 fcrb3515 IE 0.04 seocl881 IE 0.04 fcrb8445 IE 0.04 seoa8960 IE 0.04 fcrb9636 IE 0.04 fcrb2569 IE 0.04 fcrc5137 IE 0.04 fcrb6472 IE 0.04 fcrc5328 IE 0.04 fcrcO351 IE 0.04 mioa0364 IE 0.04 fcrcOββl IE 0.04 mioa2851 IE 0.04 fcrc2647 IE 0.04 fcrc(S38l IE όrϋi seob0547 IE 0.04 hfcrll89 IE 0.04 seobl906 IE 0.04 hfcr2770 IE 0.04 seob5395 IE 0.04 miob5810 IE 0.04 hfcr5865 IE 0.04 miod6025 IE 0.04 fcrc5402 IE 0.04 ncrb4869 IE 0.04 miob9228 IE 0.04 ncrb8538 IE 0.04 hfcr5970 IE 0.04 ncrcO217 IE 0.04 ncrc4079 IE 0.04 ncrcO747 IE 0.04 hfcr4046 IE 0.04 ncrcll68 IE 0.04 seob3513 IF 1.44e-05 ncrc2607 IE 0.04 miob9652 IF 9.27e-05 ncrc6416 IE 0.04 mioa2290 IF 1.00455e-04 ncrc8903 IE 0.04 mioa3987 IF 1.01e-04 seoal802 IE 0.04 hfcr4489 IF 1.17e-04 seoa3555 IE 0.04 ncr2363 IF 1.17e-O4 seoa5447 IE 0.04 mioc5633 IF 1.43481e-04 seobl9Q8 IE 0.04 fcrb4383 IF 1.45e-04 seocl230 IE 0.04 mioa2204 IF 1.61204e-04 mioal077 IE 0.04 seoa3516 IF 1.61204e-04 fcrc3958 IE 0.04 hfcr6129 IF 1.84e-04 miocl002 IE 0.04 ncrO132 IF 1.89e-O4 ncrc9440 IE 0.04 mioa8439 IF 2.36e-04 seoc3554 IE 0.04 ncr3598 IF 2.41e-04 fcrcO725 IE 0.04 fcr3936 IF 2.51e-04 seob9734 IE 0.04 mioa8864 IF 2.53933e-04 ncr2583 IE 0.04 fcrbl296 IF 2.69e-04 fcrbl775 IE 0.04 hfcr6033 IF 2.73e-04 fcrbl916 IE 0.04 ncrc3257 IF 2.77e-04 fcrb8872 IE 0.04 fcr4214 IF 2.91e-04 mioa8318 IE 0.04 hfcr2041 IF 2.92e-04 miob0487 IE 0.04 fcr6748 IF 3.0e-04 seoa4829 IE 0.04 mioa3321 IF 3.02e-04 seoc0513 IE 0.04 hfcr5991 IF 3.11e-04 fcrc2376 IE 0.04 seoa5554 IF 3.36e-04 fcrc2737 IE 0.04 mioa3392 IF 3.68e-04 fcrc4948 IE 0.04 mioa2213 IF 3.85e-04 ncrO451 IE 0.04 fcrb2784 IF 3.9297e-04 ncr3177 IE 0.04 fcrb8196 IF 3.9297e-O4 ncrc4882 IE 0.04 ncrb8530 IF 4.1e-04 seoa3670 IE 0.04 ncr3614 IF 4.14e-04 seob0483 IE 0.04 seocl025 IF 4.3e-04 seob5762 IE 0.04 ncr4975 IF 4.61e-04 fcr5425 IE 0.04 seob4075 IF 4.85728e-04 fcrb4388 IE 0.04 ncr7839 IF 5.15e-04 fcrb7885 IE 0.04 ncr3815 IF 5.17e-04 fcrc4848 IE 0.04 hfcr5942 IF 5.23e-04 fcrc5007 IE 0.04 ncr8843 IF 5.4e-04 mioall65 IE 0.04 fcr4212 IF 5.61e-04 mioa3987 IE 0.04 fcrb2334 IF 5.71e-04 miob2067 IE 0.04 hfcr2275 IF 5.9789e-04 rαiob2756 IE 0.04 mioa7465 IF 6.04e-04 miob7373 IE 0.04 miob5810 IF 6.13e-04 mioc3332 IE 0.04 hfcr5864 IF 6.22e-04 mioc7542 IE 0.04 fcrc6542 IF 6.5e-04 mioc8055 IE 0.04 fcr6708 IF 6.58e-04 miod0878 IE 0.04 mioa0311 IF 6.62324e-04 ncrcO558 IE 0.04 mioc7381 IF 6.62324e-04 ncrcllO3 IE 0.04 ncrb8419 IF 6.62324e-04 ncrcl247 IE 0.04 seob4105 IF 6.64e-04 ncrc3856 IE 0.04 miobl545 IF 7.28e-04 seoa2385 IE 0.04 fcrbl633 IF 7.31e-04 seoa4708 IE 0.04 mioal276 IF 7.32964e-04 seoa8556 IE 0.04 seoa5849 IF 7.39e-04 seoa9705 IE 0.04 fcrb5467 IF 7.45e-04 seoa2385 IF ~""7T63e-σ;r seob3697 IF 1.914664e-03 seoa0418 IF 7.66e-04 fcrb2763 IF 1.928684e-03 seob8174 IF 7.87e-04 hfcr5220 IF 1.93844e-03 mioc4835 IF 8.0e-04 seob0755 IF 2.00392e-03 miob3696 IF 8.10329e-04 ncrc3650 IF 2.009196e-03 hfcr5620 IF 8.11e-04 miod3558 IF 2.053453e-03 fcr4414 IF 8.14e-04 fcrb7113 IF 2.056425e-03 fcrb4345 IF 8.23e-04 fcrb3234 IF 2.126705e-03 seob8368 IF 8.38e-04 seoc4260 IF 2.16146e-03 seob3468 IF 8.52e-04 mioa8150 IF 2.221472e-03 mioal704 IF 8.62e-04 ncrb3369 IF 2.227846e-03 hfcr5905 IF 8.63e-04 hfcr6141 IF 2.247103e-03 seoa3392 IF 8.67e-04 seoc0068 IF 2.284571e-03 ncr2967 IF 8.69e-04 ncr3037 IF 2.287966e-03 seob4192 IF 8.69e-04 miob5888 IF 2.294368e-03 fcr4695 IF 8.71e~04 ncr6108 IF 2.294368e-03 mioa0972 IF 8.94977e-04 seob2685 IF 2.294368e-03 mioa2156 IF 9.04e-04 miob3982 IF 2.333313e-03 ncrO847 IF 9.04e-04 ncrbO513 IF 2.334478e-03 fcrc6877 IF 9.07e-04 ncr9789 IF 2.417674e-03 fcrb6465 IF 9.31e-04 ncrbO46O IF 2.498431e-03 hfcr3467 IF 9.62e~04 seoa5577 IF 2.502273e-03 mioa0364 IF 9.72e-04 ncr9933 IF 2.508241e-03 fcrc5136 IF 9.87503e-04 mioc0950 IF 2.588257e-03 ncrb5972 IF 1.013154e-03 seob4140 IF 2.589542e-03 ncrb8056 IF 1.025404e-03 mioc5603 IF 2.598809e-03 ncrbl880 IF 1.06995e-03 ncrb8437 IF 2.624852e-03 mioa4674 IF 1.08854e-03 seoc2904 IF 2.671857e-03 fcrb7324 IF 1.132955e-03 mioal868 IF 2.739644e-03 ncrb5233 IF 1.138168e-03 mioc6417 IF 2.739644e-03 mioa0909 IF 1.189492e-03 ncr3960 IF 2.739644e-03 fcrO68O IF 1.198768e-03 ncrc3908 IF 2.739644e-03 fcrb2054 IF 1.198768e-03 seob0409 IF 2.739644e-03 miob9671 IF 1.198768e-03 seob6872 IF 2.739644e-03 fcrc6228 IF 1.250123e-03 fcrb5645 IF 2.768103e-03 mioc0699 IF 1.295875e-03 seoc2633 IF 2.782919e-03 hfcr5499 IF 1.299893e-03 seob6015 IF 2.805059e-03 fcrb2246 IF 1.302022e-03 mioc7471 IF 2.877428e-03 miodl389 IF 1.318907e-03 seoal749 IF 2.894471e-03 fcr4902 IF 1.361678e-03 seob7465 IF 2.982853e-03 fcrb2254 IF 1.375926e-03 ncr4572 IF 2.989794e-03 seoa0353 IF 1.396441e-03 ncrc6871 IF 2.989794e-03 fcrcl803 IF 1.449724e-03 fcrb6715 IF 2.992521e-03 miob3757 IF 1.449724e-03 ncrcl411 IF 3.058649e-03 fcrb2162 IF 1.456955e-03 hfcr2930 IF 3.059881e-03 rαioa3693 IF 1.472788e-03 fcr3717 IF 3.061351e-03 fcrb2744 IF 1.519135e-03 miod4019 IF 3.069637e-03 ncr6137 IF 1.552827e-03 fcrb2080 IF 3.191254e-03 fcrb6508 IF 1.580622e-03 ncr9175 IF 3.251549e-03 fcrbl397 IF 1.592036e-03 hfcr6509 IF 3.257391e-03 mioa9062 IF 1.656379e-03 mioa2292 IF 3.259973e-03 hfcr6515 IF 1.683036e-03 ncrc9850 IF 3.259973e-03 ncrcO856 IF 1.694702e-03 seob2283 IF 3.259973e-03 ncr0097 IF 1.703212e-03 seoa4066 IF 3.271732e-03 mioa9891 IF 1.746709e-03 fcrbl950 IF 3.335219e-03 seoa0536 IF 1.746709e-03 seoa9690 IF 3.339303e-03 ncr3402 IF 1.757133e-03 ncrc4985 IF 3.345513e-03 miobl830 IF 1.762715e-03 ncr7945 IF 3.346416e-03 fcrb2321 IF 1.829751e-03 hfcr6043 IF 3.358515e-03 fcrcO287 IF 1.883587e-03 seob6486 IF 3.422066e-03 fcrb6868 IF 1.914664e-03 mioa0607 IF 3.45556e-03 fcrc4722 IF 1.914664e-03 fcrc5604 IF 3.457081e-03 mioa4484 IF 1.914664e-03 seob8311 IF 3.529644e-03 miob7117 IF 1.914664e-03 miob0167 IF 3.551533e-03 ncr4503 IF 3.551533e-03 ncrc0849 IF 4.961124e-03 seoal689 IF 3.551533e-03 seobl808 IF 4.961124e-03 seob7039 IF 3.551533e-03 seob5673 IF 4.961124e-03 fcrb2346 IF 3.631263e-03 seoa7605 IF 5.014096e-03 miob8373 IF 3.653412e-03 seob8483 IF 5.052725e-03 hfcr5473 IF 3.664884e-03 mioc4842 IF 5.15722e-03 ncr4009 IF 3.745829e-03 hfcr5522 IF 5.165867e-03 ncr8177 IF 3.751419e~03 hfcr6687 IF 5.175533e-03 mioblOOβ IF 3.759831e-03 fcrc2126 IF 5.191177e-03 ncr0609 IF 3.81321e-03 mioa5452 IF 5.191177e-03 seoa4829 IF 3.817245e-03 miob2093 IF 5.218484e-03 mioa2343 IF 3.845825e~03 ncrc0017 IF 5.259195e-03 ncrc6953 IF 3.845825e-03 ncrc3457 IF 5.259195e-03 fcr5425 IF 3.859974e-03 mioa8820 IF 5.382406e-03 ncr4485 IF 3.863747e-03 miod0456 IF 5.382406e-03 fcrb5639 IF 3.865895e-03 ncr3763 IF 5.382406e-03 mioa4753 IF 3.865895e-03 ncrbl670 IF 5.382406e-03 mioa9061 IF 3.865895e-03 fcrbl922 IF 5.52751e-03 miob0180 IF 3.865895e-03 fcr3269 IF 5.600028e-03 miob0189 IF 3.865895e-03 fcr2417 IF 5.638591e-03 miod0187 IF 3.865895e-03 seoa5785 IF 5.696732e-03 seoa6238 IF 3.865895e-03 seoal089 IF 5.709807e-03 seobll33 IF 3.865895e-03 fcrb2510 IF 5.834736e-03 ncrc3030 IF 3.933089e-03 miob2227 IF 5.834736e-03 seoa8547 IF 3.94569e-03 mioc2561 IF 5.834736e-03 fcr3743 IF 3.964466e-03 seoa5691 IF 5.834736e-03 mioa8984 IF 3.964466e-03 seoc2220 IF 5.834736e-03 hfcr6375 IF 4.00567e-03 ncrc7127 IF 5.867274e-03 hfcr5956 IF 4.064802e-03 mioc2019 IF 5.891644e-03 ncr3189 IF 4.13181e-03 mioa6969 IF 5.906016e-03 seob6084 IF 4.203369e-03 seob7575 IF 6.097516e-03 fcrb6870 IF 4.204555e-03 ncrc3593 IF 6.113977e-03 mioc3958 IF 4.204555e-03 ncrb4390 IF 6.170009e-03 ncrb7482 IF 4.204555e-03 fcrb6896 IF 6.199739e-03 ncrc2780 IF 4.204555e-03 hfcr5232 IF 6.232511e-03 seoc0957 IF 4.204555e-03 ncrc5019 IF 6.25157e-03 mioc0567 IF 4.232828e-03 mioc5194 IF 6.294222e-03 fcrb5194 IF 4.288194e-03 inioa3940 IF 6.302655e-03 ncr3419 IF 4.297798e-03 fcrb2315 IF 6.316326e-03 fcrc5471 IF 4.303567e-03 fcrc6374 IF 6.320005e-03 fcrc3942 IF 4.385521e-03 mioa6721 IF 6.320005e-03 seob6701 IF 4.441698e-03 miob2466 IF 6.320005e-03 seoa4056 IF 4.458253e-03 ncr8481 IF 6.320005e-03 seob8489 IF 4.541888e-03 ncrb8605 IF 6.320005e-03 fcrb2305 IF 4.556834e-03 seoa0099 IF 6.320005e-03 mioa2185 IF 4.556834e-03 seoa0388 IF 6.320005e-03 fcrO821 IF 4.569082e-03 seocl348 IF 6.320005e-03 fcr3823 IF 4.569082e-03 miob6099 IF 6.326637e-03 hfcr2295 IF 4.569082e-03 ncrc5079 IF 6.338658e-03 hfcrO522 IF 4.585758e-03 seob0586 IF 6.373817e-03 mioc7073 IF 4.595129e-03 seoa9889 IF 6.418044e-03 mioc2094 IF 4.690236e-03 fcr4699 IF 6.424174e-03 raioc2546 IF 4.717485e-03 fcr5019 IF 6.499398e-03 raiob4307 IF 4.736268e~03 miod4269 IF 6.541189e-03 fcr7656 IF 4.778951e-03 ncrbO571 IF 6.562046e-03 ncrc9855 IF 4.823976e-03 seob7419 IF 6.640174e-03 hfcr3674 IF 4.87135e-03 mioa2774 IF 6.667082e-03 fcrb5527 IF 4.893773e-03 ncr6401 IF 6.682138e-03 fcrb2207 IF 4.928943e-03 seoa7266 IF 6.701766e-03 mioc6156 IF 4.9342e~03 seob3699 IF 6.753484e-03 fcr7095 IF 4.961124e-03 fcrb2356 IF 6.825088e-03 mioa9147 IF 4.961124e-03 seob6380 IF 6.825088e-03 miob9336 IF 4.961124e-03 fcrc2050 IF 6.840188e-03 ncrb6385 IF 4.961124e-03 hfcr5237 IF 6.840188e-03 πιioa9033 IF 6.840188e-03 ncrc6846 IF 8.631293e-03 mioa9630 IF 6.840188e-03 seoa5303 IF 8.631293e-03 miob0213 IF 6.840188e-03 fcrb2133 IF 8.647588e-03 ncrcO427 IF 6.840188e-03 hfcr3149 IF 8.667419e-03 seoa6078 IF 6.840188e-03 mioc3206 IF 8.811712e-03 fcr6691 IF 6.901382e-03 ncrc3733 IF 8.814606e-03 fcrb5297 IF 6.946937e-03 miod5703 IF 8.861302e-03 fcrc3998 IF 6.962613e-03 ncrbl861 IF 8.882816e-03 ncrc9700 IF 6.962613e-03 mioc4420 IF 8.897141e-03 seob5260 IF 6.964546e-03 fcr5470 IF 8.900727e-03 ncrc4226 IF 6.994012e-03 miod5505 IF 8.920428e-03 seob4363 IF 7.032122e-03 fcrb2126 IF 9.199582e-03 ncr9502 IF 7.122344e-03 ncrcl578 IF 9.205415e-03 seoa0469 IF 7.129312e-03 hfcr3224 IF 9.219579e-03 mioa8851 IF 7.216452e-03 miod2232 IF 9.250174e-03 raioa5681 IF 7.217865e-03 seoa5578 IF 9.281899e-03 miob8947 IF 7.23621e-03 mioa6991 IF 9.31265e-03 seoa5151 IF 7.267712e-03 ncr7570 IF 9.31265e-03 fcrO751 IF 7.378041e-03 seoa0420 IF 9.31265e-03 fcrc3993 IF 7.389258e-03 seoa8750 IF 9.31265e-03 fcrb6620 IF 7.397348e-03 seobl526 IF 9.31265e-03 fcrc6892 IF 7.397348e-03 seob3322 IF 9.31265e-03 hfcrOOll IF 7.397348e-03 miob2668 IF 9.456576e-03 miob7970 IF 7.397348e-03 seocl078 IF 9.467094e-03 ncrbO262 IF 7.397348e-03 seob2163 IF 9.524093e-03 ncrcll92 IF 7.397348e-03 ncr3040 IF 9.576948e-03 seoa7369 IF 7.397348e-03 ncrb3449 IF 9.579048e-03 seocl631 IF 7.397348e-03 ncr9429 IF 9.603911e-03 mioal417 IF 7.397472e-03 ncrc4323 IF 9.65164e-03 fcrb.3079 IF 7.406491e-03 fcrc4307 IF 9.674174e-03 seoc0778 IF 7.492808e-03 seoa0137 IF 9.678908e-03 ncrb3238 IF 7.636087e-03 mioa2330 IF 9.685757e-03 mioc7662 IF 7.706037e-03 fcrb2306 IF 9.694983e-03 seoa3415 IF 7.706037e-03 hfcr5228 IF 9.697553e-03 seob0442 IF 7.706037e-03 fcrb7700 IF 9.721976e-03 ncr5975 IF 7.721088e-03 hfcr5181 IF 9.747239e-03 seoa0429 IF 7.723786e-03 ncrc3544 IF 9.74833e-03 fcrb6195 IF 7.742707e-03 ncrb8518 IF 9.75683e-03 ncr8693 IF 7.754322e-03 fcrb3192 IF 9.87471e-03 fcrl404 IF 7.795263e-03 fcr5006 IF 9.917803e-03 ncrcl871 IF 7.818883e-03 fcr5199 IF 9.921817e-03 mioal304 IF 7.968902e-03 ncrb0200 IF 9.990439e-03 fcrbO979 IF 7.993636e-03 fcrbl477 IF 0.01 mioc3580 IF 7.993636e-03 fcrbl494 IF 0.01 seoa0486 IF 7.993636e-03 mioc7444 IF 0.01 seoc5627 IF 7.99363βe-03 ncr8272 IF 0.01 fcrb6574 IF 8.038684e-03 ncrc2831 IF 0.01 miocOlβl IF 8.075165e-03 ncrc4757 IF 0.01 miod3591 IF 8.085647e-03 seoa0145 IF 0.01 hfcr5860 IF 8.106115e-03 seob0949 IF 0.01 ncrc3416 IF 8.126637e-03 seobllOO IF 0.01 miob2287 IF 8.142773e-03 seob4793 IF 0.01 seobl419 IF 8.163409e-03 seob5812 IF 0.01 fcrc0910 IF 8.3518e-03 miod2412 IF 0.01 seob2221 IF 8.390552e-03 hfcr5969 IF 0.01 seocl785 IF 8.437195e-03 miod5622 IF 0.01 ncr3276 IF 8.480045e-03 hfcr5719 IF 0.01 seoa8902 IF 8.536355e-03 seoa8814 IF 0.01 mioa8713 IF 8.544193e-03 seoa3555 IF 0.01 fcrO224 IF 8.631293e-03 ncrb6742 IF 0.01 fcr4503 IF 8.631293e-03 miob2905 IF 0.01 mioa6093 IF 8.631293e-03 fcrb6650 IF 0.01 mioa9792 IF 8.631293e-03 fcrbl689 IF 0.01 ncr5426 IF 8.631293e-03 ncrb2092 IF 0.01 fcrc0405 IF D.Ol ncr3965 IF 0.01 fcrc2014 IF 0.01 miod6467 IF 0.01 ncr5529 IF 0.01 ncrc9343 IF 0.01 seoa8754 IF 0.01 miod0807 IF 0.01 ncrc4444 IF 0.01 ncrbO328 IF 0.01 fcrbO673 IF 0.01 seobOOβl IF 0.01 seoc3426 IF 0.01 miod7011 IF 0.01 ncrcO863 IF 0.01 hfcr2693 IF 0.01 mioc4843 IF 0.01 ncrc9228 IF 0.01 fcrb2713 IF 0.01 ncrc4903 IF 0.01 ncr8995 IF 0.01 seob9480 IF 0.01 hfcrlO83 IF 0.01 fcrb6791 IF 0.01 ncrcl904 IF 0.01 mioa2038 IF 0.01 fcrc6855 IF 0.01 hfcrO383 IF 0.01 mioc7731 IF 0.01 fcrc5190 IF 0.01 seob5099 IF 0.01 fcrc5250 IF 0.01 ncrcl999 IF 0.01 hfcrl201 IF 0.01 seoc5911 IF 0.01 mioa9007 IF 0.01 miod4539 IF 0.01 mioa9935 IF 0.01 fcrOO18 IF 0.01 mioc3906 IF 0.01 fcr0990 IF 0.01 ncr9487 IF 0.01 hfcr4275 ]F 0.01 ncrcO249 IF 0.01 mioa8767 IF 0.01 seoa0045 IF 0.01 miob3426 IF 0.01 seoa6358 IF 0.01 miob4756 IF 0.01 miob5495 IF 0.01 ncr2269 IF 0.01 mioa9649 IF 0.01 ncrc3971 IF 0.01 miod2369 IF 0.01 seoa0470 IF 0.01 ncr3934 IF 0.01 seob2994 IF 0.01 hfcr5865 IF 0.01 seoc6666 IF 0.01 fcrc6476 IF 0.01 ncr3971 IF 0.01 fcrcO471 IF 0.01 seob5562 IF 0.01 seobll45 IF 0.01 seoc4888 IF 0.01 fcrc2131 IF 0.01 ncr8357 IF 0.01 mioal293 IF 0.01 seob5579 IF 0.01 ncrc5631 IF 0.01 fcrb3017 IF 0.01 mioc3045 IF 0.01 fcrcO686 IF 0.01 seob6535 IF 0.01 miocl086 IF 0.01 seob0375 IF 0.01 mioal763 IF 0.01 fcrb8187 IF 0.01 seob6879 IF 0.01 ncr6343 IF 0.01 ncr0898 IF 0.01 ncr3483 IF 0.01 ncrc3313 IF 0.01 ncrc5363 IF 0.01 fcrb2256 IF 0.01 miob4090 IF 0.01 seoa2162 IF 0.01 fcrb7443 IF 0.01 ncrc4798 IF 0.01 miod4759 IF 0.01 seob2797 IF 0.01 fcrl855 IF 0.01 ncrc9867 IF 0.01 mioc2381 IF 0.01 fcrb6106 IF 0.01 fcr5536 IF 0.01 fcrb7703 IF 0.01 fcrcO775 IF 0.01 fcrcOO42 IF 0.01 hfcr3089 IF 0.01 hfcrll89 IF 0.01 hfcr6628 IF 0.01 mioa8778 IF 0.01 miob2285 IF 0.01 mioa8970 IF 0.01 ncrc2600 IF 0.01 miob7290 IF 0.01 seoa4174 IF 0.01 miob9248 IF 0.01 seoa7478 IF 0.01 mioc2039 IF 0.01 seob9241 IF 0.01 miod2641 IF 0.01 seoc7498 IF 0.01 ncrb5940 IF 0.01 seoa9724 IF 0.01 ncrb8319 IF 0.01 seob7309 IF 0.01 seob2689 IF 0.01 miob8812 IF 0.01 seob5032 IF 0.01 seobl844 IF 0.01 seob7946 IF 0.01 seob5899 IF 0.01 ncr2926 IF 0.01 ncr4180 IF 0.01 rαioa3331 IF 0.01 seob6882 IF 0.01 'mioa21£3' IF miod4521 IF 0.01 fcrb6301 IF 0.01 ncr2381 IF 0.01 fcr0608 IF 0.01 ncr4790 IF 0.01 fcr5354 IF 0.01 ncrb8102 IF 0.01 ncr3496 IF 0.01 ncrc2685 IF 0.01 mioc6204 IF 0.01 ncrc5150 IF 0.01 ncrb6327 IF 0.01 seoal736 IF 0.01 ncrcl665 IF 0.01 seob0547 IF 0.01 fcrb7380 IF 0.01 seob7500 IF 0.01 fcrb3135 IF 0.01 seob7722 IF 0.01 ncrcO743 IF 0.01 seoc2131 IF 0.01 seob2959 IF 0.01 miob8565 IF 0.01 hfcrlll5 IF 0.01 fcr6057 IF 0.01 ncr3148 IF 0.01 fcrc5233 IF 0.01 mioa6580 IF 0.01 mioa8919 IF 0.01 seoa7926 IF 0.01 fcrb5901 IF 0.01 seoc0355 IF 0.01 ncrbO384 IF 0.01 mioa6552 IF 0.01 fcrb2792 IF 0.01 ncrc4539 IF 0.01 seob6279 IF 0.01 seoa5302 IF 0.01 mioc4730 IF 0.01 seocl791 IF 0.01 seoa2209 IF 0.01 miod4564 IF 0.01 seoc0780 IF 0.01 mioc3682 IF 0.01 seob0534 IF 0.01 fcrb2318 IF 0.01 mioa6135 IF 0.01 fcrc4456 IF 0.01 seoc0924 IF 0.01 mioc3716 IF 0.01 seoal644 IF 0.01 ncr0420 IF 0.01 seoal263 IF 0.01 ncrb4428 IF 0.01 mioc4022 IF 0.01 miob4352 IF 0.01 seoa6867 IF 0.01 seob8261 IF 0.01 mioa2970 IF 0.01 miob6442 IF 0.01 fcrb6102 IF 0.01 hfcr2657 IF 0.01 mioal388 IF 0.01 miod7421 IF 0.01 mioal921 IF 0.01 raioc7895 IF 0.01 fcrcl090 IF 0.01 mioc2110 IF 0.01 mioc4100 IF 0.01 ncrcO696 IF 0.01 seobOOOδ IF 0.01 fcrb8321 IF 0.01 fcrb5343 IF 0.01 seoc3854 IF 0.01 fcrb6279 IF 0.01 fcr5392 IF 0.01 miod4176 IF 0.01 miod2334 IF 0.01 fcrc6335 IF 0.01 miod4355 IF 0.01 fcr6279 IF 0.01 mioa3668 IF 0.01 fcrbl618 IF 0.01 fcrc7047 IF 0.01 fcrb6107 IF 0.01 miob3084 IF 0.01 inioa2173 IF 0.01 seob9898 IF 0.01 mioa5508 IF 0.01 mioc4575 IF 0.01 mioc3669 IF 0.01 seoa5658 IF 0.01 mioc3994 IF 0.01 seob3734 IF 0.01 mioc5039 IF 0.01 fcrb2596 IF 0.01 mioc6973 IF 0.01 mioa4318 IF 0.01 miod7440 IF 0.01 seoa3408 IF 0.01 ncrb8693 IF 0.01 miodl021 IF 0.01 ncrc9217 IF 0.01 ncrc3068 IF 0.01 seoa0014 IF 0.01 ssob8204 IF 0.01 seob4676 IF 0.01 mioa2537 IF 0.01 seob7569 IF 0.01 fcrb3651 IF 0.01 seob9368 IF 0.01 fcrb2208 IF 0.01 fcr2303 IF 0.01 hfcr5023 IF 0.01 ncrcO393 IF 0.01 fcrll82 IF 0.01 mioa6035 IF 0.01 fcrb8949 IF 0.01 mioc3576 IF 0.01 hfcr2984 IF 0.01 fcrb8516 IF 0.01 hfcr5987 IF 0.01 mioa5836 IF 0.01 miobl561 IF 0.01 mioc4032 IF 0.01 mioc8437 IF 0.01 seoc4093 IF 0.01 -hfcr5514 IF ■"" OTDl seob0992 IF 0.02 mioc7509 IF 0.01 seob8562 IF 0.02 seoc6862 IF 0.01 seoc2589 IF 0.02 fcrc5344 IF 0.01 fcr2306 IF 0.02 mioc7818 IF 0.01 miod3197 IF 0.02 hfcr2770 IF 0.01 ncr3197 IF 0.02 ncrb3702 IF 0.01 seoc6030 IF 0.02 fcrb3466 IF 0.01 miocl707 IF 0.02 ncrb4182 IF 0.01 miob4760 IF 0.02 hfcr3738 IF 0.01 miob3953 IF 0.02 fcr7318 IF 0.01 seoc8175 IF 0.02 ncrb75βl IF 0.01 seoa5784 IF 0.02 ncrcO288 IF 0.01 ncrc4732 IF 0.02 ncrc4508 IF 0.01 mioa4975 IF 0.02 seoc6266 IF 0.01 ncrb6261 IF 0.02 ncrc4808 IF 0.01 ncrcl402 IF 0.02 ncr7666 IF 0.01 fcrb6028 IF 0.02 seob3419 IF 0.01 fcrcO357 IF 0.02 fcrcO529 IF 0.01 seoa7530 IF 0.02 mioa7361 IF 0.01 fcr5571 IF 0.02 fcrcl745 IF 0.01 fcrc0415 IF 0.02 miod3827 IF 0.01 miod5123 IF 0.02 ncr6925 IF 0.01 seoa7546 IF 0.02 seoa7157 IF 0.01 fcrl844 IF 0.02 seoc5228 IF 0.01 fcrb7458 IF 0.02 ncrb6073 IF 0.01 fcrc3600 IF 0.02 ncrc4015 IF 0.01 seoa3665 IF 0.02 ncr0806 IF 0.01 fcrO768 IF 0.02 fcrc4054 IF 0.01 hfcr5045 IF 0.02 fcrc5351 IF 0.01 fcrb8333 IF 0.02 ncrc4620 IF 0.01 fcrc5290 IF 0.02 seoc6696 IF 0.01 hfcrO521 IF 0.02 seob9302 IF 0.01 mioa0192 IF 0.02 fcrc6089 IF 0.01 miob4574 IF 0.02 ncrb4248 IF 0.01 miob6029 IF 0.02 ncr5473 IF 0.01 ncrbO323 IF 0.02 fcrb6062 IF 0.01 ncrbO487 IF 0.02 fcr4160 IF 0.01 ncrb7516 IF 0.02 fcrc4244 IF 0.01 ncrc3121 IF 0.02 miodl265 IF 0.02 ncrc4089 IF 0.02 mioa8858 IF 0.02 seoa2051 IF 0.02 seob2188 IF 0.02 seoa7126 IF 0.02 fcrb5644 IF 0.02 seob0185 IF 0.02 seob9750 IF 0.02 seob9420 IF 0.02 seob3747 IF 0.02 ncrc2472 IF 0.02 fcrbll20 IF 0.02 seob5652 IF 0.02 fcrb7207 IF 0.02 mioal055 IF 0.02 miod7429 IF 0.02 mioal891 IF 0.02 mioc7686 IF 0.02 ncrcO259 IF 0.02 fcrl557 IF 0.02 fcrcO569 IF 0.02 fcr3005 IF 0.02 fcrbl901 IF 0.02 fcrbl678 IF 0.02 miob8515 IF 0.02 fcrc6196 IF 0.02 seocl490 IF 0.02 hfcr4007 IF 0.02 mioal392 IF 0.02 miob7135 IF 0.02 miodl718 IF 0.02 mioc2728 IF 0.02 miod6090 IF 0.02 ncrO213 IF 0.02 miob9519 IF 0.02 ncrc2415 IF 0.02 ncr3165 IF 0.02 seoalβlβ IF 0.02 miob4864 IF 0.02 seoa2690 IF 0.02 ncr7967 IF 0.02 seoa4647 IF 0.02 fcrcl209 IF 0.02 seoa5473 IF 0.02 fcrb6084 IF 0.02 seoa7178 IF 0.02 seoa4783 IF 0.02 seoa8401 IF 0.02 mioc0090 IF 0.02 mioc7296 IF ™ "0.02 ncrc5025 IF 0.02 mioal632 IF 0.02 seoa4012 IF 0.02 fcrb4109 IF 0.02 fcr7424 IF 0.02 ncrcO445 IF 0.02 fcrb7036 IF 0.02 miocl249 IF 0.02 mioa3239 IF 0.02 fcr4782 IF 0.02 miob2355 IF 0.02 ncr7973 IF 0.02 miob9073 IF 0.02 ncrbO782 IF 0.02 miocl524 IF 0.02 ncrc2273 IF 0.02 miod2570 IF 0.02 mioc4667 IF 0.02 miod3347 IF 0.02 hfcr0501 IF 0.02 ncrbO864 IF 0.02 seoc0502 IF 0.02 ncrb2400 IF 0.02 mioc7904 IF 0.02 ncrc2807 IF 0.02 fcrb2933 IF 0.02 seoa5253 IF 0.02 fcrb6968 IF 0.02 seob2938 IF 0.02 fcrb7780 IF 0.02 seoc5218 IF 0.02 fcrc5139 IF 0.02 ncrc6861 IF 0.02 fcrc5763 IF 0.02 ncr3045 IF 0.02 mioal979 IF 0.02 mioc4161 IF 0.02 mioa6252 IF 0.02 fcrb6651 IF 0.02 miob6536 IF 0.02 fcrcl014 IF 0.02 miob9441 IF 0.02 ncrc2018 IF 0.02 mioc3683 IF 0.02 fcrcl207 IF 0.02 mioc4319 IF 0.02 fcrb6236 IF 0.02 miod5030 IF 0.02 mioc7441 IF 0.02 ncr0083 IF 0.02 ncr4648 IF 0.02 ncrcO798 IF 0.02 fcrc0096 IF 0.02 ncrc4759 IF 0.02 rαioc8682 IF 0.02 seob2661 IF 0.02 seoal789 IF 0.02 seob7404 IF 0.02 ncr5484 IF 0.02 seocl476 IF 0.02 fcr4804 IF 0.02 mioa4667 IF 0.02 fcrc5060 IF 0.02 rαioc3139 IF 0.02 ncrc9284 IF 0.02 ncr8686 IF 0.02 fcr5663 IF 0.02 seoal041 IF 0.02 miob8707 IF 0.02 seoa5731 IF 0.02 ncr3397 IF 0.02 fcrb2156 IF 0.02 seob5894 IF 0.02 miob7391 IF 0.02 miod2834 IF 0.02 mioc2394 IF 0.02 fcrb5812 IF 0.02 hfcr2890 IF 0.02 ncr0210 IF 0.02 mioa0647 IF 0.02 ncr3149 IF 0.02 seoa3109 IF 0.02 miod3027 IF 0.02 mioc7774 IF 0.02 fcrb8430 IF 0.02 fcrb9671 IF 0.02 miod6134 IF 0.02 ncr6415 IF 0.02 seoc2173 IF 0.02 ncrb6557 IF 0.02 seoa5800 IF 0.02 seob8501 IF 0.02 fcrc5547 IF 0.02 seob4057 IF 0.02 fcrb2150 IF 0.02 ncrcl595 IF 0.02 fcrb4988 IF 0.02 fcr7646 IF 0.02 fcrb6663 IF 0.02 miob9325 IF 0.02 fcrc5107 IF 0.02 ncrc9978 IF 0.02 fcrc6970 IF 0.02 fcrb2396 IF 0.02 mioa2528 IF 0.02 fcrb5351 IF 0.02 miob9284 IF 0.02 fcrb5534 IF 0.02 miob9817 IF 0.02 seob3594 IF 0.02 mioc2828 IF 0.02 miobl873 IF 0.02 miod0455 IF 0.02 mioc6878 IF 0.02 miod2977 IF 0.02 seoa9501 IF 0.02 ncr3843 IF 0.02 seob9292 IF 0.02 ncr9934 IF 0.02 fcrb6986 IF 0.02 ncrcllO2 IF 0.02 seob5004 IF 0.02 seob5767 IF 0.02 fcrb8674 IF 0.02 seob7278 IF 0.02 fcrcl298 IF 0.02 seob9092 IF 0.02 seocl764 IF 0.02 miocl992 IF 0.02 mioal621 IF 0.02 seoa0913 IF 0.02 fcr3181 IF 0.02 seoa7296 IF 0.02 miob8096 IF 0.02 seoc0098 IF 0.02 hfcr2670 IF 0.02 seoa4518 IF 0.02 mioc8934 IF 0.02 ncrc6841 IF 0.02 hfcr5959 IF 0.02 fcrbl562 IF 0.02 fcr5625 IF 0.02 miob6702 IF 0.02 seoal439 IF 0.02 mioc6274 IF 0.03 fcrb5603 IF 0.02 ncrb6675 IF 0.03 miob0974 IF 0.02 fcrb4232 IF 0.03 miob6355 IF 0.02 fcrb5164 IF 0.03 fcrb5002 IF 0.02 mioal025 IF 0.03 mioc3565 IF 0.02 miob0942 IF 0.03 ncrc2701 IF 0.02 miob4956 IF 0.03 hfcr2955 IF 0.02 mioc2619 IF 0.03 hfcrll77 IF 0.02 ncrlO55 IF 0.03 fcrb0044 IF 0.02 ncr2695 IF 0.03 seoc5662 IF 0.02 ncrb4025 IF 0.03 mioc5240 IF 0.02 ncrc3104 IF 0.03 hfcrl238 IF 0.02 ncrc5088 IF 0.03 fcrl347 IF 0.02 ncrc5947 IF 0.03 ncr6920 IF 0.02 ncrc9704 IF 0.03 seoalll7 IF 0.02 seoa2900 IF 0.03 hfcr2547 IF 0.02 seoa7129 IF 0.03 ncrb2558 IF 0.02 seob0263 IF 0.03 miod6801 IF 0.02 ncrb6949 IF 0.03 fcrb6141 IF 0.02 fcrb8447 IF 0.03 hfcrll41 IF 0.02 seoa3514 IF 0.03 hfcr5383 IF 0.02 seocl642 IF 0.03 mioa4548 IF 0.02 seoc4756 IF 0.03 miob3474 IF 0.02 seoa0024 IF 0.03 miob4673 IF 0.02 fcrb5438 IF 0.03 miob6598 IF 0.02 seoc4909 IF 0.03 miocl640 IF 0.02 miobl563 IF 0.03 ncr3123 IF 0.02 ncrc2463 IF 0.03 ncr3782 IF 0.02 seoa7078 IF 0.03 ncrcl326 IF 0.02 seob6467 IF 0.03 seoal083 IF 0.02 mioc6075 IF 0.03 seoa2402 IF 0.02 mioc4077 IF 0.03 seoa3544 IF 0.02 seob9730 IF 0.03 seoa9709 IF 0.02 fcrb7237 IF 0.03 seob2775 IF 0.02 fcrc7010 IF 0.03 seob9552 IF 0.02 ncrb4015 IF 0.03 seoc0056 IF 0.02 fcrc5372 IF 0.03 fcrb2760 IF 0.02 seoa8738 IF 0.03 fcrc5297 IF 0.02 seob6198 IF 0.03 mioa7317 IF 0.02 miod4370 IF 0.03 seoal501 IF 0.02 fcr4900 IF 0.03 miod2786 IF 0.02 seob6028 IF 0.03 fcrb4542 IF 0.02 seob4087 IF 0.03 ncrcO883 IF 0.02 fcr3528 IF 0.03 hfcr6406 IF 0.02 ncrc3141 IF 0.03 ncrc5508 IF 0.02 ncrb8404 IF 0.03 ncrc5608 IF 0.02 ncrc5327 IF 0.03 ncrb0090 IF 0.02 miob9678 IF 0.03 seob5044 IF 0.02 fcrc5379 IF 0.03 seoc5134 IF 0.02 ncr4378 IF 0.03 fcr5472 IF 0.02 miobll78 IF 0.03 mioa5097 IF 0.02 mioal532 IF 0.03 ncrc3453 IF 0.02 seob6386 IF 0.03 rαiob8586 IF 0.02 fcr4846 IF 0.03 miod4857 IF 0.02 fcrb4504 IF 0.03 mioc0649 IF 0.02 mioa0840 IF 0.03 mioa2327 - TF -"• CT.03 ncrc5844 IF 0.03 mioa4770 IF 0.03 seoal480 IF 0.03 mioc3127 IF 0.03 seoa4670 IF 0.03 mioc3962 IF 0.03 seoa4675 IF 0.03 mioc6360 IF 0.03 seoa5094 IF 0.03 miodll57 IF 0.03 seoa8642 IF 0.03 miodl542 IF 0.03 seob0063 IF 0.03 ncrl780 IF 0.03 seob6177 IF 0.03 ncrc6264 IF 0.03 miod0228 IF 0.03 ncrc9044 IF 0.03 fcrb6185 IF 0.03 ncrc9237 IF 0.03 ncrb2053 IF 0.03 seoa2801 IF 0.03 fcrb2430 IF 0.03 seobl268 IF 0.03 ncrb2027 IF 0.03 seob6087 IF 0.03 fcr6386 IF 0.03 fcr4722 IF 0.03 ncrcO715 IF 0.03 fcrb9324 IF 0.03 seob6670 IF 0.03 mioa3467 IF 0.03 fcrb3283 IF 0.03 ncrb7329 IF 0.03 seoa0064 IF 0.03 fcr4795 IF 0.03 mioa3588 IF 0.03 fcr5141 IF 0.03 seoal615 IF 0.03 seob6585 IF 0.03 ncrb7386 IF 0.03 ncr3690 IF 0.03 mioc4895 IF 0.03 seob5033 IF 0.03 fcrb6596 IF 0.03 fcrb3544 IF 0.03 fcrc5831 IF 0.03 fcrc2807 IF 0.03 seoc6004 IF 0.03 seoa0044 IF 0.03 fcrb2120 IF 0.03 mioc3593 IF 0.03 ncrc2439 IF 0.03 fcrb2697 IF 0.03 seocl495 IF 0.03 hfcr5003 IF 0.03 seoa3863 IF 0.03 mioc7084 IF 0.03 ncrb3585 IF 0.03 seoc2221 IF 0.03 ncr9549 IF 0.03 seob5021 IF 0.03 fcrb7593 IF 0.03 fcrb3309 IF 0.03 fcrb9481 IF 0.03 fcrcl828 IF 0.03 fcrc2222 IF 0.03 ncrc4851 IF 0.03 fcrc6084 IF 0.03 fcrO824 IF 0.03 fcrc7214 IF 0.03 mioa4326 IF 0.03 hfcr5706 IF 0.03 seoa5685 IF 0.03 mioa4037 IF 0.03 seoa9377 IF 0.03 mioa5468 IF 0.03 miob0202 IF 0.03 mioa6102 IF 0.03 fcrb5929 IF 0.03 mioa6734 IF 0.03 seob6625 IF 0.03 mioa8074 IF 0.03 ncr0019 IF 0.03 miobll39 IF 0.03 fcrc5137 IF 0.03 miob3044 IF 0.03 fcrl914 IF 0.03 miob5647 IF 0.03 fcrbl775 IF 0.03 mioc2204 IF 0.03 ncrc6925 IF 0.03 ncr3588 ' IF 0.03 ncrb3348 IF 0.03 ncr4040 IF 0.03 ncrcl537 IF 0.03 ncr8725 IF 0.03 seoa3908 IF 0.03 ncr9469 IF 0.03 ncr9003 IF 0.03 ncrb8203 IF 0.03 seoc6099 IF 0.03 ncrc0508 IF 0.03 fcrlOβO IF 0.03 seoa24'43 IF 0.03 fcr3559 IF 0.03 seoa3429 IF 0.03 fcr4494 IF 0.03 seoa3701 IF 0.03 fcrb2190 IF 0.03 seoa7366 IF 0.03 fcrc2598 IF 0.03 seoa9711 IF 0.03 hfcr4640 IF 0.03 seob8914 IF 0.03 miob0636 IF 0.03 seoc2136 IF 0.03 miob3234 IF 0.03 mioc4472 IF 0.03 miodl908 IF 0.03 seoc0284 IF 0.03 ncrb4166 IF 0.03 ncrbl247 IF 0.03 ncrcl889 IF 0.03 hfcr2664 IF 0.03 ncrc3171 IF 0.03 miod4467 IF 0.03 "fcrb869TT~ ■-' TF ...... σ.. σ3 seoa6364 IF 0.03 seoa6930 IF 0.03 mioc3137 IF 0.03 seob4145 IF 0.03 ncrc8903 IF 0.03 fcr3367 IF 0.03 fcrb3715 IF 0.03 fcr6630 IF 0.03 fcr5190 IF 0.04 miod6292 IF 0.03 seoc5467 IF 0.04 hfcr6276 IF 0.03 mioa2783 IF 0.04 ncr3751 IF 0.03 ncr3219 IF 0.04 fcrb5272 IF 0.03 seoa9828 IF 0.04 ncrc5716 IF 0.03 miob9529 IF 0.04 hfcr2821 IF 0.03 miob8694 IF 0.04 fcrb3718 IF 0.03 seoa8300 IF 0.04 fcrb5346 IF 0.03 seob0154 IF 0.04 ncrc8881 IF 0.03 ncrcl380 IF 0.04 miob0681 IF 0.03 hfcr0370 IF 0.04 ncrc8892 IF 0.03 fcrb7255 IF 0.04 mioa3335 IF 0.03 fcrb6208 IF 0.04 ncrb8496 IF 0.03 ncrc5959 IF 0.04 mioc7781 IF 0.03 fcr5316 IF 0.04 seobl834 IF 0.03 seob4579 IF 0.04 ncrcl995 IF 0.03 miod4367 IF 0.04 miocl971 IF 0.03 fcr2442 IF 0.04 mioc2360 IF 0.03 fcr7561 IF 0.04 ncrcl751 IF 0.03 fcrbl916 IF 0.04 seoa0860 IF 0.03 fcrb3518 IF 0.04 seoa3737 IF 0.03 fcrb4231 IF 0.04 ncrc6587 IF 0.03 fcrc6997 IF 0.04 seoa4606 IF 0.03 hfcrO439 IF 0.04 seoa2244 IF 0.03 hfcrO489 IF 0.04 fcrb6949 IF 0.03 hfcr2584 IF 0.04 mioc2021 IF 0.03 mioa0601 IF 0.04 seobQ872 IF 0.03 mioa0869 IF 0.04 ncr3118 IF 0.03 mioa2079 IF 0.04 miod6032 IF 0.03 mioa7069 IF 0.04 hfcrlO37 IF 0.03 miob9714 IF 0.04 fcr4471 IF 0.03 miocll25 IF 0.04 fcr3599 IF 0.03 miod4407 IF 0.04 fcrbl854 IF 0.03 ncr3718 IF 0.04 fcrc4841 IF 0.03 ncr7093 IF 0.04 hfcr3615 IF 0.03 ncrb0033 IF 0.04 mioa8998 IF 0.03 ncrc6825 IF 0.04 mioc0206 IF 0.03 ncrc7029 IF 0.04 mioc3300 IF 0.03 seoa0207 IF 0.04 mioc6114 IF 0.03 seoa9160 IF 0.04 miod5126 IF 0.03 seobll96 IF 0.04 ncr5709 IF 0.03 seobl586 IF 0.04 ncrb3445 IF 0.03 seob8710 IF 0.04 ncrc3100 IF 0.03 seoc5888 IF 0.04 seoa5455 IF 0.03 ncr2175 IF 0.04 seoa5520 IF 0.03 fcrc4188 IF 0.04 seob8807 IF 0.03 seoa6144 IF 0.04 seoc4187 IF 0.03 fcrbl741 IF 0.04 fcrb5339 IF 0.03 ncrb3638 IF 0.04 ncrc7038 IF 0.03 seoa2219 IF 0.04 fcrb3702 IF 0.03 ncrc2128 IF 0.04 fcrc4814 IF 0.03 miodl236 IF 0.04 fcr4984 IF 0.03 seoc4038 IF 0.04 mioa7258 IF 0.03 seoa8521 IF 0.04 seob6844 IF 0.03 fcrc3640 IF 0.04 fcrb8762 IF 0.03 fcrc5699 IF 0.04 seob0674 IF 0.03 fcrb4781 IF 0.04 mioc7764 IF 0.03 miob8249 IF 0.04 miob0931 IF 0.03 ncrc3953 IF 0.04 ncrb6378 IF 0.03 ncr2994 IF 0.04 ϊniόkS'86'5'71""" IF fcrc4180 IF 0.04 fcrb6281 IF 0.04 seocl073 IF 0.04 ncrb5595 IF 0.04 seob8693 IF 0.04 fcrbl573 IF 0.04 fcrbl344 IF 0.04 seobl617 IF 0.04 mioa8973 IF 0.04 seobl757 IF 0.04 fcrb5181 IF 0.04 fcrb2034 IF 0.04 fcrcO839 IF 0.04 fcrb5763 IF 0.04 seoc0009 IF 0.04 fcrb7829 IF 0.04 fcrb6640 IF .0.04 mioal513 IF 0.04 ncrc5907 IF 0.04 ncrb6581 IF 0.04 fcrb2624 IF 0.04 miod2837 IF 0.04 seoc0619 IF 0.04 miob6098 IF 0.04 seob5889 IF 0.04 seoa6621 IF 0.04 fcrb2196 IF 0.04 ncrc6920 IF 0.04 fcrb4985 IF 0.04 miocl808 IF 0.04 fcrb8093 IF 0.04 ncrb6337 IF 0.04 fcrb8614 IF 0.04 ncrcl247 IF 0.04 fcrcO112 IF 0.04 mioc0909 IF 0.04 hfcr3091 IF 0.04 miod0878 IF 0.04 hfcrβllO IF 0.04 mioc0240 IF 0.04 ncr3686 IF 0.04 ncrO612 IF 0.04 ncrcll43 IF 0.04 seoa0040 IF 0.04 ncrc2949 IF 0.04 fcrO253 IF 0.04 ncrc6382 IF 0.04 fcrO186 IF 0.04 seoa8716 IF 0.04 fcr2218 IF 0.04 seoa9082 IF 0.04 fcrb4817 IF 0.04 seob4333 IF 0.04 fcrb6796 IF 0.04 seob4612 IF 0.04 fcrb6939 IF 0.04 seoc4288 IF 0.04 fcrb7748 IF 0.04 mioa9127 IF 0.04 mioa4721 IF 0.04 seob0261 IF 0.04 mioa8836 IF 0.04 mioa3469 IF 0.04 mioc5336 IF 0.04 ncrc7005 IF 0.04 ncr0570 IF 0.04 seob4172 IF 0.04 ncrcO936 IF 0.04 seobl093 IF 0.04 ncrc3936 IF 0.04 fcrb3160 IF 0.04 ncrc4740 IF 0.04 ncrb8383 IF 0.04 ncrc5207 IF 0.04 fcrb7528 IF 0.04 seoal584 IF 0.04 mioa8539 IF 0.04 seoa3251 IF 0.04 mioc3081 IF 0.04 seoa7250 IF 0.04 fcrcO271 IF 0.04 seoa7647 IF 0.04 ncrb8201 IF 0.04 seobl660 IF 0.04 seoc2504 IF 0.04 seob5558 IF 0.04 mioc7854 IF 0.04 seob8853 IF 0.04 seob3455 IF 0.04 ncrb3903 IF 0.04 ncrl699 IF 0.04 ncrc5016 IF 0.04 seoc4513 IF 0.04 mioa7955 IF 0.04 mioc6298 IF 0.04 hfcr3143 IF 0.04 miob6459 IF 0.04 seoal924 IF 0.04 fcrb7803 IF 0.04 ncrbl438 IF 0.04 mioc4996 IF 0.04 seoa9712 IF 0.04 seoa2936 IF 0.04 fcrc4125 IF 0.04 fcrbl380 IF 0.04 ncrc3283 IF 0.04 fcrb5294 IF 0.04 seoc5780 IF 0.04 ncrc6459 IF 0.04 ncr3483 IF 0.04 miob8711 IF 0.04 seoc0999 IF 0.04 seoa3230 IF 0.04 ncrc9286 IF 0.04 miob0496 IF 0.04 mioc8671 IF 0.04 mioc6925 IF 0.04 mioa4542 IF 0.04 seoa9792 IF 0.04 mioal687 IF 0.04 seob6008 IF 0.04 mioa4196 IF 0.04 seoa9046 IF 0.04 seob2161 IF 0.04 seob4515 IF 0.04 miob7627 IF 0.04 seoa4202 IF 0.04 mlob4712"" Tf „..,..(J-.Q..J seob0294 IG 4.757821e-03 seoa5528 IF 0.04 fcr4746 IG 5.04282e-03 ncrcl761 IF 0.04 ncrl355 IG 5.04282e-03 miod3366 IF 0.04 miod5957 IG 5.342732e-03 fcrb8495 IF 0.04 ncrbO328 IG 5.342732e-03 fcrcO487 IF 0.04 seoa3639 IG 5.342732e-03 hfcrO594 IF 0.04 seoc2646 IG 5.342732e-03 mioa0776 IF 0.04 ncrb5117 IG 5.497749e-03 mioa7015 IF 0.04 fcr6635 IG 5.658209e-03 mioa7169 IF 0.04 fcrb3808 IG 5.658209e-03 mioa8773 IF 0.04 fcrc7087 IG 5.658209e-03 miob7938 IF 0.04 miob0189 IG 5.658209e-03 miod6025 IF 0.04 mioc7372 IG 5.658209e-03 ncr8628 IF 0.04 ncr8606 IG 5.989924e-03 ncrb3980 IF 0.04 fcrc4692 IG 6.33857e-03 ncrc2763 IF 0.04 miob4574 IG 6.33857e-03 ncrc4000 IF 0.04 ncrc4757 IG 6.33857e-03 ncrc5162 IF 0.04 hfcr2756 IG 6.704863e-03 seoa5366 IF 0.04 miob9030 IG 6.704863e-03 seob8817 IF 0.04 seoa2162 IG 6.704863e-03 ncr7941 IF 0.04 fcrb6211 IG 7.089543e-03 mioa3646 IF 0.04 hfcr5260 IG 7.089543e-03 seoc2191 IF 0.04 mioa5355 IG 7.089543e-03 fcrb2015 IF 0.04 ncr5975 IG 7.493369e-03 miod6058 IF 0.04 fcrb2624 IG 7.917127e-03 ncrc3468 IF 0.04 fcrc5372 IG 7.917127e-03 seob7658 IF 0.04 ncr2861 IG 7.917127e-03 ncrc6981 IF 0.04 seoa3121 IG 7.917127e-03 fcrb4391 IF 0.04 hfcr5003 IG 8.361625e-03 miod6609 IF 0.04 mioa9357 IG 8.361625e-03 mioc8479 IG 1.54e-05 ncrb8539 IG 8.361625e-03 fcrcO559 IG 3.01e-05 seobl808 IG 8.361625e-03 seoa6560 IG 7.42e-05 fcr4306 IG 8.590878e-03 miob7985 IG 3.4914e-04 mioa6659 IG 8.827692e-03 πιiobl946 IG 4.71848e-04 miob4413 IG 8.827692e-03 ncr3527 IG 5.88272e-04 ncrc2289 IG 8.827692e-03 fcrb6815 IG 9.02252e-04 mioc0999 IG 9.316185e-03 mioc6260 IG 1.110144e-03 seoa0488 IG 9.316185e-03 hfcrββll IG 1.188468e-03 fcr2303 IG 9.827982e-03 mioa3399 IG 1.359944e-03 fcrbl769 IG 9.827982e-03 fcrb9851 IG 1.360187e-03 fcrb5100 IG 9.827982e-03 seoc2006 IG 1.360187e-03 fcrcO345 IG 9.827982e-03 ncrl699 IG 1.454132e-03 fcrc2573 IG 9.827982e-03 ncrl282 IG 1.89084e-03 fcrc5087 IG 9.827982e-03 fcrb5077 IG 2.016885e-03 miob9336 IG 9.827982e-03 mioc7444 IG 2.016885e-03 mioc3332 IG 9.827982e-03 miob0167 IG 2.291704e-03 mioc4747 IG 9.827982e-03 seoa8299 IG 2.291704e-03 fcr4160 IG 0.01 ncrO761 IG 2.441249e-03 miob9668 IG 0.01 seob3025 IG 2.766654e-03 fcrb5775 IG 0.01 seob8051 IG 3.13006e-03 miob9519 IG 0.01 mioa8970 IG 3.535205e-03 mioc5103 IG 0.01 ncrbO164 IG 3.535205e-03 ncrc3604 IG 0.01 seob5632 IG 3.535205e-03 ncrc5500 IG 0.01 seoa2233 IG 3.754676e-03 fcrb6509 IG 0.01 seob9552 IG 3.754676e-03 fcrc4408 IG 0.01 mioa0626 IG 3.986113e-03 miod4220 IG 0.01 fcrb9280 IG 4.230067e-03 ncr9105 IG 0.01 fcrc6119 IG 4.230067e-03 ncrc5150 IG 0.01 miob4956 IG 4.230067e-03 seoal736 IG 0.01 ncr4860 IG 4.487107e-03 seoa8684 IG 0.01 fcrc4506 IG 4.757821e-03 hfcr3180 IG 0.01 miob2210 IG 4.757821e-03 ncr4189 IG 0.01 seoa5833 IG 4.757821e-03 seob3090 IG 0.01 seob9574 ΓG- ' OTOl mioc2133 IG 0.02 seoc4135 IG 0.01 mioc3430 IG 0.02 miob7765 IG 0.01 ncrb6282 IG 0.02 seob0304 IG 0.01 seoal734 IG 0.02 seoc4078 IG 0.01 miob5810 IG 0.02 seoc4380 IG 0.01 mioc5643 IG 0.02 fcrb2218 IG 0.01 ncr9337 IG 0.02 mioal015 IG 0.01 ncrcl602 IG 0.02 mioal660 IG 0.01 ncrc4402 IG 0.02 mioa4009 IG 0.01 seob0483 IG 0.02 mioa5461 IG 0.01 fcrc5041 IG 0.02 mioa9792 IG 0.01 mioc7471 IG 0.02 mioc3523 IG 0.01 miodl292 IG 0.02 ncrc3856 IG 0.01 ncrcO174 IG 0.02 seocl348 IG 0.01 seoa4457 IG 0.02 seoc4484 IG 0.01 seob5748 IG 0.02 rαiob9340 IG 0.01 fcrb6188 IG 0.02 mioc0302 IG 0.01 mioa9630 IG 0.02 fcr2798 IG 0.01 miod0775 IG 0.02 fcrc5509 IG 0.01 ncrb6530 IG 0.02 mioa2522 IG 0.01 seoa5433 IG 0.02 fcrb2818 IG 0.01 seoa6695 IG 0.02 fcrcO727 IG 0.01 seob9734 IG 0.02 mioc0227 IG 0.01 fcr2598 IG 0.02 ncrb8203 IG 0.01 fcrb6281 IG 0.02 miodl200 IG 0.01 fcrb7965 IG 0.02 seob7409 IG 0.01 fcrc7243 IG 0.02 fcrb7063 IG 0.01 miob5969 IG 0.02 hfcr6486 IG 0.01 miob6245 IG 0.02 miob0180 IG 0.01 ncrc3408 IG 0.02 miob9068 IG 0.01 fcrb7861 IG 0.02 ncrb2870 IG 0.01 fcrcO967 IG 0.02 seoa370i IG 0.01 mioa3856 IG 0.02 fcrbl741 IG 0.01 miob9284 IG 0.02 mioa5614 IG 0.01 mioc7542 IG 0.02 mioc3716 IG 0.01 miod4129 IG 0.02 mioc3958 IG 0.01 ncr8171 IG 0.02 ncr8041 IG 0.01 ncrbO513 IG 0.02 ncrb8207 IG 0.01 seoa5848 IG 0.02 ncrc9304 IG 0.01 seoa7383 IG 0.02 seoc3321 IG 0.01 seob4612 IG 0.02 fcrb4712 IG 0.01 seoc2681 IG 0.02 ncrc0097 IG 0.01 fcrclO43 IG 0.03 seoc7203 IG 0.01 fcrc2683 IG 0.03 mioa8032 IG 0.01 hfcr3902 IG 0.03 miob9882 IG 0.01 mioa0909 IG 0.03 seocl077 IG 0.01 mioc8694 IG 0.03 miobl559 IG 0.02 ncrc2618 IG 0.03 hfcr5157 IG 0.02 seoa4422 IG 0.03 mioc4983 IG 0.02 seoc3302 IG 0.03 mioc7665 IG 0.02 fcrb5503 IG 0.03 mioc8423 IG 0.02 fcrb6536 IG 0.03 seob8092 IG 0.02 fcrb7645 IG 0.03 seoc2224 IG 0.02 fcrcl563 IG 0.03 fcr4308 IG 0.02 fcrc2254 IG 0.03 mioal674 IG 0.02 miob6364 IG 0.03 mioc8257 IG 0.02 miocl971 IG 0.03 miod6671 IG 0.02 mioc5664 IG 0.03 ncrcO667 IG 0.02 ncrc2404 IG 0.03 fcr3525 IG 0.02 seoa3891 IG 0.03 fcrb4579 IG 0.02 seoa9729 IG 0.03 mioa9033 IG 0.02 seobl268 IG 0.03 miob8026 IG 0.02 seob2994 IG 0.03 miob8583 IG 0.02 seob5214 IG 0.03 seoc0957 IG O .03 seoa4366 IG 0.04 fcr3714 IG 0.03 seob6156 IG 0.04 mioc3603 IG 0.03 fcrc2817 IG 0.04 ncrO612 IG 0.03 hfcr5220 IG 0.04 ncr0701 IG 0.03 ncrb5192 IG 0.04 ncrcO461 IG 0.03 seoa2087 IG 0.04 seob5342 IG 0.03 seoa3002 IG 0.04 miob5855 IG 0.03 seob0344 IG 0.04 mioc6374 IG 0.03 seocl561 IG 0.04 miod0708 IG 0.03 fcr2821 IG 0.04 miod2886 IG 0.03 fcrb4367 IG 0.04 ncrb3011 IG 0.03 fcrb5000 IG 0.04 seob2155 IG 0.03 fcrcO112 IG 0.04 seob4537 IG 0.03 fcrcO591 IG 0.04 seob9292 IG 0.03 mioa2374 IG 0.04 seocl234 IG 0.03 miod7243 IG 0.04 seoc4137 IG 0.03 ncr2486 IG 0.04 fcrb8114 IG 0.03 ncrc9237 IG 0.04 fcrc0075 IG 0.03 seoa8443 IG 0.04 fcrc2013 IG 0.03 seob0918 IG 0.04 rαioal532 IG 0.03 fcrb2633 IG 0.04 miob2293 IG 0.03 fcrb5903 IG 0.04 mioc2369 IG 0.03 hfcr2544 IG 0.04 miod7460 IG 0.03 miob4091 IG 0.04 ncrO673 IG 0.03 miob7319 IG 0.04 ncrc9343 IG 0.03 mioc4009 IG 0.04 seoa3628 IG 0.03 miodl908 IG 0.04 seobll45 IG 0.03 ncrclO31 IG 0.04 seob5113 IG 0.03 seσa3633 IG 0.04 seob5219 IG 0.03 seob9674 IG 0.04 seob8817 IG 0.03 seoc3554 IG 0.04 seoc2295 IG 0.03 fcrb5146 IG 0.04 fcr5509 IG 0.03 miob9010 IG 0.04 fcr6228 IG 0.03 mioc6946 IG 0.04 fcrb2318 IG 0.03 miod0455 IG 0.04 fcrb3474 IG 0.03 miod2211 IG 0.04 fcrb5259 IG 0.03 ncrcO262 IG 0.04 fcrc2787 IG 0.03 ncrc3093 IG 0.04 hfcrl968 IG 0.03 ncrc4384 IG 0.04 rαiod3132 IG 0.03 seoal480 IG 0.04 ncr8538 IG 0.03 seoa2899 IG 0.04 ncr8594 IG 0.03 seoa6118 IG 0.04 ncrcO185 IG 0.03 seoa6144 IG 0.04 ncrcl531 IG 0.03 miob8143 IH 9.6e-05 ncrc3324 IG 0.03 fcrb6191 IH 2.18748e-04 seoa4012 IG 0.03 fcrb4995 IH 5.08096e-04 fcrb2462 IG 0.03 hfcr4477 IH 5.46853e-04 fcrb4360 IG 0.03 miob7209 IH 7.83893e-04 fcrb5948 IG 0.03 fcrb9202 IH 9.02252e-04 miob4760 IG 0.03 ncr2472 IH 9.02252e-04 miob7794 IG 0.03 miob7554 IH 9.67277e-04 mioc4351 IG 0.03 fcrb3330 IH 1.188468e-03 ncr2926 IG 0.03 seoc2191 IH 1.188468e-03 ncrbl365 IG 0.03 miob4308 IH 1.659667e-03 seoa5392 IG 0.03 fcrb2162 IH 2.15038e-03 seobl889 IG 0.03 mioc2451 IH 2.15038e-03 seobβ446 IG 0.03 miob8572 IH 2.291704e-03 seocl278 IG 0.03 fcrc6345 IH 2.441249e-03 fcrcOββl IG 0.04 miocl203 IH 2.441249e-03 hfcrl259 IG 0.04 seob3112 IH 2.441249e-03 hfcr4497 IG 0.04 seoc3965 IH 2.599424e-03 miod7246 IG 0.04 fcrb7944 IH 3.1300βe-03 ncr3233 IG 0.04 fcrc2807 IH 3.13006e-03 ncrc5359 IG 0.04 hfcrlδll IH 3.32717e-03 mxod5703 IH -~y.32717e-03 fcrc5850 IH 0.02 ncr8628 IH 3.986113e-03 fcrb4413 IH 0.03 ncrb3957 IH 3.986113e-03 ncr8413 IH 0.03 seob8741 IH 3.986113e-03 seob4545 IH 0.03 fcr7042 IH 4.487107e-03 fcrl312 IH 0.03 mxod7421 IH 5.04282e-03 fcrc6010 IH 0.03 fcrl984 IH 5.342732e-03 mxoa9581 IH 0.03 mioc3930 IH 5.342732e-03 mxoa9649 IH 0.03 mxoc0728 IH 5.989924e-03 ncr2812 IH 0.03 seob5478 IH 7.089543e-03 seoa8851 IH 0.03 fcrc5614 IH 7.917127e-03 seob3141 IH 0.03 ncrb8343 IH 7.917127e-03 mxod5785 IH 0.03 fcr3323 IH 8.361625e-03 mxocl910 IH 0.03 fcrbl428 IH 8.827692e-03 fcrb3897 IH 0.03 mioc0669 IH 8.827692e-03 fcrb9430 IH 0.03 miod5122 IH 8.827692e-03 mxocl060 IH 0.03 ncrcO729 IH 9.316185e-03 mxod4066 IH 0.03 fcrb4226 IH 0.01 ncrb5595 IH 0.03 seoa0429 IH 0.01 seob7929 IH 0.03 seob2966 IH 0.01 fcrb4981 IH 0.03 fcrb6031 IH 0.01 fcrcl781 IH 0.03 fcrcO166 IH 0.01 mxoc2662 IH 0.03 fcrb2041 IH 0.01 ncrb8105 IH 0.03 fcrb9680 IH 0.01 seoal977 IH 0.03 hfcr3149 IH 0.01 seob0288 IH 0.03 ncr7292 IH 0.01 mxob8773 IH 0.03 ncrc6382 IH 0.01 seoa5214 IH 0.03 mioc8153 IH 0.01 fcrb2051 IH 0.04 seob3485 IH 0.01 hfcr5905 IH 0.04 mxoc7986 Ih 0.01 mxoa2073 IH 0.04 seob9406 IH 0.01 seoa3555 IH 0.04 mxob2656 IH 0.01 seob0688 IH 0.04 ncrcO217 IH 0.01 fcr7295 IH 0.04 seob5069 IH 0.01 mxoa5085 IH 0.04 mxoa6442 IH 0.01 ncrc3598 IH 0.04 seoc0945 IH 0.01 ncrc5780 IH 0.04 fcr3664 IH 0.01 seoa5787 IH 0.04 fcrb5813 IH 0.01 fcr4471 IH 0.04 mxoc3139 IH 0.01 mxob7373 IH 0.04 mxoc6937 IH 0.01 ncr5651 IH 0.04 ncrc8892 IH 0.01 ncrc5844 IH 0.04 seoa2641 IH 0.01 fcrc4916 IH 0.04 seob8321 IH 0.01 mxoc2074 IH 0.04 fcrc4380 IH 0.02 seob2959 IH 0.04 ncr6142 IH 0.02 seocl025 IH 0.04 seoa2448 IH 0.02 seoc6182 IH 0.04 seoc0499 IH 0.02 mxoa8851 IH 0.04 fcrb9686 IH 0.02 mxob9788 IH 0.04 ncr3037 IH 0.02 mxoc5695 IH 0.04 seob0497 IH 0.02 seoa9709 IH 0.04 fcrb9324 IH 0.02 seob6535 II 1.74042e-04 hfcr4007 IH 0.02 ncrcO262 II 4.7724e-04 mxoc2997 IH 0.02 seoc3965 II 5.85651e-04 ncrb4331 IH 0.02 ncr9105 II 8.16756e-04 ncrcl885 IH 0.02 seob5962 II 9.30251e-04 seob4925 IH 0.02 fcrb6202 II 1.127264e-03 mxoc0162 IH 0.02 mxoa3629 II 1.127264e-03 seoa2381 IH 0.02 fcrc3907 II 1.200832e-03 seoa5554 IH 0.02 ncr2484 II 1.200832e-03 seoc4720 IH 0.02 seob6395 II 1.200832e-03 hfcr2250 IH 0.02 fcrb8994 II 1.361048e-03 mxoa2377 IH 0.02 seoal747 II 1.44814e-03 seob4197 IH 0.02 mxoa4014 II 1.540198e-03 fcrc2670 IH 0.02 seoa5986 II 1.848665e-03 fcrO187 II I.963134e-03 miob7136 II 0.01 fcrb2759 II 1.963134e-03 miod0126 II 0.01 mioc5113 II 1.963134e-03 hfcr5691 II 0.01 fcrb6005 II 2.083894e-03 mioa0247 II 0.01 miob0178 II 2.083894e-03 mioa8818 II 0.01 seoa3322 II 2.083894e-03 mioa8899 II 0.01 ncrc2831 II 2.486944e-03 ncrc3072 II 0.01 ncr5713 II 2.63595e-03 ncrc4033 II 0.01 ncrb8538 II 2.792845e-03 fcrb7183 II 0.01 ncrc6712 II 2.792845e-03 mioa4057 II 0.01 miod0340 II 2.957987e-03 mioc0226 II 0.01 fcrb7453 II 3.131743e-03 mioc7782 II 0.01 ncrcl374 II 3.131743e-03 ncr9337 II 0.01 miodl809 II 3.314495e-03 seoal552 II 0.01 ncr2382 II 3.314495e-03 seoa8655 II 0.01 ncrbO513 II 3.314495e-03 fcrb5269 II 0.01 ncrc0304 II 3.314495e-03 ncr3368 II 0.01 ncrcl203 II 3.314495e-03 ncrc4259 II 0.01 mioa8318 II 3.506638e-03 seoa5766 II 0.01 seocl934 II 3.70858e-03 mioa9323 II 0.01 fcrbl917 II 3.920744e-03 ncrcl379 II 0.01 seoa3108 II 4.623001e-03 ncrc5500 II 0.01 seob2303 II 4.623001e-03 seoa6151 II 0.01 fcrb4995 II 5.150669e-03 fcrb9289 II 0.01 mioa0647 II 5.150669e-03 fcrb9871 II 0.01 ncrb8063 II 5.150669e-03 fcrc7358 II 0.01 seoa2854 II 5.150669e-03 mioa8774 II 0.01 hfcr4176 II 5.433839e-03 ncr4384 II 0.01 mioa3913 II 5.433839e-03 seoa2801 II 0.01 ncrclO48 II 5.433839e-03 seob0928 II 0.01 fcrb8208 II 5.730596e-03 seob4333 II 0.01 miob0795 II 5.730596e-03 seob5021 II 0.01 fcrb6829 II 6.041484e-03 fcrbO131 II 0.01 miob2601 II 6.041484e-03 fcrc2231 II 0.01 seoa5396 II 6.041484e-03 hfcr2894 II 0.01 fcrb4367 II 6.367062e-03 seoa4317 II 0.01 fcrb6728 II 6.367062e-03 fcrb2318 II 0.01 fcrc2314 II 6.367062e-03 fcrb4918 II 0.01 ncrb0045 II 6.707903e-03 mioa6585 II 0.01 fcrb3664 II 7.064602e-03 ncrcO728 II 0.01 seoc2144 II 7.064602e-03 fcrb2715 II 0.01 mioal353 II 7.437766e-03 ncr7093 II -0.01 miobl789 II 7.437766e-03 ncr8112 II 0.01 ncrl526 II 7.437766e-03 seoblOOδ II 0.01 ncrc9187 II 7.437766e-03 fcrb5000 II 0.01 fcrb4717 II 7.828021e-03 fcrb5622 II 0.01 miod5682 II 7.828021e-03 mioa4782 II 0.01 seob6139 II 7.828021e-03 ncrcl775 II 0.01 seob9193 II 7.828021e-03 fcrbl855 II 0.01 seobl052 II 8.236011e-03 fcrb2113 II 0.01 seoc7566 II 8.236011e-03 hfcr2584 II 0.01 miod0978 II 8.662397e-03 mioa2478 II 0.01 seoa5253 II 8.662397e-03 mioc2204 II 0.01 fcrbl441 II 9.107857e-03 miod5894 II 0.01 fcrb8202 II 9.107857e-03 seoc0514 II 0.01 fcrc2472 II 9.107857e-03 fcr3856 II 0.01 hfcrl646 II 9.107857e-03 fcrb7616 II 0.01 miod3160 II 9.107857e-03 fcrb7703 II 0.01 fcr7667 II 9.573086e-03 fcrcO396 II 0.01 fcrb4929 II 9.573086e-03 fcrc6460 II 0.01 fcrb9069 II 9.573086e-03 miob3199 II 0.01 mioa3693 II 0.01 miob6688 II 0.01 mioa3963 II 0.01 seoc2336 II 0.01 mioa9154 II 0.01 fcrbl644 II 0.01 ϊcrbβϊΗl"" ' Iϊ "-■O".σi seoaOlOl II 0.02 mioa3440 II 0.01 fcr2088 II 0.02 ncrc2161 II 0.01 miob8373 II 0.02 seoa2822 II 0.01 mioc6956 II 0.02 hfcr4055 II 0.01 ncrb5537 II 0.02 miocll87 II 0.01 ncrc5724 II 0.02 ncr7341 II 0.01 seoa2837 II 0.02 seoalβll II 0.01 seoa7923 II 0.02 seoa3748 II 0.01 seoc2317 II 0.02 seoa4717 II 0.01 fcrb6937 II 0.02 seoa5848 II 0.01 mioa0494 II 0.02 seob8807 II 0.01 mioa9492 II 0.02 fcrb2796 II 0.01 seoa8351 II 0.02 fcrb5087 II 0.01 seobl219 II 0.02 fcrc5799 II 0.01 fcrb9751 II 0.02 mioa5836 II 0.01 fcrc3958 II 0.02 ncr7973 II 0.01 mioc7781 II 0.02 ncrc4815 II 0.01 ncr3587 II 0.02 ncrc7016 II 0.01 ncr7666 II 0.02 seob8291 II 0.01 ncrc6796 II 0.02 seoc0951 II 0.01 fcrb6917 II 0.03 fcrl984 II 0.02 mioa9719 II 0.03 fcrb2996 Ii 0.02 miodl925 II 0.03 fcrb8936 II 0.02 ncrcl884 II 0.03 fcrc4688 II 0.02 ncrc4869 II 0.03 hfcrlδll II 0.02 fcrb3017 II 0.03 mioa6704 II 0.02 fcrc2280 II 0.03 ncrb8392 II 0.02 fcrc6940 II 0.03 seob7082 II 0.02 mioa2290 II 0.03 fcrc6972 II 0.02 mioa6471 II 0.03 seca7129 II 0.02 miobl778 II 0.03 seob4612 II 0.02 mioc8635 II 0.03 fcrbO146 II 0.02 ncrbO916 II 0.03 fcrb6759 II 0.02 fcrbl329 II 0.03 miob8487 II 0.02 hfcrlO53 II 0.03 miocl248 II 0.02 mioa4484 II 0.03 ncr2260 II 0.02 mioc2097 II 0.03 seoa2528 II 0.02 mioc9775 II 0.03 fcr0680 II 0.02 miod7246 II 0.03 fcr5120 II 0.02 seoa9656 II 0.03 fcrb5631 II 0.02 seoc5842 II 0.03 mioa2620 II 0.02 fcr2079 II 0.03 miob3911 II 0.02 fcrb9068 II 0.03 miob4057 II 0.02 fcrc6470 II 0.03 mioc2726 II 0.02 mioc7216 II 0.03 ncrb4869 II 0.02 mioc7998 II 0.03 ncrcO558 II 0.02 ncr3412 II 0.03 ncrc8863 II 0.02 ncrc6584 II 0.03 seoa5461 II 0.02 seoa4017 II 0.03 seob6417 II 0.02 seoa5156 II 0.03 seoc4960 II 0.02 fcrb4994 II 0.03 fcrb6968 II 0.02 fcrb7939 II 0.03 fcrcO892 II 0.02 fcrb9543 II 0.03 fcrc5068 II 0.02 fcrc2619 II 0.03 hfcr3921 II 0.02 hfcr2963 II 0.03 mioc0019 II 0.02 miob9201 II 0.03 ncr5168 II 0.02 mioc5692 II 0.03 ncrc2701 II 0.02 miod4938 II 0.03 ncrc5245 II 0.02 ncrbl518 II 0.03 seoa8401 II 0.02 ncrb8177 II 0.03 mioc0911 II 0.02 ncrc7171 II 0.03 miod3785 II 0.02 seoc2191 II 0.03 ncrbO163 II 0.02 fcrb4229 II 0.03 ncrb6833 II 0.02 fcrb6549 II 0.03 tiϊbrffOΩ II "" ""OTO"3 ncrbO571 II 0.04 mioa0332 II 0.03 ncrcO539 II 0.04 mioa9788 II 0.03 seob2807 II 0.04 miob6442 II 0.03 seoc4135 II 0.04 ncr2836 II 0.03 fcrO955 II 0.04 seoc2205 II 0.03 fcrb2510 II 0.04 hfcr5157 II 0.03 fcrcO677 II 0.04 miod7052 II 0.03 fcrc5831 II 0.04 ncrb0046 II 0.03 miobl734 II 0.04 ncrcl349 II 0.03 miodl792 II 0.04 seoc4513 II 0.03 ncr0007 II 0.04 fcr2542 II 0.04 ncr5055 II 0.04 fcrb4696 II 0.04 ncrb3056 II 0.04 fcrc4968 II 0.04 ncrc2319 II 0.04 fcrc5471 II 0.04 seob3419 II 0.04 hfcr3134 II 0.04 seob5379 II 0.04 mioc4641 II 0.04 seoc0861 II 0.04 mioc6269 II 0.04 mioa9581 IJ 5.11163e-04 miodl825 II 0.04 ncrb8752 IJ 5.47259e-04 miod6845 II 0.04 seoa3516 U 8.71839e-04 ncr7934 II 0.04 mioa9555 IJ 1.057776e-03 ncrb4319 II 0.04 seoc0778 IJ 1.540198e-03 ncrc4448 II 0.04 mioc5751 IJ 1.963134e-03 seoa4727 II 0.04 fcrb4890 IJ 2.083894e-03 seoa8374 II 0.04 fcrc6228 IJ 2.211242e-03 seob4122 II 0.04 seoa5911 IJ 2.345486e-03 seob6844 II 0.04 seoa0740 IJ 2.486944e-03 fcrbl995 II 0.04 seoa5554 IJ 2.792845e-03 fcrb6635 II 0.04 fcrb8187 IJ 2.957987e-03 mioa2475 II 0.04 ncrc9712 IJ 2.957987e-03 raiob0154 II 0.04 fcrb7510 IJ 3.131743e-03 mioc2451 II 0.04 fcrb4727 IJ 3.314495e-03 ncrc4531 II 0.04 mioa2185 IJ 3.314495e-03 seoa9711 II 0.04 fcrb6808 IJ 3.70858e-03 seob0248 II 0.04 seob7346 IJ 3.70858e-03 seob2149 II 0.04 fcrb4378 IJ 4.143566e-03 seoc5002 II 0.04 miod0592 IJ 4.143566e-03 fcrb5810 II 0.04 miod3306 IJ 4.377496e-03 fcrb6817 II 0.04 seoc5039 IJ 4.377496e-03 fcrcO839 II 0.04 mioa3945 IJ 4.88056e-03 fcrcl381 II 0.04 ncrc3624 IJ 5.150669e-03 miob7443 II 0.04 seoc0843 IJ 5.433839e-03 miob7985 II 0.04 hfcrO734 IJ 5.730596e-03 mioc3484 II 0.04 seoa4436 IJ 5.730596e-03 seob2185 II 0.04 ncrc4135 IJ 6.041484e-03 seob7463 II 0.04 seob4752 IJ 6.041484e-03 fcrb2596 II 0.04 mioc2074 IJ 7.064602e-03 fcrc5024 II 0.04 ncrc4089 IJ 7.064602e-03 hfcr5260 II 0.04 seocll75 IJ 7.064602e-03 ncr2472 II 0.04 ncrb3980 IJ 7.828021e-03 ncr8538 II 0.04 ncrc9944 IJ 8.236011e-03 seob3559 II 0.04 seoa7383 IJ 8.662397e-03 seobβOOO II 0.04 fcrb2452 IJ 9.107857e-03 seob7903 II 0.04 ncr2160 IJ 9.573086e-03 fcr2167 II 0.04 seoa5234 IJ 9.573086e-03 fcrb3056 II 0.04 ncrb8689 IJ 0.01 fcrb4542 II 0.04 seob0992 IJ 0.01 fcrb6740 II 0.04 miodl532 IJ 0.01 mioal015 II 0.04 miod5703 IJ 0.01 mioa6552 II 0.04 ncrc9704 IJ 0.01 mioa9989 II 0.04 mioa6093 IJ 0.01 mioc3565 II 0.04 miob2466 IJ 0.01 mioc6946 II 0.04 miob8259 IJ 0.01 ncr6144 II 0.04 seob!322 IJ 0.01 fcrb2l3"7 - "~~1J '""" ΌTOI ncrb6903 IK 6.737022e-03 ncrc4780 IJ 0.01 seoa3694 IK 6.737022e-03 seoa5157 IJ 0.01 seocl484 IK 6.737022e-03 miob3308 IJ 0.01 hfcr6700 IK 7.311813e-03 seoblOδl IJ 0.01 fcrb7760 IK 7.928973e-03 fcrcO654 IJ 0.01 ncrb7516 IK 7.928973e-03 mioc2039 IJ 0.01 miod4775 IK 8.591056e-03 miocll26 IJ 0.01 miob4684 IK 9.300726e-03 fcrbl503 IJ 0.01 mioc0852 IK 0.01 fcrbO193 IJ 0.02 mioc7910 IK 0.01 mioa8851 IJ 0.02 fcrb4789 IK 0.01 ncr7904 IJ 0.02 fcrb9286 IK 0.01 ncrc0640 IJ 0.02 ncrc5438 IK 0.01 miob9614 IJ 0.02 seob6758 IK 0.01 ncrc3936 IJ 0.02 fcrb2849 IK 0.01 fcrb2198 IJ 0.02 fcrb4656 IK 0.01 mioa4064 IJ 0.02 fcrb8680 IK 0.01 mioa0528 IJ 0.02 miod5612 IK 0.01 ncr9140 IJ 0.02 seob5342 IK 0.01 seoa9389 IJ 0.02 fcrbl441 IK 0.01 fcrb8542 IJ 0.02 ncrc8851 IK 0.01 fcrb9655 IJ 0.02 seob0937 IK 0.01 mioa6738 IJ 0.02 seobl039 IK 0.01 mioc4978 IJ 0.02 seob7392 IK 0.01 seob4270 IJ 0.02 seob8999 IK 0.01 fcrb6279 IJ 0.03 seocl305 IK 0.01 fcrcO351 IJ 0.03 miocl085 IK 0.01 fcrc4669 IJ 0.03 ncrb3314 IK 0.01 miocl205 IJ 0.03 fcrb2041 IK 0.01 seobl399 IJ 0.03 fcrc6138 IK 0.01 seob6379 IJ 0.03 ncr2484 IK 0.01 mioa5586 IJ 0.03 ncrb2200 IK 0.01 seob2169 IJ 0.03 fcrb7861 IK 0.01 ncrcO936 IJ 0.03 hfcr2295 IK 0.01 seobl411 IJ 0.03 mioc0909 IK 0.01 fcrc4161 IJ 0.03 ncrcl049 IK 0.01 mioa6102 IJ 0.03 ncrc5327 IK 0.01 fcrb3518 IJ 0.03 seocl664 IK 0.01 ncrc4994 IJ 0.03 fcrbl691 IK 0.01 miob7373 IJ 0.04 fcrc4456 IK 0.01 mioc0206 IJ 0.04 miob3426 IK 0.01 ncr2288 IJ 0.04 miod6162 IK 0.01 ncrcl567 IJ 0.04 fcrb5702 IK 0.01 seoa4485 IJ 0.04 miob9533 IK 0.01 hfcr4485 IJ 0.04 mioc2720 IK 0.01 ncrc3856 IJ 0.04 seob3367 IK 0.01 fcrb2350 IJ 0.04 fcr2821 IK 0.02 fcrb6734 IJ 0.04 fcrb5867 IK 0.02 mioa9821 IJ 0.04 fcrb7944 IK 0.02 miob8657 IJ 0.04 ncrclβO8 IK 0.02 ncrl780 IJ 0.04 seob5764 IK 0.02 mioa6832 IJ 0.04 fcrb6185 IK 0.02 seobl362 IJ 0.04 mioc0164 IK 0.02 miocOlδl IJ 0.04 mioc0530 IK 0.02 ncr4946 IJ 0.04 seoc5228 IK 0.02 ncrc4287 IJ 0.04 mioa5902 IK 0.02 fcrc2745 IJ 0.04 seoa0085 IK 0.02 seob2195 IJ 0.04 seob0787 IK 0.02 seoc5140 IK 3.392191e-03 hfcr5719 IK 0.02 seob5193 IK 4.048349e-03 mioc3593 IK 0.02 fcrb5259 IK 4.416653e-03 ncrb8332 IK 0.02 mioc6204 IK 4.814199e-03 ncrc4588 IK 0.02 fcr0604 IK 5.242925e-03 seoa0137 IK 0.02 seob7928 IK 6.202152e-03 seob0971 IK 0.02 seob5556 IK •"■"U.O2 miob3845 IK 0.04 seob3499 IK 0.02 ncrb5197 IK 0.04 fcrb6779 IK 0.02 seoa5833 IK 0.04 fcrc6551 IK 0.02 seoa9060 IK 0.04 mioc0384 IK 0.02 seobll58 IK 0.04 miod0977 IK 0.02 seoc!402 IK 0.04 ncrb2131 IK 0.02 fcr0703 IK 0.04 ncrc2857 IK 0.02 fcrb7098 IK 0.04 seoa4461 IK 0.02 fcrb7831 IK 0.04 fcrbl876 IK 0.02 mioa5812 IK 0.04 fcrb6031 IK 0.02 miob9403 IK 0.04 miob2210 IK 0.02 mioc3669 IK 0.04 ncrc9491 IK 0.02 miod6044 IK 0.04 seoal269 IK 0.02 ncr5522 IK 0.04 seoa4163 IK 0.02 ncrcO423 IK 0.04 seobl419 IK 0.02 seob2717 IK 0.04 seobl848 IK 0.02 seob4967 IK 0.04 seoc0651 IK 0.02 seob7569 IK 0.04 fcrb2979 IK 0.03 seocl203 IK 0.04 fcrb4786 IK 0.03 seoc!236 IK 0.04 miob5829 IK 0.03 fcrb3848 IK 0.04 miob9163 IK 0.03 fcrc0305 IK 0.04 mioc6937 IK 0.03 fcrc2439 IK 0.04 fcrb3627 IK 0.03 fcrc6381 IK 0.04 fcrb7244 IK 0.03 miob8989 IK 0.04 mioa4628 IK 0.03 seoa0256 IK 0.04 ncr7631 IK 0.03 seoa2854 IK 0.04 ncrc6127 IK 0.03 seoa4640 IK 0.04 seoa8401 IK 0.03 seob6096 IK 0.04 seob3892 IK 0.03 seob6386 IK 0.04 fcr3282 IK 0.03 seob8300 IK 0.04 fcrbl337 IK 0.03 fcrb7324 IL 3.7556e-04 fcrb2763 IK 0.03 ncrc5780 IL 1.774441e-03 mioa2652 IK 0.03 seocl508 IL 3.707446e-03 mioc7372 IK 0.03 ncrc9642 IL 4.048349e-03 ncrcO534 IK 0.03 seoa4802 IL 4.416653e-03 ncrcl421 IK 0.03 seob2938 IL 4.416653e-03 ncrc2080 IK 0.03 miob3072 IL 5.242925e-03 fcr7561 IK 0.03 seob2937 IL 5.704865e-03 fcrb7247 IK 0.03 fcr4328 IL 6.202152e-03 fcrc2457 IK 0.03 seoa4327 IL 6.737022e-03 fcrc3993 IK 0.03 seob5658 IL 7.311813e-03 ncr3435 IK 0.03 fcrb3476 IL 7.928973e-03 ncrc8841 IK 0.03 miod5682 IL 7.928973e-03 seoa6078 IK 0.03 ncrc5959 IL 7.928973e-03 seoa8867 IK 0.03 fcr2293 IL 8.591056e-03 seob0386 IK 0.03 mioc0238 IL 9.300726e-03 seoc0809 IK 0.03 ncrcO393 IL 0.01 fcrbl877 IK 0.04 seob8212 IL 0.01 fcrb4994 IK 0.04 mioc3523 IL 0.01 fcrcOlβO IK 0.04 mioc7471 IL 0.01 fcrc0720 IK 0.04 ncrb3329 IL 0.01 fcrc2131 IK 0.04 mioa9792 IL 0.01 miocl963 IK 0.04 fcr2088 IL 0.01 miod0355 IK 0.04 hfcr3486 IL 0.01 seoa2652 IK 0.04 fcrb8202 IL 0.01 seob3370 IK 0.04 fcrb3920 IL 0.01 seob4689 IK 0.04 fcrb5016 IL 0.01 fcr5712 IK 0.04 fcrb8236 IL 0.01 fcrbl397 IK 0.04 fcrcO775 IL 0.01 fcrbl523 IK 0.04 miob6087 IL 0.01 fcrb2051 IK 0.04 ncrb5704 IL 0.01 fcrb7803 IK 0.04 seoa9997 IL 0.01 mioa6738 IK 0.04 seob!399 IL 0.01 fcr5369 IL 0.01 ncrc6407 IL 0.04 miod5301 IL 0.01 seob6836 IL 0.04 ncr5613 IL 0.01 fcrb3217 IL 0.04 seob7747 IL 0.01 hfcr3514 IL 0.04 raioc5633 IL 0.01 miob2448 IL 0.04 seob2661 IL 0.01 seoa8501 IL 0.04 fcr4444 IL 0.02 seob9001 IL 0.04 ncrc6756 IL 0.02 fcrc0180 IL 0.04 seob5032 IL 0.02 miob8515 IL 0.04 fcrb5926 IL 0.02 ncrc2377 IL 0.04 miob9901 IL 0.02 seoc6169 IL 0.04 fcrb2380 IL 0.02 fcrc6651 IM 1.12314e-04 hfcrO285 IL 0.02 seoa8299 IM 1.913367e-03 seoal615 IL 0.02 ncr3825 IM 2.829671e-03 seoa2042 IL 0.02 fcrb3219 IM 2.830108e-03 fcrc5160 IL 0.02 seocl535 IM 3.05452e-03 raioc2592 IL 0.02 mioc5367 IM 3.825425e-03 ncr5027 IL 0.02 fcrc2807 IM 4.118173e-03 ncrcO421 IL 0.02 ncrcO821 IM 4.118173e-03 ncrc6697 IL 0.02 seoa3578 IM 4.118173e-03 seoc4288 IL 0.02 miocl279 IM 4.430531e-03 fcrb9450 IL 0.02 ncrb8224 IM 4.763601e-03 fcrc4841 IL 0.02 fcr7424 IM 5.11853e-03 mioc3413 IL 0.02 ncrbO696 IM 5.11853e-03 mioc3492 IL 0.02 seob7250 IM 6.32669e-03 ncr3869 IL 0.02 miob7435 IM 6.781521e-O3 seob5711 IL 0.02 fcrc5873 IM 7.264698e-O3 fcrb3016 IL 0.02 mioa8857 IM 7.264698e-03 fcrb6211 IL 0.02 mioc5179 IM 8.321953e-03 miocl440 IL 0.02 ncr4790 IM 8.321953e-03 mioc7444 IL 0.02 seob6582 IM 8.321953e-03 miod5349 IL 0.02 miob6485 IM 8.899102e-03 ncr3718 IL 0.02 seoc2549 IM 8.899102e-03 ncr9378 IL 0.02 miod4564 IM 9.510735e-03 ncrc5150 IL 0.02 seobl906 IM 9.510735e-03 seocl628 IL 0.02 ncr4551 IM 0.01 fcrb3074 IL 0.03 ncrc9528 IM 0.01 hfcr2367 IL 0.03 mioa0577 IM 0.01 mioa9630 IL 0.03 mioc7974 IM 0.01 mioc0276 IL 0.03 ncr8780 IM 0.01 ncr2408 IL 0.03 ncrc2495 IM 0.01 fcrb3024 IL 0.03 fcr2498 IM 0.01 miod3417 IL 0.03 fcr6329 IM 0.01 ncrb2400 IL 0.03 fcrbl446 IM 0.01 seoa7647 IL 0.03 fcrb8215 IM 0.01 fcrb5723 IL 0.03 fcrc2024 IM 0.01 m±od0625 IL 0.03 miob3149 IM 0.01 seobll44 IL 0.03 miob9901 IM 0.01 seob9552 IL 0.03 rαiob9274 IM 0.01 fcrcO456 IL 0.03 mioc2188 IM 0.01 fcrc4968 IL 0.03 fcrbl876 IM 0.01 mioa4674 IL 0.03 fcr7561 IM 0.01 miod3421 IL 0.03 ncrc5039 IM 0.01 ncrc7127 IL 0.04 seob8029 IM 0.01 fcrb4287 IL 0.04 fcrb5305 IM 0.01 fcrb7036 IL 0.04 fcrc7243 IM 0.01 fcrc5418 IL 0.04 miob6437 IM 0.01 hfcr2686 IL 0.04 ncr2145 IM 0.01 miob9284 IL 0.04 ncrcl949 IM 0.01 miod2232 IL 0.04 ncrc9530 IM 0.01 seob0564 IL 0.04 seob9218 IM 0.01 seoc2504 IL 0.04 ncrb8239 IM 0.01 fcrb8121 IL 0.04 seob8483 IM 0.01 ncrcll93 IL 0.04 mioa5054 IM 0.01 ncfb'228'2'"' ΪM 0.01 mxoa5836 IM 0.02 seob0879 IM 0.01 miod2211 IM 0.02 fcr3880 IM 0.01 ncrδlll IM 0.02 fcrc5270 IM 0.01 ncrb4435 IM 0.02 miob8320 IM 0.01 ncrc8863 IM 0.02 ncrcl999 IM 0.01 seoc0861 IM 0.02 seoa9740 IM 0.01 fcrO793 IM 0.03 seob7262 IM 0.01 fcrb6406 IM 0.03 mioa5310 IM 0.01 fcrc4067 IM 0.03 miob5647 IM 0.01 hfcr3834 IM 0.03 mioc8412 IM 0.01 mioc0181 IM 0.03 miod5310 IM 0.01 miocl749 IM 0.03 ncrb6675 IM 0.01 miod2058 IM 0.03 seoa2087 IM 0.01 ncr7088 IM 0.03 seobl860 IM 0.01 ncr8538 IM 0.03 mioc2539 IM 0.02 ncrc4985 IM 0.03 mioc4242 IM 0.02 seoa3344 IM 0.03 m±oc5240 IM 0.02 seob3307 IM 0.03 mioc8481 IM 0.02 seob6414 IM 0.03 ncrO976 IM 0.02 seocl483 IM 0.03 ncr2304 IM 0.02 fcrb8422 IM 0.03 ncrb6720 IM 0.02 fcrc2683 IM 0.03 ncrb8817 IM 0.02 ncr2996 IM 0.03 seoa7583 IM 0.02 ncrb6762 IM 0.03 seob7941 IM 0.02 miob6721 IM 0.03 fcrb2197 IM 0.02 ncr3834 IM 0.03 fcrc2800 IM 0.02 ncr7962 IM 0.03 ncrc4153 IM 0.02 ncrcl421 IM 0.03 fcrl388 IM 0.02 seoa7542 IM 0.03 fcrc4560 IM 0.02 seob6096 IM 0.03 mioa3335 IM 0.02 fcrl992 IM 0.03 miod2707 IM 0.02 fcrb6484 IM 0.03 seoal977 IM 0.02 mioa6772 IM 0.03 seob4039 IM 0.Q2 mioc5636 IM 0.03 seob8311 IM 0.02 miod0167 IM 0.03 mioa0684 IM 0.02 ncrcll68 IM 0.03 miob0785 IM 0.02 ncrcl311 IM 0.03 miodl792 IM 0.02 ncrc7171 IM 0.03 miod4540 IM 0.02 seoc0619 IM 0.03 ncrc3544 IM 0.02 seoc4078 IM 0.03 seoa4524 IM 0.02 miod5338 IM 0.03 seoa5784 IM 0.02 fcrl347 IM 0.03 seoa9814 IM 0.02 fcr2220 IM 0.03 seoc2220 IM 0.02 fcrbl690 IM 0.03 mioa4738 IM 0.02 miocβ269 IM 0.03 mioc8640 IM 0.02 miod2853 IM 0.03 miod4269 IM 0.02 seoa5528 IM 0.03 ncr4113 IM 0.02 seoa7926 IM 0.03 ncr4122 IM 0.02 seob4165 IM 0.03 ncr4993 IM 0.02 fcr0680 IM 0.03 ncr6232 IM 0.02 fcr3856 IM 0.03 ncrbO265 IM 0.02 mioa0220 IM 0.03 seob5021 IM 0.02 miobl789 IM 0.03 seob5490 IM 0.02 ncrb4437 IM 0.03 fcr3987 IM 0.02 seoa5731 IM 0.03 fcrb2100 IM 0.02 seob3226 IM 0.03 fcrc4033 IM 0.02 seoc5125 IM 0.03 fcrc4138 IM 0.02 fcrc0076 IM 0.04 mioa0247 IM 0.02 fcrc2090 IM 0.04 mioaβ543 IM 0.02 fcrc4157 IM 0.04 mioc0279 IM 0.02 miob0942 IM 0.04 ncrb6846 IM 0.02 miod0773 IM 0.04 seoa9889 IM 0.02 miod0832 IM 0.04 fcrb8485 IM 0.02 ncr5975 IM 0.04 ncrb5535 0.04 fcrc2014 IN 2.620469e-03 ncrb7350 IM 0.04 ncrbl420 IN 2.620469e-03 ncrc7092 IM 0.04 mioa4845 IN 2.830108e-03 seob6198 IM 0.04 fcrc2096 IN 3.551234e-03 mioa3602 IM 0.04 seob4363 IN 3.551234e-03 fcr4900 IM 0.04 fcrb6005 IN 3.825425e-03 fcrb3016 IM 0.04 seoc2723 IN 3.825425e-03 fcrb3760 IM 0.04 ncrc0807 IN 5.496516e-03 fcrb4231 IM 0.04 ncrc8909 IN 5.898804e-03 hfcrlllδ IM 0.04 fcrb6890 IN 6.32669e-03 mioa5461 IM 0.04 fcrb4271 IN β.781521e-03 mioa6091 IM 0.04 ncrb4319 IN 6.781521e-03 mioc3206 IM 0.04 seoa3852 IN 6.781521e-03 miod4735 IM 0.04 fcrb5550 IN 7.264698e-03 ncrcO279 IM 0.04 mioc2662 IN 7.264698e-03 ncrc2087 IM 0.04 ncrc6623 IN 7.777673e-03 ncrc3049 IM 0.04 miod.4464 IN 8.321953e-03 seob0949 IM 0.04 ncrO451 IN 8.321953e-03 seoc0523 IM 0.04 seobl285 IN 8.321953e-03 seoa0231 IM 0.04 miod6324 IN 8.899102e-03 fcr4106 IM 0.04 mioc0302 IN 9.510735e-03 fcrb6442 IM 0.04 mioc5203 IN 9.510735e-03 fcrcO332 IM 0.04 mioc6320 IN 9.510735e-03 fcrc2670 IM 0.04 ncr3316 IN 9.510735e-03 mioal775 IM 0.04 ncrcO544 IN 9.510735e-03 mioa2073 IM 0.04 ncrc2110 IN 9.510735e-03 mioa4792 IM 0.04 fcrb4321 IN 0.01 mioa5452 IM 0.04 seoa9817 IN 0.01 mioa8796 IM 0.04 ncrclO49 IN 0.01 mioa9505 IM 0.04 seoa7897 IN 0.01 miobl774 IM 0.04 seob8386 IN 0.01 miob8532 IM 0.04 fcrb3169 IN 0.01 mioc0911 IM 0.04 mioa0820 IN 0.01 mioc3430 IM 0.04 mioa3620 IN 0.01 ncrc5149 IM 0.04 miod7429 IN 0.01 seoalββl IM 0.04 ncrc6846 IN 0.01 seob0370 IM 0.04 seoa3102 IN 0.01 seob7903 IM 0.04 seoa4056 IN 0.01 fcrbl397 IM 0.04 seoa8993 IN 0.01 fcrb2851 IM 0.04 fcr5350 IN 0.01 fcrb3127 IM 0.04 fcr5836 IN 0.01 fcrc5614 IM 0.04 fcrb5219 IN 0.01 mioa3693 IM 0.04 miob9905 IN 0.01 miod5122 IM 0.04 hfcrO521 IN 0.01 ncr4416 IM 0.04 τaioc0902 IN 0.01 ncr6197 IM 0.04 miocl590 IN 0.01 ncrbl373 IM 0.04 ncr3189 IN 0.01 ncrc3551 IM 0.04 ncrc6811 IN 0.01 ncrc6587 IM 0.04 seob5478 IN 0.01 ncrc7105 IM 0.04 seoc5209 IN 0.01 seoa2135 IM 0.04 fcrb4275 IN 0.01 seoa2162 IM 0.04 mioc3663 IN 0.01 seoa8738 IM 0.04 ncrb6394 IN 0.01 seoa9445 IM 0.04 hfcr3375 IN 0.01 ncrcO539 IN 2.76601e-04 mioa9821 IN 0.01 seoc5039 IN 4.04054e-04 mioc3746 IN 0.01 fcrbl580 IN 9.05702e-04 seoa5691 IN 0.01 mioc3042 IN 1.500619e-03 fcr2218 IN 0.01 seoa3847 IN 1.500619e-03 fcrb5181 IN 0.01 seob0442 IN 1.628342e-03 ncrcO646 IN 0.01 ncrb8665 IN 1.913367e-03 ncrc6697 IN 0.01 seob4270 IN 2.071956e-03 seoa6152 IN 0.01 fcrb2452 IN 2.42476e-03 seoc3487 IN 0.01 fcrb9401 IN 2.42476e-03 ncrcl003 IN 0.01 US2005/022071
fcrO997 IN 0.02 miod2888 10 4.231915e-03 fcrc2346 IN 0.02 ncrc4815 10 4.728956e-03 mioa0252 IN 0.02 fcrc2724 10 5.141808e-03 ncrc9232 IN 0.02 ncr6343 10 5.398569e-03 seoa4040 IN 0.02 seoc5039 10 5.492474e-03 hfcr2890 IN 0.02 mioa0577 10 5.937728e-03 ncrl523 IN 0.02 fcrb5123 10 6.493198e-03 fcrl562 IN 0.02 ncrc4219 10 6.586678e-03 ncrb8721 IN 0.02 fcrb9639 10 6.645906e-03 mioa2185 IN 0.02 ncrc2859 10 6.734392e-03 ncr6141 IN 0.02 fcrb7099 10 6.749149e-03 ncrc3045 IN 0.02 mioa3725 10 7.103544e-03 ncrc5207 IN 0.02 seoa3827 10 7.157172e-03 seoal439 IN 0.02 seoc0861 10 7.27845e-03 mioc5270 IN 0.02 ncr9587 10 7.280627e-03 miod0708 IN 0.02 raioc6360 10 7.319551e-03 ncrcO863 IN 0.02 seob7765 10 7.57129e-03 ncr4648 IN 0.03 mioa3392 10 7.67629e-03 seob6853 IN 0.03 mioc3605 10 8.235796e-03 fcrb7780 IN 0.03 mioc3042 10 8.28488e-03 mioc2997 IN 0.03 fcrb5181 io 8.370927e-03 mioc3296 IN 0.03 mioa4845 10 8.41047e-03 ncrcO262 IN 0.03 seoa9409 10 8.522999e-03 seoc0780 IN 0.03 seoa8501 10 9.074357e-03 miocl783 IN 0.03 seob4363 10 9.215346e-03 ncr3713 IN 0.03 ncr9175 10 9.251372e-03 ncrb3314 IN 0.03 ncrb2125 10 9.300568e-03 seob2953 IN 0.03 seobl90β 10 9.52088e-03 fcrbl399 IN 0.03 seob5003 10 9.992094e-03 hfcr6501 IN 0.03 fcrc4157 10 0.01 mioc4994 IN 0.03' seoa8370 10 0.01 miod4629 IN 0.03 fcrb9914 10 0.01 ncrl526 IN 0.03 miod4539 10 0.01 ncrc2394 IN 0.03 fcrb4378 10 0.01 seoa6118 IN 0.03 miod6068 10 0.01 fcr3861 IN 0.04 mioc5367 10 0.01 mioa3945 IN 0.04 fcrb9401 10 0.01 mioc6374 IN 0.04 seob7764 10 0.01 seoc2518 IN 0.04 ncrb8665 10 0.01 fcrb3592 IN 0.04 seoaβlδl 10 0.01 hfci6370 IN 0.04 ncrc5672 10 0.01 mioa3471 IN 0.04 ncrcO852 10 0.01 mioa6731 IN 0.04 fcrb2306 10 0.01 miod3327 IN 0.04 seoa3102 10 0.01 ncrb6581 IN 0.04 fcrb8915 10 0.01 ncrc9637 IN 0.04 fcrc2050 10 0.01 seob6703 IN 0.04 mioa8857 10 0.01 fcrc6470 IN 0.04 fcrb2449 10 0.01 mioa9604 IN 0.04 ncrc2103 10 0.01 miod4895 IN 0.04 mioa5054 10 0.01 seoa2744 IN 0.04 mioa4532 10 0.01 seob7765 IN 0.04 seobl285 10 0.01 fcr2195 IN 0.04 mioc0424 10 0.01 ncrc2080 IN 0.04 ncrc5571 10 0.01 seoa7669 IN 0.04 ncrl235 10 0.01 seob0089 IN 0.04 ncrbl420 10 0.01 seob6584 IN 0.04 ncr3827 10 0.01 fcrcβ651 10 1.70693e-03 seoa3847 10 0.01 seoa0536 10 2.030178e-03 mioc3296 10 0.01 ncr2507 10 3.138435e-03 ncrc9052 10 0.01 fcrc2619 10 3.288685e-03 seoa0792 10 0.01 seoa8993 10 3.289707e-03 fcrb6091 10 0.01 mioa0474 10 4.026625e-03 fcrb3181 10 0.01 mioa3888 10 4.070197e-03 ncr7382 10 0.01 seob5080" 10 hfcr3531 10 0.02 seob4039 10 0.01 hfcrlllδ 10 0.02 fcrc5873 10 0.01 fcrc5577 10 0.02 fcrbβOOO 10 0.01 fcrb5550 10 0.02 seoa8299 10 0.01 ncrcO445 10 0.02 fcr2821 10 0.01 mioa5540 10 0.02 mxob0931 10 0.01 miod5058 10 0.02 fcrb2800 10 0.01 miob6459 10 0.02 seoa6129 10 0.01 seoa0085 10 0.02 fcrc3998 10 0.01 mioa5310 10 0.02 miob2569 10 0.01 mioa3963 10 0.02 fcrb9069 10 0.01 ncrclO49 10 0.02 ncrc6623 10 0.01 mxoa0307 10 0.02 fcrb9476 10 0.01 hfcr3503 10 0.02 seob4270 10 0.01 seoclββl 10 0.02 fcrb7951 10 0.01 ncrc2825 10 0.02 ncr2363 10 0.01 mioa0220 10 0.02 seocl535 10 0.01 fcrc0076 10 0.02 πιioal513 10 0.01 miod3306 10 0.02 seob0154 10 0.01 fcrb6406 10 0.02 seob6704 10 0.01 fcrc6305 10 0.02 ncrc2098 10 0.01 ncrc9020 10 0.02 miob5608 10 0.01 mioa3471 10 0.02 ncrcO279 10 0.01 fcrb4470 10 0.02 seoc5222 10 0.01 ncrcl999 10 0.02 seoc4909 10 0.01 miob2287 10 0.02 ncr3052 10 0.01 inioa6583 10 0.02 seobl787 10 0.01 fcrc2807 10 0.02 ncr7945 10 0.01 fcrc6969 10 0.02 miocl030 10 0.01 miod3327 10 0.02 fcrb3445 10 0.01 fcrc2014 10 0.02 ncrc2110 10 0.01 fcrc6028 10 0.02 ncrc6264 10 0.02 ncr3825 10 0.02 ncrc3551 10 0.02 fcrb3017 10 0.02 fcrb2516 10 0.02 seob4001 10 0.03 fcrb6005 10 0.02 fcrb6202 10 0.03 fcrO419 10 0.02 seob0089 10 0.03 ncrc0807 10 0.02 mioc5240 10 0.03 seob6272 10 0.02 seoc0009 10 0.03 hfcr2890 10 0.02 miob6485 10 0.03 fcrc2670 10 0.02 fcr5350 10 0.03 ncrb0550 10 0.02 seoa9165 10 0.03 ncr4648 10 0.02 seob8403 10 0.03 seob9869 10 0.02 ncrc3049 10 0.03 hfcr2863 10 0.02 miocl279 10 0.03 fcrcO271 10 0.02 fcrb4533 10 0.03 seoc4609 10 0.02 fcrb3056 10 0.03 miod4732 10 0.02 ncrO451 10 0.03 ncr9789 10 0.02 seoblOδl 10 0.03 ncr8588 10 0.02 mioa0820 10 0.03 fcrb2060 10 0.02 fcrb3205 10 0.03 miod2853 10 0.02 seocll75 10 0.03 seob6229 10 0.02 fcr7424 10 0.03 ncr3568 10 0.02 ncrb4319 10 0.03 fcrb2452 10 0.02 fcrb5929 10 0.03 seob5962 10 0.02 ncr4790 10 0.03 seob9764 10 0.02 fcrO997 10 0.03 mioa3672 10 0.02 ncr0046 10 0.03 ncr6072 10 0.02 mioc6341 10 0.03 mioa7069 10 0.02 ncr6170 10 0.03 seob3731 10 0.02 fcrc5311 10 0.03 seoc3443 10 0.02 hfcr2820 10 0.03 fcrc4244 10 0.02 fcrc6997 10 0.03 hfcr2547 10 0.02 mioc6320 10 0.03 mioc787'8""'""-'1O" -5'V0'3 mioa6034 10 0.04 fcrb9099 10 0.03 seoa0396 10 0.04 miob7092 10 0.03 seob0523 10 0.04 ncr2472 10 0.03 fcr2293 10 0.04 hfcrO734 10 0.03 seoc5209 10 0.04 fcrb8732 10 0.03 ncrc4994 10 0.04 ncrb5537 10 0.03 ncrb4435 10 0.04 ncr8096 10 0.03 miob3320 10 0.04 seoa3852 10 0.03 ncrc3544 10 0.04 mioa4738 10 0.03 ncrc6587 10 0.04 seocOOlβ 10 0.03 ncrc5663 10 0.04 mioa6739 10 0.03 fcrb5896 10 0.04 ncrc9877 10 0.03 fcr6427 10 0.04 mioc3573 10 0.03 seob2148 10 0.04 seocl694 10 0.03 mioc2541 10 0.04 ncrc5025 10 0.03 seob6131 10 0.04 mioc2381 10 0.03 fcrb6574 10 0.04 seoal737 10 0.03 ncrb8714 10 0.04 fcr7561 10 0.03 ncrb7350 10 0.04 fcrb4275 10 0.03 fcrc6313 10 0.04 fcrb6436 10 0.03 fcrb5070 10 0.04 miob0974 10 0.03 ncr4551 10 0.04 seob3734 10 0.03 fcrb9520 10 0.04 fci-1060 10 0.03 fcrc2376 IP 2.27e-04 ncrb6394 10 0.03 ncrc2381 IP 7.13e-04 seob9872 10 0.03 ncrb3957 IP 9.67e-04 seoa2087 10 0.03 ncrc7171 IP 1.034296e-03 seob6279 10 0.04 seoa0925 IP 1.319416e-03 fcrb2305 10 0.04 fcrcl906 IP 1.365415e-03 hfcr4176 10 0.04 miob9441 IP 1.426-28e-03 fcrc0072 10 0.04 fcrb7753 IP 1.481468e-03 ncr4122 10 0.04 mioc0621 IP 1.503579e-03 miocθ.902 10 0.04 miod5184 IP 1.661105e-03 fcrc0180 10 0.04 miod3160 IP 1.763432e-03 ncrbl899 10 0.04 seob8854 IP 1.841199e-03 miod4496 10 0.04 fcrb9963 IP 1.859959e-03 ncrbll79 10 0.04 mioa3940 IP 1.859959e-03 seoa9817 10 0.04 ncrc4219 IP 2.079782e-03 mioc0019 10 0.04 miob9403 IP 2.104585e-03 fcrb3169 10 0.04 ncr4030 IP 2.18945e-03 seob8300 10 0.04 fcrc2775 IP 2.201026e-03 seob0294 10 0.04 miod4998 IP 2.322457e-03 miob6437 10 0.04 ncrc7065 IP 2.322457e-03 fcrc2254 10 0.04 rαioc3127 IP 2.476955e-03 seoa3578 10 0.04 mioc4103 IP 2.476955e-03 fcrb8422 10 0.04 fcrc4948 IP 2.588614e-03 seoa0029 10 0.04 fcrc5134 IP 2.589996e-03 fcrb6460 10 0.04 mioa2783 IP 2.589996e-03 ncrcO508 10 0.04 ncr7813 IP 2.589996e-03 fcrb9633 10 0.04 fcrcl689 IP 2.804609e-03 fcrb5527 10 0.04 ncr8314 IP 3.121843e-03 seob0386 10 0.04 miob2492 IP 3.20844e-03 fcrb4599 10 0.04 miob8347 IP 3.20844e-03 fcrc4669 10 0.04 seoa5234 IP 3.20844e-03 fcrb2350 10 0.04 fcrb2765 IP 3.308966e-03 mioc0302 10 0.04 mioc3413 IP 3.490738e-03 seob9232 10 0.04 fcrb7380 IP 3.564101e-03 fcrb8505 10 0.04 miob5873 IP 3.585792e-03 ncrb2131 10 0.04 fcrc2280 IP 3.590799e-03 ncrc4259 10 0.04 seoc7885 IP 3.612999e-03 fcrb3896 10 0.04 seob6872 IP 3.677261e-03 fcrc5379 10 0.04 miob6688 IP 3.723139e-03 ncrb3468 10 0.04 fcrb3518 IP 3.874043e-03 fcrc6990 10 0.04 miocl438 IP 3.954153e-03 miόc'2152'""""•""TP' T:'9'5415'3"e-03 fcrb2993 IP 8.683395e-03 ncr3700 IP 3.954153e-03 fcrb8936 IP 8.683395e-03 ncrb7561 IP 4.138654e-03 miob9817 IP 8.683395e-03 ncrcO972 IP 4.19215e-03 mioc2360 IP 8.683395e-03 miob7554 IP 4.228055e-03 miod2707 IP 8.683395e-03 fcrbl202 IP 4.381373e-03 ncr3834 IP 8.683395e-03 fcrb2796 IP 4.381373e-03 ncrcO393 IP 8.683395e-03 fcrcO637 IP 4.381373e-03 ncrc5508 IP 8.683395e-03 ncrO615 IP 4.381373e-03 seobl839 IP 8.683395e-03 seoa5156 IP 4.381373e-03 seoc0780 IP 8.684081e-03 miob7188 IP 4.531729e-03 fcrcll65 IP 8.876286e-03 ncrcO829 IP 4.789246e-03 miob7209 IP 8.90642e-03 seoa9874 IP 4.817268e-03 seobl574 IP 9.211766e-03 mioaβδll IP 4.848713e-03 fcrb8432 IP 9.228453e-03 miodl454 IP 4.848713e-03 mioa9033 IP 9.277008e-03 ncrb3903 IP 4.848713e-03 ncrc9739 IP 9.281176e-03 seoc2029 IP 4.848713e-03 miod6947 IP 9.37261e-03 fcr3664 IP 4.853433e-03 seoc3277 IP 9.391913e-03 fcrc6127 IP 4.994669e-03 mioa0826 IP 9.529688e-03 miocl416 IP 5.177558e-03 ncrO791 IP 9.529688e-03 mioc2728 IP 5.300295e-03 ncrc3049 IP 9.529688e-03 fcr3559 IP 5.359298e-03 ncrc3171 IP 9.529688e-03 miob4037 IP 5.359298e-03 seob3699 IP 9.529688e-03 ncrlO55 IP 5.359298e-03 fcrb6776 IP 9.549328e-03 seoa2631 IP 5.359298e-03 fcrb8730 IP 9.812826e-03 ncrb6949 IP 5.470501e-03 seoc2785 IP 9.857578e-03 seoa8960 IP 5.592575e-03 seocl345 IP 0.01 seob0128 IP 5.718877e-03 seoa7403 IP 0.01 ncrc5405 IP 5.752711e-03 seobl667 IP 0.01 fcrc4658 IP 5.916437e-03 fcrc5850 IP 0.01 mioc7509 IP 5.916437e-03 miod5651 IP 0.01 miob4613 IP 5.926508e-03 seoa0044 IP 0.01 miob2814 IP 6.166182e-03 seoa7115 IP 0.01 mioa3467 IP 6.426036e-03 seoa8351 IP 0.01 ncrc2463 IP 6.523627e-03 seob4117 IP 0.01 seoa5392 IP 6.523627e-03 seob5843 IP 0.01 fcrc5138 IP 6.64384e-03 fcrc4045 IP 0.01 miod7486 IP 6.651906e-03 fcrb7510 IP 0.01 fcrbl492 IP 7.184558e-03 fcrb9843 IP 0.01 fcrb6667 IP 7.184558e-03 seoc2031 IP 0.01 fcrb9222 IP 7.184558e-03 mioc0899 IP 0.01 fcrc6888 IP 7.184558e-03 fcr7419 IP 0.01 mioal885 IP 7.184558e-03 fcrb7700 IP 0.01 mioa5586 IP 7.184558e-03 fcrc4971 IP 0.01 miobl833 IP 7.184558e-03 fcrc5007 IP 0.01 miod5126 IP 7.184558e-03 mioa8679 IP 0.01 miod5349 IP 7.184558e-03 miob3042 IP 0.01 ncr0420 IP 7.184558e-03 mioc6956 IP 0.01 ncr3960 IP 7.184558e-03 miod4467 IP 0.01 ncr9919 IP 7.184558e-03 ncr3404 IP 0.01 ncrc4113 IP 7.184558e-03 ncr3412 IP 0.01 seob6882 IP 7.184558e-03 ncr3527 IP 0.01 miocl245 IP 7.224439e-03 ncrb2091 IP 0.01 seob4749 IP 7.682062e-03 ncrc4079 IP 0.01 miob3411 IP 7.697079e-03 fcrb2344 IP 0.01 fcrb9611 IP 7.903117e-03 seoa0464 IP 0.01 mioc5113 IP 7.903117e-03 seobl318 IP 0.01 ncrcO744 IP 7.903117e-03 ncrcO445 IP 0.01 ncrc4531 IP 7.903117e-03 fcrbl552 IP 0.01 fcrbβlβO IP 7.966664e-03 fcrb3001 IP 0.01 seoa3634 IP 8.179896e-03 fcrcO672 IP 0.01 hfcr4028 IP 8.260407e-03 fcrc5164 IP 0.01 fcrb5753 IP 8.563533e-03 fcrc5402 IP 0.01 seob6835 IP 8.57233e-03 fcrc6282 IP 0.01 fcrc705'6"'" crσi miocl978 IP 0.01 mioal584 IP 0.01 mioc7668 IP 0.01 ncr2866 IP 0.01 ncrc5372 IP 0.01 ncr3357 IP 0.01 ncrcll53 IP 0.01 ncrcO858 IP 0.01 miob8096 IP 0.01 ncrc3842 IP 0.01 fcrb4077 IP 0.01 ncrc5844 IP 0.01 fcrb6190 IP 0,01 fcrc4408 IP 0.01 hfcr2963 IP 0.01 mioc0019 IP 0.01 hfcr6640 IP 0.01 ncrO212 IP 0.01 miob2466 IP 0.01 seob2724 IP 0.01 mioc2019 IP 0.01 ncr3434 IP 0.01 mioc5695 IP 0.01 ncrc6597 IP 0.01 ncr3944 IP 0.01 fcr6630 IP 0.01 seoa5253 IP 0.01 miob2386 IP 0.01 seoa7126 IP 0.01 miob7610 IP 0.01 seob3303 IP 0.01 mioc0310 IP 0.01 mioa9891 IP 0.01 mioc0760 IP 0.01 miob2478 IP 0.01 miocll07 IP 0.01 mioa2691 IP 0.01 mioc5210 IP 0.01 mioc3716 IP 0.01 ncr4700 IP 0.01 miodl316 IP 0.01 ncrbl518 IP 0.01 seocl476 IP 0.01 ncrc9729 IP 0.01 seocl484 IP 0.01 seob9353 IP 0.01 ncr2575 IP 0.01 seoc5134 IP 0.01 fcrb4391 IP 0.01 miob9248 IP 0.01 fcrb7525 IP 0.01 ncrc4757 IP 0.01 mioa0294 IP 0.01 ncrc5758 IP 0.01 mioa3080 IP 0.01 hfcr6611 IP 0.01 miob2686 IP 0.01 fcrb2851 IP 0.01 miob6290 IP 0.01 mioa3469 IP 0.01 miob7638 IP 0.01 mioc6391 IP 0.01 mioc0486 IP 0.01 ncr3163 IP 0.01 mioc4925 IP 0.01 ncr4545 IP 0.01 mioc6260 IP 0.01 seoa5577 IP 0.01 ncr4118 IP 0.01 seoa5843 IP 0.01 seob0219 IP 0.01 seob4896 IP 0.01 seob9282 IP 0.01 mioc4888 IP 0.01 seoc5612 IP 0.01 seob9241 IP 0.01 ncrb7465 IP 0.01 mioc3906 IP 0.01 fcrb9909 IP 0.01 fcrb9449 IP 0.01 seoc0945 IP 0.02 mioc4161 IP 0.01 seoc4078 IP 0.02 fcrc6972 IP 0.01 ncrO612 IP 0.02 seoc4187 IP 0.01 miob3595 IP 0.02 fcrc6335 IP 0.01 miob5810 IP 0.02 seoc2000 IP 0.01 seoc0551 IP 0.02 mioa9630 IP 0.01 miocl995 IP 0.02 hfcr3019 IP 0.01 fcrb3870 IP 0.02 mioa2493 IP 0.01 fcrb7951 IP 0.02 ncr3432 IP 0.01 fcrc4876 IP 0.02 ncr9933 IP 0.01 hfcrl709 IP 0.02 ncrbll63 IP 0.01 miob4144 IP 0.02 ncrb6087 IP 0.01 miob5675 IP 0.02 ncrcl595 IP 0.01 mioc2133 IP 0.02 ncrc3436 IP 0.01 miod0592 IP 0.02 ncrc3880 IP 0.01 ncr3339 IP 0.02 seoa2775 IP 0.01 ncrc3520 IP 0.02 seob7929 IP 0.01 ncrc4496 IP 0.02 fcrb9543 IP 0.01 ncrc5571 IP 0.02 fcrc2222 IP 0.01 seoa0135 IP 0.02 miob6355 IP 0.01 seob0817 IP 0.02 seob6008 IP 0.01 seobl808 IP 0.02 ncrc3443 IP 0.01 seobl908 IP 0.02 fcrb6639 IP 0.01 seoc2249 IP 0.02 ncrc3324 IP O.OZ miod0773 IP 0.02 miob8391 IP 0.02 fcrb5841 IP 0.02 seob8261 IP 0.02 fcrb9629 IP 0.02 mioc2123 IP 0.02 hfcr3067 IP 0.02 ncr3465 IP 0.02 mioa3379 IP 0.02 ncrc8865 IP 0.02 miob7922 IP 0.02 mioc7471 IP 0.02 miob9065 IP 0.02 ncrcO849 IP 0.02 mioc3574 IP 0.02 seob8607 IP 0.02 ncrO496 IP 0.02 mioc2799 IP 0.02 ncr7537 IP 0.02 seoa7542 IP 0.02 ncrcl349 IP 0.02 fcr5415 IP 0.02 ncrc4302 IP 0.02 fcrb4760 IP 0.02 ncrc6242 IP 0.02 fcrb5603 IP 0.02 seoa6238 IP 0.02 fcrb6808 IP 0.02 seob2169 IP 0.02 fcrcO376 IP 0.02 seob3189 IP 0.02 fcrcO839 IP 0.02 seob5064 IP 0.02 fcrc6868 IP 0.02 mioc7433 IP 0.02 hfcrOOll IP 0.02 ncr9469 IP 0.02 miob9714 IP 0.02 ncrc8851 IP 0.02 miod7440 IP 0.02 seoa7286 IP 0.02 miod7461 IP 0.02 miob9325 IP 0.02 ncr2994 IP 0.02 fcrb3946 IP 0.02 ncr4140 IP 0.02 seoa9409 IP 0.02 ncr6212 IP 0.02 mioc3962 IP 0.02 ncr8156 IP 0.02 fcrbl466 IP 0.02 ncrc4231 IP 0.02 fcrbl633 IP 0.02 ncrc5744 IP 0.02 fcrb3330 IP 0.02 ncrc6899 IP 0.02 fcrb4656 IP 0.02 seoa2051 IP 0.02 fcrb8080 IP 0.02 seoa3287 IP 0.02 fcrc2014 IP 0.02 seoc2131 IP 0.02 fcrc5233 IP 0.02 mioc7170 IP 0.02 mioa3987 IP 0.02 fcrb9069 IP 0.02 miob2341 IP 0.02 ncrcO341 IP 0.02 miob2944 IP 0.02 hfcr2693 IP 0.02 mioc7620 IP 0.02 fcrb6112 IP 0.02 miod2641 IP 0.02 fcrb5723 IP 0.02 miod7227 IP 0.02 fcrb6432 IP 0.02 ncrb5244 IP 0.02 mioa8945 IP 0.02 ncrcl811 IP 0.02 miocl440 IP 0.02 ncrc3856 IP 0.02 miod4220 IP 0.02 ncrc6423 IP 0.02 miod4784 IP 0.02 seoa4675 IP 0.02 miod4895 IP 0.02 seoa5387 IP 0.02 ncrllOl IP 0.02 seobl844 IP 0.02 ncr5557 IP 0.02 seob9756 IP 0.02 ncr5668 IP 0.02 seoclββl IP 0.02 ncr9378 IP 0.02 seocl856 IP 0.02 ncrb4441 IP 0.02 mioc5740 IP 0.02 ncrb5737 IP 0.02 ncr2175 IP 0.02 ncrc3121 IP 0.02 seoa8229 IP 0.02 seoa3639 IP 0.02 ncrcl310 IP 0.02 seob8839 IP 0.02 fcrc0430 IP 0.02 miocl963 IP 0.02 seoa6930 IP 0.02 miob7373 IP 0.02 ncrc7131 IP 0.02 fcrc6196 IP 0.02 ncrO159 IP 0.02 ncrc9428 IP 0.02 ncrc9517 IP 0.02 seob7444 IP 0.02 seoc4900 IP 0.03 miocll26 IP 0.02 mioc2577 IP 0.03 seob2938 IP 0.02 ncr3233 IP 0.03 mioc7686 IP 0.02 seoc4941 IP 0.03 miob4574 IP 0.02 miob5098 IP 0.03 ncrcO663 IP 0.02 seoa8640 IP 0.03 miob2432 IP 0.02 mioc0317 IP 0.03 ...∑p. fcrbl547 OT(B fcrO637 IP 0.03 fcrbl990 IP 0.03 fcr4803 IP 0.03 fcrb2713 IP 0.03 fcrbl380 IP 0.03 fcrb3726 IP 0.03 fcrbl689 IP 0.03 fcrb4311 IP 0.03 miob6562 IP 0.03 fcrb6949 IP 0.03 mioc2872 IP 0.03 fcrb8536 IP 0.03 mioc4022 IP 0.03 hfcL-2895 IP 0.03 ncr8177 IP 0.03 mioa4177 IP 0.03 ncr9324 IP 0.03 miobl873 IP 0.03 ncrc2079 IP 0.03 mioc4557 IP 0.03 ncrc2730 IP 0.03 ncrc0704 IP 0.03 ncrc3598 IP 0.03 ncrc9519 IP 0.03 seoa3422 IP 0.03 seobl385 IP 0.03 seob0815 IP 0.03 seob4057 IP 0.03 seobl766 IP 0.03 seob5054 IP 0.03 seob4095 IP 0.03 miod5218 IP 0.03 seob4333 IP 0.03 miod4449 IP 0.03 seoc2348 IP 0.03 fcrb7693 IP 0.03 seobl960 IP 0.03 fcrcO569 IP 0.03 fcr2700 IP 0.03 miob9185 IP 0.03 mioa9007 IP 0.03 fcrb2754 IP 0.03 seoa7178 IP 0.03 mioa9523 IP 0.03 seob7039 IP 0.03 ncrc7113 IP 0.03 fcrc6916 IP 0.03 seobl889 IP 0.03 ncrb2085 IP 0.03 ncr2812 IP 0.03 seob8065 IP 0.03 seob3064 IP 0.03 miob8707 IP 0.03 fcrc6452 IP 0.03 seob5032 IP 0.03 fcr5474 IP 0.03 ncrcO863 IP 0.03 fcrb2189 IP 0.03 miob8609 IP 0.03 fcrb2460 IP 0.03 hfcr2770 IP 0.03 fcrc7373 IP 0.03 mioa6585 IP 0.03 mioa0528 IP 0.03 mioa0582 IP 0.03 mioa0862 IP 0.03 ncrc9039 IP 0.03 mioa3440 IP 0.03 fcrb3237 IP 0.03 miob3427 IP 0.03 fcrb6666 IP 0.03 mioc2443 IP 0.03 hfcrl762 IP 0.03 mioc2596 IP 0.03 hfcr2287 IP 0.03 ncrb8585 IP 0.03 hfcr2629 IP 0.03 ncrcO383 IP 0.03 hfcr6613 IP 0.03 ncrc6778 IP 0.03 mioa2998 IP 0.03 ncrc7049 IP 0.03 mioa5326 IP 0.03 seoa0913 IP 0.03 mioa8796 IP 0.03 seoa7366 IP 0.03 miob2375 IP 0.03 seoa8268 IP 0.03 miob5770 IP 0.03 seoa9445 IP 0.03 ncr5168 IP 0.03 seob2139 IP 0.03 ncr7178 IP 0.03 seob3887 IP 0.03 ncrb3498 IP 0.03 seob4039 IP 0.03 ncrcO427 IP 0.03 seob5478 IP 0.03 ncrcO544 IP 0.03 seob5556 IP 0.03 ncrc2888 IP 0.03 ncr7284 IP 0.03 seoa2381 IP 0.03 mioa6580 IP 0.03 seoa2854 IP 0.03 seob4766 IP 0.03 seoa3207 IP 0.03 seob0065 IP 0.03 seob0063 IP 0.03 fcr3043 IP 0.03 seob6139 IP 0.03 seob6177 IP 0.03 seob6585 IP 0.03 seob8388 IP 0.03 seoc0651 IP 0.03 seoc0657 IP 0.03 seocl804 IP 0.03 fcrb8222 IP 0.03 mioc4117 IP 0.03 ncr9125 IP 0.03 hfcr5514 IP 0.03 seoa4422 IP 0.03 ncrcO375 IP 0.03 fcrc6413 IP 0.03 mioa4135 IP 0.04 seoa4012 IP 0.03 fcrc2126 IP 0.04 "fcfb5"384"'""" '""Ϊ'P '""""""bTθ'4 miob3320 IP 0.04 πιioc7561 IP 0.04 miocl560 IP 0.04 ncrb7340 IP 0.04 ncr3141 IP 0.04 seocl631 IP 0.04 ncr3177 IP 0.04 ncr4215 IP 0.04 ncr5568 IP 0.04 seoc0596 IP 0.04 ncr7924 IP 0.04 fcrb5902 IP 0.04 ncrb8721 IP 0.04 fcr4699 IP 0.04 ncrcO564 IP 0.04 fcrbl916 IP 0.04 ncrc3100 IP 0.04 fcrb4799 IP 0.04 ncrc3464 IP 0.04 fcrb8236 IP 0.04 ncrc6587 IP 0.04 fcrcO487 IP 0.04 ncrc9159 IP 0.04 fcrcl565 IP 0.04 ncrc9700 IP 0.04 hfcr2955 IP 0.04 seoal910 IP 0.04 mioal097 IP 0.04 seoa4167 IP 0.04 mioa2038 IP 0.04 seobl891 IP 0.04 mioa8484 IP 0.04 seob3684 IP 0.04 miob5646 IP 0.04 seocl872 IP 0.04 miocl085 IP 0.04 fcrc2231 IP 0.04 mioc2735 IP 0.04 fcrb4533 IP 0.04 ncr3963 IP 0.04 fcrc5506 IP 0.04 ncr7539 IP 0.04 mioa5729 IP 0.04 ncrcl259 IP 0.04 mioc0662 IP 0.04 ncrc3596 IP 0.04 mioc6385 IP 0.04 ncrc5633 IP 0.04 ncrc9712 IP 0.04 seoa4202 IP 0.04 seob0564 IP 0.04 seoa4717 IP 0.04 fcrb9407 IP 0.04 seobll53 IP 0.04 fcrb5763 IP 0.04 seobll96 IP 0.04 hfcr3183 IP 0.04 seob2735 IP 0.04 fcrb8014 IP 0.04 fcrb6028 IP 0.04 fcrc2849 IP 0.04 fcr2018 IP 0.04 mioc6987 IP 0.04 fcrcO835 IP 0.04 ncrc4600 IP 0.04 hfcr5383 IP 0.04 mioa6545 IP 0.04 fcrb5108 IP 0.04 seoc3487 IP 0.04 mioc4318 IP 0.04 seoc2681 ' IP 0.04 ncrclO32 IP 0.04 mioc0424 IP 0.04 miob9748 IP 0.04 mioa0857 IP 0.04 mioc9205 IP 0.04 mioc2911 IP 0.04 seoal900 IP 0.04 mioc3573 IP 0.04 fcrlO98 IP 0.04 mioc7662 IP 0.04 seob2685 IP 0.04 mioa0249 IP 0.04 seob9645 IP 0.04 miob2166 IP 0.04 fcr4503 IP 0.04 mioc0911 IP 0.04 hfcr3224 IP 0.04 mioc2726 IP 0.04 ncrc6825 IP 0.04 miod4752 IP 0.04 ncrc4267 IP 0.04 ncrc9338 IP 0.04 fcrb3654 IP 0.04 seob0261 IP 0.04 fcrc5547 IP 0.04 fcrb2715 IP 0.04 mioc4366 IP 0.04 fcrb3933 IP 0.04 mioc8423 IP 0.04 fcrb5181 IP 0.04 fcr4900 IP 0.04 fcrb6785 IP 0.04 fcrb3083 IP 0.04 fcrb7608 IP 0.04 fcrb4248 IP 0.04 fcrc6345 IP 0.04 fcrb5928 IP 0.04 mioblll5 IP 0.04 fcrb6502 IP 0.04 miob5412 IP 0.04 fcrb7723 IP 0.04 mioc0384 IP 0.04 fcrb9371 IP 0.04 miocl028 IP 0.04 fcrb9856 IP 0.04 mioc2110 IP 0.04 fcrcl740 IP 0.04 mioc6983 IP 0.04 fcrc5137 IP 0.04 miod2845 IP 0.04 hfcr5864 IP 0.04 ncrO547 IP 0.04 mioal603 IP 0.04 ncr5713 IP 0.04 mioa2261 IP 0.04 ncrbl956 IP 0.04 "ncrb7726" ip o;o4 miod3776 IQ 2.13004e-03 ncrcllO3 IP 0.04 ncrbO487 IQ 2.13004e-03 ncrc2128 IP 0.04 fcrb9017 IQ 2.158692e-03 ncrc3045 IP 0.04 ncrc4920 IQ 2.172062e-03 ncrc3735 IP 0.04 seoal720 IQ 2.200519e-03 ncrc3953 IP 0.04 miob8830 IQ 2.222443e-03 ncrc4575 IP 0.04 fcr0770 IQ 2.292688e-03 ncrc8932 IP 0.04 fcr2006 IQ 2.342381e-03 ncrc9469 IP 0.04 fcr2079 IQ 2.342381e-03 seoa2528 IP 0.04 fcr5779 IQ 2.342381e-03 seoa7295 IP 0.04 mioal015 IQ 2.342381e-03 seob3517 IP 0.04 ncrc9704 IQ 2.342381e-03 seoc2589 IP 0.04 seobll97 IQ 2.342381e-03 seoc3690 IP 0.04 seoa5235 IQ 2.36091e-03 seoc4824 IP 0.04 mioa5614 IQ 2.467117e-03 ncrO664 IP 0.04 fcr0707 IQ 2.573342e-03 seob0046 IQ 4.27e-05 ncrc3258 IQ 2.573342e-03 mioc7662 IQ 8.24e-05 seoa5433 IQ 2.733174e-03 miod5590 IQ 1.44e-04 miob5010 IQ 2.824307e-03 seob2950 IQ 1.52e-04 seob7082 IQ 2.824307e-03 hfcr3413 IQ 2.00513e-04 seob8099 IQ 2.828664e-03 fcrb2330 IQ 2.07e-04 seoc8306 IQ 2.882251e-03 miob7135 IQ 3.26e-04 fcrb6431 IQ 3.096741e-03 fcrb3454 IQ 3.56e-04 ncrO847 IQ 3.096741e-03 hfcr2616 IQ 3.60223e-04 ncrc9562 IQ 3.096741e-03 seob5324 IQ 4.3e-04 seoal419 IQ 3.096741e-03 ncrb3768 IQ 4.92e-04 seobl399 IQ 3.096741e-03 fcrb8127 IQ 5.63262e-04 seob0031 IQ 3.219186e-03 fcrb9655 IQ 5.63262e-04 seoa4795 IQ 3.26458e-03 ncrc5653 IQ 6.2804e-04 fcrO997 IQ 3.392193e-03 fcrb3763 IQ 7.78162e-04 • mioa3944 IQ 3.392193e-03 miod6068 IQ 7.78162e-04 mioa5461 IQ 3.392193e-03 seob6432 IQ 7.78162e-04 mioc0501 IQ 3.392193e-03 fcrb2094 IQ 8.2e-04 mioclβOO IQ 3.392193e-03 fcrc6127 IQ 8.95e-04 ncrcO427 IQ 3.392193e-03 fcrcO959 IQ 9.59902e-04 ncrc9793 IQ 3.392193e-03 seob6015 IQ 1.030399e-03 ncrc9855 IQ 3.392193e-03 ncrl526 IQ 1.063703e-03 seoa0792 IQ 3.392193e-03 fcrc4848 IQ 1.106641e-03 seob5886 IQ 3.592626e-03 fcrc5391 IQ 1.178958e-03 fcrb6359 IQ 3.619754e-03 miob4860 IQ 1.178958e-03 fcr5190 IQ 3.712298e-03 miod5080 IQ 1.254238e-03 fcrb3461 IQ 3.712298e-03 fcrc6228 IQ 1.304487e-03 fcrb5202 IQ 3.712298e-03 mioa0891 IQ 1.304487e-03 mioa6738 IQ 3.712298e-03 ncrcl665 IQ 1.441864e-03 mioc2577 IQ 3.727477e-03 seoa3422 IQ 1.441864e-03 mioa8767 IQ 3.739684e-03 fcrclO19 IQ 1.484277e-03 miod5218 IQ 3.808421e-03 mioc3419 IQ 1.484277e-03 ncrcO174 IQ 3.808421e-03 ncrb3957 IQ 1.484277e-03 mioc0567 IQ 3.983145e-03 ncrc5592 IQ 1.584296e-03 ncrcl751 IQ 4.026284e-03 mioa3620 IQ 1.59205e-03 fcrbl344 IQ 4.058782e-03 hfcr2536 IQ 1.605711e-03 fcrb5994 IQ 4.058782e-03 seoa5547 IQ 1.674509e-03 fcrcO654 IQ 4.058782e-03 fcr0027 IQ 1.75607e-03 mioa3548 IQ 4.058782e-03 miob2960 IQ 1.75607e-03 miob8146 IQ 4.058782e-03 mioc4270 IQ 1.75607e-03 ncrc9284 IQ 4.058782e-03 seoa0085 IQ 1.756632e-03 ncrc9438 IQ 4.058782e-03 seob3112 IQ 1.781563e-03 ncrc9712 IQ 4.058782e-03 fcrb6464 IQ 1.801576e-03 seoal410 IQ 4.058782e-03 mioc3573 IQ 1.88355e-03 seobl748 IQ 4.058782e-03 seoa4601 IQ 1.912246e-03 ncrcO798 IQ 4.123577e-03 fcr2139 IQ 1.935013e-03 ncrOOlβ IQ 4.130421e-03 ncrc4135 IQ 1.935013e-03 mioa2072 IQ 4.159968e-03 fcrc5850 IQ 1.963872e-03 seoa6106 IQ 4.168552e-03 seob0763 IQ 4.178879e-03 fcrb6738 IQ 6.800293e-03 fcrb5287 IQ 4.217128e-03 miob8825 IQ 6.800293e-03 fcrb6236 IQ 4.251227e-03 ncrb8203 IQ 6.800293e-03 mioc5661 IQ 4.281336e-03 seoa9931 IQ 6.800293e-03 fcrb0072 IQ 4.290663e-03 seob0514 IQ 6.800293e-03 ncrc5492 IQ 4.291267e-03 ncrc9795 IQ 7.052912e-03 fcrb2162 IQ 4.433463e-03 miob0644 IQ 7.214905e-03 mioa6172 IQ 4.433463e-03 hfcr0750 IQ 7.323261e-03 ncr3811 IQ 4.433463e-03 seoc3554 IQ 7.323261e-03 ncr9599 IQ 4.433463e-03 fcrb2080 IQ 7.387873e-03 ncrb8468 IQ 4.433463e-03 fcrb5092 IQ 7.387873e-03 seoa0040 IQ 4.433463e-03 fcrc0412 IQ 7.387873e-03 seob2966 IQ 4.433463e-03 hfcr3500 IQ 7.387873e-03 mioa8192 IQ 4.459835e-03 mioa6102 IQ 7.387873e-03 fcrb3644 IQ 4.811926e-03 miod0993 IQ 7.387873e-03 fcrb4868 IQ 4.821954e-03 miod7418 IQ 7.387873e-03 fcrO894 IQ 4.838255e-03 seob7015 IQ 7.387873e-03 fcrbl990 IQ 4.838255e-03 mioc2123 IQ 7,393331e-03 fcrclll5 IQ 4.838255e-03 seoa7178 IQ 7.471525e-03 raioa6476 IQ 4.838255e-03 fcrbl496 IQ 7.708383e-03 ncr5871 IQ 4.838255e-03 seob2685 IQ 7.994213e-03 ncrc4015 IQ 4.838255e-03 fcr2132 IQ 8.019199e-03 fcrb5662 IQ 5.061344e-03 fcrb3483 IQ 8.019199e-03 mioa8484 IQ 5.124865e-03 mioa3997 IQ 8.019199e-03 seob0250 IQ 5.168742e-03 mioa6807 IQ 8.019199e-03 seoc3269 IQ 5.172067e-03 mioc4731 IQ 8.019199e-03 fcrb5645 IQ 5.275168e-03 ncrc5569 IQ 8.019199e-03 fcrc5799 IQ 5.275168e-03 seoa9930 IQ 8.019199e-03 miod6058 IQ 5.275168e-03 seob2169 IQ 8.019199e-03 ncrclO37 IQ 5.275168e-03 ncrclO29 IQ 8.067263e-03 ncrc9700 IQ 5.275168e-03 ncr9934 IQ 8.203479e-03 seoa4284 IQ 5.340812e-03 seob3503 IQ 8.224653e-03 ncrcO324 IQ 5.366126e-03 fcrb6442 IQ 8.263283e-03 mioa5326 IQ 5.506529e-03 ncrbO749 IQ 8.275966e-03 seob6189 IQ 5.603216e-03 fcrb6262 IQ 8.280361e-03 fcrcO651 IQ 5.627127e-03 miod2311 IQ 8.338691e-03 ncrc5947 IQ 5.731692e-03 miod4493 IQ 8.523402e-03 fcrc2808 IQ 5.746317e-03 fcr3269 IQ 8.696926e-03 mioa3321 IQ 5.746317e-03 fcr4328 IQ 8.696926e-03 mioa6832 IQ 5.746317e-03 fcr4782 IQ 8.696926e-03 miob8657 IQ 5.746317e-03 mioc0528 IQ 8.696926e-03 miocl416 IQ 5.746317e-03 miod3509 IQ 8.696926e-03 ncr6142 IQ 5.746317e-03 seoa0429 IQ 8.696926e-03 ncrc9947 IQ 5.746317e-03 seoa9086 IQ 8.696926e-03 ncrc3408 IQ 5.837544e-03 seob6856 IQ 8.696926e-03 hfcr5991 IQ 5.941877e-03 seocl934 IQ 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mioa4183 IQ 0.04 fcrb7608 IQ 0.04 hfcr3453 IQ 0.04 fcrb8334 IQ 0.04 seoblδδO IQ 0.04 fcrcl745 IQ 0.04 ncrcO747 IQ 0.04 mioa0294 IQ 0.04 ncrc3076 IQ 0.04 mioc0999 IQ 0.04 mioal303 IQ 0.04 ncr0644 IQ 0.04 fcrb0097 IQ 0.04 ncrb7167 IQ 0.04 miob8586 IQ 0.04 ncrc3544 IQ 0.04 miob9121 IQ 0.04 seoal559 IQ 0.04 hfcr3445 IQ 0.04 seoa5977 IQ 0.04 fcrbO355 IQ 0.04 seoa9389 IQ 0.04 fcr3757 IQ 0.04 seob2987 IQ 0.04 fcrb2545 IQ 0.04 seob6133 IQ 0.04 fcrcO198 IQ 0.04 seob7530 IQ 0.04 fcrc2724 IQ 0.04 seoc0866 IQ 0.04 hfcr5381 IQ 0.04 seoc4561 IQ 0.04 hfcr6370 IQ 0.04 fcrb4929 IQ 0.04 miob3474 IQ 0.04 seob0751 IQ 0.04 mioc3904 IQ 0.04 fcrb2308 IQ 0.04 miod2556 IQ 0.04 fcrb8765 IQ 0.04 miod4686 IQ 0.04 miod7408 IQ 0.04 miod7414 IQ 0.04 seoa5552 IQ 0.04 ncrb2524 IQ 0.04 ncrcllll IQ 0.04 ncrcl885 IQ 0.04 fcrcO529 IQ 0.04 ncrc9772 IQ 0.04 miob7435 IQ 0.04 seoa0783 IQ 0.04 seoa7366 IQ 0.04 seoal739 IQ 0.04 miod5672 IQ 0.04 seoa2181 IQ 0.04 ncrb8559 IQ 0.04 seoa3821 IQ 0.04 seob4570 IQ 0.04 seoa8401 IQ 0.04 seoc7566 IR 4.46e-05 seob2283 IQ 0.04 miodl450 IR 1.6e-04 seob4197 IQ 0.04 mioc8437 IR 1.73e-04 seob5379 IQ 0.04 fcrb8940 IR 2.2e-04 seob6446 IQ 0.04 seocl311 IR 3.9e-04 seoc5612 IQ 0.04 mioc8016 IR 4.68e-04 seob0185 IQ 0.04 seob4333 IR 4.76e-04 fcrl004 IQ 0.04 fcrb5007 IR 5.07e-04 fcrb4570 IQ 0.04 ncrO429 IR 5.52e-04 mioa2013 IQ 0.04 miod5672 IR 7.23e-04 seob6572 IQ 0.04 ncrc9279 IR 7.82e-04 seocl023 IQ 0.04 seob3982 IR 9.0e-04 miob0496 IQ 0.04 fcrb8243 IR 9.59e-04 fcrb5639 IQ 0.04 ncrb8721 IR 1.162766e-03 mioa3963 IQ 0.04 seoa8979 IR 1.319654e-03 ncrcO646 IQ 0.04 fcrc5071 IR 1.418474e-03 miob5829 IQ 0.04 seob4036 IR 1.454856e-03 ncr3404 IQ 0.04 mioa9821 IR 1.580436e-03 seob6541 IQ 0.04 fcrb7505 IR 1.701834e-03 seobll55 IQ 0.04 ncr7876 IR 1.750917e-03 seob7463 IQ 0.04 ncrc5758 IR 2.140366e-03 ncrc4384 IQ 0.04 fcrO788 IR 2.965188e-03 fcrb2749 IQ 0.04 mioa5681 IR 2.965188e-03 fcrb6990 IQ 0.04 ncr3971 IR 2.965188e-03 ncr3830 IQ 0.04 fcrbl856 IR 3.128651e-03 fcrb2199 IQ 0.04 mioc2082 IR 3.266717e-03 fcrbl890 IQ 0.04 fcrb7487 IR 3.605772e-03 seob6217 IQ 0.04 seob5612 IR 3.815575e-03 hfcr6085 ""' i-k "" 3."869lS8e-03 mxoa9429 IR 0.01 mioa0936 IR 3.869158e-03 ncr4984 IR 0.01 ncr5473 IR 4.172868e-03 ncrc4996 IR 0.01 ncr2995 IR 4.406314e-03 ncrc6846 IR 0.01 seobll33 IR 4.406314e-03 seoa5303 IR 0.01 fcrb6481 IR 4.591403e-03 seob3503 IR 0.01 miob4864 IR 5.008048e-03 seoc0203 IR 0.01 fcrb4551 IR 5.070437e-03 ncr7037 IR 0.01 seob9124 IR 5.140962e-03 ncr6197 IR 0.01 fcrc0302 IR 5.314391e-03 fcrO469 IR 0.01 ncr7284 IR 5.341076e-03 seoa8432 IR 0.01 fcr3973 IR 5.680774e-03 seob0999 IR 0.01 hfcrO676 IR 5.680774e-03 fcrc0700 IR 0.01 ncr3419 IR 5.680774e-03 fcrcO287 IR 0.01 seob8562 IR 5.799323e-03 πιiob7610 IR 0.01 ncr6415 IR 5.811451e-03 seoal065 IR 0.01 seob4539 IR 5.985149e-03 ncrb5227 IR 0.01 mioc0978 IR 6.234951e-03 seoa9373 IR 0.01 ncr2160 IR 6.38343e-03 fcrb4311 IR 0.01 πuod4348 IR 6.431377e-03 fcrb5840 IR 0.01 ncr9123 IR 6.431377e-03 miobl059 IR 0.01 mioc2381 IR 6.542403e-03 m-.ob3426 IR 0.01 fcrc6228 IR 6.563288e-03 mioc6345 IR 0.01 fcrc7180 IR 6.981505e-03 miod2834 IR 0.01 fcrbβlOβ IR 7.267233e-03 ncrb8330 IR 0.01 mioa6969 IR 7.267233e-03 ncrc2463 IR 0.01 miob4346 IR 7.267233e-03 ncrc9010 IR 0.01 rrαob9073 IR 7.267233e-03 seoa7094 IR 0.01 ncr4993 IR 7.267233e-03 fcrc6397 IR 0.01 seoa8195 IR 7.267233e-03 hfcr0560 IR 0.01 seob4726 IR 7.267233e-03 ncr6893 IR 0.01 seoc0438 IR 7.267233e-03 fcrb2800 IR 0.01 seoc2131 IR 7.267233e-03 fcr5470 IR 0.01 mioc2887 IR 7.625229e-03 miocl978 IR 0.01 hfcr3922 IR 7.634307e-03 ncrb3314 IR 0.01 fcrb9859 IR 7.640161e-03 hfcr6043 IR 0.01 fcrb3550 IR 8.107057e-03 fcrb7430 IR 0.01 hfcr5383 IR 8.196224e-03 fcrb8196 IR 0.01 miob6536 IR 8.196224e-03 mioa7088 IR 0.01 ncr7768 IR 8.23771e-03 miod6552 IR 0.01 miob9495 IR 8.352358e-03 ncr5890 IR 0.01 nιioa3428 IR 8.522496e-03 ncr7097 IR 0.01 fcr3033 IR 8.949232e-03 ncrb7635 IR 0.01 miod4083 IR 8.954907e-03 ncrc0090 IR 0.01 mioa6172 IR 9.121639e-03 seoa7403 IR 0.01 ncr7852 IR 9.121639e-03 seob0872 IR 0.01 seoa5396 IR 9.201027e-03 seoc5627 IR 0.01 fcr3987 IR 9.226752e-03 fcrb6269 IR 0.01 fcrb6666 IR 9.226752e-03 mioa8796 IR 0.01 mioa8647 IR 9.226752e-03 ncrb8134 IR 0.01 ncr5709 IR 9.226752e-03 ncr7631 IR 0.01 ncrb8751 IR 9.226752e-03 fcr5092 IR 0.01 miod6234 IR 9.25374e-03 hfcrll89 IR 0.01 fcr3269 IR 9.538296e-03 fcrbl977 IR 0.01 fcr2986 IR 9.702695e-03 mioc0371 IR 0.01 ncr3402 IR 9.933523e-03 mioc3603 IR 0.01 ncrc2290 IR 9.933523e-03 ncr2581 IR 0.01 ncrc4076 IR 9.933523e-03 ncrclO03 IR 0.01 ncrO159 IR 9.971493e-03 seoa4174 IR 0.01 hfcr5970 IR 0.01 fcrb9225 IR 0.01 fcr0018 IR 0.01 miodl825 IR 0.01 fcrO288 IR 0.01 ncrc9739 IR 0.01 fcr3005 IR 0.01 fcrc5418 IR 0.01 mioa6093 IR 0.01 miod2635 IR 0.01 2005/022071
seoc092"0"~•""-'ir '—crrσi ncrbll63 IR 0.02 fcrb8728 IR 0.01 ncrb6385 IR 0.02 seoa5578 IR 0.01 ncrc9549 IR 0.02 fcrb9324 IR 0.01 seoa7899 IR 0.02 fcrO637 IR 0.01 seoa8636 IR 0.02 fcrc2829 IR 0.01 ncr7753 IR 0.02 hfcrO521 IR 0.01 seob6541 IR 0.02 mioa2079 IR 0.01 miocl002 IR 0.02 mioa2204 IR 0.01 seoc4260 IR 0.02 miobl873 IR 0.01 ncrl692 IR 0.02 raiob2355 IR 0.01 seob6781 IR 0.02 miob2858 IR 0.01 hfcr3674 IR 0.02 miob3474 IR 0.01 ncr3965 IR 0.02 miod2483 IR 0.01 fcrc6460 IR 0.02 ncrl780 IR 0.01 seoc2681 IR 0.02 ncr7570 IR 0.01 seoal747 IR 0.02 seoal883 IR 0.01 mioa8380 IR 0.02 seoa2801 IR 0.01 seob5203 IR 0.02 seoa7296 IR 0.01 fcrb5720 IR 0.02 seob4075 IR 0.01 ncrc6817 IR 0.02 seob8986 IR 0.01 hfcr0400 IR 0.02 seoc2256 IR 0.01 fcrl060 IR 0.02 miob8077 IR 0.01 fcr3599 IR 0.02 miob9020 IR 0.01 fcrc5721 IR 0.02 miocl598 IR 0.01 fcrc6582 IR 0.02 miob9788 IR 0.01 hfcr2708 IR 0.02 miodl578 IR 0.01 miob2093 IR 0.02 hfcr2642 IR 0.01 miob8572 IR 0.02 fcrc2040 IR 0.01 miod4184 IR 0.02 fcrc2619 IR 0.01 miod4998 IR 0.02 ncrb5909 IR 0.01 miod6243 IR 0.02 fcr2598 IR 0.01 ncr6326 IR 0.02 seob6256 IR 0.01 ncr7178 IR 0.02 miodl908 IR 0.01 ncrcllO2 IR 0.02 fcr0470 IR 0.01 seoa7369 IR 0.02 fcrO68O IR 0.01 seoblOβl IR 0.02 fcrb2190 IR 0.01 seobl808 IR 0.02 fcrb4570 IR 0.01 seocl535 IR 0.02 mioal603 IR 0.01 seocl406 IR 0.02 mioa5594 IR 0.01 fcrc5547 IR 0.02 miodl358 IR 0.01 ncrbO328 IR 0.02 seob5773 IR 0.01 seoc5267 IR 0.02 mioc4978 IR 0.01 fcrb9499 IR 0.02 fcrc0720 IR 0.01 mioc4290 IR 0.02 ncrc6875 IR 0.01 seoal460 IR 0.02 seoa5691 IR 0.01 seoa9302 IR 0.02 fcrb2197 IR 0.01 seob7047 IR 0.02 fcrb5867 IR 0.01 fcrb2159 IR 0.02 fcrb7723 IR 0.01 seoa4284 IR 0.02 mioc3906 IR 0.01 seob4424 IR 0.02 fcrc5154 IR 0.01 ncrc9483 IR 0.02 hfcr3207 IR 0.01 seoa5766 IR 0.02 ncrc9525 IR 0.01 fcrl068 IR 0.02 fcrO553 IR 0.02 ncrc5553 IR 0.02 fcrb8623 IR 0.02 seoa7897 IR 0.02 fcrb9602 IR 0.02 seob5730 IR 0.02 fcrc5126 IR 0.02 seob9122 IR 0.02 hfcr5719 IR 0.02 seob6415 IR 0.02 mioa4484 IR 0.02 fcrO793 IR 0.02 mioc2021 IR 0.02 fcrc5136 IR 0.02 rtιioc8412 IR 0.02 inioa0890 IR 0.02 miodl937 IR 0.02 mioa2528 IR 0.02 ncrbO487 IR 0.02 miob2668 IR 0.02 ncrb0602 IR 0.02 miob3942 IR 0.02 005/022071
ncr9779" ΓR' 0702 fcrc5850 IR 0.03 ncrbO367 IR 0.02 mioa6585 IR 0.03 seob0949 IR 0.02 mioc2961 IR 0.03 seob9871 IR 0.02 miodl885 IR 0.03 fcrl608 IR 0.02 ncrc5232 IR 0.03 fcr3181 IR 0.02 seoa7366 IR 0.03 seocl957 IR 0.02 seoa8609 IR 0.03 ncrc3526 IR 0.02 seob3545 IR 0.03 ncr6811 IR 0.02 seob6020 IR 0.03 fcrb6410 IR 0.02 seob6680 IR 0.03 fcrbl924 IR 0.02 seoa5525 IR 0.03 seoa6387 IR 0.02 mioc7296 IR 0.03 seoa0221 IR 0.02 seob6132 IR 0.03 ncrcl374 IR 0.02 seoc3269 IR 0.03 mioa0707 IR 0.02 miob8698 IR 0.03 hfcrO543 IR 0.02 mioc4769 IR 0.03 fcrc4390 IR 0.02 ncrb3638 IR 0.03 fcr3539 IR 0.02 seobl238 IR 0.03 fcr5536 IR 0.02 fcr2729 IR 0.03 fcrb2460 IR 0.02 mioc3523 IR 0.03 fcrb3169 IR 0.02 ncrc9168 IR 0.03 fcrb5440 IR 0.02 fcrb2484 IR 0.03 fcrb7885 IR 0.02 fcrb8808 IR 0.03 fcrc0042 IR 0.02 seob8639 IR 0.03 fcrc3998 IR 0.02 miob9678 IR 0.03 fcrc6560 IR 0.02 hfcrO383 IR 0.03 hfcr3446 IR 0.02 fcr3620 IR 0.03 hfcr6322 IR 0.02 fcrb3201 IR • 0.03 mioal055 IR 0.02 ncrcO744 IR 0.03 mioa6102 IR 0.02 seoc2218 IR 0.03 mioa6567 IR 0.02 fcrb2094 IR 0.03 mioc4552 IR 0.02 fcrb6785 IR 0.03 ncr3588 IR 0.02 mioc7904 IR 0.03 ncrb3768 IR 0.02 ncr3434 IR 0.03 ncrb5837 IR 0.02 ncrb8385 IR 0.03 ncrb8102 IR 0.02 ncrc2868 IR 0.03 ncrc2807 IR 0.02 ncrb8790 IR 0.03 seoa0388 IR 0.02 fcr7415 IR 0.03 seoa6510 IR 0.02 fcrb4932 IR 0.03 seob9353 IR 0.02 fcrb4995 IR 0.03 seoc2510 IR 0.02 fcrb5688 IR 0.03 seob5064 IR 0.02 fcrc5713 IR 0.03 fcr4568 IR 0.02 hfcr4007 IR 0.03 seoa2385 IR 0.02 mioa0874 IR 0.03 seoa7077 IR 0.02 miobl789 IR 0.03 fcrb8891 IR 0.02 mioc0699 IR 0.03 miob8487 IR 0.02 mioc2587 IR 0.03 fcrb4918 IR 0.02 mioc2728 IR 0.03 miob2466 IR 0.02 mioc7895 IR 0.03 seobllOO IR 0.02 ncr6232 IR 0.03 fcrc4846 IR 0.02 ncr7923 IR 0.03 fcr2935 IR 0.02 ncrb2013 IR 0.03 seob8839 IR 0.02 ncrb3444 IR 0.03 fcrcO839 IR 0.02 ncrb4402 IR 0.03 ncrc2531 IR 0.02 ncrcll92 IR 0.03 fcrb4504 IR 0.02 ncrc4411 IR 0.03 fcrc5092 IR 0.02 seoa4167 IR 0.03 seob8311 IR 0.02 seoa5366 IR 0.03 seoc2518 IR 0.02 seob3523 IR 0.03 seoaOOlO IR 0.02 seob8242 IR 0.03 fcr2293 IR 0.03 seoc4960 IR 0.03 fcrb6141 IR 0.03 seoc5134 IR 0.03 fcrb6817 IR 0.03 miocl049 IR 0.03 fcrc5113 IR 0.03 mioc3726 IR 0.03 mioa3084 IR 0.03 mioa8622 IR 0.04 mioc3962 IR 0.03 fcr3379 IR 0.04 ncrO138 IR 0.03 fcrb9151 IR 0.04 ncr8199 IR 0.03 fcrb9620 IR 0.04 seobll58 IR 0.03 hfcr3467 IR 0.04 seobl891 IR 0.03 hfcr3615 IR 0.04 fcrb4342 IR 0.03 hfcr5237 IR 0.04 hfcr3110 IR 0.03 mioa0891 IR 0.04 seoc5006 IR 0.03 mioa5531 IR 0.04 seoc2589 IR 0.03 mioa8820 IR 0.04 fcrb5199 IR 0.03 mioc2828 IR 0.04 mioa8851 IR 0.03 ncrl712 IR 0.04 ncrb7102 IR 0.03 ncrl768 IR 0.04 ncrc3100 IR 0.03 ncr7595 IR 0.04 fcr2018 IR 0.03 ncrb3373 IR 0.04 fcr2684 IR 0.03 ncrb7211 IR 0.04 miod5198 IR 0.03 seoa0207 IR 0.04 miocll26 IR 0.03 seoa5575 IR 0.04 ncr4126 IR 0.03 seob6812 IR 0.04 seob8786 IR 0.03 fcrc2745 IR 0.04 mioa0820 IR 0.03 mioa0245 IR 0.04 fcrb5114 IR 0.03 mioa0763 IR 0.04 ncr9781 IR 0.03 hfcr3444 IR 0.04 seoa5090 IR 0.03 mioc6412 IR 0.04 fcrO99O IR 0.03 ncr3339 IR 0.04 fcr3053 IR 0.03 mioc3716 IR 0.04 fcrb5564 IR 0.03 mioc7668 IR 0.04 fcrb8870 IR 0.03 fcr4272 IR 0.04 fcrc2280 IR 0.03 fcr2306 IR 0.04 fcrc5458 IR 0.03 fcrc5482 IR 0.04 hfcr2275 IR 0.03 seoc5228 IR 0.04 hfcr3962 IR 0.03 rαiod3205 IR 0.04 miob8249 IR 0.03 seob9368 IR 0.04 mioc4603 IR 0.03 fcrc.2050 IR 0.04 mioc6391 IR 0.03 fcrc4157 IR 0.04 ncrcO964 IR 0.03 mioc0301 IR 0.04 ncrc5806 IR 0.03 ncrcl326 IR 0.04 ncrc5959 IR 0.03 seoa9363 IR 0.04 ncrc5972 IR 0.03 seob4804 IR 0.04 seoa4055 IR 0.03 fcrb2124 IR 0.04 seoa9735 IR 0.03 fcr3957 IR 0.04 fcrb2292 IR 0.03 fcrO253 IR 0.04 fcrb6363 IR 0.03 fcrl562 IR 0.04 ncr7151 IR 0.03 fcr5474 IR 0.04 ncrc6687 IR 0.03 fcrc5309 IR 0.04 miod0455 IR 0.03 hfcr6700 IR 0.04 ncrc6697 IR 0.03 mioaOβOl IR 0.04 fcrc5516 IR 0.03 mioa8774 IR 0.04 mioa3018 IR 0.03 miob4368 IR 0.04 ncrc3343 IR 0.03 mioc3127 IR 0.04 fcr2167 IR 0.03 mioc6385 IR 0.04 fcrc2131 IR 0.03 mioc8635 IR 0.04 seob0219 IR 0.03 ncr8481 IR 0.04 seob6008 IR 0.03 ncr9487 IR 0.04 seob6558 IR 0.03 ncrb7167 IR 0.04 seob5458 IR 0.03 ncrcO856 IR 0.04 seocl504 IR 0.03 ncrc5738 IR 0.04 mioal971 IR 0.03 seoa4670 IR 0.04 mioc9206 IR 0.03 seobl574 IR 0.04 ncrcO262 IR 0.03 seob4105 IR 0.04 seob6139 IR 0.03 seob4498 IR 0.04 ncr9933 IR 0.03 seoc0513 IR 0.04 miod7326 IR 0.03 fcr4214 IR 0.04 miod2367 IR 0.03 fcrb6522 IR 0.04 005/022071
ncrc2443 IR 0.04 seoc2030 IR 0.04 ncr6637 IR 0.04 seoal644 IV 2.4e-07 fcrb5679 IR 0.04 fcrb4985 IV 7.42e-07 seob6028 IR 0.04 seob3307 IV 8.48e-06 seoa0238 IR 0.04 seob2775 IV 1.28e-05 fcrcO559 IR 0.04 fcrb5214 IV 2.5e-05 ncrc0803 IR 0.04 fcrb8489 IV 2.68e-05 mioc6925 IR 0.04 mioc6211 IV 3.64e-05 hfcr3224 IR 0.04 miod6234 IV 3.66e-05 ncrcO863 IR 0.04 hfcr3180 IV 4.19e-05 miob4760 IR 0.04 fcrb2256 IV 4.65e-05 seob5954 IR 0.04 ncrc9436 IV 5.3e-05 seoa5302 IR 0.04 fcrb4981 IV 5.58e-05 fcrb6301 IR 0.04 seoa9997 IV 6.35e-05 ncrcO922 IR 0.04 seoc0009 IV 6.35e-05 fcrc5007 IR 0.04 miob9805 IV 7.39e-05 fcrcl313 IR 0.04 seoa0099 IV 8.58e-05 mioc0240 IR 0.04 seob4197 IV 8.72e-05 miod7099 IR 0.04 fcr5836 lv 9.95e-05 ncrcO341 IR 0.04 fcrc5604 IV 1.06e-04 fcrO343 IR 0.04 mioa4229 IV 1.08e-04 fcr2182 IR 0.04 fcrb4781 IV 1.15081e-04 fcrbl604 IR 0.04 fcrb2137 IV 1.21e-04 fcrbl854 IR 0.04 seob2937 IV 1.23e-04 fcrc4005 IR 0.04 fcrb3966 IV 1.27e-04 hfcr3067 IR 0.04 seob2938 IV 1.52e-04 hfcr3070 IR 0.04 hfcr2287 IV 1.53205e-04 hfcr4170 IR 0.04 miod.4564 IV 1.53205e-04 hfcr4275 IR 0.04 seoal460 IV 1.53205e-04 mioa2165 IR 0.04 ncrc5019 IV 1.72e-04 mioa5902 IR 0.04 hfcr5969 IV 1.76303e-04 mioa8452 IR 0.04 mioa2993 IV 2.02535e-04 mioa8594 IR 0.04 miod5505 IV 2.02535e-04 miob5119 IR 0.04 fcrb5123 IV 2.14e-04 miod4407 IR 0.04 hfcr2844 IV 2.22e-04 miod7367 IR 0.04 miod7099 IV 2.26e-04 ncr4612 IR 0.04 fcr5339 IV 2.32274e-04 ncr5975 IR 0.04 miob9652 IV 2.32274e-04 ncr8725 IR 0.04 seoal883 IV 2.32274e-04 ncrb0090 IR 0.04 seoa5652 IV 2.32274e-04 ncrb3011 IR 0.04 seob5100 IV 2.32274e-04 ncrb7376 IR 0.04 ncrc5150 IV 2.45e-04 ncrcl653 IR 0.04 seob6217 IV 2.45e-04 ncrc6129 IR 0.04 seob7500 IV 2.45e-04 ncrc7171 IR 0.04 mioc0121 IV 2.45493e-04 seoa0064 IR 0.04 seob4306 IV 2.49e-04 seoa6238 IR 0.04 seob2950 IV 2.65e-04 seoa8266 IR 0.04 ncr2892 IV 2.65933e-04 seob8854 IR 0.04 ncrb3980 IV 2.65933e-04 seocl539 IR 0.04 seoa6178 IV 2.65933e-04 seocl764 IR 0.04 seoc3519 lv 2.65933e-04 seoc7498 IR 0.04 fcrc5160 IV 3.03966e-04 seob3485 IR 0.04 mioa3940 IV 3.03966e-04 ncrb8468 IR 0.04 seob7765 IV 3.03966e-04 fcrb6005 IR 0.04 fcr3575 IV 3.19001e-04 ncrb8539 IR 0.04 miod5030 IV 3.46873e-04 ncrc5016 IR 0.04 fcrb3544 IV 3.47e-04 ncr2288 IR 0.04 seocl235 IV 3.49e-04 fcrbl920 IR 0.04 seoa5156 IV 3.5e-04 fcrb8020 IR 0.04 ncrbO164 IV 3.54e-04 seoa4132 IR 0.04 ncrc5025 IV 3.55e-04 seoa9870 IR 0.04 seoa6661 IV 3.66e-04 seob0876 IR 0.04 ncrc4985 IV 3.68e-04 seob2990 IR 0.04 seob7622 IV 3.68e-04 fcr2860 IV 3.7e-04 fcrc7219 IV 5.78978e-04 fcr4128 IV 3.7e-04 mioc2872 IV 5.78978e-04 fcrbl539 IV 3.7e-04 ncrc2857 IV 5.78978e-04 fcrbl990 IV 3.7e-04 seob9851 IV 5.78978e-04 fcrb2330 IV 3.7e-04 mioa8380 IV 5.8e-04 fcrb3119 IV 3.7e-04 hfcrO229 IV 6.04e-04 fcrb5269 IV 3.7e-04 fcrb5351 IV 6.14e-04 fcrb5536 IV 3.7e-04 seob9614 IV 6.2227e-04 fcrb5564 IV 3.7e-04 fcrb5896 IV 6.33e-04 fcrb6251 IV 3.7e-04 fcrc2724 IV 6.33e-04 fcrb6596 IV 3.7e-04 mioc7370 IV 6.33e-04 fcrb6639 IV 3.7e-04 fcr2299 IV 6.35519e-04 fcrb7084 IV 3.7e-04 fcr3053 IV 6.55553e-04 fcrb7693 IV 3.7e-04 fcrc5137 IV 6.55553e-04 fcrc2745 IV 3.7e-04 fcrc5614 IV 6.55553e-04 fcrc2849 IV 3.7e-04 mioa3668 IV 6.55553e-04 fcrc5480 IV 3.7e-04 miod4493 IV 6.55553e-04 miobl561 IV 3.7e-04 seoa5898 IV 6.55553e-04 miob3456 IV 3.7e-04 seoa8993 IV 6.55553e-04 miob8816 IV 3.7e-04 seoa9160 IV 6.55553e-04 miob9248 IV 3.7e-04 seob2987 IV 6.55553e-04 miob9393 IV 3.7e-04 ncrc3460 IV 7.23e-04 mioc4022 IV 3.7e-04 seob3088 IV 7.31e-04 miod5622 IV 3.7e-04 fcrb9611 IV 7.41141e-04 ncrl545 IV 3.7e-04 fcrc5107 IV 7.41141e-04 ncrb2288 IV 3.7e-04 mioc3682 IV 7.41141e-04 ncrb7482 IV 3.7e-04 ncrbO749 IV 7.41141e-04 ncrc2443 IV 3.7e-04 seoc3690 IV 7.41141e-04 ncrc5363 IV 3.7e-04 seocl642 IV 7.46e-04 seoa2209 IV 3.7e-04 fcrb8340 IV 7.85e-04 seoa3230 IV 3.7e-04 miob8825 IV 7.85e-04 seoa7340 IV 3.7e-04 seob9869 IV 8.13e-04 seoa8997 IV 3.7e-04 mioc4161 IV 8.18e-04 seob0303 IV 3.7e-04 fcrb8668 IV 8.3666e-04 seob0763 IV 3.7e-04 fcrc5041 IV 8.3666e-04 seob0872 IV 3.7e-04 fcrc5086 IV 8.3666e-04 seobl426 IV 3.7e-04 fcrc6932 IV 8.3666e-04 seoclββl IV 3.7e-04 hfcr3058 IV 8.3666e-04 seoc2221 IV 3.7e-04 seob6437 IV 8.3666e-04 mioa4753 IV 3.89e-04 seob9772 IV 8.3666e-04 fcrb8114 IV 3.91e-04 seoal318 IV 8.39e-04 seoal749 IV 3.95201e-04 fcrb9420 IV 8.44e-04 miob9124 IV 4.2e-04 fcrb6715 IV 8.66e-04 fcrcO43O IV 4.28e-04 ncr2575 IV 9.04e-04 seoa5977 IV 4.43e-04 ncrc4875 IV 9.26e-04 fcrl772 IV 4.49e-04 miob9285 IV 9.36e-04 fcrc5577 IV 4.49548e-04 fcrbl684 IV 9.43108e-04 miob7794 IV 4.49548e-04 fcrb2113 IV 9.43108e-04 ncr4030 IV 4.49548e-04 fcrb3686 IV 9.43108e-04 ncrcO576 IV 4.49548e-04 mioa8864 IV 9.43108e-04 seoa0429 IV 4.68e-04 miob3695 IV 9.43108e-04 fcrb3680 IV 4.71e-04 miob4574 IV 9.43108e-04 ncrO133 IV 4.83e-04 miob8609 IV 9.43108e-04 fcrb6785 IV 5.10569e-04 seoa0799 IV 9.43108e-04 miob0764 IV 5.10569e-04 seob6379 IV 9.43108e-04 miod3827 IV 5.10569e-04 seoc4380 IV 9.43108e-04 ncrb0046 IV 5.10569e-04 seob6380 IV 9.5e-04 seobl362 IV 5.10569e-04 seob2994 IV 9.76e-04 seoc0149 IV 5.10569e-04 ncrc5039 IV 9.97e-04 fcrc4180 IV 5.27e-04 miocl354 IV 1.061561e-03 fcrb3763 IV 5.67e-04 ncrbll67 IV 1.073748e-03 ncrc3283 IV 5.67e-04 seoa3895 IV 1.119756e-03 ncrc3358 IV 5.67e-04 seocl307 IV 1.151704e-03 fcr4328 IV 5.78978e-04 seoal737 IV 1.155164e-03 ncrc9899 IV 1.170347e-03 ncrc3598 IV 1.476515e-03 fcrb3734 IV 1.170828e-03 ncrc4076 IV 1.476515e-03 seob0133 IV 1.181737e-03 ncrc5688 IV 1.476515e-03 miob3618 IV 1.193188e-03 ncrc8988 IV 1.476515e-03 miob7106 IV 1.193188e-03 seoal856 IV 1.476515e-03 miob7638 IV 1.193188e-03 seoa2768 IV 1.476515e-03 ncrbO328 IV 1.193188e-03 seoa3891 IV 1.476515e-03 ncrcO461 IV 1.193188e-03 seoa4681 IV 1.476515e-03 seob4555 IV 1.193188e-03 seoa5235 IV 1.476515e-03 seoc0034 IV 1.193188e-03 seoa6557 IV 1.476515e-03 mioa0192 IV 1.218739e-03 seoa7517 IV 1.476515e-03 miod4735 IV 1.252641e-03 seoa7530 IV 1.476515e-03 mioal906 IV 1.255827e-03 seoa8424 IV 1.476515e-03 mioa8973 IV 1.256375e-03 seoa9627 IV 1.476515e-03 seoa9724 IV 1.27253e-03 seoa9792 IV 1.476515e-03 miod.1316 IV 1.29623e-03 seob0370 IV 1.476515e-03 ncrc9159 IV 1.326356e-03 seobl947 IV 1.476515e~03 fcr3001 IV 1.339245e-03 seob5556 IV 1.476515e-03 fcrb8908 IV 1.339245e-03 seoc2722 IV 1.476515e-03 fcrcO591 IV 1.339245e-03 mioa8987 IV 1.493237e-03 fcrc2775 IV 1.339245e-03 miob7319 IV 1.494352e-03 fcrc7056 IV 1.339245e-03 mioa0311 IV 1.501088e-03 hfcr2629 IV 1.339245e-03 mioa2173 lv 1.501088e-03 miob2836 IV 1.339245e-03 ncr4135 IV 1.501088e-03 mioc0669 IV 1.339245e-03 ncrb2544 IV 1.501088e-03 miod3946 IV 1.339245e-03 ncrb4166 IV 1.501088e-03 ncrcO964 IV 1.339245e-03 seoa0536 IV 1.501088e-03 ncrc9052 IV 1.339245e-03 seob8562 IV 1.501088e-03 seoa6172 IV 1.339245e-03 hfcr2670 IV 1.548822e-03 seob4734 IV 1.339245e-03 seoa5554 lv 1.560016e-03 seob7424 IV 1.339245e-03 miob9748 IV 1.637446e-03 fcr4380 IV 1.411242e-03 hfcrO263 lv 1.680177e-03 miod3914 IV 1.413142e-03 hfcr5991 IV 1.680177e-03 miod7081 IV 1.426601e-03 hfcr6613 IV 1.680177e-03 miodl323 IV 1.440791e-03 mioa3913 IV 1.680177e-03 fcrbl922 IV 1.458522e-03 ncrb3768 IV 1.680177e-03 ncr4189 IV 1.458522e-03 fcrO748 IV 1.690349e-03 fcrl756 IV 1.476515e-03 fcrb2292 IV 1.706646e-03 fcr4494 IV 1.476515e-03 fcrc6990 IV 1.722062e-03 fcr4902 IV 1.476515e-03 seob3226 IV 1.79599e-03 fcrbl575 IV 1.476515e-03 seob2728 IV 1.80089e-03 fcrb2483 IV 1.476515e-03 hfcr3444 IV 1.835381e-03 fcrb4470 IV 1.476515e-03 fcr2102 IV 1.844189e-03 fcrb4717 IV 1.476515e-03 fcr385β IV 1.878078e-03 fcrb7036 IV 1.476515e-03 mioa8952 IV 1.878078e-03 fcrb9454 IV 1.476515e-03 miob8487 IV 1.878078e-03 fcrcO529 IV 1.476515e-03 miod5060 IV 1.878078e-03 fcrc5471 IV 1.476515e-03 miod5123 IV 1.878078e-03 fcrc7222 IV 1.476515e-03 ncr6344 IV 1.878078e-03 hfcr3647 IV 1.476515e-03 seoa0501 IV 1.878078e-03 mioal626 IV 1.476515e-03 seoa8239 IV 1.878078e-03 miob3307 IV 1.476515e-03 seoc5228 IV 1.878078e-03 miob9734 IV 1.476515e-03 fcrb2800 IV 1.898609e-03 miob9788 IV 1.476515e-03 ncrO634 IV 1.92683e-03 mioc0911 IV 1.476515e-03 seoa4795 IV 1.961962e-03 mioc7364 IV 1.476515e-03 seob7906 IV 1.97218e-03 miodl448 IV 1.476515e-03 fcrc5482 IV 2.019211e-03 miod5092 IV 1.476515e-03 ncrc5230 IV 2.026769e-03 miod7212 IV 1.476515e-03 seoa2805 IV 2.092754e-03 ncr0025 IV 1.476515e-03 fcr2276 IV 2.096472e-03 ncr2967 IV 1.476515e-03 fcrb4345 IV 2.096472e-03 ncr8538 IV 1.476515e-03 fcrcl563 IV 2.096472e-03 ncrb3638 IV 1.476515e-03 fcrc5290 IV 2.096472e-03 ncrcl780 IV 1.476515e-03 fcrc6002 lv 2.096472e-03 2005/022071
mioa'0597'"'' W 2.d"9647"2'e-03 ncrl712 IV 2.893252e-03 miob7662 IV 2.096472e-03 ncrc2161 IV 2.893252e-03 miob8320 IV 2.096472e-03 seoa2391 IV 2.893252e-03 miod5010 IV 2.096472e-03 seob4145 IV 2.893252e-03 raiod5256 IV 2.096472e-03 seob9543 IV 2.893252e-03 ncr3825 IV 2.096472e-03 ncrO491 IV 2.89732e-03 ncrb6109 IV 2.096472e-03 seocl628 IV 3.049387e-03 seoa0207 IV 2.096472e-03 seob6156 IV 3.053926e-03 ncrb2092 IV 2.134438e-03 ncrc4033 IV 3.104345e-03 fcrbl992 IV 2.209983e-03 seob8287 IV 3.182459e-03 seoa3761 IV 2.225021e-03 mioc0302 IV 3.183214e-03 miod4407 IV 2.228163e-03 mioc0621 IV 3.183214e-03 seoa5433 lv 2.23127e-03 ncr2930 IV 3.183214e-03 seoa6620 IV 2.299089e-03 mioc3369 IV 3.203555e-03 fcrb3615 IV 2.337161e-03 ncrc9877 IV 3.209695e-03 fcrb3870 IV 2.337161e-03 fcrl555 IV 3.212904e-03 fcrb5296 IV 2.337161e-03 fcr4129 IV 3.212904e-03 fcrb6508 IV 2.337161e-03 fcrb5467 IV 3.212904e-03 fcrb8119 IV 2.337161e-03 fcrb8236 IV 3.212904e-03 fcrb8215 IV 2.337161e-03 mioa7617 IV 3.212904e-03 fcrc5139 IV 2.337161e-03 miod4269 iv 3.212904e-03 miod5198 IV 2.337161e-03 ncr4656 iv 3.212904e-03 miod6560 IV 2.337161e-03 ncr7923 IV 3.212904e-03 ncr3782 IV 2.337161e-03 ncr8153 IV 3.212904e-03 ncrb0027 IV 2.337161e-03 seoal599 IV 3.212904e-03 ncrb4477 IV 2.337161e-03 seoa8912 IV 3.212904e-03 ncrb7166 IV 2.337161e-03 miocl060 IV 3.280556e-03 seoall73 IV 2.337161e-03 ncrc2675 -L V 3.327646e-03 seobl808 IV 2.337161e-03 ircr8725 IV 3.386385e-03 seob6206 IV 2.342651e-03 fcrb2713 iv 3.387122e-03 miob9087 IV 2.3779e-03 fcrb6031 IV 3.409264e-03 seob0321 iv 2.379735e-03 hfcr2536 IV 3.539273e-03 miob3594 iv 2.406345e-03 mioa9491 IV 3.555572e-03 fcrb4067 iv 2.48884e-03 fcr2940 IV 3.558947e-03 seob0937 IV 2.540573e-03 fcr5536 IV 3.563359e-03 mioc3220 IV 2.541667e-03 fcrbl731 IV 3.563359e-03 fcrb9684 IV 2.564635e-03 mioc2166 IV 3.563359e-03 seob7575 IV 2.564635e-03 ncrb0045 IV 3.563359e-03 fcrOOδl IV 2.602068e-03 ncrb0074 IV 3.563359e-03 fcr3282 IV 2.602068e-03 ncrb2517 IV 3.563359e-03 fcrcO379 IV 2.602068e-03 ncrc6846 IV 3.563359e-03 fcrc4390 IV 2.602068e-03 seoal992 IV 3.563359e-03 miob5940 IV 2.602068e-03 seoa6152 IV 3.563359e-03 mioc3603 IV 2.602068e-03 seoa8177 IV 3.563359e-03 miodl377 IV 2.602068e-03 seob0058 IV 3.563359e-03 miod5771 IV 2.602068e-03 seob6133 IV 3.563359e-03 miod6324 IV 2.602068e-03 seoc0951 IV 3.563359e-03 ncrc8863 IV 2.602068e-03 seoc3659 IV 3.563359e-03 seoal598 IV 2.602068e-03 seoc4928 IV 3.563359e-03 seob4303 IV 2.602068e-03 ncr4454 IV 3.575847e-03 fcrb6929 IV 2.604001e-03 seob3882 IV 3.664302e-03 ncrc2959 IV 2.604001e-03 seob0085 IV 3.701885e-03 fcrbl337 IV 2.697142e-03 mioa6726 IV 3.711483e-03 fcrc5690 IV 2.697142e-03 ncrb8646 IV 3.711654e-03 ncrO165 IV 2.697142e-03 seoa3717 IV 3.749508e-03 seoa6131 IV 2.721704e-03 seoa8399 IV 3.841578e-03 fcr4503 IV 2.721914e-03 fcrb5375 IV 3.881761e-03 miod4857 IV 2.870616e-03 miob9533 IV 3.895277e-03 fcrl328 IV 2.893252e-03 ncrb6742 IV 3.895528e-03 fcrc2573 IV 2.893252e-03 seoc5006 IV 3.929979e-03 fcrc5671 IV 2.893252e-03 fcrbl876 IV 3.9471e-03 hfcr5611 IV 2.893252e-03 fcrc2954 IV 3.9471e-03 mioc3669 IV 2.893252e-03 mioa2204 IV 3.9471e-03 miodOOδO IV 2.893252e-03 mioa8970 IV 3.9471e-03 mioa9604 IV 3.9471e-03 ncrc2831 IV 4.204851e-03 miob8691 IV 3.9471e-03 ncrc2919 IV 4.204851e-03 mioc7201 IV 3.9471e-03 ncrc4907 IV 4.204851e-03 miod5310 IV 3.9471e-03 ncrc5312 IV 4.204851e-03 miod6845 IV 3.9471e-03 ncrc8841 IV 4.204851e-03 ncr8481 IV 3.9471e-03 ncrc8949 IV 4.204851e-03 ncrb8239 IV 3.9471e-03 ncrc9328 IV 4.204851e-03 ncrc3855 IV 3.9471e-03 ncrc9910 IV 4.204851e-03 ncrc5376 IV 3.9471e-03 seoa0388 IV 4.204851e-03 ncrc6587 IV 3.9471e-03 seoa3147 IV 4.204851e-03 seoa0256 IV 3.9471e-03 seoa6137 IV 4.204851e-03 seob7278 IV 3.9471e-03 seoa6393 IV 4.204851e-03 miob8754 IV 3.989036e-03 seoa6497 IV 4.204851e-03 seob7584 IV 4.012618e-03 seoa6598 IV 4.204851e-03 ncrb8171 IV 4.135195e-03 seoa6718 IV 4.204851e-03 miocl416 IV 4.135238e-03 seoa7157 IV 4.204851e-03 seob0046 IV 4.135238e-03 seoa8543 IV 4.204851e-03 miob9209 IV 4.157852e-03 seoa9302 IV 4.204851e-03 fcrb2308 IV 4.204851e-03 seob0034 IV 4.204851e-03 fcrb3181 IV 4.204851e-03 seob0154 IV 4.204851e-03 fcrb3201 IV 4.204851e-03 seob0426 IV 4.204851e-03 fcrb4241 IV 4.204851e-03 seoblOδl IV 4.204851e-03 fcrb4270 IV 4.204851e-03 seob4645 IV 4.204851e-03 fcrb4294 IV 4.204851e-03 seob5319 IV 4.204851e-03 fcrb4360 IV 4.204851e-03 seob5743 IV 4.204851e-03 fcrb4417 IV 4.204851e-03 seob5748 IV 4.204851e-03 fcrb4963 IV 4.204851e-03 seob5899 IV 4.204851e-03 fcrb5688 IV 4.204851e-03 seob7474 IV 4.204851e-03 fcrb8449 IV 4.204851e-03 seob8483 IV 4.204851e-03 fcrb9371 IV 4.204851e-03 seoc0843 IV 4.204851e-03 fcrcl758 IV 4.204851e-03 fcr0903 IV 4.316218e-03 fcrc2306 IV 4.204851e-03 fcrl499 IV 4.339479e-03 fcrc5583 IV 4.204851e-03 hfcr3143 IV 4.349057e-03 hfcr3022 IV 4.204851e-03 mioc3139 IV 4.358139e-03 mioal520 IV 4.204851e-03 fcrb3627 IV 4.366761e-03 mioa3321 IV 4.204851e-03 fcrb5503 IV 4.366761e-03 mioa3673 IV 4.204851e-03 fcrcl402 IV 4.366761e-03 mioa4076 IV 4.204851e-03 fcrc5771 IV 4.366761e-03 mioa4564 IV 4.204851e-03 hfcr2895 IV 4.366761e-03 mioa5085 IV 4.204851e-03 miob2668 IV 4.366761e-03 mioa5692 IV 4.204851e-03 miod5672 IV 4.366761e-03 mioa5955 IV 4.204851e-03 ncr4416 IV 4.366761e-03 miob3953 IV 4.204851e-03 ncrbO696 IV 4.366761e-03 miocl229 IV 4.204851e-03 ncrb8585 IV 4.366761e-03 mioc2209 IV 4.204851e-03 seoalβlβ IV 4.366761e-03 mioc2348 IV 4.204851e-03 seob4293 IV 4.366761e-03 mioc2451 IV 4.204851e-03 seoc0284 IV 4.366761e-03 mioc2577 IV 4.204851e-03 seoc0742 IV 4.366761e-03 mioc4112 IV 4.204851e-03 seoc4381 IV 4.366761e-03 mioc6374 IV 4.204851e-03 fcrc5007 IV 4.388153e-03 miod2128 IV 4.204851e-03 seob0185 IV 4.388153e-03 miod3160 IV 4.204851e-03 miod7414 IV 4.474241e-03 miod6048 IV 4.204851e-03 seoc2549 IV 4.489551e-03 miod6521 IV 4.204851e-03 mioc4351 IV 4.538604e-03 raiod7324 IV 4.204851e-03 mioc8481 IV 4.678627e-03 ncrO638 IV 4.204851e-03 miod4686 IV 4.740394e-03 ncrl305 IV 4.204851e-03 fcr2972 IV 4.825136e-03 ncrl550 IV 4.204851e-03 fcrc4551 IV 4.825136e-03 ncrb0207 IV 4.204851e-03 fcrc6174 IV 4.825136e-03 ncrb3348 IV 4.204851e-03 hfcr4497 IV 4.825136e-03 ncrb8538 IV 4.204851e-03 miob2285 IV 4.825136e-03 ncrcO539 IV 4.204851e-03 mioclβOO IV 4.825136e-03 ncrcl247 IV 4.204851e-03 miod4932 IV 4.825136e-03 ncrcl595 IV 4.204851e-03 ncr8096 IV 4.825136e-03 22071
ncrc9784 IV 4.825136e-03 fcr2935 IV 5.990508e-03 seoa4107 IV 4.825136e-03 seob6851 IV 6.032512e-03 seob4105 IV 4.825136e-03 seob7309 IV 6.107204e-03 seob7866 IV 4.825136e-03 fcrb7760 IV 6.130086e-03 seoa2899 IV 4.826733e-03 ncrc5088 IV 6.197231e-03 hfcrO489 IV 4.869783e-03 seob2661 IV 6.211987e-03 fcrb5527 IV 4.888032e-03 mioc2021 IV 6.236422e-03 seob5551 IV 4.908034e-03 seocl804 IV 6.261128e-03 mioa4077 IV 4.909178e-03 miob8463 IV 6.391219e-03 ncrc9228 IV 4.93663e-03 fcrcO959 IV 6.416508e-03 fcrb3120 IV 5.081016e-03 ncr2484 IV 6.42957e-03 ncrc4448 IV 5.161235e-03 fcrb6301 IV 6.441057e-03 hfcr5865 IV 5.255535e-03 fcrl347 IV 6.462965e-03 fcrb3715 IV 5.312325e-03 fcrb5241 IV 6.462965e-03 fcrb7852 IV 5.318621e-03 fcrc5391 IV 6.462965e-03 ncrb7027 IV 5.324125e-03 mioa0497 IV 6.462965e-03 fcrl312 IV 5.325183e-03 miob3531 IV 6.462965e-03 fcrb8542 IV 5.325183e-03 miob6713 IV 6.462965e-03 fcrc6465 IV 5.325183e-03 miod0456 IV 6.462965e-03 fcrc7046 IV 5.325183e-03 ncr2869 IV 6.462965e-03 mioal473 IV 5.325183e-03 ncr7668 IV 6.462965e-03 mioa9891 IV 5.325183e-03 ncrcO249 iv 6.462965e-03 miob0167 IV 5.325183e-03 ncrc3596 IV 6.462965e-03 miob3247 IV 5.325183e-03 ncrc4654 IV 6.462965e-03 miob5751 IV 5.325183e-03 ncrc6417 IV 6.462965e-03 miod5258 IV 5.325183e-03 seoal747 IV 6.462965e-03 ncrc6588 IV 5.325183e-03 seoa2822 IV 6.462965e-03 seoa4647 IV 5.325183e-03 seoa6754 IV 6.462965e-03 seob3064 IV 5.325183e-03 seoa9363 IV 6.462965e-03 seob6096 IV 5.325183e-03 seob0201 IV 6.462965e-03 seoc0369 IV 5.325183e-03 seob3170 IV 6.462965e-03 miob9163 IV 5.338918e-03 seob7729 IV 6.462965e-03 seoa6930 IV 5.338918e-03 seob9756 IV 6.462965e-03 seob2685 IV 5.358138e-03 ncrb7350 IV 6.507615e-03 miob0974 IV 5.376436e-03 seob9970 IV 6.507615e-03 fcrb6107 IV 5.429727e-03 seoa8300 IV 6.520751e-03 seob4689 IV 5.482935e-03 fcrc4663 IV 6.53971e-03 hfcr2686 IV 5.502936e-03 hfcr3404 IV 6.5438e-03 seoa9712 IV 5.541398e-03 fcrb6363 IV 6.573178e-03 mioc2403 IV 5.546828e-03 seob5889 IV 6.584665e-03 mioc2592 IV 5.591403e-03 fcrbl767 IV 6.5959e-03 seoa4066 IV 5.634661e-03 raiob7109 IV 6.695564e-03 miob8274 IV 5.669235e-03 mioc2961 IV 6.726375e-03 mioa5558 IV 5.842704e-03 ncrc3690 IV 6.760566e-03 ncr3614 IV 5.868299e-03 seobβ272 IV 6.914972e-03 fcr6748 IV 5.870026e-03 fcrbβ776 IV 6.94408e-03 fcrb5449 IV 5.870026e-03 ncrb8802 IV 6.973937e-03 fcrc0180 IV 5.870026e-03 fcr5257 IV 7.107476e-03 mioa2038 IV 5.870026e-03 fcrb6279 IV 7.107476e-03 raioa6556 IV 5.870026e~03 fcrb7812 IV 7.107476e-03 miob2227 IV 5.870026e-03 mioa9246 IV 7.107476e-03 miob9678 IV 5.870026e-03 miobl814 IV 7.107476e-03 mioc5182 IV 5.870026e-03 miob4803 IV 7.107476e-03 miod4019 IV 5.870026e-03 miob6632 IV 7.107476e-03 ncrδlll IV 5.870026e-03 mioc0899 IV 7.107476e-03 ncrb0220 IV 5.870026e-03 mioc2619 iv 7.107476e-03 seoal834 IV 5.870026e-03 ncrl299 iv 7.107476e-03 seoa4264 IV 5.870026e-03 ncr4612 iv 7.107476e-03 seoa5138 IV 5.870026e-03 ncr7904 IV 7.107476e-03 seocl764 IV 5.870026e-03 ncrb3424 IV 7.107476e-03 seoc3374 IV 5.870026e-03 ncrc2796 IV 7.107476e-03 fcrb5254 IV 5.873716e-03 ncrc3551 IV 7.107476e-03 hfcr3844 IV 5.898035e-03 ncrc4798 IV 7.107476e-03 fcrO999 IV 5.963463e-03 ncrc9044 IV 7.107476e-03 71
n"crc9525 T\T ""•"?:T0T476e-03 mioa2374 IV 8.566036e-03 seoal736 IV 7.107476e-03 mioa3486 IV 8.566036e-03 seoal802 IV 7.107476e-03 mioa4037 IV 8.566036e-03 seoa9373 IV 7.107476e-03 mioa4177 IV 8.566036e-03 seob2953 IV 7.107476e-03 ncr3112 IV 8.566036e-03 seob7039 IV 7.107476e-03 ncr7382 IV 8.566036e-03 ncrc9483 IV 7.135877e-03 ncrb6903 IV 8.566036e-03 fcrbl867 IV 7.149399e-03 seoa4524 IV 8.566036e-03 fcrb6351 IV 7.150346e-03 seoc0317 IV 8.566036e-03 fcrc5024 IV 7.17138e-03 miob8707 IV 8.631338e-03 hfcr3436 IV 7.17138e-03 fcrc6228 IV 8.668265e-03 ncr3442 IV 7.17138e-03 fcrb2166 IV 8.677358e-03 ncrbO653 IV 7.17138e-03 fcrb8852 IV 8.677358e-03 ncrb6327 IV 7.17138e-03 fcrc6916 IV 8.677358e-03 fcrc4150 IV 7.20619e-03 seoa4608 IV 8.697131e-03 seoa0792 IV 7.296919e-03 seob6279 iv 8.785727e-03 fcrb5438 IV 7.315319e-03 ncrc9259 IV 8.793602e-03 ncr2861 IV 7.37339e-03 seob0084 iv 8.825601e-03 seoc4824 IV 7.406399e-03 fcr4224 iv 8.856803e-03 ncrc6825 IV 7.490077e-03 fcrb7785 IV 8.880688e-03 seob5684 IV 7.493924e-03 seoc8115 IV 9.082651e-03 fcrb2424 IV 7.538874e-03 mioa4738 iv 9.101947e-03 hfcr5942 IV 7.555731e-03 seoa7546 IV 9.109835e-03 miob4370 IV 7.592592e-03 miob3174 IV 9.150755e-03 seoa0221 IV 7.677171e-03 fcrb8161 IV 9.192222e-03 seoc2050 IV 7.769248e-03 seob5044 IV 9.283845e-03 fcr3861 IV 7.78102e-03 hfcr2389 IV 9.342306e-03 fcr5728 IV 7.78102e-03 fcrb2818 IV 9.351412e-03 mioc2950 IV 7.78102e-03 fcrb5918 IV 9.360485e-03 ncrc3856 lv 7.78102e-03 fcrc6016 IV 9.367048e-03 ncrc6459 IV 7.78102e-03 ncrc5146 IV 9.367346e-03 fcrb2749 lv 7.807218e-03 fcr4803 IV 9.387964e-03 fcrb5272 IV 7.807218e-03 fcr5316 IV 9.387964e-03 fcrb5389 IV 7.807218e-03 fcrb4789 IV 9.387964e-03 fcrb6549 iv 7.807218e-03 fcrb9499 IV 9.387964e-03 hfcr3518 lv 7.807218e-03 fcrc2536 IV 9.387964e-03 mioa9492 iv 7.807218e-03 fcrc5142 IV 9.387964e-03 miob3308 IV 7.807218e-03 fcrc5506 IV 9.387964e-03 miod6134 iv 7.807218e-03 fcrc7180 IV 9.387964e-03 ncrb0054 IV 7.807218e-03 hfcr5919 IV 9.387964e-03 ncrc5316 IV 7.807218e-03 miob2705 IV 9.387964e-03 ncrc6597 IV 7.807218e-03 miob4144 IV 9.387964e-03 ncrc7023 iv 7.807218e-03 mioc2110 IV 9.387964e-03 ncrc9795 IV 7.807218e-03 mioc6156 IV 9.387964e-03 seob7941 IV 7.807218e-03 miod4629 IV 9.387964e-03 seob9627 IV 7.807218e-03 miod6961 IV 9.387964e-03 ncrc6264 IV 7.987052e-03 ncrO159 IV 9.387964e-03 seoa9740 IV 7.991283e-03 ncrl523 IV 9.387964e-03 seob3326 IV 8.004907e-03 ncr4654 IV 9.387964e-03 fcrb8121 IV 8.06206e-03 ncr5065 IV 9.387964e-03 fcrb5929 IV 8.173274e-03 ncrb2085 IV 9.387964e-03 seoa7178 IV 8.208048e-03 ncrc6778 IV 9.387964e-03 miob6087 IV 8.227431e-03 seoa6658 IV 9.387964e-03 mioc0981 IV 8.229427e-03 seobll87 IV 9.387964e-03 ncrc5760 IV 8.24867e-03 seob3303 IV 9.387964e-03 mioa3084 IV 8.251328e-03 seocl948 IV 9.387964e-03 seob5903 IV 8.35087e-03 seob0089 IV 9.434517e-03 fcrbl399 IV 8.385639e-03 ncr4550 IV 9.487359e-03 seoa4056 IV 8.435426e-03 ncrc9284 IV 9.514386e-03 fcr3033 IV 8.446509e-03 mioc0530 IV 9.561475e-03 seoa2978 IV 8.471198e-03 ncrb8396 IV 9.591184e-03 ncr3965 IV 8.490122e-03 fcrO511 IV 9.682221e-03 fcrb4589 IV 8.566036e-03 fcrO837 IV 9.682221e-03 fcrb8504 IV 8.566036e-03 fcrl337 IV 9.682221e-03 22071
fcr3559" "'""~"l"v' " ■"■"•9T6F222Te-03 seobl842 IV 9.682221e-03 fcrbl729 IV 9.682221e-03 seobl898 IV 9.682221e-03 fcrb2203 IV 9.682221e-03 seob5773 IV 9.682221e-03 fcrb3024 IV 9.682221e-03 seob6560 IV 9.682221e-03 fcrb3857 IV 9.682221e-03 seoc0355 IV 9.682221e-03 fcrb3874 IV 9.682221e-03 seoc3870 IV 9.682221e-03 fcrb5537 IV 9.682221e-03 seoc4960 IV 9.682221e-03 fcrb5709 IV 9.682221e-03 seoc6703 IV 9.682221e-03 fcrb5912 IV 9.682221e-03 ncrb4248 IV 9.724687e-03 fcrb6874 IV 9.682221e-03 fcrc6877 IV 9.907979e-03 fcrb9914 IV 9.682221e-03 mioa4667 IV 0.01 fcrclO85 IV 9.682221e-03 mioc7331 IV 0.01 fcrc4722 IV 9.682221e-03 hfcr5959 IV 0.01 fcrc6452 IV 9.682221e-03 seobl862 IV 0.01 hfcr0045 IV 9.682221e-03 fcr2220 IV 0.01 hfcr5003 IV 9.682221e-03 fcrc2231 IV 0.01 mioa2413 IV 9.682221e-03 fcrc6855 IV 0.01 mioa2447 IV 9.682221e-03 fcrc6898 IV 0.01 mioa4326 IV 9.682221e-03 miob2869 IV 0.01 mioa4818 lv 9.682221e-03 miob4037 IV 0.01 miob0681 IV 9.682221e-03 miodl236 IV 0.01 miobl830 IV 9.682221e-03 ncr9324 iv 0.01 rαiob2686 IV 9.682221e-03 ncr9502 IV 0.01 miob3461 IV 9.682221e-03 ncrc4219 IV 0.01 miob3937 iv 9.682221e-03 ncrc4757 IV 0.01 miob4055 IV 9.682221e-03 ncrc6920 IV 0.01 miob4735 IV 9.682221e-03 ncrc9519 IV 0.01 miob6442 IV 9.682221e-03 seoa0186 IV 0.01 miob6615 IV 9.682221e-03 seoal552 IV 0.01 miob8572 IV 9.682221e-03 seobl420 IV 0.01 miob8694 IV 9.682221e-03 seobl906 IV 0.01 miocl086 IV 9.682221e-03 seoc0698 IV 0.01 mioc2541 IV 9.682221e-03 seoc4410 IV 0.01 mioc3206 IV 9.682221e-03 rαiob0207 IV 0.01 mioc4318 IV 9.682221e-03 miob8616 IV 0.01 miod4752 IV 9.682221e-03 mioc9881 IV 0.01 ncr2926 IV 9.682221e-03 miob2287 IV 0.01 ncr3690 iv 9.682221e-03 miod6437 IV 0.01 ncr3815 IV 9.682221e-03 seob5465 IV 0.01 ncr4332 IV 9.682221e-03 miod6938 IV 0.01 ncr8693 IV 9.682221e-03 hfcr2756 IV 0.01 ncrbO145 IV 9.682221e-03 seoal615 IV 0.01 ncrb6762 IV 9.682221e-03 mioc2385 IV 0.01 ncrcO341 IV 9.682221e-03 iniocl808 IV 0.01 ncrc0744 IV 9.682221e-03 miocl991 IV 0.01 ncrcO972 IV 9.682221e-03 seobl213 IV 0.01 ncrc2273 IV 9.682221e-03 fcrc6566 IV 0.01 ncrc3321 IV 9.682221e-03 ncrcβl27 IV 0.01 ncrc4940 IV 9.682221e-03 fcrb4378 IV 0.01 ncrc5016 IV 9.682221e-03 fcr0107 IV 0.01 ncrc5072 IV 9.682221e-03 fcrlOβO IV 0.01 ncrc6371 IV 9.682221e-03 fcrbl369 IV 0.01 ncrc8963 IV 9.682221e-03 fcrbl492 IV 0.01 seoal770 IV 9.682221e-03 fcrb6718 IV 0.01 seoa2641 IV 9.682221e-03 fcrc6372 IV 0.01 seoa3108 IV 9.682221e-03 mioa0245 IV 0.01 seoa4739 IV 9.682221e-03 mioal953 IV 0.01 seoa6315 IV 9.682221e-03 mioa8796 IV 0.01 seoa8638 IV 9.682221e-03 miob7945 IV 0.01 seob0182 IV 9.682221e-03 miob8214 IV 0.01 seob0308 IV 9.682221e-03 miob8947 IV 0.01 seob0975 IV 9.682221e-03 rαioc2216 IV 0.01 seobl323 IV 9.682221e-03 miodl291 IV 0.01 seobl574 IV 9.682221e-03 miod4332 IV 0.01 ncrcO829 IV """0TOI mioc3040 IV 0.01 ncrc8873 IV 0.01 mioc7471 IV 0.01 seoal540 IV 0.01 fcrb5336 IV 0.01 seoa4366 IV 0.01 fcrcl745 IV 0.01 seoa4670 IV 0.01 mioc0955 IV 0.01 seobl316 IV 0.01 seoa9828 IV 0.01 seobl972 IV 0.01 ncrc3045 IV 0.01 seob3361 IV 0.01 miob2656 IV 0.01 seocl495 IV 0.01 fcr2729 IV 0.01 seoc2681 IV 0.01 seoc0018 IV 0.01 fcrc2429 IV 0.01 miob9463 IV 0.01 seob0483 iv 0.01 fcr2969 IV 0.01 miod4023 IV 0.01 miocl928 IV 0.01 seoa8921 IV 0.01 fcr3957 IV 0.01 seob9674 IV 0.01 ncrc3468 IV 0.01 ncrc5061 IV 0.01 fcrc6481 IV 0.01 ncr3963 IV 0.01 fcr5560 IV 0.01 seoa9046 IV 0.01 fcr2218 IV 0.01 seob8854 IV 0.01 fcrb2426 iv 0.01 seob5490 IV 0.01 fcrb2460 IV 0.01 fcrO787 IV 0.01 fcrb4616 IV 0.01 ncrc2701 IV 0.01 fcrb5756 IV 0.01 fcrb3704 IV 0.01 fcrc5355 iv 0.01 seob2283 IV 0.01 fcrc5468 iv 0.01 fcrb6509 IV 0.01 fcrc5688 IV 0.01 fcrb4869 IV 0.01 hfcr3413 IV 0.01 seoa3105 IV 0.01 mioa0533 IV 0.01 hfcr6406 IV 0.01 mioa0826 IV 0.01 seob5778 IV 0.01 mioal343 IV 0.01 mioc7561 IV 0.01 mioa2292 IV 0.01 seob4192 IV 0.01 mioc0214 IV 0.01 seob7419 IV 0.01 mioc5072 IV 0.01 miod3205 IV 0.01 miod0443 IV 0.01 fcrb8093 IV 0.01 miod7251 IV 0.01 fcrc4529 IV 0.01 ncr8709 IV 0.01 fcrc5060 IV 0.01 ncrb2053 IV 0.01 hfcr2934 IV 0.01 ncrb6818 IV 0.01 hfcr3500 IV 0.01 seob5454 IV 0.01 mioa0820 IV 0.01 seob8333 IV 0.01 mioa5461 IV 0.01 seocl990 IV 0.01 mioa9033 IV 0.01 fcrb7487 IV 0.01 miob7850 IV 0.01 fcrb3079 IV 0.01 mioc4885 IV 0.01 seocl203 IV 0.01 miod3776 IV 0.01 ncrc9237 IV 0.01 ncr4790 IV 0.01 fcrb9636 IV 0.01 ncrb0200 IV 0.01 miob4570 IV 0.01 ncrc3324 IV 0.01 hfcr2601 IV 0.01 ncrc9793 IV 0.01 hfcr3144 IV 0.01 seoa2633 IV 0.01 fcrb2299 IV 0.01 seoa9883 IV 0.01 seob9145 IV 0.01 seob2149 IV 0.01 ncr4126 IV 0.01 seob2658 IV 0.01 fcrb5007 IV 0.01 seob3191 IV 0.01 seob6026 IV 0.01 seob7739 IV 0.01 fcrb4734 IV 0.01 seoc0957 IV 0.01 miod0625 IV 0.01 seocl217 IV 0.01 seob6835 IV 0.01 seoc4137 IV 0.01 ncr9175 IV 0.01 seoc5722 IV 0.01 fcrO824 IV 0.01 seob0755 IV 0.01 ncrc9704 IV 0.01 miod4539 IV 0.01 mioa4810 IV 0.01 seob5927 IV 0.01 ncrc8935 IV 0.01 seobl039 IV 0.01 seoa2972 IV 0.01 seoa3251 IV 0.01 fcr0707 IV 0.01 fcrc0210 IV 0.01 fcr5190 IV 0.01 2005/022071
fcrbl950 IV o.oi ncr8067 IV 0.01 fcrb6438 IV 0.01 seoa3731 IV 0.01 fcrb7602 IV 0.01 seoa6697 IV 0.01 fcrb8208 IV 0.01 seoa7366 IV 0.01 fcrc2884 IV 0.01 seoa7369 IV 0.01 hfcr2850 IV 0.01 seoa7583 IV 0.01 mioa0187 IV 0.01 seob4333 IV 0.01 mioa0684 IV 0.01 seoc0920 IV 0.01 mioa6093 IV 0.01 seoc4145 IV 0.01 mioa9154 IV 0.01 seoal720 IV 0.01 mxob8698 IV 0.01 ncrc4732 IV 0.01 mioc0090 IV 0.01 fcrc6397 IV 0.01 mioc0528 IV 0.01 mioa4606 IV 0.01 mioc3958 IV 0.01 fcr6708 IV 0.01 miodl200 IV 0.01 seob3139 IV 0.01 miod6644 IV 0.01 hfcr3634 IV 0.01 miod6671 IV 0.01 seob3009 IV 0.01 miod7225 IV 0.01 seoc4900 IV 0.01 ncrO664 IV 0.01 fcrb2804 IV 0.01 ncrc5149 IV 0.01 hfcr6256 lv 0.01 ncrc5653 IV 0.01 miob3018 IV 0.01 ncrc6623 IV 0.01 fcrc6826 lv 0.01 ncrc8865 IV 0.01 seob6041 IV 0.01 seoal979 IV 0.01 seob8257 IV 0.01 seoa2042 IV 0.01 seoal567 lv 0.01 seoa9873 IV 0.01 seoa6129 IV 0.01 seob2642 IV 0.01 seoa9060 IV 0.01 seob4122 IV 0.01 seoc5039 IV 0.01 seob6872 IV 0.01 miob3420 IV 0.01 seob9764 IV 0.01 ncr3297 IV 0.01 fcrb4409 IV 0.01 ncrc3624 IV 0.01 ncr2695 IV 0.01 fcr2195 IV 0.01 seob7229 IV 0.01 fcrb8719 IV 0.01 seoa5698 IV 0.01 fcrcO415 IV 0.01 miod6058 IV 0.01 fcrc5169 IV 0.01 ncr2391 IV 0.01 fcrc6976 IV 0.01 ncr3037 IV 0.01 mioal513 IV 0.01 fcrc6642 IV 0.01 miob2687 IV 0.01 fcrb6651 IV 0.01 miob2941 IV 0.01 ncr9956 IV 0.01 raiob4368 IV 0.01 miocll25 IV 0.01 πuob9128 IV 0.01 mioa3987 IV 0.01 miodll57 IV 0.01 ncr2421 IV 0.01 miod6488 IV 0.01 mioc3605 IV 0.01 ncrO547 IV 0.01 fcrb9017 IV 0.01 ncrl355 IV 0.01 miod5793 IV 0.01 ncr3163 IV 0.01 miob4760 IV 0.01 ncr6415 IV 0.01 ncrc2928 IV 0.01 ncr8866 IV 0.01 fcrb6255 IV 0.01 ncrbO364 IV 0.01 ncrc4440 IV 0.01 ncrc3593 IV 0.01 mioa6543 IV 0.01 seoa4829 IV 0.01 miob3358 IV 0.01 seoa5986 IV 0.01 fcrbl580 IV 0.01 seoa7373 IV 0.01 fcrb3288 IV 0.01 seobll97 IV 0.01 fcrc2705 IV 0.01 seob4075 IV 0.01 hfcr0130 IV 0.01 seob4117 IV 0.01 mioa9062 IV 0.01 seob4270 IV 0.01 mioa9719 IV 0.01 seob9282 IV 0.01 mioa9935 IV 0.01 seocl856 IV 0.01 mioblδll IV 0.01 seoc4038 IV 0.01 miob3257 IV 0.01 fcrb4271 IV 0.01 miodl056 IV 0.01 seoa9729 IV 0.01 miod4066 IV 0.01 seoa5552 IV 0.01 ncr2176 IV 0.01 fcrc6833 IV 0.01 005/022071
ncr5651 IV 0.01 seoa0823 IV 0.01 fcrc2429 IV 0.01 seob9435 IV 0.01 ncrc7085 IV 0.01 mioc4280 IV 0.01 mioa3963 IV 0.01 seoc4436 IV 0.01 seocl906 IV 0.01 mioa5059 IV 0.01 rαioa7976 IV 0.01 ncrcl606 IV 0.01 seoa9656 IV 0.01 miocl379 IV 0.01 fcrb3476 IV 0.01 miob0939 IV 0.01 ncrc5091 IV 0.01 fcr2582 IV 0.01 mioc0206 IV 0.01 fcrbO146 IV 0.01 ncr5709 IV 0.01 fcrbl689 IV 0.01 seoa5787 IV 0.01 fcrb2017 IV 0.01 seob6492 IV 0.01 fcrb3576 IV 0.01 seob8194 IV 0.01 fcrb3725 lv 0.01 fcr2908 IV 0.01 fcrb3760 lv 0.01 fcrb6181 IV 0.01 fcrb4266 lv 0.01 seoa2141 IV 0.01 fcrb4543 IV 0.01 seoa5578 IV 0.01 fcrb5448 IV 0.01 seoa4158 IV 0.01 fcrb5687 IV 0.01 seoc3277 IV 0.01 fcrb5690 IV 0.01 seoc0924 ■IV 0.01 fcrb5720 IV 0.01 ncrc6871 IV 0.01 fcrb5774 IV 0.01 seob3360 IV 0.01 fcrb6281 IV 0.01 mioc0140 IV 0.01 fcrb7871 IV 0.01 mioa6121 IV 0.01 fcrb8202 IV 0.01 ncr9373 IV 0.01 fcrb8222 IV 0.01 mioa0482 IV 0.01 fcrb8802 IV 0.01 seoa4040 IV 0.01 fcrb8829 IV 0.01 fcr2611 IV 0.01 fcrb9858 IV 0.01 fcr3599 IV 0.01 fcrcO241 IV 0.01 fcr4639 IV 0.01 fcrc3488 IV 0.01 fcrb2421 IV 0.01 fcrc4054 IV 0.01 fcrb2704 IV 0.01 fcrc5040 IV 0.01 fcrb3309 IV 0.01 fcrc5789 IV 0.01 fcrb3554 IV 0.01 fcrc6184 IV 0.01 fcrb3644 IV 0.01 hfcr3043 IV 0.01 fcrb4504 IV 0.01 hfcr3441 IV 0.01 fcrb6780 IV 0.01 hfcr3592 IV 0.01 fcrb8732 IV 0.01 hfcr5706 IV 0.01 fcrc3538 IV 0.01 hfcr5798 IV 0.01 fcrc6374 IV 0.01 mioal370 IV 0.01 hfcr5604 IV 0.01 mioal976 IV 0.01 mioa9630 IV 0.01 mioa4552 IV 0.01 miod.1532 IV 0.01 mioa5045 IV 0.01 miod.1809 IV 0.01 mioa5054 IV 0.01 miod5349 IV 0.01 mioa5355 IV 0.01 ncr5568 IV 0.01 mioa7015 IV 0.01 ncr9933 IV 0.01 miobl506 IV 0.01 ncrcO729 IV 0.01 miob2093 IV 0.01 ncrcO852 IV 0.01 miob4401 IV 0.01 ncrc9024 IV 0.01 miob5491 IV 0.01 seoa2765 IV 0.01 miobββlO IV 0.01 seoa3359 IV 0.01 miob8830 IV 0.01 seoa4587 IV 0.01 miob9714 IV 0.01 seoa8783 IV 0.01 miocl560 IV 0.01 seoa9287 IV 0.01 mioc2039 IV 0.01 seob2750 IV 0.01 rαioc2343 IV 0.01 seob3751 IV 0.01 mioc2806 IV 0.01 seob5658 IV 0.01 mioc4526 IV 0.01 seob9820 IV 0.01 mioc4839 IV 0.01 seoc0416 IV 0.01 mioc7381 IV 0.01 seocl311 IV 0.01 mioc7863 IV 0.01 seoc4132 IV 0.01 raiocL5884 IV 0.01 fcrb5000 IV 0.01 miod6162 IV 0.01 miod7066 IV 0.0Ϊ ncr6170 IV 0.02 ncr0007 IV 0.01 ncr9487 IV 0.02 ncrO912 IV 0.01 ncrbO262 IV 0.02 ncr3219 IV 0.01 ncrb8220 IV 0.02 ncr3649 IV 0.01 ncrcO856 IV 0.02 ncr3686 IV 0.01 ncrc4903 IV 0.02 ncr3948 IV 0.01 seoa6867 IV 0.02 ncr4384 IV 0.01 seobll44 IV 0.02 ncr5971 IV 0.01 seob2156 IV 0.02 ncr7295 IV 0.01 seob2966 IV 0.02 ncrcl498 IV 0.01 seoc0651 IV 0.02 ncrcl999 IV 0.01 seocl476 IV 0.02 ncrc4586 IV 0.01 ncrc4531 IV 0.02 ncrc8903 IV 0.01 seoc4135 IV 0.02 ncrc9039 IV 0.01 fcrc6888 IV 0.02 seoa2162 IV 0.01 hfcr3019 IV 0.02 seoa2244 IV 0.01 mioa8096 IV 0.02 seoa3417 IV 0.01 ncrc4882 IV 0.02 seoa4783 IV 0.01 seoa9959 IV 0.02 seoa5691 IV 0.01 seoal263 IV 0.02 seoa7543 IV 0.01 seoa7926 IV 0.02 seoa9209 IV 0.01 miob9348 IV 0.02 seob0971 IV 0.01 mioc2728 IV 0.02 seobl007 IV 0.01 ncrc4302 IV 0.02 seobl737 IV 0.01 miob8650 IV 0.02 seobl770 IV 0.01 ncrb4428 IV 0.02 seob2108 IV 0.01 ncrc4188 IV 0.02 seob3154 IV 0.01 ncrc5738 IV 0.02 seob3244 IV 0.01 seobll61 IV 0.02 seob3404 IV 0.01 seob5711 IV 0.02 seob4079 IV 0.01 fcrc6989 IV 0.02 seob5004 IV 0.01 fcrOO18 IV 0.02 seob5144 IV 0.01 fcrl984 IV 0.02 seob5225 IV 0.01 fcrbl477 IV 0.02 seob9122 IV 0.01 fcrb2624 IV 0.02 seob9574 IV 0.01 mioa0763 IV 0.02 seocl023 IV 0.01 mioal701 IV 0.02 seocl305 IV 0.01 mioa9792 IV 0.02 seoc4609 IV 0.01 miob3690 IV 0.02 ncrb8752 IV 0.01 mioc2341 IV 0.02 fcrb2133 IV 0.01 mioc7774 IV 0.02 seoa4675 IV 0.01 miod0993 IV 0.02 seoc2248 IV 0.01 ncr5485 IV 0.02 miod2845 IV 0.01 ncr5871 IV 0.02 fcrc6041 IV 0.01 ncr7351 IV 0.02 miob9176 IV 0.01 seoall02 IV 0.02 seoal559 IV 0.01 seoa6395 IV 0.02 seoc0276 IV 0.02 seoa7115 IV 0.02 ncrcl949 IV 0.02 seoa7902 IV 0.02 seob0783 IV 0.02 seoa8547 IV 0.02 miod7243 IV 0.02 seob2148 IV 0.02 fcrO529 IV 0.02 seob5395 IV 0.02 fcrbl898 IV 0.02 seocl996 IV 0.02 fcrb2719 IV 0.02 seoc2487 IV 0.02 fcrb4542 IV 0.02 miob8373 IV 0.02 fcrb4826 IV 0.02 fcrb2437 IV 0.02 fcrb7939 IV 0.02 rαiob9404 IV 0.02 hfcr4168 IV 0.02 hfcr2524 IV 0.02 miob2492 IV 0.02 seoa0238 IV 0.02 miob3898 IV 0.02 miod0500 IV 0.02 miob6226 IV 0.02 seobl787 IV 0.02 mioc2546 IV 0.02 fcrb3330 IV 0.02 mioc8206 IV 0.02 ncrc3908 IV 0.02 ncr0075 IV 0.02 fcrc6542 IV 0.02 71
miob6702 IV 0.02 seoa9445 IV 0.02 fcrc2254 IV 0.02 seoc0705 IV 0.02 seob0752 IV 0.02 miob3131 IV 0.02 fcrb2556 IV 0.02 fcrb2334 IV 0.02 seoc0203 IV 0.02 ncrc3464 IV 0.02 ncrO252 IV 0.02 miod7461 IV 0.02 ncr3483 IV 0.02 ncr2160 IV 0.02 seoa5090 IV 0.02 fcr2182 IV 0.02 seoa8902 IV 0.02 fcrbl916 IV 0.02 fcrl992 IV 0.02 fcrb9849 IV 0.02 miob9848 IV 0.02 fcrc5873 IV 0.02 seoa5766 IV 0.02 hfcrl371 IV 0.02 mioc7084 IV 0.02 mioal068 IV 0.02 fcrb8682 IV 0.02 mioa3629 IV 0.02 fcrcO287 IV 0.02 miob0189 IV 0.02 fcrO796 IV 0.02 miob3271 iv 0.02 fcrb3946 IV 0.02 miob4322 IV 0.02 fcrb5346 IV 0.02 miob6536 IV 0.02 fcrb8516 IV 0.02 miob9065 IV 0.02 fcrc2710 IV 0.02 mioc0337 IV 0.02 fcrc7057 IV 0.02 rnioc3716 IV 0.02 mioa5409 IV 0.02 mioc6987 IV 0.02 miobl493 IV 0.02 mioc7421 IV 0.02 miobl774 IV 0.02 mioc8368 IV 0.02 miob2448 IV 0.02 miod0992 IV 0.02 miob8077 IV 0.02 ncr3339 IV 0.02 mioc4089 IV 0.02 ncr8314 IV 0.02 mioc7296 IV 0.02 ncrbO624 IV 0.02 mioc8635 IV 0.02 ncrc2763 IV 0.02 miod6731 IV 0.02 ncrc3735 IV 0.02 ncr3975 IV 0.02 ncrc9557 IV 0.02 ncr9975 iv 0.02 seoa0470 IV 0.02 ncrb6581 IV 0.02 seoalllδ IV 0.02 ncrb8619 IV 0.02 seoa8229 IV 0.02 ncrc7038 IV 0.02 seoa8716 IV 0.02 ncrc8856 IV 0.02 seob3670 IV 0.02 seoa0145 IV 0.02 seob9241 IV 0.02 seoa0420 IV 0.02 seoc0780 IV 0.02 seob2081 IV 0.02 seocl504 IV 0.02 seob6879 iv 0.02 seob9898 IV 0.02 seob7505 IV 0.02 miob7435 IV 0.02 seob9617 IV 0.02 ncrc5631 IV 0.02 fcrb6834 IV 0.02 mioa4548 IV 0.02 fcrb8334 IV 0.02 ncr3944 IV 0.02 seobl238 IV 0.02 raioal660 IV 0.02 seob8241 IV 0.02 seob3025 IV 0.02 miob8391 IV 0.02 hfcr2890 IV 0.02 seoa5302 IV 0.02 fcrb3539 IV 0.02 ncr0045 IV 0.02 ncr8780 IV 0.02 mioa4318 IV 0.02 ncrcl310 iv 0.02 ncrc7049 IV 0.02 seob0678 IV 0.02 miod4759 IV 0.02 mioc8793 IV 0.02 fcrb3298 IV 0.02 fcrb2591 IV 0.02 ncrO761 IV 0.02 ncr4648 IV 0.02 miod5682 IV 0.02 ncrc7065 IV 0.02 seob8386 IV 0.02 fcrc6138 IV 0.02 ncr8199 IV 0.02 fcrb9407 IV 0.02 ncrb8649 IV 0.02 ncrb3284 IV 0.02 miob9462 IV 0.02 miob4221 IV 0.02 ncr2905 IV 0.02 seob5032 IV 0.02 mioal353 IV 0.02 ncrcO423 IV 0.02 mioa8925 IV 0.02 fcrb6990 IV 0.02 seob3131 IV 0.02 κιiob7268 IV 0.02 mioc8057 IV 0.02 seoa6226 IV 0.02 ncrc429b W ncrc2087 IV 0.02 miocl992 IV 0.02 ncr3598 IV 0.02 miocl248 IV 0.02 ncr9779 IV 0.02 miod5655 IV 0.02 mioc2536 IV 0.02 seob2555 IV 0.02 fcrc6108 IV 0.03 fcrbl733 IV 0.02 mioa4770 IV 0.03 fcrbl929 IV 0.02 ncrb2588 IV 0.03 mioa4674 IV 0.02 fcr5930 IV 0.03 mioa5695 IV 0.02 fcrlO92 IV 0.03 seob6030 IV 0.02 fcrbl962 IV 0.03 seoc2670 IV 0.02 fcrb4506 IV 0.03 miob3552 IV 0.02 fcrc5205 IV 0.03 fcrb2015 IV 0.02 hfcr2658 IV 0.03 fcrb7392 IV 0.02 mioa4064 IV 0.03 fcrc5713 IV 0.02 mioa5594 IV 0.03 hfcr2789 IV 0.02 miobl778 IV 0.03 hfcr4278 iv 0.02 miob2877 IV 0.03 mioa0577 IV 0.02 miob2900 IV 0.03 mioa6552 IV 0.02 miob4793 IV 0.03 miobl044 IV 0.02 mioc0734 iv 0.03 miob2700 IV 0.02 miocl296 iv 0.03 miob2855 IV 0.02 rαioc4387 iv 0.03 miob3411 IV 0.02 mioc7444 IV 0.03 miob6098 IV 0.02 mioc9974 IV 0.03 miocl249 IV 0.02 miod7440 IV 0.03 miod6024 IV 0.02 ncr2666 IV 0.03 miod7375 IV 0.02 ncrbll86 IV 0.03 ncr7411 IV 0.02 ncrcO8O7 IV 0.03 ncr9039 IV 0.02 ncrc2007 IV 0.03 ncrcl615 IV 0.02 ncrc4001 IV 0.03 ncrc3541 IV 0.02 ncrc4189 IV 0.03 ncrc3713 IV 0.02 ncrc4467 IV 0.03 ncrc3887 IV 0.02 seoa0288 IV 0.03 ncrc4135 IV 0.02 seoa5575 IV 0.03 ncrc4996 IV 0.02 seoa7443 IV 0.03 seoa0115 IV 0.02 seoa7647 IV 0.03 seoa0511 IV 0.02 seob0376 IV 0.03 seoa2178 IV 0.02 seobllOO IV 0.03 seoa7249 IV 0.02 seobl667 IV 0.03 seob0466 IV 0.02 seob4440 IV 0.03 seobl318 IV 0.02 seob7465 IV 0.03 seob3189 IV 0.02 seob8817 IV 0.03 seob3415 IV 0.02 seob9001 IV 0.03 seob3564 IV 0.02 fcrb9583 IV 0.03 seob6541 IV 0.02 seob7047 IV 0.03 seocl069 IV 0.02 fcrb8649 IV 0.03 ncr7934 IV 0.02 ncr2182 IV 0.03 fcrb5313 IV 0.02 fcrcβ313 IV 0.03 seoa3852 IV 0.02 ncr2866 IV 0.03 inioa3923 IV 0.02 ncrc5758 IV 0.03 seoa4317 IV 0.02 ncrc2868 IV 0.03 fcrc2683 IV 0.02 ncrcO421 IV 0.03 fcrbl618 IV 0.02 ncrc2018 IV 0.03 fcrbl320 IV 0.02 seoc2723 IV 0.03 hfcr6141 IV 0.02 seob6584 IV 0.03 hfcr3726 IV 0.02 miob3942 IV 0.03 seob6853 IV 0.02 miod7460 IV 0.03 hfcr3674 IV 0.02 fcrb4551 IV 0.03 mioc7986 IV 0.02 fcrbl921 IV 0.03 miod2641 IV 0.02 mioc0347 IV 0.03 fcrc4851 IV 0.02 fcrb2569 IV 0.03 fcrc5233 IV 0.02 miob0931 IV 0.03 seob2090 IV 0.02 seob7463 IV 0.03 fcrcO959 IV 0.02 fcrb7831 IV 0.03 mioc8412 IV 0.03 seob2810 IV 0.03 fcrb6650 IV 0.03 mxoc7665 IV 0.03 seobl538 IV 0.03 miob8531 IV 0.03 fcrc6476 IV 0.03 hfcrl238 IV 0.03 seoa5162 IV 0.03 ncr3701 IV 0.03 mioa0601 IV 0.03 ncrb3957 IV 0.03 miod4449 IV 0.03 seob3654 IV 0.03 miod5591 IV 0.03 seob3367 IV 0.03 seob5562 IV 0.03 fcrc5516 IV 0.03 fcrb8095 IV 0.03 fcrb6897 IV 0.03 fcrll82 IV 0.03 ncrc3536 IV 0.03 fcr2196 IV 0.03 fcrlOO4 IV 0.03 fcrbl411 IV 0.03 fcrbl741 IV 0.03 fcrb6436 IV 0.03 fcrb4918 iv 0.03 fcrb7593 IV 0.03 fcrlO98 IV 0.03 fcrc0730 IV 0.03 fcrl855 IV 0.03 fcrc5174 IV 0.03 fcr3121 IV 0.03 fcrc6010 IV 0.03 fcr3155 IV 0.03 hfcr2314 IV 0.03 fcr4699 IV 0.03 hfcr2963 IV 0.03 fcrbl724 IV 0.03 hfcr5150 IV 0.03 fcrb2554 IV 0.03 mioa4476 IV 0.03 fcrb2866 IV 0.03 miob2478 IV 0.03 fcrb4238 IV 0.03 mioc7225 IV 0.03 fcrb4331 IV 0.03 mioc7952 IV 0.03 fcrb4788 IV 0.03 miod0708 IV 0.03 fcrb5267 IV 0.03 miodl389 IV 0.03 fcrb6106 IV 0.03 miod5651 IV 0.03 fcrb6236 IV 0.03 miod6835 IV 0.03 fcrb6416 IV 0.03 ncr0644 IV 0.03 fcrb6636 IV 0.03 ncr3165 IV 0.03 fcrb6640 IV 0.03 ncr3830 IV 0.03 fcrb6802 IV 0.03 ncr3869 IV 0.03 fcrb6939 IV 0.03 ncr4040 lv 0.03 fcrb6968 IV 0.03 ncr8326 IV 0.03 fcrb7068 IV 0.03 ncrbO487 IV 0.03 fcrb7951 IV 0.03 ncrbl956 IV 0.03 fcrb8927 IV 0.03 ncrb5537 IV 0.03 fcrb9639 IV 0.03 ncrb7516 IV 0.03 fcrcO148 IV 0.03 ncrb8721 IV 0.03 fcrc2577 IV 0.03 ncrcl003 IV 0.03 fcrc6460 IV 0.03 ncrc3842 IV 0.03 hfcr4046 IV 0.03 ncrc6268 IV 0.03 mioa2528 IV 0.03 ncrc6996 IV 0.03 mioa2986 IV 0.03 seoa0040 IV 0.03 iaioa5231 IV 0.03 seoa4886 IV 0.03 mioa8679 IV 0.03 seoa5382 IV 0.03 mioa8984 IV 0.03 seoa5473 IV 0.03 mioa9709 IV 0.03 seoa9421 IV 0.03 miob0942 IV 0.03 seobl891 IV 0.03 miob2593 IV 0.03 seob9635 IV 0.03 miob3668 IV 0.03 seocl694 IV 0.03 miob4975 IV 0.03 seoc2172 IV 0.03 miob7274 IV 0.03 seoc3029 IV 0.03 miob8932 IV 0.03 ncrc4991 IV 0.03 miob9274 IV 0.03 miodl714 IV 0.03 mioc0107 IV 0.03 ncr2079 IV 0.03 miocl025 IV 0.03 mioc6358 IV 0.03 miocll35 IV 0.03 mioa8484 IV 0.03 rαiocl865 IV 0.03 ncrcl775 IV 0.03 mioc3243 IV 0.03 fcrb7616 IV 0.03 mioc3565 IV 0.03 fcrO833 IV 0.03 mioc3571 IV 0.03 seob4223 IV 0.03 rαioc7073 IV 0.03 seob2689 IV 0.03 mioc7542 IV 0.03 US2005/022071
mioc7910 IV 0.03 mioc0741 IV 0.03 miod0884 IV 0.03 miod7011 IV 0.03 miod5190 IV 0.03 ncr6212 IV 0.03 miod6745 IV 0.03 ncr6408 IV 0.03 ncrO927 IV 0.03 ncr7345 IV 0.03 ncr2583 IV 0.03 ncr8112 IV 0.03 ncr2848 IV 0.03 ncrb2909 IV 0.03 ncr3148 IV 0.03 ncrc3777 IV 0.03 ncr5055 IV 0.03 seoa0024 IV 0.03 ncr5301 IV 0.03 seoal419 IV 0.03 ncr9469 IV 0.03 seoa4284 IV 0.03 ncrb6432 IV 0.03 seoa4461 IV 0.03 ncrb8714 IV 0.03 seoa5894 IV 0.03 ncrcl904 IV 0.03 seob6576 IV 0.03 ncrc4263 IV 0.03 seoc6169 IV 0.03 ncrc4373 IV 0.03 ncrbl224 IV 0.03 ncrc5672 IV 0.03 fcrc2082 IV 0.03 ncrc6953 IV 0.03 fcrll50 IV 0.03 seoa0512 IV 0.03 mioc0852 IV 0.03 seoal765 IV 0.03 mioa0485 IV 0.03 seoa2809 IV 0.03 mioc4731 IV 0.03 seoa3269 IV 0.03 seoa0396 IV 0.03 seoa5494 IV 0.03 fcr5721 IV 0.03 seoa7442 IV 0.03 fcrb7632 IV 0.03 seob0787 IV 0.03 fcrb8628 IV 0.03 seob0906 IV 0.03 ncrc5553 IV 0.03 seobl372 IV 0.03 seob3520 IV 0.03 seobl793 IV 0.03 fcr3367 IV 0.03 seob2155 IV 0.03 ncr7813 iv 0.03 seob7015 IV 0.03 mioc7807 IV 0.03 seob8311 IV 0.03 seob3731 IV 0.03 seob8742 IV 0.03 miod3197 IV 0.03 seoc3876 IV 0.03 ncrl948 IV 0.03 seoc4076 IV 0.03 ncrcO115 IV 0.03 seoc4625 IV 0.03 seoa9619 IV 0.03 fcrc2013 IV 0.03 seob4140 IV 0.03 miob7922 IV 0.03 seob2724 iv 0.03 fcrb6464 IV 0.03 fcrb3629 IV 0.03 seob6316 IV 0.03 fcrb7685 IV 0.03 seob6131 IV 0.03 mioc0915 IV 0.03 seoa7587 IV 0.03 ncrb3936 IV 0.03 seoc3511 IV 0.03 ncr4194 IV 0.03 miob3757 IV 0.03 seoc0394 IV 0.03 fcr4622 IV 0.03 fcrb5726 IV 0.03 mioc0999 IV 0.03 miodl820 IV 0.03 fcrbl582 IV 0.03 seoa9131 IV 0.03 fcrb2090 IV 0.03 fcrc2108 IV 0.03 fcrb4926 IV 0.03 mioa3392 IV 0.03 fcrb5100 IV 0.03 seob8999 IV 0.03 fcrb5603 IV 0.03 mioc5740 IV 0.03 fcrb5631 IV 0.03 ncrO212 IV 0.03 fcrb5752 IV 0.03 fcr5350 IV 0.03 fcrb6460 IV 0.03 fcr7295 IV 0.03 fcrb6986 IV 0.03 fcrbl406 IV 0.03 fcrb9096 IV 0.03 fcrbl769 IV 0.03 fcrc2830 IV 0.03 fcrb2376 IV 0.03 fcrc5359 lv 0.03 fcrb4515 IV 0.03 fcrc7243 IV 0.03 fcrb4719 IV 0.03 hfcr0501 IV 0.03 fcrb5253 IV 0.03 hfcr3821 IV 0.03 fcrb6717 IV 0.03 mioa3428 IV 0.03 fcrb6870 IV 0.03 mioa9007 IV 0.03 fcrcl654 IV 0.03 miob5888 IV 0.03 fcrc4005 IV 0.03 mioc0052 IV 0.03 fcrc4176 IV 0.03 71
fcrc5452 IV 0.03 hfcrO439 IV 0.04 fcrc5699 IV 0.03 hfcr3494 IV 0.04 hfcrO345 IV 0.03 hfcr5397 IV 0.04 mioal524 IV 0.03 mioa4196 IV 0.04 raioc5141 IV 0.03 mioa7976 IV 0.04 miod7270 IV 0.03 mioa9179 IV 0.04 ncr2381 IV 0.03 miobl939 IV 0.04 ncr3380 IV 0.03 miob3314 IV 0.04 ncr4485 IV 0.03 m±ob7135 IV 0.04 ncrb3001 IV 0.03 miob9688 IV 0.04 ncrb4022 IV 0.03 mioc0486 lv 0.04 ncrb8398 IV 0.03 mioc0902 IV 0.04 ncrc5592 IV 0.03 miocll98 lv 0.04 ncrc6359 IV 0.03 mioc7668 IV 0.04 seoa0014 IV 0.03 miod0355 IV 0.04 seoa3533 IV 0.03 miod4083 IV 0.04 seoa5151 IV 0.03 ncrl876 IV 0.04 seoa8564 IV 0.03 ncr5397 IV 0.04 seob3112 IV 0.03 ncrc2110 IV 0.04 seob9285 IV 0.03 ncrc3895 IV 0.04 seoc0616 IV 0.03 ncrc4313 IV 0.04 fcr5425 IV 0.03 ncrc4620 IV 0.04 ncrcll75 IV 0.03 ncrc5207 IV 0.04 mioc4366 IV 0.03 ncrc6861 IV 0.04 seob7744 IV 0.03 ncrc8976 IV 0.04 fcrO893 IV 0.03 seoa4023 IV 0.04 miocl783 IV 0.03 seoa4600 IV 0.04 fcrb6304 IV 0.03 seoa5234 IV 0.04 seob7402 IV 0.03 seoa6621 IV 0.04 miod4522 IV 0.03 seoa6887 IV 0.04 ncr2260 IV 0.03 seob3545 IV 0.04 ncrc7016 IV 0.03 seob4263 IV 0.04 fcrb6012 IV 0.03 seob3854 IV 0.04 seob7404 IV 0.03 mioal933 IV 0.04 ncrc9766 IV 0.03 fcrclll5 IV 0.04 fcrcO695 IV 0.03 fcrb6890 IV 0.04 fcrc4649 IV 0.03 seob0325 IV 0.04 fcr2103 IV 0.03 fcrbl451 IV 0.04 fcr3528 lv 0.03 mioa0332 IV 0.04 ncr3763 IV 0.03 ncrc9758 IV 0.04 seob5493 IV 0.03 mioal603 IV 0.04 fcrc5846 IV 0.03 miod0878 IV 0.04 seoc0775 IV 0.03 fcrb5326 IV 0.04 fcrbO355 IV 0.03 ncrO238 IV 0.04 ncrb2125 IV 0.03 mioc2694 IV 0.04 seob4621 IV 0.03 fcrb8808 IV 0.04 fcrb5151 IV 0.03 seob2331 IV 0.04 seob0253 IV 0.03 ncrb3077 IV 0.04 seoa3847 IV 0.03 ncrc3068 IV 0.04 seob8204 IV 0.03 ncrc2415 IV 0.04 ncrc5744 IV 0.04 hfcr5905 IV 0.04 seob6751 IV 0.04 miod7429 IV 0.04 seob3378 IV 0.04 seob0031 IV 0.04 fcrb8485 IV 0.04 mioblll5 IV 0.04 seoa3245 IV 0.04 ncr2486 IV 0.04 mioc5103 IV 0.04 seoa2936 IV 0.04 ncrc5423 IV 0.04 fcrb5087 IV 0.04 fcrllO3 IV 0.04 ncr5472 IV 0.04 fcr0503 IV 0.04 seob9872 IV 0.04 fcr4226 IV 0.04 ncrc9004 IV 0.04 fcrb4388 IV 0.04 ncrcl608 IV 0.04 fcrb5440 IV 0.04 seob3067 IV 0.04 fcrc5483 IV 0.04 seoa5637 IV 0.04 fcrc6089 IV 0.04 seoc4762 IV 0.04 mioal392 IV 0.04 mioa2072 IV 0.04 mioc8682 IV 0.04 fcrb9401 IV 0.04 seob6812 IV 0.04 seob8261 IV 0.04 seob8425 IV 0.04 fcrb9444 IV 0.04 fcrO997 IV 0.04 miob4346 IV 0.04 fcr2573 IV 0.04 ncrc9681 IV 0.04 fcr3043 IV 0.04 seob6758 IV 0.04 fcr3286 IV 0.04 seobll33 IV 0.04 fcr4743 IV 0.04 mioa4632 IV 0.04 fcrbl466 IV 0.04 miob4378 IV 0.04 fcrb2859 IV 0.04 miod4342 IV 0.04 fcrb3080 IV 0.04 fcrb4721 IV 0.04 fcrc0810 IV 0.04 fcrb7931 IV 0.04 fcrcl790 IV 0.04 seoa0219 IV 0.04 fcrc2407 IV 0.04 fcr3287 IV 0.04 fcrc3907 IV 0.04 seoa0065 IV 0.04 fcrc4277 IV 0.04 ncrcO262 IV 0.04 fcrc6519 IV 0.04 seocl025 IV 0.04 hfcr3660 IV 0.04 seoa7075 IV 0.04 hfcr3902 IV 0.04 miod0676 IV 0.04 mioa0132 IV 0.04 seob6446 IV 0.04 mioal380 IV 0.04 ncrcll68 IV 0.04 mioa2327 IV 0.04 ncrbl420 IV 0.04 mioa2818 IV 0.04 seoa7605 IV 0.04 mioa4542 IV 0.04 seobl757 IV 0.04 mioa5508 IV 0.04 fcrl421 iv 0.04 mioa9649 IV 0.04 fcrO553 IV 0.04 miob3696 IV 0.04 fcrO793 IV 0.04 mioc0861 IV 0.04 fcrb2102 IV 0.04 mioc2173 IV 0.04 fcrb2592 IV 0.04 mioc2634 lv 0.04 fcrb7331 IV 0.04 mioc5736 IV 0.04 fcrcO795 IV 0.04 mioc6252 IV 0.04 hfcr6245 IV 0.04 mioc8531 IV 0.04 mioa9127 IV 0.04 miod2079 IV 0.04 mioa9147 IV 0.04 ncr6326 IV 0.04 miob2601 IV 0.04 ncr7286 IV 0.04 miob8308 IV 0.04 ncrc3030 IV 0.04 mioc0760 IV 0.04 ncrc6118 IV 0.04 mioc3316 IV 0.04 ncrc8881 IV 0.04 mioc4552 IV 0.04 seoa2381 IV 0.04 mioc5013 IV 0.04 seoa6223 IV 0.04 ncr0808 IV 0.04 seob3548 IV 0.04 ncr6256 IV 0.04 seob3886 IV 0.04 ncrb6261 IV 0.04 seob6467 IV 0.04 ncrc5569 IV 0.04 seob6882 IV 0.04 ncrc6193 IV 0.04 seoc5002 IV 0.04 ncrc7043 IV 0.04 seoa9566 IV 0.04 ncrc9023 IV 0.04 mioa3997 IV 0.04 seoa7077 IV 0.04 seob6008 IV 0.04 seob0514 IV 0.04 fcr0706 IV 0.04 seobl231 IV 0.04 fcrb4712 IV 0.04 seoc0098 IV 0.04 fcrc6234 IV 0.04 seocl593 IV 0.04 mioc0649 IV 0.04 fcrcO686 IV 0.04 fcrc5898 IV 0.04 mioa5773 IV 0.04 ncrc9304 IV 0.04 seoa7078 IV 0.04 ncrc3073 IV 0.04 ncrcll40 IV 0.04 hfcr3375 IV 0.04 mioc7662 IV 0.04 ncrb2798 IV 0.04 fcrb3192 IV 0.04 ncrc2756 IV 0.04 fcrb9376 IV 0.04 seob5954 IV 0.04 seob5734 IV 0.04 seoc2192 IV 0.04 seocl934 IV 0.04 ncr0920 IV 0.04 seoa3639 IV 0.04 mioal585 IV 0.04 fcrc4729 IV 0.04 fcrc5071 IV 0.04 ncrcOllo IX 3.19e-04 seobl319 IV 0.04 fcrb5002 IX 4.23e-04 ncr5168 IV 0.04 seoc4941 IX 4.34e-04 seob8660 IV 0.04 mioa3486 IX 4.38e-04 fcrb5177 IV 0.04 seoa5849 IX 4.42e-04 ncrc9530 IV 0.04 mioc6412 IX 4.44e-04 fcrb2150 IV 0.04 seob0514 IX 4.46e-04 fcrl562 IV 0.04 miob7985 IX 4.6e-04 ncr3118 IV 0.04 ncrb8396 IX 4.66e-04 ncrb2360 IV 0.04 ncrc6479 IX 4.81e-04 fcrb8940 IV 0.04 ncr7973 IX 4.85e-04 fcrbl446 IV 0.04 hfcr3183 IX 4.88e-04 seoa7295 IV 0.04 seoc2336 IX 4.94e-04 miob8146 IV 0.04 seoa9792 IX 5.09e-04 seoa7223 IV 0.04 fcrc5384 IX 5.1e-04 ncrcl349 IV 0.04 mioc2694 IX 5.18e-04 fcrb2763 IV 0.04 mioc0276 IX 5.28e-04 fcrb6747 IV 0.04 seoa5528 IX 5.38e-04 mioa0869 IV 0.04 seoa9287 IX 5.45e-04 fcrbl312 IV 0.04 seoc4380 IX 6.07e-04 inioc4834 IV 0.04 miod6162 IX 6.12e-04 miob9463 IX 3.09e-07 miod6147 IX 6.2e-04 fcrb6460 IX 8.2e-07 seob9847 IX 6.68e-04 fcrb5214 IX 1.04e-06 ncrb8220 IX 7.0e-04 fcrc2954 IX 1.71e-06 miocl025 IX 7.12e-04 fcrb3521 IX 4.14e-06 miob7850 IX 7.18e-04 seoc4135 IX 4.17e-06 seoc4971 IX 7.29e-04 mioc2039 IX 4.36e-06 fcrc5671 IX 7.36e-04 ncrc9739 IX 1.55e-05 seoc4381 IX 7.54e-04 miob9748 IX 1.57e-05 seoc4132 IX 8.23e-04 miod6961 IX 1.97e-05 mioc2514 IX 8.4e-04 fcrb4981 IX 3.15e-05 hfcr2890 IX 9.18e-04 ncrb0074 IX 3.45e-05 seob4515 IX 9.22e-04 ncr5651 IX 3.54e-05 seoa7249 IX 9.92e-04 fcrb9611 IX 3.57e-05 seocl675 IX 1.027088e-03 fcrll69 IX 3.6e-05 ncrO133 IX 1.05172e-03 seoa9389 IX 3.72e-05 miob9805 IX 1.156892e-03 ncrcll40 IX 3.81e-05 seoa9042 IX 1.158959e-03 mioc2094 IX 3.97e-05 fcrb9849 IX 1.227294e-03 mioa7976 IX 4.01e-05 miob9831 IX 1.263821e-03 ncrc4931 IX 4.13e-05 hfcr4489 IX 1.264067e-03 miod6134 IX 4.56e-05 ncr4673 IX 1.338044e-03 fcrc5482 IX 6.75e-05 fcrb7852 IX 1.34787e-03 fcrb6664 IX 7.04e-05 fcrc2573 IX 1.392684e-03 fcrc6826 IX 8.11e-05 seob3307 IX 1.439442e-03 miob8825 IX 9.0e-05 fcrc2429 IX 1.448948e-03 fcrb5855 IX 9.3e-05 fcrc5850 IX 1.455176e-03 fcrb7055 IX 1.05e-04 seob7739 IX 1.615597e-03 mioa2377 IX 1.05e-04 seob9756 IX 1.643381e-03 miod5894 IX 1.05e-04 miob9340 IX 1.687009e-03 ncrb8207 IX 1.06e-04 fcrb6469 IX 1.855844e-03 mioc0206 IX 1.09e-04 mioc2388 IX 1.89135e-03 seob9884 IX 1.24e-04 seocl561 IX 1.894109e-03 fcrc5452 IX 1.31e-04 fcrc2082 IX 1.897321e-03 miob7391 IX 1.32e-04 seob5726 IX 1.905436e-03 fcrc6174 IX 1.37e-04 seoc3690 IX 1.972802e-03 ncr8725 IX 1.44e-04 miob5434 IX 1.991278e-03 fcrb4985 IX 1.54e-04 fcrb7722 IX 2.042772e-03 mioc2021 IX 1.67e-04 ncr7286 IX 2.102525e-03 miob9614 IX 1.96e-04 miod7440 IX 2.149284e-03 seoc4928 IX 2.44e-04 seob2990 IX 2.157379e-03 fcrc4390 IX 2.52e-04 miob8026 IX 2.175933e-03 ncrb6903 IX 2.65e-04 ncr7292 IX 2.18489e-03 mioc0107 IX 2.66e-04 seoa6314 IX 2.307802e-03 seob5894 IX 2.313364e-03 miob9734 IX 5.792326e-03 ncrb8189 IX 2.367011e-03 seoc0369 IX 5.805794e-03 ncrcl003 IX 2.407968e-03 mioa4109 IX 5.817645e-03 mioa8945 IX 2.42255e-03 seoa2300 IX 5.821371e-03 fcrb7829 IX 2.47234βe-03 ncrc5375 IX 5.858062e-03 mioa6091 IX 2.513592e-03 fcrc6697 IX 5.891967e-03 mioc5740 IX 2.55859e-03 seob5240 IX 5.949332e-03 fcrc5107 IX 2.595298e-03 fcrc6898 IX 6.102757e-03 miod5703 IX 2.632329e-03 mioa4241 IX 6.102757e-03 fcrbl724 IX 2.708883e-03 πιioa4245 IX 6.102757e-03 mioa8778 IX 2.736234e-03 fcrc5355 IX 6.12294e-03 seoc4436 IX 2.749055e-03 seob4117 IX 6.194381e-03 miod2330 IX 2.768542e-03 fcrc4663 IX 6.213654e-03 miob9652 IX 2.773751e-03 seob4067 IX 6.309695e-03 seob9772 IX 2.86764e-03 seob9872 IX 6.406966e-03 seoc4137 IX 2.947972e-03 miocl088 IX 6.432154e~03 mioc7304 IX 2.982148e-03 mioa4229 IX 6.506475e-03 seocOβlβ IX 3.08994e-03 seocl432 IX 6.543795e-03 ncr3262 IX 3.123964e-03 fcrcl654 IX 6.589598e-03 ncrc5653 IX 3.128058e-03 miod5256 IX 6.623723e-03 fcrb5892 IX 3.158737e-03 mioal276 IX 6.669969e-03 mioc3261 IX 3.213251e-03 mioc7362 IX 6.725393e-03 mioc4534 IX 3.321646e-03 fcr2573 IX 6.784327e-03 seocl234 IX 3.414658e-03 fcrb5664 IX 6.784327e-03 hfcr4278 IX 3.439806e-03 raiob3314 IX 6.784327e-03 seoa8979 IX 3.449583e-03 ncr3642 IX 6.784327e-03 seoc4316 IX 3.511847e-03 seoa2272 IX 6.784327e-03 seob9635 IX 3.531378e-03 seob7409 IX 6.845355e-03 fcr2821 IX 3.593158e-03 miob5770 IX 6.867442e-03 hfcr3500 IX 3.661254e-03 fcrb3466 IX 6.869457e~03 fcr4460 IX 3.716445e-03 seoc2264 IX 6.927055e-03 hfcr4423 IX 3.861046e-03 seob6558 IX 7.08591e-03 miob5098 IX 3.86132e-03 ncrb4039 IX 7.185379e-03 mioal473 IX 3.958354e-03 fcrb3966 IX 7.202047e-03 mioal674 IX 3.965367e-03 seoa0393 IX 7.211102e-03 seocl495 IX 4.012337e-03 ncr7967 IX 7.237351e-03 miob2448 IX 4.055375e-03 ncrb0164 IX 7.331552e-03 miob8694 IX 4.122295e-03 fcrc5458 IX 7.482432e-03 fcrb3219 IX 4.135014e-03 ncr2575 IX 7.600247e-03 seoal615 IX 4.220231e-03 ncrc4295 IX 7.627364e-03 ncrcO644 IX 4.272698e-03 seobl318 IX 7.684111e-03 seoc0551 IX 4.340756e-03 fcrcO651 IX 7.699131e-03 fcrb8973 αx 4.544509e-03 fcrb6676 IX 7.719631e-03 fcrc2710 IX 4.544509e-03 fcrb8536 IX 7.719631e-03 ncr5027 IX 4.544509e-03 mioa8987 IX 7.719631e-03 seoa8960 IX 4.572134e-03 seoa0158 IX 7.719631e-03 seoa5746 IX 4.64536e-03 seoa5933 IX 7.719631e-03 fcrb4937 IX 4.846074e-03 seoa8239 IX 7.719631e-03 seocl078 IX 4.922685e-03 seob5395 IX 7.719631e-03 seoc0284 IX 5.076305e-03 miodl718 IX 8.034424e-03 mioc7952 IX 5.107777e-03 mioc3549 IX 8.160964e-03 fcrb6718 IX 5.108963e-03 seoc4260 IX 8.363871e-03 fcrbl657 IX 5.130499e-03 mioa3572 IX 8.372463e~03 ncrcO852 IX 5.205548e-03 seob9820 IX 8.388595e-03 ncrc5947 IX 5.205548e-03 mioal948 IX 8.415321e-03 seoa3359 IX 5.205548e-03 seoc2191 IX 8.433017e-03 miod4522 IX 5.216795e-03 seoa7295 IX 8.440913e-03 fcrb8187 IX 5.219732e-03 fcrb7118 IX 8.491482e-03 fcr5509 IX 5.329617e-03 mioc2872 IX 8.598424e-03 seob0918 IX 5.361021e-03 fcrl337 IX 8.6173e-03 miob7136 IX 5.450311e-03 ncrc3593 IX 8.6173e-03 miod5198 IX 5.472689e-03 ncr0420 IX 8.718204e-03 ncrb2588 IX 5.579537e-03 seoa2472 IX 8.76281e-03 mioc5179 IX 5.601724e-03 fcrb6693 IX 8.764987e-03 fcrb7072 IX 8.764987e-03 fcrb6015 IX 0.01 hfcr6256 IX 8.764987e-03 fcrc7047 IX 0.01 miob3898 IX 8.764987e-03 mioal392 IX 0.01 miob5855 IX 8.764987e-03 ncr5522 IX 0.01 miod0625 IX 8.764987e-03 fcrb9520 IX 0.01 ncrc6813 IX 8.764987e-03 fcrcO959 IX 0.01 seoa0511 IX 8.764987e-03 mioc7216 IX 0.01 mioc4843 IX 8.811966e-03 fcrb3841 IX 0.01 mioc6211 IX 8.904696e-03 ncr0107 IX 0.01 seob6256 IX 9.02746e-03 miob9325 IX 0.01 mioc0121 IX 9.163151e-03 seoal036 IX 0.01 fcrb8730 IX 9.169484e-03 mioa5231 IX 0.01 fcrb9858 IX 9.20164e-03 fcrbl909 IX 0.01 seoa4324 IX 9.212171e-03 mioa3084 IX 0.01 miob8947 IX 9.337599e-03 ncr6415 IX 0.01 miob2533 IX 9.353064e-03 mioal293 IX 0.01 mioa8831 IX 9.438617e-03 mioa6721 IX 0.01 seoc2295 IX 9.611484e-03 miob3696 IX 0.01 miod7081 IX 9.612646e-03 mioc2928 IX 0.01 seob0906 IX 9.62924e-03 ncrb3352 IX 0.01 ncrc4448 IX 9.709792e-03 ncrcl331 IX 0.01 fcrc2613 IX 9.72871e-03 ncrc3596 IX 0.01 mioc6997 IX 9.740521e-03 seoa5382 IX 0.01 seocl628 IX 9.782377e-03 seoa6203 IX 0.01 miob4668 IX 9.926235e-03 seob8291 IX 0.01 fcrc5139 IX 9.930809e-03 seob0058 IX 0.01 fcrc5468 IX 9.930809e-03 mioc2443 IX 0.01 miob3591 IX 9.930809e-03 seoc0778 IX 0.01 iαioc3107 IX 9.930809e-03 miob8639 IX 0.01 ncrcll92 IX 9.930809e-03 fcr3599 IX 0.01 seoa6661 IX 9.930809e-03 hfcr2250 IX 0.01 seoc4609 IX 9.930809e-03 ncrc3520 IX 0.01 miob7290 IX 9.963579e-03 rαioa3528 IX 0.01 ncr2861 IX 0.01 fcrbl788 IX 0.01 miob9185 IX 0.01 fcrb3782 IX 0.01 seoa2381 IX 0.01 fcrb9856 IX 0.01 fcrb5122 IX 0.01 ncrc3773 IX 0.01 miob8992 IX 0.01 ncrc4728 IX 0.01 ncr8481 IX 0.01 seoa6129 IX 0.01 seob5209 IX 0.01 seob3654 IX 0.01 ncrc5877 IX 0.01 miod4998 IX 0.01 ncrc6439 IX 0.01 seob2283 IX 0.01 ncrb3317 IX 0.01 mioa2343 IX 0.01 ncrc6708 IX 0.01 seocl218 IX 0.01 miob6029 IX 0.01 seocl236 IX 0.01 seoal598 IX 0.01 mioc0226 IX 0.01 fcrb8719 IX 0.01 mioc2074 IX 0.01 mioa8925 IX 0.01 fcrcl607 IX 0.01 seob6856 IX 0.01 seoc2681 IX 0.01 miob6373 IX 0.01 seoc0009 IX 0.01 fcrc5086 IX 0.01 fcrl347 IX 0.01 seocl203 IX 0.01 fcrb7661 IX 0.01 seoc0843 IX 0.01 fcrc7046 IX 0.01 fcrc6916 IX 0.01 hfcrll37 IX 0.01 hfcr2314 IX 0.01 miob5699 IX 0.01 rnioc2634 IX 0.01 miod0992 IX 0.01 ncr5557 IX 0.01 ncrb7177 IX 0.01 ncrbO22O IX 0.01 ncrc3011 IX 0.01 seoa8822 IX 0.01 ncrc4772 IX 0.01 fcr2986 IX 0.01 seob8807 IX 0.01 miod6835 IX 0.01 seob9368 IX 0.01 miod4564 IX 0.01 seoal736 IX 0.01 ncr8538 IX 0.01 ncrc2600 IX 0.01 miob7531 IX 0.01 mioa8096 IX 0.01 mioc3573 IX 0.01 ncrc6359 IX 0.01 fcrb8910 IX 0.01 seoa4012 IX 0.01 mioc8474 IX 0.01 fcrclll5 IX 0.01 miod5957 IX 0.01 rαioc2911 IX 0.01 seob7278 IX 0.01 seoa4181 IX 0.01 fcrb3153 IX 0.01 seoa9482 IX 0.01 seobll91 IX 0.01 seob7250 IX 0.01 miob9820 IX 0.01 mioblll5 IX 0.01 fcrbl750 IX 0.01 mioc2451 IX 0.01 ncrc2686 IX 0.01 ncrc2227 IX 0.01 fcrb3870 IX 0.01 seob7047 IX 0.01 miod6068 IX 0.01 ncrbO653 IX 0.01 miod7408 IX 0.01 miod5707 IX 0.01 fcr2299 IX 0.01 ncrc5113 IX 0.01 seob9851 IX 0.01 seobl730 IX 0.01 fcr2054 IX 0.01 seob8261 IX 0.01 fcr4727 IX 0.01 ncrl876 IX 0.01 fcrb7573 IX 0.01 seob2657 IX 0.01 fcrcO522 IX 0.01 seoc0499 IX 0.01 fcrcl402 IX 0.01 miod6090 IX 0.01 mioa3668 IX 0.01 raioc0424 IX 0.01 raioa5452 IX 0.01 miob2841 IX 0.01 rtιiob9348 IX 0.01 ncrc5423 IX 0.01 ncr3148 IX 0.01 seoa9959 IX 0.01 ncr6943 IX 0.01 miob8691 IX 0.01 ncrb3980 IX 0.01 seoal318 IX 0.01 ncrcO249 IX 0.01 mioc3648 IX 0.02 seoa8870 IX 0.01 mioc6949 IX 0.02 seoc8283 IX 0.01 ncr9919 IX 0.02 fcrb4248 IX 0.01 seoc0491 IX 0.02 ncr3248 IX 0.01 fcrb6929 IX 0.02 ncrc9412 IX 0.01 fcrb8020 IX 0.02 seocl264 IX 0.01 fcrb9649 IX 0.02 ncrc3321 IX 0.01 ncr3123 IX 0.02 seob0263 IX 0.01 miob5752 IX 0.02 miocl379 IX 0.01 miodll95 IX 0.02 mioc4103 IX 0.01 seob8660 IX 0.02 ncrc7131 IX 0.01 fcr2220 IX 0.02 fcrb9843 IX 0.01 fcr5257 IX 0.02 fcrc6642 IX 0.01 fcrb6508 IX 0.02 fcrc2817 IX 0.01 fcrcl986 IX 0.02 ncrc2675 IX 0.01 mioa0497 IX 0.02 miob3594 IX 0.01 mioal524 IX 0.02 hfcr5009 IX 0.01 mioa3160 IX 0.02 mioc3492 IX 0.01 miodl846 IX 0.02 seob8260 IX 0.01 miod6904 IX 0.02 fcrb4926 IX 0.01 ncr3496 IX 0.02 fcrb6870 IX 0.01 ncr3782 IX 0.02 fcrb8855 IX 0.01 ncr8067 IX 0.02 seoc0651 IX 0.01 ncr8794 IX 0.02 fcrc4722 IX 0.01 ncrc8976 IX 0.02 fcrb6410 IX 0.01 seoa2058 IX 0.02 ncr7668 IX 0.01 seoa3322 IX 0.02 mioa4564 IX 0.01 seoa3737 IX 0.02 fcrb5547 IX 0.01 seob0339 IX 0.02 fcrb8516 IX 0.01 seoa8902 IX 0.02 fcrb8728 IX 0.01 fcrb8570 IX 0.02 fcrc0130 IX 0.01 mioc0940 IX 0.02 hfcr3144 IX 0.01 seoc4720 IX 0.02 mioa0597 IX 0.01 hfcr2955 IX 0.02 mioa8173 IX 0.01 fcrb7693 IX 0.02 ncr4656 IX 0.01 seoa3761 IX 0.02 ncrb0045 IX 0.01 ncr3276 IX 0.02 ncrbO749 IX 0.01 ncrc6407 IX 0.02 fcrb6750 IX 0.02 mioa3467 IX 0.02 mioa5059 IX 0.02 fcrb4727 IX 0.02 seocl948 IX 0.02 fcrc5604 IX 0.02 ncrc7092 IX 0.02 fcr0503 IX 0.02 ncr3233 IX 0.02 fcr2972 IX 0.02 fcrl853 IX 0.02 fcrblδOl IX 0.02 seobl574 IX 0.02 fcrb2199 IX 0.02 mioc7668 IX 0.02 fcrb2510 IX 0.02 ncrcO259 IX 0.02 fcrc5169 IX 0.02 mioc0052 IX 0.02 fcrc6997 IX 0.02 miod5030 IX 0.02 hfcr3149 IX 0.02 seob7369 IX 0.02 miob7836 IX 0.02 fcrclO85 IX 0.02 miob9688 IX 0.02 miob3531 IX 0.02 mioc6417 IX 0.02 miod0195 IX 0.02 miod2079 IX 0.02 xaiod0977 IX 0.02 ncrb2085 IX 0.02 miocl7066 IX 0.02 ncrb5909 IX 0.02 ncrcO217 IX 0.02 ncrc5316 IX 0.02 seob2185 IX 0.02 seoa0783 IX 0.02 miocl416 IX 0.02 seoa3711 IX 0.02 fcrb9686 IX 0.02 ncrbO262 IX 0.02 seocl235 IX 0.02 seob8029 IX 0.02 fcrcO166 IX 0.02 mioc4270 IX 0.02 fcrbl689 IX 0.02 fcrb8765 IX 0.02 seob0047 IX 0.02 miod5372 IX 0.02 fcrb2113 IX 0.02 miod5190 IX 0.02 fcrb2350 IX 0.02 seob6417 IX 0.02 fcrb5964 IX 0.02 iαioc3040 IX 0.02 hfcr4168 IX 0.02 ncr3701 IX 0.02 hfcr6687 IX 0.02 mioa6807 IX 0.02 mioa2851 IX 0.02 mioa4014 IX 0.02 mioa6442 IX 0.02 seob5792 IX 0.02 miob4090 IX 0.02 fcrb4383 IX 0.02 miob8487 IX 0.02 fcrb4817 IX 0.02 miob8817 IX 0.02 fcrc3488 IX 0.02 miod3776 IX 0.02 mioa5085 IX 0.02 ncr3112 IX 0.02 ncrc4757 IX 0.02 ncr5537 IX 0.02 seoa0256 IX 0.02 seoa2824 IX 0.02 seob4560 IX 0.02 seoc0317 IX 0.02 seoc5156 IX 0.02 seoc5002 IX 0.02 fcrb2484 IX 0.02 ncrc5230 IX 0.02 fcrc5391 IX 0.02 miod7337 IX 0.02 iniob0644 IX 0.02 fcrc2131 IX 0.02 ncr5529 IX 0.02 miob8531 IX 0.02 ncrb8419 IX 0.02 fcrO824 IX 0.02 seob8911 IX 0.02 mioa4810 IX 0.02 ncr3934 IX 0.02 ncrb4248 IX 0.02 ncr3465 IX 0.02 fcrb5287 IX 0.02 fcr2542 IX 0.02 fcrc6228 IX 0.02 ncr6344 IX 0.02 miob6702 IX 0.02 miob7443 IX 0.02 seoa3147 IX 0.02 seoc2904 IX 0.02 seob2642 IX 0.02 fcrb7584 IX 0.02 seoc0220 IX 0.02 seob0678 IX 0.02 fcrb3595 IX 0.02 fcrbOO44 IX 0.02 miob9274 IX 0.02 fcr3001 IX 0.02 ncr3306 IX 0.02 fcrb2012 IX 0.02 ncrcO864 IX 0.02 fcrb4506 IX 0.02 fcr6264 IX 0.02 fcrb6099 IX 0.02 fcrbl729 IX 0.02 fcrb6779 IX 0.02 miob8345 IX 0.02 fcrb9639 IX 0.02 fcrb2254 IX 0.02 fcrc6345 IX 0.02 ncrc3076 IX 0.02 hfcr6573 IX 0.02 miocl783 IX 0.02 rαioa4628 IX 0.02 US2005/022071
mioa6174 IX 0.02 mioc4708 IX 0.03 miob3953 IX 0.02 miod4084 IX 0.03 miob6098 IX 0.02 miod7486 IX 0.03 miob7945 IX 0.02 ncr3380 IX 0.03 mioc4603 IX 0.02 ncr6811 IX 0.03 miod5184 IX 0.02 ncr7904 IX 0.03 ncr4612 IX 0.02 ncr8337 IX 0.03 ncr4946 IX 0.02 ncrcl502 IX 0.03 ncrb6357 IX 0.02 ncrc2919 IX 0.03 ncrc5887 IX 0.02 seoa0799 IX 0.03 ncrc9704 IX 0.02 seoa3121 IX 0.03 seoa4040 IX 0.02 seoa7086 IX 0.03 seoa5778 IX 0.02 seoa7157 IX 0.03 seoa9129 IX 0.02 seob6872 IX 0.03 seoa2830 IX 0.02 seob9092 IX 0.03 fcrb2849 IX 0.02 seoc0394 IX 0.03 fcrb5918 IX 0.02 seoa7605 IX 0.03 fcr4128 IX 0.02 miob9559 IX 0.03 ncr3163 IX 0.02 seoal056 IX 0.03 seob8562 IX 0.02 fcr2164 IX 0.03 seoc4022 IX 0.02 fcrb4224 IX 0.03 ncrc6423 IX 0.02 fcrb8915 IX 0.03 seoa2134 IX 0.02 fcrc5699 IX 0.03 seoa9131 IX 0.02 miob8704 IX 0.03 fcrc5007 IX 0.02 mioc0950 IX 0.03 seob3053 IX 0.02 miod4512 IX 0.03 miob4126 IX 0.02 ncr4219 IX 0.03 miob8191 IX 0.02 ncrb4402 IX 0.03 ncrl780 IX 0.02 ncrcl595 IX 0.03 ncrb4319 IX 0.02 ncrc2533 IX 0.03 fcrb5470 IX 0.02 ncrc5919 IX 0.03 fcrb6009 IX 0.02 seoa7897 IX 0.03 ncrc3953 IX 0.02 seobl770 IX 0.03 ncrc6953 IX 0.02 seob2797 IX 0.03 seoal501 IX 0.02 miob2067 IX 0.03 mioa9179 IX 0.02 miod7324 IX 0.03 fcr2106 IX 0.02 ncrb8451 IX 0.03 seoa0065 IX 0.03 ncrcl687 IX 0.03 fcrb2457 IX 0.03 seob3182 IX 0.03 fcrb5360 IX 0.03 seob3520 IX 0.03 fcr6748 IX 0.03 seoc6169 IX 0.03 mioa4792 IX 0.03 fcrb3874 IX 0.03 miob4352 IX 0.03 hfcrO676 IX 0.03 fcrb2596 IX 0.03 fcrc2126 IX 0.03 seoa8486 IX 0.03 fcrb9355 IX 0.03 fcr7171 IX 0.03 ncr2472 IX 0.03 fcrb2189 IX 0.03 rαiod4066 IX 0.03 fcrb2754 IX 0.03 πιioc3419 IX 0.03 fcrb3726 IX 0.03 ncr2176 IX 0.03 fcrb3920 IX 0.03 fcrcl745 IX 0.03 fcrb4515 IX 0.03 fcrc6084 IX 0.03 fcrb5037 IX 0.03 ncr8780 IX 0.03 fcrb9184 IX 0.03 miod6947 IX 0.03 fcrcO959 IX 0.03 hfcr5611 IX 0.03 fcrc6651 IX 0.03 miob7209 IX 0.03 mioa2620 IX 0.03 ncrc5724 IX 0.03 raioa4548 IX 0.03 fcrO787 IX 0.03 mioa5202 IX 0.03 seoaβlll IX 0.03 mioa5409 IX 0.03 fcr4743 IX 0.03 mioa9792 IX 0.03 fcrb461β IX 0.03 miob4091 IX 0.03 fcrb6011 IX 0.03 miob5873 IX 0.03 fcrcl886 IX 0.03 miob6597 IX 0.03 fcrc4896 IX 0.03 mioc0337 IX 0.03 hfcr3615 IX 0.03 hfcr5991 IX 0.03 seobl273 IX 0.03 mioa0246 IX 0.03 seob2689 IX 0.03 mioa2537 IX 0.03 seoc0034 IX 0.03 mioa8607 IX 0.03 seoc4888 IX 0.03 miob6401 IX 0.03 ncrcll93 IX 0.03 miob9176 IX 0.03 ncrbll79 IX 0.03 mioc0669 IX 0.03 fcrcβOll IX 0.03 mioc5443 IX 0.03 ncr3869 IX 0.03 miod3254 IX 0.03 fcrb3760 IX 0.03 ncr2892 IX 0.03 miob2287 IX 0.03 ncr4030 IX 0.03 mioc7863 IX 0.03 ncr4572 IX 0.03 miodl236 IX 0.03 ncr5397 IX 0.03 ncrcO185 IX 0.03 ncrc3927 IX 0.03 ncrc9947 IX 0.03 seoa0024 IX 0.03 seob0085 IX 0.03 seobl513 IX 0.03 seob2259 IX 0.03 seob3076 IX 0.03 seobβOOO IX 0.03 seob9543 IX 0.03 fcrb4869 IX 0.03 seoc4955 IX 0.03 fcrb6220 IX 0.03 seoa7250 IX 0.03 fcrc5577 IX 0.03 ncrb3056 IX 0.03 miob3348 IX 0.03 seocl898 IX 0.03 miocll25 IX 0.03 fcrb2077 IX 0.03 ncrcl421 IX 0.03 fcrb2449 IX 0.03 ncrc3100 IX 0.03 mioa2185 IX 0.03 ncrc5079 IX 0.03 ncrc3377 IX 0.03 fcrb8340 IX 0.03 seob4734 IX 0.03 fcrb8700 IX 0.03 fcrb3017 IX 0.03 ncr7934 IX 0.03 fcrb4533 IX 0.03 seoc3965 IX 0.03 fcrb4921 IX 0.03 seob8104 IX 0.03 rαiob4822 IX 0.03 seoa5691 IX 0.03 miob8143 IX 0.03 fcrc4150 IX 0.03 mioc7904 IX 0.03 seob2775 IX 0.03 seob0298 IX 0.03 seob8065 IX 0.03 mioc2188 IX 0.03 mioc3042 IX 0.04 hfcr4114 IX 0.03 seoc0149 IX 0.04 ncr2930 IX 0.03 hfcr2616 IX 0.04 miob2968 IX 0.03 seoa3717 IX 0.04 fcrb3074 IX 0.03 ncr4485 IX 0.04 ncr4545 IX 0.03 ncrbl337 IX 0.04 miod4686 IX 0.03 fcrb3086 IX 0.04 seob2974 IX 0.03 fcrc6374 IX 0.04 seob3564 IX 0.03 fcrb4249 IX 0.04 fcr3173 IX 0.03 fcrb5702 IX 0.04 fcrbl898 IX 0.03 fcr4784 IX 0.04 fcrb2546 IX 0.03 fcrb5100 IX 0.04 fcrb5077 IX 0.03 fcrcO213 IX 0.04 fcrb5754 IX 0.03 fcrcl431 IX 0.04 fcrb9499 IX 0.03 raioa0311 IX 0.04 fcrc2830 IX 0.03 miob2355 IX 0.04 fcrc5154 IX 0.03 miob9053 IX 0.04 fcrc5282 IX 0.03 mioc0902 IX 0.04 miob8657 IX 0.03 mioc3958 IX 0.04 mioc0375 IX 0.03 miodl291 IX 0.04 mioc3092 IX 0.03 miodl389 IX 0.04 mioc5226 IX 0.03 ncr3268 IX 0.04 rαiod4493 IX 0.03 ncr4215 IX 0.04 ncrlβ92 IX 0.03 ncr4550 IX 0.04 ncrbO624 IX 0.03 ncr7136 IX 0.04 ncrcl361 IX 0.03 ncr8234 IX 0.04 seoa0420 IX 0.03 ncr9502 IX 0.04 seoal924 IX 0.03 ncrbll86 IX 0.04 seoa6178 IX 0.03 ncrb2517 IX 0.04 seoa6654 IX 0.03 ncrb8539 IX 0.04 ncrcl596 IX 0.04 fcrc5835 IX 0.04 ncrcl885 IX 0.04 hfcr4477 IX 0.04 ncrc2529 IX 0.04 mioa2478 IX 0.04 ncrc3713 IX 0.04 mioa2993 IX 0.04 ncrc4016 IX 0.04 mioa4542 IX 0.04 ncrc4740 IX 0.04 mioa5511 IX 0.04 ncrc6371 IX 0.04 mioa6262 IX 0.04 ncrc9819 IX 0.04 miob3307 IX 0.04 seoa0085 IX 0.04 miob7106 IX 0.04 seoa5894 IX 0.04 mioc5661 IX 0.04 seob0266 IX 0.04 miod0080 IX 0.04 seob0949 IX 0.04 ncr3727 IX 0.04 seob4579 IX 0.04 ncr5668 IX 0.04 seob8885 IX 0.04 ncrc5738 IX 0.04 seob9193 IX 0.04 ncrc7005 IX 0.04 seoc2192 IX 0.04 ncrc9286 IX 0.04 seoc3487 IX 0.04 seoa3269 IX 0.04 ncrc9401 IX 0.04 seoa3989 IX 0.04 fcrb4570 IX 0.04 seoa9494 IX 0.04 mioa3469 IX 0.04 seob0700 IX 0.04 miobl789 IX 0.04 seob4498 IX 0.04 miocl438 IX 0.04 seob5632 IX 0.04 ncr3040 IX 0.04 seocl476 IX 0.04 ncrcO262 IX 0.04 seoc2220 IX 0.04 seoaOlOl IX 0.04 seoc5267 IX 0.04 seoa3633 IX 0.04 miob9209 IX 0.04 hfcr6406 IX 0.04 miod6038 IX 0.04 miob3809 IX 0.04 ncrb8437 IX 0.04 ncr5046 IX 0.04 fcrb5007 IX 0.04 seoa7296 IX 0.04 seob6020 IX 0.04 seob2139 IX 0.04 fcrb5199 IX 0.04 miocl808 IX 0.04 miob4760 IX 0.04 seoc4625 IX 0.04 miob9010 IX 0.04 mioc0227 IX 0.04 ncrb2125 IX 0.04 ncrcO457 IX 0.04 fcrb9680 IX 0.04 seob6835 IX 0.04 fcrbl580 IX 0.04 mioa3646 IX 0.04 ncrbO364 IX 0.04 seoa2795 IX 0.04 seob5684 IX 0.04 seoc2622 IX 0.04 mioa2319 IX 0.04 hfcr4488 IX 0.04 seob0831 IX 0.04 mioc4219 IX 0.04 ncrb2798 IX 0.04 seobll33 IX 0.04 ncrc6659 IX 0.04 miod5369 IX 0.04 seob5523 IY 5.44e-07 fcrb6643 IX 0.04 seob6853 IY 1.12e-06 mioc7260 IX 0.04 ncrcO262 IY 3.34e-06 ncrc4001 IX 0.04 seoc4484 IY 3.37e-06 ncrc9525 IX 0.04 mioal585 IY 8.5e-06 mioc4575 IX 0.04 ncrb2092 IY 8.5e-06 hfcr4497 IX 0.04 seob8368 IY 8.5e-06 mioa3399 IX 0.04 seocl203 IY 8.5e-06 mioa6545 IX 0.04 mioa3160 IY 8.88e-06 fcrbl562 IX 0.04 seob8911 IY 8.88e-06 fcrb6928 IX 0.04 miod5349 IY 9.46e-06 fcrc6631 IX 0.04 ncrb7292 IY 9.46e-06 miob6518 IX 0.04 miob0986 IY 1.09e-05 ncrc4079 IX 0.04 fcr2103 IY 1.38e-05 seoa3287 IX 0.04 fcrb3252 IY 1.38e-05 seocl804 IX 0.04 fcrc2090 IY 1.38e-05 fcrcO148 IX 0.04 miob6562 IY 1.38e-05 fcrO788 IX 0.04 mioc3430 IY 1.38e-05 fcrb3474 IX 0.04 mioc6997 IY 1.38e-05 fcrb6986 IX 0.04 ncr0025 IY 1.38e-05 fcrb7440 IX 0.04 seoa2970 IY 1.38e-05 fcrb7443 IX 0.04 seoa9373 IY 1.38e-05 seocl593 IY 1.38e-05 ncrl387 IY 2.99713e-04 fcrb8465 IY 2.03e-05 hfcr2984 IY 3.16106e-04 ncrc2888 IY 2.54e-05 ncrc9286 IY 3.16256e-04 fcr0107 IY 2.57e-05 fcrb7830 IY 3.26091e-04 fcrbl922 IY 2.57e-05 miob8694 IY 3.26091e-04 miod2665 IY 2.61e-05 seoal480 IY 3.26091e-04 fcrb7588 IY 2.76e-05 seob3189 IY 3.26091e-04 miod6238 IY 2.76e-05 seoc6703 IY 3.26091e-04 ncrc4597 IY 2.76e-05 ncr3163 IY 3.28388e-04 seoa4518 IY 2.76e-05 mioa3856 IY 3.33477e-04 seoc2029 IY 2.76e-05 ncrO258 IY 3.45611e-04 fcrb8504 IY 3.07e-05 fcr6530 IY 3.55737e-04 fcrc3358 IY 3.07e-05 fcr7403 IY 3.55737e-04 miob9209 IY 3.07e-05 fcrb2040 IY 3.55737e-04 miob9831 IY 3.07e-05 fcrb5326 IY 3.55737e-04 mioc6055 IY 3.07e-05 fcrc0076 IY 3.55737e-04 miod4142 IY 3.Q7e-05 mioal626 IY 3.55737e-04 miod4759 IY 3.07e-05 miob4308 IY 3.55737e-04 seoc2824 IY 3.07e-05 ncrO251 IY 3.55737e-04 seoa7608 IY 3.33e-05 ncr0660 IY 3.55737e-04 fcrc3993 IY 3.64e-05 seoal413 IY 3.55737e-04 ncrO176 IY 3.64e-05 seoc2976 IY 3.55737e-04 seob7432 IY 3.64e-05 miod7421 IY 3.63889e-04 ncrc8835 IY 4.15e-05 seoc2696 IY 3.63889e-04 fcrc5831 IY 4.31e-05 miocl203 IY 3.76683e-04 hfcr5970 IY 4.31e-05 fcrb9671 IY 3.8591e-04 seob6506 IY 4.31e-05 miod2556 IY 4.17224e-04 seob9960 IY 4.31e-05 seob0523 IY 4.48203e-04 miob8301 IY 5.82e-05 fcrb4616 IY 4.63478e-04 miob9533 IY 5.82e-05 fcrb7808 IY 4.91846e-04 ncr9956 IY 5.82e-05 hfcrO525 IY 4.91846e-04 seocl99β IY 5.82e-05 mioa2580 IY 4.91846e-04 mioc4343 IY 6.73e-05 ncrc3045 IY 4.91846e-04 seoc0513 IY 6.97e-05 ncrc4308 IY 4.91846e-04 seoa0288 IY 7.47e-05 seob4584 IY 4.91846e-04 fcr3101 IY 8.62e-05 ncr4180 IY 5.07682e-04 seob0200 IY 8.62e-05 miob3725 IY 5.09895e-04 miob7099 IY 1.04084e-04 fcrb2933 IY 5.21225e-04 fcrb7488 IY 1.0904e-04 fcrb3476 IY 5.21225e-04 ncrb4339 IY 1.11567e-04 fcrb9420 IY 5.21225e-04 ncrc2080 IY 1.16828e-04 mioc2021 IY 5.21225e-04 seoa4670 IY 1.16828e-04 ncrb3541 IY 5.21225e-04 seob9346 IY 1.34696e-04 seoc0619 IY 5.2253e-04 fcrll73 IY 1.52868e-04 fcrb3140 IY 5.6899e-04 ncrcl044 IY 1.52868e-04 ncrc3598 IY 5.6899e-04 seoc0920 IY 1.57502e-04 seobl853 IY 5.6899e-04 fcr3367 IY 1.63813e-04 ncrc6994 IY 6.36489e-04 ncrc4555 IY 1.63813e-04 seoa7652 IY 6.36489e-04 fcrc5937 IY 1.72728e-04 fcrb8419 IY 6.59196e-04 miob8812 IY 1.72728e-04 mioc8507 IY 6.59196e-04 ncr5971 IY 1.72728e-04 seoa0783 IY 6.59196e-04 seoa8636 IY 1.72728e-04 seob4925 IY 6.68896e-04 mioclll7 IY 2.74004e-04 mioc2891 IY 6.8067e-04 miob9404 IY 2.94903e-04 miod0894 IY 7.11238e-04 mioc9205 IY 2.94903e-04 ncrb5972 IY 7.11238e-04 fcr2700 IY 2.97843e-04 fcrc7112 IY 7.18906e-04 fcr3861 IY 2.97843e-04 miod2792 IY 7.42567e-04 fcrb3497 IY 2.97843e-04 seob3158 IY 7.46102e-04 fcrb8614 IY 2.97843e-04 seoc7785 IY 7.91035e-04 mioc0121 IY 2.97843e-04 fcrO253 IY 8.82072e-04 mioc3618 IY 2.97843e-04 seobl660 IY 8.82072e-04 seoa6203 IY 2.97843e-04 seob2283 IY 8.82072e-04 seob5748 IY 2.97843e-04 fcrb4226 IY 9.26956e-04 seocl236 IY 2.97843e-04 fcrc3416 IY 9.27715e-04 mioa2173 IY 9.48317e-04 seob5379 IZ 0.01 ncrc3554 IY 9.48767e-04 seobl513 IZ 0.01 ncrcl310 IY 9.51038e-04 hfcr3622 IZ 0.01 fcrb6478 IY 9.59421e-04 fcrc6563 IZ 0.01 ncrb6903 IY 1.001243e-03 miob4055 IZ 0.02 fcrb7495 IY 1.020987e-03 seoa7509 IZ 0.02 fcrb8422 IY 1.020987e-03 seoa6573 IZ 0.02 fcrc7219 IY 1.020987e-03 fcrb4696 IZ 0.02 seoa2949 IY 1.020987e-03 seob4805 IZ 0.02 seob4589 IY 1.071134e-03 seocl425 IZ 0.02 fcrbl492 IY 1.115562e-03 rαioc0630 IZ 0.02 hfcr2238 IY 1.115562e-03 seob9614 IZ 0.02 seob8741 IY 1.163938e-03 seob8501 IZ 0.02 mioa2073 IY 1.200074e-03 miob2687 IZ 0.02 ncrcO749 IY 1.200074e-03 ncr3778 IZ 0.02 mioc9896 IY 1.350122e-03 mioa4014 IZ 0.02 fcrb2859 IY 1.393429e-03 seob3751 IZ 0.02 fcrb3010 IY 1.393429e-03 mioc0950 IZ 0.02 fcrb8432 IY 1.393429e-03 fcrb4542 IZ 0.02 fcrc6279 IY 1.393429e-03 seocl230 IZ 0.02 miob3560 IY 1.393429e-03 fcrb8504 IZ 0.02 miob9551 IY 1.393429e-03 fcrc5351 IZ 0.02 miodl606 IY 1.393429e-03 fcrb5254 IZ 0.02 seoa8716 IY 1.393429e-03 mioc3580 IZ 0.02 fcrb3592 IY 1.430199e-03 mioa7299 IZ 0.02 ncrc5230 IY 1.443394e-03 miod4184 IZ 0.02 fcrb3015 IY 1.449119e-03 seoc2173 IZ 0.02 ncrb8273 IY 1.449119e-03 seoa6358 IZ 0.02 seoa9389 IY 1.449119e-03 fcr5222 IZ 0.02 ncr9664 IY 1.538333e-03 ncr4452 IZ 0.02 miob3161 IY 1.541015e-03 mioc0347 IZ 0.02 rαiob0644 IY 1.545534e-03 ncrc9483 IZ 0.02 seocl872 IY 1.553693e-03 mioa2333 IZ 0.02 ncr3815 IY 1.615964e-03 seoc4103 IZ 0.02 seob2085 IY 1.615964e-03 mioal062 IZ 0.03 fcrb9588 IZ 9.42678e-04 ncrc4600 IZ 0.03 ncrc9280 IZ 1.36307e-03 seobl316 IZ 0.03 fcrc6826 IZ 1.61351e-03 seobl617 IZ 0.03 seoal559 IZ 1.740315e-03 miod0884 IZ 0.03 ncrb8392 IZ 4.140103e-03 fcrl844 IZ 0.03 ncr4696 IZ 4.503228e-03 fcrb2160 IZ 0.03 ncrc9469 IZ 4.849487e-03 fcrc0604 IZ 0.03 fcrb9694 IZ 6.09468e-03 miob3456 IZ 0.03 ncr3559 IZ 7.002691e-03 miob4673 IZ 0.03 fcrbl950 IZ 7.501979e-03 miodl714 IZ 0.03 fcrb4925 IZ 7.651244e-03 fcrb6874 IZ 0.03 seoc2475 IZ 7.691468e-03 mioc7818 IZ 0.03 miodl505 IZ 8.44159e-03 mioa4057 IZ 0.03 seoa2391 IZ 9.669113e-03 fcrb9871 IZ 0.03 ncr0210 IZ 9.776908e-03 miob3928 IZ 0.03 fcrb9751 IZ 9.809161e-03 ncr3963 IZ 0.03 seob5773 IZ 0.01 miob3396 IZ 0.03 seoal552 IZ 0.01 mioal494 IZ 0.03 seoall04 IZ 0.01 miocll98 IZ 0.03 miob3354 IZ 0.01 miob4975 IZ 0.04 seobl793 IZ 0.01 mioa3629 IZ 0.04 seoc0951 IZ 0.01 fcrO665 IZ 0.04 seob3154 IZ 0.01 fcrbl382 IZ 0.04 rαioc2619 IZ 0.01 mioa0890 IZ 0.04 seob7584 IZ 0.01 ncr3434 IZ 0.04 ncrb2395 IZ 0.01 hfcr3500 IZ 0.04 fcrb7070 IZ 0.01 fcrbl992 IZ 0.04 seob6413 IZ 0.01 mioa8774 IZ 0.04 ncrcllO3 IZ 0.01 fcrb8208 IZ 0.04 ncrc4226 IZ 0.04 fcrc5351 2 0.02 seob4147 IZ 0.04 fcrb5254 2 0.02 fcr0990 IZ 0.04 mioc3580 2 0.02 fcrc7240 IZ 0.04 mioa7299 2 0.02 fcr4460 IZ 0.04 miod4184 2 0.02 miob3044 IZ 0.04 seoc2173 2 0.02 fcr4885 IZ 0.04 seoa6358 2 0.02 miob0178 IZ 0.04 fcr5222 2 0.02 seobl420 IZ 0.04 ncr4452 2 0.02 miobl493 IZ 0.04 mioc0347 2 0.02 seob9750 IZ 0.04 ncrc9483 2 0.02 fcrc2431 IZ 0.04 mioa2333 2 0.02 mioa9541 IZ 0.04 seoc4103 2 0.02 mioc8264 IZ 0.04 mioal062 2 0.03 fcrb9588 2 9.42678e-04 ncrc4600 2 0.03 ncrc9280 2 1.36307e-03 seobl316 2 0.03 fcrc6826 2 1.61351e-03 seobl617 2 0.03 seoal559 2 1.740315e-03 miod0884 2 0.03 ncrb8392 2 4.140103e-03 fcrl844 2 0.03 ncr4696 2 4.503228e-03 fcrb2160 2 0.03 ncrc9469 2 4.849487e-03 fcrc0604 2 0.03 fcrb9694 2 6.09468e-03 miob3456 2 0.03 ncr3559 2 7.002691e-03 miob4673 2 0.03 fcrbl950 2 7.501979e-03 miodl714 2 0.03 fcrb4925 2 7.651244e-03 fcrb6874 2 0.03 seoc2475 2 7.691468e-03 mioc7818 2 0.03 miodl505 2 8.44159e-03 mioa4057 2 0.03 seoa2391 2 9.669113e-03 fcrb9871 2 0.03 ncr0210 2 9.776908e-03 miob3928 2 0.03 fcrb9751 2 9.809161e-03 ncr3963 2 0.03 seob5773 2 0.01 miob3396 2 0.03 seoal552 2 0.01 mioal494 2 0.03 seoall04 2 0.01 miocll98 2 0.03 miob3354 2 0.01 miob4975 2 0.04 seobl793 2 0.01 mioa3629 2 0.04 seoc0951 2 0.01 fcrO665 2 0.04 seob3154 2 0.01 fcrbl382 2 0.04 mioc2619 2 0.01 mioa0890 2 0.04 seob7584 2 0.01 ncr3434 2 0.04 ncrb2395 2 0.01 hfcr3500 2 0.04 fcrb7070 2 0.01 fcrbl992 2 0.04 seob6413 2 0.01 mioa8774 2 0.04 ncrcllO3 2 0.01 fcrb8208 2 0.04 seob5379 2 0.01 ncrc4226 2 0.04 seobl513 2 0.01 seob4147 2 0.04 hfcr3622 2 0.01 fcr0990 2 0.04 fcrc6563 2 0.01 fcrc7240 2 0.04 miob4055 2 0.02 fcr4460 2 0.04 seoa7509 2 0.02 miob3044 2 0.04 seoa6573 2 0.02 fcr4885 2 0.04 fcrb4696 2 0.02 miob0178 2 0.04 seob4805 2 0.02 seobl420 2 0.04 seocl425 2 0.02 miobl493 2 0.04 mioc0630 2 0.02 seob9750 2 0.04 seob9614 2 0.02 fcrc2431 2 0.04 seob8501 2 0.02 mioa9541 2 0.04 miob2687 2 0.02 mioc8264 2 0.04 ncr3778 2 0.02 fcrb3217 4A 1.07e-08 mioa4014 2 0.02 fcr3053 4A 7.35e-08 seob3751 2 0.02 fcr3181 4A 7.35e-08 mioc0950 2 0.02 mioc3430 4A 2.08e-07 fcrb4542 2 0.02 seob5523 4A 7.14e-07 seocl230 2 0.02 ncrb2092 4A 8.18e-07 fcrb8504 2 0.02 fcrb3227 4A 2.16e-06 mioa9473 4A 2.16e-06 hfcr3043 4A 3.19e-05 seob6853 4A 2.44e-06 miob3953 4A 3.41e-05 seob6576 4A 2.72e-06 mioa4014 4A 3.56e-05 ncr3163 4A 3.44e-06 mioc8423 4A 3.62e-05 fcrb2554 4A 3.76e-06 seob6844 4A 3.65e-05 seoc0775 4A 3.82e-06 mioc5532 4A 3.78e-05 seoa2970 4A 4.64e-06 miodll95 4A 3.78e-05 seob0085 4A 5.05e-06 miod5349 4A 3.78e-05 mioa2788 4A 5.98e-06 ncrc8903 4A 3.78e-05 mioa7317 4A 6.38e-06 fcr2607 4A 3.82e-05 seob3326 4A 6.84e-06 fcrb3654 4A 3.82e-05 fcr2952 4A 6.92e-06 ncr0045 4A 3.82e-05 seocl203 4A 7.69e-06 ncrc5434 4A 3.82e-05 ncrc5243 4A 7.95e-06 seoa4717 4A 3.82e-05 fcrb4534 4A 8.04e-06 seoa9042 4A 3.82e-05 mioa3367 4A 8.23e-06 seobl911 4A 3.82e-05 fcrb3578 4A 8.3e-06 seob0154 4A 4.04e-05 miob6562 4A 8.53e-06 mioa0311 4A 4.07e-05 fcrc3993 4A 9.09e-06 miob6223 4A 4.11e-05 miod7414 4A 9.19e-06 ncrc6171 4A 4.13e-05 mioa9179 4A 9.26e-06 mioa2072 4A 4.19e-05 ncr7668 4A 9.93e-06 ncr8588 4A 4.45e-05 seoal427 4A 1.02e-05 seoa2585 4A 4.45e-05 miob6430 4A l.le-05 miod5123 4A 4.5e-05 fcr5075 4A 1.24e-05 seob0248 4A 4.5e-05 fcrbl892 4A 1.24e-05 seob6446 4A 4.81e-05 seoa8916 4A 1.24e-05 seoa4518 4A 5.34e-05 mioc7170 4A 1.26e-05 fcr6235 4A 5.39e-05 seob5240 4A 1.35e-05 hfcrl863 4A 5.39e-05 miob6419 4A 1.37e-05 hfcr3011 4A 5.39e-05 fcrcO487 4A 1.38e-05 mioa3939 4A 5.39e-05 seoa6133 4A 1.45e-05 mioa6130 4A 5.39e-05 mioc4022 4A 1.52e-05 ncrO258 4A 5.39e-05 mioal585 4A 1.82e-05 ncr3197 4A 5.39e-05 crO471 4A 1.83e-05 ncr3837 4A 5.39e-05 fcrbl960 4A 1.83e-05 fcrc2090 4A 5.47e-05 hfcrl.137 4A 1.83e-05 seocl593 4A 5.57e-05 hfcr8902 4A 1.83e-05 mioc8410 4A 5.58e-05 seoa0231 4A 1.83e-05 mioc7764 4A 5.6e-05 seoa8754 4A 1.83e-05 ncrO212 4A 5.86e-05 seob4427 4A 1.83e-05 seocl023 4A 6.08e-05 seob2994 4A 1.84e-05 seob9282 4A 6.18e-05 mioa2319 4A 1.93e-05 fcrl463 4A 6.32e-05 seob6004 4A 2.07e-05 miodl236 4A 6.32e-05 fcr2103 4A 2.09e-05 seob0065 4A 6.32e-05 seoa4461 4A 2.14e-05 seob4029 4A 6.32e-05 mioa0595 4A 2.21e-05 fcrb8504 4A 6.63e-05 seob3518 4A 2.35e-05 seoc2029 4A 6.66e-05 fcrb6939 4A 2.39e-05 seoa3516 4A 7.05e-05 mioa3923 4A 2.39e-05 fcr4831 4A 7.47e-05 mioa5059 4A 2.39e-05 fcrc3739 4A 7.47e-05 ncrc9899 4A 2.47e-05 miod5369 4A 7.47e-05 ncr5568 4A 2.59e-05 ncr5168 4A 7.47e-05 mioa4636 4A 2.66e-05 miobl231 4A 7.53e-05 ncrb4441 4A 2.66e-05 ncrO898 4A 7.53e-05 seoa0959 4A 2.66e-05 seob3367 4A 7.53e-05 seoal940 4A 2.66e-05 fcr5559 4A 7.65e-05 seob3189 4A 2.66e-05 fcrb6012 4A 7.67e-05 seob4303 4A 2.66e-05 seob8368 4A 7.81e-05 seobl231 4A 2.9e-05 seoa8566 4A 8.04e-05 hfcr5919 4A 2.96e-05 seob3191 4A 8.14e-05 fcr4477 4A 3.17e-05 fcrb8516 4A 8.38e-05 fcrc7388 4A 3.17e-05 mioa4009 4A 8.69e-05 miod5651 4A 3.17e-05 seob6279 4A 8.69e-05 fcrl756 4A 8.79e-05 mioc7686 4A 1.62e-04 fcrcO112 4A 8.79e-05 fcrb7693 4A 1.65e-04 fcrc3750 4A 8.79e-05 miod6238 4A 1.65e-04 seoa0288 4A 9.34e-05 seob4333 4A 1.65e-04 seoa2949 4A 9.47e-05 seob6206 4A 1.72e-04 fcrb3461 4A 9.48e-05 ncrc5844 4A 1.73e-04 mioa9510 4A 9.71e-05 seob0755 4A 1.75e-04 ncrc5569 4A 9.91e-05 fcrb9655 4A 1.79e-04 ncr2013 4A 1.01e-04 fcr7043 4A 1.91e-04 fcrbl855 4A 1.03e-04 mioa9154 4A 1.91e-04 miod5505 4A 1.03e-04 ncrO733 4A 1.91e-04 seoa3533 4A 1.03e-04 ncrb8693 4A 1.91e-04 seob5748 4A 1.03e-04 ncrc4597 4A 1.91e-04 seob7432 4A 1.03e-04 seoa0486 4A 1.91e-04 fcrb2704 4A 1.04e-04 seoa6658 4A 1.91e-04 hfcr4489 4A 1.04e-04 fcrc4054 4A 1.92e-04 mioa3913 4A 1.04e-04 mioc6075 4A 1.92e-04 mioa5695 4A 1.04e-04 seob0122 4A 1.92e-04 ncr7813 4A 1.04e-04 seoa9373 4A 1.99e-04 seoall02 4A 1.04e-04 mioa8647 4A 2.01e-04 seob2148 4A 1.04e-04 ncr0808 4A 2.02e-04 seob4669 4A 1.04e-04 seoa6497 4A 2.02e-04 ncr7904 4A 1.06e-04 seoa6598 4A 2.06e-04 iniod6845 4A 1.08e-04 seob0308 4A 2.13e-04 seoa5662 4A 1.08e-04 seoal598 4A 2.2e-04 mioc4667 4A l.le-04 fcr5720 4A 2.22e-04 mioc8057 4A 1.12e-04 seob5743 4A 2.22e-04 seob6368 4A 1.12e-04 fcr6044 4A 2.23e-04 ncr0025 4A 1.15e-04 hfcr3019 4A 2.23e-04 miod4759 4A 1.16e-04 miod4142 4A 2.23e-04 mioa6064 4A 1.17e-04 seob6670 4A 2.23e-04 fcrc6888 4A 1.21e-04 miobl326 4A 2.27e-04 mioa0535 4A 1.21e-04 seob3119 4A 2.29e-04 ncrO478 4A 1.21e-04 miob3348 4A 2.34e-04 mioa0252 4A 1.24e-04 mioc0950 4A 2.34e-04 seoa7530 4A 1.27e-04 seoa0032 4A 2.34e-04 fcrb8740 4A 1.34e-04 fcr6577 4A 2.43e-04 mioc6114 4A 1.36e-04 hfcrll63 4A 2.47e-04 miob6881 4A 1.37e-04 seob7465 4A 2.5e-04 mioall93 4A 1.41e-04 fcr2088 4A 2.54e-04 ncr2293 4A 1.41e-04 hfcr5604 4A 2.54e-04 ncr2905 4A 1.41e-04 mioa2173 4A 2.54e-04 ncr4126 4A 1.41e-04 miob4867 4A 2.54e-04 ncr8843 4A 1.41e-04 mioc7895 4A 2.54e-04 ncrc9910 4A 1.41e-04 ncrb2288 4A 2.54e-04 seoa4681 4A 1.41e-04 ncrcO715 4A 2.54e-04 seoa9537 4A 1.41e-04 ncrcll40 4A 2.54e-04 seob0376 4A 1.41e-04 ncrc6005 4A 2.54e-04 seobl318 4A 1.41e-04 seoa0221 4A 2.54e-04 seob3182 4A 1.41e-04 seoa7443 4A 2.54e-04 seob3923 4A 1.41e-04 seobl862 4A 2.54e-04 seob6835 4A 1.41e-04 seob6467 4A 2.54e-04 fcrb8080 4A 1.42e-04 seoa3717 4A 2.58e-04 ncrcO342 4A 1.42e-04 seob6272 4A 2.58e-04 seoa8232 4A 1.42e-04 fcrc2082 4A 2.59e-04 seoa9357 4A 1.43e-04 seob6751 4A 2.59e-04 seoc0999 4A 1.46e-04 seoc6169 4A 2.59e-04 seobβ851 4A 1.47e-04 mioc7665 4A 2.77e-04 fcrb3134 4A 1.48e-04 mioal662 4A 2.78e-04 seob6131 4A 1.5e-04 seoa9482 4A 2.78e-04 fcrbl733 4A 1.51e-04 seoa3105 4A 2.79e-04 ncrbO782 4A 1.51e-04 fcrb8668 4A 2.81e-04 seoa3847 4A 1.55e-04 fcrl879 4A 2.88e-04 mioa4552 4A 1.58e-04 seob4419 4A 2.9e-04 Fcfb-945-4 4A- 2.92e-U4 ncrc6953 4A 4.38e-04 fcrcO959 4A 3.0e-04 fcr3121 4A 4.4e-04 mioc5643 4A 3.0e-04 fcrb2060 4A 4.4e-04 ncrb8385 4A 3.0e-04 fcrb2536 4A 4.4e-04 ncrc5039 4A 3.0e-04 hfcrl743 4A 4.4e-04 seoa4395 4A 3.0e-04 mioa5696 4A 4.4e-04 seoa6032 4A 3.0e-04 ncrb3329 4A 4.4e-04 mioal354 4A 3.05e-04 seoa5235 4A 4.4e-04 ncr8709 4A 3.05e-04 seoa8443 4A 4.4e-04 miob9907 4A 3.06e-04 seobl879 4A 4.4e-04 fcrb3135 4A 3.14e-04 seobl908 4A 4.4e-04 fcrc6335 4A 3.14e-04 seob2810 4A 4.4e-04 mioc7331 4A 3.16e-04 seob3313 4A 4.4e-04 fcr4846 4A 3.22e-04 seob3670 4A 4.4e-04 seob6525 4A 3.26e-04 seob3854 4A 4.4e-04 mioa3963 4A 3.31e-04 seob4273 4A 4.4e-04 mioa4076 4A 3.32e-04 seob4545 4A 4.4e-04 seob8204 4A 3.32e-04 seob4579 4A 4.52e-04 fcrb4428 4A 3.35e-04 miob3763 4A 4.56e-04 seobl414 4A 3.35e-04 ncrO912 4A 4.57e-04 fcr3595 4A 3.36e-04 seoc2191 4A 4.57e-04 hfcr6687 4A 3.36e-04 fcrb3165 4A 4.58e-04 mioa0577 4A 3.36e-04 fcrb8465 4A 4.58e-04 mioa3080 4A 3.36e-04 miob8812 4A 4.58e-04 miobl062 4A 3.36e-04 seob0089 4A 4.59e-04 miob6904 4A 3.36e-04 seob6015 4A 4.63e-04 ncr2666 4A 3.36e-04 miob0189 4A 4.81e-04 ncr8975 4A 3.36e-04 miod4332 4A 4.81e-04 seobl793 4A 3.36e-04 seob9882 4A 4.82e-04 fcrbl807 4A 3.41e-04 seob9552 4A 4.83e-04 seoa4366 4A 3.43e-04 fcr5123 4A 4.86e-04 seoclllδ 4A 3.44e-04 seoc0924 4A 4.93e-04 fcrc3415 4A 3.46e-04 mioa6854 4A 4.95e-04 ncrc5079 4A 3.46e-04 ncrb2266 4A 4.95e-04 seob8501 4A 3.46e-04 ncrb7292 4A 4.99e-04 seoa5090 4A 3.52e-04 miob0167 4A 5.02e-04 fcrc4360 4A 3.65e-04 miob4475 4A 5.09e-04 fcrc5516 4A 3.67e-04 seob9946 4A 5.09e-04 mioa8852 4A 3.67e-04 mioc3716 4A 5.17e-04 ncrb4182 4A 3.67e-04 ncrc0150 4A 5.23e-04 seoa6315 4A 3.67e-04 mioc4641 4A 5.24e-04 fcrc3009 4A 3.81e-04 fcrO955 4A 5.26e-04 fcr5758 4A 3.82e-04 mioc8379 4A 5.31e-04 ncrO153 4A 3.86e-04 ncr3782 4A 5.46e-04 ncrc4079 4A 3.87e-04 mioa9891 4A 5.55e-04 fcrb9481 4A 3.89e-04 miob2705 4A 5.57e-04 ncrb0054 4A 3.94e-04 miod3468 4A 5.67e-04 fcr7419 4A 3.98e-04 fcr3559 4A 5.7e-04 mioa8622 4A 3.98e-04 mioa0407 4A 5.7e-04 seoc2824 4A 3.98e-04 fcr4763 4A 5.71e-04 hfcr5695 4A 4.0e-04 hfcrl302 4A 5.71e-04 hfcrl733 4A 4.08e-04 hfcr5228 4A 5.71e-04 seoa9828 4A 4.08e-04 mioa5692 4A 5.71e-04 mioalββO 4A 4.09e-04 ncrb3702 4A 5.71e-04 fcrbl741 4A 4.1e-04 seoa0008 4A 5.71e-04 hfcrO624 4A 4.17e-04 seoa4600 4A 5.71e-04 ncrb4957 4A 4.19e-04 seob4050 4A 5.71e-04 seoa8348 4A 4.2e-04 seob4555 4A 5.71e-04 mioc6987 4A 4.23e-04 ncrcO174 4A 5.72e-04 fcrb2933 4A 4.24e-04 seoa4053 4A 5.77e-04 seoa7555 4A 4.26e-04 seoc2264 4A 5.79e-04 fcrb6968 4A 4.27e-04 mioa8970 4A 5.84e-04 fcrcl216 4A 4.35e-04 ncr3177 4A 6.01e-04 seoa8399 4A 4.37e-04 fcrb5537 4A 6.02e-04 fcrcΪ3Λ "4A "6".TT-Te-OT" seoa7546 4A 8.85e-04 seoall73 4A 6.02e-04 miob9336 4A 8.89e-04 seoa8543 4A 6.02e-04 fcrc3240 4A 8.94e-04 seob6492 4A 6.02e-04 hfcr2287 4A 8.94e-04 fcrc4658 4A 6.2e-04 ncr2182 4A 8.98e-04 fcrb9720 4A 6.21e-04 fcrbl420 4A 9.14e-04 mioc6997 4A 6.21e-04 ncrcl402 4A 9.21e-04 ncr2486 4A 6.43e-04 seoa7647 4A 9.23e-04 mioal520 4A 6.55e-04 seoa6620 4A 9.33e-04 seoa5933 4A 6.71e-04 fcrb6009 4A 9.38e-04 ncrcOlOO 4A 6.72e-04 hfcr0130 4A 9.38e-04 fcrc3358 4A 6.88e-04 hfcrl697 4A 9.38e-04 mioc4089 4A 6.88e-04 hfcr6651 4A 9.38e-04 ncrl351 4A 6.88e-04 mioa0884 4A 9.38e-04 seob6189 4A 6.88e-04 mioal097 4A 9.38e-04 ncrc6871 4A 6.89e-04 mioa9505 4A 9.38e-04 seob0514 4A 6.89e-04 miob2492 4A 9.38e-04 fcrb5214 4A 6.9e-04 miob3898 4A 9.38e-04 fcrO253 4A 6.95e-04 ncrl563 4A 9.38e-04 seob0168 4A 7.17e-04 ncrc2776 4A 9.38e-04 seoa8640 4A 7.25e-04 ncrc5369 4A 9.38e-04 fcr2079 4A 7.35e-04 seoa7902 4A 9.38e-04 hfcr4488 4A 7.35e-04 seoa8642 4A 9.38e-04 mioa3514 4A 7.35e-04 seob3462 4A 9.38e-04 miob5495 4A 7.35e-04 seob4621 4A 9.38e-04 ncr3237 4A 7.35e-04 ncrc9877 4A 9.39e-04 ncr3368 4A 7.35e-04 mioc6312 4A 9.46e-04 seoal720 4A 7.35e-04 fcrb7113 4A 9.49e-04 seoa4586 4A 7.35e-04 ncrc9517 4A 9.57e-04 seob2936 4A 7.35e-04 fcr5470 4A 9.65e-04 ncrc9855 4A 7.36e-04 mioa6731 4A 9.71e-04 mioc4318 4A 7.51e-04 ncrc2888 4A 9.82e-04 fcrb2292 4A 7.53e-04 seob0304 4A 9.82e-04 ncrc9712 4A 7.6e-04 ncr7595 4A 9.9e-04 fcrcl849 4A .65e-04 ncrb6530 4A 9.9e-04 seoa9935 4A .66e-04 ncrl387 4A 1.015584e-03 ncrc5091 4A .81e-04 fcrb8161 4A 1.016248e-03 hfcrl265 4A ,83e-04 fcrcO529 4A 1.016248e-03 ncrb5595 4A ,83e-04 mioa6091 4A 1.016248e-03 fcr4128 4A ,85e-04 seob3419 4A 1.016248e-03 fcr6937 4A 7.85e-04 seob3307 4A 1.019572e-03 fcrb2658 4A 7.85e-04 mioa9294 4A 1.023327e-03 mioa8946 4A 7.85e-04 ncrc8841 4A 1.026636e-03 miob2836 4A 7.85e-04 seoa0931 4A 1.029283e-03 miod6947 4A 7.85e-04 fcrbl552 4A 1.033819e-03 seob2188 4A 7.85e-04 seoc3876 4A 1.036352e-03 seob2697 4A 7.85e-04 fcrcO241 4A 1.040278e-03 hfcr9613 4A 7.88e-04 seoa5577 4A 1.046877e-03 ncrc3464 4A 7.96e-04 miob5736 4A 1.05271e-03 mioa5085 4A 8.03e-04 seob9067 4A 1.059844e-03 mioa9033 4A 8.07e-04 ncrb8063 4A 1.071424e-03 mioa0702 4A 8.14e-04 seoc3588 4A 1.072097e-03 seobl757 4A 8.17e-04 fcrcO554 4A 1.073062e-03 seoa3408 4A 8.32e-04 hfcr0045 4A 1.089633e-03 seob5418 4A 8.47e-04 ncr5473 4A 1.09353e-03 seob5219 4A 8.52e-04 miod6029 4A 1.098098e-03 fcr6390 4A 8.53e-04 seoa0029 4A 1.108278e-03 fcrc2576 4A 8.68e-04 seoa7517 4A 1.113082e-03 fcr5509 4A 8.72e-04 seob2108 4A 1.137133e-03 fcr4782 4A 8.75e-04 fcrb3725 4A 1.139592e-03 mioc8619 4A 8.79e-04 seoa7094 4A 1.143856e-03 ncrb6833 4A 8.81e-04 seob5032 4A 1.149465e-03 seoa5911 4A 8.81e-04 ncrc6617 4A 1.150819e-03 seob5223 4A 8.82e-04 ncr8866 4A 1.151954e-03 fcrb'269'tr 4A -™r:r5325Ze-03 fcrb8162 4A 1.470845e-03 fcrcO839 4A 1.153252e-03 seob9772 4A 1.471975e-03 fcrc2997 4A 1.153252e-03 fcr6616 4A 1.477453e-03 mioal055 4A 1.153252e-03 fcrb7830 4A 1.477453e-03 mioc6847 4A 1.153252e-03 ncrc6981 4A 1.477453e-03 seoa8894 4A 1.153252e-03 mioa9935 4A 1.481624e-03 seob9145 4A 1.153252e-03 fcr2074 4A 1.495497e-03 ncrc5054 4A 1.171669e-03 fcr4380 4A 1.495497e-03 seoa2122 4A 1.181929e-03 fcr6497 4A 1.495497e-03 fcrO139 4A 1.189014e-03 mioa0132 4A 1.495497e-03 fcr4795 4A 1.189014e-03 mioa4241 4A 1.495497e-03 fcrb2137 4A 1.189014e-03 miob0154 4A 1.495497e-03 fcrb2249 4A 1.189014e-03 miob4836 4A 1.495497e-03 fcrb3718 4A 1.189014e-03 ncrO766 4A 1.495497e-03 hfcrl310 4A 1.189014e-03 ncr4545 4A 1.495497e-03 hfcr5207 4A 1.189014e-03 ncrb8253 4A 1.495497e-03 hfcr6406 4A 1.189014e-03 ncrc6817 4A 1.495497e-03 mioal445 4A 1.189014e-03 seoa2012 4A 1.495497e-03 mioal971 4A 1.189014e-03 seoa5986 4A 1.495497e-03 ncrb8273 4A 1.189014e-03 seoa6923 4A 1.495497e-03 ncrc4654 4A 1.189014e-03 seoa9160 4A 1.495497e-03 ncrc4991 4A 1.189014e-03 mioa0104 4A 1.503118e-03 seob3499 4A 1.189014e-03 seob4972 4A 1.542133e-03 seob4515 4A 1.189014e-03 ncrb8134 4A 1.545685e-03 seob5954 4A 1.189014e-03 seoa3628 4A 1.550842e-03 miocll22 4A 1.199347e-03 seoc3603 4A 1.622635e-03 ncrc9772 4A 1.199347e-03 mioc7260 4A 1.640617e-03 fcr3664 4A 1.221006e-03 seobl322 4A 1.640617e-03 seobl737 4A 1.225361e-03 fcrb2871 4A 1.641476e-03 fcrc6854 4A 1.225922e-03 fcr6415 4A 1.65459e-03 seoal856 4A 1.22754e-03 seoa6573 4A 1.659636e-03 mioa5468 4A 1.22757e-03 ncr4202 4A 1.662058e-03 fcrbl496 4A 1.228573e-03 fcrb7944 4A 1.668024e-03 mioc7509 4A 1.242274e-03 mioa5691 4A 1.668024e-03 ncrbO145 4A 1.249902e-03 miob8694 4A 1.668024e-03 seoa4040 4A 1.254277e-03 ncrc5672 4A 1.668024e-03 seob8065 4A 1.263192e-03 seoa7369 4A 1.668024e-03 ncrb4154 4A 1.275192e-03 fcrb2218 4A 1.669633e-03 miob5412 4A 1.279477e-03 mioc7441 4A 1.681385e-03 mioc2561 4A 1.299486e-03 ncr4140 4A 1.683401e-03 fcrb5267 4A 1.306446e-03 fcrb5389 4A 1.706779e-03 fcrb8949 4A 1.306446e-03 seoa2136 4A 1.708395e-03 mioc2880 4A 1.306446e-03 miob0877 4A 1.708607e-03 raiodl925 4A 1.306446e-03 fcrb4340 4A 1.72973e-03 seoa7935 4A 1.306446e-03 fcrcO295 4A 1.734937e-03 seob6680 4A 1.306446e-03 ncrc3624 4A 1.734937e-03 seoa3230 4A 1.307889e-03 seob7584 4A 1.749079e-03 mioc7807 4A 1.34163e-03 miodll08 4A 1.765616e-03 miob6821 4A 1.349941e-03 miod5707 4A 1.770733e-03 miob6536 4A 1.366732e-03 miod3494 4A 1.774182e-03 seoa7249 4A 1.384501e-03 seoa5465 4A 1.788591e-03 fcrb8187 4A 1.386656e-03 fcrb9147 4A 1.79589e-03 ncrl550 4A 1.389193e-03 seoa2639 4A 1.804717e-03 fcrc0094 4A 1.402006e-03 fcr4927 4A 1.819364e-03 seob9750 4A 1.412312e-03 seoa6661 4A 1.823921e-03 seoa4305 4A 1.4252e-03 miob5016 4A 1.82416e-03 mioc3726 4A 1.427533e-03 hfcr6468 4A 1.830816e-03 seoa3352 4A 1.432555e-03 fcr5779 4A 1.832202e-03 fcrc5142 4A 1.436449e-03 seoc0098 4A 1.833281e-03 seoa6137 4A 1.452067e-03 ncrO612 4A 1.850714e-03 mioc7744 4A 1.453002e-03 seob2966 4A 1.860222e-03 mioc6260 4A 1.462698e-03 fcr6016 4A 1.867933e-03 ncrc2273 4A 1.462698e-03 fcrb4241 4A 1.867933e-03 ncrc9557 4A 1.464273e-03 hfcr3928 4A 1.867933e-03 hfcr5473 4A 1.86/933e-03 mioa9717 4A 2.317623e-03 mioa0380 4A 1.867933e-03 ncrbl373 4A 2.317623e-03 ncr4550 4A 1.867933e-03 ncrbl956 4A 2.317623e-03 seoa0600 4A 1.867933e-03 ncrcO846 4A 2.317623e-03 seoa6197 4A 1.867933e-03 seoal036 4A 2.317623e~03 seoa7178 4A 1.867933e-03 seoa6152 4A 2.317623e-03 seoa7459 4A 1.867933e-03 seoa7474 4A 2.317623e-03 seob2658 4A 1.867933e-03 seob0201 4A 2.317623e-03 seob2987 4A 1.867933e-03 seobl008 4A 2.317623e-03 seob4030 4A 1.867933e-03 seobl383 4A 2.317623e-03 seob4108 4A 1.867933e-03 seob3064 4A 2.317623e-03 fcrb2813 4A 1.878952e-03 seob3169 4A 2.317623e-03 fcrcO591 4A 1.880047e-03 seob4209 4A 2.317623e-03 mioc5664 4A 1.880047e-03 seob6836 4A 2.317623e-03 ncr6072 4A 1.880047e-03 mioa2447 4A 2.335891e-03 seob5562 4A 1.880047e-03 seob5458 4A 2.351371e-03 seoc3515 4A 1.880047e-03 seoa0913 4A 2.365506e-03 mioa4548 4A 1.882387e-03 seoa2162 4A 2.367662e-03 miod0441 4A 1.888471e-03 miobl789 4A 2.370948e-03 seob0321 4A 1.899642e-03 fcrO272 4A 2.376722e-03 fcrb5164 4A 1.933006e-03 fcrc3048 4A 2.376722e-03 ncr0075 4A 1.981513e-03 mioa0909 4A 2.376722e-03 fcrb5751 4A 1.985967e-03 mioa5773 4A 2.376722e-03 miob4956 4A 1.994783e-03 seoa4460 4A 2.376722e-03 fcrO529 4A 2.00776e-03 seocl996 4A 2.376722e-03 miod6324 4A 2.008182e-03 seoa0070 4A 2.383995e-03 mioc0852 4A 2.013387e-03 mioa8864 4A 2.389963e-03 fcrb8901 4A 2.028624e-03 fcrb6990 4A 2.397748e-03 seobl385 4A 2.035324e-03 miob6988 4A 2.414644e-03 seob5319 4A 2.037611e-03 fcrc0604 4A 2.41509e-03 miob4058 4A 2.055915e-03 miocOlβl 4A 2.421189e-03 fcrb7339 4A 2.061469e-03 fcrb6191 4A 2.453588e-03 mioal276 4A 2.063329e-03 ncr6142 4A 2.463109e-03 miob3696 4A 2.07018e-03 fcr5625 4A 2.470811e-03 ncrc6795 4A 2.10605e-03 miob4743 4A 2.4917e-03 fcrb4333 4A 2.115552e-03 ncrc5150 4A 2.514664e-03 fcrcO695 4A 2.115552e-03 hfcrl914 4A 2.51783e-03 seob9960 4A 2.115552e-03 miob0974 4A 2.55067e-03 miob4055 4A 2.123865e-03 ncrO238 4A 2.554407e-03 mioa0204 4A 2.150798e-03 seoa6393 4A 2.560261e-03 miob8609 4A 2.150798e-03 seoa8300 4A 2.561393e-03 ncr7082 4A 2.150798e-03 seoa0064 4A 2.575945e-03 seoa3670 4A 2.150798e-03 inioa0494 4A 2.576066e-03 πιioa6913 4A 2.17648e-03 mioa4667 4A 2.594704e-03 hfcrl419 4A 2.190823e-03 mioc3206 4A 2.606318e-03 seobl319 4A 2.198025e-03 ncrb4390 4A 2.608575e-03 mioblll5 4A 2.207063e-03 seob3303 4A 2.613533e-03 seoa2957 4A 2.209613e-03 miocl995 4A 2.618833e-03 ncrO634 4A 2.224026e-03 seoa0003 4A 2.632563e-03 fcr0056 4A 2.244042e-03 miob7105 4A 2.652256e-03 mioa7140 4A 2.255824e-03 miob8146 4A 2.652256e-03 mioal285 4A 2.276518e-03 hfcrllOl 4A 2.652382e-03 mioa6721 4A 2.277105e-03 rnioa6093 4A 2.665895e-03 fcr2164 4A 2.280497e-03 seocl234 4A 2.665895e-03 fcr2940 4A 2.297728e-03 fcrbl750 4A 2.666763e-03 fcrb9959 4A 2.302145e-03 fcrc4390 4A 2.666763e-03 seob5778 4A 2.302145e-03 mioc7444 4A 2.666763e-03 fcrb5918 4A 2.304441e-03 ncrc9867 4A 2.672208e-03 mioc2166 4A 2.31388e-03 fcrb2592 4A 2.719238e-03 fcr2167 4A 2.317623e-03 miob2700 4A 2.726689e-03 fcr4622 4A 2.317623e-03 mioal324 4A 2.738763e-03 fcr6931 4A 2.317623e-03 fcr4699 4A 2.745569e-03 fcrb5422 4A 2.317623e-03 seoa6344 4A 2.775358e-03 mioa3812 4A 2.317623e-03 mioa2327 4A 2.800964e-03 τιioB8"932 "4"A-' - 2T8T0O83e-O3 mioc2094 4A 3.160126e-03 ncrc4808 4A 2.829236e-03 seob3170 4A 3.199615e-03 miob3411 4A 2.831754e-03 ncrc3840 4A 3.203915e-03 seoa5444 4A 2.852962e-03 ncrcOlOl 4A 3.204732e-03 fcrb9269 4A 2.853454e-03 mioa8851 4A 3.205248e-03 seob4807 4A 2.856307e-03 hfcr0750 4A 3.235107e-03 fcr2160 4A 2.857258e-03 seoa6304 4A 3.235153e-03 fcr2958 4A 2.857258e-03 miob8320 4A 3.241377e-03 fcr7004 4A 2.857258e-03 fcrb7616 4A 3.253815e-03 hfcr3149 4A 2.857258e-03 miob3461 4A 3.253815e-03 hfcr4114 4A 2.857258e-03 fcrb3515 4A 3.308692e-03 hfcr6700 4A 2.857258e-03 fcrl305 4A 3.317135e-03 hfcr9549 4A 2.857258e-03 miob8711 4A 3.324589e-03 mioal473 4A 2.857258e-03 fcrbl689 4A 3.33795e-03 mioa7323 4A 2.857258e-03 seob3869 4A 3.338116e-03 miob2855 4A 2.857258e-03 fcrb4003 4A 3.338435e-03 ncrO132 4A 2.857258e-03 fcrb9420 4A 3.338435e-03 ncr2484 4A 2.857258e-03 fcrclO19 4A 3.338435e-03 ncr4539 4A 2.857258e-03 hfcr6640 4A 3.338435e-03 ncrb2091 4A 2.857258e-03 ncrO335 4A 3.338435e-03 ncrb3585 4A 2.857258e-03 ncrO547 4A 3.338435e-03 ncrb6453 4A 2.857258e-03 seoa3761 4A 3.338435e-03 ncrcO292 4A 2.857258e-03 seocl264 4A 3.3522e-03 ncrc5553 4A 2.857258e-03 fcrc6566 4A 3.377492e-03 seoa0172 4A 2.857258e-03 seobl906 4A 3.378608e-03 seoa0511 4A 2.857258e-03 ncrb7600 4A 3.419412e-03 seoal263 4A 2.857258e-03 ncr0847 4A 3.475969e-03 seoa2428 4A 2.857258e-03 fcr6881 4A 3.501009e-03 seoa4017 4A 2.857258e-03 hfcr5691 4A 3.501009e-03 seoa4264 4A 2.857258e-03 hfcr6716 4A 3.501009e-03 seoa7295 4A 2.857258e-03 mioal427 4A 3.501009e-03 seoa9814 4A 2.857258e-03 mioa8998 4A 3.501009e-03 seob0418 4A 2.857258e-03 miob4793 4A 3.501009e-03 seob0810 4A 2.857258e-03 ncrl802 4A 3.501009e-03 seob4039 4A 2.857258e-03 ncr3713 4A 3.501009e-03 seob5213 4A 2.857258e-03 ncrbl802 4A 3.501009e-03 seob5441 4A 2.857258e-03 ncrb6087 4A 3.501009e-03 ncrc2119 4A 2.863581e-03 ncrc3536 4A 3.501009e-03 ncrc5162 4A 2.885393e-03 seoa0539 4A 3.501009e-03 hfcrl671 4A 2.887323e-03 seoa0799 4A 3.501009e-03 mioa4196 4A 2.903424e-03 seoa0844 4A 3.501009e-03 seoa3245 4A 2.907675e-03 seoa2442 4A 3.501009e-03 miob3477 4A 2.939546e-03 seoa2641 4A 3.501009e-03 seoc4416 4A 2.952743e-03 seoa3007 4A 3.501009e-03 fcrc6826 4A 2.964202e-03 seoa8610 4A 3.501009e-03 fcrb4409 4A 2.985575e-03 seob0478 4A 3.501009e-03 fcrcO396 4A 2.985575e-03 seobl426 4A 3.501009e-03 miod2635 4A 2.985575e-03 seob2803 4A 3.501009e-03 ncrcO457 4A 2.985575e-03 seob5012 4A 3.501009e-03 seoa8388 4A 2.985575e-03 fcrb6785 4A 3.51357e-03 seob6558 4A 2.985575e-03 miob9052 4A 3.544872e-03 seob8693 4A 2.985575e-03 seobl023 4A 3.566451e-03 seob9645 4A 2.985575e-03 miod2128 4A 3.574785e-03 seoc3980 4A 2.985575e-03 fcr5176 4A 3.587181e-03 seob9241 4A 2.99238e-03 fcrb4599 4A 3.597168e-03 miod6731 4A 3.045051e-03 fcrc6976 4A 3.597168e-03 fcrc2948 4A 3.06001e-03 ncrbl515 4A 3.597168e-03 miob4322 4A 3.075941e-03 ncrc0090 4A 3.597168e-03 fcrbl578 4A 3.077893e-03 ncrc9727 4A 3.597168e-03 fcrb5416 4A 3.077893e-03 mioc0899 4A 3.59844e-03 miod4348 4A 3.077893e-03 mioc3788 4A 3.62389e-03 mioa2536 4A 3.081507e-03 ncr2717 4A 3.62389e-03 fcrc0430 4A 3.131979e-03 hfcr0370 4A 3.630372e-03 mioa4738 4A 3.14495e-03 hfcr3180 4A 3.630537e-03 m.IδbΑδθT' '■""'""4'A' T."6Vq"rb2-e-U3 mxoal531 4A 4.264609e-03 seoc0276 4A 3.708025e-03 ncr3396 4A 4.264609e-03 fcrb5645 4A 3.72733e-03 ncrb6432 4A 4.264609e-03 fcrc2099 4A 3.72733e-03 ncrcO863 4A 4.264609e-03 mioa6621 4A 3.72733e-03 ncrc3141 4A 4.264609e-03 miob2375 4A 3.72733e-03 seoa0457 4A 4.264609e-03 seob0817 4A 3.72733e-03 seoa8239 4A 4.264609e-03 seob2081 4A 3.72733e-03 seob0031 4A 4.264609e-03 seob3731 4A 3.72733e-03 seob4122 4A 4.264609e-03 seob4191 4A 3.72733e-03 fcrcO654 4A 4.283627e-03 ncrc3045 4A 3.738416e-03 fcrb4391 4A 4.301677e-03 miob6437 4A 3.759775e-03 seoc7281 4A 4.325008e-03 ncrc9217 4A 3.765962e-03 miob7922 4A 4.380163e-03 seoa3852 4A 3.792684e-03 miodl389 4A 4.380163e-03 seocl348 4A 3.792955e-03 ncr9337 4A 4.380163e-03 seoc4288 4A 3.815133e-03 ncrc4296 4A 4.380163e-03 seob4896 4A 3.845442e-03 fcrb3967 4A 4.387834e-03 fcrb3244 4A 3.919128e-03 miob9905 4A 4.397546e-03 fcrb6834 4A 3.919128e-03 mioc6341 4A 4.410268e-03 hfcr2314 4A 3.926347e-03 fcrcl834 4A 4.412427e-03 fcrl337 4A 3.927065e-03 ncr6943 4A 4.466962e-03 fcrb3073 4A 3.947476e-03 mioa8952 4A 4.498195e-03 miob7638 4A 3.947803e-03 seoa9870 4A 4.498195e-03 seob9649 4A 3.956049e-03 fcrc0180 4A 4.507236e-03 ncr2792 4A 3.970785e-03 seoa4452 4A 4.518564e-03 mioa0246 4A 3.971832e-03 fcrcO166 4A 4.523453e-03 ncrclO49 4A 3.971832e-03 fcrl557 4A 4.531571e-03 ncr8893 4A 3.971909e-03 fcrb2317 4A 4.55536e-03 ncrb7329 4A 3.975596e-03 seob8300 4A 4.569266e-03 mioa0328 4A 4.030655e-03 fcrcl745 4A 4.587076e-03 seob0831 4A 4.04796e-03 seob0752 4A 4.588706e-03 fcrc5937 4A 4.057973e-03 mioal626 4A 4.607822e-03 seob6133 4A 4.064124e-03 ncr7661 4A 4.609072e-03 miob3471 4A 4.066271e-03 fcrb3017 4A 4.625568e-03 fcrb5037 4A 4.068542e-03 fcrb3219 4A 4.625568e-03 mioc8434 4A 4.07859e-03 miocl440 4A 4.625568e-03 fcrb6062 4A 4.105596e-03 ncrO438 4A 4.625568e-03 miob3426 4A 4.107551e-03 ncrc4798 4A 4.625568e-03 seoc3883 4A 4.107669e-03 seoa8979 4A 4.625568e-03 seob4117 4A 4.111207e-03 seoc2031 4A 4.625568e-03 miob5675 4A 4.125492e-03 seob0885 4A 4.636325e-03 fcrb3225 4A 4.150986e-03 miodl542 4A 4.642122e-03 fcrb7240 4A 4.155298e-03 fcrc6542 4A 4.660162e-03 fcrb9464 4A 4.155298e-03 mioc7561 4A 4.660162e-03 fcrcO379 4A 4.155298e-03 hfcr7855 4A 4.667117e-03 mioa6738 4A 4.155298e-03 seoa3863 4A 4.684362e-03 ncrO531 4A 4.155298e-03 miob9393 4A 4.704854e-03 seoa4783 4A 4.155298e-03 miob5708 4A 4.754388e-03 seoc2253 4A 4.155298e-03 miod2888 4A 4.817047e-03 seoc6703 4A 4.155298e-03 mioa9792 4A 4.824649e-03 fcrc2613 4A 4.15673e-03 miod7440 4A 4.824649e-03 seob2259 4A 4.15777e-03 ncr8153 4A 4.833622e-03 fcrc6916 4A 4.157873e-03 hfcr4423 4A 4.854665e-03 mioc6274 4A 4.205108e-03 seoa5742 4A 4.870803e-03 seob6879 4A 4.22005e-03 fcr7042 4A 4.873697e-03 miob7109 4A 4.239511e-03 fcr4984 4A 4.887289e-03 fcrb8877 4A 4.261651e-03 fcrb5812 4A 4.900673e-03 ncrb6661 4A 4.262601e-03 mioc4387 4A 4.90272e-03 seoa6621 4A 4.26298e-03 miob9209 4A 4.905985e-03 fcrl833 4A 4.264609e-03 fcrbl731 4A 4.960118e-03 fcr3983 4A 4.264609e-03 miod5698 4A 5.055279e-03 fcrb2596 4A 4.264609e-03 seoc2226 4A 5.062218e-03 hfcrO285 4A 4.264609e-03 ncrb6037 4A 5.071532e-03 hfcrll27 4A 4.264609e-03 miobl493 4A 5.083751e-03 miobδ58"3 4A ' "■5":TI893e-03 mioc8479 4A 5.706928e-03 seoc2336 4A 5.122604e-03 miod7461 4A 5.706928e-03 fcrb2591 4A 5.141567e-03 ncr3527 4A 5.706928e-03 fcrc2040 4A 5.141567e-03 ncr5613 4A 5.706928e-03 miod4342 4A 5.141567e-03 seoa3555 4A 5.706928e-03 seob2221 4A 5.141567e-03 seobl766 4A 5.706928e-03 mioc3958 4A 5.159834e-03 fcrcl660 4A 5.732649e-03 seoa4708 4A 5.163067e-03 ncrc5363 4A 5.753889e-03 fcr4214 4A 5.165418e-03 mioa4135 4A 5.753919e-03 fcrb3298 4A 5.165418e-03 fcr6181 4A 5.76259e-03 fcrb3651 4A 5.165418e-03 ncrbl337 4A 5.76259e-03 fcrb3841 4A 5.165418e-03 mioa3799 4A 5.764693e-03 hfcrO225 4A 5.165418e-03 ncrb6192 4A 5.778573e-03 hfcrO829 4A 5.165418e-03 seoa4174 4A 5.780772e-03 hfcr5150 4A 5.165418e-03 seob2797 4A 5.780772e-03 hfcr6256 4A 5.165418e-03 ncrc4633 4A 5.832721e-03 miobl337 4A 5.165418e-03 seoa4107 4A 5.832721e-03 ncrl235 4A 5.165418e-03 fcrbl320 4A 5.844972e-03 ncr7768 4A 5.165418e-03 seoa8486 4A 5.848414e-03 ncrb3284 4A 5.165418e-03 miod5894 4A 5.886058e-03 seoa0219 4A 5.165418e-03 ncrb7350 4A 5.902871e-03 seoa7608 4A 5.165418e-03 seob9730 4A 5.938749e-03 seob6229 4A 5.165418e-03 fcrc5577 4A 5.939408e-03 seob7658 4A 5.165418e-03 mioa3342 4A 5.993081e-03 seoa8397 4A 5.190451e-03 seoa5382 4A 6.021301e-03 seoa2141 4A 5.238547e-03 ncrb8665 4A 6.032318e-03 seobll97 4A 5.238547e-03 seob0466 4A 6.055754e-03 seoa4023 4A 5.272613e-03 fcrO824 4A 6.069793e-03 hfcr4046 4A 5.282479e-03 hfcrO242 4A 6.069793e-03 miob3131 4A 5.293767e-03 ncrc4531 4A 6.069793e-03 seoa6754 4A 5.293767e-03 ncrc6749 4A 6.069793e-03 mioa8912 4A 5.307914e-03 seoc4505 4A 6.069793e-03 miob9065 4A 5.307914e-03 ncr4118 4A 6.075226e-03 mioc2726 4A 5.307914e-03 ncrc3544 4A 6.143974e-03 seobll87 4A 5.307914e-03 miod2525 4A 6.144214e-03 fcrb6715 4A 5.311191e-03 mioc4895 4A 6.180041e-03 fcrbl684 4A 5.32483e-03 hfcrO517 4A 6.202657e-03 mioc0375 4A 5.32483e-03 hfcr2808 4A 6.206246e-03 ncr3751 4A 5.32483e-03 fcrO712 4A 6.222497e-03 ncrc9117 4A 5.326932e-03 fcr2299 4A 6.222497e-03 mioc0741 4A 5.330299e-03 fcr3823 4A 6.222497e-03 seoa7212 4A 5.357933e-03 fcr5112 4A 6.222497e-03 miodl448 4A 5.361348e-03 fcrb2189 4A 6.222497e-03 seoal065 4A 5.373049e-03 fcrb3704 4A 6.222497e-03 fcrb8092 4A 5.392619e-03 mioa8539 4A 6.222497e-03 seoal653 4A 5.394287e-03 mioa8811 4A 6.222497e-03 miod2025 4A 5.399416e-03 mioa9666 4A 6.222497e-03 fcr4084 4A 5.432797e-03 ncrb2256 4A 6.222497e-03 seob5203 4A 5.45952e-03 seoaOlll 4A 6.222497e-03 miod6038 4A 5.47802e-03 seoa6155 4A 6.222497e-03 miob9176 4A 5.505561e-03 seobl243 4A 6.222497e-03 ncr3778 4A 5.529327e-03 seob4118 4A 6.222497e-03 ncrO615 4A 5.540195e-03 seob5490 4A 6.222497e-03 seoa2819 4A 5.55831e-03 ncrc4757 4A 6.252367e-03 seoa6887 4A 5.55989e-03 mioal370 4A 6.257446e-03 fcrO593 4A 5.561587e-03 fcrc6513 4A 6.273392e-03 seoa3639 4A 5.567231e-03 fcrO224 4A 6.325491e-03 mioa3944 4A 5.57993e-03 fcrl421 4A 6.325491e-03 miod3592 4A 5.595543e-03 fcrb6102 4A 6.325491e-03 ncrc3856 4A 5.595893e-03 mioal891 4A 6.325491e-03 seob2336 4A 5.671489e-03 miodlδll 4A 6.325491e-03 seoa9724 4A 5.679419e-03 miodl942 4A 6.325491e-03 fcr2276 4A 5.706928e-03 miod2323 4A 6.325491e-03 hfcr2295 4A 5.706928e-03 ncrb5197 4A 6.325491e-03 htfc32Y6 """'"ϊA' "•""β";3-25'49Te-03 ncrc8873 4A 7.378867e-03 seoa4739 4A 6.325491e-03 fcr0018 4A 7.440983e-03 seoc0312 4A 6.325491e-03 seoa3108 4A 7.441703e-03 miobl506 4A 6.366876e-03 ncrc6439 4A 7.443178e-03 fcrb5317 4A 6.373009e-03 fcrl981 4A 7.456649e-03 miod7212 4A 6.387023e-03 fcr6592 4A 7.456649e-03 mioc6360 4A 6.399833e-03 fcrbl962 4A 7.456649e-03 fcrb6141 4A 6.401977e-03 mioa0582 4A 7.456649e-03 mioal701 4A 6.401977e-03 miob6456 4A 7.456649e-03 miob4037 4A 6.401977e-03 ncr8693 4A 7.456649e-03 miod6041 4A 6.401977e-03 ncrb4319 4A 7.456649e-03 seob3356 4A 6.426576e-03 ncrb6818 4A 7.456649e-03 ncrc6888 4A 6.466221e-03 ncrb8820 4A 7.456649e-03 fcrc2536 4A 6.476377e-03 ncrc2018 4A 7.456649e-03 fcrl844 4A 6.492241e-03 ncrc3468 4A 7.456649e-03 fcr6573 4A 6.501956e-03 seoa2363 4A 7.456649e-03 ncrb6073 4A 6.504705e-03 seoa8177 4A 7.456649e-03 fcrbl721 4A 6.507726e-03 seob0263 4A 7.456649e-03 miocl060 4A 6.545993e-03 seob4075 4A 7.456649e-03 fcr5339 4A 6.551892e-03 seob4835 4A 7.456649e-03 ncr3123 4A 6.581747e-03 ncrc3358 4A 7.463658e-03 hfcrO731 4A 6.583534e-03 ncrb4339 4A 7.48001e-03 ncr3684 4A 6.635942e-03 miob2227 4A 7.500266e-03 fcr2530 4A 6.689902e-03 miod7270 4A 7.513006e-03 miodl331 4A 6.733995e-03 seoa6412 4A 7.513354e-03 seoa5302 4A 6.799372e-03 ncr9108 4A 7.517374e-03 seoa2962 4A 6.824728e-03 fcr5354 4A 7.519432e-03 ncrbO696 4A 6.838407e-03 miob4803 4A 7.548582e-03 ncrcl765 4A 6.853461e-03 ncr5713 4A 7.549524e-03 miod7418 4A 6.879028e-03 ncr6335 4A 7.551784e-03 mioa8646 4A 6.88399e-03 fcrb4415 4A 7.552397e-03 hfcrO521 4A 6.893985e-03 mioa2013 4A 7.553193e-03 ncr3118 4A 6.909891e-03 seobl093 4A 7.559492e-03 seob7404 4A 6.975311e-03 mioa2851 4A 7.597834e-03 seoal584 4A 6.981622e-03 ncr9549 4A 7.605434e-03 fcrb2809 4A 6.989881e-03 ncrl912 4A 7.616232e-03 miob3531 4A 6.997741e-03 ncr6408 4A 7.649128e-03 hfcrO383 4A 6.997808e-03 miob3308 4A 7.656241e-03 fcrlO68 4A 7.001313e-03 mioa9429 4A 7.677999e-03 fcrc3704 4A 7.001313e-03 fcrb8927 4A 7.686197e-03 mioal775 4A 7.001313e-03 fcrb9649 4A 7.686197e-03 miob8214 4A 7.001313e-03 fcrc2231 4A 7.686197e-03 seob5773 4A 7.001313e-03 mioa3842 4A 7.686197e-03 seocl235 4A 7.001313e-03 ncrc6127 4A 7.686197e-03 mioa9649 4A 7.004996e-03 fcrcO651 4A 7.716354e-03 mioa3395 4A 7.018735e-03 fcrb9569 4A 7.73867e-03 mioc7421 4A 7.018735e-03 fcrc4734 4A 7.73867e-03 ncrb4538 4A 7.018735e-03 mioc2471 4A 7.73867e-03 seob4726 4A 7.018735e-03 ncrcl751 4A 7.73867e-03 hfcr3436 4A 7.022918e-03 seoal736 4A 7.73867e-03 fcrl633 4A 7.036908e-03 seoa4324 4A 7.73867e-03 miod4720 4A 7.094158e-03 seob3887 4A 7.73867e-03 ncrc5088 4A 7.096473e-03 seob8602 4A 7.73867e-03 ncrc2133 4A 7.104324e-03 mioa3379 4A 7.75818e-03 seob4752 4A 7.112274e-03 ncr6141 4A 7.805448e-03 seob0253 4A 7.113413e-03 seob0763 4A 7.806702e-03 hfcr8908 4A 7.12111e-03 miob8947 4A 7.816384e-03 miob8515 4A 7.12261e-03 fcrb5039 4A 7.832165e-03 mioc0824 4A 7.15595e-03 ncr9975 4A 7.832165e-03 seoc2030 4A 7.182844e-03 ncrb5535 4A 7.832165e-03 fcrb3483 4A 7.24584e-03 mioal532 4A 7.894273e-03 seob5044 4A 7.256079e-03 seobl052 4A 7.907861e-03 ncrO518 4A 7.375395e-03 seob0038 4A 7.942732e-03 hfcr6486 4A 7.375397e-03 mioc0090 4A 7.966285e-03 'inlό'al49"4 "' fk' ' %~.OUXlTTe-03 ncrb8817 4A 8.890462e-03 fcrb4275 4A 8.016752e-03 ncrcl999 4A 8.890462e-03 seoa9729 4A 8.054693e-03 ncrc4903 4A 8.890462e-03 seoc3463 4A 8.071299e-03 ncrc5179 4A 8.890462e-03 hfcrβllO 4A 8.079562e-03 seoa0256 4A 8.890462e-03 seoc0596 4A 8.12048e-03 seoa0549 4A 8.890462e-03 seoa7442 4A 8.198259e-03 seoa3144 4A 8.890462e-03 fcrO6O4 4A 8.225459e-03 seoa8912 4A 8.890462e-03 hfcr2789 4A 8.270865e-03 seoa9644 4A 8.902262e-03 fcrb2784 4A 8.281158e-03 seob2131 4A 8.945684e-03 ncrc3453 4A 8.291186e-03 miocl638 4A 8.998678e-03 ncrb6949 4A 8.33639e-03 ncr3811 4A 9.023625e-03 ncrc5813 4A 8.336956e-03 seob2185 4A 9.023625e-03 seoa7286 4A 8.352358e-03 seoa8902 4A 9.03728e-03 mioc6211 4A 8.361946e-03 ncrO251 4A 9.045048e-03 fcrb5204 4A 8.383698e-03 mioa4484 4A 9.084475e-03 mioc3906 4A 8.39011e-03 fcrb9253 4A 9.091981e-03 mioa2156 4A 8.3927e-03 fcrb9659 4A 9.121639e-03 miob6372 4A 8.3927e-03 miod6032 4A 9.121639e-03 ncrb8285 4A 8.404129e-03 seob3151 4A 9.164302e-03 fcrb4016 4A 8.407716e-03 fcr2939 4A 9.185386e-03 hfcr3494 4A 8.407716e-03 mioa5097 4A 9.186801e-03 seoal559 4A 8.407716e-03 mioa5461 4A 9.186801e-03 seoa5429 4A 8.407716e-03 ncr3306 4A 9.186801e-03 fcrc7180 4A 8.416489e-03 fcrc5846 4A 9.224686e-03 mioc4290 4A 8.442771e-03 seoa6695 4A 9.246123e-03 fcrb5928 4A 8.465835e-03 mioa5836 4A 9.297065e-03 ncr8827 4A 8.500115e-03 seoa3359 4A 9.339094e-03 mioa8471 4A 8.501059e-03 mioal953 4A 9.364811e-03 miob2668 4A 8.520984e-03 seoa0470 4A 9.381493e-03 miob5646 4A 8.522582e-03 fcrO999 4A 9.416219e-03 fcrb6725 4A 8.542063e-03 mioa2185 4A 9.416219e-03 mioc7370 4A 8.542063e-03 mioa8557 4A 9.416219e-03 miod0340 4A 8.542063e-03 mioc8474 4A 9.416219e-03 seoa9060 4A 8.542063e-03 miod4857 4A 9.416219e-03 seob5862 4A 8.59472e-03 ncr4656 4A 9.416219e-03 seoa0396 4A 8.622683e-03 seob3007 4A 9.416219e-03 seob2685 4A 8.638593e-03 seob9480 4A 9.416219e-03 seob5886 4A 8.655309e-03 seob8255 4A 9.416834e-03 fcrc7240 4A 8.681552e-03 seob5004 4A 9.45308e-03 ncr3035 4A 8.686913e-03 miob9201 4A 9.466193e-03 mioal976 4A 8.695473e-03 mioa4721 4A 9.472533e-03 ncrc4000 4A 8.697789e-03 seob0703 4A 9.515566e-03 ncrb8203 4A 8.703157e-03 seob6413 4A 9.517365e-03 seoa8556 4A 8.703157e-03 fcrb6460 4A 9.520838e-03 fcrbl529 4A 8.709936e-03 ncrc2472 4A 9.520838e-03 seoa4422 4A 8.746669e-03 fcrbδllO 4A 9.527098e-03 mioa6582 4A 8.752864e-03 mioc2369 4A 9.59029e-03 ncr4551 4A 8.805744e-03 seoa5977 4A 9.591902e-03 ncrb8821 4A 8.827587e-03 fcrO787 4A 9.596065e-03 fcrb2331 4A 8.866666e-03 mioc4782 4A 9.602574e-03 fcrb5439 4A 8.866666e-03 ncrc3598 4A 9.64688e-03 ncrb8189 4A 8.866666e-03 ncrO213 4A 9.687262e-03 seob8386 4A 8.866666e-03 fcrbl337 4A 9.722232e-03 seoc4380 4A 8.866666e-03 seob8311 4A 9.81703e-03 fcr3538 4A 8.890462e-03 mioa8858 4A 9.827977e-03 fcrbl547 4A 8.890462e-03 seoa0469 4A 9.846641e-03 fcrb2223 4A 8.890462e-03 miob9185 4A 9.849754e-03 hfcr2803 4A 8.890462e-03 mioc2173 4A 9.86782e-03 hfcr3500 4A 8.890462e-03 seoa7266 4A 9.86782e-03 mioa2268 4A 8.890462e-03 seob5500 4A 9.89313e-03 mioa7015 4A 8.890462e-03 rαiod7421 4A 9.917289e-03 mioa9060 4A 8.890462e-03 ncr506β 4A 9.921102e-03 ncrl526 4A 8.890462e-03 miobll39 4A 9.933523e-03 ffcrc888'ϊ ~••'■ *~*gjζ - ■*~SC9'5'7S57e-03 seob7946 4A 0.01 seoc2027 4A 9.989765e-03 fcrb2713 4A 0.01 fcr4832 4A 0.01 miob8052 4A 0.01 ncrc9055 4A 0.01 miob8012 4A 0.01 seoa6358 4A 0.01 fcr7403 4A 0.01 seocl948 4A 0.01 ncr3436 4A 0.01 seoa0949 4A 0.01 fcrb4739 4A 0.01 miob8373 4A 0.01 miod3591 4A 0.01 miod4938 4A 0.01 ncrc2685 4A 0.01 seob3520 4A 0.01 ncrb2558 4A 0.01 miod7337 4A 0.01 mioc3127 4A 0.01 ncr2892 4A 0.01 miod0443 4A 0.01 fcrb4378 4A 0.01 fcr4722 4A 0.01 seoc3993 4A 0.01 seoc6696 4A 0.01 seob3076 4A 0.01 ncrl876 4A 0.01 ncrb8102 4A 0.01 ncrc2670 4A 0.01 fcrb3519 4A 0.01 seoc2246 4A 0.01 fcr3957 4A 0.01 hfcr5220 4A 0.01 mioa9788 4A 0.01 miod0625 4A 0.01 seob3464 4A 0.01 fcrb7321 4A 0.01 seoa0135 4A 0.01 miob4064 4A 0.01 ncr2304 4A 0.01 miob9284 4A 0.01 hfcr4462 4A 0.01 seoa0464 4A 0.01 fcrb3306 4A 0.01 seobl009 4A 0.01 fcr2196 4A 0.01 mioc0734 4A 0.01 fcr2573 4A 0.01 seob0409 4A 0.01 fcrb7699 4A 0.01 seoa2041 4A 0.01 fcrc3229 4A 0.01 fcrb7392 4A 0.01 mioal882 4A 0.01 ncrcO539 4A 0.01 mioa5951 4A 0.01 mioc8682 4A 0.01 ncrb0303 4A 0.01 mioc4247 4A 0.01 seoa9930 4A 0.01 hfcrlllδ 4A 0.01 fcrb5720 4A 0.01 ncr7852 4A 0.01 ncr6920 4A 0.01 hfcr4485 4A 0.01 hfcr0489 4A 0.01 fcr5407 4A 0.01 fcrcll81 4A 0.01 mioal944 4A 0.01 seob0339 4A 0.01 mioa2395 4A 0.01 seoa4670 4A 0.01 mioa6418 4A 0.01 miob3396 4A 0.01 miob4668 4A 0.01 miod4083 4A 0.01 ncrc9368 4A 0.01 miocl205 4A 0.01 seoa8870 4A 0.01 seoc2205 4A 0.01 miob0202 4A 0.01 fcrO768 4A 0.01 ncr2966 4A 0.01 fcrll73 4A 0.01 fcrbl729 4A 0.01 fcr5288 4A 0.01 fcrO187 4A 0.01 fcr5755 4A 0.01 fcrbl687 4A 0.01 fcrb0007 4A 0.01 mioal687 4A 0.01 fcrb4252 4A 0.01 mioa2522 4A 0.01 fcrb5296 4A 0.01 mioc3618 4A 0.01 hfcr6164 4A 0.01 miod4407 4A 0.01 hfcr6336 4A 0.01 ncrbl420 4A 0.01 mioblOlδ 4A 0.01 seob7200 4A 0.01 ncrO527 4A 0.01 fcrb4367 4A 0.01 ncrb3003 4A 0.01 fcrb6896 4A 0.01 ncrb6327 4A 0.01 ncrcO393 4A 0.01 ncrb8689 4A 0.01 fcrb4238 4A 0.01 ncrc5207 4A 0.01 hfcr3441 4A 0.01 seoa4167 4A 0.01 seoa9889 4A 0.01 seoa8597 4A 0.01 seoc2144 4A 0.01 seob0298 4A 0.01 seob4197 4A 0.01 seob0745 4A 0.01 fcrb2325 4A 0.01 seob3451 4A 0.01 seoa4595 4A 0.01 seob4992 4A 0.01 mioa5131 4A 0.01 seob5645 4A 0.01 ncr3172 4A 0.01 «,,,μ ,._^. „„„ •Se&B37'4''?"'" ■• O".Ol miob7135 4A 0.01 fcr3620 4A 0.01 seob8388 4A 0.01 fcr4965 4A 0.01 ncr6401 4A 0.01 fcrc3942 4A 0.01 ncr7097 4A 0.01 mioc7471 4A 0.01 fcr6493 4A 0.01 ncr5055 4A 0.01 mioc6426 4A 0.01 ncr5488 4A 0.01 seoa5473 4A 0.01 fcrc5797 4A 0.01 seob8355 4A 0.01 ncrbl438 4A 0.01 ncrbO864 4A 0.01 ncrb8343 4A 0.01 hfcr6677 4A 0.01 fcrb6728 4A 0.01 mioc7818 4A 0.01 seoc3250 4A 0.01 mioa0891 4A 0.01 seoa4727 4A 0.01 ncrb8437 4A 0.01 miod3366 4A 0.01 mioa2413 4A 0.01 seoa2381 4A 0.01 seobl632 4A 0.01 seob7729 4A 0.01 seoa6867 4A 0.01 seoa3895 4A 0.01 fcrO842 4A 0.01 mioal388 4A 0.01 seoc3277 4A 0.01 seob3734 4A 0.01 ncrcl941 4A 0.01 seob7082 4A 0.01 ncrc2949 4A 0.01 fcrb2635 4A 0.01 seob3141 4A 0.01 mioc4366 4A 0.01 miod7375 4A 0.01 ncrclO29 4A 0.01 fcrb2208 4A 0.01 fcrc2326 4A 0.01 ncrlO55 4A 0.01 hfcr3592 4A 0.01 fcrc6582 4A 0.01 miob2067 4A 0.01 fcr2102 4A 0.01 fcrbl428 4A 0.01 fcrb4929 4A 0.01 mioc3930 4A 0.01 mioc4009 4A 0.01 fcrb3237 4A 0.01 miod6044 4A 0.01 seoc2173 4A 0.01 ncrcl593 4A 0.01 fcrblO17 4A 0.01 seob4539 4A 0.01 hfcrl202 4A 0.01 seob6156 4A 0.01 hfcr2966 4A 0.01 seob4766 4A 0.01 hfcr3022 4A 0.01 seoa9287 4A 0.01 hfcr9290 4A 0.01 seob5465 4A 0.01 mioa0308 4A 0.01 miob5810 4A 0.01 mioa3786 4A 0.01 mioa5202 4A 0.01 mioa5294 4A 0.01 fcrbl539 4A 0.01 mioa5326 4A 0.01 hfcr3453 4A 0.01 ncr8542 4A 0.01 fcr6150 4A 0.01 ncrcO856 4A 0.01 miob0939 4A 0.01 ncrc4226 4A 0.01 fcrb8855 4A 0.01 ncrc4794 4A 0.01 miob5916 4A 0.01 seoal582 4A 0.01 mioa4064 4A 0.01 seoal883 4A 0.01 fcr6902 4A 0.01 seoa4630 4A 0.01 hfcrO675 4A 0.01 seoa5894 4A 0.01 mioc2905 4A 0.01 seoa7626 4A 0.01 fcrb4725 4A 0.01 seobl646 4A 0.01 mioa2478 4A 0.01 hfcrO776 4A 0.01 ncrcl665 4A 0.01 fcr4582 4A 0.01 seocl664 4A 0.01 fcrb7685 4A 0.01 seoa5785 4A 0.01 hfcr2516 4A 0.01 miod2330 4A 0.01 mioa6999 4A 0.01 fcrb7388 4A 0.01 miob5752 4A 0.01 seoa3251 4A 0.01 miod0686 4A 0.01 fcrb5675 4A 0.01 miod3600 4A 0.01 fcrb7880 4A 0.01 ncrcO249 4A 0.01 fcrcl607 4A 0.01 seoa9855 4A 0.01 mioa9258 4A 0.01 seob5064 4A 0.01 miob9163 4A 0.01 seob9756 4A 0.01 seoa3752 4A 0.01 seob9818 4A 0.01 seob0344 4A 0.01 miob8425 4A 0.01 miob0931 4A 0.01 seob2816 4A 0.01 seob0228 4A 0.01 "ffiiδάirgg 4A ■QVϋ'l miod6658 4A 0.01 seoa2391 4A 0.01 fcrO776 4A 0.01 mioa6476 4A 0.01 fcr4012 4A 0.01 miob2593 4A 0.01 fcr6969 4A 0.01 seoa3274 4A 0.01 fcrb4988 4A 0.01 mioc0999 4A 0.01 fcrc3025 4A 0.01 seob0641 4A 0.01 ncr4384 4A 0.01 fcrb5087 4A 0.01 seoa0823 4A 0.01 fcrb5536 4A 0.01 seob2149 4A 0.01 ncrb4022 4A 0.01 seob5558 4A 0.01 seobl574 4A 0.01 seob6177 4A 0.01 ncr3402 4A 0.01 seoc2220 4A 0.01 fcrc7056 4A 0.01 mioc2152 4A 0.01 mioa7299 4A 0.01 fcrb7453 4A 0.01 seob3751 4A 0.01 hfcr3834 4A 0.01 miob4238 4A 0.01 mioa6772 4A 0.01 mioc8694 4A 0.01 miob4876 4A 0.01 seoa6106 4A 0.01 ncrbO653 4A 0.01 seob8807 4A 0.01 seob9353 4A 0.01 fcrb7751 4A 0.01 seoal992 4A 0.01 seobl399 4A 0.01 miod6024 4A 0.01 seoa0388 4A 0.01 fcrc2969 4A 0.01 mioal353 4A 0.01 ncr0740 4A 0.01 fcrb7084 4A 0.01 ncrblO85 4A 0.01 ncr7839 4A 0.01 ncrb6581 4A 0.01 ncr5939 4A 0.01 ncrc6941 4A 0.01 mioa6840 4A 0.01 seoa0145 4A 0.01 ncrc5758 4A 0.01 seob0716 4A 0.01 fcrO557 4A 0.01 fcrc7010 4A 0.01 seob3455 4A 0.01 mioa6268 4A 0.01 seob7444 4A 0.01 ncrO429 4A 0.01 miob6797 4A 0.01 fcrc6382 4A 0.01 ncrb3936 4A 0.01 mioal947 4A 0.01 crO289 4A 0.01 fcrb2380 4A 0.01 fcrO469 4A 0.01 mioc0911 4A 0.01 fcr3005 4A 0.01 fcrc6413 4A 0.01 fcr3323 4A 0.01 mioa6428 4A 0.01 fcr4057 4A 0.01 fcrb9499 4A 0.01 fcr6314 4A 0.01 hfcr5023 4A 0.01 fcrbl657 4A 0.01 mioa8987 4A 0.01 fcrbl720 4A 0.01 miob9700 4A 0.01 fcrb2308 4A 0.01 ncrc3908 4A 0.01 fcrb4375 4A 0.01 seoa6638 4A 0.01 hfcr6509 4A 0.01 mioa4944 4A 0.01 hfcr7968 4A 0.01 ncrO679 4A 0.01 mioal657 4A 0.01 fcr0990 4A 0.01 mioa3888 4A 0.01 fcrb6087 4A 0.01 mioa4321 4A 0.01 miocl354 4A 0.01 miob5713 4A 0.01 miod5159 4A 0.01 ncr7345 4A 0.01 ncr9956 4A 0.01 ncrb3942 4A 0.01 ncrc2686 4A 0.01 ncrcll41 4A 0.01 ncrcβOOO 4A 0.01 ncrc2928 4A 0.01 seob9124 4A 0.01 seoalllδ 4A 0.01 fcrb2485 4A 0.01 seoa3287 4A 0.01 hfcr4444 4A 0.01 seobl746 4A 0.01 seoal789 4A 0.01 seob2139 4A 0.01 fcrb5702 4A 0.01 seob4145 4A 0.01 ncrb3541 4A 0.01 seob4213 4A 0.01 fcrcl689 4A 0.01 seob2959 4A 0.01 miob7092 4A 0.01 ncrc2763 4A 0.01 hfcr6680 4A 0.01 fcr0860 4A 0.01 hfcr2984 4A 0.01 seobl617 4A 0.01 mioc0669 4A 0.01 mioa4704 4A 0.01 fcrb2979 4A 0.01 'talOb-34'4"3- "~"ϋ. Ul hfcrO511 4A 0.01 hfcr6931 4A 0.01 hfcr2536 4A 0.01 hfcrO476 4A 0.01 hfcr6501 4A 0.01 ncr3614 4A 0.01 mioa0533 4A 0.01 fcrO548 4A 0.01 mioa9316 4A 0.01 fcrc6002 4A 0.01 miob3307 4A 0.01 hfcr2506 4A 0.01 miob3982 4A 0.01 seob4804 4A 0.01 ncrO456 4A 0.01 ncr9596 4A 0.01 ncrcO558 4A 0.01 miob2881 4A 0.01 ncrc2940 4A 0.01 miob9495 4A 0.01 ncrc3416 4A 0.01 ncrb6557 4A 0.01 ncrc5806 4A 0.01 ncrc4586 4A 0.01 seoa0563 4A 0.01 fcrl883 4A 0.01 seoa0926 4A 0.01 fcrb2516 4A 0.01 seoa3489 4A 0.01 fcrb4542 4A 0.01 seob2807 4A 0.01 fcrcO367 4A 0.01 seoa9711 4A 0.01 fcrc6174 4A 0.01 ncrbl456 4A 0.01 mioc0052 4A 0.01 ncr3483 4A 0.01 ncr4408 4A 0.01 seoc0220 4A 0.01 ncrc4188 4A 0.01 ncrb2544 4A 0.01 seoa3633 4A 0.01 seob5927 4A 0.01 seoa5157 4A 0.01 seoa0393 4A 0.01 seob4498 4A 0.01 seoa3422 4A 0.01 seob6030 4A 0.01 seob0879 4A 0.01 seoc3487 4A 0.01 miod3854 4A 0.01 seoal089 4A 0.01 fcrO833 4A 0.01 fcrc5351 4A 0.01 seob3699 4A 0.01 fcrO792 4A 0.01 fcrc0700 4A 0.01 fcr5316 4A 0.01 miod7408 4A 0.01 fcrb4109 4A 0.01 seoc0657 4A 0.01 fcrb6181 4A 0.01 seoc8065 4A 0.01 fcrb8915 4A 0.01 fcrb8664 4A 0.01 miob8698 4A 0.01 ncrbl670 4A 0.01 miob9529 4A 0.01 fcr2842 4A 0.01 ncrc0028 4A 0.01 fcrbl763 4A 0.01 ncrc9412 4A 0.01 mioa2213 4A 0.01 seob0046 4A 0.01 ncrc5653 4A 0.01 seoa0512 4A 0.01 ncrc3434 4A 0.01 ncrc7151 4A 0.01 fcrc0350 4A 0.01 fcrbl834 4A 0.01 mioa2993 4A 0.01 ncr7945 4A 0.01 fcrb9355 4A 0.01 ncrb8239 4A 0.01 fcrc5328 4A 0.01 ncrc4015 4A 0.01 miob5119 4A 0.01 fcrb2926 4A 0.01 mioc5661 4A 0.01 hfcr4028 4A 0.01 seoa5547 4A 0.01 seob0999 4A 0.01 seoa5584 4A 0.01 miob4368 4A 0.01 seoc7018 4A 0.01 seoa9619 4A 0.01 mioc3332 4A 0.01 hfcr5383 4A 0.01 miob3330 4A 0.01 seoa4524 4A 0.01 fcr2986 4A 0.01 miob8816 4A 0.01 fcrc6970 4A 0.01 seob0787 4A 0.01 miob0496 4A 0.01 ncrO664 4A 0.01 mioc0121 4A 0.01 miod2264 4A 0.01 miocl524 4A 0.01 miob6373 4A 0.01 fcrc4157 4A 0.01 fcr7208 4A 0.01 mioa8051 4A 0.01 seoa4587 4A 0.01 miob5855 4A 0.01 seoa8684 4A 0.01 mioc3648 4A 0.01 seob9485 4A 0.01 ncr7239 4A 0.01 fcr4802 4A 0.01 seoa9740 4A 0.01 fcr4954 4A 0.01 ncr3339 4A 0.01 fcr7026 4A 0.01 fcrcO597 4A 0.01 fcrb2637 4A 0.01 ncrclδβl 4A 0.01 ,...,..4,Ά.. ••••-•oτo'T seoa3514 4A 0.01 miod3306 4A 0.01 seob3204 4A 0.01 ncrc6047 4A 0.01 seob6506 4A 0.01 ncr3830 4A 0.01 seob9574 4A 0.01 ncrcl502 4A 0.01 seoc0742 4A 0.01 ncr3496 4A 0.01 fcr2018 4A 0.01 seoal834 4A 0.01 miocl768 4A 0.01 mioc2019 4A 0.01 ncr0004 4A 0.01 ncrc6479 4A 0.01 fcrl853 4A 0.01 seoa0501 4A 0.01 mioc8481 4A 0.01 fcrOOβl 4A 0.01 ncr7712 4A 0.01 ncr4332 4A 0.01 miod7351 4A 0.01 ncrl007 4A 0.01 fcrb8536 4A 0.01 ncr3843 4A 0.01 seob7622 4A 0.01 mioal310 4A 0.01 fcrβlβl 4A 0.01 seob0221 4A 0.01 seob4087 4A 0.01 fcrb9694 4A 0.01 miob3552 4A 0.01 mioc8016 4A 0.01 seoa4202 4A 0.01 seob5209 4A 0.01 fcrb9829 4A 0.01 seobl526 4A 0.01 fcrc7228 4A 0.01 ncrcO576 4A 0.01 mioal906 4A 0.01 fcrb7367 4A 0.01 miod5372 4A 0.01 seoa8867 4A 0.01 ncrc5635 4A 0.01 seob3415 4A 0.01 seobll55 4A 0.01 mioa0482 4A 0.01 seob3564 4A 0.01 mioa6690 4A 0.01 fcrb7215 4A 0.01 ncr3037 4A 0.01 seoa8195 4A 0.01 fcrb5964 4A 0.01 fcrO779 4A 0.01 seob4172 4A 0.01 fcr2494 4A 0.01 miob9020 4A 0.01 fcr5584 4A 0.01 fcrb9118 4A 0.01 fcrb2715 4A 0.01 fcr7201 4A 0.01 fcrb5448 4A 0.01 hfcr3391 4A 0.01 fcrb5534 4A 0.01 fcrb2353 4A 0.01 hfcr6027 4A 0.01 miod2977 4A 0.01 mioa9062 4A 0.01 miob0361 4A 0.01 ncr0019 4A 0.01 fcrb5870 4A 0.01 ncr9324 4A 0.01 fcrb5438 4A 0.01 ncrb8237 4A 0.01 seob0241 4A 0.01 ncrcl501 4A 0.01 seob8742 4A 0.01 seoal497 4A 0.01 fcrb2200 4A 0.01 seoa2042 4A 0.01 fcrcl758 4A 0.01 seoa2692 4A 0.01 mioa0247 4A 0.01 seoa2777 4A 0.01 seob2724 4A 0.01 seoa4543 4A 0.01 mioa8747 4A 0.01 seoa5777 4A 0.01 fcrcl947 4A 0.01 seoa6038 4A 0.01 fcrb3119 4A 0.01 seoa6203 4A 0.01 mioc2443 4A 0.01 seobl057 4A 0.01 seobl947 4A 0.01 seob2642 4A 0.01 fcr0707 4A 0.01 seob2657 4A 0.01 mioa7239 4A 0.01 seob3551 4A 0.01 mioc6055 4A 0.01 seob5684 4A 0.01 seoa9712 4A 0.01 seob7744 4A 0.01 seocl906 4A 0.01 fcrbll90 4A 0.02 seob2750 4A 0.01 seob9152 4A 0.02 ncrc6367 4A 0.01 mioc3139 4A 0.02 miod2665 4A 0.01 mioc9732 4A 0.02 miocl865 4A 0.01 mioc8437 4A 0.02 seoal358 4A 0.01 ncrb6640 4A 0.02 seoa9997 4A 0.01 mioc7863 4A 0.02 seobll03 4A 0.01 fcrb4727 4A 0.02 fcrc4876 4A 0.01 ncr6344 4A 0.02 hfcrl314 4A 0.01 fcrO893 4A 0.02 mioc4112 4A 0.01 miob8025 4A 0.02 ... ,,..,,.47¥... ro2 fcr3575 4A 0.02 hfcr2850 4A 0.02 mioc7433 4A 0.02 mioc6970 4A 0.02 miob8565 4A 0.02 fcrb4249 4A 0.02 seob9285 4A 0.02 miod3254 4A 0.02 seob6560 4A 0.02 ncrc3256 4A 0.02 seoa3908 4A 0.02 seoa9165 4A 0.02 mioal704 4A 0.02 seob5214 4A 0.02 seoc5140 4A 0.02 seocl402 4A 0.02 seob6153 4A 0.02 miob9817 4A 0.02 miod2853 4A 0.02 seobl748 4A 0.02 mioa7602 4A 0.02 miob3725 4A 0.02 fcr2417 4A 0.02 ncr4215 4A 0.02 fcrb8829 4A 0.02 fcrb3726 4A 0.02 ncr4416 4A 0.02 rαioc4925 4A 0.02 fcrb3554 4A 0.02 fcrb6269 4A 0.02 ncr5719 4A 0.02 ncr7292 4A 0.02 ncrcl231 4A 0.02 seob5889 4A 0.02 ncrc5608 4A 0.02 hfcr6727 4A 0.02 seoa7115 4A 0.02 mioa0959 4A 0.02 seob0386 4A 0.02 fcr6619 4A 0.02 seob7886 4A 0.02 fcr4468 4A 0.02 ncrc4772 4A 0.02 ncrc6984 4A 0.02 ncrb8398 4A 0.02 hfcr7095 4A 0.02 seoa9945 4A 0.02 seocl025 4A 0.02 seobl834 4A 0.02 mioc2694 4A 0.02 seoa6654 4A 0.02 ncrb3301 4A 0..02 mioa8899 4A 0.02 ncrb3348 4A 0.02 miob0589 4A 0.02 ncrcO583 4A 0.02 miocl245 4A 0.02 fcrb3618 4A 0.02 mioc3962 4A 0.02 mioa5452 4A 0.02 ncr4590 4A 0.02 fcrc6356 4A 0,.02 ncr4122 4A 0.02 fcrc2455 4A 0..02 seoa3143 4A 0.02 fcr5618 4A 0..02 hfcrll89 4A 0.02 miob6595 4A 0,.02 seoa6172 4A 0.02 mioc5692 4A 0..02 fcrb5850 4A 0.02 ncrO223 4A 0..02 ncrcO439 4A 0.02 ncr4208 4A 0,.02 fcrb8509 4A 0.02 ncr8337 4A 0.,02 seob0937 4A 0.02 ncrc4135 4A 0,.02 fcrb8243 4A 0.02 seoal979 4A 0..02 mioc3420 4A 0.02 seob3545 4A 0.,02 miod7457 4A 0.02 seoc0866 4A 0.,02 seobl744 4A 0.02 seob4570 4A 0.02 seob4965 4A 0.02 seoc5835 4A 0.02 seob7474 4A 0.02 fcrb2843 4A 0.,02 ncrc5631 4A 0.02 miob4077 4A 0.02 seob8639 4A 0.02 fcrb3715 4A 0.02 hfcrll41 4A 0.02 fcrb6829 4A 0.02 ncr3434 4A 0.02 seoa9709 4A 0.02 ncr7267 4A 0.02 ncrb8714 4A 0.02 fcr3277 4A 0.02 mioc5740 4A 0.02 miod3456 4A 0.02 fcrbO265 4A 0.02 fcrb2040 4A 0.02 ncrcl341 4A 0.02 hfcrl795 4A 0.02 seob6415 4A 0.02 ncrO165 4A 0.02 mioa3439 4A 0.02 fcrbβOOO 4A 0.02 miob5467 4A 0.02 fcr2528 4A 0.02 seoa2300 4A 0.02 fcr6412 4A 0.02 mioa6807 4A 0.02 fcr6928 4A 0.02 fcrc2807 4A 0.02 fcrbl788 4A 0.02 fcrl499 4A 0.02 fcrb2787 4A 0.02 miob3693 4A 0.02 hfcrO415 4A 0.02 seoa5637 4A 0.02 mioal079 4A 0.02 seob2011 4A 0.02 mioa3203 4A 0.02 " ifti'Obl-54'51 "-- '41A" " ""0'It)2 seob8393 4A 0.02 miob2355 4A 0.02 seoc0416 4A 0.02 ncrO159 4A 0.02 ncrc4882 4A 0.02 ncr5149 4A 0.02 miob7268 4A 0.02 ncr7151 4A 0.02 ncrcl615 4A 0.02 ncrbO367 4A 0.02 ncr3112 4A 0.02 ncrb7726 4A 0.02 mioc4839 4A 0.02 ncrc4985 4A 0.02 fcrb2090 4A 0.02 seoalββl 4A 0.02 seoa0302 4A 0.02 seoa4806 4A 0.02 seoall04 4A 0.02 seoa6495 4A 0.02 mioc2039 4A 0.02 seoa9739 4A 0.02 fcrl393 4A 0.02 seob0992 4A 0.02 ncr2861 4A 0.02 miob9855 4A 0.02 fcrb2647 4A 0.02 fcrb3870 4A 0.02 ncrc6813 4A 0.02 fcrb9187 4A 0.02 ncr2899 4A 0.02 miob2478 4A 0.02 rαiod0971 4A 0.02 miob3234 4A 0.02 fcrcO213 4A 0.02 mioc7362 4A 0.02 seoa4012 4A 0.02 mioc8332 4A 0.02 ncr4946 4A 0.02 ncr0083 4A 0.02 ncrO266 4A 0.02 fcr0608 4A 0.02 ncr8326 4A 0.02 fcrb5202 4A 0.02 fcrb3466 4A 0.02 seob6379 4A 0.02 miod3347 4A 0.02 seoc0513 4A 0.02 ncr4113 4A 0.02 seoa0429 4A 0.02 ncr9429 4A 0.02 fcrb5503 4A 0.02 ncrcl595 4A 0.02 ncrc2080 4A 0.02 ncrc5149 4A 0.02 hfcr5798 4A 0.02 seoa7543 4A 0.02 ncrc9642 4A 0.02 ncrcll43 4A 0.02 seob2195 4A 0.02 fcrb6359 4A 0.02 fcrO566 4A 0.02 mioa3335 4A 0.02 raioc0347 4A 0.02 fcrb9007 4A 0.02 πuod2412 4A 0.02 miocll25 4A 0.02 fcrbl550 4A 0.02 seobl972 4A 0.02 mioa3856 4A 0.02 fcrcl907 4A 0.02 mioa4326 4A 0.02 ncr7382 4A 0.02 fcr3856 4A 0.02 fcrb3080 4A 0.02 fcrb4784 4A 0.02 fcrb7093 4A 0.02 seocl425 4A 0.02 mioc7998 4A 0.02 ncrc5113 4A 0.02 ncrl967 4A 0.02 seob0918 4A 0.02 fcrb4925 4A 0.02 miob6702 4A 0.02 seoc8175 4A 0.02 seob0846 4A 0.02 hfcr2544 4A 0.02 ncrb8747 4A 0.02 miod4066 4A 0.02 miob4864 4A 0.02 mioc7364 4A 0.02 ncrc5072 4A 0.02 seoal552 4A 0.02 fcrbl691 4A 0.02 rαioc6949 4A 0.02 miob2743 4A 0.02 seob5767 4A 0.02 miod3341 4A 0.02 fcrb8222 4A 0.02 mioa6545 4A 0.02 miod4449 4A 0.02 seob0303 4A 0.02 ncrb8649 4A 0.02 seoa5731 4A 0.02 ncrc5417 4A 0.02 ncrb2909 4A 0.02 fcrlO98 4A 0.02 miob9285 4A 0.02 mioc4636 4A 0.02 ncr2994 4A 0.02 mioc6983 4A 0.02 seoa3544 4A 0.02 fcrc6010 4A 0.02 fcrc7338 4A 0.02 fcr5141 4A 0.02 seob7039 4A 0.02 fcrb2196 4A 0.02 fcrb7588 4A 0.02 fcrc4948 4A 0.02 miob5647 4A 0.02 mioal337 4A 0.02 mioc4696 4A 0.02 mioa3629 4A 0.02 mioc0238 4A 0.02 mioa4427 4A 0.02 fcrb3518 4A 0.02 miod0845 4A 0.02 ncrc2098 4A 0.02 fcr2442 4A 0.02 fcrO843 4A 0.02 fcrbl990 4A 0.02 mioa2290 4A 0.02 fcrb7871 4A 0.02 seoa5994 4A 0.02 fcrb8196 4A 0.02 mioc5307 4A 0.02 fcrc2678 4A 0.02 seocl406 4A 0.02 miob8096 4A 0.02 seob4140 4A 0.02 mioc3220 4A 0.02 fcrb3584 4A 0.02 miod6919 4A 0.02 seob7903 4A 0.02 seoa0174 4A 0.02 seoc2135 4A 0.02 seoa8997 4A 0.02 seob3918 4A 0.02 seoc0809 4A 0.02 fcrb7495 4A 0.02 mioc5736 4A 0.02 fcr0703 4A 0.02 seoc4909 4A 0.02 fcrb2559 4A 0.02 ncrcllll 4A 0.02 ncrc5296 4A 0.02 fcrb3181 4A 0.02 miocl910 4A 0.02 mioa4476 4A 0.02 miob9848 4A 0.02 ncrb5254 4A 0.02 seob6584 4A 0.02 fcrb6818 4A 0.02 fcrb3702 4A 0.02 ncrc9947 4A 0.02 fcrb4270 4A 0.02 ncrl428 4A 0.02 ncrl791 4A 0.02 fcrb4345 4A 0.02 fcrO761 4A 0.02 hfcr2275 4A 0.02 fcrblO59 4A 0.02 seoa7587 4A 0.02 fcrbl312 4A 0.02 fcrb8014 4A 0.02 hfcr0470 4A 0.02 ncrll86 4A 0.02 hfcr4645 4A 0.02 miod4752 4A 0.02 hfcr5243 4A 0.02 ncrb693β 4A 0.02 mioa4057 4A 0.02 ncrc5312 4A 0.02 miob2645 4A 0.02 fcrO796 4A 0.02 ncr3465 4A 0.02 ncrl631 4A 0.02 ncrb4351 4A 0.02 ncr2472 4A 0.02 ncrb8460 4A 0.02 fcrb6463 4A 0.02 ncrb8538 4A 0.02 seoal776 4A 0.02 ncrc5019 4A 0.02 fcr5181 4A 0.02 seoal513 4A 0.02 miob4975 4A 0.02 seoa3229 4A 0.02 mioc4504 4A 0.02 seoa3400 4A 0.02 ncrb6846 4A 0.02 seoa8227 4A 0.02 seoa2765 4A 0.02 seoa8564 4A 0.02 seob0678 4A 0.02 seob0182 4A 0.02 seob5144 4A 0.02 seob5711 4A 0.02 fcrb6442 4A 0.02 seob6683 4A 0.02 mioa4229 4A 0.02 ncrb8559 4A 0.02 fcrb4226 4A 0.02 miobl059 4A 0.02 fcr5204 4A 0.02 seob6096 4A 0.02 fcrc2014 4A 0.02 miod7243 4A 0.02 mioa2158 4A 0.02 mioc8206 4A 0.02 hfcr9461 4A 0.02 ncr2862 4A 0.02 fcr2684 4A 0.02 ncrb2395 4A 0.02 hfcrO582 4A 0.02 fcrb4802 4A 0.02 seoc2741 4A 0.02 mioa3673 4A 0.02 hfcr7576 4A 0.02 seoa4601 4A 0.02 miob2709 4A 0.02 mioa4077 4A 0.02 ncrcl608 4A 0.02 ncr6343 4A 0.02 fcrb6028 4A 0.02 miocl928 4A 0.02 mioa5586 4A 0.02 ncrc3953 4A 0.02 mioc3045 4A 0.02 mioa7556 4A 0.02 seoa0536 4A 0.02 ncrc6592 4A 0.02 fcrc6234 4A 0.02 seoal540 4A 0.02 mioa8074 4A 0.02 seoa5586 4A 0.02 ncr0210 4A 0.02 mioc0164 4A 0.02 miob9734 4A 0.02 mioa8679 4A 0.02 seob8972 4A 0.02 mioa9007 4A 0.02 ncrcO115 4A 0.02 miob3044 4A 0.02 ncr3827 4A 0.02 •ncrc7β'4Θ'' -4A " ncrcO843 4A 0.03 seoa6033 4A 0.02 ncrc9944 4A 0.03 seoa0794 4A 0.02 seoa0783 4A 0.03 seoa4056 4A 0.02 seoa2899 4A 0.03 iniob3496 4A 0.02 seoa3335 4A 0.03 seob2950 4A 0.02 seoa3910 4A 0.03 seoc4187 4A 0.02 seoa6697 4A 0.03 ncr8606 4A 0.02 seoa9931 4A 0.03 ncrc7049 4A 0.02 seob0394 4A 0.03 fcrOlll 4A 0.02 seobl007 4A 0.03 fcrc3676 4A 0.02 fcr2743 4A 0.03 fcrc7047 4A 0.02 mioc3746 4A 0.03 mioc4319 4A 0.02 fcr6266 4A 0.03 ncrb3001 4A 0.02 fcrb3342 4A 0.03 seoa3038 4A 0.02 mioal213 4A 0.03 mioa8594 4A 0.02 miod3160 4A 0.03 miob6615 4A 0.02 seob2303 4A 0.03 mioc6296 4A 0.02 mioa0152 4A 0.03 ncr7876 4A 0.02 mioal303 4A 0.03 seoa8638 4A 0.02 mioa5231 4A 0.03 seob3493 4A 0.02 ncrb5192 4A 0.03 ncrcl885 4A 0.02 fcrb7533 4A 0.03 ncrc9280 4A 0.02 mioc4730 4A 0.03 seoal253 4A 0.02 seoc3269 4A 0.03 fcr4471 4A 0.02 hfcr9644 4A 0.03 mioc4888 4A 0.03 fcrb5343 4A 0.03 fcrc0130 4A 0.03 seobl844 4A 0.03 seoc4513 4A 0.03 mioc0978 4A 0.03 fcrc2830 4A 0.03 ncr8067 4A 0.03 seoal567 4A 0.03 ncr8794 4A 0.03 seob3404 4A 0.03 miob6228 4A 0.03 fcr4786 4A 0.03 mioc3369 4A 0.03 mioa2261 4A 0.03 fcrb9909 4A 0.03 mioci226 4A 0.03 seobl853 4A 0.03 ncrc9259 4A 0.03 fcrb9376 4A 0.03 seoa5520 4A 0.03 mioc5240 4A 0.03 fcrb3644 4A 0.03 ncrcl995 4A 0.03 fcrb2044 4A 0.03 fcrb2256 4A 0.03 miod0455 4A 0.03 fcrcO857 4A 0.03 fcrc6877 4A 0.03 fcrcO771 4A 0.03 miob4570 4A 0.03 fcrO2O6 4A 0.03 mioa6556 4A 0.03 seob8454 4A 0.03 mioa9492 4A 0.03 ncrc5907 4A 0.03 miocll07 4A 0.03 fcrb2876 4A 0.03 seoa0740 4A 0.03 mioc2596 4A 0.03 seob8483 4A 0.03 mioa8439 4A 0.03 ncr5509 4A 0.03 hfcr2732 4A 0.03 seobl956 4A 0.03 fcr5392 4A 0.03 ncrc3408 4A 0.03 miob7156 4A 0.03 fcrb8542 4A 0.03 fcrb4388 4A 0.03 miob9121 4A 0.03 seob3513 4A 0.03 fcr4414 4A 0.03 mioa6704 4A 0.03 fcr5958 4A 0.03 miobl734 4A 0.03 fcr6994 4A 0.03 ncr5065 4A 0.03 fcrb2426 4A 0.03 ncrc4600 4A 0.03 fcrb4248 4A 0.03 seoa5303 4A 0.03 hfcr5009 4A 0.03 fcrb5912 4A 0.03 hfcr7649 4A 0.03 seoal615 4A 0.03 mioa6252 4A 0.03 miob3690 4A 0.03 mioa7485 4A 0.03 ncr3948 4A 0.03 mioa8975 4A 0.03 ncr9933 4A 0.03 ncr3655 4A 0.03 fcr3528 4A 0.03 ncr9502 4A 0.03 fcrb9364 4A 0.03 ncrb8458 4A 0.03 fcr4902 4A 0.03 •netc4'8-'63^" *■•»-~4A ' *t)ItI3' fcr3936 4A 0.03 seob3548 4A 0.03 miod5957 4A 0.03 seoa2795 4A 0.03 miod6938 4A 0.03 fcr3033 4A 0.03 fcrc6372 4A 0.03 seoa5685 4A 0.03 ncr8725 4A 0.03 miod4370 4A 0.03 ncrb2027 4A 0.03 fcrb2818 4A 0.03 seoa3147 4A 0.03 fcrb6084 4A 0.03 fcrc7102 4A 0.03 fcrb9014 4A 0.03 seob4676 4A 0.03 mioa6442 4A 0.03 seob7346 4A 0.03 ncrcl687 4A 0.03 miocl438 4A 0.03 fcrb8732 4A 0.03 mioc0240 4A 0.03 ncr3141 4A 0.03 hfcrO237 4A 0.03 ncr2967 4A 0.03 seoa3811 4A 0.03 miobl318 4A 0.03 seob7419 4A 0.03 seoc2810 4A 0.03 fcrb3120 4A 0.03 fcrb4734 4A 0.03 fcr7059 4A 0.03 mioc6973 4A 0.03 seoc4093 4A 0.03 miodl574 4A 0.03 seoa5494 4A 0.03 miod4539 4A 0.03 seoa2087 4A 0.03 ncrb8721 4A 0.03 fcrcO496 4A 0.03 ncrc2319 4A 0.03 fcr2293 4A 0.03 ncrc2387 4A 0.03 fcrc3868 4A 0.03 mioc0625 4A 0.03 mioa0601 4A 0.03 ncrc3855 4A 0.03 mioc6925 4A 0.03 fcrb4985 4A 0.03 mioc8851 4A 0.03 hfcrl856 4A 0.03 miod2570 4A 0.03 mioal304 4A 0.03 fcrb6363 4A 0.03 seoa9209 4A 0.03 fcrc2108 4A 0.03 seoa2862 4A 0.03 mioc6902 4A 0.03 ncr7941 4A 0.03 seob6486 4A 0.03 fcrbO979 4A 0,.03 seob5193 4A 0.03 fcrb2051 4A 0,.03 fcrl372 4A 0.03 fcrb6718 4A 0(.03 mioa2722 4A 0.03 fcrcO915 4A 0..03 ncrcO964 4A 0.03 mioa8836 4A 0..03 ncrc3799 4A 0.03 miob4090 4A 0..03 fcrl957 4A 0.03 mioc3936 4Α 0,,03 miob9274 4A 0.03 mioc7570 4A 0.,03 seoal571 4A 0.03 miod5126 4A 0.,03 mioc5141 4A 0.03 ncrc2259 4A 0.,03 seob7575 4A 0.03 ncrc9562 4A 0.,03 miob6865 4A 0.03 seoc2584 4A 0.,03 fcrO531 4A 0.03 seoa7075 4A 0.03 fcrO622 4A 0.03 ncrcO324 4A 0.03 fcr4460 4A 0.03 ncrO194 4A 0.,03 fcr5861 4A 0.03 miob7290 4A 0.03 fcrbO575 4A 0.03 ncrc9304 4A 0.03 fcrb2800 4A 0.03 seob3148 4A 0.03 fcrb2859 4A 0.03 miob6438 4A 0.03 fcrb4278 4A 0.03 fcr6748 4A 0.03 hfcr2686 4A 0.03 hfcr2629 4A 0.03 mioal392 4A 0.03 fcrb3686 4A 0.03 mioa3654 4A 0.03 seob5100 4A 0.03 mioa3945 4A 0.03 seoa5665 4A 0.03 mioa5310 4A 0.03 seob3697 4A 0.03 ncrlO52 4A 0.03 fcrb4334 4A 0.03 ncr2848 4A 0.03 seob0944 4A 0.03 ncrb2108 4A 0.03 seoa3415 4A 0.03 ncrcll76 4A 0.03 miod7011 4A 0.03 ncrc4047 4A 0.03 seob6510 4A 0.03 ncrc4994 4A 0.03 ncrc6861 4A 0.03 ncrc5016 4A 0.03 fcrb3711 4A 0.03 ncrc5146 4A 0.03 ncrc6875 4A 0.03 seoa5580 4A 0.03 -se>oa5O52 4A '* U."UJ ncrOOlβ 4A 0.03 seob0629 4A 0.03 miod4365 4A 0.03 seob0872 4A 0.03 mioa0862 4A 0.03 seob2555 4A 0.03 fcrb5467 4A 0.03 seob2601 4A 0.03 miocl992 4A 0.03 seob3009 4A 0.03 mioc5198 4A 0.03 seob5454 4A 0.03 seob9872 4A 0.03 miocl296 4A 0.03 seoa0792 4A 0.03 mioc5615 4A 0.03 seob6049 4A 0.03 ncr6881 4A 0.03 miob2287 4A 0.03 ncrb5244 4A 0.03 ncrl687 4A 0.03 ncrb8056 4A 0.03 fcrbl503 4A 0.03 ncrc7169 4A 0.03 fcr4444 4A 0.03 seob0976 4A 0.03 ncrO761 4A 0.03 fcrb5903 4A 0.03 mioa3282 4A 0.03 seob0185 4A 0.03 fcrl207 4A 0.03 hfcr3902 4A 0.03 mioa4564 4A 0.03 ncr7792 4A 0.03 miod0195 4A 0.03 seoc2256 4A 0.03 seob5493 4A 0.03 seob6872 4A 0.03 fcr2601 4A 0.03 seoc3854 4A 0.03 fcr6708 4A 0.03 fcrb6734 4A 0.03 ncrb8790 4A 0.03 mioc4420 4A 0.03 seob9586 4A 0.03 fcrcO637 4A 0.03 fcrl346 4A 0.03 ncr8686 4A 0.03 ncr9587 4A 0.03 seob9871 4A 0.03 seoc0355 4A 0.03 seob4537 4A 0.03 seob4192 4A 0.03 seob9266 4A 0.03 seob5636 4A 0.03 mioc3574 4A 0.03 fcrl411 4A 0.03 fcr3287 4A 0.03 fcrc6609 4A 0.03 miocl364 4A 0.03 fcrcO456 4A 0.03 fcrb8762 4A 0.03 seob4795 4A 0.03 fcrb2744 4A 0.03 hfcr3134 4A 0.03 fcr5519 4A 0.03 hfcr2616 4A 0.03 fcrb9639 4A 0.03 ncrc6778 4A 0.03 ncrc2110 4A 0.03 seob4165 4A 0.03 ncr9469 4A 0.03 fcr4469 4A 0.03 seob5734 4A 0.03 fcr7063 4A 0.03 miob2448 4A 0.03 fcrc5744 4A 0.03 ncrc6423 4A 0.03 raioc2116 4A 0.03 seob8710 4A 0.03 mioc2341 4A 0.03 ncr2583 4A 0.03 ncr3149 4A 0.03 ncr2269 4A 0.03 ncrb7678 4A 0.03 seoa0353 4A 0.03 ncrc0097 4A 0.03 seoa0014 4A 0.03 seob4363 4A 0.03 mioa4674 4A 0.03 seob7747 4A 0.03 seob6256 4A 0.03 mioa8824 4A 0.03 seoa2402 4A 0.03 fcrb3184 4A 0.03 miodl316 4A 0.03 seoa7553 4A 0.03 ncrcl311 4A 0.03 mioc5210 4A 0.03 ncrb4869 4A 0.03 ncrc3880 4A 0.03 fcrb4918 4A 0.03 fcrc2651 4A 0.03 mioc7381 4A 0.03 fcrb6432 4A 0.03 mioc8640 4A 0.03 mioa2377 4A 0.03 ncrl699 4A 0.03 ncrc7085 4A 0.03 seobll91 4A 0.03 ncrc8949 4A 0.03 seoc2549 4A 0.03 fcrbO743 4A 0.03 seob0949 4A 0.03 seob5508 4A 0.03 mioc2343 4A 0.03 fcrO343 4A 0.03 ncrO853 4A 0.03 fcrb6236 4A 0.03 seoc4381 4A 0.03 fcrb2851 4A 0.03 fcrb7742 4A 0.03 fcr4160 4A 0.03 ncrc9850 4A 0.03 ncrc6072 4A 0.03 mioc0206 4A 0.03 mioc0910 4A 0.03 "4A ;;„..*g.:-σ3 hfcr2390 4A 0.04 fcrbl877 4A 0.03 ncrc8963 4A 0.04 ncrc8988 4A 0.03 seob7631 4A 0.04 fcrb9588 4A 0.03 seob5726 4A 0.04 ncrc5716 4A 0.03 fcrb4781 4A 0.04 fcr5627 4A 0.03 fcrb6549 4A 0.04 ncrb5965 4A 0.03 mioal015 4A 0.04 fcrb5644 4A 0.03 miob8341 4A 0.04 fcrb4921 4A 0.03 miod7429 4A 0.04 miob0764 4A 0.03 ncr4648 4A 0.04 mioa0332 4A 0.03 ncrbl899 4A 0.04 fcrb7801 4A 0.03 ncrc2087 4A 0.04 seoal644 4A 0.03 ncrc5492 4A 0.04 ncrb5909 4A 0.03 seoc0056 4A 0.04 miod0187 4A 0.03 ncrb7376 4A 0.04 fcrb6508 4A 0.03 ncrlO29 4A 0.04 mioa5681 4A 0.03 ncr5557 4A 0.04 seob3197 4A 0.03 ncr7753 4A 0.04 fcrbl876 4A 0.03 fcrb2310 4A 0.04 miobl833 4A 0.03 fcrb7829 4A 0.04 seoa2801 4A 0.03 ncrc6920 4A 0.04 ncrb3903 4A 0.03 seoal844 4A 0.04 miod5122 4A 0.04 seob6064 4A 0.04 fcrb8946 4A 0.04 seob6812 4A 0.04 miob0178 4A 0.04 seoc2263 4A 0.04 seoa29 /8 4A 0.04 seob8817 4A 0.04 ncrc0095 4A 0.04 seoc0617 4A 0.04 fcrb8383 4A 0.04 seob0261 4A 0.04 fcrclO43 4A 0.04 ncr3815 4A 0.04 seoa2443 4A 0.04 seob9820 4A 0.04 mioall40 4A 0.04 fcrb5051 4A 0.04 fcrb3169 4A 0.04 ncrc6841 4A 0.04 fcrl885 4A 0.04 seob3067 4A 0.04 f-cr2174 4A 0.04 seob2163 4A 0.04 fcr5190 4A 0.04 fcrb6031 4A 0.04 fcr7418 4A 0.04 seoa2805 4A 0.04 fcrbl441 4A 0.04 fcrb5705 4A 0.04 fcrbl724 4A 0.04 miob8143 4A 0.04 fcrb2685 4A 0.04 ncr3393 4A 0.04 hfcrO71O 4A 0.04 mioc0053 4A 0.04 hfcr2342 4A 0.04 fcrc5789 4A 0.04 hfcr3413 4A 0.04 seobl842 4A 0.04 hfcr5871 4A 0.04 fcrb2765 4A 0.04 hfcr6970 4A 0.04 mioa8289 4A 0.04 mioa0869 4A 0.04 mioc4983 4A 0.04 miob0746 4A 0.04 miob5873 4A 0.04 ncrcO139 4A 0.04 miod2792 4A 0.04 ncrc4920 4A 0.04 seob6781 4A 0.04 seoa5552 4A 0.04 fcrb6890 4A 0.04 seoa6364 4A 0.04 miod4546 4A 0.04 seobl411 4A 0.04 ncrO676 4A 0.04 seob5174 4A 0.04 miob2841 4A 0.04 fcrl328 4A 0.04 mioc4731 4A 0.04 fcrb5688 4A 0.04 fcrO926 4A 0.04 seob8562 4A 0.04 fcrb4579 4A 0.04 ncrcO797 4A 0.04 seoa5809 4A 0.04 fcrb2676 4A 0.04 ncrc5536 4A 0.04 ncrcO864 4A 0.04 fcrc5483 4A 0.04 seoc4928 4A 0.04 fcrO629 4A 0.04 seoc5789 4A 0.04 hfcr5864 4A 0.04 hfcr2844 4A 0.04 seob4925 4A 0,.04 miob8639 4A 0.04 ncrb3011 4A 0.04 ncrO265 4A 0.04 fcrbl202 4A 0.04 seob3317 4A 0.04 fcrc3488 4A 0.04 "'••'•' --IA « '"OM4' mioc5226 4A 0.04 fcrb2299 4A 0.04 ncrb8451 4A 0.04 ncrc5760 4A 0.04 seob7402 4A 0.04 fcrc5831 4A 0.04 seoc2670 4A 0.04 seob2169 4A 0.04 ncrcl669 4A 0.04 fcrb2472 4A 0.04 mioa9093 4A 0.04 ncrc3283 4A 0.04 seoa4146 4A 0.04 fcr7711 4A 0.04 fcr5663 4A 0.04 hfcr3754 4A 0.04 mioa4285 4A 0.04 miob8657 4A 0.04 ncr3397 4A 0.04 fcrc6560 4A 0.04 seobll45 4A 0.04 mioa4318 4A 0.04 fcrb3896 4A 0.04 fcrb8682 4A 0.04 seobl316 4A 0.04 ncr7631 4A 0.04 seoa0023 4A 0.04 mioa4818 4A 0.04 ncrc9286 4A 0.04 seoa9656 4A 0.04 ncr9487 4A 0.04 fcrb9796 4A 0.04 seoa6131 4A 0.04 ncrb5060 4A 0.04 ncrc5949 4A 0.04 mioc6878 4A 0.04 seob5942 4A 0.04 mioa3331 4A 0.04 ncrb5227 4A 0.04 seoc2131 4A 0.04 hfcr3922 4A 0.04 seob4036 4A 0.04 ncrcl411 4A 0.04 fcrb6502 4A 0.04 seoa6510 4A 0.04 fcrb8730 4A 0.04 seoa9783 4A 0.04 hfcrO171 4A 0.04 fcrl887 4A 0.04 miob5608 4A 0.04 ncrc4259 4A 0.04 ncr0420 4A 0.04 mioc4575 4A 0.04 ncrc6588 4A 0.04 fcrb5929 4A 0.04 seob7941 4A 0.04 fcrc4969 4A 0.04 fcrb9843 4A 0.04 ncrO673 4A 0.04 fcrb6351 4A 0.04 seoc2589 4A 0.04 seoa3417 4A 0.04 miod5785 4A 0.04 fcrbl399 4A 0.04 seob0818 4A 0.04 raioa9555 4A 0.04 seoa9689 4A 0.04 miob2900 4A 0.04 seob5574 4A 0.04 seob7907 4A 0.04 fcr0706 4A 0.04 seoa4450 4A 0.04 fcr2073 4A 0.04 fcrbl867 4A 0.04 fcr4885 4A 0.04 fcrb9135 4A 0.04 fcr6018 4A 0.04 mioa6583 4A 0.04 fcrbl775 4A 0.04 miod5612 4A 0.04 fcrbl854 4A 0.04 miod6068 4A 0.04 hfcrl694 4A 0.04 ncr2926 4A 0.04 hfcr2237 4A 0.04 ncr8628 4A 0.04 hfcr2598 4A 0.04 seoa4181 4A 0.04 hfcr3726 4A 0.04 seob0639 4A 0.04 mioal071 4A 0.04 ncrc8867 4A 0.04 mioa2708 4A 0.04 fcrc2254 4A 0.04 mioa3713 4A 0.04 mioa3620 4A 0.04 mioa8733 4A 0.04 mioa2446 4A 0.04 miobll94 4A 0.04 fcrl347 4A 0.04 miob2533 4A 0.04 miod6058 4A 0.04 miob3773 4A 0.04 ncrcl578 4A 0.04 ncr3034 4A 0.04 ncrc6264 4A 0.04 ncr4535 4A 0.04 fcr5425 4A 0.04 ncrc2413 4A 0.04 miod3234 4A 0.04 ncrc3457 4A 0.04 hfcr5987 4A 0.04 ncrc3596 4A 0.04 fcr4804 4A 0.04 ncrc3650 4A 0.04 seoa7408 4A 0.04 seoa0137 4A 0.04 fcr5199 4A 0.04 seoa5787 4A 0.04 fcrbl969 4A 0.04 seobOOβl 4A 0.04 fcrb6747 4A 0.04 seob0975 4A 0.04 mioc3208 4A 0.04 seob5673 4A 0.04 inioc5179 4A 0.04 seob8099 4A 0.04 ~hf?cf25'Θ&•»■'■",.Vi --4X" ""•- i--Q *fif4 seob0991 4A 0.04 miocl425 4A O.04 fcrb2334 4A 0.04 ncrb3424 4A O.04 miod4063 4A 0.04 ncrcl259 4A O.04 seob6246 4A 0.04 seoa5094 4A O.04 seocl490 4A 0.04 ncrbO328 4A O.04 fcrc4362 4A 0.04 ncrc6846 4A O .04 ncrcO259 4A 0.04 fcr4494 4A O.04 fcrb4515 4A 0.04 fcrc2089 4A O .04 seob4263 4A 0.04 ncrc5592 4A O.04 fcrc2126 4A 0.04 ncr3961 4A O.04 fcrc4180 4A 0.04 mioa2647 4A O.04 hfcr2657 4A 0.04 fcr2542 4A O.04 mioa3940 4A 0.04 fcrbl916 4A O.04 fcrb5259 4A 0.04 fcrb2190 4A O.04 fcrc6771 4A 0.04 miob9679 4A O.04 mioc5603 4A 0.04 ncr2866 4A O.04 mioc8945 4A 0.04 ncrc9758 4A O.04 ncrc3171 4A 0.04 mioc4534 4A O.04 ncrc4885 4A 0.04 seob9884 4A O.04 ncrc9168 4A 0.04 fcr5006 4A O.04 ncrc9440 4A 0.04 fcrc2808 4A O.04 seoa7340 4A 0.04 miob4346 4A O.04 seoc0951 4A 0.04 seoa2768 4A O.04 seob7274 4A 0.04 fcrcl298 4A O.04 hfcr4349 4A 0.04 fcrb3309 4A O.04 miob8345 4A 0.04 miob2960 4A O.04 fcrb6464 4A 0.04 miob6459 4A O.04 ncr2995 4A 0.04 mioc8917 4A O.04 seob6386 4A 0.04 ncr5192 4A O .04 ncrc6996 4A 0.04 ncr5781 4A O .04 seob9970 4A 0.04 mioc8278 4A O,.04 ncrb7166 4A 0.04 mioa0075 4A O,.04 seoc7020 4A 0.04 ncrc3321 4A O..04 ncrc6459 4A 0.04 ncrb0090 4A O,.04 miocll35 4A 0.04 fcrb2059 4A O..04 hfcr3931 4A 0.04 fcrb2350 4A O..04 fcrc2346 4A 0.04 fcrb3629 4A O..04 hfcr2930 4A 0.04 fcrb5114 4A O..04 miob3595 4A 0.04 seocl307 4A O,.04 seoc8115 4A 0.04 fcr5498 4A O..04 fcr4272 4A 0.04 fcrc5126 4A O.,04 fcrb3894 4A 0.04 fcr3772 4A O.,04 fcrc6461 4A 0.04 fcrb4365 4A O.04 miob8627 4A 0.04 ncr2288 4A O.04 seoa2244 4A 0.04 ncr2576 4A O.,04 miod6162 4A 0.04 fcr3072 4A O.04 ncr5645 4A 0.04 seoa3634 4A O.04 fcrb2722 4A 0.04 fcrb4543 4A O.04 fcrb6897 4A 0.04 mioa8773 4A O.04 mioa6739 4A 0.04 seoa7899 4A O.04 miod3106 4A 0.04 fcrb2502 4A O.04 ncrc9469 4A 0.04 seoa0186 4A O.04 seoal419 4A 0.04 mioc0226 4A O.04 seob7569 4A 0.04 mioc7352 4A 0.04 seoc5156 4A 0.04 hfcr5905 4A 0.04 miod7440 4C 2.44e-07 seoa9131 4A 0.04 fcr3053 4C 3.37e-07 ncr3297 4A 0.04 seoc2029 4C 6.23e-07 hfcr5244 4A 0.04 fcr3181 4C 7.1e-07 miod0080 4A 0.04 ncr3163 4C 2.24e-06 seob3163 4A 0.04 mioc7170 4C 2.53e-06 mioa5511 4A 0.04 mioa2072 4C 3.26e-06 mioa7169 4A 0.04 seob4303 4C 3.35e-06 fcrb5198 4A 0.04 seob5523 4C 4.23e-06 -Sgob4413* ■™4C ■4'.-ZTe-06 seob0154 4C 1.23e-04 fcrc3993 4C 4.37e-06 fcrbl960 4C 1.25e-04 seoa4461 4C 5.29e-06 seoa0231 4C 1.25e-04 seoa6032 4C 5.48e-06 seob6576 4C 1.27e-04 miod5349 4C 6.31e-06 mioa5059 4C 1.29e~04 mioc3430 4C 7.49e-06 seob6189 4C 1.32e-04 seoa8894 4C l.le-05 miod7414 4C 1.36e-04 mioa9473 4C 1.36e-05 seoal427 4C 1.38e-04 fcrb3227 4C 1.45e-05 miob6881 4C 1.44e-04 fcrb4534 4C 1.46e-05 fcrO253 4C 1.45e-04 seob2697 4C 1.66e-05 seoa2585 4C 1.47e-04 seoc0775 4C 1.67e-05 ncrO223 4C 1.5e-04 ncr3751 4C 1.95e-05 seob3313 4C 1.51e-04 seoa4518 4C 2.47e-05 seoa4717 4C 1.52e-04 seoa6133 4C 2.48e-05 seob6851 4C 1.55e-04 miob6562 4C 2.52e-05 miob8812 4C 1.56e-04 seob0085 4C 3.04e-05 fcrb3461 4C 1.6e-04 seoa8566 4C 3.22e-05 mioa4009 4C 1.63e-04 seoa8754 4C 3.62e-05 ncrc6171 4C 1.66e-04 mioa2788 4C 4.4e-05 seocl023 4C 1.66e-04 mioa3367 4C 4.43e-05 seoa0959 4C 1.68e-04 ncrc5243 4C 4.53e-05 seob4029 4C 1.69e-04 ncr7668 4C 4.63e-05 mioa4014 4C 1.73e-04 fcrb3578 4C 4.7e-05 fcrb2704 4C 1.74e-04 seob3326 4C 4.9e-05 seob3189 4C 1.76e-04 fcr2952 4C 5.1e-05 miob3953 4C 1.77e-04 miob6419 4C 5.13e-05 miod5651 4C 1.77e-04 miod5123 4C 5.49e-05 seoa2970 4C 1.77e-04 mioa9179 4C 5.61e-05 seob5748 4C 1.79e-04 seob4333 4C 5.73e-05 ncr7904 4C 1.8e-04 seob6853 4C 5.74e-05 ncr8588 4C 1.82e-04 ncrcO729 4C 5.77e-05 hfcr2390 4C 1.83e-04 seoa8916 4C 5.83e-05 seobl231 4C 1.85e-04 seob3518 4C 5.95e-05 miod5505 4C 1.89e-04 mioa3963 4C 6.04e-05 fcrb3654 4C 1.91e-04 seoa9373 4C 6.54e-05 seob6751 4C 2.02e-04 mioa4636 4C 6.55e-05 mioa6130 4C 2.04e-04 crO471 4C 6.77e-05 seoc2264 4C 2.1e-04 miob6430 4C 7.39e-05 fcr7587 4C 2.15e-04 seoal940 4C 7.43e-05 fcrb2554 4C 2.2e-04 mioa3913 4C 7.5e-05 ncrO212 4C 2.21e-04 fcrbl892 4C 7.58e-05 ncr3197 4C 2.22e-04 seoa9042 4C 7.7e-05 fcr2607 4C 2.23e-04 seoa0032 4C 8.0e-05 hfcr3256 4C 2.23e-04 mioa2319 4C 8.18e-05 fcr4846 4C 2.28e-04 seob5240 4C 8.29e-05 fcrcl381 4C 2.28e-04 ncrc9899 4C 8.35e-05 miob0167 4C 2.32e-04 fcr5075 4C 8.62e-05 mioa0311 4C 2.33e-04 ncrb2092 4C 8.8e-05 seocll75 4C 2.33e-04 hfcr8902 4C 8.84e-05 mioc4022 4C 2.34e-04 seoa9482 4C 9.27e-05 mioa8970 4C 2.35e-04 seob6004 4C 9.64e-05 seob0065 4C 2.35e-04 fcrb7339 4C 9.87e-05 seobl911 4C 2.39e-04 fcrb8516 4C 9.9e-05 mioa3939 4C 2.4e-04 hfcr3043 4C 1.0e-04 ncr0045 4C 2.44e-04 ncrc5434 4C 1.0e-04 seoc0999 4C 2.44e-04 fcr4477 4C 1.01e-04 fcrcO554 4C 2.48e-04 fcrc3739 4C 1.03e-04 seocl203 4C 2.49e-04 fcrb6939 4C 1.04e-04 fcrc0075 4C 2.58e-04 hfcr5919 4C 1.04e-04 fcr6235 4C 2.6e-04 mioa3923 4C 1.12e-04 seoc4960 AC 2.65e-04 seob4427 4C 1.16e-04 miob6223 4C 2.67e-04 ncrb4441 4C 1.22e-04 seob4191 4C 2.69e-04 hfcrll37 4C 1.23e-04 mioc2726 4C 2.7e-04 ~ftu"ob86"94~ ""4C "Z:87e-04 fcr4831 4C 5.97e-04 ncrO258 4C 2.89e-04 mioa0595 4C 6.04e-04 ncr4126 4C 2.91e-04 seob9145 4C 6.11e-04 mioc6260 4C 2.92e-04 mioal662 4C 6.2e-04 fcrb6012 4C 3.0e-04 fcrc3750 4C 6.22e-04 seoa4053 4C 3.03e-04 seoall02 4C 6.3e-04 seob6467 4C 3.03e-04 miod4759 4C 6.36e-04 fcrcO112 4C 3.06e-04 mioc7686 4C 6.41e-04 ncrO153 4C 3.07e-04 ncrb2266 4C 6.45e-04 ncrl631 4C 3.07e-04 seob6835 4C 6.58e-04 seoa4366 AC 3.08e-04 mioa2537 4C 6.62e-04 seoc6169 4C 3.11e-04 seoa3717 4C 6.62e-04 mioal585 4C 3.13e-04 fcrc3009 4C 6.69e-04 hfcr3011 4C 3.15e-04 mioc8410 4C 6.98e-04 seoa5662 4C 3.21e-04 mioa6418 4C 7.09e-04 seoa0486 4C 3.23e-04 mioc0950 4C 7.19e-04 seoa2949 4C 3.29e-04 miodl925 4C 7.23e-04 fcrcO487 4C 3.31e-04 ncrc9910 4C 7.24e-04 ncr3837 4C 3.33e-04 seoa0221 4C 7.25e-04 mioa7317 4C 3.37e-04 mioc5532 4C 7.27e-04 hfcrl863 4C 3.39e-04 mioc4089 4C 7.46e-04 fcr2573 4C 3.43e-04 mioc7764 4C 7.57e-04 seob5743 4C 3.48e-04 seoa4681 4C 7.58e-04 mioa5695 4C 3.5e-04 seob6131 4C 7.61e-04 fcrb6968 4C 3.6e-04 seob3303 4C 7.67e-04 seob7584 4C 3.63e-04 mioa4552 4C 7.78e-04 ncrcO342 4C 3.76e-04 ncr8843 4C 7.83e-04 ncrc5417 4C 3.79e-04 seoa9537 4C 7.83e-04 seob3854 4C 3.91e-04 seob9574 4C 7.88e-04 seob6279 4C 3.96e-04 seob0308 4C 7.98e-04 fcrb8504 4C 3.99e-04 mioall93 4C 8.04e-04 seob3367 4C 4.04e-04 seobl318 4C 8.04e-04 fcrb7830 4C 4.09e-04 hfcr6687 AC 8.17e-04 seob3182 4C 4.09e-04 fcrbl496 4C 8.19e-04 hfcr4489 4C 4.15e-04 miob8320 4C 8.21e-04 fcrb4428 4C 4.19e-04 seob0376 AC 8.21e-04 ncrO898 4C 4.21e-04 seoa8979 4C 8.23e-04 mioc8057 4C 4.39e-04 seoa9357 4C 8.24e-04 miobl231 4C 4.43e-04 ncrb0782 4C 8.27e-04 mioc2911 4C 4.49e-04 seob3923 4C 8.31e-04 fcr5559 4C 4.69e-04 ncr2293 4C 8.33e-04 seoa6497 4C 4.69e-04 seob0755 AC 8.35e-04 seoa6598 4C 4.73e-04 hfcr0478 4C 8.43e-04 fcrl463 4C 4.77e-04 ncrO733 4C 8.46e-04 fcr2103 4C 4.84e-04 hfcr5220 4C 8.48e-04 seob3191 4C 4.89e-04 ncrc5844 4C 8.48e-04 seobl319 4C 4.91e-04 mioa5355 4C 8.58e-04 miod6845 4C 4.99e-04 ncr8041 4C 8.61e-04 ncrc5663 4C 5.0e-04 fcrO824 4C 8.62e-04 seob6844 4C 5.04e-04 seob2994 4C 8.67e-04 mioa9510 4C 5.12e-04 seob3119 AC 8.73e-04 ncrc4597 4C 5.18e-04 fcrb8668 4C 8.75e-04 fcr3664 4C 5.26e-04 seob6206 4C 8.88e-04 seob4669 4C 5.33e-04 mioa6731 4C 8.9e-04 seob9282 4C 5..33e-04 fcrb2556 4C 8.98e-04 ncr7813 4C 5..48e-04 ncr5568 4C 9.0e-04 seoa7530 4C 5.56e-04 mioc4667 4C 9.01e-04 seob0248 4C 5.56e-04 mioa5696 4C 9.03e-04 seoa0288 4C 5,.7e-04 hfcrll63 4C 9.1e-04 mioa6064 4C 5.71e-04 miob7156 4C 9.1e-04 seoc6703 4C 5.75e-04 ncr0808 4C 9.12e-04 miob9907 4C 5.82e-04 hfcr3019 4C 9.19e-04 ncrc8903 4C 5.88e-04 fcrc3048 4C 9.26e-04 seob2148 4C 5.88e-04 fcrb9454 4C 9.3e-04 Se'oa'631'5- "4Cf' "T.33if-04 miodll95 4C 1.431152e-03 fcrb9655 4C 9.37e-04 fcrb2484 4C 1.447281e-03 ncrcO715 4C 9.39e-04 mioc6987 4C 1.45158e-03 seoa8348 4C 9.56e-04 fcrl879 4C 1.452287e-03 fcrb9420 4C 9.64e-04 mioa6721 4C 1.457877e-03 seob6272 4C 9.74e-04 seob6486 4C 1.458125e-03 fcr2798 4C 9.78e-04 seoa3516 4C 1.46015e-03 seocl996 4C 9.81e-04 ncr0025 4C 1.469905e-03 hfcr6486 4C 9.83e-04 hfcrl310 4C 1.478083e-03 ncrc9280 4C 1.0e-03 fcr3101 4C 1.487316e-03 seoa5090 4C 1.007122e-03 fcrb9959 4C 1.48762e-03 mioa2173 4C 1.00875e-03 fcrb5214 4C 1.490057e-03 ncrb5595 4C 1.013841e-03 ncrb4182 4C 1.50404e-03 fcrb2933 4C 1.016356e-03 ncrb3702 4C 1.511682e-03 fcr7043 4C 1.016688e-03 ncr7595 4C 1.511845e-03 miodl236 4C 1.028284e-03 mioa0577 4C 1.518616e-03 seob6446 4C 1.030592e-03 ncr8975 4C 1.524283e-03 seob2936 4C 1.039004e-03 seob4579 4C 1.527065e-03 ncrb8693 4C 1.046851e-03 fcr4128 4C 1.53383e-03 mioa9154 4C 1.070876e-03 ncrc5162 4C 1.553082e-03 seoa6658 4C 1.070876e-03 ncr5168 AC 1.55656e-03 ncrb2288 AC 1.076406e-03 ncrb3001 4C 1.557302e-03 fcrc7388 4C 1.081745e-03 fcrb8187 4C 1.573331e-03 fcrb9588 4C 1.089146e-03 ncrb4039 4C 1.584396e-03 miob8572 4C 1.097548e-03 miodlδll 4C 1.590472e-03 seob8204 4C 1.099016e-03 seoa9740 4C 1.598061e-03 miob3982 4C 1.105438e-03 mioa3080 4C 1.603678e-03 ncrcO174 4C 1.123083e-03 hfcr3500 4C 1.610297e-03 miod5310 4C 1.129688e-03 mioa8852 4C 1.618546e-03 hfcr5604 4C 1.140826e-03 mioa0494 4C 1.619093e-03 seoa7443 4C 1.140826e-03 seoaOlll 4C 1.622252e-03 fcrbl855 4C 1.142061e-03 ncr3782 AC 1.63287e-03 ncr3713 4C 1.150048e-03 ncrc5569 AC 1.637974e-03 fcrb9269 4C 1.150659e-03 mioc6075 4C 1.640004e-03 mioa3395 4C 1.154329e-03 seoalllδ 4C 1.653668e-03 seob8368 4C 1.154981e-03 mioa4076 4C 1.665908e-03 seob0122 4C 1.171391e-03 seoa4305 4C 1.678116e-03 mioa0535 4C 1.173792e-03 fcrc6826 4C 1.682437e-03 miobl062 4C 1.177608e-03 mioal015 4C 1.68644e-03 seobl793 4C 1.177608e-03 seob2185 4C 1.70771e-03 seocl593 4C 1.178329e-03 fcrc2090 4C 1.719751e-03 miob9336 4C 1.178419e-03 seoa9870 4C 1.728369e-03 ncrc0090 4C 1.185916e-03 seobl414 4C 1.747109e-03 seobl862 4C 1.201035e-03 seoa9828 4C 1.753504e-03 seoa3105 4C 1.211669e-03 fcrb3165 4C 1.760242e-03 seob3670 4C 1.23222e-03 seoal598 4C 1.770719e-03 hfcr6406 4C 1.239885e-03 fcrc2099 4C 1.790284e-03 miob4867 4C 1.2560936-03 seobl766 4C 1.794891e-03 ncrcll40 4C 1.256093e-03 hfcrl743 AC 1.798381e-03 fcr5720 4C 1.2614e-03 seobl908 AC 1.798381e-03 miod6238 4C 1.278208e-03 seob4545 4C 1.798381e-03 mioc7331 4C 1.301405e-03 fcrc5516 4C 1.799844e-03 ncrc6005 4C 1.305117e-03 hfcr5228 4C 1.800664e-03 miodl942 4C 1.305311e-03 mioa5692 4C 1.800664e-03 mioa0909 4C 1.312146e-03 seob4555 AC 1.800664e-03 seobl879 4C 1.347303e-03 fcrbl420 4C 1.801622e-03 miob6904 4C 1.355302e-03 fcr3595 4C 1.803204e-03 mioc2880 4C 1.356947e-03 ncr2666 4C 1.803204e-03 seob6368 4C 1.361315e-03 ncr2905 4C 1.815247e-03 ncr2472 4C 1.365497e-03 fcrb8940 4C 1.834572e-03 ncrb8385 4C 1.369106e-03 miob5495 4C 1.838333e-03 fcrl756 4C 1.392986e-03 ncr3368 4C 1.838333e-03 fcr2088 4C 1.408465e-03 fcrbl807 4C 1.859263e-03 miob3348 4C 1.428182e-03 ncrc5054 4C 1.860924e-03 Se'ob'ϋ'b'Tb •4C ■■r:86l275e-03 seoa5577 4C 2.,367648e-03 seoc0924 4C 1.861444e-03 seoa5235 4C 2.,369839e-03 seoa8399 4C 1.88316e-03 ncrb8273 4C 2.,393454e-03 fcrbδlβl 4C 1.885852e-03 fcrb3134 4C 2.402582e-03 mioal520 AC 1.887743e-03 mioa9294 4C 2.,412283e-03 fcrb8740 4C 1.892065e-03 hfcrl697 4C 2.,435634e-03 seob2810 4C 1.901388e-03 ncrc5369 4C 2.435634e-03 fcr5758 4C 1.909224e-03 seob5219 4C 2.439695e-03 mioc8423 4C 1.916815e-03 mioc6114 4C 2.46067e-03 ncrc9729 4C 1.919544e-03 rαiob8143 4C 2.489145e-03 hfcr5695 4C 1.956159e-03 seoall73 4C 2.502414e-03 seoc4380 4C 1. M ( 956915e-03 seoa3670 4C 2.543649e-03 fcrb2871 4C M CNM C (1.964974e-03 miob0189 4C 2.57178e-03 miod0592 4C 1.985622e-03 miob2705 4C 2.574869e-03 ncrc0150 4C 1.985831e-03 seoa8232 4C 2.578028e-03 fcrb2060 4C 1.99746e-03 fcr5123 4C 2.597097e-03 hfcrO415 4C 1.99746e-03 inioa6854 4C 2.602922e-03 seoa7555 4C 1.99746e-03 ncrc5061 4C 2.618499e-03 fcr2079 4C 2.016524e-03 seoc0369 4C 2.638551e-03 miod0340 4C 021229e-03 fcrO139 4C 2.651872e-03 seocl025 4C 02575e-03 fcr7004 AC 2.657357e-03 fcrb8080 4C 2.025786e-03 mioa9505 4C 2.658032e-03 mioc0824 4C 2.034483e-03 mioc8434 4C 2.661058e-03 seob0201 4C 2.050861e-03 seob7465 4C 2.711845e-03 fcrb9720 4C 2.057033e-03 fcrb4409 4C 2.722505e-03 fcr5470 4C 2.060807e-03 seoc0276 4C 2.725532e-03 ncrc4079 4C 2.066895e-03 fcrO999 4C 2.730498e-03 hferO624 4C 2.085152e-03 hfcr4488 4C 2.731117e-03 ncrb3329 4C 2.085152e-03 seoa0563 4C 2.731117e-03 seob6015 4C 2.09993e-03 fcr3559 4C 2.734761e-03 fcr4782 4C 2.115262e-03 mioal660 4C 2.749542e-03 fcrbl733 4C 2.120875e-03 seoc0513 4C 2.753054e-03 fcrb9843 4C 2.120966e-03 ncrc9712 4C 2. M C 760324e-03 ncrO238 4C 2.1386e-03 miod5707 4C 2.770552e-03 mioc7895 4C 2.143605e-03 miob8711 4C 2.809832e-03 fcrb3718 4C 2.145161e-03 seob3307 4C 2.811695e-03 seob6670 4C 155534e-03 seob9946 4C 2.84126e-03 seob8501 4C 2.166556e-03 mioc7744 4C 2.845792e-03 miod2525 4C 2.174809e-03 seoa8543 4C 2.874349e-03 mioc6312 4C 2.199817e-03 miob3898 4C 2.881671e-03 hfcrl733 4C 2.209362e-03 seoa8642 4C 881671e-03 ncrb4957 4C 2.210616e-03 mioa7140 4C 2.898369e-03 seob0089 4C 211811e-03 seoa7546 4C 2.919393e-03 hfcr6651 4C
Figure imgf000295_0001
216864e-03 fcr4763 4C 2.928101e-03 hfcrl302 4C 2.226095e-03 miob5016 4C 2.928675e-03 seob4050 4C 2.226095e-03 fcrcl849 4C 2.952953e-03 fcrb2536 4C 2.230106e-03 ncrc3464 4C 2.965232e-03 mioa8851 4C 2.23281e-03 fcrb3483 4C 2.983789e-03 fcr3121 4C 2.234303e-03 seob9649 AC 2.997767e-03 seoa2012 4C 2.255872e-03 seoa4600 AC 2.998627e-03 seoclllδ 4C 2.258564e-03 seoa0008 AC 3.007551e-03 fcr7419 4C 2.260166e-03 seob0168 4C 3.016761e-03 seob7432 4C 2.272415e-03 seob2221 4C 3.044324e-03 mioc4318 4C 2.279552e-03 ncrc5079 AC 3.050155e-03 fcrcO695 4C 2.279562e-03 fcrb9481 AC 3.074875e-03 seoa8443 4C 2.285067e-03 ncrb0054 AC 3.127244e-03 fcrc6566 4C 2.298537e-03 seoa3533 4C 3.13784e-03 miobl326 4C 2.329343e-03 miod5369 4C 3.159305e-03 miob4475 4C 2.330183e-03 seoa5683 4C 3.160612e-03 ncrc9469 4C 33257e-03 fcrb2218 4C 3.164316e-03 fcrO224 4C 2.334725e-03 mioa0407 4C 3.167049e-03 miod6467 4C 2.34447e-03 fcrb7693 4C 3.203691e-03 seob4273 4C 2.355513e-03 seoa9160 4C 3.207335e-03 hfcr6700 4C 2.362015e-03 miob4836 4C 3.218792e-03 rtctO'4J3'β"' •4C"- "™3t2451826e-03 seob3869 4C 4.093483e-03 ncrb6530 4C 3.252247e-03 seoa2641 4C 4.10684e-03 ncrcOlOO 4C 3.298912e-03 seoa6620 4C 4.118774e-03 ncrc2776 4C 3.322303e-03 mioc0741 4C 4.154883e-03 ncrb3942 4C 3.332792e-03 fcrb9694 4C 4.20251e-03 ncrbO513 4C 3.349517e-03 ncrc4531 4C 4.208322e-03 ncrl387 4C 3.356296e-03 ncrb7292 4C 4.220365e-03 ncrbO145 4C 3.359507e-03 miod4140 4C 4.240281e-03 fcrb3017 4C 3.376066e-03 hfcr0045 4C 4.261381e-03 ncrc5091 4C 3.385978e-03 ncrc9877 4C 4.26595e-03 fcrcO241 4C 3.422538e-03 seoc2191 4C 4.272909e-03 seob0514 4C 3.423122e-03 mioc3716 4C 4.281847e-03 ncr8866 4C 3.437719e-03 fcrb3725 4C 4.312352e-03 fcrc6888 4C 3.449333e-03 seoa9711 4C 4.329889e-03 miob9065 4C 3.456747e-03 ncrc9772 4C 4.399985e-03 seoa0549 4C 3.456908e-03 seoa6573 4C 4.452642e-03 mioa3514 4C 3.46245e-03 mioa0380 4C 4.489956e-03 ncr3237 4C 3.46245e-03 mioal097 4C 4.509349e-03 ncrc4991 4C 3.4643e-03 seob3462 4C 4.509349e-03 seob3499 4C 3.4643e-03 mioa8811 4C 4.52055e-03 ncrc9855 4C 3.46879e-03 fcrcO959 4C 4.530274e-03 seoal036 4C 3.477733e-03 seob3154 4C 4.531125e-03 miod6029 4C 3.517177e-03 mioa0890 4C 4.570501e-03 ncr2013 4C 3.51941e-03 fcrc6335 4C 4.58749e-03 seob4972 4C 3.531512e-03 ncrc5760 4C 4.601184e-03 hfcr0130 4C 3.533831e-03 mioal354 4C 4.601629e-03 fcrb4378 4C 3.540302e-03 mioal427 4C 4.633362e-03 mioc3726 4C 3.550472e-03 ncrc3840 4C 4.633362e-03 seob2188 4C 3.584274e-03 seoa0799 4C 4.633362e-03 seobl667 4C 3.640309e-03 seoa4040 4C 4.639931e-03 seoa6661 4C 3.644681e-03 miod4348 4C 4.655192e-03 seoal720 4C 3.649977e-03 seoa7902 4C 4.66728e-03 seob2108 4C 3.666209e-03 seoa2978 4C 4.686163e-03 ncrO478 4C 3.666381e-03 ncrc5150 4C 4.691321e-03 fcrb2249 4C 3.695741e-03 ncrl563 4C 4.707547e-03 ncrb8134 4C 3.708615e-03 seob4621 4C 4.707547e-03 seob9750 4C 3.708927e-03 fcrc7047 4C 4.744327e-03 fcr6577 4C 3.735652e-03 seob5562 4C 4.746427e-03 hfcrO676 4C 3.735778e-03 ncrb4912 4C 4.752707e-03 miob2492 4C 3.735778e-03 mioa6739 4C 4.778158e-03 mioal971 4C 3.738256e-03 seoa4395 4C 4.779169e-03 seob5954 4C 3.738256e-03 hfcr6468 4C 4.79651e-03 miob8932 4C 3.752087e-03 ncrb8253 4C 4.799003e-03 ncrc4808 4C 3.775334e-03 seoa5986 4C 4.799003e-03 mioa5085 4C 3.775842e-03 miob5736 4C 4.80808e-03 seoa4586 4C 3.780119e-03 seoa7517 4C 4.870405e-03 seoa3408 4C 3.790116e-03 mioa0826 4C 4.884238e-03 hfcr0370 4C 3.8121e-03 fcrb4415 4C 4.898061e-03 mioa8774 4C 3.812303e-03 mioa6621 4C 4.904983e-03 seobll87 4C 3.817075e-03 miocll22 4C 4.93196e-03 fcr3155 4C 3.832262e-03 ncrc6795 4C 4.933324e-03 ncr2182 4C 3.847974e-03 seoa3847 4C 4.942903e-03 ncrc9557 4C 3.849741e-03 mioa9831 4C 4.946737e-03 rαiob0974 4C 3.874121e-03 fcr5625 4C 4.99519e-03 fcrb6009 4C 3.925377e-03 ncr3684 4C 5.017654e-03 mioc5643 4C 3.926872e-03 hfcr2287 4C 5.054567e-03 miod7243 4C 3.934025e-03 fcrc2576 4C 5.063792e-03 ncrc3434 4C 3.939813e-03 fcrc0430 4C 5.084925e-03 seob0200 4C 3.979023e-03 ncrc5672 4C 5.11611e-03 hfcr9613 4C 4.011004e-03 fcr6497 4C 5.124127e-03 miob4055 4C 4.011569e-03 mioa5468 4C 5.124127e-03 miob4956 4C 4.05244e-03 seoa6923 4C 5.124127e-03 miob0154 4C 4.08718e-03 miob0865 4C 5.128184e-03 seoa2442 4C 4.093483e-03 mioa8946 4C 5.185969e-03 fct0Θ5'&" 4C" 5-":r9Cf305e-03 ncr4550 4C 6.099859e-03 ncrc5039 4C 5.197151e-03 mioc7444 4C 6.13308e-03 hfcr5009 4C 5.200779e-03 seoa3704 4C 6.146625e-03 miod2639 4C 5.218734e-03 hfcrl265 4C 6.161536e-03 fcrc4360 4C 5.250167e-03 seob0321 4C 6.178138e-03 fcrb4241 4C 5.25156e-03 fcrb7981 4C 6.206858e-03 fcrc6563 4C 5.282794e-03 seob3520 4C 6.215767e-03 seoa2122 4C 5.287317e-03 ncrcOlOl 4C 6.221193e-03 fcrO768 4C 5.301658e-03 ncrb6833 AC 6.229488e-03 miod3854 4C 5.307923e-03 ncr0075 AC 6.26855e-03 fcrcl745 4C 5.32716e-03 mioa8952 4C 6.270044e-03 mioa4196 4C 5.344108e-03 ncrO766 4C 6.276933e-03 mioc8619 4C 5.353299e-03 ncrc6817 4C 6.276933e-03 ncrc4654 4C 5.369673e-03 mioa2013 4C 6.287512e-03 seob4515 4C 5.369673e-03 seob9485 AC 6.301111e-03 ncr6232 4C 5.407943e-03 miod4142 AC 6.383134e-03 mioc2074 4C 5.414445e-03 miob4593 4C 6.402894e-03 ncrc9758 4C 5.427537e-03 fcr5779 4C 6.474618e-03 ncr4545 4C 5.438486e-03 seoa6197 4C 6.486591e-03 ncr9429 4C 5.458161e-03 seob0703 4C 6.50097e-03 fcrbβllO 4C 5.478029e-03 miob6821 4C 6.505915e-03 seoa0029 4C 5.480759e-03 ncrb8343 4C 6.552284e-03 seobOCm 4C 5.509288e-03 seoa5911 4C 6.597064e-03 mioc0222 4C 5.512684e-03 mioc2166 4C 6.609859e-03 seoal856 4C 5.531453e-03 fcrb9352 4C 6.647947e-03 fcrc6854 4C 5.540234e-03 fcrb2292 AC 6.653984e-03 fcrc2082 4C 5.550695e-03 seob4039 AC 6.713668e-03 seob2987 4C 5.569207e-03 fcrb5422 4C 6.7264e-03 mioal473 4C 5.580693e-03 ncrc2119 4C 6.756046e-03 fcrb9871 4C 5.592316e-03 seob9552 4C 6.762169e-03 seob4030 4C 5.594557e-03 fcrb3841 4C 6.780973e-03 mioa2158 4C 5.639619e-03 miob6988 4C 6.80749e-03 fcr4795 4C 5.649808e-03 hfcrl760 4C 6.835569e-03 seob5726 4C 5.65605e-03 hfcr5473 4C 6.857537e-03 fcrb7852 4C 5.667225e-03 fcrc3415 4C 6.861942e-03 ncrl550 4C 5.713507e-03 hfcr5987 4C 6.867295e-03 mioa3471 4C 5.716154e-03 seoa0926 4C 6.867921e-03 mioa0132 4C 5.738039e-03 mioblll5 AC 6.869801e-03 mioa8647 AC 5.768458e-03 fcr4380 AC 6.880921e-03 mioal445 4C 5.811166e-03 miob5412 4C 6.89987e-03 seoc3588 4C 5.826485e-03 ncr6335 4C 6.907692e-03 ncrc3468 4C 5.832748e-03 seoa3628 4C 6.949533e-03 miob9052 4C 5.833135e-03 seob9772 4C 6.956817e-03 mioc5664 4C 5.835444e-03 mioa9581 4C 6.958796e-03 fcrb2137 4C 5.84394e-03 ncr2486 4C 6.982956e-03 hfcr5207 4C 5.84394e-03 miob0877 4C 6.983468e-03 mioa2327 4C 5.859459e-03 ncr0240 4C 6.999641e-03 fcrb9147 4C 5.861796e-03 seoa6137 4C 7.003723e-03 seob5458 4C 5.862878e-03 mioal337 4C 7.012816e-03 seoa5554 4C 5.877316e-03 fcr6415 4C 7.029261e-03 fcrb5254 4C 5.890258e-03 seobl385 4C 7.029563e-03 seoal552 4C 5.896449e-03 ncrb8207 4C 7.041692e-03 seoa3230 4C 5.898784e-03 hfcrl914 4C 7.057729e-03 fcrl844 4C 5.913324e-03 ncrl351 4C 7.095309e-03 fcrb5267 4C 5.942373e-03 seob2803 AC 7.108081e-03 fcrb6896 4C 5.949973e-03 seob5012 AC 7.108081e-03 seoa4524 4C 5.958281e-03 seob7474 4C 7.109226e-03 fcrc0094 4C 5.96467e-03 mioc7818 4C 7.166952e-03 miob3684 4C 5.971717e-03 mioa4241 4C 7.17269e-03 fcr6390 4C 6.000719e-03 seoa2136 4C 7.196923e-03 seobl737 4C 6.007946e-03 seoa9935 4C 7.197022e-03 fcr2074 4C 6.018802e-03 fcr5339 4C 7.199684e-03 seoc2131 4C 6.08684e-03 miob2836 4C 7.210691e-03 fcr0608 4C 6.096137e-03 mioa0104 4C 7.212787e-03 "Sebb6«3" -#C "T.2'2'95T4e-03 miob2855 4C 8.459946e-03 ncr3948 4C 7.237837e-03 seoal263 4C 8.459946e-03 mioa6093 4C 7.268139e-03 seoa7459 4C 8.497761e-03 seoal559 4C 7.27224e-03 seob2658 4C 8.497761e-03 hfcr9549 4C 7.295285e-03 ncrc6871 4C 8.532381e-03 seobll91 4C 7.325599e-03 seoa7474 4C 8.568552e-03 miob4058 4C 7.329008e-03 ncrcl502 AC 8.575665e-03 fcr5723 4C 7.369143e-03 fcrc2997 4C 8.587588e-03 mioa0328 4C 7.377639e-03 mioa9891 4C 8.689171e-03 seoc3993 4C 7.378389e-03 hfcr3928 4C 8.732239e-03 miod5703 4C 7.424044e-03 mioa2478 4C 8.749534e-03 fcr6044 4C 7.452917e-03 seoa7509 AC 8.755591e-03 seob6560 4C 7.500236e-03 mioa6738 4C 8.769774e-03 fcrβOlβ 4C 7.53305e-03 seoa7178 4C 8.77984e-03 miod6162 4C 7.584327e-03 seob5319 4C 8.811962e-03 seoa2363 4C 7.617361e-03 fcrcO654 4C 8.812335e-03 seob2169 4C 7.634987e-03 miobl506 AC 8.830271e-03 fcr6616 4C 7.680441e-03 miodl331 AC 8.912821e-03 fcr6931 4C 7.680441e-03 seoa5547 4C 8.932311e-03 seoa6152 4C 7.680441e-03 seob3169 4C 8.97374e-03 fcr4622 4C 7.681578e-03 mioa3629 4C 8.97697e-03 mioal324 4C 7.698039e-03 miob3696 4C 8.981708e-03 fcrb7944 4C 7.767483e-03 fcrc4054 4C 8.989376e-03 ncrl802 4C 7.784697e-03 mioa2413 4C 9.000025e-03 seob7729 AC 7.786058e-03 fcr2160 4C 9.030333e-03 seob2966 4C 7.806257e-03 ncr4539 4C 9.030333e-03 seob4108 4C 7.828518e-03 mioa0707 4C 9.037843e-03 ncr2484 4C 7.879438e-03 ncr3496 4C 9.054107e-03 ncrb3585 4C 7.879438e-03 ncrcO667 4C 9.054835e-03 seoa0913 AC 7.91186e-03 seob4807 AC 9.055692e-03 ncrO612 4C 7.925456e-03 ncrc9428 AC 9.100754e-03 mioc7998 4C 7.939586e-03 mioa2652 AC 9.114417e-03 seob9872 4C 7.94076e-03 miob3461 AC 9.117224e-03 mioc0375 4C 7.952967e-03 fcrb8877 AC 9.135346e-03 fcrb6768 AC 7.961352e-03 fcr0707 AC 9.142522e-03 seoa3245 4C 7.995448e-03 mioa9649 AC 9.154014e-03 miob7309 4C 8.045055e-03 miob3938 AC 9.20224e-03 hfcr5905 4C 8.045743e-03 seoa5933 AC 9.204501e-03 miob2227 4C 8.045772e-03 miob5675 AC 9.254824e-03 fcrb7113 4C 8.05715e-03 seoa2391 4C 9.263207e-03 hfcr6501 4C 8.058052e-03 fcrb6191 4C 9.290334e-03 ncrbl373 4C 8.093663e-03 ncrcO383 4C 9.294641e-03 fcrcO295 4C 8.130412e-03 miob4743 AC 9.329707e-03 mioa9717 4C 8.135586e-03 ncrb3284 AC 9.343469e-03 seob4209 4C 8.135586e-03 ncrc6953 4C 9.345199e-03 miod2665 4C 8.152135e-03 ncrc4757 4C 9.409517e-03 fcrc3358 4C 8.156756e-03 seob5032 4C 9.43118e-03 fcr4965 4C 8.164022e-03 ncrb6453 4C 9.583937e-03 mioc6997 4C 8.185087e-03 ncrc5553 4C 9.583937e-03 miobl493 4C 8.197903e-03 seob0418 4C 9.583937e-03 fcrO593 4C 8.19812e-03 seob0810 4C 9.583937e-03 fcr4129 4C 8.215271e-03 mioa4548 4C 9.611891e-03 ncrO912 4C 8.275646e-03 fcrc5846 AC 9.622113e-03 seob0466 4C 8.285506e-03 fcrbl503 AC 9.664889e-03 mioa9630 4C 8.304775e-03 ncrcl873 AC 9.678381e-03 ncrb6192 4C 8.312148e-03 ncrcO846 AC 9.679567e-03 seoc2824 4C 8.3183e-03 seob3064 4C 9.679567e-03 seoa0070 4C 8.335344e-03 seob5778 4C 9.689819e-03 seoal736 4C 8.342294e-03 fcrb5537 4C 9.69729e-03 miob9848 4C 8.369066e-03 hfcrl419 4C 9.717051e-03 seoall04 4C 8.378822e-03 mioa6035 4C 9.789009e-03 ncrO634 4C 8.421583e-03 seoa0003 4C 9.794242e-03 seoa5465 4C 8.437929e-03 seoc4416 4C 9.841173e-03 seob8065 4C 8.459463e-03 seob6492 4C 9.84478e-03 -JfC a9".r'8-'6l3-8"6e-03 fcrb8162 4C O.01 ncrbl802 4C 9.,861386e-03 fcrb3702 4C O.01 seob8817 4C 9.,894482e-03 seoa4017 AC O.01 fcrbl578 AC 9.906745e-03 seob5441 AC O.01 fcz2164 AC 9.970513e-03 mioa9792 4C O.01 seoa4452 4C 9.989774e-03 mioa2536 4C O.01 miob8214 4C O..01 seoc3876 4C O.01 miod2837 4C O.Ol hfcr5237 4C O.01 mioc4366 4C O.Ol seoa6393 AC O.01 fcr5354 AC O.Ol mioa9033 AC O.01 fcrO955 AC O.Ol fcr3823 AC O.01 fcrc3240 AC O.Ol ncrc0304 AC O.01 fcr4699 AC O.Ol ncrb4390 AC O.01 ncrO132 AC O.Ol hfcrllOl AC O.01 seoa0511 AC O.Ol fcrc0350 AC O.01 fcrb2713 AC O.Ol fcrb4570 AC O.01 miob6797 AC O.Ol hfcrO237 AC O.01 ncrbl956 AC O.Ol hfcr6716 AC O.01 fcrc5351 AC O.Ol iniocl609 AC O.01 mioa2580 AC O.Ol ncrcO292 AC O.01 fcrb3651 AC O.Ol mioc3906 AC O.01 hfcrO829 AC O.Ol seob3007 AC O.01 seobl574 AC O.Ol miod6731 AC O.01 seoa8556 AC O.Ol fcrb7588 AC O.01 mioal285 AC O.Ol seoc2226 AC O.01 seoa2162 AC O.Ol seob8693 AC O.01 seobl383 AC O.Ol mioa4064 AC O.01 hfcr6509 AC O.Ol fcrcO396 AC O.01 ncrb0033 AC O.Ol ncrb4319 AC O.01 miob4574 AC O.Ol miob9393 AC O.01 fcrl337 AC O.Ol mioc3206 AC O.01 ncrc3624 AC O.Ol seoa7478 AC O.01 fcr2167 AC O.Ol ncrc8841 AC O.01 mioa3812 AC O. Ol fcrc6990 AC O.01 miob4037 AC O.Ol seob5927 AC O.01 seoa9814 AC O.Ol rαioal626 AC O.01 seoblOOδ AC O.Ol seoa7295 AC O.01 seob6836 AC O.Ol fcrbl202 AC O.01 ncr3402 AC O.Ol fcrcl689 AC O.01 mioa4667 AC O.Ol mioa5773 AC O.01 fcrlO68 AC O.Ol seob9882 AC O.01 mioa7015 AC O.Ol ncr3434 AC O.01 miob2743 AC O.Ol miob2700 AC O.01 seoa8300 AC O.Ol seob3170 AC O.01 fcrb6785 4C O.Ol fcrcO379 AC O.01 hfcr3149 4C O.Ol miob4673 AC O.01 fcrb5527 4C O.Ol ncrc3529 AC O.01 mioa5461 4C O.Ol mioc3958 AC O.01 ncrb8451 4C O.Ol seoa0172 AC O.01 ncrc9368 4C O.Ol seob5213 AC O.01 fcrb4109 4C O.Ol seoa6344 AC O.01 seoa4264 4C O.Ol ncrc5806 AC O.01 fcrc2948 4C O.Ol seob0478 AC O.01 inioc0121 4C O.Ol mioc4247 AC O.01 miod7457 4C O.Ol fcrb3704 AC O.01 hfcr2148 4C O.Ol mioa5691 AC O.01 seob3887 4C O.Ol ncr4202 AC O.01 ncrb6087 4C O.Ol fcrcl216 AC O.01 seobl916 4C O.Ol seob6133 AC O.01 fcrc5577 4C O.Ol hfcrl671 AC O,.01 fcrb2635 4C O.Ol hfcr6256 4C O..01 ncrcO863 AC O.Ol fcrl633 4C O.,01 seob4122 AC O.Ol fcrbl741 4C O,.01 fcrb3135 4C O.Ol seoc3603 4C O..01 -£ctb68'34" TC "σ.oi mioa8864 4C 0.01 fcr2958 4C 0.01 fcrb7453 4C 0.01 seoa2428 4C 0.01 seoa9729 4C 0.01 seoal056 4C 0.01 ncrc6617 4C 0.01 miob0496 4C 0.01 hfcrl795 4C 0.01 seob3731 4C 0.01 fcrb5588 4C 0.01 seoc2447 4C 0.01 fcrb6432 4C 0.01 hfcr4114 4C 0.01 fcrcl758 4C 0.01 mioa7323 4C 0.01 fcr6881 4C 0.01 ncrb3936 4C 0.01 seoa3007 4C 0.01 miocll98 4C 0.01 seoa2957 4C 0.01 miod4407 4C 0.01 fcrO997 4C 0.01 fcrb4388 4C 0.01 fcrcl834 4C 0.01 fcr2102 4C 0.01 fcrbl320 4C 0.01 miob6437 4C 0.01 mioa5902 4C 0.01 mioc2694 4C 0.01 hfcr7966 4C 0.01 seoa4460 4C 0.01 ncr7082 4C 0.01 ncrb6432 4C 0.01 mioa4321 4C 0.01 seobl426 4C 0.01 seoc4288 4C 0.01 seob9645 4C 0.01 fcrb8119 4C 0.01 fcr4214 4C 0.01 fcrb2813 4C 0.01 ncrO847 4C 0.01 seobl906 4C 0.01 ncrc9217 4C 0.01 mioc6847 4C 0.01 mioa5097 4C 0.01 miob4793 4C 0.01 ncrb5197 4C 0.01 seoa8912 4C 0.01 ncrc9727 4C 0.01 fcrb8901 4C 0.01 seoa2639 4C 0.01 seob4896 4C 0.01 mioa2333 4C 0.01 miob3477 4C 0.01 seoc4909 4C 0.01 seobl646 4C 0.01 seoa9619 4C 0.01 ncrc9304 4C 0.01 ncr6142 4C 0.01 ncrc3536 4C 0.01 miod4342 4C 0.01 seoa0539 4C 0.01 fcrbl689 4C 0.01 seoa0844 4C 0.01 ncrc5592 4C 0.01 mioa6476 4C 0.01 seoa7094 4C 0.01 miod7351 4C 0.01 seoa8902 4C 0.01 fcrc4658 4C 0.01 mioc0899 4C 0.01 seoc7281 4C 0.01 fcr5509 4C 0.01 mioc7807 4C 0.01 ncrc4296 4C 0.01 mioclOβO 4C 0.01 seoa4708 4C 0.01 hfcr5691 4C 0.01 mioa9093 4C 0.01 seocl230 4C 0.01 mioal279 4C 0.01 ncrb8790 4C 0.01 fcrO272 4C 0.01 miob2375 4C 0.01 hfcr2850 4C 0.01 ncrc9612 4C 0.01 fcr6937 4C 0.01 mioc7370 4C 0.01 miod3468 4C 0.01 seoa5429 4C 0.01 hfcrO521 4C 0.01 hfcr6640 4C 0.01 fcrb5675 4C 0.01 seob4570 4C 0.01 ncrc3453 4C 0.01 fcrl557 4C 0.01 miob3763 4C 0.01 ncrc9867 4C 0.01 seoa0457 4C 0.01 mioc2039 4C 0.01 fcrc6642 4C 0.01 fcrb3073 4C 0.01 miod6041 4C 0.01 ncr8290 4C 0.01 ncrc3045 4C 0.01 seoal653 4C 0.01 fcr5176 4C 0.01 ncrc6382 4C 0.01 fcrb5536 4C 0.01 miob7638 4C 0.01 ncrc2685 4C 0.01 miob4860 4C 0.01 seoa7212 4C 0.01 seob0263 4C 0.01 mioc7260 4C 0.01 mioc7509 4C 0.01 miod4539 4C 0.01 ncrc3258 4C 0.01 miob7373 4C 0.01 miob3411 4C 0.01 mioa2421 4C 0.01 mioa3646 4C 0.01 ncrl235 4C 0.01 hfcr2314 4C 0.01 ncrc9517 4C 0.01 seoa4107 4C 0.01 '■fictά7Cr85- "ifC "σ.O'i seob9480 4C O.01 fcrb3244 4C 0.01 seob4766 4C O.01 seoa0931 4C 0.01 ncrb6661 4C O.01 miob3471 4C 0.01 seob9756 4C O.01 fcrb8465 4C 0.01 fcrb6829 4C O.01 seoc2253 4C 0.01 miod.6947 4C O.01 ncr7267 4C 0.01 fcrl833 4C O.01 fcrb5092 4C 0.01 fcr3983 4C O.01 ncrb4339 4C 0.01 mioal531 4C O.01 mioc7561 4C 0.01 mioa8796 4C O.01 mioa9666 4C 0.01 seoa6621 4C O.01 ncrcl765 4C 0.01 hfcr6680 4C O.01 ncr8811 4C 0.01 fcr6386 4C O.01 fcrc5797 4C 0.01 seoa3863 4C O.01 mioal079 4C 0.01 fcrl305 4C O.01 hfcr3413 4C 0.01 fcrb8092 4C O.01 miob0973 4C 0.01 fcrb2596 4C O.01 hfcrl709 4C 0.01 ncrc3141 4C O.01 hfcrO285 4C 0.01 seoa8239 4C O.01 fcr6314 4C 0.01 fcrbllδO 4C O.01 ncrO335 4C 0.01 ncrb6327 4C O.01 fcr4927 AC 0.01 fcrc2969 4C O.01 seoc0394 4C 0.01 ncrc6127 4C O.01 seob4945 4C 0.01 rαioa8539 4C O.01 fcrb4739 4C 0.01 ncr8693 4C O.01 fcrbl750 4C 0.01 fcr6266 4C O.01 fcrcOlββ 4C 0.01 fcr4272 AC O.01 seob2011 AC 0.01 fcrb2426 AC O.01 ncrc4089 4C 0.01 seob2077 AC O.01 ncrc0803 4C 0.01 ncrc2859 4C O.01 fcrc2007 4C 0.01 fcrbl763 4C O.01 hfcr2832 4C 0.01 mioa0152 AC O.01 seoa3422 4C 0.01 seoc0866 AC O.01 miob4157 4C 0.01 fcrO712 AC O.01 ncr8893 4C 0.01 ncrb7102 AC O.01 seoa3108 4C 0.01 hfcr4423 AC O.01 mioa0246 4C 0.01 ncrc2613 AC O.01 miob7391 4C 0.01 seob7764 4C O.01 mioa6091 AC 0.01 hfcrl641 4C O.01 ncr3811 AC 0.01 miod0686 4C O.01 seob5886 4C 0.01 fcrb9856 4C O.01 mioc4641 4C 0.01 miobl789 4C O.01 seob4539 4C 0.01 ncr6141 4C O.01 mioc8379 4C 0.01 mioa9935 AC O.01 seob8104 4C 0.01 seoal979 AC O.01 fcrb6251 4C 0.01 ncr7973 AC O.01 fcrcO839 4C 0.01 seoa4727 AC O.01 ncr8067 4C 0.01 fcrc5671 AC O.01 seoa2272 4C 0.01 fcrb7392 AC O.01 seoa0469 4C 0.01 seob6229 AC O.01 ncr4118 4C 0.01 seoa2819 AC O.01 seoa2817 4C 0.01 fcrb9639 AC O.01 fcrb2020 4C 0.01 fcrb4294 AC O.01 fcrb2305 4C 0.01 fcr0990 AC O.01 ncrb8392 4C 0.01 mioa3799 AC O.01 ncr7768 4C 0.01 miob0207 AC O.01 hfcr5150 4C 0.01 seoa3144 AC O.01 seob7658 4C 0.01 seob5490 AC O.01 seobl322 4C 0.01 fcrb9680 AC O,.01 ncr9919 4C 0.01 fcrl855 AC O,.01 ncr3396 4C 0.01 seoa5742 AC O,.01 miob9185 4C 0.01 fcrc4045 AC O,.01 seoa9724 4C 0.01 seob2081 AC O,.01 ncrcll76 4C 0.01 fcr7042 AC O,.01 "M'b&l4"4O ™4C τrrσi seob0831 4C 0.02 ncrc3358 4C 0.01 ncrc9483 4C 0.02 ncrc2273 4C 0.01 seob5645 4C 0.02 ncrc5608 4C 0.01 seocl264 4C 0.02 fcr3323 4C 0.01 fcr2940 4C 0.02 ncrbO262 4C 0.01 mioa2475 4C 0.02 seoa3639 4C 0.01 fcrcl660 4C 0.02 mioc4534 4C 0.01 seob9067 4C 0.02 ncrc9023 4C 0.01 ncr5713 4C 0.02 miob4238 4C 0.01 seob0745 4C 0.02 ncrO615 4C 0.01 seoa4023 4C 0.02 seob7744 4C 0.01 seob8807 4C 0.02 ncrc3072 4C 0.01 fcrbl529 4C 0.02 fcrb3298 4C 0.01 seoa3251 4C 0.02 mioa2156 4C 0.01 ncrc5363 4C 0.02 fcrc6174 4C 0.01 seob0763 4C 0.02 ncrcl952 4C 0.01 fcrb4238 4C 0.02 seoa5586 4C 0.02 miod2330 4C 0.02 seoa2962 4C 0.02 miod0441 4C 0.02 hfcrO383 4C 0.02 hfcr3441 4C 0.02 miod5698 4C 0.02 seocl539 4C 0.02 hfcr3592 4C 0.02 miob3396 4C 0.02 fcrbl381 4C 0.02 mioc2539 4C 0.02 seobl617 4C 0.02 ncr7672 4C 0.02 fcrO018 4C 0.02 miob6372 4C 0.02 mioc4925 4C 0.02 fcrb2591 4C 0.02 seobl243 4C 0.02 seob2807 4C 0.02 ncrb6037 4C 0.02 fcrc4390 4C 0.02 seob3533 4C 0.02 hfcr3404 4C 0.02 fcrbl550 4C 0.02 fcrb2308 4C 0.02 miodl448 4C 0.02 fcrb2160 4C 0.02 miob4712 4C 0.02 fcrc4896 4C 0.02 ncrb3301 4C 0.02 miob3044 4C 0.02 fcrb2658 4C 0.02 fcrcl763 4C 0.02 ncrc4885 4C 0.02 seoa6887 4C 0.02 seoa7935 4C 0.02 ncrc7043 4C 0.02 seoc4052 4C 0.02 ncrc3276 4C 0.02 seoa0256 4C 0.02 miob4975 4C 0.02 seoa0219 4C 0.02 seob6680 4C 0.02 mioa0647 4C 0.02 fcrb2690 4C 0.02 mioc4766 4C 0.02 mioal062 4C 0.02 mioa0582 4C 0.02 fcr2859 4C 0.02 seob6041 4C 0.02 hfcr2598 4C 0.02 mioal055 4C 0.02 ncrc3953 4C 0.02 ncrc5088 4C 0.02 fcrbl721 4C 0.02 miob4570 4C 0.02 mioc5307 4C 0.02 fcrcO529 4C 0.02 fcrb3519 4C 0.02 miod2128 4C 0.02 fcr5204 4C 0.02 miob2687 4C 0.02 seob0253 4C 0.02 ncr4416 4C 0.02 mioc4290 4C 0.02 fcrb5204 4C 0.02 fcr5618 4C 0.02 ncrb8063 4C 0.02 seoa3555 4C 0.02 miob7109 4C 0.02 seob0872 4C 0.02 fcr2299 4C 0.02 ncrc6981 4C 0.02 seobl834 4C 0.02 ncrc3544 4C 0.02 fcr7403 4C 0.02 hfcr5181 4C 0.02 fcrb6062 4C 0.02 seoal584 4C 0.02 ncr5939 4C 0.02 seob2797 4C 0.02 seocl948 AC 0.02 seob9635 4C 0.02 miobl337 4C 0.02 seocl664 4C 0.02 seoa7608 4C 0.02 fcrb9499 4C 0.02 miob8657 4C 0.02 seob4612 4C 0.02 fcr2276 4C 0.02 seob7622 4C 0.02 hfcrO225 4C 0.02 seoa8486 4C 0.02 if ' !! !. I! "-. U I! •'••■hd>r30S-""Ii™'-ic :":""ci:O2" ncrcO883 4C 0.02 ncr2717 4C 0.02 fcr5425 4C 0.02 seoal582 4C 0.02 miobl734 4C 0.02 mioa7602 4C 0.02 ncr5066 4C 0.02 mioc0090 4C 0.02 fcr6573 4C 0.02 mioc3930 4C 0.02 fcr4634 4C 0.02 hfcr6366 4C 0.02 ncr9165 4C 0.02 fcrb9751 4C 0.02 mioa8773 4C 0.02 mioal058 4C 0.02 fcr5889 4C 0.02 seob3356 4C 0.02 ncrc2413 4C 0.02 seob4075 4C 0.02 fcrb5702 4C 0.02 ncrb8665 4C 0.02 mioc2219 4C 0.02 seoa6038 4C 0.02 seobl052 4C 0.02 fcrbl528 4C 0.02 miocl205 4C 0.02 seoa5382 4C 0.02 mioa3342 4C 0.02 seoa6155 4C 0.02 mioal071 4C 0.02 ncrc5072 4C 0.02 seob4752 4C 0.02 seoa9817 4C 0.02 hfcr2295 4C 0.02 fcrcl965 4C 0.02 ncr2408 4C 0.02 mioc2806 4C 0.02 miob0202 4C 0.02 mioa0204 4C 0.02 hfcr3180 4C 0.02 seoc4505 4C 0.02 seoa6412 4C 0.02 hfcr3436 4C 0.02 seoa8597 4C 0.02 miob8609 4C 0.02 fcrbl657 4C 0.02 fcrb2330 4C 0.02 seob2139 4C 0.02 ncrcl402 4C 0.02 seoa9873 4C 0.02 fcrc4485 4C 0.02 miod3591 4C 0.02 ncr2792 4C 0.02 fcr4885 4C 0.02 ncrc6439 4C 0.02 fcr3620 4C 0.02 hfcr6164 4C 0.02 mioa4285 4C 0.02 seob3451 4C 0.02 ncrc3416 4C 0.02 seoai883 4C 0.02 ncr3843 4C 0.02 fcr5112 4C 0.02 seoal789 4C 0.02 mioa5614 4C 0.02 seoa4158 4C 0.02 seob9430 4C 0.02 fcrc7228 4C 0.02 fcr6534 4C 0.02 ncrb8820 4C 0.02 miod6068 4C 0.02 seob5174 4C 0.02 fcrb4252 4C 0.02 fcrbl962 4C 0.02 seob2642 4C 0.02 mioc3208 4C 0.02 fcrb2189 4C 0.02 miob6610 4C 0.02 seob4118 4C 0.02 seob9266 4C 0.02 seoa8397 4C 0.02 seob4726 4C 0.02 seoa5302 4C 0.02 seoal065 4C 0.02 seob9614 4C 0.02 seob5044 4C 0.02 miob5646 4C 0.02 ncrc2018 4C 0.02 seoal571 4C 0.02 ncrc4313 4C 0.02 hfcrlllδ 4C 0.02 fcrb6436 4C 0.02 seob3464 4C 0.02 ncr3118 4C 0.02 ncrc9117 4C 0.02 seob8255 4C 0.02 miob0926 4C 0.02 seob6096 4C 0.02 ncrbO328 4C 0.02 ncrb7726 4C 0.02 ncrcl751 4C 0.02 fcrb2223 4C 0.02 fcrcO651 4C 0.02 hfcr2803 4C 0.02 ncr3778 4C 0.02 ncrc7131 4C 0.02 hfcrl856 4C 0.02 mioc2173 4C 0.02 hfcr6932 4C 0.02 mioa6913 4C 0.02 seoblllδ 4C 0.02 ncrb6818 4C 0.02 ncrbO696 4C 0.02 seoa8177 4C 0.02 fcrbl312 4C 0.02 fcrc2670 4C 0.02 fcrb6734 4C 0.02 seoa5894 4C 0.02 seoa2620 4C 0.02 ncr9549 4C 0.02 ncrc0640 4C 0.02 mioa2851 4C 0.02 ncr9108 4C 0.02 ncrcl231 4C 0.02 seob9353 4C 0.02 fcr5316 4C 0.02 "-fdtbmr1""■"••"4C •'-"Orσ2 miod7337 4C 0.03 miob8341 4C O.02 fcrc2678 4C 0.03 ncrc2888 4C O.02 seoa8401 4C 0.03 hfcr2629 4C O.02 hfcrO476 AC 0.03 seoal089 4C O.02 seob2134 AC 0.03 mioa5531 4C O.02 seob7404 AC 0.03 fcrcl947 4C O.02 fcrb8949 4C 0.03 ncrb8751 4C O.02 ncrc4903 4C 0.03 miob6456 4C O.02 seob2750 AC 0.03 ncrc2472 AC O.02 fcrb5928 AC 0.03 seoa0949 4C O.02 ncr3465 AC 0.03 fcrb8014 4C O.02 seoc2173 4C 0.03 seob2816 4C O.02 mioc4420 4C 0.03 seoa8640 4C O.02 seob7946 4C 0.03 ncrb8437 4C O.02 mioc2950 4C 0.03 miod6560 4C O.02 seoc3980 AC 0.03 mioblOOl AC O.02 mioa0862 AC 0.03 fcrbl720 4C O.02 seoa9855 AC 0.03 seoa3287 4C O.02 mioa2522 AC 0.03 miob4876 4C O.02 seoc4187 AC 0.03 seob4992 4C O .02 ncrb8203 AC 0.03 miob8947 4C O.02 ncrc5019 AC 0.03 miob3690 4C O.02 seob0182 4C 0.03 fcrbl547 4C O.02 hfcr3634 4C 0.03 miodl542 4C O.02 ncr2966 4C 0.03 ncrl912 4C O.02 ncrb8649 4C O.'03 fcrc5160 4C O .02 ncrb8821 4C 0.03 fcrb4275 4C O.02 fcrb6460 4C 0.03 mioa4738 4C O.02 fcrb7616 4C 0.03 ncrcO583 4C O .02 ncr8995 4C 0.03 ncr5488 4C O,.02 fcrb3306 4C 0.03 fcrcO591 4C O,.02 fcrb4599 4C 0.03 mioal494 4C 0..02 fcrb9253 AC 0.03 ncrcl595 4C 0,.02 seoa7442 AC 0.03 fcr6592 4C 0,.02 ncr4551 AC 0.03 seob4835 4C 0..02 seoa9930 AC 0.03 fcrb7951 4C 0..03 hfcr2820 4C 0.03 mioc2019 4C 0.,03 mioc0317 AC 0.03 fcrb2715 4C 0.,03 seobl770 AC 0.03 seoa7223 4C 0.,03 seob3468 AC 0.03 seob2959 4C 0.,03 mioa6582 AC 0.03 seob9241 4C 0.,03 miob3531 AC 0.03 fcrclO19 4C 0.03 seoa9889 AC 0.03 seob4480 4C 0.03 seoa7587 AC 0.03 raioa0308 4C 0.03 mioa2268 AC 0.03 ncr8542 4C 0.03 fcrb6102 AC 0.03 fcrb6185 4C 0.03 ncrl876 AC 0.03 mioa5836 4C 0.03 seoa3359 AC 0.03 seocl234 4C 0.03 mioa8984 AC 0.03 seob3485 4C 0.03 seob0298 AC 0.03 seob3076 4C 0.03 ncrb2544 AC 0.03 fcrb6747 4C 0.03 fcrb5416 AC 0.03 fcrb2040 4C 0.03 fcrb2198 AC 0.03 hfcr2789 4C 0.03 miod5030 AC 0.03 ncrl526 4C 0.03 seoa7249 AC 0.03 ncrcl999 4C 0.03 ncrc4047 AC 0.03 fcrb4696 4C 0.03 seob8311 AC 0.03 ncrc8881 4C 0.03 ncrO976 AC 0.03 fcrb2353 4C 0.03 mioa8548 AC 0.03 mioa6174 4C 0.03 fcr5222 AC 0.03 seoa9389 4C 0.03 seoa3910 AC 0.03 seoa2777 4C 0.03 ncrcO793 AC 0.03 seob6558 4C 0.03 mioc8694 AC 0.03 seoa2381 4C 0.03 fcrb3169 AC 0.03 '•Ϊc't65r42-"•""TC ■'- σ."0"3 miod2025 4C O.03 hfcr2966 4C 0.03 ncrl352 4C O.03 fcrc6970 4C 0.03 fcrb5296 4C O.03 miob5699 4C 0.03 fcrbO265 4C O.03 fcrO946 4C 0.03 seoal460 4C O.03 ncr4140 4C 0.03 seob8099 4C O.03 fcrb2592 4C 0.03 mioc3574 4C O.03 mioa9060 4C 0.03 fcrb2090 4C O.03 mioal953 4C 0.03 miob0178 AC O.03 fcrb2325 4C 0.03 ncrc5631 AC O.03 fcrb4781 4C 0.03 fcrb4248 AC O.03 miocl928 4C 0.03 ncr0019 AC O.03 seob3513 4C 0.03 mioa3620 AC O.03 fcrb8664 4C 0.03 fcrb2472 AC O.03 mioa3944 4C 0.03 fcrb3515 AC O.03 seobl660 4C 0.03 mioal380 AC O.03 ncrc2940 4C 0.03 ncrb8239 AC O.03 ncrcO413 4C 0.03 fcr2196 AC O.03 seob5004 4C 0.03 mioa8912 AC O.03 mioa2818 4C 0.03 ncrbl438 AC O.03 ncrc4633 4C 0.03 mioa6268 AC O.03 seob3455 4C 0.03 seoa9739 AC O.03 mioc5740 4C 0.03 seoc2246 AC O.03 seob7981 4C 0.03 ncr6072 AC O.03 fcrc6609 4C 0.03 miob0361 AC O.03 ncrO194 4C 0.03 miob6373 AC O.03 ncrc4794 4C 0.03 ncr3268 AC O.03 fcr6150 4C 0.03 miob2067 AC O..03 ncrb3003 4C 0.03 ncrcO856 AC O.03 fcrb8908 4C 0.03 fcrb7321 AC O.03 ncr3172 4C 0.03 fcrb3083 AC O,.03 fcrb5645 4C 0.03 mioblOlβ AC O,.03 hfcrO489 AC 0.03 seoa8814 AC O,.03 mioa4721 4C 0.03 hfcr4462 AC O,.03 ncrcl885 4C 0.03 ncr4656 AC O,.04 miodl558 4C 0.03 mioal621 AC O..04 miod6437 4C 0.03 fcr5288 AC O..04 miob3911 AC 0.03 fcr4954 AC O..04 ncr2861 AC 0.03 mioc2592 AC O..04 fcrO529 AC 0.03 hfcr7968 AC O.,04 mioa8886 AC 0.03 ncrc5207 AC O.,04 ncrc4985 AC 0.03 miob9284 AC O.,04 mioc3127 AC 0.03 miod4449 AC O.,04 ncrcO259 AC 0.03 seob4197 AC O.04 fcrb6725 AC 0.03 seob4492 AC O.,04 ncrc9055 AC 0.03 seoa8870 AC O.,04 mioa4135 AC 0.03 fcrb6874 AC O.04 ncr3339 AC 0.03 ncr2269 AC O.04 ncr4452 AC 0.03 miod2635 AC O.04 seoa0174 AC 0.03 seob0846 AC O.OA fcrll73 AC 0.03 fcrb3237 AC O.04 fcr5755 AC 0.03 hfcr4444 AC O.04 seoa4167 AC 0.03 miodl389 AC O.04 mioc8682 AC 0.03 fcrO842 AC O.04 fcrcO771 AC 0.03 fcrb5751 AC O.04 seob0228 AC 0.03 fcrb8852 AC O.04 ncrc4600 AC 0..03 ncrcl049 AC O.04 seob8355 AC 0.03 fcrb5177 AC O.04 fcrb6715 4C 0.03 seobll45 AC O.04 fcrb2800 4C 0.03 fcrb9390 AC O.04 mioc7360 4C 0,.03 fcrO779 AC O.04 mioa5326 AC 0,.03 seob3699 AC O.04 hfcrll89 AC 0..03 fcrb7036 AC O.04 seobl093 AC 0,.03 ncr7852 AC O.04 "fc'tb96«9-"~" "4'C """ "ϋϊ'04 seoc5627 4C O.04 ncrc4226 4C 0,.04 fcr0860 4C O.04 seoa7626 4C 0..04 fcrblO17 4C O.04 ncrc2928 AC 0..04 fcrOOβl 4C O.04 fcrbl834 AC 0.,04 hfcr3022 4C O.04 miob3426 4C 0..04 hfcr9290 4C O.04 ncrc5758 4C 0..04 fcrO986 4C O.04 fcr2018 4C 0..04 mioa3439 4C O.04 seoa3489 4C 0.,04 miod6044 4C O.04 hfcr2516 4C 0.,04 seob0046 4C O.04 crO682 4C 0.04 ncrc0704 4C O.04 miob6595 4C 0.,04 fcrb5639 4C O.04 seoa9131 4C 0.,04 seoal497 4C O.04 ncrO547 4C 0.04 seobl057 4C O.04 fcrO776 4C 0.04 ncrbl456 4C O.04 fcrb4333 4C 0.04 fcr7026 4C O.04 hfcrl304 4C 0.04 seoa0740 4C O.04 fcr5006 4C 0.04 mioc4696 4C O.04 fcrb4340 4C 0.04 fcrb4375 4C O.04 mioc8479 4C 0.04 hfcrO242 4C O.04 miodll08 4C 0.04 mioa5586 AC O.04 mioa9007 4C 0.04 ncrb4538 4C O.04 seoa2041 4C 0.04 hfcr5383 4C O.04 seob3751 4C 0.04 miod2412 4C O.04 hfcrl202 4C 0.04 fcrb5449 4C O.04 ncrc5296 4C 0.04 seobl972 AC O.04 seob3734 4C 0.04 ncrc4000 AC O.04 ncrO531 4C 0.04 fcrb2350 4C O.04 ncrc8976 4C 0.04 miod2323 4C O.04 fcrcO559 4C 0.04 mioa8314 4C O.04 miob3131 4C 0.04 mioa7299 4C O.04 fcrb7584 4C 0.04 hfcrO496 4C O.04 seocll59 4C 0.04 ncrc9331 AC O,.04 seoc0778 4C 0.04 mioal276 AC O.04 miob0939 4C 0.04 ncr7345 AC O,.04 ncr9337 4C 0.04 hfcr2544 AC O,.04 fcrb4270 4C 0.04 hfcrl314 4C O,.04 ncrl437 4C 0.04 seoa2141 AC O..04 ncr9596 4C 0.04 fcrb9714 AC O..04 ncrc4875 4C 0.04 ncr3037 AC O..04 ncr3614 4C 0.04 miod7440 4E 2.,44e-07 seob0497 4C 0.04 seoc2029 4E 6.,23e-07 hfcrO675 4C 0.04 mioc7170 4E 2.,53e-06 miob3443 4C 0.04 mioc2561 4E 3.,17e-06 fcrbl724 4C 0.04 mioa2072 4E 3.,26e-06 seobl748 4C 0.04 seob5523 4E 4.,23e-06 seobl420 4C 0.04 seob4419 4E 4.27e-06 hfcr6931 4C 0.04 fcrc3993 4E 4.37e-06 seoc5285 4C 0.04 seoa4461 4E 5.29e-06 seob9543 4C 0.04 seoaβ032 4E 5.48e-06 fcr4832 4C 0.04 miod5349 4E 6.31e-06 mioa3786 4C 0.04 mioc3430 4E 7.49e-06 mioc3618 4C 0.04 seoa8894 4E 1.le-05 seob6582 4C 0.04 fcrb4534 4E 1.46e-05 fcr6412 4C 0.04 seob2697 4E 1.66e-05 iniob9121 4C 0.04 seoa4518 4E 2.47e-05 fcrc4948 4C 0.04 seoa6133 4E 2.48e-05 ncr2304 4C 0.04 miobβ562 4E 2.52e-05 seoa3335 4C 0.04 seoa8566 4E 3.22e-05 fcr4057 4C 0.04 ncr7668 4E 4.63e-05 seobl746 4C 0.04 fcrb3578 4E 4.7e-05 seob4213 4C 0.04 fcr2952 4E 5.le-05 ncrc4247 4C 0.04 miod5123 4E 5.49e-05 seoa6697 4C 0.04 seob4333 4E 5.73e-05 "sdo'b6-&-53" •""-•••|E ''rJ":"5i:"74e-b5 fcrb8504 4E 3.99e-04 mioa3963 4E 6.04e-05 fcr5779 4E 4.02e-04 miod7421 4E 6.08e-05 miob7922 4E 4.07e-04 mioa2327 4E 6.21e-05 fcrb7830 4E 4.09e-04 seoa9373 4E 6.54e-05 fcrb4428 4E 4.19e-04 mioa2319 4E 8.18e-05 mioc8057 4E 4.39e-04 ncrb2092 4E 8.8e-05 fcrl463 4E 4.77e-04 mioal370 4E 8.87e-05 fcr2103 4E 4.84e-04 mioc8410 4E 9.18e-05 miod6845 4E 4.99e-04 seoa9482 4E 9.27e-05 ncrc5663 4E 5.0e-04 mioa3492 4E 9.61e-05 seob6844 4E 5.04e-04 fcrb7339 4E 9.87e-05 ncrc4597 4E 5.18e-04 fcrb8516 4E 9.9e-05 ncrc4757 4E 5.23e-04 fcr4477 4E 1.01e-04 seob9282 4E 5.33e-04 fcrc3739 4E 1.03e-04 seoa7530 4E 5.56e-04 fcrbO265 4E 1.2e-04 seob0248 4E 5.56e-04 seob6576 4E 1.27e-04 seoc6703 4E 5.75e-04 seob6189 4E 1.32e-04 miob9907 4E 5.82e-04 mioa5085 4E 1.33e-04 fcr2607 4E 5.84e-04 miod7414 4E 1.36e-04 ncrc8903 4E 5.88e-04 mioc2094 4E 1.38e-04 seoa4739 4E 5.91e-04 seoal427 4E 1.38e-04 fcr4831 4E 5.97e-04 seoa2585 4E 1.47e-04 mioa0595 4E 6.04e-04 fcrb2536 4E 1.53e-04 seob9145 4E 6.11e-04 seob6851 4E 1.55e-04 mioal662 4E 6.2e-04 fcrb5850 4E 1.56e-04 fcrc3750 4E 6.22e-04 miob8812 4E 1.56e-04 miod4759 4E 6.36e-04 fcrb3461 4E 1.6e-04 fcrc6826 4E 6.39e-04 seocl023 4E 1.66e-04 mioc7686 4E 6.41e-04 seob4029 4E 1.69e-04 ncrb2266 4E 6.45e-04 seoa0536 4E 1.71e-04 ncrclO49 4E 6.5e-04 πιiod5651 4E 1.77e-04 ncre3453 4E 6.5e-04 seoa2S70 4E 1.77e-04 fcr2573 4E 6.62e-04 seob5748 4E 1.79e-04 mioa2537 4E 6.62e-04 miod5505 4E 1.89e-04 fcrc3009 4E 6.69e-04 seob6751 4E 2.02e-04 mioa6418 4E 7.09e-04 seoc2264 4E 2.1e-04 mioc0950 4E 7.19e-04 fcrb2554 4E 2.2e-04 miodl925 4E 7.23e-04 fcrcl381 4E 2.28e-04 mioc5532 4E 7.27e-04 miob0167 4E 2.32e-04 mioc4089 4E 7.46e-04 mioc4022 4E 2.34e-04 mioc7362 4E 7.52e-04 seob0065 4E 2.35e-04 mioc7764 4E 7.57e-04 fcrc5937 4E 2.36e-04 seob9960 4E 7.63e-04 seoc0999 4E 2.44e-04 seoa8388 4E 7.64e-04 fcrcO554 4E 2.48e-04 fcrc6976 4E 7.65e-04 seocl203 4E 2.49e-04 seob3303 4E 7.67e-04 hfcrlO73 4E 2.54e-04 ncrO853 4E 7.96e-04 seob4191 4E 2.69e-04 fcrbl496 4E 8.19e-04 mioc2726 4E 2.7e-04 seoa8979 4E 8.23e-04 miob8694 4E 2.87e-04 ncrc2080 4E 8.53e-04 mioc6260 4E 2.92e-04 mioa5355 4E 8.58e-04 seoa4053 4E 3.03e-04 ncr8041 4E 8.61e-04 fcrcO112 4E 3.06e-04 seob2994 4E 8.67e-04 ncrO153 4E 3.07e-04 fcrb8668 4E 8.75e-04 ncrl631 4E 3.07e-04 ncrc9784 4E 8.77e-04 seoa4366 4E 3.08e-04 fcrc7102 4E 8.87e-04 seoc6169 4E 3.11e-04 mioa6731 4E 8.9e-04 mioal585 4E 3.13e-04 ncr5568 4E 9.0e-04 seoa2949 4E 3.29e-04 mioc4667 4E 9.01e-04 fcrcO487 4E 3.31e-04 ncr0808 4E 9.12e-04 mioa7317 4E 3.37e-04 hfcr3019 4E 9.19e-04 seob5743 4E 3.48e-04 fcrc3048 4E 9.26e-04 seob7584 4E 3.63e-04 fcrb9454 4E 9.3e-04 ncrcO342 4E 3.76e-04 miod5369 4E 9.71e-04 se"oc'ϊ'9W "* '4E'' "•-r:SIe-04 ncr2905 4E 1.815247e-03 hfcr6486 4E 9.83e-04 seob6506 4E 1.826439e-03 ncrb5595 4E 1.013841e-03 ncrc5054 4E 1.860924e-03 fcrb7240 4E 1.015969e-03 seob6525 4E 1.861275e-03 fcrb2933 4E 1.016356e-03 fcrb8161 4E 1.885852e-03 hfcr5381 4E 1.016874e-03 mioal520 4E 1.887743e-03 miodl236 4E 1.028284e-03 ncr4009 4E 1.89041e-03 seob6446 4E 1.030592e-03 fcrb8740 4E 1.892065e-03 miob7105 4E 1.073216e-03 mioc8423 4E 1.916815e-03 fcrc7388 4E 1.081745e-03 ncrc9729 4E 1.919544e-03 miob3982 4E 1.105438e-03 fcrb2871 4E 1.964974e-03 seoa0740 4E 1.112372e-03 miod0592 4E 1.985622e-03 fcrbl855 4E 1.142061e-03 ncrc0150 4E 1.985831e-03 fcrb9269 4E 1.150659e-03 miod5894 4E 2.014527e-03 mioa3395 4E 1.154329e-03 miod0340 4E 2.021229e-03 seob8368 4E 1.154981e-03 seob4560 4E 2.021887e-03 seob0122 4E 1.171391e-03 fcrb8080 4E 2.025786e-03 mioa0535 4E 1.173792e-03 mioc0824 4E 2.034483e-03 fcrbl539 4E 1.177578e-03 ncrb8330 4E 2.048755e-03 seocl593 4E 1.178329e-03 fcrb5675 4E 2.051294e-03 miob9336 4E 1.178419e-03 mioa8899 4E 2.051294e-03 seob3191 4E 1.178567e-03 fcrb9720 4E 2.057033e-03 ncrc0090 4E 1.185916e-03 miocll07 4E 2.070013e-03 miod6238 4E 1.278208e-03 miob9163 4E 2.097664e-03 miodl942 4E 1.305311e-03 miob5432 4E 2.108077e-03 ncr4539 4E 1.309997e-03 fcrbl733 4E 2.120875e-03 mioa0909 4E 1.312146e-03 fcrb9843 4E 2.120966e-03 seoal844 4E 1.345317e-03 fcrc6916 4E 2.121527e-03 rαioc2880 4E 1.356947e-03 ncrO238 4E 2.1386e-03 seob6368 4E 1.361315e-03 mioa3598 4E 2.13862e-03 mioc2348 4E 1.386736e-03 mioc7895 4E 2.143605e-03 fcrl756 4E 1.392986e-03 seob6670 4E 2.155534e-03 miod6038 4E 1.395436e-03 seob8501 4E 2.166556e-03 miodOOδO 4E 1.423952e-03 miod2525 4E 2.174809e-03 miob3348 4E 1.428182e-03 fcrb2624 4E 2.174826e-03 miodll95 4E 1.431152e-03 fcrb6187 4E 2.177878e-03 ncr7753 4E 1.444958e-03 seoa0115 4E 2.17824e-03 mioc6987 4E 1.45158e-03 seocl906 4E 2.1992e-03 seoa3516 4E 1.46015e-03 mioc6312 4E 2.199817e-03 ncr0025 4E 1.469905e-03 fcr3121 4E 2.234303e-03 fcr3101 4E 1.487316e-03 seoclll8 4E 2.258564e-03 ncr7595 4E 1.511845e-03 fcr7419 4E 2.260166e-03 fcr4128 4E 1.53383e-03 seob7432 4E 2.272415e-03 ncrc5162 4E 1.553082e-03 fcrcO695 4E 2.279562e-03 ncr5168 4E 1.55656e-03 seoal065 4E 2.312579e-03 fcrb8187 4E 1.573331e-03 fcrcβOlβ 4E 2.3165e-03 miodlβll 4E 1.590472e-03 miobl326 4E 2.329343e-03 seoa9740 4E 1.598061e-03 miob4475 4E 2.330183e-03 mioa3080 4E 1.603678e-03 fcrb3134 4E 2.402582e-03 mioc0052 4E 1.624699e-03 ncrb5254 4E 2.446454e-03 miob9209 4E 1.634228e-03 mioc6114 4E 2.46067e-03 ncrc5569 4E 1.637974e-03 fcrc5142 4E 2.464685e-03 mioc6075 4E 1.640004e-03 seoa3670 4E 2.543649e-03 seob0376 4E 1.666731e-03 seoa8232 4E 2.578028e-03 seoa4305 4E 1.678116e-03 ncrcl578 4E 2.579378e-03 mioal015 4E 1.68644e-03 miod4332 4E 2.601957e-03 seob0304 4E 1.693307e-03 ncrc5061 4E 2.618499e-03 fcrc2090 4E 1.719751e-03 seocl402 4E 2.660096e-03 fcrb3165 4E 1.760242e-03 mioc8434 4E 2.661058e-03 seoal598 4E 1.770719e-03 seob7465 4E 2.711845e-03 fcrc2099 4E 1.790284e-03 fcrb4409 4E 2.722505e-03 seobl766 4E 1.794891e-03 seoc0276 4E 2.725532e-03 fcrc5516 4E 1.799844e-03 mioal660 4E 2.749542e-03 fcrbl420 4E 1.801622e-03 seob3307 4E 2.811695e-03 ffii;όc3§"62"'" ■'■■•"""2"."8'2%Ttf7e-03 seoc3515 4E 4.159057e-03 seoa9814 4E 2.836239e-03 seoa8844 4E 4.195788e-03 seob9946 4E 2.84126e-03 ncrb7292 4E 4.220365e-03 seob4545 4E 2.842281e-03 fcrb9649 4E 4.270145e-03 mioc7744 4E 2.845792e-03 seoc2191 4E 4.272909e-03 fcrb4391 4E 2.852331e-03 mioc3716 4E 4.281847e-03 fcrO224 4E 2.870823e-03 seoa9711 4E 4.329889e-03 seoa7478 4E 2.876929e-03 fcrb8467 4E 4.415815e-03 miob8146 4E 2.888517e-03 mioc0347 4E 4.417094e-03 mioa7140 4E 2.898369e-03 seoa6573 4E 4.452642e-03 ncrc6359 4E 2.898846e-03 seob0168 4E 4.459519e-03 seoc3854 4E 2.908161e-03 fcrcO959 4E 4.530274e-03 ncrc3464 4E 2.965232e-03 fcrc6335 4E 4.58749e-03 mioa0702 4E 2.977964e-03 mioal354 4E 4.601629e-03 seob9649 4E 2.997767e-03 fcrb2318 4E 4.630688e-03 seob2221 4E 3.044324e-03 miod4686 4E 4.657868e-03 ncrc5079 4E 3.050155e-03 ncrc5150 4E 4.691321e-03 fcrb9481 4E 3.074875e-03 fcrb7931 4E 4.703478e-03 seoa6304 4E 3.095502e-03 seoa7129 4E 4.719984e-03 ncrb0054 4E 3.127244e-03 seob5562 4E 4.746427e-03 seoa3533 4E 3.13784e-03 hfcr5498 4E 4.758155e-03 mioa2185 4E 3.147097e-03 mioa2691 4E 4.758155e-03 mioa0407 4E 3.167049e-03 ncr3369 4E 4.770221e-03 fcrb7693 4E 3.203691e-03 mioa6739 4E 4.778158e-03 fcrbl552 4E 3.209013e-03 seoa4395 4E 4.779169e-03 ncrO438 4E 3.245826e-03 mioa0826 4E 4.884238e-03 ncrb6530 4E 3.252247e-03 mioa6621 4E 4.904983e-03 mioa0187 4E 3.279663e-03 fcrb5438 4E 4.92339e-03 fcrc3211 4E 3.326366e-03 seoa3847 4E 4.942903e-03 fcr5075 4E 3.353508e-03 mioa9831 4E 4.946737e-03 ncrbO145 4E 3.359507e-03 fcrc6174 4E 4.953862e-03 fcrb3017 4E 3.376066e-03 fcrb3192 4E 4.985296e-03 ncr8866 4E 3.437719e-03 ncr3684 4E 5.017654e-03 mioc7441 4E 3.437924e-03 hfcr2287 4E 5.054567e-03 ncrc7040 4E 3.438068e-03 seobl318 4E 5.055898e-03 seoc7104 4E 3.438068e-03 fcrc2576 4E 5.063792e-03 fcrc6888 4E 3.449333e-03 fcrc0430 4E 5.084925e-03 miob9065 4E 3.456747e-03 miob8583 4E 5.097523e-03 fcrb2307 4E 3.463155e-03 ncrc5672 4E 5.11611e-03 miob7290 4E 3.492188e-03 ncr2862 4E 5.163569e-03 ncr2013 4E 3.51941e-03 miob6364 4E 5.172374e-03 seob4972 4E 3.531512e-03 ncrc5500 4E 5.175143e-03 mioc3726 4E 3.550472e-03 seob3204 4E 5.176722e-03 seob2188 4E 3.584274e-03 mioa8946 4E 5.185969e-03 seob2797 4E 3.6022e-03 ncrc5039 4E 5.197151e-03 seoal749 4E 3.637644e-03 miod2639 4E 5.218734e-03 seoa6661 4E 3.644681e-03 fcrc4360 4E 5.250167e-03 ncrO478 4E 3.666381e-03 miod3592 4E 5.253602e-03 fcrb2249 4E 3.695741e-03 ncrbl398 4E 5.295501e-03 ncrO451 4E 3.707725e-03 miod3854 4E 5.307923e-03 fcr6577 4E 3.735652e-03 miod3254 4E 5.326719e-03 fcr6018 4E 3.762242e-03 mioc8619 4E 5.353299e-03 ncrc4808 4E 3.775334e-03 ncrc9758 4E 5.427537e-03 hfcr0370 4E 3.8121e-03 fcrbδllO 4E 5.478029e-03 seobll87 4E 3.817075e-03 fcr0990 4E 5.490991e-03 fcr3155 4E 3.832262e-03 fcrbl684 4E 5.495549e-03 ncr2182 4E 3.847974e-03 fcrb6650 4E 5.525357e-03 mioc5643 4E 3.926872e-03 fcrc6854 4E 5.540234e-03 mioc7084 4E 3.93591e-03 ncrdl382 4E 5.540547e-03 seoall73 4E 3.950326e-03 fcrc2082 4E 5.550695e-03 ncrb4025 4E 4.052138e-03 rαioc3296 4E 5.567015e-03 miob4956 4E 4.05244e-03 fcrb7852 4E 5.667225e-03 miod6018 4E 4.139368e-03 fcrO253 4E 5.72015e-03 mioc0741 4E 4.154883e-03 mioa8647 4E 5.768458e-03 fcrb'5Ϊ6'4w "--"'4E'1"""-"'k'iS1Α19e-03 hfcrO517 4E 7.704251e-03 seoc3588 4E 5.826485e-03 fcrb7944 4E 7.767483e-03 mioc5664 4E 5.835444e-03 fcrb9655 4E 7.792172e-03 fcrb9147 4E 5.861796e-03 ncrc4226 4E 7.824984e-03 ncrc4654 4E 5.866006e-03 seob6177 4E 7.843092e-03 fcrb5267 4E 5.942373e-03 ncrbl670 4E 7.921435e-03 fcrbl687 4E 5.960627e-03 mioc7998 4E 7.939586e-03 fcrc0094 4E 5.96467e-03 seob2775 4E 7.953567e-03 fcr6390 4E 6.000719e-03 miob2227 4E 8.045772e-03 seoa0470 4E 6.100317e-03 fcrb7113 4E 8.05715e-03 ncr3827 4E 6.109319e-03 fcrcO295 4E 8.130412e-03 hfcrl265 4E 6.161536e-03 miod2665 4E 8.152135e-03 fcrb7529 4E 6.171216e-03 fcrc3358 4E 8.156756e-03 seob3520 4E 6.215767e-03 fcr4965 4E 8.164022e-03 ncrbl337 4E 6.21911e-03 mioc6997 4E 8.185087e-03 ncrbl515 4E 6.341156e-03 miobl493 4E 8.197903e-03 miod4142 4E 6.383134e-03 seoc2220 4E 8.240295e-03 miob4593 4E 6.402894e-03 ncrb3077 4E 8.265993e-03 ncrc6712 4E 6.41394e-03 ncrO912 4E 8.275646e-03 ncrcO139 4E 6.437649e-03 seob0466 4E 8.285506e-03 mioa5836 4E 6.490511e-03 seoc2824 4E 8.3183e-03 mioc0301 4E 6.490511e-03 hfcr5737 4E 8.332211e-03 ncr5719 4E 6.490511e-03 seoa4070 4E 8.351138e-03 seoa5473 4E 6.490511e-03 ncrO634 4E 8.421583e-03 seod2243 4E 6.490511e-03 seob8065 4E 8.459463e-03 seob0703 4E 6.50097e-03 ncrc6871 4E 8.532381e-03 seoa5911 4E 6.597064e-03 ncrcl502 4E 8.575665e-03 mioc2166 4E 6.609859e-03 fcrc2997 4E 8.587588e-03 ncr4545 4E 6.618737e-03 fcr0770 4E 8.634112e-03 seoa4333 4E 6.629988e-03 mioa3856 4E 8.663706e-03 mioal303 4E 6.633139e-03 mioa9891 4E 8.689171e-03 fcrb2292 4E 6.653984e-03 fcrbl835 4E 8.733177e-03 seob5500 4E 6.672454e-03 ncrc7016 4E 8.733177e-03 ncr4384 4E 6.700762e-03 seob6836 4E 8.733177e-03 ncr7631 4E 6.713476e-03 miobl506 4E 8.830271e-03 seob9552 4E 6.762169e-03 fcrcl834 4E 8.876728e-03 fcrcl607 4E 6.799098e-03 miodl331 4E 8.912821e-03 hfcrl760 4E 6.835569e-03 fcrc4054 4E 8.989376e-03 fcrc3415 4E 6.861942e-03 mioa2413 4E 9.000025e-03 miob5412 4E 6.89987e-03 fcrb2299 4E 9.0502e-03 seob9772 4E 6.956817e-03 seob3415 4E 9.05062e-03 ncrc2495 4E 6.966429e-03 mioa2652 4E 9.114417e-03 ncr2486 4E 6.982956e-03 miob3461 4E 9.117224e-03 mioal337 4E 7.012816e-03 fcr0707 4E 9.142522e-03 ncrc6813 4E 7.043646e-03 miodl574 4E 9.196145e-03 hfcrl914 4E 7.057729e-03 seoa5933 4E 9.204501e-03 mioal427 4E 7.073426e-03 seoa2391 4E 9.263207e-03 ncrl351 4E 7.095309e-03 fcrb6191 4E 9.290334e-03 seoa6923 4E 7.09741e-03 ncrcOlOl 4E 9.315893e-03 seob7474 4E 7.109226e-03 ncrc6953 4E 9.345199e-03 mioa0249 4E 7.184179e-03 seoc0098 4E 9.429093e-03 seoa9935 4E 7.197022e-03 seob5032 4E 9.43118e-03 miob2836 4E 7.210691e-03 seocl234 4E 9.440983e-03 mioa6093 4E 7.268139e-03 mioa4548 4E 9.611891e-03 ncrc6687 4E 7.304834e-03 fcrc5846 4E 9.622113e-03 ncre0769 4E 7.328795e-03 seob4140 4E 9.654697e-03 miob4058 4E 7.329008e-03 fcrb5537 4E 9.69729e-03 fcrb8542 4E 7.350932e-03 ncr4113 4E 9.724485e-03 fcr6044 4E 7.452917e-03 mioa6035 4E 9.789009e-03 seob6560 4E 7.500236e-03 fcrb3466 4E 9.829055e-03 mioa4009 4E 7.528646e-03 seob6492 4E 9.84478e-03 ncrc5972 4E 7.541349e-03 ncrc7038 4E 9.905278e-03 miob4221 4E 7.572537e-03 miob8214 4E 0.01 ncr7099 4E 7.606402e-03 mioc4366 4E 0.01 ""fcfcl39"4 ■-'■ ^E "" 0'."Dl mioc4247 4E 0.01 fcrc3283 4E 0.01 seoc2670 4E 0.01 fcrc5154 4E 0.01 mioa5691 4E 0.01 fcrO955 4E 0.01 ncr4202 4E 0.01 fcrc3240 4E 0.01 fcrcl216 4E 0.01 fcrbl787 4E 0.01 seob3189 4E 0.01 mioa2580 4E 0.01 miob4743 4E 0.01 fcrb4988 4E 0.01 seob4732 4E 0.01 ncrb0033 4E 0.01 fcrbl741 4E 0.01 ncrd4619 4E 0.01 seoc3603 4E 0.01 ncrc3624 4E 0.01 miob0496 4E 0.01 miob4037 4E 0.01 fcrb4832 4E 0.01 mioa5692 4E 0.01 seob3731 4E 0.01 ncr3402 4E 0.01 miod4407 4E 0.01 seob4036 4E 0.01 mioc2541 4E 0.01 fcrlO68 4E 0.01 fcr2102 4E 0.01 mioa6585 4E 0.01 seoa4460 4E 0.01 mioc3618 4E 0.01 ncrb5244 4E 0.01 fcrb6785 4E 0.01 mioc0560 4E 0.01 mioa5461 4E 0.01 seob9645 4E 0.01 ncrb8451 4E 0.01 ncrb5197 4E 0.01 seoa7266 4E 0.01 ncrc9727 4E 0.01 fcrb4109 4E 0.01 seoa2639 4E 0.01 fcrb2818 4E 0.01 mioal657 4E 0.01 fcrc2948 4E 0.01 mioc3523 4E 0.01 miod7457 4E 0.01 seoc3116 4E 0.01 seob3887 4E 0.01 ncr6142 4E 0.01 hfcr9397 4E 0.01 ncrc5592 4E 0.01 seob6467 4E 0.01 seoa7094 4E 0.01 fcr2598 4E 0.01 seoa8902 4E 0.01 mioa6135 4E 0.01 mioa4014 4E 0.01 mioc4009 4E 0,.01 ncrc4296 4E 0.01 seoc2336 4E 0,.01 fcrc4948 4E 0.01 fcrb3135 4E 0..01 fcrO272 4E 0.01 fcrb8162 4E 0,.01 mioc7471 4E 0.01 fcrb3702 4E 0,.01 miobl829 4E 0.01 fcrb8432 4E 0,.01 fcr6937 4E 0.01 mioa9792 4E 0..01 miod3468 4E 0.01 ncrd6949 4E 0..01 fcrc7069 4E 0.01 fcrb5389 4E 0..01 ncrb5737 4E 0.01 seoc3876 4E 0..01 ncrc3453 4E 0.01 fcrc6041 4E 0..01 miob3763 4E 0.01 mioc7553 4E 0..01 miob2492 4E 0.01 ncrO954 4E 0.,01 miod6041 4E 0.01 seoa5456 4E 0.,01 fcr5176 4E 0.01 mioa9033 4E 0..01 fcrb5536 4E 0.01 seob3007 4E 0.,01 mioa8622 4E 0.01 fcrb7588 4E 0.,01 mioc7260 4E 0.01 seoc2226 4E 0.01 miod4539 4E 0.01 seob8693 4E 0.01 mioa2421 4E 0.01 fcrcO396 4E 0.01 ncrb6087 4E 0.01 seoal332 4E 0.01 mioc6358 4E 0.01 miob9393 4E 0.01 ncrc9517 4E 0.01 fcrcllδl 4E 0.01 mioa8864 4E 0.01 mioc3206 4E 0.01 fcrb3886 4E 0.01 mioa6721 4E 0.01 seoa9729 4E 0.01 ncrc8841 4E 0.01 ncrc6617 4E 0.01 ncrb6833 4E 0.01 seoc3463 4E 0.01 fcrbl202 4E 0.01 ncrl428 4E 0.01 mioa5773 4E 0.01 fcrcl758 4E 0.01 seob9882 4E 0.01 seoa2957 4E 0.01 fcrcO379 4E 0.01 miob5708 4E 0.01 mioc3958 4E 0.01 miod3494 4E 0.01 ncrc3116 4E 0.01 ncr7082 4E 0.01 ■'"SebaS^S""- ~4'E "~-:iσ.'tfi miod0625 4E 0.01 fcrb2813 4E 0.01 ncrOOlβ 4E 0.01 mioc6847 4E 0.01 ncrc6407 4E 0.01 fcr0280 4E 0.01 ncrd7976 4E 0.01 fcrb8901 4E 0.01 seob2149 4E 0.01 mioa5231 4E 0.01 seob4039 4E 0.01 miob3477 4E 0.01 seoc0698 4E 0.01 ncrOO45 4E 0.01 ncrb3341 4E 0.01 seob4213 4E 0.01 seobl322 4E 0.01 fcrc4658 4E 0.01 ncr9919 4E 0.01 seoc7281 4E 0.01 fcrb2996 4E 0.01 seob2938 4E 0.01 seob8301 4E 0.01 fcrcl745 4E 0.01 ncr8827 4E 0.01 fcrll85 4E 0.01 seob9480 4E 0.01 fcr5755 4E 0.01 miod6947 4E 0.01 mioc7807 4E 0.01 fcrb2094 4E 0.01 ncrb8790 4E 0.01 fcrc7056 4E 0.01 miob2375 4E 0.01 fcrl305 4E 0.01 mioa0891 4E 0.01 fcrc2969 4E 0.01 mioc7370 4E 0.01 ncrc6127 4E 0.01 seoa9344 4E 0.01 fcr5509 4E 0.01 seoa5429 4E 0.01 seob3419 4E 0.01 hfcr6640 4E 0.01 ncrc2859 4E 0.01 fcrb2784 4E 0.01 mioa0152 4E 0.01 ncr8290 4E 0.01 seoc0866 4E 0.01 mioc4839 4E 0.01 fcrc3229 4E 0.01 mioa9258 4E 0.01 hfcr4423 4E 0.01 mioc7509 4E 0.01 ncr4126 4E 0.01 miob3411 4E 0.01 miob7607 4E 0.01 mioa3646 4E 0.01 miod0686 4E 0.01 seoa4107 4E 0.01 fcrb2596 4E 0.01 seoa0931 4E 0.01 miobl789 4E 0.01 ncrc5813 4E 0.01 ncr6141 4E 0.01 seoa3737 4E 0.01 mioa9935 4E 0.01 seoa6658 4E 0.01 seoal979 4E 0.01 hfcrl419 4E 0.01 fcrc5671 4E 0.01 fcrb8465 4E 0.01 seob6229 4E 0.01 seoc2253 4E 0.01 ncrbl956 4E 0.01 mioc7561 4E 0.01 hfcr3011 4E 0.01 ncrc6861 4E 0.01 fcrb4294 4E 0.01 fcrbl917 4E 0.01 seoc8175 4E 0.01 seob7941 4E 0.01 mioa3799 4E 0.01 miob7531 4E 0.01 mioa6999 4E 0.01 seobl783 4E 0.01 miob0207 4E 0.01 seoa4324 4E 0.01 seob5490 4E 0.01 ncrO335 4E 0.01 miod0167 4E 0.01 seoc3870 4E 0.01 seob2081 4E 0.01 raiod0963 4E 0.01 miocl440 4E 0.01 fcrb7833 4E 0.01 ncrc2273 4E 0.01 ncrc0803 4E 0.01 ncrc5608 4E 0.01 fcr4927 4E 0.01 fcrb8196 4E 0.01 seob3464 4E 0.01 hfcr6243 4E 0.01 mioa0246 4E 0.01 ncr3219 4E 0.01 mioa6091 4E 0.01 ncre5960 4E 0.01 mioc4641 4E 0.01 fcrb7751 4E 0.01 seocl278 4E 0.01 seoc4625 4E 0.01 seob4539 4E 0.01 fcr4795 4E 0.01 mioc8379 4E 0.01 mioa7169 4E 0.01 fcrcO839 4E 0.01 fcrbl582 4E 0.01 fcrb7505 4E 0.01 fcrc6106 4E 0.01 fcrb3217 4E 0.01 mioc8474 4E 0.01 mioa2722 4E 0.01 seob4570 4E 0.01 mioa6034 4E 0.01 fcr7705 4E 0.01 mioc0978 4E 0.01 ncr7876 4E 0.02 "■seba29"β2-"".„„, .tt ,».,.,>,σ;O,2,. ncrc6981 4E 0.02 fcrb2926 4E 0.02 fcr0027 4E 0.02 fcr6616 4E 0.02 ncr2293 4E 0.02 miob3307 4E 0.02 ncrc6047 4E 0.02 fcrb2658 4E 0.02 seoc0593 4E 0.02 ncrc5653 4E 0.02 seob7622 4E 0.02 mioc2546 4E 0.02 seob9818 4E 0.02 seoa7935 4E 0.02 seoa3752 4E 0.02 seoc7546 4E 0.02 seoc5780 4E 0.02 hfcr2505 4E 0.02 ncrc9387 4E 0.02 seoa0221 4E 0.02 ncr2717 4E 0.02 seoc4052 4E 0.02 fcr2530 4E 0.02 seob0763 4E 0.02 fcr3269 4E 0.02 mioal055 4E 0.02 fcrb8719 4E 0.02 seob4192 4E 0.02 hfcrl774 4E 0.02 miob4570 4E 0.02 fcrbl528 4E 0.02 fcrcO529 4E 0.02 ncre5807 4E 0.02 miod2128 4E 0.02 seob9730 4E 0.02 ncr4416 4E 0.02 mioc9919 4E 0.02 miod0971 4E 0.02 seoc4505 4E 0.02 ncrb8063 4E 0.02 hfcrO256 4E 0.02 miob7109 4E 0.02 miob8609 4E 0.02 seobl834 4E 0.02 seoa4586 4E 0.02 fcr7403 4E 0.02 bfcs0043 4E 0.02 mioa8858 4E 0.02 ncrcl402 4E 0.02 seob0831 4E 0.02 ncr2792 4E 0.02 seocl264 4E 0.02 seoa4634 4E 0.02 fcr2940 4E 0.02 mioa3940 4E 0.02 seob9067 4E 0.02 fcrb5720 4E 0.02 fcrb7118 4E 0.02 fcrcO677 4E 0.02 miod0441 4E 0.02 mioa6147 4E 0.02 seoa6867 4E 0.02 miob7611 4E 0.02 fcrb2913 4E 0.02 ncrb3284 4E 0.02 fcrb3201 4E 0.02 seoa4158 4E 0.02 fcrb9578 4E 0.02 seoa4670 4E 0.02 seoa5214 4E 0.02 miob9855 4E 0.02 seoc3426 4E 0.02 mioc2799 4E 0.02 ncrd4701 4E 0.02 mioa8548 4E 0.02 miob3396 4E 0.02 ncrc4586 4E 0.02 fcr3043 4E 0.02 ncrc9117 4E 0.02 fcrb2591 4E 0.02 seob5624 4E 0.02 fcrc4390 4E 0.02 ncrcl751 4E 0.02 hfcr3404 4E 0.02 fcrcO651 4E 0.02 ncrc4001 4E 0.02 ncrcl687 4E 0.02 ncrcO292 4E 0.02 hfcrl856 4E 0.02 ncrc3276 4E 0.02 fcrb5393 4E 0.02 rαiob4975 4E 0.02 ncr9108 4E 0.02 mioc0214 4E 0.02 fcr4634 4E 0.02 miod4857 4E 0.02 miod0053 4E 0.02 seob6680 4E 0.02 fcr2587 4E 0.02 miob9533 4E 0.02 miob3330 4E 0.02 fcrb2690 4E 0.02 mioc2019 4E 0.02 seoa3628 4E 0.02 fcrc5721 4E 0.02 fcr2859 4E 0.02 hfcr2295 4E 0.02 seoc6266 4E 0.02 ncr2408 4E 0.02 mioc5226 4E 0.02 hfcr3180 4E 0.02 mioc5307 4E 0.02 ncr8975 4E 0.02 fcrb6990 4E 0.02 ncrb2798 4E 0.02 fcrb3519 4E 0.02 fcr3620 4E 0.02 seoc2030 4E 0.02 seoc0416 4E 0.02 miob8274 4E 0.02 seoa8912 4E 0.02 seoc4380 4E 0.02 fcrc7228 4E 0.02 fcr5618 4E 0.02 miob6610 4E 0.02 seoa3555 4E 0.02 seob4726 4E 0.02 '"se'bB82'5ST ΪE "™ ncrc9633 4E 0.03 seob6096 4E O.02 hfcr5797 4E 0.03 fcrb4365 4E O.02 seoal789 4E 0.03 fcrc2670 4E O.02 miob3531 4E 0.03 seoa8867 4E O.02 miod7270 4E 0.03 ncrcl231 4E O.02 fcrb6102 4E 0.03 ncrc2888 4E O.02 miob7223 4E 0.03 seobl947 4E O .02 fcrb5416 4E 0.03 mioa7088 4E O.02 ncrd3839 4E 0.03 mioc5695 4E O.02 seoa7249 4E 0.03 ncr3052 4E O.02 fcr5222 4E 0.03 ncrcl537 4E O.02 fcrb8910 4E 0.03 seob3009 4E O.02 seoa5447 4E 0.03 mioa5531 4E O.02 fcrc6970 4E 0.03 mioa6840 4E O.02 ncr5374 4E 0.03 ncrc9436 4E O.02 seob6030 4E 0.03 miob4876 4E O.02 miod7461 4E 0.03 ncrO491 4E O.02 fcr0860 4E 0.03 fcrl724 4E O.02 fcrO946 4E 0.03 fcr7042 4E O.02 mioc3959 4E 0.03 mioa4738 4E O.02 ncr4140 4E 0.03 ncr5557 4E O.02 mioc3994 4E 0.03 fcrcO591 4E O.02 fcr5369 4E 0.03 fcrb7951 4E O.03 seoa5528 4E 0.03 ncr3968 4E O.03 mioa4704 4E 0.03 fcrclO19 4E O.03 fcrb8664 4E 0.03 hfcr5971 4E O.03 inioa3944 4E 0.03 fcrb6747 4E O.03 ncrc4620 4E 0.03 fcrb2040 4E O.03 mioa2818 4E 0.03 seoa2679 4E O.03 ncrc4633 4E 0.03 fcrO553 4E O.03 ncrb0303 4E 0.03 fcrb2353 4E O.03 ncrc6439 4E 0.03 seob4079 4E O.03 fcr4306 4E 0.03 ncr3237 4E O,.03 seoa5554 4E 0.03 hfcrO734 4E O.03 fcrO843 4E 0.03 hfcr3500 4E O.03 ncrb2151 4E 0.03 seoa9060 4E O,.03 mioa0497 4E 0.03 seob3064 4E O..03 fcrb4340 4E 0.03 seobl889 4E O,.03 fcrbβlll 4E 0.03 seoc0616 4E O..03 fcrc6108 4E 0.03 fcrb8949 4E O..03 mioa9492 4E 0.03 fcr0206 4E O..03 miob6952 4E 0.03 ncrcO439 4E O.,03 mioc3648 4E 0.03 mioc6902 4E O.,03 ncrbl719 4E 0.03 seob7946 4E O.,03 ncrc0608 4E 0.03 seoc3980 4E O.,03 ncreO516 4E 0.03 fcrb5296 4E O.03 seob6584 4E 0.03 fcrcδOlO 4E O.,03 seob7668 4E 0.03 ncr2866 4E O.03 seoc3443 4E 0.03 ncr3380 4E O.03 seod0773 4E 0.03 ncrc2670 4E O.03 seob3170 4E 0.03 seob3404 4E O.03 seoa3544 4E 0.03 seoa9855 4E O.03 fcrb5645 4E 0.03 ncr3690 4E O.03 fcrb4016 4E 0.03 seoc4187 4E O.03 seob0885 4E 0.03 miob7850 4E O.03 seob5558 4E 0.03 ncrb4441 4E O.03 ncr9664 4E 0.03 ncrb8649 4E O.03 fcrO529 4E 0.03 mioa3361 4E O.03 seob0650 4E 0.03 fcrb7616 4E O.03 seodl506 4E 0.03 ncr8995 4E O.03 miob3354 4E 0.03 fcrb4599 4E O.03 mioa2447 4E 0.03 seob8082 4E O.03 fcrb6725 4E 0.03 seoa2978 4E O.03 ncr5939 4E 0.03 ~hdr21T5 !"""--"4E "~"Λ~0.rϋ3' ncr4550 4E 0..04 seoa7369 4E 0.,03 seoa2041 4E 0..04 miod6048 4E 0.,03 ncrO531 4E 0.,04 seob0344 4E 0.03 miod6213 4E 0.,04 seoa6598 4E 0.03 seob3455 4E 0..04 mioc4504 4E 0.03 ncr9337 4E 0.,04 fcrb4479 4E 0.03 fcrb7490 4E 0.,04 seoa8642 4E 0.03 fcrc0039 4E 0.,04 mioal380 4E 0.03 fcrcO676 4E 0.04 mioa6583 4E 0.03 hfcrO723 4E 0.04 fcr2196 4E 0.03 mioal079 4E 0.,04 mioa8912 4E 0.03 miob4793 4E 0.04 ncr5065 4E 0.03 miob7550 4E 0.04 seoc2246 4E 0.03 mioc5679 4E 0.04 ncr6072 4E 0.03 ncrc6382 4E 0.04 fcrc4876 4E 0.03 seoa7509 4E 0.04 hfcr5987 4E 0.03 seobll33 4E 0.04 ncrb3001 4E 0.03 fcrb3283 4E 0.04 ncr3268 4E 0.03 seoc5285 4E 0.04 seob8300 4E 0.03 mioa6556 4E 0.04 fcr6708 4E 0.03 mioc4086 4E 0.04 seoa9042 4E 0.03 fcrb8908 4E 0.04 seoa7923 4E 0.03 seob6582 4E 0.04 ncr4656 4E 0.04 ncrc9039 4E 0.04 ncre2208 4E 0.04 ncrO132 4E 0.04 fcr3338 4E 0.04 mioc6341 4E 0.04 fcrb5422 4E 0.04 fcrb4333 4E 0.04 fcrb7761 4E 0.04 seoc2256 4E 0.04 hfcr9140 4E 0.04 fcrβlδl 4E 0.04 mioa4810 4E 0.04 miod4370 4E 0.04 mioa6471 4E 0.04 miob2668 4E 0.04 mioc5646 4E 0.04 seob3394 4E 0.04 ncrb5145 4E 0.04 ncrb2992 4E 0.04 ncrc5845 4E 0.04 seobl009 4E 0.04 seoa5455 4E 0.04 ncrb8539 4E 0.04 seob0288 4E 0.04 hfcr4470 4E 0.04 seob6630 4E 0.04 miod6044 4E 0.04 miob3018 4E 0.04 seob0046 4E 0.04 seoa4055 4E 0.04 miod6731 4E 0.04 miob9284 4E 0.04 fcrb2762 4E 0.04 seob2169 4E 0.04 ncr2591 4E 0.04 miob5608 4E 0.04 fcrb5639 4E 0.04 miod2635 4E 0.04 hfcrlO55 4E 0.04 fcr4984 4E 0.04 seoc0742 4E 0.04 miod4564 4E 0.04 mioc4696 4E 0.04 ncr4538 4E 0.04 fcr7518 4E 0.04 ncrc4798 4E 0.04 fcrb7880 4E 0.04 ncre2942 4E 0.04 ncrb4538 4E 0.04 fcrb5751 4E 0.04 seobl972 4E 0.04 fcrb5177 4E 0.04 ncrc4000 4E 0.04 seob5465 4E 0.04 seoa2795 4E 0.04 miob2743 4E 0.04 miod4546 4E 0.04 miob5752 4E 0.04 miob7913 4E 0.04 seoa5258 4E 0.04 fcr3053 4E 0.04 seoa3761 4E 0.04 seob6198 4E 0.04 mioc8206 4E 0.04 miodl792 4E 0.04 miob3426 4E 0.04 fcrb2015 4E 0.04 seoc7016 4E 0.04 fcrc7014 4E 0.04 miob6595 4E 0.04 miob7518 4E 0.04 mioa2268 4E 0.04 seoa8638 4E 0.04 fcrb7402 4E 0.04 mioal276 4E 0.04 ncrO547 4E 0.04 seob0817 4E 0.04 mioc8479 4E 0.04 ncrl235 4E 0.04 miodllOδ 4E 0.04 hfcrl314 4E 0.04 ■Se'baOδ'dS '~°■ -tE ΌYOT fcr4582 4G 2.27e-04 miod7440 4G 2.44e-07 fcrcl381 4G 2.28e-04 seoc2029 4G 6.23e-07 miob0167 4G 2.32e-04 mioc7170 4G 2.53e-06 mioc4022 4G 2.34e-04 mioc2561 4G 3.17e-06 seob0065 4G 2.35e-04 mioa2072 4G 3.26e-06 fcrc5937 4G 2.36e-04 seob5523 4G 4.23e-06 seoc0999 4G 2.44e-04 seob4419 4G 4.27e-06 fcrcO554 4G 2.48e-04 fcrc3993 4G 4.37e-06 seocl203 4G 2.49e-04 seoa4461 4G 5.29e-06 seob4191 4G 2.69e-04 seoa6032 4G 5.48e-06 mioc2726 4G 2.7e-04 miod5349 4G 6.31e-06 mioc8882 4G 2.89e-04 mioc3430 4G 7.49e-06 mioc6260 4G 2.92e-04 seoa8894 4G l.le-05 seoa4053 4G 3.03e-04 fcrb4534 4G 1.46e-05 fcrcO112 4G 3.06e-04 seob2697 4G 1.66e-05 ncrO153 4G 3.07e-04 ncr3751 4G 1.95e-05 ncrl631 4G 3.07e-04 seoa4518 4G 2.47e-05 seoa4366 4G 3.08e-04 seoa6133 4G 2.48e-05 seoc6169 4G 3.11e-04 miob6562 4G 2.52e-05 mioal585 4G 3.13e-04 seoa8566 4G 3.22e-05 ncrb8714 4G 3.16e-04 ncr3902 4G 3.39e-05 seoa2949 4G 3.29e-04 ncr7668 4G 4.63e-05 fcrcO487 4G 3.31e-04 fcrb3578 4G 4.7e-05 mioa7317 4G 3.37e-04 fcr2952 4G 5.1e-05 fcr2573 4G 3.43e-04 miod5123 4G 5.49e-05 seob5743 4G 3.48e-04 seob4333 4G 5.73e-05 ncrcO342 4G 3.76e-04 seob6853 4G 5.74e-05 fcrb8504 4G 3.99e-04 mioa3963 4G 6.04e-05 fcr5779 4G 4.02e-04 miod7421 4G 6.08e-05 mioa8820 4G 4.06e-04 seoa9373 4G 6.54e-05 miob7922 4G 4.07e-04 fcrb8908 4G 7.67e-05 fcrb7830 4G 4.09e-04 mioa2319 4G 8.18e-05 fcrb4428 4G 4.19e-04 ncrb2092 4G 8.8e-05 fcrc6010 4G 4.34e-04 mioal370 4G 8.87e-05 mioc8057 4G 4.39e-04 mioc8410 4G 9.18e-05 fcr4784 4G 4.64e-04 fcrb7339 4G 9.87e-05 fcrl463 4G 4.77e-04 fcrb8516 4G 9.9e-05 fcr2103 4G 4.84e-04 fcr4477 4G 1.01e-04 miod6845 4G 4.99e-04 fcrc3739 4G 1.03e-04 seob6844 4G 5.04e-04 fcrbO265 4G 1.2e-04 ncrc4597 4G 5.18e-04 seob6576 4G 1.27e-04 seob9282 4G 5.33e-04 seob6189 4G 1.32e-04 seob0248 4G 5.56e-04 miod7414 4G 1.36e-04 seoc6703 4G 5.75e-04 πιioc2094 4G 1.38e-04 miob9907 4G 5.82e-04 seoal427 4G 1.38e-04 fcr2607 4G 5.84e-04 ncr3262 4G 1.4e-04 ncrc8903 4G 5.88e-04 seoa2585 4G 1.47e-04 seoa4739 4G 5.91e-04 ncrO223 4G 1.5e-04 fcr4831 4G 5.97e-04 seoa7652 4G 1.5e-04 mioa0595 4G 6.04e-04 seob6851 4G 1.55e-04 seob9145 4G 6.11e-04 fcrb5850 4G 1.56e-04 mioal662 4G 6.2e-04 miob8812 4G 1.56e-04 fcrc3750 4G 6.22e-04 fcrb3461 4G 1.6e-04 miod4759 4G 6.36e-04 seocl023 4G 1.66e-04 fcrc6826 4G 6.39e-04 seob4029 4G 1.69e-04 mioc7686 4G 6.41e-04 miod5651 4G 1.77e-04 ncrb2266 4G 6.45e-04 seoa2970 4G 1.77e-04 mioa2537 4G 6.62e-04 seob5748 4G 1.79e-04 fcrc3009 4G 6.69e-04 hfcr2390 4G 1.83e-04 mioa6418 4G 7.09e-04 rαiod5505 4G 1.89e-04 mioc0950 4G 7.19e-04 seob6751 4G 2.02e-04 miodl925 4G 7.23e-04 seoc2264 4G 2.1e-04 mioc5532 4G 7.27e-04 fcrb2554 4G 2.2e-04 mioc4089 4G 7.46e-04 mibc73"βr"-"-~ξ<3 ' mioc6987 4G 1.45158e-03 mioc7764 4G 7.57e-04 seoa3516 4G 1.46015e-03 seob9960 4G 7.63e-04 ncr0025 4G 1.469905e-03 seoa8388 4G 7.64e-04 fcr3101 4G 1.487316e-03 fcrc6976 4G 7.65e-04 seob4579 4G 1.499276e-03 seob3303 4G 7.67e-04 ncr7595 4G 1.511845e-03 seob9574 4G 7.88e-04 fcr4128 4G 1.53383e-03 hfcrO478 4G 8.43e-04 ncrc5162 4G 1.553082e-03 mioa5355 4G 8.58e-04 ncr5168 4G 1.55656e-03 fcrO824 4G 8.62e-04 ncrb3001 4G 1.557302e-03 seob2994 4G 8.67e-04 fcrb8187 4G 1.573331e-03 fcrb8668 4G 8.75e-04 mioal380 4G 1.588271e-03 ncrc9784 4G 8.77e-04 miodlδll 4G 1.590472e-03 fcrc7102 4G 8.87e-04 seoa9740 4G 1.598061e-03 mioa6731 4G 8.9e-04 mioa3080 4G 1.603678e-03 fcrb2556 4G 8.98e-04 mioc0052 4G 1.624699e-03 fcrcl298 4G 9.0e-04 ncrc5569 4G 1.637974e-03 ncr5568 4G 9.0e-04 mioc6075 4G 1.640004e-03 mioc4667 4G 9.01e-04 ncrcl367 4G 1.650171e-03 ncr0808 4G 9.12e-04 seob0376 4G 1.666731e-03 hfcr3019 4G 9.19e-04 ncr5719 4G 1.677652e-03 fcrc3048 4G 9.26e-04 seoa4305 4G 1.678116e-03 fcrb9454 4G 9.3e-04 mioal015 4G 1.68644e-03 mioc6212 4G 9.34e-04 seob0304 4G 1.693307e-03 fcrb9420 4G 9.64e-04 fcrc2090 4G 1.719751e-03 miod5369 4G 9.71e-04 miob6834 4G 1.736843e-03 fcr2798 4G 9.78e-04 mioblll5 4G 1.756956e-03 seocl996 4G 9.81e-04 fcrb3165 4G 1.760242e-03 hfcr6486 4G 9.83e-04 seoal598 4G 1.770719e-03 ncrb5595 4G 1.013841e-03 fcrc2099 4G 1.790284e-03 fcrb7240 4G 1.015969e-03 seobl766 4G 1.794891e-03 fcrb2933 4G 1.016356e-03 fcrc5516 4G 1.799844e-03 hfcr5381 4G 1.016874e-03 fcrbl420 4G 1.801622e-03 miodl236 4G 1.028284e-03 ncr2905 4G 1.815247e-03 seob6446 4G 1.030592e-03 ncrc5054 4G 1.860924e-03 miob7105 4G 1.073216e-03 seob6525 4G 1.861275e-03 fcrc7388 4G 1.081745e-03 fcrb6949 4G 1.872239e-03 fcrc5134 4G 1.086904e-03 fcrb8161 4G 1.885852e-03 miod5310 4G 1.129688e-03 mioal520 4G 1.887743e-03 fcrbl855 4G 1.142061e-03 fcrb8740 4G 1.892065e-03 fcrb9269 4G 1.150659e-03 mioc8423 4G 1.916815e-03 mioa3395 4G 1.154329e-03 fcrb2871 4G 1.964974e-03 seob8368 4G 1.154981e-03 ncrc0150 4G 1.985831e-03 seob0122 4G 1.171391e-03 fcr7381 4G 1.992663e-03 mioa0535 4G 1.173792e-03 ncr5397 4G 2.00365e-03 seocl593 4G 1.178329e-03 miod5894 4G 2.014527e-03 miob9336 4G 1.178419e-03 miod0340 4G 2.021229e-03 mioc7664 4G 1.185788e-03 seob4560 4G 2.021887e-03 seoa9060 4G 1.185788e-03 fcrb8080 4G 2.025786e-03 ncrc0090 4G 1.185916e-03 mioc0824 4G 2.034483e-03 hfcr6406 4G 1.239885e-03 fcrl760 4G 2.050359e-03 miod6238 4G 1.278208e-03 fcrb9720 4G 2.057033e-03 miodl942 4G 1.305311e-03 miocll07 4G 2.070013e-03 fcrl883 4G 1.308793e-03 fcrbl733 4G 2.120875e-03 ncr4539 4G 1.309997e-03 fcrc6916 4G 2.121527e-03 mioa0909 4G 1.312146e-03 ncrO238 4G 2.1386e-03 mioc2880 4G 1.356947e-03 mioc7895 4G 2.143605e-03 seob6368 4G 1.361315e-03 seob6670 4G 2.155534e-03 mioc2348 4G 1.386736e-03 seob8501 4G 2.166556e-03 fcrl756 4G 1.392986e-03 miod2525 4G 2.174809e-03 miod6038 4G 1.395436e-03 fcrb2624 4G 2.174826e-03 miob3348 4G 1.428182e-03 mioc6312 4G 2.199817e-03 miodll95 4G 1.431152e-03 seoc2101 4G 2.226943e-03 ncr7753 4G 1.444958e-03 fcr3121 4G 2.234303e-03 s'eόb2Ϊ$'β"~"—4G-"~"2":'iA2"222e-03 ncrd6628 4G 3.732487e-03 seoclllθ 4G 2.258564e-03 fcr6577 4G 3.735652e-03 fcr7419 4G 2.260166e-03 fcr6018 4G 3.762242e-03 seob7432 4G 2.272415e-03 ncrc4808 4G 3.775334e-03 fcrcO695 4G 2.279562e-03 seoal480 4G 3.790975e-03 miobl326 4G 2.329343e-03 hfcr0370 4G 3.8121e-03 πιiob4475 4G 2.330183e-03 seobll87 4G 3.817075e-03 miod6467 4G 2.34447e-03 fcr3155 4G 3.832262e-03 fcrb3134 4G 2.402582e-03 ncr2182 4G 3.847974e-03 mioal976 4G 2.452224e-03 seob0876 4G 3.851453e-03 mioc6114 4G 2.46067e-03 ncrc6888 4G 3.861264e-03 fcrc5142 4G 2.464685e-03 seoa6573 4G 3.875968e-03 miob8143 4G 2.489145e-03 mioc5643 4G 3.926872e-03 seoa3670 4G 2.543649e-03 ncrc3434 4G 3.939813e-03 seoa8232 4G 2.578028e-03 seoall73 4G 3.950326e-03 ncrcl578 4G 2.579378e-03 miob3461 4G 3.955005e-03 miod4332 4G 2.601957e-03 fcrb9476 4G 3.999792e-03 mioc7974 4G 2.623765e-03 ncrb2200 4G 3.999792e-03 mioc8434 4G 2.661058e-03 raiob4956 4G 4.05244e-03 seob7465 4G 2.711845e-03 fcrcO559 4G 4.077564e-03 fcrb4409 4G 2.722505e-03 ncrc5492 4G 4.081573e-03 seoc0276 4G 2.725532e-03 mioc0741 4G 4.154883e-03 mioal660 4G 2.749542e-03 seoc3515 4G 4.159057e-03 mioc3962 4G 2.824707e-03 ncrc4531 4G 4.208322e-03 seob9946 4G 2.84126e-03 fcrb8974 4G 4.209704e-03 seob4545 4G 2.842281e-03 ncrb7292 4G 4.220365e-03 mioc7744 4G 2.845792e-03 miod4140 4G 4.240281e-03 miob8146 4G 2.888517e-03 seoc2191 4G 4.272909e-03 mioa7140 4G 2.898369e-03 mioc0567 4G 4.279318e-03 ncrc6359 4G 2.898846e-03 mioc3716 4G 4.281847e-03 seoc3094 4G 2.921585e-03 seoa9711 4G 4.329889e-03 fcrb3872 4G 2.952114e-03 fcrb8467 4G 4.415815e-03 ncrc3464 4G 2.965232e-03 mioc0347 4G 4.417094e-03 ncrc5079 4G 3.050155e-03 fcrl997 4G 4.429139e-03 fcrb9481 4G 3.074875e-03 fcr5681 4G 4.429139e-03 seoa6304 4G 3.095502e-03 ncrc0640 4G 4.429139e-03 ncrb0054 4G 3.127244e-03 fcrcO959 4G 4.530274e-03 seoa3533 4G 3.13784e-03 fcrc6335 4G 4.58749e-03 mioa2185 4G 3.147097e-03 mioal354 4G 4.601629e-03 mioa3856 4G 3.151528e-03 miod4348 4G 4.655192e-03 seoa5683 4G 3.160612e-03 miod4686 4G 4.657868e-03 mioa0407 4G 3.167049e-03 ncrc5150 4G 4.691321e-03 fcrb7693 4G 3.203691e-03 seob5562 4G 4.746427e-03 fcrbl552 4G 3.209013e-03 ncrb4912 4G 4.752707e-03 ncrc6423 4G 3.223469e-03 fcr0707 4G 4.772856e-03 ncrO438 4G 3.245826e-03 seoa4395 4G 4.779169e-03 mioa2374 4G 3.293401e-03 mioa0826 4G 4.884238e-03 fcr5075 4G 3.353508e-03 mioa6621 4G 4.904983e-03 ncrbO145 4G 3.359507e-03 seob9649 4G 4.907044e-03 fcrb3017 4G 3.376066e-03 seoa5235 4G 4.928397e-03 miob8274 4G 3.404337e-03 seoa3847 4G 4.942903e-03 fcrl462 4G 3.423782e-03 ncr3684 4G 5.017654e-03 ncr8866 4G 3.437719e-03 fcrbl202 4G 5.050003e-03 mioc7441 4G 3.437924e-03 hfcr2287 4G 5.054567e-03 fcrc6888 4G 3.449333e-03 fcrc2576 4G 5.063792e-03 miob9065 4G 3.456747e-03 fcrb8942 4G 5.08231e-03 ncr2013 4G 3.51941e-03 fcrc0430 4G 5.084925e-03 seob4972 4G 3.531512e-03 miob8583 4G 5.097523e-03 mioc3726 4G 3.550472e-03 ncrc5672 4G 5.11611e-03 seob2188 4G 3.584274e-03 ncr2862 4G 5.163569e-03 seob2797 4G 3.6022e-03 ncr3313 4G 5.18529e-03 seoal749 4G 3.637644e-03 mioa8946 4G 5.185969e-03 ncrO478 4G 3.666381e-03 ncrc5039 4G 5.197151e-03 fcrb2249 4G 3.695741e-03 fcrc4360 4G 5.250167e-03 ItIiOd-EiS1S"""" "'""4G •"'"■'"'"5":!2'>53602e--03 ncr6335 4G 6.907692e-03 ncrbl398 4G 5.295501e-03 seob0783 4G 6.916463e-03 miod3854 4G 5.307923e-03 ncr9956 4G 6.920421e-03 miod3254 4G 5.326719e-03 seob9772 4G 6.956817e-03 mioc8619 4G 5.353299e-03 ncr2486 4G 6.982956e-03 ncrc0508 4G 5.409373e-03 mioal337 4G 7.012816e-03 ncr6637 4G 5.411789e-03 ncrb8207 4G 7.041692e-03 ncr4009 4G 5.457222e-03 hfcrl914 4G 7.057729e-03 fcrc6854 4G 5.540234e-03 miocl910 4G 7.062221e-03 fcrc2082 4G 5.550695e-03 mioal427 4G 7.073426e-03 fcrc6560 4G 5.571911e-03 ncrl351 4G 7.095309e-03 ncrc9280 4G 5.58653e-03 seob7474 4G 7.109226e-03 fcrb7852 4G 5.667225e-03 miob3161 4G 7.124632e-03 ncre5923 4G 5.691762e-03 seoa9935 4G 7.197022e~03 fcrO253 4G 5.72015e-03 miob2836 4G 7.210691e-03 nιioa8647 4G 5.768458e-03 fcrc6925 4G 7.233504e-03 fcrb5164 4G 5.814619e-03 mioa6093 4G 7.268139e-03 seoc3588 4G 5.826485e-03 fcr7646 4G 7.271984e-03 hfcrO724 4G 5.834821e-03 seobll91 4G 7.325599e-03 mioc5664 4G 5.835444e-03 miob4058 4G 7.329008e-03 fcrb9147 4G 5.861796e-03 fcr5723 4G 7.369143e-03 fcrb6640 4G 5.9216e-03 miob4667 4G 7.37305e-03 ncrc4654 4G 5.92837e-03 seoc3993 4G 7.378389e-03 fcrb5267 4G 5.942373e-03 fcr6044 4G 7.452917e-03 fcrc0094 4G 5.96467e-03 mioc6925 4G 7.512586e-03 fcrll85 4G 5.966352e-03 mioa4009 4G 7.528646e-03 fcrc6470 4G 5.968954e-03 ncrc3936 4G 7.54805e-03 seoa7775 4G 5.986336e-03 ncrbl670 4G 7.580391e-03 fcr6390 4G 6.000719e-03 seob2169 4G 7.634987e-03 fcr5536 4G 6.049129e-03 ncrc6087 4G 7.662274e-03 ncrcO663 4G 6.085718e-03 hfcrO517 4G 7.704251e-03 fcrb6791 4G 6.08683e-03 fcrb7944 4G 7.767483e-03 fcrO6O8 4G 6.096137e-03 seob6177 4G 7.843092e-03 seoa0470 4G 6.100317e-03 seoa0913 4G 7.91186e-03 hfcrl265 4G 6.161536e-03 mioc7998 4G 7.939586e-03 ncrc9778 4G 6.18188e-03 mioc0375 4G 7.952967e-03 seob3520 4G 6.215767e-03 fcrb7113 4G 8.05715e-03 ncrbl337 4G 6.21911e-03 fcrcO295 4G 8.130412e-03 fcrb8915 4G 6.26297e-03 miod2665 4G 8.152135e-03 seobl302 4G 6.26297e-03 fcrc3358 4G 8.156756e-03 ricrbl515 4G 6.341156e-03 fcr4965 4G 8.164022e-03 fcrb8959 4G 6.342616e-03 mioc6997 4G 8.185087e-03 miod4142 4G 6.383134e-03 fcrO593 4G 8.19812e-03 miob4593 4G 6.402894e-03 seoc2220 4G 8.240295e-03 ncrcβOOO 4G 6.444479e-03 ncrO912 4G 8.275646e-03 ncrc9779 4G 6.449226e-03 seoc2824 4G 8.3183e-03 hfcr4176 4G 6.494359e-03 seoal736 4G 8.342294e-03 seob0703 4G 6.50097e-03 ncrO634 4G 8.421583e-03 fcrO211 4G 6.552321e-03 seob8065 4G 8.459463e-03 fcr4469 4G 6.567393e-03 ncrc6264 4G 8.530558e-03 seoa5911 4G 6.597064e-03 ncrc6871 4G 8.532381e-03 mioc2166 4G 6.609859e-03 fcrb6928 4G 8.55153e-03 ncr4545 4G 6.618737e-03 seob0688 4G 8.565542e-03 mioal303 4G 6.633139e-03 fcrc2997 4G 8.587588e-03 fcrb9352 4G 6.647947e-03 mioa9891 4G 8.689171e-03 fcrb2292 4G 6.653984e-03 mioal708 4G 8.695664e-03 ncr7631 4G 6.713476e-03 mioa2818 4G 8.722275e-03 seob9552 4G 6.762169e-03 πιioa6738 4G 8.769774e-03 fcrb8536 4G 6.791086e-03 miobl506 4G 8.830271e-03 hfcrl760 4G 6.835569e-03 seoa4332 4G 8.870675e-03 fcrc3415 4G 6.861942e-03 fcrcl834 4G 8.876728e-03 seob0716 4G 6.894599e-03 miodl331 4G 8.912821e-03 seoc8850 4G 6.894599e-03 ncrl550 4G 8.917489e-03 miob5412 4G 6.89987e-03 fcrc4054 4G 8.989376e-03 fcrb3894 4G ""T."δ"3812e-03 mioa9033 4G 0.01 fcrb2299 4G 9.0502e-03 fcrcl660 4G 0.01 seob3415 4G 9.05062e-03 ncrc0304 4G 0.01 rαioa2652 4G 9.114417e-03 ncrc5930 4G 0.01 fcr5029 4G 9.19539e-03 fcrb4570 4G 0.01 miodl574 4G 9.196145e-03 seob3007 4G 0.01 seoa2391 4G 9.263207e-03 fcrb7588 4G 0.01 fcrb6191 4G 9.290334e-03 seoc2226 4G 0.01 ncrc6953 4G 9.345199e-03 seob8693 4G 0.01 seoc0098 4G 9.429093e-03 ncrb7635 4G 0.01 seob5032 4G 9.43118e-03 fcrcO396 4G 0.01 seocl234 4G 9.440983e-03 fcrcllδl 4G 0.01 seoal737 4G 9.470911e-03 mioc3206 4G 0.01 mioc4835 4G 9.495048e-03 mioa6721 4G 0.01 ncrO159 4G 9.535725e-03 ncrc8841 4G 0.01 mioa4548 4G 9.611891e-03 ncrb6833 4G 0.01 fcrb6220 4G 9.620246e-03 seob7409 4G 0.01 seob4140 4G 9.654697e-03 ncr0004 4G 0.01 fcrb5537 4G 9.69729e-03 mioa5773 4G 0.01 ncr4113 4G 9.724485e-03 seob9882 4G 0.01 fcr3367 4G 9.80502e-03 ncrc5959 4G 0.01 fcrb3466 4G 9.829055e-03 fcrcO379 4G 0.01 seob6492 4G 9.84478e-03 miob4673 4G 0.01 fcrbl578 4G 9.906745e-03 mioa3945 4G 0.01 mioc4366 4G 0.01 raioc3958 4G 0.01 fcrO955 4G 0.01 mioc4247 4G 0.01 fcrc3240 4G 0.01 mioa5691 4G 0.01 seobl574 4G 0.01 ncr4202 4G 0.01 fcrb4988 4G 0.01 fcrcl21β 4G 0.01 fcr0903 4G 0.01 seob6836 4G 0.01 ncrc3624 4G 0.01 fcrl633 4G 0.01 miob8415 4G 0.01 fcrbl741 4G 0.01 miob4037 4G 0.01 seobll33 4G 0.01 ncr3402 4G 0.01 seoc3603 4G 0.01 fcrc6294 4G 0.01 miob0496 4G 0.01 fcrlO68 4G 0.01 seob3731 4G 0.01 miob2743 4G 0.01 seoc2447 4G 0.01 mioc3618 4G 0.01 miod4407 4G 0.01 fcrb6785 4G 0.01 miob8373 4G 0.01 miob7651 4G 0.01 fcr2102 4G 0.01 fcrb5918 4G 0.01 seoa4460 4G 0.01 mioa5461 4G 0.01 ncrb2131 4G 0.01 seoa7266 4G 0.01 miod6961 4G 0.01 fcrb5100 4G 0.01 mioc0560 4G 0.01 fcrb4109 4G 0.01 seob9645 4G 0.01 mioc0121 4G 0.01 fcrb7808 4G 0.01 miod7457 4G 0.01 ncrb5197 4G 0.01 seob3887 4G 0.01 ncrc9727 4G 0.01 seob6467 4G 0.01 seoa2639 4G 0.01 mioa6135 4G 0.01 mioa2213 4G 0.01 mioa8852 4G 0.01 ncr6142 4G 0.01 mioc4009 4G 0.01 miob4370 4G 0.01 seoc2336 4G 0.01 miod4342 4G 0.01 fcrcO654 4G 0.01 ncrc6171 4G 0.01 fcr4763 4G 0.01 seoa7094 4G 0.01 fcrb8542 4G 0.01 seoa8902 4G 0.01 fcrb8162 4G 0.01 fcr5509 4G 0.01 hfcr5970 4G 0.01 ncrc4296 4G 0.01 miod4464 4G 0.01 fcrO272 4G 0.01 fcrb3702 4G 0.01 seob3684 4G 0.01 fcrb5389 4G 0.01 fcr2341 4G 0.01 seoc3876 4G 0.01 miod7243 4G 0.01 fcrb3135 4G 0.01 seob4293 4G 0.01 hfcr5237 4G 0.01 seoa5552 4G 0.01 ncrb2288 4G 0.01 fcrb5644 4G 0.01 fcr6937 4G 0.01 seoa0536 4G 0.01 miod3468 4G 0.01 ncrc5813 4G 0.01 ncrb5737 4G 0.01 seoa6658 4G 0.01 fcrb5675 4G 0.01 hfcrl419 4G 0.01 ncrc3453 4G 0.01 fcrb8465 4G 0.01 miob3763 4G 0.01 seoc2253 4G 0.01 miob2492 4G 0.01 miob6419 4G 0.01 miod6041 4G 0.01 mioc7561 4G 0.01 fcr5176 4G 0.01 seob9847 4G 0.01 fcrb5536 4G 0.01 fcrb7584 4G 0.01 mioa8622 4G 0.01 miob6030 4G 0.01 ncrc2685 4G 0.01 ncrblO85 4G 0.01 hfcr5383 4G 0.01 seoa4324 4G 0.01 mioc7260 4G 0.01 ncrO335 4G 0.01 miod4539 4G 0.01 seoc3870 4G 0.01 seob7747 4G 0.01 ncr2967 4G 0.01 ncrc9517 4G 0.01 fcrbl750 4G 0.01 seoa9729 4G 0.01 seob0418 4G 0.01 ncrc6617 4G 0.01 seobl788 4G 0.01 seob0928 4G 0.01 seoa3429 4G 0.01 fcrb5588 4G 0.01 fcr4927 4G 0.01 miod2264 4G 0.01 mioal062 4G 0.01 ncrl428 4G 0.01 seob3464 4G 0.01 fcrcl758 4G 0.01 miob8025 4G 0.01 seoa2957 4G 0.01 miod5372 4G 0.01 mioa4944 4G 0.01 mioa6091 4G 0.01 miob5708 4G 0.01 mioc4641 4G 0.01 seoc7684 4G 0.01 fcr3032 4G 0.01 fcrb2190 4G 0.01 seob4539 4G 0.01 seob5007 4G 0.01 mioc8379 4G 0.01 miod3494 4G 0.01 seoa2272 4G 0.01 miob5119 4G 0.01 miob2905 4G 0.01 ncr7082 4G 0.01 seoc5612 4G 0.01 ncrO851 4G 0.01 fcr3861 4G 0.01 ncrel414 4G 0.01 ncr9779 4G 0.01 fcrb2813 4G 0.01 seobl322 4G 0.01 mioc6847 4G 0.01 ncr9919 4G 0.01 fcrcO637 4G 0.01 ncr8827 4G 0.01 fcrb8901 4G 0.01 seob9480 4G 0.01 miob3477 4G 0.01 fcrcO726 4G 0.01 ncr0045 4G 0.01 miod6947 4G 0.01 seoc4748 4G 0.01 fcrb3497 4G 0.01 fcrc4658 4G 0.01 fcrl305 4G 0.01 fcrb6968 4G 0.01 fcrc2969 4G 0.01 fcrcl745 4G 0.01 hfcr2797 4G 0.01 mioc7807 4G 0.01 mioa7512 4G 0.01 miob2375 4G 0.01 miob3131 4G 0.01 mioa0891 4G 0.01 ncrc6127 4G 0.01 fcr6841 4G 0.01 seob3419 4G 0.01 mioc7370 4G 0.01 mioa0152 4G 0.01 seoa5429 4G 0.01 fcrb2054 4G 0.01 hfcr6640 4G 0.01 seoc0866 4G 0.01 fcrb9456 4G 0.01 ncr4126 4G 0.01 mioc2039 4G 0.01 fcr2700 4G 0.01 fcrb2784 4G 0.01 seoa8348 4G 0.01 ncr8290 4G 0.01 miod0686 4G 0.01 fcr0508 4G 0.01 fcrb2596 4G 0.01 mioc7509 4G 0.01 seob0751 4G 0.01 ncrc3258 4G 0.01 miobl789 4G 0.01 miob3411 4G 0.01 ncr6141 4G 0.01 mioa3646 4G 0.01 mioa9935 4G 0.01 seoa4107 4G 0.01 seoal979 4G 0.01 seoa0931 4G 0.01 miod7238 4G 0.01 fcrb6759 ""'"4G '""" 0.01 fcrcO839 4G 0.02 ncrc9024 4G 0.01 miod0441 4G 0.02 fcrb7392 4G 0.01 ncr5781 4G 0.02 mioa3799 4G 0.01 miob3396 4G 0.02 mioa6999 4G 0.01 fcr3043 4G 0.02 mioc0940 4G 0.01 seoc3980 4G 0.02 seob5490 4G 0.01 fcrb2591 4G 0.02 seob2081 4G 0.01 fcrc4390 4G 0.02 miocl440 4G 0.01 ncrb3003 4G 0.02 ncrc2273 4G 0.01 fcrc4896 4G 0.02 ncrc5608 4G 0.01 seoe4862 4G 0.02 miob4238 4G 0.01 miocl580 4G 0.02 mioc8474 4G 0.01 hfcr4007 4G 0.02 fcr5803 4G 0.01 miob4975 4G 0.02 fcrb3008 4G 0.01 miod4857 4G 0.02 seob0182 4G 0.01 seob6680 4G 0.02 seob9044 4G 0.01 fcrb2690 4G 0.02 mioa6807 4G 0.01 seoa3628 4G 0.02 seoa2962 4G 0.02 fcr2859 4G 0.02 hfcr4488 4G 0.02 ncrc2888 4G 0.02 fcrb2926 4G 0.02 ncrc6076 4G 0.02 fcrbl381 4G 0.02 fcrb6990 4G 0.02 fcr6616 4G 0.02 fcrb3519 4G 0.02 seoa3634 4G 0.02 miob2709 4G 0.02 seob3533 4G 0.02 mioa0220 4G 0.02 seob5551 4G 0.02 seoc2030 4G 0.02 fcrb2658 4G 0.02 seoc6099 4G 0.02 ncrc4885 4G 0.02 mioc4290 4G 0.02 seob5080 4G 0.02 seoa3555 4G 0.02 seoa7935 4G 0.02 ncrc6981 4G 0.02 hfcr2505 4G 0.02 fcr0027 4G 0.02 seoc4052 4G 0.02 miod7011 4G 0.02 seob6156 4G 0.02 ncrc9284 4G 0.02 mioal055 4G 0.02 mioc5736 4G 0.02 mioc8278 4G 0.02 fcrc7331 4G 0.02 seob4192 4G 0.02 ncrO212 4G 0.02 miodl675 4G 0.02 fcrb9499 4G 0.02 fcrcO529 4G 0.02 fcr0064 4G 0.02 miod2128 4G 0.02 fcrbO623 4G 0.02 ncr3149 4G 0.02 seoa3752 4G 0.02 ncrb8063 4G 0.02 ncr2717 4G 0.02 miob7109 4G 0.02 fcr5507 4G 0.02 seoa7917 4G 0.02 fcrbO598 4G 0.02 seoa8776 4G 0.02 hfcr5045 4G 0.02 seobl834 4G 0.02 seob8999 4G 0.02 fcr7403 4G 0.02 seoa6887 4G 0.02 fcrb6062 4G 0.02 hfcr6366 4G 0.02 mioa8858 4G 0.02 fcr5836 4G 0.02 mioaOβOl 4G 0.02 seoa2381 4G 0.02 mioc4270 4G 0.02 seob2657 4G 0.02 fcr2276 4G 0.02 miod5397 4G 0.02 seob0831 4G 0.02 seoc4505 4G 0.02 seocl264 4G 0.02 fcrb6725 4G 0.02 fcrb3539 4G 0.02 ncrcl402 4G 0.02 fcr2940 4G 0.02 ncr2792 4G 0.02 seob9122 4G 0.02 ncrc6439 4G 0.02 miob9821 4G 0.02 fcrcl669 4G 0.02 mioa2970 4G 0.02 seoc4103 4G 0.02 miob2116 4G 0.02 mioa3940 4G 0.02 seob9067 4G 0.02 miod6068 4G 0.02 seob4492 4G 0.02 seob2163 4G 0.02 fcrb2704 4G 0.02 ncrc9117 4G 0.02 miod7052 4G 0.02 ncrcl751 4G 0.02 miocl416 4G 0.02 fcrcO651 4G 0.02 ncr3778 4G 0.02 fcrb3857 4G 0.02 seoa8132 4G 0.02 fcr6234 4G 0.02 fcrO793 4G 0.02 fcrcl947 4G 0.02 miob6459 4G 0.02 ncr3237 4G 0.02 fcrO622 4G 0.02 ncrbl518 4G 0.02 seoc3443 4G 0.02 miob5010 4G 0.02 seob3189 4G 0.02 miob4876 4G 0.02 ncrc7040 4G 0.02 ncr3219 4G 0.02 ncr9108 4G 0.02 fcrc6228 4G 0.02 ncre0075 4G 0.02 fcrcl311 4G 0.02 seoa3910 4G 0.02 fcrc6542 4G 0.02 seoa5784 4G 0.02 mioc8264 4G 0.02 seob5225 4G 0.02 ncrb7111 4G 0.02 seoc3032 4G 0.02 ncrc6480 4G 0.02 seob8873 4G 0.02 miodl542 4G 0.02 seoa5157 4G 0.02 fcr7042 4G 0.02 miod0053 4G 0.02 mioa4738 4G 0.02 miob3330 4G 0.02 fcrcO591 4G 0.02 mioc7910 4G 0.02 seob9241 4G 0.03 seoal992 4G 0.02 fcrclO19 4G 0.03 fcrc5721 4G 0.02 seob4480 4G 0.03 hfcr2295 4G 0.02 mioa5836 4G 0.03 hfcr3180 4G 0.02 fcrb6747 4G 0.03 ncr8975 4G 0.02 fcrb2040 4G 0.03 seoa9873 4G 0.02 miobll39 4G 0.03 miob8711 4G 0.02 ncrb3314 4G 0.03 ncrO251 4G 0.02 fcrb9843 4G 0.03 fcr3620 4G 0.02 hfcr3436 4G 0.03 hfcr3834 4G 0.02 fcrO787 4G 0.03 seob0089 4G 0.02 fcrc5138 4G 0.03 seoc0416 4G 0.02 seob4079 4G 0.03 fcrc4949 4G 0.02 mioc0978 4G 0.03 miob4760 4G 0.02 hfcrO734 4G 0.03 seoc8420 4G 0.02 seod0608 4G 0.03 fcrb6693 4G 0.02 fcrc7125 4G 0.03 fcrc7228 4G 0.02 ncrcll92 4G 0.03 mioc3208 4G 0.02 seob6558 4G 0.03 seoa8564 4G 0.02 mioc0235 4G 0.03 ncrcO115 4G 0.02 fcrb6249 4G 0.03 fcrb6208 4G 0.02 fcrb8949 4G 0.03 seob4726 4G 0.02 fcrl421 4G 0.03 seoal065 4G 0.02 miocl596 4G 0.03 hfcr3494 4G 0.02 seob4001 4G 0.03 fcrb5439 4G 0.02 fcr4433 4G 0.03 seob8255 4G 0.02 seob7946 4G 0.03 seoc2810 4G 0.02 mioa0862 4G 0.03 ncrb3341 4G 0.02 seoc4187 4G 0.03 seob6096 4G 0.02 ncrb8649 4G 0.03 mioc3871 4G 0.02 fcrb7616 4G 0.03 ncrd2232 4G 0.02 seoa7383 4G 0.03 ncrc7131 4G 0.02 ncr0429 4G 0.03 seoc6666 4G 0.02 fcrb4599 4G 0.03 miob3291 4G 0.02 seobl367 4G 0.03 ncrcl231 4G 0.02 ncrb2013 4G 0.03 ncrb8496 4G 0.02 fcrO578 4G 0.03 fcr5316 4G 0.02 mioa8864 4G 0.03 miocOlβl 4G 0.02 miod4895 4G 0.03 seoa8854 4G 0.02 mioa7088 4G 0.03 seoaOlOl 4G 0.02 fcr2684 4G 0.03 mioa5511 4G 0.02 seobl219 4G 0.03 seobl947 4G 0.02 seoal789 4G 0.03 ncrc6455 4G 0.02 miob3531 4G 0.03 ncrcl341 4G 0.02 rαiob4057 4G 0.03 miob0865 4G 0.02 ncrc9642 4G 0.03 ncrc9259 4'G' 0.03 miob3432 4G 0.03 fcrb3003 4G 0.03 fcrb2090 4G 0.03 fcrb5416 4G 0.03 miod6324 4G 0.03 miob5810 4G 0.03 seoa6598 4G 0.03 ncrc8851 4G 0.03 mioc4504 4G 0.03 seoa7249 4G 0.03 seob0650 4G 0.03 seoa8738 4G 0.03 fcr2196 4G 0.03 hfcr7686 4G 0.03 seoc2246 4G 0.03 seoc2031 4G 0.03 ncr6072 4G 0.03 fcrc6970 4G 0.03 seob0787 4G 0.03 seob6030 4G 0.03 hfcr2381 4G 0.03 fcrO946 4G 0.03 mioc7433 4G 0.03 ncr4140 4G 0.03 ncrl007 4G 0.03 seoc3883 4G 0.03 ncrcl615 4G 0.03 fcrb8664 4G 0.03 fcrb3083 4G 0.03 mioa3944 4G 0.03 hfcr4423 4G 0.03 ncrc4620 4G 0.03 ncr4656 4G 0.04 hfcrO731 4G 0.03 fcrb4718 4G 0.04 seob7866 4G 0.03 fcrb6481 4G 0.04 hfcr6634 4G 0.03 ncrc4000 4G 0.04 ncrc4633 4G 0.03 seob0128 4G 0.04 mioa6913 4G 0.03 fcrb7133 4G 0.04 ncrbO303 4G 0.03 ncr3934 4G 0.04 ncrd5465 4G 0.03 miob9284 4G 0.04 hfcr5797 4G 0.03 mioc3936 4G 0.04 mioa6595 4G 0.03 miodl792 4G 0.04 miob0746 4G 0.03 ncrcl593 4G 0.04 ncrb6818 4G 0.03 seoa3748 4G 0.04 fcrb2198 4G 0.03 seodl325 4G 0.04 fcrc7046 4G 0.03 hfcr3518 4G 0.04 τniob3044 4G 0.03 seoe0722 4G 0.04 ncrb4538 4G 0.03 hfcrO439 4G 0.04 seob5144 4G 0.03 miod2635 4G 0.04 seob2717 4G 0.03 seoc3854 4G 0.04 fcr6321 4G 0.03 seodl802 4G 0.04 fcrb5645 4G 0.03 ncrclO49 4G 0.04 fcrb4016 4G 0.03 hfcr5522 4G 0.04 ncrb6337 4G 0.03 ncrcll65 4G 0.04 seob0885 4G 0.03 miod5707 4G 0.04 fcr5560 4G 0.03 ncrb8285 4G 0.04 seob5558 4G 0.03 fcrb9649 4G 0.04 seoa3038 4G 0.03 mioc4131 4G 0.04 ncrc4302 4G 0.03 miocl995 4G 0.04 fcrO529 4G 0.03 fcr5941 4G 0.04 seoc7581 4G 0.03 fcrb6818 4G 0.04 ncrc9055 4G 0.03 seoa3949 4G 0.04 ncrlO55 4G 0.03 seoa3761 4G 0.04 seoa4163 4G 0.03 fcr5571 4G 0.04 seoal332 4G 0.03 hfcr3902 4G 0.04 fcrcO771 4G 0.03 fcrb6363 4G 0.04 seob8355 4G 0.03 miob3426 4G 0.04 seoa2679 4G 0.03 fcrb6715 4G 0.04 ncr6212 4G 0.03 fcrb7402 4G 0.04 ncrc3856 4G 0.03 ncrb5514 4G 0.04 mioc7360 4G 0.03 ncrO547 4G 0.04 seoa7369 4G 0.03 fcrb4340 4G 0.04 miod2696 4G 0.03 mioc8479 4G 0.04 miod2025 4G 0.03 miodll08 4G 0.04 ncrl352 4G 0.03 ncr4550 4G 0.04 ncrc5088 4G 0.03 seoa2041 4G 0.04 hfcr2850 4G 0.03 ncrO531 4G 0.04 seobl243 4G 0.03 hfcr2535 4G 0.04 fcrc3605 4G 0.03 ncrb2909 4G 0.04 ncrb8189 4G 0.03 seoa4571 4G 0.04 fcr0490 4G 0.04 seoa4461 41 5.29e-06 fcrbβlll 4G 0.04 seoa6032 41 5.48e-06 mioc4782 4G 0.04 miod5349 41 6.31e-06 miod6016 4G 0.04 mioc3430 41 7.49e-06 seob4147 4G 0.04 seoa8894 41 l.le-05 ncr9337 4G 0.04 mioa9473 41 1.36e-05 ncrc4875 4G 0.04 fcrb3227 41 1.45e-05 seoa5258 4G 0.04 fcrb4534 41 1.46e-05 fcrb9714 4G 0.04 seob2697 41 1.66e-05 seobl748 4G 0.04 seoc0775 41 1.67e-05 ncrc5738 4G 0.04 ncr3751 41 1.95e-05 seoe4484 4G 0.04 seoa4518 41 2.47e-05 fcrb5656 4G 0.04 seoa6133 41 2.48e-05 mioc6960 4G 0.04 miob6562 41 2.52e-05 ncrb3541 4G 0.04 seob0085 41 3.04e-05 miod6658 4G 0.04 seoa8566 41 3.22e-05 mioc2728 4G 0.04 seoa8754 41 3.62e-05 fcrc4948 4G 0.04 mioa2788 41 4.4e-05 ncrO132 4G 0.04 mioa3367 41 4.43e-05 seob4363 4G 0.04 ncrc5243 41 4.53e-05 ncr2304 4G 0.04 seob3326 41 4.9e-05 fcrb4333 4G 0.04 miob6419 41 5.13e-05 fcr6181 4G 0.04 miod5123 41 5.49e-05 seob2131 4G 0.04 mioa9179 41 5.61e-05 fcrO843 4G 0.04 seob4333 41 5.73e-05 fcrb5275 4G 0.04 seob6853 41 5.74e-05 fcrb9382 4G 0.04 ncrcO729 41 5.77e-05 miob2668 4G 0.04 seoa8916 41 5.83e-05 miod0126 4G 0.04 seob3518 41 5.95e-05 miod6044 4G 0.04 mioa3963 41 6.04e-05 seob0046 4G 0.04 seoa9373 41 6.54e-05 miod6731 4G 0.04 mioa4636 41 6.55e-05 fcr3706 4G 0.04 crO471 41 6.77e-05 fcr5812 4G 0.04 miob6430 41 7.39e-05 fcrb0130 4G 0.04 seoal940 41 7.43e-05 seoa8921 4G 0.04 mioa3913 41 7.5e-05 seoa0740 4G 0.04 fcrbl892 41 7.58e-05 fcr5930 4G 0.04 seoa9042 41 7.7e-05 mioc4696 4G 0.04 seoa0032 41 8.0e-05 miod2412 4G 0.04 mioa2319 41 8.18e-05 ncr2583 4G 0.04 seob5240 41 8.29e-05 seoa2795 4G 0.04 ncrc9899 41 8.35e-05 fcr6881 4G 0.04 fcr5075 41 8.62e-05 fcrcO129 4G 0.04 ncrb2092 41 8.8e-05 fcrb7829 4G 0.04 hfcr8902 41 8.84e-05 miob9700 4G 0.04 mioal370 41 8.87e-05 ncrcO644 4G 0.04 seoa9482 41 9.27e-05 ncrd9362 4G 0.04 seob6004 41 9.64e-05 seoe3634 4G 0.04 fcrb8516 41 9.9e-05 seoal567 4G 0.04 hfcr3043 41 1.0e-04 seoc2589 4G 0.04 ncrc5434 41 1.0e-04 ncre5107 4G 0.04 fcr4477 41 1.01e-04 hfcrl314 4G 0.04 fcrc3739 41 1.03e-04 seoa2141 4G 0.04 fcrb6939 41 1.04e-04 fcr3053 41 3.37e-07 hfcr5919 41 1.04e-04 seoc2029 41 6.23e-07 mioa3923 41 1.12e-04 fcr3181 41 7.1e-07 seob4427 41 1.16e-04 ncr3163 41 2.24e-06 ncrb4441 41 1.22e-04 mioc7170 41 2.53e-06 hfcrll37 41 1.23e-04 mioa2072 41 3.26e-06 seob0154 41 1.23e-04 seob4303 41 3.35e-06 fcrbl960 41 1.25e-04 seob5523 41 4.23e-06 seoa0231 41 1.25e-04 seob4419 41 4.27e-06 seob6576 41 1.27e-04 fcrc3993 41 4.37e-06 mioa5059 41 1.29e-04 seob6189 41 1.32e-04 seoa4053 41 3.03e-04 miod7414 41 1.36e-04 seob6467 41 3.03e-04 mioc2094 41 1.38e-04 fcrcO112 41 3.06e-04 miob6881 41 1.44e-04 ncrO153 41 3.07e-04 fcrO253 41 1.45e-04 seoa4366 41 3.08e-04 seoa2585 41 1.47e-04 seoc6169 41 3.11e-04 seoa7652 41 1.5e-04 mioal585 41 3.13e-04 seob3313 41 1.51e-04 hfcr3011 41 3.15e-04 seoa4717 41 1.52e-04 ncrb8714 41 3.16e-04 seob6851 41 1.55e-04 seoa5662 41 3.21e-04 fcrb5850 41 1.56e-04 seoa0486 41 3.23e-04 miob8812 41 1.56e-04 fcrcO487 41 3.31e-04 fcrb3461 41 1.6e-04 ncr3837 41 3.33e-04 mioa4009 41 1.63e-04 mioa7317 41 3.37e-04 ncrc6171 41 1.66e-04 hfcrl863 41 3.39e-04 seocl023 41 1.66e-04 fcr2573 41 3.43e-04 seoa0959 41 1.68e-04 seob5743 41 3.48e-04 seob4029 41 1.69e-04 mioa5695 41 3.5e-04 mioa4014 41 1.73e-04 fcrb6968 41 3.6e-04 fcrb2704 41 1.74e-04 seob7584 41 3.63e-04 seob3189 41 1.76e-04 fcrb3578 41 3.7e-04 miob3953 41 1.77e-04 ncrcO342 41 3.76e-04 miod5651 41 1.77e-04 ncrc5417 41 3.79e-04 seoa2970 41 1.77e-04 seob3854 41 3.91e-04 seob5748 41 1.79e-04 seob6279 41 3.96e-04 ncr7904 41 1.8e-04 fcrb8504 41 3.99e-04 ncr8588 41 1.82e-04 seob3367 41 4.04e-04 seobl231 41 1.85e-04 seob3182 41 4.09e-04 miod5505 41 1.89e-04 hfcr4489 41 4.15e-04 fcrb3654 41 1.91e-04 fcrb4428 41 4.19e-04 seob6751 41 2.02e-04 ncrO898 41 4.21e-04 mioa6130 41 2.04e-04 mioc8057 41 4.39e-04 seoc2264 41 2.1e-04 miobl231 41 4.43e-04 fcr7587 41 2.15e-04 fcr5559 41 4.69e-04 fcrb2554 41 2.2e-04 seoa6497 41 4.69e-04 ncrO212 41 2.21e-04 seoa6598 41 4.73e-04 ncr3197 41 2.22e-04 fcrl463 41 4.77e-04 fcr2607 41 2.23e-04 fcr2103 41 4.84e-04 fcr4582 41 2.27e-04 seob3191 41 4.89e-04 fcr4846 41 2.28e-04 seobl319 41 4.91e-04 fcrcl381 41 2.28e-04 miod6845 41 4.99e-04 miob0167 41 2.32e-04 ncrc5663 41 5.0e-04 mioa0311 41 2.33e-04 seob6844 41 5.04e-04 seocll75 41 2.33e-04 mioa9510 41 5.12e-04 mioc4022 41 2.34e-04 mioc2602 41 5.18e-04 mioa8970 41 2.35e-04 ncrc4597 41 5.18e-04 seob0065 41 2.35e-04 ncrc4757 41 5.23e-04 fcrc5937 41 2.36e-04 fcr3664 41 5.26e-04 seobl911 41 2.39e-04 seob4669 41 5.33e-04 mioa3939 41 2.4e-04 seob9282 41 5.33e-04 ncr0045 41 2.44e-04 ncr7813 41 5.48e-04 seoc0999 41 2.44e-04 seoa7530 41 5.56e-04 fcrcO554 41 2.48e-04 seob0248 41 5.56e-04 seocl203 41 2.49e-04 seoa0288 41 5.7e-04 hfcrlO73 41 2.54e-04 mioa6064 41 5.71e-04 £crc0075 41 2.58e-04 miob9907 41 5.82e-04 fcr6235 41 2.6e-04 ncrc8903 41 5.88e-04 seoc4960 41 2.65e-04 seob2148 41 5.88e-04 miob6223 41 2.67e-04 fcr4831 41 5.97e-04 miob8694 41 2.87e-04 mioa0595 41 6.04e-04 ncrO258 41 2.89e-04 seob9145 41 6.11e-04 ncr4126 41 2.91e-04 mioal662 41 6.2e-04 mioc6260 41 2.92e-04 fcrc3750 41 6.22e-04 fcrb6012 41 3.0e-04 seoall02 41 6.3e-04 miod475''9'"" 41 """' 6m.36e-04 seob6272 41 9.74e-04 fcrc6826 41 6.39e-04 seoa5090 41 1.007122e-03 mioc7686 41 6.41e-04 mioa2173 41 1.00875e-03 ncrb2266 41 6.45e-04 fcrb7240 41 1.015969e-03 seob6835 41 6.58e-04 fcr7043 41 1.016688e-03 mioa2537 41 6.62e-04 miodl236 41 1.028284e-03 seoa3717 41 6.62e-04 seob6446 41 1.030592e-03 fcrc3009 41 6.69e-04 seob2936 41 1.039004e-03 mioc8410 41 6.98e-04 ncrb8693 41 1.046851e-03 mioa6418 41 7.09e-04 mioa9154 41 1.070876e-03 mioc0950 41 7.19e-04 seoa6658 41 1.070876e-03 miod.1925 41 7.23e-04 ncrb2288 41 1.076406e-03 ncrc9910 41 7.24e-04 fcrc7388 41 1.081745e-03 seoa0221 41 7.25e-04 fcrb9588 41 1.089146e-03 mioc5532 41 7.27e-04 miob8572 41 1.097548e-03 mioc4089 41 7.46e-04 seob8204 41 1.099016e-03 mioc7764 41 7.57e-04 miod5894 41 1.100493e-03 seoa4681 41 7.58e-04 ncrcO174 41 1.123083e-03 seob6131 41 7.61e-04 hfcr5604 41 1.140826e-03 seob9960 41 7.63e-04 seoa7443 41 1.140826e-03 seoa8388 41 7.64e-04 fcrbl855 41 1.142061e-03 seob3303 41 7.67e-04 ncr3713 41 1.150048e-03 mioa4552 41 7.78e-04 fcrb9269 41 1.150659e-03 mioa4484 41 7.8e-04 seob8368 41 1.154981e-03 ncr8843 41 7.83e-04 seob0122 41 1.171391e-03 seoa9537 41 7.83e-04 mioa0535 41 1.173792e-03 seob9574 41 7.88e-04 miobl062 41 1.177608e-03 seob0308 41 7.98e-04 seobl793 41 1.177608e-03 mioall93 41 8.04e-04 seocl593 41 1.178329e-03 seobl318 41 8.04e-04 miob9336 41 1.178419e-03 hfcr6687 41 8.17e-04 seobl862 41 1.201035e-03 fcrbl496 41 8.19e-04 seoa3105 41 1.211669e-03 miob8320 41 8.21e-04 seob3670 41 1.23222e-03 seob0376 41 8.21e-04 miob4867 41 1.256093e-03 seoa8979 41 8.23e-04 ncrcll40 41 1.256093e-03 seoa9357 41 8.24e-04 fcr5720 41 1.2614e-03 ncrbO782 41 8.27e-04 miod6238 41 1.278208e-03 seob3923 41 8.31e-04 mioc7331 41 1.301405e-03 ncr2293 41 8.33e-04 ncrc6005 41 1.305117e-03 seob0755 41 8.35e-04 miodl942 41 1.305311e-03 ncrO733 41 8.46e-04 mioa0909 41 1.312146e-03 hfcr5220 41 8.48e-04 seobl879 41 1.347303e-03 ncrc5844 41 8.48e-04 miob6904 41 1.355302e-03 raioa5355 41 8.58e-04 mioc2880 41 1.356947e-03 ncr8041 41 8.61e-04 seob6368 41 1.361315e-03 seob2994 41 8.67e-04 ncr2472 41 1.365497e-03 seob3119 41 8.73e-04 ncrb8385 41 1.369106e-03 fcrb8668 41 8.75e-04 fcrl756 41 1.392986e-03 ncrc9784 41 8.77e-04 miod6038 41 1.395436e-03 seob6206 41 8.88e-04 fcr2088 41 1.408465e-03 mioa6731 41 8.9e-04 miob3348 41 1.428182e-03 ncr5568 41 9.0e-04 miodll95 41 1.431152e-03 mioc4667 41 9.01e-04 fcrl879 41 1.452287e-03 mioa5696 41 9.03e-04 mioa6721 41 1.457877e-03 hfcrll63 41 9.1e-04 seoa3516 41 1.46015e-03 ncr0808 41 9.12e-04 ncr0025 41 1.469905e-03 hfcr3019 41 9.19e-04 hfcrl310 41 1.478083e-03 fcrc3048 41 9.26e-04 fcrb9959 41 1.48762e-03 fcrb9454 41 9.3e-04 fcrb5214 41 1.490057e-03 seoa6315 41 9.33e-04 ncrb4182 41 1.50404e-03 fcrb9655 41 9.37e-04 ncrb3702 41 1.511682e-03 ncrcO715 41 9.39e-04 mioa0577 41 1.518616e-03 seoa8348 41 9.56e-04 ncr8975 41 1.524283e-03 fcrb9420 41 9.64e-04 seob4579 41 1.527065e-03 T7US2005/022071
fcr4128 "41 1.53383e-03 mioc6987 41 2.076534e-03 ncrc5162 41 1.553082e-03 hfcrO624 41 2.085152e-03 ncr5168 41 1.55656e-03 ncrb3329 41 2.085152e-03 ncrb3001 41 1.557302e-03 seob6015 41 2.09993e-03 miob3308 41 1.566301e-03 fcr4782 41 2.115262e-03 fcrb8187 41 1.573331e-03 fcrbl733 41 2.120875e-03 ncrb4039 41 1.584396e-03 ncrO238 41 2.1386e-03 miodlθll 41 1.590472e-03 mioc7895 41 2.143605e-03 hfcr3500 41 1.610297e-03 fcrb3718 41 2.145161e-03 mioa8852 41 1.618546e-03 seob6670 41 2.155534e-03 mioa0494 41 1.619093e-03 seob8501 41 2.166556e-03 seoaOlll 41 1.622252e-03 miod2525 41 2.174809e-03 ncr3782 41 1.63287e-03 fcrb6187 41 2.177878e-03 ncrc5569 41 1.637974e-03 mioc6312 41 2.199817e-03 mioc6075 41 1.640004e-03 hfcrl733 41 2.209362e-03 mioa4076 41 1.665908e-03 ncrb4957 41 2.210616e-03 seoa4305 41 1.678116e-03 seob0089 41 2.211811e-03 seob2185 41 1.70771e-03 hfcr6651 41 2.216864e-03 fcrc2090 41 1.719751e-03 hfcrl302 41 2.226095e-03 seoa9870 41 1.728369e-03 seob4050 41 2.226095e-03 seobl414 41 1.747109e-03 fcrb2536 41 2.230106e-03 seoa9828 41 1.753504e-03 rαioa8851 41 2.23281e-03 fcrb3165 41 1.760242e-03 seoa2012 41 2.255872e-03 fcrc2099 41 1.790284e-03 seoclllδ 41 2.258564e-03 seobl766 41 1.794891e-03 fcr7419 41 2.260166e-03 hfcrl743 41 1.798381e-03 seob7432 41 2.272415e-03 seobl908 41 1.798381e-03 mioc4318 41 2.279552e-03 seob4545 41 1.798381e-03 fcrcO695 41 2.279562e-03 fcrc5516 41 1.799844e-03 fcr3121 41 2.285067e-03 hfcr5228 41 1.800664e-03 seoa8443 41 2.285067e-03 rαioa5692 41 1.800664e-03 seoal065 41 2.312579e-03 seob4555 41 1.800664e-03 miob4475 41 2.330183e-03 fcrbl420 41 1.801622e-03 ncrc9469 41 2.33257e-03 fcr3595 41 1.803204e-03 ncrc6592 41 2.352328e-03 ncr2666 41 1.803204e-03 seob4273 41 2.355513e-03 fcrb8940 41 1.834572e-03 hfcr6700 41 2.362015e-03 miob5495 41 1.838333e-03 seoa5577 41 2.367648e-03 ncr3368 41 1.838333e-03 seoa5235 41 2.369839e-03 fcrbl807 41 1.859263e-03 ncrb8273 41 2.393454e-03 ncrc5054 41 1.860924e-03 fcrb3134 41 2.402582e-03 seob6525 41 1.861275e-03 mioa9294 41 2.412283e-03 seoc0924 41 1.861444e-03 hfcrl697 41 2.435634e-03 seoa8399 41 1.88316e-03 ncrc5369 41 2.435634e-03 fcrb8161 41 1.885852e-03 seob5219 41 2.439695e-03 mioal520 41 1.887743e-03 seoa5444 41 2.479655e-03 fcrb8740 41 1.892065e-03 seoall73 41 2.502414e-03 seob2810 41 1.901388e-03 miob0189 41 2.57178e-03 fcr5758 41 1.909224e-03 miob2705 41 2.574869e-03 mioc8423 41 1.916815e-03 seoa8232 41 2.578028e-03 hfcr5695 41 1.956159e-03 fcr5123 41 2.597097e-03 seoc4380 41 1.956915e-03 mioa6854 41 2.602922e-03 fcrb2871 41 1.964974e-03 fcrO139 41 2.651872e-03 ncrc0150 41 1.985831e-03 fcr7004 41 2.657357e-03 fcrb2060 41 1.99746e-03 mioa9505 41 2.658032e-03 hfcrO415 41 1.99746e-03 seob7465 41 2.711845e-03 seoa7555 41 1.99746e-03 fcrb4409 41 2.722505e-03 fcr2079 41 2.016524e-03 seoc0276 41 2.725532e-03 seob4560 41 2.021887e-03 fcrO999 41 2.730498e-03 seocl025 41 2.02575e-03 hfcr4488 41 2.731117e-03 fcrb8080 41 2.025786e-03 fcr3559 41 2.734761e-03 seob0201 41 2.050861e-03 mioal660 41 2.749542e-03 fcrb9720 41 2.057033e-03 seoc0513 41 2.753054e-03 fcr5470 41 2.060807e-03 ncrc9712 41 2.760324e-03 ncrc4079 41 2.066895e-03 miod5707 41 2.770552e-03 miob8711 41 2.809832e-03 seoa6661 41 3.644681e-03 seob3307 41 2.811695e-03 seoal720 41 3.649977e-03 seoa9814 41 2.836239e-03 seob2108 41 3.666209e-03 seob9946 41 2.84126e-03 ncrO478 41 3.666381e-03 mioc7744 41 2.845792e-03 fcrb2249 41 3.695741e-03 seoa8543 41 2.874349e-03 seob4117 41 3.700072e-03 miob3898 41 2.881671e-03 ncrb8134 41 3.708615e-03 seoa8642 41 2.881671e-03 seob9750 41 3.708927e-03 mioa7140 41 2.898369e-03 miob2492 41 3.735778e-03 fcr4763 41 2.928101e-03 mioal971 41 3.738256e-03 rαiob5016 41 2.928675e-03 seob5954 41 3.738256e-03 fcrcl849 41 2.952953e-03 miob8932 41 3.752087e-03 fcrbl731 41 2.954229e-03 ncrc4808 41 3.775334e~03 fcrb3483 41 2.983789e-03 mioa5085 41 3.775842e-03 seoa4600 41 2.998627e-03 seoa4586 41 3.780119e-03 seoa0008 41 3.007551e-03 seoa3408 41 3.790116e-03 seob0168 41 3.016761e-03 mioa8774 41 3.812303e-03 seob2221 41 3.044324e-03 seoa7546 41 3.830009e-03 fcrb2933 41 3.049655e-03 fcr3155 41 3.832262e-03 ncrc5079 41 3.050155e-03 ncr2182 41 3.847974e-03 fcrb9481 41 3.074875e-03 ncrc9557 41 3.849741e-03 ncrbOO54 41 3.127244e-03 miob0974 41 3.874121e-03 seoa3533 41 3.13784e-03 seoa6573 41 3.875968e-03 miod5369 41 3.159305e-03 fcrb2350 41 3.883582e-03 seoa5683 41 3.160612e-03 fcrb6009 41 3.925377e-03 mioa0407 41 3.167049e-03 miod7243 41 3.934025e-03 fcrb7693 41 3.203691e-03 ncrc3434 41 3.939813e-03 seoa9160 41 3.207335e-03 hfcr9613 41 4.011004e-03 fcrbl552 41 3.209013e-03 miob4055 41 4.011569e-03 miob4836 41 3.218792e-03 miob0154 41 4.03718e-03 ncrO438 41 3.245826e-03 seoa2442 41 4.093483e-03 mioa0187 41 3.279663e-03 seob3869 41 4.093483e-03 mioa2374 41 3.293401e-03 seoa2641 41 4.10684e-03 ncrcOlOO 41 3.298912e-03 seoa6620 41 4.118774e-03 seob8741 41 3.317878e-03 seoc3515 41 4.159057e-03 ncrc2776 41 3.322303e-03 fcrb9694 41 4.20251e-03 ncrb3942 41 3.332792e-03 ncrb7292 41 4.220365e-03 ncrbO513 41 3.349517e-03 miod4140 41 4.240281e-03 ncrl387 41 3.356296e-03 hfcr0045 41 4.261381e-03 fcrb3017 41 3.376066e-03 ncrc9877 41 4.26595e-03 ncrc5091 41 3.385978e-03 seoc2191 41 4.272909e-03 miob8274 41 3.404337e-03 mioc0567 41 4.279318e-03 fcrcO241 41 3.422538e-03 mioc3716 41 4.281847e-03 seob0514 41 3.423122e-03 fcrb3725 41 4.312352e-03 ncr8866 41 3.437719e-03 ncrc9772 41 4.399985e-03 mioc7441 41 3.437924e-03 raiob8226 41 4.470203e-03 fcrc6888 41 3.449333e-03 mioa0380 41 4.489956e-03 miob9065 41 3.456747e-03 mioal097 41 4.509349e-03 mioa3514 41 3.46245e-03 seob3462 41 4.509349e-03 ncr3237 41 3.46245e-03 mioa8811 41 4.52055e-03 hfcr6406 41 3.4643e-03 fcrcO959 41 4.530274e-03 ncrc4991 41 3.4643e-03 seob3154 41 4.531125e-03 seob3499 41 3.4643e-03 mioa0890 41 4.570501e-03 ncrc9855 41 3.46879e-03 fcrc6335 41 4.58749e-03 miod6029 41 3.517177e-03 ncrc5760 41 4.601184e-03 ncr2013 41 3.51941e-03 mioal427 41 4.633362e-03 seob4972 41 3.531512e-03 ncrc3840 41 4.633362e-03 hfcr0130 41 3.533831e-03 seoa4040 41 4.639931e-03 fcrb4378 41 3.540302e-03 miod4348 41 4.655192e-03 ncrcl643 41 3.548484e-03 seoa7902 41 4.66728e-03 mioc3726 41 3.550472e-03 ncrc5150 41 4.691321e-03 seob2188 41 3.584274e-03 ncrl563 41 4.707547e-03 seob2797 41 3.6022e-03 seob4621 41 4.707547e-03 seobl667 41 3.640309e-03 fcrc7047 41 4.744327e-03 "seob55"62"' '"'""41 4.746427e-03 mioa8647 41 5.768458e-03 seoa4395 41 4.779169e-03 mioal445 41 5.811166e-03 hfcr6468 41 4.79651e-03 seoc3588 41 5.826485e-03 ncrb8253 41 4.799003e-03 ncrc3468 41 5.832748e-03 seoa5986 41 4.799003e-03 miob9052 41 5.833135e-03 raiob5736 41 4.80808e-03 mioc5664 41 5.835444e-03 seob3151 41 4.834042e-03 fcrb2137 41 5.84394e-03 seob9292 41 4.848082e-03 hfcr5207 41 5.84394e-03 seoa7517 41 4.870405e-03 mioa2327 41 5.859459e-03 fcrb4415 41 4.898061e-03 fcrb9147 41 5.861796e-03 mioa6621 41 4.904983e-03 seob5458 41 5.862878e-03 seob9649 41 4.907044e-03 seoa5554 41 5.877316e-03 miocll22 41 4.93196e-03 seoal552 41 5.896449e-03 ncrc6795 41 4.933324e-03 seoa3230 41 5.898784e-03 seoa3847 41 4.942903e-03 fcrl844 41 5.913324e-03 fcr5625 41 4.99519e-03 fcrb5267 41 5.942373e-03 ncr3684 41 5.017654e-03 fcrb6896 41 5.949973e-03 ncrb2091 41 5.039376e-03 fcrc0094 41 5.96467e-03 hfcr2287 41 5.054567e-03 miob3684 41 5.971717e-03 fcrc2576 41 5.063792e-03 fcr6390 41 6.000719e-03 seoa0501 41 5.071997e-03 seobl737 41 6.007946e-03 fcrc0430 41 5.084925e-03 fcr2074 41 6.018802e-03 hfcr0750 41 5.09128e-03 fcrl883 41 6.048593e-03 mioa3379 41 5.102551e-03 fcr5536 41 6.049129e-03 ncrc5672 41 5.11611e-03 seoc2131 41 6.08684e-03 fcr6497 41 5.124127e-03 ncr4550 41 6.099859e-03 mioa5468 41 5.124127e-03 seoa0470 41 6.100317e-03 seoa6923 41 5.124127e-03 fcrb2218 41 6.116835e-03 ncrc5500 41 5.175143e-03 mioc7444 41 6.13308e-03 mioa8946 41 5.185969e-03 seoa3704 41 6.146625e-03 fcr0056 41 5.190905e-03 seob0321 41 6.178138e-03 ncrc5039 41 5.197151e-03 ncrbl337 41 6.21911e-03 hfcr5009 41 5.200779e-03 ncrcOlOl 41 6.221193e-03 fcrc4360 41 5.250167e-03 ncrb6833 41 6.229488e-03 fcrb4241 41 5.25156e-03 ncr0075 41 6.26855e-03 mioc0824 41 5.254723e-03 mioa8952 41 6.270044e-03 fcrc6563 41 5.282794e-03 ncrO766 41 6.276933e-03 seoa2122 41 5.287317e-03 ncrc6817 41 6.276933e-03 fcrO768 41 5.301658e-03 mioa2013 41 6.287512e-03 ncrc6479 41 5.320657e-03 seob9485 41 6.301111e-03 miod3254 41 5.326719e-03 miod4142 41 6.383134e-03 fcrcl745 41 5.32716e-03 ncrc6712 41 6.41394e-03 mioa4196 41 5.344108e-03 fcr5779 41 6.474618e-03 mioc8619 41 5.353299e-03 seoa6197 41 6.486591e-03 ncrc4654 41 5.369673e-03 seob0703 41 6.50097e-03 seob4515 41 5.369673e-03 miob6821 41 6.505915e-03 mioc2074 41 5.414445e-03 ncrb8343 41 6.552284e-03 ncr4545 41 5.438486e-03 seoa5911 41 6.597064e-03 seoa0029 41 5.480759e-03 fcrb2292 41 6.653984e-03 fcr0990 41 5.490991e-03 seob4191 41 6.654737e-03 fcrbl684 41 5.495549e-03 fcrb2765 41 6.689668e-03 seob0031 41 5.509288e-03 miod5612 41 6.698915e-03 seoal856 41 5.531453e-03 ncr4384 41 6.700762e-03 fcrc6854 41 5.540234e-03 seob4039 41 6.713668e-03 fcrc2082 41 5.550695e-03 fcrb5087 41 6.714694e-03 seob2987 41 5.569207e-03 fcrb5422 41 6.7264e-03 mioal473 41 5.580693e-03 ncrc2119 41 6.756046e-03 seob4030 41 5.594557e-03 seob9552 41 6.762169e-03 fcr3282 41 5.608041e-03 fcrb3841 41 6.780973e-03 fcr4795 41 5.649808e-03 miob6988 41 6.80749e-03 seob5726 41 5.65605e-03 hfcr5473 41 6.857537e-03 fcrb7852 41 5.667225e-03 fcrc3415 41 6.861942e-03 ncrl550 41 5.713507e-03 fcr4380 41 6.880921e-03 mioa0132 41 5.738039e-03 ncr6335 41 6.907692e-03 miod3"θ7~9"""* I1""""™6"r'9'l873e-03 seoc2220 41 8.240295e-03 seoa3628 41 6.949533e-03 ncrO912 41 8.275646e-03 seob9772 41 6.956817e-03 seob0466 41 8.285506e-03 mioa9581 41 6.958796e-03 mioa9630 41 8.304775e-03 ncr2486 41 6.982956e-03 ncrb6192 41 8.312148e-03 miob0877 41 6.983468e-03 seoc2824 41 8.3183e-03 seoa6137 41 7.003723e-03 seoa0070 41 8.335344e-03 fcr6415 41 7.029261e-03 seoal736 41 8.342294e-03 seobl385 41 7.029563e-03 seoa4070 41 8.351138e-03 ncrb8207 41 7.041692e-03 hfcr7855 41 8.373048e-03 ncrl351 41 7.095309e-03 seoall04 41 8.378822e-03 seob2803 41 7.108081e-03 ncrO634 41 8.421583e-03 seob5012 41 7.108081e-03 seoa5465 41 8.437929e-03 ncrcllO3 41 7.109167e-03 miob2855 41 8.459946e-03 mioa4241 41 7.17269e-03 seoal263 41 8.459946e-03 seoa2136 41 7.196923e-03 seoa0913 41 8.473584e-03 fcr5339 41 7.199684e-03 seoa7459 41 8.497761e-03 miob2836 41 7.210691e-03 seob2658 41 8.497761e-03 mioa0104 41 7.212787e-03 ncrc6871 41 8.532381e-03 ncr3948 41 7.237837e-03 seoa7474 41 8.568552e-03 rαioa8471 41 7.255595e-03 fcrc2997 41 8.587588e-03 mioa6093 41 7.268139e-03 ncr2905 41 8.640653e-03 seoal559 41 7.27224e-03 mioa9891 41 8.689171e-03 hfcr9549 41 7.295285e-03 hfcr3928 41 8.732239e-03 miod6213 41 7.325332e-03 seoa7509 41 8.755591e-03 miob4058 41 7.329008e-03 seob6026 41 8.755905e-03 mioa0328 41 7.377639e-03 mioa6738 41 8.769774e-03 seoc3993 41 7.378389e-03 seoa7178 41 8.77984e-03 miod5703 41 7.424044e-03 fcrc0604 41 8.785023e-03 fcr6044 41 7.452917e-03 seob5319 41 8.811962e-03 seoa7078 41 7.471779e-03 fcrcO654 41 8.812335e-03 fcrβOlβ 41 7.53305e-03 miobl506 41 8.830271e-03 miob4221 41 7.572537e-03 fcrcl834 41 8.876728e-03 seoa2363 41 7.617361e-03 miodl331 41 8.912821e-03 fcr6616 41 7.680441e-03 seob3169 41 8.97374e-03 fcr6931 41 7.680441e-03 mioa3629 41 8.97697e-03 seoa6152 41 7.680441e-03 miob3696 41 8.981708e-03 fcr4622 41 7.681578e-03 fcrc4054 41 8.989376e-03 mioal324 41 7.698039e-03 fcr2160 41 9.030333e-03 fcrb7944 41 7.767483e-03 mioa0707 41 9.037843e-03 seob7729 41 7.786058e-03 ncr3496 41 9.054107e-03 seob2966 41 7.806257e-03 ncrcO667 41 9.054835e-03 seoa0600 41 7.828518e-03 seob4807 41 9.055692e-03 seob4108 41 7.828518e-03 ncrc9428 41 9.100754e-03 seob6177 41 7.843092e-03 fcrb8877 41 9.135346e-03 ncr2484 41 7.879438e-03 mioa9649 41 9.154014e-03 ncrb3585 41 7.879438e-03 miod5672 41 9.160361e-03 ncrO612 41 7.925456e-03 seoa5933 41 9.204501e-03 mioc7998 41 7.939586e-03 miob5675 41 9.254824e-03 seob9872 41 7.94076e-03 ncrcO383 41 9.294641e-03 raioc0375 41 7.952967e-03 miob4743 41 9.329707e-03 fcrb6768 41 7.961352e-03 ncrb3284 41 9.343469e-03 seoa3245 41 7.995448e-03 ncrc6953 41 9.345199e-03 hfcr5905 41 8.045743e-03 seoc0098 41 9.429093e-03 miob2227 41 8.045772e-03 seocl234 41 9.440983e-03 fcrb7113 41 8.05715e-03 fcr4084 41 9.52855e-03 hfcrδSOl 41 8.058052e-03 ncrb6453 41 9.583937e-03 ncrbl373 41 8.093663e-03 ncrc5553 41 9.583937e-03 mioa9717 41 8.135586e-03 seob0418 41 9.583937e-03 seob4209 41 8.135586e-03 seob0810 41 9.583937e-03 fcrc3358 41 8.156756e-03 mioa4548 41 9.611891e-03 fcrbl329 41 8.164602e-03 fcrc5846 41 9.622113e~03 miocβ997 41 8.185087e-03 fcrbl503 41 9.664889e-03 fcr4129 41 8.215271e-03 ncrcl873 41 9.678381e-03 ncrcO846 41 ~'"""9"7"679"5"67e-03 ncrcO863 41 0.01 seob3064 41 9.679567e-03 seob4122 41 0.01 seob5778 41 9.689819e-03 fcrb3135 41 0.01 fcrb5537 41 9-.69729e-03 fcrb8162 41 0.01 hfcrl419 41 9.717051e-03 fcrb3702 41 0.01 mioa6035 41 9.789009e-03 seoa4017 41 0.01 seoa0003 41 9.794242e-03 seob5441 41 0.01 seoc4416 41 9.841173e-03 mioa9792 41 0.01 mioa8998 41 9.861386e-03 mioa2536 41 0.01 ncrbl802 41 9.861386e-03 fcrb5389 41 0.01 seob8817 41 9.894482e-03 miob5412 41 0.01 fcrbl578 41 9.906745e-03 seoc3876 41 0.01 fcr2164 41 9.970513e-03 miob4803 41 0.01 seoa4452 41 9.989774e-03 seoa6393 41 0.01 miob8214 41 0.01 mioa9033 41 0.01 mioc4366 41 0.01 fcr3823 41 0.01 fcr5354 41 0.01 ncrb4390 41 0.01 fcrO955 41 0.01 hfcrllOl 41 0.01 fcrc3240 41 0.01 fcrc0350 41 0.01 fcr4699 41 0.01 fcrb4570 41 0.01 ncrO132 41 0.01 hfcr6716 41 0.01 seoa0511 41 0.01 ncrcO292 41 0.01 fcrb2713 41 0.01 mioc3906 41 0.01 miob6797 41 0.01 miod6731 41 0.01 ncrbl956 41 0.01 seoc2226 41 0.01 fcrb3651 41 0.01 seob8693 41 0.01 hfcr0829 41 0.01 rαioa4064 41 0.01 seobl574 41 0.01 fcrcO396 41 0.01 seoa8556 41 0.01 miob9393 41 0.01 mioal285 41 0.01 ncrc8841 41 0.01 fcr2418 41 0.01 mioal626 41 0.01 seoa2162 41 0.01 seoa7295 41 0.01 seob3520 41 0.01 ncrO491 41 0.01 seobl383 41 0.01 seob9882 41 0.01 hfcr6509 41 0.01 ncr3434 41 0.01 itιiob4574 41 0.01 miob4322 41 0.01 fcri337 41 0.01 miob2700 41 0.01 ncrc3624 41 0.01 seob3170 41 0.01 fcr2167 41 0.01 miob4673 41 0.01 mioa3812 41 0.01 ncrc3529 41 0.01 seobl008 41 0.01 mioc3958 41 0.01 seob6836 41 0.01 seoa0172 41 0.01 mioa4667 41 0.01 seob5213 41 0.01 fcrl068 41 0.01 seoa6344 41 0.01 raioa7015 41 0.01 ncrc5806 41 0.01 seoa8300 41 0.01 fcrcO496 41 0.01 mioa6585 41 0.01 seob0478 41 0.01 mioal394 41 0.01 mioc6925 41 0.01 hfcr3149 41 0.01 fcrb3704 41 0.01 fcrb5527 41 0.01 mioa5691 41 0.01 fcrb5918 41 0.01 ncrc5653 41 0.01 seob3699 41 0.01 ncr4202 41 0.01 ncrb8451 41 0.01 fcrcl216 41 0.01 seoa7266 41 0.01 seob6133 41 0.01 fcrb3584 41 0.01 hfcrl671 41 0.01 ncr7668 41 0.01 hfcr6256 41 0.01 seoa4264 41 0.01 fcrl633 41 0.01 fcrc2948 41 0.01 fcrbl741 41 0.01 hfcr2148 41 0.01 fcrb6834 41 0.01 seob3887 41 0.01 fcr2958 41 0.01 ncrb6087 41 0.01 seoa2428 41 0.01 mioa6135 41 0.01 ncrc6888 41 0.01 fcrc5577 41 0.01 seob3731 41 0.01 seoc2336 41 0.01 hfcr4114 41 0.01 mioa7323 '""''4I "" 0.01 fcrc6119 41 0.01 fcrb4388 41 0.01 ncrc9304 41 0.01 miob6437 41 0.01 ncrc3536 41 0.01 seoa4460 41 0.01 seoa0539 41 0.01 ncrb6432 41 0.01 seoa0844 41 0.01 seobl426 41 0.01 mioa6476 41 0.01 miod6961 41 0.01 miod7351 41 0.01 seob9645 41 0.01 fcrc4658 41 0.01 fcr4214 41 0.01 seoc7281 41 0.01 ncrO847 41 0.01 seob2938 41 0.01 ncrc9217 41 0.01 mioc7807 41 0.01 mioa5097 41 0.01 mioclOβO 41 0.01 ncrb5197 41 0.01 hfcrll27 41 0.01 seoa2639 41 0.01 hfcr5691 41 0.01 mioa2333 41 0.01 seocl230 41 0.01 seoc4909 41 0.01 miod0187 41 0.01 ncr6142 41 0.01 miob2375 41 0.01 miod4342 41 0.01 mioc7370 41 0.01 fcrbl689 41 0.01 hfcr6640 41 0.01 seoa7094 41 0.01 seob4570 41 0.01 mioc0899 41 0.01 fcrl557 41 0.01 fcrb3897 41 0.01 fcrb3073 41 0.01 seoa4708 41 0.01 seoal653 41 0.01 mioa9093 41 0.01 mioc2546 41 0.01 seob0344 41 0.01 miob7638 41 0.01 hfcr2850 41 0.01 miob4860 41 0.01 fcr6937 41 0.01 mioa9258 41 0.01 miod3468 41 0.01 seob0263 41 0.01 hfcrO521 41 0.01 ncrc3408 41 0.01 miocl248 41 0.01 miob3411 41 0.01 ncrc3453 41 0.01 mioa3646 41 0.01 miob3763 41 0.01 hfcr2314 41 0.01 seoa0457 41 0.01 fcrb3244 41 0.01 fcrc6642 41 0.01 seoa0931 41 0.01 ncrc3045 41 0.01 fcrb2559 41 0.01 mioa8622 41 0.01 seob7907 41 0.01 seoa7212 41 0.01 ncrc5813 41 0.01 mioc7260 41 0.01 rαiob3471 41 0.01 miod4539 41 0.01 fcrb8465 41 0.01 miob7373 41 0.01 seoc2253 41 0.01 ncrl235 41 0.01 ncr7267 41 0.01 ncrc9517 41 0.01 ncrb4339 41 0.01 ncrc6617 41 0.01 mioc7561 41 0.01 hfcrl795 41 0.01 mioa9666 41 0.01 fcrb5588 41 0.01 ncrcl765 41 0.01 seoc3463 41 0.01 ncr8811 41 0.01 fcrb6432 41 0.01 fcrc5797 41 0.01 fcr6881 41 0.01 mioal079 41 0.01 seoa3007 41 0.01 hfcr3413 41 0.01 seoa2957 41 0.01 miob0973 41 0.01 fcrbl320 41 0.01 hfcrO285 41 0.01 miod3494 41 0.01 seoa4324 41 0.01 hfcr7966 41 0.01 seoc3870 41 0.01 ncrb5264 41 0.01 fcr4927 41 0.01 seoc4288 41 0.01 seoc0394 41 0.01 fcrb8119 41 0.01 seob4945 41 0.01 fcrb2813 41 0.01 fcrb4739 41 0.01 seobl906 41 0.01 fcrbl750 41 0.01 mioc6847 41 0.01 fcrcO166 41 0.01 miob4793 41 0.01 seob2011 41 0.01 seoa8912 41 0.01 ncrc4089 41 0.01 fcrb8901 41 0.01 seoc0657 41 0.01 seob4896 41 0.01 fcrc2007 41 0.01 seobl646 41 0.01 ncrcO457 41 0.01 'seόa3422 41 0.01 ncrb7102 41 0.01 mioal062 41 0.01 hfcr4423 41 0.01 seob3464 41 0.01 seoa5578 41 0.01 miob4157 41 0.01 mioc6296 41 0.01 ncr8893 41 0.01 miod0686 41 0.01 seoa3108 41 0.01 fcrb9856 41 0.01 miob7391 41 0.01 ncr6141 41 0.01 mioa6091 41 0.01 ncr7973 41 0.01 ncr3811 41 0.01 seoa4727 41 0.01 seob5886 41 0.01 fcrc5671 41 0.01 mioc4641 41 0.01 seob6229 41 0.01 mioc8379 41 0.01 ncrc9024 41 0.01 ncrc6708 41 0.01 seoa2819 41 0.01 seob8104 41 0.01 fcrb9639 41 0.01 seoa2381 41 0.01 seoa3144 41 0.01 fcrb6251 41 0.01 mioa4628 41 0.01 fcrcO839 41 0.01 fcrb9680 41 0.01 seoa2272 41 0.01 seoa5742 41 0.01 fcrb4995 41 0.01 fcrc4045 41 0.01 ncr4118 41 0.01 fcr7042 41 0.01 fcrb2020 41 0.01 ncrc3358 41 0.01 ncrb8392 41 0.01 ncrc2273 41 0.01 ncr7768 41 0.01 fcr3323 41 0.01 hfcr5150 41 0.01 ncrbO262 41 0.01 seob7658 41 0.01 seoa3639 41 0.01 seobl322 41 0.01 seoa3670 41 0.01 ncr3396 41 0.01 ncrc9023 41 0.01 miob9185 41 0.01 ncrO615 41 0.01 seoa9724 41 0.01 seob7744 41 0.01 mioc0140 41 0.01 ncrc3072 41 0.01 ncrcll76 41 0.01 fcrb3298 41 0.01 seob5592 41 0.01 mioa2156 41 0.01 seob9480 41 0.01 fcrc6174 41 0.01 seobl526 41 0.01 ncrcl952 41 0.01 ncrb6661 41 0.01 hfcrO383 41 0.02 seob9756 41 0.01 ncrb7166 41 0.02 miod.6947 41 0.01 miod5698 41 0.02 fcrl833 41 0.01 hfcr3592 41 0.02 fcr3983 41 0.01 seob2685 41 0.02 mioal531 41 0.01 seobl617 41 0.02 mioa8796 41 0.01 fcr0018 41 0.02 seoa6621 41 0.01 seobl243 41 0.02 hfcr6680 41 0.01 ncrb6037 41 0.02 fcr6386 41 0.01 seob3533 41 0.02 miobl946 41 0.01 seoa3352 41 0.02 seoa3863 41 0.01 miodl448 41 0.02 fcrl305 41 0.01 miob4712 41 0.02 fcrb8092 41 0.01 seobl513 41 0.02 fcrb2596 41 0.01 fcrb2658 41 0.02 ncrc3141 41 0.01 mioc0630 41 0.02 seoa8239 41 0.01 hfcr2505 41 0.02 ncrb6327 41 0.01 seoc4052 41 0.02 fcrb3550 41 0.01 seoa0219 41 0.02 mioa8539 41 0.01 mioa0647 41 0.02 fcr5509 41 0.01 mioc4766 41 0.02 ncr8693 41 0.01 mioa0582 41 0.02 ncrc9159 41 0.01 mioal055 41 0.02 seob2077 41 0.01 ncrc5088 41 0.02 seob3419 41 0.01 seob4192 41 0.02 ncrc2859 41 0.01 fcrcO529 41 0.02 mioc0741 41 0.01 miod2128 41 0.02 fcrbl763 41 0.01 miob2687 41 0.02 mioa0152 41 0.01 fcrb5204 41 0.02 fcrO712 41 0.01 ncr3598 41 0.02 ncrb8063 41 0.02 seob9818 41 0.02 hfcr5232 41 0.02 seoa8486 41 0.02 fcr2299 41 0.02 ncr2717 41 0.02 seob3370 41 0.02 seoal582 41 0.02 fcrb6062 41 0.02 mioc0090 41 0.02 ncr5939 41 0.02 mioc3930 41 0.02 seoa9389 41 0.02 hfcr6366 41 0.02 miobl337 41 0.02 fcrb9751 41 0.02 seoa7608 41 0.02 seob3356 41 0.02 miob8657 41 0.02 seob4075 41 0.02 ncrb4319 41 0.02 ncrb8665 41 0.02 hfcrO225 41 0.02 seoa6038 41 0.02 seob0831 41 0.02 fcrb2866 41 0.02 ncrc9483 41 0.02 seoa5382 41 0.02 seob5645 41 0.02 seoa6155 41 0.02 fcr2940 41 0.02 ncrc5072 41 0.02 mioa2475 41 0.02 seoa9817 41 0.02 fcrcl660 41 0.02 seob7180 41 0.02 hfcrO351 41 0.02 seoc4505 41 0.02 seob9067 41 0.02 hfcr3436 41 0.02 ncr5713 41 0.02 ncrcl402 41 0.02 seob0745 41 0.02 fcrc4485 41 0.02 seoa4023 41 0.02 ncr2792 41 0.02 fcrbl529 41 0.02 ncrc6439 41 0.02 ncrc5363 41 0.02 seob3451 41 0.02 seob0763 41 0.02 seoal883 41 0.02 fcrb4238 41 0.02 fcr5112 41 0.02 miod2330 41 0.02 fcrb6676 41 0.02 hfcr7576 41 0.02 seob9430 41 0.02 seob3415 41 0.02 fcr6534 41 0.02 fcrc4037 41 0.02 ncrc7040 41 0.02 hfcr3441 41 0.02 miod6068 41 0.02 fcrb2876 41 0.02 mioc2799 41 0.02 miob3396 41 0.02 fcrb4252 41 0.02 miob6372 41 0.02 seob5490 41 0.02 fcrb2591 41 0.02 seob2642 41 0.02 seob2807 41 0.02 seob7764 41 0.02 fcrc4390 41 0.02 fcrb2189 41 0.02 hfcr3404 41 0.02 seob4118 41 0.02 fcrb2308 41 0.02 seoa8397 41 0.02 miod3302 41 0.02 seoa5302 41 0.02 ncrO335 41 0.02 miob5646 41 0.02 fcrc4896 41 0.02 seoal571 41 0.02 seob4676 41 0.02 ncr7151 41 0.02 fcrcl763 41 0.02 hfcrlllδ 41 0.02 seoa6887 41 0.02 ncrc9117 41 0.02 ncrc7043 41 0.02 ncrbO328 41 0.02 ncrc3276 41 0.02 ncrcl751 41 0.02 fcrb2690 41 0.02 fcrcO651 41 0.02 fcrO593 41 0.02 seoblllδ 41 0.02 hfcr2598 41 0.02 ncrbO696 41 0.02 ncrc3953 41 0.02 fcrbl312 41 0.02 seocl934 41 0.02 fcrb6734 41 0.02 fcrbl721 41 0.02 ncrc0640 41 0.02 seoc2030 41 0.02 ncr5066 41 0.02 seob0253 41 0.02 fcr6573 41 0.02 fcrc2131 41 0.02 fcr4634 41 0.02 seob0872 41 0.02 ncr9165 41 0.02 ncrc6981 41 0.02 mioa8773 41 0.02 ncrc3544 41 0.02 ncrc2413 41 0.02 hfcr5181 41 0.02 seobl052 41 0.02 seoal584 41 0.02 miocl205 41 0.02 seocl664 41 0.02 fcrb2784 41 0.02 seob4612 41 0.02 fcrc5721 41 0.02 "mioa3342'"" 4Ϊ" .......,g™fø seoa3287 41 0.02 mioal071 41 0.02 seob4992 41 0.02 seob4752 41 0.02 miob8947 41 0.02 hfcr2295 41 0.02 miob3690 41 0.02 fcrb2430 41 0.02 seoa3556 41 0.02 seoa6412 41 0.02 fcrbl547 41 0.02 seoa8597 41 0.02 seoa0135 41 0.02 seob6153 41 0.02 miodl542 41 0.02 fcrbl657 41 0.02 ncrl912 41 0.02 seob2139 41 0.02 fcrb4275 41 0.02 miob0931 41 0.02 mioa8747 41 0.02 miod3591 41 0.02 ncr5488 41 0.02 ncrb2798 41 0.02 fcrcO591 41 0.02 ncrc3416 41 0.02 mioal494 41 0.02 ncr3843 41 0.02 ncrcl595 41 0.02 seoa4158 41 0.02 fcr6592 41 0.02 ncrb8820 41 0.02 seob4835 41 0.02 fcrbl962 41 0.02 fcrb2715 41 0.03 ncrcO115 41 0.02 seob2959 41 0.03 seob9266 41 0.02 seob9241 41 0.03 seob4424 41 0.02 mioa6428 41 0.03 ncrc5635 41 0.02 fcrclO19 41 0.03 seob5044 41 0.02 seob4480 41 0.03 ncrc2018 41 0.02 fcr7374 41 0.03 ncrc4033 41 0.02 mioa0308 41 0.03 fcrb2546 41 0.02 ncr8542 41 0.03 ncrc4313 41 0.02 miob7554 41 0.03 fcrb5439 41 0.02 seob3485 41 0.03 fcrb6436 41 0.02 seob3076 41 0.03 ncr3118 41 0.02 fcrb6747 41 0.03 ncr8112 41 0.02 fcrb2040 41 0.03 ncrb7726 41 0.02 hfcr2789 41 0.03 fcrb4921 41 0.02 ncrl526 41 0.03 fcrb2223 41 0.02 ncrcl999 41 0.03 hfcr2803 41 0.02 ncr3705 41 0.03 ncrc7131 41 0.02 fcrb4696 41 0.03 mioc2173 41 0.02 seoc0312 41 0.03 ncrb6818 41 0.02 ncrc8881 41 0.03 seoa8177 41 0.02 ncrcO576 41 0.03 fcrc2670 41 0.02 ncrcl665 41 0.03 seoa5894 41 0.02 seob4537 41 0.03 fcrb6426 41 0.02 seoa2777 41 0.03 ncr9549 41 0.02 seoa9060 41 0.03 fcr5316 41 0.02 fcrb2208 41 0.03 fcrbl337 41 0.02 seob6558 41 0.03 miob8341 41 0.02 ncr5149 41 0.03 ncrc2888 41 0.02 miod7337 41 0.03 hfcr2629 41 0.02 ncrc3321 41 0.03 seoal089 41 0.02 fcrc2678 41 0.03 hfcr2686 41 0.02 seoa2830 41 0.03 mioa5531 41 0.02 seoa8401 41 0.03 hfcr0370 41 0.02 hfcrO476 41 0.03 fcrcl947 41 0.02 fcrc2807 41 0.03 ncrb8751 41 0.02 fcrb8949 41 0.03 miob6456 41 0.02 fcrl421 41 0.03 ncrc2472 41 0.02 seob2750 41 0.03 seoa0949 41 0.02 fcrb5928 41 0.03 fcrb8014 41 0.02 mioc0910 41 0.03 miod6560 41 0.02 ncr3465 41 0.03 hfcrO776 41 0.02 miod5962 41 0.03 mioblOOl 41 0.02 mioc4420 41 0.03 seob6773 41 0.02 seoa0014 41 0.03 ncrc3551 41 0.02 miod6234 41 0.03 fcrbl720 41 0.02 fcrl887 41 0.03 ...» ιι,,,» .„„„ .. ncrb'8203' 41 " 0".'03 ncrc4794 41 0.03 ncrc5019 41 0.03 fcr6150 41 0.03 seob0182 41 0.03 ncrb3003 41 0.03 ncr2966 41 0.03 fcrb5645 41 0.03 ncrb8821 41 0.03 seoa0469 41 0.03 hfcr6727 41 0.03 hfcrO489 41 0.03 fcrb6460 41 0.03 mioa4721 41 0.03 ncrO429 41 0.03 seob5558 41 0.03 fcrb3306 41 0.03 ncrcl885 41 0.03 fcrb9253 41 0.03 miob3911 41 0.03 seoa7442 41 0.03 fcrO529 41 0.03 miob9666 41 0.03 seocl898 41 0.03 ncr4551 41 0.03 ncrc4985 41 0.03 seoa9930 41 0.03 ncrbO864 41 0.03 ncrc6861 41 0.03 mioc3127 41 0.03 seob3468 41 0.03 mioc4888 41 0.03 mioa6582 41 0.03 mioa2447 41 0.03 seoa9889 41 0.03 fcrb6725 41 0.03 mioa2268 41 0.03 seoa7157 41 0.03 fcrb6102 41 0.03 miob5916 41 0.03 ncrl876 41 0.03 ncr3339 41 0.03 seoa3359 41 0.03 ncr4452 41 0.03 mioa8984 41 0.03 fcrll73 41 0.03 seob0298 41 0.03 fcr5755 41 0.03 ncrb2544 41 0.03 mioc8682 41 0.03 fcrb2198 41 0.03 miob2293 41 0.03 seoa7249 41 0.03 ncrc4600 41 0.03 ncrc4047 41 0.03 mioa0533 41 0.03 seob8311 41 0.03 seob8355 41 0.03 mioa8548 41 0.03 fcrb2800 41 0.03 fcr5222 41 0.03 seoa7369 41 0.03 seoa3910 41 0.03 mioa5326 41 0.03 seoc.2031 41 0.03 hfcrll89 41 0.03 hfcrl856 41 0.03 fcrb4219 41 0.03 fcrb3169 41 0.03 ncrb2558 41 0.03 fcrc5142 41 0.03 ncrl352 41 0.03 hfcr2966 41 0.03 ncrlO55 41 0.03 ncr4140 41 0.03 fcrb5296 41 0.03 seob9847 41 0.03 ncrb8189 41 0.03 fcrb2592 41 0.03 seoal460 41 0.03 mioa9060 41 0.03 seob8099 41 0.03 fcrb4832 41 0.03 mioc3574 41 0.03 mioal953 41 0.03 fcrb4727 41 0.03 fcrb2325 41 0.03 miod6324 41 0.03 seob7575 41 0.03 fcrb4248 41 0.03 miob9495 41 0.03 ncr0019 41 0.03 seoc3883 41 0.03 fcrb2472 41 0.03 seob3513 41 0.03 ncrb2272 41 0.03 fcrb8664 41 0.03 miocl910 41 0.03 seoa9644 41 0.03 fcr2196 41 0.03 seoblββO 41 0.03 ncrbl438 41 0.03 mioa8836 41 0.03 mioc4387 41 0.03 ncrc2940 41 0.03 seoa9739 41 0.03 mioa6913 41 0.03 ncr6072 41 0.03 fcrb4719 41 0.03 ncr3268 41 0.03 ncrb0303 41 0.03 raiob2067 41 0.03 mioc5740 41 0.03 ncrcO856 41 0.03 seobl316 41 0.03 fcrb7321 41 0.03 fcr4306 41 0.03 mioblOlβ 41 0.03 seob7981 41 0.03 seob7946 41 0.03 mioa3203 41 0.03 seoa8814 41 0.03 miod7486 41 0.03 hfcr4462 41 0.03 fcrb5720 41 0.03 ncr3830 41 0.04 ncrO194 41 0.03 ncr4656 41 0.04 hfcr5023 41 0.04 seobl748 41 0.04 seob4150 41 0.04 ncrc5738 41 0.04 fcr5288 41 0.04 fcrb3283 41 0.04 ncrc4296 41 0.04 seobl420 41 0.04 fcr4954 41 0.04 fcr6708 41 0.04 ncrc4000 41 0.04 seoc5285 41 0.04 mioc2592 41 0.04 seob5193 41 0.04 hfcr7968 41 0.04 ncrc3391 41 0.04 mioa5461 41 0.04 mioa3786 41 0.04 ncrc5207 41 0.04 seob2555 41 0.04 seoa2135 41 0.04 fcr6412 41 0.04 ncr3934 41 0.04 mioc6274 41 0.04 mioa2647 41 0.04 fcr5176 41 0.04 seob4197 41 0.04 ncr2304 41 0.04 seob4492 41 0.04 fcrb4333 41 0.04 seoa8870 41 0.04 fcr4057 41 0.04 fcrb6874 41 0.04 seobl746 41 0.04 ncrc0320 41 0.04 seob4213 41 0.04 miob5855 41 0.04 seob8454 41 0.04 seob0846 41 0.04 fcrl657 41 0.04 fcrb3237 41 0.04 seoal358 41 0.04 ncrc4798 41 0.04 ncrc4247 41 0.04 fcrb5751 41 0.04 seoa6697 41 0.04 seoa7605 41 0.04 ncr9975 41 0.04 seobll45 41 0.04 fcrblO17 41 0.04 fcrO779 41 0.04 fcr0061 41 0.04 mioal944 41 0.04 miob2668 41 0.04 ncrc4226 41 0.04 hfcr3022 41 0.04 seoa7626 41 0.04 hfcr9290 41 0.04 seoa3761 41 0.04 seoa5303 41 0.04 seoa9935 41 0.04 mioa3439 41 0.04 ncrc2928 41 0.04 seoal497 41 0.04 miob3426 41 0.04 seobl057 41 0.04 seoa3489 41 0.04 fcrb2744 41 0.04 hfcr2516 41 0.04 fcr7026 41 0.04 seobl081 41 0.04 fcr4444 41 0.04 crO682 41 0.04 fcr5930 41 0.04 seoc0551 41 0.04 fcrc6382 41 0.04 ncrO547 41 0.04 fcrb4375 41 0.04 mioa0891 41 0.04 seob0999 41 0.04 miob0746 41 0.04 fcrc7240 41 0.04 hfcrl304 41 0.04 mioa5586 41 0.04 mioc6983 41 0.04 fcrb5449 41 0.04 fcrO511 41 0.04 fcrc2455 41 0.04 mioc8479 41 0.04 fcr5519 41 0.04 miod.1108 41 0.04 miod2323 41 0.04 seoa2041 41 0.04 ncrc4994 41 0.04 seob3751 41 0.04 mioa7299 41 0.04 seob0185 41 0.04 ncrO176 41 0.04 hfcrl202 41 0.04 hfcrO496 41 0.04 seob3734 41 0.04 seoa8921 41 0.04 ncrO531 41 0.04 ncrc9331 41 0.04 fcrcO559 41 0.04 ncr7345 41 0.04 miob3131 41 0.04 hfcr2544 41 0.04 fcrb7584 41 0.04 hfcrl314 41 0.04 seocll59 41 0.04 seoa2141 41 0.04 seoc0778 41 0.04 fcrb9714 41 0.04 miob0939 41 0.04 miod7440 4K 2.44e-07 fcrb2754 41 0.04 seoc2029 4K 6.23e-07 fcrc4068 41 0.04 mioc7170 4K 2.53e-06 ncr9596 41 0.04 mioc2561 4K 3.17e-06 ncrc4875 41 0.04 mioa2072 4K 3.26e-06 seob0497 41 0.04 seob4419 4K 4.27e-06 fcrbl724 41 0.04 fcrc3993 4K 4.37e-06 seoa6032 4K 5.48e-06 mioa6418 4K 7.09e-04 miod5349 4K 6.31e-06 miodl925 4K 7.23e-04 seoa8894 4K l.le-05 mioc4089 4K 7.46e-04 seob2697 4K 1.66e-05 mioc7362 4K 7.52e-04 ncr3751 4K 1.95e-05 seob9960 4K 7.63e-04 seoa4518 4K 2.47e-05 seoa8388 4K 7.64e-04 miob6562 4K 2.52e-05 fcrc6976 4K 7.65e-04 seoa8566 4K 3.22e-05 seob3303 4K 7.67e-04 ncr7668 4K 4.63e-05 seob9574 4K 7.88e-04 fcrb3578 4K 4.7e-05 fcrbl496 4K 8.19e-04 miod5123 4K 5.49e-05 hfcrO478 4K 8.43e-04 seob4333 4K 5.73e-05 ncrc2080 4K 8.53e-04 mioa3963 4K 6.04e-05 mioa5355 4K 8.58e-04 miod7421 4K 6.08e-05 ncr8041 4K 8.61e-04 seoa9373 4K 6.54e-05 fcrO824 4K 8.62e-04 mioal370 4K 8.87e-05 ncrc9784 4K 8.77e-04 mioc8410 4K 9.18e-05 fcrc7102 4K 8.87e-04 fcrb7339 4K 9.87e-05 fcrb2556 4K 8.98e-04 fcrb8516 4K 9.9e-05 mioc4667 4K 9.01e-04 fcrc3739 4K 1.03e-04 miob7156 4K 9.1e-04 fcrbO265 4K 1.2e-04 fcrc3048 4K 9.26e-04 seob6189 4K 1.32e-04 miod5369 4K 9.71e-04 miod7414 4K 1.36e-04 fcr2798 4K 9.78e-04 mioc2094 4K 1.38e-04 hfcr6486 4K 9.83e-04 fcrO253 4K 1.45e-04 fcrb7240 4K 1.015969e-03 ncrO223 4K 1.5e-04 hfcr5381 4K 1.016874e-03 fcrb2536 4K 1.53e-04 miob7105 4K 1.073216e-03 seob6851 4K 1.55e-04 miob3982 4K 1.105438e-03 fcrb5850 4K 1.56e-04 miod5310 4K 1.129688e-03 miob8812 4K 1.56e-04 mioa3395 4K 1.154329e-03 fcrb3461 4K 1.6e-04 fcrbl539 4K 1.177578e-03 seob5748 4K 1.79e-04 miob9336 4K 1.178419e-03 hfcr2390 4K 1.83e-04 hfcr6406 4K 1.239885e-03 hfcr3256 4K 2.23e-04 miodl942 4K 1.305311e-03 fcrcl381 4K 2.28e-04 ncr4539 4K 1.309997e-03 miob0167 4K 2.32e-04 seoal844 4K 1.345317e-03 seob0065 4K 2.35e-04 mioc2880 4K 1.356947e-03 fcrc5937 4K 2.36e-04 mioc2348 4K 1.386736e-03 seoc.0999 4K 2.44e-04 miod6038 4K 1.395436e-03 fcrcO554 4K 2.48e-04 ncr7753 4K 1.444958e-03 hfcrlO73 4K 2.54e-04 mioc6987 4K 1.45158e-03 seob4191 4K 2.69e-04 seob6486 4K 1.458125e-03 mioc2726 4K 2.7e-04 fcr3101 4K 1.487316e-03 miob8694 4K 2.87e-04 ncr7595 4K 1.511845e-03 mioc6260 4K 2.92e-04 fcr4128 4K 1.53383e-03 seoa4053 4K 3.03e-04 fcrb8187 4K 1.573331e-03 ncrl631 4K 3.07e-04 seoa9740 4K 1.598061e-03 seoa2949 4K 3.29e-04 mioa3080 4K 1.603678e-03 fcr2573 4K 3.43e-04 mioa0494 4K 1.619093e-03 seob7584 4K 3.63e-04 mioc0052 4K 1.624699e-03 fcr5779 4K 4.02e-04 miob9209 4K 1.634228e-03 miob7922 4K 4.07e-04 mioc6075 4K 1.640004e-03 fcrb4428 4K 4.19e-04 mioal015 4K 1.68644e-03 mioc2911 4K 4.49e-04 seob0304 4K 1.693307e-03 fcrl463 4K 4.77e-04 fcrb3165 4K 1.760242e-03 ncrc4757 4K 5.23e-04 fcrc2099 4K 1.790284e-03 seob9282 4K 5.33e-04 seobl766 4K 1.794891e-03 seoa7530 4K 5.56e-04 fcrc5516 4K 1.799844e-03 seoc6703 4K 5.75e-04 seob6506 4K 1.826439e-03 seoa4739 4K 5.91e-04 fcrb8940 4K 1.834572e-03 seob9145 4K 6.11e-04 fcrb8161 4K 1.885852e-03 fcrc6826 4K 6.39e-04 ncrc9729 4K 1.919544e-03 mioa2537 4K 6.62e-04 seoc4380 4K 1.956915e-03 fcrc3009 4K 6.69e-04 ncrc0150 4K 1.985831e-03 ncr5397 4K 2Ϊcfθ365e-O3 miod4686 4K 4.657868e-03 miod5894 4K 2.014527e-03 ncrc5150 4K 4.691321e-03 seob4560 4K 2.021887e-03 seoa7129 4K 4.719984e-03 mioc0824 4K 2.034483e-03 ncrb4912 4K 4.752707e-03 miocll07 4K 2.070013e-03 mioa6621 4K 4.904983e-03 miob9163 4K 2.097664e-03 fcrb5438 4K 4.92339e-03 fcrc6916 4K 2.121527e-03 fcrc6174 4K 4.953862e-03 ncrO238 4K 2.1386e-03 fcrb3192 4K 4.985296e-03 miod2525 4K 2.174809e-03 seobl318 4K 5.055898e-03 fcrb2624 4K 2.174826e-03 fcrc0430 4K 5.084925e-03 fcrb6187 4K 2.177878e-03 fcrb2310 4K 5.087043e-03 seoa0115 4K 2.17824e-03 miob8583 4K 5.097523e-03 seocl906 4K 2.1992e-03 ncr2862 4K 5.163569e-03 fcr3121 4K 2.234303e-03 miob6364 4K 5.172374e-03 fcrcO695 4K 2.279562e-03 ncrc5500 4K 5.175143e-03 fcrc6016 4K 2.3165e-03 seob3204 4K 5.176722e-03 miobl326 4K 2.329343e-03 mioa8946 4K 5.185969e-03 miob4475 4K 2.330183e-03 miod2639 4K 5.218734e-03 fcrc5142 4K 2.464685e-03 miod3592 4K 5.253602e-03 miob8143 4K 2.489145e-03 fcrc6563 4K 5.282794e-03 seoa3670 4K 2.543649e-03 ncrbl398 4K 5.295501e-03 ncrcl578 4K 2.579378e-03 miod3854 4K 5.307923e-03 miod4332 4K 2.601957e-03 miod3254 4K 5.326719e-03 ncrc5061 4K 2.618499e-03 fcrcl745 4K 5.32716e-03 seoc0369 4K 2.638551e-03 mioc8619 4K 5.353299e-03 seoc0276 4K 2.725532e-03 ncrc9758 4K 5.427537e-03 fcrO999 4K 2.730498e-03 ncr9429 4K 5.458161e-03 mioc3962 4K 2.824707e-03 fcr0990 4K 5.490991e-03 fcrb4391 4K 2.852331e-03 mioc0222 4K 5.512684e-03 fcrO224 4K 2.870823e-03 fcrb6650 4K 5.525357e-03 miob8146 4K 2.888517e-03 fcrb7852 4K 5.667225e-03 mioa7140 4K 2.898369e-03 mioa3471 4K 5.716154e-03 ncrc6359 4K 2.898846e-03 fcrb5164 4K 5.814619e-03 mioa0702 4K 2.977964e-03 seoa4524 4K 5.958281e-03 seob9649 4K 2.997767e-03 fcrbl687 4K 5.960627e-03 seoa6304 4K 3.095502e-03 fcr6390 4K 6.000719e-03 mioa2185 4K 3.147097e-03 fcr0608 4K 6.096137e-03 seoa5683 4K 3.160612e-03 mioc7444 4K 6.13308e-03 seoa8979 4K 3.203255e-03 ncrcO342 4K 6.197336e-03 mioa0187 4K 3.279663e-03 miob4593 4K 6.402894e-03 ncrbO145 4K 3.359507e-03 ncrcO139 4K 6.437649e-03 fcrb3017 4K 3.376066e-03 seob0703 4K 6.50097e-03 miob9065 4K 3.456747e-03 mioc2166 4K 6.609859e-03 fcrb2307 4K 3.463155e-03 ncr4545 4K 6.618737e-03 miob7290 4K 3.492188e-03 mioal303 4K 6.633139e-03 seob4972 4K 3.531512e-03 fcrb9352 4K 6.647947e-03 seob2797 4K 3.6022e-03 seob5500 4K 6.672454e-03 ncrO451 4K 3.707725e-03 ncr4384 4K 6.700762e-03 seob9750 4K 3.708927e-03 ncr7631 4K 6.713476e-03 fcr6018 4K 3.762242e-03 fcrcl607 4K 6.799098e-03 hfcr0370 4K 3.8121e-03 hfcrl760 4K 6.835569e-03 seobll87 4K 3.817075e-03 hfcr5987 4K 6.867295e-03 ncr2182 4K 3.847974e-03 ncr6335 4K 6.907692e-03 mioc7084 4K 3.93591e-03 seoa4366 4K 6.910667e-03 seob0200 4K 3.979023e-03 miod3079 4K 6.91873e-03 miod6018 4K 4.139368e-03 ncrc2495 4K 6.966429e-03 ncrc4531 4K 4.208322e-03 ncr0240 4K 6.999641e-03 miod4140 4K 4.240281e-03 mioal337 4K 7.012816e-03 fcrb9649 4K 4.270145e-03 ncrc6813 4K 7.043646e-03 fcrb8467 4K 4.415815e-03 hfcrl914 4K 7.057729e-03 mioc0347 4K 4.417094e-03 seob7474 4K 7.109226e-03 mioa0890 4K 4.570501e-03 mioa0249 4K 7.184179e-03 mioa0909 4K 4.589875e-03 mioa6093 4K 7.268139e-03 fcrb2318 4K 4.630688e-03 ncrc6687 4K 7.304834e-03 seobll91 4K 7.325599e-03 miod7457 4K 0.01 miob4058 4K 7.329008e-03 hfcr2148 4K 0.01 fcrb8542 4K 7.350932e-03 seob3887 4K 0.01 fcr5723 4K 7.369143e-03 fcr2598 4K 0.01 seoc3993 4K 7.378389e-03 mioa6135 4K 0.01 seob6560 4K 7.500236e-03 mioc4009 4K 0.01 mioc6925 4K 7.512586e-03 miod4464 4K 0.01 seob2169 4K 7.634987e-03 fcrb8432 4K 0.01 hfcrO517 4K 7.704251e-03 miob5412 4K 0.01 ncrc4226 4K 7.824984e-03 hfcr5237 4K 0.01 seob6177 4K 7.843092e-03 ncrc0304 4K 0.01 mioc7998 4K 7.939586e-03 fcrb7588 4K 0.01 mioc0375 4K 7.952967e-03 seob8693 4K 0.01 miob7309 4K 8.045055e-03 fcrcllδl 4K 0.01 miod2665 4K 8.152135e-03 seoa7478 4K 0.01 fcr4965 4K 8.164022e-03 mioa6721 4K 0.01 fcrO593 4K 8.19812e-03 fcrc6990 4K 0.01 seoc2220 4K 8.240295e-03 fcrbl202 4K 0.01 ncrb3077 4K 8.265993e-03 mioa5773 4K 0.01 hfcr5737 4K 8.332211e-03 ncrc3529 4K 0.01 seoa4070 4K 8.351138e-03 fcrb9481 4K 0.01 fcrc7388 4K 8.574251e-03 seoc2670 4K 0.01 ncrcl502 4K 8.575665e-03 fcrl633 4K 0.01 fcr0770 4K 8.634112e-03 seobll33 4K 0.01 mioa3856 4K 8.663706e-03 seoal056 4K 0.01 mioa6738 4K 8.769774e-03 seoc2447 4K 0.01 miodl331 4K 8.912821e-03 mioc2541 4K 0.01 seoa5547 4K 8.932311e-03 fcr2102 4K 0.01 mioa0707 4K 9.037843e-03 mioc2694 4K 0.01 fcrb2299 4K 9.0502e-03 mioa5097 4K 0.01 seob3415 4K 9.05062e-03 mioa2213 4K 0.01 mioa2652 4K 9.114417e-03 ncrc5592 4K 0.01 fcr0707 4K 9.142522e-03 seoa7094 4K 0.01 miodl574 4K 9.196145e-03 mioa4014 4K 0.01 miob3938 4K 9.20224e-03 fcr5509 4K 0.01 seoa2391 4K 9.263207e-03 mioal279 4K 0.01 ncrcOlOl 4K 9.315893e-03 fcrO272 4K 0.01 seoal737 4K 9.470911e-03 mioc7471 4K 0.01 fcrc5846 4K 9.622113e-03 seoa3109 4K 0.01 seob4140 4K 9.654697e-03 miobl829 4K 0.01 fcr3367 4K 9.80502e-03 ncrb5737 4K 0.01 fcrb3466 4K 9.829055e-03 miod6041 4K 0.01 ncrc7038 4K 9.905278e-03 ncrc2685 4K 0.01 fcrbl578 4K 9.906745e-03 miod4539 4K 0.01 miod2837 4K 0.01 seoa9729 4K 0.01 mioc4366 4K 0.01 fcrb5588 4K 0.01 fcrbl787 4K 0.01 miod2264 4K 0.01 mioa2580 4K 0.01 ncrl428 4K 0.01 seobl574 4K 0.01 fcrcl758 4K 0.01 fcrb4988 4K 0.01 miob5708 4K 0.01 seob3520 4K 0.01 mioa5902 4K 0.01 miod5651 4K 0.01 seoa5235 4K 0.01 ncrb0033 4K 0.01 fcr0280 4K 0.01 miob4037 4K 0.01 mioa5231 4K 0.01 mioa5692 4K 0.01 miob3477 4K 0.01 ncr3402 4K 0.01 seoc4748 4K 0.01 seob4036 4K 0.01 seob2938 4K 0.01 fcrlO68 4K 0.01 miob2375 4K 0.01 miob2743 4K 0.01 mioa0891 4K 0.01 mioa6585 4K 0.01 ncrc9612 4K 0.01 mioc3618 4K 0.01 mioc7370 4K 0.01 fcrb5527 4K 0.01 hfcr6640 4K 0.01 mioa5461 4K 0.01 mioc2039 4K 0.01 fcrb2818 4K 0.01 fcrb2784 4K 0.01 seocl307 4K 0.01 fcrbl582 4K 0.01 ncr8290 4K 0.01 fcr7705 4K 0.01 mioa9258 4K 0.01 ncr7876 4K 0.02 ncrc3258 4K 0.01 seoa5586 4K 0.02 miob3411 4K 0.01 seoa2962 4K 0.02 mioa3646 4K 0.01 fcrb2926 4K 0.02 ncrc7085 4K 0.01 miob3307 4K 0.02 seoa0536 4K 0.01 ncr2013 4K 0.02 ncrc5813 4K 0.01 seob5551 4K 0.02 seoa3737 4K 0.01 ncrc4885 4K 0.02 fcrb5092 4K 0.01 hfcr2505 4K 0.02 ncrc6861 4K 0.01 seoa0221 4K 0.02 fcrbl917 4K 0.01 seoc4052 4K 0.02 seob7941 4K 0.01 seob0763 4K 0.02 mioa0595 4K 0.01 seob4192 4K 0.02 miob0973 4K 0.01 miob4570 4K 0.02 seobl783 4K 0.01 seocl948 4K 0.02 seoc3870 4K 0.01 seob0831 4K 0.02 fcrb7833 4K 0.01 seocl264 4K 0.02 seob0418 4K 0.01 fcr6967 4K 0.02 fcrc2007 4K 0.01 mioc2539 4K 0.02 mioal062 4K 0.01 fcr3043 4K 0.02 miob4157 4K 0.01 hfcr3404 4K 0.02 miob7391 4K 0.01 fcrc7046 4K 0.02 fcrcO839 4K 0.01 ncrc4001 4K 0.02 fcrb7505 4K 0.01 ncrO335 4K 0.02 ncr8067 4K 0.01 ncrcO292 4K 0.02 seoa2272 4K 0.01 fcrc4896 4K 0.02 ncrb3341 4K 0.01 fcrcl763 4K 0.02 fcr2088 4K 0.01 ncrc7043 4K 0.02 ncr9919 4K 0.01 mioc0214 4K 0.02 seob8301 4K 0.01 miod4857 4K 0.02 ncr8827 4K 0.01 miob9533 4K 0.02 seob9480 4K 0.01 ncrc6871 4K 0.02 fcrb3497 4K 0.01 fcrb3519 4K 0.02 fcrl305 4K 0.01 fcr0027 4K 0.02 mioa0152 4K 0.01 miodl811 4K 0.02 seoc0866 4K 0.01 ncr2293 4K 0.02 fcrc3229 4K 0.01 ncrc6047 4K 0.02 seoall73 4K 0.01 seob9635 4K 0.02 hfcr4423 4K 0.01 seoa3752 4K 0.02 mioc6296 4K 0.01 seoc5780 4K 0.02 miob7607 4K 0.01 ncrc9387 4K 0.02 miod0686 4K 0.01 fcr3269 4K 0.02 fcrb2596 4K 0.01 hfcr6366 4K 0.02 miobl789 4K 0.01 fcrb8719 4K 0.02 fcr3575 4K 0.01 mioal058 4K 0.02 seoal979 4K 0.01 fcrbl528 4K 0.02 seoa4727 4K 0.01 hfcr0130 4K 0.02 seob6229 4K 0.01 fcrcl965 4K 0.02 hfcr3011 4K 0.01 seob9730 4K 0.02 seoc8175 4K 0.01 mioc2806 4K 0.02 mioa6999 4K 0.01 ncrl344 4K 0.02 miob0207 4K 0.01 mioa3940 4K 0.02 seob2283 4K 0.01 seob9430 4K 0.02 seob5490 4K 0.01 fcr6534 4K 0.02 miod0167 4K 0.01 mioc2799 4K 0.02 fcrl855 4K 0.01 fcrb4252 4K 0.02 ncr3483 4K 0.01 ncr3968 4K 0.02 miocl440 4K 0.01 ncrc4586 4K 0.02 fcrb8196 4K 0.01 ncrb5065 4K 0.02 mioc4534 4K 0.01 ncrc9117 4K 0.02 miob4238 4K 0.01 seob5624 4K 0.02 fcr4795 4K 0.01 ncrcl687 4K 0.02 "hfcria-fβ" 4K 0.02 mioa3944 4K 0.03 fcrb5393 4K 0.02 mioa2818 4K 0.03 seob9353 4K 0.02 fcr4306 4K 0.03 ncrcO883 4K 0.02 fcrO843 4K 0.03 fcr4634 4K 0.02 seob7981 4K 0.03 seoa5157 4K 0.02 mioa0497 4K 0.03 miod0053 4K 0.02 ncrc6416 4K 0.03 fcr2587 4K 0.02 fcrb5645 4K 0.03 miob3330 4K 0.02 seoc4470 4K 0.03 mioc2219 4K 0.02 seob0885 4K 0.03 fcrc5721 4K 0.02 ncr9664 4K 0.03 ncr2408 4K 0.02 ncr2861 4K 0.03 mioall65 4K 0.02 mioa4135 4K 0.03 ncrb2798 4K 0.02 fcrcO771 4K 0.03 fcr3620 4K 0.02 miob9581 4K 0.03 seoc0416 4K 0.02 seob8355 4K 0.03 seoa8912 4K 0.02 miocl758 4K 0.03 fcrc6513 4K 0.02 hfcr2584 4K 0.03 mioc3208 4K 0.02 ncr2175 4K 0.03 seoal065 4K 0.02 seoa3489 4K 0.03 fcrb4365 4K 0.02 seoa2936 4K 0.03 seoa7926 4K 0.02 mioc3574 4K 0.03 seoa8854 4K 0.02 fcrb2090 4K 0.03 miob8341 4K 0.02 mioc4504 4K 0.03 seobl947 4K 0.02 fcrb4479 4K 0.03 mioa5531 4K 0.02 mioa3620 4K 0.03 ncrb8751 4K 0.02 mioal380 4K 0.03 seoa8640 4K 0.02 fcrb7661 4K 0.03 miob3594 4K 0.02 ncrb8239 4K 0.03 ncrO491 4K 0.02 fcr2196 4K 0.03 fcrl724 4K 0.02 fcrc4876 4K 0.03 fcr7042 4K 0.02 miob0361 4K 0.03 ncr5557 4K 0.02 ncrb3001 4K 0.03 fcrb7951 4K 0.03 seoa9042 4K 0.03 seob4480 4K 0.03 fcrb3083 4K 0.03 mioa5836 4K 0.03 mioa2343 4K 0.03 seob3485 4K 0.03 miod6781 4K 0.04 fcrc2008 4K 0.03 seob0122 4K 0.04 mioc7339 4K 0.03 fcr4984 4K 0.04 fcrb2353 4K 0.03 ncr4538 4K 0.04 seob4079 4K 0.03 miobl746 4K 0.04 ncr3237 4K 0.03 fcrb9390 4K 0.04 hfcrO734 4K 0.03 seob5465 4K 0.04 hfcr3500 4K 0.03 fcrb7036 4K 0.04 seob3064 4K 0.03 hfcrl743 4K 0.04 fcrb3946 4K 0.03 seoa8239 4K 0.04 seocOβlβ 4K 0.03 seoc7472 4K 0.04 seob7946 4K 0.03 ncrcl421 4K 0.04 mioc2950 4K 0.03 ncrc5758 4K 0.04 mioa0862 4K 0.03 miob6595 4K 0.04 mioa2522 4K 0.03 fcr5006 4K 0.04 ncrb4441 4K 0.03 seob7082 4K 0.04 ncr8995 4K 0.03 miod0455 4K 0.04 seoa2978 4K 0.03 seoa9160 4K 0.04 mioc0317 4K 0.03 seob9302 4K 0.04 seobl770 4K 0.03 ncrl437 4K 0.04 seoal789 4K 0.03 seoa8716 4K 0.04 miob3531 4K 0.03 ncrc4875 4K 0.04 ncrcO793 4K 0.03 miod5190 4K 0.04 miod7461 4K 0.03 mioa0908 4K 0.04 miob5699 4K 0.03 seoc5285 4K 0.04 mioc3994 4K 0.03 ncr8843 4K 0.04 fcr5369 4K 0.03 mioc4086 4K 0.04 fcrb8664 4K 0.03 fcr4832 4K 0.04 seob6582 4K 0.04 ncrc5663 4M 5.0e-04 ncrc9039 4K 0.04 ncrc4757 4M 5.23e-04 fcrc4948 4K 0.04 seob9282 4M 5.33e-04 ncrO132 4K 0.04 seoa4739 4M 5.91e-04 miod4998 4K 0.04 fcr2573 4M 6.62e-04 mioc2133 4K 0.04 fcrc3009 4M 6.69e-04 seobl737 4K 0.04 mioc4089 4M 7.46e-04 seoc5627 4K 0.04 mioc7362 4M 7.52e-04 fcrO86O 4K 0.04 fcrc6976 4M 7.65e-04 seob3394 4K 0.04 seob9574 4M 7.88e-04 seob0046 4K 0.04 seoa8979 4M 8.23e-04 ncrc0704 4K 0.04 hfcrO478 4M 8.43e-04 miod6731 4K 0.04 ncr8041 4M 8.61e-04 ncr2591 4K 0.04 ncrc9784 4M 8.77e-04 seoa0740 4K 0.04 fcrc7102 4M 8.87e-04 mioc4696 4K 0.04 mioa6731 4M 8.9e-04 fcr7518 4K 0.04 fcrb2556 4M 8.98e-04 seob6198 4K 0.04 miob7156 4M 9.1e-04 mioa8314 4K 0.04 fcr2798 4M 9.78e-04 ncr2994 4K 0.04 hfcr6486 4M 9.83e-04 fcrb2015 4K 0.04 fcrb7240 4M 1.015969e-03 mioa5326 4K 0.04 hfcr5381 4M 1.016874e-03 hfcrl314 4K 0.04 miob7105 4M 1.073216e-03 miod7440 4M 2.44e-07 miob3982 4M 1.105438e-03 mioc7170 4M 2.53e-06 mioa3395 4M 1.154329e-03 mioc2561 4M 3.17e-06 ncrc0090 4M 1.185916e-03 fcrc3993 4M 4.37e-06 hfcr6406 4M 1.239885e-03 seoa6032 4M 5.48e-06 ncr4539 4M 1.309997e-03 miod5349 4M 6.31e-06 seoal844 4M 1.345317e-03 seoa8894 4M l.le-05 mioc2348 4M 1.386736e-03 seob2697 4M 1.66e-05 miod6038 4M 1.395436e-03 ncr3751 4M 1.95e-05 ncr7753 4M 1.444958e-03 ncr7668 4M 4.63e-05 seob6486 4M 1.458125e-03 fcrb3578 4M 4.7e-05 fcr3101 4M 1.487316e-03 miod7421 4M 6.08e-05 ncrb3001 4M 1.557302e-03 mioal370 4M 8.87e-05 miodlδll 4M 1.590472e-03 mioc8410 4M 9.18e-05 seoa9740 4M 1.598061e-03 seoa9482 4M 9.27e-05 mioa3080 4M 1.603678e-03 fcrb7339 4M 9.87e-05 mioa0494 4M 1.619093e-03 fcr4477 4M 1.01e-04 mioc6075 4M 1.640004e-03 fcrbO265 4M 1.2e-04 ncrcl367 4M 1.650171e-03 seob6189 4M 1.32e-04 mioal015 4M 1.68644e-03 miod7414 4M 1.36e-04 seob0304 4M 1.693307e-03 ncr3262 4M 1.4e-04 seob6853 4M 1.718429e-03 fcrO253 4M 1.45e-04 fcrb3165 4M 1.760242e-03 ncrO223 4M 1.5e-04 seoal598 4M 1.770719e-03 seob6851 4M 1.55e-04 fcrc5516 4M 1.799844e-03 fcrb3461 4M 1.6e-04 fcrb8940 4M 1.834572e-03 hfcr3256 4M 2.23e-04 fcrb8161 4M 1.885852e-03 fcr4582 4M 2.27e-04 ncrc9729 4M 1.919544e-03 fcrcl381 4M 2.28e-04 seoc4380 4M 1.956915e-03 seob0065 4M 2.35e-04 miod0592 4M 1.985622e-03 seoc0999 4M 2.44e-04 ncr5397 4M 2.00365e-03 mioc2726 4M 2.7e-04 seob4560 4M 2.021887e-03 mioc6260 4M 2.92e-04 mioc0824 4M 2.034483e-03 fcrcO112 4M 3.06e-04 miocll07 4M 2.070013e-03 ncrl631 4M 3.07e-04 fcrb9843 4M 2.120966e-03 ncrb8714 4M 3.16e-04 fcrc6916 4M 2.121527e-03 seoa2949 4M 3.29e-04 miod2525 4M 2.174809e-03 fcr5779 4M 4.02e-04 fcrb2624 4M 2.174826e-03 miob7922 4M 4.07e-04 fcr3121 4M 2.234303e-03 fcrb7830 4M 4.09e-04 fcrcO695 4M 2.279562e-03 mioc2911 4M 4.49e-04 miobl326 4M 2.329343e-03 fcrl463 4M 4.77e-04 miod6467 4M 2.34447e-03 mioal976 4M 2.452224e-03 ncrbl398 4M 5.295501e-03 fcrc5142 4M 2.464685e-03 miod3854 4M 5.307923e-03 seoa3670 4M 2.543649e-03 miod3254 4M 5.326719e-03 ncrcl578 4M 2.579378e-03 mioc8619 4M 5.353299e-03 miod4332 4M 2.601957e-03 ncrc9758 4M 5.427537e-03 ncrc5061 4M 2.618499e-03 ncr9429 4M 5.458161e-03 seoc0369 4M 2.638551e-03 mioc0222 4M 5.512684e-03 fcrb4409 4M 2.722505e-03 fcrc6560 4M 5.571911e-03 fcrO999 4M 2.730498e-03 mioa3471 4M 5.716154e-03 mioc3962 4M 2.824707e-03 fcrb5164 AU 5.814619e-03 seob4545 4M 2.842281e-03 seoa4524 4M 5.958281e-03 fcrb4391 4M 2.852331e-03 fcr5536 4M 6.049129e-03 mioa7140 4M 2.898369e-03 ncrcO663 4M 6.085718e-03 ncrc6359 4M 2.898846e-03 mioc7444 4M 6.13308e-03 ncrc3464 4M 2.965232e-03 ncrcO342 4M 6.197336e-03 mioa0702 4M 2.977964e-03 seob3520 4M 6.215767e-03 seob9649 4M 2.997767e-03 ncrbl337 4M 6.21911e-03 seoa6304 4M 3.095502e-03 ncrbl515 4M 6.341156e-03 mioa2185 4M 3.147097e-03 ncrcβOOO 4M 6.444479e-03 mioa3856 4M 3.151528e-03 hfcr4176 4M 6.494359e-03 mioa0187 4M 3.279663e-03 fcrb8187 4M 6.498087e-03 mioa2374 4M 3.293401e-03 seob0703 4M 6.50097e-03 fcrb3017 4M 3.376066e-03 fcr4469 4M 6.567393e-03 miob8274 4M 3.404337e-03 mioc2166 4M 6.609859e-03 miob9065 4M 3.456747e-03 ncr4545 4M 6.618737e-03 seob4972 4M 3.531512e-03 mioal303 4M 6.633139e-03 seob2797 4M 3.6022e-03 ncr7631 4M 6.713476e-03 seoal749 4M 3.637644e-03 fcrb8536 4M 6.791086e-03 ncr0451 4M 3.707725e-03 hfcr5987 4M 6.867295e-03 fcr6018 4M 3.762242e-03 ncr9956 '4M 6.920421e-03 seoal480 4M 3.790975e-03 ncr0240 4M 6.999641e-03 hfcr0370 4M 3.8121e-03 mioal337 4M 7.01281βe-03 seobll87 4M 3.817075e-03 ncrb8207 4M 7.041692e-03 fcr3155 4M 3.832262e-03 hfcrl914 4M 7.057729e-03 ncr2182 4M 3.847974e-03 mioal427 4M 7.073426e-03 seoa6573 4M 3.875968e-03 mioa6093 4M 7.268139e-03 seob0200 4M 3.979023e-03 seob6560 4M 7.500236e-03 ncrc5492 4M 4.081573e-03 mioc6925 4M 7.512586e-03 mioc0741 4M 4.154883e-03 ncrc3936 4M 7.54805e-03 ncrc4531 4M 4.208322e-03 ncrc6087 4M 7.662274e-03 miod4140 4M 4.240281e-03 hfcrO517 4M 7.704251e-03 mioc0567 4M 4.279318e-03 fcrb9655 4M 7.792172e-03 mioc3716 4M 4.281847e-03 seob6177 4M 7.843092e-03 fcrb8467 4M 4.415815e-03 miob7309 4M 8.045055e-03 mioc0347 4M 4.417094e-03 fcrcO295 4M 8.130412e-03 fcrcO959 4M 4.530274e-03 fcr4965 4M 8.164022e-03 mioa0890 4M 4.570501e-03 raiobl493 4M 8.197903e-03 miod4686 4M 4.657868e-03 fcrO593 4M 8.19812e-03 ncrc5150 4M 4.691321e-03 seoc2220 4M 8.240295e-03 ncr3369 4M 4.770221e-03 fcrcO228 4M 8.284667e-03 fcr0707 4M 4.772856e-03 hfcr5737 4M 8.332211e-03 mioa6739 4M 4.778158e-03 seoa4070 4M 8.351138e-03 mioa6621 4M 4.904983e-03 fcrbl684 4M 8.355442e-03 mioa9831 4M 4.946737e-03 ncrc6264 4M 8.530558e-03 fcrbl202 4M 5.050003e-03 fcrc7388 4M 8.574251e-03 fcrb8942 4M 5.08231e-03 ncrcl502 4M 8.575665e-03 fcrc0430 4M 5.084925e-03 mioa6738 4M 8.769774e-03 fcrb2310 4M 5.087043e-03 miobl506 4M 8.830271e-03 miob8583 4M 5.097523e-03 fcrcl834 4M 8.876728e-03 ncr2862 4M 5.163569e-03 seoa5547 4M 8.932311e-03 ncr3313 4M 5.18529e-03 mioa2413 4M 9.000025e-03 mioa8946 4M 5.185969e-03 fcrb3894 4M 9.03812e-03 miod3592 4M 5.253602e-03 fcrb2299 4M 9.0502e-03 fcrc6563 4M 5.282794e-03 seob3415 4M 9.05062e-03 mioa2652 4M J~'T.TΪ44Ϊ7e-03 mioa4014 4M 0.01 fcr5029 4M 9.19539e-03 fcr5509 4M 0.01 miodl574 4M 9.196145e-03 mioal279 4M 0.01 miob3938 4M 9.20224e-03 mioc7471 4M 0.01 ncrcOlOl 4M 9.315893e-03 seob3684 4M 0.01 seoal737 4M 9.470911e-03 seoa3109 4M 0.01 mioc4835 4M 9.495048e-03 seoa5552 4M 0.01 fcrb6220 4M 9.620246e-03 ncrb5737 4M 0.01 fcrc5846 4M 9.622113e-03 ncrc3453 4M 0.01 ncr4113 4M 9.724485e-03 mioc8016 4M 0.01 ncrc2780 4M 9.75612e-03 miob2492 4M 0.01 fcr3367 4M 9.80502e-03 mioc4788 4M 0.01 mioc4022 4M 9.984425e-03 miod6041 4M 0.01 miob8214 4M 0.01 ncrc2685 4M 0.01 miod2837 4M 0.01 miod4539 4M 0.01 mioc4366 4M 0.01 mioa2421 4M 0.01 miod4142 4M 0.01 seoa2639 4M 0.01 mioa2580 4M 0.01 seob7747 4M 0.01 seobl574 4M 0.01 seoa9729 4M 0.01 fcrb4988 4M 0.01 ncrl428 4M 0.01 miod5651 4M 0.01 mioa4944 4M 0.01 fcr0903 4M 0.01 miob5708 4M 0.01 ncrc3624 4M 0.01 mioa5902 4M 0.01 miob4037 4M 0.01 miob5119 4M 0.01 ncr3402 4M 0.01 seoa5235 4M 0.01 miob2743 4M 0.01 seoc4748 4M 0.01 fcrb5918 4M 0.01 seob8321 4M 0.01 ncrb8451 4M 0.01 miob2375 4M 0.01 fcrb5100 4M 0.01 mioa0891 4M 0.01 hfcr2148 4M 0.01 ncrc9612 4M 0.01 seob3887 4M 0.01 fcrb2784 4M 0.01 fcr2598 4M 0.01 ncr8290 4M 0.01 mioa6135 4M 0.01 miod3448 4M 0.01 seoa0003 4M 0.01 miob4860 4M 0.01 mioc4009 4M 0.01 miob3411 4M 0.01 fcrb3135 4M 0.01 seoa0799 4M 0.01 hfcr5970 4M 0.01 ncrc7085 4M 0.01 miod4464 4M 0.01 seoa0536 4M 0.01 fcrb3702 4M 0.01 seoa3737 4M 0.01 miob5412 4M 0.01 miob6419 4M 0.01 hfcr5237 4M 0.01 fcrb5092 4M 0.01 ncrc5930 4M 0.01 miob0973 4M 0.01 fcrb5422 4M 0.01 seoc3870 4M 0.01 fcrb4570 4M 0.01 seob0418 4M 0.01 seoa7478 4M 0.01 ncrc0803 4M 0.01 mioa6721 4M 0.01 fcrc2007 4M 0.01 mioa5773 4M 0.01 miod5372 4M 0.01 ncrc5959 4M 0.01 miob7391 4M 0.01 seob6368 4M 0.01 fcr3032 4M 0.01 miob4673 4M 0.01 fcrcO839 4M 0.01 ncrc3529 4M 0.01 fcrb7505 4M 0.01 fcrb9481 4M 0.01 ncr8067 4M 0.01 seoal056 4M 0.01 fcr2088 4M 0.01 fcrcll32 4M 0.01 seob9480 4M 0.01 fcrb7588 4M 0.01 miod6947 4M 0.01 mioc2541 4M 0.01 fcrb3497 4M 0.01 mioc2694 4M 0.01 fcrl305 4M 0.01 miod6961 4M 0.01 miod5123 4M 0.01 fcrb7808 4M 0.01 hfcr4423 4M 0.01 mioa5097 4M 0.01 ncrc2613 4M 0.01 mioa2213 4M 0.01 seob7764 4M 0.01 ncrc5592 4M 0.01 mioc4730 4M 0.01 ncrc6171 4M 0.01 miob7607 4M 0.01 seoa7094 4M 0.01 miod0686 4M 0.01 miobl789 4M Q.'OΪ fcrb5393 4M 0.02 ncrc9024 4M 0.01 ncrcO883 4M 0.02 fcrb4294 4M 0.01 seob8873 4M 0.02 mioa3799 4M 0.01 seoa5157 4M 0.02 mioa6999 4M 0.01 miod0053 4M 0.02 miob0207 4M 0.01 miob3330 4M 0.02 mioc0940 4M 0.01 mioc2219 4M 0.02 fcrl855 4M 0.01 mioc7910 4M 0.02 seoa4518 4M 0.01 fcrc5721 4M 0.02 seob2081 4M 0.01 miod0977 4M 0.02 miocl440 4M 0.01 ncr2408 4M 0.02 mioc4534 4M 0.01 mioall65 4M 0.02 mioa0601 4M 0.01 ncrb7292 4M 0.02 seoa5586 4M 0.02 mioc7119 4M 0.02 seoa2962 4M 0.02 seoc0416 4M 0.02 fcrb2926 4M 0.02 fcrc4949 4M 0.02 miob3307 4M 0.02 seoa4158 4M 0.02 seob3533 4M 0.02 mioc3208 4M 0.02 seob5551 4M 0.02 miob6610 4M 0.02 ncrb3301 4M 0.02 hfcr3494 4M 0.02 hfcr2505 4M 0.02 fcrb5439 4M 0.02 mioc8278 4M 0.02 seob8255 4M 0.02 seob4192 4M 0.02 fcrc2670 4M 0.02 miob4570 4M 0.02 ncr9549 4M 0.02 ncr4416 4M 0.02 ncrcl231 4M 0.02 seobl834 4M 0.02 seoa7926 4M 0.02 seoa9389 4M 0.02 seoa8854 4M 0.02 seocl948 4M 0.02 seobl947 4M 0.02 mioc4270 4M 0.02 mioa5531 4M 0.02 fcr2276 4M 0.02 ncrcl341 4M 0.02 seocl264 4M 0.02 seoc2144 4M 0.02 miob2116 4M 0.02 ncrb8751 4M 0.02 fcr6967 4M 0.02 miob3594 4M 0.02 fcrb2704 4M 0.02 miob4876 4M 0.02 miob3396 4M 0.02 fcrc6228 4M 0.02 fcr3043 4M 0.02 seoal213 4M 0.02 hfcr3404 4M 0.02 fcr7042 4M 0.02 ncrcO292 4M 0.02 mioa4738 4M 0.02 fcrcl763 4M 0.02 ncrb8248 4M 0.02 fcrb3010 4M 0.02 ncr5557 4M 0.02 ncrc7043 4M 0.02 ncrcO583 4M 0.02 hfcr4007 4M 0.02 mioa5836 4M 0.03 miod4857 4M 0.02 fcrb6747 4M 0.03 mioal062 4M 0.02 mioc7339 4M 0.03 mioc5307 4M 0.02 hfcrO734 4M 0.03 fcrb4360 4M 0.02 fcr4433 • 4M 0.03 fcr5618 4M 0.02 seob7946 4M 0.03 fcr0027 4M 0.02 mioc2950 4M 0.03 seob7622 4M 0.02 miod6234 4M 0.03 seoa3752 4M 0.02 fcrbl556 4M 0.03 hfcr6366 4M 0.02 ncr8995 4M 0.03 mioal058 4M 0.02 seobl770 4M 0.03 fcrbl528 4M 0.02 miob3531 4M 0.03 fcrcl965 4M 0.02 mioc2451 4M 0.03 mioc0052 4M 0.02 miob5810 4M 0.03 ncrc6439 4M 0.02 ncrcO793 4M 0.03 ncrl344 4M 0.02 mioc8694 4M 0.03 mioa3940 4M 0.02 fcrc6970 4M 0.03 fcr6534 4M 0.02 miob5699 4M 0.03 fcrb4252 4M 0.02 ncr4140 4M 0.03 ncr3968 4M 0.02 fcrb5438 4M 0.03 seob2163 4M 0.02 mioc2110 4M 0.03 ncrc9117 4M 0.02 ncr8413 4M 0.03 fcrb5603 4M 0.02 mioa2818 4M 0.03 seob7981 4M 0.03 mioa2072 40 3.26e-06 ncr3404 4M 0.03 seob4303 40 3.35e-06 fcrb4016 4M 0.03 seob5523 40 4.23e-06 seob0885 4M 0.03 seoa4461 40 5.29e-06 ncr2861 4M 0.03 miod5349 40 6.31e-06 fcrb2306 4M 0.03 ncr3751 40 1.95e-05 mioa2038 4M 0.03 ncrcO729 40 5.77e-05 miob4090 4M 0.03 mioa3963 40 6.04e-05 hfcr2584 4M 0.03 ncr2472 40 6.28e-05 ncr0045 4M 0.03 seoa9373 40 6.54e-05 mioc7360 4M 0.03 mioa3913 40 7.5e-05 fcrb4219 4M 0.03 seoa0032 40 8.0e-05 mioc6385 4M 0.03 mioal370 40 8.87e-05 mioc4504 4M 0.03 seocll75 40 1.16e-04 mioa3620 4M 0.03 miob6881 40 1.44e-04 mioal380 4M 0.03 fcrO253 40 1.45e-04 ncrb8239 4M 0.03 seoa7652 40 1.5e-04 miob0361 4M 0.03 seob3313 40 1.51e-04 ncrcl615 4M 0.03 seob6851 40 1.55e-04 fcrb3083 4M 0.03 seoc4960 40 1.55e-04 ncrc4000 4M 0.04 mioa4009 40 1.63e-04 ncr3934 4M 0.04 miod5894 40 1.73e-04 miob9284 4M 0.04 fcrb2704 40 1.74e-04 seob0122 4M 0.04 miod5651 40 1.77e-04 ncr2269 4M 0.04 hfcr2390 40 1.83e-04 miod2635 4M 0.04 ncrc9280 40 1.93e-04 ncrb0027 4M 0.04 mioal015 40 2.07e-04 miobl746 4M 0.04 seoc2264 40 2.1e-04 fcrb5177 4M 0.04 fcr7587 40 2.15e-04 fcrb9390 4M 0.04 fcr4582 40 2.27e-04 fcrb7036 4M 0.04 miob0167 40 2.32e-04 hfcr5522 4M 0.04 mioa8970 40 2.35e-04 seoa8239 4M 0.04 hfcrlO73 40 2.54e-04 mioc4131 4M 0.04 fcrc0075 40 2.58e-04 fcrb6818 4M 0.04 mioc6260 4O~ "2/92e-04 ncrc5758 4M 0.04 seob6467 40 3.03e-04 miob6595 4M 0.04 ncrO153 40 3.07e-04 mioal947 4M 0.04 ncrb8714 40 3.16e-04 seoa3443 4M 0.04 seoa0486 40 3.23e-04 ncrl437 4M 0.04 ncr6232 40 3.38e-04 ncrc5738 4M 0.04 ncrc5417 40 3.79e-04 seobl420 4M 0.04 seob3854 40 3.,91e-04 seoc5285 4M 0.04 fcrO224 40 4..62e-04 seoa0174 4M 0.04 seobl319 40 4.91e-04 fcrb2346 4M 0.04 ncrc5663 40 5.0e-04 mioal294 4M 0.04 mioc2602 40 5.18e-04 mioc3618 4M 0.04 ncrc4757 40 5.23e-04 seob6582 4M 0.04 fcr3664 40 5.26e-04 seob6198 4M 0.04 seob9145 40 6.11e-04 seoc5627 4M 0.04 fcrc3750 40 6.22e-04 fcr0860 4M 0.04 fcrc6826 40 6.39e-04 seoa0137 4M 0.04 ncrb2266 40 6.45e-04 ncrcO7O4 4M 0.04 fcr2573 40 6.62e-04 miod5126 4M 0.04 mioa2537 40 6.62e-04 mioa8536 4M 0.04 fcrc3009 40 6.69e-04 miob9248 4M 0.04 ncrcl643 40 6.73e-04 fcrb5639 4M 0.04 fcrb9588 40 6.74e-04 seoa0740 4M 0.04 mioa6418 40 7.09e-04 fcr5930 4M 0.04 miob0865 40 7.17e-04 mioc4696 4M 0.04 mioc7362 40 7.52e-04 miod2412 4M 0.04 ncrb4039 40 7.64e-04 seobl972 4M 0.04 seob3303 40 7.67e-04 fcrb2080 4M 0.04 mioa4484 40 7.8e-04 ncr3163 40 2.24e-06 seob9574 40 7.88e-04 US2005/022071 t,. ,1 ,. 1U'... ncrbO513 40 8.14e-04 ncrc6592 40 2.352328e-03 miob8320 40 8.21e-04 miob8143 40 2.489145e-03 seoa8979 40 8.23e-04 seoall73 40 2.502414e-03 hfcrO478 40 8.43e-04 ncrb6087 40 2.508309e-03 hfcr5220 40 8.48e-04 fcr3121 40 2.565184e-03 mioa5355 40 8.58e-04 seocl230 40 2.583324e-03 fcrO824 40 8.62e-04 fcrb7981 40 2.62789e-03 ncrc9784 40 8.77e-04 ncrc3408 40 2.651987e-03 seoa7509 40 9.42e-04 fcr7004 40 2.657357e-03 seoa8348 40 9.56e-04 mioc8434 40 2.661058e-03 fcrb9420 40 9.64e-04 miobl946 40 2.682567e-03 seob8501 40 9.81e-04 mioc3332 40 2.68477e-03 fcrb7240 40 1.015969e-03 seoa6923 40 2.71229e-03 seob2936 40 1.039004e-03 seoa0563 40 2.731117e-03 seob9292 40 1.045712e-03 seoc0513 40 2.753054e-03 miob8572 40 1.097548e-03 fcrb5254 40 2.802941e-03 ncr3713 40 1.150048e-03 seobl316 40 2.802941e-03 miob9336 40 1.178419e-03 seob8104 40 2.802941e-03 seob8368 40 1.179921e-03 miob8711 40 2.809832e-03 hfcr6700 40 1.205756e-03 mioa9033 40 2.817963e-03 miob4956 40 1.21517e-03 seoa7546 40 2.919393e-03 hfcr640β 40 1.239885e-03 fcrbl731 40 2.954229e-03 mioa0909 40 1.312146e-03 fcrb3483 40 2.983789e-03 ncrc9304 40 1.328255e-03 fcrb2933 40 3.049655e-03 miod6038 40 1.395436e-03 mioa0890 40 3.084414e-03 fcrb2484 40 1.447281e-03 seoa5683 40 3.160612e-03 mioa6721 40 1.457877e-03 fcrb2218 40 3.164316e-03 seoa3516 40 1.46015e-03 mioa0187 40 3.279663e-03 hfcrl310 40 1.478083e-03 mioa2374 40 3.293401e-03 fcrb9959 40 1.48762e-03 seob8741 40 3.317878e-03 ncrb3001 40 1.557302e-03 ncrb3942 40 3.332792e-03 miob3308 40 1.566301e-03 ncrbO145 40 3.359507e-03 miodlδll 40 1.590472e-03 fcrb3017 40 3.376066e-03 hfcr3500 40 1.610297e-03 ncrc9023 40 3.400776e-03 mioa0494 40 1.619093e-03 miob8274 40 3.404337e-03 seoaOlll 40 1.622252e-03 miob9065 40 3.456747e-03 seoalll8 40 1.653668e-03 fcrb4378 40 3.540302e-03 seoa4305 40 1.678116e-03 mioc3726 40 3.550472e-03 miob4221 40 1.682038e-03 mioc0950 40 3.586197e-03 seob2185 40 1.70771e-03 miod5672 40 3.586197e-03 seoa9870 40 1.728369e-03 seob2797 40 3.6022e-03 ncrc9469 40 1.762334e-03 seobl667 40 3.640309e-03 seoal598 40 1.770719e-03 hfcrO676 40 3.735778e-03 fcrb8940 40 1.834572e-03 mioa8774 40 3.812303e-03 seobl513 40 1.842974e-03 fcr3155 40 3.832262e-03 seoc4380 40 1.956915e-03 seoa6573 40 3.875968e-03 miod0592 40 1.985622e-03 fcrb2350 40 3.883582e-03 hfcrO415 40 1.99746e-03 fcrb6785 40 3.927728e-03 miod0340 40 2.021229e-03 miod7243 40 3.934025e-03 seob4560 40 2.021887e-03 seob0200 40 3.979023e-03 seocl025 40 2.02575e-03 seoa2442 40 4.093483e-03 seob0201 40 2.050861e-03 seob3869 40 4.093483e-03 fcr5470 40 2.060807e-03 seoa2641 40 4.10684e-03 fcr4782 40 2.115262e-03 seoa5554 40 4.201202e-03 fcrb9843 40 2.120966e-03 fcrb9694 40 4.20251e-03 fcrb6187 40 2.177878e-03 ncrc4531 40 4.208322e-03 mioa8851 40 2.23281e-03 mioc0567 40 4.279318e-03 fcr7419 40 2.260166e-03 mioc3716 40 4.281847e-03 fcrcO695 40 2.279562e-03 mioc0630 40 4.288784e-03 ncrcllO3 40 2.30949e-03 fcrl657 40 4.312428e-03 fcrc6119 40 2.31135e-03 ncr7973 40 4.312428e-03 seob5743 40 2.31135e-03 mioa2475 40 4.318525e-03 seoal065 40 2.312579e-03 seoa9711 40 4.329889e-03 miod6467 40 2.34447e-03 miob8226 40 4.470203e-03 mioa8811 40 4.52055e-03 ncrc6712 40 6.41394e-03 seob3154 40 4.531125e-03 seoc0394 40 6.498391e-03 ncrc5760 40 4.601184e-03 ncrb8343 40 6.552284e-03 mioal427 40 4.633362e-03 mioa9581 40 6.573012e-03 ncrc3840 40 4.633362e-03 seoa5911 40 6.597064e-03 seoa2978 40 4.686163e-03 mioal303 40 6.633139e-03 fcrc7047 40 4.744327e-03 seob4191 40 6.654737e-03 ncrb4912 40 4.752707e-03 seoal559 40 6.671142e-03 fcr0707 40 4.772856e-03 fcrb2765 40 6.689668e-03 mioa6739 40 4.778158e-03 ncr4384 40 6.700762e-03 seob9756 40 4.787117e-03 fcrb5087 40 6.714694e-03 miob4673 40 4.802836e-03 seob9552 40 6.762169e-03 seob3151 40 4.834042e-03 fcrb3841 40 6.780973e-03 mioa0826 40 4.884238e-03 seoal529 40 6.829367e-03 fcrb4415 40 4.898061e-03 hfcrl760 40 6.835569e-03 seob0185 40 4.919496e-03 miod6560 40 6.859782e-03 rαioa9831 40 4.946737e-03 seoa0926 40 6.867921e-03 ncrb8207 40 5.048159e-03 miod3079 40 6.91873e-03 fcrb4579 40 5.048574e-03 fcrb8852 40 6.991416e-03 seoa0501 40 5.071997e-03 ncrc4600 40 7.01528e-03 fcrc0430 40 5.084925e-03 hfcrl914 40 7.057729e-03 ncrc5500 40 5.175143e-03 miob0636 40 7.05796e-03 hfcr5009 40 5.200779e-03 fcrb9751 40 7.132232e-03 fcrb4241 40 5.25156e-03 fcrb8908 40 7.17729e-03 fcrc6563 40 5.282794e-03 fcr5339 40 7.199684e-03 fcrO768 40 5.301658e-03 ncr3948 40 7.237837e-03 miod5703 40 5.307234e-03 miod6213 40 7.325332e-03 seob8092 40 5.307234e-03 seobll91 40 7.325599e-03 miod3254 40 5.326719e-03 seoc3993 40 7.378389e-03 seoa5396 40 5.362874e-03 ncr3035 40 7.454459e-03 mioc2074 40 5.414445e-03 seoa7078 40 7.471779e-03 miocl609 40 5.451025e-03 seob6560 40 7.500236e-03 seobl318 40 5.452083e-03 mioc6925 40 7.512586e-03 seob0031 40 5.509288e-03 fcrc6642 40 7.539399e-03 seob5726 40 5.548229e-03 miod6162 40 7.584327e-03 miod4539 40 5.585447e-03 seoa2363 40 7.617361e-03 seoa0219 40 5.585447e-03 miob4574 40 7.632732e-03 fcrb9871 40 5.592316e-03 seob2169 40 7.634987e-03 fcr3282 40 5.608041e-03 seocl898 40 7.715363e-03 mioa2158 40 5.639619e-03 seob7729 40 7.786058e-03 mioa9630 40 5.670246e-03 seob6177 40 7.843092e-03 ncrc3468 40 5.832748e-03 seoa0913 40 7.91186e-03 miob9052 40 5.833135e-03 fcr4634 40 7.917348e-03 fcrl496 40 5.876385e-03 miob8812 40 7.917348e-03 seoa2135 40 5.876385e-03 seobl793 40 7.917348e-03 miob9666 40 5.888003e-03 miob3911 40 7.927059e-03 seoal552 40 5.896449e-03 mioc7998 40 7.939586e-03 fcrl844 40 5.913324e-03 seob9872 40 7.94076e-03 fcrb6896 40 5.949973e-03 mioc0375 40 7.952967e-03 seoa4524 40 5.958281e-03 fcrb6768 40 7.961352e-03 miob3684 40 5.971717e-03 hfcr5905 40 8.045743e-03 fcr5536 40 6.049129e-03 miob2227 40 8.045772e-03 seoc2131 40 6.08684e-03 hfcr6501 40 8.058052e-03 fcrO6O8 40 6.096137e-03 fcrbl329 40 8.164602e-03 mioc7444 40 6.13308e-03 fcrc5160 40 8.176648e-03 seoa3704 40 6.146625e-03 ncrc9483 40 8.178505e-03 miod5612 40 6.180534e-03 fcr4129 40 8.215271e-03 mioa4057 40 6.188583e-03 seoc2220 40 8.240295e-03 ncrb6833 40 6.229488e-03 fcrb6676 40 8.311707e-03 ncrc2635 40 6.269334e-03 fcrc2678 40 8.311707e-03 mioa2013 40 6.287512e-03 ncrb6192 40 8.312148e-03 seob9485 40 6.301111e-03 seoa4070 40 8.351138e-03 fcrc6174 40 6.350026e-03 seoall04 40 8.378822e-03 ncr8811 40 6.352352e-03 ncrc4089 40 8.390818e-03 US2005/022071 :. I, .. •!,..!■ •...,)! ii,.,ii seoa8232 40 8.450517e-03 fcr0990 40 0.01 seob8065 40 8.459463e-03 mioc5103 40 0.01 seob4972 40 8.59936e-03 fcrc0350 40 0.01 hfcrl795 40 8.723061e-03 miob8627 40 0.01 ncrc4079 40 8.723061e-03 fcrb5100 40 0.01 ncrl282 40 8.738728e-03 ncr5781 40 0.01 seob6026 40 8.755905e-03 hfcr2148 40 0.01 seocl234 40 8.859639e-03 fcrcβOll 40 0.01 fcrcl834 40 8.876728e-03 seob3887 40 0.01 fcrbl503 40 8.94593e-03 mioa6135 40 0.01 seocl023 40 8.957311e-03 seobl916 40 0.01 mioa3629 40 8.97697e-03 fcrb3135 40 0.01 mioa0707 40 9.037843e-03 miod4464 40 0.01 ncr3496 40 9.054107e-03 fcrb3702 40 0.01 ncrcO667 40 9.054835e-03 miob5412 40 0.01 ncrc9428 40 9.100754e-03 seoa2141 40 0.01 seob4945 40 9.152008e-03 seoa9389 40 0.01 mioa9649 40 9.154014e-03 miod0884 40 0.01 seoa5933 40 9.204501e-03 hfcr5237 40 0.01 seoa2391 40 9.263207e-03 fcrb5422 40 0.01 ncrcO383 40 9.294641e-03 fcrb4570 40 0.01 ncrcOlOl 40 9.315893e-03 fcrb8653 40 0.01 miob5855 40 9.472774e-03 ncr3037 40 0.01 mioc8619 40 9.511612e-03 mioa4064 40 0.01 ncr8199 40 9.599163e-03 seoa7478 40 0.01 seoa6070 40 9.602104e-03 fcrc6990 40 0.01 ncrcl873 40 9.678381e-03 mioc3603 40 0.01 ncrc2780 40 9.75612e-03 mioa5773 40 0.01 mioa6035 40 9.789009e-03 ncrO491 40 0.01 ncr9337 40 9.841178e-03 fcrb6185 40 0.01 miocl248 40 9.846887e-03 seob9882 40 0.01 seob8817 40 9.894482e-03 seob6368 40 0.01 fcrbl578 40 9.906745e-03 seob7637 40 0.01 ncrc6382 40 9.927817e-03 ncrc3529 40 0.01 ncrc4226 40 9.989082e-03 mioc3958 40 0.01 miob8214 40 0.01 fcrb9481 40 0.01 mioc4366 40 0.01 ncrc5806 40 0.01 ncr6141 40 0.01 fcrcO496 40 0.01 seobl420 40 0.01 fcrb3704 40 0.01 fcr5354 40 0.01 ncrc5653 40 0.01 miob6797 40 0.01 fcrb2460 40 0.01 seobl574 40 0.01 mioal062 40 0.01 fcr2418 40 0.01 seob4805 40 0.01 ncrc4247 40 0.01 seobll33 40 0.01 seob3520 40 0.01 miob0496 40 0.01 hfcr4055 40 0.01 miodl714 40 0.01 hfcr6509 40 0.01 fcrbO146 40 0.01 ncrb0033 40 0.01 miob8373 40 0.01 fcr0903 40 0.01 fcrb4388 40 0.01 miob8992 40 0.01 mioc2694 40 0.01 mioa9792 40 0.01 rαiod6961 40 0.01 seob9847 40 0.01 mioc0560 40 0.01 mioa2333 40 0.01 seoc5228 40 0.01 fcrb8623 40 0.01 mioa5097 40 0.01 mioa7015 40 0.01 seoc0312 40 0.01 miob2743 40 0.01 fcr4885 40 0.01 mioa6585 40 0.01 miob9848 40 0.01 miob7391 40 0.01 ncrcl050 40 0.01 mioal394 40 0.01 seoa2830 40 0.01 fcrb5527 40 0.01 ncr4452 40 0.01 seob3699 40 0.01 seoc4909 40 0.01 ncrb8451 40 0.01 ncrc5592 40 0.01 fcrO511 40 0.01 mioa4014 40 0.01 fcrb3584 40 0.01 fcrb3897 40 0.01 1,. i ,. I ln.,i mioa9093 40 "'" 6".6i ncrbO328 40 0.01 seob0344 40 0.01 fcrb4739 40 0.01 hfcr2850 40 0.01 fcrb2754 40 0.01 fcrc4045 40 0.01 seob2011 40 0.01 fcrb5675 40 0.01 seoc0657 40 0.01 seoa8566 40 0.01 hfcr2832 40 0.01 mioc8016 40 0.01 seoa3422 40 0.01 seoa7157 40 0.01 seob3464 40 0.01 fcrc6854 40 0.01 ncrc6708 40 0.01 ncr2035 40 0.01 seoa2381 40 0.01 ncrc2685 40 0.01 fcrb6251 40 0.01 miob9228 40 0.01 ncr8067 40 0.01 miob7373 40 0.01 seob9543 40 0.01 mioa2421 40 0.01 seoa2272 40 0.01 miocl085 40 0.01 fcrb6698 40 0.01 miod6238 40 0.01 seoc2646 40 0.01 seob0523 40 0.01 fcrb4995 40 0.01 seob6576 40 0.01 seoa0469 40 0.01 fcrb6432 40 0.01 seoa2817 40 0.01 fcrcl758 40 0.01 fcrb2020 40 0.01 fcrO997 40 0.01 fcrb2305 40 0.01 fcrcO559 40 0.01 seoa8300 40 0.01 mioa5902 40 0.01 ncrb8392 40 0.01 miob2687 40 0.01 ncrb3468 40 0.01 hfcr7966 40 0.01 ncrc4296 40 0.01 ncrb5264 40 0.01 ncrb8665 40 0.01 fcrb2536 40 0.01 mioa0088 40 0.01 fcrb8119 40 0.01 miob9185 40 0.01 seobl646 40 0.01 mioc0140 40 0.01 miod7351 40 0.01 ncrcll76 40 0.01 seob2938 40 0.01 ncr8538 40 0.01 fcrb9714 40 0.01 seob5592 40 0.01 miod0187 40 0.01 miob0468 40 0.01 ncrb8790 40 0.01 seob9480 40 0.01 mioa8998 40 0.01 ncr4550 40 0.01 mioa2290 40 0.01 mioa8796 40 0.01 seob4570 40 0.01 mioc3220 40 0.01 ncrc9867 40 0.01 hfcr6680 40 0.01 ncr9165 40 0.01 seob7432 40 0.01 miob3044 40 0.01 fcr6386 40 0.01 mioc2039 40 0.01 fcrb3497 40 0.01 seob5379 40 0.01 miob5122 40 0.01 seocl307 40 0.01 ncrc5768 40 0.01 miob4860 40 0.01 mioc0728 40 0.01 raioa9258 40 0.01 ncr4860 40 0.01 fcrb9680 40 0.01 fcrb3518 40 0.01 seob4612 40 0.01 ncr5557 40 0.01 ncrc3258 40 0.01 ncrc6480 40 0.01 fcrb6406 , 40 0.01 fcrbllβO 40 0.01 miob7554 40 0.01 fcrb3550 40 0.01 ncrc7085 40 0.01 fcr4272 40 0.01 seob5636 40 0.01 ncrc9159 40 0.01 seob7907 40 0.01 fcrb2426 40 0.01 ncrc5813 40 0.01 seob2077 40 0.01 ncr7267 40 0.01 miod6134 40 0.01 seoc0705 40 0.01 fcrbl729 40 0.01 fcrb4542 40 0.01 ncrc2859 40 0.01 mioal079 40 0.01 miob0189 40 0.01 hfcr3413 40 0.01 ncrc4033 40 0.01 miob0973 40 0.01 mioa0152 40 0.01 hfcrl709 40 0.01 ncrb7102 40 0.01 fcr5222 40 0.01 ncr0808 40 0.01 seoc3870 40 0.01 seob9334 40 0.01 fcrb2353 40 0.01 hfcrl641 40 0.01 T7US2005/022071 U K •■ i. «...„ seoal353 40 0.01 miob3396 40 0.02 seoa5578 40 0.01 seoa3093 40 0.02 fcrb9856 40 0.01 miob3928 40 0.02 miod6044 40 0.01 ncr7666 40 0.02 mioc4983 40 0.01 seoal979 40 0.02 fcrb4696 40 0.01 ncr7672 40 0.02 seob5773 40 0.01 fcrb7487 40 0.02 fcrb6693 40 0.01 seob2807 40 0.02 ncrc9024 40 0.01 fcrc4390 40 0.02 fcrb9639 40 0.01 hfcr3404 40 0.02 fcrb4294 40 0.01 fcrb2308 40 0.02 πdoal520 40 0.01 miod3302 40 0.02 mioa4628 40 0.01 fcrb2160 40 0.02 mioc6949 40 0.01 fcrc4896 40 0.02 miod2707 40 0.01 fcrcl763 40 0.02 ncrc2273 40 0.01 fcrb3010 40 0.02 ncrc5608 40 0.01 ncrc2182 40 0.02 ncrbO262 40 0.01 fcrc4408 40 0.02 seob7744 40 0.01 hfcr3622 40 0.02 ncrc3072 40 0.01 miob9533 40 0.02 ncrcl952 40 0.01 seoa7223 40 0.02 ncrc7092 40 0.02 hfcr2598 40 0.02 ncrb7166 40 0.02 seocl934 40 0.02 fcrbl381 40 0.02 fcrcO332 40 0.02 seobl617 40 0.02 mioc5307 40 0.02 miob8487 40 0.02 mioc2997 40 0.02 mioc4766 40 0.02 miob9209 40 0.02 seob3533 40 0.02 seob8807 40 0.02 miob4712 40 0.02 seob4030 40 0.02 seoc3965 40 0.02 miob0178 40 0.02 ncrc4885 40 0.02 miod6048 40 0.02 hfcr3834 40 0.02 seob0872 40 0.02 seoc0551 40 0.02 hfcr5181 40 0.02 mioa8548 40 0.02 mioc2116 40 0.02 ncrO176 40 0.02 mioa8836 40 0.02 seoa2970 40 0.02 hfcr6366 40 0.02 mioc8057 40 0.02 fcrc4068 40 0.02 hfcr2505 40 0.02 ncrc6479 40 0.02 seoc4052 40 0.02 seob4492 40 0.02 mioa0647 40 0.02 fcrb4921 40 0.02 seob6041 40 0.02 ncrbOO74 40 0.02 seob4192 40 0.02 ncrb8220 40 0.02 miodl389 40 0.02 seoa6038 40 0.02 fcrb6937 40 0.02 ncrl235 40 0.02 ncr3598 40 0.02 fcrb2866 40 0.02 fcrb9859 40 0.02 ncrO791 40 0.02 hfcr5232 40 0.02 fcrb8504 40 0.02 fcrl404 40 0.02 miod6845 40 0.02 ncr5713 40 0.02 seoa9817 40 0.02 seob3370 40 0.02 fcrcl965 40 0.02 ncr5939 40 0.02 seob7180 40 0.02 seocl948 40 0.02 seoal571 40 0.02 fcrc4421 40 0.02 seoc4505 40 0.02 ncr5168 40 0.02 fcrb3083 40 0.02 seobl008 40 0.02 mioc0052 40 0.02 miob8657 40 0.02 fcrb2330 40 0.02 mioc0347 40 0.02 ncrcl402 40 0.02 ncr4538 40 0.02 fcrc4485 40 0.02 ncrb4319 40 0.02 ncrc6439 40 0.02 hfcrO351 40 0.02 fcrb6928 40 0.02 ncrb7726 40 0.02 ncrl344 40 0.02 miod2330 40 0.02 ncrcO646 40 0.02 fcrc4037 40 0.02 mioa5614 40 0.02 fcrb2876 40 0.02 ncrcl361 40 0.02 seob9430 40 0.02 fcrc2670 40 0.02 fcrb4252 40 0.02 fcrb6426 40 0.02 hfcr5706 40 0.02 ncrcl231 40 0.02 miob3757 40 0.02 ncr9549 40 0.02 fcrb9634 40 0.02 hfcr2629 40 0.02 seoa2300 40 0.02 mioa5531 40 0.02 seob7764 40 0.02 fcr4927 40 0.02 seob9614 40 0.02 fcrcl947 40 0.02 seob2163 40 0.02 fcrc2131 40 0.02 ncrc9117 40 0.02 ncrclO32 40 0.02 miob0926 40 0.02 ncrb8751 40 0.02 fcrb8901 40 0.02 fcr3714 40 0.02 fcrb3086 40 0.02 seoa8640 40 0.02 hfcr6932 40 0.02 mioblOOl 40 0.02 seoblllδ 40 0.02 ncrc3551 40 0.02 fcrb6099 40 0.02 miob3690 40 0.02 fcrbl312 40 0.02 seob4689 40 0.02 fcrb6734 40 0.02 seoa3556 40 0.02 seoa6658 40 0.02 fcr4128 40 0.02 ncrc0640 40 0.02 seoa0023 40 0.02 fcr5425 40 0.02 ncrcl595 40 0.02 fcrl772 40 0.02 mioc7260 40 0.02 mioa8773 40 0.02 fcrc2431 40 0.02 fcr5889 40 0.02 miod6835 40 0.02 ncrc2413 40 0.02 ncrc4903 40 0.02 fcrb8445 40 0.02 seob4147 40 0.02 fcrc5504 40 0.02 fcrb2715 40 0.03 miob3456 40 0.02 seob5080 40 0.03 miod0686 40 0.02 ncr5149 40 0.03 ncrc9004 40 0.02 fcr7374 40 0.03 fcrb8226 40 0.02 fcrc6413 40 0.03 seob6872 40 0.02 fcrb6747 40 0.03 mioc2219 40 0.02 seoc2173 40 0.03 fcrc5721 40 0.02 ncr3705 40 0.03 mioal071 40 0.02 ncrb3957 40 0.03 fcrb2430 40 0.02 fcrb2208 40 0.03 ncrb7292 40 0.02 mioa3379 40 0.03 seob2188 40 0.02 seoa2822 40 0.03 seob6153 40 0.02 seoa5721 40 0.03 miocl808 40 0.02 seob3064 40 0.03 seoa9873 40 0.02 seoa8401 40 0.03 mioc0530 40 0.02 hfcrO476 40 0.03 seoa4023 40 0.02 seob2134 40 0.03 ncrb2798 40 0.02 seob2750 40 0.03 seoa4158 40 0.02 fcr2798 40 0.03 fcrbl720 40 0.02 ncr3465 40 0.03 fcrc7228 40 0.02 miod5962 40 0.03 mioa0684 40 0.02 fcrb3874 40 0.03 miod7367 40 0.02 fcr2952 40 0.03 ncrc9855 40 0.02 fcrb0097 40 0.03 mioc3208 40 0.02 mioa0862 40 0.03 fcr4460 40 0.02 fcrb2102 40 0.03 seobl081 40 0.02 miod6234 40 0.03 seob9266 40 0.02 mioa2522 40 0.03 ncr4859 40 0.02 ncr0045 40 0.03 seob4424 40 0.02 seoa4132 40 0.03 seoa8229 40 0.02 ncrc5019 40 0.03 fcrb2546 40 0.02 seob0182 40 0.03 ncrc4313 40 0.02 hfcr3634 40 0.03 fcrb6436 40 0.02 ncr0018 40 0.03 ncr8112 40 0.02 ncrb6818 40 0.03 mioc4420 40 0.02 seoc4470 40 0.03 fcrb5449 40 0.02 fcrc6345 40 0.03 fcrb3554 40 0.02 seoa9930 40 0.03 5 022071
ncrO775 40 0.03 ncrl352 40 0.03 ncrc6861 40 0.03 fcrcO775 40 0.03 seob3468 40 0.03 fcr3599 40 0.03 mioa0245 40 0.03 miod5591 40 0.03 mioa8984 40 0.03 seoal460 40 0.03 fcrO986 40 0.03 seob8099 40 0.03 seoc4720 40 0.03 mioc3574 40 0.03 ncrc2675 40 0.03 fcrb2090 40 0.03 fcrb5416 40 0.03 fcrb4248 40 0.03 fcrb2198 40 0.03 fcrb8740 40 0.03 miod5030 40 0.03 seob8489 40 0.03 ncrc4047 40 0.03 fcrb9602 40 0.03 seoc0778 40 0.03 fcrb2472 40 0.03 ncr7967 40 0.03 seob5081 40 0.03 seoa3910 40 0.03 ncrb2272 40 0.03 hfcrl856 40 0.03 ncrc9394 40 0.03 fcrb3169 40 0.03 miob6442 40 0.03 fcrc6970 40 0.03 miob0361 40 0.03 miod5123 40 0.03 seoa0462 40 0.03 seoc2510 40 0.03 fcrb2120 40 0.03 seob7575 40 0.03 fcrb7099 40 0.03 seobl660 40 0.03 fcrc5282 40 0.03 ncrcO413 40 0.03 ncr3527 40 0.03 mioc0915 40 0.03 seoa8814 40 0.03 seoa0014 40 0.03 mioal621 40 0.04 hfcr2544 40 0.03 mioa0132 40 0.04 ncrc5491 40 0.03 miob2841 40 0.04 fcrbl344 40 0.03 fcrc3907 40 0.04 fcrb4719 40 0.03 seoa8916 40 0.04 mioc5740 40 0.03 ncrc4000 40 0.04 ncrb8820 40 0.03 seoa9916 40 0.04 mioa9492 40 0.03 mioc2592 40 0.04 seob6535 40 0.03 ncrc2103 40 0.04 miod7486 40 0.03 miob7627 40 0.04 fcrb8243 40 0.03 ncr3934 40 0.04 ncrO194 40 0.03 fcrb4760 40 0.04 ncrc6416 40 0.03 ncrc5230 40 0.04 seocl856 40 0.03 mioc4103 40 0.04 fcr6748 40 0.03 mioc5678 40 0.04 ncrbll86 40 0.03 seoc0809 40 0.04 hfcr6164 40 0.03 seoc4955 40 0.04 fcrc2954 40 0.03 fcrcl381 40 0.04 mioa0595 40 0.03 miob4975 40 0.04 seoa5778 40 0.03 ncrc0320 40 0.04 seoc7566 40 0.03 ncr2269 40 0.04 hfcr3921 40 0.03 mioa3469 40 0.04 ncr5046 40 0.03 ncrcl631 40 0.04 ncrc6597 40 0.03 ncrc5296 40 0.04 ncrcl885 40 0.03 mioc7360 40 0.04 mioc0593 40 0.03 seob7409 40 0.04 mioa8886 40 0.03 fcrb5751 40 0.04 ncrcO259 40 0.03 seobll45 40 0.04 fcrcO771 40 0.03 fcrb9390 40 0.04 ncrc5738 40 0.03 mioc0315 40 0.04 miob9581 40 0.03 rαioc0227 40 0.04 miob2293 40 0.03 seob9649 40 0.04 seob8355 40 0.03 fcrl797 40 0.04 fcrb2800 40 0.03 hfcr5045 40 0.04 fcrb9069 40 0.03 seoa9935 40 0.04 fcrb4219 40 0.03 fcrc5071 40 0.04 fcr5509 40 0.03 hfcr3180 40 0.04 ncrc9351 40 0.03 ncrb6742 40 0.04 seoa5849 40 0.03 seoc2224 40 0.04 ncr3815 40 0.03 ncrc8892 40 0.04 mioc2184 40' 0".'04 hfcrO242 40 0.04 fcrb6416 40 0.04 ncrc9729 40 0.04 ncr9175 40 0.04 ncr3284 40 0.04 crO682 40 0.04 mioc7818 40 0.04 ncrO252 40 0.04 miob3461 40 0.04 ncr2363 40 0.04 ncrc4931 40 0.04 fcrb5672 40 0.04 miob7985 40 0.04 hfcrl304 40 0.04 fcr2167 40 0.04 fcrb2746 40 0.04 seob6198 40 0.04 fcrb7593 40 0.04 mioa8314 40 0.04 fcrb8898 40 0.04 seob9946 40 0.04 ncrc2827 40 0.04 mioc2451 40 0.04 ncrc8976 40 0.04 hfcrO496 40 0.04 miob3131 40 0.04 miob0487 40 0.04 fcrb7584 40 0.04 mioc2290 40 0.04 fcrb2203 40 0.04 seoa8921 40 0.04 fcrb3578 40 0.04 fcrb4856 40 0.04 fcrc6566 40 0.04 seob7981 40 0.04 seoc0596 40 0.04 ncrc9331 40 0.04 seocll59 40 0.04 miob9688 40 0.04 ncrb6357 40 0.04 fcrc2376 40 0.04 ncrl437 40 0.04 seoa2620 40 0.04 ncrc4875 40 0.04 seoc4187 40 0.04 fcrb2060 40 0.04 seob3722 40 0.04 seob0497 40 0.04 seoa5552 40 0.04 fcrbl724 40 0.04 seoc2029 4Q 6.23e-07 mioc2887 40 0.04 mioc7170 4Q 2.53e-06 seobl748 40 0.04 mioc2561 4Q 3.17e-06 fcrb3283 40 0.04 mioa2072 4Q 3.26e-06 fcrb7944 40 0.04 seob4419 4Q 4.27e-06 fcr6708 40 0.04 fcrc3993 4Q 4.37e-06 mioa8946 40 0.04 seoa6032 4Q 5.48e-06 seoc5285 40 0.04 rαiod5349 4Q 6.31e-06 mioal584 40 0.04 seoa8894 4Q l.le-05 fcr4832 40 0.04 ncr3751 4Q 1.95e-05 fcrb3015 40 0.04 seoa4518 4Q 2.47e-05 miob3618 40 0.04 seoa8566 4Q 3.22e-05 miob8191 40 0.04 fcrb3578 4Q 4.7e-05 miocl357 40 0.04 seoa9373 4Q 6.54e-05 hfcr2890 40 0.04 mioal370 4Q 8.87e-05 seob4197 40 0.04 seoa9482 4Q 9.27e-05 ncr2304 40 0.04 fcr4477 4Q 1.01e-04 mioc2133 40 0.04 fcrbO265 4Q 1.2e-04 ncr7151 40 0.04 inioc2094 4Q 1.38e-04 ncrc9491 40 0.04 ncr3262 4Q 1.4e-04 fcr2861 40 0.04 ncrO223 4Q 1.5e-04 seob0818 40 0.04 fcrb2536 4Q 1.53e-04 fcrb2199 40 0.04 seob6851 4Q 1.55e-04 seoc3277 40 0.04 fcrb5850 4Q 1.56e-04 seocl058 40 0.04 miob8812 4Q 1.56e-04 seobl848 40 0.04 miod5651 4Q 1.77e-04 miod5126 40 0.04 hfcr2390 4Q 1.83e-04 ncrb3585 40 0.04 fcr4582 4Q 2.27e-04 fcrb2301 40 0.04 miob0167 4Q 2.32e-04 mioa9604 40 0.04 fcrc5937 4Q 2.36e-04 seoc0698 40 0.04 seoc0999 4Q 2.44e-04 fcrb8668 40 0.04 fcrcO554 4Q 2.48e-04 fcrc4663 40 0.04 hfcrlO73 4Q 2.54e-04 mioal906 40 0.04 seob4191 4Q 2.69e-04 seoal802 40 0.04 mioc2726 4Q 2.7e-04 fcr5930 40 0.04 miob8694 4Q 2.87e-04 seoa4264 40 0.04 seoa4053 4Q 3.03e-04 seob4999 40 0.04 fcrcO112 4Q 3.06e-04 miod6058 40 0.04 ncrl631 40 3.07e-04 ncrb8714 4Q 3.16e-04 ncrc0150 4Q 1.985831e-03 fcr2573 4Q 3.43e-04 miod5894 4Q .014527e-03 seob7584 4Q 3.63e-04 miod0340 4Q 2.021229e-03 fcr5779 4Q 4.02e-04 seob4560 4Q 2.021887e-03 ncrc5663 4Q 5.Oe-04 mioc0824 4Q 2.034483e-03 ncrc4757 4Q 5.23e-04 miob9163 4Q 2.097664e-03 seoa7530 4Q 5.56e-04 fcrb9843 4Q 2.120966e-03 seoc6703 4Q 5.75e-04 miod2525 4Q 2.174809e-03 seob9145 4Q 6.lle-04 fcrb2624 4Q 2.174826e-03 mioa2537 4Q 6.62e-04 fcrb6187 4Q 2.177878e-03 mioa6418 4Q 7.09e-04 seoa0115 4Q 2.17824e-03 mioc7362 4Q 7.52e-04 seocl906 4Q 2.1992e-03 seob9960 4Q 7.63e-04 fcr3121 4Q 2.234303e-03 seoa8388 4Q 7.64e-04 fcrcO695 4Q 2.279562e-03 fcrc6976 4Q 7.65e-04 fcrc6016 4Q 2.3165e-03 seob3303 4Q 7.67e-04 miobl326 4Q 2.329343e-03 seob9574 4Q 7.88e-04 miob4475 4Q 2.330183e-03 seoa8979 4Q 8.23e-04 miod6467 4Q 2.34447e-03 hfcrO478 4Q 8.43e-04 mioal976 4Q 2.452224e-03 ncrc2080 4Q 8.53e-04 fcrc5142 4Q 2.464685e-03 mioa5355 4Q 8.58e-04 miob8143 4Q 2.489145e-03 ncr8041 4Q 8.61e-04 seoa3670 4Q .543649e-03 fcrO824 4Q 8.62e-04 ncrcl578 4Q 2.579378e-03 ncrc9784 4Q 8.77e-04 miod4332 4Q .601957e-03 fcrc7102 4Q 8.87e-04 ncrc5061 4Q .618499e-03 rαioa6731 4Q 8.9e-04 seocl402 4Q 2.660096e-03 fcrb2556 4Q 8.98e-04 seoc0276 4Q 2.725532e-03 mioc4667 4Q 9.Ole-04 seob3307 4Q 2.811695e-03 miob7156 4Q 9.le-04 seoa9814 4Q 2.836239e-03 fcrc3048 4Q 9.26e-04 seob4545 4Q 2.842281e-03 fcr2798 4Q 9.78e-04 fcrb4391 4Q 2.852331e-03 seocl996 4Q 9,.81e-04 fcrO224 4Q 2.870823e-03 hfcr6486 4Q 9,.83e-04 miob8146 4Q 2.888517e-03 hfcr5381 4Q 1..016874e-03 mioa7140 4Q 2MM CM C C,.898369e-03 miod5310 4Q 1,.129688e-03 ncrc3464 4Q 2,.965232e-03 mioa3395 4Q 1,.154329e-03 mioa0702 4Q 2,.977964e-03 fcrbl539 4Q 1,,177578e-03 seob9649 4Q 2..997767e-03 miob9336 4Q 1.,178419e-03 seob2221 4Q 3,,044324e-03 ncrc0090 4Q 1.,185916e-03 mioa2185 4Q 3.,147097e-03 hfcr6406 4Q 1.,239885e-03 mioa3856 4Q 3.,151528e-03 miodl942 4Q 1.,305311e-03 seoa5683 4Q 3.,160612e-03 ncr4539 4Q 1.,309997e-03 mioa0187 4Q 3.,279663e-03 seoal844 4Q 1.345317e-03 mioa2374 4Q 3.,293401e-03 mioc2880 4Q 1.,356947e-03 fcr5075 4Q 3.,353508e-03 mioc2348 4Q 1.386736e-03 ncrbO145 4Q 3.359507e-03 mioc6987 4Q 1.,45158e-03 raiob8274 4Q 3.,4043376-03 fcr3101 4Q 1.487316e-03 miob9065 4Q 3.456747e-03 ncr7595 4Q 1.511845e-03 fcrb2307 4Q 3.463155e-03 fcr4128 4Q 1.53383e-03 miob7290 4Q 3.492188e-03 ncrb3001 4Q 1.557302e-03 seob2797 4Q 3.6022e-03 fcrb8187 4Q 1.573331e-03 seoal749 4Q 3.637644e-03 miodlβll 4Q 1.590472e-03 ncrO451 4Q 3.707725e-03 mioa3080 4Q 1.603678e-03 fcr6018 4Q 3.762242e-03 mioc0052 4Q 1.624699e-03 seoal480 4Q 3.790975e-03 miob9209 4Q 1.634228e-03 hfcr0370 4Q 3.8121e-03 ncrcl367 4Q 1.650171e-03 seoa6573 4Q 3.875968e-03 ncr5719 4Q 1.677652e-03 mioc7084 4Q 3.93591e-03 mioal015 4Q 1.68644e-03 seob0200 4Q 3.979023e-03 seob6853 4Q 1.718429e-03 ncrc5492 4Q 4.081573e-03 fcrc2099 4Q 1.790284e-03 miod6018 4Q 4.139368e-03 seobl766 4Q 1.794891e-03 πιioc0741 4Q 4.154883e-03 seob6506 4Q 1.826439e-03 ncrc4531 4Q 4.208322e-03 ncrc9729 4Q 1.919544e-03 miod4140 4Q 4.240281e-03 miod0592 4Q 1.985622e-03 fcrb9649 40 4.270145e-03 'mioc05SV"" ~"jfr
Figure imgf000358_0001
mioa0249 4Q 7.184179e-03 fcrcO959 4Q 4.530274e-03 seoa9935 4Q 7.197022e-03 fcrb2318 4Q 4.630688e-03 ncrc6687 4Q 7.304834e-03 miod4686 4Q 4.657868e-03 seobll91 4Q 7.325599e-03 seoa7129 4Q 4.719984e-03 miob4058 4Q 7.329008e-03 ncrb4912 4Q 4.752707e-03 fcrb8542 4Q 7.350932e-03 ncr3369 4Q 4.770221e-03 fcr5723 4Q 7.369143e-03 fcr0707 4Q 4.772856e-03 seoc3993 4Q 7.378389e-03 mioa6739 4Q 4.778158e-03 seob6560 4Q 7.500236e-03 fcrb5438 4Q 4.92339e-03 mioc6925 4Q 7.512586e-03 mioa9831 4Q 4.946737e-03 ncrc3936 4Q 7.54805e-03 fcrb3192 4Q 4.985296e-03 miob4221 4Q 7.572537e-03 ncr3684 4Q 5.017654e-03 seob2169 4Q 7.634987e-03 seobl318 4Q 5.055898e-03 ncrc6087 4Q 7.662274e-03 fcrb8942 4Q 5.08231e-03 hfcrO517 4Q 7.704251e-03 fcrc0430 4Q 5.084925e-03 fcrb9655 4Q 7.792172e-03 miob8583 4Q 5.097523e-03 ncrc4226 4Q 7.824984e-03 ncrc5672 4Q 5.11611e-03 seob6177 4Q 7.843092e-03 miob6364 4Q 5.172374e-03 mioc7998 4Q 7.939586e-03 ncrc5500 4Q 5.175143e-03 miob7309 4Q 8.045055e-03 seob3204 4Q 5.176722e-03 fcrcO295 4Q 8.130412e-03 ncr3313 4Q 5.18529e-03 miod2665 4Q 8.152135e-03 miod2639 4Q 5.218734e-03 miobl493 4Q 8.197903e-03 miod3592 4Q 5.253602e-03 fcrO593 4Q 8.19812e-03 fcrc6563 4Q 5.282794e-03 ncrb3077 4Q 8.265993e-03 miod3854 4Q 5.307923e-03 hfcr5737 4Q 8.332211e-03 miod3254 4Q 5.326719e-03 seoa4070 4Q 8.351138e-03 fcrcl745 4Q 5.32716e-03 fcrbl684 4Q 8.355442e-03 ncrc9758 4Q 5.427537e-03 ncrc6264 4Q 8.530558e-03 fcr0990 4Q 5.490991e-03 ncrcl502 4Q 8.575665e-03 fcrb6650 4Q 5.525357e-03 fcr0770 4Q 8.634112e-03 fcrc2082 4Q 5.550695e-03 miobl506 4Q 8.830271e-03 fcrb7852 4Q 5.667225e-03 miodl331 4Q 8.912821e-03 fcrO253 4Q 5.72015e-03 mioa2413 4Q 9.000025e-03 fcrb5164 4Q 5.814619e-03 fcrb3894 4Q 9.03812e-03 fcrbl687 4Q 5.960627e-03 fcrb2299 4Q 9.0502e-03 fcrl883 4Q 6.048593e-03 miob3461 4Q 9.117224e-03 fcr5536 4Q 6.049129e-03 miodl574 4Q 9.196145e-03 ncrcO663 4Q 6.085718e-03 fcrb7339 4Q 9.198522e-03 fcr0608 4Q 6.096137e-03 seoa2391 4Q 9.263207e-03 ncrcO342 4Q 6.197336e-03 ncrcOlOl 4Q 9.315893e-03 ncrbl337 4Q 6.21911e-03 seoal737 4Q 9.470911e-03 ncrbl515 4Q 6.341156e-03 mioc4835 4Q 9.495048e-03 ncrcO139 4Q 6.437649e-03 fcrb6220 4Q 9.620246e-03 ncrcβOOO 4Q 6.444479e-03 fcrc5846 4Q 9.622113e-03 hfcr4176 4Q 6.494359e-03 fcr3367 4Q 9.80502e-03 seob0703 4Q 6.50097e-03 fcrb3466 4Q 9.829055e-03 fcr4469 4Q 6.567393e-03 ncrc7038 4Q 9.905278e-03 mioc2166 4Q 6.609859e-03 mioc4022 4Q 9.984425e-03 mioal303 4Q 6.633139e-03 miob8214 4Q 0.01 fcrb9352 4Q 6.647947e-03 mioc4366 4Q 0.01 seob5500 4Q 6.672454e-03 miod4142 4Q 0.01 fcrb8536 4Q 6.791086e-03 fcrb2713 4Q 0.01 fcrcl607 4Q 6.799098e-03 fcrbl787 4Q 0.01 hfcrl760 4Q 6.835569e-03 mioa2580 4Q 0.01 hfcr5987 4Q 6.867295e-03 fcrb4988 4Q 0.01 ncr6335 4Q 6.907692e-03 seob3520 4Q 0.01 seoa4366 4Q 6.910667e-03 fcr0903 4Q 0.01 miod3079 4Q 6.91873e-03 miob4037 4Q 0.01 ncrc2495 4Q 6.966429e-03 mioa5692 4Q 0.01 ncrb8207 4Q 7.041692e-03 seob4036 4Q 0.01 ncrc6813 4Q 7.043646e-03 fcrlO68 4Q 0.01 mioal427 4Q 7.073426e-03 mioa2993 4Q 0.01 seob7474 4Q 7.109226e-03 miob2743 4Q 0.01 mioc3618 4Q 0.01 ncr8290 4Q 0.01 fcrb5918 4Q 0.01 mioa9258 4Q 0.01 mioa5461 4Q 0.01 ncrc3258 4Q 0.01 ncrb8451 4Q 0.01 seoa0799 4Q 0.01 fcrb5100 4Q 0.01 fcrb3165 4Q 0.01 fcrb2818 4Q 0.01 mioa3646 4Q 0.01 miod7457 4Q 0.01 seoa0536 4Q 0.01 seob3887 4Q 0.01 miob6419 4Q 0.01 fcr2598 4Q 0.01 ncrc6861 4Q 0.01 seoa0003 4Q 0.01 fcrbl917 4Q 0.01 fcrb3135 4Q 0.01 seob7941 4Q 0.01 hfcr5970 4Q 0.01 mioa0595 4Q 0.01 miod4464 4Q 0.01 seobl783 4Q 0.01 fcrb3702 4Q 0.01 seoc3870 4Q 0.01 fcrb8432 4Q 0.01 fcrb7833 4Q 0.01 hfcr5237 4Q 0.01 fcrbl750 4Q 0.01 ncrc0304 4Q 0.01 seob0418 4Q 0.01 ncrc5930 4Q 0.01 ncrc0803 4Q 0.01 fcrb5422 4Q 0.01 mioal062 4Q 0.01 fcrb7588 4Q 0.01 miod5372 4Q 0.01 seob8693 4Q 0.01 fcrb7505 4Q 0.01 fcrbl202 4Q 0.01 seoa2272 4Q 0.01 ncrc5959 4Q 0.01 ncrb3341 4Q 0.01 seob6368 4Q 0.01 ncr9919 4Q 0.01 miob4673 4Q 0.01 seob8301 4Q 0.01 seoc2670 4Q 0.01 fcrb3497 4Q 0.01 mioa5691 4Q 0.01 miod5123 4Q 0.01 seobll33 4Q 0.01 seob3419 4Q 0.01 seoc2447 4Q 0.01 ncrc2859 4Q 0.01 fcrcll32 4Q 0..01 fcrc3229 4Q 0,.01 mioc2541 4Q 0,.01 seoall73 4Q 0,.01 fcr2102 4Q 0,.01 hfcr4423 4Q 0,.01 miod6961 4Q 0..01 mioc6296 4Q 0,.01 fcrb7808 4Q 0..01 mioc4730 4Q 0,.01 mioa2213 4Q 0.,01 miob7607 4Q 0..01 ncrc5592 4Q 0..01 fcrb2596 4Q 0..01 ncrc6171 4Q 0..01 fcr3575 4Q 0..01 mioa4014 4Q 0.,01 seoa4727 4Q 0..01 fcr5509 4Q 0.,01 seob6229 4Q 0..01 mioc7471 4Q 0.,01 ncrbl956 4Q 0.,01 seob3684 4Q 0.,01 ncrc9024 4Q 0.,01 miobl829 4Q 0.01 hfcr3011 4Q 0.,01 seob5748 4Q 0.01 fcrb4294 4Q 0.,01 seoa5552 4Q 0.01 mioa3799 4Q 0.,01 ncrc3453 4Q 0.01 mioa6999 4Q 0.01 mioc8016 4Q 0.01 mioc0940 4Q 0.01 miob2492 4Q 0.01 seob5490 4Q 0.01 miod6041 4Q 0.01 miod0167 4Q 0.01 mioa2421 4Q 0.01 ncr3483 4Q 0.01 seob7747 4Q 0.01 seob2081 4Q 0.01 ncrc9517 4Q 0.01 ncr3432 4Q 0.01 seoa9729 4Q 0.01 fcrb8196 4Q 0.01 ncrc6617 4Q 0.01 miob4238 4Q 0.01 miod2264 4Q 0.01 fcrb7751 4Q 0.01 miob5119. 4Q 0.01 fcr4795 4Q 0.01 seoa5235 4Q 0.01 fcrbl582 4Q 0.01 fcr0280 4Q 0.01 fcr7705 4Q 0.01 fcrb8901 4Q 0.01 ncr7876 4Q 0.02 mioa5231 4Q 0.01 seoa2962 4Q 0.02 seoc4748 4Q 0.01 miob3307 4Q 0.02 seob2938 4Q 0.01 ncr2013 4Q 0.02 mioa0891 4Q 0.01 seob3533 4Q 0.02 mioc7370 4Q 0.01 seob5551 4Q 0.02 mioc2039 4Q 0.01 ncrc4885 4Q 0.02 "•S'e&e4θb'2""*' -"Q-.©2 fcrc2670 4Q 0.02 seob0763 4Q 0.02 ncrcl231 4Q 0.02 mioc8278 4Q 0.02 seoa8854 4Q 0.02 miob4570 4Q 0.02 ncrcl341 4Q 0.02 ncr4416 4Q 0.02 seoa8640 4Q 0.02 seoa8776 4Q 0.02 miob4876 4Q 0.02 seobl834 4Q 0.02 fcrc6228 4Q 0.02 seoa9389 4Q 0.02 fcrl724 4Q 0.02 mioa0601 4Q 0.02 fcr7042 4Q 0.02 mioc4270 4Q 0.02 mioa4738 4Q 0.02 seob0831 4Q 0.02 fcrb7951 4Q 0.03 seocl264 4Q 0.02 ncr3968 4Q 0.03 miob2116 4Q 0.02 seob4480 4Q 0.03 fcrb2704 4Q 0.02 fcrb2040 4Q 0.03 miob3396 4Q 0.02 ncr3237 4Q 0.03 fcr3043 4Q 0.02 hfcrO734 4Q 0.03 ncrO335 4Q 0.02 hfcr3500 4Q 0.03 ncrcO292 4Q 0.02 seob3064 4Q 0.03 fcrc4896 4Q 0.02 fcrb3946 4Q 0.03 hfcr4007 4Q 0.02 seoc0616 4Q 0.03 miob9533 4Q 0.02 seoc2172 4Q 0.03 ncrc6871 4Q 0.02 miocl596 4Q 0.03 mioc5307 4Q 0.02 fcr4433 4Q 0.03 fcrb3519 4Q 0.02 seob7946 4Q 0.03 fcr5618 4Q 0.02 mioa0862 4Q 0.03 fcr0027 4Q 0.02 miod6234 4Q 0.03 ncr2293 4Q 0.02 ncrb4441 4Q 0.03 ncrc6047 0.02 seoa7383 4Q 0.03 seob7622 4Q 0.02 ncr8995 4Q 0.03 seoc5780 4Q 0.02 ncrO429 4Q 0.03 ncrc9387 4Q 0.02 seoa2978 4Q 0.03 fcr3269 40 0.02 ncrc9633 4Q 0.03 hfcr6366 4Q 0.02 seoal789 4Q 0.03 fcrb8719 4Q 0.02 miob3531 4Q 0.03 ncrc5491 4Q 0.02 mioc2451 4Q 0.03 fcrbl528 4Q 0.02 miob5810 4Q 0.03 fcrcl965 4Q 0.02 seoa8738 4Q 0.03 mioc2799 4Q 0.02 seoa0470 4Q 0.03 ncrc4586 4Q 0.02 fcr5369 4Q 0.03 seob2163 4Q 0.02 fcrb8664 4Q 0.03 ncrc9117 4Q 0.02 mioa5452 4Q 0.03 seob5624 4Q 0.02 ncrc4620 4Q 0.03 ncrcl687 4Q 0.02 mioa2818 4Q 0.03 hfcrl856 4Q 0.02 fcrO843 4Q 0.03 fcrb5393 4Q 0.02 mioa0497 4Q 0.03 fcr4634 4Q 0.02 ncrc6416 4Q 0.03 seob8873 4Q 0.02 miob9901 4Q 0.03 fcr2587 4Q 0.02 seob0885 4Q 0.03 hfcrlO37 4Q 0.02 fcr5560 4Q 0.03 hfcr2295 4Q 0.02 ncr9664 4Q 0.03 seoa9699 4Q 0.02 fcrb2306 4Q 0.03 ncrb7292 4Q 0.02 ncrc9055 4Q 0.03 ncrb2798 4Q 0.02 fcrcO771 4Q 0.03 fcr3620 4Q 0.02 miob9581 4Q 0.03 fcrc4949 4Q 0.02 seob8355 4Q 0.03 seoa8912 4Q 0.02 ncr2175 4Q 0.03 fcrc6513 4Q 0.02 ncr0045 4Q 0.03 miod0894 4Q 0.02 mioc7360 4Q 0.03 mioc3208 4Q 0.02 fcrb2090 4Q 0.03 miob6610 4Q 0.02 fcrb4479 4Q 0.03 hfcr3494 4Q 0.02 mioal380 4Q 0.03 fcrb5439 4Q 0.02 fcr2196 4Q 0.03 seob8255 4Q 0.02 fcrc4876 4Q 0.03 fcrb4365 40 0.02 ncrl007 40 0.03 "s"eόa'90""4"_r" ..... ^ seoa6032 4S 5.48e-06 ncrcl615 4Q 0.03 miod5349 4S 6.31e-06 fcrb3083 4Q 0.03 seoa8894 4S 1. le-05 mioa2343 4Q 0.03 fcrb4534 4S 1.46e-05 ncrc4000 4Q 0.04 seob2697 4S 1.66e-05 ncr3934 4Q 0.04 seoa4518 4S 2.47e-05 miob9284 4Q 0.04 miob6562 4S 2.52e-05 fcr4984 4Q 0.04 seoa8566 4S 3.22e-05 ncr4538 4Q 0.04 miod5123 4S 5.49e-05 ncrbl833 4Q 0.04 seob4333 4S 5.73e-05 fcrb5177 4Q 0.04 miod7421 4S 6.08e-05 seob5465 4Q 0.04 mioal370 4S 8.87e-05 fcrb8467 4Q 0.04 fcrb7339 4S 9.87e-05 hfcr5522 4Q 0.04 fcr4477 4S 1.Ole-04 miob5752 4Q 0.04 fcrbO265 4S 1.2e-04 mioc4131 4Q 0.04 seob6189 4S 1.32e-04 ncrc6459 4Q 0.04 mioc2094 4S 1.38e-04 fcrb6818 4Q 0.04 fcrO253 4S 1.45e-04 seoa3121 4Q 0.04 seoa7652 4S 1.5e-04 miob6595 4Q 0.04 fcrb2536 4S 1.53e-04 miod0455 4Q 0.04 fcrb5850 4S 1.56e-04 seob0266 4Q 0.04 miob8812 4S 1.56e-04 seoa0860 4Q 0.04 fcrb3461 4S 1.6e-04 ncrO531 4Q 0.04 seob4029 4S 1.69e-04 fcrclO19 4Q 0.04 miod5651 4S 1.77e-04 ncrc4875 4Q 0.04 seob5748 4S 1.79e-04 ncrc5738 4Q 0.04 hfcr2390 4S 1.83e-04 ncrcO217 4Q 0.04 miod5505 4S 1.89e-04 fcrb3283 4Q 0.04 seoc2264 4S 2. le-04 hfcr6515 4Q 0.04 fcr4582 4S 2.27e-04 mioc4086 4Q 0.04 fcrcl381 4S 2.28e-04 seob6582 4Q 0.04 miob0167 4S 2.32e-04 ncrc9039 4Q 0.04 seocll75 4S 2.33e-04 ncrO132 4Q 0.04 fcrc5937 4S 2.36e-04 fcr6181 4Q 0.04 fcrcO554 4S 2.48e-04 miob2668 4Q 0.04 seob4191 4S 2.69e-04 seob3394 4Q 0.04 miob8694 4S 2.87e-04 miod5126 4Q 0.04 ncrO153 4S 3.07e-04 miod6731 4Q 0.04 seoa4366 4S 3.08e-04 seoa0792 4Q 0.04 seoc6169 4S 3.lle-04 miob9248 4Q 0.04 ncrb8714 4S 3.16e-04 ncr2591 4Q 0.04 fcr2573 4S 3.43e-04 fcrb5639 4Q 0.04 fcrb3578 4S 3.7e-04 fcr5930 4Q 0.04 fcr5779 4S 4.02e-04 seoa4606 4Q 0.04 ncrc5663 4S 5. Oe-04 mioc4696 4Q 0.04 mioc2602 4S 5.18e-04 fcr7518 4Q 0.04 seoc6703 4S 5.75e-04 fcrb7880 4Q 0.04 fcr2607 4S 5.84e-04 ncrcl751 4Q 0.04 seoa4739 4S 5.91e-04 miod2412 4Q 0.04 seob9145 4S 6.lle-04 seobl972 4Q 0.04 mioa6418 4S 7.09e-04 miob7913 4Q 0.04 miodl925 4S 7.23e-04 fcr3053 4Q 0.04 mioc4089 4S 7.46e-04 seob6198 4Q 0.04 mioc7362 4S 7.52e-04 fcrb2015 4Q 0.04 seob9960 4S 7.63e-04 mioa5326 4Q 0.04 seoa8388 4S 7.64e-04 hfcrl314 4Q 0.04 seob3303 4S 7.67e-04 seoc2029 4S 6.23e-07 ncrc2080 4S 8.53e-04 mioc7170 4S 2.53e-06 mioa5355 4S 8.58e-04 mioa2072 4S 3.26e-06 ncr8041 4S 8.61e-04 seob5523 4S 4.23e-06 ncrc9784 4S 8.77e-04 seob4419 4S 4.27e-06 fcrc7102 4S 8.87e-04 fcrc3993 4S 4.37e-06 mioa6731 4S 8.9e-04 seoa4461 4S 5.29e-06 mioc4667 4S 9.Ole-04 Ycfc30ϊff- TS■ ■™5T2Εe-σg seob4972 4S 3.531512e-03 fcrb9420 4S 9.64e-04 mioc3726 4S 3.550472e-03 seocl996 4S 9.81e-04 seob2797 4S 3.6022e-03 hfcr5381 4S 1.016874e-03 fcr6018 4S 3.762242e-03 fcrbl539 4S 1.177578e-03 fcr3155 4S 3.832262e-03 miob9336 4S 1.178419e-03 ncr2182 4S 3.847974e-03 miodl942 4S 1.305311e-03 mioc7084 4S 3.93591e-03 mioa0909 4S 1.312146e-03 miod6018 4S 4.139368e-03 mioc2880 4S 1.356947e-03 miod4140 4S 4.240281e-03 mioc2348 4S 1.386736e-03 fcrb9649 4S 4.270145e-03 miod6038 4S 1.395436e-03 seoa9711 4S 4.329889e-03 fcr3101 4S 1.487316e-03 miob8226 4S 4.470203e-03 fcr4128 4S 1.53383e-03 mioa0890 4S 4.570501e-03 ncrc5162 4S 1.553082e-03 fcrb2318 4S 4.630688e-03 ncrb3001 4S 1.557302e-03 miod4686 4S 4.657868e-03 miodlδll 4S 1.590472e-03 seoa7129 4S 4.719984e-03 seoa9740 4S 1.598061e-03 mioa6739 4S 4.778158e-03 mioa0494 4S 1.619093e-03 mioa0826 4S 4.884238e-03 mioc0052 4S 1.624699e-03 mioa6621 4S 4.904983e-03 miob9209 4S 1.634228e-03 fcrb5438 4S 4.92339e-03 ncrcl367 4S 1.650171e-03 mioa9831 4S 4.946737e-03 seoa4305 4S 1.678116e-03 fcrc6174 4S 4.953862e-03 seob0304 4S 1.693307e-03 fcrb3192 4S 4.985296e-03 fcrb3165 4S 1.760242e-03 ncr3684 4S 5.017654e-03 fcrc2099 4S 1.790284e-03 seobl318 4S 5.055898e-03 seobl766 4S 1.794891e-03 fcrc0430 4S 5.084925e-03 fcrc5516 4S 1.799844e-03 ncrc5672 4S 5.11611e-03 seob6506 4S 1.826439e-03 ncr2862 4S 5.163569e-03 ncrc5054 4S 1.860924e-03 miob6364 4S 5.172374e-03 seoc4380 4S 1.956915e-03 ncrc5500 4S 5.175143e-03 miod0592 4S 1.985622e-03 seob3204 4S 5.176722e-03 ncrcO15O 4S 1.985831e-03 miod3592 4S 5.253602e-03 miod5894 4S 2.014527e-03 mioc0824 4S 5.254723e-03 miod0340 4S 2.021229e-03 ncrbl398 4S 5.295501e-03 seob4560 4S 2.021887e-03 miod3254 4S 5.326719e-03 miob9163 4S 2.097664e-03 mioc8619 4S 5.353299e-03 fcrb9843 4S 2.120966e-03 fcr0990 4S 5.490991e-03 ncrO238 4S 2.1386e-03 fcrbl684 4S 5.495549e-03 miod2525 4S 2.174809e-03 fcrb6650 4S 5.525357e-03 fcrb2624 4S 2.174826e-03 fcr3282 4S 5.608041e-03 seoa0115 4S 2.17824e-03 fcrb7852 4S 5.667225e-03 fcr3121 4S 2.234303e-03 ncrc4654 4S 5.866006e-03 fcr7419 4S 2.260166e-03 fcrbl687 4S 5.960627e-03 fcrcO695 4S 2.279562e-03 fcr6390 4S 6.000719e-03 seoal065 4S 2.312579e-03 fcrl883 4S 6.048593e-03 fcrc6016 4S 2.3165e-03 fcr5536 4S 6.049129e-03 miob4475 4S 2.330183e-03 hfcr3500 4S 6.054096e-03 ncrcl578 4S 2.579378e-03 seoa0470 4S 6.100317e-03 mioc8434 4S 2.661058e-03 ncrc6712 4S 6.41394e-03 seoc0276 4S 2.725532e-03 ncrcO139 4S 6.437649e-03 fcrO999 4S 2.730498e-03 fcrb8187 4S 6.498087e-03 fcrb4391 4S 2.852331e-03 mioal303 4S 6.633139e-03 fcrO224 4S 2.870823e-03 fcrb2765 4S 6.689668e-03 mioa7140 4S 2.898369e-03 miod5612 4S 6.698915e-03 ncrc6359 4S 2.898846e-03 fcrb5087 4S 6.714694e-03 seob2221 4S 3.044324e-03 fcrcl607 4S 6.799098e-03 fcrb2933 4S 3.049655e-03 hfcrl760 4S 6.835569e-03 mioa2185 4S 3.147097e-03 miod3079 4S 6.91873e-03 seoa5683 4S 3.160612e-03 ncrc2495 4S 6.966429e-03 mioa2374 4S 3.293401e-03 hfcrl914 4S 7.057729e-03 ncrb0145 4S 3.359507e-03 mioal427 4S 7.073426e-03 fcrb3017 4S 3.376066e-03 mioa0249 4S 7.184179e-03 miob8274 4S 3.404337e-03 seoa9935 4S 7.197022e-03 fcrb2307 4S 3.463155e-03 mioa6093 4S 7.268139e-03 Ufidb'405'8 —V:55SoUBe-03 fcrb3897 4S 0.01 fcrb8542 4S 7.350932e-03 fcrc4948 4S 0.01 seoc3993 4S 7.378389e-03 mioc7471 4S 0.01 seoa7078 4S 7.471779e-03 seob0344 4S 0.01 miob4221 4S 7.572537e-03 miobl829 4S 0.01 hfcrO517 4S 7.704251e-03 mioc3092 4S 0.01 ncrc4226 4S 7.824984e-03 ncrb5737 4S 0.01 mioc7998 4S 7.939586e-03 fcrb5675 4S 0.01 fcrbl329 4S 8.164602e-03 mioc8016 4S 0.01 miobl493 4S 8.197903e-03 mioa2421 4S 0.01 ncrb3077 4S 8.265993e-03 ncrc9517 4S 0.01 hfcr5737 4S 8.332211e-03 ncrl428 4S 0.01 seoa4070 4S 8.351138e-03 fcrcl758 4S 0.01 seob8065 4S 8.459463e-03 fcr0280 4S 0.01 ncrl631 4S 8.505128e-03 fcrb8901 4S 0.01 fcrO770 4S 8.634112e-03 mioa5231 4S 0.01 ncr2905 4S 8.640653e-03 seoc7281 4S 0.01 mioa6738 4S 8.769774e-03 fcrcl745 4S 0.01 miodl331 4S 8.912821e-03 miod0187 4S 0.01 mioa0707 4S 9.037843e-03 ncrb8790 4S 0.01 fcrb2299 4S 9.0502e-03 mioc7370 4S 0.01 seob3415 4S 9.05062e-03 miob4860 4S 0.01 seoa5933 4S 9.204501e-03 ncrc3258 4S 0.01 ncrcOlOl 4S 9.315893e-03 miob3411 4S 0.01 seob4140 4S 9.654697e-03 mioa3646 4S 0.01 mioa6035 4S 9.789009e-03 ncrc7085 4S 0.01 fcrb3466 4S 9.829055e-03 seoa0536 4S 0.01 mioc4366 4S 0.01 seobl783 4S 0.01 fcrbl787 4S 0.01 seoc3870 4S 0.01 seob3520 4S 0.01 fcrb7833 4S 0.01 fcrlO68 4S 0.01 seob0418 4S 0.01 miob2743 4S 0.01 seoc0657 4S 0.01 mioa6585 4S 0.01 fcrc2007 4S 0.01 mioc3618 4S 0.01 mioal062 4S 0.01 fcrb5527 4S 0.01 seob3464 4S 0.01 ncrb8451 4S 0.01 miob4157 4S 0.01 fcrb3584 4S 0.01 fcrcO839 4S 0.01 fcrb5100 4S 0.01 fcrb7505 4S 0.01 fcrb2818 4S 0.01 seoa2272 4S 0.01 hfcr2148 4S 0.01 ncr9919 4S 0.01 seob3887 4S 0.01 seob8301 4S 0.01 fcr2598 4S 0.01 mioc0140 4S 0.01 mioa6135 4S 0.01 fcrb3550 4S 0.01 fcrb3135 4S 0.01 ncrc2859 4S 0.01 fcrb3702 4S 0.01 itιioa0152 4S 0.01 mioa9792 4S 0.01 fcrc3229 4S 0.01 fcrb5422 4S 0.01 seoa5578 4S 0.01 seoc2226 4S 0.01 mioc6296 4S 0.01 fcrcllδl 4S 0.01 miod0686 4S 0.01 mioa6721 4S 0.01 fcr3575 4S 0.01 fcrbl202 4S 0.01 seob6229 4S 0.01 seob9882 4S 0.01 ncrbl956 4S 0.01 ncrc3529 4S 0.01 ncrc9024 4S 0.01 seoc2670 4S 0.01 fcrb4294 4S 0.01 mioc6925 4S 0.01 seob2283 4S 0.01 mioa5691 4S 0.01 mioa4628 4S 0.01 ncrc5653 4S 0.01 ncrc2273 4S 0.01 fcrl633 4S 0.01 ncrc5608 4S 0.01 miob0496 4S 0.01 fcrb8196 4S 0.01 mioc2541 4S 0.01 fcrb7751 4S 0.01 miob8373 4S 0.01 fcrbl582 4S 0.01 seoa4460 4S 0.01 fcr7705 4S 0.01 miod6961 4S 0.01 ncr7876 4S 0.02 mioc0560 4S 0.01 seoa2962 4S 0.02 '■τϊcfb7rβ&-' -41S' ncrb8751 4S 0.02 fcrb2926 4S 0.02 seoa8640 4S 0.02 seobl617 4S 0.02 seob6773 4S 0.02 ncr2013 4S 0.02 seoa3556 4S 0.02 seob3533 4S 0.02 fcrb7951 4S 0.03 seoa0221 4S 0.02 seob4480 4S 0.03 seoc4052 4S 0.02 seob3485 4S 0.03 miod2128 4S 0.02 fcrb6747 4S 0.03 seoa9389 4S 0.02 seob4079 4S 0.03 ncr4538 4S 0.02 ncr3237 4S 0.03 fcrc4037 4S 0.02 hfcrO734 4S 0.03 fcr3043 4S 0.02 seoc2172 4S 0.03 seoa3093 4S 0.02 mioc6902 4S 0.03 fcrc4390 4S 0.02 fcr2952 4S 0.03 hfcr3404 4S 0.02 miod6234 4S 0.03 miod3302 4S 0.02 ncrO429 4S 0.03 ncrc4001 4S 0.02 ncr4551 4S 0.03 fcrc4896 4S 0.02 mioc0472 4S 0.03 ncrc7043 4S 0.02 fcr5222 4S 0.03 seocl934 4S 0.02 fcrc6970 4S 0.03 fcrcO332 4S 0.02 fcr5369 4S 0.03 mioc5307 4S 0.02 ncrc4620 4S 0.03 fcrb3519 4S 0.02 fcrb4719 4S 0.03 mioc2997 4S 0.02 fcr4306 4S 0.03 mioc5736 4S 0.02 fcrO843 4S 0.03 seoc5780 4S 0.02 seob9851 4S 0.03 fcr3269 4S 0.02 seob7981 4S 0.03 hfcr6366 4S 0.02 miod7486 4S 0.03 fcrb8719 4S 0.02 ncrc6416 1 4S 0.03 ncrc5491 4S 0.02 hfcr3454 4S 0.03 fcrb2866 4S 0.02 ncr9664 4S 0.03 ncrO791 4S 0.02 miob2293 4S 0.03 hfcr0130 4S 0.02 seob8355 4S 0.03 seoc4505 4S 0.02 ncrl352 4S 0.03 ncrcl402 4S 0.02 mioc3574 4S 0.03 ncrl344 4S 0.02 fcrb4479 4S 0.03 mioa3940 4S 0.02 mioal380 4S 0.03 seob9430 4S 0.02 mioa6583 4S 0.03 fcr6534 4S 0.02 ncrb2272 4S 0.03 miod6068 4S 0.02 fcr2196 4S 0.03 mioc2799 4S 0.02 fcrc4876 4S 0.03 fcrb4252 4S 0.02 hfcr5987 4S 0.03 ncrc4586 4S 0.02 seob8300 4S 0.03 seob2163 4S 0.02 seoa9042 4S 0.03 seob5624 4S 0.02 seob4150 4S 0.04 hfcrl856 4S 0.02 ncrc4000 4S 0.04 seob8873 4S 0.02 ncr3934 4S 0.04 fcr2587 4S 0.02 seoc3854 4S 0.04 miob3330 4S 0.02 fcrb5751 4S 0.04 fcrc5721 4S 0.02 miobl746 4S 0.04 ncr2408 4S 0.02 seob5465 4S 0.04 ncrb7292 4S 0.02 fcrb8467 4S 0.04 fcrc6513 4S 0.02 miob5752 4S 0.04 seoa4158 4S 0.02 miod0455 4S 0.04 fcrc7228 4S 0.02 seobll55 4S 0.04 seob4424 4S 0.02 ncr4550 4S 0.04 fcrb4365 4S 0.02 miob7106 4S 0.04 fcrc2670 4S 0.02 ncrc4875 4S 0.04 fcrb6426 4S 0.02 seobl748 4S 0.04 ncrcl231 4S 0.02 fcrb3283 4S 0.04 fcr5316 4S 0.02 mioa0908 4S 0.04 miob8341 4S 0.02 fcr6708 4S 0.04 mioa5531 4S 0.02 mioc4086 4S 0.04 fcrcl947 4S 0.02 fcr0860 4S 0.04 il-mibb2"δ€8-lr ;-"-"4S fcrb2556 40 8.98e-04 fcr5930 4S 0.04 fcrb9420 40 9.64e-04 fcr7518 4S 0.04 hfcr5381 40 1.016874e-03 fcrb7880 4S 0.04 hfcr6406 40 1.239885e-03 fcr3053 4S 0.04 miodl942 40 1.305311e-03 seob6198 4S 0.04 mioc2348 40 1.386736e-03 nιioa8314 4S 0.04 miod6038 40 1.395436e-03 seoa8921 4S 0.04 fcr3101 40 1.487316e-03 hfcrl314 4S 0.04 ncrb3001 40 1.557302e-03 mioc7170 4U 2.53e-06 seoa9740 40 1.598061e-03 mioa2072 4U 3.26e-06 mioa0494 40 1.619093e-03 seoa4461 4U 5.29e-06 ncrcl367 40 1.650171e-03 seoa6032 4U 5.48e-06 ncr5719 40 1.677652e-03 seoa8894 4U l.le-05 seoa4305 40 1.678116e-03 seob2697 4U 1.66e-05 seob0304 40 1.693307e-03 ncr3751 4U 1.95e-05 fcrb3165 40 1.760242e-03 miod7421 4U 6.08e-05 fcrc5516 40 1.799844e-03 seoa9373 4U 6.54e-05 seoc4380 40 1.956915e-03 mioal370 4U 8.87e-05 ncrc0150 40 1.985831e-03 seoa9482 4U 9.27e-05 miod0340 40 2.021229e-03 fcrb7339 4U 9.87e-05 miod2525 40 2.174809e-03 fcr4477 4U 1.01e-04 fcrb2624 40 2.174826e-03 fcrbO265 4U 1.2e-04 fcrb6187 40 2.177878e-03 seob6189 4U 1.32e-04 fcr7419 40 2.260166e-03 ncr3262 4U 1.4e-04 seoal065 40 2.312579e-03 fcrO253 4U 1.45e-04 fcrc6016 40 2.3165e-03 seoa7652 4ϋ 1.5e-04 miod6467 40 2.34447e-03 seob6851 40 1.55e-04 mioal976 40 2.452224e-03 fcrb3461 4ϋ 1.6e-04 miob8143 40 2.489145e-03 miod5651 4U 1.77e-04 seoa3670 40 2.543649e-03 hfcr2390 4U 1.83e-04 ncrcl578 40 2.579378e-03 miod5505 4U 1.89e-04 mioc8434 40 2.661058e-03 secb6751 40 2.02e-04 fcrb4409 40 2.722505e-03 seoc2264 40 2.1e-04 fcrO999 40 2.730498e-03 fcrcl381 4ϋ 2.28e-04 seob3307 40 2.811695e-03 seocll75 40 2.33e-04 seoa9814 40 2.836239e-03 seoc0999 40 2.44e-04 mioa7140 40 2.898369e-03 hfcrlO73 40 2.54e-04 ncrc6359 40 2.898846e-03 mioc6260 40 2.92e-04 miob0167 40 2.975083e-03 fcrcO112 40 3.06e-04 fcrb2933 40 3.049655e-03 ncrO153 40 3.07e-04 mioa2185 40 3.147097e-03 fcr5779 40 4.02e-04 mioa3856 40 3.151528e-03 fcrb7830 40 4.09e-04 seoa5683 40 3.160612e-03 ncrc5663 40 5.0e-04 mioa0187 40 3.279663e-03 mioc2602 40 5.18e-04 mioa2374 40 3.293401e-03 ncrc4757 40 5.23e-04 ncrbO145 40 3.359507e-03 fcr2607 40 5.84e-04 fcrb3017 40 3.376066e-03 seoa4739 40 5.91e-04 miob8274 40 3.404337e-03 seob9145 40 6.11e-04 seob4972 40 3.531512e-03 fcrc3750 40 6.22e-04 seob2797 40 3.6022e-03 ncrb2266 40 6.45e-04 seoal749 40 3.637644e-03 raioa2537 40 6.62e-04 fcr6018 40 3.762242e-03 mioa6418 40 7.09e-04 seoal480 40 3.790975e-03 miodl925 40 7.23e-04 fcr3155 40 3.832262e-03 mioc4089 40 7.46e-04 ncr2182 40 3.847974e-03 mioc7362 40 7.52e-04 seoa6573 40 3.875968e-03 seob3303 40 7.67e-04 ncrc4531 40 4.208322e-03 seoa8979 40 8.23e-04 miod4140 40 4.240281e-03 hfcrO478 40 8.43e-04 mioc0567 40 4.279318e-03 mioa5355 40 8.58e-04 seoa9711 40 4.329889e-03 ncr8041 4U 8.61e-04 mioc0347 40 4.417094e-03 fcrO824 40 8.62e-04 miob8226 40 4.470203e-03 fcrc7102 40 8.87e-04 fcrcO959 40 4.530274e-03 mioa6731 40 8.9e-04 mioa0890 40 4.570501e-03 !i ;»" 'UiLΪΛ- I! I! 'i.. ''-m_i-oa09-09-'"" AU ;i-Hr58'984-5e-03 fcrb3894 4U 9.03812e-03 rαiod4686 4U 4.657868e-03 fcrb2299 4U 9.0502e-03 ncrb4912 4U 4.752707e-03 seob3415 4U 9.05062e-03 mioa6739 4U 4.778158e-03 seoal737 4U 9.470911e-03 mioa0826 4U 4.884238e-03 mioc4835 4U 9.495048e-03 mioa6621 4ϋ 4.904983e-03 fcrc5846 4U 9.622113e-03 mioa9831 4U 4.946737e-03 ncr4113 4U 9.724485e-03 fcrb8942 4U 5.08231e-03 mioa6035 4U 9.789009e-03 fcrc0430 4U 5.084925e-03 fcr3367 4U 9.80502e-03 ncr2862 4U 5.163569e-03 miob8214 4U 0.01 ncrc5500 4U 5.175143e-03 mioc4366 4ϋ 0.01 ncr3313 4U 5.18529e-03 miob9336 4U 0.01 miod3592 4U 5.253602e-03 seobl574 4U 0.01 ncrbl398 4U 5.295501e-03 miob4037 4U 0.01 miod3254 4U 5.326719e-03 mioa2993 4U 0.01 mioc8619 4U 5.353299e-03 mioa6585 4U 0.01 fcr0990 4U 5.490991e-03 fcrb5527 4U 0.01 fcrc6560 4U 5.571911e-03 fcrb5918 4U 0.01 fcr3282 4U 5.608041e-03 ncrb8451 4U 0.01 seoc3588 4U 5.826485e-03 fcrb3584 40 0.01 fcr5536 4U 6.049129e-03 fcrb5100 4U 0.01 ncrcO663 4U 6.085718e-03 hfcr2148 4U 0.01 fcr0608 4U 6.096137e-03 fcr2598 4U 0.01 ncrbl337 4U 6.21911e-03 inioa6135 4U 0.01 ncrc6712 4ϋ 6.41394e-03 hfcr5970 4U 0.01 ncrcβOOO 4U 6.444479e-03 miod4464 4U 0.01 fcrb8187 4U 6.498087e-03 fcrb5389 4U 0.01 seob0703 4U 6.50097e-03 hfcr5237 4U 0.01 fcr4469 4U 6.567393e-03 fcrb5422 4U 0.01 mioal303 4U 6.633139e-03 seoc2226 4U 0.01 fcrb2765 4U 6.689668e-03 mioa6721 4U 0.01 miod5612 4U 6.698915e-03 ncrc5959 4U 0.01 fcrb5087 4U 6.714694e-03 ncrc3529 4U 0.01 fcrb8536 4U 6.791086e-03 fcrb9481 4U 0.01 hfcrl760 4U 6.835569e-03 ncrc5653 4ϋ 0.01 ncr6335 4U 6.907692e-03 seobll33 4U 0.01 miod3079 4U 6.91873e-03 miob0496 4U 0.01 ncr9956 4U 6.920421e-03 fcrb7588 4U 0.01 ncrb8207 4U 7.041692e-03 miob8373 4U 0.01 hfcrl914 4U 7.057729e-03 seoa4460 4U 0.01 mioal427 4U 7.073426e-03 mioc0560 4U 0.01 seoa9935 4U 7.197022e-03 fcrb7808 4U 0.01 mioa6093 4U 7.268139e-03 mioa5097 4U 0.01 miod6213 4U 7.325332e-03 mioa2213 4U 0.01 miob4058 4U 7.329008e-03 miod4342 4U 0.01 seoa7078 4U 7.471779e-03 ncrc6171 4U 0.01 mioc6925 4U 7.512586e-03 seoa7094 4U 0.01 ncrc3936 4U 7.54805e-03 fcrb3897 4U 0.01 seob2169 4U 7.634987e-03 mioc7471 4U 0.01 ncrc6087 4U 7.662274e-03 seob0344 4ϋ 0.01 hfcrO517 4U 7.704251e-03 seoa5552 4ϋ 0.01 fcrb9655 4U 7.792172e-03 ncrb5737 4U 0.01 seoa0913 4U 7.91186e-03 fcrb5675 4ϋ 0.01 mioc7998 4U 7.939586e-03 mioc4788 4U 0.01 fcrbl329 4U 8.164602e-03 seoa2639 4U 0.01 miobl493 4U 8.197903e-03 seob7747 4U 0.01 fcrcO228 4U 8.284667e-03 miod2264 4U 0.01 seoa4070 4U 8.351138e-03 ncrl428 4U 0.01 fcrbl684 4U 8.355442e-03 fcrcl758 4U 0.01 seob8065 4ϋ 8.459463e-03 mioa4944 4U 0.01 ncrc6264 4U 8.530558e-03 miob5119 4U 0.01 mioa6738 4U 8.769774e-03 seoc4748 4U 0.01 miobl506 4ϋ 8.830271e-03 seob2938 4U 0.01 mioa0707 4U 9.037843e-03 fcrcl745 4U 0.01 '•ffifbdOW'P"--1'••-40 mioa3940 4U 0.02 mioa8998 4U O.01 seob9430 40 0.02 mioc2039 4ϋ O.01 fcr6534 4U 0.02 mioc2546 4U O.01 fcrb8901 4U 0.02 miob4860 4U O.01 seob8873 4U 0.02 mioa9258 4U O.01 miod6387 4U 0.02 ncrc3258 4U O.01 miob3330 4U 0.02 miob3411 4U O.01 fcrc5721 4ϋ 0.02 ncrc7085 4U O.01 hfcr2295 4U 0.02 seoa0536 4U O.01 mioc7119 4U 0.02 miob6419 4U O.01 seoa9873 4U 0.02 miob0973 4U O.01 ncrb2798 4U 0.02 seoc3870 4U O.01 fcrc4949 4U 0.02 ncr2967 4U O.01 fcrc7228 4CJ 0.02 fcrbl750 4U O.01 mioc3208 4U 0.02 seob0418 4U O.01 seob4424 4U 0.02 ncrc0803 4U O.01 fcrb5439 4U 0.02 seoc0657 4U O.01 fcrc2670 4U 0.02 fcrc2007 4U O.01 fcrb6426 4U 0.02 seob3464 4U O.01 ncrcl231 4U 0.02 miod5372 4ϋ O.01 mioa5531 4ϋ 0.02 seoa2272 4ϋ O.01 seoc2144 4U 0.02 mioc0140 4U O.01 fcrcl947 4U 0.02 fcrb3497 4U O.01 ncrb8751 4U 0.02 fcr4272 4U O.01 seob6773 4U 0.02 mioa0152 4ϋ O.01 seoa3556 4U 0.02 mioc4730 4U O.01 fcrc6228 4ϋ 0.02 miod0686 4U O.01 ncrb8248 4U 0.02 ncrc9024 4ϋ O.01 fcrb6747 4U 0.03 mioa6999 4U O.01 seob4079 4U 0.03 mioc0940 4U O.01 mioc2116 4ϋ 0.03 mioa4628 4U O.01 hfcrO734 4ϋ 0.03 ncrc2273 4U O.01 fcr2952 4U 0.03 ncrc5608 4U O.01 fcr4433 4U 0.03 miob4238 4U O.01 mioa0862 4U 0.03 mioaOβOl 4ϋ O.01 miod6234 4U 0.03 ncrb7166 4U O.02 fcrbl556 4ϋ 0.03 fcrb2926 4U O.02 ncr8199 4U 0.03 fcrbl381 4U O.02 seoa7383 4U 0.03 seobl617 4U O.02 mioc2451 4U 0.03 seob3533 4U O.02 fcrb5416 4U 0.03 seoa3352 4U O.02 seoa8738 4U 0.03 ncrc4885 4U O.02 fcrc6970 4U 0.03 seoc4052 4ϋ O.02 ncrc4620 4U 0.03 seoa9389 4ϋ O.02 fcrb4719 4U 0.03 fcrb2704 4U O.02 seob7981 4U 0.03 fcrc4037 4U O.02 miod7486 4U 0.03 seoa3093 4U O.02 ncrc6416 4U 0.03 fcrc4390 4U O.02 seob0885 4ϋ 0.03 hfcr3404 4ϋ O.02 seoa3038 4U 0.03 miod3302 4U O.02 ncrc9055 4U 0.03 fcrc4896 4U O.02 fcrcO771 4U 0.03 ncrc7043 4U O.02 ncrc5738 4U 0.03 miob9533 4U O.02 miob9581 4U 0.03 mioal062 4U O.02 miob2293 4U 0.03 seocl934 4U O.02 ncrl352 4U 0.03 fcrc2131 4U O.02 mioc3574 4U 0.03 mioc5736 4U O.02 fcrb2090 4U 0.03 hfcr6366 4U O.02 ncrb2272 4U 0.03 ncrO791 4U O.02 ncrl007 4U 0.03 seob5761 4U O.02 ncrcl615 4U 0.03 seoc4505 4U O.02 ncrc4000 4U 0.04 mioc0052 4U O.02 ncr2269 4U 0.04 ncrcl402 4U O.02 seoc3854 4U 0.04 i Il ' Il ■ I! Il 1I „• :! ti " - • ~ "••ftιroblT46""'■ 40 "■— '"ϋ :!tf4 " seob3163 7A 6.585485e-03 seob5465 4U 0.04 fcrl657 7A 6.595853e-03 hfcr5522 4U 0.04 ncrc5716 7A 6.616725e-03 miod3027 4ϋ 0.04 fcrb8393 7A 6.674381e-03 seobll55 4U 0.04 ncrcl469 7A 6.823212e-03 seoa3443 4U 0.04 fcrb3330 7A 6.930815e-03 ncrc4875 4U 0.04 seoa2391 7A 6.95326e-03 seobl748 4U 0.04 mioc5678 7A 6.998647e-03 fcrb3283 4U 0.04 fcrbl807 7A 7.006094e-03 fcr6708 4U 0.04 seoa7926 7A 7.014042e-03 seoc5285 4U 0.04 fcrb6472 7A 7.107374e-03 seob0976 4U 0.04 fcrbl920 7A 7.188322e-03 miob9248 4U 0.04 ncrb0550 7A 7.225645e-03 fcr5930 4U 0.04 fcrb2754 7A 7.264187e-03 miod6058 4U 0.04 miob4956 7A 7.349209e-03 mioa8314 4U 0.04 seob6414 7A 7.368386e-03 seoa8921 4U 0.04 fcrb8485 7A 7.481387e-03 hfcr2955 7A 5.09e-05 ncrc3161 7A 7.607648e-03 mioal055 7A 3.84e-04 mioa9033 7A 7.801274e-03 fcrb5840 7A 3.87e-04 hfcr6141 7A 7.924398e-03 seob9302 7A 5.06e-04 fcrl724 7A 7.976219e-03 ncrO836 7A 5.56e-04 seoc0394 IK 7.98437e-03 fcrb4413 7A 1.011219e-03 mioc3523 7A 8.137321e-03 miob0636 7A 1.023089e-03 seoa4333 7A 8.239225e-03 seoa5848 7A 1.02893e-03 mioa5902 7A 8.26574e-03 mioc7444 7A 1.056907e-03 ncrc4323 7A 8.456349e-03 fcrc2455 7A 1.247088e-03 seob7180 7A 8.878316e-03 mioa9246 7A 1.742382e-03 ncrl437 7A 8.894272e-03 miob8704 7A 1.816261e-03 mioc7471 7A 8.933176e-03 mioa8594 7A 2.071556e-03 ncrc8963 7A 9.097822e-03 seob8212 7A 2.146053e-03 ncrcl952 7A 9.151906e-03 fcrb5187 7A 2.160316e-03 mioa3428 7A 9.325496e-03 seob8321 7A 2.411238e-03 fcrb4400 7A 9.397631e-03 ncrb6087 7A 2.447161e-03 seoc3426 7A 9.422754e-03 ncr9175 7A 2.614876e-03 mioc5664 7A 9.457355e-03 fcr3714 7A 2.947117e-03 ncr9371 7A 9.632014e-03 hfcr3197 7A 3.090889e-03 seoal812 7A 9.984721e-03 hfcr2295 7A 3.281442e-03 fcrb2160 7A 0.01 seocl664 7A 3.357752e-03 seobll45 7A 0.01 ncrO791 7A 3.83397e-03 hfcr5604 7A 0.01 ncrc9642 7A 3.889141e-03 miod0878 7A 0.01 fcrb9096 7A 3.931911e-03 miod0355 7A 0.01 ncrc3049 7A 4.065059e-03 fcrb4415 7A 0.01 seob6535 7A 4.182541e-03 seob2797 7A 0.01 ncrcO729 7A 4.212215e-03 mioa2377 7A 0.01 fcrc0075 7A 4.248397e-03 fcrb3704 7A 0.01 miob4574 7A 4.280838e-03 seoa5785 7A 0.01 seob5213 7A 4.291681e-03 ncrcO217 7A 0.01 miod6560 7A 4.342561e-03 seobl319 7A 0.01 hfcrββll 7A 4.494123e-03 seoa2641 7A 0.01 seocl535 7A 4.804365e-03 seob6316 7A 0.01 seoc2510 7A 4.948173e-03 ncrc5959 7A 0.01 ncrbO513 7A 5.005288e-03 fcr3880 7A 0.01 seoa8521 7A 5.057401e-03 fcrb2190 7A 0.01 ncrc5150 7A 5.168054e-03 fcrb8680 7A 0.01 fcrbl697 7A 5.247909e-03 miob4221 7A 0.01 ncrc9469 7A 5.631261e-03 seoa0462 7A 0.01 fcrb7944 7A 5.840758e-03 fcrO788 7A 0.01 fcrb5241 7A 5.986816e-03 seoa6530 7A 0.01 seoa5843 7A 6.002825e-03 miod0676 7A 0.01 fcrb9068 7A 6.207763e-03 fcrc2829 7A 0.01 seob6096 7A 6.274797e-03 ncrc9633 7A 0.01 fcrb5259 7A 6.483437e-03 seob8104 7A 0.01 ncrcO185 7A 6.570639e-03 seoa5698 7A 0.01
Figure imgf000369_0001
fcrbl580 7A 0.02 ncrc5025 7A 0.01 ncr6578 7A 0.02 fcrb5775 7A 0.01 hfcr2658 7A 0.02 miocl928 7A 0.01 ncr5065 7A 0.02 ncrc6796 7A 0.01 fcrb6871 7A 0.02 seoa3102 7A 0.01 seoc3883 7A 0.02 ncrOOlδ 7A 0.01 seoa4181 7A 0.02 fcrb8334 7A 0.01 miob5012 7A 0.02 fcrbl731 7A 0.01 seoa6598 7A 0.02 fcrc2099 7A 0.01 ncrb8530 7A 0.02 fcrb3808 7A 0.01 fcrb8061 7A 0.02 fcrb2495 7A 0.01 ncrcl379 7A 0.02 miob0399 7A 0.01 seob6198 7A 0.02 miob2163 7A 0.01 fcrb8690 7A 0.02 fcrb4280 7A 0.01 fcrb2592 7A 0.02 ncrc5016 7A 0.01 seob3371 7A 0.02 ncrc6479 7A 0.01 ncr9337 7A 0.02 ncrc3080 7A 0.01 fcrc5004 7A 0.02 mioc3958 7A 0.01 seob5004 7A 0.02 fcrb2376 7A 0.01 seoal552 7A 0.02 seocl025 7A 0.01 miob5467 7A 0.02 miob4975 7A 0.01 miodl030 7A 0.02 mioa3514 7A 0.01 seocl023 7A 0.02 fcrb4832 7A 0.01 fcr4634 7A 0.02 fcrbl690 7A 0.01 ncr5192 7A 0.02 mioc0317 7A 0.01 fcrb2126 7A 0.02 ncr3397 7A 0.01 miodl236 7A 0.02 fcr2897 7A 0.01 seob4191 7A 0.02 fcrcO367 7A 0.01 miod3826 7A 0.02 mioc2451 7A 0.01 fcrc5831 7A 0.02 seob9552 7A 0.01 mioc2662 7A 0.02 ncrO761 7A 0.01 seob5478 7A 0.02 miob3247 7A 0.01 seocl627 7A 0.02 seob6437 7A 0.01 seob4001 7A 0.02 ncrc3408 7A 0.01 fcr3282 7A 0.02 fcrb5761 7A 0.01 fcrb8898 7A 0.02 fcrbl916 7A 0.01 seob9872 7A 0.02 ncr3934 7A 0.01 fcr6708 7A 0.02 fcrb9841 7A 0.01 hfcr2275 7A 0.02 seob8807 7A 0.01 seob3697 7A 0.02 ncrb8468 7A 0.01 fcrb3285 7A 0.02 miod5682 7A 0.01 ncrl658 7A 0.02 seoa7897 7A 0.01 miob7672 7A 0.02 ncrc4780 7A 0.01 seoa2962 7A 0.02 ncrb2798 7A 0.01 ncrc5417 7A 0.02 seob2689 7A 0.01 mioa0247 7A 0.02 mioa0582 7A 0.01 ncrc8932 7A 0.02 seob7409 7A 0.01 ncr9983 7A 0.02 fcrb3476 7A 0.01 mioa9630 7A 0.03 miodl291 7A 0.01 seocl278 7A 0.03 seoa4586 7A 0.01 miob8077 7A 0.03 ncrc9153 7A 0.01 seoa0014 7A 0.03 fcrc6470 7A 0.01 miob0167 7A 0.03 fcrc5071 7A 0.02 fcrb9751 7A 0.03 seob6484 7A 0.02 ncr3369 7A 0.03 fcrb4321 7A 0.02 mioa9792 7A 0.03 ncrcl050 7A 0.02 fcrb2200 7A 0.03 ncr3718 7A 0.02 ncrc2827 7A 0.03 ncrc3733 7A 0.02 fcrc6028 7A 0.03 seob9241 7A 0.02 fcrb6416 7A 0.03 ncrOOlβ 7A 0.02 inioa9604 7A 0.03 mioa4674 7A 0.02 seob5044 7A 0.03 ncr3869 7A 0.02 mioc2184 7A 0.03 ncrc8892 7A 0.02 mioa7957 7A 0.03 -s6occ3'&8-8"••"---7'A *-"""OTO3 hfcr2046 7A 0.04 miob9163 7A O.03 fcrb9118 7A 0.04 hfcr3592 7A O.03 fcrc4408 7A 0.04 seoall04 7A O.03 miob2210 7A 0.04 seoal776 7A O.03 seoa2824 7A 0.04 miob0180 7A O.03 ncrc6941 7A 0.04 ncr4656 7A O.03 seob3009 7A 0.04 hfcr5003 7A O.03 m±ob6437 7A 0.04 fcrclO14 7A O.03 fcrb7487 7A 0.04 miodl825 7A O.03 seoa4598 7A 0.04 fcrl347 7A O.03 fcrb4579 7A 0.04 miob0189 7A O.03 mioc8278 7A 0.04 miod2225 7A O.03 ncrb8437 7A 0.04 fcrb3169 7A O.03 ncrc6348 7A 0.04 fcrb4995 7A O.03 mioa8852 7A 0.04 mioa0890 7A O.03 fcrc2577 7A 0.04 fcrb5467 7A O.03 seoc0491 7A 0.04 ncrb8330 7A O.03 mioc3413 7A 0.04 fcrcO559 7A O.03 ncrc9612 7A 0.04 seob0928 7A O.03 ncrc9023 7A 0.04 ncr2575 7A O.03 fcrb3518 7A 0.04 seob4492 7A O.03 seoa5554 7A 0.04 fcrc5699 7A O.03 fcrb8829 7A 0.04 fcrb2015 7A O.03 hfcr2955 7B 5.09e-05 fcrc6570 7A O.03 mioal055 7B 3.84e-04 seob5379 7A O.03 fcrb5840 7B 3.87e-04 seocl230 7A O.03 seob9302 7B 5.06e-04 seob5214 7A O.03 ncrO836 7B 5.56e-04 seoclββl 7A O.03 fcrb4413 7B 1.011219e-03 seob0497 7A O.03 miob0636 7B 1.023089e-03 miob6373 7A O.03 seoa5848 7B 1.02893e-03 ncr9934 7A O.03 mioc7444 7B 1.056907e-03 miob6290 7A O.03 fcrc2455 7B 1.247088e-03 mioal276 7A O.03 mioa9246 7B 1.742382e-03 mioall65 7A O.03 miob8704 7B 1.816261e-03 ncrc9681 7A O.03 mioa8594 7B 2.071556e-03 ncrc4757 7A O.03 seob8212 7B 2.146053e-03 fcrb9324 7A O.03 fcrb5187 7B 2.160316e-03 seob3191 7A O.04 seob8321 7B 2.411238e-03 seob6020 7A O.04 ncrb6087 7B 2.447161e-03 mioal353 7A O.04 ncr9175 7B 2.614876e-03 mioc3716 7A O.04 fcr3714 7B 2.947117e-03 hfcrO521 7A O.04 hfcr3197 7B 3.090889e-03 ncr7973 7A O.04 hfcr2295 7B 3.281442e-03 seoc0268 7A O.04 seocl664 7B 3.357752e-03 seob0047 7A O.04 ncrO791 7B 3.83397e-03 seoa3633 7A O.04 ncrc9642 7B 3.889141e-03 fcrb5092 7A O.04 fcrb9096 7B 3.931911e-03 fcrbl687 7A O.04 ncrc3049 7B 4.065059e-03 seob5064 7A O.04 seob6535 7B 4.182541e-03 fcrb9520 7A O.04 ncrcO729 7B 4.212215e-03 seob9946 7A O.04 fcrc0075 7B 4.248397e-03 mioc3930 7A O.04 miob4574 7B 4.280838e-03 seobl316 7A O.04 seob5213 7B 4.291681e-03 ncr2256 7A O.04 miod6560 7B 4.342561e-03 seoa6144 7A O.04 hfcrββll 7B 4.494123e-03 ncrcO592 7A O.04 seocl535 7B 4.804365e-03 miob2093 7A O.04 seoc2510 7B 4.948173e-03 fcrb6734 7A O.04 ncrbO513 7B 5.005288e-03 fcrb8910 7A O.04 seoa8521 7B 5.057401e-03 seoa4158 7A O.04 ncrc5150 7B 5,.168054e-03 seoa6393 7A O.04 fcrbl697 7B 5,.247909e-03 miob4064 7A O.04 ncrc9469 7B 5,.631261e-03 mioc2694 7A O.04 fcrb7944 7B 5,.840758e-03 *•■•■-:drto521Sl1" 'r9868l6e-03 fcrO788 7B O.01 seoa5843 7B 6.002825e-03 seoa6530 7B O.01 fcrb9068 7B 6.207763e-03 miod0676 7B O.01 seob6096 7B 6.274797e-03 fcrc2829 7B O.01 fcrb5259 7B 6.483437e-03 ncrc9633 7B O.01 ncrcO185 7B 6.570639e-03 seob8104 7B O.01 seob3163 7B 6.585485e-03 seoa5698 7B O.01 fcrl657 7B 6.595853e-03 fcrb9588 7B O.01 ncrc5716 7B 6.616725e-03 ncrc5025 7B O.01 fcrb8393 7B 6.674381e-03 fcrb5775 7B O.01 ncrcl469 7B 6.823212e-03 miocl928 7B O.01 fcrb3330 7B 6.930815e-03 ncrc6796 7B O.01 seoa2391 7B 6.95326e-03 seoa3102 7B O.01 mioc5678 7B 6.998647e-03 ncrOOlδ 7B O.01 fcrbl807 7B 7.006094e-03 fcrb8334 7B O.01 seoa7926 7B 7.014042e-03 fcrbl731 7B O.01 fcrb6472 7B 7.107374e-03 fcrc2099 7B O.01 fcrbl920 7B 7.188322e-03 seob7346 7B O.01 ncrb0550 7B 7.225645e-03 fcrb3808 7B O.01 fcrb2754 7B 7.264187e-03 fcrb2495 7B O.01 miob4956 7B 7.349209e-03 miob0399 7B O.01 seob6414 7B 7.368386e-03 miob2163 7B O.01 fcrb8485 7B 7.481387e-03 fcr2861 7B O.01 ncrc3161 7B 7.607648e-03 fcrb4280 7B O.01 mioa9033 7B 7.801274e-03 ncrc5016 7B O.01 hfcr6141 7B 7.924398e-03 ncrc6479 7B O.01 fcrl724 7B 7.976219e-03 ncrc3080 7B O.01 seoc0394 7B 7.98437e-03 mioc3958 7B O.01 mioc3523 7B 8.137321e-03 fcrb2376 7B O.01 seoa4333 7B 8.239225e-03 seocl025 7B O.01 mioa5902 7B 8.26574e-03 miob4975 7B O.01 ncrc4323 7B 8.456349e-03 seob5081 7B O,.01 seob7180 7B 8,.878316e-03 mioa3514 7B O,.01 ncrl437 7B 8,.894272e-03 fcrb4832 7B O,.01 rαioc7471 7B 8,.933176e-03 seoa5746 7B O..01 ncrc8963 7B 9,.097822e-03 fcrbl690 7B O,.01 ncrcl952 7B 9..151906e-03 mioc0317 7B O..01 mioa3428 7B 9,,325496e-03 ncr3397 7B O..01 fcrb4400 7B 9.,397631e-03 fcr2897 7B O.,01 seoc3426 7B 9,,422754e-03 fcrcO367 7B O..01 mioc5664 7B 9,,457355e-03 mioc2451 7B O.,01 ncr9371 7B 9.,632014e-03 seob9552 7B O.,01 seoal812 7B 9..984721e-03 ncrO761 7B O.,01 fcrb2160 7B 0.,01 miob3247 7B O.Ol seobll45 7B 0.,01 seob6437 7B O.,01 hfcr5604 7B 0.,01 ncrc3408 7B O.Ol miod0878 7B 0..01 fcrb5761 7B O.Ol miod0355 7B 0.,01 fcrbl91β 7B O.Ol fcrb4415 7B 0.,01 ncr3934 7B O.Ol seob2797 7B 0.01 fcrb9841 7B O.Ol mioa2377 7B 0.01 seob8807 7B O.Ol fcrb3704 7B 0.01 ncrb8468 7B O.Ol fcrbl720 7B 0.01 miod5682 7B O.Ol seoa5785 7B 0.01 seoa7897 7B O.Ol ncrcO217 7B 0.01 ncrc4780 7B O.Ol seobl319 7B 0.01 ncrb2798 7B O.Ol seoa2641 7B 0.01 seob2689 7B O.Ol seob6316 7B 0.01 mioa0582 7B O.Ol ncrc5959 7B 0.01 seob7409 7B O.Ol fcr3880 7B 0.01 fcrb3476 7B O.Ol fcrb2190 7B 0.01 miodl291 7B O.Ol fcrb8680 7B 0.01 seoa4586 7B O.Ol miob4221 7B 0.01 ncrc9153 7B O.Ol seoa0462 7B 0.01 fcrc6470 7B O.Ol B "... -■•fcrc50-7t- '••" ™?B fcrb9751 7B 0.03 seob6484 7B 0.02 ncr3369 7B 0.03 ncrcl050 7B 0.02 mioa9792 7B 0.03 ncr3718 7B 0.02 fcrb2200 7B 0.03 ncrc3733 7B 0.02 ncrc2827 7B 0.03 seob9241 7B 0.02 fcrc6028 7B 0.03 ncrOOlβ 7B 0.02 fcrb6416 7B 0.03 mioa4674 7B 0.02 mioa9604 7B 0.03 ncr3869 7B 0.02 seob5044 7B 0.03 ncrc8892 7B 0.02 mioc2184 7B 0.03 fcrbl580 7B 0.02 mioa7957 7B 0.03 ncr6578 7B 0.02 seoc3588 7B 0.03 hfcr2658 7B 0.02 miob9163 7B 0.03 ncr5065 7B 0.02 hfcr3592 7B 0.03 fcrb6871 7B 0.02 seoall04 7B 0.03 seoc3883 7B 0.02 seoal776 7B 0.03 seoa4181 7B 0.02 miob0180 7B 0.03 miob5012 7B 0.02 ncr4656 7B 0.03 seoa6598 7B 0.02 hfcr5003 7B 0.03 ncrb8530 7B 0.02 fcrclO14 7B 0.03 fcrb8061 7B 0.02 miodl825 7B 0.03 ncrcl379 7B 0.02 miob0189 7B 0.03 seob6198 7B 0.02 miod2225 7B 0.03 fcrb8690 7B 0.02 fcrb3169 7B 0.03 fcrb2592 7B 0.02 fcrb4995 7B 0.03 mioa3588 7B 0.02 ncr8420 7B 0.03 seob3371 7B 0.02 mioa0890 7B 0.03 ncr9337 7B 0.02 fcrb5467 7B 0.03 fcrc5004 7B 0.02 ncrb8330 7B 0.03 seob5004 7B 0.02 fcrcO559 7B 0.03 seoal552 7B 0.02 seob0928 7B 0.03 miob5467 7B 0.02 ncr2575 7B 0.03 miodl030 7B 0.02 seob4492 7B 0.03 seocl023 7B 0.02 fcrc5699 7B 0.03 fcr4634 7B 0.02 fcrb2015 7B 0.03 ncr5192 7B 0.02 fcrc6570 7B 0.03 fcrb2126 7B 0.02 seob5379 7B 0.03 miodl236 7B 0.02 seocl230 7B 0.03 seob4191 7B 0.02 seob5214 7B 0.03 miod3826 7B 0.02 seoclββl 7B 0.03 fcrc5831 7B 0.02 seob0497 7B 0.03 mioc2662 7B 0.02 miob6373 7B 0.03 seob5478 7B 0.02 ncr9934 7B 0.03 seocl627 7B 0.02 miob6290 7B 0.03 seob4001 7B 0.02 mioal276 7B 0.03 fcr3282 7B 0.02 mioall65 7B 0.03 seob9872 7B 0.02 ncrc9681 7B 0.03 fcr6708 7B 0.02 ncrc4757 7B 0.03 seoc2518 7B 0.02 fcrb9324 7B 0.03 hfcr2275 7B 0.02 seob3191 7B 0.04 seob3697 7B 0.02 seob6020 7B 0.04 fcrb3285 7B 0.02 mioal353 7B 0.04 ncrl658 7B 0.02 mioc3716 7B 0.04 miob7672 7B 0.02 hfcrO521 7B 0.04 seoa2962 7B 0.02 ncr7973 7B 0.04 ncrc5417 7B 0.02 fcrb3074 7B 0.04 mioa0247 7B 0.02 seob0047 7B 0.04 ncrc8932 7B 0.02 seoa3633 7B 0.04 ncr9983 7B 0.02 fcrb5092 7B 0.04 mioa9630 7B 0.03 seob8031 7B 0.04 seocl278 7B 0.03 fcrbl687 7B 0.04 miob8077 7B 0.03 seob5064 7B 0.04 seoa0014 7B 0.03 fcrb9520 7B 0.04 miob0167 7B 0.03 seob9946 7B 0.04 "-mi'oc393θ"" "•σ.w' ■"''•" miobl059 7C 3.273784e-03 seobl316 7B 0.04 fcrl657 7C 3.33072e-03 ncr2256 7B 0.04 seob6879 7C 3.527558e-03 seoa6144 7B 0.04 ncr3869 7C 3.790137e-03 ncrcO592 7B 0.04 miob0180 7C 3.857035e-03 miob2093 7B 0.04 hfcrββll 7C 4.095694e-03 fcrb6734 7B 0.04 seoc3426 7C 4.1094e-03 fcrb8910 7B 0.04 miob0636 7C 4.169115e-03 seoa4158 7B 0.04 mioc3523 7C 4.183983e-03 seoa6393 7B 0.04 ncrcl608 7C 4.401747e-03 miob4064 7B 0.04 ncrcO729 7C 4.516067e-03 mioc2694 7B 0.04 ncr0791 7C 4.561328e-03 fcrb5198 7B 0.04 seobl319 7C 4.576012e-03 hfcr2046 7B 0.04 seob8104 7C 4.730107e-03 fcrc4408 7B 0.04 mioa9246 7C 4.789915e-03 miob2210 7B 0.04 miocl978 7C 4.797667e-03 seoa2824 7B 0.04 miodl908 7C 4.876308e-03 ncrc6941 7B 0.04 mioa5468 7C 4.907885e-03 seob3009 7B 0.04 seob5032 7C 4.975292e-03 miob6437 7B 0.04 miob4378 7C 5.078973e-03 fcrb7487 7B 0.04 fcrb3330 7C 5.374198e-03 seoa4598 7B 0.04 seoc4748 7C 5.469684e-03 fcrb4579 7B 0.04 mioc3962 7C 5.503304e-03 mioc8278 7B 0.04 mioc3958 7C 5.585154e-03 ncrb8437 7B 0.04 fcrcO367 7C 5.737407e-03 ncrc6348 7B 0.04 fcrb2754 7C 5.762075e-03 mioa8852 7B 0.04 miob9087 7C 5.972077e-03 fcrc2577 7B 0.04 fcrb5187 7C 5.978955e-03 seoc0491 7B 0.04 seob3533 7C 6.085375e-03 mioc3413 7B 0.04 mioa9792 7C 6.087453e-03 miob7290 7B 0.04 hfcr3197 7C 6.106227e-03 ncrc9612 7B 0.04 seoc2510 7C 6.174005e-03 ncrc9023 7B 0.04 mioal380 7C 6.442511e-03 mioc8945 7B 0.04 ncrc5716 7C 6.68224e-03 mioal025 7B 0.04 ncrc9469 7C 6.77346e-03 seoa5554 7B 0.04 fcrb4413 7C 6.787171e-03 fcrb8829 7B 0.04 ncrc9633 7C 6.817579e-03 ncr8199 7B 0.04 miob0189 7C 7.012368e-03 fcrb6620 7C 1.77e-04 seoc0394 7C 7.095938e-03 miob4574 7C 1.83e-04 ncr8910 7C 7.351684e-03 mioc7444 7C 2.56e-04 fcrb7255 7C 7.43613e-03 ncrc5150 7C 3.13e-04 miob7794 7C 7.579496e-03 seoa3344 7C 4.55e-04 fcrcO345 7C 7.916289e-03 ncrb6087 7C 4.71e-04 seocl025 7C 7.956279e-03 ncrO438 7C 5.19e-04 seoa4524 7C 8.152986e-03 mioa9033 7C 5.75e-04 ncrc3080 7C 8.155422e-03 fcrb5840 7C 8.51e-04 ncr0045 7C 8.177432e-03 ncrO761 7C 1.385274e-03 miob0167 7C 8.26841e-03 fcrc2455 7C 1.385374e-03 ncrcO185 7C 8.347022e-03 fcr3714 7C 1.392876e-03 seob8321 7C 8.40953e-03 miob2227 7C 1.414e-03 mioc7542 7C 8.570606e-03 mioc7471 7C 1.435651e-03 fcrb9841 7C 8.62362e-03 fcrl724 7C 1.477911e-03 ncr9123 7C 9.047244e-03 miob8704 7C 1.797807e-03 mioa9630 7C 9.267681e-03 ncr9175 7C 1.845832e-03 mioc0317 7C 9.415542e-03 seoblδOl 7C 1.975048e-03 ncrbO513 7C 9.541507e-03 seob5778 7C 2.02244e-03 fcrc0604 7C 9.581387e-03 fcrbl720 7C 2.141799e-03 seoc4145 7C 9.588973e-03 fcrbl920 7C 2.220558e-03 seob9241 7C 9.629249e-03 hfcr2295 7C 2.485914e-03 miob4221 7C 9.796545e-03 ncrc4757 7C 2.527142e-03 miob4956 7C 0.01 fcrb6734 7C 2.588863e-03 hfcrO521 7C 0.01 miob3456 7C 2.79378e-03 fcrb4280 7C 0.01 seob9552 7C 3.172751e-03 seobll45 7C 0.01 "•iftibc26S4-" ....O.;trr seoal552 7C O.Ol seoa2641 7C 0.01 seoa5848 7C O.Ol miod2388 7C 0.01 fcr4634 7C O.Ol fcrbl807 1C 0.01 mioa9604 7C O.Ol fcrO843 1C 0.01 ncr3718 7C O.02 fcrb7944 1C 0.01 seob5099 7C O.02 fcrb5775 1C 0.01 seoc4288 7C O.02 fcrb5202 1C 0.01 fcrcO496 7C O.02 miob6391 1C 0.01 mioc5643 7C O.02 ncrc5959 1C 0.01 miobl946 7C O.02 fcr3525 1C 0.01 seob7180 7C O.02 miocl928 1C 0.01 seob3244 7C O.02 seob6041 1C 0.01 mioa5540 7C O.02 miod0878 1C 0.01 fcrb2160 7C O.02 miodl714 1C 0.01 miob2163 7C O.02 fcrb9914 1C 0.01 miob2589 7C O.02 fcrb3808 1C 0.01 ncrc5417 7C O.02 fcrb3704 1C 0.01 fcrb3476 7C O.02 fcr2842 1C 0.01 seob0038 7C O.02 seob5478 1C 0.01 fcrb2536 7C O.02 seoa3102 1C 0.01 mioc3413 7C O.02 seocl023 1C 0.01 mioa4674 7C O.02 ncrc6871 1C 0.01 hfcrO439 7C O.02 hfcr2732 1C 0.01 seob8212 7C O.02 seoa5698 1C 0.01 seocl664 7C O.02 fcrb9588 1C 0.01 seoc3883 7C O.02 miod3546 1C 0.01 miod3826 1C O.02 fcrbl690 1C 0.01 fcrb6871 1C O.02 ncrcl952 1C 0.01 fcrb7487 1C O.02 fcrb3119 1C 0.01 seoa5843 1C O.02 fcrb8061 1C 0.01 fcrb6661 1C O.02 miob9336 1C 0.01 mioa8987 1C O.02 ncr4946 1C 0.01 fcrb2592 1C O.02 seoa8443 1C 0.01 miob8578 1C O.02 mioc5664 1C 0.01 fcr2897 1C O.02 seoc0523 1C 0.01 ncr4859 1C O.02 ncrb0550 1C 0.01 fcrb5259 1C O.02 miod0355 1C 0.01 miob9284 1C O.02 mioc9008 1C 0.01 seoa5746 1C O.02 seoa2391 1C 0.01 mioa7140 1C O.02 fcrb2041 1C 0.01 fcr5141 1C O.02 miodl030 1C 0.01 seoall04 1C O.02 ncrcl050 1C 0.01 fcrb8341 1C O.02 seob2689 1C 0.01 miob8211 1C O.02 fcrc2099 1C 0.01 ncrOOlδ 1C O.02 seob7474 1C 0.01 miocll22 1C O.02 ncrc6479 1C 0.01 ncrO266 1C O.02 seoa4598 1C 0.01 miocl940 1C O.02 seoa3121 1C 0.01 fcrcO549 1C O.02 fcr0788 1C 0.01 inioc4552 1C O.02 ncr3843 1C 0.01 ncrc4575 1C O.02 mioa5045 1C 0.01 fcrb5077 1C O.02 ncr6920 1C 0.01 miob4975 1C O.02 seoc0276 1C 0.01 seob4492 1C O.02 fcrc2807 1C 0.01 fcrb7833 1C O.02 ncrcO217 1C 0.01 fcrb4995 1C O.02 ncrb8821 1C 0.01 seob4191 1C O.02 ncr4118 1C 0.01 seoal776 1C O.02 seoc6169 1C 0.01 miob7913 1C O.02 mioal775 1C 0.01 miob2918 1C O.02 seoa0014 1C 0.01 fcrbO359 1C O.03 ncrc8892 1C 0.01 ncrcl589 1C O.03 fcrb9520 1C 0.01 mioa0482 1C O.03 fcrb4415 1C 0.01 mioc2394 1C O.03 -s^oa4327"il--s --7C "~"*O::Ό3' ncr3141 7C 0..04 seoa6175 7C 0.,03 ncrc3129 7C 0.04 miob4413 7C 0.03 miob3928 7C 0.04 fcrc5071 7C 0.03 raioc5198 7C 0.,04 miob3552 1C 0.03 miod5275 7C 0.04 seoal857 7C 0.03 ncrb8063 7C 0.04 fcrb5902 7C 0.03 fcrb2495 7C 0.04 seob4216 7C 0.03 fcrc6997 7C 0.04 ncr2930 7C 0.03 fcrb4321 7C 0.04 hfcr3180 7C 0.03 fcrc0771 7C 0.04 miob7276 7C 0.03 seoal559 7C 0.04 fcr3181 7C 0.03 ncrc4153 7C 0.04 ncrl007 7C 0.03 miod2834 7C 0.04 mioc7433 7C 0.03 mioc3716 7C 0.04 fcrb4696 7C 0.03 ncrcO336 7C 0.04 mioc2596 1C 0.03 mioc3906 7C 0.04 seoc3836 1C 0.03 ncrc9280 7C 0.04 ncrb4428 1C 0.03 seob6773 7C 0.04 ncr7151 1C 0.03 ncrc9023 7C 0.04 rαiob9441 1C 0.03 ncrc4263 7C 0.04 miob9710 1C 0.03 fcrb5675 7C 0.04 ncrcl687 1C 0.03 mioa5461 7C 0.04 seoa5392 1C 0.03 ncrb8530 7C 0.04 seoa5151 1C 0.03 seoc7203 7C 0.04 fcrO986 1C 0.03 seob8660 7C 0.04 ncrb2091 1C 0.03 fcrb4400 7C 0.04 ncrcO174 1C 0.03 ncrb7600 7C 0.04 fcr2106 1C 0.03 miob9163 7C 0.04 ncrb6282 1C 0.03 fcrb6620 7D 1.77e-04 ncrcO457 7C 0.03 miob4574 7D 1.83e-04 seoa5743 7C 0.03 mioc7444 7D 2.56e-04 seob4079 7C 0.03 ncrc5150 7D 3.13e-04 seob6028 1C 0.03 seoa3344 7D 4.55e-04 miodl825 7C 0.03 ncrb6087 7D 4.71e-04 fcrcO559 7C 0.03 ncrO438 7D 5.19e-04 seoa6144 7C 0.03 mioa9033 7D 5.75e-04 mioa2475 7C 0.03 fcrb5840 7D 8.51e-04 fcrb6031 7C 0.03 ncrO761 7D 1.385274e-03 fcrc4692 1C 0.03 fcrc2455 7D 1.385374e-03 mioc6298 7C 0.03 fcr3714 7D 1.392876e-03 ncrc9022 7C 0.03 miob2227 7D 1.414e-03 ncrc8865 7C 0.03 mioc7471 7D 1.435651e-03 miob3560 7C 0.03 fcrl724 7D 1.477911e-03 fcrc0308 7C 0.03 miob8704 7D 1.797807e-03 mioc5661 7C 0.03 ncr9175 7D 1.845832e-03 seob6492 7C 0.03 seoblδOl 7D 1.975048e-03 ncr3435 7C 0.03 seob5778 7D 2.02244e-03 fcrb5761 7C 0.04 fcrbl720 7D 2.141799e-03 seob2148 7C 0.04 fcrbl920 7D 2.220558e-03 fcrb9475 7C 0.04 hfcr2295 7D 2.485914e-03 seob2728 7C 0.04 ncrc4757 7D 2.527142e-03 miob5012 7C 0.04 fcrb6734 7D 2.588863e-03 seoc5228 7C 0.04 miob3456 7D 2.79378e-03 seob8853 7C 0.04 seob9552 7D 3.172751e-03 ncrl437 7C 0.04 fcrl657 7D 3.33072e-03 ncrb7586 7C 0.04 seob6879 7D 3.527558e-03 ncrcllO2 7C 0.04 ncr3869 7D 3.790137e-03 fcr2293 7C 0.04 miob0180 7D 3.857035e-03 mioc2662 7C 0.04 hfcrββll 7D 4.095694e-03 ncrlO55 7C 0.04 seoc3426 7D 4.1094e-03 fcrb9871 7C 0.04 miob0636 7D 4.169115e-03 fcrb3298 7C 0.04 mioc3523 7D 4.183983e-03 ncr2575 7C 0.04 ncrcl608 7D 4.401747e-03 ncr9105 7C 0.04 ncrcO729 7D 4.516067e-03 Ml.ncJt07M-»■'••» "L
Figure imgf000376_0001
miod0878 7D 0.01 seobl319 7D 4.576012e-03 miodl714 7D 0.01 mioa9246 7D 4.789915e-03 fcrb9914 7D 0.01 miocl978 7D 4.797667e-03 fcrb3808 7D 0.01 miodl908 7D 4.876308e-03 fcrb3704 7D 0.01 seob5032 7D 4.975292e-03 fcr2842 7D 0.01 miob4378 7D 5.078973e-03 seob5478 7D 0.01 fcrb3330 7D 5.374198e-03 seoa3102 7D 0.01 seoc4748 7D 5.469684e-03 seocl023 7D 0.01 mioc3962 7D 5.503304e-03 ncrc6871 7D 0.01 mioc3958 7D 5.585154e-03 hfcr2732 7D 0.01 fcrcO367 7D 5.737407e-03 seoa5698 7D 0.01 fcrb2754 7D 5.762075e-03 fcrb9588 7D 0.01 miob9087 7D 5.972077e-03 miod3546 7D 0.01 fcrb5187 7D 5.978955e-03 fcrbl690 7D 0.01 seob3533 7D 6.085375e-03 ncrcl952 7D 0.01 mioa9792 7D 6.087453e-03 fcrb3119 7D 0.01 hfcr3197 7D 6.106227e-03 fcrb8061 7D 0.01 seoc2510 7D 6.174005e-03 miob9336 7D 0.01 mioal380 7D 6.442511e-03 ncr4946 7D 0.01 ncrc5716 7D 6.68224e-03 seoa8443 7D 0.01 ncrc9469 7D 6.77346e-03 mioc5664 7D 0.01 fcrb4413 7D 6.787171e-03 ncrb0550 7D 0.01 ncrc9633 7D 6.817579e-03 miod0355 7D 0.01 miob0189 7D 7.012368e-03 seob7952 7D 0.01 seoc0394 7D 7.095938e-03 ncr9975 7D 0.01 ncr8910 7D 7.351684e-03 mioc9008 7D 0.01 fcrb7255 7D 7.43613e-03 seoa2391 7D 0.01 miob7794 7D 7.579496e-03 fcrb2041 7D 0.01 fcrcO345 7D 7.916289e-03 miodl030 7D 0.01 seocl025 7D 7.956279e-03 ncrcl050 7D 0.01 seoa4524 7D 8.15298βe-03 seob2689 7D 0.01 ncrc3080 7D 8.155422e-03 ncrc6479 7D 0.01 ncr0045 7D 8.177432e-03 seoa4598 7D 0.01 miob0167 7D 8.26841e-03 seoa3121 7D 0.01 ncrcO185 7D 8.347022e-03 fcrO788 7D 0.01 seob8321 7D 8.40953e-03 ncr3843 7D 0.01 mioc7542 7D 8.570606e-03 mioa5045 7D 0.01 fcrb9841 7D 8.62362e-03 ncr6920 7D 0.01 ncr9123 7D 9.047244e-03 seoc0276 7D 0.01 mioa9630 7D 9.267681e-03 fcrc2807 7D 0.01 mioc0317 7D 9.415542e-03 ncrcO217 7D 0.01 ncrbO513 7D 9.541507e-03 ncrb8821 7D 0.01 fcrc0604 7D 9.581387e-03 ncr4118 7D 0.01 seoc4145 7D 9.588973e-03 seoc6169 7D 0.01 seob9241 7D 9.629249e-03 seoa0014 7D 0.01 miob4221 7D 9.796545e-03 ncrc8892 7D 0.01 miob4956 7D 0.01 fcrb9520 7D 0.01 hfcrO521 7D 0.01 fcrb4415 7D 0.01 fcrb4280 7D 0.01 seoal552 7D 0.01 seobll45 7D 0.01 seoa5848 7D 0.01 mioc2694 7D 0.01 fcr4634 7D 0.01 seoa2641 7D 0.01 mioa9604 7D 0.01 miod2388 7D 0.01 ncr3718 7D 0.02 fcrO843 7D 0.01 seob5099 7D 0.02 fcrb7944 7D 0.01 seoc4288 7D 0.02 seobOOβl 7D 0.01 fcrcO496 7D 0.02 fcrb5775 7D 0.01 mioc5643 7D 0.02 fcrb5202 7D 0.01 seob7180 7D 0.02 miob6391 7D 0.01 seob3244 7D 0.02 ncrc5959 7D 0.01 mioa5540 7D 0.02 fcr3525 7D 0.01 fcrb2160 7D 0.02 miocl928 7D 0.01 miob2163 7D 0.02 seob6041 7D 0.01 miob2589 7D 0.02 ""-■' "W '-"•U".'0'2 seoa5392 7D 0.03 fcrb3476 7D 0.02 seoa5151 7D 0.03 seob0038 7D 0.02 fcrO98β 7D 0.03 fcrb2536 7D 0.02 ncrb2091 7D 0.03 mioc3413 7D 0.02 ncrcO174 7D 0.03 mioa4674 7D 0.02 fcr2106 7D 0.03 hfcrO439 7D 0.02 ncrb6282 7D 0.03 seob8212 7D 0.02 ncrcO457 7D 0.03 seocl664 7D 0.02 seoa5743 7D 0.03 seoc3883 7D 0.02 seob4079 7D 0.03 miod3826 7D 0.02 seob6028 7D 0.03 fcrb6871 7D 0.02 miodl825 7D 0.03 fcrb7487 7D 0.02 fcrcO559 7D 0.03 seoa5843 7D 0.02 mioa6093 7D 0.03 fcrb6661 7D 0.02 seoa6144 7D 0.03 mioa8987 7D 0.02 mioa2475 7D 0.03 fcrb2592 7D 0.02 fcrb6031 7D 0.03 miob8578 7D 0.02 fcrc4692 7D 0.03 fcr2897 7D 0.02 seoa2141 7D 0.03 ncr4859 7D 0.02 ncrc3161 7D 0.03 fcrb5259 7D 0.02 mioc6298 7D 0.03 miob9284 7D 0.02 ncrc9022 7D 0.03 seoa5746 7D 0.02 ncrc8865 7D 0.03 mioa7140 7D 0.02 miob3560 7D 0.03 fcr5141 7D 0.02 fcrc0308 7D 0.03 seoall04 7D 0.02 seob4795 7D 0.03 miob8211 7D 0.02 ncr3435 7D 0.03 ncr0018 7D 0.02 seob4972 7D 0.04 ncrO266 7D 0.02 fcrb5761 7D 0.04 miocl940 7D 0.02 mioa6556 7D 0.04 fcrcO549 7D 0.02 miob5012 7D 0.04 mioc4552 7D 0.02 seoc5228 7D 0.04 fcrb5077 7D 0.02 seoa5366 7D 0.04 miob4975 7D 0.02 seob8853 7D 0.04 seob4492 7D 0.02 ncrl437 7D 0.04 ncrb8607 7D 0.02 fcr2293 7D 0.04 fcrb7833 7D 0.02 mioc2662 7D 0.04 fcrb4995 7D 0.02 ncrlO55 7D 0.04 seob4191 7D 0.02 fcrb3298 7D 0.04 seoal776 7D 0.02 ncr2575 7D 0.04 miob7913 7D 0.02 ncr9105 7D 0.04 miob2918 7D 0.02 ncr3141 7D 0.04 fcrbO359 7D 0.03 miob3928 7D 0.04 ncrcl589 7D 0.03 mioc5198 7D 0.04 mioa0482 7D 0.03 hfcr2820 7D 0.04 mioc2394 7D 0.03 ncrb8063 7D 0.04 seoa4327 7D 0.03 seob0991 7D 0.04 seoa6175 7D 0.03 fcrb2495 7D 0.04 miob4413 7D 0.03 fcrcβ997 7D 0.04 fcrc5071 7D 0.03 fcrb4321 7D 0.04 fcrb2356 7D 0.03 fcrcO771 7D 0.04 seoal857 7D 0.03 seoal559 7D 0.04 seob4216 7D 0.03 ncrc9280 7D 0.04 ncr2930 7D 0.03 seob6773 7D 0.04 fcr3181 7D 0.03 ncrc9023 7D 0.04 ncrl007 7D 0.03 ncrc4263 7D 0.04 fcrb4696 7D 0.03 mioa5461 7D 0.04 mioc2596 7D 0.03 ncrb8530 7D 0.04 seoc3836 7D 0.03 seoc7203 7D 0.04 ncrb4428 7D 0.03 fcrb4400 7D 0.04 ncr7151 7D 0.03 miob9163 7D 0.04 miob9441 7D 0.03 seoa9642 7D 0.04 miob9710 7D 0.03 rαioc7444 7E 2.84e-04 ncrcl687 7D 0.03 fcrb5840 7E 2.9e-04 ■•■•fcrc24!'35-"- fcrb2592 7E 6.650881e-03 miob8704 7E 3.62e-04 fcrb8061 7E 6.707705e-03 hfcr2295 7E 6.84e-04 ncrOOlδ 7E 6.815386e-03 ncrcO729 7E 9.66e-04 fcrb2160 7E 7.080557e-03 ncrO791 7E 1.004132e-03 hfcrO439 7E 7.265513e-03 mioa9246 7E 1.030127e-03 seob5478 7E 7.35909e-03 ncr9175 7E 1.15365e-03 miob4975 7E 7.545395e-03 hfcr3197 7E 1.334495e-03 seob8807 7E 7.623702e-03 fcrb4413 7E 1.512275e-03 seocl023 7E 7.656351e-03 fcrb3330 7E 1.538504e-03 miod6560 7E 7.670992e-03 ncrc5716 7E 1.541187e-03 fcrb6871 7E 7.735804e-03 fcrl657 7E 1.733038e-03 mioa4674 7E 7.735804e-03 ncrc9469 7E 1.756246e-03 fcr4634 7E 7.920675e-03 fcrb2754 7E 1.806486e-03 seoa0014 7E 7.920675e-03 miob0636 7E 1.838326e-03 fcrb5259 7E 7.930389e-03 seob8321 7E 1.949298e-03 seob4191 7E 7.998537e-03 seoc0394 7E 1.949298e-03 seoal552 7E 8.032912e-03 ncrbO513 7E 2.189358e-03 fcrc5071 7E 8.290755e-03 fcrbl920 7E 2.530312e-03 ncrc5417 7E 8.322313e-03 mioa9033 7E 2.603375e-03 fcrb8542 7E 8.458985e-03 ncrcO185 7E 2.830703e-03 seoc2510 7E 8.539969e-03 seoa2641 7E 2.853121e-03 miodl030 7E 8.596627e-03 seobl319 7E 2.864895e-03 fcrb3476 7E 8.741568e-03 ncrc5959 7E 3.174673e-03 seoall04 7E 8.741568e-03 ncrc5150 7E 3.19704e-03 fcrb4415 7E 8.886086e-03 miob4956 7E 3.255589e-03 seob9552 7E 8.953387e-03 mioc3523 7E 3.258033e-03 mioa9630 7E 8.968651e-03 fcrb7944 7E 3.286402e-03 fcrcO559 7E 0.01 fcrb3704 7E 3.434732e-03 ncrc9633 7E 0.01 miob4221 7E 3.434732e-03 ncr3718 7E 0.01 fcrb5775 7E 3.436382e-03 ncrO761 7E 0.01 miod0878 7E 3.502172e-03 miodl291 7E 0.01 miob4574 7E 3.51632e-03 ncrcl050 7E 0.01 seoa3102 7E 3.925908e-03 fcrb8485 7E 0.01 miod0355 7E 3.969358e-03 fcrb4995 7E 0.01 ncrc6479 7E 4.043396e-03 mioc0317 7E 0.01 fcrb3808 7E 4.159535e-03 seoa2087 7E 0.01 fcrO788 7E 4.266475e-03 fcrb9520 7E 0.01 fcrbl690 7E 4.266475e-03 ncrl437 7E 0.01 fcrb9588 7E 4.344385e-03 seob6879 7E 0.01 seoa2391 7E 4.406167e-03 seob5081 7E 0.01 fcrl724 7E 4.506649e-03 fcr3880 7E 0.01 miocl928 7E 4.7287e-03 seoa5848 7E 0.01 fcrbl720 7E 4.8283e-03 mioa9604 7E 0.01 fcr3714 7E 4.924205e-03 mioa9792 7E 0.01 seob2689 7E 5.142134e-03 seoa5577 7E 0.01 ncrb0550 7E 5.278292e-03 ncrcO217 7E 0.01 fcrc2099 7E 5.410153e-03 fcrbl807 7E 0.01 fcrb5187 7E 5.560147e-03 miocOlβl 7E 0.01 seoa5698 7E 5.600034e-03 fcrb2495 7E 0.01 ncr3869 7E 5.84742e-03 seoa6144 7E 0.01 seob8104 7E 5.84742e-03 seob7474 7E 0.01 ncrc8892 7E 5.855135e-03 hfcrββll 7E 0.01 seob8212 7E 5.958133e-03 fcrb7487 7E 0.01 seoa5843 7E 6.103701e-03 ncr2575 7E 0.01 fcrb9841 7E 6.15574e-03 fcrb6734 7E 0.01 seocl025 7E 6.156515e-03 ncrcl608 7E 0.01 miob2163 7E 6.163764e-03 mioc7764 7E 0.01 seobll45 7E 6.478216e-03 ncrc3529 7E 0.01 seocl664 7E 6.478216e-03 mioa6174 7E 0.01 fcr2897 7E 6.484868e-03 seoa9150 7E 0.01 ncrc3733 7E 6.532461e-03 miod2330 7E 0.01 seoc3883 7E 6.616268e-03 seoa5554 7E 0.01 mioc7471 7E 6.642579e-03 seoa8945 7E 0.01 ;:fcrl-76βir ™'!"- "-0-;Ol ncrcl952 7E 0.02 seob7941 7E 0.01 miocl978 7E 0.02 seob4079 7E 0.01 seob5379 7E 0.02 miob3456 7E 0.01 ncrc9023 7E 0.02 miob5012 7E 0.01 mioc3081 7E 0.02 fcrb4400 7E 0.01 seob9241 7E 0.02 mioc7360 7E 0.01 seobl316 7E 0.02 seoal559 7E 0.01 mioc3413 7E 0.02 seob7180 7E 0.01 seob8817 7E 0.02 fcrbl527 7E 0.01 ncrc9280 7E 0.02 ncrc3080 7E 0.01 fcrO843 7E 0.02 fcrb7871 7E 0.01 mioc5141 7E 0.02 mioa4484 7E 0.01 fcrb6661 7E 0.02 seob4492 7E 0.01 miob4760 7E 0.02 ncrO252 7E 0.01 miod3826 7E 0.02 ncrO836 7E 0.01 ncrb6087 7E 0.02 seob6856 7E 0.01 ncrc6264 7E 0.02 mioa2475 7E 0.01 hfcr2250 7E 0.02 ncrc6659 7E 0.01 ncrc4575 7E 0.02 seob4689 7E 0.01 mioa0890 7E 0.02 mioc6970 7E 0.01 ncrc0174 7E 0.02 ncrc3520 7E 0.01 seoa2815 7E 0.02 mioc7433 7E 0.01 fcrb2308 7E 0.02 fcrb5761 7E 0.01 miob2227 7E 0.02 hfcr4485 7E 0.01 ncrc9739 7E 0.02 seob9122 7E 0.01 mioal055 7E 0.02 seoblδOl 7E 0.01 miob7985 7E 0.02 mioc4731 7E 0.01 seoc5010 7E 0.02 fcr2861 7E 0.01 miob7276 7E 0.02 ncrc4219 7E 0.01 miob4673 7E 0.02 seoal776 7E 0.01 mioa8852 7E 0.02 fcrc4408 7E 0.01 miob2210 7E 0.02 mioc6298 7E 0.01 ncrbO328 7E 0.02 miob9688 7E 0.01 seoa5094 7E 0.02 seoa9916 7E 0.01 rαioa8970 7E 0.02 seoc5228 7E 0.01 fcrcO549 7E 0.02 ncrc3408 7E 0.01 ncr7178 7E 0.02 seob6773 7E 0.01 seob7346 7E 0.02 fcrb6968 7E 0.01 ncrb8343 7E 0.02 ncrb8530 7E 0.01 fcrb6031 7E 0.02 seobl891 7E 0.01 fcrbl529 7E 0.02 miob7109 7E 0.01 ncr4378 7E 0.02 mioa5902 7E 0.01 fcrcl834 7E 0.02 fcrb2203 7E 0.01 seoa5746 7E 0.02 seoa2962 7E 0.01 seob8241 7E 0.02 seoa3415 7E 0.01 seob6535 7E 0.02 miob8932 7E 0.01 seoc4748 7E 0.02 fcrb8393 7E 0.01 fcrb2460 7E 0.02 miob9304 7E 0.02 fcrb3521 7E 0.02 ncrcO829 7E 0.02 seoa6393 7E 0.02 seocl230 7E 0.02 mioc7782 7E 0.02 fcrb4696 7E 0.02 seocll67 7E 0.02 miob3928 7E 0.02 miob3618 7E 0.02 mioc4888 7E 0.02 itιioa8778 7E 0.02 ncr3793 7E 0.02 mioa8824 7E 0.02 ncrclO32 7E 0.02 mioal584 7E 0.02 seoa5392 7E 0.02 itιiob8515 7E 0.02 seob7729 7E 0.02 seoc3394 7E 0.02 fcrb2041 7E 0.02 ncrc8937 7E 0.02 seob5080 7E 0.02 miocl995 7E 0.02 fcrb9135 7E 0.02 seoa6598 7E 0.02 seob4030 7E 0.02 fcrb4280 7E 0.02 fcrc2807 7E 0.02 seob6872 7E 0.02 seob6096 7E 0.02 seob9334 7E 0.02 ■•-nete68"7.T" .........7E •'■-■ "Θ".ϋr2 seob6386 7E 0.03 ncrc4135 7E 0.02 πιiob6373 7E 0.03 hfcr6700 7E 0.02 seoa9870 7E 0.03 ncrc7040 7E 0.02 ncrc5768 7E 0.03 mioa2303 7E 0.02 ncrc9557 7E 0.03 miobl946 7E 0.02 ncrc9483 7E 0.03 seob3533 7E 0.02 fcrb2102 7E 0.03 fcrb6620 7E 0.02 ncr8041 7E 0.03 mioc7910 7E 0.02 mioa9891 7E 0.03 seoc2518 7E 0.02 seob4303 7E 0.03 mioc3958 7E 0.02 fcrb2849 7E 0.03 seob5764 7E 0.02 fcrb6896 7E 0.03 miob2833 7E 0.02 miob5855 7E 0.03 mioc3716 7E 0.02 ncrc9351 7E 0.03 seoa7509 7E 0.02 ncrc9232 7E 0.03 ncrc4757 7E 0.02 mioa4318 7E 0.03 seoa7212 7E 0.03 mioa4077 7E 0.03 seob4001 7E 0.03 fcr4308 7E 0.03 fcr7386 7E 0.03 ncr7826 7E 0.03 fcrb6406 7E 0.03 seob5726 7E 0.03 miob4368 7E 0.03 ncr8420 7E 0.04 fcrb8162 7E 0.03 seob8972 7E 0.04 seob0442 7E 0.03 fcrb6834 7E 0.04 seoc2681 7E 0.03 mioc2290 7E 0.04 mioc5336 7E 0.03 mioc5561 7E 0.04 mioa3588 7E 0.03 ncrb3301 7E 0.04 miod4564 7E 0.03 seoc2135 7E 0.04 fcrb9871 7E 0.03 miob9882 7E 0.04 fcrb8465 7E 0.03 miod0845 7E 0.04 seob2797 7E 0.03 ncrb8821 7E 0.04 seoa4181 7E 0.03 miod6845 7E 0.04 fcrb8674 7E 0.03 mioc0315 7Ξ 0.04 ncr3035 7E 0.03 ncr0045 7E 0.04 seoa3852 7E 0.03 seoal065 7E 0.04 miob0167 7E 0.03 fcrc6826 7E 0.04 seoc2584 7E 0.03 seob5418 7E 0.04 fcrO793 7E 0.03 seob7409 7E 0.04 miob3044 7E 0.03 seoa3908 7E 0.04 mioal655 7Ξ 0.03 fcrbl731 7E 0.04 fcrO986 7E 0.03 seob8031 7E 0.04 fcrb4360 7E 0.03 hfcr3445 7E 0.04 mioc3962 7E 0.03 mioa5468 7E 0.04 fcrb6106 7E 0.03 fcrc5831 7E 0.04 fcr3043 7E 0.03 mioa6659 7E 0.04 mioa2377 7E 0.03 mioa8925 7E 0.04 seob3370 7E 0.03 mioc8945 7E 0.04 seoa4366 7E 0.03 ncrc4600 7E 0.04 fcrb9680 7E 0.03 miodl316 7E 0.04 seoa5992 7E 0.03 ncr5522 7E 0.04 fcrl404 7E 0.03 ncrb0054 7E 0.04 seob3699 7E 0.03 ncrO634 7E 0.04 fcrc6335 7E 0.03 mioa9492 7E 0.04 miocl963 7E 0.03 miod3079 7E 0.04 ncr9096 7E 0.03 fcrb4275 7E 0.04 ncrO638 7E 0.03 hfcr3180 7E 0.04 mioc4766 7E 0.03 miob9233 7E 0.04 fcrbl787 7E 0.03 seoa3429 7E 0.04 miod7442 7E 0.03 seob7022 7E 0.04 ncrc4263 7E 0.03 miod0676 7E 0.04 seob2169 7E 0.03 miod4867 7E 0.04 fcrb5202 7E 0.03 miob2855 7E 0.04 miob9284 7E 0.03 seob9302 7E 0.04 seoa5151 7E 0.03 seoa5382 7E 0.04 miob9718 7E 0.03 fcrb5077 7E 0.04 Veδa95'efi:-■ :'-:: -¥E O--.&-4" --• fcrbl550 7H 1.744779e-03 seob5465 7E 0.04 seobl793 7H 1.746788e-03 seob9872 7E 0.04 seoa8642 7H 1.818748e-03 seoc2224 7E 0.04 mioa0243 7H 1.821004e-03 seoc3690 7E 0.04 ncr7768 7H 1.970833e-03 seoclββl 7E 0.04 ncr7753 7H 1.973156e-03 ncrl912 7E 0.04 fcrO689 7H 2.095317e-03 seoc3426 7E 0.04 ncr5939 7H 2.197427e-03 seoa4670 7E 0.04 seob0674 7H 2.200729e-03 ncrcllO3 7E 0.04 hfcr3022 7H 2.206323e-03 seoa9482 7E 0.04 hfcr6993 7H 2.229516e-03 miob9010 7E 0.04 mioa9510 7H 2.272997e-03 mioc4103 7E 0.04 mioblOOl 7H 2.272997e-03 fcrb5902 7E 0.04 ncrb8253 7H 2.272997e-03 fcrb7833 7E 0.04 seoa0913 7H 2.272997e-03 mioc3332 7E 0.04 seoa3207 7H 2.47998e-03 seocl957 7E 0.04 seoa8945 7H 2.524774e-03 seoc2589 7E 0.04 ncrc4991 7H 2.855331e-03 ncrb6282 7E 0.04 hfcr6774 7H 2.898816e-03 fcrc6413 7E 0.04 fcrb4367 7H 3.033643e-03 fcr2842 7E 0.04 mioa6130 7H 3.033643e-03 ncrc6941 7E 0.04 miob4836 7H 3.033643e-03 ncrb8539 7E 0.04 seoa3143 7H 3.033643e-03 seob4612 7E 0.04 seob4696 7H 3.033643e-03 ncrcO292 7E 0.04 ncrl912 7H 3.052263e-03 fcrb5198 7E 0.04 ncr7151 7H 3.075649e-O3 rαioc4747 7E 0.04 seoa6620 7H 3.154417e-03 mioc5661 7E 0.04 miob6870 7H 3.165971e-03 seob6560 7E 0.04 seoa0743 7H 3.165971e-03 fcrb6740 7E 0.04 seoal979 7H 3.165971e-03 ncrb3314 7E 0.04 ncrc6953 7H 3.209568e-03 mioa0294 7E 0.04 mioa5059 7H 3.287457e-03 mioc6956 7E 0.04 fcr4084 7H 3.620032e-03 seoa4640 7E 0.04 ncr2160 7H 3.659353e-03 seob3545 7E 0.04 ncrb8689 7H 3.664539e-03 seocl051 7E 0.04 seoa0844 7H 3.799382e-03 mioc7542 7E 0.04 seoa3088 7H 3.807614e-03 hfcr2657 7E 0.04 seob2148 7H 3.808191e-03 ncrc4885 7E 0.04 seoa0729 7H 3.868396e-03 seoa8229 7E 0.04 mioal097 7H 4.002165e-03 fcrb2554 7H 1.07e-04 ncrc4654 7H 4.002165e-03 seobl981 7H 1.16e-04 seoa6923 7H 4.002165e-03 seob0261 7H 2.48e-04 fcr0059 7H 4.081208e-03 ncr7668 7H 3.42e-04 ncrcllll 7H 4.153399e-03 seob3670 7H 6.03e-04 fcr6395 7H 4.209092e-03 hfcr6256 7H 7.36e-04 seob3204 7H 4.269661e-03 ncr5149 7H 7.85e-04 seoal940 7H 4.313312e-03 seoa2063 7H 7.9e-04 seoa5090 7H 4.496516e-03 fcr7070 7H 9.21e-04 seoa0869 7H 4.525435e-O3 ncrb8670 7H 1.012853e-03 fcr6386 7H 4.599e-03 seob5064 7H 1.227443e-03 fcrl372 7H 4.619861e-03 seob3313 7H 1.22901e-03 mioa0183 7H 4.629944e-03 ncrO429 7H 1.239629e-03 ncr7066 7H 4.691957e-03 mioa0869 7H 1.243955e-03 seoa0783 7H 4.707784e-03 ncrc6817 7H 1.346072e-03 fcr5190 7H 4.713307e-03 fcrb5424 7H 1.375646e-03 mioa2528 7H 4.723266e-03 fcrbl733 7H 1.394885e-03 seob3923 7H 4.727937e-03 seoall32 7H 1.460318e-03 seoa0085 7H 4.797498e-03 seob4612 7H 1.612891e-03 fcrbl494 7H 4.878e-03 fcrll73 7H 1.614256e-03 mioa0577 7H 5.082016e-03 fcrb3024 7H 1.653831e-03 hfcrO618 7H 5.164571e-03 miob6713 7H 1.653831e-03 seoal253 7H 5.203408e-03 ncrcOlOO 7H 1.665894e-03 fcr0056 7H 5.22227e-03 seoa9945 7H 1.682301e-03 fcrb5422 7H 5.22227e-03 τϊct258-3 '■"-""7Η iL:;:;15Y22227''g-03 miob4058 7H 8.775787e-03 ncrb2092 7H 5.22227e-03 ncr4332 7H 8.916581e-03 ncrb8820 7H 5.22227e-03 seoal067 7H 9.014358e-03 ncrc6846 7H 5.22227e-03 seoa8783 7H 9.08216e-03 seob4273 7H 5.22227e-03 mioa9506 7H 9.085175e-03 seob4363 7H 5.22227e-03 mioal708 7H 9.103454e-03 seob4427 7H 5.22227e-03 seob4555 7H 9.185799e-03 ncr9502 7H 5.282745e-03 fcr3367 7H 9.208963e-03 fcr6016 7H 5.3249e-03 ncrc9709 7H 9.40138e-03 mioa0380 7H 5.332482e-03 fcrO776 7H 9.450352e-03 fcrb2744 7H 5.389299e-03 mioa0363 7H 9.450352e-03 seob5751 7H 5.571231e-03 miob6372 7H 9.450352e-03 fcrO469 7H 5.580897e-03 miob6771 7H 9.450352e-03 fcrb5761 7H 5.636668e-03 ncrO223 7H 9.450352e-03 hfcrl234 7H 5.636668e-03 ncrc9022 7H 9.450352e-03 ncrc3840 7H 5.656048e-03 seoal672 7H 9.450352e-03 mioa0535 7H 5.674734e-03 seoa5234 7H 9.450352e-03 seob0321 7H 6.009287e-03 seoa6495 7H 9.450352e-03 seoa3219 7H 6.01363e-03 seoa6718 7H 9.450352e-03 seoal747 7H 6.029416e-03 seoa9373 7H 9.450352e-03 seob5418 7H 6.258276e-03 seoa9421 7H 9.450352e-03 mioa3673 7H 6.356021e-03 seob6703 7H 9.450352e-03 fcr7026 7H 6.36597e-03 fcrO253 7H 9.482077e-03 seoal392 7H 6.552582e-03 fcr5075 7H 9.65907e-03 fcrO551 7H 6.608617e-03 ncrb5940 7H 9.851067e-03 seoa6867 7H 6.615019e-03 hfcr6992 7H 9.872218e-03 seoal069 7H 6.734347e-03 seoa5777 7H 0.01 fcr5112 7H 6.743796e-03 fcrbl496 7H 0.01 fcrb2189 7H 6.743796e-03 seob0787 7H 0.01 ncrc4296 7H 6.743796e-03 ncrc6005 7H 0.01 ncrc6367 7H 6.743796e-03 fcrb4696 7H 0.01 seob0629 7H 6.743796e-03 mioa3649 7H 0.01 seob7499 7H 6.743796e-03 miob6881 7H 0.01 mioa3963 7H 6.845147e-03 mioa9670 7H 0.01 ncr3944 7H 7.147067e-03 seob5624 7H 0.01 seoa0486 7H 7.349414e-03 seoa8177 7H 0.01 seoalβlβ 7H 7.393112e-03 seoa7608 7H 0.01 seoa6621 7H 7.502464e-03 ncrbl802 7H 0.01 mioa9154 7H 7.54023e-03 ncrbO782 7H 0.01 fcrO712 7H 7.628512e-03 ncrb5264 7H 0.01 hfcr5150 7H 7.923243e-03 ncrcll40 7H 0.01 ncrcO846 7H 7.950466e-03 fcr6596 7H 0.01 miob5122 7H 7.951944e-03 fcr3772 7H 0.01 seoa0575 7H 8.076369e-03 fcr5141 7H 0.01 fcrbl527 7H 8.180547e-03 fcr6314 7H 0.01 fcrb2592 7H 8.226603e-03 fcr7042 7H 0.01 ncrc2650 7H 8.238043e-03 hfcr6099 7H 0.01 seoa0577 7H 8.284474e-03 mioa8471 7H 0.01 seoal460 7H 8.576166e-03 miobl318 7H 0.01 fcr2340 7H 8.62297e-03 miob3825 7H 0.01 fcrb2060 7H 8.62297e-03 ncr2666 7H 0.01 fcrb4375 7H 8.62297e-03 ncr3793 7H 0.01 fcrb4725 7H 8.62297e-03 ncr4545 7H 0.01 mioa8858 7H 8.62297e-03 ncrb4248 7H 0.01 miob0877 7H 8.62297e-03 ncrc6480 7H 0.01 ncr0210 7H 8.62297e-03 seoa0511 7H 0.01 ncr7813 7H 8.62297e-03 seoal039 7H 0.01 ncrc4226 7H 8.62297e-03 seoa2178 7H 0.01 seoa0172 7H 8.62297e-03 seoa2641 7H 0.01 seoal992 7H 8.62297e-03 seoa4146 7H 0.01 seob6004 7H 8.62297e-03 seoa5662 7H 0.01 seoa0186 7H 8.724488e-03 seoa7443 7H 0.01 ncr3655 7H 8.739078e-03 seoa8754 7H 0.01 miob2879 7H 8.775107e-03 seobl318 7H 0.01 '•g'eόb'is's'y-'1""" "7Η' '-•Tr.oi seoa3102 7H 0.01 seobl911 7H 0.01 fcr6493 7H 0.01 seob8104 7H 0.01 ncrcl421 7H 0.01 fcr3121 7H 0.01 seoal644 7H 0.01 ncrc9020 7H 0.01 seob0716 7H 0.01 seoal789 7H 0.01 fcrbl657 7H 0.01 seoa0853 7H 0.01 seoa4102 7H 0.01 fcrb2483 7H 0.01 seoa5461 7H 0.01 fcrbO131 7H 0.01 ncrc6888 7H 0.01 fcrOOβl 7H 0.01 fcrbO623 7H 0.01 hfcrO596 7H 0.01 fcr0990 7H 0.01 seob7077 7H 0.01 fcr2598 7H 0.01 seob3307 7H 0.01 fcr3322 7H 0.01 mioa4721 7H 0.01 fcr7059 7H 0.01 fcrb2495 7H 0.01 hfcr8957 7H 0.01 seoa6497 7H 0.01 mioa4064 7H 0.01 seoa5578 7H 0.01 ncr3649 7H 0.01 ncrb4319 7H 0.01 ncr5871 7H 0.01 seoa2042 7H 0.01 ncrb6742 7H 0.01 seob6279 7H 0.01 ncrcl631 7H 0.01 seob3464 7H 0.01 seoa0501 7H 0.01 hfcr6600 7H 0.01 seoal496 7H 0.01 hfcr8429 7H 0.01 seoa4708 7H 0.01 seoal427 7H 0.01 seoa9712 7H 0.01 seob4117 7H 0.01 seob3520 7H 0.01 seoa0158 7H 0.01 seob5574 7H 0.01 seob5193 7H 0.01 mioa4318 7H 0.01 seoa5577 7H 0.01 seoa8894 7H 0.01 seoal535 7H 0.01 mioa9792 7H 0.01 seoa5382 7H 0.01 seoa8443 7H 0.01 fcr0206 7H 0.01 hfcrl863 7H 0.01 fcr2079 7H 0.01 miob3471 7H 0.01 fcr4128 7H 0.01 seobl023 7H 0.01 fcr7345 7H 0.01 seoal571 7H 0.01 fcrbl441 7H 0.01 mioa9722 7H 0.01 fcrb2344 7H 0.01 seoa4681 7H 0.01 fcrb4719 7H 0.01 ncrc2413 7H 0.01 fcrb5534 7H 0.01 fcrb5725 7H 0.01 hfcr5719 7H 0.01 fcrO768 7H 0.01 hfcr5871 7H 0.01 fcrO842 7H 0.01 hfcr7855 7H 0.01 fcr4699 7H 0.01 mioal531 7H 0.01 fcrbl962 7H 0.01 mioa2647 7H 0.01 fcrb2059 7H 0.01 mioa3080 7H 0.01 fcrb3848 7H 0.01 miobl062 7H 0.01 hfcrO225 7H 0.01 ncr3197 7H 0.01 mioal370 7H 0.01 ncr3483 7H 0.01 mioa2791 7H 0.01 ncr3948 7H 0.01 mioa5695 7H 0.01 ncr8975 7H 0.01 miob6223 7H 0.01 ncr9599 7H 0.01 ncrO898 7H 0.01 ncrbO462 7H 0.01 ncr5971 7H 0.01 ncrb2091 7H 0.01 ncrb3056 7H 0.01 ncrb4039 7H 0.01 ncrb8404 7H 0.01 ncrcO793 7H 0.01 ncrc5243 7H 0.01 ncrcO856 7H 0.01 seoa0014 7H 0.01 ncrc4047 7H 0.01 seoa2442 7H 0.01 ncrc4153 7H 0.01 seoa4586 7H 0.01 ncrc5434 7H 0.01 seoa4727 7H 0.01 seoa0545 7H 0.01 seoa7555 7H 0.01 seob0007 7H 0.01 seoa8397 7H 0.01 seob3462 7H 0.01 seobl879 7H 0.01 hfcr6727 7H 0.01 seob2685 7H 0.01 ncrc6171 7H 0.01 seob4419 7H 0.01 ncr3811 7H 0.01 seob7432 7H 0.01 lS'eoa'27'3'4""- ' -?H" „.... ,Q...w , mioblOlβ 7H 0.02 ncr5568 7H 0.01 fcr4795 7H 0.02 seoal736 7H 0.01 fcrO343 7H 0.02 fcr4802 7H 0.01 fcrO589 7H 0.02 seob0975 7H 0.01 fcr3125 7H 0.02 hfcr2672 7H 0.01 fcrbl539 7H 0.02 ncrO238 7H 0.01 fcrbl835 7H 0.02 fcr6616 7H 0.01 fcrb2094 7H 0.02 hfcr9811 7H 0.01 fcrb2321 7H 0.02 ncrb2151 7H 0.01 fcrb4316 7H 0.02 ncrbO696 7H 0.01 hfcr5706 7H 0.02 seob4451 7H 0.01 hfcr6498 7H 0.02 mioa6089 7H 0.01 hfcr8982 7H 0.02 fcrbll20 7H 0.01 miob6865 7H 0.02 hfcr5604 7H 0.01 ncr3435 7H 0.02 mioa5420 7H 0.01 ncrc4757 7H 0.02 mioa3654 7H 0.01 seoal575 7H 0.02 hfcr6677 7H 0.01 seoa6521 7H 0.02 ncrc4597 7H 0.01 seoa9131 7H 0.02 seoa8921 7H 0.01 seob0572 7H 0.02 ncr7876 7H 0.01 seob5174 7H 0.02 seob0371 7H 0.01 seob6812 7H 0.02 fcrb3127 7H 0.01 seob7505 7H 0.02 hfcr9320 7H 0.01 ncrb4472 7H 0.02 seob5558 7H 0.02 ncr7136 7H 0.02 fcr3539 7H 0.02 seob4621 7H 0.02 seoal582 7H 0.02 ncrO612 7H 0.02 seoalllδ 7H 0.02 mioa3335 7H 0.02 ncrO851 7H 0.02 seoa0824 7H 0.02 fcrb2302 7H 0.02 ncr3149 7H 0.02 hfcrO366 7H 0.02 seob2108 7H 0.02 seoa0860 7H 0.02 fcrb3704 7H 0.02 fcr6412 7H 0.02 fcr7095 7H 0.02 fcrbl320 7H 0.02 ncr3827 7H 0.02 fcrb2596 7H 0.02 fcr5560 7H 0.02 fcrb4249 7H 0.02 seoalβll 7H 0.02 hfcrOO41 7H 0.02 fcrb2933 7H 0.02 hfcr2295 7H 0.02 miob0682 7H 0.02 hfcr4488 7H 0.02 fcr0018 7H 0.02 hfcr6921 7H 0.02 fcrO796 7H 0.02 mioal213 7H 0.02 fcr2160 7H 0.02 mioa2993 7H 0.02 fcr3701 7H 0.02 mioa4883 7H 0.02 fcr5220 7H 0.02 mioa5692 7H 0.02 fcrβlβl 7H 0.02 mioa5696 7H 0.02 fcrb3134 7H 0.02 mioa6064 7H 0.02 hfcr0023 7H 0.02 miob3794 7H 0.02 hfcrO383 7H 0.02 miob4248 7H 0.02 hfcrO776 7H 0.02 miob6878 7H 0.02 hfcr2803 7H 0.02 ncr2966 7H 0.02 hfcr3602 7H 0.02 ncr3163 7H 0.02 hfcr4444 7H 0.02 ncr4535 7H 0.02 hfcr5956 7H 0.02 seoa0099 7H 0.02 hfcr7151 7H 0.02 seoal598 7H 0.02 hfcr7317 7H 0.02 seoa7647 7H 0.02 mioa0595 7H 0.02 seobl737 7H 0.02 mioa0884 7H 0.02 ncrcllO3 7H 0.02 miob2645 7H 0.02 ncrc6348 7H 0.02 miob6562 7H 0.02 hfcr3956 7H 0.02 ncrO335 7H 0.02 seoa6573 7H 0.02 ncr2182 7H 0.02 hfcrO511 7H 0.02 ncr5264 7H 0.02 miob3042 7H 0.02 ncr7852 7H 0.02 ncrl550 7H 0.02 ncrb4441 7H 0.02 hfcr8540 7H 0.02 ncrcO139 7H 0.02 "ϋ lf~ IS I' Il " Wctό34-64' o-.fe- ncr8542 7H 0.03 ncrc3650 7H 0.02 ncrbl956 7H 0.03 ncrc4994 7H 0.02 ncrcO583 7H 0.03 ncrc8841 7H 0.02 seoaOlll 7H 0.03 seoa0600 7H 0.02 seoall02 7H 0.03 seoa0956 7H 0.02 seoa6393 7H 0.03 seoal358 7H 0.02 seoa6612 7H 0.03 seoal875 7H 0.02 seoa6658 7H 0.03 seoa2467 7H 0.02 seobβOOO 7H 0.03 seoa4012 7H 0.02 seob6883 7H 0.03 seoa5473 7H 0.02 seob7764 7H 0.03 seoa7295 7H 0.02 ncrcO324 7H 0.03 seoa7553 7H 0.02 fcr5958 7H 0.03 seob0122 7H 0.02 seob5726 7H 0.03 seob3169 7H 0.02 fcrb2396 7H 0.03 seob5537 7H 0.02 mioa8039 7H 0.03 hfcr8864 7H 0.02 fcrb3026 7H 0.03 seoa0549 7H 0.02 seob0063 7H 0.03 seob0688 7H 0.02 mioa2522 7H 0.03 seoal883 7H 0.02 ncr3673 7H 0.03 fcr3549 7H 0.02 fcr4566 7H 0.03 miob6437 7H 0.02 fcrb2041 7H 0.03 seobl853 7H 0.02 hfcr2941 7H 0.03 fcrblO17 7H 0.02 hfcr2963 7H 0.03 mioa4009 7H 0.02 hfcr8804 7H 0.03 mioa3672 7H 0.02 mioal380 7H 0.03 ncr0107 7H 0.02 mioal947 7H 0.03 fcrb2749 7H 0.02 mioa4076 7H 0.03 seoa4783 7H 0.02 mioa6772 7H 0.03 ncrO258 7H 0.02 mioa9648 7H 0.03 seoa3408 7H 0.02 ncr8337 7H 0.03 ncrOO45 7H 0.02 ncrb0200 7H 0.03 seoal765 7H 0.02 ncrb4437 7H 0.03 seoa5303 7H 0.02 ncrb4555 7H 0.03 ncrc5113 7H 0.02 ncrcl380 7H 0.03 ncrb5244 7H 0.02 ncrcl595 7H 0.03 seob7658 7H 0.02 seoal540 7H 0.03 miob4408 7H 0.02 seoa3109 7H 0.03 hfcrll41 7H 0.03 seoa9140 7H 0.03 seoa2135 7H 0.03 seoa9930 7H 0.03 seoa2889 7H 0.03 seob0386 7H 0.03 fcrO837 7H 0.03 seob0442 7H 0.03 ncrc0320 7H 0.03 seob2011 7H 0.03 hfcr4114 7H 0.03 seob4466 7H 0.03 seob3545 7H 0.03 fcr0620 7H 0.03 ncrcO529 7H 0.03 miob6419 7H 0.03 fcr7114 7H 0.03 fcr6142 7H 0.03 seoa8300 7H 0.03 seoaOlOl 7H 0.03 hfcr2390 7H 0.03 mioa5329 7H 0.03 fcr2494 7H 0.03 seoa0990 7H 0.03 fcrb2299 7H 0.03 miob2705 7H 0.03 fcrb2647 7H 0.03 ncr2176 7H 0.03 fcrb3192 7H 0.03 seoal901 7H 0.03 fcrb4333 7H 0.03 hfcr3224 7H 0.03 fcrb5297 7H 0.03 ncrc9727 7H 0.03 hfcrl302 7H 0.03 seob3326 7H 0.03 hfcr3540 7H 0.03 hfcr5691 7H 0.03 mioa3812 7H 0.03 ncrb4957 7H 0.03 mioa3987 7H 0.03 ncrb8343 7H 0.03 mioa6913 7H 0.03 seoa6661 7H 0.03 mioa9666 7H 0.03 fcr7128 7H 0.03 miob2687 7H 0.03 mioa2536 7H 0.03 ncr3148 7H 0.03 seob7444 7H 0.03 ncr4539 7H 0.03 ncrb7376 7H 0.03 •s'Sόal6Ψ6""', .,.„..TH.. .,.™ J3-..(J3 seoa0065 7H 0.04 ncrclO31 7H 0.03 hfcr2287 7H 0.04 seob2631 7H 0.03 seob6678 7H 0.04 fcrb2077 7H 0.03 seoal232 7H 0.04 seoa0044 7H 0.03 ncrc2776 7H 0.04 fcrO179 7H 0.03 fcr5758 7H 0.04 fcrl242 7H 0.03 ncrb3498 7H 0.04 fcr2860 7H 0.03 seob4191 7H 0.04 fcrbl689 7H 0.03 seoa9935 7H 0.04 fcrb5311 7H 0.03 seoal080 7H 0.04 hfcr5883 7H 0.03 fcrb3702 7H 0.04 mioal971 7H 0.03 fcr7146 7H 0.04 mioa7415 7H 0.03 fcrl951 7H 0.04 miob0178 7H 0.03 fcr2167 7H 0.04 miobl337 7H 0.03 fcr2442 7H 0.04 miob2492 7H 0.03 fcr3983 7H 0.04 miob2836 7H 0.03 fcr4385 7H 0.04 miob4867 7H 0.03 fcr4927 7H - 0.04 ncr0019 7H 0.03 fcr7667 7H 0.04 ncr3368 7H 0.03 fcrb2426 7H 0.04 ncrβ619 7H 0.03 fcrb2575 7H 0.04 ncr7178 7H 0.03 hfcr5919 7H 0.04 ncrbl515 7H 0.03 hfcr7357 7H 0.04 ncrb6675 7H 0.03 hfcr9290 7H 0.04 ncrcO341 7H 0.03 hfcr9296 7H 0.04 ncrcO457 7H 0.03 mioa0245 7H 0.04 ncrc3457 7H 0.03 mioa3160 7H 0.04 ncrc4411 7H 0.03 miobll94 7H 0.04 ncrc8903 7H 0.03 miob6432 7H 0.04 seoa0003 7H 0.03 ncrO266 7H 0.04 seoa0219 7H 0.03 ncrb2558 7H 0.04 seoal900 7H 0.03 ncrb7726 7H 0.04 seoa3670 7H 0.03 ncrc6423 7H 0.04 seoa5302 7H 0.03 seoa4327 7H 0.04 seobl410 7H 0.03 seoa7249 7H 0.04 seob3119 7H 0.03 seoa9828 7H 0.04 seob4440 7H 0.03 seoa9998 7H 0.04 seob4965 7H 0.03 seob0999 7H 0.04 seob5684 7H 0.03 seobl008 7H 0.04 seob6630 7H 0.03 seobl052 7H 0.04 seob7744 7H 0.03 seobl426 7H 0.04 seob8287 7H 0.03 seobl842 7H 0.04 ncrc4362 7H 0.03 seob4570 7H 0.04 fcrbl687 7H 0.03 seob5441 7H 0.04 ncrc6417 7H 0.03 hfcrl697 7H 0.04 seob0376 7H 0.03 seoa0512 7H 0.04 seob5711 7H 0.03 seob2085 7H 0.04 fcr4743 7H 0.03 seoa2449 7H 0.04 fcr5472 7H 0.03 seob8241 7H 0.04 miob3120 7H 0.03 seoa0221 7H 0.04 seoa4264 7H 0.04 seob4570 7H 0.04 mioal062 7H 0.04 fcr6887 7H 0.04 seoa7064 7H 0.04 ncr7088 7H 0.04 seoa0840 7H 0.04 seobl746 7H 0.04 seoa6734 7H 0.04 hfcr8475 7H 0.04 ncr4416 7H 0.04 ncrc5061 7H 0.04 seob5493 7H 0.04 ncr8843 7H 0.04 fcrbO624 7H 0.04 fcrO237 7H 0.04 mioa6690 7H 0.04 seoa5698 7H 0.04 mioa8713 7H 0.04 miob6228 7H 0.04 seob0763 7H 0.04 fcr5625 7H 0.04 hfcr3019 7H 0.04 fcrb2315 7H 0.04 hfcr0750 7H 0.04 hfcrO676 7H 0.04 ncrcO292 7H 0.04 hfcr2535 7H 0.04 'Kf&f52"4'4 7rtf " 0".b"4" - fcr2188 71 5.437357e-03 hfcr5381 7H 0.04 miod3826 71 6.215714e-03 hfcr6243 7H 0.04 seob0084 71 6.215714e-03 hfcr6971 7H 0.04 seob0782 71 6.215714e-03 hfcr7631 7H 0.04 seob4928 71 6.215714e-03 hfcr9743 7H 0.04 seoa6178 71 6.305954e-03 mioa0890 7H 0.04 seob2658 71 7.212877e-03 mioa2295 7H 0.04 ncrcO749 71 8.040483e-03 mioa4057 7H 0.04 ncrb8035 71 8.36773e-03 mioa6595 7H 0.04 ncrc6774 71 8.36773e-03 mioa7522 7H 0.04 seob4029 71 8.879458e-03 mioa9062 7H 0.04 ncrc4302 71 9.231614e-03 ncrll22 7H 0.04 ncr3397 71 9.740651e-03 ncr2288 7H 0.04 seoa5698 71 9.777088e-03 ncr3588 7H 0.04 mioc6341 71 0.01 ncr4040 7H 0.04 miob9830 71 0.01 ncr7097 7H 0.04 fcrbll58 71 0.01 ncr8413 7H 0.04 fcrb6791 71 0.01 ncr9587 7H 0.04 seoa5743 71 0.01 ncrb7465 7H 0.04 seobl617 71 0.01 ncrcl665 7H 0.04 fcrO187 71 0.01 seoal776 7H 0.04 fcrb5422 71 0.01 seoa3287 7H 0.04 miod6554 71 0.01 seoa6466 7H 0.04 ncrc9394 71 0.01 seoa6637 7H 0.04 fcrc0350 71 0.01 seoa6677 7H 0.04 ncr3587 71 0.01 seoa6754 7H 0.04 seob5397 71 0.01 seob0085 7H 0.04 fcrl394 71 0.01 seob0755 7H 0.04 fcrb2996 71 0.01 seob2067 7H 0.04 miobl833 71 0.01 seob4333 7H 0.04 ncre5706 71 0.01 seob5336 7H 0.04 mioa0291 71 0.01 hfcr8495 7H 0.04 fcrO535 71 0.01 hfcr3846 7H 0.04 fcrb2715 71 0.01 seoal483 7H 0.04 ncrb8160 71 0.01 seoa9603 7H 0.04 ncrc3864 71 0.01 mioa9179 7H 0.04 ncrd4954 71 0.01 seoa3344 7H 0.04 seoa9930 71 0.01 mioa8861 7H 0.04 seob0928 71 0.02 ncrc5054 7H 0.04 mioc7509 71 0.02 hfcr6534 7H 0.04 crO491 71 O.02 seob6368 7H 0.04 seob9435 71 0.02 mioa8594 7H 0.04 mioc6973 71 0.02 fcrb2545 7H 0.04 ncrd7372 71 0.02 seoa0799 7H 0.04 ncr0004 71 0.02 miob5632 7H 0.04 seocl305 71 0.02 mioa4014 7H 0.04 seob9614 71 0.02 miob3911 71 4.41e-04 ncrd4929 71 0.02 seob2067 71 6.57e-04 fcrbl769 71 0.02 ncrc4772 71 8.82e-04 fcrb5441 71 0.02 seoc0149 71 2.085221e-03 fcrb6469 71 0.02 ncrc4759 71 2.552738e-03 fcrb9124 71 0.02 ncrb0074 71 2.926149e-03 miod4539 71 0.02 fcrb9565 71 3.208495e-03 ncr7852 71 0.02 fcrc2431 71 3.223683e-03 ncrc4132 71 0.02 hfcrl826 71 3.257445e-03 ncrc6749 71 0.02 miob3120 71 3.257445e-03 seoa8979 71 0.02 seoa9209 71 3.257445e-03 seocl025 71 0.02 soa0550 71 4.178366e-03 seoa2381 71 0.02 miod6038 71 4.186981e-03 ncrc9168 71 0.02 ncrc3690 71 4.482529e-03 fcrb8664 71 0.02 fcrb4718 71 4.540706e-03 ncrll22 71 0.02 seoa9656 71 4.540706e-03 miod2330 71 0.02 mioc6269 71 5.370316e-03 ncrb6846 71 0.02 • tτbf910$ seoc2136 71 0.03 fcr2498 71 0.02 miob9533 71 0.03 hfcr0130 71 0.02 fcrb6738 71 0.03 miob4221 71 0.02 fcr4725 71 0.04 mioc8254 71 0.02 fcrb8236 71 0.04 fcrc6010 71 0.02 fcrb6723 71 0.04 fcrl225 71 0.02 fcrl463 71 0.04 fcrl994 71 0.02 miob8694 71 0.04 fcrb7723 71 0.02 seoa7917 71 0.04 mioa4810 71 0.02 ncrc9530 71 0.04 miob5412 71 0.02 seob6558 71 0.04 mioc3574 71 0.02 mioa5468 71 0.04 ncrcO32O 71 0.02 miod6324 71 0.04 ncrc5762 71 0.02 fcrbO131 71 0.04 ncrd3638 71 0.03 fcrcO839 71 0.04 fcrb4383 71 0.03 hfcr6336 71 0.04 fcrb7699 71 0.03 mioa5202 71 0.04 fcrb3153 71 0.03 ncr7668 71 0.04 ncrb3468 71 0.03 ncrc5492 71 0.04 fcrc5506 71 0.03 seob4065 71 0.04 seob7082 71 0.03 seob9420 71 0.04 miob8992 71 0.03 seob9818 71 0.04 fcrc7243 71 0.03 seoc4748 71 0.04 hfcr3019 71 0.03 hfcr2275 71 0.04 ncrd3708 71 0.03 seoa8018 71 0.04 seoal776 71 0.03 ncrcO728 71 0.04 seoal883 71 0.03 fcrb3080 71 0.04 seoa5580 71 0.03 Table#8B AffySpot Tablet pvalue AffySpot Tablet pvalue 201794_s_at IAA 1.68e-06 208137 x at IAA 8.08e-04 212640_at IAA 4.74e-06 219734"_at IAA 8.16e-04 205060_at IAA 4.18e-05 91684 <g_at IAA 8.64e-04 20468β_at IAA 6.09e-05 213694_at IAA 8.67e-04 212648_at IAA 6.57e-05 200867" at IAA 8.76e-04 224369_s_at IAA 6.68e-05 202545'_at IAA 8.9e-04 221306_at IAA 1.23e-04 207314"_x_at IAA 8.9e-04 22490β_at IAA .35e-04 211733" x at IAA 9.02e-04 203810_at IAA .37e-04 202184^ s at IAA 9.34e-04 208248_x_at IAA .67e-04 214109'_at IAA 9.39e-04 221582_at IAA .85e-04 214743"_at IAA 9.48e-04 216907_x_at IAA 1.92e-04 221419"_s_at IAA 9.48e-04 211935_at IAA 2.21e-04 211926" s at IAA 9.48e-04 212718_at IAA 2.37e-04 211945"_s at IAA 9.48e-04 203254_s_at IAA 2.53e-04 200649"_at IAA 9.48e-04 215049_x_at IAA 2.69e-04 216041" x at IAA 9.79e-04 202443_x_at IAA 2.69e-04 204675'_at IAA 9.94e-04 208772_at IAA 2..69e-04 219279"_at IAA 9.95e-04 214173_x_at IAA 2..72e-04 218566" s at IAA 1.00946e-03 230243_at IAA 2..91e-04 205292" s at IAA 1.028944e-03 201935_s_at IAA 3..21e-04 202461"_at IAA 1.056849e-03 224859_at IAA 3..63e-04 208686" s at IAA 1.056894e-03 206059_at IAA 3..84e-04 211987"_at IAA 1.070779e-03 204573_at IAA 4..09e-04 208791"_at IAA 1.073209e-03 218236_s_at IAA 4..12e-04 215606"_s_at IAA 1.075892e-03 200709_at IAA 4..2e-04 217815"_at IAA 1.09023e-03 202783_at IAA 4..24e-04 213440"_at IAA 1.108962e-03 203037_s_at IAA 4..3e-04 216997"_x_at IAA 1.110697e-03 206632_s_at IAA 4..35e-04 212763"_at IAA 1.17205e-03 1564785_at IAA 4..37e-04 210428"_s at IAA 1.172917e-03 200697_at IAA 4..56e-04 218501"_at IAA 1.182111e-03 202638_s_at IAA 4..65e-04 224658" x at IAA 1.212659e-03 202786_at IAA 4..73e-04 200610"_s_at IAA 1.220998e-03 208704_x_at IAA 4..78e-04 219470" x at IAA 1.256633e-03 212863_x_at IAA 4..91e-04 208777" s at IAA 1.284705e-03 213906_at IAA 4..93e-04 219777""at IAA 1.29045e-03 209585_s_at IAA 5..43e-04 212590""at IAA 1.290851e-03 203392_s_at IAA 5..55e-04 203640""at IAA 1.3161e-03 41577_at IAA 6..09e-04 202910"_s_at IAA 1.333828e-03 202471_s_at IAA 6..18e-04 203537""at IAA 1.335028e-03 202610_s_at IAA 6..26e-04 224764""at IAA 1.363715e-03 200046_at IAA 6..37e-04 208896""at IAA 1.363848e-03 200742__s_at IAA 6..39e-04 225558""at IAA 1.375945e-03 204497_at IAA 6..55e-04 211571"_s_at IAA 1.375945e-03 204396_s_at IAA 6..59e-04 200845" s at IAA 1.375945e-03 203970_s_at IAA 6..62e-04 218098"~at IAA 1.42754e-03 201118_at IAA 6..72e-04 244498"x at IAA 1.435389e-03 201687_s_at IAA 6..86e-04 243750 x at IAA 1.478026e-03 209960__at IAA 7.13e-04 218223 s at IAA 1.479659e-03 212500_at IAA 7.33e-04 204860 s at IAA 1.487699e-03 217862_at IAA 7.33e-04 224991 "at IAA 1.543497e-03 217970_s_at IAA 7.43e-04 214306 "at IAA 1.620502e-03 204806__x_at IAA 7.43e-04 218568 "at IAA 1.620526e-03 203264__s_at IAA 7.48e-04 218311".at IAA 1.622558e-03 219183_s__at IAA 7.53e-04 218172 "s at IAA 1.632171e-03 213291_s_at IAA 7.54e-04 221505 "at IAA 1.663732e-03 220999_s__at IAA 7.75e-04 202739 s at IAA 1.67119e-03 202096_s_at IAA 7.86e-04 45749 at IAA 1.712874e-03 202720_at IAA 7.88e-04 45526 g_at IAA 1.712874e-03 217197 x at IAA 8.02e-04 37028 at IAA 1.712874e-03 18 !l "I..» if Il at'-••""••'"-f'KA iT.'ifl'-?li:4e-03 203574 at IAA 1.712874e-03 33646 g at IAA 1.712874e-03 203733_at IAA 1.712874e-03 41160 at IAA 1.712874e-03 204119 s at IAA 1.712874e-03 41469 at IAA 1.712874e-03 204798_at IAA 1.712874e-03 33850 at IAA 1.712874e-03 203926 x at IAA 1.712874e-03 91682 at IAA 1.712874e-03 204565 at IAA 1.712874e-03 218963_s_at IAA 1.712874e-03 203691_at IAA 1.712874e-03 219675 s at IAA 1.712874e-03 204732 s at IAA 1.712874e-03 218580 x at IAA 1.712874e-03 203746_s_at IAA 1.712874e-03 220015_at IAA 1.712874e-03 203535_at IAA 1.712874e-03 219931 s at IAA 1.712874e-03 203616 at IAA 1.712874e-03 218854 at IAA 1.712874e-03 203281_s_at IAA 1.712874e-03 220305 at IAA 1.712874e-03 221875 x at IAA 1.712874e-03 219506_at IAA 1.712874e-03 220784 s at IAA 1.712874e-03 218540_at IAA 1.712874e-03 220785 at IAA 1.712874e-03 220370 s at IAA 1.712874e-03 222360_at IAA 1.712874e-03 219375 at IAA 1.712874e-03 221790_s_at IAA 1.712874e-03 219512 at IAA 1.712874e-03 220684_at IAA 1.712874e-03 218750_at IAA 1.712874e-03 221903 s at IAA 1.712874e-03 219049_at IAA 1.712874e-03 211506_s_at IAA 1.712874e-03 218810__at IAA 1.712874e-03 211919 s at IAA 1.712874e-03 219960_s at IAA 1.712874e-03 210069_ati IAA 1.712874e-03 213524 s at IAA 1.712874e-03 210361 s at IAA 1.712874e-03 214472 at IAA 1.712874e-03 209456 s at IAA 1.7128.74e-03 212983 at IAA 1.712874e-03 210985 s at IAA 1.712874e-03 214005 at IAA 1.712874e-03 212209_at IAA 1.712874e~03 214132_at IAA 1.712874e-03 209964 s at IAA 1.712874e-03 213671_s at IAA 1.712874e-03 210119_at IAA 1.712874e-03 213798_s_at IAA 1.712874e-03 209840 s at IAA 1.712874e-03 213754_s_at IAA 1.712874e-03 209771 x at IAA 1.712874e-03 213476_x_at IAA 1.712874e-03 210873 x at IAA 1.712874e-03 214733_s_at IAA 1.712874e-03 211962_s_at IAA 1.712874e-03 213811 x at IAA 1.712874e-03 211333 s at IAA 1.712874e-03 213587 s at IAA 1.712874e-03 211941 s at IAA 1.712874e-03 214290 s at IAA 1.712874e-03 210042 s at IAA 1.712874e-03 213988 s at IAA 1.712874e-03 210216 x at IAA 1.712874e-03 214318 s at IAA 1.712874e-03 211991_s at IAA 1.712874e-03 214241 at IAA 1.712874e-03 211942 x at IAA 1.712874e-03 218164 at IAA 1.712874e-03 202035_s_at IAA 1.712874e-03 218149_s_at IAA 1.712874e-03 202607_at IAA 1.712874e-03 216841 s at IAA 1.712874e-03 203233_at IAA 1.712874e-03 218197 s at IAA 1.712874e-03 203202 at IAA 1.712874e-03 218376_s_at IAA 1.712874e-03 202081_at IAA 1.712874e-03 216304 x at IAA 1.712874e-03 203141 s at IAA 1.712874e-03 216979_at IAA 1.712874e-03 202014_at IAA 1.712874e-03 218037_at IAA 1.712874e-03 202467 s at IAA 1.712874e-03 218166 s at IAA 1.712874e-03 202475_at IAA 1.712874e-03 215718 s at IAA 1.712874e-03 202393 s at IAA 1.712874e-03 216379 x at IAA 1.712874e-03 202224 at IAA 1.712874e-03 216526 x at IAA 1.712874e-03 202241_at IAA 1.712874e-03 215498 s at IAA 1.712874e-03 203021_at IAA 1.712874e-03 215667 x at IAA 1.712874e-03 202692 s at IAA 1.712874e-03 215737 x at IAA 1.712874e-03 203081_at IAA 1.712874e-03 217872_at IAA 1.712874e-03 202631 s at IAA 1.712874e-03 217094 s at IAA 1.712874e-03 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S at IAA 1.712874e-03 217992 s at IAA 2.60699e-03 201502" S at IAA 1.712874e-03 221081 s_at IAA 2.64645e-03 201464" X at IAA 1.712874e-03 1553678 a at IAA 2.659966e-03 201129"_at IAA 1.712874e-03 205026 at IAA 2.72873e-03 200961" at IAA 1.712874e-03 203645 s at IAA 2.730785e-03 200869"_at IAA 1.712874e-03 204079 at IAA 2.730785e-03 201788"_at IAA 1.712874e-03 203239 s at IAA 2.730785e-03 201556" S at IAA 1.712874e-03 209367 at IAA 2.730785e-03 201003" X at IAA 1.712874e-03 200743 s at IAA 2.730785e-03 201482""at IAA 1.712874e-03 201814 at IAA 2.730785e-03 200999" S at IAA 1.712874e-03 204494 s at IAA 2.745809e-03 208488" S at IAA 1.713106e-03 214196 s at IAA 2.755786e-03 219854""at IAA 1.719652e-03 218356 at IAA 2.767857e-03 210116""at IAA 1.734931e-03 207483 s_at IAA 2.78493e-03 201304""at IAA 1.756437e-03 217956 s at IAA 2.848563e-03 201824 _at IAA 1.77109e-03 217106 x at IAA 2.859526e-03 221918 "at IAA 1.774096e-03 200721 s_at IAA 2.916261e-03 204252""at IAA 1.78859e-03 203585 at 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7.18868e-03 212296 at IAA 6.268081e-03 208815 x_at IAA 7.293509e-03 209884 s at IAA 6.277709e-03 230252 [at IAA 7.304007e-03 214937 x at IAA 6.288803e-03 218212 s at IAA 7.304279e-03 205571 at IAA 6.288806e-03 200948""at IAA 7.315435e-03 211615 s_at IAA 6.309927e-03 212833""at IAA 7.3307e-03 202602 s_at IAA 6.34029e-03 201589 "at IAA 7.331303e-03 213064 at IAA 6.343167e-03 202956 "at IAA 7.35006e-03 B i! 2-0325'9jsr-g't:'""'TM rr.;3Εrδ79e-03 213867 X at IAA 7.557581e-03 74694 s_at IAA 7.378723e-03 213897_s_at IAA 7.557581e-03 32099 at IAA 7.399579e-03 212537 X at IAA 7.557581e-03 211063 s at IAA 7.414502e-03 213170_at IAA 7.557581e-03 200873 s at IAA 7.437178e-03 213969 X at IAA 7.557581e-03 212252_at IAA 7.438057e-03 212638 S at IAA 7.557581e-03 218229_s at IAA 7.444908e-03 214526 X at IAA 7.557581e-03 206991 s at IAA 7.447433e-03 212501_at IAA 7.557581e-03 37145 at IAA 7.456097e-03 217896 S at IAA 7.557581e-03 212199_at IAA 7.469951e-03 218084 X at IAA 7.557581e-03 208644_at IAA 7.475064e-03 216903_s at IAA 7.557581e-03 218700_s at IAA 7.485134e-03 216657_at IAA 7.557581e-03 208392 x at IAA 7.486348e-03 217414 X at IAA 7.557581e-03 201320_at IAA 7.54139e-03 217792_at IAA 7.557581e-03 209187 at IAA 7.556946e-03 218381 S at IAA 7.557581e-03 266 s at IAA 7.557581e-03 217950_at IAA 7.557581e-03 49485 at IAA 7.557581e-03 217733 S at IAA 7.557581e-03 38964 r at IAA 7.557581e-03 215785 S at IAA 7.557581e~03 37232 at IAA 7.557581e-03 215354 S at IAA 7.557581e-03 36711 at IAA 7.557581e-03 217967 S at IAA 7.557581e-03 76897 s at IAA 7.557581e-03 217740 X at IAA 7.557581e-03 57588_at IAA 7.557581e-03 217028_at IAA 7.557581e-03 AFFX- 217739 S at IAA 7.557581e-03 hum alu at IAA 7.557581e-03 217232 X at IAA 7.557581e-03 218616_at IAA 7.557581e-03 217747 S at IAA 7.557581e-03 219678 x at IAA 7.557581e-03 215313 X at IAA 7.557581e-03 220202 s at IAA 7.557581e-03 204470_at IAA 7.557581e-03 219007_at IAA 7.557581e-03 204501_at IAA 7.557581e-03 219399_at IAA 7.557581e-03 204621 S at IAA 7.557581e-03 219620 x at IAA 7.557581e-03 203630 S at IAA 7.557581e-03 220113 x at IAA 7.557581e-03 204777 S at IAA 7.557581e-03 219112_at IAA 7.557581e-03 204638_at IAA 7.557581e-03 219392 x at IAA 7.557581e-03 204218_at IAA 7.557581e-03 218870_at IAA 7.557581e-03 204567 S at IAA 7.557581e-03 218649 x at IAA 7.557581e-03 203504 S at IAA 7.557581e-03 219448_at IAA 7.557581e-03 204021 S at IAA 7.557581e-03 219646 at IAA 7.557581e-03 204564_at IAA 7.557581e-03 219957 at IAA 7.557581e-03 204029 at IAA 7.557581e-03 218531_at IAA 7.557581e-03 203410_at IAA 7.557581e-03 219191 s at IAA 7.557581e-03 204495 s_at IAA 7.557581e-03 218476_at IAA 7.557581e-03 203852 S at IAA 7.557581e-03 219376_at IAA 7.557581e-03 204265 s_at IAA 7.557581e-03 219111 s at IAA 7.557581e-03 204615 X at IAA 7.557581e-03 219843_at IAA 7.557581e-03 203607"at IAA 7.557581e-03 220071 x at IAA 7.557581e-03 204393 s_at IAA 7.557581e-03 218913 s at IAA 7.557581e-03 203665 at IAA 7.557581e-03 218817_at IAA 7.557581e-03 204118_at IAA 7.557581e-03 213564' x at IAA 7.557581e-03 204610 S at IAA 7.557581e-03 212549_at IAA 7.557581e-03 203775_at IAA 7.557581e-03 213687 s at IAA 7.557581e-03 203752 S at IAA 7.557581e-03 214467_at IAA 7.557581e-03 221555 X at IAA 7.557581e-03 212391 x at IAA 7.557581e-03 220947 S at IAA 7.557581e-03 212581 x at IAA 7.557581e-03 220721_at IAA 7.557581e-03 214219_x at IAA 7.557581e-03 220924 S at IAA 7.557581e-03 214746_s at IAA 7.557581e-03 221277 S at IAA 7.557581e-03 212862 at IAA 7.557581e-03 221619_s_at IAA 7.557581e-03 213414_s_at IAA 7.557581e-03 220597 S at IAA 7.557581e-03 214143 x at IAA 7.557581e-03 220704_at IAA 7.557581e-03 213892_s at IAA 7.557581e-03 220485 S at IAA 7.557581e-03 213932 x at IAA 7.557581e-03 221700 S at IAA 7.557581e-03 213084 x at IAA 7.557581e-03 220960 X at IAA 7.557581e-03 213023 at IAA 7.557581e-03 211073 X at IAA 7.557581e-03 212335_at IAA 7.557581e-03 210556_at IAA 7.557581e-03 213939 s at IAA 7.557581e-03 212039 X at IAA 7.557581e-03 'x™at "*ΪAA"Υ755158ϊ"e-03 206545 _at IAA 7.557581e-03 209549" S at IAA 7.557581e-03 206559" x at IAA 7.557581e-03 211429" S at IAA 7.557581e-03 204916""at IAA 7.557581e-03 211956" S at IAA 7.557581e-03 206060" s_at IAA 7.557581e-03 210244"_at IAA 7.557581e-03 205249" at IAA 7.557581e-03 209584" X at IAA 7.557581e-03 204959" at IAA 7.557581e-03 210162" S at IAA 7.557581e-03 206115"_at IAA 7.557581e-03 210286" S at IAA 7.557581e-03 204961" s at IAA 7.557581e-03 209841" S at IAA 7.557581e-03 206687" s at IAA 7.557581e-03 211696" X at IAA 7.557581e-03 208632" at IAA 7.557581e-03 211345" X at IAA 7.557581e-03 208821"_at IAA 7.557581e-03 209497"_s_at IAA 7.557581e-03 207936" x at IAA 7.557581e-03 212063" at IAA 7.557581e-03 207783" x at IAA 7.557581e-03 211990"_at IAA 7.557581e-03 208921" s at IAA 7.557581e-03 210638" S at IAA 7.557581e-03 208641" s_at IAA 7.557581e-03 210254"_at IAA 7.557581e-03 208082" x at IAA 7.557581e-03 211750" X at IAA 7.557581e-03 209118"[s_at IAA 7.557581e-03 211745" X at IAA 7.557581e-03 207545" s at IAA 7.557581e-03 211982" X at IAA 7.557581e-03 208812" x at IAA 7.557581e-03 209995" S at IAA 7.557581e-03 208829""at IAA 7.557581e-03 211972" X at IAA 7.557581e-03 208680"_at IAA 7.557581e-03 211734"_s_at IAA 7.557581e-03 208089" s at IAA 7.557581e-03 210646" X at IAA 7.557581e-03 208770"_s_at IAA 7.557581e-03 210241" S at IAA 7.557581e-03 207551" s_at IAA 7.557581e-03 211699" X at IAA 7.557581e-03 207500" at IAA 7.557581e-03 209791"_at IAA 7.557581e-03 209116" x at IAA 7.557581e-03 209484"_s at IAA 7.557581e-03 208616" s_at IAA 7.557581e-03 211316" X at IAA 7.557581e-03 208834" x at IAA 7.557581e-03 202349"_at IAA 7.557581e-03 209221" s at IAA 7.557581e-03 202842" S at IAA 7.557581e-03 209134" s_at IAA 7.557581e-03 203112" S at IAA 7.557581e-03 200704"[at IAA 7.557581e-03 202541"_at IAA 7.557581e-03 200031" s_at IAA 7.557581e-03 202064"_s_at IAA 7.557581e-03 200017" at IAA 7.557581e-03 202906" S at IAA 7.557581e-03 200725" x at IAA 7.557581e-03 202414"_at IAA 7.557581e-03 200002"[at IAA 7.557581e-03 202843" at IAA 7.557581e-03 200038" s at IAA 7.557581e-03 202768"_at IAA 7.557581e-03 200077" s at IAA 7.557581e-03 202021" X at IAA 7.557581e-03 200003" s at IAA 7.557581e-03 202626" S at IAA 7.557581e-03 200062" s at IAA 7.557581e-03 202737" S at IAA 7.557581e-03 200029"[at IAA 7.557581e-03 202643" S at IAA 7.557581e-03 200819" s at IAA 7.557581e-03 203175""at IAA 7.557581e-03 200030" s at IAA 7.557581e-03 202388""at IAA 7.557581e-03 200022""at IAA 7.557581e-03 202859" X at IAA 7.557581e-03 200026""at IAA 7.557581e-03 203243" S at IAA 7.557581e-03 200644 [at IAA 7.557581e-03 202934 [at IAA 7.557581e-03 200781" s_at IAA 7.557581e-03 202068" S at IAA 7.557581e-03 200078" s at IAA 7.557581e-03 202649 X at IAA 7.557581e-03 200645""at IAA 7.557581e-03 202029 X at IAA 7.557581e-03 200024""at IAA 7.557581e-03 202387 -at IAA 7.557581e-03 200674 s at IAA 7.557581e-03 202912 "at IAA 7.557581e-03 200099" s at IAA 7.557581e-03 202632""at IAA 7.557581e-03 200007"[at IAA 7.557581e-03 202431~_s at IAA 7.557581e-03 200794 x at IAA 7.557581e-03 202917 S at IAA 7.557581e-03 200025 s at IAA 7.557581e-03 203107" X at IAA 7.557581e-03 200092 s at IAA 7.557581e-03 202968 S at IAA 7.557581e-03 200834 s at IAA 7.557581e-03 205119"_s_at IAA 7.557581e-03 200705"s at IAA 7.557581e-03 205134 S at IAA 7.557581e-03 200018 "at IAA 7.557581e-03 205667 [at IAA 7.557581e-03 200715 x at IAA 7.557581e-03 204892 X at IAA 7.557581e-03 200623 s at IAA 7.557581e-03 204924 "at IAA 7.557581e-03 200033""at IAA 7.557581e-03 205922 [at IAA 7.557581e-03 200021 [at IAA 7.557581e-03 205353 S at IAA 7.557581e-03 200666[s_at IAA 7.557581e-03 205040""at IAA 7.557581e-03 200019 s at IAA 7.557581e-03 '■20i40(Par;";:
Figure imgf000396_0001
202675 at IAA 8.205811e-03 201236" s_at IAA 7 557581e-03 209146 at IAA 8.209195e-03 201071" x_at IAA 7 557581e-03 208848 at IAA 8.212483e-03 201426" s at IAA 7 557581e-03 212692_s_at IAA .234798e-03 201594" s at IAA 7 557581e-03 205212 s at IAA 8.258769e-03 200926""at IAA 7 557581e-03 205312 at IAA 8.271037e-03 201217" x_at IAA 7 557581e-03 202761 s at IAA 8.287775e-03 201254" x_at IAA 7 557581e-03 208619_at IAA 8.312131e-03 200963" x at IAA 7 557581e-03 200866 s at IAA 8.320536e-03 201356"JLt IAA 7 557581e-03 218152 at IAA 8.33309e-03 201331" s at IAA 7 557581e-03 202846 s at IAA 8.338187e-03 201258"_at IAA 7 557581e-03 207734 at IAA 8.367267e-03 201143" s_at IAA CO7 557581e-03 202923 s at IAA 8.409531e-03 201953" at IAA 7 557581e-03 202856 s at IAA 8.441958e-03 201642"_at IAA 7 557581e-03 207983 s at IAA 8.45187e-03 200871" s_at IAA 7 557581e-03 53071 s at IAA 8.519703e-03 201315" x at IAA 7 557581e-03 209906 at IAA 8.585797e-03 201180" s at IAA 7 557581e-03 217990 at IAA 8.592255e-03 200936"_at IAA 7 557581e-03 217827 s at IAA 8.593364e-03 200937" s_at IAA 7 557581e-03 221808 at IAA 8.595504e-03 201172" x at IAA 7 557581e-03 201536 at IAA 8.599995e-03 200905" x_at IAA 7 557581e-03 218136 s at IAA 8.61194e-03 201737" s at IAA 7 557581e-03 213159 at IAA 8.647098e-03 200949" x at IAA 7 557581e-03 217822 at IAA 8.66955e-03 201257" x at IAA 7 557581e-03 201277 s at IAA 8.672249e-03 200933" x at IAA 7 557581e-03 205627 at IAA 8.707481e-03 201094" at IAA 7 557581e-03 209994 s at IAA 8.765132e-03 201859" at IAA 7 557581e-03 218128_at IAA 8.804967e-03 201041" s at IAA 7 557581e-03 208876 s at IAA 8.807416e-03 201010" s_at IAA 7 557581e-03 201175 at IAA 8.813446e-03 201492" s at IAA 7 557581e-03 218570 at IAA 8.81458e-03 201466" s_at IAA 7 557581e-03 203113 s at IAA 8.81582e-03 201049" s_at IAA 7 557581e-03 202451 at IAA 8.821909e-03 201030" x at IAA 7 557581e-03 1553644 at IAA 8 CO.834419e-03 200858" s_at IAA 7 557581e-03 226354 at IAA 8.838035e-03 204354"_at IAA 7 558249e-03 AFFX-HUMGAPDH/ 217985" s at IAA 7 578506e-03 M33197 IAA 8.838035e-03 202124" s_at IAA 7 578961e-03 218950 at IAA 8.838035e-03 208485" x at IAA 7 633872e-03 213046 at IAA 8.838035e-03 218929" at IAA 7 651591e-03 206978 at IAA 8.838035e-03 229510"_at IAA 7 667683e-03 204908 s at IAA 8.838035e-03 205608" s at IAA 7 689956e-03 201040 at IAA 8.838035e-03 201503" at IAA 7 719202e-03 201590 x at IAA 8.838035e-03 204061""at IAA 7 732518e-03 201666 at IAA 8.838035e-03 204299"~at IAA 7 744186e-03 214118 x at IAA 8.867592e-03 207668" x_at IAA 7 744199e-03 211675 s at IAA 8.875114e-03 211924" s at IAA 7 7605e-03 201753 s at IAA 8.891296e-03 230370" x at IAA 7 767939e-03 200654 at IAA 8.918234e-03 203778"JIt IAA 7 780889e-03 205324 s at IAA 8.920427e-03 202232" s_at IAA 7 788512e-03 220052_s_at IAA 8.938102e-03 211971" s at IAA 7 831334e-03 208843 s at IAA 8.943604e-03 222409"JLt IAA 7 873742e-03 201661 s at IAA 8.958128e-03 212459" x_at IAA 7 88595e-03 204474 at IAA 8.990666e-03 200800" s at IAA 7 921623e-03 217371 s at IAA 8.991858e-03 201933""at IAA 7 930456e-03 201251 at IAA 9.019572e-03 220776""at IAA 7 947444e-03 209798 at IAA 9.044367e-03 208860" s_at IAA 7 993649e-03 203261 at IAA 9.102636e-03 204714" s_at IAA 8 0145e-03 208117 s at IAA 9.108323e-03 211135" x at IAA 8 023124e-03 210039 s at IAA 9.147124e-03 213019[at IAA 026806e-03 218383 at IAA 9.199698e-03 210240" s at IAA 8 112782e-03 208800 at IAA 9.204127e-03 213590""at IAA 8 156202e-03 242056 at IAA 9.215e-03 219446""at IAA 8 166354e-03 37950 at IAA 9.225131e-03 202141 s at IAA 175627e-03 204297 at IAA 9.225131e-03 '2"222Od'" s at
Figure imgf000397_0001
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210093"~s~at IAA 9.567413e-03 218966 at IAA 0.01 210395" x at IAA 9.591381e-03 222587 s at IAA 0.01 203166""at IAA 9.594938e-03 204190 at IAA 0.01 219296" at IAA 9.626187e-03 218589 at IAA 0.01 214450""at IAA 9.656924e-03 201634 s at IAA 0.01 202136"_at IAA 9.678885e-03 220019 s_at IAA 0.01 203302""at IAA 9.680746e-03 221080 s at IAA 0.01 222243" s at IAA 9.685251e-03 202747 s_at IAA 0.01 211936""at IAA 9.733779e-03 201517 at IAA 0.01 202683""s_at IAA 9.763329e-03 201334 s at IAA 0.01 208730" x at IAA 9.79081e-03 211071 s_at IAA 0.01 202162"~s_at IAA 9.818255e-03 218165 at IAA 0.01 203049" s at IAA 9.933102e-03 202020 s at IAA 0.01 219819" s at IAA 9.937498e-03 204639 at IAA 0.01 201873" s at IAA 9.938002e-03 219563 at IAA 0.01 218548" x at IAA 9.957915e-03 212168 at IAA 0.01 51200 at IAA 9.959403e-03 201836 s at IAA 0.01 203102 s at IAA 9.974563e-03 1555609 a at IAA 0.01 204172""at IAA 0.01 220237 at IAA 0.01 212973" at IAA 0.01 210962 s at IAA 0.01 206284" x at IAA 0.01 210543 s_at 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IAA 0.02 202589""at IAA 0.02 209155" s at IAA 0.02 202395"_at IAA 0.02 207564"x at IAA 0.02 203053" at IAA 0.02 209103"_s_at IAA 0.02 203132""at IAA 0.02 208887~[at IAA 0.02 203186" s at IAA 0.02 208929"x_at IAA 0.02 202595" s_at IAA 0.02 207614 s_at IAA 0.02 202809 ~s_at IAA 0.02 208728 s at IAA 0.02 202985" s at IAA 0.02 208424"s at IAA 0.02 202644" s at IAA 0.02 208828""at IAA 0.02 202295 s at IAA 0.02 209058""at IAA 0.02 202069" s at IAA 0.02 208363 s at IAA 0.02 202704""at IAA 0.02 208909 "at IAA 0.02 202573""at IAA 0.02 208736 "at IAA 0.02 202529 "at IAA 0.02 207536 _s_at IAA 0.02 202016 "at IAA 0.02 208718.at IAA 0.02 202499"s at IAA 0.02 208313"s at IAA 0.02 202342 s at IAA 0.02 208695 s at IAA 0.02 202887 s at IAA 0.02 208648 _at IAA 0.02 202444 s at IAA 0.02 209345 s at IAA 0.02 203034 s at IAA 0.02 208689 s at IAA 0.02 202672 ]s_at IAA 0.02 208678 ~at IAA 0.02 202925 s at IAA 0.02 208964 s at IAA 0.02 208904' S at"* IAA θiθ'2 217356_s_at IAA 0.02 200034" S at IAA 0.02 212597 s at IAA 0.02 200732" S at IAA 0.02 207945 s at IAA 0.02 200675"_at IAA 0.02 211559 s at IAA 0.02 200097" s_at IAA 0.02 211762 s at IAA 0.02 200089" S at IAA 0.02 218724_s_at IAA 0.02 200096" S at IAA 0.02 209574 s at IAA 0.02 200754" X at IAA 0.02 201298 s at IAA 0.02 200087" S at IAA 0.02 218526 s at IAA 0.02 200716" X at IAA 0.02 212263_at IAA 0.02 200040""at IAA 0.02 201231 s at IAA 0.02 200012" X at IAA 0.02 221692 s at IAA 0.02 200686" S at IAA 0.02 208453 s at IAA 0.02 200013"_at IAA 0.02 220796 x at IAA 0.02 200805"_at IAA 0.02 225819 at IAA 0.02 200023" S at IAA 0.02 208638 at IAA 0.02 200036" s_at IAA 0.02 214662 at IAA 0.02 200009" at IAA 0.02 206513 at IAA 0.02 200826"_at IAA 0.02 1569349 at IAA 0.02 200717" X at IAA 0.02 71933 at IAA 0.02 200633"_at IAA 0.02 202147 s at IAA 0.02 200763" S at IAA 0.02 212918_at IAA 0.02 200032" s__at IAA 0.02 213775 x at IAA 0.02 200768" S at IAA 0.02 1554770 x at IAA 0.02 200037" S at IAA 0.02 217794_at IAA 0.02 200642" at IAA 0.02 203449 s_at IAA 0.02 200064"_at IAA 0.02 238148 s at IAA 0.02 200810" S at IAA 0.02 225634 at IAA 0.02 200823" X at IAA 0.02 204369_at IAA 0.02 201357" S at IAA 0.02 218286 s at IAA 0.02 201354" S at IAA 0.02 204655 at IAA 0.02 200965" S at IAA 0.02 226283_at IAA 0.02 201605" X at IAA 0.02 213322 at IAA 0.02 201446" S at IAA 0.02 206118 at IAA 0.02 200889" S at IAA 0.02 223009_at IAA 0.02 201033" X at IAA 0.02 202074 s_at IAA 0.02 201296" S at IAA 0.02 221724 s at IAA 0.02 201457" X at IAA 0.02 206448_at IAA 0.02 201948" at IAA 0.02 219487_at IAA 0.02 201218"_at IAA 0.02 202449 s_at IAA 0.02 200909" S at IAA 0.02 212006_at IAA 0.02 201853" S at IAA 0.02 213128 s at IAA 0.02 201154" X at IAA 0.02 201079_at IAA 0.02 201891" S at IAA 0.02 201800 s at IAA 0.02 201550" X at IAA 0.02 202542 s_at IAA 0.02 201569" S at IAA 0.02 224693_at IAA 0.02 201633" S at IAA 0.02 213261 at IAA 0.02 201548 S at IAA 0.02 34764 at IAA 0.02 200919""at IAA 0.02 209153 s at IAA 0.02 201551" S at IAA 0.02 200759 x at IAA 0.02 201988" S at IAA 0.02 219156_at IAA 0.02 201751""at IAA 0.02 211784 s at IAA 0.02 201429" S at IAA 0.02 201216_at IAA 0.02 201433" S at IAA 0.02 220775 s at IAA 0.02 200868" S at IAA 0.02 206219 s_at IAA 0.02 201468" S at IAA 0.02 202642 s at IAA 0.02 200982 S at IAA 0.02 207812 s at IAA 0.02 218527 "at IAA 0.02 200714' x at IAA 0.02 201088"[at IAA 0.02 235327' x at IAA 0.02 222201 S at IAA 0.02 214473 x at IAA 0.02 209267 _s_at IAA 0.02 200020'_at IAA 0.02 235388 "at IAA 0.02 207687'_at IAA 0.02 218747" S at IAA 0.02 207791 s at IAA 0.02 221711 S at IAA 0.02 201925' s at IAA 0.02 218889"at "IAA'"oTδ2 202868_s__at IAA 0.02 219206"x at IAA 0.02 201500_s_at IAA 0.02 202813~"at IAA 0.02 203140_at IAA 0.02 204203""at IAA 0.02 215813_s_at IAA 0.02 220661"s_at IAA 0.02 213528_at IAA 0.02 223011"s at IAA 0.02 202127_at IAA 0.02 221269"s at IAA 0.02 225361_x_at IAA 0.02 210705 s_at IAA 0.02 212733_at IAA 0.02 219176 at IAA 0.02 217751_at IAA 0.02 203893""at IAA 0.02 201669_s_at IAA 0.02 224560"~.at IAA 0.02 201877_s_at IAA 0.02 226387 "at IAA 0.02 207986_x_at IAA 0.02 218659 _at IAA 0.02 36019_at IAA 0.02 220367 s at IAA 0.02 203035_s_at IAA 0.02 218941 "_at IAA 0.02 214315_x__at IAA 0.02 213405 "at IAA 0.02 208729_x_at IAA 0.02 212377 s_at IAA 0.02 203615_x_at IAA 0.02 203528 at IAA 0.02 216268_s_at IAA 0.02 221484 "at IAA 0.02 220560_at IAA 0.02 222985 at IAA 0.02 218633_x_at IAA 0.02 202878 s at IAA 0.02 206649_s_at IAA 0.02 203236 s at IAA 0.02 208667_s_at IAA 0.02 205012 s at IAA 0.02 203973_s_at IAA 0.02 200640 "at IAA 0.02 212742_at IAA 0.02 201861 s at IAA 0.02 202855_s_at IAA 0.02 201422 at IAA 0.02 218999_at IAA 0.02 201864 j≡lt IAA 0.02 217811_at IAA 0.02 201635 s at IAA 0.02 220143_x__at IAA 0.02 236027 at IAA 0.02 225319_s_at IAA 0.02 209054 s at IAA 0.02 202990_at IAA 0.02 201425 at IAA 0.02 204616_at IAA 0.02 202123 s at IAA 0.02 34031_i_at IAA 0.02 200964 at IAA 0.02 211576_s_at IAA 0.02 31845 at IAA 0.02 211015_s_at IAA 0.02 213626 at IAA 0.02 203839_s_at IAA 0.02 217834 s at IAA 0.02 204786_s_at IAA 0.02 221498 at IAA 0.02 1560339_s_at IAA 0.02 210732 s at IAA 0.02 221058_s_at IAA 0.02 205419 at IAA 0.02 202206_at IAA 0.02 220248 x at IAA 0.02 201300_s_at IAA 0.02 201054 at IAA 0.02 202679_at IAA 0.02 1556744 a at IAA 0.02 227388_at IAA 0.02 201784 s at IAA 0.02 225976_at IAA 0.02 1554899_s at IAA 0.02 200648_s_at IAA 0.02 203363 s at IAA 0.02 205099_s_at IAA 0.02 218256 s at IAA 0.02 205462_s_at IAA 0.02 207358 x at IAA 0.02 202795_x_at IAA 0.02 217981 s at IAA 0.02 204351_at IAA 0.02 219151 s at IAA 0.02 211988_at IAA 0.02 217978 s at IAA 0.02 203303_at IAA 0.02 219848 s at IAA 0.02 231809_x_at IAA 0.02 50965 at IAA 0.02 207289_at IAA 0.02 214866 at IAA 0.02 222052_at IAA 0.02 212602 at IAA 0.02 200050_at IAA 0.02 203353 s at IAA 0.02 211955_at IAA 0.02 202621 at IAA 0.02 222631_at IAA 0.02 219672 at IAA 0.02 218420_s_at IAA 0.02 214995 s at IAA 0.02 221718_s_at IAA 0.02 38241 at IAA 0.02 212904_at IAA 0.02 212657 s at IAA 0.02 213452_at IAA 0.02 219200 at IAA 0.02 225081_s_at IAA 0.02 202351 at IAA 0.02 209131_s_at IAA 0.02 202155 s at IAA 0.02 202658_at IAA 0.02 211747 s at IAA 0.02 221059 s at IAA 0.02 210152_at IAA 0.02 218610 s at IAA 0.02 218522" s at IAA 0.02 205047" s at IAA 0.02 219690"_at IAA 0.02 219821" s at IAA 0.02 202577" s_at IAA 0.02 203646"_at IAA 0.02 210555" s_at IAA 0.02 201584" s_at IAA 0.02 206846" s at IAA 0.02 207667" s at IAA 0.02 224984" at IAA 0.02 201960" s at IAA 0.02 218514" at IAA 0.02 221847"_at IAA 0.02 209065"_at IAA 0.02 206571" s_at IAA 0.02 217737" x at IAA 0.02 209770" at IAA 0.02 218175"_at IAA 0.02 203401" at IAA 0.02 218109" s_at IAA 0.02 208982" at IAA 0.02 210582" s_at IAA 0.02 211698" at IAA 0.02 214298" x at IAA 0.02 223620" at IAA 0.02 202355" s at IAA 0.02 202754"_at IAA 0.02 218018" at IAA 0.02 208997" s at IAA 0.02 201955"_at IAA 0.02 202779" s_at IAA 0.02 208673" s at IAA 0.02 231747" at IAA 0.02 203234" at IAA 0.02 203801" at IAA 0.02 219505" at IAA 0.02 202822"_at IAA 0.02 218019" s at IAA 0.02 208454" s at IAA 0.02 212383" at IAA 0.02 212351" at IAA 0.02 210114"_at IAA 0.02 209467" s at IAA 0.02 203647" s at IAA 0.02 202915" s at IAA 0.02 214421" x at IAA 0.02 218503" at IAA 0.02 219212" at IAA 0.02 220566" at IAA 0.02 218163"_at IAA 0.02 218632"_at IAA 0.02 200915" x_at IAA 0.02 218882" s at IAA 0.02 208802" at IAA 0.02 201692"_at IAA 0.02 218696" at IAA 0.02 208741" at IAA 0.02 218244"_at IAA 0.02 212599" at IAA 0.02 212037"_at IAA 0.02 224733"_at IAA 0.02 219235" s_at IAA 0.02 224160" s_at IAA 0.02 200694" s at IAA 0.02 225406^_at IAA 0.02 201136"_at IAA 0.02 243 g at IAA 0.02 206491" s at IAA 0.02 228234 at IAA 0.02 218062" x at IAA 0.02 235181" at IAA 0.02 204520" x_at IAA 0.02 220288" at IAA 0.02 218324" s at IAA 0.02 217903" at IAA 0.02 201000" at IAA 0.02 218066"_at IAA 0.02 201007" at IAA 0.02 203825" at IAA 0.02 201501" s at IAA 0.02 203718 at IAA 0.02 200799" at IAA 0.02 221485 "at IAA 0.02 201365"_at IAA 0.02 209479""at IAA 0.02 217940" s at IAA 0.02 202896"_s_at IAA 0.02 203514"jit IAA 0.02 202532" s at IAA 0.02 208961" s at IAA 0.02 207719"x_at IAA 0.02 205821"_at IAA 0.02 208018 _s_at IAA 0.02 218239 s at IAA 0.02 208773" s at IAA 0.02 204839""at IAA 0.02 200614""at IAA 0.02 202757 "at IAA 0.02 155296"ϊ at IAA 0.02 203335""at IAA 0.02 200814 _at IAA 0.02 201274 "at IAA 0.02 200852"x at IAA 0.02 208796 s at IAA 0.02 201552 [at IAA 0.02 201494 "at IAA 0.02 203137"[at IAA 0.02 35156 at IAA 0.02 218298" s at IAA 0.02 209188 x at IAA 0.02 218056[at IAA 0.02 1555522I s at IAA 0.02 203195 s_at IAA 0.02 220035 _at IAA 0.02 213009 _s_at IAA 0.02 204049 _s_at IAA 0.02 223658 "at IAA 0.02 209476[at IAA 0.02 218594 [at IAA 0.02 203362" s at IAA 0.02 222199 s_at IAA 0.02 202510 s_at IAA 0.02 205763 s_at IAA 0.02 206809 s at IAA 0.02 219913 s at IAA 0.02 219774_at IAA 0.02 207606 s at IAA 0.03 213331 s_at IAA 0.02 219717 at IAA 0.03 211271 x at IAA 0.02 202874 s at IAA 0.03 201053 s at IAA 0.02 39402 at IAA 0.03 218697_at IAA 0.02 218140_x at IAA 0.03 235057 at IAA 0.02 218449 at IAA 0.03 205594_at IAA 0.02 217880 at IAA 0.03 218405 at IAA 0.02 203741 s at IAA 0.03 201135 at IAA 0.02 227379 at IAA 0.03 210038 at IAA 0.02 220199 s at IAA 0.03 204436 at IAA 0.02 202888 s at IAA 0.03 218050_at IAA 0.02 208767 s at IAA 0.03 203139 at IAA 0.02 202321 at IAA 0.03 208655_at IAA 0.02 209701 at IAA 0.03 202259 s at IAA 0.02 211004 s at IAA 0.03 218454_at IAA 0.02 212855 at IAA 0.03 220495 s at IAA 0.02 211521 s at IAA 0.03 201515 s at IAA 0.02 204529 s at IAA 0.03 1554464 a at IAA 0.02 205583 s at IAA 0.03 210579 s at IAA 0.03 226761 at IAA 0.03 219231 at IAA 0.03 226404 at IAA 0.03 206734 at IAA 0.03 207300 s at IAA 0.03 241955 at IAA 0.03 204084 s at IAA 0.03 218512_at IAA 0.03 201202 at IAA 0.03 202634_at IAA 0.03 223474 at IAA 0.03 201110 s at IAA 0.03 203529 at IAA 0.03 220371 s_at IAA 0.03 212239 at IAA 0.03 209198 s at IAA 0.03 206914 at IAA 0.03 205621 at IAA 0.03 203062 s at IAA 0.03 218397 at IAA 0.03 221920 s at IAA 0.03 205323_s_at IAA 0.03 203484 at IAA 0.03 223124_s_at IAA 0.03 202962 at IAA 0.03 204594 s at IAA 0.03 204992 s at IAA 0.03 205917_at IAA 0.03 208931 s at IAA 0.03 223350 x_at IAA 0.03 207601 at IAA 0.03 218571 s at IAA 0.03 205839 s at IAA 0.03 214709 s_at IAA 0.03 204072 s at IAA 0.03 210561 s at IAA 0.03 225364 at IAA 0.03 208799_at IAA 0.03 221036 s at IAA 0.03 211395 x at IAA 0.03 228648 at IAA 0.03 219421_at IAA 0.03 218026 at IAA 0.03 202558 s at IAA 0.03 1553594 a at IAA 0.03 201004 at IAA 0.03 226249 at IAA 0.03 207275 s at IAA 0.03 221708 s_at IAA 0.03 203460 s at IAA 0.03 206655 s at IAA 0.03 213743_at IAA 0.03 1557905 s at IAA 0.03 203412_at IAA 0.03 209486 at IAA 0.03 200753 x at IAA 0.03 204283 at IAA 0.03 202380 s_at IAA 0.03 208892 s at IAA 0.03 203104 at IAA 0.03 201423 s at IAA 0.03 201858 s at IAA 0.03 208308 s at IAA 0.03 218781_at IAA 0.03 1559883__s_at IAA 0.03 209828 s_at IAA 0.03 200673 at IAA 0.03 205081 at IAA 0.03 208270 s at IAA 0.03 57163 at IAA 0.03 212792 at IAA 0.03 205063_at IAA 0.03 201276 at IAA 0.03 213475 s_at IAA 0.03 201611 s at IAA 0.03 218838_s_at IAA 0.03 201321 s at IAA 0.03 202121 s at IAA 0.03 219303 at IAA 0.03 200738 s at IAA 0.03 215706 x at IAA 0.03 215228_at IAA 0.03 204800 s at IAA 0.03 212034 s at IAA 0.03 203791 at IAA 0.03 214054 at IAA 0.03 220235 s at IAA 0.03 203373 at IAA 0.03 214687 x at IAA 0.03 205176" s_at IAA 0.03 201795 at IAA 0.03 200048" s_at IAA 0.03 211609 X at IAA 0.03 208403" x_at IAA 0.03 205644~S at IAA 0.03 214629" x at IAA 0.03 218174" S ~at IAA 0.03 214369" s at IAA 0.03 215806 X at IAA 0.03 203079" s_at IAA 0.03 220712""at IAA 0.03 212825" at IAA 0.03 205408 "at IAA 0.03 204640" s at IAA 0.03 202860""at IAA 0.03 203432" at IAA 0.03 207064 S at IAA 0.03 205170"_at IAA 0.03 225649 S at IAA 0.03 221060" s_at IAA 0.03 203116 S at IAA 0.03 210146" x at IAA 0.03 209604"S at IAA 0.03 201930"_at IAA 0.03 212757"S at IAA 0.03 241715" x at IAA 0.03 201697"s_at IAA 0.03 48531 ;at IAA 0.03 208656 S at IAA 0.03 204246 s at IAA 0.03 203487"S at IAA 0.03 222601" at IAA 0.03 218288 S at IAA 0.03 201526" at IAA 0.03 201360 at IAA 0.03 203501" at IAA 0.03 203513 "at IAA 0.03 209286" at IAA 0.03 208581 X at IAA 0.03 200004" at IAA 0.03 218924 S at IAA 0.03 212381"_at IAA 0.03 210095"S at IAA 0.03 222975" s at IAA 0.03 203409 "at IAA 0.03 204630" s_at IAA 0.03 217966 S at IAA 0.03 201832" s at IAA 0.03 202042 IAA 0.03 221826"_at IAA 0.03 204220 "at IAA 0.03 204019"_s_at IAA 0.03 207351 S at IAA 0.03 202746""at IAA 0.03 212413 at IAA 0.03 211684" s_at IAA 0.03 210538 s_at IAA 0.03 208249"_s_at IAA 0.03 209060 X at IAA 0.03 206738"_at IAA 0.03 204060 S at IAA 0.03 233467" s_at IAA 0.03 223115 "at IAA 0.03 226629"_at IAA 0.03 225425 S at IAA 0.03 205690" s_at IAA 0.03 225899 X at IAA 0.03 213418"_at IAA 0.03 239381 at IAA 0.03 211561" x at IAA 0.03 213716 S at IAA 0.03 202174"_s_at IAA 0.03 217993 S at IAA 0.03 231868""at IAA 0.03 217336 at IAA 0.03 202641" at IAA 0.03 203591 s_at IAA 0.03 218395" at IAA 0.03 211178 s_at IAA 0.03 219066"_at IAA 0.03 211948 X at IAA 0.03 217299" s_at IAA 0.03 210951 X at IAA 0.03 210088"_x_at IAA 0.03 202424 at IAA 0.03 223283" s at IAA 0.03 202435 S at IAA 0.03 200953" s_at IAA 0.03 202100 at IAA 0.03 220537" at IAA 0.03 205685 at IAA 0.03 200837"_at IAA 0.03 206976 s_at IAA 0.03 209852" x at IAA 0.03 208771 s_at IAA 0.03 200655"_s_at IAA 0.03 208945 S at IAA 0.03 212789""at IAA 0.03 207761 S at IAA 0.03 201631" s at IAA 0.03 208610 s_at IAA 0.03 209150"_s_at IAA 0.03 208998 at IAA 0.03 227180""at IAA 0.03 208634 S at IAA 0.03 202150" s at IAA 0.03 1552264 a at IAA 0.03 226510""at IAA 0.03 1554240_a at IAA 0.03 217724""at IAA 0.03 200859 X at IAA 0.03 203629 s at IAA 0.03 201137 S at IAA 0.03 203943"[at IAA 0.03 219037 at IAA 0.03 211727" s at IAA 0.03 211922 S at IAA 0.03 218967" s at IAA 0.03 202164 s_at IAA 0.03 59644 at IAA 0.03 200853 at IAA 0.03 217989 _at IAA 0.03 213875 X at IAA 0.03 207677"s at IAA 0.03 205672 at IAA 0.03 202330" s at IAA 0.03 35617 at IAA 0.03 231540_at IAA'"0.03 203658_at IAA 0.03 208720_s_at IAA 0.03 219700__at IAA 0.03 204336_s_at IAA 0.03 213915__at IAA 0..0033 222708_s_at IAA 0.03 52164_at IAA 0..0033 217726_at IAA 0.0033 203317_at IAA 0.0033 207338_s_at IAA 0.0033 204960_at IAA 0.0033 224281_s_at IAA 0.0033 226378_s_at IAA 0.0033 212213_x_at IAA 0.0033 206586_at IAA 0.0033 117_at IAA 0.0033 204244_s_at IAA 0.0033 217797_at IAA 0.0033 220760_x_at IAA 0.0033 218259_at IAA 0.03 201323_at IAA 0.0033 210912_x_at IAA 0 03 205241_at IAA 0.0033 208705_s_at IAA 0.03 217865_at IAA 0.0033 204446_s_at IAA 0.03 200706_s_at IAA 0.0033 207691_x_at IAA 0.03 53720_at IAA 0.0033 210479_s_at IAA 0.03 202530_at IAA 0.0033 217826_s_at IAA 0.03 1552553_a_at IAA 0.03 201950_x_at IAA 0.03 200057_s_at IAA 0.03 206377_at IAA 0.03 213049_at IAA 0.03 203291_at IAA 0.03 217912_at IAA 0.03 212014_x_at IAA 0.03 216689_x_at IAA 0.03 209455_at IAA 0.03 202491_s_at IAA 0.03 243196_s_at IAA 0.03 211961_s_at IAA 0.03 205299_s_at IAA 0.03 207541_s_at IAA 0.03 219217_at IAA 0.03 220980_s_at IIAAAA 00..0033 221580_s_at IAA 0.03 223034_s_at IIAAAA 00..0033 218111_s_at IAA 0.03 200978_at IIAAAA 00..0033 201089_at IAA 0.03 209005_at IAA 0.03 206158_s_at IAA 0.03 208684_at IAA 0.03 205383_s_at IAA 0.03 1487_at IAA 0.03 200782_at IAA 0..03 201477_s_at IAA 0.03 223637 s_at IAA 0.03 202060_at IAA 0.03 226994~at IAA 0..0033 218979_at IAA 0.03 202853_s_at IAA 0..0033 218646_at IAA 0..0033 203572_s_at IAA 0.03 1552301_a_at IAA 0..0033 214224_s_at IAA 0.03 202665_s_at IAA 0..0033 221002_s_at IAA 0.03 218249_at IAA 0..0033 205609_at IAA 0..0033 202221_s_at IAA 0..0033 202207_at IAA 0..0033 201144_s_at IAA 0..0033 219041_s_at IAA 0..0033 211270_x_at IAA 0..0044 218699_at IAA 0..0033 205006_s_at IAA 0.04 204700_x_at IAA 0..0033 217995_at IAA 0.04 201804_x_at IAA 0..0033 202364_at IAA 0..0044 214011_s_at IAA 0..0033 221020_s_at IAA 0..0044 200744_s_at IAA 0..0033 222014_x_at IAA 0.04 203508_at IAA 0.03 212539_at IAA 0.04 217207_s_at IAA 0.03 201178_at IIAAAA 00.,04 202565_s_at IAA 0.03 208919_s_at IIAAAA 00.,04 225392_at IAA 0,.03 202361_at IAA 0.04 201413_at IAA 0,.03 206324_s_at IAA 0.04 202817_s_at IAA 0.03 211702_s_at IAA 0 04 214314_s_at IAA 0,.03 202557_at IAA 0 04 218735_s_at IAA 0,.03 203252_at IAA 0 04 221581_s_at IAA 0,.03 204867_at IAA 0 04 204788_s_at IAA 0.03 210317_s_at IAA 0 04 222623_s_at IAA 0.03 201082_s_at IAA 0 04 213239_at IAA 0..03 214583_at IAA 0, 04 201985_at IAA 0,.03 204050_s_at IAA 0.04 219802_at IAA 0.03 207969_x_at IAA 0, 04 212453_at IAA 0.03 211385_x_at IAA 0.04 204949_at IAA 0.03 202897_at IAA 0.04 202666_s_at IAA 0.03 225390_s_at IAA 0.04 64408_s_at IAA 0.,03 206420_at IAA 0.04 216396 s at IAA 0.03 204971 at IAA 0.04 220261 s_at IAA 0.04 207535 s at IAA 0.04 210858" x at IAA 0.04 217772 s at IAA 0.04 203900""at IAA 0.04 208918 s at IAA 0.04 207078"_at IAA 0.04 225177 at IAA 0.04 201012" at IAA 0.04 224619_at IAA 0.04 204176"_at IAA 0.04 224374 s at IAA 0.04 214511" x at IAA 0.04 219126_at IAA 0.04 220477"~s_at IAA 0.04 213318_s_at IAA 0.04 211760" s at IAA 0.04 217835_x_at IAA 0.04 225032" at IAA 0.04 215783_s_at IAA 0.04 219317" at IAA 0.04 217858 s at IAA 0.04 223506" at IAA 0.04 218217_at IAA 0.04 226489" at IAA 0.04 204467_s_at IAA 0.04 219353"_at IAA 0.04 210835 s at IAA 0.04 204387" x at IAA 0.04 206174 s at IAA 0.04 210772"]at IAA 0.04 208174 x at IAA 0.04 217763" s at IAA 0.04 209141_at IAA 0.04 217960" s at IAA 0.04 209435_s_at IAA 0.04 208579" x at IAA 0.04 200722 s at IAA 0.04 201715"_s_at IAA 0.04 1555948 s at IAA 0.04 201090" x at IAA 0.04 201647_s_at IAA 0.04 220659" s at IAA 0.04 220148_at IAA 0.04 209949"_at IAA 0.04 214339_s_at IAA 0.04 202527" s at IAA 0.04 203110__at IAA 0.04 200676"~s_at IAA 0.04 227268 at IAA 0.04 231896" s at IAA 0.04 218146_at IAA 0.04 201074" at IAA 0.04 212407_at IAA 0.04 203692" s at IAA 0.04 209615__s at IAA 0.04 225378" at IAA 0.04 211725 s at IAA 0.04 209111" at IAA 0.04 209233_at IAA 0.04 202182"_at IAA 0.04 215548 s at IAA 0.04 224209" s at IAA 0.04 200864 s at IAA 0.04 201463"~s_at IAA 0.04 214291_at IAA 0.04 208622" s at IAA 0.04 217216 x at IAA 0.04 218177"~at IAA 0.04 34206 at IAA 0.04 207674" at IAA 0.04 36030 at IAA 0.04 214578" s_at IAA 0.04 202194_at IAA 0.04 203126" at IAA 0.04 201392_s at IAA 0.04 204208"_at IAA 0.04 213287 s at IAA 0.04 201779" s at IAA 0.04 211139_s_at IAA 0.04 201302""at IAA 0.04 201721__s at IAA 0.04 232914" s at IAA 0.04 202801 at IAA 0.04 206183""s at IAA 0.04 220307_at IAA 0.04 227647""at IAA 0.04 202645 s at IAA 0.04 209662" at IAA 0.04 204683_at IAA 0.04 201889"_at IAA 0.04 218773_s at IAA 0.04 212194" s at IAA 0.04 211505_s_at IAA 0.04 204493""at IAA 0.04 1405 i_at IAA 0.04 219253"_at IAA 0.04 212305 s at IAA 0.04 224391" s at IAA 0.04 215691__x at IAA 0.04 217100" s at IAA 0.04 203765_at IAA 0.04 205171""at IAA 0.04 210201 x at IAA 0.04 209265" s at IAA 0.04 218367 x at IAA 0.04 219816" s at IAA 0.04 202189 x at IAA 0.04 218315" s at IAA 0.04 209135 at IAA 0.04 41329 at IAA 0.04 203523_at IAA 0.04 208864 _s_at IAA 0.04 221208 s_at IAA 0.04 202604" x at IAA 0.04 202422 s_at IAA 0.04 202484" s at IAA 0.04 213699 s_at IAA 0.04 210164""at IAA 0.04 200696 s at IAA 0.04 1558136 s at IAA 0.04 205856~at IAA 0.04 209499 x at IAA 0.04 217941 s at IAA 0.04 202408 s at IAA 0.04 205831"at IAA 0.04 227143" s at IAA 0.04 208688 x at IAA 0.04 202477_s_at ~"1AA""O ,04 M33197 IAA 0..04 219860_at IAA 0.04 635_s_at IAA 0..04 204070_at IAA 0.04 AFFX-HUMGAPDH/ 212100_s_at IAA 0.04 M33197 IAA 0,.04 217830_s_at IAA 0 ,04 52169_at IAA 0,,04 222503_s_at IAA 0 ,04 53968_at IAA 0.,04 218456_at IAA 0 ,04 57532_at IAA 0,.04 201591_s_at IAA 0 ,04 65591_at IAA 0.,04 209444_at IAA 0 ,04 219952_s_at IAA 0.,04 211801_x_at IAA 0 04 218713_at IAA 0..04 214439_x_at IAA 0 04 218708_at IAA 0.04 201512_s_at IAA 0 ,04 219129_s_at IAA 0.,04 233759_s_at IAA 0 04 220054_at IAA 0, 04 212262_at IAA 0 04 218660_at IAA 0.04 218167_at IAA 0 04 218637_at IAA 0.04 217964_at IAA 0 04 219256_s_at IAA 0.04 221677_s_at IAA 0 04 218992_at IAA 0.04 209128_s_at IAA 0 04 218648_at IAA 0.04 217947_at IAA 0 04 219575_s_at IAA 0.04 217861_s_at IAA 0 04 220086__at IAA 0.04 212642_s__at IAA 0 04 219724__s_at IAA 0.04 202538_s__at IAA 0 04 219137_s_at IAA 0.04 201965_s_at IAA 0.04 218575__at IAA 0.04 200049_at IAA 0.04 218607_s_at IAA 0.04 207549_x_at IAA 0 04 219667__s_at IAA 0.04 221219_s_at IAA 0 04 218777_at IAA 0.04 217870_s_at IAA 0, 04 219540_at IAA 0.04 201823_s_at IAA 0, 04 219571_s_at IAA 0.04 201189_s_at IAA 0, 04 219806_s_at IAA 0.04 57539_at IAA 0, 04 218739_at IAA 0.04 221290_s_at IAA 0.04 218982_s_at IAA 0.04 217842_at IAA 0, 04 218625_at IAA 0.04 211970_x_at IAA 0.04 218474~_s_at IAA 0.04 225623_at IAA 0, 04 219429_at IAA 0.04 212130_x_at IAA 0.04 219570_at IAA 0.04 1553297_a_at IAA 0.04 219649_at IAA 0.04 213507_s_at IAA 0.04 219679_s_at IAA 0.04 230214_at IAA 0.04 219648_at IAA 0.04 211558_s_at IAA 0.04 219073_s_at IAA 0.04 219248_at IAA 0.04 218802_at IAA 0.04 217885_at IAA 0.04 220032_at IAA 0.04 1553562_at IAA 0.04 219345_at IAA 0.04 202654_x_at IAA 0.04 218978_s_at IAA 0.04 207569_at IAA 0.04 219118_at IAA 0.04 213491_x_at IAA 0.04 219528_s_at IAA 0.04 217980_s_at IAA 0.04 218482_at IAA 0.04 209514_s_at IAA 0.04 219812_at IAA 0.04 46256_at IAA 0.04 219983_at IAA 0.04 35626_at IAA 0.04 218611_at IAA 0.04 51228_at IAA 0.04 219163_at IAA 0.04 50221_at IAA 0.04 218822_s_at IAA 0.04 33323_r_at IAA 0.04 219133_at IAA 0.04 39817_s_at IAA 0.04 219286_s_at IAA 0.04 33778_at IAA 0.04 220366_at IAA 0.04 32259_at IAA 0.04 220399_at IAA 0.04 45288_at IAA 0.04 212790_x_at IAA 0.04 35436_at IAA 0.04 212558_at IAA 0.04 32069 at IAA 0.04 212588_at IAA 0.04 32032_at IAA 0.04 212629_s_at IAA 0.04 51192_at IAA 0.04 212333_at IAA 0.04 89948 at IAA 0.04 212773_s_at IAA 0.04 AFFX-HUMISGF3A/ 214513_s_at IAA 0.04 M9793 IAA 0.04 212840_at IAA 0.04 AFFX-HUMGAPDH/ 212675 s at IAA 0.04 212764 at IAA 0.04 217838 S at IAA 0.04 213538"_at IAA 0.04 217824" at IAA 0.04 214334" X at IAA 0.04 218150" at IAA 0.04 214869" X at IAA 0.04 217286" S at IAA 0.04 214496" X at IAA 0.04 218003" S at IAA 0.04 212826" S at IAA 0.04 217816" S at IAA 0.04 212348" S at IAA 0.04 217817" at IAA 0.04 212818" S at IAA 0.04 218391"_at IAA 0.04 212585""at IAA 0.04 218104"_at IAA 0.04 212864" at IAA 0.04 217147" S at IAA 0.04 213623"_at IAA 0.04 218076" S at IAA 0.04 213470" S at IAA 0.04 218309"_at IAA 0.04 212798" S at IAA 0.04 218233" S at IAA 0.04 213859" X at IAA 0.04 215947" s_at IAA 0.04 212369"_at IAA 0.04 215990" s_at IAA 0.04 214544" s_at IAA 0.04 217718" S at IAA 0.04 212852" S at IAA 0.04 218411" S at IAA 0.04 214459" X at IAA 0.04 217938" S at IAA 0.04 212971" at IAA 0.04 218297"_at IAA 0.04 214146" S at IAA 0.04 215230" X at IAA 0.04 213151" S at IAA 0.04 218089"_at IAA 0.04 213154" S at IAA 0.04 216591" S at IAA 0.04 213650""at IAA 0.04 217917" S at IAA 0.04 214097"_at IAA 0.04 217918"_at IAA 0.04 213594" X at IAA 0.04 218132" S at IAA 0.04 214263" X at IAA 0.04 218088" S at IAA 0.04 214119" s_at IAA 0.04 218103"_at IAA 0.04 212457" at IAA 0.04 217078" S at IAA 0.04 213080" X at IAA 0.04 217864" s_at IAA 0.04 214258" X at IAA 0.04 217807" S at IAA 0.04 212543"Vt IAA 0.04 215884" S at IAA 0.04 214246" X at IAA 0.04 217986" S at IAA 0.04 213012" at IAA 0.04 217800" S at IAA 0.04 213083" at IAA 0.04 218304" s_at IAA 0.04 214230" at IAA 0.04 217742" S at IAA 0.04 213349"_at IAA 0.04 218455""at IAA 0.04 213588" X at IAA 0.04 217877" S at IAA 0.04 213047" X at IAA 0.04 218116""at IAA 0.04 212508""at IAA 0.04 217939" S at IAA 0.04 213578""at IAA 0.04 218093" S at IAA 0.04 212910" at IAA 0.04 203436"_at IAA 0.04 213137" S at IAA 0.04 203455" at IAA 0.04 212542" s_at IAA 0.04 204312" X at IAA 0.04 212257" S at IAA 0.04 203517""at IAA 0.04 214895" S at IAA 0.04 203538" at IAA 0.04 212958" X at IAA 0.04 204024" at IAA 0.04 212953" X at IAA 0.04 204140"_at IAA 0.04 213088" S at IAA 0.04 204245" S at IAA 0.04 214908 s_at IAA 0.04 204116"_at IAA 0.04 212519"_at IAA 0.04 204290" S at IAA 0.04 218426" S at IAA 0.04 204445" S at IAA 0.04 217499 X at IAA 0.04 204808" S at IAA 0.04 217819 "at IAA 0.04 203378""at IAA 0.04 217972"_at IAA 0.04 203975" S at IAA 0.04 218048"_at IAA 0.04 203274""at IAA 0.04 218041" X at IAA 0.04 204151" X at IAA 0.04 218145""at IAA 0.04 203590""at IAA 0.04 217644 S at IAA 0.04 204098"_at IAA 0.04 218082" S at IAA 0.04 204794 _at IAA 0.04 216438 S at IAA 0.04 203945" at IAA 0.04 217887" s_at IAA 0.04 203831""at IAA 0.04 217926""at IAA 0.04 204391 X at IAA 0.04 218346" S at IAA 0.04 204479 _at IAA 0.04 218258 "at IAA 0.04 203437 at IAA 0.04 204748 at IAA 0.04 211023 at IAA 0.04 204243_at IAA 0.04 209795"^at IAA 0.04 203408 s at IAA 0.04 211883" x_at IAA 0.04 203932_at IAA 0.04 211100" x_at IAA 0.04 203654 s_at IAA 0.04 211824" x_at IAA 0.04 203874 s at IAA 0.04 210629" x_at IAA 0.04 204706_at IAA 0.04 211710" x_at IAA 0.04 203685_at IAA 0.04 210338" s_at IAA 0.04 204820_s_at IAA 0.04 209706" at IAA 0.04 221951 at IAA 0.04 210685" s_at IAA 0.04 220711_at IAA 0.04 211433" x_at IAA 0.04 221220 s_at IAA 0.04 209722" s_at IAA 0.04 221476 s_at IAA 0.04 212227" x_at IAA 0.04 220964 s at IAA 0.04 211951" at IAA 0.04 220753 s at IAA 0.04 210607""at IAA 0.04 221985_at IAA 0.04 210775" x_at IAA 0.04 221218 s at IAA 0.04 209571"^at IAA 0.04 220748 s at IAA 0.04 210396" s_at IAA 0.04 221867 at IAA 0.04 210774" s_at IAA 0.04 222010 at IAA 0.04 211672" s_at IAA 0.04 221249 s at IAA 0.04 211005""at IAA 0.04 222290_at IAA 0.04 212184" s_at IAA 0.04 221791_s_at IAA 0.04 210172" at IAA 0.04 221601 s at IAA 0.04 211090" s_at IAA 0.04 221427 s at IAA 0.04 202907" s_at IAA 0.04 221486_at IAA 0.04 202394" s_at IAA 0.04 220731 s at IAA 0.04 202325" s_at IAA 0.04 220757 s at IAA 0.04 202158" s_at IAA 0.04 221293 s at IAA 0.04 202524"_s_at IAA 0.04 220974 x at IAA 0.04 203078""at IAA 0.04 220703_at IAA 0.04 202777""at IAA 0.04 222108_at IAA 0.04 203124" s_at IAA 0.04 221558 s at IAA 0.04 202392" s_at IAA 0.04 221230 s at IAA 0.04 202144"_s_at IAA 0.04 222021 x at IAA 0.04 202379" s_at IAA 0.04 220918_at IAA 0.04 203190""at IAA 0.04 221829 s at IAA 0.04 203169"~.at IAA 0.04 220791 x at IAA 0.04 202646" s_at IAA 0.04 221532 s at IAA 0.04 202367" at IAA 0.04 211503 s at IAA 0.04 202698"_x_at IAA 0.04 211749 s_at IAA 0.04 203120""at IAA 0.04 210128 s at IAA 0.04 202226"_s_at IAA 0.04 210190 at IAA 0.04 202816" s_at IAA 0.04 209510_at IAA 0.04 202725" at IAA 0.04 210187_at IAA 0.04 202836"_s_at IAA 0.04 209899 s at IAA 0.04 202433""at IAA 0.04 210017_at IAA 0.04 202592"'at IAA 0.04 211755 s at IAA 0.04 202689" at IAA 0.04 211168 s at IAA 0.04 202824" s_at IAA 0.04 211995 x at IAA 0.04 202832""at IAA 0.04 210105 s at IAA 0.04 202298" at IAA 0.04 211983 x at IAA 0.04 202581""at IAA 0.04 210541 s at IAA 0.04 202518""at IAA 0.04 212249 at IAA 0.04 203012" x_at IAA 0.04 212061_at IAA 0.04 202065" s_at IAA 0.04 210996 s at IAA 0.04 202556" s_at IAA 0.04 209778_at IAA 0.04 203048""s_at IAA 0.04 210786 s at IAA 0.04 202201""at IAA 0.04 209953 s at IAA 0.04 202579" x_at IAA 0.04 211681 s at IAA 0.04 202844" s_at IAA 0.04 211378 x at IAA 0.04 203044"~.at IAA 0.04 210205 at IAA 0.04 202244"~at IAA 0.04 211938_at IAA 0.04 202058" s_at IAA 0.04 209474 s at IAA 0.04 202848" s at IAA 0.04 202723" s at IAA 0.04 206181 _at IAA 0.04 202090" s at IAA 0.04 207334" S at IAA 0.04 203203" s at IAA 0.04 205041" S at IAA 0.04 202858"~at IAA 0.04 205804" S at IAA 0.04 202596"_at IAA 0.04 205732" S at IAA 0.04 202736" s at IAA 0.04 205077" s_at IAA 0.04 202268" s at IAA 0.04 205996" S at IAA 0.04 202505"_at IAA 0.04 204986" S at IAA 0.04 202092" s at IAA 0.04 207266" X at IAA 0.04 202313""at IAA 0.04 205733"_at IAA 0.04 202419"_at IAA 0.04 207988" S at IAA 0.04 202031" s at IAA 0.04 207843" X at IAA 0.04 202735" at IAA 0.04 209329" X at IAA 0.04 202974"_at IAA 0.04 209203" s_at IAA 0.04 203023"_at IAA 0.04 208103" s_at IAA 0.04 202032" s at IAA 0.04 208078" S at IAA 0.04 202810"_at IAA 0.04 209197""at IAA 0.04 203168"_at IAA 0.04 207460"_at IAA 0.04 205395" s at IAA 0.04 207688" S at IAA 0.04 205436" s at IAA 0.04 208948" S at IAA 0.04 207104" x at IAA 0.04 207980" S at IAA 0.04 205158" at IAA 0.04 208611" S at IAA 0.04 207283"_at IAA 0.04 208713 "at IAA 0.04 205861"_at IAA 0.04 209089"_at IAA 0.04 206855" s at IAA 0.04 208374" S at IAA 0.04 205219" s at IAA 0.04 207525" s_at IAA 0.04 205934"_at IAA 0.04 208826" X at IAA 0.04 207419" s at IAA 0.04 209337"JIt IAA 0.04 205039" s at IAA 0.04 208970" s_at IAA 0.04 205045"_at IAA 0.04 208782"_at IAA 0.04 206689" x at IAA 0.04 208858" S at IAA 0.04 207001" x at IAA 0.04 208763" S at IAA 0.04 205184"_at IAA 0.04 208091" S at IAA 0.04 207128" s at IAA 0.04 209274" S at IAA 0.04 206968" s at IAA 0.04 207657" X at IAA 0.04 206761"_at IAA 0.04 208822 S at IAA 0.04 207319" s at IAA 0.04 209240"JIt IAA 0.04 207164" s at IAA 0.04 208296" X at IAA 0.04 206722" s at IAA 0.04 208758""at IAA 0.04 205596" s at IAA 0.04 208629" S at IAA 0.04 206471"_s at IAA 0.04 208677" S at IAA 0.04 205382" s at IAA 0.04 208398" S at IAA 0.04 206336"_at IAA 0.04 208707""at IAA 0.04 207023" x at IAA 0.04 208926"_at IAA 0.04 205446" s at IAA 0.04 208022" s_at IAA 0.04 205788" s at IAA 0.04 208206" S at IAA 0.04 205238"_at IAA 0.04 208658"_at IAA 0.04 205097"_at IAA 0.04 208112" X at IAA 0.04 206245" s at IAA 0.04 207907""at IAA 0.04 205232" s at IAA 0.04 207753"JIt IAA 0.04 205803" s at IAA 0.04 208768" X at IAA 0.04 205074"_at IAA 0.04 209006" S at IAA 0.04 206676" at IAA 0.04 209185" S at IAA 0.04 207216""at IAA 0.04 208987"J3_at IAA 0.04 205370"_x at IAA 0.04 208523" X at IAA 0.04 205126"_at IAA 0.04 208992" S at IAA 0.04 205416" s at IAA 0.04 207764" S at IAA 0.04 206061" s at IAA 0.04 209127" S at IAA 0.04 204912""at IAA 0.04 209149" S_at IAA 0.04 206429"~at IAA 0.04 208540 X at IAA 0.04 205403"_at IAA 0.04 208698" S at IAA 0.04 206036" s at IAA 0.04 207782 S at IAA 0.04 206949" s at IAA 0.04 209140 X at IAA 0.04 205849" s at IAA 0.04 208804" S at IAA 0.04 '"209115_at IAA"OTbI 201008 s at IAA 0.04 207872" s at IAA 0.04 201683" x at IAA 0.04 208184" s_at IAA 0.04 201227" s at IAA 0.04 208450*_at IAA 0.04 201256"_at IAA 0.04 200745 s at IAA 0.04 201383" s at IAA 0.04 200818" at IAA 0.04 201332" s at IAA 0.04 200811" at IAA 0.04 201957""at IAA 0.04 200663_at IAA 0.04 201535" at IAA 0.04 200607" s at IAA 0.04 201507"_at IAA 0.04 200778" s at IAA 0.04 201329" s at IAA 0.04 200741" s at IAA 0.04 201971" s_at IAA 0.04 200840'_at IAA 0.04 201359"_at IAA 0.04 200599"_s_at IAA 0.04 201994" at IAA 0.04 200829 x at IAA 0.04 201441"_at IAA 0.04 200626" s at IAA 0.04 200882" s at IAA 0.04 200051"_at IAA 0.04 201377" at IAA 0.04 200801" x at IAA 0.04 201519"_at IAA 0.04 200045"_at IAA 0.04 201522" x_at IAA 0.04 200748" s_at IAA 0.04 201553" s at IAA 0.04 200767" s at IAA 0.04 201513" at IAA 0.04 200055""at IAA 0.04 201573" s at IAA 0.04 200849" s_at IAA 0.04 201486"_at IAA 0.04 200809" x at IAA 0.04 201337" s_at IAA 0.04 200718" s at IAA 0.04 201490" s at IAA 0.04 200650" s_at IAA 0.04 201393" s at IAA 0.04 200813" s at IAA 0.04 200872" at IAA 0.04 200668" s at IAA 0.04 213504"_at IAA 0.04 200815" s at IAA 0.04 204405" x at IAA 0.04 200689" x at IAA 0.04 202125" s at IAA 0.04 200091' s at IAA 0.04 227276" at IAA 0.04 201709" s at IAA 0.04 225968" at IAA 0.04 201002" s at IAA 0.04 239412"_at IAA 0.04 201018" at IAA 0.04 208759"_at IAA 0.04 201119" s at IAA 0.04 218927" s at IAA 0.04 201307" at IAA 0.04 222790" s at IAA 0.04 201288"_at IAA 0.04 203429" s_at IAA 0.04 201742" x at IAA 0.04 219169" s at IAA 0.04 201023" at IAA 0.04 218023" s at IAA 0.04 201165"_s_at IAA 0.04 35254 at IAA 0.04 201707"_at IAA 0.04 201731 s at IAA 0.04 200900" s_at IAA 0.04 1552320 a at IAA 0.04 201305" x at IAA 0.04 205518 s at IAA 0.04 201729" s at IAA 0.04 214299"~at IAA 0.04 201874"_at IAA 0.04 200723" s_at IAA 0.04 201160" s at IAA 0.04 208893" s at IAA 0.04 201152" s_at IAA 0.04 224049""at IAA 0.04 201306" s at IAA 0.04 218799""at IAA 0.04 201922"_at IAA 0.04 202138 x at IAA 0.04 201637" s at IAA 0.04 200843" s at IAA 0.04 201242" s_at IAA 0.04 208885"_at IAA 0.04 201237" at IAA 0.04 244710""at IAA 0.04 201646" at IAA 0.04 200000 s at IAA 0.04 200989" at IAA 0.04 202534 x at IAA 0.04 200921"_s_at IAA 0.04 200757" s at IAA 0.04 201656"_at IAA 0.04 200944 s at IAA 0.04 201230" s at IAA 0.04 207081""s_at IAA 0.04 201017"_at IAA 0.04 210658" s at IAA 0.04 201665" x_at IAA 0.04 37943 at IAA 0.04 200958" s at IAA 0.04 216295 s at IAA 0.04 201013" s at IAA 0.04 203347 s at IAA 0.04 201678" s at IAA 0.04 201886.at IAA 0.04 200912" s_at IAA 0.04 201400 [at IAA 0.04 201682" at IAA 0.04 200595 s at IAA 0.04 201771" at IAA 0.04 213004 "at IAA 0.04 '''202077^at '"""ΪMCOTQT 205400 at IAA 0.04 201343_at IAA 0.04 201921"_at IAA 0.04 204568_at IAA 0.04 203913" s at IAA 0.04 218805_at IAA 0.04 218946"_at IAA 0.04 205548_s_at IAA 0.04 218285" s at IAA 0.04 227259_at IAA 0.04 208749" x_at IAA 0.04 204588_s_at IAA 0.04 212202"_s_at IAA 0.04 212610_at IAA 0.04 202197"_at IAA 0.04 1552976_at IAA 0.04 212637" s at IAA 0.04 202034_x_at IAA 0.04 209538" at IAA 0.04 215089_s_at IAA 0.04 204481"_at IAA 0.04 202193_at IAA 0.04 203905"_at IAA 0.04 218435_at IAA 0.04 212266" s at IAA 0.04 217936_at IAA 0.04 229686"_at IAA 0.04 218499_at IAA 0.04 201156" s_at IAA 0.04 78047_s_at IAA 0.04 201651" s at IAA 0.04 221495_s_at IAA 0.04 222537" s at IAA 0.04 212255_s_at IAA 0.04 222881" at IAB 5.9e-06 201833_at IAA 0.04 209795"_at IAB 3.4e-05 201963_at IAA 0.04 225005"_at IAB 3.99e-05 225604_s_at IAA 0.04 223217" s at IAB 4.22e-05 216565_x_at IAA 0.04 209395"_at IAB 5.59e-05 212860_at IAA 0.04 200020"_at IAB 8.04e-05 207605_x_at IAA 0.04 222412" s at IAB 9.72e-05 211953_s_at IAA 0.04 210889" s at IAB 1.3e-04 218473_s_at IAA 0.04 219312" s at IAB 1.37e-04 209943_at IAA 0.04 205349"~at IAB 1.37e-04 218200_s_at IAA 0.04 205770" at IAB 1.82e-04 235241_at IAA 0.04 209625""at IAB 2.15e-04 202760_s_at IAA 0.04 203769" s at IAB 2.69e-04 204528_s_at IAA 0.04 209099" x at IAB 2.99e-04 217781_s_at IAA 0.04 206039"_at IAB 3.47e-04 1554503_a_at IAA 0.04 210458" s at IAB 3.95e-04 209107__x_at IAA 0.04 204161" s at IAB 4.02e-04 207618_s_at IAA 0.04 228512"jat IAB 4.22e-04 204023_at IAA 0.04 216945" x at IAB 5.04e-04 221210_s_at IAA 0.04 220974" x at IAB 5.1e-04 208238_x_at IAA 0.04 201694" s at IAB 5.33e-04 202635_s_at IAA 0.04 227195"_at IAB 5.77e-04 206472_s_at IAA 0.04 217956" s at IAB 5.98e-04 212731_at IAA 0.04 202466""at IAB 6.14e-04 218240_at IAA 0.04 201008" s at IAB 7.72e-04 200842_s_at IAA 0.04 208960" s at IAB 7.8e-04 208933_s_at IAA 0.04 220940" at IAB 8.2e-04 219966_x_at IAA 0.04 222985""at IAB 8.25e-04 1553155_x_at IAA 0.04 210017" at IAB 9.91e-04 219110_at IAA 0.04 201626""at IAB 1.029633e-03 228034_x_at IAA 0.04 222088" s at IAB 1.051096e-03 217802_s_at IAA 0.04 201695" s at IAB 1.103947e-03 200785_s_at IAA 0.04 208961 s at IAB 1.110124e-03 206011_at IAA 0.04 201693" s at IAB 1.116156e-03 214590_s_at IAA 0.04 206323 x at IAB 1.220281e-03 218465_at IAA 0.04 225789 _at IAB 1.29213e-03 202131_s_at IAA 0.04 209360 s at IAB 1.303325e-03 201313_at IAA 0.04 226168 at IAB 1.322951e-03 209657_s_at IAA 0.04 224768"jat IAB 1.322951e-03 209303_at IAA 0.04 224511" s at IAB 1.322951e-03 210944_s_at IAA 0.04 226896 "at IAB 1.322951e-03 210176_at IAA 0.04 224595""at IAB 1.322951e-03 203603_s_at IAA 0.04 226053 "at IAB 1.322951e-03 218797_s_at IAA 0.04 224602 "at IAB 1.322951e-03 205042_at IAA 0.04 227844""at IAB 1.322951e-03 218823_s_at IAA 0.04 223639"s at IAB 1.322951e-03 200730 s at IAA 0.04 225187""at IAB 1.322951e-03 223993" s at IAB T."32295Te-03 201010 s at IAB 1.322951e-03 225976""at IAB 1.322951e-03 201464 x at IAB 1.322951e-03 226987" at IAB 1.322951e-03 201319_at IAB 1.322951e-03 225489"_at IAB 1.322951e-03 201625 s at IAB 1.47524e-03 226452" at IAB 1.322951e-03 226001_at IAB 1.632895e-03 225252" at IAB 1.322951e-03 218793 s at IAB 1.640947e-03 225849" s at IAB 1.322951e-03 220742 s at IAB 1.768548e-03 224504" s at IAB 1.322951e-03 210180 s at IAB 1.975261e-03 227960" s at IAB 1.322951e-03 207922_s_at IAB 2.12638e-03 235568""at IAB 1.322951e-03 208448 x at IAB 2.476264e-03 232009" at IAB 1.322951e-03 213957 s at IAB 2.551905e-03 231406"_at IAB 1.322951e-03 204190_at IAB 2.635083e-03 239146" at IAB 1.322951e-03 224374_s_at IAB 2.839516e-03 228234""at IAB 1.322951e-03 219892_at IAB 2.852895e-03 218586" at IAB 1.322951e-03 201566 x at IAB 3.231828e-03 219041" s at IAB 1.322951e-03 224892_at IAB 3.346532e-03 218532" s at IAB 1.322951e-03 201739_at IAB 3.431494e-03 218530""at IAB 1.322951e-03 201009 s at IAB 3.618289e-03 218737"_at IAB 1.322951e-03 220187_at IAB 3.819191e-03 212636" at IAB 1.322951e-03 AFFX-HUMGAPDH/ 217207" s_at IAB 1.322951e-03 M33197 IAB 3.882062e-03 204881"_s_at IAB 1.322951e-03 221819_at IAB 3.967163e-03 203737* s at IAB 1.322951e-03 225091_at IAB 4.021522e-03 204159""at IAB 1.322951e-03 201466 s at IAB 4.055734e-03 204286" s at IAB 1.322951e-03 234978__at IAB 4.058318e-03 204278" s at IAB 1.322951e-03 225772 s at IAB 4.096824e-03 204319""s at IAB 1.322951e-03 224585 x at IAB 4.173929e-03 204794" at IAB 1.322951e-03 224764_at IAB 4.196051e-03 203504" s_at IAB 1.322951e-03 213418_at IAB 4.339437e-03 203471" s at IAB 1.322951e-03 204254 s at IAB 4.393802e-03 203778" at IAB 1.322951e-03 212190_at IAB 4.428875e-03 223049"_at IAB 1.322951e-03 235113__at IAB 4.581375e-03 223007" s at IAB 1.322951e-03 235020_at IAB 4.722601e-03 222753" s at IAB 1.322951e-03 202092 s at IAB 4.751901e-03 221893"~s_at IAB 1.322951e-03 220236_at IAB 4.763481e-03 223186" at IAB 1.322951e-03 201328 at IAB 4.84355e-03 223244"_s_at IAB 1.322951e-03 224904 at IAB 4.907504e-03 220942" x at IAB 1.322951e-03 226796_at IAB 4.907504e-03 223081""at IAB 1.322951e-03 224628_at IAB 4.907504e-03 222707" s at IAB 1.322951e-03 225049__at IAB 4.907504e-03 223208"_at IAB 1.322951e-03 225648_at IAB 4.907504e-03 220937" s at IAB 1.322951e-03 224177 s at IAB 4.907504e-03 223176""at IAB 1.322951e-03 224796_at IAB 4.907504e-03 210527" x at IAB 1.322951e-03 226080_at IAB 4.907504e-03 209538""at IAB 1.322951e-03 224800 at IAB 4.907504e-03 210847" x at IAB 1.322951e-03 226012_at IAB 4.907504e-03 210119" at IAB 1.322951e-03 225945 at IAB 4.907504e-03 209892" at IAB 1.322951e-03 224578_at IAB 4.907504e-03 210742" at IAB 1.322951e-03 224159 x at IAB 4.907504e-03 209457""at IAB 1.322951e-03 226354_at IAB 4.907504e-03 209511" at IAB 1.322951e-03 225071 at IAB 4.907504e-03 209595"_at IAB 1.322951e-03 226639 at IAB 4.907504e-03 202497" x at IAB 1.322951e-03 226963_at IAB 4.907504e-03 202599" s at IAB 1.322951e-03 225390 s at IAB 4.907504e-03 202458""at IAB 1.322951e-03 225662 at IAB 4.907504e-03 202860" at IAB 1.322951e-03 223740_at IAB 4.907504e-03 206792" x at IAB 1.322951e-03 224413_s_at IAB 4.907504e-03 208676" s at IAB 1.322951e-03 225799_at IAB 4.907504e-03 208795" s at IAB 1.322951e-03 224596 at IAB 4.907504e-03 208763" s_at IAB 1.322951e-03 225748 at IAB 4.907504e-03 200035" at IAB 1.322951e-03 231023_at IAB 4.907504e-03 200049" jat IAB 1.322951e-03 229553 at IAB 4.907504e-03 200030 s at IAB 1.322951e-03 238765 at IAB 4.907504e-03 200948""at IAB 1.322951e-03 235022 at IAB 4.907504e-03 4.907504e-03 222409_at IAB 8.135654e-03 4.907504e-03 223158 s at IAB 8.306031e-03 4.907504e-03 201465 s at IAB 8.358797e-03 4.907504e-03 213524 s at IAB 8.390193e-03 4.907504e-03 200953 s at IAB 8.493784e-03 4.907504e-03 202436 s_at IAB 8.512986e-03 4.907504e-03 228454 at IAB 8.522209e-03 4.907504e-03 212099_at IAB 8.835484e-03 4.907504e-03 206488 s at IAB 8.955465e-03 4.907504e-03 210556 at IAB 9.113215e-03 4.907504e-03 225120_at IAB 9.137252e-03 4.907504e-03 219403 s at IAB 9.183209e-03 4.907504e-03 219439_at IAB 9.198299e-03 4.907504e-03 218522 s at IAB 9.221278e-03 4.907504e-03 216979 at IAB 9.243684e-03 4.907504e-03 224902_at IAB 9.329874e-03 4.907504e-03 203176 s at IAB 9.451655e-03 4.907504e-03 205214_at IAB 9.564607e-03 4.907504e-03 222768 s at IAB 9.696902e-03 4.907504e-03 204030 s at IAB 9.815159e-03 4.907504e-03 223209 s at IAB 9.972465e-03 4.907504e-03 214590 s at IAB 9.984321e-03 4.907504e-03 204912_at IAB 0.01 4.907504e-03 218813 s at IAB 0.01 4.907504e-03 207445_s_at IAB 0.01 4.907504e-03 219052 at IAB 0.01 4.907504e-03 225132_at IAB 0.01 4.907504e-03 225850_at IAB 0.01 4.907504e-03 202644 s at IAB 0.01 4.907504e-03 209288 s at IAB 0.01 4.907504e-03 219423 x at IAB 0.01 4.907504e-03 217826 s at IAB 0.01 4.907504e-03 37145 at" IAB 0.01 4.907504e-03 221192 x at IAB 0.01 4.907504e-03 226333_at IAB 0.01 4.907504e-03 213816 s at IAB 0.01 5.004687e-03 217764_s at IAB 0.01 5.186583e-03 91684 g_at IAB 0.01 5.479163e-03 222982 x at IAB 0.01 5.487119e-03 221060 s at IAB 0.01 5.69183e-03 209385 s at IAB 0.01
Figure imgf000418_0001
5.69183e-03 235458 at IAB 0.01 204669 s at IAB 5.708117e-03 203668_at IAB 0.01 204748 IAB 6.008283e-03 207113 s at IAB 0.01 204691Ixatat IAB 6.096943e-03 222243 s at IAB 0.01 200053 at IAB 6.125805e-03 224639_at IAB 0.01 207075 at IAB 6.143416e-03 233168 s at IAB 0.01 218515_at IAB 6.148687e-03 223152 at IAB 0.01 202325 s at IAB 6.550801e-03 202522 at IAB 0.01 202934_at IAB 6.740621e-03 229723_at IAB 0.01 233970 s at IAB 6.813443e-03 238077_at IAB 0.01 210264 at IAB 7.00613e-03 202435_s at IAB 0.01 221867 at IAB 7.080955e-03 203297_s_at IAB 0.01 207728_at IAB 7.188962e-03 223003_at IAB 0.01 222436 s at IAB 7.254512e-03 216383 at IAB 0.01 219368_at IAB 7.271604e-03 225702 at IAB 0.01 209458_x_at IAB 7.557581e-03 225814 at IAB 0.01 223216 x at IAB 7.595591e-03 224952_at IAB 0.01 206834 at IAB 7.65295e-03 226518_at IAB 0.01 219426 at IAB 7.683749e-03 225535 s at IAB 0.01 225309 at IAB 7.718759e-03 227208_at IAB 0.01 214762_at IAB 7.837633e-03 225196 s at IAB 0.01 212860 at IAB 7.948432e-03 225198_at IAB 0.01 218310 at IAB 8.034698e-03 225956 at IAB 0.01 225065 x at '"'IAB"oToϊ 212124 at IAB 0.01 225406_at IAB 0.01 202100" at IAB 0.01 226730_s at IAB 0.01 202021" x at IAB 0.01 225581 s at IAB 0.01 202541"_at IAB 0.01 224573_at IAB 0.01 202499' s_at IAB 0.01 224439 x at IAB 0.01 203049' s at IAB 0.01 227322 s at IAB 0.01 202428" x at IAB 0.01 224575_at IAB 0.01 202888" s at IAB 0.01 227784_s_at IAB 0.01 202906" s at IAB 0.01 224395 s at IAB 0.01 202379" s at IAB 0.01 224479 s at IAB 0.01 202247" s_at IAB 0.01 233167_at IAB 0.01 202340" x at IAB 0.01 232520 s at IAB 0.01 205170" at IAB 0.01 35820 at IAB 0.01 205896'_at IAB 0.01 229940 at IAB 0.01 205495" s_at IAB 0.01 242352_at IAB 0.01 205012" s at IAB 0.01 230748_at IAB 0.01 208640'_at IAB 0.01 233575 s at IAB 0.01 209257" s_at IAB 0.01 34868_at IAB 0.01 209137" s at IAB 0.01 AFFX-HUMGAPDH/ 209028" s_at IAB 0.01 M33197 IAB 0.01 209160" at IAB 0.01 218896 s at IAB 0.01 209286"_at IAB 0.01 219734 at IAB 0.01 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218175_at IAE 0.03 204228_at IAE 0.03 215452_x_at IAE 0.03 216941_s_at IAE 0.03 217967_s_at IAE 0.03 208420_x_at IAE 0.03 204461_x_at IAE 0.03 204314_s_at IAE 0.03 204828_at IAE 0.03 204366_s_at IAE 0.03 222529_at IAE 0.03 212111_at IAE 0.03 222981_s_at IAE 0.03 212862_at IAE 0.03 220990_s_at IAE 0.03 218153_at IAE 0.03 223014_at IAE 0.03 212315_s_at IAE 0.03 222891_s_at IAE 0.03 226199_at IAE 0.03 210754_s_at IAE 0.03 244546_at IAE 0.03 209515_s_at IAE 0.03 235896_s_at IAE 0.03 210621_s_at IAE 0.03 203660_s_at IAE 0.03 210774_s_at IAE 0.03 203264_s_at IAE 0.03 211005_at IAE 0.03 204672_s_at IAE 0.03 211372_s_at IAE 0.03 230421_at IAE 0.03 210951_x_at IAE 0.03 213314_at IAE 0.03 212037_at IAE 0.03 204401_at IAE 0.03 202450_s_at IAE 0.03 205053_at IAE 0.03 202461_at IAE 0.03 219434_at IAE 0.03 202241_at IAE 0.03 220578_at IAE 0.03 205839_s_at IAE 0.03 206088_at IAE 0.03 207038_at IAE 0.03 205189_s_at IAE 0.03 207008_at IAE 0.03 205987_at IAE 0.03 205931_s_at IAE 0.03 218703_at IAE 0.03 205568_at IAE 0.03 214974_x_at IAE 0.03 206208_at IAE 0.03 223304_at IAE 0.03 207559_s_at IAE 0.03 202106_at IAE 0.03 208985_s_at IAE 0.03 202153_s_at IAE 0.03 209369_at IAE 0.03 205041_s_at IAE 0.03 209395_at IAE 0.03 210140_at IAE 0.03 209186_at IAE 0.03 219001_s_at IAE 0.03 1555167_s_at IAE 0.03 224948_at IAE 0.03 200706_s_at IAE 0.03 209099_x_at IAE 0.03 1554690_a_at IAE 0.03 207231_at IAE 0.03 1553718_at IAE 0.03 21168l_s_at IAE 0.03 1553984_s_at IAE 0.03 217966_s_at IAE 0.03 201133_s_at IAE 0.03 200665_s_at IAE 0.03 201696_at IAE 0.03 206959_s_at IAE 0.03 201748_s_at IAE 0.03 227721_at IAE 0.04 201093_x_at IAE 0.03 225743_at IAE 0.04 200871_s_at IAE 0.03 204501_at IAE 0.04 201156 s at IAE 0.03 205173 x at IAE 0.04 '"'"2224if s at ΪAE" 0.04 213572 s_at IAE 0.04 214945'_at IAE 0.04 213811" x at IAE 0.04 234305" s_at IAE 0.04 213988" s at IAE 0.04 227189'_at IAE 0.04 213175" s_at IAE 0.04 219234' x at IAE 0.04 213370"_s_at IAE 0.04 204278" s at IAE 0.04 214049" x at IAE 0.04 219403" s__at IAE 0.04 212295' s at IAE 0.04 222717" at IAE 0.04 212501"_at IAE 0.04 202626" s at IAE 0.04 212988" x at IAE 0.04 217728"_at IAE 0.04 214553" s at IAE 0.04 206129" s at IAE 0.04 214459" x at IAE 0.04 203189" s_at IAE 0.04 213084" x_at IAE 0.04 208729"_x_at IAE 0.04 218238" at IAE 0.04 204624"_at IAE 0.04 218280" x at IAE 0.04 201522" x at IAE 0.04 217299" s at IAE 0.04 205297" s_at IAE 0.04 218414" s_at IAE 0.04 221712" s at IAE 0.04 216547"_at IAE 0.04 1552472 a at IAE 0.04 217947"_at IAE 0.04 231173 at IAE 0.04 217731" s_at IAE 0.04 206929" s at IAE 0.04 218364"_at IAE 0.04 224467" s at IAE 0.04 215909" x at IAE 0.04 213340"_s_at IAE 0.04 215109"_at IAE 0.04 230518"_at IAE 0.04 218098" at IAE 0.04 220643" s_at IAE 0.04 204554" at IAE 0.04 214552" s_at IAE 0.04 204081" at IAE 0.04 207728"_at IAE 0.04 204258" at IAE 0.04 214629" x_at IAE 0.04 203827" at IAE 0.04 205763"_s_at IAE 0.04 203574" at IAE 0.04 203205" at IAE 0.04 204872" at IAE 0.04 204106" at IAE 0.04 204668" at IAE 0.04 204142"_at IAE 0.04 204222" s_at IAE 0.04 203853" s at IAE 0.04 203335"_at IAE 0.04 203814" s at IAE 0.04 203992" s at IAE 0.04 222125" s_at IAE 0.04 222557"_at IAE 0.04 222841" s at IAE 0.04 222480" at IAE 0.04 221558" s at IAE 0.04 222547"_at IAE 0.04 223042 _s_at IAE 0.04 222133 s at IAE 0.04 209486"[at IAE 0.04 211995"x at IAE 0.04 210633"x at IAE 0.04 211686" s_at IAE 0.04
Figure imgf000430_0001
211999 "at IAE 0.04 225986 x at IAE 0.04 211962 s at IAE 0.04 224690 "at IAE 0.04 212075 s at IAE 0.04 227569 "at IAE 0.04 210356 x at IAE 0.04 227385 [at IAE 0.04 202100 [at IAE 0.04 239660 "at IAE 0.04 202431 s at IAE 0.04 231940 "at IAE 0.04 202200 s at IAE 0.04 235027 "at IAE 0.04 203182 s_at IAE 0.04 230836 "at IAE 0.04 202577 _s_at IAE 0.04 218555 [at IAE 0.04 202460 s at IAE 0.04 219110 [at IAE 0.04 205323 s at IAE 0.04 218647 s_at IAE 0.04 206515 "at IAE 0.04 219910 [at IAE 0.04 204959 [at IAE 0.04 219037 [at IAE 0.04 206060 _s_at IAE 0.04 220175 s at IAE 0.04 206559 x at IAE 0.04 "'20584 r r at "' "ΪAE" 0.04 211944_at IAG 7.04e-04 205256_at IAE 0.04 203590 at IAG 7.63e-04 206752_s_at IAE 0.04 212050 at IAG 7.93e-04 207936_x_at IAE 0.04 64486 at IAG 8.14e-04 207509_s_at IAE 0.04 219956_at IAG 8.17e-04 207549_x_at IAE 0.04 209653 at IAG 8.2e-04 208885_at IAE 0.04 212384_at IAG 8.3e-04 208625_s_at IAE 0.04 216652_s_at IAG 8.71e-04 208611_s_at IAE 0.04 212079 s at IAG 9.98e-04 209384_at IAE 0.04 216251 s at IAG 1.033006e-03 207783_x_at IAE 0.04 212102 s at IAG 1.063658e-03 208039_at IAE 0.04 201303_at IAG 1.08727e-03 1553749_at IAE 0.04 221607 x at IAG 1.121754e-03 1570410_at IAE 0.04 216526 x at IAG 1.12702e-03 200768_s_at IAE 0.04 209009_at IAG 1.134826e-03 1554153_a_at IAE 0.04 35626 at IAG 1.155905e-03 200801_x_at IAE 0.04 201569 s at IAG 1.15748e-03 200077_s_at IAE 0.04 201753 s at IAG 1.159953e-03 201315_x_at IAE 0.04 202818 s at IAG 1.187457e-03 201872_s_at IAE 0.04 214553 s at IAG 1.263653e-03 201849_at IAE 0.04 217796 s at IAG 1.294393e-03 201013_s_at IAE 0.04 202638 s at IAG 1.329477e-03 201726_at IAE 0.04 212287 at IAG 1.547918e-03 201272_at IAE 0.04 209313_at IAG 1.589471e-03 201258_at IAE 0.04 220349 s at IAG 1.601492e-03 201570_at IAE 0.04 209668 x at IAG 1.644875e-03 201529_s_at IAE 0.04 203656_at IAG 1.654945e-03 201054_at IAE 0.04 204640 s at IAG 1.666068e-03 223380_s_at IAE 0.04 201072 s at IAG 1.698658e-03 227022_at IAE 0.04 200702 s_at IAG 1.699015e-03 217853_at IAE 0.04 211069_s_at IAG 1.771339e-03 209835_x_at IAE 0.04 211000_s_at IAG 1.809716e-03 220777_at IAE 0.04 219821 s at IAG 1.84602e-03 222067_x_at IAE 0.04 213998 s at IAG 1.896524e-03 214422_at IAE 0.04 201414_s_at IAG 1.907781e-03 209583_s_at IAE 0.04 221514 at IAG 2.003432e-03 212291_at IAE 0.04 203965_at IAG 2.035138e-03 205863_at IAE 0.04 213916_at IAG 2.03615e-03 208369_s_at IAE 0.04 203124 s at IAG 2.046937e-03 202917_s_at IAE 0.04 210426 x at IAG 2.048308e-03 221600_s_at IAE 0.04 204212_at IAG 2.053087e-03 214273_x_at IAE 0.04 201587 s at IAG 2.139946e-03 210627_s_at IAE 0.04 204046_at IAG 2.171377e-03 203674_at IAG 1.01e-04 201633 s at IAG 2.174744e-03 202874_s_at IAG l.\31e-04 203630 s at IAG 2.18879e-03 211085_s_at IAG 1.42e-04 202492 at IAG 2.200711e-03 218754_at IAG 1.73e-04 212451_at IAG 2.226808e-03 212213_x_at IAG 1.77e-04 218135 at IAG 2.253272e-03 207618_s_at IAG 2.48e-04 218659_at IAG 2.302978e-03 208835_s_at IAG 2.81e-04 218315 s at IAG 2.313581e-03 214843_s_at IAG 3.88e-04 208051 s at IAG 2.319686e-03 212249_at IAG 4.14e-04 222343 at IAG 2.34273e-03 212629_s_at IAG 4.2e-04 200051_at IAG 2.373327e-03 218649_x_at IAG 4.41e-04 218807_at IAG 2.497948e-03 218521_s_at IAG 4.48e-04 203147 s at IAG 2.508271e-03 200767_s_at IAG 4.58e-04 200772 x at IAG 2.51093e-03 212218_s_at IAG 5.01e-04 204544_at IAG 2.519817e-03 210731_s_at IAG 5.26e-04 202605 at IAG 2.573875e-03 208450_at IAG 5.59e-04 208325 s at IAG 2.600413e-03 202131_s_at IAG 5.83e-04 207198 s at IAG 2.601674e-03 214853_s_at IAG 5.95e-04 202626 s at IAG 2.624949e-03 203514_at IAG 5.98e-04 200871_s_at IAG 2.682533e-03 200957_s_at IAG 6.5e-04 212643_at IAG 2.690275e-03 203978 at IAG 6.83e-04 213398 s at IAG 2.720827e-03 i ir ■■ ii » 1S , iϊ ii H . „ ""204209""_at ""ΪAG""1'."7"49"70Te-03 204366_s_at IAG 6.131504e-03 218314 s at IAG 2.751347e-03 209203_s_at IAG 6.170708e-03 208817 at IAG 2.755885e-03 212588_at IAG 6.236877e-03 219112 "at IAG 2.777065e-03 200797_s_at IAG 6.294021e-03 219695"at IAG 2.879129e-03 219543_at IAG 6.380925e-03 210048 at IAG 2.994828e-03 221263_s_at IAG 6.382976e-03 203723""_at IAG 3.001734e-03 213214_x_at IAG 6.468144e-03 201965"s at IAG 3.109621e-03 208661__s_at IAG 6.479928e-03 203746 s at IAG 3.139861e-03 204493_at IAG 6.524928e-03 214730 s at IAG 3.153765e-03 203132_at IAG 6.552763e-03 203054 s_at IAG 3.218631e-03 44146_at IAG 6.742311e-03 202600 s at IAG 3.408642e-03 209001_s_at IAG 6.760429e-03 208152"s at IAG α3π .452256e-03 200060_s_at IAG 6.884239e-03 202361 "_at IAG 3.469357e-03 203380_x_at IAG 6.893403e-03 216438 s at IAG 3.628385e-03 221563_at IAG 6.913372e-03 58367 si_at IAG 3.629673e-03 214315_x_at IAG 6.92735e-03 208624 s at IAG 3.73572e-03 204669_s_at IAG 7.116652e-03 221481 x_at IAG 3.870349e-03 48612_at IAG 7.26617e-03 208684""at IAG 4.061779e-03 220748_s_at IAG 7.318219e-03 210208 x at IAG 4.064727e~03 200895_s_at IAG 7.360774e-03 45526 g at IAG 4.093793e~03 219540_at IAG 7.369946e-03 201737 s at IAG 4.138295e-03 207780_at IAG 7.372413e-03 208663 s_at IAG 4.148347e-03 200708_at IAG 7.389545e-03 204028 s at IAG 4.223007e-03 212007_at IAG 7.396832e-03 219593 "at IAG 4.230516e-03 201956_s_at IAG 7.430838e-03 201085 s at IAG 4.267906e-03 201198_s_at IAG 7.579847e-03 212047 s at IAG 4.268395e-03 221864_at IAG 7.594666e-03 212638 "s at IAG 4.316116e-03 220969_s_at IAG 7.607487e-03 203335 at IAG 4.341122e-03 210951_x_at IAG 7.66351e-03 219878 s at IAG 4.372931e-03 203345_s_at IAG 7.729657e-03 222105 "s_at IAG 4.388161e-03 222244_s_at IAG 7.808859e-03 216915 _s_at IAG 4.464781e-03 201545_s_at IAG 7.840022e-03 220646 s at IAG 4.52665e-03 208946_s_at IAG 7.855961e-03 202658 'at IAG 4.54126e-03 202509_s_at IAG 7.862325e-03 208660 at IAG 4.626556e-03 209453_at IAG 7.912131e-03 200958 s_at IAG 4.653266e-03 201494_at IAG 8.052669e-03 213650 "at IAG 4.741285e-03 204478_s_at IAG 8.087901e-03 211270 x at IAG 4.768334e-03 208688_x_at IAG 8.182216e-03 219870 at IAG 4.789132e-03 203185_at IAG 8.206065e-03 209669 s_at IAG 4.78971e-03 205783_at IAG 8.274557e-03 208859 s at IAG 4.82024e-03 206515_at IAG 8.356337e-03 34858 at IAG 4.862957e-03 206219_s_at IAG 8.363564e-03 212587 s at IAG 5,067577e-03 208974__x_at IAG 8.365858e-03 217499 x at IAG ,079111e-03 200861_at IAG 8.396369e-03 218398 at IAG 5.101508e-03 33760_at IAG 8.402577e-03 217732 s at IAG 5.117337e-03 208042_at IAG 8.483872e-03 208961 s_at IAG 5.242419e-03 218378__s__at IAG 8.497553e-03 220735 s at IAG 5.332079e-03 220355_s_at IAG 8.58496e-03 213579 s at IAG 5.337012e-03 219809__at IAG 8.633956e-03 218806 s at IAG 5.383513e-03 219399_at IAG 8.64639βe-03 218168 s at IAG 5.402092e-03 220945_x_at IAG 8.656949e-03 217644 s at IAG 5 ,442792e-03 204638_at IAG 8.705879e-03 201770 at IAG 5 ,443484e-03 205462_s_at IAG 8.748628e-03 218941 at IAG 5 ,462263e-03 217775_s_at IAG 8.760763e-03 210479 s at IAG 5 ,538178e-03 202157_s_at IAG 8.789768e-03 200710 at IAG 5.610092e-03 202631_s_at IAG 8.8214e-03 208003 s at IAG 5.64428e-03 218116_at IAG 8.822656e-03 200045 at IAG 5 ,702059e-03 207104_x_at IAG 8.82447e-03 203377 s at IAG 5 ,739837e-03 203247_s_at IAG 8.857666e-03 37796 at IAG 5.793659e-03 203351_s_at IAG 8.92903e-03 208900 s_at IAG 5 ,817748e-03 201491__at IAG 8.930601e-03 211725 s at IAG 5.846683e-03 201859_at IAG 8.985382e-03 219189 at IAG 5.88903e-03 219334_s_at IAG 9.004632e-03 220800 s at IAG 6 ,031258e-03 218187 s at IAG 9.012184e-03 •20^724" s_at 213011_s_at IAG 0.01 205383" s at IAG 9.187547e-03 213887_s_at IAG 0.01 201557""at IAG 9.200722e-03 218082_s_at IAG 0.01 218160"_at IAG 9.210103e-03 210172_at IAG 0.01 200015" s at IAG 9.286343e-03 200998_s_at IAG 0.01 205585" at IAG 9.376354e-03 201231_s_at IAG 0.01 202304"_at IAG 9.445583e-03 201417_at IAG 0.01 201541" s at IAG 9.471858e-03 207320_x_at IAG 0.01 205340"_at IAG 9.664981e-03 33148_at IAG 0.01 201182" s_at IAG 9.68555e-03 200097_s_at IAG 0.01 212152] x_at IAG 9.714539e-03 212814_at IAG 0.01 AFFX- 201544_x_at IAG 0.01 hum alu at IAG 9.780781e-03 212599_at IAG 0.01 221506 s_at IAG 9.787779e-03 200694_s_at IAG 0.01 212320" at IAG 9.820953e-03 204352_at IAG 0.01 201861" s at IAG 9.927428e-03 221216_s_at IAG 0.01 217959" s at IAG 0.01 202335_s_at IAG 0.01 209682" at IAG 0.01 204089_x_at IAG 0.01 200049"_at IAG 0.01 217899_at IAG 0.01 217783" s at IAG 0.01 221553_at IAG 0.01 212220" at IAG 0.01 217944_at IAG 0.01 200647" x at IAG 0.01 212897_at IAG 0.01 200072" s at IAG 0.01 203831_at IAG 0.01 213980"_s_at IAG 0.01 202021_x_at IAG 0.01 216304" x at IAG 0.01 206968_s_at IAG 0.01 205305" at IAG 0.01 205105_at IAG 0.01 218912"_at IAG 0.01 210010_s_at IAG 0.01 215416" s at IAG 0.01 32029_at IAG 0.01 210057" at IAG 0.01 212369_at IAG 0.01 200920" s at IAG 0.01 208056_s_at IAG 0.01 219202"_at IAG 0.01 218474_s_at IAG 0.01 201550"_x_at IAG 0.01 202135_s_at IAG 0.01 208895" s at IAG 0.01 218966_at IAG 0.01 210649" s_at IAG 0.01 203923_s_at IAG 0.01 212055" at IAG 0.01 205839_s_at IAG 0.01 205475 _at IAG 0.01 204085_s_at IAG 0.01 201469"_s_at IAG 0.01 55705_at IAG 0.01 211962" s at IAG 0.01 217751_at IAG 0.01 204479""at IAG 0.01 205419_at IAG 0.01 212363"x at IAG 0.01 210639_s_at IAG 0.01 205988""at IAG 0.01 202261_at IAG 0.01 203006""at IAG 0.01 214012_at IAG 0.01 218258"_at IAG 0.01 221804_s_at IAG 0.01 218709 s at IAG 0.01 217961_at IAG 0.01 202423 "at IAG 0.01 211983_x_at IAG 0.01 217840""at IAG 0.01 201016_at IAG 0.01 201010"s at IAG 0.01 213378_s_at IAG 0.01 200744 s_at IAG 0.01 203321_s_at IAG 0.01 208992" s at IAG 0.01 55872_at IAG 0.01 211065 x at IAG 0.01 202129_s_at IAG 0.01 208676 s at IAG 0.01 204808_s_at IAG 0.01 204060 _s_at IAG 0.01 202230_s_at IAG 0.01 212429~_s_at IAG 0.01 207727_s_at IAG 0.01 213603"s at IAG 0.01 210926_at IAG 0.01 203992 s at IAG 0.01 204873_at IAG 0.01 202855 s_at IAG 0.01 221516_s_at IAG 0.01 208106 x at IAG 0.01 211671_s_at IAG 0.01 220097 s at IAG 0.01 203900_at IAG 0.01 207604 s at IAG 0.01 202068_s_at IAG 0.01 211970 x at IAG 0.01 210105_s_at IAG 0.01 207416 _s_at IAG 0.01 201511_at IAG 0.01 218967 s at IAG 0.01 205955_at IAG 0.01 210428"s at IAG 0.01 200011_s_at IAG 0.01 212636 "at IAG 0.01 203760 s at IAG 0.01 T7US2005/022071
Figure imgf000434_0001
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208812 X at IAG 0.04 212152 X at IAH 3.12e-04 203575 ~.at IAG 0.04 212590 at IAH 3.29e-04 211417 X at IAG 0.04 200701_at IAH 3.3e-04 211160 X at IAG 0.04 202322 S at IAH 3.31e-04 204169 "at IAG 0.04 200055 at IAH 3.41e-04 220525 S at IAG 0.04 212332_at IAH 3.55e-04 219452"at IAG 0.04 200759 X at IAH 3.59e-04 200598 S at IAG 0.04 221519 at IAH 4.39e-04 219904 at IAG 0.04 64486 at IAH 4.49e-04 214543 X at IAG 0.04 201151 S at IAH 4.56e-04 220775 S at IAG 0.04 221498'_at IAH 4.61e-04 204372 S at IAG 0.04 20121Ϊ S at IAH 4.67e-04 205047"S at IAG 0.04 202014'_at IAH 4.81e-04 202527 S at IAG 0.04 211090' S at IAH 4.98e-04 204193 at IAG 0.04 202475 5.0e-04 212333 at IAG 0.04 208127'Ia IAH Stat IAH 5.1e-04 201873 S at IAG 0.04 36994 'at IAH 5.24e-04 208591 S at IAG 0.04 209515 S at IAH 5.26e-04 202940 at IAG 0.04 217911 s_at IAH 5.42e-04 208832 at IAG 0.04 214496 X at IAH 5.56e-04 216550 X at IAG 0.04 201106 5.56e-04 204434 at IAG 0.04 21315ΪIa IAH Stat IAH 5.58e-04 221764 at IAG 0.04 202373' 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203162" S at IAH 9.38e-04 210338 s_at IAH 2.510447e-03 202647" S at IAH 9.4e-04 204674"_at IAH 2.532299e-03 220408" X at IAH 9.64e-04 212819"_at IAH 2.532506e-03 213011" S at IAH 9.81e-04 201070" X _at IAH 2.540944e-03 200049""at IAH 9.99e-04 217814" at IAH 2.548306e-03 202433" at IAH 1.032391e-03 212899" at IAH 2.578141e-03 212246"_at IAH 1.061817e-03 208610" S at IAH 2.589557e-03 220371" S at IAH 1.076086e-03 204554"_at IAH 2.605118e-03 204387" X at IAH 1.100921e-03 217742" s_at IAH 2.630281e-03 202841" X at IAH 1.142776e-03 208003" S "at IAH 2.644008e-03 217903" at IAH 1.155974e-03 207485" X [at IAH 2.650736e-03 200707" at IAH 1.170017e-03 212456"_at IAH 2.678915e-03 200001"_at IAH 1.206512e-03 209852" X at IAH 2.784473e-03 203055" S at IAH 1.275228e-03 200634""at IAH 2.785202e-03 214698""at IAH 1.277253e-03 208363" s_at IAH 2.79416e-03 215089" S at IAH 1.37965e-03 220132" S "at IAH 2.838911e-03 201737" S at IAH 1.397871e-03 218287" S at IAH 2.851596e-03 217958"~at IAH 1.402409e-03 200753" X at IAH 2.881376e-03 205921" s_at IAH 1.424529e-03 209002" S "at IAH 2.893392e-03 200796" S at IAH 1.434059e-03 214246" X at IAH 2.924157e-03 201001" S at IAH 1.434819e-03 202442"_at IAH 2.954902e-03 206748" S at IAH 1.473216e-03 201174" S at IAH 2.986236e-03 221472""at IAH 1.487701e-03 210948" S "at IAH 2.988439e-03 209007" s_at IAH 1.570838e-03 203659" S at IAH 3.056346e-03 214773" X at IAH 1.599299e-03 204864" S at IAH 3.06234e-03 201778" S at IAH 1.649058e-03 220580"_at IAH 3.085114e-03 209241" X at IAH 1.673338e-03 215227" X at IAH 3.087278e-03 218250" S at IAH 1.708811e-03 208800"_at IAH 3.100941e-03 208704" X at IAH 1.755075e-03 218490" S at IAH 3.101842e-03 214908" S at IAH 1.764647e-03 207186" S at IAH 3.107555e-03 210639" S at IAH 1.780565e-03 210818" s_at IAH 3.107949e-03 201698" s_at IAH 1.78194e-03 213198" at IAH 3.121459e-03 205335" S at IAH 1.788501e-03 200660"_at IAH 3.159362e-03 208698" S at IAH 1.797558e-03 213507" s_at IAH 3.171993e-03 218917" S at IAH 1.797685e-03 202742" S at IAH 3.195616e-03 217781" S at IAH 1.799191e-03 200760" S at IAH 3.206042e-03 214369" s_at IAH 1.808523e-03 213708" S at IAH 3.220002e-03 200960" X at IAH 1.809744e-03 201182" S at IAH 3.237033e-03 203718""at IAH 1.81334e-03 208420" x_at IAH 3.237036e-03 205098"_at IAH 1.862791e-03 206710 S at IAH 3.265669e-03 201299" s_at IAH 1.889336e-03 202691 "at IAH 3.266349e-03 203087" S at IAH 1.915197e-03 212550"[at IAH 3.284182e-03 218817" at IAH 1.929522e-03 201090" X at IAH 3.296247e-03 202298""at IAH 1.951101e-03 200041 S at IAH 3.310673e-03 202047" S at IAH 1.9702e-03 211072" X at IAH 3.341956e-03 201073" S at IAH 1.979403e-03 209274" S at IAH 3.346408e-03 49077 at IAH 2.027635e-03 202638" S at IAH 3.371305e-03 218846 _at IAH 2.034446e-03 209388""at IAH 3.517622e-03 200806" S at IAH 2.035872e-03 214268" S at IAH 3.528264e-03 202979" S at IAH 2.108364e-03 216997"x_at IAH 3.531333e-03 206254 "at IAH 2.122556e-03 207697 X at IAH 3.614509e-03 204109" S at IAH 2.129863e-03 201945"[at IAH 3.635594e-03 221776 S at IAH 2.131976e-03 221483 S at IAH 3.653082e-03 211416 X at IAH 2.174786e-03 202428 X at IAH 3.69354e-03 213446" S at IAH 2.195027e-03 209102 s_at IAH 3.734488e-03 201389"[at IAH 2.243542e-03 212286 "at IAH 3.765184e-03 202195" S at IAH 2.25556e-03 205411 "at IAH 3.771645e-03 2m165Js'jft"""TKH 201032_at IAH 5.716524e-03 202424_at IAH 3.798648e-03 214752 x at IAH 5.725605e-03 202443 x at IAH 3.81239e-03 204971 at IAH 5.739726e-03 208721 s at IAH 3.833304e-03 201425 at IAH 5.771751e-03 202039_at IAH 3.852019e-03 219119_at IAH 5.821061e~03 209067 s at IAH 3.897249e-03 202524 s at IAH 5.884169e-03 217913_at IAH 3.913726e-03 202093 s at IAH 5.892617e-03 214864_s_at IAH 3.924689e-03 203095__at IAH 5.89674e-03 200714_x at IAH 3.987747e-03 205996_s_at IAH 5.96618e-03 200843 s at IAH 3.993471e-03 202816 s at IAH 5.970092e-03 212674 s at IAH 4.006902e-03 217780_at IAH 6.023683e-03 202209_at IAH 4.023358e-03 210532_s_at IAH 6.044999e-03 221550 at IAH 4.04064e-03 217787 s at IAH 6.057369e-03 203322_at IAH 4.083446e-03 201225_s_at IAH 6.058904e-03 222035 s_at IAH 4.202559e-03 215691 x at IAH 6.05935e-03 208909_at IAH 4.216243e-03 207594_s_at IAH 6.065375e-03 215739 s at IAH 4.268406e-03 200861_at IAH 6.073417e-03 33768 at IAH 4.376498e-03 221419 s at IAH 6.10644e-03 217801_at IAH 4.403061e-03 213038_at IAH 6.128582e-03 219939 s at IAH 4.410518e-03 202215_s_at IAH 6.155271e-03 208735 s at IAH 4.425718e-03 209667_at IAH 6.233312e-03 211395 x at IAH 4.431005e-03 215357_s_at IAH 6.289516e-03 203261_at IAH 4.446614e-03 206470_at IAH 6.297921e-03 201635 s at IAH 4.457207e-03 203020 at IAH 6.319318e-03 217898_at IAH 4.47796e-03 201557_at IAH 6.410401e-03 217576 x_at IAH 4.493191e-03 219035 s at IAH 6.426541e-03 208447_s_at IAH 4.493271e-03 212209 at IAH 6.482727e-03 202292 x at IAH 4.508012e-03 218251_at IAH 6.491708e-03 219767 s at IAH 4.510862e-03 209790 s at IAH 6.528971e-03 206471 s at IAH 4.563079e-03 212371 at IAH 6.711204e-03 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s_at IAH 0.03 203529_at IAH 0.04 219843"_at IAH 0.03 218037_at IAH 0.04 210149 s at IAH 0.03 218632_at IAH 0.04 219613' s_at IAH 0.03 206222_at IAH 0.04 203643" at IAH 0.03 214472_at IAH 0.04 201478' s at IAH 0.03 219620_x_at IAH 0.04 218171"_at IAH 0.03 208982_at IAH 0.04 210732' s_at IAH 0.03 217718_s_at IAH 0.04 203652'_at IAH 0.03 200786_at IAH 0.04 210629" x at IAH 0.03 214119_s_at IAH 0.04 218217" at IAH 0.03 203943_at IAH 0.04 202771' at IAH 0.03 201859_at IAH 0.04 201817"_at IAH 0.03 219774_at IAH 0.04 216071" x at IAH 0.03 211913_s_at IAH 0.04 215109"_at IAH 0.03 202383_at IAH 0.04 207131" x_at IAH 0.03 215000_s_at IAH 0.04 207057" at IAH 0.03 204370_at IAH 0.04 200898" s at IAH 0.03 210213_s_at IAH 0.04 212408" at IAH 0.03 205546_s_at IAH 0.04 202957"_at IAH 0.03 221938_x_at IAH 0.04 208750" s at IAH 0.03 201861 s at IAH 0.04 2iϊ2O2'-s""at W"O;i:t)r <! 218091 at IAH 0.04 217826 S at IAH 0.04 201942 s at IAH 0.04 218580 X at IAH 0.04 204155 s at IAH 0.04 219015 S at IAH 0.04 212930 at IAH 0.04 212323 S at IAH 0.04 204197 s at IAH 0.04 217370 X at IAH 0.04 220305 at IAH 0.04 202449 s_at IAH 0.04 217475 s at IAH 0.04 217720_at IAH 0.04 211085 s at IAH 0.04 205322 S at IAH 0.04 212860 at IAH 0.04 207668 X at IAH 0.04 201317 s at IAH 0.04 211025 X at IAH 0.04 202511 s at IAH 0.04 202964 S at IAH 0.04 212747 at IAH 0.04 204542_at IAH 0.04 221205 at IAH 0.04 204417_at IAH 0.04 210142 x at IAH 0.04 203470 S at IAH 0.04 204313 s at IAH 0.04 212008 at IAH 0.04 219185 at IAH 0.04 211575 S at IAH 0.04 221849 s at IAH 0.04 202853 S at IAH 0.04 206513 at IAH 0.04 45749 at IAH 0.04 201089 at IAH 0.04 210235 S at IAH 0.04 208628 s at IAH 0.04 215222 X at IAH 0.04 209414 at IAH 0.04 217865_at IAH 0.04 217938 s at IAH 0.04 217489 S at IAH 0.04 215785 s at IAH 0.04 52651 at IAH 0.04 209329 x at IAH 0.04 218854_at IAH 0.04 219878 s at IAH 0.04 209635_at IAH 0.04 216205 s at IAH 0.04 203253 s_at IAH 0.04 202978 s at IAH 0.04 219146 at IAH 0.04 219679 s at IAH 0.04 218425_at IAH 0.04 202101 s at IAH 0.04 207764 s_at IAH 0.04 218607 s at IAH 0.04 214544 S at IAH 0.04 217825 s at IAH 0.04 208478 s_at IAH 0.04 203654 s at IAH 0.04 210878 S at IAH 0.04 200812 at IAH 0.04 207223 S at IAH 0.04 205988 at IAH 0.04 203387 S at IAH 0.04 204613 at IAH 0.04 214666 X at IAH 0.04 218256 s at IAH 0.04 219761 at IAH 0.04 202943 s at IAH 0.04 202290_at IAH 0.04 213743 at IAH 0.04 201772 at IAH 0.04 204185 x at IAH 0.04 212420_at IAH 0.04 208917 x at IAH 0.04 200011 S at IAH 0.04 202990 at IAH 0.04 204669 S at IAH 0.04 200811 at IAH 0.04 205627_at IAH 0.04 209231 s at IAH 0.04 211584 S at IAH 0.04 203080 s_at IAH 0.04 204131 S at IAH 0.04 211048 s at IAH 0.04 212181 S at IAH 0.04 202584 at IAH 0.04 209296~at IAH 0.04 209457 at IAH 0.04 220924 S at IAH 0.04 202809 s at IAH 0.04 211015 S at IAH 0.04 217824 at IAH 0.04 219155~at IAH 0.04 218582 at IAH 0.04 211043 S at IAH 0.04 206613 s at IAH 0.04 212207 at IAH 0.04 200935 at IAH 0.04 64432 at IAH 0.04 200042 at IAH 0.04 213128 S at IAH 0.04 202167 s at IAH 0.04 203579 S at IAH 0.04 216593 s at IAH 0.04 219040 at IAH 0.04 221267 s_at IAH 0.04 219860 at IAH 0.04 202033 s at IAH 0.04 218889_at IAH 0.04 218950 at IAH 0.04 212281 S at IAH 0.04 201255 x at IAH 0.04 202592_at IAH 0.04 220307 at IAH 0.04 213940 s_at IAH 0.04 207408 at IAH 0.04 202781 S at IAH 0.04 212702 s at IAH 0.04 219639 X at IAH 0.04 209941 at IAH 0.04 212919 at IAH 0.04 203155 at IAH 0.04 %l§3'7_air""' ,..or , .... 213957 _s_at IAI 4.92e-04 207132_x_at IAH 0.04 214003" x_at IAI 4.92e-04 200621_at IAH 0.04 214487" s_at IAI 4.92e-04 206113_s_at IAH 0.04 212616" at IAI 4.92e-04 214252_s_at IAH 0.04 212391" x at IAI 4.92e-04 217829_s_at IAI 9.7e-06 217902" s at IAI 4.92e-04 207688_s_at IAI 1.53e-05 218173" s_at IAI 4.92e-04 202779_s_at IAI 1.91e-05 203758" at IAI 4.92e-04 204470_at IAI 2.3e-05 221800" s at IAI 4.92e-04 201739_at IAI 2.63e-05 221628" s at IAI 4.92e-04 224365_s_at IAI 3.27e-05 203008" x_at IAI 4.92e-04 221555_x_at IAI 3.68e-05 202081" at IAI 4.92e-04 224946_s_at IAI 5.89e-05 207198" s at IAI 4.92e-04 235113_at IAI 5.89e-05 205096"~at IAI 4.92e-04 228556_at IAI 5.89e-05 204995"_at IAI 4.92e-04 242774_at IAI 5.89e-05 208663" s at IAI 4.92e-04 219582_at IAI 5.89e-05 200761" s at IAI 4.92e-04 213932_x_at IAI 5.89e-05 201429" s at IAI 4.92e-04 204748_at IAI 5.89e-05 200925" at IAI 4.92e-04 203547_at IAI 5.89e-05 201895" at IAI 4.92e-04 221791_s_at IAI 5.89e-05 237813" at IAI 5.21e-04 202416_at IAI 5.89e-05 232517"_s_at IAI 5.67e-04 206559_x_at IAI 5.89e-05 220072" at IAI 5.67e-04 205511_at IAI 5.89e-05 66053 jat IAI 6.02e-04 209140_x_at IAI 5.89e-05 228147 at IAI 6.22e-04 207783_x_at IAI 5.89e-05 211983" x at IAI 6.46e-04 1552644_a_at IAI 5.89e-05 224963" at IAI 6.95e-04 1553538_s_at IAI 5.89e-05 203147"_s_at IAI 7.21e-04 200682_s_at IAI 5.89e-05 202859" x at IAI 7.27e-04 201060_x_at IAI 5.89e-05 205041" s at IAI 8.22e-04 203338_at IAI 9.58e-05 224585" x at IAI 8.39e-04 206323_x_at IAI 1.18e-04 217892" s_at IAI 8.48e-04 238701_x_at IAI 1.22e-04 202412" s at IAI 9.4e-04 203223_at IAI 1.22e-04 207229""at IAI 9.51e-04 209189_at IAI 1.53e-04 228155""at IAI 9.67e-04 222737_s_at IAI 1.55e-04 238810"_at IAI 9.79e-04 224474_x_at IAI 1.88e-04 201041" s_at IAI 1.033142e-03 225713_at IAI 1.88e-04 223018" at IAI 1.052826e-03 232188_at IAI 1.88e-04 201714"_at IAI 1.052826e-03 212869_x_at IAI 1.88e-04 207993" s at IAI 1.109268e-03 213998_s_at IAI 1.88e-04 225092" at IAI 1.113154e-03 218166_s_at IAI 1.88e-04 231776"_at IAI 1.113154e-03 215313_x_at IAI 1.88e-04 233575" s at IAI 1.113154e-03 210479_s_at IAI 1.88e-04 AFFX-HSACO7/ 205072_s_at IAI 1.88e-04 X00351 _M IAI 1.113154e-03 208812_x_at IAI 1.88e-04 219097" x_at IAI 1.113154e-03 1554997_a_at IAI 1.88e-04 214179" s at IAI 1.113154e-03 225523_at IAI 2.23e-04 213214"x at IAI 1.113154e-03 207525_s_at IAI 2.23e-04 215043" s_at IAI 1.113154e-03 213867_x_at IAI 2.32e-04 216231"_s_at IAI 1.113154e-03 222748_s_at IAI 2.47e-04 218334""at IAI 1.113154e-03 219818_s_at IAI 2.75e-04 217733" s_at IAI 1.113154e-03 210014_x_at IAI 2.94e-04 204690"_at IAI 1.113154e-03 235449_at IAI 3.55e-04 204085" s at IAI 1.113154e-03 223226_x_at IAI 3.58e-04 223346" at IAI 1.113154e-03 212966_at IAI 3.9e-04 223339""at IAI 1.113154e-03 213775_x_at IAI 4.27e-04 211970"_x_at IAI 1.113154e-03 226739_at IAI 4.92e-04 202348 _s_at IAI 1.113154e-03 235106_at IAI 4.92e-04 202662" s at IAI 1.113154e-03 33322_i_at IAI 4.92e-04 203029" s at IAI 1.113154e-03 AFFX- 202092" s_at IAI 1.113154e-03 hum_alu_at IAI 4.92e-04 202444 _s_at IAI 1.113154e-03 219885_at IAI 4.92e-04 205996"_s_at IAI 1.113154e-03 218750 at IAI 4.92e-04 207426 s at IAI 1.113154e-03 L2072'89'""at * ■~'ΪAΪ l":iϊ3l5-4e-03 212790 x at IAI 2.253684e-03 208997 s at IAI 1.113154e-03 215718 s_at IAI 2.253684e-03 208904 s_at IAI 1.113154e-03 216526 x at IAI 2.253684e-03 208729 x at IAI 1.113154e-03 217028 at IAI 2.253684e-03 207980 s at IAl 1.113154e-03 217924 at IAI 2.253684e-03 1569594 a at IAI 1.113154e-03 203989 x_at IAI 2.253684e-03 1555611_s_at IAI 1.113154e-03 223223 at IAI 2.253684e-03 1554152_a_at IAI 1.113154e-03 211944 at IAI 2.253684e-03 201318 s at IAI 1.113154e-03 211333 s at IAI 2.253684e-03 201492 s_at IAI 1.113154e-03 202535 at IAI 2.253684e-03 201137 s at IAI 1.113154e-03 202697 at IAI 2.253684e-03 201049 s_at IAI 1.113154e-03 202750 s_at IAI 2.253684e-03 201140 s at IAI 1.113154e-03 203012 x at IAI 2.253684e-03 201631 s_at IAl 1.113154e-03 202388 at IAI 2.253684e-03 201469 s at IAI 1.113154e-03 202760 s at IAI 2.253684e-03 215948 x at IAI 1.164581e-03 202078 at IAI 2.253684e-03 200763 s_at IAI 1.165665e-03 202919 at IAI 2.253684e-03 210627 s_at IAI 1.198112e-03 205327 s_at IAI 2.253684e-03 212988 x at IAI 1.268911e-03 204892 x at IAI 2.253684e-03 206405 x at IAI 1.269832e-03 209396 s at IAI 2.253684e-03 228993 s_at IAI 1.282065e-03 208720 s at IAI 2.253684e-03 201085 s at IAI 1.373927e-03 208815 x at IAI 2.253684e-03 234981 x at IAI 1.411073e-03 208998 at IAI 2.253684e-03 221607 x at IAI 1.466337e-03 1553962 s at IAI 2.253684e-03 219096 at IAI 1.530057e-03 1552323 s at IAI 2.253684e-03 202551 s_at IAI 1.530057e-03 1552541_at IAI 2.253684e-03 221550 at IAI 1.548827e-03 1555407 s at IAI 2.253684e-03 215111 s at IAI 1.556807e-03 201721 s at IAI 2.253684e-03 209357 at IAI 1.608023e-03 201891 s_at IAI 2.253684e-03 224727 at IAI 1.678702e-03 201316 at IAI 2.253684e-03 211084 x at IAI 1.698177e-03 201257 x at IAI 2.253684e-03 204524__at IAI 1.716415e-03 229061 s at IAI 2.25651e-03 208873 s_at IAI 1.718394e-03 220328 at IAI 2.31088e-03 225751 at IAI 1.744718e-03 203983 at IAI 2.349556e-03 200077 s at IAI 1.790433e-03 213476 x at IAI 2.434679e-03 223230 at IAI 1.8364e-03 203606 at IAI 2.461504e-03 229272 at IAI 1.852157e-03 219467 at IAI 2.504392e-03 204937 s_at IAI 1.914311e-03 200047 s at IAI 2.506856e-03 210757 x at IAI 1.924219e-03 37201 at IAI 2.554042e-03 206336 at IAI 1.924219e-03 208643 s at IAI 2.560821e-03 235463 s at IAI 1.940561e-03 218887 at IAI 2.604001e-03 225617 at IAI 1.946318e-03 215100 at IAI 2.604001e-03 217753 s at IAI 1.94799e-03 225994 at IAI 2.666423e-03 215260 s at IAI 1.986657e-03 232209 x at IAI 2.704677e-03 236614 at IAI 2.013973e-03 200769 s at IAI 2.721252e-03 212945 s at IAI 2.104781e-03 209599 s at IAI 2.849272e-03 205510 s at IAI 2.11391e-03 1568934 at IAI 2.942078e-03 202487 s at IAI 2.181556e-03 222602 at IAI 3.005566e-03 224906 at IAI 2.253684e-03 1567080 s at IAI 3.051238e-03 223915 at IAI 2.253684e-03 211956 s at IAI 3.123759e-03 225277 at IAI 2.253684e-03 214908 s at IAI 3.130551e-03 225640 at IAI 2.253684e-03 219557 s at IAI 3.148341e-03 225833 at IAI 2.253684e-03 203107 x at IAI 3.151716e-03 234915 s at IAI 2.253684e-03 210718 s at IAI 3.229165e-03 232008 s_at IAI 2.253684e-03 225507 at IAI 3.303458e-03 228087 at IAI 2.253684e-03 200812 at IAI 3.431653e-03 229742 at IAI 2.253684e-03 213224 s at IAI 3.440986e-03 219237 s_at IAI 2.253684e-03 238462 at IAI 3.475246e-03 219906 at IAI 2.253684e-03 213702 x at IAI 3.498378e-03 219774 at IAI 2.253684e-03 230214 at IAI 3.50832e-03 218972 at IAI 2.253684e-03 201010 s at IAI 3.533007e-03 220363 s_at IAI 2.253684e-03 210338 s at IAI 3.584344e-O3 214459 x at IAI 2.253684e-03 203333 at IAI 3.608958e-03 214012 at IAI 2.253684e-03 221466 at IAI 3.608958e-03 ■2'ilό'7i'ms_at IAI'1'''3T60"819'58e-03 1555643_s_at IAI 4.184226e-03 218987 at IAI 3.650537e-03 201044 x at IAI 4.184226e-03 225420 at IAI 3.662468e-03 201405 s_at IAI 4.184226e-03 243927 x at IAI 3.662468e-03 201236 s at IAI 4.184226e-03 202166 s at IAI 3.745021e-03 201811 x at IAI 4.184226e-03 224614 at IAI 3.751643e-03 201409 s at IAI 4.184226e-03 200871 s_at IAI 3.792931e-03 1568856 at IAI 4.204851e-03 214321 at IAl 3.793306e-03 1555091_at IAI 4.263479e-03 202840 at IAI 3.820156e-03 201699 at IAI 4.27499e-03 203899 s at IAI 3.855093e-03 210426 x at IAI 4.525997e-03 203535 at IAI 3.870907e-03 212856 at IAI 4.657532e-03 202090 s at IAI 3.888617e-03 91684 g_at IAI 4.732743e-03 231863 at IAI 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Figure imgf000465_0001
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3.25715e-04 207320 x at IS 3.81e-04 207616" s at IS 3.25715e-04 206687_s_at IS 3.81e-04 209276"~s at IS 3.25715e-04 218280_x_at IS 3.85e-04 209194" at IS 3.25715e-04 202546__at IS 3.87e-04 208640"_at IS 3.25715e-04 218603_at IS 3.9e-04 209027" s at IS 3.25715e-04 211749 s_at IS 3.9e-04 208882" s_at IS 3.25715e-04 200941_at IS 3.9e-04 200829" x at IS 3.25715e-04 200990_at IS 3.92e-04 200634"_at IS 3.25715e-04 219326 s at IS 3.93443e-04 200839" s__at IS 3.25715e-04 214523_at IS 3.93443e-04 200625" s at IS 3.25715e-04 208873_s at IS 3.96e-04 200728" at IS 3.25715e-04 202315 s at IS 3.97e-04 200614" at IS 3.25715e-04 213526 s at IS 3.98e-O4 201007"_at IS 3.25715e-04 212780_at IS 3.99e-04 200889" s at IS 3.25715e-04 217773 s at IS 4.0e-04 201643" x at IS 3.25715e-04 202471 s at IS 4.0e-04 201725" at IS 3.25715e-04 31826 at IS 4.07e-04 201642""at IS 3.25715e-04 200848_at IS 4.08e-04 201670" s at IS 3.25715e-04 208669 s at IS 4.1e-O4 201359" at IS 3.25715e-04 202469 s at IS 4.11e-04 201493"_s__at IS 3.25715e-04 203788 s at IS 4.13e-04 201163" s at IS 3.25715e-04 221816 s at IS 4.16e-04 201593"_s_at IS 3.25715e-04 202173 IS 4.17e-04 201091" s at IS 3.25715e-04 200097IssZaatt IS 4.18e-04 201483" s at IS 3.25715e-04 221474 at IS 4.25e-04 200980" s at IS 3.25715e-04 202581 at IS 4.25e-04 201097" s_at IS 3.25715e-04 209384_at IS 4.25e-04 201277" s at IS 3.25715e-04 200073 s at IS 4.31e-04 201817" at IS 3.25715e-04 210840 s at IS 4.36e-04 201200" at IS 3.25715e-04 33323 'r_at IS 4.37e-04 201903" at IS 3.25715e-04 214733 s at IS 4.42332e-04 201186" at IS 3.25715e-04 217427 s at IS 4.42332e-04 201487""at IS 3.25715e-04 211271 x at IS 4.46e-04 201807""at IS 3.25715e-04 214435 x at IS 4.47e-04 213867" x at IS 3.26e-04 212697 at IS 4.5e-04 212740""at IS 3.28e-04 204605 at IS 4.52e-04 211746" x at IS 3.32e-04 212584_at IS 4.57e-04 201687" s at IS 3.32e-04 216221 s at IS 4.6e-04 210739" x at IS 3.39e-04 200964'_at IS 4.61e-04 218334"JIt IS 3.41e-04 213318 s at IS 4.65e-04 217845"_x at IS 3.42e-04 AFFX-HUMGAPDH/ 216526" x at IS 3.43e-04 M33197 IS 4.67e-04 214467""at IS 3.45e-04 218364_at IS 4.67e-04 204509""at IS 3.47e-04 201322 at IS 4.69e-04 ' '"2"OISO1 s at ""i'S ;'-4r7ϊi-o4":" 202636 at IS 5.02887e-04 4.72e-04 202770 s_at IS 5.02887e-04 4.78e-04 202956 at IS 5.02887e-04 4.87e-04 202304 at IS 5.02887e-04 4.88e-04 202811 at IS 5.02887e-04 4.88e-04 202506 at IS 5.02887e-04 4.9e-04 202568 s at IS 5.02887e-04 4.91e-04 202430 s at IS 5.02887e-04 4.91e-04 207168 s at IS 5.02887e-04 4.99e-04 205575 at IS 5.02887e-04 5.0e-04 205842 s at IS 5.02887e-04 5.02887e-04 206618 at IS 5.02887e-04 5.02887e-04 205027 s at IS 5.02887e-04 5.02887e-04 205513 at IS 5.02887e-04 5.02887e-04 206150 at IS 5.02887e-04 5.02887e-04 205607 s at IS 5.02887e-04 5.02887e-04 209295 at IS 5.02887e-04 5.02887e-04 208284 x at IS 5.02887e-04 5.02887e-04 208540 x at IS 5.02887e-04 5.02887e-04 208709 s_at IS 5.02887e-04 5.02887e-04 209281 s_at IS 5.02887e-04 5.02887e-04 209020 at IS 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Figure imgf000470_0001
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Figure imgf000471_0002
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209089"_at IS 7.57566e-04 218071 s at IS 7.57566e-04 207545" s at IS 7.57566e-04 203371 s at IS 7.57566e-04 208970" s at IS 7.57566e-04 203897'~at IS 7.57566e-04 208660""at IS 7.57566e-04 204308 s at IS 7.57566e-04 209018" s_at IS 7.57566e-04 203887 s at IS 7.57566e-04 200838"_at IS 7.57566e-04 204314 s at IS 7.57566e-04 200696" s at IS 7.57566e-04 203512_at IS 7.57566e-04 200739" s_at IS 7.57566e-04 204859 s at IS 7.57566e-04 200744" s at IS 7.57566e-04 203362 s at IS 7.57566e-04 200765" x at IS 7.57566e-04 203594 at IS 7.57566e-04 200799" at IS 7.57566e-04 204646_at IS 7.57566e-04 200039"_s_at IS 7.57566e-04 203319 s at IS 7.57566e-04 200732" s at IS 7.57566e-04 220947 s at IS 7.57566e-04 200668" s_at IS 7.57566e-04 221539 at IS 7.57566e-04 200785" s at IS 7.57566e-04 221749_at IS 7.57566e-04 200070""at IS 7.57566e-04 221263 s at IS 7.57566e-04 201863"_at IS 7.57566e-04 222077 s at IS 7.57566e-04 201173" x at IS 7.57566e-04 221059 s at IS 7.57566e-04 201172" x at IS 7.57566e-04 221778_at IS 7.57566e-04 200870""at IS 7.57566e-04 221006' s at IS 7.57566e-04 200896 x at IS 7.57566e-04 209549 s at IS 7.57566e-04 201086 x at IS 7.57566e-04 211936'_at IS 7.57566e-04 201412""at IS 7.57566e-04 210644' s at IS 7.57566e-04 201343 "at IS 7.57566e-04 210200'_at IS 7.57566e-04 201773"[at IS 7.57566e-04 209901 x at IS 7.57566e-04 201221 s at IS 7.57566e-04 2"0l8"62"''s2' at"""I1S 201518 at IS 9.51e-04 201866 S at IS 7.57566e-04 202428 x at IS 9.56e-04 201772 at IS 7.57566e-04 209388 at IS 9.63e-04 201651 S at IS 7.57566e-04 209514 s_at IS 9.74e-04 201237 at IS 7.57566e-04 217943 s at IS 9.79e-04 201761 at IS 7.57566e-04 219869 !s_at IS 9.7915e-04 200960 X at IS 7.57566e-04 217786 at IS 9.7915e-04 200059 S at IS 7.71e-04 208546 x_at IS 9.7915e-04 200976 S at IS 7.79e-04 229632 s at IS 9.81e-04 1553514 a_at IS 7.89e-04 212904 at IS 9.92e-04 205898 at IS 7.93e-04 214875 x at IS 9.96e-04 201201 at IS 7.94e-04 220162 s_at IS 1.012362e-03 200822 X at IS 7.98e-04 214813 at IS 1.012362e-03 201271 s_at IS 7.99e-04 213851 at IS 1.012362e-03 202795 X at IS 8.05e-04 201936 s at IS 1.012362e-03 223136 at IS 8.07e-04 218139 s_at IS 1.015459e-03 200808 S at IS 8.11e-04 221808 at IS 1.017818e-03 200058 s_at IS 8.12e-04 225294 s at IS 1.019488e-03 210616 S at IS 8.22e-04 211784 s_at IS 1.021721e-03 213475 S at IS 8.25e-04 201209 at IS 1.041552e-03 200053 at IS 8.26e-04 200760 s at IS 1.043083e-03 219929 S at IS 8.3371e-04 202334 s at IS 1.047015e-03 208926 at IS 8.3371e-04 218205 s_at IS 1.049849e-03 218716 X at IS 8.38e-04 202089 s_at IS 1.050113e-03 202413 S at IS 8.42e-04 204467 s at IS 1.052826e-03 235568 at IS 8.44e-04 209960 at IS 1.052826e-03 221495 'S at IS 8.46e-04 205034 at IS 1.052826e-03 202593 S at IS 8.47e-04 212629 s_at IS 1.054481e-03 204892 X at IS 8.47e-04 210031 at IS 1.054481e-03 201582 at IS 8.48e-04 212025 s at IS 1.054481e-03 202272 S at IS 8.5e-04 202764 at IS 1.054481e-03 205644 S at IS 8.59e-04 202807 s at IS 1.054481e-03 212452 X at IS 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1.11587e-03 209103 s_at IS 1.11587e-03 212261 "at IS 1.11587e-03 209286 at IS 1.11587e-03 214629 x at IS 1.11587e-03 209076 s at IS 1.11587e-03 213292 s at IS 1.11587e-03 200620 at IS 1.11587e-03 212663""at IS 1.11587e-03 200753 x at IS 1.11587e-03 217745 s_at IS 1.11587e-03 200087 s at IS 1.11587e-03 218237"[s_at IS 1.11587e-03 201535 at IS 1.11587e-03 217886 "at IS 1.11587e-03 201181 at IS 1.11587e-03 218223"s_at IS 1.11587e-03 201823 s_at IS 1.11587e-03 218383 "at IS 1.11587e-03 201606 s at IS 1.11587e-03 218323 ~at IS 1.11587e-03 201705 at IS 1.11587e-03 218404 "at IS 1.11587e-03 201662 s_at IS 1.11587e-03 217800 s at IS 1.11587e-03 201114 x at IS 1.11587e-03 203732"[at IS 1.11587e-03 201133 s at IS 1.11587e-03 204082"[at IS 1.11587e-03 214894 x at IS 1.11701e-03 203253"s at IS 1.11587e-03 221190 s at IS 1.13543e-03 204683[at IS 1.11587e-03 201726 at IS 1.138291e-03 203880"at IS 1.11587e-03 207573 x at IS 1.14614e-03 203292 "s at IS 1.11587e-03 202753 at IS 1.152508e-03 203518 "at IS 1.11587e-03 209510 at IS 1.15528e-03 221829 s_at IS 1.11587e-03 201113 at IS 1.168348e-03 221235 s at IS 1.11587e-03 208655 at IS 1.169594e-03 222143 s at IS 1.11587e-03 214268 s at IS 1.170918e-03 221516 s at IS 1.11587e-03 219324 at IS 1.173072e-03 220939 s at IS 1.11587e-03 217864 s at IS 1.193296e-03 221482 s_at IS 1.11587e-03 200052 s at IS 1.195567e-03 211043 s at IS 1.11587e-03 201584 s_at IS 1.19567e-03 212072 s at IS 1.11587e-03 201743 at IS 1.197455e-03 211742 _s_at IS 1.11587e-03 204767 s at IS 1.197841e-03 211783 s at IS 1.11587e-03 209831 x at IS 1.197841e-03 210340 s_at IS 1.11587e-03 203513 at IS 1.198038e-03 211730 s at IS 1.11587e-03 222992 s at IS 1.199595e-03 209860 s at IS 1.11587e-03 211978 x at IS 1.205097e-03 202182""at IS 1.11587e-03 218799 at IS 1.205427e-03 202629"~.at IS 1.11587e-03 208315 x_at IS 1.205427e-03 202478""at IS 1.11587e-03 216304 x at IS 1.207234e-03 202532 s at IS 1.11587e-03 208113 x at IS 1.210316e-03 202553 s at IS 1.11587e-03 200859 x_at IS 1.211266e-03 203202 [at IS 1.11587e-03 212085 at IS 1.211655e-03 202635 s at IS 1.11587e-03 213503 x at IS 1.213017e-03 202097 'at IS 1.11587e-03 213001 at IS 1.22434e-03 203039 s at IS 1.11587e-03 207755 at IS 1.230329e-03 202660 "at IS 1.11587e-03 202021 x at IS 1.235081e-03 202277 ~at IS 1.11587e-03 1554503 a_at IS 1.237978e-03 202540 s at IS 1.11587e-03 204882 at IS 1.248186e-03 202663 "at IS 1.11587e-03 202129 s_at IS 1.253074e-03 206571 s at IS 1.11587e-03 201568 at IS 1.25367e-03 205003 [at IS 1.11587e-03 200936 at IS 1.258805e-03 206592 s at IS 1.11587e-03 222447 at IS 1.262136e-03 205370 x at IS 1.11587e-03 204116 at IS 1.267524e-03 206183 s at IS 1.11587e-03 217769 s_at IS 1.282403e-03 207205 [at IS 1.11587e-03 213360 s at IS 1.292952e-03 207922 s at IS 1.11587e-03 219329 s at IS 1.293961e-03 208684 "at IS 1.11587e-03 214606 at IS 1.303157e-03 207857 [at IS 1.11587e-03 203857 s at IS 1.303157e-03 208943 s at IS 1.11587e-03 208238 x at IS 1.303157e-03 208767"s at IS 1.11587e-03 208931 s at IS 1.303234e-03 71
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Figure imgf000475_0001
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202592_at IS 1.403139e-03 217916 s_at IS 1.610118e-03 200816 S at IS 1.405705e-03 218157 x at IS 1.610118e-03 210427 X at IS 1.409107e-03 218142 s at IS 1.610118e-03 217938 S at IS 1.413929e-03 218085 at IS 1.610118e-03 208876 S at IS 1.424401e-03 217815_at IS 1.610118e-03 209330 S at IS 1.424963e-03 218202 x at IS 1.610118e-03 210629 X at IS 1.426183e-03 218039_at IS 1.610118e-03 210438 X at IS 1.444186e~03 218394_at IS 1.610118e-03 212020 S at IS 1.450182e-03 218037_at IS 1.610118e-03 221718 S at IS 1.45094e-03 218426 s at IS 1.610118e-03 207630 S at IS 1.469697e-03 204446 s at IS 1.610118e-03 220953_s_at IS 1.470998e-03 203884 s at IS 1.610118e-03 218597 S at IS 1.476515e-03 203445 s at IS 1.610118e-03 210504_at IS 1.476515e-03 203433_at IS 1.610118e-03 204192_at IS 1.477643e-03 203566 s at IS 1.610118e-03 201530_X at IS 1.481414e-03 204246 s at IS 1.610118e-03 219070_s_at IS 1.482855e-03 204177 s at IS 1.610118e-03 204409 S at IS 1.482855e-03 203992 s at IS 1.610118e-03 210692 S at IS 1.482855e-03 203814 s at IS 1.610118e-03 209083_at IS 1.491044e-03 204206_at IS 1.610118e-03 200778 S at IS 1.496911e-03 220761_s_at IS 1.610118e-03 213746 S at IS 1.498903e-03 221046 s at IS 1.610118e-03 201356_at IS 1.512759e-03 221234 s at IS 1.610118e-03 214428 X at IS 1.517482e-03 209555 s at IS 1.610118e-03 221014 S at IS 1.517557e-03 210140_at IS 1.610118e-03 201389_at IS 1.518076e-03 210117_at IS 1.610118e-03 200729_s_at IS 1.532065e-03 211433 x at IS 1.610118e-03 214736 S at IS 1.534792e-03 211984 at IS 1.610118e-03 200807 S at IS 1.539466e-03 209943_at IS 1.610118e-03 218671 S at IS 1.540044e-03 209579_s_at IS 1.610118e-03 208667_s_at IS 1.545634e-03 210145_at IS 1.610118e-03 204599_s_at IS 1.550807e-03 209574 s at IS 1.610118e-03 201366_at IS 1.553303e-03 210288 at IS 1.610118e-03 200712 S at IS 1.554922e-03 203110_at IS 1.610118e-03 213418_at IS 1.571944e-03 202569 s at IS 1.610118e-03 216194 S at IS 1.575452e-03 202025 x at IS 1.610118e-03 ""20'22Ot"_έt "'"Is "■■~r.'"6T0il8e-03 212600 s at IS 1.72914e-03 202163"s_at IS 1.610118e-03 225985"_at IS 1.729971e-03 202373"s_at IS 1.610118e-03 223144" s at IS 1.741482e-03 202650" S at IS 1.610118e-03 203134" at IS 1.746851e-03 202462 S at IS 1.610118e-03 212852" s at IS 1.75304e-03 202645 S at IS 1.610118e-03 219165""at IS 1.754922e-03 202874 S at IS 1.610118e-03 37425 <3_at IS 1.760333e-03 205174" S at IS 1.610118e-03 205322 s_at IS 1.772867e-03 205510 S at IS 1.610118e-03 212515" s at IS 1.775946e-03 206061" S at IS 1.610118e-03 218996"_at IS 1.786039e-03 205992" S at IS 1.610118e-03 227651"_at IS 1.787389e-03 206026 S at IS 1.610118e-03 200065" s at IS 1.808089e-03 205603 S at IS 1.610118e-03 201901" s at IS 1.809818e-03 209380 S at IS 1.610118e-03 201032""at IS 1.809888e-03 209130 "at IS 1.610118e-03 219672"_at IS 1.82034e-03 208875 S at IS 1.610118e-03 205270" s_at IS 1.829613e-03 208834 X at IS 1.610118e-03 208695"_s_at IS 1.832185e-03 207719 X at IS 1.610118e-03 215535" s at IS 1.835358e-03 208825 X at IS 1.610118e-03 217020" at IS 1.84107e-03 208895 S at IS 1.610118e-03 215719" x at IS 1.84501e-03 208678 "at IS 1.610118e-03 218789" s at IS 1.848115e-03 208056 S at IS 1.610118e-03 210875" s at IS 1.848115e-03 209186 "at IS 1.610118e-03 1565162 s at IS 1.864471e-03 200786 ~.at IS 1.610118e-03 217718 s_at IS 1.865634e-03 200594 X at IS 1.610118e-03 212901" s at IS 1.866093e-03 200794 X at IS 1.610118e-03 202531"~at IS 1.877039e-03 201009 s_at IS 1.610118e-03 211883" x at IS 1.884678e-03 200983 X at IS 1.610118e-03 201232" s at IS 1.885814e-03 201527 "at IS 1.610118e-03 218471" s at IS 1.886379e-03 201491 "at IS 1.610118e-03 207808" s at IS 1.886379e-03 201574 "at IS 1.610118e-03 225208" s "at IS 1.89058e-03 201456 s_at IS 1.610118e-03 224789"_at IS 1.891129e-03 201176 S at IS 1.610118e-03 201549" x at IS 1.899587e-03 201594 S at IS 1.610118e-03 212794" s at IS 1.90119e-03 201088 at IS 1.610118e-03 218172" s_at IS 1.910669e-03 201089 ~.at IS 1.610118e-03 218979" at IS 1.911121e-03 201865 X at IS 1.610118e-03 214866"_at IS 1.911121e-03 202899 S at IS 1.613723e-03 213127" s at IS 1.911121e-03 220078 "at IS 1.617918e-03 213133" s at IS 1.911121e-03 212259 S at IS 1.617918e-03 213301" x at IS 1.911121e-03 213958 "at IS 1.617918e-03 221867"_at IS 1.911121e-03 212634 at IS 1.617918e-03 201053" s at IS 1.911121e-03 212462 at IS 1.617918e-03 212144"_at IS 1.91218e-03 218006 S at IS 1.617918e-03 206342" x at IS 1.912404e-03 204275 at IS 1.617918e-03 207571" x at IS 1.914575e-03 221601 _s_at IS 1.617918e-03 213622""at IS 1.923172e-03 210981 S at IS 1.617918e-03 203221"_at IS 1.928441e-03 202208 S at IS 1.617918e-03 208734" x at IS 1.934221e-03 203247 S at IS 1.617918e-03 213798" s at IS 1.946406e-03 201959 S at IS 1.617918e-03 202166" s at IS 1.957746e-03 200968 S at IS 1.617918e-03 208656" s at IS 1.962171e-03 217899 at IS 1.62424e-03 205310"_at IS 1.963055e-03 212015 X at IS 1.644751e-03 204031" s_at IS 1.969584e-03 204361 S at IS 1.649168e-03 211581" x at IS 1.973504e-03 211975 at IS 1.655613e-03 201280" s at IS 1.991158e-03 209022 at IS 1.661241e-03 214039" s at IS 1.991777e-03 203579 S at IS 1.665808e-03 204118" at IS 1.995424e-03 218852 at IS 1.671324e-03 204265" s at IS 2.011001e-03 208296 X at IS 1.676114e-03 201953"}at IS 2.014553e-03 202018 S at IS 1.678702e-03 204568" at IS 2.019864e-03 200761 S at IS 1.694908e-03 206978""at IS 2.023406e-03 202257 S at IS 1.712874e-03 207339" s at IS 2.028846e-03 205040 at IS 1.712874e-03 200833" s at IS 2.065948e-03 211378 X at IS 1.725211e-03 201696" at IS 2.074503e-03 -IS ""T.Or7'i5304"e-03 210249 s at IS 2.279612e-03 IS 2 088784e-03 202047"~s_at IS 2.279612e-03 IS 2 10639e-03 203186" s at IS 2.279612e-03 IS 2.10975e-03 202241"_at IS 2.279612e-03 IS 2.111444e-03 202181" at IS 2.279612e-03
Figure imgf000477_0001
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IS 0.01 209165"_at IS 0.01 204316_at IS 0.01 200022"_at IS 0.01 205197 s at IS 0.01 201319" at IS 0.01 207686 s at IS 0.01 200958" s at IS 0.01 201276 at IS 0.01 201824"_at IS 0.01 32042 at IS 0.01 200891" s at IS 0.01 227233_at IS 0.01 201407" s at IS 0.01 218178 s at IS 0.01 201566" x at IS 0.01 222991 s at IS 0.01 217754""at IS 0.01 238439_at IS 0.01 208637" x at IS 0.01 206006 s at IS 0.01 208675" s at IS 0.01 207431 s at IS 0.01 223009""at IS 0.01 209426 s at IS 0.01 208852" s at IS 0.01 201956 s at IS 0.01 209232" s at IS 0.01 201924 at IS 0.01 214438""at IS 0.01 217879_at IS 0.01 32811 at IS 0.01 210186 s at IS 0.01 200819 s at IS 0.01 213620 s at IS 0.01 219176"_at IS 0.01 212007 at IS 0.01 217846""at IS 0.01 212076_at IS 0.01 201993 x at IS 0.01 206652_at IS 0.01 202391""at IS 0.01 205323 s at IS 0.01 236283" x at IS 0.01 201742 x at IS 0.01 206662 at IS 0.01 202545 at IS 0.01
Figure imgf000487_0001
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Figure imgf000529_0001
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s at IZ 0.04 213773 x at IZ 0.04 201584" s at IZ 0.04 208436 s at IZ 0.04 218970" s at IZ 0.04 201829 at IZ 0.04 218873" at IZ 0.04 204542 at IZ 0.04 201833"_at 3A 1.89e-09 212663 at IZ 0.04 211734" s at 3A 1.41e-08 202695 s at IZ 0.04 206240"_s_at 3A 5.19e-07 204176 at IZ 0.04 212227" x at 3A 8.64e-07 1558775 s at IZ 0.04 213326"_at 3A 1.44e-06 44111 at IZ 0.04 220661" s at 3A 1.48e-06 208705 s at IZ 0.04 208998"_at 3A 1.75e-06 209186 at IZ 0.04 215994" x at 3A 2.57e-06 218454 at IZ 0.04 202078"_at 3A 6.05e-06 210962 s at IZ 0.04 212451"_at 3A 8.02e-06 235645 at IZ 0.04 213480"_at 3A 8.76e-06 215785 s_at IZ 0.04 212130" x at 3A 1.09e-05 217958 at IZ 0.04 212088" at 3A 1.75e-05 208255 s at IZ 0.04 212706" at 3A 1.82e-05 221502 at IZ 0.04 201013" s at 3A 1.82e-05 206133 at IZ 0.04 222125" s at 3A 2.33e-05 209674 at IZ 0.04 201140" s at 3A 2.8e-05 219624 at IZ 0.04 221935" s at 3A 3.24e-05 218036 x_at IZ 0.04 220127" s at 3A 3.27e-05 222311 s at IZ 0.04 219299" at 3A 4.26e-05 212452 x at IZ 0.04 218448" at 3A 5.52e-05 212286 at IZ 0.04 204686" at 3A 6.09e-05 208486 at IZ 0.04 203685"_at 3A 6.26e-05 202918 s at IZ 0.04 224369] s at 3A 6.68e-05 216088 s at IZ 0.04 209702"_at 3A 7.73e-05 210948 s at IZ 0.04 204599" s at 3A 1.11075e-04 203654 s at IZ 0.04 202356" s_at 3A 1.32223e-04 218347 at IZ 0.04 224906" at 3A 1.35e-04 222218 s at IZ 0.04 203810"_at 3A 1.37e-04 1558136_s_at IZ 0.04 205608" s at 3A 1.37e-04 201528 at IZ 0.04 209366" x at 3A 1.47862e-04 218064 s at IZ 0.04 208410" x at 3A 1.54092e-04 239212 at IZ 0.04 209398""at 3A 1.75e-04 204190 at IZ 0.04 221582"_at 3A 1.85e-04 209949 at IZ 0.04 212718" at 3A 1.88e-04 1553271_at IZ 0.04 205707"jat 3A 2.54266e-04 211251 x at IZ 0.04 205632" s at 3A 2.55409e-04 204995 at IZ 0.04 214173" x at 3A 2.72e-04 218478 s at IZ 0.04 202717" s at 3A 2.86995e-04 227286 at IZ 0.04 230243"_at 3A 2.91e-04 221920 s at IZ 0.04 217388" s at 3A 2.96058e-04 202778 s_at IZ 0.04 212305" s at 3A 3.13517e-04 214783 s at IZ 0.04 201935" s at 3A 3.21e-04 206028 s at IZ 0.04 201967""at 3A 3.31e-04 209089 at IZ 0.04 201524" x at 3A 3.41382e-04 215038 s at IZ 0.04 212213" x at 3A 3.44e-04 213322 at IZ 0.04 208994""s_at 3A 3.44e-04 219774 at IZ 0.04 215718" s at 3A 3.58262e-04 219528 s at IZ 0.04 201545 3A 3.59384e-04 211982 x at IZ 0.04 220890"-ssIaatt 3A 3.88094e-04 209148 at IZ 0.04 200613""at 3A 3.89591e-04 48531 at IZ 0.04 36019 at 3A 3.91668e-04 203429 s at IZ 0.04 214688 at 3A 4.0804e-04 217869 at IZ 0.04 156478Jϊ at 3A 4.37e-04 5 x at 3A 1.602123e-03 3 s at 3A 1.606842e-03 4_at 3A 1.635446e-03
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Figure imgf000581_0001
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3A 1.186408e-03 213624 at 3A 2.591333e-03 218715 at 3A 1.194611e-03 217232 x at 3A 2.591333e-03 228770 at 3A 1.197841e-03 202623 at 3A 2.591333e-03 224859 at 3A 1.217275e-03 208997 s at 3A 2.591333e-03 200033 at 3A 1.230407e-03 210873 x at 3A 2.593918e-03 219180 s at 3A 1.294092e-03 214736 s_at 3A 2.603873e-03 235057 at 3A 1.309919e-03 217992 s at 3A 2.60699e-03 201236 s at 3A 1.315072e-03 1553678_a at 3A 2.659966e-03 202910 s at 3A 1.333828e-03 219191 s_at 3A 2.680476e-03 224764 at 3A 1.363715e-03 211945 s at 3A 2.773187e-03 201272 at 3A 1.407352e-03 205098 at 3A 2.845937e-03 200909 s at 3A 1.411331e-03 200649 at 3A 2.876317e-03 218208 at 3A 1.412234e-03 234919 s at 3A 2.888184e-03 244498 x at 3A 1.435389e-03 213453 x at 3A 2.91297e-03 222858 s at 3A 1.444148e-03 234981 x at 3A 2.919568e-03 1553575_at 3A 1.444148e-03 208703 s at 3A 2.927498e-03 221339 at 3A 1.465537e-03 203585 at 3A 2.949576e-03 243750 x at 3A 1.478026e-03 218961 s at 3A 2.969483e-03 218223 s at 3A 1.479659e-03 208900 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3.659711e-03 1555390_at 3A 5.459697e-03 217197 x at 3A 3.681948e-03 222407 s at 3A 5.477366e-03 203758 at 3A 3.698604e-03 225043 at 3A 5.478715e-03 202683 s at 3A 3.704599e-03 213056 at 3A 5.497483e-03 225981 at 3A 3.748011e-03 209367 at 3A 5.51047e-03 1569371_at 3A 3.782714e-03 225058 at 3A 5.518419e-03 212007 at 3A 3.833435e-03 208723 at 3A 5.535505e-03 204858 s at 3A 3.956677e-03 222915 s at 3A 5.549285e-03 220953 s at 3A 3.96668e-03 219788_at 3A 5.580137e-03 209619 at 3A 3.971671e-03 AFFX-HUMGAPDH/ 202443 x at 3A 3.971671e-03 M33197 3A 5.610949e-03 205832 at 3A 3.971671e-03 214658 at 3A 5.630888e-03 226630 at 3A 3.992608e-03 218747 s at 3A 5.632497e-03 202469 s at 3A 4.133154e-03 234987 at 3A 5.648675e-03 201587 s at 3A 4.147674e-03 222024 s at 3A 5.648675e-03 216041 x at 3A 4.177596e-03 1556744 a at 3A 5.651615e-03 204748 at 3A 4.269554e-03 210926 at 3A 5.669452e-03 201635 s at 3A 4.283599e-03 210317 s at 3A 5.70779e-03 203432 at 3A 4.291123e-03 212192 at 3A 5.742785e-03 217822 at 3A 4.29819e-03 203054 s at 3A 5.778652e-03 203011 at 3A 4.363336e-03 225401 at 3A 5.816251e-03 200064 at 3A 4.374784e-03 214919 s at 3A 5.824778e-03 200850 s at 3A 4.401119e-03 1552667 a at 3A 5.849357e-03 213314 at 3A 4.401923e-03 1553906 s at 3A 5.930907e-03 200818 at 3A 4.402216e-03 214054 at 3A 5.931645e-03 217976 s at 3A 4.425296e-03 215313 x at 3A 5.931645e-03 209766 at 3A 4.426057e-03 222529 at 3A 5.931645e-03 207347 at 3A 4.449694e-03 200801 x_at 3A 5.931645e-03 202794 at 3A 4.460039e-03 209396 s at 3A 6.050255e-03 223658 at 3A 4.47733e-03 200602 at 3A 6.086068e-03 224600 at 3A 4.509862e-03 201376 s_at 3A 6.088482e-03 201094 at 3A 4.524903e-03 222859 s at 3A 6.134707e-03 1554503_a_at 3A 4.56796e-03 208174 x at 3A 6.134736e-03 209067 s at 3A 4.580566e-03 1552790 a at 3A 6.23872e-03 229390 at 3A 4.602307e-03 201977 s at 3A 6.249651e-03 210240 s_at 3A 4.-621529e-03 229391 s_at 3A 6.250172e-03 244710 at 3A 4.644132e-03 214168 s at 3A 6.252128e-03 222679 s at 3A 4.759123e-03 213064 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Figure imgf000599_0001
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7.29e-05 208174" x at 3D 1.23e-04 202917 s at 3D 7.32e-05 201805""at 3D 1.26e-04 217928 s at 3D 7.35e-05 212576"_at 3D 1.28e-04 208039 at 3D 7.35e-05 204038" s at 3D 1.28e-04 207783 x at 3D 7.5e-05 221511" x at 3D 1.28e-04 201389 at 3D 7.64e-05 200797] s at 3D 1.28e-04 224616 at 3D 7.68e-05 204714" s at 3D 1.3e-04 220446 s_at 3D 7.82e-05 203370" s at 3D 1.3e-04 213867 x at 3D 7.91e-05 211504" x at 3D 1.31e-04 209057 x at 3D 8.16e-05 202624" s_at 3D 1.33e-04 201315 x at 3D 8.19e-05 212988" x at 3D 1.35e-04 205513 at 3D 8.23e-05 206834"_at 3D 1.42e-04 207730 x at 3D 8.38e-05 208919" s at 3D 1.45e-04 '20'8"74'9l x at '3D"" 1.46e-04 202607 at 3D 2.21e-04 231922 at 3D 1.48e-04 203752 s at 3D 2.22e-04 216231 s at 3D 1.48e-04 202388 at 3D 2.22e-04 1570033_at 3D 1.48e-04 202902 s_at 3D 2.22e-04 225649 s at 3D 1.5e-04 208695 s at 3D 2.22e-04 227467 at 3D 1.51e-04 200663 at 3D 2.26e-04 211995 x at 3D 1.53e-04 209773 s_at 3D 2.3e-04 227233 at 3D 1.55e-04 211275 s at 3D 2.32e-04 201395 at 3D 1.56e-04 205557 at 3D 2.33e-04 202573 at 3D 1.57e-04 214523 at 3D 2.37e-04 203198 at 3D 1.57e-04 202379 s at 3D 2.38e-04 203455_s at 3D 1.59e-04 210556 at 3D 2.4e-04 AFFX-HUMGAPDH/ 205068 s_at 3D 2.4e-04 M33197 3D 1.6e-04 220949 s at 3D 2.41e-04 200650 s at 3D 1.6e-04 201721 s at 3D 2.41e-04 222217 s at 3D 1.61e-04 221804 s_at 3D 2.43e-04 200031 s at 3D 1.61e-04 201605 x at 3D 2.44e-04 225374 at 3D 1.63e-04 209806 at 3D 2.45e-04 211742 s at 3D 1.63e-04 202187 s at 3D 2.45e-04 205863 aϊ 3D 1.63e-04 202682 s at 3D 2.45e-04 213453 x at 3D 1.65e-04 219033 at 3D 2.46e-04 219371 s at 3D 1.66e-04 203318 s at 3D 2.46e-04 221619 s at 3D 1.67e-04 212307 s at 3D 2.49e-04 32259 at 3D 1.68e-04 205554 s at 3D 2.49e-04 218776 s at 3D 1.69e-04 225640 at 3D 2.5e-04 205865 at 3D 1.69e-04 209762 x at 3D 2.5e-04 217817 at 3D 1.71e-04 200077 s at 3D 2.52e-04 206209 s at 3D 1.71e-04 204959 at 3D 2.53e-04 232946 s at 3D 1.72e-04 203502 at 3D 2.54e-04 37796 at 3D 1.72e-04 1554892 a at 3D 2.56e-04 218595 s at 3D 1.72e-04 213515 x at 3D 2.6e-04 211657 at 3D 1.72e-04 218578 at 3D 2.63e-04 219359 at 3D 1.73e-04 203110 at 3D 2.67e-04 74694 s_at 3D 1.8e-04 160020 at 3D 2.69e-04 210858 x at 3D 1.81e-04 203254 s_at 3D 2.7e-04 220238 s at 3D 1.84e-04 212222 at 3D 2.73e-04 213034 at 3D 1.85e-04 202990 at 3D 2.83e-04 203282 at 3D 1.86e-04 219797 at 3D 2.88e-04 1553569_at 3D 1.87e-04 213198 at 3D 2.91e-04 201492 s at 3D 1.87e-04 38710 at 3D 2.92e-04 221208 s at 3D 1.88e-04 203523 at 3D 2.92e-04 202466 at 3D 1.88e-04 1554503 a at 3D 2.98e-04 206257 at 3D 1.91e-04 220416 at 3D 2.99e-04 208651 x at 3D 1.94e-04 37028 at 3D 3.0e-04 224250 s at 3D 1.99e-04 207801 s at 3D 3.01e-04 37652 at 3D 2.0e-04 219156 at 3D 3.02e-04 213280 at 3D 2.01e-04 221474 at 3D 3.07e-04 1552553 a at 3D 2.01e-04 200985 s at 3D 3.07e-04 218600 ,at 3D 2.03e-04 210004 at 3D 3.11e-04 202107 s at 3D 2.04e-04 212203 x at 3D 3.11e-04 201576 s at 3D 2.08e-04 225250 at 3D 3.16e-04 203535 at 3D 2.09e-04 214084 x at 3D 3.16e-04 227094 at 3D 2.1e-04 218231 at 3D 3.16e-04 218039 at 3D 2.11e-04 205400 at 3D 3.16e-04 201071 x at 3D 2.11e-04 208898 at 3D 3.16e-04 203034 s at 3D 2.12e-04 221269 s at 3D 3.18e-04 218599 at 3D 2.13e-04 1557915_s_at 3D 3.18e-04 215832 x at 3D 2.15e-04 200905 x at 3D 3.18e-04 227905 s at 3D 2.16e-04 213119 at 3D 3.21e-04 211883 x at 3D 2.16e-04 238593 at 3D 3.22e-04 200059 s at 3D 2.16e-04 202431 s at 3D 3.22e-04 206790 s at 3D 2.17e-04 208486 at 3D 3.22e-04 201903 at 3D 2.17e-04 225294 s at 3D 3.3e-04 210186 s at 3D 2.21e-04 218950 at 3D 3.3e-04 ■l2"309f St""""-13'D' "-3""."35'e-d4" 222825 at 3D 4.64e-04 208012 x at 3D 3.35e-04 225251 at 3D 4.7e-04 202681_at 3D 3.37e-04 222156 x at 3D 4.7e-04 AFFX-HSAC07/ 220881 at 3D 4.7e-04 X00351 M 3D 3.38e-04 205950 s at 3D 4.75e-04 200763 s_at 3D 3.41e-04 208825 x at 3D 4.77e-04 220408 x at 3D 3.42e-04 204446 s at 3D 4.83e-04 204192 at 3D 3.42e-04 211581 x at 3D 4.84e-04 203459 s at 3D 3.45e-04 205237 at 3D 4.84e-04 201356 at 3D 3.45e-04 227207 x at 3D 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4.683474e-03 226496""at 3E 5.076622e-03 201140_s_at 3E 4.687528e-03 1554167 a at 3E 5.09145e-03 222244 s at 3E 4.688094e-03 208018 _s_at 3E 5.102881e-03 203167_at 3E 4.690373e-03 61734Jit 3E 5.103541e-03 201311 s at 3E 4.702239e-03 212518 at 3E 5.118549e-03 204093 at 3E 4.71371e-03 217978"_s_at 3E 5.118549e-03 202874_s_at 3E 4.71371e-03 203388""at 3E 5.121324e-03 210386 s at 3E 4.724591e-03 218154""at 3E 5.125262e-03 213243 at 3E 4.726282e-03 207782" s at 3E 5.128182e-03 208094_s_at 3E 4.739907e-03 221203" s at 3E 5.13632e-03 218996_at 3E 4.754378e-03 219859""at 3E 5.152967e-03 215633_x at 3E 4.761173e-03 219392" x at 3E 5.173231e-03 201999 s at 3E 4.761173e-03 204549"_at 3E 5.184351e-03 227981_at 3E 4.762498e-03 210624" s at 3E 5.184351e-03 201390 s at 3E 4.763501e-03 204387" x at 3E 5.185893e-03 226353_at 3E 4.766474e-03 225737" s at 3E 5.208082e-03 53720 at 3E 4.766474e-03 201104" x at 3E 5.208403e-03 201331 s at 3E 4.766474e-03 236267]"at 3E 5.209906e-03 219892 at 3E 4.78141e-03 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Figure imgf000671_0001
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Figure imgf000675_0001
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Figure imgf000683_0001
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4 203885 at 31 1.042091e-03 233571 x at 31 1.64e-04 205603 s at 31 1.048481e-03 207549 x at 31 1.7e-04 1568609 s at 31 1.077832e-03 218999_at 31 1.74e-04 209475_at 31 1.105684e-03 201719 s at 31 1.74e-04 202729 s_at 31 1.122465e-03 224956_at 31 1.76e-04 235121 at 31 1.170878e-03 210971 s_at 31 1.84e-04 224797 at 31 1.177637e-03 202459 s at 31 2.27e-04 226537 at 31 1.189017e-03 204581_at 31 2.29e-04 224416 s at 31 1.232364e-03 218559 s at 31 2.32e-04 205750 at 31 1.236271e-03 209853 s at 31 2.36e-04 222436 s at 31 1.256479e-03 221770 at 31 2.46e-04 228217 s at 31 1.269167e-03 223437_at 31 2.71e-04 208541 x_at 31 1.273347e-03 215719 x at 31 2.75e-04 1553096 s at 31 1.304149e-03 210145_at 31 2.99e-04 211330 s at 31 1.307258e-03 224787 s at 31 3.21e-04 201370"s at 31 1.308184e-03 1552312 a at 31 3.55e-04 205715 at 31 1.313228e-03 203178_at 31 3.76e-04 218883 s at 31 1.322241e-03 44822 s at 31 3.8e-04 219607 s at 31 1.328232e-03 204493 at 31 3.8e-04 208707 at 31 1.343036e-03 1555021 a at 31 3.83e-04 200796 s_at 31 1.347213e-03 216652 s at 31 3.86e-04 214572 s at 31 1.354042e-03 205960 at 31 4.0e-04 222224 at 31 1.372396e-03 54037 at 31 4.02e-04 219111 s at 31 1.393832e-03 205067_at 31 4.04e-04 35626 at 31 1.411227e-03 203471 s_at 31 4.09e-04 201416 at 31 1.430808e-03 221724 s at 31 4.13e-04 206222 at 31 1.468495e-03 238638_at 31 4.26e-04 219066 at 31 1.47293e-03 210904 s at 31 4.28e~04 200669 s__at 31 1.472956e-03 214012 at 31 4.33e-04 218315 s_at 31 1.486619e-03 205127_at 31 4.81e-04 207446 at 31 1.486813e-03 216915 s_at 31 4.84e-04 202356 s_at 31 1.499649e-03 203029 s at 31 4.86e-04 223584 s at 31 1.517385e-03 41160 at 31 4.93e-04 207164 s at 31 1.527344e-03 33304 at 31 4.94e-04 207761 s at 31 1.536713e-03 216252 x at 31 4.98e-04 207738 s at 31 1.557217e-03 217974_at 31 5.0e-04 222636 at 31 1.567428e-03 201798 s at 31 5.12e-04 202374 s_at 31 1.57783e-03 233878 s at 31 5.26e-04 220235 s at 31 1.667164e-03 39402 at 31 5.38e-04 1563088 a_at 31 1.712432e-03 211574 s at 31 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" "'"" 218627_at 3P 3.806343e-03 201939"_at 3N 0. 04 222516_at 3P 3.876379e-03 209517" s at 3N 0 . 04 219232 s at 3P 3.926731e-03 211282" x_at 3N 0 . 04 201108 s_at 3P 3.941696e-03 209892" at 3N 0. 04 228087_at 3P 4.025039e-03 203049" s at 3P 4 . 13e-05 204958 at 3P 4.094795e-03 202599" s at 3P 5 . 37e-05 224596 at 3P 4.105954e-03 226896" at 3P 6. 21e-05 201739_at 3P 4.271043e-03 225814" at 3P 9. 39e-05 216305 s at 3P 4.287734e-03 208960" s_at 3P 1 . 31e-04 224643_at 3P 4 . 645467e-03 223186" at 3P 1. 43e-04 203502_at 3P 4.700955e-03 226080" at 3P 1 . 45e-04 204333_s at 3P 4.889466e-03 220940" at 3P 1 . 79e-04 206618_at 3P 4.951513e-03 217956" s at 3P 2 . 01e-04 209006_s_at 3P 4.961649e-03 202241" at 3P 3 . 33e-04 202458 at 3P 5.028416e-03 210742"_at 3P 3 . 46e-04 224833 at 3P 5.037106e-03 207667" s at 3P 4 . 19e-04 200948__at 3P 5.07136e-03 226168"_at 3P 4 . 36e-04 223007 s at 3P 5.096519e-03 202499" s_at 3P 4 . 59e-04 35150 at 3P 5.350011e-03 205171" at 3P 5 . 16e-04 227548_at 3P 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Figure imgf000735_0001
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Figure imgf000737_0001
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226321 at 4D 0.01 219770 at 4D 0.01 225719 s_at 4D 0.01 221036 s at 4D 0.01 227587 at 4D 0.01 214934 at 4D 0.01 40255 at 4D 0.01 223070 at 4D 0.01 218588 s_at 4D 0.01 207036 x at 4D 0.01 218440 at 4D 0.01 204861 s at 4D 0.01 212222 at 4D 0.01 238858 at 4D 0.01 202838 at 4D 0.01 224787 s at 4D 0.01 205921 s at 4D 0.01 204483 at 4D 0.01 1554428 s at 4D 0.01 219504 s at 4D 0.01 200798 x at 4D 0.01 201365 at 4D 0.01 210117 at 4D 0.01 223866 at 4D 0.01 1555255 a at 4D 0.01 206701"x at 4D 0.01 200039 s at 4D 0.01 218753 at 4D 0.01 200059 s at 4D 0.01 230144 at 4D 0.01 201859 at 4D 0.01 214585 s at 4D 0.01 210240 s at 4D 0.01 211734 s at 4D 0.01 1552419_s_at 4D 0.01 218467 at 4D 0.01 221987 s at 4D 0.01 221638 s at 4D 0.01 220123 at 4D 0.01 1555446_s_at 4D 0.01 203871 at 4D 0.01 227391 x at 4D 0.01 222955 s at 4D 0.01 1556200 a at 4D 0.01 238606 at 4D 0.01 238635 at 4D 0.01 229426 at 4D 0.01 243475 at 4D 0.01 209475 at 4D 0.01 1553865 a at 4D 0.01 210907 s_at 4D 0.01 218495 at 4D 0.01 223835 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8.805661e-03 220969 s at 4H 8.442591e-03 200741_s_at 4H 8.815963e-03 219292 at 4H 8.453002e-03 218531_at 4H 8.829484e-03 207657 x at 4H 8.469504e-03 200833 s at 4H 8.842189e-03 202963 at 4H 8.479324e-03 203653 s at 4H 8.875272e-03 201243 s at 4H 8.489524e-03 211826_s_at 4H 8.875511e-03 221962 s at 4H 8.496875e-03 219603_s_at 4H 8.882653e-03 238982 at 4H 8.506385e-03 212925 at 4H 8.896087e-03 2Z5876 ':1S1F-""""9ΪT •&V5;4UO^3έ-03 214441 at 4H 9.664197e-03 205642 at 4H 8.98855e-03 241337 at 4H 9.673732e-03 219149 x at 4H 8.993054e-03 228089 x at 4H 9.675312e-03 204054 at 4H 9.000952e-03 223370 at 4H 9.676023e-03 227363 s_at 4H 9.012122e-03 205644 s at 4H 9.682812e-03 1553192_at 4H 9.013928e-03 222157 s at 4H 9.690659e-03 230363 s at 4H 9.014103e-03 233483 at 4H 9.707584e-03 204372 s at 4H 9.014332e-03 212025 s at 4H 9.729761e-03 221481 x at 4H 9.017832e-03 239205 s at 4H 9.740759e-03 221014 s at 4H 9.032119e-03 206515 at 4H 9.746822e-03 205681 at 4H 9.040702e-03 235923 at 4H 9.75252e-03 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2.22e-05 206177 s at 4J 4.09e-04 218441 s at 4J 3.28e-05 208779 x at 4J 4.13e-04 1562321_at 4J 3.51e-05 217094 s at 4J 4.18e-04 206495 s at 4J 4.54e-05 207665 at 4J 4.43e-04 204510 at 4J 5.25e-05 220784 s at 4J 4.5e-04 207979 s at 4J 5.63e-05 229665 at 4J 4.67e-04 224550 s_at 4J 6.06e-05 205193 at 4J 4.86e-04 206841 at 4J 8.16e-05 225505 s at 4J 4.92e-04 206205 at 4J 9.02e-05 203789 s at 4J 4.92e-04 224969 at 4J 1.02e-04 213846 at 4J 4.96e-04 209696 at 4J 1.09e-04 206218 at 4J 4.98e-04 203440 at 4J l.le-04 224661 at 4J 5.01e-04 222108 at 4J 1.38e-04 222656 at 4J 5.02e-04 "223-94B"1'at -■4J*-57We-(Jt 236040 at 4J 1.040131e-03 207768 at 4J 5.26e-04 226764 at 4J 1.042415e-03 205067 at 4J 5.27e-04 204924 at 4J 1.048244e-03 242727 at 4J 5.36e-04 223639 s_at 4J 1.04825e-03 203472 s at 4J 5.38e-04 206729 at 4J 1.04825e-03 202747 s_at 4J 5.45e-04 1556744 a_at 4J 1.04825e-03 228512 at 4J 5.55e-04 200874 s at 4J 1.04825e-03 231406 at 4J 5.61e-04 215952 s_at 4J 1.053544e-03 204507 s at 4J 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at 4J 5.667609e-03 1558412_at 4J 6.250458e-03 223528 s_at 4J 5.677638e-03 207429 at 4J 6.283588e-03 215773 x at 4J 5.698293e-03 207813 s at 4J 6.285898e-03 208705 s at 4J 5.710494e-03 201492 s at 4J 6.292787e-03 202833 s at 4J 5.718934e-03 223413 s at 4J 6.309734e-03 205612 at 4J 5.720436e-03 213084 x at 4J 6.312072e-03 227850 x at 4J 5.729189e-03 214100 x at 4J 6.324142e-03 208834 x at 4J 5.776766e-03 203765 at 4J 6.329595e-03 203310 at 4J 5.779868e-03 1555002_at 4J 6.333506e-03 1555073 at 4J 5.786475e-03 209008 x at 4J 6.343148e-03 224137 at 4J 5.799284e-03 219613 s at 4J 6.353338e-03 232986 at 4J 5.801876e-03 208825 x_at 4J 6.360815e-03 203828 s at 4J 5.811107e-03 219486 at 4J 6.380111e-03 1553666_at 4J 5.820463e-03 201181 at 4J 6.404226e-03 216915 s_at 4J 5.827268e-03 207389 at 4J 6.407492e-03 228134 at 4J 5.832548e-03 222875 at 4J 6.413032e-03 211298 s at 4J 5.835257e-03 217739 s_at 4J 6.421782e-03 231991 at 4J 5.84114e-03 220704 at 4J 6.422833e-03 201577 at 4J 5.842712e-03 1555167 s at 4J 6.424255e-03 222235 s. at 4J 5.846832e-03 239382 at 4J 6.464706e-03 226296 s at 4J 5.851257e-03 1558549 s at 4J 6.466751e-03 223065 s at 4J 5.874954e-03 218680 x at 4J 6.482812e-03 206995 x at 4J 5.897546e-03 204327 s_at 4J 6.512933e-03 221156 x at 4J 5.904115e-03 210397 at 4J 6.512993e-03 217766 s at 4J 5.911645e-03 201094 at 4J 6.525788e-03 203012 x at 4J 5.912613e-03 213607 x at 4J 6.530206e-03 201000 at 4J 5.91725e-03 217912 at 4J 6.541377e-03 213398 s_at 4J 5.924707e-03 202770 s_at 4J 6.558034e-03 227286 at 4J 5.946058e-03 221676 s at 4J 6.58496e-03 202100 at 4J 5.955068e-03 219644 at 4J 6.597195e-03 236151 at 4J 5.956865e-03 220078 at 4J 6.599114e-03 238205 at 4J 5.956983e-03 218492 s at 4J 6.600177e-03 219434 at 4J 5.958132e-03 200734 s_at 4J 6.60699e-03 1552921 a at 4J 5.964279e-03 218196 at 4J 6.610252e-03 225177 at 4J 5.969115e-03 226784 at 4J 6.617708e-03 217738 at 4J 5.999666e-03 218404 at 4J 6.630326e-03 227753 at 4J 6.007975e-03 225165 at 4J 6.651581e-03 220990 s at 4J 6.027546e-03 218366 x at 4J 6.656176e-03 204011 at 4J 6.03316e-03 208694 at 4J 6.657899e-03 218578 at 4J 6.035681e-03 1553096 s at 4J 6.688249e-03 244398 x_at 4J 6.041987e-03 209001 s at 4J 6.69906e-03 1555497 a_at 4J 6.062011e-03 212520 s at 4J 6.70109e-03 201625 s_at 4J 6.062179e-03 212603 at 4J 6.701294e-03 208768 x at 4J 6.078722e-03 204185 x at 4J 6.703793e-03 206748 s at 4J 6.080401e-03 222403 at 4J 6.735932e-03 219190 s at 4J 6.083717e-03 225659 at 4J 6.747741e-03 205438 at 4J 6.086626e-03 227029 at 4J 6.748551e-03 206522 at 4J 6.090804e-03 226018 at 4J 6.753555e-03 200010 at 4J 6.104062e-03 217898 at 4J 6.763116e-03 209486 at 4J 6.112382e-03 206632 s_at 4J 6.777252e-03 206350 at 4J 6.128485e-03 219787 s at 4J 6.785551e-03 217303 s at 4J 6.139645e-03 227601 at 4J 6.794109e-03 218968 s at 4J 6.140106e-03 212254 s at 4J 6.794109e-03 221288 at 4J 6.14637e-03 211594 s_at 4J 6.794109e-03 218559 s at 4J 6.147895e-03 205015 s at 4J 6.809682e-03 212334 at 4J 6.147895e-03 213752 at 4J 6.833658e-03 222670 s at 4J 6.147895e-03 201359 at 4J 6.85541e-03 206789 s at 4J 6.147895e-03 231530 s at 4J 6.855756e-03 20'4'29&' s at 4J ■"6":8t67βe-03 231405 at 4J 7.45326e-03 206004 at 4J 6.879814e-03 1552423 at 4J 7.459343e-03 200852 x at 4J 6.891839e-03 208053 at 4J 7.46862e-03 1554800_at 4J 6.905639e-03 229974 at 4J 7.47168e-03 1555243 x at 4J 6.912454e-03 205558 at 4J 7.473238e-03 202112 at 4J 6.915469e-03 202464 s at 4J 7.487016e-03 205963 s_at 4J 6.921137e~03 200875 s_at 4J 7.493837e-03 219641 at 4J 6.931672e-03 219504 s_at 4J 7.495336e-03 222140 s at 4J 6.946492e-03 212809 at 4J 7.496523e-03 204559 s_at 4J 6.966896e-03 203936 s at 4J 7.497688e-03 1557165 s at 4J 6.967149e-03 216252 x at 4J 7.529774e-03 203420 at 4J 6.972707e-03 219768 at 4J 7.574087e-03 209366 x at 4J 6.973536e-03 224435 at 4J 7.593718e-03 223650 s at 4J 6.974647e-03 222218 s_at 4J 7.603157e-03 209308 s at 4J 6.979122e-03 235472 at 4J 7.603484e-03 219854 at 4J 7.024855e-03 205957 at 4J 7.605619e-03 220397 at 4J 7.027515e-03 232891 at 4J 7.605817e-03 224380 s at 4J 7.056585e-03 233338 at 4J 7.607793e-03 205384 at 4J 7.069631e-03 207913 at 4J 7.613092e-03 213506 at 4J 7.079267e-03 203152 at 4J 7.618989e-03 212295 s at 4J 7.080972e-03 221850 x_at 4J 7.623637e-03 217729 s at 4J 7.080972e-03 229250 at 4J 7.631541e-03 223139 s at 4J 7.080972e-03 223018 at 4J 7.631645e-03 207186 s at 4J 7.080972e-03 1553363_at 4J 7.648955e-03 209218 at 4J 7.080972e-03 204774 at 4J 7.662341e-03 209066 x at 4J 7.080972e-03 224331 s at 4J 7.665632e-03 218575 at 4J 7.08278e-03 224352 s_at 4J 7.683432e-03 229063 s at 4J 7.086439e-03 209233 at 4J 7.686533e-03 242520 s at 4J 7.091594e-03 213460 x at 4J 7.687122e-03 200834 s_at 4J 7.117368e-03 223334 at 4J 7.701091e-03 201723 s at 4J 7.122702e-03 237107 at 4J 7.707475e-03 219476 at 4J 7.125082e-03 228034 x at 4J 7.707475e-03 200763 s at 4J 7.130244e-03 214822 at 4J 7.707838e-03 223569 at 4J 7.142454e-03 208517 x at 4J 7.70955e-03 203768 s at 4J 7.164616e-03 209241 x at 4J 7.722806e-03 212047 s at 4J 7.167143e-03 222409 at 4J 7.725475e-03 207826 s at 4J 7.209094e-03 223336 s at 4J 7.72584e-03 203053 at 4J 7.210742e-03 31807 at 4J 7.73627e-03 226470 at 4J 7.21978e-03 209007 s at 4J 7.739402e-03 204658 at 4J 7.224667e-03 200029 at 4J 7.766145e-03 1559833_at 4J 7.231516e-03 205310 at 4J 7.796406e-03 224513 s at 4J 7.236742e-03 203904 x at 4J 7.801834e-03 207529 at 4J 7.250736e-03 229518 at 4J 7.806149e-03 225255 at 4J 7.25327e-03 205844 at 4J 7.81201e-03 225061 at 4J 7.278615e-03 219947 at 4J 7.82023e-03 218033 s at 4J 7.281665e-03 220443 s_at 4J 7.837043e-03 213820 s at 4J 7.298176e-03 200781 s at 4J 7.840771e-03 201532 at 4J 7.320226e-03 211924 s at 4J 7.852992e-03 223227 at 4J 7.329261e-03 200819 s at 4J 7.863911e-03 219176 at 4J 7.331163e-03 220404 at 4J 7.881705e-03 209426 s at 4J 7.349213e-03 225359 at 4J 7.897615e-03 211721 s at 4J 7.353999e-03 223531 x at 4J 7.914791e-03 210190 at 4J 7.356452e-03 225092 at 4J 7.938666e-03 218882 s at 4J 7.360597e-03 204781 s at 4J 7.94056e-03 219119 at 4J 7.376369e-03 205191 at 4J 7.941193e-03 36711 at 4J 7.381213e-03 221164 x at 4J 7.954436e-03 232287 at 4J 7.383557e-03 210254 at 4J 7.981397e-03 210370 s at 4J 7.384418e-03 211163 s at 4J 8.002702e-03 210904 s at 4J 7.39243e-03 40149 at 4J 8.01536e-03 205489 at 4J 7.417685e-03 218447 at 4J 8.018548e-03 202536 at 4J 7.428387e-03 204571 x_at 4J 8.025586e-03 200909 s at 4J 7.430079e-03 205547 s at 4J 8.030388e-03 223433 at 4J 7.435555e-03 200003 s at 4J 8.038911e-03 204387 x at 4J 7.44723e-03 203497 at 4J 8.059161e-03 '220241_at 4J εi.064969e-03 231940 at 4J 8 . 60406e-03 205496_at 4J ES.06614e-03 201479 at 4J 8.615676e-03 206952_at 4J εi.072446e-03 207325 x at 4J 8.622023e-03 201812 s at 4J E!.091384e-03 222745 s at 4J 8.63988e-03 1566991_at 4J 8i.093912e-03 206983 at 4J 8.64054e-03 225872__at 4J ES.094094e-03 218226 s_at 4J 8.65139e-03 221602 s at 4j εi.097514e-03 223880 x at 4J 8.653511e-03 217529 at 4J Ei.l08429e-03 201421 s at 4J 8.673036e-03 1557172 x at 4J εi.H6965e-03 204766 s at 4J 8.673051e-03 214461_at 4J ES.12734e-03 229576 s at 4J 8.67608e-03 226329 s at 4J εi.l32013e-03 208548 at 4J 8.6851e-03 49077 at 4J E!.132013e-03 215649 s at 4J 8.711336e-03 216894 x at 4J εL132013e-03 217926 at 4J 8.71796e-03 218286_s_at 4J ES.132013e-03 213045 at 4J 8.719014e-03 218331 s at 4J εi.l32013e-03 210613 s_at 4J 8.722378e-03 210152 at 4J EJ.132013e-03 229251 s at 4J 8.733177e-03 200629 at 4J ε?.132013e-03 1568667 s at 4J 8.744653e-03 33304 at 4J Ei.l41175e-03 227822 at 4J 8.752635e-03 210999 s at 4J EJ.1486e-03 204894 s at 4J 8.771513e-03 234362 s_at 4J EΪ.16894e-O3 201519 at 4J 8.793668e-03 228332 s at 4J E3.172131e-03 218646 at 4J 8.794438e-03 211534 x at 4J E!.17817e-03 203868 s at 4J 8.794805e-03 200002 at 4J Eϊ.204457e-03 200741 s at 4J 8.815963e-03 235076 at 4J E3.209135e-03 218531 at 4J 8.829484e-03 230587 at 4J Ei.21701e-03 1564295_at 4J 8.835587e-03 218074_at 4J E3.220834e-03 202342 s_at 4J 8.845878e-03 222657 s at 4J Ei.238347e-03 203653 s at 4J 8.875272e-03 219968_at 4J Eϊ.242525e-03 211826 s at 4J 8.875511e-03 215181_at 4J ES.257802e-03 217977 at 4J 8.882674e-03 222223 s at 4J E3.302767e-03 212925 at 4J 8.896087e-03 202787 s__at 4J E3.315621e-03 227946 at 4J 8.902323e-03 217748 at 4J E3.335896e-03 225876 at 4J 8.940093e-03 38671 at 4J E?.33888e-03 211763 s at 4J 8.967476e-03 218699_at 4J Eϊ.363388e-03 205642 at 4J 8.98855e-03 227796_at 4J E3.417114e-03 219149""x at 4J 8.993054e-03 201119 s at 4J E3.419653e-03 227363 s_at 4J 9.012122e-03 200703~at 4J E3.419663e-03 230363 s at 4J 9.014103e-03 204644_at 4J E3.43002e-03 221014 s at 4J 9.032119e-03 211711 s at 4J E3.439389e-03 205681 at 4J 9.040702e-03 204193_at 4J E3.441571e-03 200916 at 4J 9.063015e-03 220969 s at 4J Ei.442591e-03 213457 at 4J 9.077279e-03 227233_at 4J E?.443841e-03 200744 s_at 4J 9.099021e-03 219292_at 4J Eϊ.453002e-03 1553793 a at 4J 9.104546e-03 229881 at 4J Eϊ.455885e-03 214518 at 4J 9.115701e-03 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Figure imgf000859_0001
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Figure imgf000867_0001
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6.215714e-03 226267 at 4N 5.082878e-03 227516 at 4N 6.215714e-03 215389 s at 4N 5.110902e-03 227160 s_at 4N 6.215714e-03 217196 s at 4N 5.144156e-03 221522_at 4N 6.215714e-03 202857 at 4N 5.161095e-03 221499 s at 4N 6.215714e-03 202621 at 4N 5.175548e-03 201831 s at 4N 6.215714e-03 225399 at 4N 5.206619e-03 212686 at 4N 6.219669e-03 222728 s at 4N 5.21592e-03 204190 at 4N 6.2314e-03 1553861 at 4N 5.222568e-03 1553645_at 4N 6.232125e-03 220273 at 4N 5.243243e-03 223413 s at 4N 6.309734e-C3 202047 s at 4N 5.262023e-03 1555002_at 4N 6.333506e-03 206603 at 4N 5.291538e-03 219613 s at 4N 6.353338e-03 214965 at 4N 5.328578e-O3 219486 at 4N 6.380111e-03 219056 at 4N 5.3433βe-03 201599 at 4N 6.401525e-03 1553328 a at 4N 5.359563e-03 218631 at 4N 6.403031e-03 208092 s_at 4N 5.384326e-03 201181 at 4N 6.404226e-03 218559 s_at 4N 5.394742e-03 220704 at 4N 6.422833e-03 1569490_at 4N 5.399113e-03 1555167 s at 4N 6.424255e-03 226956 at 4N 5.409694e-03 239382 at 4N 6.464706e-03 240082 s_at 4N 5.426981e-03 210397 at 4N 6.512993e-03 225686 at 4N 5.474608e-03 212330 at 4N 6.533381e-03 225043 at 4N 5.477734e-03 223212 at 4N 6.553897e-03 218319 at 4N 5.486629e-03 221676 s at 4N 6.58496e-03 200708 at 4N 5.49409e-03 220078 at 4N 6.599114e-03 203675 at 4N 5.504814e-03 218492 s at 4N 6.600177e-03 224347 x at 4N 5.506369e-03 200734 s_at 4N 6.60699e-03 236270 at 4N 5.553057e-03 218196 at 4N 6.610252e-03 203269 at 4N 5.558709e-03 226784 at 4N 6.617708e-03 207857 at 4N 5.601415e-03 225165 at 4N 6.651581e-03 225414 at 4N 5.619139e-03 209001 s at 4N 6.69906e-03 219345 at 4N 5.64503e-03 212603 at 4N 6.701294e-03 213168 at 4N 5.663681e-03 227029 at 4N 6.748551e-03 219544 at 4N 5.667609e-03 217898 at 4N 6.763116e-03 209168 at 4N 5.706822e-03 206632 s at 4N 6.777252e-03 202833 s at 4N 5.718934e-03 235896 s at 4N 6.778859e-03 205612 at 4N 5.720436e-03 205015 s at 4N 6.809682e-03 209184 s at 4N 5.727574e-03 201359 at 4N 6.85541e-03 227850 x at 4N 5.729189e-03 231530 s_at 4N 6.855756e-03 228009 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Figure imgf000888_0001
9.132905e-03 1568924_a at 4N 0.01 224458_at 4N 9.14197e-03 202068 s_at 4N 0.01 1553802_a_at 4N 9.145221e-03 209867 s at 4N 0.01 209864_at 4N 9.172634e-03 210272 at 4N 0.01 211676_s_at 4N 9.195651e-03 223136 at 4N 0.01 213630_at 4N 9.229797e-03 209179 s_at 4N 0.01 1553311_at 4N 9.280361e-03 209619 at 4N 0.01 225741_at 4N 9.284525e-03 202933 s at 4N 0.01 243851_at 4N 9.299401e-03 208391 s at 4N 0.01 201622_at 4N 9.33153e-03 223548 at 4N 0.01 210094_s_at 4N 9.376535e-03 222396 at 4N 0.01 1566109_at 4N 9.384769e-03 209081 s at 4N 0.01 205395_s_at 4N 9.432996e-03 210750 s_at 4N 0.01 1553175_s_at 4N 9.450283e-03 206370 at 4N 0.01 217755_at 4N 9.480162e-03 212550 at 4N 0.01 225489_at 4N 9.493753e-03 217807 s at 4N 0.01 225331_at 4N 9.530547e-03 205256 at 4N 0.01 211574_s_at 4N 9.573203e-03 214264 s at 4N 0.01 225969_at 4N 9.577028e-03 212106 at 4N 0.01 201166_s_at 4N 9.588989e-03 203066 at 4N 0.01 22034l_s_at 4N 9.616116e-03 203508 at 4N 0.01 208579_x_at 4N 9.621186e-03 224735 at 4N 0.01 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214585 s_at 4N 0.02 227230 s at 4N 0.02 219451 at 4N 0.02 213370 s at 4N 0.02 216962 at 4N 0.02 209875 s_at 4N 0.02 203534 at 4N 0.02 229886 at 4N 0.02 219030 at 4N 0.02 220149 at 4N 0.02 221896 s_at 4N 0.02 203820 s at 4N 0.02 202887 s at 4N 0.02 207844 at 4N 0.02 203474 at 4N 0.02 212719 at 4N 0.02 209306 s_at 4N 0.02 202428 x_at 4N 0.02 224430 s at 4N 0.02 212333 at 4N 0.02 1553340 s at 4N 0.02 1562386'_s_at 4N 0.02 223626 x at 4N 0.02 204555 s_at 4N 0.02 1552974_at 4N 0.02 236728 at 4N 0.02 1552316 a at 4N 0.02 1552743_at 4N 0.02 205690 s at 4N 0.02 212630 at 4N 0.02 207018 s at 4N 0.02 1552921 a at 4N 0.02 205327 s at 4N 0.02 203316 s at 4N 0.02 202838 at 4N 0.02 227548 at 4N 0.02 224836 at 4N 0.02 225134 at 4N 0.02 1557322_at 4N 0.02 238609 at 4N 0.02 206250 x at 4N 0.02 218602 s at 4N 0.02 218476 at 4N 0.02 220319 s at 4N 0.02 229211 at 4N 0.02 212669 at 4N 0.02 203831 at 4N 0.02 214467 at 4N 0.02 233496 s at 4N 0.02 212648 at 4N 0.02 213501 at 4N 0.02 203823 at 4N 0.02 206454 s 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223394 at 4N 0.02 206185' at 4N 0.02 223749 at 4N 0.02 201568'_at 4N 0.02 205371 s at 4N 0.02 212681_at 4N 0.02 202468 s at 4N 0.02 209202' s at 4N 0.02 218116 at 4N 0.02 202475_at 4N 0.02 202302"_s_at 4N 0.02 218964_at 4N 0.02 208190" s at 4N 0.02 202818_s_at 4N 0.02 215708" s at 4N 0.02 229069_at 4N 0.02 233587" s at 4N 0.02 219651_at 4N 0.02 224871" at 4N 0.02 214144_at 4N 0.02 203835" at 4N 0.02 238488_at 4N 0.02 200827" at 4N 0.02 226306_at 4N 0.02 223097"_at 4N 0.02 233454_at 4N 0.02 219146"_at 4N 0.02 205340_at 4N 0.02 221365" at 4N 0.02 230362_at 4N 0.02 219526"_at 4N 0.02 212689_s_at 4N 0.02 212034" s at 4N 0.02 230774_at 4N 0.02 224584"_at 4N 0.02 226975_at 4N 0.02 209513" s at 4N 0.02 226117_at 4N 0.02 218807"_at 4N 0.02 204328_at 4N 0.02 233868" x at 4N 0.02 1553193_at 4N 0.02 214358"_at 4N 0.02 1553550_at 4N 0.02 202620" s at 4N 0.02 200018_at 4N 0.02 244523""at 4N 0.02 207835_at 4N 0.02 205876"_at 4N 0.02 202651_at 4N 0.02 212872" s at 4N 0.02 204310_s_at 4N 0.02 224630" at 4N 0.02 212331_at 4N 0.02 225346"_at 4N 0.02 208734_x_at 4N 0.02 224333" s at 4N 0.02 204063_s_at 4N 0.02 234299" s at 4N 0.02 202064_s_at 4N 0.02 235007" at 4N 0.02 1553165_at 4N 0.02 219343"_at 4N 0.02 232892_at 4N 0.02 220140" s at 4N 0.02 1569206_at 4N 0.02 219324" at 4N 0.02 207055_at 4N 0.02 213160"_at 4N 0.02 220054_at 4N 0.02 212842" x at 4M 0.02 204423_at 4N 0.02 21804C""at 4N 0.02 202654_x_at 4N 0.02 218181" s at 4N 0.02 218715_at 4N 0.02 217952" x at 4N 0.02 207ό38_at 4N 0.02 203958" s at 4N 0.02 209207_s_at 4N 0.02 203776"_at 4N 0.02 1553039_a_at 4N 0.02 209515" s at 4N 0.02 223139_s_at 4N 0.02 209681""at 4N 0.02 205144_at 4N 0.02 203162" s at 4N 0.02 222088_s_at 4N 0.02 202521" at 4N 0.02 205922_at 4N 0.02 205436" s at 4N 0.02 205248_at 4N 0.02 205981" s_at 4N 0.02 203117_s_at 4N 0.02 205171"_at 4N 0.02 206170_at 4N 0.02 208741" at 4N 0.02 219818_s_at 4N 0.02 200044""at 4N 0.02 226829_at 4N 0.02 201612""at 4N 0.02 241408_at 4N 0.02 201901" s at 4N 0.02 223942_x_at 4N 0.02 201358" s at 4N 0.02 201642_at" 4N 0.02 201928" at" 4N 0.02 207991_x_at 4N 0.02 236316" at 4N 0.02 222984_at 4N 0.02 218795""at 4N 0.02 219904_at 4N 0.02 201176"js_at 4N 0.03 203355_s_at 4N 0.02 226487""at 4N 0.03 235296_at 4N 0.02 209089""at 4N 0.03 212213_x_at 4N 0.02 235425""at 4N 0.03 223441_at 4N 0.02 231522"_at 4N 0.03 217763_s_at 4N 0.02 201630" s at 4N 0.03 214487_s_at 4N 0.02 200843" s at 4N 0.03 202739_s_at 4N 0.02 220206"_at 4N 0.03 244118_at 4N 0.02 224250" s at 4N 0.03 225475_at 4N 0.02 217743" s at 4N 0.03 208674_x_at 4N 0.02 203617" x at 4N 0.03 203478 at 4N 0.02 232897" at 4N 0.03 1553202: at ™4N -Q:b'3 244738_at 4N 0.03 224831 at 4N 0.03 207969_x__at 4N 0.03 219734 "at 4N 0.03 1568954_s__at 4N 0.03 201328.at 4N 0.03 213190_at 4N 0.03 225088"!at 4N 0.03 220894_x_at 4N 0.03 204303"s_at 4N 0.03 231863_at 4N 0.03 208932""at 4N 0.03 211160__x_at 4N 0.03 218302 "at 4N 0.03 202102_s_at 4N 0.03 1570414: x at 4N 0.03 1552867_at 4N 0.03 202394 s at 4N 0.03 208778__s_at 4N 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0.01 208840" s at 5B 9.402334e-03 202244 at 5B 0.01 208791" at 5B 9.402334e-03 215332 s_at 5B 0.01 209388"_at 5B 9.402334e-03 225620 at 5B 0.01 200733" s at 5B 9.402334e-03 204091 at 5B 0.01 1554365 a at 5B 9.402334e-03 202393 s at 5B 0.01 200664 s at 5B 9.402334e-03 203137 at 5B 0.01 201670" s at 5B 9.402334e-03 207351 s_at 5B 0.01 201853"_s at 5B 9.402334e-03 200649 at 5B 0.01 201669" 3 at 5B 9.402334e-03 221711 s at 5B 0.01 215158" s_at 5B 9.46404e-03 233167 at 5B 0.01 219231"_at 5B 9.523656e-03 201245 s at 5B 0.01 210555' s at 5B 9.54544e-03 210453 x_at 5B 0.01 209694""at 5B 9.626425e-03 235266 at 5B 0.01 203044""at 5B 9.6763e-03 1553876_at 5B 0.01 200033"_at 5B 9.767332e-03 222803 at 5B 0.01 217747" s_at 5B 9.782413e~03 226510 at 5B 0.01 208762" at 5B 9.832176e~03 211780 x_at 5B 0.01 219174""at 5B 9.88652e-03 212459 x at 5B 0.01 32032 at 5B 9.937147e-03 203262 s at 5B 0.01 227887 _at 5B 0.01 225680 at 5B 0.01 230337" at 5B 0.01 222880 at 5B 0.01 214009""at 5B 0.01 218421 at 5B 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219259 at 5G 3.669783e-03 221012 s at 5G 2.717792e-03 212919 at 5G 3.677154e-03 224918 x at 5G 2.724227e-03 225665 at 5G 3.677384e-03 202719 s at 5G 2.724993e-03 222826 at 5G 3.692728e-03 223044 at 5G 2.770833e-03 208248 x at 5G 3.69469e-03 204759 at 5G 2.788526e-03 217975 at 5G 3.714591e-03 226071 at 5G 2.796859e-03 217507 at 5G 3.717264e-03 209089 at 5G 2.801067e-03 211742 s at 5G 3.726552e-03 200678 x at 5G 2.807655e-03 202443 x_at 5G 3.728633e-03 203822 s_at 5G 2.838055e-03 201222 s at 5G 3.750931e-03 209513 s_at 5G 2.85124e-03 212588 at 5G 3.780378e-03 228747 at 5G 2.855371e-03 222981 s at 5G 3.781231e-03 213397 x at 5G 2.864213e-03 212377 s_at 5G 3.838633e-03 201743 at 5G 2.894772e-03 206209 s_at 5G 3.847383e-03 209369 at 5G 2.89f>851e-03 218807 at 5G 3.863644e-03 209380 s at 5G 2.910181e-03 218733 at 5G 3.863676e-03 225142 at 5G 2.945288e-03 206515 at 5G 3.890768e-03 203501 at 5G 2.953915e-03 1554503_a_at 5G 3.899988e-03 207713 s at 5G 2.95711e-03 204174 at 5G 3.900449e-03 223993 s at 5G 2.963854e-03 214430 at 5G 3.91591e-03 212543 at 5G 2.989119e-03 220944 at 5G 3.92112e-03 212830 at 5G 3.005315e-03 214629 x at 5G 3.939983e-03 208438 s_at 5G 3.039072e-03 223397 s_at 5G 3.984295e-03 203127 s at 5G 3.03949e-03 205441 at 5G 4.016109e-03 210951 x at 5G 3.048323e-03 218092 s at 5G 4.023892e-03 209102 s_at 5G 3.078204e-03 212662 at 5G 4.024836e-03 214150 x_at 5G 3.08242e-03 206548 at 5G 4.065087e-03 201819 at 5G 3.091549e-03 208018 s at 5G 4.072412e-03 224998 at 5G 3.092275e-03 235812 at 5G 4.072689e-03 209061 at 5G 3.096917e-03 225672 at 5G 4.088933e-03 204908 s at 5G 3.143433e-03 222706 at 5G 4.094072e-03 202138 x at 5G 3.206554e-03 210796 x at 5G 4.096774e-03 210081 at 5G 3.209845e-03 203234 at 5G 4.098825e-03 219033 at 5G 3.250319e-03 211548 s at 5G 4.123081e-03 217473 x at 5G 3.255021e-03 226711 at 5G 4.129635e-03 230058 at 5G 3.260747e-03 202187 s at 5G 4.131408e-03 202948 at 5G 3.267394e-03 229231 at 5G 4.154846e-03 217886 at 5G 3.299559e-03 221602 s at 5G 4.182544e-03 213513 x at 5G 3.306722e-03 215603 x_at 5G 4.19999e-03 35436 at 5G 3.323006e-03 209896 s_at 5G 4.20482e-03 202445 s at 5G 3.351245e-03 210401 at 5G 4.208206e-03 223082 at 5G 3.354071e-03 225863 s_at 5G 4.208692e-03 212585 at 5G 3.36084e-03 202777 at 5G 4.250159e-03 205896 at 5G 3.368276e-03 206483 at 5G 4.276073e-03 200899 s at 5G 3.374407e-03 224632 at 5G 4.305948e-03 213065 at 5G 3.383229e-03 218913 s at 5G 4.322338e-03 221613 s at "'5G 4."333038e-03 211999_at 5G 5.371321e-03 217742 s at 5G 4.356495e-03 202437" s_at 5G 5.379465e-03 226026" at 5G 4.364805e-03 210817" s_at 5G 5.421859e-03 216944" s_at 5G 366031e-03 220712"_at 5G 5.424213e-03 212864" at 5G 376921e-03 200677"_at 5G 5.427092e-03 205936" s at 5G 4.385385e-03 222522" x_at 5G 5.430585e-03 204308" s at 5G 4.391567e-03 218502" s at 5G 5.499355e-03 206687" s at 5G 4.409406e-03 219862" s at 5G 5.505531e-03 46270 ϊat 5G 4.422549e-03 201531"_at 5G 5.529556e-03 201097 _s_at 5G 427931e-03 203455" s_at 5G 5.533299e-03 221036" s at 5G 495547e-03 205067" at 5G 5.537373e-03 216041" x at 5G 4.500312e-03 231736" x at 5G 5.546817e-03 216298""at 5G J4 J=..-.513808e-03 217990"_at 5G 5.561014e-03 1555278 a at 5G 4.520194e-03 202412" s at 5G 5.566652e-03 226064 s at 5G 4.521711e-03 209234" at 5G 5.572882e-03 221485" at 5G 525296e-03 206546"_at 5G 5.573502e-03 203140"_at 5G 4.530462e-03 217739" s at 5G 5.583325e-03 204265" s at 5G 540627e-03 200731" s_at 5G 5.591111e-03 214733" s at 5G 4.549689e-03 201238" s at 5G 5.600295e-03 208864""s at 5G 4.595356e-03 218454"_at 5G 5.601515e-03 206371" at 5G 4.599595e-03 210774" s_at 5G 5.60723e-03 205865" at 5G 4.600741e-03 214002" at 5G 5.609735e-03 202313"_at 5G 4.609099e-03 212663"_at 5G 5.614671e-03 228950" s at 5G 4.613781e-03 218376" s_at 5G 5.62458e-03 218483" s at 5G 4.624149e-03 207785" s at 5G 5.656382e-03 206571" s at 5G 4.66241e-03 225133" at 5G 5.664578e-03 226169" at 5G 4.665471e-03 227220"_at 5G 5.666444e-03 234988" at 5G 4.674062e-03 215832" x at 5G 5.681455e-03 210216" x at 5G 4.681025e-03 239740"_at 5G 5.68279e-03 200791" s at 5G 4.717882e-03 209042" s at 5G 5.713951e-03 220329" s at 5G 4.73904e-03 235061""at 5G 5.73405e-03 201642"_at 5G 4.744612e-03 208704"_x_at 5G 5.749544e-03 205367" at 5G 4.757076e-03 222396""at 5G 5.793002e-03 214743" at 5G 4.760609e-03 212055" at 5G 5.817044e-03 230748" at 5G 768913e-03 203175"_at 5G 5.819962e-03 209409"_at 5G 4.781648e-03 221762" s at 5G 5.856388e-03 200625" s_at 5G 4.828003e-03 202199"_s_at 5G 5.857114e-03 227450" at 5G 4.840227e-03 212404" s at 5G 5.868897e-03 217738"_at 5G 855411e-03 204099"_at 5G 5.874936e-03 201411" s at 5G 4.859703e-03 224480" s_at 5G 5.884682e-03 226219"_at 5G 4.868196e-03 223482"_at 5G 5.934472e-03 230100" x at 5G 4.885429e-03 203435" s_at 5G 5.958806e-03 202277" at 5G 4.909062e-03 202353" s_at 5G 5.963632e-03 202388" at 5G 4.91109e-03 205174" s at 5G 5.970948e-03 201319"_at 5G 4.991389e-03 212075" s at 5G 5.977781e-03 217783" s at 5G 002927e-03 202266"_at 5G 5.990959e-03 244268" x at 5G 5.062955e-03 202923" s at 5G 6.022177e-03 227215"_at 5G 5.100448e-03 217995""at 5G 6.041089e-03 201925" s at 5G 105388e-03 203276"_at 5G 6.060517e-03 206464""at 5G 5.111337e-03 213198" at 5G 6.084599e-03 215716" s at 5G 115104e-03 205781"_at 5G 6.089225e-03 219191" s at 5G 14685e-03 234471" s at 5G 6.102353e-03 224707""at 5G 16287e-03 213151" s at 5G 6.13525e-03 202792" s at 5G 179506e-03 201023""at 5G 6.137974e-03 222870" s at 5G 182053e-03 235699""at 5G 6.146371e-03 209486"_at 5G 5.195814e-03 229770"JIt 5G 6.155037e-03 201899" s at 5G 222081e-03 207992" s at 5G 6.199207e-03 208284"x at 5G 257716e-03 224414" s at 5G 6.200945e-03 215416" s at 5G 289056e-03 210826 x at 5G 6.210047e-03 201288 _at 5G 5.296457e-03 201118""at 5G 6.213884e-03 228674" s at 5G 5.318695e-03 225798""at 5G 6.222803e-03 213590 "at 5G 334795e-03 203176 s at 5G 6.234926e-03 218734""at 5G 5.33527e-03 205277 "at 5G 6.256708e-03 204446" s at 5G 5.370164e-03 226050 "at 5G 6.271711e-03 T7US2005/022071
235435'"at ~5G 8"gr2f3"5"0Te-03 218795_at 5G 7.300241e-03 218153 at 5G 6.281648e-03 209514 s at 5G 7.302499e-03 205448 s_at 5G 6.286486e-03 207243~s at 5G 7.308517e-03 225899 x at 5G 6.31288e-03 210058_at 5G 7.321092e-03 210793 s at 5G 6.322131e-03 213341_at 5G 7.33349e-03 201475 x_at 5G 6.324995e-03 224659_at 5G 7.386032e-03 200871 s_at 5G 6.325658e-03 207677_s_at 5G 7.387996e-03 205416 s at 5G 6.391214e-03 211961 s at 5G 7.388883e-03 229933 at 5G 6.46532e-03 225198_at 5G 7.415665e-03 221156 x at 5G 6.512028e-03 218364 at 5G 7.424853e-03 213453 x_at 5G 6.512242e-03 217104 at 5G 7.447783e-03 203765 at 5G 6.55302e-03 32091 at 5G 7.466911e-03 225602 at 5G 6.553322e-03 213153_at 5G 7.497494e-03 204385 at 5G 6.563792e-03 205041 s at 5G 7.515714e-03 212737 at 5G 6.589082e-03 220371 s at 5G 7.544945e-03 200782 at 5G 6.595308e-03 219921 s_t G 7.565672e-03 206649 s at 5G 6.598419e-03 208921I 5 s aat 5G 7.575101e-03 217883 at 5G 6.598885e-03 212926 at 5G 7.596489e-03 217825 s at 5G 6.612292e-03 203274_at 5G 7.61046e-03 203233 at 5G 6.620998e-03 208756 at 5G 7.628381e-03 203278 s at 5G 6.626014e-03 203759 at 5G 7.656412e-03 203725 at 5G 6.633665e-03 224761 at 5G 7.682743e-03 55065 at 5G 6.664611e-03 227741_at 5G 7.683409e-03 203691 at 5G 6.674861e-03 212024 x at 5G 7.709197e-03 200714 x at 5G 6.676488e-03 222472~at 5G 7.729696e-03 203255 at 5G 6.698682e-03 224958 at 5G 7.739098e-03 212602 at 5G 6.70291e-03 212549_at 5G 7.750272e-03 221799 at 5G 6.718253e-03 210145 at 5G 7.77283e-03 209367 at 5G 6.730988e-03 206308_at 5G 7.796478e-03 219892 at 5G 6.752149e-03 207988 s at 5G 7.81254e-03 230563 at 5G 6.753672e-03 220137_at 5G 7.82669e-03 225367 at 5G 6.754755e-03 200958_s_at 5G 7.837071e-03 213836 s at 5G 6.769868e-03 212274_at 5G 7.884136e-03 206028 s_at 5G 6.777004e-03 208749 x at 5G 7.88449e-03 211509 s at 5G 6.786775e-03 223474 at 5G 7.888795e-03 210648 x at 5G 6.794883e-03 222447_at 5G 7.919646e-03 216902 s at 5G 6.795824e-03 217718 s at 5G 7.932398e-03 201350 at 5G 6.796554e-03 201669 s at 5G 7.957811e-03 200886__s_at 5G 6.831201e-03 225662_at 5G 7.964523e-03 217868 s at 5G 6.865884e-03 221718_s_at 5G 7.96788e-03 219622 at 5G 6.928436e-03 209141_at 5G 7.973343e-03 219383 at 5G 6.932283e-03 218313 s at 5G 7.988213e-03 202299 s at 5G 6.936129e-03 215158 s at 5G 8.002329e-03 200797 s__at 5G 6.944513e-03 202096 s at 5G 8.014763e-03 1555812_a_at 5G 6.954192e-03 225524_at 5G 8.025028e-03 204009 s at 5G 6.956424e-03 209643 s at 5G 8.058061e-03 220001 at 5G 6.973618e-03 214665 s at 5G 8.159597e-03 226743 at 5G 6.995762e-03 52169 at 5G 8.213059e-03 218095 s at 5G 6.996333e-03 203359 s at 5G 8.275454e-03 226353 at 5G 7.023061e-03 205119 s at 5G 8.278085e-03 218404 at 5G 7.031888e-03 218111 s at 5G 8.305941e-03 225251 at 5G 7.053391e-03 212229 s at 5G 8.306376e-03 209515 s at 5G 7.058743e-03 236846_at 5G 8.332185e-03 1555522 s at 5G 7.092673e-03 211960 s at 5G 8.346639e-03 212757 s at 5G 7.111649e-03 208703 s at 5G 8.465235e-03 209296 at 5G 7.141183e-03 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s at 5G 9.64344e-03 201834_at 5G 0.01 210129 s at 5G 9.685817e-03 202228_s_at 5G 0.01 210142 x at 5G 9.695538e-03 201854_s_at 5G 0.01 227268 at 5G 9.758064e-03 217967_s_at 5G 0.01 202907 s at 5G 9.76609e-03 218660_at 5G 0.01 202241 at 5G 9.77037e-03 204202_at 5G 0.01 205213 at 5G 9.780573e-03 224377_s_at 5G 0.01 212625 at 5G 9.811029e-03 203897_at 5G 0.01 204811 s at 5G 9.831761e-03 204410_at 5G 0.01 217989 at 5G 9.855033e-03 219031_s_at 5G 0.01 213206 at 5G 9.875773e-03 219394 at 5G 0.01 1555728_a_at 5G 0.01 200639_s_at 5G 0.01 203044_at 5G 0.01 205681_at 5G 0.01 225618_at 5G 0.01 207168_s_at 5G 0.01 204336_s_at 5G 0.01 55093_at 5G 0.01 212271_at 5G 0.01 222984_at 5G 0.01 226726_at 5G 0.01 203605_at 5G 0.01 207801_s_at 5G 0.01 208130_s_at 5G 0.01 225921_at 5G 0.01 203907_s__at 5G 0.01 200872_at 5G 0.01 1558549_s_at 5G 0.01 210428_s_at 5G 0.01 203104_at 5G 0.01 210102_at 5G 0.01 202333_s_at 5G 0.01 1558027_s_at 5G 0.01 219004_s_at 5G 0.01 205930_at 5G 0.01 221234_s_at 5G 0.01 214098_at 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0.01 227276_at 5G 0.01 223280 x at 5G 0.01 228813_at 5G 0.01 201412 at 5G 0.01 212332_at 5G 0.01 204554 at 5G 0.01 222408_s_at 5G 0.01 200033 at 5G 0.01 228114_x_at 5G 0.01 214746_s_at 5G 0.01 214273_x_at 5G 0.01 219460 s at 5G 0.01 221541_at 5G 0.01 218392 x at 5G 0.01 217811_at 5G 0.01 212315 s at 5G 0.01 209580_s_at 5G 0.01 232964 at 5G 0.01 200948_at 5G 0.01 231271 x at 5G 0.01 218149_s_at 5G 0.01 221490 at 5G 0.01 204158_s__at 5G 0.01 209222 s at 5G 0.01 204021_s_at 5G 0.01 201393 s at 5G 0.01 226817_at 5G 0.01 207655 s at 5G 0.01 214290_s__at 5G 0.01 205590 at 5G 0.01 207008_at 5G 0.01 205300 s at 5G 0.01 210980_s__at 5G 0.01 200704 at 5G 0.01 212717_at 5G 0.01 217992 s at 5G 0.01 213125_at 5G 0.01 219161 s at 5G 0.01 212457_at 5G 0.01 225604 s at 5G 0.01 218383_at 5G 0.01 220088 at 5G 0.01 235396_at 5G 0.01 212764_at 5G 0.01 222527_s_at 5G 0.01 204096 s at 5G 0.01 212262_at 5G 0.01 208640 at 5G 0.01 217799_x_at 5G 0.01 203460 s at 5G 0.01 203041_s_at 5G 0.01 205016 at 5G 0.01 216210_x_at 5G 0.01 201017 at 5G 0.01 222656_at 5G 0.01 207122 x at 5G 0.01 203559_s_at 5G 0.01 210999 s at 5G 0.01 1552386_at 5G 0.01 38710 at 5G 0.01 212501_at 5G 0.01 224356 x at 5G 0.01 208771_s_at 5G 0.01 218880_at 5G 0.01 205568_at 5G 0.01 202180 s at 5G 0.01 200071_at 5G 0.01 204959 at 5G 0.01 220066_at 5G 0.01 233167 at 5G 0.01 219906_at 5G 0.01 202424 at 5G 0.01 222235_s_at 5G 0.01 237056 at 5G 0.01 205702_at 5G 0.01 224298 s at 5G 0.01 229873_at 5G 0.01 229743 at 5G 0.01 203742_s_at 5G 0.01 209135 at 5G 0.01 219392_x_at 5G 0.01 201921 at 5G 0.01 234000_s_at 5G 0.01 209310 s at 5G 0.01 219150_s_at 5G 0.01 218461 at 5G 0.01 201725_at 5G 0.01 1553133 at 5G 0.01 205049_s_at 5G 0.01 210981 s at 5G 0.01 204563_at 5G 0.01 205052 at 5G 0.01 200667_at 5G 0.01 202425 x at 5G 0.01 212099_at 5G 0.01 203266 s at 5G 0.01 222488_s_at 5G 0.01 208919 s at 5G 0.01 219227_at 5G 0.01 201237 at 5G 0.01 1552472_a__at 5G 0.01 219717 at 5G 0.01 219035_s_at 5G 0.01 200083 at 5G 0.01 201829_at 5G 0.01 200800 s at 5G 0.01 205798_at 5G 0.01 217938_s_at 5G 0.01 209005_at 5G 0.01 208861 s at 5G 0.01 219023_at 5G 0.01 210279 at 5G 0.01 219889_at 5G 0.01 217781 s at 5G 0.01 203370_s_at 5G 0.01 202399 s at 5G 0.01 204524_at 5G 0.01 207093 s at 5G 0.01 205247_at 5G 0.01 230170 at 5G 0.01 205546_s_at 5G 0.01 202362 at 5G 0.01 207691 x at 5G 0.01 202193 at 5G 0.01 202100" at 5G :df - 219284 at 5G 0.01 209022""at 5G 0.01 226298 at 5G 0.01 225919" s at 5G 0.01 229251 s_at 5G 0.01 209288" s at 5G 00..01 207943 x at 5G 0.01 224327" s at 5G 00..01 218821 at 5G 0.01 221778""at 5G 00..01 211702 s at 5G 0.01 203416""at 5G 00..01 1568704 a at 5G 0.01 223288" at 5G 00..01 201788 at 5G 0.01 207500""at 5G 00..01 228573 at 5G 0.01 215299" x at 5G 00..01 223437 at 5G 0.01 218812" s at 5G 00..01 218012 at 5G 0.01 209069" s at 5G 00..01 213911 s at 5G 0.01 210057"_at 5G O00r-..01 221216 s at 5G 0.01 211072" x at 5G 00..01 211009 s at 5G 0.01 204669" s at 5G 00..01 205322 s at 5G 0.01 209314" s at 5G 00..01 1569887 a at 5G 0.01 214879" x at 5G 00..01 208933 s_at 5G 0.01 202939""at 5G 00..01 219458 s at 5G 0.01 213633"_at 5G 00..01 219055 at 5G 0.01 200996"_at 5G 00..01 210153 s at 5G 0.01 200956" s at 5G 00..01 202188 "at 5G 0.01 218123""at 5G 00..01 242916 at 5G 0.01 213545" x at 5G 00..01 214181 x at 5G 0.01 219577" s at 5G 00..01 223608 at 5G 0.01 228034" x at 5G 00..01 225434 at 5G 0.01 204683""at 5G 00..01 212407 at 5G 0.01 218047" at 5G 00..01 235022 at 5G 0.01 1552553_a_at 5G 0,.01 203445 s at 5G 0.01 222895_s_at 5G 0..01 223005 s at 5G 0.01 223583_at 5G 0..01 220939 s at 5G 0.01 214047_s_at 5G 0..01 202727 s_at 5G 0.01 212901_s_at 5G 0..01 218062 x at 5G 0.01 200794_x_at 5G 0,.01 206324 s at 5G 0.01 I552398_a_at 5G 0..01 232087 at" 5G 0.01 205577_at 5G 0..01 201760 s at 5G 0.01 220788_s_at 5G 0..01 203887 s at 5G 0.01 218669_at 5G 0..01 218865 at 5G 0.01 200988_s_at 5G 0..01 201455 s at 5G 0.01 203526_s_at 5G 0,.01 203498 at 5G 0.01 223254_s_at 5G 0,,01 224796 at 5G 0.01 202097_at 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' w r,...» 210878_s_at 5G 0.01 224760_at 5G 0.02 5G 0.01 216841_s__at 5G 0.02 1552263_at 201774_s_at 5G 0.02 204787_at 5G 0.01 210582_s_at 5G 0.01 208076_at 5G 0.02 205147_x_at 5G 0.01 204046_at 5G 0.02 5G 0.01 217028_at 5G 0.02 35820_at 223132_s_at 5G 0.01 209162_s_at 5G 0.02 221484_at 5G 0.02 203658_at 5G 0.01 201997_s_at 5G 0.01 203185_at 5G 0.02 0.01 218713_at 5G 0.02 201358__s_at 5G 212680_x_at 5G 0.02 200004_at 5G 0.01 244498_x_at 5G 0.01 204588_s_at 5G 0.02 217418_x_at 5G 0.02 215399_s__at 5G 0.01 229560_at 5G 0.02 235089_at 5G 0.01 .01 224638_at 5G 0.02 208588_at 5G 0 224616_at 5G 0.02 217827_s_at 5G 0.01 .01 218318_s__at 5G 0.02 218465_at 5G 0 224596_at 5G 0.02 202957_at 5G 0.01 0.01 222146_s_at 5G 0.02 209791_at 5G 201276__at 5G 0.02 79005_at 5G 0.01 G 0.01 218528__s_at 5G 0.02 224965_at 5 202397__at 5G 0.02 221572_s_at 5G 0.01 5G 0.01 201859_at 5G 0.02 205763_s_at 202682_s_at 5G 0.02 202925_s_at 5G 0.01 5G 0.01 224608_s_at 5G 0.02 20107l_x_at 201132_at 5G 0.01 204662_at 5G 0.02 201901_s__at 5G 0.02 226053_at 5G 0.01 201089_at 5G 0.02 209839_at 5G 0.01 205981_s_at 5G 0.01 203591_s_at 5G 0.02 225306_s_at 5G 0.01 200942_s_at 5G 0.02 206707_x_at 5G 0.01 221269_s_at 5G 0.02 0.01 226333_at 5G 0.02 222993_at 5G 204604_at 5G 0.02 226968_at 5G 0.01 204206_at 5G 0.02 202214_s_at 5G 0.02 47105_at 5G 0.02 227094_at 5G 0.02 206995_x_at 5G 0.02 203394_s_at 5G 0.02 210293_s_at 5G 0.02 202016_at 5G 0.02 5G 0.02 218004_at 5G 0.02 213154 s_at 205475_at 5G 0.02 202641_at 5G 0.02 238825_at 5G 0.02 202197_at 5G 0.02 220252_x_at 5G 0.02 205291_at 5G 0.02 5G 0.02 207094_at 5G 0.02 209451_at 216071_x_at 5G 0.02 218078_s_at 5G 0.02 218498_s_at 5G 0.02 206110_at 5G 0.02 0.02 220987_s_at 5G 0.02 209394_at 5G 218249_at 5G 0.02 235514_at 5G 0.02 221069_s_at 5G 0.02 225032_at 5G 0.02 200683_s_at 5G 0.02 230075_at 5G 0.02 5G 0.02 203535_at 5G 0.02 224739_at 201085_s_at 5G 0.02 AFFX-HUMGAPDH/ M33197 5G 0.02 202337_at 5G 0.02 202592_at 5G 0.02 213440_at 5G 0.02 209118_s_at 5G 0.02 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Figure imgf001076_0001
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5H 4.024385e-03 217478 s at 5H 3.106696e-03 1553569_at 5H 4.026166e-03 211995 x at 5H 3.12535e-03 206138 s at 5H 4.032197e-03 218831 s at 5H 3.128917e-03 201859 at 5H 4.051128e-03 209380 s_at 5H 3.161945e-03 219933 at 5H 4.064344e-03 219988 s at 5H 3.167843e-03 204923 at 5H 4.065519e-03 201954 at 5H 3.173862e-03 225315 at 5H 4.094947e-03 220015 at 5H 3.178883e-03 218845 at 5H 4.107748e-03 209852 x at 5H 3.186534e-03 223152 at 5H 4.115325e-03 222867 s at 5H 3.230613e-03 200600 at 5H 4.11612e-03 224920 x_at 5H 3.230619e-03 201864 at 5H 4.119392e-03 211716 x at 5H 3.246724e-03 202469 s at 5H 4.133154e-03 202039 at 5H 3.266421e-03 213045 at 5H 4.135242e-03 201319 at 5H 3.280975e-03 218389 s at 5H 4.137371e-03 206592 s_at 5H 3.327841e-03 201315 x at 5H 4.138459e-03 1555889 a at 5H 3.333763e-03 202102 s at 5H 4.138817e-03 234919 s at 5H 3.334616e-03 216526 x at 5H 4.152e-03 204206 at 5H 3.432991e-03 1555811_at 5H 4.172558e-03 205172 x at 5H 3.434254e-03 200031 s_at 5H 4.195934e-03 204209 at 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4.204851e-03 201082 s at 5H 3.664313e-03 225133 at 5H 4.204851e-03 43544 at 5H 3.666506e-03 223609 at 5H 4.204851e-03 224988 at 5H 3.671126e-03 226306 at 5H 4.204851e-03 212661 x at 5H 3.68701e-03 226298 at 5H 4.204851e-03 204079 at 5H 3.688966e-03 227075 at 5H 4.204851e-03 205081 at 5H 3.692935e-03 226314 at 5H 4.204851e-03 227466 at 5H 3.721951e-03 224707 at 5H 4.204851e-03 211940 x at 5H 3.723167e-03 226761 at 5H 4.204851e-03 225231 at 5H 3.748877e-03 226219 at 5H 4.204851e-03 1555812 a at 5H 3.777277e-03 224046 s at 5H 4.204851e-03 201346 at 5H 3.794679e-03 224963 at 5H 4.204851e-03 218972 at 5H 3.809121e-03 226691 at 5H 4.204851e-03 217780 at 5H 3.822859e-03 225222 at 5H 4.204851e-03 209945 s at 5H 3.835035e-03 225210 s at 5H 4.204851e-03 206855 s_at 5H 3.838712e-03 226276 at 5H 4.204851e-03 227855 at 5H 3.839502e-03 226267 at 5H 4.204851e-03 203445 s at 5H 3.844633e-03 225252 at 5H 4.204851e-03 220326 s at 5H 3.847314e-03 227811 at 5H 4.204851e-03 1556059 s at 5H 3.867257e-03 225177 at 5H 4.204851e-03 "22'Miδ~'a£ " •u""4"r2"0'4'85le-03 213735 _s_at 5H 4.204851e-03 225849 s at 5H 4.204851e-03 212270" x at 5H 4.204851e-03 226906~s_at 5H 4.204851e-03 213911" s at 5H 4.204851e-03 227143 s at 5H 4.204851e-03 213503" x at 5H 4.204851e-03 225076 s at 5H 4.204851e-03 214054"_at 5H 4.204851e-03 227647_at 5H 4.204851e-03 214707" x at 5H 4.204851e-03 223857 x at 5H 4.204851e-03 214150" x at 5H 4.204851e-03 223591_at 5H 4.204851e-03 212463""at 5H 4.204851e-03 226957 x at 5H 4.204851e-03 212625"_at 5H 4.204851e-03 224782_at 5H 4.204851e-03 213566"_at 5H 4.204851e-03 226091 s at 5H 4.204851e-03 214895" s_at 5H 4.204851e-03 225063~at 5H 4.204851e-03 213571" s at 5H 4.204851e-03 226629 at 5H 4.204851e-03 213414"~s at 5H 4.204851e-03 225251 at 5H 4.204851e-03 213418"_at 5H 4.204851e-03 224733 at 5H 4.204851e-03 213620" s_at 5H 4.204851e-03 38290 at 5H 4.204851e-03 212877" at 5H 4.204851e-03 48825 at 5H 4.204851e-03 214201" x at 5H 4.204851e-03 233252 s at 5H 4.204851e-03 213356" x at 5H 4.204851e-03 236259_at 5H 4.204851e-03 212491" s at 5H 4.204851e-03 38964 r at 5H 4.204851e-03 213348""at 5H 4.204851e-03 45749 at 5H 4.204851e-03 214730" s_at 5H 4.204851e-03 38892 at 5H 4.204851e-03 212415"_at 5H 4.204851e-03 228648_at 5H 4.204851e-03 212717" at 5H 4.204851e-03 233842 x at 5H 4.204851e-03 213733"_at 5H 4.204851e-03 229089 at 5H 4.204851e-03 213671" s at 5H 4.204851e-03 243 g at 5H 4.204851e-03 213875" x at 5H 4.204851e-03 35160 at 5H 4.204851e-03 212914"_at 5H 4.204851e-03 44822 s at 5H 4.204851e-03 213545" x at 5H 4.204851e-03 229390 at 5H 4.204851e-03 214567" s at 5H 4.204851e-03 235359_at 5H 4.204851e-03 213318" s at 5H 4.204851e-03 228915 at 5H 4.204851e-03 212288""at 5H 4.204851e-03 229723_at 5H 4.204851e-03 212519" at 5H 4.204851e-03 34031 i_at 5H 4.204851e-03 213932" x at 5H 4.204851e-03 40446 at 5H 4.204851e-03 213185" at 5H 4.204851e-03 229967 at 5H 4.204851e-03 218022" at 5H 4.204851e-03 55093 at 5H 4.204851e-03 217955" at 5H 4.204851e-03 55705_at 5H 4.204851e-03 218171" at 5H 4.204851e-03 AFFX- 218130" at 5H 4.204851e-03 hum alu at 5H 4.204851e-03 215127" s at 5H 4.204851e-03 57163 at 5H 4.204851e-03 215148" s_at 5H 4.204851e-03 91952 at 5H 4.204851e-03 217751"_at 5H 4.204851e-03 218509_at 5H 4.204851e-03 218463" s at 5H 4.204851e-03 219324 at 5H 4.204851e-03 218357" s at 5H 4.204851e-03 219706 at 5H 4.204851e-03 218132" s at 5H 4.204851e-03 218517_at 5H 4.204851e-03 218385"_at 5H 4.204851e-03 218528 s at 5H 4.204851e-03 215706" x at 5H 4.204851e-03 219673 at 5H 4.204851e-03 217737" x at 5H 4.204851e-03 218987_at 5H 4.204851e-03 218078" s at 5H 4.204851e-03 218797 s at 5H 4.204851e-03 217905" at 5H 4.204851e-03 218782 s at 5H 4.204851e-03 217764" s at 5H 4.204851e-03 218673 s_at 5H 4.204851e-03 217763" s at 5H 4.204851e-03 218803_at 5H 4.204851e-03 216438" s at 5H 4.204851e-03 219035 s at 5H 4.204851e-03 218471""s_at 5H 4.204851e-03 219350 s at 5H 4.204851e-03 215193" x at 5H 4.204851e-03 219111 s at 5H 4.204851e-03 218205" s at 5H 4.204851e-03 218474 s at 5H 4.204851e-03 217984" at 5H 4.204851e-03 218961 s at 5H 4.204851e-03 218020"_s_at 5H 4.204851e-03 219259_at 5H 4.204851e-03 216231" s at 5H 4.204851e-03 219679 s_at 5H 4.204851e-03 217750" s at 5H 4.204851e-03 219913 s at 5H 4.204851e-03 217923""at 5H 4.204851e-03 212971~at 5H 4.204851e-03 215089" s at 5H 4.204851e-03 214321 at 5H 4.204851e-03 218302""at 5H 4.204851e-03 212552_at 5H 4.204851e-03 217414" x at 5H 4.204851e-03 213453 x at 5H 4.204851e-03 215737" x at 5H 4.204851e-03 '2"l3cfO8'"-ar' ~"5h n":"2D"l85le-03 222985 at 5H 4.204851e-03 218099"_at 5H 4.204851e-03 223034 s at 5H 4.204851e-03 203665"[at 5H 4.204851e-03 220615 s at 5H 4.204851e-03 204571" x at 5H 4.204851e-03 211750 x at 5H 4.204851e-03 204218 "at 5H 4.204851e-03 211025 x at 5H 4.204851e-03 203547""at 5H 4.204851e-03 210418 s_at 5H 4.204851e-03 204566""at 5H 4.204851e-03 211752 s_at 5H 4.204851e-03 203816""at 5H 4.204851e-03 210453 x_at 5H 4.204851e-03 204098" at 5H 4.204851e-03 210982 s at 5H 4.204851e-03 203514" at 5H 4.204851e-03 211999 at 5H 4.204851e-03 203523""at 5H 4.204851e-03 211745 x at 5H 4.204851e-03 203971""at 5H 4.204851e-03 211576 s_at 5H 4.204851e-03 203667""at 5H 4.204851e-03 209786 at 5H 4.204851e-03 204501"_at 5H 4.204851e-03 209482 at 5H 4.204851e-03 203262"_s_at 5H 4.204851e-03 211609 x at 5H 4.204851e-03 204820" s at 5H 4.204851e-03 209906 at 5H 4.204851e-03 203546" at 5H 4.204851e-03 211289 x at 5H 4.204851e-03 204691" x at 5H 4.204851e-03 211271 x at 5H 4.204851e-03 203342""at 5H 4.204851e-03 211270 x_at 5H 4.204851e-03 203501""at 5H 4.204851e-03 212129 at 5H 4.204851e-03 203648"_at 5H 4.204851e-03 209511 at 5H 4.204851e-03 203320" at 5H 4.204851e-03 209701 at 5H 4.204851e-03 203668" at 5H 4.204851e-03 212203 x at 5H 4.204851e-03 204372" s at 5H 4.204851e-03 209949 at 5H 4.204851e-03 204646"_at 5H 4.204851e-03 209947 at 5H 4.204851e-03 203311" s at 5H 4.204851e-03 211433 x at 5H 4.204851e-03 203317""at 5H 4.204851e-03 210057 at 5H 4.204851e-03 203286""at 5H 4.204851e-03 210443 x_at 5H 4.204851e-03 204872"_at 5H 4.204851e-03 209880 s at 5H 4.204851e-03 203359" s at 5H 4.204851e-03 209643 s at 5H 4.204851e-03 204214" s at 5H 4.204851e-03 202833 s at 5H 4.204851e-03 204446""s at 5H 4.204851e-03 202197 at 5H 4.204851e-03 204524""at 5H 4.204851e-03 203064 s at 5H 4.204851e-03 221597"_s_at 5H 4.204851e-03 202317 s at 5H 4.204851e-03 221214" s at 5H 4.204851e-03 202497 x at 5H 4.204851e-03 220750" s at 5H 4.204851e-03 202860 at 5H 4.204851e-03 222912"[at 5H 4.204851e-03 202297 s at 5H 4.204851e-03 221666" s at 5H 4.204851e-03 202428 x_at 5H 4.204851e-03 223445"_at 5H 4.204851e-03 202876 s at 5H 4.204851e-03 222483"[at 5H 4.204851e-03 202386 s at 5H 4.204851e-03 223380" s_at 5H 4.204851e-03 202487 s at 5H 4.204851e-03 222980"_at 5H 4.204851e-03 203206 at 5H 4.204851e-03 222804" x at 5H 4.204851e-03 202160 at 5H 4.204851e-03 223048" at 5H 4.204851e-03 202596 at 5H 4.204851e-03 223053" x at 5H 4.204851e-03 203045 at 5H 4.204851e-03 223376 s at 5H 4.204851e-03 202426 s_at 5H 4.204851e-03 222064"[s_at 5H 4.204851e-03 202189 x at 5H 4.204851e-03 222984"[at 5H 4.204851e-03 202300 at 5H 4.204851e-03 222218" s at 5H 4.204851e-03 202360 at 5H 4.204851e-03 221866" at 5H 4.204851e-03 202716 at 5H 4.204851e-03 222578" s_at 5H 4.204851e-03 203174 s at 5H 4.204851e-03 222406" s at 5H 4.204851e-03 202138 x at 5H 4.204851e-03 222396" at 5H 4.204851e-03 202024 at 5H 4.204851e-03 223064"_at 5H 4.204851e-03 202841 x at 5H 4.204851e-03 222524" s_at 5H 4.204851e-03 203168 at 5H 4.204851e-03 221849"_s_at 5H 4.204851e-03 204928 s at 5H 4.204851e-03 222826"_at 5H 4.204851e-03 206968 s at 5H 4.204851e-03 223145" s at 5H 4.204851e-03 205898 at 5H 4.204851e-03 223066"_at 5H 4.204851e-03 205812 s at 5H 4.204851e-03 223398""at 5H 4.204851e-03 205786 s at 5H 4.204851e-03 223070"[at 5H 4.204851e-03 207094 at 5H 4.204851e-03 221269" s at 5H 4.204851e-03 205546 s at 5H 4.204851e-03 223000" s_at 5H 4.204851e-03 204961 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8.64e-04 211926 s at 51 2.122415e-03 225401 at 51 8.65e-04 207064 s_at 51 2.218871e-03 202545 at 51 8.9e-04 235181 at 51 2.231016e-03 208900 s at 51 8.95e-04 203680 at 51 2.249407e-03 219342 at 51 9.51e-04 215228 at 51 2.253684e-03 203585 at 51 9.53e-04 219032 x_at 51 2.262873e-03 209458 x at 51 9.77e-04 228770 at 51 2.311178e-03 208753 s at 51 9.95e-04 216304 x at 51 2.324379e-03 205292 s at 51 1.028944e-03 60471 at 51 2.334388e-03 208686 s at 51 1.056894e-03 222516 at 51 2.343346e-03 200661 at 51 1.113154e-03 212140 at 51 2.377205e-03 208772 at 51 1.164431e-03 212451 at 51 2.410531e-03 212763 at 51 1.17205e-03 200845 s at 51 2.417445e-03 210428 s_at 51 1.172917e-03 204689 at 51 2.444067e-03 200077 s at 51 1.184205e-03 200712 s_at 51 2.444697e-03 204495 s at 51 1.188115e-03 208704 x at 51 2.48151e-03 222067 x_at 51 1.192247e-03 230720 at 51 2.536609e-03 224859 at 51 1.217275e-03 230252 at 51 2.536609e-03 202910 s at 51 1.333828e-03 211404 s at 51 2.556026e-03 234987 at 51 1.339241e-03 221782 at 51 2.591333e-03 214743 at 51 1.355453e-03 217992 s at 51 2.60699e-03 211699 x at 51 1.428696e-03 218682 s_at 51 2.626922e-03 244498 x at 51 1.435389e-03 229390 at 51 2.687115e-03 226464 at 51 1.445135e-03 223262 s at 51 2.697461e-03 210387 at 51 1.478899e-03 222024 s at 51 2.697461e-03 218223 s_at 51 1.479659e-03 203087 s at 51 2.697461e-03 212869 x at 51 1.481921e-03 207724 s at 51 2.697461e-03 235479 at 51 1.509475e-03 200838 at 51 2.697461e-03 204361 s at 51 1.509475e-03 201299 s_at 51 2.697461e-03 203401 at 51 1.509475e-03 214196 s at 51 2.755786e-03 223243 s_at 51 1.509475e-03 214544 s_at 51 2.758271e-03 221676 s at 51 1.509475e-03 211945 s at 51 2.773187e-03 222752 s at 51 1.509475e-03 223204 at 51 2.774137e-03 208703 s at 51 1.509475e-03 219368 at 51 2.790064e-03 204164 at 51 1.528357e-03 211240 x at 51 2.871138e-03 1553906 s at 51 1.528357e-03 214414 x at 51 2.898974e-03 218030 at 51 1.530057e-03 204018 x_at 51 2.898974e-03 225640 at 51 1.53902e-03 206283 s at 51 2.898974e-03 226422 at 51 1.53902e-03 200674 s at 51 2.898974e-03 224625 x at 51 1.53902e-03 200963 x at 51 2.898974e-03 AFFX-HUMGAPDH/ 222407 s at 51 2.940086e-03 M33197 51 1.53902e-03 1557000_at 51 2.943603e-03 203560 at 51 1.53902e-03 218961 s at 51 2.969483e-03 205140 at 51 1.53902e-03 212322 at 51 2.979906e-03 206545 at 51 1.53902e-03 209760 at 51 2.993398e-03 208834 x at 51 1.53902e-03 1553575_at 51 3.128658e-03 208985 s_at 51 1.53902e-03 200742 s at 51 3.132568e-03 200740 s at 51 1.53902e-03 212238 at 51 3.149936e-03 1553185_at 51 1.53902e-03 207206 s at 51 3.189314e-03 1553551 s at 51 1.53902e-03 207198 s at 51 3.207421e-03 201030 x at 51 1.53902e-03 224636 at 51 3.229165e-03 201138 s at 51 1.53902e-03 204650 s at 51 3.229165e-03 225501 at 51 1.555653e-03 202886 s at 51 3.229165e-03 203362 s at 51 1.624429e-03 225051 at 51 3.252648e-03 200633 at 51 1.693254e-03 227410 at 51 3.252648e-03 224616 at 51 1.712874e-03 243750 x at 51 3.252648e-03 T7US2005/022071 Tl .■ ii « 'I-- Il !1 !' , 2"l3084"'x at -5f~ 228167 _at 51 4.548173e-03 214119 s at 51 3.252648e-03 230690""at 51 4.548173e-03 212988 x at 51 3.252648e-03 214109""at 51 4.548173e-03 213356 x at 51 3.252648e-03 215038" s at 51 4.548173e-03 218106 s at 51 3.252648e-03 214953" s at 51 4.548173e-03 203535 at 51 3.252648e-03 222243" s at 51 4.548173e-03 223441 at 51 3.252648e-03 221027" s at 51 4.548173e-03 221607 x at 51 3.252648e-03 221080" s at 51 4.548173e-03 221002 s at 51 3.252648e-03 202708 ~s~at 51 4.548173e-03 209628 at 51 3.252648e-03 206218" at 51 4.548173e-03 202503 s_at 51 3.252648e-03 207815"_at 51 4.548173e-03 205060 at 51 3.252648e-03 200665" s at 51 4.548173e-03 1553588'__at 51 3.252648e-03 201977" s at 51 4.548173e-03 209019 s_at 51 3.259099e-03 209067" s at 51 4.580566e-03 203254 s_at 51 3.306318e-03 201351" s_at 51 4.701574e-03 214974 x at 51 3.321229e-03 212933" x_at 51 4.72716e-03 212839 s at 51 3.421987e-03 218208"_at 51 4.72716e-03 212381 at 51 3.42507e-03 206976" s at 51 4.72716e-03 213932 x at 51 3.42507e-03 201257" x_at 51 4.72716e-03 215313 x at 51 3.42507e-03 229563" s at 51 4.758959e-03 200781 s at 51 3.42507e-03 224642""at 51 4.774748e-03 211657 at 51 3.428964e-03 40420 at 51 4.791452e-03 204209 at 51 3.442004e-03 200709 _at 51 4.851706e-03 200867 at 51 3.467121e-03 208901" s_at 51 4.930879e-03 207426 s_at 51 3.481172e-03 209060" x_at 51 4.945685e-03 222065 s_at 51 3.490721e-03 218243"_at 51 4.997573e-03 207759 s at 51 3.496057e~03 234491" s_at 51 5.121073e-03 201954 at 51 3.560819e-03 201492" s at 51 5.162609e-03 202803 s_at 51 3.630412e-03 208438""s at 51 5.164089e-03 203076 s at 51 3.659711e~03 201814""at 51 5.211282e-03 212570 at 51 3.662468e~03 206095""s at 51 5.236653e-03 202105 at 51 3.662468e~03 224416""s_at 51 5.24094e-03 205686 s_at 51 3.670646e-03 235032""at 51 5.24094e-03 235675 at 51 3.680046e-03 235241"~at 51 5.24094e-03 1553214 a at 51 3.706738e-03 217106" x at 51 5.24094e-03 225981 at 51 3.748011e-03 209761" s at 51 5.25896e-03 218972 at 51 3.809121e-03 AFFX-HSACO7/ 230561 s at 51 3.96668e-03 X00351 5 51 5.259211e-03 203734 at 51 3.96668e-03 201676" x at 51 5.293918e-03 214519 s at 51 3.971671e-03 202611" s at 51 5.314152e-03 204777 s at 51 4.001203e-03 204407""at 51 5.342017e-03 204193 at 51 4.031241e-03 203309" s at 51 5.421248e-03 223234 at 51 4l031241e-03 201157 s at 51 5.466996e-03 211040 x_at 51 4.031241e-03 225043""at 51 5.478715e-03 200817 x_at 51 4.031241e-03 225058""at 51 5.518419e-03 200012 x at 51 4.031241e-03 202082" s at 51 5.523317e-03 215399 s at 51 4.046273e-03 205789""at 51 5.523317e-03 211571 s at 51 4.088e-03 208904" s at 51 5.523317e-03 216041 x at 51 4.09008e-03 222464" s at 51 5.531342e-03 202469 s at 51 4.133154e-03 222915~ s at 51 5.549285e-03 201587 s at 51 4.147674e-03 219788"_at 51 5.580137e-03 208146 s at 51 4.184226e-03 222858~ s_at 51 5.699403e-03 201461 s at 51 4.184226e-03 200890" s at 51 5.713964e-03 204109 s at 51 4.254184e-03 203054" s at 51 5.778652e-03 1552386 at 51 4.254628e-03 203332" s at 51 5.789461e-03 212552 at 51 4.255644e-03 213867"x at 51 5.815971e-03 211937 at 51 4.473712e-03 155266',L_a_at 51 5.849357e-03 217845 x at 51 4.477109e-03 209457 _at 51 5.931645e-03 217020 at 51 4.479149e-03 201108' s at 51 5.931645e-03 222409 at 51 4.501129e-03 201020"_at 51 5.953591e-03 204079 at 51 4.508542e-03 208035".at 51 6.050429e-03 224600 at 51 4.509862e-03 218129" s at 51 6.065943e-03 201094 at 51 4.524903e-03 201121""s_at 51 6.069638e-03 225997 at 51 4.548173e-03 209984""at 51 6.088482e-03 005/022071 B • I! ii it , a a i » 208174 "x at 6\l34736e-03 212685 s__at 51 7.301532e-03 221170 at 51 6.150485e-03 212459"x at 51 7.301532e-03 225132 at 51 6.202404e-03 217942""at 51 7.301532e-03 224585 x at 51 6.202404e-03 204222 s at 51 7.301532e-03 224815 at 51 6.202404e-03 203562""at 51 7.301532e-03 226361 at 51 6.202404e-03 203605"jit 51 7.301532e-03 232001 at 51 6.202404e-03 222673" x at 51 7.301532e-03 218491 s at 51 6.202404e-03 210314" x at 51 7.301532e-03 218583 s at 51 6.202404e-03 209911" x at 51 7.301532e-03 213687 s at 51 6.202404e-03 210786 s at 51 7.301532e-03 213881 x_at 51 6.202404e-03 208721" s_at 51 7.301532e-03 217728 at 51 6.202404e-03 200679"x at 51 7.301532e-03 218364 at 51 6.202404e-03 156308"E3_a_at 51 7.301532e-03 203303 at 51 6.202404e-03 201065_ s__at 51 7.301532e-03 204219 s_at 51 6.202404e-03 200911" s at 51 7.301532e-03 203675 at 51 6.202404e-03 200866""s at 51 7.301532e-03 222987 s at 51 6.202404e-03 201106"_at 51 7.301532e-03 222995 s at 51 6.202404e-03 220329" s_at 51 7.301554e-03 221478 at 51 6.202404e-03 212833"_at 51 7.3307e-03 202437 s_at 51 6.202404e-03 221516" s at 51 7.335469e-03 202135 s_at 51 6.202404e-03 32099 at 51 7.399579e-03 206559 x at 51 6.202404e-03 204405 x_at 51 7.417291e-03 205255_x at 51 6.202404e-03 211542" x_at 51 7.429492e-03 208692 at 51 6.202404e-03 225251" at 51 7.456857e-03 200062 s at 51 6.202404e-03 225651" at 51 7.462533e-03 200634 at 51 6.202404e-03 205863""at 51 7.462533e-03 200016 x at 51 6.202404e-03 200021"_at 51 7.462533e-03 1553132 a at 51 6.202404e-03 201110" s at 51 7.462533e-03 200949 x_at 51 6.202404e-03 203932" at 51 7.520029e-03 224002 s_at 51 6.255613e-03 219570" at 51 7.557581e-03 224391 s at 51 6.266065e-03 207569 ~at 51 7.557581e-03 212334 at 51 6.302992e-03 229304" s at 51 7.589657e-03 201256 at 51 6.335751e-03 242284""at 51 7.589657e-03 213064"at 51 6.343167e-03 242730" at 51 7.589657e-03 210574 s at 51 6.431043e-03 219648"_at 51 7.589657e-03 208720 s at 51 6.431043e-03 217965" s at 51 7.589657e-03 209134 s at 51 6.431043e-03 216521" s at 51 7.589657e-03 201429 s at 51 6.431043e-03 218198" at 51 7.589657e-03 200684 s at 51 6.431206e-03 203725" at 51 7.589657e-03 208994 s_at 51 6.495645e-03 51 7.589657e-03 1553530 a at 51 6.524162e-03 51 7.589657e-03 208557 at 51 6.583341e-03 51 7.589657e-03 226064 s at 51 6.602656e-03 51 7.589657e-03 223609 at 51 6.602656e-03 51 7.667683e-03 204025 s at 51 6.602656e-03 51 7.691524e-03 224991 at 51 6.633632e-03 51 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Figure imgf001091_0001
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s at 51 0.01 1553644_at 51 8.834419e-03 225231 at 51 0.01 200654_at 51 8.918234e-03 226629 at 51 0.01 204892_x_at 51 8.922208e-03 224840 at 51 0.01 217371_s_at 51 8.991858e-03 39248 at 51 0.01 223259_at 51 9.055294e-03 220330 s_at 51 0.01 205425_at 51 9.075769e-03 212790 x_at 51 0.01 201980_s_at 51 9.132353e-03 212665 at 51 0.01 224658_x_at 51 9.172376e-03 213528 at 51 0.01 213587_s_at 51 9.228217e-03 214146 s at 51 0.01 211133_x_at 51 9.254602e-03 218426 s at 51 0.01 204634_at 51 9.320938e-03 215109 at 51 0.01 225898_at 51 9.326245e-03 217918 at 51 0.01 211806_s_at 51 9.393818e-03 218116 at 51 0.01 208138_at 51 9.418943e-03 203947 at 51 0.01 208325_s_at 51 9.451779e-03 223022 s_at 51 0.01 234985_at 51 9.503389e-03 221505 at 51 0.01 217961_at 51 9.601474e-03 223305 at 51 0.01 201277_s_at 51 9.641971e-03 220960 x at 51 0.01 202764_at 51 9.682221e-03 223236 at 51 0.01 1553901_x_at 51 9.682221e-03 222992 s at 51 0.01 201122_x_at 51 9.684298e-03 222891 s at 51 0.01 220925_at 51 9.706741e-03 210646 x at 51 0.01 208144_s_at 51 9.716663e-03 210116 at 51 0.01 223176_at 51 9.733599e-03 210959 s at 51 0.01 211936_at 51 9.733779e-03 202165 at 51 0.01 201931_at 51 9.787274e-03 203048 s at 51 0.01 227960_s_at 51 9.816571e-03 203221 at 51 0.01 225686_at 51 9.833434e-03 202907 s__at 51 0.01 219543_at 51 9.95002e-03 208997 s at 51 0.01 217947_at 51 9.95002e-03 208825 x_at 51 0.01 200032_s_at 51 9.95002e-03 208730 x at 51 0.01 230550__at 51 9.955728e-03 200663 at 51 0.01 203021_at 51 9.991261e-03 200085 s at 51 0.01 204172__at 51 0.01 200091 s at 51 0.01 214449_s_at 51 0.01 200022 at 51 0.01 202043_s_at 51 0.01 200026 at 51 0.01 202412_s_at 51 0.01 1553569_at 51 0.01 209835_x_at 51 0.01 201656 at 51 0.01 208598_s_at 51 0.01 204190 at 51 0.01 222555_s_at 51 0.01 209154 at 51 0.01 1555037_a_at 51 0.01 206222 at 51 0.01 209384 at 51 0.01 202747 s at 51 0.01 2l5"8l3""s at '"31 215646 s_at 51 0.01 212168 at 51 0.01 209520 s at 51 0.01 226823 at 51 0.01 203388 at 51 0.01 228652 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0.01 218989 x at 51 0.01 213039 at 51 0.01 223506 at 51 0.01 202719 s at 51 0.01 223014 at 51 0.01 211783 s at 51 0.01 217756 x at 51 0.01 1554202 x at 51 0.01 203414 at 51 0.01 226330 s at 51 0.01 224977 at 51 0.01 203834 s at 51 0.01 222125 s at 51 0.01 203989 x at 51 0.01 222607 s at 51 0.01 201631 s at 51 0.01 210579 s at 51 0.01 209367 at 51 0.01 202283 at 51 0.01 209007 s at 51 0.01 206861 s at 51 0.01 211207 s~at 51 0.01 209207 s at 51 0.01 208696 at 51 0.01 203831 at 51 0.01 206157 at 51 0.01 213046 at 51 0.01 201444 s_at 51 0.01 203991 s at 51 0.01 210371 s at 51 0.01 217746 s at 51 0.01 227791 at 51 0.01 207018 s at 51 0.01 201064 s at 51 0.01 209083 at 51 0.01 212719 at 51 0.01 200828 s_at 51 0.01 206875 s at 51 0.01 212244 at 51 0.01 208910 s at 51 0.01 211714 x at 51 0.01 211015 s at 51 0.01 31826 at 51 0.01 208742 s at 51 0.01 203594_at 51 0.01 226230 at 51 0.01 205197 s at 51 0.01 212814 at 51 0.01 212467 at 51 0.01 204297 at 51 0.01 224900_at 51 0.01 223857 x at 51 0.01 203708_at 51 0.01 202583 s at 51 0.01 203066 at 51 0.01 215758 x at 51 0.01 206049 at 51 0.01 221509 at 51 0.01 227180_at 51 0.01 218123 at 51 0.01 200806 s at 51 0.01 202659 at 51 0.01 225470_at 51 0.01 219757 s at 51 0.01 225153 at 51 0.01 202173 s_at 51 0.01 228071_at 51 0.01 219165 at 51 0.01 232024_at 51 0.01 218919 at 51 0.01 234107 s at 51 0.01 207559 s at 51 0.01 218735 s at 51 0.01 221437 s at 51 0.01 219528 s at 51 0.01 203258 at" 51 0.01 218750_at 51 0.01 230264 s at 51 0.01 218740 s_at 51 0.01 218034 at 51 0.01 214143 x at 51 0.01 216526 x at 51 0.01 212413_at 51 0.01 203899 s at 51 0.01 213080 x at 51 0.01 220368 s at 51 0.01 217800 s at 51 0.01 213738 s_at 51 0.01 218252_at 51 0.01 201967 at 51 0.01 218346 s at 51 0.01 204490 s at 51 0.01 217843 s at 51 0.01 221744 at 51 0.01 217801 at 51 0.01 201059 at 51 0.01 218049_s_at 51 0.01 219105 x at 51 0.01 204102 s at 51 0.01 201475 x at 51 0.01 204118_at 51 0.01 201165 s at 51 0.01 204489 s at 51 0.01 228747 at 51 0.01 222580 at 51 0.01 1553533_at 51 0.01 221558_s_at 51 0.01 202882 x at 51 0.01 209452 s at 51 0.01 206900 x at 51 0.01 210873 x at 51 0.01 222473 s at 51 0.01 210532 s at 51 0.01 213352 at 51 0.01 209515 s at 51 0.01 205013 s at 51 0.01 206914_at 51 0.01 214500 at 51 0.02 205849 s at 51 0.01 211536 x at 51 0.02 209440_at 51 0.01 219402 s_at 51 0.02 207515 s at 51 0.01 226334 s at 51 0.02 209066 x at 51 0.01 209476 at 51 0.02 200618 at 51 0.01 203923 s at 51 0.02 1553856 s at 51 0.01 207655 s at 51 0.02 200716 x at 51 0.01 206380 s at 51 0.02 201049 s at 51 0.01 213056 at 51 0.02 201623 s at 51 0.01 207108 s at 51 0.02 201358 s at 51 0.01 221222 s at 51 0.02 201153 s at 51 0.01 220439 at 51 0.02 203827_at 51 0.01 1552316 a at 51 0.02 202582 s at 51 0.01 212681 at 51 0.02 58916 at 51 0.01 219938 s at 51 0.02 219294_at 51 0.01 217216 x at 51 0.02 214552 s at 51 0.01 203556 at 51 0.02 211251 x at 51 0.01 228393 s at 51 0.02 201534 s at 51 0.01 201743 at 51 0.02 200737 at 51 0.01 31874 at 51 0.02 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235007 at 51 0.02 218883 s at 51 0.02 221507 at 51 0.02 218803"~at 51 0.02 222507 s at 51 0.02 226018" at 51 0.02 1552739_s_at 51 0.02 223608" at 51 0.02 206478 at 51 0.02 225091" at 51 0.02 205105 at 51 0.02 223686" at 51 0.02 235388 at 51 0.02 224823" at 51 0.02 205074 at 51 0.02 236356"_at 51 0.02 217356 s at 51 0.02 220367" s_at 51 0.02 218724 s at 51 0.02 218878" s at 51 0.02 1568934_at 51 0.02 212584" at 51 0.02 231540 at 51 0.02 213501" at 51 0.02 201231 s_at 51 0.02 212720"_at 51 0.02 222600 s at 51 0.02 213579" s at 51 0.02 220796 x at 51 0.02 215158"_s_at 51 0.02 225819 at 51 0.02 217963" s at 51 0.02 1554770 x at 51 0.02 218217"_at 51 0.02 238148 s at 51 0.02 215832" x at 51 0.02 225634 at 51 0.02 204613" at 51 0.02 204369 at 51 0.02 203485" at 51 0.02 225794 s_at 51 0.02 203817"_at 51 0.02 212391 x at 51 0.02 204396"_s_at 51 0.02 211995 x at 51 0.02 221776" s at 51 0.02 202029 x at 51 0.02 209717" at 51 0.02 200092 s at 51 0.02 202934"_at 51 0.02 201476 s at 51 0.02 203102" s at 51 0.02 226283 at 51 0.02 202084" s at 51 0.02 202131 s at 51 0.02 202606" s at 51 0.02 205895 s at 51 0.02 207761"_s_at 51 0.02 219289 at 51 0.02 207957" s at 51 0.02 225707 at 51 0.02 209382"~at 51 0.02 200856"x at 51 0.02 209027" s at 51 0.02 212006 at 51 0.02 200071""at 51 0.02 203344 s at 51 0.02 200782"_at 51 0.02 201079 at 51 0.02 201392" s at 51 0.02 213261 at 51 0.02 217978" s at 51 0.02 232520 s at 51 0.02 201563""at 51 0.02 205861 at 51 0.02 229553""at 51 0.02 204207 s at 51 0.02 223978" s at 51 0.02 201216 at 51 0.02 226076"~s at 51 0.02 220775 s at 51 0.02 217104""at 51 0.02 211955 at 51 0.02 222790"~s_at 51 0.02 208917 x_at 51 0.02 209765""at 51 0.02 235327 x at 51 0.02 206261"_at 51 0.02 207791 s_at 51 0.02 200999" s at 51 0.02 219206 x_at 51 0.02 201037""at 51 0.02 205763 s at 51 0.02 200870""at 51 0.02 220661 s at 51 0.02 210616~ s at 51 0.02 223011 s_at 51 0.02 203528""at 51 0.02 219512 at 51 0.02 206522~~at 51 0.02 210705 s at 51 0.02 211681" s at 51 0.02 241820 at 51 0.02 226354""at 51 0.02 201590 x at 51 0.02 203107" x at 51 0.02 236027 at 51 0.02 206493"_at 51 0.02 202717 s at 51 0.02 208812" x at 51 0.02 211686 s at 51 0.02 201665" x at 51 0.02 31845 at 51 0.02 201173" x at 51 0.02 205462 s at 51 0.02 208729" x at 51 0.02 201409 s at 51 0.02 218633" x at 51 0.02 208923 at 51 0.02 203973" s at 51 0.02 201784 s at 51 0.02 201426" s at 51 0.02 209069 s at 51 0.02 220112"_at 51 0.02 1554149 at 51 0.02 218999"_at 51 0.02 235306 at 51 0.02 217811" at 51 0.02 200833 s at 51 0.02 200678 x at 51 0.02 206150 at 51 0.02 217743 s at 51 0.02 212160 at 51 0.02 224698_at 51 0.02 228405 at 51 0.02 225626_at 51 0.02 221565 s at 51 0.02 224892 at 51 0.02 220386 s at 51 0.02 219097 x_at 51 0.02 204793 at 51 0.02 220232_at 51 0.02 224674 at 51 0.02 218723_s_at 51 0.02 219824 at 51 0.02 220283_at 51 0.02 204385 at 51 0.02 213513 x at 51 0.02 223168 at 51 0.02 217491 x at 51 0.02 202964 s at 51 0.02 217398 x at 51 0.02 1568720_at 51 0.02 218143 s at 51 0.02 227388 at 51 0.02 203413_at 51 0.02 225976 at 51 0.02 221476_s_at 51 0.02 223602 at 51 0.02 222757 s at 51 0.02 227379 at 51 0.02 223047_at 51 0.02 223303 at 51 0.02 221700 s_at 51 0.02 217673 x at 51 0.02 223059_s_at 51 0.02 212403 at 51 0.02 220740 s at 51 0.02 224392 s at 51 0.02 223301 s at 51 0.02 204513 s at 51 0.02 221775 x at 51 0.02 212904 at 51 0.02 211983 x at 51 0.02 225081 s_at 51 0.02 212239 at 51 0.02 207350 s at 51 0.02 210996_s_at 51 0.02 201566 x at 51 0.02 211339 s at 51 0.02 219690 at 51 0.02 202595 s at 51 0.02 219497 s at 51 0.02 202090 s at 51 0.02 203457 at 51 0.02 202524 s at 51 0.02 211048 s_at 51 0.02 203224 at 51 0.02 210504 at 51 0.02 202557 at 51 0.02 202355 s_at 51 0.02 202544 at 51 0.02 218018 at 51 0.02 202850_at 51 0.02 214501 s at 51 0.02 206323 x_at 51 0.02 212063 at 51 0.02 205323 s at 51 0.02 218649 x at 51 0.02 205831 at 51 0.02 208975 s at 51 0.02 205726_at 51 0.02 1553528 a at 51 0.02 208845_at 51 0.02 212255 s at 51 0.02 209332 s_at 51 0.02 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201760" s at ■51"" 0.03 218156 s at 51 0.03 201063"_at 51 0.03 218032""at 51 0.03 201054"_at 51 0.03 2O195θ]_x_at 51 0.03 200774"_at 51 0.03 AFFX-HUMGAPDH/ 200033"_at 51 0.03 M33197 51 0.03 217781" s at 51 0.03 236621 _at 51 0.03 223283] s at 51 0.03 202542" s at 51 0.03 214421" x at 51 0.03 204158" s at 51 0.03 209002" s at 51 0.03 212014" x at 51 0.03 200613" at 51 0.03 212692"_s_at 51 0.03 219126"_at 51 0.03 2I8III" s_at 51 0.03 217724"_at 51 0.03 203262" s at 51 0.03 226901" at 51 0.03 223637" s at 51 0.03 219045" at 51 0.03 226994"~at 51 0.03 238793" at 51 0.03 222690" s at 51 0.03 218435"_at 51 0.03 241715" x at 51 0.03 215357" s at 51 0.03 220643" s at 51 0.03 223307" at 51 0.03 206377""at 51 0.03 201824" at 51 0.03 53720 at 51 0.03 201652"_at 51 0.03 209517 s at 51 0.03 211609"_x_at 51 0.03 205609""at 51 0.03 202006" at 51 0.03 201040"_at 51 0.03 205685" at 51 0.03 225370"_at 51 0.03 218008"_at 51 0.03 214074" s at 51 0.03 226387""at 51 0.03 213566""at 51 0.03 202487" s_at 51 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Figure imgf001103_0001
200077 s at 5J 0.04 005/022071
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1.879563e-03 200597 at 5L 5.182022e-03 211594 s at 5L 1.932941e-03 204821 "at 5L 5.229281e-03 1555154_a_at 5L 1.941173e-03 207541~ s at 5L 5.281982e-03 205988 at 5L 1.944422e-03 217552" x at 5L 5.356731e-03 224364 s at 5L 2.154815e-03 213906"~at 5L 5.373054e-03 224177 s at 5L 2.171076e-03 214551" s_at 5L 5.398732e-03 38241 at 5L 2.198847e-03 201672" s_at 5L 5.40822e-03 201017 at 5L 2.217283e-03 205590" at 5L 5 . 608797 e-O3 209184 s at 5L 2.246344e-03 220241" at 5L 5.634476e-03 205255 x at 5L 2.358794e-03 211952" at 5L 5.73552e-03 205291 at 5L 2.511356e-03 219428" s at 5L 5.746051e-03 219423 x at 5L 2.702052e-03 36084 at 5L 5.8364e-03 226389 s_at 5L 2.74995e-03 220740 s at 5L 5.960655e-03 212910 at 5L 2.782487e-03 203408"_s_at 5L 5.970748e-03 205462 s at 5L 2.795772e-03 226329" s at 5L 5.986283e-03 1552737 s at 5L 2.802112e-03 204748" at 5L 6.008283e-03 207651 at 5L 2.833213e-03 215990" s_at 5L 6.026918e-03 227927 at 5L 2.833723e-03 221081" s at 5L 6.083185e-03 204405 x_at 5L 2.862991e-03 218515" at 5L 6.148687e-03 202466 at 5L 2 . 897922e-03 200064" at 5L 6.264302e-03 215415 s at 5L 2.902488e-03 202251"_at 5L 6.312564e-03 224988 at 5L 3.196318e-03 210947" s at 5L 6.348207e-03 216100 s at 5L 3.253064e-03 222804" x_at 5L 6.386431e-03 212737 at 5L 3.33387e-03 204639" at 5L 6.44712e-03 1557000 at 5L 3 . 357671e-03 201583" s at 5L 6.566825e-O3 209197 at 5L 3.370651e-03 207159" x_at 5L 6.567432e-03 204559 s at 5L 3.402858e-03 201024" x at 5L 6.59954e-03 201013 s at 5L 3.411488e-03 204985" s_at 5L 6.683113e-03 201739 at 5L 3.431494e-03 203427" at 5L 6.858742e-03 202513 s at 5L 3.499235e-03 235310" at 5L 6.967223e-03 222108 at 5L 3.532146e-03 221867"_at 5L 7.080955e-03 201697 s at 5L 3.612621e-03 208249" s at 5L 7.089012e-03 212544 at 5L 3.735757e-03 219581""at 5L 7.123443e-03 201569 s at 5L 3.762888e-03 223221"_at 5L 7.148118e-03 57516 at 5L 3.790351e-03 218757" s at 5L 7.22167e-03 211665 s at 5L 3.80477e-03 208646"~at 5L 7.231181e-03 220187 at 5L 3.819191e-03 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0.03 214030 at 5M 0.03 209520 s at 5M 0.03 202543 s at 5M 0.03 212037 at 5M 0.03 223304 at 5M 0.03 206261 at 5M 0.03 201464 x at 5M 0.03 222557 at 5M 0.03 224196 x at 5M 0.03 211666 x at 5M 0.03 233511 at 5M 0.03 32137 at 5M 0.03 240159 at 5M 0.03 219680 at 5M 0.03 238488 at 5M 0.03 222749 at 5M 0.03 233126 s at 5M 0.03 210301 at 5M 0.03 218530 at 5M 0.03 207384 at 5M 0.03 217817 at 5M 0.03 208255 s at 5M 0.03 203271 s at 5M 0.03 1494 f at 5M 0.03 204191 at 5M 0.03 1559977 a at 5M 0.03 205359 at 5M 0.03 89476 r_at 5M 0.03 208797 s at 5M 0.03 229120 s at 5M 0.03 1554202 x at 5M 0.03 202934 at 5M 0.03 207571 x at 5M 0.03 226599 at 5M 0.03 201298 s at 5M 0.03 212293 at 5M 0.03 233461 x at 5M 0.03 61734 at 5M 0.03 207809 s at 5M 0.03 204131 s at 5M 0.03 1555562 a at 5M 0.03 206174 s at 5M 0.03 201662 s at 5M 0.03 1554112 a at 5M 0.03 204949 at 5M 0.03 213012 at 5M 0.03 210042 s at 5M 0.03 224866 at 5M 0.03 202033 s at 5M 0.03 200985 s at 5M 0.03 I i! !! > Il Il n"23'D626"'at""•:"5M;;"-crrόbr 227449 at 5M 0.04 203831 at 5M 0.03 220134 x at 5M 0.04 203582 s at 5M 0.03 218439 s at 5M 0.04 203297 s at 5M 0.03 203264 s at 5M 0.04 208662 s at 5M 0.04 204453 at 5M 0.04 217805 at 5M 0.04 201272 at 5M 0.04 204908 s at 5M 0.04 225621 at 5M 0.04 217728 at 5M 0.04 200020 at 5M 0.04 200965 s at 5M 0.04 32209 at 5M 0.04 205041 s at 5M 0.04 203931 s at 5M 0.04 225841 at 5M 0.04 52169 at 5M 0.04 206031 s at 5M 0.04 218088 s at 5M 0.04 202626 s at 5M 0.04 212381 at 5M 0.04 223130 s_at 5M 0.04 228019 s at 5M 0.04 203312 x at 5M 0.04 225487 at 5M 0.04 206553 at 5M 0.04 215416 s at 5M 0.04 209460 at 5M 0.04 202097 at 5M 0.04 202449 s_at 5M 0.04 226521 s at 5M 0.04 235516 at 5M 0.04 204184 s at 5M 0.04 205645 at 5M 0.04 216941 s at 5M 0.04 204275 at 5M 0.04 206668 s at 5M 0.04 201853 s_at 5M 0.04 221764 at 5M 0.04 218373 at 5M 0.04 1555736 a at 5M 0.04 205632 s at 5M 0.04 203752 s at 5M 0.04 220925 at 5M 0.04 35436 at 5M 0.04 214687 x at 5M 0.04 210160 at 5M 0.04 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0.04 205986 at 5N 9.78e-04 201917 s at 5M 0.04 208426 x_at 5N 9.78e-04 217977 at 5M 0.04 201968 s at 5N 9.78e-04 202488 s at 5M 0.04 213133 s at 5N 1.0e-03 208488 s_at 5M 0.04 243851 at 5N 1.015442e-03 214430 at 5M 0.04 230563 at 5N 1.097802e-03 205227 at 5M 0.04 222392 x at 5N 1.172511e-03 219093 at 5M 0.04 211275 s_at 5N 1.172511e-03 224304 x at 5M 0.04 1554624 a at 5N 1.172511e-03 202492 at 5M 0.04 203753 at 5N 1.179893e-03 207234 at 5M 0.04 207855 s_at 5N 1.274009e-03 202203 s at 5M 0.04 219281 at 5N 1.300628e-03 238429 at 5M 0.04 224782 at 5N 1.399786e-03 226353 at 5M 0.04 224630 at 5N 1.399786e-03 241372 at 5M 0.04 219980 at 5N 1.399786e-03 218770 s at 5M 0.04 211612 s_at 5N 1.399786e-03 213603 s at 5M 0.04 200780 x at 5N 1.399786e-03 202736 s_at 5M 0.04 1554240 a at 5N 1.399786e-03 217725 x at 5M 0.04 207809 s at 5N 1.490826e-03 201836 s at 5M 0.04 208646 at 5N 1.500204e-03 202682 s at 5M 0.04 224048 at 5N 1.534407e-03 218669 at 5M 0.04 211721 s at 5N 1.55142e-03 217398 x at 5M 0.04 55872 at 5N 1.612875e-03 201112 s at 5M 0.04 225799 at 5N 1.664589e-03 225965 at 5M 0.04 200611 s_at 5N 1.664589e-03 209102 s at 5M 0.04 1553612_at 5N 1.664589e-03 224652 at 5M 0.04 203554 x at 5N 1.682083e-03 222891 s at 5N 6.34e-06 223609 aϊ 5N 1.716003e-03 230058 at 5N 2.19e-05 34031 i_at 5N 1.799376e-03 210347 s at 5N 6.0e-05 1554456 a at 5N 1.87136e-03 210356 x at 5N 7.69e-05 223658 at 5N 1.896522e-03 200863 s_at 5N 1.24e-04 204280 at 5N 1.955509e-03 240159 at 5N 1.29e-04 212540 at 5N 1.971979e-03 215336 at 5N 1.95e-04 203936 s at 5N 1.971979e-03 207665 at 5N 1.95e-04 203958 s at 5N 1.971979e-03 202695 s at 5N 2.43e-04 1569679_at 5N 1.971979e-03 203725 at 5N 3.01e-04 219657 s at 5N 2.052216e-03 212129 at 5N 3.01e-04 212807 s at 5N 2.052216e-03 204116 at 5N 3.7e-04 226817 at 5N 2.271128e-03 209583 s at 5N 4.18e-04 200857 s at 5N 2.285887e-03 222146 s at 5N 4.31e-04 211245 x at 5N 2.308125e-03 225178 at 5N 4.52e-04 38710 at 5N 2.327529e-03 48659 at 5N 4.54e-04 219574 at 5N 2.327529e-03 202061 s at 5N 4.54e-04 214721 x at 5N 2.327529e-03 209217 s at 5N 4.54e-04 214290 s at 5N 2.327529e-03 1555688 s at 5N 4.69e-04 220702 at 5N 2.327529e-03 209339 at 5N 5.33e-04 211543 s at 5N 2.327529e-03 217418 x at 5N 5.54e-04 211858 x_at 5N 2.327529e-03 206245 s at 5N 5.54e-04 202117 at 5N 2.327529e-03 1558111_at 5N 5.54e-04 206848 at 5N 2.327529e-03 200752 s at 5N 5.54e-04 200640 at 5N 2.327529e-03 Il i! 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■■-'51N τ""O'TO'Ϊ 219981 x at 5N 0.01 1552302_at 5N 0.01 219667 s at 5N 0.01 200692 s_at 5N 0.01 218721 s at 5N 0.01 201795 at 5N 0.01 213956 at 5N 0.01 206965 at 5N 0.01 215111 s_at 5N 0.01 204214 s at 5N 0.01 217882 at 5N 0.01 207286 at 5N 0.01 203546 at 5N 0.01 239891 x at 5N 0.01 220940 at 5N 0.01 210718 s at 5N 0.01 208759 at 5N 0.01 218149 s_at 5N 0.01 1552667 a at 5N 0.01 209367 at 5N 0.01 1554770 x at 5N 0.01 206586 at 5N 0.01 1552773_at 5N 0.01 208091 s at 5N 0.01 200700 s at 5N 0.01 218383 at 5N 0.01 1569302_at 5N 0.01 220486 x at 5N 0.01 201700 at 5N 0.01 235253 at 5N 0.01 202119 s at 5N 0.01 225394 s_at 5N 0.01 208546 x at 5N 0.01 1570571_at 5N 0.01 205395 s at 5N 0.01 208002 s at 5N 0.01 201840 at 5N 0.01 210225 x at 5N 0.01 238783 at 5N 0.01 201708 s at 5N 0.01 210886 x at 5N 0.01 207655 s at 5N 0.01 209049 s_at 5N 0.01 1557944_s_at 5N 0.01 230645 at 5N 0.01 222212 s at 5N 0.01 203379 at 5N 0.01 231824 at 5N 0.01 217825 s at 5N 0.01 218709 s_at 5N 0.01 203076 s at 5N 0.01 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s at 5N 0.01 205600_x_at 5N 0.02 AFFX-HUMGAPDH/ 206478_at 5N 0.02 M33197 5N 0.01 207707_s_at 5N 0.02 201884 at 5N 0.01 1556743_at 5N 0.02 226507 at 5N 0.01 207936_x_at 5N 0.02 201078 at 5N 0.01 200973_s_at 5N 0.02 215535 s_at 5N 0.01 222848_at 5N 0.02 229666 s at 5N 0.01 201494_at 5N 0.02 208728 s at 5N 0.01 203315_at 5N 0.02 225616 at 5N 0.01 202696_at 5N 0.02 224871 at 5N 0.01 203317_at 5N 0.02 241731 x at 5N 0.01 223135_s_at 5N 0.02 218362 s_at 5N 0.01 201176_s_at 5N 0.02 217918 at 5N 0.01 226350_at 5N 0.02 210057 at 5N 0.01 223049_at 5N 0.02 203047 at 5N 0.01 222977_at 5N 0.02 205627 at 5N 0.01 222406_s_at 5N 0.02 1553099' at 5N 0.01 205411_at 5N 0.02 1553960_at 5N 0.01 220728_at 5N 0.02 201318 s at 5N 0.01 201129_at 5N 0.02 201866 s at 5N 0.01 55705_at 5N 0.02 221188 s at 5N 0.01 201625_s_at 5N 0.02 203642 s_at 5N 0.01 218060_s_at 5N 0.02 200868 s at 5N 0.01 207357_s_at 5N 0.02 33768 at 5N 0.01 219846_at 5N 0.02 220941 s at 5N 0.01 202200_s_at 5N 0.02 202331 at 5N 0.01 209398_at 5N 0.02 200625 s at 5N 0.01 219380_x_at 5N 0.02 219984 s_at 5N 0.01 202167_s_at 5N 0.02 1554769 at 5N 0.01 224369_s_at 5N 0.02 210172 at 5N 0.01 205359_at 5N 0.02 221705 s_at 5N 0.01 1552362_a_at 5N 0.02 234631 at 5N 0.01 1554980_a_at 5N 0.02 202264 s at 5N 0.01 219938_s_at 5N 0.02 200693 at 5N 0.01 211916_s_at 5N 0.02 242349 at 5N 0.02 226929_at 5N 0.02 213446 s at 5N 0.02 213175_s_at 5N 0.02 223457 at 5N 0.02 212204_at 5N 0.02 208829 at 5N 0.02 222408_s_at 5N 0.02 203143 s at 5N 0.02 226794_at 5N 0.02 204804 at 5N 0.02 224656_s_at 5N 0.02 225860 at 5N 0.02 227485_at 5N 0.02 210088 x at 5N 0.02 228964_at 5N 0.02 204480 s at 5N 0.02 235744_at 5N 0.02 221541 at 5N 0.02 220173_at 5N 0.02 225250 at 5N 0.02 212895_s_at 5N 0.02 205633 s_at 5N 0.02 213151_s_at 5N 0.02 214299 at 5N 0.02 218215_s_at 5N 0.02 209249 s at 5N 0.02 203255_at 5N 0.02 206257 at 5N 0.02 204174 at 5N 0.02 Q.t.!,..,> ..-.'SN«. 227098_at 5N 0.02 209514 s_at 5N 0.02 64432 ]at 5N 0.02 203203~s_at 5N 0.02 217720_at 5N 0.02 206061 s at 5N 0.02 203394" s_at 5N 0.02 205220""at 5N 0.02 223138" s at 5N 0.02 207734""at 5N 0.02 223243"[[s_at 5N 0.02 155524]"_at 5N 0.02 220796" x at 5N 0.02 1552862i_a_at 5N 0.02 211202"_s_at 5N 0.02 155575S)_a_at 5N 0.02 211676" s at 5N 0.02 214130 s at 5N 0.02 210742"_at 5N 0.02 216054 x at 5N 0.02 209685" s at 5N 0.02 212262"[at 5N 0.02 202292" x at 5N 0.02 201828"x at 5N 0.02 205645"_at 5N 0.02 236696^"at 5N 0.02 206209"_s_at 5N 0.02 221490""at 5N 0.02 205511"_at 5N 0.02 160020"_"at 5N 0.02 208591"_s_at 5N 0.02 222842 [at 5N 0.02 208039"_at 5N 0.02 225252 _at 5N 0.02 208742"~s_at 5N 0.02 220949"s at 5N 0.02 204059"_s_at 5N 0.02 202000"[at 5N 0.02 217865"_at 5N 0.02 205328 at 5N 0.02 204501'_at 5N 0.02 244766 [at 5N 0.02 201883' s at 5N 0.02 201172"x at 5N 0.02 238660""at 5N 0.02 201136 "at 5N 0.02 207876" s at 5N 0.02 206361 [at 5N 0.02 219635'_at 5N 0.02 201090"x at 5N 0.02 215158" s at 5N 0.02 237563 s at 5N 0.02 219594"_at 5N 0.02 218509 [at 5N 0.02 217949"_s_at 5N 0.02 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219161_s_at 5N 0.03 203989_x_at 5N 0.04 219137_s_at 5N 0.03 207426_s_at 5N 0.04 214847_s_at 5N 0.03 1554168_a_at 5N 0.04 212318_at 5N 0.03 210740_s_at 5N 0.04 214894_x_at 5N 0.03 202443_x_at 5N 0.04 215952_s_at 5N 0.03 214473_x_at 5N 0.04 218304_s__at 5N 0.03 219751_at 5N 0.04 215948_x_at 5N 0.03 214377_s_at 5N 0.04 218037_at 5N 0.03 213842_x_at 5N 0.04 217879_at 5N 0.03 215046_at 5N 0.04 211433_x_at 5N 0.03 220953_s_at 5N 0.04 202488_s_at 5N 0.03 205214_at 5N 0.04 202841_x_at 5N 0.03 201047_x_at 5N 0.04 207198_s_at 5N 0.03 209616_s_at 5N 0.04 208200_at 5N 0.03 1553647_at 5N 0.04 200761_s_at 5N 0.03 211945_s_at 5N 0.04 1565162_s_at 5N 0.03 230574_at 5N 0.04 1555803_a_at 5N 0.03 217938_s_at 5N 0.04 200617_at 5N 0.03 204715_at 5N 0.04 201028_s_at 5N 0.03 216565_x_at 5N 0.04 201626_at 5N 0.03 219033_at 5N 0.04 228253_at 5N 0.03 225693_s_at 5N 0.04 226536_at 5N 0.03 224707_at 5N 0.04 222104_x_at 5N 0.03 232720_at 5N 0.04 221257_x_at 5N 0.03 238346_s_at 5N 0.04 228774_at 5N 0.03 230337_at 5N 0.04 211918_x_at 5N 0.03 219571_s_at 5N 0.04 203081_at 5N 0.03 219859_at 5N 0.04 213453_x_at 5N 0.03 212792_at 5N 0.04 202100_at 5N 0.03 213557_at 5N 0.04 203844_at 5N 0.03 217796_s_at 5N 0.04 200059_s_at 5N 0.03 217811_at 5N 0.04 205863_at 5N 0.03 204714_s_at 5N 0.04 209085_x_at 5N 0.03 211372_s_at 5N 0.04 210461_s_at 5N 0.03 209595_at 5N 0.04 201356_at 5N 0.03 202300_at 5N 0.04 211716_x_at 5N 0.03 207416_s_at 5N 0.04 223915_at 5N 0.03 209301_at 5N 0.04 203672_x_at 5N 0.03 208082_x_at 5N 0.04 200774_at 5N 0.03 209331_s_at 5N 0.04 202074_s_at 5N 0.03 208637_x_at 5N 0.04 228506_at 5N 0.03 208268_at 5N 0.04 200039_s_at 5N 0.03 200767_s_at 5N 0.04 226739_at 5N 0.03 201929_s_at 5N 0.04 223626_x_at 5N 0.03 201954_at 5N 0.04 238858_at 5N 0.03 225527_at 5N 0.04 211416_x_at 5N 0.03 200799_at 5N 0.04 205232_s_at 5N 0.03 227223_at 5N 0.04 209702_at 5N 0.03 223050_s_at 5N 0.04 1553681_a_at 5N 0.03 223276_at 5N 0.04 203416_at 5N 0.03 224843_at 5N 0.04 213501_at 5N 0.03 206491_s_at 5N 0.04 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207470 at 5N 0.04 222218 s at 5N 0.04 200832 s at 5N 0.04 200859 x at 5N 0.04 211101 x at 5N 0.04 213594 x at 5N 0.04 219307 at 5N 0.04 201029 s_at 5N 0.04 200007 at 5N 0.04 202120 x at 5N 0.04 206363 at 5N 0.04 208662 s_at 5N 0.04 219382" at 5N 0.04 205786 s at 5N 0.04 202596 at 5N 0.04 206342 x at 5N 0.04 38964 r at 5N 0.04 201363 s at 5N 0.04 223148 at 5N 0.04 200931 s at 5N 0.04 203136 at 5N 0.04 1553292 s at 5N 0.04 1555735 a at 5N 0.04 213011 s at 5N 0.04 202939 at 5N 0.04 222343 at 5N 0.04 219922 s at 5N 0.04 200070 at 5N 0.04 226975 at 5N 0.04 240859 at 5N 0.04 220944 at 5N 0.04 201582 at 5N 0.04 21"?212 s at 5N 0.04 1554670_at 5N 0.04 201975 at 5N 0.04 201248 s at 5N 0.04 221506 s_at 5N 0.04 209116 x at 5N 0.04 208785 s_at 5N 0.04 1553297 a at 5N 0.04 222024 s at 5N 0.04 218107 at 5N 0.04 218854 at 5N 0.04 206035 at 5N 0.04 223451 s_at 5N 0.04 219678 x at 5N 0.04 224651 at 5N 0.04 218102 at 5N 0.04 224636 at 5N 0.04 207654 x_at 5N 0.04 217892 s at 5N 0.04 208928 at 5N 0.04 218224 at 5N 0.04 224377 s at 5N 0.04 204169 at 5N 0.04 201416 at 5N 0.04 204075 s at 5N 0.04 227068 at 5N 0.04 204882 at 5N 0.04 205211 s at 5N 0.04 221222 s at 5N 0.04 208943 s_at 5N 0.04 211657 at 5N 0.04 200976 s at 5N 0.04 202922 at 5N 0.04 AFFX-HUMGAPDH/ 202364 at 5N 0.04 M33197 5N 0.04 202501 at 5N 0.04 201883 s_at 50 5.39e-09 202845 s at 5N 0.04 218679 s at 50 5.25e-08 202727 s at 5N 0.04 224625 x at 50 3.5e-07 205831 at 5N 0.04 209860 s at 50 8.51e-07 209430 at 5N 0.04 200763 s_at 50 8.63e-07 209184 s at 5N 0.04 221700 s at 50 9.27e-07 200639 s at 5N 0.04 209806 at 50 l.lle-06 201737 s at 5N 0.04 200075 s at 50 1.21e-06 201590 x at 5N 0.04 218301 at 50 1.48e-06 241955 at 5N 0.04 202090 s at 50 1.52e-06 234919 s at 5N 0.04 1554249 a at 50 1.64e-06 202978 s at 5N 0.04 213453 x at 50 1.71e-06 221158 at 5N 0.04 1552734_at 50 1.78e-06 205554 s at 5N 0.04 212895 s at 50 2.11e-06 II'" ," Il !l >!"»■ in, is" gig .i..J< .....;>5O '■ 208625_s_at 50 1.39e-05 207988 s at 50 2.21e-06 210981_s_at 50 1.4e-05 217733 s at 50 2.28e-06 214687_x_at 50 1.42e-05 224585 x at 50 2.33e-06 212581_x_at 50 1.42e-05 AFFX-HUMGAPDH/ 212220_at 50 1.43e-05 M33197 50 2.72e-06 200634_at 50 1.47e-05 AFFX-HUMGAPDH/ 202201_at 50 1.48e-05 M33197 50 2.77e-06 208815_x_at 50 1.5e-05 210286 s at 50 2.78e-06 201231_s_at 50 1.53e-05 201558 at 50 2.91e-06 212024_x_at 50 1.54e-05 200839 s at 50 3.01e-06 200680_x_at 50 1.59e-05 203254 s at 50 3.11e-06 213446_s_at 50 1.62e-05 221474 at 50 3.54e-06 211983_x_at 50 1.62e-05 226739 at 50 3.81e-06 238996_x_at 50 1.69e-05 218208 at 50 3.9e-06 202343_x_at 50 1.69e-05 1559883 s at 50 4.06e-06 206748_s_at 50 1.7e-05 214003 x at 50 4.74e-06 203535_at 50 1.71e-05 201318 s at 50 4.91e-06 209665_at 50 1.76e-05 201492 s at 50 4.97e-06 217398_x_at 50 1.78e-05 208042 at 50 4.99e-06 200012_x_at 50 1.85e-05 210312 s at 50 5.54e-06 224733_at 50 1.88e-05 233571 x at 50 5.82e-06 223039_at 50 1.99e-05 207168 s at 50 5.9e-06 200025_s_at 50 2.0e-05 201094 at 50 6.33e-06 241820_at 50 2.03e-05 223440 at 50 6.51e-06 200718_s_at 50 2.05e-05 208904 s at 50 6.87e-06 202460_s_at 50 2.1e-05 228253 at 50 7.17e-06 1553588_at 50 2.1e-05 214938 x at 50 7.21e-06 202917_s_at 50 2.13e-05 205012 s_at 50 7.25e-06 201828_x_at 50 2.16e-05 203034 s at 50 7.36e-06 201576_s_at 50 2.16e-05 222442 s at 50 7.61e-06 226091_s_at 50 2.28e-05 238199 x at 50 7.81e-06 222600_s_at 50 2.28e-05 204236 at 50 7.95e-06 217817_at 50 2.35e-05 200033 at 50 8.4e-06 200003_s_at 50 2.35e-05 201721 s at 50 8.48e-06 202655_at 50 2.36e-05 200966 x at 50 8.54e-06 213735_s_at 50 2.43e-05 200925 at 50 8.64e-06 235461_at 50 2.45e-05 231735 s at 50 8.96e-06 213932_x_at 50 2.49e-05 211040 x at 50 9.35e-06 226177_at 50 2.56e-05 212988 x at 50 9.42e-06 217863_at 50 2.57e-05 201082 s at 50 9.51e-06 207783_x_at 50 2.57e-05 209034 at 50 9.65e-06 211995_x_at 50 2.58e-05 218431 at 50 9.66e-06 213214_x_at 50 2.6e-05 37793 r_at 50 9.8e-06 217801_at 50 2.68e-05 208770 s at 50 9.94e-06 202029_x_at 50 2.68e-05 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1.479827e-03 203175 at 50 1.210291e-03 225498_at 50 1.480044e-03 208912 s at 50 1.215589e-03 201475 x at 50 1.485798e-03 218580 x at 50 1.217293e-03 224447 s at 50 1.491447e-03 201863 at 50 1.217862e-03 207585 s at 50 1.494504e-03 1553252 a at 50 1.235933e-03 202442"at 50 1.494573e-03 211752 s_at 50 1.239998e-03 235802_at 50 1.505093e-03 207167 at 50 1.243776e-03 215038 s at 50 1.508568e-03 212740 at 50 1.247038e-03 218113~at 50 1.509433e-03 202423 at 50 1.2523e-03 214780 s at 50 1.510196e-03 208248 x at 50 1.257175e-03 227207 x at 50 1.521402e-03 217232 x at 50 1.258298e-03 204995_at 50 1.52819e-03 209076 s at 50 1.268234e-03 201345 s at 50 1.543129e-03 202288 at 50 1.275887e-03 235430~at 50 1.564588e-03 209354 at 50 1.280063e-03 203943_at 50 1.566891e-03 222544 s_at 50 1.280489e-03 202692 s at 50 1.567904e-03 219639 x at 50 1.281073e-03 208982_at 50 1.577327e-03 221698 s at 50 1.282749e-03 210966 x at 50 1.583557e-03 208678 at 50 1.282925e-03 218255 s at 50 1.591393e-03 219186 at 50 1.287026e-03 225899 x at 50 1.595734e-03 206061 s_at 50 1.302228e-03 215535 s at 50 1.602928e-03 200822 x at 50 1.305261e-03 200073~s at 50 1.624528e-03 200958 s at 50 1.311416e-03 214048 at 50 1.626609e-03 225707 at 50 1.320928e-03 219028 at 50 1.627593e-03 231059 x at 50 1.323182e-03 203315 at 50 1.630611e-03 200097 s at 50 1.332701e-03 209005 at 50 1.639799e-03 226762 at 50 1.334398e-03 212576_at 50 1.646651e-03 201896 s at 50 1.340737e-03 202163 s at 50 1.647361e-03 221506 s at 50 1.344768e-03 207305 s at 50 1.652021e-03 213590 at 50 1.35623e-03 203054_s~at 50 1.654901e-03 200773 x at 50 1.356812e-03 200971 s at 50 1.65498e-03 201306 s at 50 1.357224e-03 222992 s at 50 1.65869e-03 222728 s at 50 1.360819e-03 204206 at 50 1.667178e-03 210996 s at 50 1.362823e-03 218010 x at 50 1.682305e-03 219161 s at 50 1.369652e-03 205237 at 50 1.684407e-03 219620 x at 50 1.372531e-03 201575_at 50 1.69335e-03 224782 at 50 1.375585e-03 221498_at 50 1.697064e-03 208845 at 50 1.382102e-03 208628 s at 50 1.7057e-03 202299 s at 50 1.386137e-03 212501_at 50 1.707475e-03 218198 at 50 1.387673e-03 206792 x at 50 1.713748e-03 212181 s at 50 1.38808e-03 209435 s at 50 1.716327e-03 204937 s at 50 1.394832e-03 202521~at 50 1.719994e-03 219077 s at 50 1.396814e-03 214663 at 50 1.720598e-03 212542 s at 50 1.402311e-03 212718' at 50 1.729144e-03 219350 s at 50 1.406424e-03 212484' at 50 1.731994e-03 211696 x at 50 1.420041e-03 202753 at 50 1.735593e-03 207180-'S-11Sf'•••»30 ""•T-."713'8!523'e-03 210395 x at 50 2.194123e-03 208308" s at 50 1.741928e-03 213557 at 50 2.198202e-03 202155"~s_at 50 1.743632e-03 224658 x at 50 2.213792e-03 218134" s at 50 1.745136e-03 217788 s at 50 2.216618e-03 206284" x at 50 1.746708e-03 221744 at 50 2.216834e-03 218032" at 50 1.749145e-03 224656 s at 50 2.227455e-03 1552976 at 50 1.754913e-03 201472 at 50 2.23823e-03 227981 _at 50 1.755562e-03 218097 s at 50 2.246e-03 218109" s at 50 1.759273e-03 201201 at 50 2.248176e-03 214743"_at 50 1.775166e-03 225267 at 50 2.254341e-03 211730" s at 50 1.803431e-03 225243 s_at 50 2.256821e-03 217028""at 50 1.804037e-03 208780 x at 50 2.260906e-03 225649" s at 50 1.81094e-03 211416 x_at 50 2.263084e-03 203912" s_at 50 1.811083e-03 225205 at 50 2.267947e-03 211178" s at 50 1.815287e-03 222790 s at 50 2.274816e-03 222977""at 50 1.815894e-03 209296 at 50 2.277323e-03 206361" at 50 1.858059e-03 200001 at 50 2.287929e-03 202161" at 50 1.861936e-03 218291 at 50 2.29451e-03 212630"_at 50 1.876643e-03 201331 s at 50 2.301831e-03 217941" s at 50 1.879283e-03 212836 at 50 2.320438e-03 223451" s at 50 1.892465e-03 218606 at 50 2.329312e-03 209148"_at 50 1.894297e-03 219001 s at 50 2.331626e-03 222104" x_at 50 1.896059e-03 225788 at 50 2.335472e-03 200650" s at 50 1.904172e-03 200678 x at 50 2.341008e-03 221891" x at 50 1.911401e-03 203258 at 50 2.342362e-03 200017"_at 50 1.913979e-03 218581 at 50 2.344156e-03 215158" s at 50 1.922202e-03 201553 s_at 50 2.345098e-03 203167"_at 50 1.930439e-03 1557915 s at 50 2.356148e-03 208704" x at 50 1.93861e-03 212540 at 50 2.362515e-03 217877" s at 50 1.943328e-03 200736 s at 50 2.366817e-03 219035" s at 50 1.960869e-03 225181 at 50 2.375811e-03 212467"_at 50 1.962625e-03 35974 at 50 2.37639e-03 209448" at 50 1.964231e-03 218935 at 50 2.379003e-03 56829 at 50 1.96842e-03 202698 x at 50 2.38691e-03 204099 _at 50 1.976203e-03 225251 at 50 2.39615e-03 220856" x_at 50 1.982311e-03 209224 s at 50 2.405983e-03 225065" x at 50 1.995758e-03 225626 at 50 2.418505e-03 201413"_at 50 2.014519e-03 222991 s at 50 2.41968e-03 214800" x_at 50 2.014749e-03 213475 s at 50 2.431177e-03 207857" at 50 2.019601e-03 201332 s at 50 2.432158e-03 201172" x at 50 2.02697e-03 212888 at 50 2.435879e-03 219594" at 50 2.02765e-03 203090 at 50 2.441286e-03 201450" s at 50 2.029887e-03 209418 s at 50 2.444644e-03 225434" at 50 2.048018e-03 33322 i_at 50 2.450021e-03 205851""at 50 2.048205e-03 210648 x at 50 2.456367e-03 217286" s at 50 2.059427e-03 220796 x at 50 2.457008e-03 207564" x at 50 2.066038e-03 201527 at 50 2.458465e-03 205865" at 50 2.074418e-03 201095 at 50 2.493996e-03 202681"_at 50 2.07851e-03 207677 s_at 50 2.495524e-03 217865 _at 50 2.084127e-03 222641 s_at 50 2.498953e-03 203243" s at 50 2.0938e-03 1563088 a at 50 2.507206e-03 204774 _at 50 2.093903e-03 47105 at 50 2.509119e-03 223305"_at 50 2.094944e-03 211537 x at 50 2.510386e-03 220659" s at 50 2.10089e-03 1555735 a at 50 2.512517e-03 217959 s at 50 2.104826e-03 202180 s at 50 2.513009e-03 202880 s at 50 2.105111e-03 204254 s at 50 2.530558e-03 213119""at 50 2.107168e-03 231716 at 50 2.538025e-03 203249"[at 50 2.126696e-03 212561 at 50 2.55178e-03 221500" s at 50 2.153907e-03 223000 s at 50 2.55401e-03 218983 "at 50 2.156407e-03 202300 at 50 2.556985e-03 215631""s_at 50 2.179553e-03 203459 s at 50 2.558754e-03 217737" x at 50 2.180738e-03 217934 x at 50 2.576327e-03 200797" s at 50 2.191367e-03 200816 s at 50 2.580285e-03 227129 x at 50 2.19211e-03 203606 at 50 2.581194e-03 ! \i " I! !1 !I 1'. , 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205726 "at 50 2.724876e-03 218809"_at 50 3.159477e-03 207072 "at 50 2.724928e-03 201090" x at 50 3.171928e-03 207485 x at 50 2.728773e-03 204387" x_at 50 3.182405e-03 202205 "at 50 2.73173e-03 200066"_at 50 3.188806e-03 1569206I at 50 2.73888e-03 215424" s at 50 3.193729e-03 223047 at 50 2.739059e-03 208759" at 50 3.201163e-03 204418 x at 50 2.740041e-03 203945" at 50 3.2189e-03 207574"s at 50 2.741939e-03 212139"_at 50 3.226076e-03 203232"s_at 50 2.742229e-03 212955" s at 50 3.243566e-03 212249 "at 50 2.745158e-03 225783"~at 50 3.248039e-03 208640 ~.at 50 2.745252e-03 212377^s at 50 3.248527e-03 209058 ~at 50 2.745732e-03 1569594 a at 50 3.249805e-03 220127 s at 50 2.74754e-03 206380 s at 50 3.255747e-03 205627 "at 50 2.751958e-03 200911" s at 50 3.256974e-03 20442.5 "at 50 2.751959e-03 217764" s at 50 3.279877e-03 64486 at 50 2.765645e-03 207389" at" 50 3.290182e-03 212457 at 50 2.782524e-03 202176" at 50 3.332978e-03 201700 "at 50 2.801004e-03 217750" s at 50 3.337524e-03 223394 "at 50 2.801397e-03 213733" at 50 3.357072e-03 201526 "at 50 2.811557e-03 210695" s at 50 3.359327e-03 200640 ~at 50 2.82073e-03 210007" s at 50 3.362175e-03 203364"s at 50 2.827646e-03 201409" s_at 50 3.367903e-03 211025 x at 50 2.832754e-03 206120" at 50 3.38134e-03 225414 ^at 50 2.849336e-03 222984" at 50 3.398119e-03 208030 s at 50 2.851703e-03 204225" at 50 3.401109e-03 227066 ~at 50 2.864737e-03 204646" at 50 3.406148e-03 212156 "at 50 2.878257e-03 218380" at 50 3.406268e-03 201954 ~at 50 2.878323e-03 227521""at 50 3.421802e-03 217763 s at 50 2.88059e-03 210129" s at 50 3.421824e-03 225309 ~at 50 2.887121e-03 219016" at 50 3.429273e-03 212593 s at 50 2.899786e-03 224736"jat 50 3.443632e-03 224680 at 50 2.917051e-03 208749" x at 50 3.444419e-03 224608 s at 50 2.922239e-03 224748"_at 50 3.447339e-03 219095 "at 50 2.924932e-03 206649" s at 50 3.457101e-03 207654 x at 50 2.930816e-03 210443" x at 50 3.459323e-03 207907 "at 50 2.947945e-03 200949" x at 50 3 . 462446e-03 206200 s at 50 2.95032e-03 238701" x at 50 3.478522e-03 221479 s at 50 2.953713e-03 203379""at 50 3.483154e-03 238365 s at 50 2.964221e-03 200625"_s_at 50 3.484636e-03 227651 at 50 2.970862e-03 201894" s at 50 3.501157e-03 218334 at 50 2.983823e-03 200891" s at 50 3.514227e-03 218950 at 50 2.985879e-03 212193" s at 50 3.517423e-03 212437 at 50 2.988055e-03 213022" s at 50 3.533741e-03 224374 s at 50 2.988376e-03 231764 _at 50 3.54513e-03 204265 s at 50 2.996408e-03 200856"x at 50 3.557182e-03 215785 s at 50 2.996894e-03 200668 s at 50 3.56101e-03 '!2O14'631' ":5O ""•3:r5T7757e-03 217720 at 50 4.208423e-03 200997"_at 50 3.586697e-03 218829 s at 50 4.214838e-03 218392" x_at 50 3.592966e-03 225120 "at 50 4.236656e-03 223591" at 50 3.619262e-03 201048 x_at 50 4.242745e-03 208736" at 50 3.623207e-03 232520 s at 50 4.266476e-03 202096"_s__at 50 3.626309e-03 212947 "at 50 4.276137e-03 200809" x at 50 3.643128e-03 216041 x at 50 4.292038e-03 202130""at 50 3.645053e-03 202298 at 50 4.301033e-03 222495"_at 50 3.674858e-03 209879 at 50 4.311186e-03 204620" s at 50 3.676219e-03 61734 at 50 4.317185e-03 217740" x at 50 3.696577e-03 208374 s at 50 4.331621e-03 207196" s at 50 3.697954e-03 218019 s at 50 4.368827e-03 201973" s at 50 3.699692e-03 212015 x_at 50 4.381639e-03 202725"""at 50 3.713726e-03 214022 s at 50 4.391736e-03 226707" at 50 3.720545e-03 219807 x at 50 4.405311e-03 200022" at 50 3.728954e-03 200920 s at 50 4.416062e-03 219096"_at 50 3.734027e-03 200619 at 50 4.425383e-03 219452"_at 50 3.755888e-03 32099 at 50 4.43372e-03 210453" x at 50 3.763072e-03 213998 s at 50 4.442299e-03 220605" s at 50 3.763413e-03 1554624_a_at 50 4.453449e-03 218520""at 50 3.766221e-03 211948 x at 50 4.458296e-03 225117" at 50 3.772596e-03 231579 s_at 50 4.49889e-03 50 3.774193e-03 207253 s at 50 4.516721e-03 50 3.793064e-03 201369 s at 50 4.530375e-03 50 3.793477e-03 218323 at 50 4.531398e-03 50 3.794609e-03 218387 s at 50 4.591156e-03 50 3.794741e-03 210142 x at 50 4.606513e-03 50 3.8086e-03 1553220 at 50 4.622698e-03 50 3.809857e-03 202624 s_at 50 4.624105e-03 50 3.812006e-03 200024 at 50 4.658832e-03 50 3.813045e-03 217874 at 50 4.659085e-03 50 3.815684e-03 200651 at 50 4.660943e-03 50 3.840145e-03 213923 at 50 4.689806e-03 50 3.864394e-03 227740 at 50 4.706887e-03 50 3.867077e-03 201009 s at 50 4.72795e-03 50 3.879695e-03 1555811_at 50 4.733598e-03
Figure imgf001145_0001
50 3.895715e-03 213507 s at 50 4.737346e-03 225738"_at 50 3.901304e-03 200929 at 50 4.751364e-03 208919" s at 50 3.908148e-03 222468 at 50 4.783133e-03 203050" at 50 3.911621e-03 205682 x at 50 4.783284e-03 218354" at 50 3.914733e-03 203655 at 50 4.78663e-03 200737"_at 50 3.931524e-03 204034 at 50 4.795974e-03 209350" s at 50 3.936463e-03 207181 s at 50 4.800651e-03 202364"_at 50 3.948243e-03 213208 at 50 4.802997e-03 217844" at 50 3.97237e-03 217496 s at 50 4.805206e-03 200649" at 50 3.988739e-03 218961 s at 50 4.808722e-03 201758"_at 50 4.026694e-03 201850 at 50 4.812175e-03 206662" at 50 4.030689e-03 219599 at 50 4.813807e-03 203269" at 50 4.034793e-03 223637 s at 50 4.818813e-03 204971"_at 50 4.039602e-03 210944 s at 50 4.830964e-03 211765" x at 50 4.065628e-03 31874 at 50 4.841245e-03 222531" s at 50 4.067908e-03 202600 s at 50 4.844644e-03 225980"_at 50 4.091515e-03 209934 s at 50 4.891473e-03 200804""at 50 4.105991e-03 211975 at 50 4.896703e-03 201200"_at 50 4.114112e-03 218243 at 50 4.897391e-03 155304^ 50 4.12467e-03 201001 s at 50 4.927188e-03 201637Lsa_aatt 50 4.14399e-03 208616 s at 50 4.929751e-03 200799""at 50 4.155429e-03 212066 s at 50 4.961231e-03 201098"_at 50 4.165984e-03 204674 at 50 4.963226e-03 223592" s at 50 4.168414e-03 1554553 s at 50 4.983619e-03 200643""at 50 4.168477e-03 233049 x at 50 4.992155e-03 224969 "at 50 4.173047e-03 217752 s at 50 4.995443e-03 214177" s at 50 4.174622e-03 204978 at 50 5.030513e-03 225068 "at 50 4.199333e-03 226144 at 50 5.073331e-03 ' II- ,'• Il .'.g!1fe"Il -222231"Jil g>l".".
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2.240388e-03 238593 at 5S 0.04 226639" at 5T 2.256617e-03 202666 s_at 5S 0.04 226642"_s_at 5T 2.318445e-03 202875 s_at 5S 0.04 220974" x at 5T 2.432416e-03 202419 at 5S 0.04 218470"_at 5T 2.440835e-03 224871 at 5S 0.04 206323" x at 5T 2.489127e-03 217877 s_at 5S 0.04 210017" at 5T 2.548474e-03 218846 at 5S 0.04 212636" at 5T 2.596391e-03 207638 at 5S 0.04 224892"_at 5T 2.710688e-03 219371 s at 5S 0.04 224374" s at 5T 2.73506e-03 204647 at 5S 0.04 200049" at 5T 2.76785e-03 205297 s at 5S 0.04 226668"_at 5T 2.78668e-03 204479 at 5S 0.04 210458" s at 5T 2.850227e-03 207187 at 5S 0.04 201659" s at 5T 2.907721e-03 213998 s at 5S 0.04 226963"_at 5T 2.995428e-03 212536 at 5S 0.04 201008" s at 5T 3.00599e-03 202135 s at 5S 0.04 201694" s at 5T 3.051616e-03 201597 at 5S 0.04 214590"_s_at 5T 3.058209e-03 212926 at 5S 0.04 224595""at 5T 3.17682e-03 209799 at 5S 0.04 222088^ s at 5T 3.289886e-03 1558747_at 5S 0.04 204440" at 5T 3.355815e-03 223306 at 5S 0.04 226119" at 5T 3.751211e-03 201866 s_at 5S 0.04 227195"~at 5T 3.81338e-03 202599 s_at 5T 5.37e-05 222516""at 5T 3.876379e-03 226896 at 5T 6.21e-05 223049" at 5T 4.010906e-03 222881 at 5T 6.52e-05 228087" at 5T 4.025039e-03 225814 at 5T 9.39e-05 204286" s at 5T 4.101158e-03 208960 s at 5T 1.31e-04 224596] at 5T 4.105954e-03 223186 at 5T 1.43e-04 202466""at 5T 4.174077e-03 226080 at 5T 1.45e-04 201739""at 5T 4.271043e-03 223217 s at 5T 1.61e-04 204190""at 5T 4.393204e-03 220940 at 5T 1.79e-04 224643""at 5T 4.645467e-03 217956 s at 5T 2.01e-04 201010" s at 5T 4.751511e-03 209795 at 5T 2.9e-04 222985""at 5T 4.824169e-03 225005 at 5T 3.01e-04 226001""at 5T 5.024678e-03 202241 at 5T 3.33e-04 224833" at 5T 5.037106e-03 210742 at 5T 3.46e-04 200948""at 5T 5.07136e-03 219312 s at 5T 3.83e-04 223007" s at 5T 5.096519e-03 226168 at 5T 4.36e-04 35150 at 5T 5.350011e-03 202499 s at 5T 4.59e-04 238356 _at 5T 5.43878e-03 222633 at 5T 5.7e-04 203471" s at 5T 5.468657e-03 200020 at 5T 6.38e-04 213524"" s at 5T 5.617408e-03 224800 at 5T 6.48e-04 219041" s at 5T 5.752768e-03 208961 s at 5T 6.85e-04 201464" x at 5T 5.802582e-03 222412 s at 5T 7.61e-04 213551" x at 5T 5.815369e-03 204161 s at 5T 8.32e-04 218530" jat 5T 5.92386e-03 218793 s at 5T 8.33e-04 205292" s at 5T 6.130072e-03 203573 s at 5T 8.51e-04 221819""at 5T 6.151346e-03 222745 s~at 5T 8.8e-04 218532" s at 5T 6.353362e-03 210889 s at 5T 1.064448e-03 219342""at 5T 6.71928e-03 AFFX-HUMGAPDH/ 203504" s at 5T 6.87941e-03 M33197 5T 1.194136e-03 225772""s_at 5T 6.89832e-03 235568 at 5T 1.288626e-03 201566" x at 5T 6.922124e-03 228512 at 5T 1.325361e-03 204159""at 5T 6.969213e-03 225819 at 5T 1.353797e-03 210556""at 5T 7.036023e-03 200823 x at 5T 1.379915e-03 204405"x at 5T 7.383409e-03 218401 s at 5T 1.471538e-03 37028 at 5T 7.402498e-03 209625 at 5T 1.678108e-03 202675 _at 5T 7.677822e-03 209457 at 5T 1.687095e-03 226554 -at 5T 7.694483e-03 238077 at 5T 1.761137e-03 227784"" s_at 5T 7.715122e-03 202960 s at 5T 1.803103e-03 224585"x at 5T 7.724745e-03 227856 at 5T 1.860785e-03 224535" s at 5T 7.793172e-03 202124 s at 5T 2.039726e-03 204881" s at 5T 7.796537e-03 "8"d'9165e"-03 200035_at 5T 0.01 220937_s_at 5T 226354_at 5T 0.01 202924_s_at 5T 7 . 833327e-03 206472_s_at 5T 0.01 225789_at 5T 7 . 920387e-03 201466_s__at 5T 0.01 224511_s_at 5T 8 . 21506e-03 202934_at 5T 0.01 224639_at 5T 8 . 25684e-03 222495_at 5T 0.01 210070_s_at 5T 8 . 419941e-03 225463_x_at 5T 0.01 202014_at 5T 8 . 580444e-03 226386_at 5T 0.01 222631_at 5T 8 . 588068e-03 1e-03 223320_s_at 5T 0.01 205114_s_at 5T 8 . 6106 53146e-03 223740_at 5T 0.01 201009_s_at 5T 8 . 6 . 009632e-03 217106_x_at 5T 0.01 210180_s_at 5T 9 9 . 11427e-03 206959_s_at 5T 0.01 202497_x_at 5T 228958_at 5T 0.01 204691_x_at 5T 9 . 178052e-03 215718_s_at 5T 0.01 228234_at 5T 9 . 55312e-03 208676_s_at 5T 0.01 204319_s_at 5T 9 . 56191e-03 224768_at 5T 0.01 225945_at 5T 9 . 682531e-03 03 204669_s_at 5T 0.01 224513_s_at 5T 9 . 739683e- 74981e-03 225976_at 5T 0.01 225132_at 5T 9 . 7 . 815159e-03 222021_x_at 5T 0.01 204030_s_at 5T 9 218586_at 5T 0.01 227065_at 5T 9 . 992174 e-03 226452_at 5T 0.01 201626_at 5T 0 . 01 209305_s_at 5T 0.01 218771_at 5T 0 . 01 224159__x_at 5T 0.01 227796_at 5T 0 . 01 202325_s_at 5T 0.01 201319_at 5T 0 . 01 226425_at 5T 0.01 AFFX-HUMGAPDH/ 226521_s_at 5T 0.01 M33197 5T 0.01 216997_x_at 5T 0.01 210982_s_at 5T 0.01 212495_at 5T 0.01 219892_at 5T 0.01 223294_at 5T 0.01 220236_at 5T 0.01 223081_at 5T 0.01 233080_s_at 5T 0.01 235048_at 5T 0.01 210190_at 5T 0.01 201502_s_at 5T 0.01 224764_at 5T 0.01 225049_at 5T 0.01 222753_s_at 5T 0.01 225187~_at 5T 0.01 207922_s_at 5T 0.01 244710_at 5T 0.01 224504_s_at 5T 0.01 203737_s_at 5T 0.01 227442_at 5T 0.01 203385_at 5T 0.01 225849_s_at 5T 0.01 204103_at 5T 0.01 235113_at 5T 0.01 203926_x_at 5T 0.01 203910_at 5T 0.01 218813_s_at 5T 0.01 226987_at 5T 0.01 202957_at 5T 0.01 208448_x_at 5T 0.01 222436_s_at 5T 0.01 209511_at 5T 0.01 223167_s_at 5T 0.01 226053_at 5T 0.01 212190_at 5T 0.01 201565_s_at 5T 0.01 209827_s_at 5T 0.01 217780_at 5T 0.01 223129__x_at 5T 0.02 222661_at 5T 0.01 207559_s_at 5T 0.02 223208_at 5T 0.01 209019_s_at 5T 0.02 200027_at 5T 0.01 226711_at 5T 0.02 224602_at 5T 0.01 235056_at 5T 0.02 223531_x_at 5T 0.01 210527_x_at 5T 0.02 201346_at 5T 0.01 225648_at 5T 0.02 225956_at 5T 0.01 212078_s_at 5T 0.02 225984_at 5T 0.01 207075_at 5T 0.02 239146_at 5T 0.01 225091_at 5T 0.02 206157_at 5T 0.01 234978_at 5T 0.02 225957_at 5T 0.01 223003__at 5T 0.02 233970_s_at 5T 0.01 217826_s_at 5T 0.02 222464_s_at 5T 0.01 223441__at 5T 0.02 226529_at 5T 0.01 213418_at 5T 0.02 213957_s_at 5T 0.01 228446_at 5T 0.02 223993_s_at 5T 0.01 218737_at 5T 0.02 202092_s_at 5T 0.01 218066_at 5T 0.02 233575_s_at 5T 0.01 219368__at 5T 0.02 202436 s at 5T 0.01 222875_at 5T 0.03 235020_at 5T 0 . 02 214455_at 5T 0.03 224796__at 5T 0.02 216591_s_at 5T 0.02 38158_at 5T 0.03 202925_s_at 5T 0.02 217792_at 5T 0.03 205004_at 5T 0.03 200913_at 5T 0.02 219066_at 5T 0.02 204805_s_at 5T 0.03 202860_at 5T 0.02 224902_at 5T 0.03 224680_at 5T 0.02 37986_at 5T 0.03 207113_s_at 5T 0.02 37145_at 5T 0.03 224345_x_at 5T 0.02 212860_at 5T 0.03 224628_at 5T 0.02 225309_at 5T 0.03 204278_s_at 5T 0.03 224578__at 5T 0.02 225919_s_at 5T 0.03 209892_at 5T 0.02 231023_at 5T 0.03 202643_s_at 5T 0.02 5T 0.03 204192_at 5T 0.02 207728_at 228106_at 5T 0.03 204269__at 5T 0.02 222768_s_at 5T 0.03 226753_at 5T 0.02 225850_at 5T 0.02 202072_at 5T 0.03 205667_at 5T 0.03 208763_s_at 5T 0.02 225252_at 5T 0.02 227844_at 5T 0.03 204172_at 5T 0.02 225799_at 5T 0.03 218319_at 5T 0.02 225581_s_at 5T 0.03 203927_at 5T 0.02 212730_at 5T 0.03 207535_s_at 5T 0.02 209595_at 5T 0.03 225997_at 5T 0.02 225904_at 5T 0.03 218194_at 5T 0.03 201670_s_at 5T 0.02 218522_s_at 5T 0.03 228009_x_at 5T 0.02 218640_s_at 5T 0.03 213816_s_at 5T 0.02 223158_s_at 5T 0.03 223176_at 5T 0.02 209766_at 5T 0.02 217764_s_at 5T 0.03 212227_χ_at 5T 0.02 228029_at 5T 0.03 227960_s_"at 5T 0.02 223168_at 5T 0.03 210779 _x_at 5T 0.03 225392_at 5T 0.02 232009_at 5T 0.02 235067"_at 5T 0.03 224840_at 5T 0.02 220123_at 5T 0.03 200030_s_at 5T 0.02 209137_s_at 5T 0.03 228061_at 5T 0.02 205096_at 5T 0.03 206792_x_at 5T 0.03 200053_at 5T 0.02 221192_x_at 5T 0.03 228859_at 5T 0.02 202617_s_at 5T 0.03 201328_at 5T 0.02 223276_at 5T 0.02 201577_at 5T 0.03 222409_at 5T 0.02 201264_at 5T 0.03 220942_x_at 5T 0.02 220306__at 5T 0.03 222707_s_at 5T 0.02 209808_x_at 5T 0.03 208795_s_at 5T 0.03 229553_at 5T 0.02 221558_s_at 5T 0.03 224904_at 5T 0.02 223835_x_at 5T 0.02 204917_s_at 5T 0.03 230297_x_at 5T 0.03 229632_s_at 5T 0.02 228454_at 5T 0.02 ...212099_at 5T 0.04 200000_s__at 5T 0.04 218073_s_at 5T 0.02 225629_s_at 5T 0.04 227447_at 5T 0.02 216945_x_at 5T 0.04 215493_x_at 5T 0.02 207445_s__at 5T 0.04 203176_s_at 5T 0.02 202945_at 5T 0.02 219439_at 5T 0.04 219371_s_at 5T 0.02 222602_at 5T 0.04 235327_x_at 5T 0.04 207628_s_at 5T 0.02 201924_at 5T 0.02 235258_at 5T 0.04 209288_s_at 5T 0.04 222989_s_at 5T 0.03 222982_x_at 5T 0.04 204912_at 5T 0.03 223559_s_at 5T 0.04 226994_at 5T 0.03 213129_s_at 5T 0.03 225232_at 5T 0.04 201669_s_at 5T 0.03 212737_at 5T 0.04 226012_at 5T 0.03 225053_at 5T 0.04 223216 x at 5T 0.03 230707 at 5T 0.04 218777 at 5T 0.04 242352 at 5U 2.580149e-03 221481" x at 5T 0.04 212636"_at 5U 2.596391e-03 215346"_at 5T 0.04 224892"_at 5U 2.710688e-03 222984"_at 5T 0.04 224374" s at 5U 2.73506e-03 210847" x at 5T 0.04 200049"_at 5U 2.76785e-03 209286""at 5T 0.04 220760" x at 5U 2.780704e-03 217756" x at 5T 0.04 206181"_at 5U 2.79716e-03 224413" s at 5T 0.04 210458" s_at 5U 2.850227e-03 202340" x at 5T 0.04 217864" s_at 5U 2.857427e-03 219052""at 5T 0.04 201138" s at 5U 2.924937e-03 222646" s at 5T 0.04 201008" s at 5U 3.00599e-03 227322" s at 5T 0.04 224595"_at 5U 3.17682e-03 202644" s_at 5T 0.04 222088" s__at 5U 3.289886e-03 224622"_at 5T 0.04 218627"_at 5U 3.806343e-03 205255" x at 5T 0.04 227195"_at 5U 3.81338e-03 203049" s at 5U 4.13e-05 219232" s_at 5U 3.926731e-03 202599" s at 5U 5.37e-05 201108" s at 5U 3.941696e-03 226896""at 5U 6.21e-05 223049""at 5U 4.010906e-03 222881"_at 5U 6.52e-05 204958"_at 5U 4.094795e-03 208960" s_at 5U 1.31e-04 204286" s_at 5U 4.101158e-03 223186" at 5U 1.43e-04 202466"_at 5U 4.174077e-03 226080"_at 5U 1.45e-04 216305" s_at 5U 4.287734e-03 223217" s at 5U 1.61e-04 204190" at 5U 4.393204e-03 220940""at 5ϋ 1.79e-04 203502"_at 5U 4.700955e-03 217956" s at 5U 2.01e-04 201010" s at 5U 4.751511e-03 209795" at 5U 2.9e-04 222985"_at 5U 4.824169e-03 225005" at 5U 3.01e-04 204333" s at 5U 4.889466e-03 202241""at 5U 3.33e-04 206618"_at 5U 4.951513e-03 210742"_at 5U 3.46e-04 209006" s at 5U 4 . 961649e-03 219312" s at 5U 3.83e-04 226001" at 5U 5.024678e-03 207667" s at 5U 4.19e-04 202458" at 5U 5.028416e-03 205171""at 5U 5.16e-04 227548" at 5U 5.362002e-03 200020" at 5U 6.38e-04 AFFX-HUMGAPDH/ 224800"_at 5U 6.48e-04 M33197 5U 5.363142e-03 208961" s at 5U 6.85e-04 203471 s at 5U 5.468657e-03 222412" s at 5U 7.61e-04 225507"_at 5U 5.477105e-03 204161" s at 5ϋ 8.32e-04 222199" s at 5U 5.558808e-03 218793" s_at 5U 8.33e-04 240413" at 5U 5.71187e-03 201456" s at 5U 1.01578e-03 203481"_at 5U 5.757702e-03 2lO889" s at 5U 1.064448e-03 201464" x at 5U 5.802582e-03 AFFX-HUMGAPDH/ 218530" at 5U 5.92386e-03 M33197 5ϋ 1.194136e-03 221819"_at 5U 6.151346e-03 226952 _at 5U 1.254263e-03 200065" s at 5U 6.218724e-03 218313" s at 5U 1.268675e-03 218532" s at 5U 6.353362e-03 235568"_at 5U 1.288626e-03 65493 at 5U 6.432789e-03 211727" s at 5U 1.319217e-03 203504 s at 5U 6.87941e-03 228512"~at 5U 1.325361e-03 225772" s at 5U 6.89832e-03 242814""at 5U 1.468964e-03 201566" x at 5U 6.922124e-03 209625""at 5U 1.678108e-03 204159" at 5U 6.969213e-03 213038" at 5U 1.679953e-03 210556" at 5U 7.036023e-03 209457""at 5U 1.687095e-03 207515" s at 5U 7.201661e-03 213353"_at 5U 1.71586e-03 225487" at 5U 7.412761e-03 202124" s at 5U 2.039726e-03 235409""at 5U 7.468945e-03 203769" s at 5U 2.050299e-03 212076"_at 5U 7.529746e-03 205005" s_at 5U 2.071487e-03 208797" s at 5U 7.610512e-03 201273"_s_at 5U 2.109166e-03 224585" x at 5U 7.724745e-03 201110" s at 5ϋ 2.149469e~03 211675" s_at 5U 7.767158e-03 223244" s at 5U 2.240388e-03 204881" s_at 5U 7.796537e-03 226639 at 5U 2.256617e-03 220937" s at 5U 7.809165e-03 36564 at 5U 2.286795e-03 203513" at 5U 7.815482e-03 218501 _at 5U 2.403645e-03 225789 "at 5U 7.920387e-03 220974"x at 5U 2.432416e~03 224182 x at 5U 8.064449e-03 206323"x at 5U 2.489127e-03 225717""at 5U 8.109819e-03 210017 "at 5U 2.548474e-03 200761" s at 5U 8.206545e-03 205087 at 5U 8.2'09298e-UJ 213957_s_at 5 224511"_s_at 5U 8.21506e-03 223993_s_at k*o 0.01 50 0.01 209724" s at 5U 8.362353e-03 202092_s_at 50 0.01 202247" s_at 5U 8.384605e-03 202436_s_at 50 0.01 210070" s_at 5U 8.419941e-03 200035_at 50 0.01 215332" s at 5U 8.480133e-03 212681_at 50 0.01 226968"_at 5U 8.532941e-03 205988_at 50 0.01 205114" s at 5U 8.61061e-03 226354_at 50 0.01 201009" s at 5U 8.653146e-03 202742_s_at 50 0.01 201109' s at 5U 8.761842e-03 212124_at 50 0.01 220446"~s at 5U 8.786318e-03 202519_at 50 0.01 218911" at 5U 8.79051e-03 210264_at 50 0.01 205855"_at 5U 8.894484e-03 201466_s_at 50 0.01 201695" s at 5U 8.921441e-03 202934_at 50 0.01 219095"_at 5ϋ 8.978441e-03 204132_s_at 50 0.01 206536" s_at 5U 9.006068e-03 203865_s_at 50 0.01 210180" s at 5U 9.009632e-03 208676_s_at 50 0.01 202497" x at 5U 9.11427e-03 224768_at 50 0.01 201631" s at 5U 9.461623e-03 204669_s_at 50 0.01 228234"_at 5U 9.55312e-03 225976_at 50 0.01 204319" s at 5U 9.56191e-03 225738_at 50 0.01 201851" at 5U 0.01 218586_at 50 0.01 211725" s at 50 0.01 220613_s_at 50 0.01 201626""at 5U 0.01 226452_at 50 0.01 206448"_at 5ϋ 0.01 206710_s_at 50 0.01 209334" s at 5U 0.01 217819_at 50 0.01 224209" s at 5U 0.01 202325_s_at 50 0.01 209702" at 5U 0.01 218067_s_at 50 0.01 201319"_at 5U 0.01 225585_at 50 0.01 206061" s at 5U 0.01 212914_at 50 0.01 217810^x at 5U 0.01 212840_at 50 0.01 AFFX-HUMGAPDH/ 204038_s_at 50 0.01 M33197 5ϋ 0.01 202239_at 50 0.01 202912 at 5U 0.01 203356_at 50 0.01 219892""at 50 0.01 221229_s_at 50 0.01 220236"_at 50 0.01 222209_s_at 50 0.01 204254" s at 50 0.01 223081_at 50 0.01 205006" s_at 50 0.01 200791_s_at 50 0.01 218019" s at 50 0.01 225187_at 50 0.01 224764" at 50 0.01 38241_at 50 0.01 221485" at 50 0.01 203594_at 50 0.01 213212" x at 50 0.01 203737_s_at 50 0.01 207922" s at 50 0.01 203362_s_at 50 0.01 224504" s at 50 0.01 203233_at 50 0.01 226098"_at 50 0.01 213677_s_at 50 0.01 225849" s at 50 0.01 203060_s_at 50 0.01 235113"~at 50 0.01 222436_s_at 50 0.01 226987""at 50 0.01 236341_at 50 0.01 202118" s at 50 0.01 220195_at 50 0.01 208448" x at 50 0.01 202807_s_at 50 0.01 200644"_at 50 0.01 208670_s_at 50 0.01 209511""at 50 0.01 210038_at 50 0.01 207700" s at 50 0.01 212190_at 50 0.01 209258 s_at 50 0.01 209827_s_at 50 0.01 226053" at 50 0.01 202613_at 50 0.02 221830""at 50 0.01 209019_s_at 50 0.02 201753" s_at 50 0.01 203051_at 50 0.02 201565" s at 50 0.01 235056_at 50 0.02 225672 "at 50 0.01 210527_x_at 50 0.02 223208""at 50 0.01 225648_at 50 0.02 228788""at 50 0.01 212078_s_at 50 0.02 210114 [at 50 0.01 225091_at 50 0.02 233970 s_at 50 0.01 209984_at 50 0.02 218552 "at 50 0.01 234978 at 50 0.02 212561_at 5U 0T02 222409_at 50 0.02 207131 x at 5U 0.02 219356_s_at 50 0.02 213418'_at 5ϋ 0.02 218310_at 50 0.02 213293 s at 5U 0.02 220942_x_at 50 0.02 205349 at 5U 0.02 222707_s_at 50 0.02 223620'_at 5U 0.02 200896_x_at 50 0.02 219368' at 5U 0.02 226026_at 50 0.02 219496__at 5U 0.02 201850_at 50 0.02 202379 s at 5U 0.02 224904_at 50 0.02 235020'_at 5U 0.02 209028_s_at 50 0.02 224796_at 5ϋ 0.02 212638__s_at 50 0.02 206297" at 5U 0.02 210555_s_at 50 0.02 222557'_at 5U 0.02 205338_s_at 50 0.02 219304'_s_at 5U 0.02 206804_at 50 0.02 202860"_at 5U 0.02 203189_s_at 50 0.02 207606 s at 5U 0.02 203176_s_at 50 0.02 204638"_at 5U 0.02 202460_s_at 50 0.02 216862"_s_at 5U 0.02 202945_at 50 0.02 212136"_at 5U 0.02 219371_s_at 50 0.02 209770' at 50 0.02 211240_x_at 50 0.02 230972"_at 50 0.02 217301_x_at 50 0.02 203297" s at 50 0.02 44822_s__at 50 0.02 213389"_at 50 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223176 "at 50 0.02 37145_at 50 0.03 242916 "_at 50 0.02 239283_at 50 0.03 212227"x at 50 0.02 212860_at 50 0.03 218393 _s_at 50 0.02 208845__at 50 0.03 220558 _x_at 50 0.02 201034_at 50 0.03 224250 s at 50 0.02 225309__at 50 0.03 202741 "at 50 0.02 213446_s_at 50 0.03 201490 s at 50 0.02 212331_at 50 0.03 232009 "at 50 0.02 202412_s_at 50 0.03 217886 "at 50 0.02 204278_s_at 50 0.03 203221 at 50 0.02 202258_s_at 50 0.03 200030 s at 50 0.02 222768_s_at 50 0.03 212332 at 50 0.02 227844_at 50 0.03 208767 s at 50 0.02 209416_s_at 50 0.03 200053 at 50 0.02 225799_at 50 0.03 223276 at 50 0.02 204401 at 50 0.03 209595 at 5U 0.03 209761_s_at 50 0.04 209732" at 5U 0.03 226730" s at 50 0.04 217122" s at 5ϋ 0.03 201389" at 50 0.04 203338"_at 5U 0.03 203827"jat 50 0.04 202435" s at 5U 0.03 204970" s at 50 0.04 218628" at 5U 0.03 231779" at 50 0.04 220521" s at 5U 0.03 203456"_at 50 0.04 218522" s at 5U 0.03 205061"_s_at 50 0.04 225630"_at 5U 0.03 200078"~s_at 5U 0.04 218640" s at 5U 0.03 224836""at 50 0.04 219635""at 5U 0.03 210288"_at 50 0.04 219151" s at 5U 0.03 237107"_at 50 0.04 223158" s at 5U 0.03 212484"_at 50 0.04 204420""at 5U 0.03 200779"_at 50 0.04 217764" s at 5U 0.03 214696""at 5U 0.04 212318""at 5U 0.03 209099" x at 50 0.04 204065""at 5U 0.03 204873"_at 50 0.04 212055""at 5U 0.03 219248"_at 50 0.04 205967""at 5ϋ 0.03 219052" at 50 0.04 219507""at 5U 0.03 200072" s at 50 0.04 224480"_s_at 5U 0.03 209604" s at 5U 0.04 206792" x_at 5U 0.03 204255" s at 50 0.04 221192" x_at 5U 0.03 217299" s at 50 0.04 222258"_s_at 5U 0.03 219110" at 50 0.04 212629" s at 5U 0.03 213191"_at 50 0.04 202577" s_at 5U 0.03 204710" s at 50 0.04 209657" s at 5U 0.03 200630" x at 5V 1.79e-06 205294"_at 5ϋ 0.03 201202"_at 5V 4.76e-06 203542" s at 5U 0.03 202295" s at 5V 4.99e-06 203048" s at 5U 0.03 210314" x at 5V 6.97e-06 220306"_at 5U 0.03 201002" s at 5V 7.04e-06 221670" s at 5U 0.03 205202"_at 5V 8.56e-06 221532" s at 5U 0.03 208705" s_at 5V 1.3e-05 2.04131" s_at 5U 0.03 212643"_at 5V 1.43e-05 202378" s at 5U 0.03 217414" x at 5V 1.74e-05 202437" s at 5U 0.03 217910" x at 5V 2.89e-05 218494" s at 5U 0.04 208248" x at 5V 2.91e-05 203182" s at 5U 0.04 211696" x_at 5V 3.01e-05 202794"_at 5U 0.04 201586" s at 5V 3.05e-05 224395" s at 5U 0.04 217949""s at 5V 3.52e-05 205668" at 5U 0.04 202596""at 5V 3.87e-05 203152"_at 5U 0.04 204417""at 5V 3.94e-05 222747" s at 5U 0.04 221419"~s at 5V 3.98e-05 201627" s at 5U 0.04 200709"_at 5V 4.58e-05 202541"_at 5U 0.04 201891"~s_at 5V 4.66e-05 200759" x at 5U 0.04 211745" x at 5V 5.14e-05 205036" at 5U 0.04 205669""at 5V 5.81e-05 226099""at 5U 0.04 219444""at 5V 6.21e-05 214561""at 5U 0.04 208909""at 5V 6.24e-05 201136""at 50 0.04 214414" x_at 5V 6.37e-05 207687""at 50 0.04 203430"~_at 5V 6.59e-05 224790""at 50 0.04 204665""at 5V 6.71e-05 223887""at 50 0.04 204351""at 5V 6.86e-05 207121" s at 50 0.04 201128" s at 5V 7.09e-05 223767"_at 5U 0.04 204342""at 5V 7.86e-05 201625"_s_at 5U 0.04 201119" s at 5V 7.86e-05 223482" at 50 0.04 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3.22e-04 209771_x_at 5V 1.25e-04 201090_x_at 5V 3.23e-04 208921_s_at 5V 1.25e-04 208702_x_at 5V 3.26e-04 212251_at 5V 1.26e-04 218648_at 5V 3.27e-04 209116_x_at 5V 1.31e-04 208736_at 5V 3.41e-04 201020_at 5V 1.33e-04 200735_x_at 5V 3.45e-04 202162_s__at 5V 1.34e-04 211070_x_at 5V 3.48e-04 203721_s_at 5V 1.35e-04 202917_s_ar 5V 3.48e-04 204018_x_at 5V 1.37e-04 205786_s_at 5V 3.52e-04 201426_s_at 5V 1.39e-04 221873_at 5V 3.57e-04 216379_x_at 5V 1.43e-04 212250_at 5V 3.6e-04 208689_s_at 5V 1.45e-04 212377_s_at 5V 3.68e-04 211571_s_at 5V 1.47e-04 220980_s_at 5V 3.69e-04 202951_at 5V 1.49e-04 210996_s_at 5V 3.69e-04 208642_s_at 5V 1.52e-04 201583_s_at 5V 3.7e-04 208643 _s_at 5V 1.52e-04 200918_s_at 5V 3.72e-04 212543]_at 5V 1.56e-04 207157_s__at 5V 3.76e-04 201894_s_at 5V 1.57e-04 1.64e-04 200743_s"_at 5V 3.78e-04 2C0030_s_at 5V 210638_s_at 5V 3.79e-04 211609_x_at 5V 1.74e-04 205690_s_at 5V 3.81e-04 209369_at 5V 1.8e-04 203922_s_at 5V 3.82e-04 211699_x_at 5V 1.81e-04 221249_s_at 5V 3.82e-04 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Figure imgf001198_0001
5.13e-04 202442_at 5V 7.39e-04 202121 s at 5V 5.15e-04 201553 s at 5V 7.39e-04 203508_at 5V 5.18e-04 211787 s_at 5V 7.4e-04 200693_at 5V 5.25e-04 201351 s at 5V 7.41e-04 213453 x at 5V 5.3e-04 201470_at 5V 7.47e-04 200639 s at 5V 5.39e-04 208821_at 5V 7.56e-04 208641 s at 5V 5.41e-04 218506 x at 5V 7.59e-04 216988 s at 5V 5.45e-04 202859 x at 5V 7.66e-04 200048_s at 5V 5.46e-04 214430_at 5V 7.79e-O4 208739 x at 5V 5.5e-04 217931_at 5V 7.81e-04 200006_at 5V 5.54e-04 211760 s at 5V 7.84e-04 200864 s at 5V 5.57e-04 209499_x_at 5V 7.92e-04 217978 s at 5V 5.61e-04 201954_at 5V 7.99e-04 201105_at 5V 5.62e-04 217743 s at 5V 8.0e-04 213735 s at 5V 5.63e-04 218034_at 5V 8.01e-04 200650 s at 5V 5.64e-04 221339_at 5V 8.01e-04 213526 s at 5V 5.65e-04 211678 s at 5V 8.03e-04 200660_at 5V 5.71e-04 219014_at 5V 8.04e-04 212168 at 5V 5.75e-04 208628 s at 5V 8.14e-04 200804 at 5V 5.77e-04 200007_at 5V 8.32e-04 206111 at 5V 5.8e-04 215313 x at 5V 8.36e-04 202298_at 5V 5.82e-04 201318 s at 5V 8.36e-04 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Figure imgf001203_0001
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0.04 222975 s at 7G 1.22e-04 201730_s_at 7F 0.04 209142 s_at 7G 1.37e-04 202303__x_at 7F 0.04 210361 s at 7G 1.38e-04 228391_at 7F 0.04 203330 s at 7G 1.79e-04 231579_s_at 7F 0.04 208854 s at 7G 2.1e-04 217743_s_at 7F 0.04 225956 7G 2.11e-04 212988_x_at 7F 0.04 204620 s at 7G 2.2e-04 219081_at 7F 0.04 213046 at 7G 2.36e-04 1553528_a_at 7F 0.04 235388_at 7G 2.6e-04 219284_at 7F 0.04 226711 at 7G 2.64e-04 224637_at 7F 0.04 1567458 s at 7G 2.82e-04 212116_at 7F 0.04 222633_at 7G 2.98e-04 206244_at 7F 0.04 205539_at 7G 3.36e-04 208974_x_at 7F 0.04 227716_at 7G 3.62e-04 212552_at 7F 0.04 218092 s at 7G 3.64e-04 204181_s_at 7F 0.04 208055 s at 7G 3.66e-04 203012_x__at 7F 0.04 222566_at 7G 3.79e-04 209185_s_at 7F 0.04 217903_at 7G 3.84e-04 201238_s_at 7F 0.04 218304 s at 7G 4.7e-04 212700_x_at 7F 0.04 204156_at 7G 4.99e-04 227219__x_at 7F 0.04 223031 s at 7G 5.01e-04 209640_at 7F 0.04 201715 s at 7G 5.02e-04 33778_at 7F 0.04 202492_at 7G 5.05e-04 224930_x__at 7F 0.04 202156 s at 7G 5.11e-04 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7G 1.946051e-03 222530" s at 7G 8.44e-04 238063 at 7G 1.963185e-03 211771" s at 7G 8.84e-04 212262 at 7G 1.981321e-03 207700" s at 7G 8.9e-04 224600 at 7G 1.994706e-03 212263"~at 7G 9.1e-04 202621 at 7G 2.004203e-03 223006" s_at 7G 9.27e-04 225872 at 7G 2.005224e-03 200011" s at 7G 9.31e-04 227143 s at 7G 2.019375e-03 210649" s at 7G 9.33e-04 238587 at 7G 2.028167e-03 211505" s at 7G 9.38e-04 209615 s at 7G 2.082712e-03 218725" at 7G 9.42e-04 210176 at 7G 2.099732e-03 229723"_at 7G 9.43e-04 217864 s at 7G 2.125982e-03 202215" s_at 7G 9.58e-04 226208 at 7G 2.131266e-03 208761" s at 7G 9.59e-04 230566 at 7G 2.150993e-03 203392" s_at 7G 9.7e-04 1553155 x at 7G 2.160039e-03 210266" s at 7G 9.77e-04 222473 s at 7G 2.169914e-03 209748"~at 7G 9.78e-04 218387 s_at 7G 2.220917e-03 219878" s at 7G 1.0e-03 228408 s at 7G 2.256531e-03 241620"_at 7G 1.033318e-03 234981 x at 7G 2.26723e-03 217762" s at 7G 1.04252e-03 225099 at 7G 2.273557e-03 208671" at 7G 1.048413e-03 201078 at 7G 2.279169e-03 209205" s at 7G 1.064756e-03 1562619_at 7G 2.322361e-03 235461"_at 7G 1.07118e-03 213351 s at 7G 2.337437e-03 203007" x at 7G 1.078962e-03 200774 at 7G 2.343916e-03 202443" x at 7G 1.083961e-03 211960 s at 7G 2.345747e-03 202626" s_at 7G 1.096887e-03 218035 s at 7G 2.349549e-03 224866" at 7G 1.099624e-03 225888 at 7G 2.351372e-03 203893 "at 7G 1.10465e-03 212553 at 7G 2.399652e-03 201074""at 7G 1.119561e-03 206500 s at 7G 2.435164e-03 223439"_at 7G 1.130739e-03 214119 s at 7G 2.441004e-03 204286 s at 7G 1.165535e-03 1553575_at 7G 2.494476e-03 222562" s_at 7G 1.18629e-03 1555948_s_at 7G 2.502228e-03 207002" s_at 7G 1.216709e-03 216550 x at 7G 2.523184e-03 205836" s at 7G 1.248079e-03 202247 s at 7G 2.527421e-03 210886 x_at 7G 1.281116e-03 206053 at 7G 2.533881e-03 226334" s_at 7G 1.292351e-03 218298 s at 7G 2.53798e-03 200821"_at 7G 1.306155e-03 209486 at 7G 2.553487e-03 225859 at 7G 1.322266e-03 202538 s at 7G 2.57107e-03 1552329 at 7G 1.322873e-03 211750 x at 7G 2.572612e-03 201619 _at 7G 1.37214e-03 233080 s at 7G 2.576251e-03 201997" s at 7G 1.380516e-03 224692 at 7G 2.602365e-03 209342 s at 7G 1.384057e-03 203117 s at 7G 2.606842e-03 217717 s at 7G 1.395713e-03 207979 s_at 7G 2.617386e-03 209896" s at 7G 1.406964e-03 209251 x at 7G 2.636512e-03 238122""at 7G 1.433665e-03 224665 at 7G 2.668737e-03 202187" s at 7G 1.440547e-03 218303 x at 7G 2.684492e-03 218166 "s at 7G 1.489965e-03 202662 s at 7G 2.690322e-03 • Ii a ■ i 2'Ϊ8fCfl 's"' at™*"1& "?.T£^8*6^e"-03 212636 at 7G 3.868233e-03 209479 at 7G 2.699212e-03 204220_at 7G 3.892751e-03 1553594 a at 7G 2.700196e-03 204849 at 7G 3.910303e-03 200851 s at 7G 2.720034e-03 1558136 s at 7G 3.915475e-03 1552734_at 7G 2.737748e-03 206488 s_at 7G 3.931093e-03 202124 s at 7G 2.749338e-03 214988 s at 7G 3.944372e-03 224560 at 7G 2.763595e-03 220248 x at 7G 3.973015e-03 224676 at 7G 2.772513e-03 222728 s at 7G 3.988415e-03 210740 s at 7G 2.791039e-03 218422 s at 7G 3.992025e-03 228099 at 7G 2.801191e-03 202026 at 7G 4.000259e-03 207966 s at 7G 2.818099e-03 212919 at 7G 4.018331e-03 1555780 a at 7G 2.82043e-03 212397 at 7G 4.042751e-03 219234 x at 7G 2.84707e-03 203690 at 7G 4.054399e-03 225334 at 7G 2.848614e-03 37986 at 7G 4.061278e-03 223014 at 7G 2.854823e-03 202092 s_at 7G 4.103606e-03 202033 s at 7G 2.859225e-03 202277 at 7G 4.172589e-03 235653 s at 7G 2.863675e-03 201898 s_at 7G 4.18978e-03 213352 at 7G 2.864835e-03 209515 s at 7G 4.193598e-03 225852 at 7G 2.875419e-03 200864 s at 7G 4.208059e-03 208990 s at 7G 2.899081e-03 226763 at 7G 4.22235e-03 223075 s at 7G 2.930571e-03 200684 s_at 7G 4 . 237933e-03 204527 at 7G 2.930719e-03 224730 at 7G 4.251208e-03 203620_s at 7G 2.937521e-03 201075 s at 7G 4.260801e-03 214048 at 7G 2.96066e-03 208825 x_at 7G 4.287251e-03 222652 s at 7G 2.971575e-03 225351 at 7G 4 . 294671e-03 201901 s_at 7G 2.977288e-03 223380 s at 7G 4.342403e-03 210840 s at 7G 2.97952e-03 220287 at 7G 4.350997e-03 215646 s at 7G 2.981381e-03 203544 s at 7G 4.355691e-03 203669 s at 7G 2.983935e-03 218683 at 7G 4.397762e-03 201408 at 7G 2.997337e-03 200976 s at 7G 4.411401e-03 222408 s at 7G 3.061643e-03 201104 x at 7G 4.425808e-03 215933 s_at 7G 3.089145e-03 244268 x at 7G 4.430358e-03 226786 at 7G 3.133372e-03 226239 at 7G 4.434928e-03 200940 s at 7G 3.145501e-03 218156 s at 7G 4.448925e-03 209015 s_at 7G 3.172354e-03 33322 i_at 7G 4.473218e-03 201628 s at 7G 3.177497e-03 224754 at 7G 4.475312e-03 220173 'at 7G 3.196469e-03 223566 s_at 7G 4.480779e-03 202838 at 7G 3.204864e-03 222574 s at 7G 4.486754e-03 222697 s_at 7G 3.21517e-03 210695 s at 7G 4.523609e-03 220746 s_at 7G 3.215912e-03 204045 at 7G 4.538201e-03 201908 at 7G 3.217173e-03 33323 r_at 7G 4.541969e-03 227740 at 7G 3.225991e-03 242706 s at 7G 4.571985e-03 203105 s at 7G 3.256672e-03 218583 s_at 7G 4.588818e-03 212616 at 7G 3.267421e-03 214743 at 7G 4.607314e-03 238860 at 7G 3.280843e-03 201917 s at 7G 4.650587e-03 201311 s at 7G 3.314954e-03 210756 s_at 7G 4.66251e-03 218701 at 7G 3.315001e-03 244495 x at 7G 4.665198e-03 208892 s at 7G 3.33511e-03 209306 s at 7G 4.674824e-03 223111 x at 7G 3.338351e-03 202822 at 7G 4.676585e-03 208663 s at 7G 3.448971e-03 205105 at 7G 4.69456e-03 222442 s at 7G 3.453233e-03 203320 at 7G 4.706835e-03 202532 "s_at 7G 3.462748e-03 200927 s_at 7G 4.722341e-03 212192 at 7G 3.468153e-03 225658 at 7G 4.724503e-03 216993 s at 7G 3.53807e-03 225267 at 7G 4.776906e-03 200848 at 7G 3.561366e~03 205961 s at 7G 4.789523e-03 222657 s at 7G 3.582237e-03 211240 x_at 7G 4.835823e-03 202860 at 7G 3.583862e-03 222496 s at 7G 4.868025e-03 207956 x at 7G 3.662877e-03 201816 s_at 7G 4.876981e-03 218521 s at 7G 3.669462e-03 1568780 at 7G 4.897193e-03 203883 s at 7G 3.67962e-03 226300 at 7G 4.921213e-03 204183 s at 7G 3.693068e-03 202829 s_at 7G 4.958157e-03 201238 s at 7G 3.78534e-03 200742 s at 7G 4.958339e-03 212552 at 7G 3.832307e-03 202811 at 7G 4.968832e-03 205078 at 7G 3.842742e-03 208420 x at 7G 4 . 987676e-03 '"4"."9"9"6"2l9e-03 201450 s_at 7G 5.971559e-03 5.033783e-03 219952 s at 7G 5.998747e-03 5.041782e-03 207719 x at 7G 6.038937e-03 5.067091e-03 229251 s_at 7G 6.040244e-03 5.096595e-03 201797 s at 7G 6.051782e-03
Figure imgf001253_0001
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5.658106e-03 201073 s at 7G 6.797927e-03 212698 s at 7G 5.669498e-03 204278 s at 7G 6.851292e-03 34260 at 7G 5.727672e-03 208878 s_at 7G 6.854999e-03 221749 at 7G 5.729569e-03 201285 at 7G 6.864023e-03 210754 s at 7G 5.739285e-03 213958 at 7G 6.889728e-03 215823 x at 7G 5.740132e-03 213940 s at 7G 6.907691e-03 1552480 s_at 7G 5.744115e-03 1554168 a at 7G 6.941333e-03 209027 s at 7G 5.775804e-03 224790 at 7G 6.94545e-03 222586 s at 7G 5.794024e-03 219023 at 7G 6.963252e-03 204493 at 7G 5.801896e-03 201085 s at 7G 6.963505e-03 203300 x_at 7G 5.812309e-03 219171 s at 7G 6.999477e-03 212515 s at 7G 5.829012e-03 212251 at 7G 7.003329e-03 201794 s at 7G 5.831058e-03 218004 at 7G 7.028753e-03 209115 at 7G 5.847314e-03 208684 at 7G 7.087344e-03 212193 s at 7G 5.867782e-03 215749 s at 7G 7.158162e-03 226144 at 7G 5.87234e-03 210543 s at 7G 7.193038e-03 218947 s at 7G 5.87694e-03 209011 at 7G 7.213132e-03 221579 s at 7G 5.920907e-03 212224 at 7G 7.235978e-03 202606 s at 7G 5.93267e-03 227741 at 7G 7.255443e-03 202610 s_at 7G 5.93496e-03 208644 at 7G 7.256069e-03 218426 s_at 7G 5.945642e-03 205248 at 7G 7.269026e-03 203799 at 7G 5.951579e-03 202745 at 7G 7.270596e-03 T.T4'2"§X8e-03 203535 at 7G 8.766418e-03 200600_at 7G 7.353331e-03 202670 at 7G 8.7673e-03 200035_at 7G 7.371156e-03 201133 s_at 7G 8.853643e-03 201118_at 7G 7.37319e-03 211081 "s_at 7G 8.860602e-03 201395_at 7G 7.376066e-03 209636 at 7G 8.876074e-03 213154_s_at 7G 7.376513e-03 201945 at 7G 8.879773e-03 208785_s_at 7G 7.393118e-03 207564 x at 7G 8.891543e-03 223268_at 7G 7.398776e-03 235348 at 7G 8.893457e-03 202545_at 7G 7.412623e-03 227276 at 7G 8.89493e-03 202317_s_at 7G 7.491872e-03 236953 s at 7G 8.905658e-03 AFFX- 211571 s_at 7G 8.9057e-03 hum_alu_at 7G 7.496815e-03 219132 at 7G 8.933895e-03 221804_s_at 7G 7.528905e-03 224797 at 7G 8.935349e-03 224984_at 7G 7.559275e-03 218842 at 7G 8.93689e-03 205191_at 7G 7.592533e-03 205895 s_at 7G 8.952419e-03 202161_at 7G 7.61666e-03 212207 at 7G 8.966703e-03 226242_at 7G 7.663158e-03 223106 "at 7G 8.99941e-03 222212_s_at 7G 7.675786e-03 203202 at 7G 9.005831e-03 209318_x_at 7G 7.677573e-03 213000 at 7G 9.027035e-03 205931_s_at 7G 7.73852e-03 223281 s at 7G 9.035259e-03 211352_s_at 7G 7.738984e-03 226510 at 7G 9.035399e-03 206649_s_at 7G 7.751933e-03 221471 at 7G 9.07607e-03 202266_at 7G 7.769322e-03 203445 s at 7G 9.088279e-03 210466_s_at 7G 7.776495e-03 218962 s_at 7G 9.098223e-03 201583_s_at 7G 7.784755e-03 223243 s at 7G 9.113267e-03 217862_at 7G 7.811456e-03 214869 x at 7G 9.114459e-03 214459_x_at 7G 7.83105e-03 204759 at 7G 9.139701e-03 201299_s_at 7G 7.836687e-03 224853 at 7G 9.181536e-03 209599_s_at 7G 7.846628e-03 201032 at 7G 9.185321e-03 212814_at 7G 7.847631e-03 224963 at 7G 9.204285e-03 218107_at 7G 7.960951e-03 220370 s at 7G 9.225939e-03 208783_s_at 7G 7.976797e-03 212706 at 7G 9.39267e-03 209448_at 7G 7.979431e-03 212869 x at 7G 9.408828e-03 202917_s_at 7G 7.982154e-03 218113 at 7G 9.419878e-03 230141_at 7G 7.999197e-03 208772 at 7G 9.420042e-03 220038_at 7G 8.020359e-03 220924 s at 7G 9.44545e-03 2251B3_at 7G 8.028685e-03 203419 at 7G 9.448125e-03 200096_s_at 7G 8.150044e-03 225957 at 7G 9.515466e-03 207785_s_at 7G 8.185329e-03 217831 s at 7G 9.516652e-03 232001_at 7G 8.208539e-03 218268 at 7G 9.535328e-03 203244_at 7G 8.221662e-03 221631 at 7G 9.536735e-03 1569022_a_at 7G 8.2224e-03 224956 at 7G 9.606943e-03 221873_at 7G 8.231898e-03 201604 s at 7G 9.618775e-03 208945_s_at 7G 8.282003e-03 1554464 a at 7G 9.637657e-03 121_at 7G 8.291248e-03 212006 at 7G 9.64373e-03 208859_s_at 7G 8.315141e-03 201014 s at 7G 9.653345e-03 217865_at 7G 8.346703e-03 203674 at 7G 9.670483e-03 209352_s_at 7G 8.356888e-03 221428 s at 7G 9.752488e-03 201321_s_at 7G 8.374692e-03 203752 s_at 7G 9.782186e-03 217765_at 7G 8.375427e-03 213404 s at 7G 9.784005e-03 204099_at 7G 8.394245e-03 202630 at 7G 9.861399e-03 224760_at 7G 8.420689e-03 225593 at 7G 9.864401e-03 218251_at 7G 8.499251e-03 203262 s at 7G 9.867506e-03 226307_at 7G 8.534597e-03 215452 x at 7G 9.93914e-03 208310_s_at 7G 8.534661e-03 203521 s at 7G 9.961605e-03 215739_s_at 7G 8.544756e-03 202654 x_at 7G 9.968632e-03 235440_at 7G 8.560755e-03 202539 s at 7G 9.982958e-03 217297_s_at 7G 8.606691e-03 221472 at 7G 9.994508e-03 202360_at 7G 8.671719e-03 229560 at 7G 0.01 202029_x_at 7G 8.739479e-03 202996 at 7G 0.01 218458_at 7G 8.743488e-03 225661 at 7G 0.01 213867_x_at 7G 8.747479e-03 200922 at 7G 0.01 207734_at 7G 8.749524e-03 212779 at 7G 0.01 223310 x at 7G 8.762925e-03 203156 at 7G 0.01
Figure imgf001255_0001
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235054 at 7G 0.02 218989 x at 7G 0.02 201940 at 7G 0.02 200876 s at 7G 0.02 209467 s at 7G 0.02 224046 s at 7G 0.02 229272 at 7G 0.02 214552 s at 7G 0.02 201057 s_at 7G 0.02 204645 at 7G 0.02 204426 at 7G 0.02 224076 s_at 7G 0.02 219546 at 7G 0.02 200041 s at 7G 0.02 226366 at 7G 0.02 202375 at 7G 0.02 217788 s at 7G 0.02 201857 at 7G 0.02 205370 x at 7G 0.02 1555803_a_at 7G 0.02 232188 at 7G 0.02 216438 s at 7G 0.02 223002 s at 7G 0.02 202059 s at 7G 0.02 209538 at 7G 0.02 201369 s at 7G 0.02 211698 at 7G 0.02 1553167 a_at 7G 0.02 213626 at 7G 0.02 223250_at 7G 0.02 217122 s at 7G 0.02 AFFX-HSACO7/ 201498 at 7G 0.02 X00351 M 7G 0.02 220338 at 7G 0.02 201337 s_at 7G 0.02 208695 s at 7G 0.02 218751 s at 7G 0.02 223190 s at 7G 0.02 226202 at 7G 0.02 208047 s at 7G 0.02 219015 s at 7G 0.02 206240 s at 7G 0.02 202583 s at 7G 0.02 •21&57Ϊ" s at -""71G ' DTO'2 218845_at 7G 0.02 209717"at 7G 0.02 208095_s_at 7G 0.02 227046" at 7G 0.02 202607_at 7G 0.02 200707" at 7G 0.02 217990_at 7G 0.02 219296" at 7G 0.02 212219_at 7G 0.02 208654" s at 7G 0.02 211023_at 7G 0.02 203142" s_at 7G 0.02 214500_at 7G 0.02 218791" s at 7G 0.02 212203_x_at 7G 0.02 225475" at 7G 0.02 203885_at 7G 0.02 225984" at 7G 0.02 203645_s_at 7G 0.02 201664" at 7G 0.02 206120_at 7G 0.02 205022" s at 7G 0.02 203042_at 7G 0.02 225665"_at 7G 0.02 201336__at 7G 0.02 213360" s at 7G 0.02 216457_s_at 7G 0.02 238794""at 7G 0.02 201221_s_at 7G 0.02 244871"_s_at 7G 0.02 212584_at 7G 0.02 202104" s at 7G 0.02 1555334_s_at 7G 0.02 203855"[at 7G 0.02 210878_s_at 7G 0.02 204772" s at 7G 0.02 202194_at 7G 0.02 204935" at 7G 0.02 201883_s_at 7G 0.02 209259" s at 7G 0.02 201094_at 7G 0.02 207394"_at 7G 0.02 218733_at 7G 0.02 215438" x at 7G 0.02 228170_at 7G 0.02 213414""s_at 7G 0.02 210286_s_at 7G 0.02 230264" s at 7G 0.02 1555091_at 7G 0.02 226111" s at 7G 0.02 228751_at 7G 0.02 219822""at 7G 0.02 222984_at 7G 0.02 227131" at 7G 0.02 217763_s_at 7G 0.02 212261" at 7G 0.02 204605_at 7G 0.02 209009" at 7G 0.02 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Figure imgf001261_0001
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Figure imgf001265_0001

Claims

WHAT IS CLAIMED: 1. A method for screening molecular markers to identify classifiers, the method comprising: a. obtaining, for members of a first training population, first molecular marker data reflective of the expression in blood of each of a plurality of molecular markers, wherein said first training population comprises a first trait subgroup and a second trait subgroup; b. selecting a plurality of candidate molecular markers from among said plurality of molecular markers based on a determination of the ability of said first molecular marker data to discriminate between members of said first trait subgroup and members of said second trait subgroup; c. obtaining, for members of a second training population, second molecular marker data reflective of the expression in blood of all or a portion of said plurality of candidate molecular markers, wherein said second training population comprises said first trait subgroup and said second trait subgroup; d. generating a plurality of combinations of molecular markers from said candidate molecular markers; e. generating a plurality of classifiers by applying a mathematical model to said second molecular marker data for each of said plurality of combinations of molecular markers; and f. selecting one or more classifiers from said plurality of classifiers based on a determination of the ability of each classifier of said plurality of classifiers to discriminate between members of said first trait subgroup and members of said second trait subgroup. 2. The method of claim 1, wherein a subset of said plurality of candidate molecular markers of (b) is selected based on a determination of the ability of molecular marker data of said candidate molecular markers to discriminate between members of said first trait subgroup and members of said second trait subgroup. 3. The method of any of claims 1 to 2, wherein said determination of the ability to discriminate is made on the basis of a measure of statistical significance. 4. The method of any of claims 1 to 2, wherein said determination of the ability to discriminate is made on the basis of differential fold change. 5. The method of claim 3, wherein said determination of the ability to discriminate is further made on the basis of differential fold change. 67" The'iriletttdd' of any of claims 4 or 5, wherein said selected candidate molecular markers demonstrate a differential fold change of greater than 2.0. 7. The method of any of claims 4 or 5, wherein said selected candidate molecular markers demonstrate a differential fold change of greater than 3.0. 8. The method of claim 3, wherein said determination of statistical significance is a p value and said p value is set such that the number of selected candidate molecular markers is less than 100. 9. The method of claim 3, wherein said determination of statistical significance is a p value and said p value is set such that the number of selected candidate molecular markers is less than 50. 10. The method of claim 3, wherein said determination of statistical significance is a p value and said molecular markers are selected if the molecular marker data results in a p value of less than 0.05. 11. The method of claim 3, wherein said determination of statistical significance is a p value and said molecular markers are selected the molecular marker data results in a p value of less than 0.01. 12. The method of any of claims 1 or 2, wherein said determination of the ability to discriminate is made on the basis of a WaId- Wolfowitz runs test, a Mann- Whitney U test, a Kolmogorov-Smirnov two-sample test, a Significant Analysis of Microarrays technique, or Manduchis' algorithm for assigning confidence to differentially expressed genes. 13. The method of any one of claims 2 to 12, wherein said determination of the ability of each of said subset of candidate molecular markers to discriminate between said members of said first trait subgroup and said second trait subgroup is made using second molecular marker data. 14. The method of claim 1, wherein said first training population and said second training population have zero or more members in common. 15. The method of claim 1, wherein said plurality of combinations includes all possible combinations of said candidate molecular markers. 16. The method of claim 1, wherein said plurality of combinations includes all possible combinations of two of said candidate molecular markers. 17. The method of claim 1, wherein said plurality of combinations includes all possible combinations of three of said candidate molecular markers. 18. The method of claim 1, wherein said plurality of combinations includes all possible combinations of four of said candidate molecular markers. ϊ'9. THe nietftϋd'bf claim 1, wherein said selecting one or more classifiers from said plurality of classifiers comprises: i obtaining for each member of a scoring population, third molecular marker data reflective of the expression in blood of molecular markers within said plurality of classifiers, wherein said scoring population comprises members of said first trait subgroup and said second trait subgroup; ii assigning a score, for each classifier in said plurality of classifiers, based on an ability of the respective classifier to discriminate between members of said first trait subgroup and members of said second trait subgroup in said scoring population using said third data; and iii selecting one or more classifiers from among said plurality of classifiers based on the score assigned to the selected classifier. 20. The method of claim 19, wherein said scoring population and said first and second training populations have zero or more members in common and said third data corresponds with said first and second data accordingly. 21. The method of claim 19, wherein said selecting one or more classifiers based on score comprises: i ranking each classifier in the plurality of classifiers on the basis of the score assigned to said classifier; and ii selecting the top 10 ranking classifiers. 22. The method of any of claims 19 and 21, wherein said score, for each respective classifier in said plurality of classifiers, is a receiver operator curve (ROC) score determined by an area under a receiver operator curve obtained by applying the respective classifier to said scoring population. 23. The method of claim 22, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.5. 24. The method of claim 22, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.65. 25. The method of claim 22, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.80. 2'6>. The'm'etrϊod'όf cMm 22, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.90. 27. The method of any of claims 1 to 26, wherein said first and second molecular marker data is obtained from one or more available databases. 28. The method of claim 1, wherein said first molecular marker data is obtained using a data collection method that allows for the collection of expression data corresponding to molecular markers for a major portion of the genome. 29. The method of claim 1, wherein said first molecular marker data is obtained using a microarray. 30. The method of claim 1, wherein said second molecular marker data is obtained using quantitative RT-PCR. 31. The method of claim 1, wherein said mathematical model is one of a regression model, a neural network, a clustering model, principal component analysis, nearest neighbor classifier analysis, linear discrimination analysis, quadratic discriminant analysis, a support vector machine, a decision tree, a genetic algorithm, a projection pursuit, or weighted voting. 32. The method of claim 31, wherein said mathematical model is optimized using bagging, boosting, or the Random Subspace Method. 33. The method of claim 1, wherein the number of candidate molecular markers selected comprises less than 100 molecular markers. 34. The method of claim 1, wherein the number of candidate molecular markers selected comprises less than 50 molecular markers. 35. The method of claim 1, the method further comprising: a. obtaining, for a test subject, fourth molecular marker data reflective of the expression in blood of candidate molecular markers of said one or more selected classifiers; b. applying said one or more classifiers to said fourth molecular marker data to thereby classify said test subject into either said first trait subgroup or said second trait subgroup. 36. The method of claim 35, wherein said fourth molecular marker data is received over the Internet from a remote source. 37. A method for identifying classifiers for a trait, the method comprising: a. obtaining, for members of a training population, molecular marker data reflective of the expression in blood of all or a portion of a plurality of candidate molecular 'maifkdrsrivherein said plurality of candidate molecular markers are those molecular markers in a Table selected from Tables 1 A-7I; wherein said Table is selected based on said trait and said training population comprises at least a first trait subgroup and a second trait subgroup for said trait as disclosed in Table F. b. generating a plurality of combinations of molecular markers from said candidate molecular markers; c. generating a plurality of classifiers by applying a mathematical model to said molecular marker data for each of said plurality of combinations; and d. selecting one or more classifiers from said plurality of classifiers based on a determination of the ability of said one or more classifiers to discriminate between members of said first trait subgroup and members of said second trait subgroup. 38. The method of claim 37 wherein a subset of said plurality of candidate molecular markers of the Table selected in (a) is selected based on a determination of the ability of the molecular marker data of said subset of candidate molecular markers to discriminate between members of said first trait subgroup and members of said second trait subgroup. 39. The method of claim 38 wherein said molecular marker data is said second data. 40. The method of claim 37, wherein said plurality of combinations includes all possible combinations of molecular markers wherein said molecular markers are those identified in the selected Table. 41. The method of claim 37, wherein said plurality of combinations includes all possible combinations of pairs of molecular markers wherein said molecular markers are those identified in the selected Table. 42. The method of claim 37, wherein said plurality of combinations includes all possible combinations of three molecular markers wherein said molecular markers are those identified in the selected Table. 43. The method of claim 37, wherein said plurality of combinations includes all possible combinations of four molecular markers wherein said molecular markers are those identified in the selected Table. 44. The method of claim 38, wherein said plurality of combinations includes all possible combinations of said subset of molecular markers of said selected Table of molecular markers. 45. TKe" πføthOd'bf cllrim 38, wherein said plurality of combinations includes all possible combinations of pairs molecular markers of said subset of molecular markers. 46. The method of claim 38, wherein said plurality of combinations includes all possible combinations of three molecular markers of said subset of molecular markers. 47. The method of claim 38, wherein said plurality of combinations includes all possible combinations of four molecular markers of said subset of molecular markers. 48. The method of any of claims 37 or 38, wherein said determination of the ability to discriminate is made on the basis of a measure of statistical significance. 49. The method of any of claims 37 or 38, wherein said determination of the ability to discriminate is made on the basis of differential fold change. 50. The method of claim 48, wherein said determination of the ability to discriminate is further made on the basis of differential fold change. 51. The method of any of claims 49 or 50, wherein said selected candidate molecular markers have molecular marker data which demonstrate a differential fold change of greater than 2.0. 52. The method of any of claims 49 or 50, wherein said selected candidate molecular markers have molecular marker data which demonstrate a differential fold change of greater than 3.0. 53. The method of claim 48, wherein said determination of statistical significance is a p value and said p value is set such that the number of selected candidate molecular markers is less than 100. 54. The method of claim 48, wherein said determination of statistical significance is a p value and said p value is set such that the number of selected candidate molecular markers is less than 50. 55. The method of claim 48, wherein said determination of statistical significance is a p value and said molecular markers are selected if they have a p value of less than 0.05. 56. The method of claim 48, wherein said determination of statistical significance is a p value and said molecular markers are selected if they have a p value of less than 0.01. 57. The method of any of claims 37 or 38, wherein said determination of the ability to discriminate is made on the basis of a WaId- Wolfowitz runs test, a Mann- Whitή'ey'U' test,''a'Kbl'mdgorov-Smimov two-sample test, a Significant Analysis of Microarrays technique, or Manduchis' algorithm for assigning confidence to differentially expressed genes. 58. The method of any one of claims 38 to 57, wherein said determination of the ability of each of said subset of candidate molecular markers to discriminate between said members of said first trait subgroup and said second trait subgroup is made using said second data. 59. The method of claim 37, wherein said first training population and said second training population have zero or more members in common. 60. The method of claim 37, wherein said selecting one or more classifiers from said plurality of classifiers comprises: i obtaining for each member of a scoring population, third data reflective of the expression in blood of molecular markers within said plurality of classifiers, wherein said scoring population comprises members of said first trait subgroup and said second trait subgroup; ii assigning a score, for each classifier in said plurality of classifiers, based on an ability of the respective classifier to discriminate between members of said first trait subgroup and members of said second trait subgroup in said scoring population using said third data; and iii selecting one or more classifiers from among said plurality of classifiers based on the score assigned to the selected classifier. 61. The method of claim 60, wherein said scoring population and said first and second training populations have zero or more members in common and said third data corresponds with said first and second data accordingly. 62. The method of claim 60, wherein said selecting one or more classifiers based on score comprises: i ranking each classifier in the plurality of classifiers on the basis of the score assigned to said classifier; and ii selecting the top 10 ranking classifiers. 63. The method of any of claims 60 and 62, wherein said score, for each respective classifier in said plurality of classifiers, is a receiver operator curve (ROC) score determined by an area under a receiver operator curve obtained by applying the respective classifier to said scoring population. 64. "Ttie* method irf claim 63, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.5. 65. The method of claim 63, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.65. 66. The method of claim 63, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.80. 67. The method of claim 63, wherein said selecting based on score comprises selecting those classifiers in said plurality of classifiers that have an ROC score greater than 0.90 68. The method of any of claims 37 to 67, wherein said molecular marker data is obtained from one or more available databases 69. The method of claim 37, wherein said molecular marker data is obtained using quantitative RT-PCR 70. The method of claim 37, wherein said mathematical model is one of a regression model, a neural network, a clustering model, principal component analysis, nearest neighbor classifier analysis, linear discrimination analysis, quadratic discriminant analysis, a support vector machine, a decision tree, a genetic algorithm, a projection pursuit, or weighted voting. 71. The method of claim 71, wherein said mathematical model is optimized using bagging, boosting, or the Random Subspace Method. 72. The method of claim 37, wherein the number of candidate molecular markers selected comprises less than 100 molecular markers. 73. The method of claim 37, wherein the number of candidate molecular markers selected comprises less than 50 molecular markers. 74. The method of claim 37, the method further comprising: a. obtaining, for a test subject, second molecular marker data reflective of the expression in blood of candidate molecular markers of said one or more selected classifiers; b. applying said one or more classifiers to said second molecular marker data to thereby classify said test subject into either said first trait subgroup or said second trait subgroup. 75. TMe rriethOcHbf daim75, wherein said second molecular marker data is received over the Internet from a remote source. 76. A system for analysing the blood of a test subject, the system comprising: a. obtaining, for said test subject, data reflective of the expression in blood of each molecular marker related to a classifier generated according to the method of any of claims 1 or 37; b. applying said classifier to said data to thereby classify said test subject into a first trait subgroup or a second trait subgroup. 77. A composition useful for diagnosing a trait of interest said composition comprising a plurality of isolated polynucleotides each of said plurality of isolated polynucleotides selectively hybridizing to a molecular marker product so as to permit said plurality of isolated polynucleotides to generate molecular marker data for a combination of molecular markers, wherein said combination of molecular markers are selected from a Table chosen from one of Tables IA to 71 and wherein said combination of molecular markers are derived using the method of claim 63 and results in a ROC score of greater than 0.6. 78. The composition of claim 78 wherein said trait of interest is selected from those traits disclosed in Table F. 79. A system for analysing the blood of a test subject, the system comprising: a. a biochemical device for obtaining, for a test subject, data reflective of the expression in blood of each molecular marker in a classifier derived according to the method of claim 1; b. a computing device for applying said classifier to said data to thereby classify said test subject into either a first trait subgroup or a second trait subgroup; and c. a display for indicating to a user the result of said classification. 80. A system for screening molecular markers to identify classifiers, the system comprising a processor and being characterized by: a. means for obtaining, for members of a first training population, first data reflective of the expression in blood of each of a plurality of molecular markers, wherein said first training population comprises a first trait subgroup and a second trait subgroup; b. means for selecting a plurality of candidate molecular markers from among said plurality of molecular markers based on a determination of the ability of said ώbrdcύ'laϊ'1ήiώ-kέrs';t6"δis;criminate between members of said first trait subgroup and members of said second trait subgroup using said first data; c. means for obtaining, for members of a second training population, second data reflective of the expression in blood of all or a portion of said plurality of candidate molecular markers, wherein said second training population comprises said first trait subgroup and said second trait subgroup d. means for generating a plurality of combinations of molecular markers from said candidate molecular markers; e. means for generating a plurality of classifiers by applying a mathematical model to each of said plurality of combinations of molectular markers using said second data; and f. means for selecting one or more classifiers from said plurality of classifiers based on a determination of the ability of each classifier of said plurality of classifiers to discriminate between members of said first trait subgroup and members of said second trait subgroup. 81. A system for identifying classifiers for a trait, the system comprising a processor and being characterized by: a. means for obtaining, for members of a training population, data reflective of the expression in blood of all or a portion of a plurality of candidate molecular markers, wherein said plurality of candidate molecular markers are those molecular markers in a Table selected from Tables 1 A-7I; wherein said Table is selected based on said trait and said training population comprises at least a first trait subgroup and a second trait subgroup for said trait. b. means for generating a plurality of combinations of molecular markers from said candidate molecular markers; c. means for applying a mathematical model to each of said plurality of combinations, using said second data, to derive a plurality of classifiers; d. means for selecting one or more classifiers from said plurality of classifiers based on a determination of the ability of said plurality of classifiers to discriminate between members of said first trait subgroup and members of said second trait subgroup.
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