dataesr/scientific-paragraphs-categorization huggingface dataset:
Platforms for repertoire data management and statistical analysis Several platforms have been developed and rely mostly on CDR3 spectratyping and sequencing data, with recent developments to manage and analyze NGS data. The ISEApeaks strategy and software were developed in order to satisfy the needs for efficient automated electrophoresis data retrieval and management  (160, 166). ISEApeaks extracts peak area and length data generated by software used to determine fragment intensity and size. CDR3 spectratype raw data, consisting of peak areas and nucleotide lengths for each V-(D)-J-C combination, is extracted, smoothed, managed, and analyzed. The repertoires of different samples are gathered in a peak database and CDR3 spectratypes can be analyzed by different perturbation indices and multivariate statistical methods implemented in ISEApeaks.
Rainbow trout IEL TRBV transcripts were highly diverse and polyclonal in adult naive individuals, in sharp contrast with the restricted diversity of IEL oligoclonal repertoires described in birds and mammals  (102). More recently, our study of the CD8 + and CD8 -αβ T cell repertoire suggests different regulatory patterns of those T cell patterns in fish and in mammals  (168). ISEApeaks was also used to implement a new statistically based strategy for quantification of repertoire diversity  (159). Kepler et al. described another original statistical approach for CDR3 spectratype analysis, using complex procedures for testing hypotheses regarding differences in antigen receptor distribution and variable repertoire diversity in different treatment groups. This approach is based on the derivation of probability distributions directly from spectratype data instead of using ad hoc measures of spectratype differences  (169).
A software (called SpA) implementing this method has been developed and made available online  (170). This approach has been used in a longitudinal analysis of TRBV repertoire during acute GvHD after stem cell transplantation  (171). Another group  (163)  reported the development of a new software platform, REPERTOIRE, which allows handling of CDR3 spectratyping data. This software implements a perturbation index based upon an expected normal Gaussian distribution of CDR3 length profiles. Owing to the complexity and diversity of the immune system, immunogenetics represents one of the greatest challenges for data interpretation: a large biological expertise, a considerable effort of standardization, and the elaboration of an efficient system for the management of the related knowledge were required.
To answer that challenge, IMGT®, the international ImMuno-GeneTics information system®(http://www. imgt. org), was created in 1989 by one of the authors  (146). Overtime, it developed standards that, since 1995, have been endorsed by the World Health Organization-International Union of Immunological Societies (WHO-IUIS) Nomenclature Committee and by the WHO-International Nonproprietary Names (INN)  (172) (173) (174) (175). IMGT® comprises seven databases (sequence, gene, and structure databases), 17 online tools and more than 15,000 pages of web resources. Among the databases, IMGT/LIGM-DB, the database for nucleotide sequences (170,685 sequences from 335 species as of July 2013) and IMGT/GENE-DB, the gene database (3,081 genes and 4,687 alleles) are of great interest for repertoire analysis.
Most of these indices are implemented in the free software application Estimates (http://purl. oclc. org/estimates) (186). Altogether, these diversity indices constitute a collection of tools with their own sensitivity to the variety and the relative abundances of the species that are perfectly suitable for assessing immune repertoire diversity. Indeed, the very famous index of variability proposed by Kabat and Wu  (187)  corresponds to the ratio of Species Richness and Berger-Parker indices. In 1990, Jores et al. showed that the resolving power of this Wu-Kabat variability coefficient can be enhanced by increasing the weight on the frequency distribution of the amino acids in the formula  (188). This approach inspired Stewart et al. (189)  to use the Shannon entropy to demonstrate that TR amino acid composition is significantly more diverse than that of IG.
ACKNOWLEDGMENTS This work was supported by  French  state funds within the  Investissements d'Avenir program  ( ANR-11-IDEX-0004-02 ;  LabEx Transimmunom) , the  European Research Council   Advanced grant  ( TRiPoD ), the European  PCRDT7  ( Lifecycle program ), the  RNSC (ImmunoComplexiT network) ,  CNRS (PEPS BMI) ,  INRA  and  Université Pierre and Marie Curie.
Study participants Clinical characteristics and data available for the studied populations are reported in Table  S1. Genotyping of SNPs rs6921438 and rs10738760 was conducted in several study samples: -French T2D case-control study. We analysed 6,920 unrelated French individuals with T2D ascertained from the French T2D family study collected by the CNRS-UMR8090 unit, from the Endocrinology-Diabetology Department of the Corbeil-Essonnes Hospital  [13] , from the Diabhycar/Diab2-Nephrogene study  [14] , from the D. E. S. I. R. cohort which is a longitudinal French general population sample  [15] , and from the SU. VI. MAX study, which is fully described elsewhere  [16]. We used 3,875 unrelated normoglycemic adults (age at exam $45 years), ascertained from the D. E. S. I. R. and the SU. VI. -MAX studies, as controls.
Glycemic status was defined according to 1997 American Diabetes Association criteria  [20] : normal glucose, defined as fasting plasma glucose ,6. 1 mmol/l without hypoglycemic treatment; and T2D, defined as fasting plasma glucose $7. 0 mmol/l and/or treatment with hypoglycemic agents. Clinical data relating to nephropathy were defined as follows: i/ Stage 2: microalbuminuria defined as ACR $30 mg/g [3. 5 mg/ mmol] and ,300 mg/g [35 mg/mmol] (at three consecutive measurements); ii/Stage 3: macroalbuminuria defined as ACR $300 mg/g [35 mg/mmol] (at three consecutive measurements); and iii/Stages $4: eGFR ,30 ml/min/1. 73 m 2 , or patients undergoing dialysis or having received a kidney transplant.
Furthermore, we did not observe any significant association between rs6921438 or rs10738760 and macular edema in patients from the D2NG study or Corbeil cohort (data not shown). Of note, use of subject age instead of T2D duration in the adjusted regression model did not modify the results for either diabetic retinopathy or diabetic nephropathy (data not shown). Finally, we assessed the association of both SNPs and the variation in quantitative traits related to T2D microvascular complications (i. e. eGFR and ACR) in a total of 3,480 French T2D participants from Corbeil and D2NG. No significant association was observed in these analyses (P. 0. 05; Table  4 ).
Acknowledgments We are sincerely indebted to all participants in the genetic study. The  Diab2-Nephroge  `ne study participating centers are acknowledged here: Poitiers -Diabetology clinic (coordinating center):  F Duengler ,  R Mare  ´chaud,  D Gendre ,  F Torremocha ,  L Labbe ,  S Hadjadj  (Principal Investigator); Poitiers -Nephrology clinic:  G Touchard ,  F Bridoux ,  A Thierry ,  R Abou-Ayache ,  S Belmouaz ;  Poitiers -Cardiovascular  disease prevention center:  D Herpin ,  P Sosner , Angers -Diabetology clinic:  G Guilloteau ,  B Bouhanick ;  P Ducluzeau ,  P Ritz ; Angouleme -Diabetology clinic: AM  Chameau-Lagarde ,  M Bonnefoy ; Corbeil-Essonnes -Diabetology clinic:  G Charpentier ,  JP Riveline ,  D Dardari ,  C Petit ; Limoges -Diabetology clinic:  F Archambeaux-Mouveroux ,  S Gallina ; Nancy -Diabetology clinic:  P Bohme ,  B Guerci ,  I Glatz ,  H Hallage ;  Narbonne -Diabetology  clinic:  N Vigier , JP Courreges; Niort -Nephrology clinic:  A Se  ´chet,  M Baron ;  Paris Bichat -Diabetology  clinic:  A Ankotche ,  R Roussel ,  M Marre ;  Rennes -Diabetology clinic: C Faure-Gerard ,  JY Poirier ,  D Maugendre ;  St Mande-HIA Begin -Diabetology clinic :  B Bauduceau ,  O Dupuy ,  H Mayaudon ,  J Margery ,  R Poyet ; Toulouse -Diabetology clinic:  P Gourdy ,  M Matta ,  H Hanaire ;  Tours-CIC, Diabetology clinic and Nephrology clinic: JM Halimi ,  P Lecomte ,  M Franc  ¸ois; Valenciennes -Diabetology clinic:  O Verier-Mine ; Valenciennes -Nephrology clinic:  P Vanhile.
This study was supported by the  BioIntelligence  project. The  Diab2-Nephrogene study  was supported by grants from  French  minister of health (PHRC-Poitiers 2004 and  PHRC-IR 2008 ) and  AFD  ( Research Grant   2003 ). The association  GEMMS (Groupe d'Etude des Maladies Metaboliques et Systemiques [Poitiers, France ] ) also supported this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The strongest evidence was found for modification of the association with LSP1-rs3817198 by number of births in parous women (P interaction per birth increase in parous women = 2. 4610 26 ) (Table  4 ; Figure  1  showing individual study results). Since this interaction was previously assessed in BCAC, we reassessed the interaction in 6266 cases and 3899 controls not included in the previous report  [7]. The SNP association still varied significantly with number of births in parous women (P interaction = 1. 6610 23 ), thus independently replicating the previous finding. The results were consistent across studies (P heterogeneity = 0. 37) (Figure  1B ). In the overall dataset, the per-allele OR (95% confidence interval) for rs3817198 ranged from 1. 03 (0. 96-1. 10) in parous women with one birth to 1.
Discussion We carried out a comprehensive evaluation of potential geneenvironment interactions between 23 established common susceptibility variants for breast cancer and 10 established environmental risk factors, using 18 variables. Compared to the previous analysis, the present dataset from BCAC included 5 new population-based studies as well as additional study participants from some studies  [7]. We examined additional environmental risk factors (14 variables), and 11 additional recently identified common susceptibility loci. In our previous report, the strongest evidence of effect modification (P-value = 0. 002) was observed for LSP1-rs3817198 by number of births  [7]. The highly consistent and significant finding based on the present analysis of only additional cases and controls provided clear independent replication.
Mammographic density has also been found to be reduced after a full-term pregnancy, particularly with greater number of births  [13, 14]. We also replicated the strongest finding reported in the Million Women Study based on 7,610 cases and 10,196 controls  [6]. In that study, the per-allele OR of CASP8-rs1045485 (or rs17468277 in our dataset) was 0. 99 (0. 92-1. 07) in those who reported ,1 drink/day and 1. 23 (1. 09-1. 38) in those who reported $1 drink/ day (P-value = 0. 003). Our observation of an increased per-allele OR of 1. 45 (1. 14-1. 85) for those who reported high alcohol intake $20 g/day and 0. 91 (0. 84-0. 98) for those who consume less provides independent replication of this SNP-environmental interaction. Although one drink corresponds to an intake of approximately 10 g alcohol, the Million Women study reported the strongest risk increase in breast cancer for women consuming at least 15 drinks per week (RR 1.
Author Summary Breast cancer involves combined effects of numerous genetic, environmental, and behavioral risk factors that are unique to each individual. High risk genes, such as BRCA1 and BRCA2, account for only a small proportion of disease occurrence. Recent genome-wide research has identified more than 20 common genetic variants, which individually alter breast cancer risk very moderately. We undertook an international collaborative study to determine whether the effect of these genetic variants vary with environmental factors, such as parity, body mass index (BMI), height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, and physical activity, which are known to affect risk of developing breast cancer. Using pooled data from 24 studies of the Breast Cancer Association Consortium (BCAC), we provide first convincing evidence that the breast cancer risk associated with a genetic variant in LSP1 differs with the number of births and that the risk associated with a CASP8 variant is altered by high alcohol consumption.
Study participants and risk factor data We used primary data from the studies in BCAC. All studies had approval from the relevant ethics committees and all participants gave informed consent. A centralized BCAC database of information about common risk factors and tumor characteristics was constructed to facilitate studies of potential modifications of SNP associations by other risk factors. A multi-step data harmonization procedure was used to reconcile differences in individual study questionnaires. The reference age for cohort studies was calculated at time of enrollment and for case-control studies at date of diagnosis for cases and at date of interview for controls. All time-dependent variables were assessed at reference age. This analysis included only subjects of European ancestry that had genotype data for at least 3 SNPs and provided information on at least one of the established risk factors.
SNP selection and genotyping We included 21 SNPs found to be associated with overall breast cancer risk at genome-wide statistical significance (p,5610 27 )   [10, 25, 34]  and SNPs for TGFB1 and CASP8 from candidate gene studies  [17]  (Table  S3 ). For three loci, 14q24. 1/RAD51L1, 12p11, CASP8, a surrogate SNP in high linkage disequilibrium (LD) (r 2 = 1 in HapMap CEU) was genotyped in a subset (Table  3  footnote)  [19, 25, 35]. Genotyping was performed in the framework of BCAC by Taqman and iPlex assays and underwent quality control as described previously  [10, 19, 25, 34, 36, 37]. Genotype data were excluded from analysis on a study-by-study basis according to the following BCAC quality control (QC) guidelines: 1) any sample that consistently failed for. 20% of the SNPs within a genotyping round, 2) all samples on any one plate that had a call rate ,90% for any one SNP, 3) all genotype data for any SNP where overall call rate was ,95%, 4) all genotype data for any SNP where duplicate concordance was ,98%.
Subjects with missing data for a particular SNP or environmental factor were excluded from the respective analysis. We also calculated stratum specific per-allele ORs for each SNP: age at menarche (#11, 12-13, $14 years), number of births (1,2,3, $4), age at first birth (,20, 20-24, 25-29, $30 years), usual BMI (,25, 25-29, $30), height (,160, 160-164, 165-169, $170 cm), duration of oral contraceptive use and of menopausal hormone use (0,. 0-,5, 5-,10, $10 years), mean lifetime alcohol intake (0, 0-,10, 10-,20, $20 g/day), pack-years of smoking (0, 1-,10, 10-,20, $20), and physical activity (0,. 0-,3. 5, $3. 5-,7, $7 h/week). For SNP-environment interactions with associated P-value,10 23 , we also compared results after adjusting for additional covariates. We performed a total of 414 (23 SNPs x 18 risk variables) tests.
Effect heterogeneity by ER status was tested using case-case analysis. Data harmonization was performed using an ACCESS database and transformation of the data elements was performed using SAS (Release 9. 2). All other data analyses were conducted using SAS (Release 9. 2) and the R programming language  [40]. Table  S7  Gene-environment interactions between SNPs and breast cancer risk factors in Caucasians with interaction pvalue,10 24 , overall and by ER status, adjusted for additional covariates. (PDF) Table  S8  Gene-environment interactions between SNPs and breast cancer risk factors in Caucasians with interaction pvalue,10 24 , overall and by ER status, restricted to populationbased studies. (PDF) Table  S9  False-positive reporting probability (FPRP) for interactions of SNPs and environmental risk factors of breast cancer showing interaction p-value,10 22.
Acknowledgments We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians, and administrative staff who have enabled this work to be carried out. In particular, we thank:  Maggie Angelakos ,  Judi Maskiell ,  Gillian Dite ,  Sonja Oeser ,  Silke Landrith ,  Eileen Williams ,  Elaine Ryder-Mills ,  Kara Sargus , The GENICA network:  Dr. Margarete Fischer-Bosch - Institute of Clinical Pharmacology, Stuttgart, and University of Tu ¨bingen, Germany [Christina Justenhoven, HB]; Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany [Yon-Dschun Ko, Christian Baisch]; Institute of Pathology, University of Bonn, Bonn, Germany [Hans-Peter Fischer ];  Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ) ,  Heidelberg, Germany [Ute Hamann]; Institute for Prevention and Occupational Medicine of the German Social Accident Insurance (IPA) ,  Bochum, Germany [TB ,  Beate Pesch ,  Sylvia Rabstein ,  Anne Lotz ] and  Institute and Outpatient Clinic of Occupational Medicine, Saarland University Medical Center and Saarland University Faculty of Medicine, Homburg, Germany  [VH],  Eija Myo  ¨ha ¨nen,  Helena Kemila ¨inen ,  Heather Thorne ,  Eveline Niedermayr  and the  kConFab Clinical Follow-Up Study , the  AOCS Management Group  ( D Bowtell ,  G Chenevix-Trench ,  A deFazio ,  D Gertig ,  A Green ,  P Webb ), the  ACS Management Group  ( A Green ,  P Parsons ,  N Hayward ,  P Webb ,  D Whiteman ); The  Australian Cancer Study Management Group  ( A.
BCAC is funded by  Cancer Research UK  [ C1287/A10118 ,  C1287/A12014 ] and by the  European Community 's  Seventh Framework Programme  under grant agreement  223175  ( HEALTH-F2-2009-223175 ) (COGS). Meetings of the BCAC have been funded by the  European Union   COST programme  [ BM0606 ]. DF Easton is a  Principal Research Fellow of CR-UK. RL Milne is supported by  Fondo de Investigacion Sanitario  ( PI11/00923 ). The  Australian Breast Cancer Family Registry  (ABCFR; 1992-1995) was supported by the  Australian NHMRC , the  New South Wales Cancer Council , and the  Victorian Health Promotion Foundation (Australia). The  Breast Cancer Family Registry (BCFR)  was supported by the  National Cancer Institute, National Institutes of Health , under RFA #  CA-06-503 , and through cooperative agreements with members of the BCFR and Principal Investigators, including  Cancer Care Ontario  ( U01 CA69467 ),  Columbia University  ( U01 CA69398 ),  Fox Chase Cancer Center  ( U01 CA69631 ),  Huntsman Cancer Institute  ( U01 CA69446 ),  Cancer Prevention Institute of California  ( U01 CA69417 ),  University of Melbourne  ( U01 CA69638 ), and  Georgetown University Medical Center Informatics Support Center  ( HHSN261200900010C ).
The CECILE study was funded by the  Fondation de France , the  French National Institute of Cancer (INCa) ,  The National League against Cancer , the  National Agency for Environmental and Occupational Health and Food Safety (ANSES) , the  National Agency for Research (ANR) , and the  Association for Research against Cancer (ARC). The CGPS was supported by the  Chief Physician Johan Boserup and Lise Boserup Fund , the  Danish Medical Research Council , and  Herlev Hospital. The CTS was supported by the  California Breast Cancer  Act of 1993,  National Institutes of Health  (grants  R01 CA77398  and the  Lon V Smith Foundation  [LVS39420]), and the  California Breast Cancer Research Fund.
Data collection When a patient is recruited for the study, the GP will need to enter the patient's clinical data and prescription into an electronic case report form (e-CRF) (Figure  2 ). After three months of patient follow-up, the GP will have to collect data on ADE occurrence, patient ADE reporting and prescription modifications, for entry into the e-CRF. Also after three months of patient follow-up, clinical research associates are to interview patients using a standardised phone questionnaire dealing with ADE occurrence, situations presenting a risk of ADEs, ADEs that occurred without being detected by GPs, and patient skills and satisfaction concerning their medical care. The associates will have to enter the corresponding data into the e-CRF and will have to provide a detailed explanation of all drug prescriptions made by the GP.
Using these quantitative proteomic approaches, a set of 148 proteins with modified abundance was identified. The bioinformatics analysis (Ingenuity Pathway Analysis) of each protein dataset originating from the different time-point comparisons revealed that four major functions were altered during the course of WNV-infection in mouse brain tissue: i) modification of cytoskeleton maintenance associated with virus circulation; ii) deregulation of the protein ubiquitination pathway; iii) modulation of the inflammatory response; and iv) alteration of neurological development and neuronal cell death. The differential regulation of selected host protein candidates as being representative of these biological processes were validated by western blotting using an original fluorescence-based method.
CyDye Labeling Samples were subjected to 2-D clean-up kit (GE healthcare), concentrated by precipitation with acetone (Sigma, St. Louis, MI), and the protein pellet was resuspended at a protein concentration of 2. 5 mg/mL in standard cell lysis buffer (UTC buffer) containing 8 M urea, 2 M thiourea, 4% (w/v) CHAPS and 30 mM Tris, adjusted to pH 8. 5 as previously described  [30]. Sample quality and protein amount was checked by loading 10 mg of each sample onto a 10% SDS-PAGE precast gel (Bio-Rad) stained with Imperial TM Protein Stain solution (Fisher Scientific) (data not shown). Proteins in each sample were minimally labeled with CyDye according to the manufacturer's recommended protocols and as previously described  [25]. Briefly, 50 mg of each protein sample were labeled with 400 pmol of either Cy3 or Cy5, freshly dissolved in anhydrous dimethylformamide, and incubated on ice for 30 min in the dark.
Image Analysis After electrophoresis, the gels with Cydye-labeled proteins were scanned three times with a Typhoon TM Trio Image scanner (GE Healthcare UK) each time at different excitation wavelengths (Cy3, 580 BP 30/green (532 nm); Cy5, 670 BP 30/red (633 nm); Cy2, 520 BP 40/blue (488)). Pre-scans were performed to adjust the photomultiplier tube (PMT) voltage to obtain images with a maximum intensity of 60 000 to 80 000 U. Images were cropped with ImageQuant TM software (GE Healthcare UK) and further analyzed using the software package Progenesis SameSpot v2 software (Nonlinear Dynamics, Newcastle upon Tyne, UK). The gel images were aligned by automated calculation of alignment vectors after assigning landmark vectors. Background subtraction and spot intensity normalization were automatically performed by Progenesis SameSpots.
Mass Spectrometry Analysis of Peptide Mixture from Gel Elution and MS Data Analysis The samples were subjected to nanoscale capillary liquid chromatography-tandem mass spectrometry (nano LC-MS/MS) analysis with a QTOF apparatus (Q-TOF Ultima, Waters, MA) as previously described  [25]. The peak lists generated in the micromass pkl format, were then fed into a local search engine Mascot Daemon v2. 2. 2 (Matrix Science, London, UK) against a mixed Mus musculus and West-Nile virus homemade protein database (SwissProt). Search parameters were set in order to allow one missed tryptic cleavage site, the carbamidomethylation of cysteine, and the possible oxidation of methionine; precursor and product ion mass error tolerance was ,0. 2 Da. All identified proteins have a Mascot score greater than 34 (Mixed: Mus musculus and West-Nile virus, 16414 sequences June 26 th , 2012), corresponding to a statistically significant (p,0.