dataesr/scientific-paragraphs-categorization huggingface dataset:
The simplest explanation is that our original observation in the GWAS data represents a false positive association. However, the eQTL and gene expression data argue against this possibility. Explanations for a false negative finding in our replication collections include: (1) lack of power, especially if the effect size observed in the GWAS represents an over-estimate of the true effect size (the Winner's Curse) -we estimate that we had 32% and 17% power (at P = 0. 05) to detect an association in the Portuguese and Japanese sample collections, respectively; (2) clinical heterogeneity, which is always a possibility in pharmacogenetic studies, especially those conducted in different countries; and (3) ethnic differences, including different patterns of LD between the underlying causative allele (which is as yet unknown) and marker SNPs tested in our study.
S. Treatment of Early Aggressive RA (TEAR, N = 109). We refer to these collections as the American College of Rheumatology Research and Education Foundation (REF) collection, as funding for GWAS genotyping was provided by the ''Within Our Reach'' project. We included additional samples from BRAGGSS (N = 595)  [12] ; the Dutch Rheumatoid Arthritis Monitoring registry (DREAM) in the Netherlands, and the ApotheekZorg (AZ) database (which facilitates the Dutch distribution of adalimumab; N = 880)  [23, 24] , together referred to as DREAM; and the French Research in Active Rheumatoid Arthritis (ReAct, N = 272)  [25]. Additional samples were collected for replication of SNPs in the 1q23 locus. These included the Rheumatic Diseases Portuguese Register (Reuma. pt, N = 378) from the Portuguese Society of Rheumatology (SPR), which captures more than 90% of patients treated with biological therapies and managed in rheumatology departments across Portugal  [26].
Additional replication samples (N = 374) of East Asian ancestry were included from the IORRA and Kyoto University Hospital registries, part of the Japanese Genetics and Allied research in Rheumatic diseases Networking consortium (GARNET)  [27]. Clinical data were collected in each cohort, including disease activity scores at baseline and at least one time point after treatment, gender, age, methotrexate use, as well as autoantibody status (RF or CCP). The composite disease activity scores for 28 joints (DAS28) included laboratory values for erythrocyte sedimentation rate (ESR) for most samples and C-reactive protein (CRP) for 191 samples in the REF collection (ABCoN, BRASS and eRA cohorts). DAS28 values were available at baseline and at 3-12 months after initiating anti-TNF therapy. Our primary phenotype was defined as DDAS = baseline DAS -end DAS, and responder status was also determined according to EULAR criteria for start and end DAS  [15].
Genotyping and data processing A total of eleven genotyping batches were processed separately. (1) BRASS samples were genotyped using Affymetrix 6. 0 chip  [28] ; (2) WTCCC samples were genotyped on Affymetrix 500K chip  [12]. All other cohorts were genotyped using Illumina platform arrays (see Table  1 ). Our American College of Rheumatology Research Education Fund (REF) collection was made up of smaller cohorts from throughout North America and Europe, including BRASS samples. Also included in REF:  (3)  ABCoN  [13]  and (4) EIRA  [29]  were separately genotyped on the Illumina 317K genotyping array; (5) eRA on the Illumina 550K chip; and (6) GENRA, BeSt, BRAGGSS (a subset of N = 53 samples), KI and LUMC were genotyped in one batch, and (  7 ) BRAGGSS (N = 87) and TEAR were genotyped in a second batch, both using Illumina 660k chips, at the Broad Institute  (8) (9) (10).
DREAM and AZ samples were genotyped in three batches, one on 550K chip and two on 660K chips (manuscript in preparation), and (11) ReAct samples were genotyped on Illumina OmniExpress chips. Quality control (QC) filtering was done in each genotyping batch, including filtering individuals with. 5% missing data, and filtering SNPs with. 1% missing data, minor allele frequency (MAF) ,1% and Chi-squared test of Hardy Weinberg equilibrium P HWE ,10 25. We then used individualpairwise identity-by-state estimates to remove occasional related and potentially contaminated samples. Data processing and QC were performed in PLINK  [30]. Principal Components Analysis (PCA) was performed using EIGENSTRAT  [31]  (default settings) on the combined dataset using 20,411 SNPs genotyped across all datasets. Ethnicity outliers including all individuals of non-European decent were identified and removed, and the first three eigenvectors were used as covariates in GWAS.
Imputation was conducted on each of eleven datasets separately, using the IMPUTE v1 software  [32]  and haplotype-phased HapMap Phase 2 (release 22) European CEU founders as a reference panel. Imputation of BRASS and EIRA was previously reported  [28, 33] , and we followed the same imputation procedures for the remaining datasets. Imputation yielded posterior genotype probabilities as well as imputation quality scores at SNPs not genotyped with a minor allele frequency $1% in HapMap CEU. We removed imputed SNPs with imputation 'info' scores ,0. 5 or MAF ,1% in any of the datasets.
Acknowledgments We thank  Lars Klareskog  and  Lars Alfredsson  for their contribution to the EIRA cohort, and the  Swedish Rheumatology Register and Biologics Register (ARTIS)  for providing follow-up data for the patients in the EIRA and KI study groups. We thank participants in the  Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS).
$ This work was supported by  AiresSud  (project  7015 ),  Coadvise  (project  FP7-PEOPLE-IRSES-2008 No. 230833 ) and Treasure (an  Inria  Euromed  3+3  project, http: //project. inria. fr/treasure/). This article is an extension of the IFAC 2011 proceeding [3].
Association Studies and QTL Detection In the selective genotyping approach (survival-associated QTL), marker-trait association was inferred by testing difference in marker allele frequencies among the two population tails. The Pearson x 2 test (1 dl) was performed at every marker used for the genome scan to search for association between the issue of infectious challenge (dead/surviving) and the genotype (R vs S grand-parental allele). Bonferroni correction was considered for multiple tests at the chromosome-wide level. Detection for VREFT-associated QTL was performed using QTLMap software  [53]. An interval mapping method described by Elsen et al. [54]  was applied, scanning for QTL every 1 cM in the genome. To take into account the uniparental origin of the DH families, they were considered as full-sib families where each fish was assigned a virtual unknown parent different for every individual.
A whole post challenge life-time dataset was built. Individual data were the surviving status (dead or alive at the end of the challenge) and the time to death (TTD, in days after infection) of each fish that died. Surviving fish corresponded to 'censored' observations, i. e. that the expected event (death) was not recorded during the observation period. Fish not genotyped yet (dead after the first 10% and, for DH-F00, survivors not previously sampled) were genotyped for the markers of the target region. Data were pre-corrected for fixed effect (aquarium). QTL detection was performed with QTLMap software using an interval mapping method for survival phenotypes (non-normal distribution and presence of censored data) based on Cox model and described by  [57]. Pedigree design was encoded as previously described and methods to test the presence of QTL and to determine thresholds were the same.
The QTL effects (% of phenotypic variance explained) were estimated by testing the within family effect of the alternative alleles at the marker closest to the QTL location (ANOVA with SAS software). QTL effect on survival was estimated using TTD, assuming a Gaussian distribution and fixing the maximum TTD at day 28 in DH-F98 (day of the last mortality recorded in the family). The same limit was chosen in DH-00 to facilitate comparison and to avoid a too high dispersion of TTD values. Ethics Statements. Animals were handled in accordance with the national and European guidelines and regulations on laboratory animals care in force at the time of the experiments. Animal work was approved by the French Veterinary Services (personal authorizations numbers 78-67 and 32-57 for Edwige QUILLET and Michel DORSON respectively).
In fact, most of the resistance to VHSV infection was explained by the same genomic region in both families. Few additional regions were also involved, but with effects more disparate and of lesser magnitude. In order to refine the estimation of the major QTL effect (RT31), an analysis including life-time data throughout the course of the infectious challenge was performed in a subset of three aquaria of 120-125 fish per family. Individuals were genotyped for the same 10 microsatellites of the RT31 linkage group. Results were very consistent with the results of the selective approach (Figure  4 ), the QTL being again highly significant in both families (P,0. 01 at the genome-wide level), and located at the same position, at the end of the linkage group, close to OMM5005. Because of this distal position, no clear support interval could be established for the QTL.
The Major Survival-associated QTL was Detected in Two Additional Families Results of the two DH families suggested that a common major QTL plays a pivotal role in the resistance/susceptibility to VHSV in different genetic backgrounds. To provide additional support to this result, we searched for the survival-associated QTL in two additional DH families. The two families (DH-F01 ad DH-F21) had been produced according to the same procedure as DH-F98 and DH-F00 (F2 progeny from breeders selected for VREFT values) but were not used in the first phase of the QTL detection Analyses for the survival-associated QTL were performed with the complete post challenge life-time dataset (3 aquaria per family, i. e. 354 fish in HD-F98 family and 360 fish in HD-F00 family, heterozygous individuals excluded).
For VREFT-associated QTL, 133 and 300 fish respectively were analysed in the two families. A total of 8 and 10 markers located on RT31 were genotyped in DH-F98 and DH-F00 respectively. Marker names and positions are available on supplementary Table  1S. Likelihood values were obtained using the QTLMap software. Likelihood thresholds for significant QTL (P,0. 01 at the genome-wide level) were determined from LRT distribution of 10000 simulations under the null hypothesis (no QTL). Survivalassociated QTL locate at 105 and 106 cM in DH-F98 and DH-F00 respectively. VREFT-associated QTL locate at 106 cM in both families (confidence intervals estimated at about 1 cM in length). doi:10. 1371/journal. pone. 0055302. g004 because of a shortage of surviving juveniles. In addition to the selection on VREFT values, the additional F0 breeders were chosen to have no relatedness with the ones previously tested, with the exception of one for which the pedigree was unknown.
Acknowledgments Authors thank the staff of  INRA  experimental facilities (fish farm in Gournay-sur-Aronde, and platform of infectiology in Jouy-en-Josas) for help in fish management, Jerome GARRIGUE and  Alix SAUSSET  for contribution to genotyping, and  Dr Carole MORENO  for helpful advice in using QTLMap software. We thank an anonymous reviewer for sharing with us information about the EST containing the OMM5005 marker.
The research leading to these results has received co-funding from  European Community (IFOP)  and  French Ministry of Research  (grant agreement  DPMA/CIPA/INRA No. 2004/190 ,  AGENOP  project) and funding from the  European Community 's  Seventh Framework Programme  (FP7, 2007-2013),  Research Infrastructures action , under the grant agreement No. FP7-228394  (NADIR). E. R. VERRIER is a recipient of a PhD fellowship co-funded by  Animal Genetics and Animal Health Divisions of INRA. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
In 2011, we assessed data from maternity centres of Bangui and found that more than 75% of pregnant women accepted HIV testing. This finding is in accordance with the recommended threshold of acceptance  [32]. Our preliminary evaluation showed a relatively high rate of HIV infection (7. 3%) among pregnant women. The profile of HIV serological status and rates of prevention of motherto-child transmission (MTCT) are detailed in Table  1. To prevent opportunistic infections, co-trimoxazole is given to all women at WHO clinical stages 2, 3 and 4 and those at stage 1 with a CD4+ count <350/mm 3 from the second trimester and throughout pregnancy  [33]. In the Central African Republic, two treatment schemes are recommended for the management of HIV infections during pregnancy  [32] : Antiretroviral treatment is offered to all pregnant women with severe or clinically advanced disease, regardless of their CD4+ count, and for those with a count of ≤350 cells/mm 3 , regardless of their symptoms.
Data collected At baseline, the following data will be collected: sociodemographic characteristics, clinical data, obstetric history, gestational age estimated from the date of the last menstrual period and/or measured by uterine fundal height, and biological end-points (CD4 count, HIV viral load (VL), malaria diagnosis, and blood cell count). During follow-up, standard clinical examinations will be performed monthly. Adverse events will be recorded and any suspected malaria episode will be investigated by laboratory testing. Blood will be taken from women presenting with a malaria episode, and CD4 count and HIV VL will be determined. Polymerase chain reaction (PCR) for malaria diagnosis and identification of resistant strain markers (dhfr and dhps) will be performed for all episodes of Plasmodium infection.
Outcome measures The primary end-point of the trial is placental parasitaemia at delivery. Data on this variable will be coded as positive or negative results. Other outcome measures are adherence to co-trimoxazole treatment, adverse events related to co-trimoxazole and SP-IPT, the incidence of malaria episodes during pregnancy and complications due to malaria: anaemia (hemoglobinaemia <10g/dl), abortion (delivery <28 weeks of amenorrhoea), stillbirth, prematurity (birth <37 weeks of amenorrhoea) and low birth weight (<2500 g body mass), mother-to-child HIV transmission and umbilical blood parasitaemia. At delivery, maternal, placental and newborn umbilical blood will be sampled to detect the asexual form of Plasmodium.
Statistical analysis The primary end-point will be analysed as intention-totreat; deaths and women lost to follow-up will be considered failures. Analysis of the 'per protocol' population will be conducted among women who complete the study until delivery and undergo the recommended initial treatment. The data will be recorded in an ACCESS™ database (Microsoft Corp. , Redmond, WA, USA) and will be analysed with STATA™ software (version 12; Statacorp, College Station, TX, USA). The analyses will be carried out after stratification according to the maternity clinic and gravidity (primigravidae versus multigravidae). The chisquared test or Fisher's exact test will be used to compare categorical variables between the two groups, including the number of positive placental malaria infections (primary end-point), cumulated incidence of malaria episodes during pregnancy, proportion of parasite strains with dhfr mutations, maternal anaemia and maternal morbidity or mortality.
The A-Train observations provide an unprecedented opportunity for the production of high quality dataset describing cloud properties. We illustrate in this study the use of one year of coincident POLDER (Polarization and Directionality of the Earth Reflectance), MODIS (MODerate Resolution Imaging Spectroradiometer) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations to establish a reference dataset for the description of cloud top thermodynamic phase at global scale. We present the results of an extensive comparison between POLDER and MODIS cloud top phase products and discuss those in view of cloud vertical structure and optical properties derived simultaneously from collocated CALIOP active measurements. These results allow to identify and quantify potential biases present in the 3 considered dataset.
(2010). A logical and reasonable decision tree is so required in processing of the final cloud phase product from different combined methods. This requires a better understanding of both strengths and weaknesses of each retrieval method. The A-Train satellites give us an unprecedented opportunity for a joint study of these different cloud phase retrieval techniques. Past studies of cloud phase from sensors of the A-Train (e. g. POLDER, MODIS, CALIOP or AIRS) have mainly focused on individual case analysis or radiative transfer simulation  (Riedi et al. , 2010; Chylek et al. , 2006; Cho et al. , 2009; Kahn et al. , 2011)   case study. Moreover, we hope to improve the phase inversion and create a much needed high confidence cloud thermodynamic phase dataset on which could rely future work on cloud phase parameterizations in cloud resolving and climate models.
A brief overview of the different cloud phase retrieval algorithms used for POLDER, MODIS and CALIOP and a description of associated datasets is provided in the second section. A third section is devoted to the global comparison of cloud phase products between POLDER and MODIS. Further assessment of the two datasets obtained from the two passive sensors is done against CALIOP and results are provided in section four. The potential biases and analysis of additional information on cloud structure are discussed in section five. A summary of main findings and outlook is provided in the last section.
Dataset and algorithm descriptions Two datasets obtained from POLDER, MODIS and CALIOP and created by the ICARE data and services center are used for this study. The first one is a joint POLDER/MODIS (PM) dataset containing all Aqua/MODIS collection 5 level 2 cloud products and PARA-SOL/POLDER3 collection 2 level 2 cloud products collocated and reprojected on a common integerized sinusoidal grid  (Zeng et al. , 2011). POLDER single orbit files are used as reference for the collocation of the coincident MODIS granules using a sinusoidal grid centered on POLDER ascending node longitude. This new dataset preserves completely POLDER cloud products while the MODIS products are averaged or mixed). In other situations where both liquid and ice are present within a POLDER superpixel, either liquid or ice dominated phase, or mixed phase is labeled depending on the number of liquid and ice pixels.
The second dataset called CALTRACK is a subset of products from POLDER, MODIS and different other sensors or meteorological reanalysis extracted along the CALIOP curtain. The level 2 CALTRACK products contain coincident data issued from several sensors under the CALIOP tracks at 5km horizontal resolution. These coincident products of different sensors are sampled and filtered using the official CALIOP products files as a reference space for collocation again using the nearest pixel approximation. Note that the CALTRACK dataset sample is much smaller than the PM one because of the narrow CALIOP swath. The period considered for the following study ranges from December 2007 to November 2008.
Global distribution observed from POLDER and MODIS The first part of the analysis is dedicated to a statistical and global comparison of cloud phase observed from POLDER and MODIS. From the PM dataset, each pixel observed by both instruments can be attributed one of the three phase index (ice, liquid or mixed) from POLDER or MODIS. There is therefore 9 (3 × 3) potential situations observed from the two sensors. We describe in the following, the geographical distribution and statistical occurrence of these 9 situations. Introduction.
In summary, CALIOP γ-δ features are very consistent with MODIS and POLDER phase products when those two agree. In cases where POLDER and MODIS have inconsistent decisions, CALIOP tends to agree with POLDER more often. This is most probably because both POLDER and CALIOP use somehow similar proxy for phase retrieval as both are based on the polarization analysis and are in fact particle shape determination methods. The statistics provided here represent a first quantitative assessment of POLDER and MODIS phase products for various cloud cover scenario and demonstrate the high quality of the joint dataset calling for a synergistic use of both products when trying to build global statistics of cloud thermodynamic phase.
Discussion From the above analysis, we have seen that the passive and active sensors are in excellent agreement when both passive sensors agree with each other. However situations where the passive sensors disagree are more complex to understand but there is a high interest in understanding these differences as it can provide ground basis for building a meaningful joint dataset even in case of opposed decision between POLDER and MODIS. The strengths and weaknesses of each retrieval algorithm are not direct and depend on many factors. In this section, we go further to discuss some of problems in phase detection for these sensors and support the analysis with the help of CALIOP to better describe the vertical structure of the observed cloud cover.
Observation geometry One of the challenging issues when building a global dataset from satellite sensors is the need to prevent any sort of systematic bias related to observation geometries. The retrieved cloud phase corresponding to certain geometry may be impacted by the 3-D effects and/or incorrect assumption about cloud microphysics used for the retrievals. Also, algorithms which depend on the observation of cloud under specific geometries are inherently subject to potential angular biases. This is the case for the POLDER algorithm for which the nature and number of applicable tests vary with accessible geometries. Also related, the observation of oriented plates by CALIOP motivated the change in viewing configuration of the lidar to prevent biases caused by specular reflection on oriented ice crystals  (Hu et al.
6. The fraction of MODIS-ice and POLDER-mixed clouds reaches 10 % when cirrus OT is between 0. 6 and 1. 4. In single layered cloud systems, the fraction of MODIS-mixed and POLDER-ice clouds reaches to a maximum of 10 % when cirrus OT is about 0. 2. The fraction of MODIS-liquid and POLDER-ice clouds reaches to a maximum of 30 % when cirrus OT is about 0. 3. This confirms that the sensitivity to thin cirrus is different between POLDER and MODIS. This first quantitative evaluation of their respective sensitivity in case of multilayered clouds provides useful information again for using the two datasets in a synergistic way.
From this study we have seen that POLDER can erroneously detect broken clouds scenes and aerosols overlaying water clouds as mixed or ice phase. However it can correctly identify cloud phase in case of single layered thin cirrus, water clouds over snow and the supercooled water clouds. Compared to POLDER, MODIS detects better the thin cirrus above water clouds and demonstrates better skills in identifying correctly the phase of broken clouds. CALIOP agrees for more than 95 % of the confident phase cases while for inconsistent and less confident phases, CALIOP partially agrees with either POLDER or MODIS, but preferentially with POLDER certainly because of both instruments extract phase information from polarization characteristics of clouds. This analysis and the joint POLDER/MODIS dataset that has been created open numerous perspectives for a better description of cloud thermodynamic phase at global scale.