dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis SSR markers used in this study showed high reproducibility in PCR amplification and ABI/Licor runs based on the allele sizes produced by control panel entries that were included in every PCR run. SagaGT software (Licor, USA) was used for allele scoring for the markers genotyped at CIRAD. At ICRISAT and CAAS, fragment analysis of PCR products was carried out using GeneScan and Genotyper 3. 7 software packages (Applied Biosystems, USA). PCR amplicon sizes were scored in base pairs (bp) based on migration relative to the internal ROX-400 size standard. At ICRISAT these raw allele calls were further processed through the AlleloBin software program (available at http://www. icrisat. org/bt-software-d-allelobin. htm) to provide adjusted allele calls. AlleloBin uses a standard repeat motif length (following the step-wise mutation model  [38] ) and a least squares algorithm to call allele sizes to integer values as suggested by Idury and Cardon  [39] , adjusting for imperfections in the co-migration of size standards and PCR products.
Marker data for 7 SSR markers (gpsb069, gpsp089, gpsb148, gpsb151, Xcup62, Xtxp295 and Xtxp33) were removed from the final analysis due to incomplete data or low quality genotyping. Finally, 3367 accessions were retained for further analysis across 41 markers (Table  1 ). Data files were assembled in a database (Sagacity v. 10, Rami, in preparation) and allele sizes were checked for congruency and adjusted according to the allelic references provided in the SSR kit  [24]. Descriptors of observed genetic diversity, such as allele number per marker, observed heterozygosity (Ho) and gene diversity (expected heterozygosity, He) were calculated using PowerMarker v3. 25 software  [40]. Allelic richness and private alleles by locus were estimated using ADZE software  [41]. Genetic distance between groups, estimated by F st statistics, was calculated with hierfstat R package  [42].
Mann-Whitney (MW) tests were used to determine whether estimates were significantly different between groups. To identify the pair-wise genetic relationships between the accessions of this sorghum global composite germplasm collection, a genetic dissimilarity matrix was calculated using simple matching with DARwin v5 software  [43]  (available at http://darwin. cirad. fr/darwin/Home. php). An overall representation of the diversity structure was obtained by a factorial analysis using the distance matrix, while individual relations were analyzed with a tree construction based on Neighbor Joining (NJ) method, as implemented in DARwin v5. In order to test for sample clustering in conjunction with admixture between sub-groups, Bayesian statistics based on Monte Carlo Markov Chain algorithm were used.
Although the Instruct software package  [44]  was developed to handle specifically species with a high level of inbreeding, as expected for sorghum, it was not used here because it cannot handle such a large number of samples. STRUCTURE software v. 2. 3. 3  [45]  was thus preferred. One hundred replicates were performed for each K, the number of clusters considered. Each run used a burn-in period of 100,000 iterations followed by 200,000 iterations. For each K, the 10 runs presenting the highest maximum likelihood value were kept, and sample assignation to groups was performed with CLUMPP software (up to K = 6, greedy algorithm, 1000 repeats, over K = 6, large K greedy algorithm, 1000 permutations) in order to deal with label switching or multimodalities. Estimate of the best cluster number was performed following  [46]  with a R (http://www.
rproject. org/) script modified from  [47]. It was compared to information given by each cluster, and identified when no new individual presented a majority of ancestry in a new cluster (threshold 0. 7). Genome plot representations were performed using a specifically developed R script (available upon request). A Reference Set of 383 sorghum accessions including S. bicolor subspecies bicolor and wild S. bicolor subspecies verticilliflorum was chosen among the publically available accessions to best represent genetic diversity as well as geographic origins. Maximum Length Subtree function of DARwin v5 software  [43]  was used to deal with genetic diversity. It is based on successive elimination of samples, each eliminated sample presenting a minimal reduction of overall diversity, measured as branch length of a tree.
Since in the GCGC collections, phenotyping data were already available on a subset of diverse accessions (  [11] ,  [4] ), this subset was first analyzed to reduce redundancy. Widely used breeding lines completed it. A first run of completion of these accessions was performed on S. bicolor only, checking that all geographic origins are conserved. The same process was performed for wild accessions, and both datasets were merged to represent the Sorghum Reference Set.
Community Resources Data. Data generated in the present study was deposited in the GCP central registry (http://generationcp. org/research/ research-themes/crop-information-systems, using Sorghum as a 'crop' filter, file G2005-01c_Sb_3393accX41SSR_V2. xlsx) and is accessible to the global community. They come in addition to passport data that are available in the germplasm banks and occasional evaluation data that may have been produced as part of searches for donors of specific traits to be used in breeding programs. The data can serve as a reference since it was obtained with an easily accessible kit of markers  [24]  that can be used on any new material for comparison. Reference set of sorghum. We used the marker data and population structure of the sorghum GCGC from this study to identify a much smaller representative subset of accessions, called the 'Reference Set'.
This Reference Set provides an entry point to sorghum germplasm globally, to identify geographic regions and racial subgroups from which sorghum accessions exhibiting interesting variability in a particular trait can be found. The general value of an internationally agreed set of representative germplasm to serve as a common reference for focussing characterization has been highlighted elsewhere  [59]. This proposed Reference Set consists of 383 accessions, includes important germplasm lines used in crop breeding programs, wild accessions and a mini-core collection of genetically diverse accessions for which considerable phenotypic data is already available. Five basic morphological types, ten intermediate ones and wild/weedy accessions from nearly all geographic origins were captured in this sorghum Reference Set.
Seeds are maintained by ICRISAT and available upon request. All passport data published in the System Wide Information Network on Genetic Resources (SINGER), including Sorghum, are available in Genesys (http://www. genesys-pgr. org/ ), which aims at being the global information system on the germplasm held ex situ. Perspectives. The core reference set is expected to stimulate links among sorghum scientists. The data have been analysed with several methods, which provide marker-based keys to germplasm classification and are meant to serve as a reference. Any new material can easily be compared to this reference; these markers are easily applicable for local studies with local questions in local laboratories, and yielding results that are comparable to other studies performed elsewhere, thanks to the use of a common kit of markers and standards.
This will be very useful for identifying germplasm action priorities, for enriching global collections if novel types are uncovered or for broadening the basis of a given breeding program. Having the data available for the whole GCGC for 41 SSR loci provides a considerable backup for mining germplasm diversity. Molecular data can serve for complementing reference materials with additional germplasm targeted towards particular applications depending on the operational constraints, the biological constraints (e. g. phenology) and statistical power. Typically SSR data can be used to adjust a sample to a target size with the view to minimizing population structure in order to maximize resolution power in a given association analysis; the Maximum Length Subtree function of the DARwin software can help do this easily, quickly and rigorously  [43].
(EPS) Table  S1  List of accessions comprising the sorghum global composite germplasm (GCCG) collection and characterization. It includes each accession number, institution originally providing the material, biological status, species, subspecies, race, geographic origin (country), geographic origin (continent), subgroup assignation (K value and Cluster), included or not in the Reference Set. Countries are given in standard ISO 3166-1 alpha-3 codes. Continents are AUS (Australia), CAfrica (Central Africa), EAfrica (Eastern Africa), EAsia (Eastern Asia), Europe, IND (India), MedB (Mediterranean Basin), MidE (Middle East), NAmerica (Northern America), SAfrica (Southern Africa), SAmerica (Southern America), WAfrica (Western Africa). Subgroup assignations correspond to Structure K values combined with Cluster assignations.
The authors thank the  Generation Challenge Programme (GCP)  for their financial support to this project. The research fellowship provided to PR by the  Council of Scientific and Industrial Research (CSIR), New Delhi, India , is gratefully acknowledged. Part of this work was carried out by using the resources of the  Computational Biology Service Unit from Cornell University  partially funded by  Microsoft Corporation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Part of our work was carried out by using the resources of  Cornell University's Computational Biology Service Unit. It was partially funded by  Microsoft Corporation.
DNA Extraction. DNA was isolated from peripheral blood leukocyte using salting out method  [14]  or Qiagen kit DNA extraction. Genotyping for 14 available individuals was determined using 3 microsatellite markers spanning a 1. 43 Mb interval that overlaps the XPC gene region (D3S3602, D3S1585, and D3S3613). Microsatellite markers were selected from the genetic maps available on NCBI browsers and the CEPH genotype database (http://www. cephb. fr/en/cephdb/) on the basis of their heterozygosity percentage and closeness to the XPC gene. Genomic DNA amplification was carried using specific primers (Table  2 ), and the fragments were amplified in a volume of 25 L containing 10 ng of DNA, 10X PCR buffer, 0. 2 mM of each dNTP, 1. 5 mL of MgCl 2 , 0. 5 M of one of each primer, and 0. 5 U Amplitaq DNA polymerase (Invitrogen).
It likely abolishes the exon 6 splicing donor site as shown by the following bioinformatic tools: "BDGP" (http://www. fruitfly. org/seq tools/splice. html) and "Human splicing finder mutation" (http://www. umd. be/HSF/). This mutation probably leads to a premature stop codon at position 260 that results in a truncated XPC protein (p. Trp260X). To note, this mutation is not present in the public dbSNP database (http://www. ncbi. nlm. nih. gov/snp). In addition, Mendelian inheritance of this mutation was confirmed (Figure  2 ). The second mutation is a G to T substitution at position 14521 of the genomic DNA (g. 18810G>T; c. 850G>T) present at a homozygous state in (XP28SFA, XP16KEB, and XP45GB) patients. This founder mutation probably leads to a premature stop codon at position 284 of XPC protein (p284X).
Acknowledgments The authors would like to thank the patients and their families as well as the patients' support group " Helping Xeroderma Pigmentosum children " (http://www. xp-tunisie. org. tn/) for their collaboration. This work was supported by the Tunisian BioMed Research International 7  Ministry of Higher Education and Scientific Research (Laboratory on Biomedical Genomics and Oncogenetics ,  LR 11 IPT 05 ) and the  Tunisian Ministry of Public Health.
Quantification of B Cell Follicle Regression Upon DT Treatment LN immunofluorescence images were segmented into B220 + B cell areas in control and DT-treated chimeras. Such B cell areas were measured using Image J software (National Institutes of Health). The percentage of B cell follicle regression in DT-treated chimeras (as opposed to control mice) was calculated by dividing the total B cell area in control mice by the total B cell area in DTtreated mice. Of note, we observed a strict correlation between such ratio and the ratio of B cell percentages recorded by flow cytometry in the LNs of control and DT-treated mice. These ratios were then used to extrapolate the size that each B cell follicle occupied before DT treatment. As an example, if DT treatment induced a 70% reduction in the size of B cell follicles, we extrapolated that B cell follicles in DT-treated mice were 70% bigger before the treatment.
Immunostaining LNs were harvested and fixed in a 0. 05 M phosphate buffer containing 0. 1 M L-lysine (pH 7. 4), 2 mg/ml NaIO 4 , and 10 mg/ ml paraformaldehyde (PLP) for 12 h, then washed in phosphate buffer and dehydrated in 30% sucrose in phosphate buffer. The 20 mm frozen sections were cut and then stained with the indicated antibodies as previously described  [36]. Immunofluorescence confocal microscopy was performed with a Leica SP5 confocal microscope. Separate images were collected for each fluorochrome and overlaid to obtain a multicolor image. Final image processing was performed with Imaris software (Bitplane) and Adobe Photoshop.
Flow Cytometry LN cells were digested with collagenase I (Life Technologies, Villebon sur Yvette, France) for 30 min at 37uC and stained in 96well V-bottomed plates. Cells were blocked with 2-4G2 for 15 min at 4uC and stained with antibodies in PBS 1% FCS 2 mM EDTA for 45 min at 4uC. Data were acquired on FACS-LSRII UV (Becton Dickinson) and were analyzed on DIVA software (Becton Dickinson).
Micro-Array Hybridization Data Normalization and Analysis Biotinylated double-strand cDNA targets were prepared, starting from 0. 3 to 3 ng of total RNA using the NuGEN Ovation Pico WTA System V2 Kit (Cat. No. 3302) and the NuGEN Encore Biotin Module Kit (Cat. No. 4200) according to NuGEN recommendations. Following fragmentation and endlabeling, 2 mg of cDNAs were hybridized for 16 h at 45oC on GeneChip Mouse Gene 1. 0 ST arrays (Affymetrix) interrogating 28. 853 genes represented by approximately 27 probes spread across the full length of the gene. The chips were washed and stained in the GeneChip Fluidicsstation 450 (Affymetrix) and scanned with the GeneChip Scanner 3000 7G (Affymetrix) at a resolution of 0. 7 mm. Raw data (. CEL Intensity files) were extracted from the scanned images using the Affymetrix GeneChip Command Console (AGCC) version 3.
2. Normalization of the raw Affymetrix expression data was performed by Robust Multi-chip Analysis (RMA) through Bioconductor (release 2. 9) in the R statistical environment (version 2. 14) via the Affy package. Probesets over a normalized expression value of 4 (log 2 scale) across all arrays were considered as expressed and were included in the analysis. ProbeSets with a standard deviation. 1 for each condition and that were not associated with a gene symbol were filtered out. At the end, 14,430 probes were kept for the subsequent analyses. In order to select the regulated probes between conditions, we used limma package (Linear Model for Microarray Data) using an empirical Bayes method. Genes having a log 2 ratio superior to 1. 5 and a p value inferior to 0. 05 was considered as up-regulated in CD21 2 RFP + cells.
This work was supported by a grant from the  Agence Nationale de la Recherche (ANR)  ( ANR-08-JCJC-0134   RCS ; ANR-12-JSV3-0001   STROMA  and  ANR-10-INBS-04-01   France Bio Imaging ) and  NIH   R01 DK 57731  (NHR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Biotech). Stringent criteria for RNA quality were applied to rule out degradation, especially a 28s/18s ratio above 1. 8 for microarray. • cRNA probe production and labeling: 3 mg of total RNA were amplified and labeled according to the manufacturer's one--cycle target labeling protocol (http://www. affymetrix. com). 10 mg of cRNA were used per hybridization (GeneChip Fluidics Station 400; Affymetrix, Santa Clara, CA). The labeled cRNAs were hybridized to HG--U133 plus 2. 0 Affymetrix GeneChip arrays (Affymetrix, Santa Clara, CA). Chips were scanned with a Affymetrix GeneChip Scanner 3000 and subsequent images analyzed using GCOS 1. 4 (Affymetrix). • Affymetrix chips quality control: we used the R package affyQCReport to generate a QC report for all chips (CEL files). All the chips that did not pass this QC filtering step were removed from further analysis.
• Normalization: raw feature data from Affymetrix HG--U133A Plus 2. 0 GeneChip TM microarrays are normalized using Robust Multi--array Average (RMA) method (R package affy)  (2). Methylome data from the discovery cohort: Whole--genome DNA methylation was analyzed in 84 HNSCC samples using the Illumina Infinium HumanMethylation27 assay. In brief, genomic DNA was bisulfite--converted using the EZ--96 DNA Methylation Kit (Zymo Research, Orange, CA). Bisulfite-converted DNA was whole--genome amplified, enzymatically fragmented, and hybridized to the BeadChip arrays according to the manufacturer's instructions. Bisulfite conversion and microarray experiments were performed by Integragen SA (Evry, France, http://www. integragen. com). The HumanMethylation27 BeadChip examines the DNA methylation status of 27,578 CpG sites at promoter regions of 14,495 protein--coding genes.
Each CpG site is represented by two beads on the array, measuring the levels of methylated (M) and unmethylated (U) DNA. The beta value DNA methylation scores for each locus are then calculated as M/(M+U). Detailed information on HumanMethylation27 BeadChip can be obtained at http://www. illumina. com. Data were exported as standard report files and normalized using le R lumi package (v2. 15). Methylome data from the validation cohort: Whole--genome DNA methylation was analyzed in 101 HNSCC samples using the Illumina Infinium HumanMethylation450 assay that examines the DNA methylation status of 485,000 CpG sites (covering 99% of RefSeq genes and 96% of CpG islands). Detailed information about this technology can be obtained at http://www. illumina. com. In brief, genomic DNA was extracted using either the Manual--MagNa Pure LC DNA II kit (Roche) or the Manual--Gentra Puregene kit (Qiagen) and bisulfite--converted using the EZ--96 DNA Methylation Kit (Zymo Research, Orange, CA).
DNA was then whole--genome amplified, enzymatically fragmented and hybridized (500 ng) to the BeadChip arrays according to the manufacturer's instructions. Bisulfite conversion and microarray experiments were performed by Integragen SA (Evry, France, http://www. integragen. com). The BeadChips were scanned using the Illumina HiScan SQ scanner and raw images data were imported into the GenomeStudio (v2011. 1) methylation module (v1. 9. 2. ) which was used to extract and transform the fluorescent signal intensities into beta--value (range: 0 --unmethylated site--to 1 --fully methylated site--). Data were exported as standard report files and normalized using le R lumi package (v2. 15). miRNome data: MicroRNA sequencing were obtained for 64 HSNCC samples by the Illumina sequencing process HiSeq2000.
Convert *bcl files into compressed FASTQ files. To do some quality control checks on raw sequence data, fastqc software was used. Finally, the script "Trim_adapter", provided by mirExpress software, handled the sequence file which contained adapter or not according the input of adaptor sequence. The sequence adaptor was trimmed on sequence data. For each sample, FASTA files were processed by miRanalyzer0. 3 software (5) to obtain counts for each mir of mirBase v18  (6). Genome data: 88 HSNCC samples were analyzed with Illumina HumanCNV370--Quad v3. 0 chips, containing 373,397 probes. Hybridization was performed by IntegraGen (Evry, France), according to the instructions provided by the array manufacturer. Raw fluorescent signals were imported and normalized into Illumina BeadStudio software as previously described  (7)  to obtain the log R ratio (LRR) and B Allele Frequency (BAF) for each SNP.
Omics Analysis Except where indicated, all transcriptome and genome analysis are carried out using either an assortment of R system software  (15)  packages including those of Bioconductor (9) (V2. 9) or original R codes. R packages and versions are indicated where appropriate. 1--Unsupervised classification Three methods were used to find unsupervised clusters within the four omics. • A model--based clustering method called RPMM for "Recursively Partitioned Mixture Model"  (16). This method models beta distributions specifically for the beta--values of methylation data and it also models gaussian distributions for the other data types. RPMM estimates parameters of mixture models via recursive Expectation--Maximization algorithms and it determines the final clustering thanks to Bayesian Information Criteria (BIC).
See (  16 ) for more details. • A consensus clustering method proposed by  Monti et al. in (17). This method perturbs the original data via resampling techniques. For each perturbed data set, a clustering algorithm is performed. A consensus clustering among the multiple runs are then assessed by taking the average over the connectivity matrices of every perturbed dataset. See  (17)  for more details. The Bioconductor R package ConsensusClusterPlus was then used. • In  (18)  we presented another method performing a consensus clustering. This method can be detailed in the six following steps. This method was used in a subgroup of our transcriptomic data in (1): o Unsupervised probe set selection : Probe set's unsupervised selection was based on the two following criteria: a p--value of a variance test (see below) less than 0.
01, a "robust" coefficient of variation (rCV) less than 10 and superior to a given rCV percentile. Eight rCV percentile thresholds were used (60%; 70%; 80%; 90%; 95%; 97. 5%; 99%; 99. 5%) yielding 8 lists of probe sets. o Variance test: For each probe set (P) we tested whether its variance across samples was different from the median of the variances of all the probe sets. The statistic used was ((n--1)×Var(P) / Var med ), where n refers to the number of samples. This statistic was compared to a percentile of the Chi--square distribution with (n--1) degrees of freedom and yielded a p--value for each probe set. This criteria is the same used in the filtering tool of BRB ArrayTools software (linus. nci. nih. gov/BRB--ArrayTools. html), described in the User's Manual. o Robust coefficient of variation: For each probe set, the rCV is calculated as follows: having ordered the intensity values of the n samples from min to max, we eliminate the minimum value and the maximum value and calculate the coefficient of variation (CV) for the rest of the values.
2--Differential Analysis Since the different studied omics have different density distributions, several differential analysis methods were used. Transcriptome data: Based on the RMA log 2 single--intensity expression data, we used moderate T-tests to identify genes differentially expressed between groups of samples, using limma R package. The H1 proportion of T--tests over the set of measured transcripts was estimated using B Storey method : (1 -2 × mean { if(p i > 0. 5) then 1 else 0} probe set i:1. 55K )  (21). Anova models were used for multigroup comparison. To control for multiple testing we measured the local false discovery rate. Methylome data: Because of the beta distribution of the beta values, Wilcoxon tests were used to identify genes differentially methylated between groups of samples.