dataesr/scientific-paragraphs-categorization huggingface dataset:
For the H1hesc cell line, we downloaded these additional broadpeak genomic intervals: H3K9ac, CHD1, EZH2, NANOG and OCT4. Most of the data correspond to the release 3 (August 2012) of the Broad histone track, downloaded from: http://hgdownload. cse. ucsc. edu/goldenPath/hg19/encodeDCC/ wgEncodeBroadHistone/ The NANOG and OCT4 data corresponds to the release 3 (September 2012) of the HAIB TFBS track. Tables were downloaded from: http://hgdownload. cse. ucsc. edu/goldenPath/hg19/encodeDCC/wgEncodeHaibTfbs/ All these tables in hg19 coordinates were converted to hg18 coordinates using LiftOver. Note that cell line H1hesc (part of ENCODE Tier 1) is the only embryonic stem cell line for which we could gather a large chromatin mark dataset as described above. Hence, we could not include in the present work the analysis of another ESC line to assess whether the unique properties observed in H1hesc are also valid for other ESC lines.
Principal component analysis Principal component analysis was performed on the rank transformed dataset using the function dudi. pca from the R package ade4 (see http://pbil. univ-lyon1. fr/ADE-4 and  [101] ) with the option scale = TRUE (i. e. each variable was centered and normalized before the PCA computation). The first four components were retained which accounts for 86% of the dataset variance (S2A and A' Fig. ). Clustering was performed in this 4D space.
Clustering strategy We used Clara algorithm  [94]  which is an optimization of k-means for large data set. We used the clara function implemented in the R package cluster. The options were set to: stand = FALSE, sampsize = 500, samples = 20, metric = euclidean. For the shared differentiated cells, the number of clusters was set to the number of prevalent chromatin states detected in  [92]. Previously to the merging of dataset into one shared epigenetic space, we checked that, when applied to each cell individually, the analysis pipeline led to qualitatively the same epigenetic states (data not shown). Poorly clustered data points were removed from the set of chromatin states. The silhouette value  [102]  is a way to quantify how well a point is clustered. Definition 1 Given a particular clustering, C 1 , C 2 ,.
Mean replication timing data and replication U-domain coordinates Mean replication timing (MRT) determined in 100 kb non-overlapping windows in hg18 coordinates for an ESC line (BG02), a lymphoblastoid cell line (GM06990), a skin fibroblast cell line (BJ), an immature myeloid cell line K562 and a HeLa cell line were obtained from the authors  [77]. The coordinates of the 1534 (BG02), 882 (GM06990), 1150 (BJ), 876 (K562) and 1498 (HeLa) replication U-domains were also obtained from these authors. Replication data in BG02, GM06990 and BJ were used as surrogates of replication data in H1hesc, GM12878 and Nhdfad, respectively. We had previously observed a very strong conservation of MRT between lymphoblastoid cell lines including GM06990  [75] , as well as between fibroblast cell lines BJ and IMR90 (unpublished).
DNase Hypersentive site data DNaseI hypersensitive sites (DHSs) data were downloaded in the Encode standard format "narrowpeaks" (http://genome. ucsc. edu/FAQ/FAQformat. html). DHS narrowpeaks are genomic intervals indentified as hypersentive zones to DNaseI within a false discovery rate of 0. 5%. We downloaded the tables from: http://hgdownload. cse. ucsc. edu/goldenPath/hg18/ encodeDCC/wgEncodeUwDnaseSeq/.
Annotation and Expression data As human gene coordinates, we used the UCSC Known Genes table. When several genes presenting the same orientation overlapped, they were merged into one gene whose coordinates correspond to the union of all the overlapping gene coordinates, resulting in 23818 distinct genes. Expression data were retrieved from the Genome Browser of the University of California Santa Cruz (UCSC). We downloaded expression values from the release 2 of Caltech RNA-seq track (ENCODE project at UCSC): http://hgdownload. cse. ucsc. edu/goldenPath/hg18/ encodeDCC/wgEncodeCaltechRnaSeq/ To determine the genome coordinates of each gene (labeled by its RefSeq identifier), we used RefSeq Genes track. For genes associated to more than one splicing variant, we merged exons coordinates by taking their union.
Acknowledgments We are very grateful to  Y. d'Aubenton-Carafa ,  A. Baker ,  JC. Cadoret ,  E. Cascales ,  CL. Cheng ,  L. Duret ,  A. Goldar ,  O. Hyrien ,  M. Kahli ,  N. Petryk ,  F. Picard ,  MN. Prioleau ,  C. Thermes  and  C. Vaillant  for helpful discussions.
XMT was performed according to the protocols reported in the literature  [12] [13] [14]. We used a Viscom X8050-16 µCT scanner at the Centre for Microtomography of the University of Poitiers (France). Scans were performed at 100 kV and 34/32 mA. The geometry was set to obtain an 84. 2-62. 9 µm voxel size in the reconstructed three-dimensional images. The reconstruction was performed using the software IMAGEJ (http://imagej. nih. gov) [15]  and the FDK algorithms of DIGICT v. 2. 4. 3 (Digisens with plugin SnapCT, acceleration in GPU). The dataset consists of 2160 projections (720 × 3) taken over 360 • for the whole body and 900 projections for the head of the specimen. Direct volume rendering was used to visualize the sub-set of selected voxels of the prey, the skeleton and the jaw in AVIZO v.
Acknowledging that the power of fixed effects meta-analysis can be greater than that of random effects, even under heterogeneity  7  , we also performed fixed effect metaanalysis, and report loci exceeding significance thresholds by either random or fixed effects analysis. Analysis included   3 ). We considered loci with Po1. 25 Â 10 À 8 to be genome-wide significant, based on the standard for genome-wide significance (P ¼ 5 Â 10 À 8 )  8  and multiplicity correction for CFTR genotype (all genotypes versus p. Phe508del homozygotes) and model (random versus fixed effects). Associations in five regions (chr3q29, chr5p15, chr6p21, chr11p12-p13 and chrXq22-q23) exceeded genome-wide significance (Fig. 1 ). Only the chr11p12-p13 locus provides significant evidence of interaction with CFTR (P ¼ 0.
As expected, this association with lung disease was not present when we restricted analysis to the p. Phe508del homozygotes (n ¼ 3,815; P ¼ 0. 1604, from a permutation-based test that is then meta-combined using Stouffer's Z-score method). eQTLs for significant SNPs. To determine whether the most significantly associated GWAS SNPs show evidence as expression quantitative trait loci (eQTLs) in lung or immune cells, we examined three available databases for eQTLs (Genotype-      2  (shown as purple diamonds and labelled with dbSNP ID). The purple diamond in the chr6 region denotes the SNP that has independent genotype confirmation, but the top imputed SNP is also indicated by a dbSNP ID (rs number). On the right side of the five panels are forest plots of the relative effect sizes for the most significant SNP in each of the 13 subgroups, ordered by size.
Samples with a call rate o98%, or sex discordance were excluded. Unintentional duplicates of GWAS2 and GWAS1 samples, and individuals missing a Consortium lung phenotype were also excluded. After exclusions, 2,921 GWAS2 samples remained for the analysis. There were 473,514 SNPs present on both the 660W and Omni5 platforms. Imputation. MaCH/Minimac software (http://www. sph. umich. edu/csg/abecasis/ MACH/index. html) was used for phasing and imputing the genotyped data. Phase I, Version 3 haplotype data from 1000 Genomes project (ftp://ftp-trace. ncbi. nih. gov/ 1000genomes/ftp/release/20110521/) was used as the reference. Approximately 5% of our patients were deemed as not of European ancestry by principal components (Supplementary Fig. 4 ), so all 1000 Genomes reference samples were included.
The remaining imputed SNP (rs9268947) has the lowest P value in the region, but lacks independent genotype confirmation. Thus, rs116003090, the SNP with the most extensive and best concordance with independent genotyping, was selected as the representative associated SNP from the HLA region. Estimating population structure with principal components. Eigensoft (http://www. hsph. harvard. edu/alkes-price/software/) was utilized to obtain principal components to serve as covariates to adjust for population stratification. Genotyped SNPs common in all platforms with a high minor allele frequency and low linkage disequilibrium were included. Principal components were generated separately for each subgroup. To account for relatives, a maximal set of unrelated individuals were first identified by choosing a random member from each family for principal component analysis.
Then we calculated an average r 2 , weighted by sample size. eQTL assessment for most significantly associated GWAS SNPs. We searched for eQTLs for the top-ranked SNP in each of five loci, and for local SNPs in r 2 (LD)40. 6 with the top-ranked GWAS SNPs, using three publicly available databases (Genotype-Tissue Expression, GTEx, lung and whole blood; University of North Carolina at Chapel Hill seeQTL, HapMap LCL and Zeller monocyte; University of Chicago SNP and CNV Annotation Database, SCAN, HapMap CEU LCL). Genes and transcripts within 100 kb of the top-ranked SNPs in five loci were included. Results are presented as nominal P values for each region, except Q values are given for the results from UNC seeQTL (Supplementary Table  3 ). GWAS1 þ 2 comparison with prior candidate associations.
A group of 17 strains were isolated from Erysiphe necator (formerly Uncinula necator) infecting grapevine. The remaining strains came from powdery mildew species infecting various host plant species, none of which was widely sampled. Overall, our dataset of 637 Ampelomyces strains contained 394 strains infecting apple powdery mildew (APM) and 243 non-APM strains. We chose B. graminis, A. mougeotii and E. necator to be the most intensively sampled non-APM mycohost species because these three powdery mildews have been shown to be representative of the diversity of non-APM species  [56].
Each gel contained GenScan-500LIZ Size Standards (35 to 500 bp, Applied Biosystems), for the analysis of microsatellite data with GeneMapper Software (Applied Biosystems). Genetic analyses: SplitsTree, correspondence analyses, linkage disequilibrium, and structure. SplitsTree 4  [57]  (http://www. splitstree. org/) was used to visualize differentiation and the occurrence of recombination, (i) for the total dataset, with the five microsatellite markers yielding amplification products for all samples, and (ii) on the 209 non-APM strains that could be genotyped with all eight markers. Factorial correspondence analyses (FCA) were performed with GENETIX v4. 05  [58]. F ST and linkage disequilibrium between the five markers and population differentiation were assessed online with Genepop  [59, 60] , http://genepop.
curtin. edu. au/. We used the Genclone software  [61]  to determine the number of different multilocus genotypes (MLGs) in the datasets and for the estimation, by permutation, of the expected number of MLGs for different numbers of markers. We used the individual-based Bayesian clustering methods implemented in STRUCTURE 2. 3. 3  [62]  to infer population structure. STRUCTURE makes use of Markov Chain-Monte Carlo (MCMC) simulations to infer the proportion of ancestry of genotypes from K distinct clusters. The underlying algorithms attempt to minimize deviations from Hardy-Weinberg and linkage disequilibria. Ten independent analyses were carried out for each number of clusters, from K = 2 to K = 10, with admixture models and 500,000 MCMC iterations, after a burnin of 500,000 steps. Outputs were processed with CLUMPP v1.
Genetic diversity and population structure in Ampelomyces strains We first investigated the population structure of Ampelomyces strains with STRUCTURE  [66]. SplitsTree analyses were also carried out, separately for the genotypes of all the strains based on five microsatellite markers (Fig. 1 ), and for the 209 non-APM strains genotyped with the eight markers (Fig. 2 ). STRUCTURE yielded well defined clusters at K values of up to 6 (Fig. 3  and S1 Fig. ), indicating the existence of six genetically differentiated clusters. For values of K of 7 and above, each new cluster included only admixed genotypes, indicating a lack of further structure. The deltaK value  [67]  confirmed that the split into six populations corresponded to a peak in the strength of the structure in the dataset, the strongest peak being at K = 4 (S2 Fig.
Recombination footprints In the dataset based on five markers and 637 strains, only 93 MLGs were detected, with a mean of 6. 7 strains per MLG. These identical MLGs probably did not correspond entirely to clones, resulting instead from the insufficiently high discriminatory power of the five markers, as a permutation estimating the number of MLGs as a function of the number of markers used did not reach a plateau (S4 Fig. ); actually, most of these identical MLGs could be differentiated using eight markers. In the dataset based on eight markers and 209 strains, 91 MLGs were detected, with a mean of only 2. 3 strains per MLG, and a single abundant MLG for 17 strains in the APM cluster. In this analysis, the number of MLGs as a function of the number of markers used did begin to reach a plateau (S4 Fig.
Discussion In this study, we developed eight new polymorphic microsatellite markers for investigating the population structure and diversity of Ampelomyces strains isolated from many powdery mildew species infecting very different host plant species and representative of the genetic diversity in Erysiphales. Five out of eight of our newly developed microsatellite markers could be amplified in both AMP and non-AMP strains, which was not the case for previous microsatellite markers  [11]. The newly developed markers will be useful for detecting and monitoring Ampelomyces strains released as commercial biological control agents in vineyards  [42]  and in agricultural fields, because they were amplified reliably from environmental samples as well as from strains maintained in culture. Various analyses, using a comprehensive dataset with the genotypes of APM and non-APM strains, confirmed that there was a high level of genetic differentiation between these two groups, with no evidence of gene flow.
Neither SplitsTree nor STRUCTURE analyses however confirmed here the distinction of the non-APM strains according to the MOTUs defined in these previous works  [38] [39] [40] 53]. STRUCTURE analyses further revealed that the Ampelomyces strains included in this work could be split into six distinct populations (Fig. 3 ). The non-APM strains formed two genetically differentiated populations that could be accounted for by geography, as one of the clusters contained mostly Hungarian strains. This differentiation of Hungarian strains probably reflects their abundance in the dataset, and further geographical clustering may be revealed within the other clusters after additional sampling. The APM strains formed four different populations that also displayed some geographical clustering. Most of the strains, both APM and non-APM, came from Hungary and two regions of France, again potentially explaining why STRUCTURE differentiated these three regions.
Sepsis is a major cause of AKI, which develops in one-fourth of all patients with sepsis and half of patients with bacteremia or shock  [9]. Sepsis-related AKI is associated with high mortality rates of up to 70%  [9, 10, 11, 12]. Whether diabetes increases the risk of AKI during sepsis is controversial  [13, 14]. However, diabetes is an established risk factor for both AKI and sepsis  [2, 3, 4]. Here, our objective was to evaluate the impact of diabetes on kidney function in patients with severe sepsis or septic shock. We used a case-control design to compare the incidence of AKI, use of renal replacement therapy (RRT), and renal recovery in diabetic and nondiabetic patients with severe sepsis or septic shock enrolled in a prospective database by 12 intensive care units (ICUs).
Study design and data source Outcomerea is a prospective observational multicenter database established between January 1997 and June 2009 by 12 French ICUs. To avoid selection bias and ensure external validity, a random sample of patients older than 16 years of age and admitted for >24 hours were entered into the database each year. Participating centers could enroll either consecutive patients admitted to a prespecified number of beds throughout each year or all consecutive patients admitted during a prespecified month. The number of beds or month of enrolment were decided yearly by the database steering committee. Detailed clinical and outcome data were recorded prospectively for each patient, on a daily basis, by senior ICU physicians with assistance from trained study monitors in each participating ICU.
Data were recorded at baseline (including demographic characteristics, comorbidities, baseline severity, admission diagnosis, admission category, and patient location just before ICU admission) then on each ICU day (including diagnostic and therapeutic procedures, laboratory parameters, organ failures, sepsis, iatrogenic events, and treatment-limitation decisions). For each patient, the data were entered into an electronic case-report form using VIGIREA and RHEA data-capture software (Outcomerea, Rosny-sous-Bois, France) and all case-report forms were then entered into the Outcomerea data warehouse. The data-capture software automatically conducted multiple checks for internal consistency of most of the variables at entry into the database. Queries generated by these checks were resolved with the source ICU before incorporation of the new data into the database.
At each participating ICU, data quality was controlled by having a senior physician from another participating ICU check a 2% random sample of the study data. A 1-day coding course was held annually with the study investigators and study monitors. In accordance with French law, approval for the development and maintenance of the Outcomerea database was obtained from the data confidentiality agency Commission Nationale de l'Informatique et des Libertés. The study was approved by the ethics committee in Clermont-Ferrand, France. Given the observational study design, absence of effect of data collection on patient management, and use of anonymized data for the statistical analyses, the ethics committee waived the requirement for informed consent from study patients. The senior physicians and centers that participated in Outcomerea are listed in the acknowledgment section.
Diabetic patients admitted for sepsis severe or septic shock should have explorations including urinary sediment and nephrotoxic drugs should be used with caution. Our study has several limitations however. First, although these patients were extracted from a large multicenter database, we cannot exclude risk of bias related to our matching approach and to differences in patient's management across sites. Second, diabetic status was assessed based on previous medical history as reported by patients, relatives or consultants, so we did not have information on HBA1C levels or exhaustive diabetes complications. Whether having a poorly controlled diabetes is of worse prognosis or not will be the object of a further Table  3. Odds ratios for acute kidney injury and renal replacement therapy associated with diabetes mellitus in the matched case-control analysis of patients with severe sepsis or septic shock: unadjusted and adjusted conditional logistic regression models.
Matched patients After adjustment study. Third, we excluded patients with CKD. However, some of patients may have been missed, as detailed information on pre-ICU renal function was not always available. In addition, some cases of prerenal AKI, or rapidly reversible AKI, may have been missed. Fourth, AKI was diagnosed according to the KGDIGO guidelines but only based on serum creatinine variations, as urinary output was not available in the database, which can not be corrected retrospectively  [24]. Fifth, the use of RRT was not based on standardized criteria but was instead upon clinicians' decision as standardized recommendations do not exist and as strategies differ across sites, like in most previous studies. Finally, no long-term follow-up was available in this database, which is a limitation as previous studies have shown that renal recovery must be evaluated no earlier than one year after an acute kidney injury episode  [25].
French Research vessels have been collecting thermo-salinometer (TSG) data since 1999 to contribute to the Global Ocean Surface Underway Data (GOSUD) programme. The instruments are regularly calibrated and continuously monitored. Water samples are taken on a daily basis by the crew and later analysed in the laboratory. We present here the delayed mode processing of the 2001-2013 dataset and an overview of the resulting quality. Salinity measurement error was a few hundredths of a unit or less on the practical salinity scale (PSS), due to careful calibration and instrument maintenance, complemented with a rigorous adjustment on water samples. In a global comparison, these data show excellent agreement with an ARGObased salinity gridded product. The Sea Surface Salinity and Temperature from French REsearch SHips (SSST-FRESH) dataset is very valuable for the 'calibration and validation' of the new satellite observations delivered by the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions.
01 PSS and the resolution close to 0. 001 PSS, which is largely sufficient to capture the surface variability. However, siting of the instrument, stability of the electronics, sufficient flow through the conductivity cell, air bubbles and contamination by fouling and deposits may dramatically increase the measurement errors and even lead to discarding the data. The Global Ocean Surface Underway Data programme (GOSUD) of the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) and the International Oceanographic Data and Information Exchange (IODE) began in the early 2000's with the main objective of developing the ocean surface measurements and assembling qualified datasets. The continuous data acquisition of thermosalinometer data on board French Research Vessels was initiated in 1999.
Calibration uncertainties are calculated according to  8  for salinity values. On board ships operated by Ifremer/Genavir (at the present time all ships except the Beautemps-Beaupré), the installation and the operation of the instruments is performed by the crew. A similar system is applied on the ship operated by other partners. Instruments are cleaned at the beginning of each cruise and water samples are taken on a daily basis and later analysed in the SHOM laboratory. Reduced data are transmitted in real time to the Coriolis datacentre and full resolution data are archived on board and provided to the SISMER/Ifremer datacentre after the cruises. When the samples for a full year have been analysed, the delayed mode processing is performed. This includes a visual quality control of temperature and salinity, and the estimation of a correction to produce the corresponding 'adjusted' variable.
Methods We present here the delayed mode processing performed on the dataset collected by the Research Vessels presently in the project (see Table  1 ). All data available up to October 2014 have been taken into account and we plan to update the database annually. The data processing follows the method developed at SO-SSS (http://www. legos. obs-mip. fr/ observations/sss) and relies on the software package TSG-QC which is freely available (http://www. ird. fr/us191/spip. php?article63). Further developments have been included over time and the processed datasets were used for studies in the Pacific Ocean  9, 10  and in the Atlantic  11, 12. We describe here the specific choices made to process the Research Vessels dataset. The full processing cycle is sketched in Fig. 1.