dataesr/scientific-paragraphs-categorization huggingface dataset:
In-Silico Analysis of CRVs We combined multiple sources of in silico functional annotation from public databases to help identify potential functional SNPs and target genes. To investigate functional elements enriched across the region encompassing the strongest CRVs, we analysed chromatin biofeatures data from the Encyclopedia of DNA Elements (ENCODE) Project29, Roadmap Epigenomics Projects30 and other data obtained through the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) namely: Chromatin State Segmentation by Hidden Markov Models (chromHMM), DNase I hypersensitive and histone modifications of epigenetic markers H3K4, H3K9, and H3K27 in Human Mammary Epithelial (HMEC) and myoepithelial (MYO) cells, T47D and MCF7 breast cancer cells and TF ChIP-seq in a range of breast cell lines (Supplementary Table  13 ).
eQTL analyses Expression QTL analyses were performed using data from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) projects9,33. The TCGA eQTL analysis was based on 458 breast tumours that had matched gene expression, copy number, and methylation profiles together with the corresponding germline genotypes available. All 458 individuals were of European ancestry as ascertained using the genotype data and the Local Ancestry in adMixed Populations (LAMP) software package (LAMP estimate cut-off >95% European)34. Germline genotypes were imputed into the 1000 Genomes Project reference panel (October 2014 release) using IMPUTE223,35. Gene expression had been measured on the Illumina HiSeq 2000 RNA-Seq platform (gene-level RSEM normalized counts36), copy number estimates were derived from the Affymetrix SNP 6.
INQUISIT We developed a computational pipeline, integrated expression quantitative trait and in silico prediction of GWAS targets (INQUISIT), to interrogate publically available data for the prioritisation of candidate target genes. Data used for INQUISIT-Chromatin interaction data from ENCODE ChIA-PET analysis in MCF-7 cells for RNApolII, ERalpha, and CTCF factors were downloaded using UCSC Table  Browser40. Hi-C data derived from HMECs were obtained from Rao et al. 41, using "interaction loops" as defined in the publication. Data were reformatted to facilitate intersection of query SNPs using BEDTools "intersect"42. For all interactions, termini were intersected with promoters using GENCODE v1943 Basic gene annotations, where we defined promoters as -1. 0 kb -+0. 1 kb surrounding a transcription start site.
Enhancer-target gene predictions by several computational algorithms were collected. Each of these datasets assigns genes to enhancers. We used all MCF-7 and HMEC enhancer predictions (low and high stringency) made by PreSTIGE44, IM-PET enhancer-gene predictions in MCF-7, HMEC and HCC1954 cell lines45. Enhancer-transcription start site (E-TSS) links were identified from the FANTOM5 Consortium were identified46, and enhancers detected in mammary epithelial cells were intersected with E-TSS links. We also collected typical and super-enhancers in MCF-7, HMEC and HCC1954 cells defined by Hnisz et al. 47. TF ChIP-seq peak data for ESR1, FOXA1, GATA3, TCF7L2 and E2F1 from MCF-7, T47D and MCF-10A cells were downloaded in narrowPeak format from ENCODE. H3K4me3 and H3K9ac (characteristic of promoters) histone modification ChIP-seq peak data for all breast cells were obtained from ENCODE and Roadmap Epigenomics Project.
ChromHMM data for breast cell samples (HMEC and myoepithelial: E027, E028 and E119) were downloaded from Roadmap Epigenomics. Expression QTL analyses were conducted as described above. In the interpretation of the eQTL results for INQUISIT (and in general) we focused on the overlap between the CRVs (risk signal) and the top eQTL variants for a given gene (eQTL signal). If the eQTL P-value for a CRV was the same as, or within 1/100 th of the eQTL P-value of the SNP most significantly associated with expression of a particular gene, that gene and the corresponding CRV were assigned a point for being an eQTL in INQUISIT. Topologically-associated domain (TAD) boundaries were derived from Hi-C data41. Genomic intervals corresponding to "contact domains" from eight human cell types were merged using BEDTools "merge" resulting in annotation of regions most likely to encompass TAD units.
Inter-TAD boundaries were identified using BEDTools "complement". Gene level RNA-seq expression data generated under multiple experimental conditions in MCF-7 and normal mammary epithelial cells were downloaded from ENCODE. The FPKM (Fragments Per Kilobase of exon per Million fragments Mapped) values for each gene were extracted using the metagene R package48 and averaged across all experiments to give an approximation of expression in breast cells. Accession numbers are given in Supplementary Table  30.
INQUISIT pipeline Candidate target genes were evaluated by assessing each CRV's potential impact on regulatory or coding features. Scores categorised by 1) distal gene regulation, 2) proximal gene regulation, or 3) impact on protein coding were calculated using the following criteria (see also Supplementary Table  17 ). Genomic annotation data for target gene predictions (chromatin interaction and computational enhancer-promoter assignment), ChIP-seq, histone modification, and chromHMM were curated into a BED formatted database. We intersected the chromosomal positions of CRVs with each category of genomic annotation data using BEDTools "intersect" (minimum 1 bp overlap), resulting in annotation of SNP-gene pairs with presence or absence of multiple classes of genomic data. Each gene was scored using a custom R script on the basis of the following criteria:.
Second, we hypothesised that this enrichment would be stronger with progressively higher INQUISIT scores. We categorised all 20,243 protein coding genes into four levels based on their INQUIST scores (level 1: coding score 2, promoter score 3-4, distal score >4; level 2: coding 1, promoter 1-2, distal 1-4; level 3: any score >0 but <1; level 4: score 0 i. e. not a predicted target). The gene nearest to a risk locus is frequently assigned as a candidate target gene in GWAS in the absence of additional functional analysis59. We observed that seven of the 147 drivers were among the genes nearest to a previously or newly identified breast cancer risk locus. Therefore, we used logistic regression, including data for all target genes predicted by INQUISIT, with driver status as outcome, and evaluated INQUISIT score level and nearest gene status as potential predictors of driver status (Supplementary Table  21 ).
3C analyses were performed in three independent 3C libraries from each cell line with each experiment quantified in duplicate. BAC clones covering each region were used to create artificial libraries of ligation products in order to normalize for PCR efficiency. Data were normalized to the signal from the BAC clone library and, between cell lines, by reference to a region within GAPDH. All qPCR products were electrophoresed on 2% agarose gels, gel purified and sequenced to verify the 3C product.
The total amount of transfected DNA was kept constant at 600 ng for each construct by the addition of pUC19 as a carrier plasmid. Luciferase activity was measured 24 hr posttransfection by the Dual-Glo Luciferase Assay System. To correct for any differences in transfection efficiency or cell lysate preparation, Firefly luciferase activity was normalized to Renilla luciferase, and the activity of each construct was measured relative to the reference promoter constructs, which had a defined activity of 1. Statistical significance was tested by log transforming the data and performing 2-way ANOVA, followed by Dunnett's multiple comparisons test in GraphPad Prism.
Global Genomic Enrichment Analyses We performed stratified LD score regression analyses17 for overall breast cancer as well as stratified by ER status using the summary statistics based on the meta-analyses of the OncoArray, GWAS and iCOGS datasets. We restricted analysis to all SNPs present on the HapMap version 3 dataset that had a MAF > 1% and an imputation quality score R 2 >0. 3 in the OncoArray data. LD scores were calculated using the 1000 Genomes Project Phase 3 EUR reference panel. We first created a "full baseline model" as previously described that included 24 non-cell type specific publicly available annotations as well as 24 additional annotations that included a 500-bp window around each of the 24 main annotations17. Additionally, we also included 100-bp windows around ChIP-seq peaks as well as one annotation containing all SNPs leading to a total of 53 overlapping annotations.
Pathway Analyses The pathway gene set database (http://download. baderlab. org/EM_Genesets, file Human_GOBP_AllPathways_no_GO_iea_April_01_2017_symbol. gmt)13 from the Bader lab dated April 1, 2017 was used in all analyses. This database contains pathways from Reactome61, NCI Pathway Interaction Database62, GO (Gene Ontology) biological process63, HumanCyc64, MSigdb65, NetPath66 and Panther67. For GO, terms inferred from electronic annotation were excluded from our analyses. The same pathway may be defined in two or more databases with potentially different sets of genes. All versions of such 'duplicate' pathways were included. To provide more biologically meaningful results and reduce false positives, only pathways that contained between 10 and 200 genes were used. Pathway size was determined by the total number of genes in the pathway that could also be mapped to the genes included in the GWAS dataset (actual pathway size may be larger).
e. chr6:27657944 for HIST1, chr1:149219841, for HIST2, chr1: 228517406 for HIST3, chr12: 14871747 for HIST4). The gene set enrichment analysis (GSEA) algorithm as implemented in the GenGen package69 was used to perform pathway analysis. Wang et al. 70 modified the original GSEA algorithm to work with GWAS datasets, using SNP significance and SNP-to-gene mapping instead of gene expression data. Briefly, the algorithm calculates an enrichment score (ES) for each pathway based on a weighted Kolmogorov-Smirnov statistic (refer to 70 for more details). Pathways that have most of their genes at the top of the ranked list of genes obtain higher ES values. Note that only the largest positive ES was considered as opposed to largest absolute ES (i. e. largest deviation from zero). This modification (recommended by the GenGen authors for GWAS analysis) was performed to include only pathways that are significantly affected between cases and controls and ignore those with significant negative ES values (this may happen if a pathway is significantly less altered than expected by chance).
Genotyping of the OncoArray was principally funded from three sources: the  PERSPECTIVE  project, funded from the  Government of Canada through Genome Canada  and the  Canadian Institutes of Health Research, the Ministère de l'Économie, de la Science et de l'Innovation du Québec through Genome Québec , and the  Quebec Breast Cancer Foundation ; the  NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative and Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE)  project ( NIH  Grants  U19 CA148065  and  X01HG007492 ); and  Cancer Research UK  ( C1287/A10118  and. C1287/A16563 ). BCAC is funded by  Cancer Research UK  [ C1287/A16563 ], by the  European Community 's  Seventh Framework Programme  under grant agreement  223175  ( HEALTH-F2-2009-223175 ) (COGS) and by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreements  633784  (B-CAST) and 634935 (BRIDGES).
Genotyping of the iCOGS array was funded by the  European Union  ( HEALTH-F2-2009-223175 ),  Cancer Research UK  ( C1287/ A10710 ), the  Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program , and the  Ministry of Economic Development, Innovation and Export Trade of Quebec  -grant #  PSR-SIIRI-701. Combining the GWAS data was supported in part by The  National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative  grant  U19 CA 148065  (DRIVE, part of the  GAME-ON initiative ). For a full description of funding and acknowledgments, see Supplementary Note.
Centrifugal Test The emulsion stability was examined by centrifugation of 5 cm 3 of the emulsion for 10 min at 2500 rpm. The emulsion stability (ESt) was determined according to: 0 0. 100, % V V ESt V   (2) wherein: ESt -emulsion stability,%, V0emulsion volume, cm 3 , V -separated layer volume, cm 3. The obtained data showed that the stability of the emulsion increased with the increase in the ESt (stability) value  (Dłużewska et al. , 2004; McClements, 2007; Reineccius, 1994).
   (4) wherein: dln -mean arithmetic diameter, μm; Mn -number of oil globules in a group; dmeanaverage diameter of the oil phase globules in a group, μm; Σn -total amount of oil phase globules. The amount of oil phase globules was defined using DinoCapture software. Temperature Test Three tubes, each containing 10 cm 3 of the emulsion were placed at three different temperatures -4 °C, 23 °C and 45 °C for 24 h  (Tan and WuHolmes 1988). The appearance of a visible oil phase ring showed that the emulsion was unstable. The creaming degree was evaluated as: "+" if there was an oil phase ring, "+/-" suspected creaming and "-" absence of an oil phase ring.
ToF-SIMS for surface elemental analysis through the chemical sequence. The Poplar samples were analyzed using a ToF. SIMS 5 (Muenster, Germany) and Measurement Explorer ionTOF software. The high mass resolution spectra were obtained by randomly rastering a ++ Bi 3 primary ion beam across a 500 μm 2 area at 200 scans and 128 × 128 pixels. The fragmentation ion peaks correlating to cellulose, G lignin, and S lignin were selected from the calibrated spectra and normalized in relation to the total ions detected (see the supplementary materials). The average normalized ion intensities were determined by observing 5 randomly selected locations on the sample (the EHA sample had 4 detection sites). The high spatial resolution images were formed by rastering the Bi 3 primary ion beam across areas ranging from 20 μm 2 to 100 μm 2 with 200 scans at 256 × 256 pixels.
Acknowledgements We would like to thank  David Graham  at  ORNL  for reviewing the manuscript, and  Damien Chaudanson at CINaM  for SEM images. This work was sponsored by the  BioEnergy Science Center (BESC)  of the  Oak Ridge National Laboratory (ORNL). The BESC is a  US Department of Energy (DOE) Bioenergy Research Center  supported by the  Office of Biological and Environmental Research in the DOE Office of Science. ORNL is managed by  UT-Battelle, LLC , for the  US DOE  under contract  DE-AC05-00OR22725.
The reference was placed on the patella. Low impedance (<5 kΩ) was obtained by shaving, gently abrading the skin and then cleaning it with isopropyl alcohol. EMG signals were amplified with an octal bio-amplifier (ML138, ADInstruments, Bella Vista, Australia), bandpass filtered (5-500 Hz) and analog-to-digitally converted at a sampling rate of 2000 Hz by PowerLab System (16/35, ADInstruments). All data were analyzed offline using Labchart 8 software (ADInstruments).
Data Analysis As results for M-wave, MEP and TMEP peak-to-peak amplitudes and areas behaved similarly, only areas are reported. M-wave, MEP and TMEP areas were measured between cursors placed at the beginning, i. e. , initial deflection from baseline, and the end, i. e. , second horizontal crossing, of the evoked potentials (see Figure  1D  in  Martin et al. , 2006). Evoked EMG data (M-wave, MEP, TMEP and SP) are presented as the average values of all responses collected pre-and post-intervention. A representative trace of the three types of evoked potentials obtained during a submaximal voluntary contraction at 50% MVC for PRE and POST sessions in LV is displayed in Figure  3. PNS M-wave areas were measured from electrical nerve stimulations in both relaxed (M max ) and contracted VL and RF (M max_50 and M max_MVC elicited during 50% and 100% MVC, respectively).
TMS VA TMS was measured by the modified twitch interpolation technique  (Todd et al. , 2003). In brief, the estimated resting twitch (ERT) was determined as the y-intercept of a linear regression of SIT amplitude and absolute voluntary force during the two series of contractions at 100, 75 and 50% MVC (Figure  3B ), i. e. , one ERT derived from six data points. The regression of voluntary torque and the SIT torque was linear (i. e. , r > 0. 9;  Hunter et al. , 2006)  for all series (0. 91 < r < 0. 99) so no data were excluded from VA TMS statistical analysis. VA TMS was then assessed with the equation: VA TMS = (1 -SIT MVC /ERT) × 100 (1) where SIT MVC is the mean SIT evoked during the two contractions at 100% MVC. The area of VL and RF MEPs elicited by TMS during 100, 75 and 50% MVC contractions were normalized to the M-wave measured at the same contraction intensity.
Statistical analyses Post hoc analyses were performed using Newman-Keuls testing when the ANOVA identified significant differences. Partial eta square (pη 2 ) was reported as an estimate of effect size, with pη 2 ≥ 0. 07 and pη 2 ≥ 0. 14 used as moderate and large effects, respectively  (Cohen, 1988). We also calculated difference scores for each session and report their mean and 95% confidence interval. Statistical significance was set at p < 0. 05. All data are presented in the text and figures as mean ± standard deviation (SD).
, 2003; Giesebrecht et al. , 2010)  and impaired efficacy in synaptic transmission during strong contractions  (Petersen et al. , 2003). Hence, we believe that a reduction in intrinsic motoneuronal excitability is the most likely explanation for our observed decrease in TMEP size following prolonged LV. Such acute modulations in motoneuronal properties may be a plausible explanation to the functional effects reported after longer-term use  (Lapole and Pérot, 2010; Iodice et al. , 2011; Lapole et al. , 2013; Tankisheva et al. , 2015; Souron et al. , 2017). However, these results should be interpreted with caution since TMEP data were collected from only 11 young subjects, reducing the statistical power for this analysis. Also, it remains to be investigated whether or not such adaptations could occur in old subjects.
ACKNOWLEDGMENTS The present study was supported by a doctoral research grant ( ENS 2014-343 ) from the  University Jean Monnet of Saint-Etienne (France)  assigned to RS. The authors sincerely thank  John Temesi  and  Saied Jalal Aboodarda  for technical assistance as well as  Douglas Doyle-Baker  for English editing. The authors thank  Techno Concept ©  for the loan of the vibratory device.
Some authors suggest that Bison schoetensacki could be the ancestor of Bison bonasus  [1] , but others consider that Bison bonasus is derived from an unknown form of Bison priscus  [19, 20]. The lack of genomic data for Bison schoetensacki has so far prevented any conclusive view on this point. As part of an effort to obtain genetic data on animals consumed by the extinct cave hyena (Crocuta crocuta), we initiated the analysis of cave hyena coprolites  [21]. In the present study, we found a coprolite containing mitochondrial Bovinae DNA that was different from Bos primigenius, Bison priscus and Bison bonasus mitochondrial sequences. Because the cave site (Grotte-aux-Ours, Fig. 1a ) that yielded this coprolite is located close to the Siréjol cave, we also analyzed DNA extracted from a Siréjol Bison.
Illumina sequencing data analysis Reads were trimmed for adapter sequences, N's and low quality stretches on the 3' end, using a software based on the FASTX-Toolkit package (http://hannonlab. cshl. edu/ fastx_toolkit) and designed by Genoscope. After this step, sequences shorter than 20 nucleotides were discarded using an in-house Python script, yielding a dataset of 601,509,879 DNA reads.
Initial analysis of the Illumina library content To confirm the identity of the coprolite producer and gain insight into the animal's diet, the 601,509,879 Illumina reads were aligned simultaneously to a set of 49 mitochondrial genomes, including the reference mitochondrial genomes for the extinct cave hyena (Crocuta crocuta, GenBank accession number NC_020670. 1) and for several potential prey species (Additional file 1: Table  S3 ). The alignment was performed using BWA version 0. 7. 12  [24]. Only the reads matching perfectly, without indel or mismatch, to a unique genome were taken into account.
Assembling simultaneously the bovine and the cave hyena mitochondrial genomes The sequence assembly was performed in several steps. First the Illumina reads, ranging in size from 20 to 101 nucleotides, were aligned simultaneously to the reference mitochondrial genomes of the extant European bison (Bison bonasus, GenBank accession number NC_014044. 1) and extinct cave hyena (Crocuta crocuta) using BWA version 0. 7. 12  [24]  with default options except for -o (gap opening) and -n (maximum edit distance), which were set to 1 and 0. 1, respectively, allowing for a maximum edit distance that increases with read length, from one mismatch for 20-26 nucleotide-long reads to four mismatches for 88-101 nucleotide-long reads. In this first round, the number of reads mapping to the Bison bonasus genome (20,384) was of the same order of magnitude as the number of reads mapping to the Crocuta crocuta genome  (13, 318).
Our AMS radiocarbon studies agree with this time frame and greatly increase the accuracy of dating analysis of the Siréjol material. Dating analysis of the coprolite failed because the sample completely dissolved during the pretreatment for AMS measurement. PCR analysis of the coprolite DNA for the mitochondrial cytB fragments demonstrated the presence of cave hyena DNA and of a Bovinae sequence different from all of those recorded in GenBank up to March 2016. The 82-bp Bovinae cytB fragment displayed 2 to 5 mismatches with the Bison bonasus, Bison priscus, and Bos primigenius mitochondrial reference sequences. Analysis of the Siréjol bone fragment with the same Bovinae primers yielded a sequence identical to the one obtained from the coprolite. These PCR studies, and subsequent quantitative PCR analyses (Additional file 1: Supplementary methods and Figure  S1 ) also indicated far higher amounts of bovine mitochondrial DNA in the coprolite than in the bone sample.
While our manuscript was under review, two articles were published  [28, 39]  that describe several mitochondrial genomes from Late Pleistocene/Holocene bison specimens related to Bison bonasus. We therefore performed a phylogenetic analysis including all these ancient mitochondrial genomes along with the mitochondrial genomes of Bison bonasus specimens. We also considered Bison priscus specimens from these studies and a previous report from our laboratory  [12] , as well as the reference mitochondrial genomes of Bos primigenius, Bison bison, Bos grunniens (yak) and Bubalus bubalis (swamp buffalo) (Additional file 1: Table  S6 ). Phylogenetic trees were constructed from this dataset with the Maximum Likelihood (ML), Minimum Evolution and Neighbor-Joining methods that all yielded similar topologies.
Analysis of Bison schoetensacki nuclear sequences Soubrier et al. [28]  analyzed a set of ~10,000 genomewide bovine SNPs from several specimens of their Bison bonasus-related CladeX (only one of which, A006, yielded a sufficient amount of information for further analysis), from two Bison bonasus specimens (a historical one killed in 1911 and an ancient one, dated >55 kyr) and from two Bison priscus specimens (dated 30 and >50 kyr)  [28]. In order to compare the bison nuclear sequences of the coprolite to these data, the library reads were mapped against the taurine cattle reference genome and the genotypes of the bovine SNPs were determined. The genotypes of the SNPs for the nuclear genome of a Bison bison specimen were also extracted from published genomic data (see Methods). Analysis by multidimensional scaling (Fig.