dataesr/scientific-paragraphs-categorization huggingface dataset:
The distribution of PRS among cases is right-shifted by σ 2 so that the overall mean PRS is 1. 0  71. The risk distribution was also performed assuming all common variation, using σ 2 ¼ logðλ 2 sib Þ, where λ sib = 1. 79, as determined using the heritability estimate from GCTA. Pathway analysis. SNPs were assigned to genes as described in the functional annotation section. The genes that mapped to genome-wide significant CRC risk SNPs were analysed using InBio Map, a manually curated database of proteinprotein interactions. Gene set enrichment was calculated using GenGen. Enrichment scores were calculated using the meta-analysis results and were based on 10 3 permutations on the χ 2 values between SNPs. Pathway definitions were obtained from the Bader Lab  33  , University of Toronto, July 2018 release.
This data contained pathway information from Gene Ontology (GO), Reactome, HumanCyc, MSigdb C2 (curated dataset), NCI Pathway, NetPath and PANTHER for a total of 7269 pathways. GO annotations that were inferred computationally were excluded. To avoid biasing the results, the meta-analysis SNPs were pruned to only those with an r 2 < 0. 1 and a distance greater than 500 kb. Pathways were visualised using Cytoscape v3. 6. 1, together with the EnrichmentMap v3. 1. 0 and AutoAnnotate v1. 2 plugins. Only pathways with an FDR <0. 05 and edges with a similarity coefficient (number of shared genes between pathways) >0. 55 were displayed. the Edinburgh CRUK Cancer Research Centre. In Birmingham, funding was provided by Cancer Research UK (C6199/A16459). We are grateful to many colleagues within UK Clinical Genetics Departments (for CORGI) and to many collaborators who participated in the VICTOR, QUASAR2 and SCOT trials.
Acknowledgements At the  Institute of Cancer Research , this work was supported by  Cancer Research UK  ( C1298/A25514 ). Additional support was provided by the  National Cancer Research Network. In Edinburgh , the work was supported by  Programme  Grant funding from  Cancer Research UK  ( C348/A12076 ) and by funding for the infrastructure and staffing of We also thank colleagues from the  UK National Cancer Research Network (for NSCCG). Support from the  European Union  [ FP7/207-2013 , grant  258236 ] and  FP7 collaborative project SYSCOL  and  COST Action  in the UK is also acknowledged [BM1206]. The COIN and COIN-B trials were funded by  Cancer Research UK  and the  Medical Research Council  and were conducted with the support of the  National Institute of Health Research Cancer Research Network.
COIN and COIN-B translational studies were supported by the  Bobby Moore Fund from Cancer Research UK, Tenovus , the  Kidani Trust, Cancer Research Wales  and the  National Institute for Social Care and Health Research Cancer Genetics Biomedical Research Unit  ( 2011-2014 ). We thank the  High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  090532/Z/09/Z ) and the  Edinburgh Clinical Research Facility (ECRF) Genetics Core ,  Western General Hospital, Edinburgh , for the generation of genotyping data. We thank the  Lothian Birth Cohorts ' members, investigators, research associates, and other team members. We thank the  Edinburgh Clinical Research Facility (ECRF) Genetics Core ,  Western General Hospital, Edinburgh , for genotyping.
Lothian Birth Cohorts' data collection is supported by the  Disconnected Mind  project (funded by  Age UK) , and the  Biotechnology and Biological Sciences Research Council (BBSRC , for genotyping;  BB/F019394/1 ) and undertaken within the  University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology  (funded by the  BBSRC and Medical Research Council RC  as part of the  LLHW [MR/K026992/1 ]). ET was supported by  Cancer Research UK CDF  ( C31250/A22804 ). This research has been conducted using the UK Biobank Resource under Application Number  7441. Generation Scotland received core support from the  Chief Scientist Office of the Scottish Government Health Directorates  [ CZD/16/6 ] and the  Scottish Funding Council  [ HR03006 ]. Genotyping of the GS:SFHS samples was carried out by the  Genetics Core Laboratory at the Clinical Research Facility, University of Edinburgh  and was funded by the  Medical Research Council UK  and the  Wellcome Trust  ( Wellcome Trust Strategic Award "STratifying Resilience and Depression Longitudinally" (STRADL)  [ 104036/Z/14/Z ]).
CFR was supported by a  Marie Sklodowska-Curie Intra-European Fellowship Action  and received considerable help from many staff in the  Department of Endoscopy at the John Radcliffe Hospital in Oxford. In Finland, this work was supported by grants from the  Academy of Finland  [ Finnish Center of Excellence Program   2012-2017 ,  250345  and  2018-2025 ,  312041 ], the  Jane and Aatos Erkko Foundation , the  Finnish Cancer Society  [personal grant to K. P. ], the  European Research Council  [ERC; 268648], the  Sigrid Juselius Foundation , SYSCOL, the  Nordic Information for Action eScience Center (NIASC) , the  Nordic Center of Excellence  financed by  NordForsk  [project  62721 , personal grant to K. P. ] and  State Research Funding of Kuopio University Hospital  [B1401]. We acknowledge the computational resources provided by the ELIXIR node, hosted at the  CSC-IT Center for Science, Finland , and funded by the  Academy of Finland  [grants 271642 and 263164], the  Ministry of Education and Culture, Finland.
V. S. was supported by the  Finnish Academy  [grant number  139635 ] and the  Finnish Foundation for Cardiovascular Research. J. -P. M. was funded by The  Finnish Cancer Foundation  and  The Jane and Aatos Erkko Foundation. Sample collection and genotyping in the Finnish Twin Cohort has been supported by the  Wellcome Trust Sanger Institute, ENGAGE-European Network for Genetic and Genomic Epidemiology ,  FP7-HEALTH-F4-2007 ; [grant agreement number 201413], the  National Institute of Alcohol Abuse and Alcoholism  [grants  AA-12502  and  AA-00145 ; to R. J. R. and  K02AA018755  to D. M. D. ] and the  Academy of Finland  [grants  100499 ,  205585 ,  265240  and  263278  to J. K. ]. The work of the  Colon Cancer Family Registry (CCFR)  was supported by the  National Cancer Institute (NCI)  of the  National Institutes of Health (NIH)  under Award number  U01 CA167551.
The  CCFR Illumina GWAS  was supported by the  NCI/NIH  under Award Numbers  U01 CA122839  and  R01 CA143237  to G. C. The content of this manuscript does not necessarily reflect the views or policies of the  NCI  or any of the collaborating centres in the CCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the  US Government  or the CCFR. The CORSA study was funded by  FFG BRIDGE  (grant  829675 , to A. G. ), the " Herzfelder'sche Familienstiftung " (grant to A. G. ) and was supported by  COST  Action  BM1206. We kindly thank all individuals who agreed to participate in the CORSA study. Furthermore, we thank all cooperating physicians and students and the  Biobank Graz of the Medical University of Graz. The DACHS study was supported by grants from the  German Research Council (Deutsche Forschungsgemeinschaft ,  BR 1704/6-1 ,  BR 1704/6-3 ,  BR 1704/6-4 ,  BR 1704/ 6-6  and  CH 117/1-1 ), and the  German Federal Ministry of Education and Research  ( 01KH0404 ,  01ER0814 ,  01ER0815  and  01ER1505A ,  01ER1505B ).
wtccc. org. uk. The BCAC study would not have been possible without the contributions of the following:  Manjeet K. Bolla ,  Qin Wang ,  Kyriaki Michailidou  and  Joe Dennis. BCAC is funded by  Cancer Research UK  ( C1287/A10118 ,  C1287/A16563 ). For the BBCS study, we thank  Eileen Williams ,  Elaine Ryder-Mills ,  Kara Sargus. The BBCS is funded by  Cancer Research UK  and  Breast Cancer Now and acknowledges NHS  funding to the  National Institute of Health Research (NIHR) Biomedical Research Centre (BRC)  and the  National Cancer Research Network (NCRN). We thank the participants and the investigators of  EPIC (European Prospective Investigation into Cancer and Nutrition). The coordination of EPIC is financially supported by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer.
We thank the  SEARCH  and EPIC teams, which were funded by a programme grant from  Cancer Research UK  ( C490/A10124 ) and supported by the  UK NIHR BRC  at the  University of Cambridge. We thank  Breast Cancer Now  and the  Institute of Cancer Research  ( ICR ) for support and funding of the UKBGS, and the study participants, study staff, and the doctors, nurses and other health-care providers and health information sources who have contributed to the study. Genotyping of the  PRACTICAL consortium OncoArray  was funded by the  US National Institutes of Health (NIH)  [ U19   CA 148537 for ELucidating Loci Involved in Prostate cancer SuscEptibility (ELLIPSE)  project and  X01HG007492  to the  Center for Inherited Disease Research (CIDR)  under contract number  HHSN268201200008I ]. Additional analytic support was provided by  NIH NCI   U01 CA188392  (PI: Schumacher).
Data availability The SCOT data can be requested through the TransSCOT committee according to the ethical permissions obtained as part of the clinical trial approval. The PRACTICAL and BCAC consortium control data are available through the respective Data Access Coordination Committees (http://practical. icr. ac. uk and http://bcac. ccge. medschl. cam. ac. uk/) and the Heinz Nixdorf Recall Study control data can be requested through https:// www. uni-due. de/recall-studie/die-studien/hnr/. UK Biobank data can be obtained through http://www. ukbiobank. ac. uk/. The Colon Cancer Family Registry data can be obtained through http://coloncfr. org/. Finnish cohort samples can be requested from THL Biobank https://thl. fi/en/web/thlbiobank. Hi-C, CHi-C, and histone ChIPseq sequencing data have been deposited in the European Genome-phenome Archive (EGA) under the accession code EGAS00001001946.
2012; Haines et al. 2012; Koulouridis et al. 2014) , supporting the presence of an in-falling population, probably triggered by galaxy mergers  (Fassbender et al. 2012; Ehlert et al. 2015). The excess was recently confirmed by a spectroscopic study  (Koulouridis et al. 2018)   Notes. ( a) SPT name: SPT-CLJ0615-5746. ( b) Masses published in the SPT catalogue  (Bleem et al. 2015 ). ( c) The Obs. ID in bold refers to the longest observation. ( d) The exposure times reported in normal and bold refer to the merged datasets and the longest Obs. ID, respectively. The former and latter were used for point source detection and flux measurement, respectively. ray selected clusters of the XXL survey  (Pierre et al. 2016 ) up to z = 0. 5. However, it was correlated only with the less massive half of the sample (M 500 < 10 14 M ⊙ ), probably because of the high-velocity dispersions in massive clusters that may effectively reduce the galaxy merging rate (see also  Arnold et al.
Sample selection and data analysis 2. 1. Galaxy cluster sample. The sample contains the five most massive, M S Z 500 ≥ 5 × 10 14 M ⊙ , and distant, z > 0. 9, galaxy clusters in the SPT and Planck catalogues. All five objects have been observed by Chandra using the Advanced CCD Imaging Camera  (Garmire et al. 2003)  and XMM-Newton using the European Photon Imaging Camera  (Turner et al. 2001; Strüder et al. 2001). In this work, we use the results of the analysis that characterised the spatial and thermodynamic properties of the five objects by combining the two instrument datasets  (Bartalucci et al. 2017 (Bartalucci et al. , 2018)). In particular, we use the measurements of R Y x 500 reported in Table  1  of  Bartalucci et al. (2018).
Point source analysis. Chandra datasets were reprocessed and cleaned from flares using the Chandra Interactive Analysis of Observations  (Fruscione et al. 2006 ) (CIAO) version 4. 9 and calibration database version 4. 7. 3  (Bartalucci et al. 2017 ). After the cleaning step, we merged multiple observations of the same object when available to maximise the statistic. We ran the CIAO wavdetect tool  (Freeman et al. 2002)  on exposure-corrected images in the [0. 5-2], [2-8], and [0. 5-8] keV bands and merged the resulting catalogues. We inspected the merged catalogue for missed or false detections by eye. We measured each point source flux with the following scheme: (i) the first estimate was obtained using the CIAO srcflux tool folding an absorbed power law to model the point source emission.
The excess of X-ray point sources in the outskirts is statistically significant at the 99. 9% confidence level (we here always use the confidence level for small numbers of events in astrophysical data  (Gehrels 1986 )). This strongly indicates an excess of AGN in our clusters, correlated with the galaxy population in their outskirts, between 2 and 2. 5R Y x 500. The respective results obtained using different luminosity thresholds for the X-ray point sources are presented in Fig. A. 1 in the appendix. Error bars indicate the 1σ confidence limits for small numbers of events in astrophysical data. Except for the bottom panel, a significant X-ray point source excess is found between 2R Y x 500 and 2. 5R Y x 500 at the 99. 9% confidence level (99% for SPT-CL0546-5345). To clarify if the detected excess is a general property of our sample or is due to a large number of sources in just one or two clusters, we plot in Fig.
AMH measurement can be easily obtained from a blood sample collected whatever the day in the menstrual cycle. In the female foetus, AMH is secreted by the ovarian granulosa since 36 weeks' gestation. Increase in plasma AMH concentration reaches a first peak between 7 and 10 yo and a second one between age 20 and 25 yo  (Kelsey et al, 2011). Decrease in plasma AMH concentration begins around 25 yo and AMH becomes undetectable before menopause  (Freeman et al, 2012). Thus, after 25 yo, the AMH blood level appears to be a more reliable marker of the ovarian pool size than the civil age. Moreover, the decline in AMH level precedes the increase in FSH and inhibin levels and can be regarded as an early predictor for progressive reduction of the ovarian reserve. Nevertheless, a recent individual patient data meta-analysis  (Depmann et al, 2018) , reported only a slight added value of AMH combined with age as a predictor of menopause.
Statistical Analysis Statistical analysis was performed using SPSS Statistics v. 19. Firstly, we planned to compare AMH blood levels between cases and controls regardless of age. Noteworthy, no sample size calculation was done: before our study was designed, only one study had reported similar AMH levels between 34 REM women and 10 controls  (Prakash et al, 2006). A model of AMH concentration from conception to menopause clearly demonstrated an AMH peak at ~25 yo, after a rise and before a decline in AMH concentrations  (Kelsey et al, 2011). Consequently, we planned to check whether the distribution of AMH blood levels according to age also depicted a peak ~25 yo. This would allow to do separate analyses in the subgroups of cases and controls <25 yo and ≥25 yo, when the sample size was sufficient (i.
05 and absolute logFC > 1. GO enrichment analysis was performed with goseq (1. 28. 0) using the Wallenius method to calculate a probability weighting function for top differential genes as a function of a gene's median transcript length. GO terms with FDR < 0. 01 were collapsed using REVIGO  57. CREBBP, SMARCB1, BAP1 analysis. Raw data for SMARCB1 and WT were downloaded from GEO (GSE75515). RNA-seq analysis was performed as described previously. Only genes common to all datasets were used in the analysis. Significance of overlap for significantly upregulated and downregulated genes between all three KOs was determined applying a one-sided Fisher's exact test (alternative hypothesis = greater). Chromatin immunoprecipitation. For ChIPs experiment, cell confluence and amount of starting material were kept constant by plating defined number of cells 2 days before cross-linking.
2. 9) using default parameters. PCR duplicates were removed with Picard Tools MarkDuplicates (1. 97; http://broadinstitute. github. io/picard). Total uniquely mapping reads were calculated using RSeQC bam_stat. py (2. 6. 4). BAM files were filtered to exclude common artifact regions (merged consensus artifact regions: http://mitra. stanford. edu/kundaje/akundaje/ release/blacklists/hg19-human/). Reads were counted in bins of length 25, RPKM normalized, and converted to bigWig format using DeepTools bamCoverage (2. 4. 1) for all heatmaps. Scores upstream and downstream of transcription start sites (TSSs) were computed from normalized bigWig files with deepTools computeMatrix (2. 4. 1) using reference-point mode. TSS plots were generated with deepTools plotHeatmap (2. 4. 1). To identify regions that gain or lose H2AK119ub1, differential analysis was performed using SICER (1.
PolII, BAP1 and ASXL1 ChIP-seq analysis. Raw data were downloaded from GEO (GSE40723, GSE36027, GSE51673, and GSE31477). FASTQ files were merged for samples with multiple runs and mapping was performed as previously described to hg19 or mm10. bigWig files were generated with deepTools bamCoverage (2. 4. 1). Artifact regions were excluded and read counts were normalized to log2(sample/ input). Average values 2 kb around the TSS were computed using deepTools multiBigwigSummary and correlation plots for these regions were generated with deepTools plotCorrelation (--corMethod pearson -removeOutliers -skipZeros). Merged consensus blacklists for hg19 and mm10 were obtained from the Kundaje lab at Stanford University (Stanford, CA, USA). Antibodies. BAP1 (1/500 dilution; C-4; sc-28383), FOXK1 (1/500 dilution; G-4; sc-373810), YY1 (1/500 dilution; sc-7341) and RAR-alpha (1/500 dilution; sc-551 × ) antibodies were purchased from Santa-Cruz; FLAG (1/1000 dilution; M2; F1804) was purchased from Sigma; HCFC1 (1/1000 dilution; A301-400A) and RNF2 (1/1000 dilution; A302-869A) antibodies were purchased from Bethyl Laboratories; Lamin B1 (1/3000 dilution; ab16048); RING1A (1/1000 dilution; 2820S), RING1B (1/1000 dilution; D22F2; 5694S) H2AK119ub1 (1/3000 dilution; D27C4; 8240S), H3K27me3 (1/3000 dilution; C36B11, 9733S), H3K4me3 (1/3000 dilution; C42D8, 9751) and H4 (1/3000 dilution; 2935S) antibodies, were purchased from Cell Signaling Technology; H2A.
Uncropped western blot data are provided in Supplementary Figs. 7-14. Chromatography analysis. For analysis of endogenous protein profiles in HAP1 cells, nuclear extracts (high salt) were first dialyzed against BP100 (50 mM potassium phosphate pH 6. 8, 100 mM NaCl, 1 mM EDTA, 1 mM DTT, protease inhibitors) and clarified by high-speed centrifugation. Samples were then purified by ion exchange chromatography using a HiTrap SP HP 5 ml column (GE Healthcare). Elution was performed by step elution with increasing NaCl concentration. In all, 500 mM elution was concentrated 5× time on centricon (Millipore, cut-off 10 kDa) and analyzed on Superose 6 PC3. 2 increase column (GE Healthcare). The native molecular size markers used for column calibration were thyroglobulin (669 kDa), ferritin (440 kDa), and aldolase (158 kDa).
The MS2 ion count target was set to 10 4 and the max injection time was 100 ms. Only those precursors with charge state 2-7 were sampled for MS2. The dynamic exclusion duration was set to 60 s with a 10 ppm tolerance around the selected precursor and its isotopes. The instrument was run in top speed mode with 3-s cycles. Data were searched against the uniprot-Human database, using Sequest HT from Proteome Discoverer 1. 4 (thermo Scientific). Enzyme specificity was set to trypsin and a maximum of two miss cleavages was allowed. Oxidized methionine, N-terminal acetylation, and carbamidomethyl cysteine were set as variable modifications. The mass tolerances in MS and MS/MS were set to 10 ppm and 0. 6 Da, respectively. The resulting files were further processed using myProMS  58. The Sequest HT target and decoy search results were validated at 1% FDR with Percolator.
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011808  59. Methylation analysis. Genomic DNA was extracted following manufacturer protocol (DNA easy, Qiagen), treated with RNAse A and RNAse T1, purified again and quantified using nanodrop. One microgram of DNA was then digested by the degradase plus (Zymoresearch), ethanol precipitated and the supernatant was then evaporated on speedvac. Samples were then reconstituted in 10 μl of solution A' (2% methanol, 0. 1% HCOOH), vortex-mixed, centrifuged and transferred to a high-performance liquid chromatography vial for micoLC-MS/MS analysis. Fractions were used directly in solution A' and analyzed (5 μl) using the RSLCnano system connected to the Orbitrap Fusion mass spectrometer.
Sample separation was achieved on a C18 column (4. 6 × 100 mm, packed with ZORBAX Eclipse XDB C18, 1. 8 µm particles, Agilent Technologies) after 5 min loading in solvent A', with a linear gradient of 10 min (from 0 to 30% (v/v) of solvent B' (80% MeCN, 0. 1% HCOOH)) at 500 μl/min. Data acquisition was performed in the Orbitrap on the 200-300 m/z range with the resolution set to a value of 240,000 at m/z 200. To determine the intensity of each nucleosides, we extracted from the MS survey of microLC-MS/MS raw files the extracted ion chromatogram (XIC) signal by using the retention time and m/z values of the well-characterized synthetic nucleoside ions using the Xcalibur softwares (manually). XIC areas were integrated in Xcalibur under the QualBrowser interface using the ICIS algorithm. Reporting summary.
Acknowledgements The  Labex DEEP, AAP EpiG and Cancer, SIRIC, "Programme Incitatif et Coopératif" (PIC) Uveal Melanoma  and the  Institut Curie supported work in R. M. 's laboratory. A. C. and M. W. were recipients of fellowships from the  "Association pour la Recherche contre le Cancer (ARC) ". This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  666003. Mass spectrometry analyses were performed by the  Institut Curie Protein Mass Spectrometry Laboratory , supported by grants from  "Region Ile-de-France " and the  FRM. High-throughput sequencing was performed by the  NGS platform of the Institut Curie , supported by grants  ANR-10-EQPX-03  and  ANR-10-INBS-09-08  from the  Agence Nationale de le Recherche  (investissements d'avenir) and by the  Canceropôle.
Data availability ChIP-seq and RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under the accession codes GSE110133 (ChIP-seq) and GSE110142 (RNA-seq) [https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc = GSE110143]. The raw protomics data are available via Proteo-meXchange with identifier PXD011808. Raw data are provided for all western blots in Supplementary Figs. 7 8 9 10 11 12 13 14. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding authors upon reasonable request. A Reporting Summary for this Article is available as a Supplementary Information file.
PATIENTS AND METHODS As in previous studies  (8) (9) (10) (11) (12) (13) , questionnaires were administered by CSA Santé (CSA Health Institute) and the same methodology was used. Data were collected between April and May. A sample of 3,012 individuals was selected based on criteria that allow representation of the Indian population aged 15 and older. The participants were selected by the quota method (based on gender, age, occupation of the family head, agglomeration level, and region). Systematic monitoring of interviews was conducted with a repeat call to 20% of those interviewed. In case this process could lead to a different outcome of any type, even for only one survey, all interviews conducted by the same interviewer were double-checked by another individual. No error was detected. Interviews were conducted in English or local languages: Hindi, Marathi, Gujarati, Bengali, or Tamil.