dataesr/scientific-paragraphs-categorization huggingface dataset:
Study samples Genotype data from six OCAC and two CIMBA genotyping projects were used for these analyses (Supplementary table  1 ). All participating studies were approved by the relevant research ethics committee and all participants provided written, informed consent. OCAC-The OCAC OncoArray data comprised 63 genotyping project/case-controls sets (Supplementary table  1 ). Some studies (e. g. SEARCH) contributed samples to more than one genotyping project and some case-control sets are a combination of multiple individual studies. The following numbers are for the subjects of European ancestry that passed QC. The analyses included 66,450 samples from seven genotyping projects: 40,941 controls, 22,406 invasive cases and 3,103 borderline cases. The number of cases by histotype were serous borderline (1,954), mucinous borderline (1,149), LGSOC (1,012), HGSOC (13,037), ENOC (2,810), CCOC (1,366) and other EOC (2,764).
CIMBA-Eligibility in CIMBA is restricted to females aged 18 years or older with pathogenic variants in BRCA1 or BRCA2. The majority of the participants were sampled through cancer genetics clinics, including some related participants. Sixty-three studies contributed OncoArray and iCOGS genotype data on 31,448 mutation carriers. For the samples genotyped on OncoArray, after quality control (see below), data were available on 15,694 BRCA1 mutation-carriers and 10,988 BRCA2 mutation carriers, of whom 2,372 and 849, respectively, were affected with EOC (Supplementary table  2 ). We also obtained genotype data on 3,342 (561 affected) BRCA1 and 1,424 (105 affected) BRCA2 nonoverlapping samples genotyped using the iCOGS array  1, 2.
Genotype data and Quality Control (QC) Data from all the genotyping projects apart from the OCAC and CIMBA OncoArray projects have been published previously  1, [3] [4] [5] [6]. Genotypes for OCAC samples were preferentially selected from the different projects in the following order: OncoArray, Mayo GWAS, COGS, and other GWAS. Genotyping was performed at five centers: University of Cambridge, Center for Inherited Disease Research (CIDR), National Cancer Institute (NCI), Genome Quebec and Mayo Clinic. OncoArray sample QC was similar to that carried out for the other projects (as described  5  ). We excluded samples if they had a genotyping call rate < 95%, excessively low or high heterozygosity, if they were not female, or were duplicates (cryptic or intended). Duplicates and close relatives were identified using in-house software that calculates a concordance matrix for all individuals.
Ancestry analysis Intercontinental ancestry was calculated for the OCAC samples using the software package FastPop (http://sourceforge. net/projects/fastpop/) 8 developed specifically for the OncoArray. Only the samples with >80% European ancestry were used for these analyses. For the CIMBA samples 33,661 weakly correlated autosomal SNPs (pair-wise r2 less than 0. 1) were used to compute the genomic kinship between all pairs of individuals, along with 267 HapMap samples (CHB, JPT, YRI and CEU). These were converted to distances and subjected to multidimensional scaling. Using the first two components, we calculated the proportion of European ancestry for each individual and excluded samples with >27% non-European ancestry to ensure that samples of Ashkenazi Jewish ancestry were included in the final sample.
Principal components analysis Principal component analysis for the OncoArray data was carried out using data from 33,661 uncorrelated SNPs (pair-wise r2 less than 0. 1) with minor allele frequency greater than 0. 05 using an in house program (available at http://ccge. medschl. cam. ac. uk/software/ pccalc/). Principal components analysis for the other genotype data sets was carried out as previously described. 1, 5.
Association analyses OCAC We evaluated the association between genotype and disease using the imputed genotype dosage in a logistic regression model. We carried out initial, genome-wide analyses separately for OncoArray, COGS and the five GWAS datasets and pooled the results using a fixed effects meta-analysis. The analyses were adjusted for study and for population substructure by including the eigenvectors of project-specific principal components as covariates in the model (nine for OncoArray, five for COGS, two for UK GWAS, and two for the US, BWH and POL GWAS, and a single PC for the MAY GWAS). The number of eigenvectors chosen was based on the inflection point of a scree plot. After one-step imputation of the genotypes in the regions of interest we used genotype dosages in a single logistic regression model with adjustment for each genotyping project/study combination and nineteen principal components.
Principal components were set to zero for samples not included in a given project. We used custom written software for the analysis. EOC is a heterogeneous phenotype with five major histotypes for invasive disease -HGSOC, LGSOC, MOC, ENOC and CCOC -and two histotypes of borderline diseaseserous and mucinous. The pattern of association across the different histotypes varies for the known EOC risk loci. We therefore carried out the association analysis on the following nine histotypes: all invasive disease; HGSOC; LGSOC; all invasive serous; serous borderline; LGSOC and borderline serous combined; ENOC; CCOC; and mucinous invasive/mucinous borderline combined.
OCAC/CIMBA meta-analysis We conducted a meta-analysis of the EOC associations in BRCA1, BRCA2 carriers and OCAC samples using an inverse variance approach assuming fixed effects. We combined the logarithm of the per-allele hazard ratio estimate for the association with EOC risk in BRCA1 and BRCA2 mutation carriers and the logarithm of the per-allele odds ratio estimate for the association with EOC (any-subtype) and serous EOC in OCAC. The number of BRCA1 and BRCA2 samples with tumor histology information was too small to allow for subgroup analyses. However, previous studies have demonstrated that the vast majority of EOCs in BRCA1 and BRCA2 mutation carriers are HGSOC. Meta-analyses were carried out using the software "metal", 2011-03-25 release  14. We evaluated whether there is evidence for multiple independent association signals in the region around each newly identified locus by evaluating the associations of genetic variants in the region while adjusting for the SNP with the smallest meta-analysis p-value in the respective region.
Functional annotation of risk associated variants Expression and methylation quantitative trait loci analyses-A TCGA data set  15  was available for 326 HGSOC tumors in women of European ancestry. Ancestry was estimated using the Local Ancestry in adMixed Populations (LAMP,  16  ) software package and individuals with > 95% European descent were retained for further analyses. Matched gene expression (measured on the Agilent 1M microarray), CpG methylation (measured on the Illumina Infinium HumanMethylation27 BeadChip), copy number alteration (called using the Affymetrix SNP 6. 0), and germline genotype (called using the Affymetrix SNP 6. 0) were also available. A Mayo Clinic data set was available for 209 serous EOC tumors and 32 ENOC tumors in women of European ancestry. Matched gene expression (measured on the Agilent whole human genome 4x44K expression microarray), CpG methylation (measured on the Illumina Infinium HumanMethylation450), copy number alteration (called using the OncoArray), and germline genotype (called using the OncoArray) were available for all of these samples.
Genotypes were imputed into the 1000 Genomes October 2014 (Phase 3, version 5)  9  European reference panel for both data sets. Expression QTL analyses were performed using linear regression as implemented in the R package Matrix eQTL  17. Only variants with imputation accuracy R 2 > 0. 3 were analyzed. Prior to eQTL analyses the effects of tumor copy number and methylation on gene expression were regressed out as previously described  18. For the Mayo data set, we performed separate analyses on the HGSOC and ENOC samples. Results for the two HGSOC data sets were combined in a random effects meta-analysis. We focused on local or cis-acting eQTLs between SNPs in the 1:100 list of potentially causal variants and all genes up to 1 Mb on either side of these SNPs. mQTL analyses for the 1:100 potentially causal SNPs in regions of interest (1 MB on either side of the index SNP) were conducted using the Mayo dataset only, because methylation was assayed with the much denser 450K array and the Mayo sample included histologies other than HGSOC.
Analysis of ChIP-seq data-ChIP-seq data were processed using MACS2 with p value cutoff of 0. 001. The smaller of input or signal was linearly scaled to the same depth as the larger dataset. In order to control the irreproducible discovery rate in ChIPseq analysis, we used IDR version 2. 0 pipeline. 20 A standard IDR threshold p< 0. 05 was applied. Functional annotation of variants-We used shell scripts with bedtools (http:// bedtools. readthedocs. org/en/latest/) to generate overlap data between all variants in each associated region including likely causal SNPs and bed file versions of all the data represented in Figure  2  and Supplementary Table  6. In addition we included 3'UTRs, 5'UTRs, miRcode high confidence conserved microRNA target sites, high confidence microRNA target sites from microRNA.
org, and all coding exons. The overlap data thus obtained were converted to matrix form by means of python scripts. MicroRNA target sites were only considered that overlapped untranslated (UTR) gene regions. Exonic variants were further assessed for missense or nonsense mutations by Mutect software  21. The NHGRI-EBI GWAS catalog was used to identify SNPs among the potentially causal set with other genome-wide signification associations (Supplementary table  14 ). Locus-specific tissue enrichment of variants-H3K27 acetylation peaks were collated from public sources (for HeLa-S3, HCT116, UCSD Ovary, UCSD Sigmoid Colon, Colon Crypt) or from in-house data (IOSE4, IOSE11, FT33, FT246, EEC16, CaOV3, UWB1. 289, OAW42, GFTR230, MCAS) (Supplementary table 5 and Supplementary table  16 ). Overlaps were counted for the all SNPs against which genotypes were imputed in 1000 genomes for each H3K27Ac dataset.
The fraction of causal SNPs with overlaps was then tested for significance against this background for each cell type in the H3K27ac datasets using the hypergeometric distribution. Finally, p values were adjusted for multiple comparisons using Bonferroni's method. Association Consortium (http://ocac. ccge. medschl. cam. ac. uk/). A subset of the OncoArray germline genotype data for the CIMBA studies will be made publically available through dbGAP. The complete dataset will not be made publically available due to restraints imposed by the ethics committees of individual studies; requests for further data can be made to the collaborators, health care providers and health information sources who have contributed to the many studies contributing to this manuscript. A full list is provided in the Supplementary Notes.
Acknowledgments The OCAC Oncoarray genotyping project was funded through grants from the  U. S. National Institutes of Health  ( CA1X01HG007491-01 (C. I. A. ) ,  U19-CA148112 (T. A. S. ) ,  R01-CA149429  (C. M. P. ) and  R01-CA058598  (M. T. G. );  Canadian Institutes of Health Research  ( MOP-86727  (L. E. K. ) and the  Ovarian Cancer Research Fund  (A. B. ). Funding for the CIMBA Oncoarray genotyping was provided by the  Government of Canada through Genome Canada  and the  Canadian Institutes of Health Research , the  Ministere de l'Economie, de la Science et de l'Innovation du Quebec through Genome Québec , the  Quebec Breast Cancer Foundation  for the  PERSPECTIVE  project, the  US National Institutes of Health  ( CA1X01HG007491-01 (C. I. A. ) ),  Odense University Hospital Research Foundation  (M.
Data availability OncoArray germline genotype data for OCAC studies will be available through dbGap (www. ncbi. nlm. nih. gov/gap). Summary results are available from the Ovarian Cancer Data Access Coordination Committee (http://cimba. ccge. medschl. cam. ac. uk/). ChipSeq data are available from the Gene Expression Omnibus (www. ncbi. nlm. nih. gov/ geo), GEO accession number GSE68104.
In particular, for the DFT calculations we adopted the BLYP  (Becke, 1988; Lee et al. , 1998) , B3LYP  (Becke, 1988; Lee et al. , 1998; Stephens et al. , 1994; Hertwig & Koch, 1997) ,  VSXC (Van Voorhis & Scuseria, 1998)  and B1B95  (Becke, 1988; Becke, 1996)  functionals, which are GGA (generalized gradient approximation), hybrid-GGA, meta-GGA and hybrid-meta-GGA functionals, respectively. All these traditional quantum chemistry computations were carried out using the GAUSSIAN09 package  (Frisch et al. , 2009). 3. 1. 2. Structure factor computation. After determination of the CCSD/6-311++G(2d,2p) molecular electron densities, for each system we computed theoretical X-ray structure factor amplitudes as analytic Fourier transforms up to a resolution sin/ = 2. 0 A ˚À1. Reciprocal space was sampled with different unit-cell sizes, either cubic with a large unit-cell edge or adapted to the molecular volume and shape.
0 with a step of 0. 5. Furthermore, as well as performing calculations with only the complete set of reflections (sin/ 2. 0 A ˚À1 ), six further subsets of decreasing maximum resolution were also selected (sin/ 1. 5, 1. 2, 0. 9, 0. 7, 0. 5, 0. 25 A ˚À1 ) in our X-ray constrained computations. Since in all the considered cases only purely theoretical structure factor amplitudes were used as constraints, the standard uncertainties h and the scale factor [see equation (  3 )] were set equal to 1. 0. Furthermore, treatment of the thermal motion has been completely neglected (i. e. anisotropic displacement parameters set to 0. 0). All the XC-RHF calculations were carried out using the free software TONTO  (Jayatilaka & Grimwood, 2003).
Quantitative The secondary data related to Sp-TB patients registered between January to March 2015 were extracted out of TB treatment cards and registers and reviewed. These patients were visited at their homes and the information regarding child contacts were verified. Patients with child contacts were interviewed in Hindi (local language) using a structured, pretested questionnaire. It had questions on sociodemographic profile of the index patient and child contacts and questions related to programmatic implementation of IPT. Those who could not be contacted even after two attempts by the investigator were not interviewed and hence their child contacts if any could not be included in the study. Operational definitions used for data collection are given in Box 1, which are in line with national guidelines  [17, 22, 23].
Quantitative Quantitative data were double-entered, validated and analysed using EpiData software (EpiData version 3. 1 for entry and version 2. 2. 2. 182 for analysis EpiData Association, Odense, Denmark). The association with key analytic outputs were calculated using Relative Risk (RR) and 95% confidence intervals (CIs). The STROBE guideline was used for reporting the quantitative component of the study  [24].
F U N D I N G This work was supported by the Department for International Development (DFID), UK, The Union, MSF and La Fondation Veuve Emile Metz-Tesch (Luxembourg). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. In accordance with WHO's open-access publication policy for all work funded by WHO or authored/co-authored by WHO staff members, the WHO retains the copyright of this publication through a Creative Commons Attribution IGO licence (http://creativecommons. org/licenses/by/3. 0/igo/legalcode), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
ACKNOWLEDGMENTS We thank  W. Halteman ,  J. Gill , and  M. Hunter Jr. for advice and guidance,  A. Black ,  K. Shute , and  E. Ray  for assistance in the field, the  Maine Department of Agriculture, Conservation, and Forestry  for logistical support,  Bigelow Preserve ,  Mount Abraham Ecological Reserve ,  Grafton Notch State Park, Mahoosuc Ecological Reserve , and the  White Mountain National Forest  for allowing us to conduct this work, and  M. Guyette ,  D. Morris , and four anonymous reviewers for their input. This research was funded by the  Maine Outdoor Heritage Fund  (project  141-01-04 ). Additional support was provided by the  University of Maine  and MDIFW through the Cooperative Agreement with the  U. S. Geological Survey Maine Cooperative Fish and Wildlife Research Unit  and the  University of Maine Graduate Student Government.
Oven temperature program was as follows: from 70 to 150 • C at a rate of 30 • /min, and from 150 to 320 • C at a rate of 5 • /min. The final temperature was 320 • C and was kept for 10 min (total time of each run: 46. 7 min). Peak areas were integrated using the GC ChemStation software and corrected manually. Compounds were identified by injecting a blend of standards (C20-C40) in the GC and by calculating the Equivalent Chain Length of the peak in the cuticular hydrocarbon extracts. Additionally, GC-MS analyses were done on a pool of eight foundresses and another of eight intraspecific parasites using an Agilent 5890 GC system coupled with a 5989A Mass Spectrometer (HP Chemstation software; temperature program as above). Finally, we validated peak identity with identifications from previous work  (Bagnères et al.
, 1996; Lorenzi et al. , 1997; Uboni et al. , 2012). GC data were analyzed focusing on the concentration of hydrocarbons on the female cuticle, on the ratio of branched to linear hydrocarbons and the ratio of long-chained to short hydrocarbons. First, we calculated the relative proportion of branched to linear hydrocarbons as the sum of peak areas of branched hydrocarbons divided by the total integration area of hydrocarbons in that extract. Second, we calculated the relative proportion of long-chained to short-chained hydrocarbons as the sum of peak area of long-chained hydrocarbons divided by the total integration area of hydrocarbons in the extract. We classified hydrocarbons as short-chained if they eluted as peaks 1-26 (peak 26 was n-C 29 ) and as long-chained if they eluted as peaks 27-63 [This classification is based on analyses on P.
Statistical Analyses We analyzed the differences in the ratio of branched to linear hydrocarbons and the ratio of long-to short-chained hydrocarbons on the female cuticle by running two different Generalized Linear Models GZLMs (for normally distributed data, identity link), one on the proportion of branched hydrocarbons and one on the proportion of long-chained hydrocarbons. In both analyses, proportion data were arcsinesquare-root-transformed to account for normality (Parallel GZLMs on non-transformed data were also run because our data fell in the middle of the range-between 0. 2 and 0. 7-where the relationship between data and predictors is approximately linear; such models yielded results substantially similar to those run on arcsine-sqrt-transformed data. ) We used female reproductive strategy (two levels: foundress or intraspecific parasite), population (three levels), and year (two levels) as fixed factor effects and the date of collection (as the day of the year) and the number of brood in the nest as covariates (to account for differences due to date and/or colony size).
Data collection and patient selection The data on patients over 18 years of age with a diagnosis of de novo or secondary AML transplanted with a related or unrelated donor were available from the EBMT registry. The latter is a voluntary working group of more than 450 transplant centres reporting regularly on their transplant activity. Only patients having available cytogenetics and transplanted between 1 January 2000 and 31 December 2013 have been selected. Patients with second allo-SCT have been excluded as well as those receiving a haplo-identical transplantation. Audits are routinely performed to insure the quality of the reported data. All patients provided informed consent on the use of their data in retrospective studies. The Review Board of EBMT approved this study. We identified a dataset of 10,799 patients with 5495 patients displaying an abnormal karyotype.
HLA typing was determined at 10 loci (A, B, C, DRB1, DQB1) by high-resolution techniques, although not all the centres report complete data on HLA. All unrelated donors were defined as HLA matched (10/10) or mismatched at 1 locus (9/10). Additional data have been collected on the therapy of relapsing patients when available. HLA data on cord blood (CB) were not captured in this study. Methods and definitions were similar to other studies performed by the Acute Leukemia Working Party of the EBMT  [30] [31] [32].
In those studies, an independent effect of abn(17p) has not been found  [13] [14] [15]. In fact, genomic losses of 17p, together with losses of 5q and 7q, and gains 11q and 8q, are the most frequent cytogenetic abnormalities described in CK  [34]. Nevertheless, our study cohort represents a more homogeneous population than the one addressed in the studies evaluating MK and CK AML. On the other hand, we were not able to find a significant effect of the presence of MK, probably because 83% of patients displayed MK at diagnosis. NRM was only 17%, probably reflecting the positive selection effect in this population, enriched with responsive patients to previous chemotherapy. In fact, these 139 patients represent only 1. 3% of 10,799 patients with an available karyotype in the EBMT database, a lower proportion than the expected rate of 5-10% in general AML population  [19].
Untreated surface state of rolled-up systems For practical applications, the state of the inner surface of the capillary is of utmost importance. We performed AFM measurements of the surface of our systems. The surface of chitosan is uniform with a root mean square roughness R q = 1. 85nm (data not shown). The plasma oxidized surface has a similar roughness R q = 1. 53nm. However, cracking of the surface occurs as shown on figure  3. The depth of those cracks is approximately the thickness of the oxide layer and their width is of some microns. In figure  3 , we also provide the typical profile of one of those cracks. For practical reasons, this cracking of the surface can be a problem. However, it can be circumvented by coating it with a supplementary PDMS layer before rolling. As the bending rigidity of a film exhibits a cubic scaling in thickness and linear in elastic modulus, one can easily make the mechanical impact of this film negligible by making it thin and softfor example by diminishing the cross-linker concentration in PDMS-.
Size measurements and imaging Tube were encased in PDMS and optically imaged with an optical transmission microscope. The images were treated with imageJ software to measure simultaneously the thickness of the film and the diameter of the tube. Patterned systems in inset of figure  2a  were imaged with an Dino-lite numerical microscope. Both the thickness of the film and the 3dimensionnal shape of the patterns were measured with an optical interferometer. The surface state of the systems were imaged with an AFM Dimension V (Digital Instruments / Veeco-Bruker , Santa Barbara, CA, USA) in contact mode. Fluorescence was imaged with an Olympus Fluoview FV1000 inverted confocal microscope.