dataesr/scientific-paragraphs-categorization huggingface dataset:
2010) , we only considered single-covariate binomial GAMs. To account for varying cetacean detectability between segments, we used a complementary log-log link function and the logarithm of the effective area surveyed as an offset  (Kery and Royle 2015). We used the restricted maximum likelihood method for automated selection of smoothing parameters  (Wood 2011). We set the basis dimension to 20 for the smoother in each model to allow for potentially complex nonlinear relationships. All models were fitted using R mgcv package ver. 1. 8-26  (Wood 2018). BW models, defined as follows, included a single global smoother with a thin plate regression spline basis with shrinkage, allowing covariates with non-significant relationships to be identified: P j j ∼ Binomial m ( ) log l og - - ( ) ( ) = + ( ) + ( ) log 1 0 m j j j b A f x where: P j is the observed presence/absence in each segment j, assumed to be binomially distributed µ j is the estimated probability of presence for segment j b 0 is the intercept term A j is the effective segment area for j (model offset) f is the smooth function of environmental covariate x RI models were defined as BW models, but included regionspecific intercepts: log l og - - ( ) ( ) = + + ( ) + ( ) ĺog 1 0 m j r j j j b b r A f x where r is a factor variable representing the region (WNA or ENA) and b r is the region-specific intercept.
Discussion As more studies address broad-scale marine resource management and conservation issues by developing basinwide SDMs combining survey data from multiple regions  (Mannocci et al. 2015 , Cañadas et al. 2018 , Virgili et al. 2019) , it is becoming critical to examine whether the modeled habitat relationships vary between regions. We tested for geographical variations in habitat relationships for a suite of cetaceans across the North Atlantic basin. Overall, cetacean occurrences were better modeled with region-specific than with global habitat relationships, as revealed by the better fits of RS models over BW models (Table  1 ). The better fits of RS models over RI models for many taxa and covariates (Table  1 ) further provided statistical evidence for differences in the shapes of region-specific relationships.
As combining disparate survey datasets will remain a challenge  (Virgili et al. 2019) , we call for increased standardization of data collection protocols between both sides of the North Atlantic basin. In addition, cetacean habitat relationships were estimated from summer data only and from a limited number of years. Further replications of surveys on both sides of the North Atlantic would be needed to assess how relationships vary across seasons and years. Incorporating surveys from larger latitudinal extents should also allow sampling broader ranges of environmental conditions experienced by cetaceans across their distributional ranges. Continuing data sharing (e. g. via the OBIS-SEAMAP repository  (Halpin et al. 2006 )) will be essential for continuing to collate high quality survey data on cetacean distributions across ocean basins.
Habitat relationships may be sensitive to sampling design in the species distribution data. In at-sea surveys of cetaceans and other marine species, poor weather conditions are one of the biggest factors that can prevent the realization of planned surveys, often for entire days or longer periods of time, leading to potentially unsystematical survey designs. In Supplementary material Appendix 7, we assessed the sensitivity of region-specific relationships to the survey design by applying jackknife procedures that excluded survey samples of 1 d or 7 d, refitting the models and examining the resulting relationships. In both regions, the shapes of resulting relationships were consistent with the shapes of overall relationships estimated from the full dataset, with very few of them falling outside the 95% confidence intervals of overall relationships (Supplementary material Appendix 7 Fig.
A7 -1, A7-2). These results underlined the robustness of habitat relationships to sampling design based on subsampling of both 1-d and 7-d survey periods. Finally, habitat preferences of cetaceans result from complex mechanisms that are both environmentally-mediated (in response to resource availability) and behaviorally-mediated (arising from social, foraging or reproductive activities)  (Palacios et al. 2013). These mechanisms cannot be fully elucidated using correlative SDMs, especially when survey datasets cover limited geographical extents. While SST and PP are classical dynamic predictors in cetacean SDMs  (Roberts et al. 2016 , Tobeña et al. 2016 , Lambert et al. 2017 , Mannocci et al. 2017 , Pennino et al. 2017 , Rogan et al. 2017 , Fiedler et al. 2018 , Becker et al. 2019 , Chavez-Rosales et al.
Conclusions As large marine survey programs implemented over recent decades have opened up new possibilities for developing SDMs at basin scales, there is a risk for combining datasets from regions where species exhibit contrasting habitat relationships. Our statistical framework based on hierarchical GAMs allows testing for geographical variations in habitat relationships. Our findings revealed substantial differences in the relationships of cetaceans with three commonly included covariates across an ocean basin. Our proposed hypotheses provide a roadmap for better understanding geographical variations in the habitat relationships of cetaceans and other highly mobile marine species at the scale of ocean basins.
Acknowledgements -We thank the numerous researchers who collected and shared line transect data with us. D. Palka  and  T. Cole  contributed the NEFSC surveys;  L. Garrison  contributed the SEFSC surveys;  P. Hammond  and  C. Lacey  contributed the  Univ. of St Andrews surveys. LM thanks Institut de Recherche pour le Development  for supporting her time in writing this manuscript. We thank  Damiano Righetti , an anonymous reviewer, and the subject editor for constructive feedback on previous versions of this manuscript. We thank  Marie Louis  for an enriching discussion about local adaptation in cetaceans.
Fluorescence microscopy and image analysis Pneumococcal precultures grown in C+Y medium at 37˚C to an OD 550 of 0. 1 were induced with either CSP (100 ng mL À1 ) or BIP (250 ng mL À1 ) peptide. At indicated times post-induction, 1 mL samples were collected, cooled down by addition of 500 mL cold medium, pelleted (3 min, 3000 g), and resuspended in 1 mL C+Y medium. Of this suspension, 2 mL were spotted on a microscope slide containing a slab of 1. 2% C+Y agarose as previously described  (Bergeet al. , 2013)  before imaging. Unless stated, images were visualized 15 min after competence induction, at the peak of competence gene expression. To generate videos, images were taken of the same fields of vision at varying time points during incubation at 37˚C. Images were captured and processed using the Nis-Elements AR software (Nikon).
Acknowledgements We thank  Nathalie Campo  and  Mathieu Berge  ´for critical reading of the manuscript, and the rest of the  Polard lab  for helpful discussions. We thank  Jerome Rech  for help creating Videos. We thank  Jan-Willem Veening  for kind donation of the pMK111 plasmid. This work was funded by the  Agence Nationale de la Recherche  (Grants  ANR-10-BLAN-1331  and  ANR-13-BSV8-0022 ).
Sequence reads were mapped to reference genome hg19 using Bowtie 1. 0. 0 with the following parameters -m 1 --strata --best -y -S -l 40 -p 2. Peak detection was performed using MACS2 (model based analysis for ChIP-Seq v2. 1. 0. 20140616) software under settings where an input sample was used as a negative control. We used a default cut-off and -B option for broad peaks. ChromHMM was used to identify chromatin states. The genome was analysed at 200 bp intervals and the tool was used to learn models from the six histone marks, CTCF ChIP-Seq reads files and corresponding Input controls. A model of 10 states was selected and applied on all samples. 8 of the 10 states identified were then given functional annotation based on histone marks enrichment. RNA Sequencing and analysis. RNA-Seq of control and STAG2-silenced RT112 cells was performed in triplicates (1x10 6 cells per sample).
Biotinylated DNA was pulled down with Dynabeads MyOne T1 streptavidin beads. End-repair, A-tailing and the Illumina adaptors ligation were performed on beads. Libraries were amplified by 10 cycles of PCR and purified using AMPure XP beads. The concentration and size distribution of the Hi-C library after PCR amplification were determined using a Qbit fluorometer and visual exploration in an agarose gel. Hi-C libraries were then paired-end sequenced on an Illumina NextSeq500 (200M reads per library). Data were processed for read quality control, mapping, interaction detection, filtering, and matrix normalization using TADbit (Serra et al. 2017) (Supplementary Figure  6A ). First, reads were qualitycontrolled using the TADbit implementation of FastQC for Hi-C datasets. Average PHRED scores were >25 throughout paired-end reads, indicative of good quality (Supplementary Figure  6B ).
Assortativity of regions altered upon STAG2 silencing. To assess whether the chromatin contact regions lost or gained upon STAG2 silencing, or the regions coding for genes deregulated upon STAG2 silencing, are located in proximity in 3D, chromatin assortativity (ChAs) analysis was used  (Pancaldi et al. 2016 ). We represent the genome as a network of nodes (chromatin fragments, here corresponding to Hi-C bins) which are connected if a Hi-C contact between them is observed. Networks were displayed using Cytoscape organic layout. To select significant Hi-C contacts we identified significant Hi-C interactions at specific binning resolution (20kb, 100kb, 1mb) by comparing each dataset to the background of the same datasets. Each sample is considered as independent. We can thus define a control network and compare it to the STAG2 silenced network and identify connections that are lost and gained from one condition to the next.
DATA ACCESS The following secure token has been created to allow review of record GSE111913 encompassing the ChIP-Seq data on cohesin subunits while it remains in private status: gdstsouqtdendir. ChIP-Seq data on histone modifications and CTCF for RT112 are deposited in the GEO database under accession number GSE104804. RNA-Seq data for RT112 cells next-generation sequencing data are available in NCBI GEO, under accession number GSE154878. Hi-C data are available in NCBI GEO, under accession number GSE155380. The authors declare that all other data supporting the findings of this study are within the manuscript and its supplementary files. "STAG2 low" cases as those with STAG2 levels in the first quartile. (E) GSEA enrichment plots for genes down-regulated in STAG2-silenced cells in "STAG2 high" versus "STAG2 low" tumor samples.
ACKNOWLEDGMENTS We thank all the staff of the GenomEast high throughput sequencing facility of the  IGBMC in Strasbourg  ( CNRS UMR7104 , Inserm  U1258 ,  Université de Strasbourg)  for the sequencing of ChIPs of histone marks and CTCF; the UROMOL investigators and  Lars Dyrskjot  for providing RNA-Seq data;  Ana Losada  for providing reagents; and  Mark Kalisz ,  Ana Losada , and  Ana Cuadrado  for critical review of the manuscript.
FUNDING This work was supported, in part, by a research grant and a  Postdoctoral Fellowship  from  Fundación Científica de la Asociación Española Contra el Cáncer  to FXR and EL, respectively. VP is supported Chromosome Number of interactions: 280,756,911 Percentage of cis interactions: 83% Min interactions: 0 Max interactions: 131,766 Number of interactions:  239 , 055 , 787  Percentage of cis interactions: 82% Min interactions: 0 Max interactions: 106,202 Number of interactions: 297,834,889 Percentage of cis interactions:  81%  Min interactions: 0 Max interactions: 132,792.
Material and Methods Approximately 20 wells have penetrated the top of the Upper Burma Limestone. Four wells (YAD-1, YAD-2, YAD-3 and YAD-4), located in the western half of the buildup (Fig. 2 ), have been selected in this study for a detailed sedimentological study. A total of 343 m has been investigated in the present work (YAD-1: 87. 5 m, YAD-2: 84 m YAD-3: 59. 5 m YAD-4: 112 m). The relevant cores are described in terms of textures and biological attributes. Detailed microscopic examination of 700 thin-sections provided the backbone of microfacies analysis identification. Thin-sections have been stained with blue colored epoxy resin for porosity evaluation and with Alizarin Red to differentiate dolomite from calcite. The studied dataset includes 2D and 3D seismic surveys acquired by TOTAL.
The prestack time migrated data have a vertical resolution of around 20 m within the carbonates. Amplitude and seismic attributes (coherency) have been used to highlight macro-scale platform morphology. In addition to the gas water contact (GWC) that forms a well-identified flat-spot, 5 key-seismic reflectors respectively named TOP UBL, H9, H9A, H10B and H10 have been interpreted within the carbonate buildup. Petrographical analysis of thin sections combined with identification of sedimentary structures, depositional textural and biological assemblages in cores, has allowed distinct facies to be recognized. These facies have been interpreted in terms of depositional environments by reference to modern and ancient analogues. The biological composition of carbonate rocks has been quantified by using point-counting on thin-sections.
Acknowledgements Acknowledgements are first due to the Department to which I belong for research that have granted travels and accommodations to the senior author in Rodrigues: UMR CBGP (Internal call for proposals 2018). Many thanks are also due to owners of Apartment  Jean Margot  for housing and several facilities. Thanks are also due to the plant protection service of Rodrigues and especially  Mr. Jérôme Félicité  for visiting some plots on some farms of the Island. I would like to thank also National Authorities of Mauritius and  Rodrigues Islands  for the signature of a Memorandum of agreement for the supply of biological material by  Government of Mauritius  and also for the Phytosanitary certificate. Thanks also to the  I-SITE Montpellier Université d'Excellence (MUSE)  for the international mobility support to the junior author (Explore # 2 , the  MUSE   International Mobility program ,  2019 ).
A FITC-labelled control scrambled siRNA was designed using InvivoGen siRNA Wizard software http://www. sirnawizard. com/scrambled. php (see Key Resources Table ). All sequences were subjected to NCBI BLAST to ensure gene-specificity and to avoid mis-targeting. Rescue experiments used a plasmid encoding a fusion protein of mature human PDIA1 (18-508), tagged at its N-terminus with a bovine pre-pro-trypsinogen signal peptide (bPPTSP) and a FLAG-M1 epitope that is exposed after cleavage of the signal peptide (kind gift of Prof David Ron, Department of Clinical Biochemistry, University of Cambridge)  (Zito et al. , 2010).
Primer design Transcript sequences for selected genes were obtained via the National Center for Biotechnology Information (NCBI) GenBank and Ensembl. Primer pairs for each transcript were designed using the Primer-Blast tool available from the NCBI (http://www. ncbi. nlm. nih. gov/tools/primer-blast/). Primers were selected according to the following rules: (i) primer length 17-30 base pairs; (ii) CG content 50-60%; (iii) melting temperature 55-80˚C; (iv) resulting amplification product 400-1200 base pairs. All potential primers were checked against the G. gallus (taxid:9031) genomic database using the Basic Local Alignment Search Tool (BLAST) from NCBI. The outputs from this last step were used to exclude all primers giving more than one significant region of identity (80% cut-off) against the whole chicken genome, or sharing more than 70% similarity with other genes.
Acknowledgements We thank  C Stern ,  M Bate ,  C Holt ,  RB Heap  and  O Paulsen  for comments on the manuscript. The initial parts of this work were supported by grants from the  Medical Research Council , the  Wellcome Trust , and the  Howard Hughes Medical Institute , and more recently by grants from the  Rosetrees Trust  and  Trinity College, Cambridge. JS was also supported by a studentship from the  International Spinal Research Trust ; EW was in receipt of an  Undergraduate Summer Vacation Research Scholarship  from the  Anatomical Society ; GR-V was in receipt of an  Amgen Foundation Summer Research Scholarship.
For some surveys the target density is uniform across the selected area whilst for others the target density varies significantly. For exposure times we give both the mean and median values. LR 3 are galaxy cluster surveys, where LR 2 represents cluster main galaxies and LR 3 illustrates cluster member galaxies. The mock catalogue of the clusters is based on the MultiDark dark matter only N-body simulations  (Klypin et al. 2016). The construction of the mock is described in  Comparat et al. (2019). LR 4 represents the eRosita AGN mock catalogue and LR 5 is the mock catalogue of TiDES host galaxies. The distribution of targets in LR 4 and LR 5 is uniform in the current mock catalogues. The last survey, LR 6, is a galaxy redshift survey constructed based on the SDSS data 5. The selection of these surveys is arbitrary and in the current paper they are used to analyse the main aspects of the target allocation problem.
Acknowledgements. This work has made use of the development effort for 4MOST, an instrument under construction by the  4MOST  Consortium (https: //www. 4most. eu/cms/consortium/)for the  European Southern Observatory (ESO). Part of this work was supported by institutional research funding  IUT26-2  and  IUT40-2  of the  Estonian Ministry of Education and Research. We acknowledge the support by the  Centre of Excellence "Dark side of the Universe"  ( TK133 ) and by the grant  MOBTP86  financed by the  European Union  through the  European Regional Development Fund. P. N. acknowledges support from the  STFC  through  ST/P000541/1  and the  Royal Society  through the award of a  University Research Fellowship. M. -R. L. C. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement nr.
, 2017; Milne et al. , 2017). SNPs associated with complex traits are more likely to be in regulatory regions than in protein-coding regions, and many of these SNPs are also associated with expression levels of nearby genes  (Nicolae et al. , 2010). For example, breast cancer GWAS-identified variants at 6q25. 1 regulate ESR1, but also coregulate other local genes such as RMND1, ARMT1, and CCDC170  (Dunning et al. , 2016, p. 1). These results suggest that by integrating genotype, phenotype, and gene expression, we can identify novel trait-associated genes and understand biological mechanisms. However, due to costs and tissue availability, acquiring GWAS and gene expression data for the same set of individuals remains challenging. A recently published approach, referred to as transcriptome-wide association study (TWAS;  Gamazon et al.
, 2015; Gusev et al. , 2016) , overcomes these difficulties by using a relatively small set of reference individuals for whom both gene expression and SNPs have been measured to impute the cis-genetic component of expression for a much larger set of individuals from their GWAS summary statistics. The association between the predicted gene expression and traits can then be tested. This method has been shown to have greater power relative to GWAS; and has identified 1,196 trait-associated genes across 30 complex traits in a recently performed multitissue TWAS  (Mancuso et al. , 2017). To date, three TWAS of breast cancer have been conducted  (Gao, Pierce, Olopade, Im, & Huo, 2017; Hoffman et al. , 2017; Wu et al. , 2018). A fourth study linked expression quantitative loci (eQTL) data across multiple tissues and breast cancer GWAS results using EUGENE, a statistical approach that sums evidence for association with disease across eQTLs regardless of directionality.
| Gene expression reference panel The transcriptome and high-density genotyping data used to build the gene expression model (reference panel) were retrieved from GTEx  (GTEx Consortium, 2015) , a consortium collected high-quality gene expression RNA-seq data across 44 body sites from 449 donors, and genome-wide genetic information. For the current study, we included 67 women of European ancestry who provided normal breast mammary tissues. RNA samples extracted from tissues were sequenced to generate data on 12,696 transcripts. Genomic DNA samples were genotyped using Illumina OMNI 5 M or 2. 5 M arrays, processed with a standard GTEx protocol. Briefly, SNPs with call rates <98%, with differential missingness between the two array experiments (5 M or 2. 5 M), with Hardy-Weinberg equilibrium p < 10 -6 or showing batch effects were excluded.
| Breast cancer meta-GWAS data The GWAS breast cancer summary-level data were mainly provided by the Breast Cancer Association Consortium (BCAC;  Michailidou et al. , 2017) , as well as the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). BCAC conducted the largest breast cancer meta-GWAS to date (referred as the overall breast cancer GWAS analysis). The BCAC included 122,977 cases and 105,974 controls of European ancestry. Among these, 46,785 cases and 42,892 controls were genotyped using the Illumina iSelect genotyping array (iCOGS) on 211,155 SNPs; and 61,282 cases and 45,494 controls were genotyped using the Illumina OncoArray on 570,000 SNPs  (Yovel, Franz, Stilz, & Schnitzler, 2008). The study also included data from 11 other GWAS on 14,910 cases and 17,588 controls. Genetic data for all individual participating studies were imputed to the 1000 Genomes Project Phase 3 v5 EUR reference panel Logistic regression was fitted to estimate per-allele odds ratios (ORs), adjusting for country and top principal components (PCs).
| Transcriptome-wide association study (TWAS) analyses Using the functional weights of those 901 genes and summary level GWAS data, we assessed the association between predicted gene expression and breast cancer risk. We performed summary-based imputation using the ImpG-Summary algorithm  (Pasaniuc et al. , 2014). Briefly, let Z be a vector of standardized association statistics (z scores) of SNPs for a trait at a given cis locus, Σ s s , be the LD matrix from reference genotype data and let W w w w w = ( … ) j 1 2 3 be the weights from the expression prediction model precompiled using the reference panel. Under the null hypothesis that none of the SNPs with w i ≠ 0 is associated with disease, the test statistic wz w w /( Σ ) ′ s s , 1/2 follows a normal distribution with mean = 0 and variance = 1.
| DISCUSSION We conducted a TWAS analysis using GTEx mammary tissue gene expression data and GWAS summary data from the largest meta-analysis for breast cancer risk. We assessed associations between overall breast cancer risk and ER+ versus ER-disease. We found 30 genes significantly associated with overall breast cancer risk, 20 genes associated with the ER+ subtype, and six genes with the ER-subtype. These results are consistent with previous reports from TWAS or similar gene-based approaches, which used various algorithms to build gene expression models. For example, of the 30 genes that we found significantly related to overall breast cancer risk, 23 were also significant in  Wu et al. (2018)  with very similar test statistics (correlation = 0. 96 for the z scores between our and Wu's results), and six were significant in  Ferreira et al.