dataesr/scientific-paragraphs-categorization huggingface dataset:
Publicly available data MSK-IMPACT data  (13) , including clinical data, mutations, and raw segmentation data were retrieved from cBioPortal in November 2018  (27). Only female ER-positive patients having experienced a relapse event were selected. Patients with ILC and IDC were identified according to their oncotree codes (ILC and IDC, BRCANOS, BRCNOS, CSNOS, respectively). Possible driver mutations and copy-number profiles were determined using the same protocols as for EuroILC.
Availability of data and materials The BAM files generated in this study are available on Genome-Phenome Archive (EGA) platform (https://ega-archive. org) under accession number EGAS00001004641. In addition, processed data including clinical, mutation, and copy-number aberration (CNA) data used in this analysis are available in Supplementary Tables  S1, S3 , and S4, respectively. More detailed methods can be found in the Data Supplement.
Acknowledgments This work has been supported by " Les Amis de Bordet," the "Fondation contre le cancer"  ( FAF-C/2016/762 ), "The  Breast Cancer Research Foundation ," and the  Belgian Fonds National de la Recherche Scientifique (F. R. S-FNRS ). F. Richard is supported by a grant from the  "Fondation Cancer Luxembourg " ( FC/2018/07 ). C. Marchio  was supported in part by a grant from the  Mayent-Rothschild Foundation to Institut Curie. The authors thank the patients and their families, the  Biobanks  from all participating hospitals,  David Brown ,  Sophia Leduc ,  Imane Bachir ,  Yacine Bareche ,  Marion Maetens ,  Floriane Dupont ,  Dominique Roels , and  Jeanne Letor  for their technical and clinical support. The costs of publication of this article were defrayed in part by the payment of page charges.
Additionally, experts were also asked in the surveys to assign weights to four criteria that addressed the rapidity of recovery, number of nodes without service and volume of water lost. Results from the rankings were evaluated with use of Visual Prometheea multicriteria decision aid software, and weights based on the recommendation by the experts. Calculation of hydraulic parameters and evaluation of the final solution based on the six predefined criteria were performed using the Epanet-Matlab toolkit  (Eliades et al. , 2016). •  Li et al. (2018)  proposed a two-stage WDN restoration method based on Epanet-Matlab toolkit  (Eliades et al. , 2016). In the first stage, a shortest path algorithm and greedy algorithm were used to gain the top priority recovery action for a quick response to the disaster.
Firstly, Dijkstra algorithm was used to calculate the shortest path from water source to hospital and fire point. The flow could be guaranteed to these locations by repairing the damaged point on the path and closing the valves of the damaged pipeline closest to the path. Then the greedy algorithm was used to obtain the restoration order of the remaining pipes. In the second stage, Particle Swarm Optimization algorithm was used to minimize the total amount of water loss during the restoration process. judgement (allowing the assumption that in real-life a crew would not be asked to deal with damages spread across the whole network). Simulations were run using the EPANET Programmer"s Toolkit linked with the MATLAB optimisation tool. •  Santonastaso et al. (2018)  adopted a strategy to restore the water service after an earthquake following two phases: 1) identification of hydraulic segments, that provided which valves had to be closed to isolate the pipe that needed to be repaired  (Creaco et al.
PanScan Ⅲ used previously genotyped controls, mostly from cohort studies (ATBC, CPS-II, EPIC, HPFS, MCCS, MEC, NHS, PLCO, WHI). To facilitate stratified analyses by study design (cohort versus case-control) and evaluate potential survival bias, we excluded 654 cases and 434 controls from PanScan Ⅲ studies because they did not have comparable controls or cases by study designs because PanScan III used previously genotyped controls (e. g. , exclusion of cases from case-control or case series studies that used cohort controls). We only included participants of European ancestry to avoid confounding by population stratification. Our final analytic data set included 8,384 (2,320 cohort, 6,064 case-control) PDAC cases and 11,955 (6,121 cohort, 5,834 case-control) controls. Each participating study obtained written informed consent from participants and approval from their local Institutional Review Board.
GWAS summary statistics Genotype imputation across the four study phases was based on the 1000 Genomes Project (Phase 3, v1) reference dataset  (23)  and IMPUTE2 (http://mathgen. stats. ox. ac. uk/impute/ impute_v2. html) (24) as previously described  (21). Due to the large overlap of variants on the arrays (Illumina HumanHap550 Infinium II, Human 610-Quad) used for PanScan I and II, these studies were combined and jointly analyzed, whereas PanScan III (OmniExpress, Omni1M, Omni2. 5M and Omni5M) and PanC4 (Illumina HumanOmniExpressExome-8v1) were each analyzed separately. For quality control, SNPs with minor allele frequency (MAF) < 5% and low-quality imputation score (IMPUTE2 INFO score < 0. 3) were excluded  (21). For each GWAS phase, SNPTEST (http://mathgen. stats. ox. ac. uk/genetics_software/snptest/ snptest.
Acknowledgements This research is supported by the  Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. The content of the article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank  Laurie Burdett ,  Aurelie Vogt ,  Belynda Hicks ,  Amy Hutchinson ,  Meredith Yeager  and other staff at the  National Cancer Institute's Division of Epidemiology and Genetics (DECG) Cancer Genomics Research Laboratory (CGR)  for GWAS genotyping. The  IARC/Central Europe study  was supported by a grant from the  US National Cancer Institute at the National Institutes of Health  ( R03 CA123546-02 ) and grants from the  Ministry of Health of the Czech Republic  ( NR 9029-4/2006 ,  NR9422-3 ,  NR9998-3 ,  MH CZ-DRO-MMCI 00209805 ).
"We thank  Kathy J. Helzlsouer  for her contribution in CLUE. We also thank all the CLUE participants. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further augmented by  Australian National Health and Medical Research Council  grants  209057 ,  396414  and  1074383  and by infrastructure provided by  Cancer Council Victoria. Cases and their vital utilized the high-performance computational capabilities of the Biowulf Linux cluster at the  NIH, Bethesda, MD, USA  (http://biowulf. nih. gov). The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. The data used for the analyses described in this manuscript were obtained from the  GTEx Portal  on September 24, 2019.
aeruginosa TKU002 22 kDa) and substrate specificity varies, but the dimeric arrangement is highly preserved  (Wang et al. , 2006; Setlhare et al. , 2019). C12DOs from Acinetobacter sp. , Burkholderia sp. , and Pseudomonas sp. were crystallized in different complexes with substrates other than catechol (ex. 1,2-ethanediol), which confirm that this enzyme can bind a variety of substrates in its catalytic site  (Vetting and Ohlendorf, 2000; Earhart et al. , 2005; Micalella et al. , 2011, PDB entries: 2XSR 5UMH, 5TD3, and 5VXT ). An alternative to the dimeric state in C12DOs was discovered in Acinetobacter radioresistens, in which Iso A forms an active trimer in solution under low ionic strength conditions  (Briganti et al. , 1997 (Briganti et al. , , 2000)) , representing the first example of a C12DO in which the oligomeric state is affected by the biochemical environment.
Genome Taxonomy and Phylogenetics Taxonomic annotation was performed employing Kraken version 2. 0. 7-beta (Valenzuela-González et al. , 2016)  and MetaPhlAn2 v2. 2. 0  (Truong et al. , 2015). The 16S rRNA gene was predicted with RNAmmer v1. 2  (Lagesen et al. , 2007)  and annotated by BLAST with the NCBI Bacterial 16S Ribosomal RNA RefSeq database (BioProject PRJNA33175), RDP  (Cole et al. , 2014) , SILVA ribosomal RNA gene database  (Quast et al. , 2012)  and Greengenes database  (DeSantis et al. , 2006). Wholegenome comparison and determination of ANI/AAI values were performed using the Microbial Genome Atlas (MiGA)  (Rodriguez-R et al. , 2018)  and NCBI prokaryotic databases. An ANI heatmap was generated with pyani  (Pritchard et al. , 2015)  using all the genomes of P. stutzeri available as of Nov 2019 in GenBank, including Pseudomonas sp.
Identification and Comparison of Catechol Dioxygenases From Pseudomonas Species The sequences of a C23DO from Pseudomonas alkylphenolica (EC 1. 13. 11. 2) and C12DO from P. putida (EC 1. 13. 11. 1)  (Zeyer et al. , 1986)  were used to search for dioxygenase genes in the genome of P. stutzeri GOM2 using BLASTP. Just one putative C12DO sequence was found with 73. 72% percent identity and 99% coverage. The identity of the sequence was verified in the non-redundant (nr) database through BLASTP.
Catechol Dioxygenase Phylogeny A phylogeny was made with the sequence of amino acids identified as close homologs in the nr database for Pseudomonas sp. R2A2, and we also included additional C12DO sequences reported by  Caglio et al. (2009) ,  Guzik et al. (2013), and Nazmi et al. (2019)  (Figure  2  and Supplementary Table  S2 ). Evolutionary analyses were conducted in MEGA X  (Kumar et al. , 2018)  with the maximum likelihood statistical method, 1,000 bootstrap replications and the Jones-Taylor-Thornton (JTT) model  (Jones et al. , 1992).
Determination of Kinetic Constants and Substrate Specificity The catalytic parameters (K m , k cat , and k cat /K m ) were determined by calculating the initial velocity of reactions varying catechol concentrations (0-150 µM) in 50 mM buffer glycine-NaOH, pH 8. 5, at 40 • C. The data were fitted by non-linear regression to the typical Michaelis-Menten equation employing the software GraphPad Prism version 6. 01 (GraphPad Software Inc. ). The K cat value was calculated based on the molecular mass of the enzyme subunit (37. 5 kDa). To examine substrate specificity, the catechol derivatives 3-methylcatechol (3-MC), 4-methylcatechol (4-MC), and 4chlorocatechol (4-CC) were assayed as substrates for PsC12DO. The reactions were performed with 100 µM substrate under standard enzyme assay conditions.
Genome Assembly, Taxonomic Annotation, and Phylogenetic Analysis The GOM2 genome was assembled with a total of 7,106,750 sequences with an average quality of 33. 59. The improved assembly included 24 scaffolds with an N50 of 419,118 bp contained in four scaffolds and an N90 of 114,516 bp contained in 12 scaffolds. A total of 96. 75% of the raw reads were mapped to the assembly, and 93. 75% were properly paired and mapped. The final genome size was 4. 94 Mb with a G + C content of 63. 54% and 108× coverage (Figure  1A ). Genome completeness was 100%, and contamination was 9%. Raw sequences have been made available in the SRA database under accession number PRJNA592574, and the genome has been deposited into DDBJ/ENA/GenBank under accession number WOUM00000000. The version described in this paper is version WOUM01000000.
The taxonomic annotation was confirmed with different methods. Eight copies of the 16S rRNA gene were predicted to have 99. 8% identity with P. stutzeri annotated with different 16S rRNA databases (Supplementary Table  S4 ). In addition, 96. 64% of the sequences coincided with a k-mer spectrum of Pseudomonas at the genus level. Single-copy markers classified it as P. stutzeri unclassified (99. 37% sequences), and ANI/AAI indicated Pseudomonas sp. R2A2 (97. 61% ANI and 98. 4% AAI) (Figure  1B ). To compare the GOM2 strain at the complete-genome level, we calculated the average nucleotide identity of the 277 P. stutzeri genomes available in GenBank at the date of manuscript submission and included the Pseudomonas sp. R2A2 strain, which MiGA identified as the most correlated strain via the NCBI Prok database.
Identification and Comparison of Catechol Dioxygenases From Pseudomonas Species We searched two dioxygenase genes previously reported in Pseudomonas genomes, but we did not find EC 1. 13. 11. 2 in the GOM2 genome; we found only EC 1. 13. 11. 1 with 73. 72% percent identity and 99% coverage compared to a sequence from P. putida (A5W235). In the nr database, we found that this sequence was a C12DO and had high percent identity to sequences from other Pseudomonas strains and another P. stutzeri (Supplementary Table  S2 ). The best hit was the TPA sequence: C12DO of Pseudomonas sp. (HAJ87005), to which it had 99% identity and 100% coverage. We searched nr excluding the Pseudomonas taxon and found that species such as Acinetobacter, Marinobacterium, Marinobacter, and Streptococcus had identity values of over 57% to the C12DO of P.
Catechol Dioxygenase Phylogeny For evolutionary analysis of the C12DOs, we used the sequences identified in the nr database and the sequences listed by  Caglio et al. (2009) ,  Guzik et al. (2013), and Nazmi et al. (2019) , which includes sequences of C12DOs, chlorocatechol 1,2-dioxygenases (CC12DOs) and hydroxyquinone 1,2dioxygenase from different organisms (HQ12DOs) (Figure  2  and Supplementary Table  S2 ). In the tree, C12DO from P. stutzeri GOM2 is grouped with Pseudomonas and Stenotrophomonas maltophilia C12DO sequences. This group contains C12DOs that bind substrates such as catechol but also 3-chlorocatechol (3-CC), 4-chlorocatechol (4-CC), 3-methylcatechol (3-MC) and 4-methylcatechol (4-MC), as in the case of P. knackmussii (before Pseudomonas sp. B13)  (Guzik et al. , 2013). These enzymes are type I catechol dioxygenases.
DISCUSSION Phylogenetics and Bioinformatics of P. stutzeri GOM2 Through taxonomic characterization, we confirmed that the strain GOM2 belongs to the species P. stutzeri, as demonstrated by several methods, including k-mer, single-copy markers and 16S rRNA gene prediction. Considering the results obtained by ANI via MiGA and comparing all available genomes of P. stutzeri in GenBank, the two strains with the highest ANI proportions were Pseudomonas sp. R2A2, with an ANI of 97. 65%, and P. stutzeri UBA7601, with an ANI of 97. 37%. Pseudomonas sp. R2A2 (GenBank accession: CP029772. 1) was isolated from groundwater in Taiwan, and this strain has a role in arsenic transformation reactions. P. stutzeri UBA7601 (GenBank accession: GCA_002477745. 1) is a genome isolated from a metagenomic sample of wood in New York City.
2c ). Previous studies suggested that antigen-presenting cells are initial targets to EBOV  26, 27. Blood analysis revealed a severe deficit of total dendritic cells (DCs) (HLA-DR + , Lin -) (p = 0. 01, for the Mann-Whitney U test), mostly due to a deficit of plasmacytoid DCs (pDCs) (HLA-DR + /Lin -/CD45RA + /CD123 + ) (p = 0. 006, for the Mann-Whitney U test), whereas conventional DC (cDC) levels remained in the normal range. Higher levels of expression were observed for the activation marker CD40 in both cDCs (p = 0. 03, for the Mann-Whitney U test) and pDCs (p = 0. 02, for the Mann-Whitney U test), with higher levels of HLA-ABC expression in pDCs (p = 0. 02, for the Mann-Whitney U test) (Fig. 2d ). We also evaluated frequency of monocytes using CD14 + and CD16 + markers. Classical (CD14 ++ CD16 -) and nonclassical (CD14 + CD16 ++ ) monocytes were not significantly different between the two groups (data not shown).
1. Amphiregulin (#DAR-00), FABP2/IFABP (#DFBP20), and haptoglobin (#DHAPGO) were determined with Human Quantikine ELISA Kits (R&D Systems), according to the manufacturer's instructions. Cell phenotyping. Immune phenotyping was performed with an LSRII Fortessa 4laser (488, 640, 561, and 405 nm) flow cytometer (BD Biosciences), and Diva software version 6. 2. FlowJo software version 9. 9. 6 (Tree Star Inc. ) was used for data analysis. Multiparametric flow cytometry panel was performed using a battery of antibodies: anti-CD38 FITC #340909 (dilution: 1/10), anti-HLA-DR PE #347401 (dilution: 1/10), anti-CD4 BV421 #562424 (dilution: 1/33), anti-CD8 APCH7 #560179 (dilution: 1/20), anti-CD3 Alexa 700 #557943 (dilution: 1/100), anti-CCR7 Alexa 647 #557734 (dilution: 1/33), CD21 PE #555422 (dilution: 1/10), CD27 APC #337169 (dilution: 1/100), CD45 Alexa 700 #560566 (dilution: 1/100), anti-CD56 PECF594 #564849 (dilution: 1/500), anti-HLA-DR BV605 #562845 (dilution: 1/50), anti-CD33 BV421 #562854 (dilution: 1/20), anti-CD141 BV711 #563155 (dilution: 1/20), anti-CD45RA PerCpCy5.
FastQ files were generated from. bcl files on Illumina BaseSpace sequence hub. After trimming (QPhred score ≥25), reads were aligned to the hg19 human reference genome, using STAR -2. 5. 3ar, and quantified relative to annotation model hg19 -GENCODE Genes -release 19, with Partek E/M (Partek® Flow® software, Copyright ©; 2019 Partek Inc. , St. Louis, MO, USA). Counts were normalized by the counts per million (+0. 0001) method. PCA was carried out using the Partek software. Differentially expressed genes were identified by using normalized read counts as input for Gene-specific analysis (GSA) performed on Partek® Flow® software. Only genes with adjusted p values (false discovery rate) ≤0. 05 and a foldchange in expression ≥1. 5 were considered to be differentially expressed. The differentially expressed genes were subjected to functional enrichment analysis with the Ingenuity Pathway software.
Statistical analysis. GraphPad Prism software version 8 was used for nonparametric statistics and plots, as described in the figure legends. Correlations between phenotypic, seric, and genetic markers were analyzed by Spearman's rank correlations. A PCA with a Spearman's correlation matrix was performed. Statistical analyses were carried out with SAS (version 9. 3 or higher, SAS Institute, Cary, NC, USA), R (version 3. 6, The R Foundation for Statistical Computing, Vienna, Austria), and XLSTAT (version 2011. 4. 04, Addinsoft, Paris, France). Associations between markers and clinical symptoms have been explored by Wilcoxon's ranksum test and then using random forests to capture nonlinear relationships (data not shown). Final adjusted analyses were performed by using logistic regressions. Fits have been evaluated by Hosmer-Lemeshow tests.
Acknowledgements We thank The  French Task Force against Ebola, INSERM , and  IRD  for institutional support, and the  Postebogui team  for their daily work and the survivors. We also thank  Aminata Diallo , head of the immunology laboratory at  Institut National de Santé Publique , and  Nathan Peiffer-Smadja  and  Maxime Schvartz  for their help in setting up a laboratory in Conakry. This work was supported by  INSERM  and by the  Investissements d'Avenir program ,  Vaccine Research Institute (VRI) , managed by the  ANR  under reference  ANR-10-LABX-77-01.
We amplified partial sequences of two mitochondrial (12S and 16S ribosomal rRNA genes) and two nuclear loci (recombination activating protein 1, RAG1; brain derived neurotrophic factor, BDNF) using the PCR primers and cycling conditions outlined in  Nielsen et al. (2018). PCR success was evaluated via 1. 5% agarose gel electrophoresis, then amplicons were sent to GeneWiz or the University of Michigan sequencing core for Sanger sequencing. We then assembled and quality trimmed raw sequences using Geneious v. 8 (Biomatters; http://www. geneious. com). Sequences were submitted to GenBank (Table  1 ). Uncorrected mean pairwise sequence divergence (p) values were calculated for both 12S and 16S (Table  2 ) using MEGA v. 6. 0  (Tamura et al. 2013).
Phylogenetics Datasets (concatenated and partitioned by locus/codon) of all samples were analyzed using maximum likelihood (RAxML v. 8. 2; Stamatakis 2014) and Bayesian (MrBayes v. 3. 2; Ronquist et al. 2012 ) methods via the CIPRES Science Gateway 3. 1 for online phylogenetic analysis  (Miller et al. 2010 ; http://www. phylo. org/index. php/portal/). Maximum likelihood analyses were performed using the default settings for RAxML using the GTRGAMMA model of sequence evolution  (Stamatakis 2006 ) and ceasing bootstrapping when extended majority rule bootstrapping criteria had been reached. An appropriate partitioning strategy and molecular models for Bayesian analyses were chosen using PartitionFinder 2  (Lanfear et al. 2017) , which assessed all possible candidate positions (e. g. , each codon in the nuclear DNA) using the Bayesian information criterion.
Advertisement calls Advertisement calls were recorded in the field using an Samsung Galaxy Note 3 cellphone at a sampling rate of 44100 kHz, and analyzed using Sound Ruler Acoustic Analysis v. 0. 9. 6. 0 using default settings (Gridi-Papp 2007) and graphical presentations of calls were produced with the R package seewave  (Sueur et al. 2008). Only a single male call was recorded from the Cuanavale River source lake (PEM A12800) on 24 October 2016. The call was compared to that of B. mossambicus and B. poweri from Ribaue, Mozambique, and to other published call data  (Minter 1997 (Minter , 2003)). We further compared our call to that of B. adspersus provided by Du  Preez and Carruthers (2017). The small number of calls did not allow for statistical analysis but the following standard measurements were taken: call duration, call interval, number of pulses per call, and dominant frequency in kilohertz (kHz).
Phylogenetics Our concatenated, multi-locus dataset was 1,852 bp long, of which 390 characters were parsimony informative. Phylogenetic analyses resulted in a well-supported species-level phylogeny and high support that Breviceps is monophyletic (bootstrap [bs] 100, posterior probability [pp] 1. 0; Fig. 1B ). All Angolan samples were recovered as monophyletic with high support (bs 100, pp 1. 0), sister to B. poweri (bs 86, pp 1. 0), and thus embedded within the B. mossambicus group (bs 99, pp 1. 0). We also failed to recover any nuclear haplotype sharing among taxa (Fig. 1C ). We recovered high genetic divergence (≥ 9-11% 12S/16S uncorrected p-distances; Table  2 ) between the Angolan material and the three most closely related (and potentially sympatric and/or morphologically similar) taxa, B.
Sampling information including specimens/field IDs (Museum Abbreviations: MCZ, Museum of Comparative Zoology, Harvard University, USA; MH-NCUP, Natural History and Science Museum of the University of Porto, Portugal; MVZ, Museum of Vertebrate Zoology, University of California, Berkeley, USA; PEM, Port Elizabeth Museum, South Africa; SAIAB, South African Institute for Aquatic Biodiversity, South Africa), GPS coordinates, and GenBank accession details for the samples included in our analyses.
Acknowledgements We are grateful to  Mariana P. Marques ,  Adam Ferguson ,  Ben Marks ,  John Cavagnaro ,  Philip Pastor ,  Suzana Bandeira ,  Ilola Jorge ,  Alvaro "Varito" Baptista ,  Ninda Baptista ,  Kerllen Costa ,  James Harvey ,  Roger Bills ,  Götz Neef , and  Luke Verburgt  for their invaluable field assistance, support, advice, and companionship. John Cavagnaro  provided the life photo of the holotype. Mohamad Beidoun  assisted with molecular work. This work was supported in part by the  US National Science Foundation  ( DEB-1556255 ,  1556559  and  DEB-1556585  to DCB, AMB, and MPH), the  JRS Biodiversity Foundation  (to DCB and AMB), and the  National Geographic Society  ( Okavango Wilderness  Project  EC0715-15  to WC). The funders had no role in study design, data collection, and analysis, the decision to publish, or manuscript preparation.