dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability. The FAME module has been developed in Python version 3 and tested under version 3. 5. 1. The code is made available under the GNU General Public License https://www. gnu. org/licenses/gpl. html (last access: 30 August 2018) and is uploaded in the Supplement of this paper. Data availability. The World Ocean Atlas datasets used are available to all users directly from the provider. Derivation of the reference δ 18 O sw dataset is detailed in Appendix A. The masks' file used in the latter procedure is provided in the Supplement to the paper.
(D) Mean F ST for all pairwise comparisons between the seven clades. Fisher test was used to analyze the association with geographical structure (p(country-clade) = 0. 006), body site of isolation (p(site-clade) = 0. 157), and mating type (p(mating-clade) = 6. 064e-05). See also Figures  S1  and S2. variation between distant C. glabrata clades is higher than the amount of genetic variation among distant clades in humanassociated C. albicans (average of 3. 7 SNPs/kb)  [10]. Most clades were present across distant locations and in different body sites, but they were generally enriched in one of the two mating types (Figure  1 ; Figure  S2A ). After these analyses were performed, several other studies reported the sequences of 20 additional strains  [20] [21] [22]. To confirm the representativeness of our dataset, we repeated the population structure analyses on an expanded dataset of 53 strains, which include these 20 additional strains (Figure  S2B ).
All the new strains clustered within our already delimited clades, indicating that they cover a large fraction of the diversity of C. glabrata clinical isolates. Of note, we observed that in the expanded dataset clade IV and clade V merged, justifying our previously discussed low F ST values between these two clades. Importantly, the expanded dataset includes the pyruvate-producing strain C. glabrata CCTCC M202019  [20]. The parental of this strain has been isolated from fertile soil  [23] , and thus it represents the only available genome sequence from a strain not isolated from the human body. Our re-analysis of this strain (see STAR Methods) identifies relatively few SNPs when compared against the reference CBS138 (1,026 SNPs, 0. 08 SNPs/kb), which situates this strain within clade V and as the most similar to the reference genome.
Genomic Plasticity Enables Large Phenotypic Differences between and within Clades To assess whether the observed genomic plasticity was reflected at the phenotypic level, we measured several relevant phenotypes of the sequenced strains. Specifically, we tested biofilm formation properties and antifungal drug susceptibility, and measured growth under stress conditions such as high and low pH, high temperature, and presence of DTT, sodium chloride, or hydrogen peroxide (see STAR Methods, Figure  6 , and Table  S3 ). Most conditions showed important differences between some strains of the same clade (Figure  6 ). In fact, for most conditions and most clades, intra-clade variation was of a similar range as inter-clade variation (Figures  6A  and 6C ). Using the R package Growthcurver, we obtained a table with the main growth curve parameters (Table  S3 ).
A principal component analysis using those values showed that clades do not cluster by phenotype, underscoring the high phenotypic plasticity even within a similar genetic background (Figure  6B ). We next surveyed private mutations, that is, SNPs and CNVs present in a single strain of a clade (Data S2). This resource may help to identify the genetic bases of phenotypic differences in strains behaving drastically different from their close relatives, as well as identifying common mutations in distant strains that show similar behaviors in a given condition. Three strains (M6, M7, and M17) showed reduced sensitivity to one or more antifungal drugs, among eight tested (Table  S4 ). Each carried a unique, private mutation in PDR1 (leading to amino acid exchanges I390K in M6, I378T in M7, and N306S in M17), a known regulator of pleiotropic drug response  [31].
We next focused on differences in adherence properties, a virulent trait that may vary depending on the repertoire of proteins attached to the cell wall. We noted that three strains showed high (F03013) or moderately high (CST35 and F15021) ability to form biofilm on polystyrol (Figure  6D ). These three biofilm-forming strains shared independent duplications of PWP4 and deletions of AWP13, two GPI-anchored adhesins  [24]. Although these and other genotype-phenotype relationships enabled by the current dataset can provide useful hints, further experiments are needed to assess what genomic alterations underlie a given phenotypic variation. We report extensive phenotypic and genetic variation, even between closely related strains, indicating fast evolutionary dynamics and a potential for fast adaptation in C.
Genome assembly Reads were pre-processed previous to assembly to trim at the first undetermined base or at the first base having PHRED quality below 10 using Trimmomatic v0. 36  [32]. The pairs with reads shorter than 31 bases after trimming were not included from the assembly process. SOAPdenovo2  [35]  and SPAdes v3. 1. 1  [36]  with default parameters was used to assemble paired-ends reads into chromosomes. AUGUSTUS software  [37]  was used to predict genes and after that, were clustered based on gene similarity using orthoMCL software  [38]  in order to obtain core genome analysis. Strains CST34, CST35, M17 and F2229 were removed from the analysis because of the low quality of the raw reads.
SNP calling Reads were aligned onto the reference assembly of the CBS138 strain  [19] 5using BWA, with the BWA-MEM algorithm with 16 as number of threads  [39]. As no raw reads are publicly available for the recently-sequenced pyruvate-producing strain C. glabrata CCTCC M202019  [20] , the Wgsim software v0. 3. 1-r13 (https://github. com/lh3/wgsim) was used to simulate reads from the assembled genome sequence using defaults parameters, except for the rate of mutations and fraction of indels that was set to 0 and number of read pairs that was set to 100000000. We identified SNPs using GATK v3. 3  [40] [41] [42] 5with an haploid model, filtering out clusters of 5 variants within 20 bases and low quality variants, and using thresholds for mapping quality and read depth (> 40 and > 30 respectively).
Experimental validation of CNVs The deletion of a region comprising three genes (corresponding to deletion numbers 33, 34 and 35 (Data S1) was experimentally confirmed by means of PCR and Sanger sequencing in the following strains: EF1620Sto, F11, F15, EG01004Sto, F03013, F15021 and CBS138. Because the investigated fragment was 8,134 base pairs long, four different sets of primers were designed to be able to capture the whole fragment (Figure  S4A ). First PCRs were performed with CBS138 (control strain) and one of the investigated strains (EF1620Sto) and primers FWD1:REV1, FWD2:REV2, FWD3:REV3 to validate the absence of the deletion in the control strain and the feasibility of the primers and PCR reactions (Figure  S4B ). In this case amplicons from the three primer pairs are expected only in the control strain.
Recombination estimates We used the interval program implemented in the LDhat v2. 2 package  [45]  to estimate population-scale recombination rates for each chromosome separately. The program was executed for 5 million iterations with sampling every 2500 iterations as recommended in the user manual. The output of interval was summarized using stats software from LDhat v2. 2 as indicated by the authors. The amount of ancestral and recent recombination for all chromosomes was estimated using fastGEAR software  [46]  using default parameters. To specifically estimate recombination in the deleted regions and nearby, we used a two-steps pipeline. First, we select deletions that appeared in more than one clade. Second, for each selected deletion we extracted genomic regions located 1Kb upand downstream of the affected gene.
Population Genomics We used the software STRUCTURE v2. 3. 4 to study the genetic structure of the population  [55]. In addition, we used popGenome to estimate F ST between different clades  [56]. We recorded the number of SNPs in C. glabrata population using the 33 strains. We obtained the number of SNP also using 32 different strains from C. albicans, as indicated above for C. glabrata, and we obtained SNP data from Saccharomyces cerevisiae using dbSNP  [62] 5as of October 2015. We calculated the ratio of non-synonymous and synonymous nucleotide diversity (p N /p S ) assuming that 3 / 4 of all sites are non-synonymous, and 1 / 4 synonymous.
All experiments were run in triplicate. A total of six different growth conditions were tested: the oxidative stress was assessed by the growth of the cultures on YPD medium supplemented with 10 mM H 2 O 2 , reductive stress with 2. 5 mM DTT and osmotic stress with 1 M NaCl. We also measured the impact of elevated temperature (41. 5 C), pH = 2 and pH = 9 along with the control growth on YPD itself. Cultures were grown in 96-well plates at 37 C or 41. 5 C, shaking, for 24 or 72 h depending on the growth rate in each condition, and monitored to determine the optical density at 600 nm every ten min by a TECAN Infinite M200microplate reader. Finally, results from growth conditions were analyzed using an R package called Growthcurver v0. 2. 1  [57].
QUANTIFICATION AND STATISTICAL ANALYSIS All statistical details (statistical test used, number of samples, and p values) for each experiment can be found in the text and in the Figure  labels. Fisher tests were computed using R and plots were obtained using the ggplot2 package for R  [58]. Multiple Correspondence Analysis (MCA) was performed using ade4 package for R to establish the main relationships between all sequenced strains and the reference  [59]. MCA is a technique similar to principal component analysis (PCA) but specific for nominal categorical data, which is used to detect and represent underlying structures in datasets. Principal Component Analysis (PCA) was used to understand the relationship between the analysis from the growth curves and the distribution of our genomes using stats package for R.
ACKNOWLEDGMENTS The T. G. group acknowledges the support of the  Spanish Ministry of Economy and Competitiveness grants ''Centro de Excelencia Severo Ochoa   2013-2017'' SEV-2012-0208  and  BFU2015-67107  cofunded by the  European Regional Development Fund (ERDF) ;  European Union  and  ERC Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  ERC-2012-StG-310325 ;  Catalan Research Agency (AGAUR)   SGR857 ;  CERCA Programme/Generalitat de Catalunya ; and a grant from the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement  H2020-MSCA-ITN-2014-642095. C. F. 's and T. G. 's groups acknowledge support from the  GDRI ''iGenolevures'' of the French CNRS  for travel and meeting funds. T. G. , O. B. , and E. G. -M. acknowledge funding from the  European Union  under grant agreement  FP7-PEOPLE-2013-ITN-606786  '' ImresFun.
Data collection Larval development (biometry), mortality, and gene expression of selected genes, corresponding to specific molecular mechanisms were followed from hatch and throughout the endogenous feeding stage (2, 4, 6, 8, 10, and 12 dph). All endogenously feeding larvae of European eel were anesthetized using tricaine methanesulfonate (MS-222; Sigma-Aldrich, Missouri, USA) prior to digital imaging and euthanized post-sampling by using an MS-222 overdose  [13]. All images were taken using a digital camera (Digital Sight DS-Fi2, Nikon Corporation, Japan) attached to a zoom stereomicroscope (SMZ1270i, Nikon Corporation, Japan), while NIS-Elements D analysis software (Version 3. 2) was used to analyze the larval images (Nikon Corporation, Japan). Mortality and biometry. Every day, dead larvae were counted and removed from all experimental units.
The ef1α and rps18 genes were chosen as housekeeping genes, since qBase+ software revealed that these mRNA levels were stable throughout analyzed samples (M < 0. 4); M gives the gene stability and M < 0. 5 is typical for stably expressed reference genes  [40]. The expression levels of 16 target and 2 reference (ef1α, rps18) genes were determined by quantitative real-time PCR (RT-qPCR), using specific primers (Table  2 ). Primers were designed using primer 3 software v 0. 4. 0 (http://frodo. wi. mit. edu/primer3/) based on cDNA sequences available in GenBank Nucleotide, the European eel transcriptome database (EeelBase 2. 0, http:// compgen. bio. unipd. it/eeelbase/) or the available European eel genome (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA73577). All primers were designed for an amplification size ranging from 75 to 200 nucleotides and optimal Tm of 60˚C.
Statistical analyses All data were analyzed using SAS statistical software (version 9. 1; SAS Institute Inc. , Cary, North Carolina). Residuals were tested for normality using the Shapiro-Wilk test and homogeneity of variances was tested using a plot of residuals versus fit values (PROC GLOT, SAS Institute 2003). Data were log 10 or arcsine square-root-transformed when data deviated from normality or homoscedasticity  [43]. The effect of salinity on larval deformities, growth rate, oil-drop utilization, and growth efficiency on 12 dph was determined using a series of one-way ANOVA models, where experimental cross was considered a random factor (SAS PROC MIXED; SAS Institute 2003). Tukey's post-hoc analyses were used to compare least-squares means between treatments. Furthermore, statistical models were used to investigate salinity effects on larval body area, mortality and gene expression throughout early larval development (from 2 to 12 dph).
Acknowledgments We would like to thank  Johanna Sarah Kottmann ,  Elisa Benini  and  Maria Kru ¨ger-Johnsen  for broodstock management and assistance during experiments,  Prof. Einar Eg Nielsen  and  Prof. Thorsten Reusch  for providing laboratory space and  Dorte Meldrup  as well as  Brian Klitgaard  for assistance during laboratory work.
name Abbreviation Function Database Accession Number Primer sequence (5' 3') (F: Forward; R: Reverse) Na + K + 2Cl -Cotransporter 1α nkcc1α Ion transport GenBank Nucleotide AJ486858 F: CCAAGGCTCAGATCTTCCTG R: TTTCCGAATGGTAACCGAAG Na + K + 2Cl -Cotransporter 2α nkcc2α Ion transport GenBank Nucleotide AJ564602 F: ACGTGGTTGGGTTTTCAGAG R: GTGAGATCCCCAAAAGCAAA Na + K + 2Cl -Cotransporter 2β nkcc2β Ion transport GenBank Nucleotide AJ564603 F: AGCCAAAGTGGTGGATGTTC R: TGTCAGCCTCTCCAGTTCCT Aquaporin 3 aqp3 Water transport GenBank Nucleotide AJ319533 F: GCTCTCATGGCTTGTTCCTC R: AAGGTCACAGTGGGGTTCAG Aquaporin 1 duplicate aqp1dup Water transport GenBank Nucleotide AJ564421 F: GAATTCCTGGCAACCTTTCA R: CAAGATGACCCAGACCCACT Aquaporin e aqpe Water transport GenBank Nucleotide AJ784153 F: TGGGCAGCTGACAGTAACAG R: AATCACCTGGTCCACAAAGC Heat Shock Protein 70 hsp70 Stress response GenBank AZBK01685255 F: TCAACCCAGATGAAGCAGTG WGS R: GCAGCAGATCCTGAACATTG Heat Shock Protein 90 hsp90 Stress response GenBank AZBK01838994 F: ACCATTGCCAAGTCAGGAAC WGS R: ACTGCTCATCGTCATTGTGC ATP Synthase F0 subunit 6 atp6 Energy metabolism GenBank Nucleotide NC_006531 F: GGCCTGCTCCCATACACATT R: GACTGGTGTTCCTTCTGGCA Cytochrome C Oxidase 1 cox1 Energy metabolism GenBank Nucleotide NC_006531 F: CTACTCCTCTCCCTGCCAGT R: CTTCTGGGTGGCCGAAGAAT Deiodinase 1 dio1 Thyroid metabolism EeelBase 2.
0 Eeel2-c186 F: AGCTTTGCCAGAACGACTGT R: TTCCAGAACTCTTCGCACCT Deiodinase 2 dio2 Thyroid metabolism Eel genome website g12347 F: GAAGAGGAGGATCGCCTACC R: GCACTCTACCTCCGTCCAAA Deiodinase 3 dio3 Thyroid metabolism EeelBase 2. 0 Eeel-c22164 F: TACGGGGCGTATTTTGAGAG R: GCTATAACCCTCCGGACCTC Thyroid Hormone Receptor α A thrαA Thyroid metabolism GenBank Nucleotide KY082904 F: GCAGTTCAACCTGGACGACT R: CCTGGCACTTCTCGATCTTC Thyroid Hormone Receptor α B thrαB Thyroid metabolism GenBank Nucleotide KY082905 F: GAAGCCTTCAGCGAGTTCAC R: ACAGCCTTTCAGGAGGATGA Thyroid Hormone Receptor β B thrβB Thyroid metabolism GenBank Nucleotide KY082907 F: GAAGACTGAGCCCTGAGGTG R: AGGTAATGCAGCGGTAATGG Elongation Factor 1 α ef1a Housekeeping GenBank Nucleotide EU407824 F: CTGAAGCCTGGTATGGTGGT R: CATGGTGCATTTCCACAGAC 40S Ribosomal S18 rps18 Housekeeping GenBank TSA GBXM01005349 F: TGACCGATGATGAGGTTGAG R: GTTTGTTGTCCAGACCGTTG https://doi.
Operational forecasts/analysis are also inhomogeneous due to the constant improvements of the involved models. Wave data (1° resolution) and wind data (~0. 7°) from the ERA-Interim reanalysis  (Dee et al. , 2011)  are available every six hours from 1979 onwards. This homogeneous dataset is used to identify event periods and to drive the model chain. A new reanalysis called ERA5 is in production and hourly wave/ wind data at 0. 36°/0. 28° between 2010 and 2016 were available at the time of this study. Both wave reanalysis are too coarse at the coast to represent the wave properties sufficiently in shallow water for the purpose of this study. 2013 ERA-Interim and ERA5 data were tested for model calibration and for sensitivity studies of the influence of ocean currents on the wave height. Indeed, the impact of ocean/coastal currents and mud banks on the waves is not taken into account in the Météo-France operational model chain yet.
Reanalysis data are used in this study to be able to produce a homogeneous set of events over a 38 years period. The model chain is calibrated for the application with operational atmospheric forcing data. As the ERA-Interim and ERA5 reanalysis have a coarser resolution than the wind data used for the operational setup, the wind distributions can differ in regard to the operational forcing. For this reason, several parameters have been adapted for calibration and the setup has been validated against observations. Along track altimeter data for 2013 from the Ifremer CERSAT data base (available from ftp://ftp. ifremer. fr/ifremer/cersat/products/swath/altimeters/waves/data/) has been used for this purpose. This includes the instruments JASON1, JASON2, CRYOSAT and SARAL. The measured CERSAT significant wave height data, which are subject to different corrections described by  Queffeulou (2004) , are applied in this study.
Measurements along track that are falling into predefined 0. 5°x0. 5° grid cells are averaged and collocated to the nearest model grid point with a maximum time delay of 1h30. Data below 0. 5 m and above 13 m are excluded. Objective scores calculated between the collocated observation-model pairs are used for model validation. The bias and scatter index  (Zambresky, 1989)  in the North Atlantic ocean, where distant swells are generated, are shown in Figure  1. Using the same wave model parameters as in the operational setup leads to a negative bias of about 21 cm (Figure  1a ) nearly all over the Atlantic, including the northern coast of South America around French Guiana. As MFWAM is used here to provide boundary conditions for WWIII, model parameters have been adapted to reduce the bias in the region where the open boundaries of WWIII are located.
The application of running windows of different sizes over randomly chosen grid points in the ERA-Interim significant wave height data over the entire period showed that fitting a stationary GPD is reasonable. Aarnes et al. (2015)  discussed a jump in 1991 in ERA-Interim wave data. It corresponds to the introduction of altimeter data assimilation, and is also visible in the reanalysis in the order of 7 cm in the mean near French Guiana. As this effect is small, and since ERA-Interim wave data are only used for the preparation of an event list, it is assumed not to affect the results. For the extreme value statistics, a homogeneous time series is necessary. To avoid to introduce inhomogeneities into the hindcast sample, data assimilation for MFWAM and WWIII is not applied in this study, as observations are not available over the entire 38 years.
ACKNOWLEDGEMENTS This study was financed by the  Direction de l'Environnement, de l'Aménagement et du Logement (DEAL) of French Guiana. The setup of the WWIII model for French Guiana was created in cooperation between the SHOM and  Météo France  in the framework of the  Homonim  project with support from the  DGPR  and the DGSCGC. This study has been conducted using the CERSAT data base of Ifremer for the altimeter's measurements and  E. U. Copernicus Marine Service Information  and contains modified  Copernicus Climate Change Service Information. We would like to thank  Héloïse Michaud  from the  SHOM  and  Antoine Gardel  from the  CNRS  for discussions concerning mud banks,  Anne-Lise Beaulant  and  Marie Minvielle  for a preparatory literature study, and  Ali Bel Madani  and  Nicolas Morie  for comments on the manuscript.
Plant material All the accessions of Aeschynomene used in this study, their geographical origin and source data are listed in Additional file 1: Table  S1. Seeds were scarified with sulphuric acid for germination and plants were grown in pots filled with compost under greenhouse conditions (temperature: 26-36 °C, relative humidity: 70%-80%, insect-proof screens) as detailed  [16]. Interspecific crosses were performed according to the protocol developed earlier and the nature of the resulting hybrids was checked using SSR markers (data not shown)  [16]. Fertility of the F1 plants was assessed by recording the number of successfully developed pods per flowering axis and the number of seeds in each pod.
Gene sequencing and sequence analysis The nuclear ribosomal internal transcribed spacer region (ITS: ITS1-5. 8S rDNA gene-ITS2) and five low copy nuclear genes CYP1, eiF1α, Sucrose Synthase, SUI1 and a gene homolog to Glyma. 07G136800 and Glyma. 18G187300 identified in Glycine max were amplified with the primers listed in Additional file 2: Table  S2. PCR amplifications, cloning and sequencing of PCR products were performed as already described  [17, 18]. The DNA sequences generated in this study were deposited in Genbank (Additional file 3: Table  S3 ) and additional sequences are available in Additional file 4: Doc. S1. For the phylogenetic analyses, the gene sequences were aligned in ClustalX, version 1. 81b and the alignments were checked in Genedoc v2. 7. Phylogenetic reconstructions were performed with the MEGA v7 program using the Neighbor Joining approach and the Tamura 3-parameter model with a 1000 x bootstrap.
SSR data analysis Genotyping data files were assembled in a single database that was used to determine genetic relationships among the accessions. For this, a distance-based approach was applied by calculating with a shared allele index a genetic dissimilarity matrix in DARwin v5 software  [25]. Then, individual relations were separately analyzed for each ploidy level (2×, 4×, 6×) with a tree construction based on an unweighted Neighbor Joining method while genetic affinities between diploid and polyploid species were investigated by a factorial analysis as implemented in DARwin v5. The genetic diversity was evaluated by computing the number of alleles per locus (Na) and the observed heterozygosities Ho for each SSR locus and for different accession groups (Additional files 12 and 13: Tables  S8, S9 ).
Acknowledgements We are grateful to the different germplasm banks for provision of seeds:  AusPGRIS (Australia) ,  CIAT (Colombia) ,  ILRI (Ethiopia) ,  IRRI (Philippines) ,  Kew Gardens  (UK) and  USDA (USA). We are also very grateful to the many herbaria and botanists who provided additional relevant plant material that significantly contributed our sampling. The present work has benefited from the facilities and expertise of the cytometry facilities of  Imagerie-Gif  (http://www. i2bc. paris-saclay. fr/spip. php?rubri-que184) and of the molecular cytogenetic facilities of the  AGAP laboratory  (https:// umr-agap. cirad. fr/en/platforms/plateformes/genotyping).