dataesr/scientific-paragraphs-categorization huggingface dataset:
Ant sampling methods During the IBISCA-Panama project four surveys were conducted spanning the dry and the wet seasons (September 2003 -November 2004)  (Basset et al. , 2012 (Basset et al. , , 2015)). Because the San Lorenzo rainforest remains humid and experiences almost no loss of canopy cover during the January-April dry season, the ground-level fauna is not thought to suffer much from drought stress (see  Roisin et al. , 2006 for termites). A combination of 11 sampling methods, resulting in an exceptionally large dataset, was used (Table  1 ; Fig. S1 ). (1) Berlese funnel. Three trees per site were sampled. For each tree, 16 soil cores of 15 cm 3 were gathered at ground level (n=8) and at the bases of main branches (n=8) and placed in Berlese funnels for 2 days. (2) Winkler. This sampling was conducted on 51 quadrats of 1 m 2 distributed at intervals of 5 m around each 20 x 20 m plot.
Complementarity of each sampling method and its pertinence for studying the entire ant assemblage To estimate the rate of species accumulation by each method (all strata pooled) or by strata (all methods pooled), species rarefaction curves were plotted on the species occurrences data matrices using the Mao Tau algorithm in EstimateS 9. 1. 0 software  (Colwell, 2016)  with 100 randomizations of the sampling order without replacement. Methods were grouped based on their compositional similarity according to the UPGMA algorithm with the Bray-Curtis similarity index available in the PAST 3. 26 software.
Horizontal beta diversity within strata To measure how species composition changed horizontally, we performed an additive partition of the species richness of each horizontal stratum  (Barton et al. , 2013 , Veech et al. , 2002) :  = +  where  is the total species richness, the average species richness within sites and the average difference in species richness between sites. This analysis was based on datasets from the three most efficient multi-strata sampling methods: aFITs (three strata), Berlese (two strata) and beating (two strata). We used the software PARTITION3  (Veech, & Crist, 2009)  to compare the observed diversity value to a corresponding null distribution (individual-based randomization method, 1000 randomizations). The mean observed richness is equal to the expected richness if the distribution of richness is spatially homogeneous.
This allows us to test the significance of the observed value as either a significantly high or low value. In addition, we followed the approach developed by  Baselga (2010)  to separate beta diversity into its turnover (species change) and nestedness (species gain/loss) components. The total compositional variation between assemblages was calculated using the Jaccard pairwise dissimilarity index. For each combination of strata and method, pairwise beta diversity measures were calculated using the "betapart" package  (Baselga, & Orme, 2012)  in R software (R core Team 2015).
Differences in horizontal beta diversity between strata Whatever the method considered, there was no significant difference between strata in horizontal beta diversity between sites, (aFIT: F ( 2,12 )=0. 39, P=0. 69; Berlese: t=-0. 84, P=0. 41; beating: t=0. 29, P=0. 78) (Fig. 5 ). Furthermore, the observed  diversity between sites was significantly higher than expected with randomized datasets, except in the understorey for aFITs (Fig. 5 ), suggesting some site effect on horizontal species distribution. Species turnover was the main contributor to beta diversity (88 + 6%, n= 7) while nestedness contributed to 12 + 6%.
Acknowledgments IBISCA-Panama is an initiative of  Pro-Natura International ,  Océan Vert , the  University Blaise Pascal, Clermont-Ferrand, France , the  University of Panama , and the  Smithsonian Tropical Research Institute (STRI). We thank  N. Springate ,  R. Kitching ,  L. Fagan , F. Odegaard for their contribution to the collection of ant specimens. I. Bachy  helped with the design of figures. We are grateful to  J. Herrera ,  E. Andrade ,  M. Samaniego ,  S. J. Wright ,  N. Baiben ,  S. Bechet ,  J. Belleguic ,  T. Aubert ,  K. Jordan ,  G. Ebersolt ,  D. Cleyet-Marrel ,  L. Pyot ,  O. Pascal ,  P. Basset , and  E. Bauhaus  who helped with logistics in the field and to  A. Yockey  for proofreading the manuscript. We also wish to thank two reviewers for their useful suggestions and  J.
Rain data were extracted from BDOH database  (Gouy et al. , 2015). A 3-month simulation was performed on a winter period characterized by long and intense rain events (670 mm cumulated). Potential evapotranspiration (PET) data were obtained from MeteoFrance for the neighbouring site of Liergues  (MeteoFrance, 2008). Data were averaged over 10-day periods and corrected by a factor -11 % to match La Morcille site  (Durand, 2014; Caisson, 2019). Rain and PET data for the simulation are summarized in Figure  3. Although virtual, we aimed at setting initial conditions as plausible as possible for this scenario. Initial water table  levels  were deduced from piezometric data on a neighbouring hillslope. Data from several piezometers were available on a transect, perpendicular to the river. Data were extrapolated over the virtual hillslope width on both sides of the river.
General formulation Random forest  (Breiman, 2001)  belongs to ensemble machine learning techniques. It consists in averaging results from an ensemble of K decision trees created independently. A decision tree is composed of an ensemble of discriminatory binary conditions contained in nodes. Such conditions are hierarchically applied from a root node to a terminal node (tree leaf)  (Rodriguez-Galiano et al. , 2014). The input space is therefore successively partitioned into smaller groups that correspond to the nodes according to a response variable. Such splitting goes on until reaching a minimum threshold of members per node. As each individual decision tree is very sensitive to the input dataset, bagging is used to avoid correlations between them and to ensure model stability. It consists in training each decision tree from a different training dataset smaller than the original one.
The power of the test α was set to 1% and 100 bootstrap replicates were used. Then, ranking was performed on the new 1,000-point LHS obtained from the reduced set of input parameters. The R code provided in De Lozzo and  Marrel (2016)  to compute HSIC measure was adapted in Python to perform both screening and ranking. Finally, sensitivity indices deduced from Random Forest (RF, Breiman 2001) surrogate model were also computed. A Random Forest model is an empirical input/output relationship based on an ensemble of decision trees. The relative importance of each input parameter in the RF building (called feature importance measure) can be easily deduced from it. More precisely, an input parameter X i is considered important if when breaking the link between X i and the output Y by permutation, the RF prediction error increases.
The metric for measuring prediction error can be defined in different ways and we chose the original formulation of Mean Decrease Accuracy from  Breiman (2001). These feature importance measures constitute a kind of sensitivity indices that can be calculated (almost) for free as soon as the RF structure is available. In this study, we used the randomForestSRC R package  (Ishwaran and Kogalur, 2020)  to obtain feature importance measures once again from the 1,000-point LHS. The number of trees used to train the RF was set to 500. As RF building already includes a bootstrap step to build each tree, applying another bootstrap could thus affect the consistency of the OOB sample. We then used a subsampling approach without replacement with subsample size set to 800 to get error bounds on feature importance measures.
As deducing the RF structure from a reduced dataset may lead to limited performances, we primarily checked that RF performances were reasonably decreased when shifting the training dataset size from 1,000 to 800. The same procedure was applied to estimate error bounds on HSIC indices as 800 points was supposed to be far enough to compute consistent estimators. 100 replications were used for all resampling methods. In a second time, aggregated sensitivity indices were computed to summarize all site/local sensitivity indices. In that case, the objective function was no longer a scalar but rather a vector that gathered a given cumulated variable for all HUs over the catchment. Aggregated Sobol' indices were calculated following  Gamboa et al. (2013).
The PESHMELBA model is an open-source model coded in Python and Fortran 90 and embedded in the OpenPALM coupler. The code for the OpenPALM coupler is available from www. cerfacs. fr/globc/PALM_WEB/user. html#download after registration. The full PESHMELBA code is freely made available on request from the corresponding author, Emilie Rouzies (emilie. rouzies@inrae. fr). UQLab (www. uqlab. com) is an open-source Matlab software for uncertainty quantification. The Python scripts used 620 to compute HSIC sensitivity indices are freely made available on request from the corresponding author. The Python Scikit-learn package is available at https://scikit-learn. org. All the data produced in this study can also be made available on request from the corresponding author.
Libraries were prepared using a Nextera XT kit (Illumina) and sequenced on an Illumina NextSeq500 (2 × 75 cycles). Genome assembly. Raw reads were trimmed using Trimmomatic v0. 39  25  to remove adapters and low-quality reads. We assembled reads using the metaspades option of SPAdes/3. 14. 0 26 and megahit v1. 2. 9 27 with default parameters. Scaffolds were queried against the NCBI non-redundant protein database 28 using DIA-MOND v2. 0. 4  29. Among other putative viruses (hits summarized in Supplementary Table  3 ), the Sarbecovirus genomes identified were verified and corrected by iterative mapping using CLC Assembly Cell v5. 1. 0 (Qiagen). Aligned reads were manually inspected using Geneious prime v2020. 1. 2 (2020) (https:// www. geneious. com/), and consensus sequences were generated using a minimum of 3× read-depth coverage to make a base call.
The genomes are nearly identical, presenting three nucleotides difference between them: g12196a, c20040t, and t24572c). We used Ivar  30  to estimate the frequency of minor variants (iSNV) from the coronavirus reads. Coverage depth and iSNVs are reported in Supplementary Fig. 4. The sequence of the spike gene of each virus was confirmed by Sanger sequencing, using primers listed in Supplementary Table  4. The sequence of Rhinolophus shameli ACE2 gene was similarly reconstructed from the reads. Dataset. Complete genome sequence data and metadata of representative SARSlike viruses were retrieved from GenBank, ViPR 31 , and GISAID. Sequences were aligned by MAFTT v. 7. 467  32  , and the alignment checked for accuracy using MEGA v7  33. Accession numbers of all 39 sequences are available in Supplementary Table  5.
Prior to the tree reconstruction, the ModelFinder application  38  , as implemented in IQ-TREE, was used to select the best-fitting nucleotide substitution model. Bayesian phylogenies were also inferred using MrBayes v3. 2. 7  39  , using the GTR substitution model. Ten million steps were run and parameters were sampled every 1000 steps. Structure modeling. The three-dimensional structure of the RBD of RshSTT200 was modeled using the SWISS-MODEL program  40  , using SARS-CoV-2 (PDB: 6yla. 1) structure as it was the best hit for the RshSTT200 amino acid sequence input. Pseudovirus entry assay. HEK293T cells (Sigma) were maintained in complete medium (DMEM, Gibco) with 10% fetal bovine serum (Gibco) and 1% penicillinstreptomycin (Gibco). The sequence of Rhinolophus shameli ACE2 gene, codon-optimized for human expression, was synthetized (GeneArt, ThermoFischer) and cloned into an expression plasmid pLenti-puro-RshACE2.
Acknowledgements We thank the  government of Cambodia  for permission to conduct this work. We thank also  General Directorate of Animal Health and Production, Department of Wildlife and Biodiversity, Forestry Administration, Ministry of Agriculture, Forestry and Fisheries, Communicable Disease Control Department, Ministry of Health, the Wildlife Conservation Society  teams and all students who helped collecting field samples. We extend our gratitude to the  Virology Unit team at Institut Pasteur du Cambodge  for technical support in laboratory diagnostic, and to  Gabor Csorba  for providing three Rhinolophus shameli samples. We are grateful to all researchers who have kindly shared genome data on the International Nucleotide Sequence Database Collaboration or on the GISAID. Supplementary Table 6 lists the originating and contributing laboratories of the sequences retrieved on the GISAID for this work.
This study was made possible by the generous support of the  American  people through the  United States Agency International Development (USAID)   Emerging Pandemic Threats PREDICT project  (cooperative agreement number  GHN-A-OO-09-00010-00  and  AID-OAA-A-14-00102 ), with a specific extension for the testing reported here. V. H. is supported by a scholarship from the  French Government  (BGF) for his Ph. D. E. S. L. acknowledges funding from the  French Government 's  Investissement d'Avenir program , ' INCEPTION ' ( ANR-16-CONV-0005 ), and  Laboratoire d'Excellence ' Integrative Biology of Emerging Infectious Diseases ' ( ANR-10-LABX-62-IBEID ). In 2010, the fieldwork was supported by the  National Authority for Preah Vihear, UNESCO , " Société des amis du Muséum et du Jardin des Plantes" , and the  Muséum national d'Histoire naturelle.
Data availability The data generated in this study have been deposited in the European Nucleotide Archive database under accession code PRJEB42502. The consensus sequences of RshSTT182 and RshSTT200 are also available at the GISAID  43  database with accession numbers: EPI_ISL_852604 and EPI_ISL_852605 [https://www. gisaid. org/]. The sequence of Rhinolophus shameli ACE2 gene has been deposited under GenBank accession number MZ851782. Source data are provided with this paper.
Phylogenetic analysis The EndoPG, Alta-1 and OPA 10-2 sequence datasets generated in our study were supplemented with data from previous studies. The sequence datasets that enabled taxonomic identification of isolates in these previous studies  (Armitage et al. , 2015; Gur, Reuveni & Cohen, 2017; Harteveld, Akinsanmi & Drenth, 2013; Rotondo et al. , 2012; Woudenberg et al. , 2015a)  were used as a reference in our phylogenetic analysis. As we performed molecular identification using an MLST approach, we decided to use the taxonomy of the Alternaria section Alternaria proposed by  Woudenberg et al. (2015a) , which consists of 11 phylogenetic species and one species complex. In other words, we used the Alternaria alternata phylogenetic species without including any results of morphospecies (e.
The taxonomic identification of 100 isolates using the concatenated trees EndoPg/Alta-1 and EndoPg/Alta-1/OPA 10-2 was compared with the function tanglegram implemented in DENDROSCOPE 3. 2. 10 (Huson & Scornavacca, 2012). A subset of single-locus sequence data for the corresponding loci was submitted to Genbank (accession nos. MN975269-MN975340, Supplemental Information 1).
The results from each experiment were analyzed separately at three data collection times: 4, 7 and 10 days post-inoculation (dpi). Two types of analysis were performed. For data from 4 dpi, a zero-inflated Poisson general linear mixed model (GLMM) was used to assess the number of lesions per leaf. The model included the following explanatory variables: the taxon of the tested isolate (A. alternata or A. arborescens SC), the apple cultivar (Gala or Golden Delicious), and an experiment repeat variable (each isolate was tested twice in independent experiments). An isolate effect was taken into account as a random variable. For data from 7 and 10 dpi, zero-inflated beta GLMMs were performed. On both 7 and 10 dpi the response variable was the proportion of the diseased leaf area on detached leaves that corresponds to the lesion size.
The diseased leaf area proportion (necrosis) was assessed by visual inspection and coded between 0 and 1. The model included the same explanatory and random effect variables used for the 4 dpi data model: taxon, cultivar, repeat and isolate (random effect). All GLMM analyses (on 4, 7 and 10 dpi) took into account only isolates of A. alternata and A. arborescens SC, as only one A. brassicicola isolate was used, which is not enough to be included in the isolate random effect. All the models were run in the R environment (version 4. 0. 3) using the glmmTMB package  (Brooks et al. , 2017). Excess zeros were in all cases tested with the function testZeroInflation of the DHARMa R package  (Hartig, 2017) , which compares the distribution of expected zeros in the data with the observed zeros. Model residual diagnostics of all models were performed with the DHARMa package.
ACKNOWLEDGEMENTS We would also like to thank the French apple producers who supplied leaf and fruit samples, and are grateful to  Pr. Thomas Guillemette ,  Pr. Barry Pryor ,  Pr. Andrea Patriarca ,  Dr. Olufemi A. Akinsanmi ,  Dr. Moshe Reuveni ,  Dr. Lior Gur ,  Dr. Marina Collina  and  Ms. Jacqueline Hubert  for sharing Alternaria strains. We are also grateful to the LSV ANSES team members who helped to perform some of the experiments, in particular  Maurane Pagniez  for the purification of single-spore strains and  Eugenie Vuittenez  for the pathogenicity assays.
Open Field Test To evaluate anxiety-like behavior  (34) , mice were placed in a field of 40 x 20 cm (surrounded by walls to prevent escape) including a new object in the center, for 10 min. The amount of time the mouse spent in the arena center (15 x 7. 5 cm around the new object, i. e. the "exposed field") and in the arena periphery (the rest of the field, i. e. the "protected field") were measured using an automated video-tracking device (Smart, Panlab Harvard Apparatus, Barcelona, Spain). Data recorded during the last 5 min were analyzed.
Forced-Swim Test To evaluate depressive-like behavior  (34) , mice were placed in Plexiglas cylinders (20 cm high, 15 cm in diameter) filled with 35°C water to a depth of 15 cm (preventing mice from touching the bottom of the cylinder). During 6 min, the duration of immobility (i. e. the time during which mice made only the smallest movements necessary to keep their heads above water) was scored using an automated video-tracking device and the last 4-minute-data were analyzed.
Interferon-Inducible Genes Expression Profiling RNeasy Protect Mini Kit (Qiagen, Maryland, USA) was used for RNA extraction of mandibular lymph nodes, according to the manufacturer's instructions. Quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, USA). The Mouse Type 1 Interferon Response RT² Profiler PCR Array, RT² First Strand Kit and RT² Real-Time SYBR Green PCR Master Mix (Qiagen, Maryland, USA) were used according to the manufacturer's instructions and using the data analysis web portal at http://www. qiagen. com/geneglobe. Quantitative real-time PCR array was performed (using 0. 5 μg of extracted RNA/mouse) on the ABI 7500 Real-Time PCR system (Applied Biosystems, Foster City, USA). Samples were assigned to Controls (NT, PBS and KLH mice) and test (IFN-K-treated mice) groups.
Statistics Means (± standard error of the mean) of MuSSDAI, SFR, phenol red thread, Von Frey, cold plate, sucrose consumption, forcedswim and open field tests, as well as histological scoring were analyzed between control (NT, PBS and KLH groups) and treated (IFN-K) mice by the Mann-Whitney U test. These parameters and cells' response assays results were also analyzed by the Kruskal-Wallis test, mainly to assess the lack of differences between the 3 control groups, followed by Dunn tests to compare individually the treated groups with their respective control groups. Survival and proteinuria data were analyzed using Kaplan-Meier graphs and log-rank tests. For IFN-inducible genes expression, the p values were calculated based on a Student's t-test of the replicate 2 (-DCT) values for each gene in the Control and IFN-K-treated groups.
Participant Recruitment and Phenotyping This is a multicenter study comprising a GWAS of 2 independent cohorts sourced from specialized headache clinics; one from the UK and one from Sweden (Supplementary Table  S1 ). UK Cohort. Recruitment occurred between 2006 and 2018, starting at the National Hospital for Neurology and Neurosurgery, London, UK. Ethical approval was obtained for 4 additional UK sites (RAC#2060008 and University College London Hospitals 04/N034). All were specialist-led headache clinics. A diagnosis was made using the International Classification of Headache Disorders-3beta edition (ICHD-3b). 2 Review by 2 independent neurologists was required if 1 ICHD-3b criterion was not met. Control genotype data consisted of a cohort of UK individuals without headache (n = 463), the 1958 birth cohort (n = 2,699) from the Wellcome Trust Case Control Consortium (WTCCC), and the National Blood Survey cohort (NBS; n = 2,501).
Swedish Cohort. Recruitment occurred between 2014 and 2017 at the Karolinska University Hospital neurology clinic, Stockholm, Sweden. Ethical approval was obtained from the Stockholm Regional Ethical Review Board (registration number 2014/656-31). Patients (n = 643) fulfilling the ICHD-3b diagnostic criteria were included. 2 Diagnosis was confirmed by headache specialists and through medical records. Additional information was derived using a diagnostic questionnaire. Control genotype data were obtained from neurologically healthy controls from the Immunomodulation and Multiple Sclerosis Epidemiology study (IMSE, https://ki-se. proxy. kib. ki. se/cns/imse) (n = 1,299).
Single Variant Association Testing Single variant association testing was performed using Scalable and Accurate Implementation of Generalized Mixed Model (SAIGE),  17  fitting a null logistic mixed model. Imputed genotype data were used for all markers, and only autosomal SNPs were analyzed. In the UK cohort, principal component analysis (PCA) axes, generated with PLINK to identify population stratification, were used as covariates. 12 PCA axes 1-6 and sex were used as covariates in the Swedish cohort. In the merged analysis, we used separately imputed data from UK and Sweden, which were again subjected to a PCA after merging. The first 10 axes were used as covariates for combined SAIGE analysis. In each linkage disequilibrium (LD) cluster of SNPs containing GWS hits, the SNP with the lowest p value in case versus control comparisons was defined as the lead SNP.
Gene Expression and Expression Quantitative Trait Locus Analysis 6] [27]  Gene mapping and expression quantitative trait loci (eQTLs) of interest were determined through FUMA using default settings and eQTL data from the brain, spleen, and vascular and immunological tissue based on GTEx v8 and the eQTL catalogue. 20 Only cis-eQTLs with multiple testing correction false discovery rate (FDR) < 0. 05 were included in downstream analysis.
Genetic Colocalization Analysis Bayesian colocalization analysis was used to determine shared causal regions or lead variants between CH and the migraine GWAS from the UK Biobank GWAS database, publicly available at http:// www. nealelab. is/uk-biobank, using the R package coloc. 32 Causal regions were defined as the range of positions of SNPs between 2 recombination hotspots. Coloc reports posterior probability for each of 5 hypotheses tested: H0, neither CH nor migraine had a genetic association within the tested region; H1, only CH had a genetic association within the tested region; H2, only migraine had a genetic association within the tested region; H3, both CH and migraine had a genetic association within the tested region, but did not share causal variants; and H4, both CH and migraine shared a single causal variant with the tested region.