dataesr/scientific-paragraphs-categorization huggingface dataset:
This possibility is, indeed, suggested by a higher representation in the southern hemisphere of UNITE SH assigned to Cadophora sp. 1, Cadophora sp. 4, Cadophora sp. 5, C. echinata, C. cf. interclivum, C. ferruginea, C. luteo-olivacea, C. cf. meredithiae, and C. variabilis (Table  S1 ). However, northern countries like USA and China, amongst the most represented by fungal ITS sequences in GenBank, did not yield as many BLAST matches as European countries did, thus adding support to the distribution patterns we observed. Just considering our isolates, all species with more than two isolates had representatives at separate locations over Europe, while often co-existing in the same site with isolates from other Cadophora species. This reproduces observations made by  Grünig et al. (2002)  and  Piercey et al.
Conclusions Our dataset is probably the most comprehensive considering currently recognized Cadophora species. It shows an unexpectedly high diversity of species in roots of Microthlaspi spp. , which led to the description of seven species new to science in this study and enabled a new and a more complete view of the life history of members of this helotialean genus. Our results also show current knowledge gaps in the phylogenetic relationships within Cadophora and with other, related lineages. Further sampling efforts to discover new species, as well as further descriptions and sequence data from species types are necessary to resolve the systematics of this fascinating group of fungi.
Availability of data and material Living cultures of all isolates have been deposited in the IPF fungal culture collection hosted at Goethe University Frankfurt, and a representative subset of the isolates were also deposited in the CBS culture collection (Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands). Dried cultures weree deposited in the Herbarium Senckenbergianum Frankfurt am Main (FR). The DNA alignments and the phylogenetic trees are available at TreeBASE under accession S25942. All data and code used in this study is available online at https://github. com/jgmv/helotialean_root_endophytes. Potential worldwide distribution of the Cadophora species target of this study as inferred by BLAST comparisons against the NCBI GenBank's nucleotide database (nt), and selected high-throughput ITS amplicon sequencing datasets available at the sequence reads archive (SRA; Table  S3 ).
A, Maps showing the worldwide distribution of BLAST matches for selected Cadophora species (see Fig. S4  for maps with all the species in this study). Each map shows the BLAST search results for all the isolates within each species. Points represent the geographic locations of BLAST matches, either from the nt or the SRA database (indicated by point shape). Only BLAST matches with percent identity above 97 % are shown, with points color indicating percent identity value. B, Species distribution ranges based on BLAST results at 98, 99, or 100 % percent identity. Ranges correspond to the farthest distance between two BLAST matches in the map, at each percent identity. For each species, each point corresponds to the range calculated for each isolate within the species.
Acknowledgments We thank  Claudia Nesselhauf  for her assistance in the curation of the isolates' dry cultures. This study was supported by  LOEWE  ( Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz)  of the state of Hesse and was conducted within the framework of the  Cluster for Integrative Fungal Research  (IPF). J. G. M. -V. acknowledges support from the  German Research Foundation  under grant  MA7171/1-1. O. K. thanks to institutional support for science and research provided by the  Ministry of Education, Youth and Sports of the Czech Republic.
Funding This study was supported by  LOEWE  ( Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz)  of the state of Hesse and was conducted within the framework of the  Cluster for Integrative Fungal Research  (IPF). J. G. M. -V. acknowledges support from the  German Research Foundation  under grant  MA7171/1-1. O. K. thanks to institutional support for science and research provided by the  Ministry of Education, Youth and Sports of the Czech Republic.
Type. Bulgaria, Nepraznentsi, 42. 67 N 022. 84 E, 740 m a. s. l. , endophytic in roots of Microthlaspi sp. , 14 May 2013, col. by T. Ali and S. Ploch, isol. by K. Glynou, FR 0255157 (ex-type culture: P1751 = CBS 146371; GenBank accessions: ITS = KT269023, LSU = MN339369, tef1-α = MN325874, rpb2 = MN367280).
Type. Spain, Puebla de Don Fadrique, 38. 05 N 002. 54 W, 1,612 m a. s. l. , endophytic in roots of M. perfoliatum (L. ) F. K. Mey. , 2 May 2013, col. by J. G. Maciá-Vicente, isol. by J. G. Maciá-Vicente, FR 0255199 (ex-type culture: P6045 = CBS 146383; GenBank accessions: ITS = KT270239, LSU = MN339428, tef1α = MN325932, rpb2 = MN367267). Additional material. Croatia, Gospić, 44. 46 N 015. 40 E, 755 m a. s. l. , endophytic  in roots of M. erraticum (Jord. ) T. Ali & Thines, 4 May 2013, col. by T. Ali, isol. by K. Glynou, FR 0255154 (culture: P1518). Bulgaria, Divlya, 42. 57 N 022. 69 E, 685 m a. s. l. , endophytic in roots of M. erraticum, 14 May 2013, col. by T. Ali and S. Ploch, isol. by K. Glynou, FR 0255159 (culture: P1823). Bulgaria, Divlya, 42. 57 N 022. 69 E, 685 m a. s. l. , endophytic in roots of M.
The third scenario corresponds to the survival of populations in different refugia where we predict: i) the existence of clearly distinct genetic clusters, and ii) postglacial gene flow with signatures of secondary contacts, with contact zones displaying higher genetic diversity through postglacial admixture between different genomic backgrounds. In order to test these alternative scenarios, we generated genome-wide data from nearly 2,000 Coho salmon from California to Alaska, one of the most extensive genomic datasets for a non-model vertebrate species to date. First, to resolve the species demographic history, we used a modelling approach that accounts both for barriers to gene flow affecting migration locally, and for linked selection affecting the rate of drift. Second, we hypothesized that demographic history shaped the pattern of deleterious mutation load, both within and among populations.
The genetic relationship among all salmon was assessed using a PCA with the R package ade4  [88]  based on the LDpruned dataset  (40, 632 SNPs). We used a 1% minor allele frequency (MAF) threshold and allowed less than 4% missing data. Formal tests of admixture were performed using Treemix  [52]  using the LD-pruned dataset of 40,632 SNPs and without any MAF threshold. An MDS was also constructed using plink and plotted with the ggplot2 [89] R package. We ran Treemix allowing up to 20 migration events and performed 500 bootstrap replicates of the best model to obtain robustness of the nodes. The "best" model was inferred after inspecting the relevant migration edges by measuring the percentage of variance explained migration edges were added to the tree, as well as by assessing the p-value associated to each migration edge.
A total 500 bootstrap replicate runs were performed under the "best" model and under a model without migration to infer the robustness of the nodes. The scripts are available on GitHub at https://github. com/QuentinRougemont/treemix_workflow Explicit demographic inferences. We tested alternative hypotheses of divergence from a single southern or northern refugium versus divergence from two or more refugia (e. g. Alaska & California). In the first case, by comparing different regional groups, we expected a model of divergence with ongoing migration (IM) to be supported, or alternatively a model of divergence with ancient migration if gene-flow has stopped recently (AM) following the divergence and recent colonization of rivers. In the case of divergence into multiple refugia we expected models of secondary contact between putative groups to be best supported.
∂a∂i uses the SFS as a summary of the data. For a given demographic model, the SFS is computed using diffusion approximation and compared to the empirical SFS using AIC. Here, we started from the whole file containing 200,000 SNPs and used one single SNP per GBS locus and filtered the data to minimize missing data. No MAF filter was used and singletons were kept to avoid ascertainment bias in estimates of demographic parameters. For each model, 20 independent replicate runs were performed and only models with the lowest AIC and ΔAIC were kept. A model was classified as "ambiguous" and not used for parameter estimation if ΔAIC between the best model and second-best model was below 10. The Godambe information criteria was initially used to evaluate parameter uncertainties based on 100 bootstrapped datasets, which resulted in large confidence intervals in a few pairwise comparisons.
Therefore, we then performed parametric bootstrap. More specifically, we converted ∂a∂i estimates into ms  [91]  estimates to construct sets of 100 bootstrapped datasets over the different categories of the SFS (i. e. neutral SFS, SFS with reduced rate of migration, SFS in low recombination areas undergoing linked selection, SFS in areas of normal recombination) that were summed and then fitted with ∂a∂i. The new estimates were then converted into demographic units to obtain the 95% confidence intervals. The whole pipeline is available at https://github. com/QuentinRougemont/DemographicInference.
Genetic load estimation from the GBS data set Estimating ancestral and derived alleles We identified derived and ancestral allele using the genomes of three outgroup species, the chinook salmon, the rainbow trout, and the Atlantic salmon, to classify SNPs as ancestral or derived. These informations were subsequently used to analyze the load of deleterious mutation. Whole genome data for the chinook salmon (n = 3 individuals) were provided by one of us (B. Koop, unpublished), whereas rainbow trout (n = 5) and Atlantic salmon (n = 5) data were downloaded from NCBI Sequence Read Archive (rainbow trout, SRA, Bioproject : SRP117091; Salmo salar SRA Bioproject: SRP059652). Every individual was aligned against the Coho salmon V1 reference genome (GCF_002021745. 1) using GATK UnifiedGenotyper and calling every SNP using the EMIT_ALL_SITES modes.
Measuring the deleterious load through piN/piS ratio The ratio of non-synonymous diversity to synonymous diversity is a commonly used metric to quantify the deleterious mutation load  [66]. To do so, we first generated aligned fasta sequences and then computed summary statistics. More specifically, for every individual, we ran GATK v4. 1. 2. 0 on the bam file generated above following GATK best practices. We generated individual gVCF files then combined individuals into populations using the CombinedGVCFs module from GATK and finally performed a joint genotyping using the all-site options. We then reconstructed fasta sequences from VCF files for every individual by reconstructing two genomic sequences with a pipeline modified from  [92]. We then used cutSeqGff. py to subsample and clean sequences considering several genomic features (CDS, introns, intergenic regions) from gff files.
The resulting alignment was then used to compute the πN/πS ratio in sequences containing 4-fold degenerate codons only. We then tested a number of correlations between the load (πN/πS) and 1) πS used as a proxy of the long-term effective population size; 2) Tajima's D used as a proxy for historical change in population size and 3) distance to the Southernmost site. Significance of the correlation were tested using the lm() function in R. Plots were drawn using ggplot2  [87]. All pipelines to run GATK and compute deleterious load are available at :https://github. com/QuentinRougemont/gatk_haplotype and https://github. com/QuentinRougemont/piNpiS.
Measuring damaging impact of non-synonymous alleles While the computation above provides insights into the deleterious mutation load, it does not allow distinguishing among recessive and additive load nor testing for differences in allelic frequencies. We therefore further tested differences in mutation load among populations as follows. The software Provean  [68]  was used to predict whether a given non-synonymous mutation was deleterious with a threshold score of -2. 5 or less using the pipeline available at https://github. com/QuentinRougemont/gbs_synonymy_with_genome. We analysed the data in two ways: first we counted the total number of putative homozygous deleterious alleles per individual as well as the total number of deleterious alleles (both in homozygous and heterozygous states) using: Ntotal = 2 Χ Nhomo + Nhetero  [34].
Ethic Statement A permit number SIRUL 111722 was obtained to work on DNA sequences. (i) No specific guidelines were followed as we did not work with animals per se and obtained all of our samples from a tissue/DNA repository of the department of Fisheries and oceans Canada; (ii) the study was approved by the following committee "Comité de protection des animaux de l'Université Laval (CPAUL)" (iii) the approval number "SIRUL 111722" was the project label at University Laval. Following answering the CPAUL questionnaire, we did not get a permit because we did not need one since there were no animals manipulations involved. Raw data will be deposit on NCBI together with Short Read Archive (SRA) accession number. All relevant vcf and jSFS files will be deposited on dryad.
Acknowledgements: We thank  B. Bougas ,  A. Perrault-Payette ,  C. Hernandez  for laboratory support and  K. Wellband ,  H. Cayuela ,  F. Hartmann  for their constructive comments. TL and QR thank  Benoit Nabholz  for discussions regarding the deleterious mutation loads and  Hill-Robertson  effects. We want to thank three reviewers (Christelle Fraïsse & two anonymous reviewer) and Associate Editors for their thoughtful comments and efforts towards improving a previous version of our manuscript. Computations were mainly performed on Colosse ( Calcul Quebec), Graham and Cedar (Compute Canada ) servers. TL is also grateful to the  Genotoul Bioinformatics Platform Toulouse Midi-Pyrenees (Bioinfo Genotoul ) and the  Biogenouest BiRD core facility  ( Universit é de Nantes ) for providing computing and storage resources.
Genotyping by Sequencing A total of 2,088 individuals were collected from 58 sample sites located along the Pacific coast from California to Alaska (S1 Table  and Figure   1 ). DNA was extracted from all individuals and sequenced using a GBS method (protocol detailed in  [78] ). Reads were aligned to the Coho salmon reference genome v1 (GCF_002021745. 1) using bwa-mem 0. 7. 13  [79]. Samtools v1. 7  [80]  was used to keep reads with a mapping quality above 20, remove supplementary alignment and unmapped read. Variants were then called with Stacks v1. 46  [81]. To do so, the module "pstacks" was used with a minimum depth of 5, and up to three mismatches were allowed in catalog assembly. The module "populations" was run to produce a vcf file that was filtered with a custom python script. We performed stringent filtering to remove SNPs that were 1) genotyped in less than 60% of the individuals; 2) at a mean depth of sequencing (averaged across all individuals) below 7, and 3) with observed heterozygosity above 0.
60, thus resulting in 93,000 SNPs. The pipeline for SNP calling is available on github at https://github. com/enormandeau/stacks_workflow/releases/tag/coho_demography_paper. Next, we removed any individuals with more than 5% missing data and finally only kept SNPs present in at least 95% of the individuals yielding a total of 82,772 filtered SNPs for 1,957 individuals. Remaining filtration was done according to the requirement of each analysis performed below. To perform explicit demographic analyses based on the site frequency spectrum, the likelihood approach implemented in ANGSD v0. 930  [82]  was used instead of the commonly used Stacks pipeline because it is less biased than the genotype calling approach  [83]. We first used the SAMTools model  [80]  in ANGSD to estimate genotype likelihoods from BAM files using only reads with a minimal base quality score of 20 and a minimal mapping quality score of 30 across all individuals.
Genetic diversity and ancestral populations For each sampling location we estimated the observed heterozygosity and π using vcftools 0. 1. 16 [84]  and Hierfstat  [85]. The most likely ancestral populations were identified using βST  [42]. A total of 1,000 bootstraps was performed to obtain the 95% confidence intervals around the βST. Weir and Cockerham's FST estimator θ  [46]  was computed in vcftools. We measured the relationship between observed heterozygosity, βST, FST and the distance to the southernmost site using linear models with the lm() function implemented in R. We also verified the relationship between FST and the distance to the southernmost site using Mantel tests with 10,000 permutations. Vcftools was also used to identify singletons (i. e. variants present in one single individual across the whole dataset).
The power to discover singletons is however dependent upon the sample size. Therefore, we reduced the size of each population to the smallest size of 13 by randomly sampling individuals in each population. We repeated the procedure 200 times to obtain standard deviations of the distribution of singletons (S3 Fig). We then computed the averaged number of singletons at the regional level as well as levels of private and shared polymorphisms (S2 Fig and  S3Fig. ). We tested the significance of differences in singletons distribution acrross the 200 replicated using a pairwise Wilcoxon test corrected with the Benjamini Hochberg method for multiple tests  [86]. The scripts are available on GitHub at https://github. com/QuentinRougemont/utility_scripts Population structure, admixture and gene flow Levels of ancestry and admixture proportions were inferred with the snmf function implemented in the R package LEA  [50] , allowing only less than 5% of missing data.
Chickens were then sampled randomly at the day of collection. The first group was the control group. The second one received CpG ODN 2007 injected by subcutaneous route: 20 mg of CpG ODN 2007 (Sequence 5'-TCG TCG TTG TCG TTT TGT CGT T-3 0 ), Avecia Biotechnology Inc. (USA). The dose of 20 mg/bird was selected based on literature and internal dose-response studies (data not shown). As described in Table  1 , 3 h (D013h), 3 D (D3), or 6 D (D6) after injection, chicks (up to 8-12/group) were bled by intracardial route sampling and euthanized (by injection of Dol ethal by intracardiac route  [IC]  in accordance with site Standard Operating Procedure). Blood was collected into Liheparin tubes (BD Vacutainer Plus sterile N 368886). Chick sex was determined manually and confirmed by molecular biology.
NMR Bioanalyses The NMR analyses were performed on 600 MHz Avance III HD spectrometer equipped with 5 mm QCI probehead and SampleJet autosampler from Bruker (Germany). To preserve the sample's metabolites integrity before analysis, the autosampler was thermostated at 5 C to 6 C. All samples were analyzed using noe-sygppr1d acquisition sequence for one dimensional proton spectra and sequentially were processed with the help of the Topspin 3. 2 software from Bruker. For each analysis, 128 scans were cumulated within 14 ppm spectral width and 32 k time domain points. The mixing time and the relaxation delay were 80 ms and 4 s, respectively. The FID data were processed into 64 k final data points using 0. 3 Hz exponential apodization and zero filling. Then, all NMR spectra were phase-and baselinecorrected before metabolites quantification.
However, because variance was homogeneous across batches, a simple linear model was used. After filtration, 19,666 transcripts (94. 5% of the transcripts) were kept for the final analysis. The distribution of raw and normalized data stratified by gender and batches is given in the Supplementary Text. Metabolomics Data Preprocessing For LC-HRMS, the extraction and annotation of MS features were performed using Biotracs-OpenMS, an open-source bioinformatics tool based on OpenMS software  (Sturm et al. , 2008; R€ ost et al. , 2016 ) that we recently published on GitHub (https://github. com/bioaster/biotracs-mopenms). Metabolite extraction was based on peak picking algorithms to annotate the most significant peaks using their mass-to-charge ratio (m/z) and retention time (RT). Peaks below a predetermined backgroundintensity threshold of 1e4 were removed from data.
This threshold was chosen in agreement with the spectrometer specifications. The resulting list of features was then used for QC analysis to correct other analytical factors, in particular intra-batch and inter-batch drifts in BioTracs-Mimosa, a Matlab application designed for the processing of metabolomics data (https://github. com/bioaster/ biotracs-m-mimosa). For intra-batch drift correction, the analytical drift is modeled using QC samples  (Wehrens et al. , 2016). Linear correction models were built without the first QC samples that are injected to stabilize the chromatographic separation  (Gika et al. , 2007). Inter-batch drift correction was performed by centering and scaling the 4 batches  (Johnson et al. , 2007)  to remove additive and multiplicative inter-batch effects. Next, the coefficient of variation (CV 5 standard deviation/mean) of each feature was computed to assess their analytical stability in QC samples.
Features having CVs. 0. 2 were considered as unstable in our analytical conditions and were removed from the study resulting in 509 m/z-RT features. Drift corrections was implemented using in-house bioinformatics solutions developed with Matlab. We assumed that correlated co-eluting features (RT shift , 10 s, correlation. 0. 80) across all samples potentially correspond to the same metabolite and were denoted "isofeatures" here. Subsequently, the average signal intensity of each group of isofeatures was calculated for further statistical treatment. This allowed reducing the initial feature set to 334 isofeature groups. Within each isofeature group, putative adduct forms were assigned according to exact masses. The identification of the lipids was performed using the LipidMatch software that we wrapped in the BioTracs-LipidMatch application.
This application is available online on GitHub (https://github. com/bioaster/biotracs-m-lipidmatch). Among all the putative metabolites extracted, 116 lipids  For NMR, the spectra were exported into the Chenomx NMR Suite software (version 8. 2) for metabolites quantification. Based on Chenomx library spectra, the absolute concentrations of polar metabolites were quantified according to DSS internal standard and exported for all samples into an Excel table. All quantified metabolites concentrations were normalized and corrected according to the dilution factors applied during the sample preparation. For the ambiguous metabolites confirmation, additional analyses were performed by spiking authentic standards. Transcriptomics Data Analysis After correction of confounding effects, DEGs were identified using LIMMA, which relies on a moderated t-statistic  (Smyth, 2004)  and is well suited for small studies.
Pathway Enrichment and Functional Analysis Owing to the lack of custom chicken pathway database, the functional analysis was done by orthology from wellcharacterized species. In this way, pathway enrichment was performed using Ingenuity Pathway Analysis (IPA, QIAGEN) on differentially expressed genes (DEG) while differentially expressed metabolites (DEM) were mapped onto the KEGG metabolic pathways. The choice of IPA was motivated by its ability to directly carry out enrichment from Affymetrix probesets. In our study, 10,975 out of 20,804 transcripts (about the half) were mapped in IPA and subsequently used for enrichment. The pathway enrichment step was performed using the Fisher exact test with a standard P-value threshold P 0. 05.