dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic Tree To better understand the population structure of each population, we used the 804-SNP dataset to conduct a phylogenetic analysis based on Provesti's distance. The inferred neighbor-joining phylogenetic tree (Figure  1B ) showed that most of the Ontario samples (in red in Figure  1B ) were different from the rest of the North American populations. Some samples from Ontario were found to be different from each others and more similar to those of central states. Such a pattern can also be observed in the PCA analysis (Figure  1A ).
Isolation by Distance The analysis of the relationship between genetic distance [F ST /(1-F ST )] and geographic distance (km) for all pairs of populations revealed a significant (p = 0. 012) but weak effect of isolation by distance, with a Mantel correlation (r) of only 0. 135. However, when analyzing the effect of isolation by distance at the local scale, a different pattern was found. With respect to US states with recent SCN history, a strong and significant positive correlation was observed between the genetic and geographic distances in North Dakota (r = 0. 430, p = 0. 013) and Minnesota (r = 0. 730, p = 0. 006). In contrast, no correlation was found among the populations from Illinois (r = -0. 170, p = 0. 767). To further explore the contribution of isolation by distance to the genetic differentiation of United States and Canada populations of SCN, we compared the genetic distance of each population from a theoretical ancestor population (MO1) using the 245-SNP dataset.
This analysis included 38 independent populations that were separated by 273 to 1,367 km from MO1 (Figure  2A ). The genetic-distance ratio to the "oldest population" varied from 0. 06 for IL1 (386 km apart) to 0. 52 for FIGURE 2 | Distribution of soybean cyst nematode populations used in this study (small dots) and identification of the "oldest" sample in the dataset (MO1, large dot) (A) as well as the relationship between genetic distance [F ST /(1-F ST )] and geographic distance (B). Linear regression line is shown with 95% confidence intervals estimated using 1,000 bootstrap replicates. ND6 (1,327 km apart) (Supplementary Table  2 ) and an overall moderate (r s = 0. 535) but significant (p = 0. 001) relationship was observed between genetic distance and geographic distance (Figure  2B ).
This multicopy region has been detected in a number races of P. tritici-repentis isolates collected from around the world  (Moolhuijzen et al. , 2018). The size of the primer pair was restricted to 99-bp to ensure comparable amplicon sizes between P. tritici-repentis and Pa. nodorum. Pa. nodorum DNA was amplified using primers modified from those previously described by  Oliver et al. (2008). Additional 3-bp was included at the beginning of each primer sequence to allow the probe to overcome the issue of primerdimer association  (Barbisin et al. , 2009). Pa. nodorum primers amplify a 112-bp fragment of a highly conserved anonymous gene (SNOG_01116•1). This gene has no significant similarity to any other sequences in the publically available genome databases  (Oliver et al. , 2008). All primers were designed using OligoArchitect TM primer analyzer (Sigma, Life Science) and scanned against the National Center for Biotechnology Information GenBank database using basic local alignment search tools to ensure their specificity.
4%), giving from 4 to 12 biological replicates per LPI and per plant (mean: 6. 58 replicates). The first step of the harvest consisted in separating the urediniospores produced from the leaf disk. Infected leaf disks were placed in 2 mL Eppendorf tubes with 2 mL of Isoton R II isotonic buffered diluent (Beckman Coulter). The tubes were vigorously shaken for 20 s at 4 m s -1 on a MP FastPrep R -24 homogenizer, in order to release all urediniospores from the uredinia. The second step consisted in measuring the size of the uredinia (or lesion size): photographs of the spore-free uredinia were taken under a stereomicroscope (25× magnification) coupled to a digital camera. Pictures were analyzed using ImageJ version 1. 5i with a dedicated script (Supplementary Note S1). To measure the number of yellow pixels corresponding to the sporulating area, the Hue Saturation and Brightness was used: pixels with a hue under 37 • were counted.
Leaf Content Measurements: Total Nitrogen and Total Carbon Contents, Sugars, Phenolic Compounds All leaf content measurements were conducted on the uninfected leaves, stored at -80 • C just after disk excision. Total nitrogen and total carbon concentrations in uninfected leaves were analyzed using an elemental analyser (Thermo Quest, Type NCS 2500). Measurements, data analysis, data record, and weighing record (Mettler Tolédo MT5 microbalance) were performed using the EAGER 200 software. Soluble sugars were extracted from 20 mg of dry leaf powder with 1 mL of 70% (v/v) methanol/water for 10 min and then centrifuged at 17,000 g for 5 min at 4 • C. This step was repeated twice and the resulting supernatants were pooled together. The supernatants, containing soluble sugars, were dried overnight with a vacuum evaporator (Maxi-Dry Plus; Hetomodel DW1, 0-110, Heto-HOLTEN A/S) to eliminate methanol.
Quantification was performed by measuring the area under each peak at 280, 320, or 350 nm, depending on the lambda max of each molecule. Due to coeluting signal at 280 nm, catechin quantification was realized by measuring the area under peak at m/z 289, corresponding to the [M-H]-of the molecule. Experimental exact masses and MS fragments were compared to metabolomics databases (Respect 1 ; MassBank 2 , DNP 3 ) and data available in the literature in order to identify the nature of the metabolites. Sixteen phenolic compounds were identified. According to their structure, these 16 metabolites could be grouped into four phenolic subclasses, which are Flavan-3-ols (catechin, proanthocyanidin dimer), hydroxycinnamic acid esters (chlorogenic acid, caffeoyl shikimate, caffeoyl glucose isomers (×2), coumaroyl glucose isomers (×2)), flavonols (rutin, quercitrin), and salicinoids (salicine, salicortin, acetylsalicortin, homaloside D, tremulacin, and a putative populoside) (Supplementary Figure  S2 ).
Disease assessment two different symptoms are commonly observed when inoculating D. alata leaves with C. gloeosporioides: the typical lesion (large necroses, dark brown, coalescent, high expansion rate) and the pinpoint lesion [punctual necroses of less than 2 mm in diameter, black, not coalescent;  Winch et al. (1984) ]. Percentages of healthy, typical lesion and pinpoint lesion areas were visually estimated from the first to the 11th day after inoculation. In order to estimate strain aggressiveness, we use the analytical framework detailed in  Frézal et al. (2012) , which transforms the evolution of disease severity over time into one index, Ag. The construction of the Ag index is based the analysis of the symptom dynamics using a linear mixed-effects regression (PROC MIXED procedure;  Laird and Ware, 1982)  with two-knots, using SAS software  (SAS Institute, 1987).
A minimum spanning tree based on the 23 ALFP markers was constructed for the full sample (222 isolates) using Bionumerics 5. 1 with 100,000 resampling and partitioning of two majority trees. To determine population subdivision without any a priori knowledge, a second analysis was performed on the clone-corrected dataset using the Bayesian clustering method implemented in the software Structure version 2. 3. 3  (Pritchard et al. , 2000). We used admixture and non-admixture models to identify genetic groups, both with distinct allele frequencies  (Falush et al. , 2003) , and we assumed uniform priors for the vector of proportion (qi) of the individual i's genome in each cluster. The scores of individuals in the genetic groups (i. e. , the posterior estimates of the qi) correspond to the probability of ancestry in each one of them.
We varied K from 1 to 10, with 10 replicates for each K, and with each simulation consisting in 800,000 Monte-Carlo Markov Chain (MCMC) iterations preceded by a burn-in period of 200,000 iterations. The most probable structure was determined by computing the posterior probability for each K using the distribution of maximum likelihoods and K distribution  (Evanno et al. , 2005 ; Supplementary Figure  S2 ), which is a quantity, related to the second order rate of change of the log probability of data with respect to the number of genetic clusters. Finally, to determine population subdivision without any a priori knowledge and without making any assumptions regarding the population genetics model, we described the genetic clusters from the clone-corrected dataset using the discriminant analysis of principal components (DAPC,  Jombart et al.
, 2010)  implemented in the R package adegenet 2. 1. 1  (Jombart and Ahmed, 2011; R Development Core Team, 2015). Pairwise  Weir and Cockerham (1984)  values were calculated between all pairs of populations using GENETIX 4. 05  (Belkhir et al. , 1996 (Belkhir et al. , /2004 ). Populations were defined as field samples and as subsets of individuals collected on the same yam cultivar, i. e. , Kabusah, Tahiti and Pacala. Then, the significance of the pairwise-Fst was tested by 10,000 random permutations of the haplotypes (i. e. , individuals) between the populations using Multilocus 1. 3b software  (Agapow and Burt, 2001). The number of genotypes (Gs) was calculated in the whole sample and per filed from the clone-corrected dataset. As recommended by  Arnaud-Haond et al. (2007)  we calculated the modified index of clonal diversity, R from  Dorken and Eckert (2001)  as R=(G-1)/(N-1) , with G, the number of identical multilocus genotypes and N, the number of individuals.
The degree of association between loci was estimated on the clone-corrected dataset by using the index of association, which is the ratio between the observed variance of the number of differences between pairs of strains and the expected variance under the hypothesis of absence of linkage disequilibrium (LD)  (Agapow and Burt, 2001). We used a modified version of this index (r D ), corrected for the dependence to the number of loci used. r D has an expected value of 0 if there is no association of alleles at unlinked loci, as is expected in a randomly mating population. The significance of r D was tested by a randomization procedure (1,000 times) by comparing the observed value to that expected under the null hypothesis of complete random mating  (Agapow and Burt, 2001). Value calculation and tests were performed by Multilocus 1.
Aggressiveness Analysis Analyses of variance were conducted for the factors host cultivar, field of origin, genetic group using the General Linear Models Procedure of SAS software (SAS Institute, Inc. , Cary, NC, United States). A standard analysis of variance (ANOVA) of the aggressiveness indices on each of the four hosts was performed using the SAS software (SAS Institute, Inc. , Cary, NC, United States) to test the effect of the host cultivar, the field and the genetic group on strain aggressiveness. Moreover, the mean-values of aggressiveness indices (i. e. , Ag-K, Ag-T, Ag-P, Ag-Pl), of initial lesion expansion rate (α Na ) and of final lesion area (Na) were compared using the Student's t-test. Means values were considered as different for a probability under the threshold of 0. 01.
Genetic Diversity A total of 222 monoconidial strains of C. gloeosporioides were isolated from three host cultivars (Figure  1 ). The AFLP analysis generated 89 clearly reproducible bands. Among the 89 clearly reproducible markers, 23 were polymorphic, independent and had a frequency between 0. 05 and 0. 95, i. e. , non-rare alleles. All the markers were amplified from several isolates originating from at least three different fields. Among the 222 isolates, 197 haplotypes were identified, which correlated with high R ratios (i. e. , modified G/N ratio, where the number of identical multilocus genotypes, G, is divided by the number of individuals, N) recorded for the entire dataset, 0. 89, and in each fieldpopulation, from 0. 83 to 0. 93 (Table  1 ). Sixteen haplotypes were found in two, three or four copies, but the few isolates sharing the same haplotype were always recovered from a unique field (Supplementary Figure  S3 ).
PCR products were then diluted to 1:10 in sterile water and 3 µL of this dilution were mixed with 0. 05 µL of GeneScan 500 LIZ Size Standard (Applied Biosystems) and 5 µL of formamide (Applied Biosystems). Analyses of PCR products were conducted on a ABI Prism R 3130xl sequencer (Applied Biosystems). Allele sizes were determined with the automatic calling and binning module of GeneMapper v4. 1 (Applied Biosystems), and complemented by a manual examination of irregular results. Samples showing dubious genotypes were tested again. Genetic diversity in each H. schachtii population was estimated using GENETIX 4. 05. 2  (Belkhir et al. , 2004)  as the unbiased gene diversity (Hnb)  (Nei, 1978). Hnb corresponds to the average probability across loci to draw at random different alleles in the same population.
Statistical Analyses To understand how temperature and coinfection affected variation in the asexual life-history traits -time from inoculation to germination, time from germination to sporulation and time from sporulation to mature chasmothecia -we used survival models as implemented in R-package survival  (Therneau, 2015)  with Cox Proportional Hazards model  (Cox, 1972). All survival models had temperature, first strain identity, coinfection and their respective interactions with the temperature as fixed explanatory effects. The identity of the second coinfecting strain and the leaf were considered as random factors. To understand how temperature and coinfection affected variation in the sexual life-history traits -total number of chasmothecia and proportion of viable produced chasmotheciawe fitted generalized linear mixed models.
Genetic Characteristics of Strains In a first step, the strains with the private allele at microsatellite locus BC6 associated with the cryptic species B. pseudocinerea   (Walker et al. , 2011)  were removed from further analyses. This private allele is found only in B. pseudocinerea strains and never in B. cinerea strains. Then, in order to compare the B. cinerea strains sampled from the different reservoirs, several indices of genetic diversity were used. The software FSTAT version 2. 9. 3  (Goudet, 1995)  was used to compute allelic richness (corrected for the smallest sample size) and unbiased gene diversity per locus. Unbiased gene diversity (Hnb) and allelic richness (average over the nine loci) were computed separately for the strains collected in the different reservoirs with the Genetix software  (Belkhir et al.
, 1996 (Belkhir et al. , -2004)). The number of different multilocus haplotypes (MLH) was computed with GenClone 1. 0 software  (Arnaud-Haond and Belkhir, 2007). We used the index of haplotypic diversity (based on the number of individuals and the number of distinct MLH), which estimates the proportion of haplotypes present in a population and takes a value of 1 when a population is composed exclusively of unique haplotypes  (Arnaud-Haond et al. , 2007).
Phylogenetic Relationships Between Strains From Non-agricultural Habitats and From Crops To assess the relationships between the different strains, we computed a neighbor-joining (NJ) tree with the program POPULATIONS (version 1. 2. 32 provided by Olivier Langella, Quantitative Genetics and Evolution, Gif-sur-Yvette, France). The NJ-tree was based on the distances of Cavalli-Sforza and  Edwards (1967) , computed from our microsatellite loci. The tree was visualized and edited with TREEVIEW  (Page, 1996). All analyses were conducted on data sets excluding clone replicates. In addition, Arlequin version 3. 5  (Excoffier et al. , 2005 ) was used to assess genetic differentiation between strains collected from non-agricultural habitats (NH) and from crops by computing RST values as suggested by  Slatkin (1995)  for microsatellite data.
Life-History Traits Measurements Three days after the formation of the first sporangia -checked by daily observations under a magnifying glass -or 5 days after inoculation (depending on the experiment, Table  1 ), newly formed sporangia were washed from each leaflet in 10 mL Isoton II (saline buffer; Beckman Coulter, Villepinte, France). Suspensions were kept in glass tubes at -20 • C until the counting of sporangia number (SN) produced using a Coulter Z2 counter (Beckman Coulter) equipped with a 100-µm aperture tube. Sporangia size (SS) was determined under a microscope fitted with a camera and using the image analysis software Histolab R v8. 1. 0 (Microvision Instruments, Evry, France). For this purpose, drops of the sporangial suspension were placed under the microscope and we used the camera measurement software to determine length and width of 100 randomly chosen sporangia.
ACKNOWLEDGMENTS The authors thank  Drs. Thomas Baum  and  Andrew Severin  for graciously making SCN genome sequences available to us before public release on scnbase. org. The authors also thank  Tom Welacky  (AAFC) and  Terry Niblack  ( OSU ) for providing SCN populations. The authors wish to acknowledge the contribution of  Génome Québec ,  Genome Canada , the  Government of Canada , the  Ministère de l'Économie, Science et Innovation du Québec, Semences Prograin Inc. ,  Syngenta Canada Inc. ,  Sevita Genetics ,  La Coop Fédérée ,  Grain Farmers of Ontario ,  Saskatchewan Pulse Growers ,  Manitoba Pulse  and  Soybean Growers , the  Canadian Field Crop Research Alliance  and  Les Producteurs de grains du Québec.
ACKNOWLEDGMENTS Jérémy Pétrowski  is acknowledged for his excellent technical help in poplar plant production. We thank  Jacqueline Marchand  and  Carole Antoine  ( Université de Lorraine, AgroParisTech, INRA, SILVA, Nancy, France ) for the poplar leaf content analyses,  Aude Fauvet  for the analysis of total phenolics, and  Didier Le Thiec  for the lyophilization of the samples. We also thank  Irène Hummel  for advices in statistical analyses, Rantanplan for inspiration,  Josselin Montarry ,  Elisabeth Fournier ,  Stéphane De Mita ,  Claire Fourrey , and  Katherine Hayden  for valuable comments on earlier drafts of this manuscript.
ACKNOWLEDGMENTS The research leading to these results has received funding from  INRA department of Plant Health and Environment. We are grateful to  Dr. Philippe Nicot  for extremely useful discussions throughout this research project and to  Dr. Caroline Monteil ,  Jean-Charles Bouvier ,  Caroline Guilbaud ,  Charlotte Chandeysson , and  Magali Duffaud  for their valuable help in samples collection. Part of this work was carried out by using the resources of the  Molecular Biology Platform of INRA-PACA center. Research on plant biology depends heavily on the cultivation of plants under experimental conditions that are controlled, monitored, and repeatable. For ensuring a perfect plant production, the contribution of the Plant Production and Environmental Management Service of the Plant Pathology Research Unit was greatly appreciated.
ACKNOWLEDGMENTS A part of this work presented in this paper first appeared in the NM thesis, which can be accessed online (see the reference list). We gratefully acknowledge the financial support by  INRA   ACCAF Metaprogramme  ( CLIF  project) and ARIMnet (PoH- MED  project;  KBBE 219262/ANR ). We also warmly thank  Prof. Zouaoui Bouznad  and  Sihem Belkhiter  ( ENSA, Algiers ) for providing Algerian isolates and  Mathilde Bodiou  for help with the sampling of P. infestans isolates. We express our thanks to  E. Rolland  and  C. Piriou  for their help with isolate maintenance and biological experiments. We are grateful to the  INRA Genetic Resources Center BrACySol of Ploudaniel  for providing the plant material used in the tests. NM was supported by a  Ph. D. project cofunded by  INRA  and  ACVNPT (Association des Créateurs de Variétés Nouvelles de Pomme de Terre)  through a  CIFRE grant  ( Conventions Industrielles de Formation par la Recherche ).
FUNDING This work was supported by grants from the  French National Research Agency  (Grant No. ANR-13-BSV7-0011 ,  FunFit  project;  ANR-11-LABX-0002-01 , Cluster of Excellence ARBRE). AM and MP were supported by  Ph. D. fellowships from the  Region Lorraine and INRA , and from the  French Ministry of Education and Research (MESR) , respectively. FUNDING This work was funded by the  French National Institute of Agronomical Research (INRA)  and the  Antilles-Guyane region. FUNDING This work was funded by grants from the  Academy of Finland  ( 296686 ), and the  European Research Council  ( Consolidator Grant   RESISTANCE 724508 ) to A-LL.
t1 Mediator of RNA polymerase II transcription subunit 13-like protein 1. 20e-133 Deletion [Ascaris suum] TP226227 g1982. t1 85/88 kDa calcium-independent phospholipase A2 [Toxocara canis] 8. 00e-29 SNP TP226227 g14545. t1 Glycoside hydrolase domain containing protein [Haemonchus 1. 10e-162 deletion, deletion contortus] TP226227 g12205. t1 Kazal domain and organic anion transporter polypeptide (OATP) family 3. 00e-36 deletion, 3 complexes and MFS [Strongyloides ratti] TP226227 g1979. t1 Serine/threonine-protein kinase haspin [Haemonchus contortus] 1. 40e-62 insertion TP252086 g18812. t1 Bestrophin/UPF0187 family-containing protein [Strongyloides ratti] 2. 30e-44 deletion TP252086 g18825. t1 mtN3/saliva family protein [Oesophagostomum dentatum] 1. 30e-55 deletion, SNP, complex TP333577 g16696.
4 (Illumina). The clustering and sequencing steps were repeated for three runs to generate enough reads per sample. The raw sequencing data were preprocessed using bcl2fastq software and the quality assessed using FastQC tools. 24 Cutadapt 25 was used to remove 3′ end nucleotides below 20 Phred quality score and extraneous adapters; additionally, reads below 25 nucleotides after trimming were discarded. Reads were then processed and aligned to the UCSC Homo sapiens reference genome (Build hg19) using STAR V. 2. 5. 2b. 26 Two-pass mapping with default alignment parameters were used. To produce the quantification data, we used RSEM V. 1. 2. 31 27 resulting in gene level expression estimates (Transcripts Per Million, TPM and read counts). A sample would pass the RNA single QC if (1) the number of reads mapped to the genes was more than 7 million and (2) the RIN (RNA integrity number) value was higher than 7.
Immunophenotyping Immunophenotyping was performed after blood collection in Duraclone tubes (Beckman Coulter) specifically designed and optimised for the PRECISESADS study. Harmonisation of instruments used for flow cytometry analysis was described elsewhere, providing evidence of high-reproducible data in different centres. 28 WB samples from patients and controls were collected in tubes with to ready-to-use unitised, dry format antibody cocktails and locally analysed within 24 hours. Flow cytometers were regularly (every 3 months) calibrated to obtain the same target mean fluorescence intensities of a reference instrument and whose coefficient of variation proved to be lower than 3. 4% throughout the study. 28 In addition, prior to the inclusion of any patient, a supplementary daily procedure was adopted to ensure that the deviation was below 5% for each sample.