dataesr/scientific-paragraphs-categorization huggingface dataset:
Papers reporting occupational HBM data on bisphenols of interest, biomarkers and analytical methods were selected. To establish a final set of papers, the two reviewers compared their findings to identify consistencies and discrepancies. Any differences pointed out were resolved mutually. One relevant paper on BPA was missed  (Wang et al. , 2012). Thus, 37, 5 and 3 papers were identified respectively for BPA, BPS and BPF. The different phases of the selection of papers are described through the diagrams (Figs. S1, S2 and S3). The quality of each study selected (i. e. 37 [BPA], 5 [BPS] and 3 [BPF]) was assessed according to a modified version of the LaKind scoring criteria (Table  1 )  (LaKind et al. , 2014). These criteria were used to highlight the overall quality of the occupational biomonitoring (BM) studies on bisphenols.
Model implementation The phenotypic regression and multi-SAD regression models can be implemented using the ASReml software  [28]  and the OWN Fortran program developed by David, which are freely available on the Zenodo platform  [29]. The degrees of the polynomials of time for each function can be selected by comparing nested models using likelihood ratio tests. In the multi-SAD regression model, to reduce the number of models to be compared, we selected the specification of the antedependence and innovation variances for each trait independently, and then the cross-antedependence specification. For the first part of the selection process, we started with the most parsimonious model (smallest degree for the polynomial functions of time), and then increasing the degree of the polynomial functions of time (alternating the antedependence parameter and the innovation variance) until B =    I ADG 0 I MBW 0 0 I BF b eADF /FI b eMBW /FI b eBF /FI I FI   , b s/FI =            -b u,s1 -θ eFI,2 b u,s1 • • • - j � k=2 θ eFI,k b u,s1 • • • -b u,sj.
Application to pig data We used data from 2435 French Large White boars, castrated males, and gilts from nine generations of two lines that were divergently selected for RFI (called hereafter high RFI line and low RFI line). All pigs were raised after weaning on the Rouillé experimental farm (Vienne, France, https:// doi. org/ 10. 15454/1. 55724 15481 18584 7E12)  [21]. The animals were selected based on their RFI over an 18-week test period, starting at ~ 10 weeks of age, which was obtained by a defined phenotypic regression of FI on ADG, MBW, and BF over the test period (i. e. one observation per animal). From each farrowing batch, 48 pigs from at least six litters were moved to a growingfinishing room at ~ 10 weeks of age, with four groups of 12 animals (boars separated from females and castrated males) placed in pens that were each equipped with a single-place electronic feeder ACEMA 64 (Pontivy, France;  [31] ).
For longitudinal analyses, phenotypes were computed at weekly intervals. Average daily gain of animal i at week j was calculated over a 4-week period following  [32]  as ADG ij = BW i(j+2) -BW i(j-2) age i(j+2) -age i(j-2) and MBW was calculated as MBW ij = BW 0. 6 ij. BF ij corresponded to the measure- ments of BF on animal i at week j. Outliers, as defined in Huynh-Tran et al. [15] , were removed from the analysis. Data for the first week in the growing-finishing room (11 weeks of age), which was considered to be an adaptation period for the animals, and for weeks corresponding to 25 and 26 weeks of age, as the number of pigs weighed per week decreased at the end of the test period (animals already slaughtered), were also removed from the dataset. The final data set included production and FI records over a 10-week period from ~ 13 to ~ 22 weeks of age, which will be denoted as weeks 1 to 10, hereafter.
The condition number (square root of the ratio of the first to the last eigenvalue) of the information matrix I ω of the SAD model used in our study was low, i. e. 26, which suggests that there was no practical identifiability issue for the data analyzed. Using results from the multi-SAD model, we estimated RFI that is independent of production traits at the genetic level by regressing FI on production traits using genetic regression coefficients. Different RFI traits can be estimated from the results of the multi-SAD model. On the one hand, estimates of phenotypic regression coefficients corrected for fixed effects (i. e. extracted from the estimated phenotypic covariance matrix  [9] ) can be used to obtain a measure of RFI that is phenotypically but not genetically independent of production traits.
Thus, these changes in coefficients might have limited impacts on covariances of the trait with FI and on the resulting RFI. For the purpose of comparison, missing phenotypes for production traits were replaced by their linear interpolations for both models. As stated in the Methods section, the multi-SAD regression model can cope with a substantial number of missing values for the production traits, which is another advantage of this model. In the phenotypic regression model, if one of the production traits is missing at a given time point for an animal, RFI is also missing, which would be problematic if many phenotypes were missing for the different production traits. In our case, applying the phenotypic regression model to the original dataset (i. e. the set with numerous missing phenotypes for production traits) led to spurious variance estimates [estimates of heritability of RFI were very high, up to 0.
78, (see Additional file 7 Figure  S5 )]. Results of the multi-SAD regression model applied to the original dataset (the same multi-SAD regression model) are provided in Additional file 7: Figure  S5  and Additional file 8: Figure  S6. The heritability estimates that were obtained with the multi-SAD regression model applied to the initial dataset did not differ from those obtained with the dataset using linear interpolation of missing phenotypes. Patterns of estimates of the genetic and environmental regression coefficients over time were also similar between the two datasets, as were the slope estimates, although we did note a tendency for steeper slopes when phenotypes were missing. The main differences between the two datasets were evident for the SBV. When phenotypes were missing, the correlations between SBV obtained with the phenotypic regression and the multi-SAD regression model were weaker than the correlations obtained when models were applied to data with no missing phenotypes: 0.
Development of genome-wide SNPs Demultiplexed FASTQ R1 and R2 files were uploaded to the Galaxy web platform  [28]. R1 and R2 read datasets for each sample were first compiled into a single list of dataset pairs. Raw data quality within each list was assessed using the FastQC tool  [29]. Reads were then trimmed to remove low quality sequences and the sequencing and index adapters. Once cleaned, reads were aligned to the S. haematobium (Egyptian strain) reference genome ShV2_May19_020001-666. fa (of 370 Mb and which contains 666 scaffolds; available on Gen-Bank BioProject PRJNA78265) using bowtie2  [30]  using default parameters settings. Aligned reads from each sample were merged together in a unique. BAM file and alignment statistics were retrieved using the Samtools flagstat tool  [31]. The total numbers of reads before and after preprocessing together with individual alignment rate are provided in S1 Table.
Genomic analyses Clustering analysis. To analyse the global structure of genetic variation, a principal component analysis (PCA) was performed based on the 56,181 filtered SNPs using the dudi. pca command from the "ade4" package  [34]  implemented in R version 4. 0. 2 [35]. Isolates were plotted according to their coordinates on the first two PCA axes using the "factoextra" package in R  [36]. To detect possible finer genetic structure among isolates, three additional PCA plots were performed to represent the spatial distribution of each isolates within three other dimensional spaces including the two following PCA axes (i. e. Axes 2 and 3; Axes 2 and 4 and Axes 3 and 4). Within and between species genomic diversity. Based on the 56,181 initial filtered SNPs, individual observed heterozygosity (Hobs) was first calculated for each isolate.
Third, Tajima's Ds were estimated for each species. To statistically compare Tajima's D between species, a series of Tajimas'Ds was computed along non-overlapping 50-kb sliding windows overall genomic scaffolds within each species. Windows that contained less than eight SNPs were discarded and only the 50-kb sliding windows in which Tajima's Ds could be estimated for S. bovis and S. haematobium (i. e. with at least one segregating site within each species) were kept (N = 4035). The series of estimated Tajima's Ds obtained for each species were next compared using a paired Wilcoxon test. Finally, overall absolute genomic divergence (Dxy) between S. bovis and S. haematobium was estimated. All nucleotide diversity and divergence estimates were computed using the 'PopGenome' R Package  [37]. Spatial genomic structure within the S.
bovis and S. haematobium clusters. To investigate any potential spatial genetic structure among isolates within each species, we computed pairwise Nei's genetic distances matrices  [38] , between each isolate within each species (i. e. S. haematobium = all 11 nominal S. haematobium isolates + the Corsican hybrid isolate; and S. bovis = the six nominal S. bovis isolates), based on the initial filtered 56,181 SNPs and using the dartR package  [39]. Correlation between the resulting matrices of pairwise Nei's distances and pairwise geographical distances computed between the geographical origin of each isolate were next tested within each species using two independent Mantel tests as implemented in the 'ape' package  [40]  in R. Significance of correlations were assessed by comparing the obtained Z-scores to 10,000 Z-values generated after bootstrapping.
Clustering analysis Overall, 56,181 biallelic SNPs distributed over 239 of the 665 nuclear genomic scaffolds (i. e. 36%) of the S. haematobium reference genome (Egyptian strain) were developed into a highquality SNP dataset. Using this dataset, we could clearly distinguish between isolates categorized as either S. haematobium or S. bovis based on a principal component analysis (i. e. PCA; Fig  2A ). In this regard, the overall absolute genomic divergence between S. bovis and S. haematobium reaches 0. 24. As expected, the laboratory generated first generation S. haematobium-bovis hybrid (F1) fell mid way between the two parental strains used for its construction, i. e. S. bovis Genome-wide genetic diversity and genetic differentation within S. haematobium and S. bovis. A. Spatial representation of all isolates in a PCA dimension defined along the two first axis that explain 63.
Acknowledgments We would like to warmly thank all the people that have helped during the sample collection including people from  G. Riveau 's team ' Espoir pour la Sante' in Senegal, FeliciteFlore Djuikwo-Teukeng from Cameroon, Etienne Angora from Ivory Coast and Kalkidan Mekete from Ethiopia. We thank  Dr Aidan Emery  (Principal Investigator of  SCAN, Schistosomiasis Collection at the Natural History Museum ) for his support for the project. We are grateful to  Muriel Rabone  ( SCAN, NHM ) for assistance with sample databasing and retrieval. Material provided via SCAN was originally collected between 1985 and 2006 and we acknowledge the support and generosity of our collaborators and partners involved in the collections, particularly in relation to the endemic countries involved, and past colleagues that maintained the live material.
We would also like to thank the  Biomedical Research Center  for kindly providing the S. haematobium sample from the  Egyptian stain. Finally , the authors warmly thank the  Bio-Environnement platform (University of Perpignan ) for sequencing and bioinformatics services. This study is set within the framework of the  "Laboratoire d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ).
This work was supported by a  "Research Quality Grant " from the  University of Perpignan  (awarded to OR) and by the program  HySWARM  ( ANR-18-CE35-0001 )  ANR  (awarded to JB). The Schistosomiasis Collection at the  Natural History Museum (SCAN)  is part funded by the  Wellcome Trust  (grant  104958/Z/14/Z ). EMB received a salary from the  ANR  ( ANR-18-CE35-0001 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical Analysis The data from the experiments were entered into the Microsoft Excel 2010 software and analyzed using R 3. 6. 3 software  [29]. Statistical analyses were performed using the results of the prevalence of haematobium infestations in snails. Statistical analyses comparing the quantitative variables were carried out using the Pearson χ 2 test with the R software. For all the analyses, a threshold of 5% was chosen as statistically significant.
Cryptic species can present a significant challenge to the application of systematic and biogeographic principles, especially if they are invasive or transmit parasites or pathogens. Detecting cryptic species requires a pluralistic approach in which molecular markers facilitate the detection of coherent taxonomic units that can then be analyzed using various traits (e. g. , internal morphology) and crosses. In asexual or self-fertilizing species, the latter criteria are of limited use. We studied a group of cryptic freshwater snails (genus Galba) from the family Lymnaeidae that have invaded almost all continents, reproducing mainly by self-fertilization and transmitting liver flukes to humans and livestock. We aim to clarify the systematics, distribution and phylogenetic relationships of these species with an integrative approach that includes morphology (shell and reproductive anatomy), molecular markers, wide-scale sampling across America, and data retrieved from GenBank (to include Old World samples).
5 µl of Taq PCR Master Mix (Qiagen), 2. 5 µl of each primer (10 mM) and 2 µl of DNA in an Eppendorf Thermal Cycler with an initial denaturation step at 95 ºC for 15 minutes; followed by 35 cycles of denaturation at 95 ºC for 30 seconds, annealing at 50 ºC for one minute, extension at 72 ºC for one minute; and a final elongation at 60 ºC for 30 minutes. The presence and size of amplification products were electrophoretically confirmed in 1% agarose gels stained with EZ-Vision. DNA sequencing was performed by Eurofins Genomics (Ebersberg, Germany) using PCR-amplified products as templates. All sequences were uploaded to GenBank (Table  S1 ) and assigned to a species using the phylogenetic reconstruction.
Type localities for all eight of the widely-recognized species in the genus Galba, either as originally stated or as subsequently restricted, are listed in Table  1. COI sequences from samples of all the populations inhabiting these localities have been previously uploaded to GenBank, and most have ITS1, ITS2 or 16S sequences available as well. Table  1. Type localities of Galba species and sequences of individuals recovered in those localities. References mentioned are the ones in which sequences were provided and not in which species were described, except for articles describing Galba meridensis and Galba neotropica were the authors also provided sequences  (Bargues et al. 2007 (Bargues et al. , 2011b)). The type localities for Galba cubensis and Galba viator were here restricted because original description of the species did not provide a locality, authors simply stated "Cuba" and "Patagonia", respectively (D  'Orbigny 1835; Pfeiffer 1839).
Retrieving data on Galba spp. distribution from published work We searched the literature and GenBank for sequence data at four genes (COI, ITS1, ITS2, and 16S) apparently attributable to lymnaeids of the genus Galba. Coordinates were provided for most sites by the authors. When coordinates were not provided, we inferred them from the locality data using GoogleEarth. We found 132 New World sites in which Galba species have been molecularly characterized (Table  S2 ), and 45 sites in the Old World (Table  S3 , Fig. S3 ). The specific nomina attributed to these sequences by their depositors in GenBank were 157 truncatula, 152 schirazensis, 70 neotropica, 57 cubensis, 44 viator, 20 cousini, 9 humilis, 6 meridensis, and 2 others. This corresponds to 166 COI, 163 ITS2, 118 ITS1, and 70 16S sequences.
Phylogenetic and ancestral reconstruction study Phylogenetic analyses were conducted on the ITS2 and COI sequences obtained in this study, together with ITS2, COI, ITS1, and 16S sequences retrieved from GenBank. Most of these sequences have been previously published (Tables  S2-S3 ). Ultimately, we included 796 sequences in our analyses: 90 for 16S, 251 for COI, 122 for ITS1, and 333 for ITS2. Some GenBank accession numbers appear more than once in Tables  S2-S3  because individuals with identical sequences have been registered under the same GenBank accession number. Alignment was performed individually for each gene using MAFFT  (Katoh and Standley 2013). Ambiguously aligned sites were excluded using GBLOCKS with default settings for a less stringent selection  (Castresana 2000). The number of positions in the final sequences was 412 for 16S (83% of the original 493 positions), 609 for COI (86% of 707), 435 for ITS1 (48% of 888), and 333 for ITS2 (23% of 1,429).
We examined levels of saturation for each gene and for the first and second versus third codon positions of COI using DAMBE  (Xia 2017). We did not find evidence of saturation in the four genes analyzed, including all codon positions of COI. We partitioned codon positions and unlinked substitution models in phylogenetic analyses. We used Bayesian inference in Beast2  (Bouckaert et al. 2014)  for five reasons: (1) to assign sequences to species; (2) to validate, and to mend if necessary, species identity for sequences retrieved from GenBank; (3) to delimit species; (4) to reconstruct the phylogeny and estimate time divergence; and (5) to assess the ancestral phenotypic state of Galba species. Because we did not have identical sets of individuals (or populations) across genes it was necessary to build four unlinked gene trees to address questions (1) and (  2 ).
The best-fitting models of sequence evolution for each gene were selected using bModelTest  (Bouckaert and Drummond 2017). We estimated a model for each COI partition (1 st , 2 nd , and 3 rd position). The best model describing the evolution of 16S was HKY+G+I, 123424+G+I for COI (1 st codon), 121321+G+I for COI (2 nd codon), TN93+G+I for COI (3 rd codon), 123424+G+I for ITS1, and 121323 for ITS2. We linked trees for the three COI codon partitions. The analyses were run using four gamma categories and a proportion of 0. 5 invariant sites. Uncorrelated relaxed-clock models were chosen for all loci. The relative clock mean priors were all lognormal (M = 0, S = 1). We used a birth-death model as priors with lognormal birth and death rates. These gene trees allowed us to evaluate species names and validate or mend species identity for all sequences, whether obtained in this study or retrieved from GenBank.
2014). We used Nested Sampling implemented in the NS package to calculate the marginal likelihoods necessary to obtain BF and also an estimate of the variance of the marginal likelihoods  (Russel et al. 2019). We also applied two other species-delimitation methods: Automatic Barcode Gap Detection (ABGD;  Puillandre et al. 2012)  and Species Tree and Classification Estimation in Beast2 (STACEY, Jones 2017). ABGD was run for each gene using the default settings (https://bioinfo. mnhn. fr/abi/public/abgd/). The prior on intraspecific divergence defines the threshold between intra-and interspecific pairwise distances and is iterated from minimum to maximum through ten steps  (Puillandre et al. 2012). Given the wide genetic diversity observed within some species  (Lounnas et al. 2017) , we chose the partition that showed high prior on intraspecific divergence (the penultimate partition).
For the STACEY delimitation method, we used only the ITS2 and COI sequences because these both genes were better represented in the dataset than ITS1 and 16S. Since the hypothetical number of species in STACEY ranges from one to the number of individuals, each of our 113 snail populations was considered as a minimal cluster. The method distinguishes very shallow species divergences with a statistic called "collapseHeight," which we set to a small value (0. 0001) following  Jones (2017). Given the results obtained with ABGD and Nested Sampling analyses, we ran three multispecies coalescence models with different collapseWeight parameters: (1) a lumping model with five Galba species with 1/X distribution (initial value: 0. 975, between 0 and 1), (2) a splitting model with nine species with 1/X distribution (initial value: 0.
952 between 0 and 1), and (3) a no-prior-taxonomic model with a Beta distribution (α = 2, β = 2). The initial value of the lumping and splitting models were calculated following  Matos-Maravi et al. (2018). All other parameters were set as in the Nested Sampling analysis. We ran the three models for 250,000,000 generations with storing every 25,000 generations. To address our question (4), regarding species topology and divergence time, we reran the multispecies tree model that showed the highest Bayes Factor in StarBeast2. We used the same parameters as in the Nested Sampling analysis, but we ran the MCMC for a longer time (250,000,000 generations stored every 25,000 generations). The MCMC output was visualized using Tracer  (Rambaut et al. 2018 ) and tree samples summarized by TreeAnnotator (utility program distributed with the Beast package) using a 10% burn-in.
S5-S6 ) but clustered tightly with the cubensis type population and similar populations in the nuclear gene trees (Figs. S7-S8 ). The mitochondrial sequences from Ethiopia (one per gene) also formed a long branch, although nuclear sequences were not reported in GenBank for that population. Within cluster VI, we found that the cubensis and neotropica type populations were located in separate subclades for the nuclear genes but clustered together in the mitochondrial trees (Figs. S5-S8 ). Gene trees (Figs. S5-S8 ) and haplotype networks (Figs. S9-S12) showed that genetic diversity varied among the six clusters and four genes. Cluster II (schirazensis) showed reduced variation, while cluster VI (cubensis, neotropica, and viator) was larger and more diverse. Mitochondrial genes seemed more diverse than nuclear genes.
This observation may be biased, however, by a structural correlation in our data between genes sequenced and regions sampled. Most of the sequences uploaded to GenBank identified as one of the eight species of Galba were accurately clustered into the six clades containing their type populations. However, there were some exceptions. Eight sequences of COI uploaded as G. truncatula from France appeared in cluster II with the G. schirazensis type population (Table  S3 ; Fig. S5 ). We reidentified these sequences as belonging to G. schirazensis. The COI sequence from Ethiopia, uploaded as G. truncatula, clustered at the base of truncatula clade I with low posterior probability (Fig. S5 ). The other Ethiopian sequence (16S), also identified as G. truncatula, did not cluster with any of the Galba clades (Fig.
Galba comprises six clusters Here we report the largest study published to date of Galba systematics and distribution, based on extensive sampling at a very large geographical scale and integration of phenotypic and molecular approaches across all DNA sequences available in GenBank for four genes  (Dayrat 2005). The widespread occurrence of self-fertilization in these populations voids the biological species concept  (Coyne and Orr 2004) , and the absence of any reliable morphological distinction obviates the typological one, with the exception of G. cousini/meridensis. Thus, we are left with a phylogenetic approach, which suggests the existence of six clusters, perhaps corresponding to as many as eight species, or as few as five. These findings reinforce several previously-published works that have involved fewer genes and smaller sample sizes  (Correa et al.