dataesr/scientific-paragraphs-categorization huggingface dataset:
Faecal microbiota data preprocessing The DADA was also implemented using the DADA2 plug-in for QIIME 2 (v. 2021. 2) to perform quality filtering and chimaera removal and construct a feature table consisting of reads abundance per ASV by sample. 107  Taxonomic assignments were given to ASVs by importing both SILVA 16S rRNA full-length database (release 138; https://www. arb-silva. de/documentation/release-138/) and SILVA 16S rRNA region-specific (515F/806R) database (release 138, 99% identity; Silva 138 99% OTUs from 515F/806R region of sequences) to QIIME 2 and classifying representative ASVs using the naive Bayes classifier plug-in. 130  The full-length and region-specific annotation tables were combined, avoiding redundancy. Subsequently, for each ASV with an unidentified genus, homology-based identifications were performed using blast (v.
2. 9. 0) on the NCBI 16S ribosomal RNA reference database (released on  May 25 th  , 2020) belonging to archaea and bacteria for classification in Greedy run mode allowing for maximum three mismatches with an E-value smaller than 1e À8. The decontam (v. 1. 14. 0) R package was used to identify and visualise possible contaminating DNA features in the negative control samples. The function isContaminnat() was used to determine the distribution of the frequency of each contaminant feature as a function of the input DNA concentration. Then, to further circumvent the problem of false-positive species predictions due to misalignment and contamination, we defined an abundance threshold of 0. 01% using the filterfun_sample() function in the phyloseq R package. In sum, 93 ASVs (out of 51,623) were statistically classified (p < 0.
1% and a prevalence threshold of 95% in the microbiome R package (v. 1. 15. 3; http://microbiome. github. io). This microbiome R package produced the measures of evenness, dominance, divergences, and abundance. The gut a-diversity indices between groups and time points were compared using a generalised linear model (glmer() function of the lme4 113 R package (v. 1. 1-27. 1)), where the group, day, faecal pH, faecal DNA concentration, age, and the box were included as fixed effects and individuals as a random effect. Bray-Curtis dissimilarity and unweighted and weighted UniFrac distances were calculated using the phyloseq R package to estimate b-diversity. For this, samples were rarefied at 16875 reads of depth (minimum sampling depth in our data) to allow an equal depth using the rarefy_even_depth() function of the phyloseq R package.
The minimal sequencing depth was sufficient for accurately profiling bacterial composition, as predicted by calculating the rarefaction curve for observed richness, Chao1, and Shannon index (which accounts for both abundance and evenness; Supplementary information). The b-diversity was visualised using the non-metric dimensional scaling (NMDS) in the vegan R package (v. 2. 5. 7) 114 using the metaMDS() function. The stress value was calculated to determine the dimensions for each NMDS. The partitioning of b-diversity into turnover (b TURN ) and nestedness (b NEST ) components was performed with the betapart R package (v. 1. 5. 6). 115  The PerMANOVA test (a non-parametric method of multivariate analysis of variance based on pairwise distances) implemented in the adonis2() function from the vegan R package allowed testing of the global association between ecological community structure and groups across time.
The strata option (strata = horse) was used to account for the repeated sampling of individual horses. Specifically, we tested the effects of group and time corrected by age, faecal pH, faecal DNA concentration, and the box on the variation of total dissimilarity between faecal microbiota. The significance of the effect of group and time was assessed in an F-test based on the sequential sum of squares estimated from a 10,000 permutations procedure. The significance threshold was chosen at an adjusted p< 0. 05. Pairwise comparisons of mean Bray-Curtis distances to group centroids among horse faecal samples were calculated using the permutational analysis of multivariate dispersion, permdisp() function in the vegan package. The effect size of the host and environmental variables on the ASV level community ordination was tested using the envfit() function in the vegan R package (10,000 permutations, Benjamini-Hochberg adj p < 0.
The contribution of each bacterial genus to the ecosystem's temporal dynamics through groups MaAsLin 2 R package (v. 1. 8. 0), 116 a multivariate statistical framework utilising generalised linear models, was used to analyse the effect of group and time points jointly with associated covariates on genera abundances, allowing formulation of both fixed effects and random effects (for within-subject correlations) in a single unified framework. Age was not considered a potential confounding factor in the MaAsLin analyses. Multiple time points per subject were considered by including the subject in the model as random effects. MaAsLin transformed the raw abundance of each genus with an arcsine square root transformation before testing for associations. The significance threshold was set at adj p < 0.
05 for all MaAsLin tests, with boosting and quality control steps enabled. for the confounding dispersion effect. The one-way ANOVA with Tukey's honest significant differences (HSD) method for pair-wise comparisons was performed using the TukeyHSD() function in the stats R package (v. 3. 6. 2). Pyrantel treatment effect on cyathostomin and microbial community: Differential abundance analysis and multispatial convergent cross-mapping (CCM) Pyrantel efficacy was estimated from the Faecal Egg Count Reduction 14 days after pyrantel treatment 131 using the eggCounts R package 118 (v. 2. 3. 2). Egg Reappearance Period (ERP) in ponies' faeces was considered the day when observed FEC reduction fell below 90% of that measured at 14 days post-treatment. 131  To investigate the pyrantel treatment effect on the cyathostomin community, we first focused on the difference in community composition observed on day 0 and day 42 after treatment, using available data from five untreated and seven treated ponies.
Alpha diversity coefficients (estimated with the estimate_richness() function of the phyloseq package) were compared between the two-time points within each group using a t-test. The group and day effects were estimated using a PerMANOVA on Jaccard distance on species presence/absence and Bray-Curtis dissimilarity on relative abundances (using the ordinate() function of the phyloseq package), using the adonis2(), function of the vegan R package (1,000 permutations and accounting for pony effect using the strata option). To isolate parasite species with significant variation in abundance between the two-time points, count data were modelled using the DESeq2 package for the same 12 samples. This analysis was restricted to 15 parasite species found in at least four individuals with more than 50 counts to avoid convergence issues.
To establish how pyrantel was affecting microbial species interaction, we inferred causal relationships between measured FEC, faecal pH and 43 faecal microbiota genera (with more than 20,000 counts; 93. 8% of total abundance) using the multi-spatial Convergent Cross-Mapping procedure through the multispa-tialCCM R package (v. 1. 2). 68  This extension of the classical CCM 36 leverages a bootstrapping approach to infer the causative effect of one interactor on the other from their respective replicated time series. 68  It is well adapted to shorter time series (less than 30-time points). For this analysis, the time-series data consisted of the replicate observations (n = 20 horses in the treated or untreated groups) gathered on day 0 and the weekly collected data after the expected egg reappearance period (days 28, 35, and 42) as parasites were not expected to re-appear before 28 days after treatment.
For the genes of interest, linear regression of gene count upon the treatment and time effects and blocking for the random individual effect was applied with the lme4 R package. For specific comparison between groups (between high and low-shedders on day 0, between treated and untreated high-shedders on day 1) and to avoid relying on a single analytical framework, gene counts were handled with the DESeq2 R package (v. 1. 30. 1). 108  Analyses were applied to genes with at least ten counts across n individuals, n referring to the least considered group size, e. g. , 12 (infection effect at day 0) or six (any other comparisons). Gene expression levels were compared between high and low-shedders before treatment (n = 12 individuals in each group) to identify the impact of cyathostomin egg excretion in the host.
ACKNOWLEDGMENTS We are grateful to  Yvan Gaude ,  Thierry Blard , and  Philippe Barrie  `re for participating in the sample collection and organisation during the project. We thank the  Genotoul bioinformatics facility (Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12;  Toulouse, France ) and  MIGALE bioinformatics facility  (https://doi. org/10. 15454/1. 5572390655343293E12  Jouy en Josas, France ) for providing help, computing, and storage resources. This work was funded by the  French Institute for Horse and Riding (IFCE,   CYATHOMIX grant ). Michel Boisseau  is a grateful recipient of an  IFCE  and  Fonds E ´peron PhD  stipend. RNAseq and metabarcoding data were produced by the  GeT core facility, Toulouse, France  (GeT,https://doi. org/10. 15454/1. 5572370921303193E12), supported by  France Genomique National infrastructure  through core funding of the '' Investissement d'avenir'' program  (contract  ANR-10-INBS-09 ).
ncbi. nlm. nih. gov/sra/PRJNA819964). The accession numbers are listed in the key resources table. The associated metadata for this project is available at the INRAE institutional data repository powered by Dataverse and is publicly available as of the date of publication with DOI: https://doi. org/10. 57745/AS7NTU. ITS-2 metabarcoding sequences are available in the DDBJ/EMBL/GenBank: PRJNA849212 at the NCBI Sequence Read Archive (SRA) (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA849212). The accession numbers are listed in the key resources table. The associated metadata for this project is available at the INRAE institutional data repository powered by Dataverse and is publicly available as of the date of publication with DOI: https://doi. org/10. 57745/AS7NTU. The RNAseq raw data are available in the DDBJ/EMBL/GenBank: PRJNA849212 at the NCBI Sequence Read Archive (SRA) (https://www.
ncbi. nlm. nih. gov/bioproject/PRJNA849212). The accession numbers are listed in the key resources table. The associated metadata for this project is available at the INRAE institutional data repository powered by Dataverse and is publicly available as of the date of publication with DOI: https://doi. org/10. 57745/AS7NTU. STAR+METHODS Data and code availability KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER Biological samples Blood and fecal samples used for Author's collection (INRAE, National https://www. inrae. fr/en transcriptome, microbiome and Research Institute for Agriculture, Food nemabiome analysis and Environment Chemicals, peptides, and recombinant proteins Strongidâ oral paste Zoetis REF#: 5414736010373 Dipyralgineâ Med'Vet REF#: 08713184045331 Critical commercial assays Stool DNA Kit Omega Bio-Tek REF#: M6399-01 Preserved Blood RNA Purification Kit I Norgen Biotek Corp.
Cat. #: 43400 Illumina Truseq Stranded mRNA Illumina 20020595 MiSeq 500 cycle reagent kit v3 Illumina N/A Deposited data 16S rRNA amplicon sequences This manuscript Data available in the DDBJ/EMBL/GenBank from 520 fecal samples under the BioProject PRJNA819964 and accessions from SRR18489300 to SRR18489819. The accession numbers of the BioSamples included here are SAMN26941952 to SAMN26942471 ITS-2 amplicon sequences from This manuscript Data available in the DDBJ/EMBL/GenBank under 66 fecal samples the BioProject PRJNA849212, and the accession codes from SRR19660475 to SRR19660667, and BioSamples codes from SAMN29052165 to SAMN29052167 Illumina RNAseq sequences from This manuscript Data available in the DDBJ/EMBL/GenBank under 72 blood samples the BioProject PRJNA849212, and the accession codes from SRR20644566 to SRR20644637, and Biosamples codes from SAMN29961920 to SAMN29961866 Nematode ITS-2 database https://www.
nemabiome. ca/ v1. 3 its2-database. html SILVA 16S rRNA full-length https://www. arb-silva. de/ release 138 database documentation/release-138/ SILVA 16S rRNA region-specific Silva 138 99% OTUs from 515F/ release 138 (515F/806R) 806R region of sequences NCBI 16S ribosomal RNA https://www. ncbi. nlm. nih. gov/ released on May 25 th , 2020 reference database refseq/targetedloci/16S_process/ Equus caballus reference transcriptome Ensembl v. 103 v3 Experimental models: Organisms/strains 40 adult pony females Bred in-house at Animal https://doi. org/10. 15454/1. 5573896321728955E12 (Equus caballus) Physiology Facility Oligonucleotides 16S rRNA forward: This paper N/A GTGCCAGCMGCCGCGGTAA 16S rRNA reverse: This paper N/A GGACTACHVGGGTWTCTAAT (Continued on next page) d RESOURCE AVAILABILITY B Lead contact B Materials availability B Data and code availability d EXPERIMENTAL MODEL AND SUBJECT DETAILS B Ethics statement B Animals B Experimental design and pyrantel administration d METHOD DETAILS B Faecal sampling, faecal pH measurement, and coprology B Cyathostomin larval culture d QUANTIFICATION AND STATISTICAL ANALYSIS B Microorganisms and cyathostomin larval DNA extraction from faecal samples B V3-V4 16S rRNA and cyathostomin ITS-2 barcodes amplification B ITS-2 barcode data preprocessing and inferences on community composition B Faecal microbiota data preprocessing B Microbiota biodiversity and richness analysis: Core genera, a-and b-diversity B d d d d The original R-scripts used have been deposited at the INRAE institutional data repository powered by Dataverse and are publicly available as of the date of publication with DOI: https://doi.
org/10. 57745/ AS7NTU. d The key resources table lists all packages and programs used for data analysis. d The derived data supporting the findings of this study are available in the INRAE institutional data repository powered by Dataverse. They are publicly available as of publication date with DOI: https://doi. org/ 10. 57745/AS7NTU. d Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request.
Bio-informatic analysis De-multiplexing was performed using bcl2fastq2 Conversion Software (Illumina). Alignment was performed on University of California Santa Cruz human genome reference build 19 using the Burrows-Wheeler Aligner (v0. 7. 17). Genome Analysis Toolkit (GATK v 4. 1. 8. 0) and PICARD (v 2. 22. 8) tools were used for base quality score recalibration (BaseRecalibrator) and identified duplicate reads (MarkDuplicates). Variant calling was performed by GATK HaplotypeCaller and by GATK Mutect and Freebayes (v1. 2. 0). Annotations were added by Ensembl VariantEffectPredictor (v96. 0) and Annovar (8 Jun 2020). Copy number variation analysis was performed using ExomeDepth (v 1. 1. 12). The entire coding sequence and intron/exon junctions (positions up to -20 and + 6) of PTEN gene were analyzed in all samples (blood, tumoral and non-tumoral).
The entire 5'UTR and 3'UTR regions up to position *20 were analyzed from blood samples only. Due to the clinical presentation, no other gene was analyzed from all samples explored. Variants were filtered on mapping quality, call quality and minimum depth of coverage of 30 × for blood sample and 200 × for tumor samples. The detection threshold for constitutional analysis was 20% of reads. The detection threshold for tumor analyses was 10% of reads. The technical sensitivity was greater than 95%. Variant interpretation was performed using ALAMUT Visual 2. 11 (Interactive BioSoftware), and relevant databases (ClinVar). Splice variants were interpreted using SpliceAI (v1. 3) and SPIP (v1. 0) softwares. Variants were classified according to the American College of Medical Genetics (ACMG) recommendations.
Data Analysis A list of the species collected in the samplings was compiled, which were grouped according to two criteria. The first took into account the way in which beetles use and relocate dung (paracoprids, telecoprids, and endocoprids)  [60] , and the second was recorded according to body size, with species divided into small (<10 mm), medium (10 to 18 mm), and large (>18 mm)  [61] , thus establishing nine functional groups  [6] : small paracoprids (Pp), small telecoprids (Tp), small endocoprids (Ep), medium paracoprids (Pm), medium telecoprids (Tm), medium endocoprids (Em), large paracoprids (Pg), large telecoprids (Tg), and large endocoprids (Eg). Then, we estimated the sample completeness for each livestock management using a sample coverage analysis with iNEXT package (see  [62, 63] ) in R version 3.
We used the Poisson error for species richness, which is a negative binomial error distribution with log link function for abundance, as these data showed over-dispersion, and we used the Gaussian error for biomass  [65]. These analyses were undertaken using the "glmer. nb" function in the LME4 package in R version 3. 2. 1  [64]. We used the indicator value method following Dufrêne and Legendre  [66] , to identify associations between dung beetle species and types of bait (see  [67] ). We used 5000 randomizations to determine the statistical significance of the observed indicator value (Monte Carlo test; p < 0. 05). This analysis was performed with the 'indicspecies' package in R software  [64, 68].
Acknowledgments: We thank the owners of the farms involved in this study. We thank  Andrés Pérez  and  Eder Flores  for their support in the fieldwork. We thank  Jaime Mercado Ordóñez  for his knowledge and support in the statistical analysis. We thank  José Ramon Verdú  for his valuable comments and suggestions to the manuscript. We thank  Julián Clavijo Bustos  for the taxonomic confirmation of the Aphodiinae and for valuable comments regarding the manuscript. We thank the  El Piñal Technical Agricultural Educational Institution in Sucre (Colombia) , for providing the necessary time slots to perform this work. PALB was supported by a Master's scholarship from the  Minas Gerais State Agency for Research and Development (FAPEMIG).
Results One hundred and fifteen studies reporting data on any of the 4 FBTs of focus (Fasciola spp. , Paragonimus spp. , Clonorchis sp. , and Opisthorchis spp. ) were included in the final selection. Opisthorchiasis was the most commonly reported and researched FBT, with recorded study prevalence ranging from 0. 66% to 88. 7% in Asia, and this was the highest FBT prevalence overall. The highest recorded study prevalence for clonorchiasis was 59. 6%, reported in Asia. Fascioliasis was reported in all regions, with the highest prevalence of 24. 77% reported in the Americas. The least data was available on paragonimiasis, with the highest reported study prevalence of 14. 9% in Africa. WHO Global Health Observatory data indicated 93/224 (42%) countries reported at least 1 FBT and 26 countries are likely co-endemic to 2 or more FBTs.
The WHO Foodborne Disease Burden Epidemiology Reference Group attributes 200,000 illnesses and more than 7,000 deaths to FBTs annually  [9]. In 2015, this was estimated to result in 1,066 thousand years disability-adjusted life years (DALYs) lost globally  [10]. FBTs largely affect populations with poor sanitation and poor public health awareness  [11, 12]. The diversity of FBTs, range of clinical presentation, lack of sensitive diagnostic tools and dedicated surveillance systems, differences in host susceptibility, and different cultural food habits contribute to the underestimation and underreporting of these pathogens  [2, 6]. The true burden remains unknown as accurate prevalence data are scarce, and many endemic countries do not have national surveillance programmes in place  [2]. FBTs are targeted for control as part of the WHO NTD road map 2021-2030, with mapping and surveillance and advocacy, capacity, and awareness building identified as critical actions required to reach the 2030 targets.
Search strategy and selection criteria of published scientific literature An initial broad-based search of PubMed, IRIS, Web of Science, Science Direct, and Cochrane electronic databases was performed using a combination of search terms, including each FBT and the terms "burden," "prevalence," "incidence," and "cases. " A complete list of the specific search terms used can be found in Box A in S1 Supplementary material. No language restrictions were set, although all search terms were in English. All references published from January 2010 through February 2020 were included in the review. Additional records were identified through a snowballing approach, whereby the bibliographies of full text articles included from the initial literature search were screened, and any reference published after 2010 was reviewed using the same inclusion/exclusion criteria.
Records were initially screened based on title and abstract and were excluded if the abstract focused on an animal population and did not identify a human population of interest, and if the abstract did not mention 1 of the 4 trematode species of interest (Fasciola spp. , Paragonimus spp. , Clonorchis sp. , and Opisthorchis spp. ). Records were retained for full-text review if they identified a human population of interest and identified 1 or more of the 4 FBTs of interest. Full-text articles were evaluated for inclusion, and a second reviewer was consulted where there was ambiguity. Records were excluded if there was no prevalence or incidence recorded for a human population, for 1 or more of the 4 FBTs of interest. Records were also excluded if they did not specify the geographical area where data was collected, or if no diagnostic method was identified for determining prevalence.
Extracting quantitative and presence only data The prevalence of the 4 FBTs (i. e. , fascioliasis, paragonimasis, clonorchiasis, and opisthorchiasis) were extracted. Prevalence data at national level were scarce; therefore, prevalence studies of smaller spatial areas were recorded for the purpose of this review. Where multiple records were available for a specific country and parasite, the minimum and maximum prevalence were recorded. The full list of countries with WHO data and the selected studies and prevalence reported are included in Table A in S1 Supplementary material. Presence only data on FBTs reported between 2010 and 2019 were extracted from the WHO Global Heath Observatory  [16].
Mapping endemicity and co-endemicity of FBTs Reported "Presence only" data from the WHO Global Health Observatory were mapped using Adobe Illustrator CS5, version 15. 1. 0 on WHO official template of world map, to represent the combinations of FBTs reported to the WHO Global Health Observatory. Countries that had reported more than 2 FBTs were considered to have potential for geographical co-endemicity. WHO records were compared with the records extracted through the review process to identify countries that has conducted epidemiological studies on the FBTs bteween 2010 and 2020 even if the records were not submitted to WHO.
Global health observatory Among the 224 countries and territories reporting data to the WHO Global Health Observatory from 2010 to 2019, 93/224 (42%) countries reported at least 1 of the 4 FBTs (i. e. , 131/224 (58%) did not report any), and 2019 was the last available year of data as the process of reporting and validation of NTD surveillance data to WHO entails approximately 1-year lag in data.