dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 18. 1 with minimal bootstrap support of 50%. This last step relied on the ITS-2 rDNA database for nematodes v. 1. 3. 0  (Workentine et al. , 2020). Building on previous work  (Poissant et al. , 2021) , 22 truncated sequences and a dubious Cyathostomum catinatum entry (accession number Y08619. 1) were removed from the whole database which was left untouched otherwise. In the end, 264 equine strongylid sequences were available for analysis.
Analytical pipelines for community structure inference using the COI amplicon For the COI barcode, we built a custom COI barcode sequence database for Cyathostominae and Strongylinae species collected from Genbank, BOLD database using the Primerminer package v. 0. 18  (Elbrecht & Leese, 2017b ) and concatenated into a single fasta file. Sequences were subsequently edited to remove elephant Cyathostominae species (Quilonia sp, Murshidia sp, Kilonia sp, and Milulima sp) using the seqtk v. 1. 3 subseq option (https://github. com/lh3/seqtk). Some entry names (n = 18) consisted of an accession number that was manually back-transformed to the corresponding species name. Duplicate entries were removed with the rmdup option of the seqkit software v. 0. 16. 0  (Shen et al. , 2016)  and sequences were dereplicated using the usearch v11.
0. 667 -fastx_uniques option  (Edgar, 2010). To reduce the database complexity and promote primary alignment, the most representative sequences were further determined using the usearch -cluster_smallmem option, considering two identity thresholds of 97% and 99%. The final database consisted of 241 sequences corresponding to 32 equine strongylid species (seven large strongyle species), including all species considered further in our mock communities. Amplicon analysis relied on a mapping approach to the custom COI sequence database implemented using the minimap2 software v. 2-2. 11 (Li, 2018)  as described elsewhere  (Ji et al. , 2020). First, paired-end reads were merged into amplicon sequences using the usearch software v11. 0. 667 and the -fastq_mergepair option  (Edgar, 2010). Merged amplicon sequences were subsequently mapped onto the COI sequence database using the minimap2  (Li, 2018)  short read mode (-ax sr).
Statistical Analyses Statistical analyses were run with R v. 4. 0. 2 (R Core Team, 2016). Community compositional data were imported and handled with the phyloseq package v1-34. 0 (McMurdie & Holmes, 2013). Abundance data (read count for the ITS-2 rDNA region, or scaled read depth for COI) were aggregated at the species level using the taxglom() function of the phyloseq package v1-34. 0 and converted to relative abundances for further analysis. These data were used to compare each barcode and pipeline predictive ability in the first comparison. After the most appropriate bioinformatics parameters were identified for each barcode, ASVs with outlier representation, i. e. , less than 100 reads per base pair for the COI barcode or 40 reads for the ITS-2 rDNA barcodes, were regarded as likely contaminants and further discarded.
e. , a trade-off between precision and recall  (Hleap et al. , 2021). Alpha diversity was estimated using the Shannon and Simpson's indices using the estimate_richness() function of the phyloseq package  (McMurdie & Holmes, 2013). The difference between the expected mock community expected and observed alpha diversity values was further considered to compare conditions and barcodes. The divergence between the inferred community species composition and the true mock community composition was estimated from a between-community distance matrix based on species presence/absence (Jaccard distance) or species relative abundances (Bray-Curtis dissimilarity) using the vegdist() function of the vegan package  (Oksanen et al. , 2017). Compositional differences between sample types were visualised with a non-metric multidimensional scaling (NMDS) with two dimensions using Jaccard and Bray-Curtis dissimilarity.
The considered life stage has limited effect on the inference of community diversity Differential worm fecundity or larval development may also contribute to distort the correlation between relative abundances measured from different sample types for some species. To test this hypothesis, nemabiome metabarcoding data were generated from six horses for three life stages of strongylids (eggs, infective larvae and adult worms collected after pyrantel treatment). The total number of species present in this population remains unknown to date. The COI barcode retrieved 12 species, of which C. radiatus was not found with the ITS-2 barcode. This latter barcode allowed the retrieval of 13 species and four additional amplicon sequence variants that were assigned at the genus level only (Fig. 3 ). ITS-2 data also recovered C.
This certainly owes to amplification biases associated with the multiple amplification steps and a reduced specificity of the lower mapping stringency. However, past experiments dealing with arthropods  (Krehenwinkel et al. , 2017)  or microbial species  (Sipos et al. , 2007)  did not find evidence of significant biases due to the increased number of PCR cycles. On the contrary, the universal ITS-2 primers yield an amplicon suitable for paired-end sequencing with a substantial overlap between both fragments. This combined with consistent amplification efficiency across species makes it more suitable than the chosen COI barcode for metabarcoding purpose. Besides, the development of an extensive ITS-2 database offers an easy-to-use tool for taxonomy assignment that covers a wide breadth of the equine strongylid diversity.
For the COI barcode, the gathered sequences encompassed fewer species than the ITS-2 database, although all the species of interest were included. The PCR amplification showed slight variation for the ITS-2 but these were not validated in the independent samples used for validation. Of note, additional factors such as the type of DNA polymerase could also affect the metabarcoding approach and warrant further investigations  (Nichols et al. , 2018). The designed COI barcode did not outperform the ITS-2 rDNA region in terms of species detection or Jaccard-based (species presence or absence) diversity estimates. Nonetheless, inclusion of C. coronatus in mock communities may have yielded less favourable results for the ITS-2 rDNA. Indeed, this species entertains a close phylogenetic relationship with C.
calicatus that may decrease the rate of correct taxonomy assignment  (Bredtmann et al. , 2019). In this respect, the COI barcode would offer additional specificity and should be considered further for the metabarcoding of cyathostomin species through other strategies. It remains unclear however why C. leptostomum and C. labratus showed good amplification in the first batch but were not found by the metabarcoding approach. This may indicate mis-assignation of sequencing reads to other closely related species. While the described approach was suboptimal, other strategies targeting the mitochondrial genome could still be applied  (Liu et al. , 2016; Ji et al. , 2020). First, bulk shotgun sequencing of cyathostomin populations could be used for mapping against a reference database of mitogenomes as applied to arthropods  (Ji et al.
Funding This work was supported by the  Institut Français du Cheval et de l'Équitation  and the  Fonds Éperon. The GeT core facility, Toulouse, France (GeT, https://doi. org/10. 15454/1. 5572370921303193E12) was supported by  France Génomique National infrastructure  through core funding of the " Investissement d'avenir" program  (contract  ANR-10-INBS-09 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Collection Blood and milk sampling, clinical measurements, and behavioral observations were performed at regular intervals from 24 h prechallenge up to 48 h postchallenge (Figure  1 ). Two veterinarians and 1 technician performed blood samples, 1 experienced veterinarian blind to cow treatment performed the clinical measurements, and 2 trained observers blind to cow treatment performed the behavioral observations, of which 1 observer performed the in-freestall observations and the other performed the at-milking observations.
ACKNOWLEDGMENTS This work is part of the  LongHealth  project that received financial support from  INRAE (National French Research Institute on Agriculture and Environment ;  Gestion Intégrée de la Santé Animale metaprogram ) and from the  Conseil Régional Auvergne-Rhône-Alpes  (CPER 2015-2020 PhD grant). The NSAID used was kindly supplied by  Ceva Santé Animale. We thank all the staff at the  Herbipôle-Marcenat research  farm for their excellent husbandry of the cows,  Anne-Lise Lainé  ( INRAE, Nouzilly, France ) for handling the cortisol analyses, and  Marie-Laure Delignette-Muller  ( Lyon, France ) and  Emmanuelle Gilot-Fromont  ( Lyon, France ) for their precious help with the statistical analyses. We also acknowledge the help we received for drafting this article through the  UMRH (Mixed Research Unit on Herbivores ) Scientific Writing Workshop chaired by  Isabelle Veissier  and  Isabelle Ortigues-Marty  ( INRAE ,  Ginger  et al.
Immediately after washing, the slides were scanned on Agilent G2505C Microarray Scanner using Agilent Scan Control A. 8. 5. 1 software. The fluorescence signal was extracted using Agilent Feature Extraction software v10. 10. 1. 1 with default parameters. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  [39]  and are accessible through GEO Series accession number GSE215200 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE215200).
Blood haematological assays and faecal measurements As described by Ref. [19] , blood samples were also collected at the beginning of the first month (T0) and at the end of the ninth month (T2) of the behavioural analysis part of the study. Whole blood samples were taken in EDTA and lithium heparin tubes (BD Vacutainer ®, 10 mL) for haematological assays. Blood was stirred for 15 min at room temperature to facilitate oxygenation. The erythron was assessed from peripheral blood samples by calculating the number of circulating red blood cells (RBC), haemoglobin concentration (HB), packed cell volume (PCV), volumetric indices, such as mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin concentration (MCHC). The leukon was assessed from data derived from the total and differential count of white blood cells (WBC) and the analysis of WBC morphology.
Statistical analyses Microarray data were analysed using R (R version 4. 1. 3, R Core Team, 2018) and Bioconductor packages (http://www. bioconductor. org, v. 3. 15) [49]  as described in GEO accession GSE215200. Raw data (median signal intensity) were filtered, log 2 transformed, corrected for batch effects (microarray washing bath serials), and quantile normalized  [50]  using the limma package (v. 3. 50. 3)  [51]. The differential analysis was performed using the limma R package. A linear model was fitted for each gene using the limma lmFit() function  [51] , considering microarray slide positions as a blocking factor. Pair-wise comparisons between contrasted horses for each behavioural indicator (High vs Low, Supplementary Fig. S1 ) were applied using specific contrasts. As previous analyses showed that some factors specifically influence each behavioural indicator  [4] , contrasted horses from experimental groups were balanced for these factors as shown in Supplementary Table  S3 , Supplementary Table  S2.
A correction for multiple testing was applied using the Benjamini-Hochberg procedure (BH)  [52] , to control the False Discovery Rate (FDR). Probes with FDR ≤0. 05 were considered to be differentially expressed between conditions. For the annotation of the Agilent chip probes, we used the annotation provided by Agilent and for the non-annotated probes, the annotation was performed using Nucleotide BLAST (https://blast. ncbi. nlm. nih. gov/). These BLASTs were performed with the following parameters: Organism = Equus caballus; minimum cover = 95%, minimum identity = 95%. The relationships between gene expression and welfare indicators were analysed using the R package mixOmics v. 6. 18. 1  [53]. Principal component analysis (PCA), sparse Partial Least Square (sPLS), and sparse Partial Least Square Discriminant Analysis (sPLSDA) were performed to explore the data set structure, characterise the correlation between gene and behavioural indicators expressions and identify the genes that contributed the most to explaining differences between contrasted horses for each behavioural indicator (High vs Low, Supplementary Fig.
S1 ). The functions of the genes selected through sPLS and sPLSDA were annotated using GeneCards (http://www. genecards. org/), and each list of genes was analysed using Ingenuity Pathway Analysis (IPA, QIAGEN Inc. , https://www. qiagenbioinformatics. com/products /ingenuitypathway-analysis). A gene set enrichment analysis using the R package GAGE  [54]  was also performed to identify if genes belonging to specific GO terms or KEGG pathways were over-represented in the gene data set. Significance was set at an FDR ≤0. 05.
The stress value was calculated to determine the dimensions for each NMDS. Stress value measures how much the distances in the reduced ordination space depart from the distances in the original p-dimensional space. High-stress values indicate a greater possibility that the structuring of observations in the ordination space is entirely unrelated to the actual full-dimensional area. The other data sets (the "explanatory variables") were then fitted to the ordination plots using the "envfit" function in the vegan R package  [55]  with 10,000 permutations. The "envfit" function performs multivariate analysis of variance (MANOVA) and linear correlations for categorical and continuous variables. The effect size and significance of each covariate were determined by comparing the difference in the centroids of each group relative to the total variation, and all of the p-values derived from the "envfit" function were Benjamini-Hochberg adjusted.
The obtained r 2 gives the proportion of variability (that is, the main dimensions of the ordination) that can be attributed to the explanatory variables. The N-integration algorithm DIABLO of the mixOmics R package was used as a second integration approach. It is to be noted that, in the case of the N-integration algorithm DIABLO, the variables of all the data sets were also centred and scaled to unit variance before integration. In this case, the relationships among all data sets were studied by adding a categorical variable, i. e. , the stereotypical behaviour of horses. For this analysis, 11 horses with missing values for one or more covariables were excluded. We also excluded one horse which was considered as an outlier. Thus, the analysis was performed on 33 horses. Horses with no stereotypical behaviours during the experiment (n = 20) were compared to horses that had expressed stereotypical behaviour at least once across the investigation (n = 13).
DIABLO seeks to estimate latent components by modelling and maximising the correlation between pairs of prespecified datasets to unravel similar functional relationships between them  [56]. A complete weighted design was considered, and the "block. splsda" function was used to predict the number of latent components and discriminants. The model was first fine-tuned using leave-one-out cross-validation by splitting the data into training and testing. Then, classification error rates were calculated using balanced error rates (BERs) between the predicted latent variables with the centroid of the class labels (i. e. , stereotypies vs non-stereotypies) using the "max. dist" function. BERs account for differences in the number of samples between different categories. Only interactions with r ≥ |0.
Correlation between behaviour and transcriptomic data In the first analysis, we categorized the horses according to a global welfare score combining the scores obtained for each of the four behavioural indicators, i. e. , stereotypies, aggressiveness towards humans, unresponsiveness to the environment, and alert postures. We reasoned that the horses cumulating the highest scores in the various behavioural troubles would display the highest welfare impairment. We found no differentially expressed probes using a false discovery rate of 5% or a threshold non-adjusted p-value <0. 005 when comparing the groups of the contrasted horses. To gather information about the global structure of the behavioural indicators, a Principal Component Analysis (PCA) was carried out on the expressed behaviours for the 187 horses.
Within the sPLS analysis for "aggressiveness towards humans", we detected 356 probes correlated with this behaviour which correspond to 205 unique genes. Within those genes, 99 were negatively and 106 positively correlated (correlation value ≥ 0. 5). We then used Ingenuity Pathway Analysis (IPA) software to understand these genes' functional implications better and reveal the molecular pathways and upregulators underpinning this gene set. Supplementary Fig. S3  shows that the over-represented biological process terms were strongly associated with inflammatory response (e. g. , cytokines IL-1A, IL1B, IL-5 expression, T cell responses, adhesion of immune cells) as well as cell differentiation pathways (differentiation of embryonic tissue, tumour cell lines, bone cells, etc. ). Using the upstream regulator analysis of the IPA software, we detected 64 upstream regulators with a p-value less than 0.
Integration of faecal microbiota composition and gene expression is associated to the expression of stereotypies Because faecal microbiota and circulating biochemical parameters had been measured on the same 45 horse population, we combined these datasets with the transcriptomic analysis and the behavioural indicators to characterise the holobiont. These analyses did not detect associations between whole blood transcriptome, gut microbiota composition and aggressiveness. This was not unexpected because using the full cohort of individuals (n = 187  [19] ) the "Aggressiveness towards humans" did not relate to overall gut microbiota composition. Still, two lactate-producing bacteria (Streptococcus and Butyrivibrio) were correlated to aggressiveness  [19]. This correlation was not found here, probably due to the lower number of horses analysed.
Data and model availability statement Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  [39]  and are accessible through GEO Series accession number GSE215200 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE215200). This Targeted Locus Study project has been deposited at DDBJ/ EMBL/GenBank under the BioProject PRJNA543287. The corresponding BioSamples accession numbers were SAMN11666402 to SAMN11666784.
Survey data (results) The survey data received was fully anonymised prior to its analysis. All product and animal test data were randomised and any information that could identify individuals or their employers was redacted. The anonymised dataset was shared with an international group of experts in the production, regulation and quality control of biologicals to support data analysis and interpretation and who are co-authors of this paper. The anonymised data from the survey analysis is available as supplementary materials (file: Supplementary materials_manufacturers survey raw data. xslx).
Ethical Aspects Human samples were obtained at the Tropical Medicine Unit of Hospital Universitario Poniente (Almeria, Spain) for diagnostic purposes as part of the standard protocol care for sub-Saharan patients attending such a unit. Anonymized data were collected retrospectively. This study was conducted in accordance with the guidelines of the As fixation is known to affect the size and shape of helminth eggs, the egg material used in the present study was analyzed without prior fixation, suspended in 0. 9% NaCl saline solution, preserved in darkness at 4 • C until required, and digitalized in the shortest possible time, within two weeks after collection [24,25].
Ethical Aspects Human samples were obtained at the Tropical Medicine Unit of Hospital Universitario Poniente (Almeria, Spain) for diagnostic purposes as part of the standard protocol care for sub-Saharan patients attending such a unit. Anonymized data were collected retrospectively. This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee (protocol code PI_19_30 on 25 September 2019). Informed consent was obtained from all subjects involved in the study. At the end of the study, all participants were informed about their parasitological results, and free treatment was offered to those who tested positive for schistosomiasis (single 40 mg/kg dose of praziquantel).
The software Sequencher version 5. 4. 6 (Gene Codes Co. , Ann Arbor, MI, USA) was used to edit and assemble the sequences, and ClustalW to align them by means of default parameters in MEGA X  [32]. A careful inspection of all nucleotide positions in the electropherograms (ABI format) allowing the detection of sequence polymorphisms between S. haematobium and S. bovis, was performed to identify possible heterogeneity, as previously described  [4, 33, 34]. Homologies were performed using the BLASTN program from the National Center for Biotechnology Information website (http://www. ncbi. nlm. nih. gov/ BLAST, accessed on 29 September 2022).
Schistosome Egg Digitalization and Measurements Schistosome egg measurements were performed on 162 samples (Table  1 ). Each egg was individualized in a 0. 9% NaCl drop on a slide and photographed using a microscope (Leica DMR 72-89663) with 400 magnifications and a high-resolution camera (Leica DFC450C) controlled by the LAS LEICA software version 4. 3 (Leica Microsystems, Heerbrugg, Switzerland). All pictures were taken without a coverslip. No pressure was applied. In addition, the lack of a coverslip allowed the recovery of individual eggs and their preservation in EtOH at 70 • C for posterior molecular characterization. For morphologic characterization, the Image Pro Plus version 5. 1 software (Media Cybernetics Inc. , Silver Spring, MD, USA) was used. Following the already standardized methodology  [23, 25, 35] , the 17 non-redundant measurements used in this study were (Figure  3  All pictures were taken without a coverslip.
No pressure was applied. In addition, the lack of a coverslip allowed the recovery of individual eggs and their preservation in EtOH at 70 ℃ for posterior molecular characterization. For morphologic characterization, the Image Pro Plus version 5. 1 software (Media Cybernetics Inc. , Silver Spring, MD, USA) was used. Following the already standardized methodology  [23, 25, 35] , the 17 non-redundant measurements used in this study were (Figure  3 ): Egg Area (EA), Egg Perimeter (EP), Radius max (Rmax), Radius min (Rmin), Egg Length (EL), Egg Width (EW), Egg Roundness (ER), width at 20 µm from Blunt End (BE20), width at 35 µm from Blunt End (BE35), width at 50 µm from Blunt End (BE50), width at 20 µm from Spine End (SE20), width at 35 µm from Spine End (SE35), width at 40 µm from Spine End (SE40), Spine Length (SL), Spine Base Width (SBW), Spine Medium Width (SMW) and Spine End Width (SEW).
Statistical Data and Analyses Statistical analyses were conducted using R version 4. 0. 0 software (The R Project for Statistical Computing; http://www. rproject. org, accessed on 20 October 2022). Generalized Linear Mixed Models (GLMM) were calculated. All models included patient identification as a random intercept to account for the lack of independence of observations (host sample size effects). Measurements were used as a response variable, and host sample size effect and geographical location were used as independent variables. The 'glmer' function of the lme4 package was used. The relevance of the terms was evaluated with the second-order Akaike Information Criterion (AICc) to account for small sample sizes  [36, 37]. When the inclusion of each term did not reduce AICc values by 2 or more units.