dataesr/scientific-paragraphs-categorization huggingface dataset:
High throughput sequencing analyses To determine the gastrointestinal nematode species composition, we amplified ITS2 rDNA (NC1: 5 0 -ACGTCTGGTT CAGGGTTGTT-3 0 and NC2: 5 0 -TTAGTTTCTTTTCCTCCGCT-3 0 ) with 1:10 diluted DNA and each sample was tagged with a unique combination of barcode identifiers to allow pooling into a single 100 ng amplicon library, following the protocol of  Avramenko et al. (2015). The library was run on an Illumina MiSeq Desktop Sequencer. We processed the raw sequence reads through a DADA2-based pipeline  (Callahan et al. , 2016 ; see https://www. nemabiome. ca for specific details). Following  Beaumelle et al. (2021) , the gastrointestinal nematodes were identified with three different methods of assignation: BLASTn  (Altschul et al. , 1990)  against the NCBI database (13 April 2021), AssignTaxonomy  (Wang et al.
, 2007; Callahan et al. , 2016)  and IDTaxa  (Murali et al. , 2018)  against the nematode ITS2 rDNA database 1. 1. 0 (Workentine et al. , 2020). We chose to attribute a confidence level to taxonomic identification at species level: high or moderate confidence if the three or two methods, respectively, were congruent (Supplementary Table  S1 ). The resulting classification of amplicon sequence variants (ASVs) was processed further to remove singletons, potential contaminants and samples with less than 1000 reads, following with the procedure set out in  Taberlet et al. (2018). For more details, see the procedure of Beaumelle et al. (  2021 ). Only gastrointestinal nematode ASVs were included in the analyses (e. g. ASVs classified as Vare-strongylus capreoli, Cystocaulus ocreatus and Neostrongylus linearis were removed from the dataset before analysis).
For the H. contortus population genetic study, a partial nad4 fragment ($307 bp) was amplified using the primers HD4_F (5 0 -GTTATTGGAGAATTTTATCATA-3 0 ) and ND4_R (5 0 -TGAAAGTCA GAAAATATTATAC-3 0 ). PCR set-up and cycling parameters were the same as those used for the ITS2 amplicon except an annealing temperature (T a ) of 54 °C was used. Nad4 amplicons were sequenced on an Illumina MiSeq Desktop Sequencer and were generated with the DADA2-based pipeline  (Callahan et al. , 2016)  following the same process as the ITS2. We only kept amplicons identified as H. contortus based on a similarity of >97% and a query coverage of 100% against NCBI database sequences.
Roe deer spatial data To investigate how the proximity of domestic animals influences the nemabiome of roe deer, the proportion of home range that overlaps with sheep or cattle pastures for each roe deer was estimated. To do that, we first calculated the area of home ranges of roe deer that have been monitored in our study area with GPS collars (one location every 6 h) between 2005 and 2017, as presented by  Malagnino et al. (2021). We used the GPS locations recorded from November to December to index the spatial behavior of adult roe deer in autumn and winter, a period corresponding to non-territorial and non-reproductive stages for this species. We estimated the home range using the fixed kernel method  (Worton, 1989)  with the Least Squares Cross Validation (LSCV) method for the smoothing factor h  (Seaman et al.
, 1985)  model according to ModelTest function  (Posada and Crandall, 1998; Schliep, 2011). We visualised the ASV phylogeny with the ggtree package  (Yu et al. , 2017) , with Bunostomum sp. as the root. We tested the variation in roe deer nemabiomes according to the presence of livestock pasture in their virtual home range using linear models with alpha (Shannon index: consider richness and evenness) and beta (weighted UniFrac index in phyloseq package, (McMurdie and Holmes, 2013)) diversity as dependent variables. The UniFrac index incorporates information on the relative relatedness of community members by incorporating phylogenetic distances between observed organisms in the computation. For both alpha and beta diversity, we considered a set of independent variables that we considered would influence GIN diversity, the date of sampling, and the overlap of roe deer home range with sheep and cattle pastures, i.
Statistical analyses on measures of genetic populations of H. contortus All analyses were carried out using R 3. 6 (R Core team, 2020. https://www. R-project. org/) unless otherwise specified. The nad4 amplicon was used to study the population genetic structure of H. contortus among sheep farms and roe deer with high/low risk of contact with sheep pasture. The sequences were aligned with the function AlignSeqs of the package DECIPHER  (Wright, 2016). We assessed the diversity of nad4 haplotypes per sample as the proportion of nad4 haplotypes in the samples weighted by the number of H. contortus L3s. We estimated this number as the proportion of H. contortus obtained with the ITS2 multiplied by the number of L3s used for DNA extraction. We set up the data by grouping the samples together into populations belonging to each sheep farm or roe deer category.
Acknowledgements The authors warmly thank all the professionals from the  Office Français de la Biodiversité  and all the trainees for data collection. In addition, we thank the two reviewers and the editor who provided relevant comments which helped to improve this paper. This project was founded by  Results Driven Agriculture Research  ( RDAR, Canada ); grant no. 2017R021R ),  University of Calgary (Canada) , the  Office Français de la Biodiversité , the  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery Grant  (grant no. RGPIN-2015-03976 ), the  NSERC CREATE (Canada) Host Parasite Interactions (HPI)  graduate training program and  VetAgro Sup -Pôle d'Expertise Vétérinaire et Agronomique des Animaux Sauvages (EVAAS, France ; http://evaas. vetagrosup. fr/; DGAL-VetAgro Sup INRAE funding).
Thermo. raw files were converted to. mzml format using Protewizard  (34)  with peaking filter applied. Features were detected using the XCMS package  (35)  with the centWave method (ppm tolerance 1⋅0  (36)  ). The signal-to-noise threshold was set to 5, noise was set to 1 × 10 6 and pre-filter was set to six scans with a minimum 5000 intensity. Retention time correction was conducted using the obiwarp method  (37). Grouping of features was set to those present in at least 25 % of all samples (retention time deviation 10 s; m/z width 0⋅015). The 'fillPeaks' function with default settings. Remaining zeros values were imputed with two thirds the minimum value on a per mass basis. Compounds were identified by accurate mass, comparison of retention times to authentic standards or by accurate mass and also comparison of fragmentation patterns to MS/MS databases  (38).
Chimeric sequences were detected by samples using UCHIME algorithm  (43). A total of 464 858 reads were merged and processed, and 437 504 sequences were kept after chimera removal. Singleton OTUs were excluded and the remaining 2311 OTUs were affiliated with the SILVA 138. 1 database using BLAST algorithm (97 % sequence identity threshold). Statistical analysis. The number of animals enrolled in this study was limited by the experimental facility capacity and the criteria retained for animal inclusion in each experimental group. More precisely, ewes had to be gestating with two fetuses and each experimental group was balanced by ewe's body score condition, live weight, age and parity. According to these criteria, fourteen ewes were enrolled in each experimental group. Graphical representations and statistical analyses were performed using GraphPad Prism 9.
7 (b)). After 12 h, no differences between the groups were measured. The biosynthetic pathway for both Neu5Ac OS and Neu5Gc OS has been reconstructed from the non-targeted LC-MS data (Fig. 8 ). A Neu5Ac residue is loaded onto a cytosine-monophosphate (CMP) molecule, which can transfer the Neu5Ac to the 3 ′ or 6 ′ position of lactose or N-acetyllactosamine. Alternatively, CMP-Neu5Ac can be hydroxylated enzymatically to CMP-Neu5Gc. The statistical analysis of the reconstructed Neu5Gc pathway was performed on colostrum samples collected 5 h post-partum, as the statistical significance was the highest at that sampling time. No significant difference was observed between C and SC groups for Neu5Ac, lactose, N-acetyllactosamine nor the Neu5Ac OS. CMP-Neu5Ac was also not altered by supplementation.
Acknowledgments The authors would like to thank  Catherine Dhainaut  and  Raphaele Gresse  from  UMR MEDIS  and  Yu Jeaju  from the  University of Guelph  for their skilled technical assistance;  Mickaël Bernard ,  Aline Cercy ,  Loic Gaillard ,  Bruno Viallard  and  Pierre Chirent  from  UE Herbipôle  for their very valuable help in the animal experiment. E. F. and F. C. D. conceptualized the study; L. D. , J. R. , E. F. , and F. C. D. devised the study methodology; L. D. , J. R. , and C. A. prepared the software; L. D. , J. R. , and F. C. D. conducted the formal analysis; L. D. , J. R. , M. S. , E. F. , and F. C. D. carried out the investigations; L. D. was involved in the activity of data curation; L. D. and F. C. D. wrote the original draft; J. R. , M. S. , M. C. , C. A. , and E. F. conducted the review and did the editing; L.
Study design A cross-sectional study was conducted from March to June 2018, among stakeholders located inside and outside the OD area. The sampling frame was based on databases of hunters and pig farmers provided by the regional authorities. In the case of hunters, a list containing 200 names of the leaders of the local hunting teams has been provided by Regional Association of hunters from Ardèche. A total of 30 hunters were randomly drawn from a list of 200 names: 20 hunters from the OD area and 10 hunters from outside this area. As the participation to interviews was volunteer, if some people drawn refused the interview, the next names on the list were selected. At the time of the study, the local Animal Health authority could not provide us with an anonymous list of the total of number of outdoor pig farmers in the region (n=81).
Acknowledgements This works was funded by the project EPIDEWILD in the Meta-program GISA (https://colloque. inrae. fr/metaprograms-workshops_eng/ Metaprograms/GISA)-Sustainable management of Animal Health  (Grant  INRAE French Institute for Agriculture, Food and Environment). We would like to acknowledge la  Fédération des Chasseurs de Ardèche (Fabrice Etienne) , the hunters and farmers that were interviewed and the local authorities of animal health and production (DSPP).
Rhythm analyses The same rhythm parameters as in our previous study  (Parker et al. 2019)  were calculated and compared for each behavior, feeding and locomotor. Briefly, the period (duration of a full cycle) and amplitude (a measure of how much of the activity or food intake that does occur is actually rhythmic) were measured using the periodogram of the rhythm, calculated by the Clocklab software (v. 2. 72, extension of Matlab v. R2013a). The interdaily stability, intradaily variability and onset of the least active 5 hours were calculated by the ActiWatch software (v. 7. 31). The interdaily stability quantifies the variability among successive days, its values (arbitrary units) decreasing with higher day-to-day variation of the activity or feeding patterns, thus ranging from 0 for Gaussian noise to 1 for maximal interdaily stability.
Ethics statement This is a retrospective, non-interventional study and the data were anonymized for all patients. We only collected data necessary for the purpose of our study. The data collected will remain in an archive for 15 years. This research was in compliance with the law "Informatique et Libertes" of January 6, 1978 as amended and the law No. 2018-493 of June 20, 2018 on the protection of personal data. Is also in compliance with European Parliament guidelines in April 27, 2016. Finally, data were transferred and collected in accordance with the reference methodology MR003 of the Commission Nationale de l'Informatique et des Libertes (CNIL) for which the Centre Hospitalier de Cayenne has signed a compliance commitment. The database was declared to the CNIL and approved on April 19, 2019 (declaration number: 2212828v0).
Data collection and analysis The data was collected and anonymized on Excel. For the analysis, we used the software Stata (version 12). Incidence assessment was based on the census of the National Institute of Statistics and Economic Studies (INSEE). For binary qualitative variables, Chi 2 or Fisher test were used. Finally, we used Mapinfo software (version 12) to make a distribution map of respiratory isolates, according to the geographical definition of the 4 regions in French Guiana: Coastal center, Savannas, West and East French Guiana.
Acknowledgments The autor thanks  Mathieu Nacher  ( Inserm 1424, French Guiana ),  Hugo Testaert  ( Pneumology Department, University Hospital of Guadeloupe ) and  Sadia Khan  ( Bordeaux University, INSERM, Bordeaux Population Health Research Center, team: EPICENE, UMR1219, Bordeaux, France ) for correcting the English. The autor thanks  Gilles Chaptal  for the realization of the map of French Guiana. The autor also thanks the French Guiana PNTM working group:  Philippe Abboud ,  Houari Aissaoui ,  Alain Berlioz-Arthaud ,  Bastien Bidaud ,  Denis Blanchet ,  Anne-Marie Bourbigot ,  Mathilde Boutrou ,  Jean-Michel Cauvin ,  Fabrice Flament ,  Claire Grenier ,  Hatem Kallel ,  Anthony Le Labourier ,  Dominique Louvel ,  Aude Lucarelli ,  Aba Mahamat ,  Alessia Melzani ,  Balthazar N'tab ,  Richard Naldjinan-Kodbaye ,  Milko Sobeski ,  Antoine Talarmin ,  Stephanie Thomas ,  Valentine Travers ,  Vincent Vantilke ,  Tania Vaz ,  Guillaume Vesin , Gae ¨lle Walter.
Like Firmin et al. [61] , we used an open coding strategy with a line-by-line analysis approach to explore data for recurring ideas  [66] [67] [68] , and developed clusters of meaning into themes. Only the principal investigator conducted the qualitative analysis and did it twice 3 months apart to confirm that the same themes emerged from the data. Also, the relevance of the themes and the mapping that emerged from the initial coding were discussed with the other researchers. We then counted the number of themes mentioned by each handler (a response could concern several themes) and finally calculated the percentages of each theme on the total number of themes cited for each question. These analyses respect the 15-point checklist of criteria for thematic analysis underlined by Braun and Clark  [69].
To be useful in a quantitative context, the factors impacting relationships between KEs must be sufficiently understood. AOP modelling methodologies range from statistical/machine learning approaches to mechanistic approaches using ordinary differential equations to individual-based models, and should be chosen according to the questions being asked and the data available  [15, 16]. Thus, there is a need to identify, extract and utilise reliable sources of data and information to inform these quantitative considerations in AOP development. An extensive and growing range of electronic, digital resources (hereafter named e-resources) are available to support the modelling of qAOPs  [17, 18]. Digital resources can be defined as web tools/interfaces, datasets/databases, mathematical models that have been conceived and created digitally or by converting chemical and biological data and information to a digital format.
Key learnings when exploring qAOP development for three different adverse outcomes During the one-week workshop, three case studies were elaborated covering different AOs, namely skin sensitisation, neurotoxicity and carcinogenicity. The AOPs were used to analyse and model selected KERs of the three different AOs. First, the participants mapped out available resources (datasets, modelling methodologies) to build quantitative KERs between KEs, including their typical applications and data requirements (a full list of these resources is available in the Supplementary Material). Additional discussions focused on which considerations should be taken into account for assessment of confidence (i. e. weight of evidence) in the supporting data and its relationship with quantification. With regard to potential regulatory applications, the role of transparent documentation of the proposed approach was also discussed.
Datasets & e-resources In this case study the wealth of freely available data related to skin sensitisation was considered. They span human data, in vivo data from regulatory studies as well as data from in chemico and in vitro alternative methods. Several datasets with skin sensitisation data from human, local lymph node assay (LLNA), and individual methods representing KEs are available in the literature. To investigate the development of the qAOP, the dataset published by Urbisch et al. [24]  and evaluated by Hoffmann et al. [25]  was investigated. This was the largest dataset available presenting a full data matrix across the AOP including information for more than 120 chemicals, i. e. human data characterising KE1, KE2, and KE3, and LLNA data to investigate the AO. Studies on skin sensitisation endpoints for several chemicals are also available in curated e-resources such as OECD QSAR Toolbox (links in Table  1 ).
Datasets & e-resources In this case study, it was noted that an extensive data collection exercise would be required due to the limited availability of precompiled data. Quantitative data extracted from literature needed to be compiled, annotated and organised in a structured and appropriate format to ensure compatibility with a suitable modelling technique. The Developmental NeuroToxicity Data Integration and Visualization Enabling Resource (DNT-DIVER) by National Toxicology Program provides a web-based curated collection of datasets from multiple DNT assays (link in Table  1 ). In this e-resource, benchmark concentration analysis was applied to compare data obtained in assays using different species (e. g. human-based cell assays or animal models such as zebrafish and planaria) and addressing various endpoints (e.
g. neuron outgrowth, protein accumulation and behaviour in fish)  [27]. The assessment of variability between different assays was identified as a major obstacle. The same endpoints can be investigated in different test systems (e. g. animal models or human samples) or by using different experimental techniques. For example, synaptogenesis can be assessed by using imaging techniques or measuring protein content. Therefore, it is useful to have access to the experimental protocols to understand how data were generated. Moreover, as part of the data processing, data points have to be converted to the same unit of measurement (e. g. BDNF concentration as % of control). Another important consideration is the weight to be assigned to individual studies or data points in a single study, for example, where multiple concentrations are tested in different test systems.
Datasets & e-resources The first step in data collection was to identify (chemical) stressors that are relevant for the AOP of interest. For the selected KEs and KERs of AOP 220, most of the quantitative data were retrieved from chemical assessment reports from regulatory agencies, which included peerreview of relevant toxicological studies. Alternatively, data could be derived directly from the scientific literature, provided that results are sufficiently documented, and the study is adequately designed (preferably conducted according to relevant OECD Test Guidelines). Robust tumour incidence data were relatively easily available, while data on histopathological measurements of cell toxicity and data on regenerative proliferation, at relevant time periods were more difficult to identify. This was partly due to the fact that regenerative proliferation is not a requirement in the cancer bioassay  [30].
Briefly, quantitative data for a list of chemical stressors, associated with a possible cancer adverse outcome, were collected from several tools and databases, including some of the e-resources identified in the workshop, such as OECD's eChemPortal and EFSA's OpenFoodTox database (links in Table  1 ). In addition, PubMed, EFSA's Peer Review Conclusions on Pesticides and Health Canada's Assessment Reports, were also taken into account. In this investigation, most of the quantitative data were retrieved from chemical risk assessment reports, where a peer-review evaluation of toxicological studies was already conducted, and therefore the related data quality was assumed to be high. Thus, for the quantification of the AOP, the relevant dose-response data for quantitative characterisation of the response-response function between different KEs and the AO were collated.
Since all datasets were from animal studies (due to the selection of KERs for quantification), the study design and associated dataset were carefully reviewed. The relevance of having well-defined inclusion criteria for studies to be used for quantification was acknowledged. Another key learning was the type of dose-response information to be used: preferably, quantification of KERs should be based on datasets that include KEs at multiple levels of biological organisation and with significant increases over controls for at least two doses/concentrations. Finally, species/sex and strain, differences in exposure period and exposure routes was recommended. The case study made very clear the lack of uniformity in data reporting among the different data sources, contributed to significant gaps and consequent uncertainty.
According to what is considered the required functional unit of prediction (step two), the modelling challenge is simplified by selecting the "more important or more relevant" nodes in the AOP or AOP network. The choice of nodes (KEs) in a simplified network can be informed by network analytics. The degree of connectedness and centrality of KEs can express the extent to which the KEs represent important nodes of convergence of the pathway, in the context of the problem formulation  [17]. Information on highly-connected KEs from network analytics needs to be weighed against the availability and quality of data, as well as the cost-effectiveness of generating new experimental data. Step three consists of identifying sources of information (from the literature and/or databases) that contain potentially useful data for quantifying the KERs that will be derived from pairs of KEs.
The datasets should meet the FAIR data principles  [19]  and interventions at both data and metadata level could support this FAIRification process for newly created or existing datasets (https://www. go-fair. org/fair-princi ples/fairification-process/). The fourth step consists of reviewing the information and extracting that considered to be reliable and relevant. At present, this is a manual and time-consuming process, but this could eventually be partially automated. For example, steps 3 and 4 could be facilitated by using online e-resources such as Abstract Sifter  [44] , Swift Review  [45] , Dis-tillerSR  [46]  (see  [47]  for additional examples). Easy access to the protocols underlying the data and the definition of the inclusion and exclusion criteria for studies and associated datasets would also facilitate the review and extraction of the data.
In the OECD programme, the value and potential modification/application of tools for the systematic consideration of available data are being considered as a basis to increase efficiency and transparency in AOP development, paving the path for new Artificial Intelligence tools to be developed to facilitate data extraction. Recent activities are oriented to promote the use ontologybased annotations of KEs and establish semantic mapping of AOP Wiki, to facilitate the extraction of AOP content, support computational analysis and allow better interoperability with other resources  [48] [49] [50] [51]  (see Supplementary Material). In the fifth step, the (manually) extracted data are codified and annotated in a machine-readable format, suitable for modelling. Attention should be paid to relevant meta data, including the correct units of measurement.