dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolic assessment of PD and FNS applied to our experiment To assess the relative contributions of parasite dilution and forage niche sharing in our experiment, we used Eqs. (  1 ) to (  11 ) with the experimental dataset 1. To fit the conditions of the experiment, gs referred to six different configurations, defined by two types of grazing (monospecific and mixed grazing), and three periods. These periods correspond to: i) spring (June) before flowering of major sward components, (ii) summer (late July) when sward heterogeneity was expected to be maximal and (iii) autumn (September (or early October)) when cumulative grazing treatment effects were expected to be maximal. To assess the gains from FNS, p i,gs was evaluated from the percentages of mouthfuls according to niches recorded during behaviour observations (details in  Joly et al.
Metabolic assessment in the mixed-grazing experiment The application of our metabolic assessment to the experimental dataset is summarized in Fig. 2. It indicates that the ME content of the diet (green bar in Fig. 2 ) is slightly above the requirements in June (blue bar), when vegetation is young and nutritive, for both monospecific and mixed grazing systems. In July and September, when vegetation ages, the ME ingested is slightly below the requirements, with more pronounced differences in monospecific than mixed-grazing. The ME cost of parasitism (red bar) is moderate compared to the ingested ME in June and July, but it becomes high in September, where it represents 100% of the ME requirements in the monospecific system (11 MJ/day), and 23% in the mixed-grazing one (3 MJ/day) (Fig. 2 ). As a result, the ME balance is negative in July and September for both systems (grey bar), while it should equal zero if ingestion had met requirements and compensated parasitism costs.
Application of the metabolic assessment to the experimental dataset The application of the metabolic assessment to our experimental  dataset helps explain the sheep growth patterns observed in monospecific and mixed grazing; and assess the relative strength of PD and FNS. We first observe that the cost of parasitism appears to be very high compared to animal requirements. Under monospecific sheep grazing, this cost represents 100% of the ME requirements and 57% of the CP requirements, in the late grazing season (September). It thus doubles the requirements of ME and importantly increases those of CP. As a result, metabolic requirements are not met and balances are extremely unfavourable, which can explain the liveweight losses at the end of the grazing season in the monospecific grazing system we observed in our experiment  (Joly et al.
Then, a 10x multiplex PCR was performed using ten validated primers extracted from the previously published panel  [44]  to target S. scabiei mites (  Sarms 33, 34, 35, 36, 37, 38, 40, 41, 44, 45)  following the PCR protocol of Soglia et al. [39]. Capillary electrophoresis was performed with an Applied Biosystems SeqStudio TM. The software GeneMapper 4. 0 (Applied Biosystems, Foster City, CA, USA) allowed the allele calls and microsatellite visualization. After molecular analysis, mites that did not fulfil the required criteria (eight detectable loci out of the ten analyzed) were excluded from the genetic analysis. Two population genetics analyses were applied to the 81 mite microsatellite outputs: i) Bayesian clustering and ii) principal component analysis (PCA). The first one requires Hardy-Weinberg equilibrium (HWE), while no assumptions are required for the PCA.
Descriptive statistics, such as observed and expected heterozygosis (Ho and He, respectively), allelic richness (R) and HWE analysis, were carried out with software R 4. 0 using the packages Adegenet 2. 1. 3 [16]. P-values for the HWE test were based on Monte Carlo permutations of alleles. The Bayesian assignment test was computed with the software STRUCTURE 2. 3. 4. Burn-in and run lengths of Markov chains were 10,000 and 100,000, respectively, and five independent runs for each K (for K = 1-20) were run. The ancestry model selected was the admixture type. Clusters were estimated as suggested by Evanno  [6] , using the DK method.
These cases referred to different  epidemiological scenarios in which infected dogs, foxes or wild felids' natural prey were pinpointed as the likely source of infection (Table  1 ). Recently, nuclear molecular markers such as microsatellites have been proven valuable for investigating population genetic differences and putative transmission pathways within S. scabiei  [21, 23, 31, 33]. To the authors' knowledge, this is the first study to apply such tools to domestic/wild felid-derived mites, indicating, on a molecular basis, what is historically reported, namely that a var. felis of S. scabiei may not exist. This conclusion needs confirmation by further molecular analyses based, in parallel, on microsatellite markers and innovative genomic techniques  [9, 18, 22, 46]. A larger dataset of mites from diversified geographical areas and carnivore communities is similarly recommended.
Data collection Data on horse age, breed, sex, use, living environment and region, and clinical signs, and data on sample type (TW vs BAL), time of sampling and shipping temperature were recovered from preformatted record sheets filled in by veterinarians. By default, the sheet recorded four age classes (≤6 months,]6-18 months],]18 months-5 years [, ≥5 years), three sex classes (male, gelding, or female), three living-environment classes (stable, outdoor, or mixed), and two sample shippingtemperature classes (ambient or refrigerated). The clinical signs recorded were spontaneous cough, coughing during TW, epistaxis, tracheal blood, tracheal mucus, dyspnoea, abnormal breath sounds, hyperthermia, and nasal discharge. Data regarding the detection of microorganisms other than Mycoplasma spp. and cytological examinations was recovered from the LAB ÉO results database, which is filled in regularly as part of the diagnostic workflow in response to veterinarian requests for analyses on respiratory samples.
Data analysis The raw collected data was further formatted for purposes of analysis. Sex, age, and breed were reassembled into two categories each (male and female, age <5 years and ≥5 years, racehorses and others), as previously described  (Martineau et al. , 2023). Use of the horses was categorized into two modalities: athletes (e. g. for racing, dressage, training) and non-athletes (e. g. retired, club riding, breeding). The sampling period was split into two semesters. Shipping time of samples was stratified into four classes: [6-12 h],]12-24 h],]24-48 h], and > 48 h. A cumulative clinical score was calculated for sampled horses with details on nasal discharge, cough, dyspnoea and hyperthermia, which are described as significant clinical signs for respiratory disorders  (Couetil et al.
The fact that prevalence of M. equirhinis remained stable across clinical scores points strongly to a non-pathogenic bacteria, in comparison to other bacterial species with a known clinical role as opportunistic or primary pathogens, i. e. S. zoo and R. equi  (Rodríguez-Lázaro et al. , 2006; Waller, 2014). However, this result contradicts a previous study that hypothesised, based on of a limited number of observations, that M. equirhinis may be implicated in coughs in thoroughbred horses  (Uchida-Fujii et al. , 2021). The main limitation of our approach was that we did not design a specific study but instead benefited from available diagnostic laboratory data. First, we did not always have an exhaustive record of clinical signs per horse, which decreased the size of the 'usable' study population (from 1948 to 1245 clinical specimens), although the population was still very robust given the large size of our original database.
Histological analyses of intestinal and colon tissues Colon, ileum, jejunum, and duodenum samples were fixed in 10 % buffered formalin for 24 h, processed, and embedded in paraffin wax by an automatic sample preparation system (LOGOS One, Milestone Medical, Sorisole, Italy). Serial histological sections of 4 μm thickness were cut, deparaffinized, rehydrated, and stained with May Grünwald-Giemsa (MGG) for morphometric analysis under a light microscope (DM5500B; Leica Microsystems, Nanterre, France). For steady-state experiments, measurements of duodenal, jejunal, and ileal villus height, crypt depth, and colonic mucosal surface area were performed using ImageJ software. At least 50 well-oriented mucosa, villi, and crypts were measured in at least 5 individual mice from each group of the study.
Analysis of sequencing data Bioinformatic analyses were performed using the QIIME2 pipeline (version 2020. 2)  (Bolyen et al. , 2019). The Divisive Amplicon Denoising Algorithm (DADA-2) plug-in in QIIME2 was used to filter, dereplicate, identify chimeric sequences, and merge paired-end reads to obtain a set of amplicon sequence variants (ASVs) for each sample  (Callahan et al. , 2016). Then representative sequences were selected for each ASV. The classify-sklearn plug-in in QIIME2, with the Silva database (version 132), was applied to assign a taxonomic annotation to each representative ASV sequence. In the next step, ASVs identified as eukaryotic contamination (none), and external contamination identified with the decontam package (57 ASVs; 3745 reads), were filtered out  (Davis et al. , 2018).
Diversity metrics (α and β diversity) were obtained with the QIIME2 core-metrics-phylogenetic plug-in, with p-sampling depth parameter fixed to 14,616 reads which corresponded to the total frequency that each sample should be rarefied to prior to computing diversity metrics. This sampling depth allowed retention of >70 % of reads and only two samples were discarded. Tests for differential relative abundance were performed with ANCOM and corncob at the order, family, and genus levels  (Mandal et al. , 2015; Martin et al. , 2020). To retrieve functional profiles for the ASV data, sequences and abundance tables were used as inputs for Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) software analysis  (Douglas et al. , 2020). The differentially abundant Metacyc pathways were identified using ALDEx2  (Fernandes et al.
Statistical analyses Data are presented as the mean ± standard error of the mean. Comparisons between different treatment groups were performed using the nonparametric Mann-Whitney U test. Spearman's correlation and other statistical analyses were performed using Prism 8 software (GraphPad, San Diego, CA, USA). Sequencing data was analyzed using the Wilcoxon Rank Sum test, with Benjamini-Hochberg correction for multiple testing. Statistical significance was defined as p < 0. 05.
Functional gene analysis The bioinformatic software package PICRUSt2 was used to predict the MetaCyc pathways of microbiota and the significantly changed MetaCyc pathways are shown in Fig. 7A. Six pathways were enriched in the control CRC group compared with the 100 μg DON group, including the superpathway of methanogenesis from acetate (METH-ACETATE-PWY; effect size -1. 8; p = 0. 02) and two pathways related to toluene degradation (PWY-5182 and PWY-5180; effect size -1. 1; p = 0. 01 for both). Twelve Metacyc pathways were identified as enriched in DONexposed mice. Among them, three were related to fucose and rhamnose degradation (FUC-RHAMCAT-PWY, RHAMCAT-PWY, and FUCCAT-PWY; effect sizes 3, 2. 6, and 2. 3 respectively; p = 0. 001). The pathway with highest enrichment, the superpathway of fucose and rhamnose degradation enzymes (FUC-RHAMCAT-PWH), was three-fold more abundant in the 100 μg DON group compared with the control CRC group (p < 0.
Authors' contribution MBM and MD performed the study conceptualization. Fucose and rhamnose measurements by GC/MS were performed by KL. Fucosidase activity assay was performed by AS and M S. D. SC, DB and DH performed microbiota analyses. Other experiments, data collection, and analyses were performed by MD, CW, MBM, and CV. MD developed endoscopy methodology. CV, LD, and DL contributed to supervision and funding acquisition. The draft of the manuscript was written by MBM. All authors read and approved the final manuscript.
Abattoir survey: F. hepatica determination in blood, stool, bile and condemned livers from slaughtered cattle The surveys consisted in four weekly visits to the abattoir of Ponte Leccia between July and August 2020, being at that time the major cattle abattoir in all of Corsica. Whenever possible, samples of blood, stool and bile were collected from slaughtered animals. In addition, we were allowed to inspect (although we were not allowed to completely and thoroughly cut and dissect the livers) only the condemned livers with traces of distomatosis (infected by the liver flukes F. hepatica or Dicrocoelium dendriticum) observed in the abattoir sanitary inspection. All collected samples were tagged with their bovine host ID in order to retrieve data at the host individual level (code of each animal, age, weight, race, name of the farmer and the municipality).
Data analyses. Overall and per-age interval (>1 year-old and < 1 year-old), rates of infection with 95% confidence intervals derived from the application of each method to the different samples were estimated as the percentage of positive samples. Test of proportions were used to compare infection rates between methods in Statistica v. 12 (StatSoft. Inc. , Tulsa, OK, USA 2014). Positive (by either previous serological and/or abattoir survey -see sections 2. 1. 1 and 2. 1. 2) and negative municipalities were mapped using MapInfo v. 15 (Pitney Bowes Software Inc. , New York, USA, 2015) for a spatial account of F. hepatica infection.
(2011) ) using the primers described in  Folmer et al. (1994). Amplicons were sent and sequenced at the GenoScreen platform (Lille, France) using the LCO1490 primer. The resulting sequences were analysed using Mega v. 5. 0  (Tamura et al. , 2011)  and were further deposited in GenBank (accession numbers: ON792318, ON792319). Briefly, multiple alignments were performed with the Clustal algorithm against sequences of P. peregra, A. balthica, A. auricularia, A. lagotis revised and annotated by  Aksenova et al. (2018) , as it constitutes the largest and most inclusive study concerning Amphipepleinae. Sequences were manually trimmed and used to construct a Neighbour Joining tree with the Jukes-Cantor model and 1000 of bootstrap replicates. Details of the PCR assays are listed in Table  1.
hepatica control in addition to the band corresponding to the internal control. In addition, as the presence of other (unidentified) trematodes besides Fasciola was observed when dissecting lymnaeid snails, all collected individuals from the given site were further screened by PCR using a set of primers designed to amplify around 220 bp of a specific region within the 16S rDNA gene of Trematoda  (Douchet et al. , 2022). Further details on the PCR conditions are listed in Table  1. DNA from adult F. hepatica was used as positive control, whereas two negative controls were also included: one consisting of DNA from non-infected lab-reared snail, and another where water instead of DNA template was added to the mix. Obtained amplicons were further sequenced by the GenoScreen platform (Lille, France) using the forward primer, blasted-x against the NCBI nonredundant databases to search for sequence identity and deposited in GenBank (accession numbers: ON814774-ON814777).
Conclusions In the present study, we show that animal fasciolosis in Corsica is characterised by a significant circulation and a favourable epidemiological scenario for transmission to occur. Infection by F. hepatica was patent in livestock and DNA of the parasite was amplified in lymnaeid snails. The exploration of husbandry practices points to a low perception of F. hepatica and its impact among farmers and to risky management strategies. Further studies should be devoted to providing in-depth characterization of the occurrence of fasciolosis in Corsica and to identification of the different hotspots for transmission throughout Corsica. Supplementary data to this article can be found online at https://doi. org/10. 1016/j. fawpar. 2023. e00188.
Acknowledgements We are most thankful to all the staff of the abattoir of  Ponte Leccia , particularly to  Francois Guerrini , for their help and assistance during sampling. We are also grateful to the two laboratories of the Corsican regional authority (especially Mich`ele Riera and  Jean-Marc Santini ) for treating the blood samples. Last but not least, we would like to acknowledge the valuable contribution of all the Corsican farmers who agreed to take part in the study. The present study was partially funded as a postdoctoral fellowship of the  University of Corsica Pasquale Paoli  and the  Culletivitta di Corsica  granted to AA. This research is part of the  GERHYCO  interdisciplinary project dedicated to water management, ecology and hydro-ecosystem services in an insular context, was financially supported by the  Culletivitta di Corsica  and is set within the framework of the  "Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ).
, 1984; Reijnders and Verschuuren, 1987; Spencer et al. , 2002; Stewart et al. , 1970). In general, subacute studies in animals did not indicate any neurological effects. As a consequence, the interest in GEs neurotoxicity has dropped. However, neurological effects have not been systematically examined for glycol ethers. Out of >80 registered GEs, only TEGME (CAS:112-35-6) has undergone an OECD TG (TG408) test, and only motor activity and histopathology were included as endpoints. For few GEs where neurotoxicity was reported in the registration dossier, data were very limited. Therefore, considering that GEs are widely used organic solvents, and that for many GEs there is no toxicological information, GEs' possible neurotoxicity cannot be ruled out and needs to be explored. Furthermore, neurotoxic effects of chronic exposure could be of further concern as the link between chronic exposure to organic solvent mixtures, including GEs, and encephalopathy has been well-documented  (Keski-Säntti et al.
Over the past decade there has been a burst of publications on IVIVE  (Loizou et al. , 2021; Noorlander et al. , 2022; Punt et al. , 2019; Shi et al. , 2020; Wang et al. , 2017) , and IVIVE is beginning to gain acceptance in the risk assessment of chemicals domain  (Chang et al. , 2022). Our aim was to predict an exposure concentration for PGME that would not induce neurotoxicity in humans by integrating repeateddose in vitro data with in silico data. Our first objective was to obtain concentration-response curves using AGGR to determine if PGME could potentially produce adverse effects in the brain, and at what concentrations. In vitro neurotoxicity testing was also done on EGME, as a positive control, and on PGBE, a widely used and supposedly non-neurotoxic GE (ECETOC, 2005b), for comparison.
45), and K b was the red blood cell concentration to the unbound plasma concentration ratio. K b was predicted using the linear regression formula between log ((1f u,p )/ f u,p ) and logK b for neutral compounds, with intercept and slope values of 0. 249 and 0. 683, respectively. Model parameter values were from  Hopf et al. (2012). All additional physico-chemical and physiological parameters used in our model are listed in Table  2. The urinary excretion-related parameters and metabolismrelated parameters were adjusted for aging participants (>58 years), following the approach of  Hopf et al. (2012)  and  Tomicic and Droz (2009). The more time-intensive rich datasets among the in vivo datasets available in the literature were used for parameter estimation. Michaelis-Menten maximum rates and constants for phase I metabolism were fitted to blood data from  Jones et al.
(1997)  and  Devanthéry et al. (2002). Phase II metabolism parameters (V max2 , K m2 ) and PGME renal clearance were fitted to  Tomicic and Vernez (2014)  and  Devanthéry et al. (2002)  urine data. Key information on the in vivo datasets used for model calibration is summarized in Table  3. GetData Graph Digitizer (v. 2. 26. 0. 20) was used to extract pharmacokinetic data from figures in published studies. The model was run in Berkley Madonna software, version 8. 3. 18, University of California, USA. Parameter fitting was done with Berkeley Madonna curve-fitting tool.
Model evaluation The model was evaluated using the pharmacokinetic datasets that were not applied in the model calibration. Key information on each of the pharmacokinetic datasets used for model evaluation is summarized in Table  3. Model performance was assessed using the WHO criteria that considers a model acceptable if its predicted values generally match with the experimental kinetic profiles within a two-fold difference  (WHO and IPCS, 2010). Goodness-of-fit between observed and simulated concentration values was assessed by linear regression, and was considered acceptable when the coefficient of determination (R 2 ) was equal or higher than 0. 75  (Yuan et al. , 2022). Goodness-of-fit was also assessed by mean absolute percentage error (MAPE) based on these criteria: acceptable if MAPE <50 %, good if MAPE <20 % and > 10 %, excellent if MAPE <10 %  (Lin et al.
Model calibration The TK model was used to simulate PGME concentrations in blood and urine under different exposure durations and concentrations according to different datasets listed in Table  3. V max1 , K M1 , CL ur, V max2 , and K M2 values were fitted to the datasets selected for calibration. The values of fitted parameters are listed in Table  2. Model predictions for the different datasets are shown in Fig. 4. Overall, the model adequately simulated human pharmacokinetic data of different studies with different exposure doses, exposure duration, and age of the participants, with a coefficient of determination (R 2 ) of 0. 92 and a MAPE of 29 %.
AUC urtot was also highly sensitive to phase II metabolism parameters (V max2 , K M2 ). Among the parameters with high impact, those with medium uncertainty were V max1 , K M1. The values for these parameters had been determined in rats, and scaled to humans by  Corley et al. (2005). Blood-toplasma ratio (BP) was estimated to have high uncertainty because it was calculated using a predictive approach that had been verified for drug data in humans. All the other parameters with high impact (QCC rest , BW, K ur , R pulm , CL ur ) had low uncertainty.
TK model Our TK model could successfully predict PGME blood and urine concentrations from different studies with different participants' age and different exposure concentrations and durations. The model was validated with several different toxicokinetic datasets, showing good results. The uncertainty in brain:blood partition coefficient and in blood-to-plasma ratio suggest that there is some uncertainty in model predictions for brain concentrations. Our TK model predictions of human brain concentrations of unbound PGME upon occupational exposure to PGME vapors at the OEL for one week were 31 times lower than the brain NOAEC for neurotoxicity derived from the AGGR tests (10. 2 mM or 1000 ppm). This suggests that occupational exposure to PGME vapors at the OEL for one week may activate the CNS immune response but may not produce any direct adverse effect on brain cells.
The salary of Marjolein Heymans and Sara Wellens was funded by the European Commission within the framework of Marie Sklodowska-Curie Innovative Training Networks: BtRAIN (H2020-MSCA-ITN-2015, no 675619) and in3 (H2020-MSCA-ITN-2016, no 721975). CRediT authorship contribution statementElena Reale: Code, validation, formal analysis, Writing -Original draft; Jenny Sandström: Investigation, writingreview & editing; Maxime Culot: Methodology, investigation, writingreview & editing; Julie Hechon: Investigation, writingreview & editing; Sara Wellens: Investigation, writingreview & editing; Marjolein Heymans: Investigation, writingreview & editing; Florianne Tschudi-Monnet: Resources, supervision, writingreview & editing; David Vernez: Conceptualization, methodology; Nancy B. Hopf: Conceptualisation, methodology, formal analysis, writingreview & editing.