dataesr/scientific-paragraphs-categorization huggingface dataset:
On the contrary, the threshold for FB application is not identified as a key parameter influencing AllLameTime, for the best class of MLD (between 4 and 8 weeks) because classes for time spent for FB application are always 1 or 2. On the contrary, AllLameTime is sensitive to the threshold for FB application (change of class in a given column), especially for the MLD class between 8 and 11 weeks, where FB application classes range from 2 to 9. To sum up, an interaction-like behavior is described for the variable threshold for FB application with the variables MLD and AllLameTime. A color code was applied to make the interpretation easier. Less desirable scenario More desirable scenario. More desirable scenario. Animals 2023,  13, 1988  9 of 20 The Tornado diagram describing GM (highest at top) for 24 combinations of FB and LD (Figure  3 ), in the context where the panel of obtained GM is the greatest (i.
This effect, however, was not observed in germ-free infected chickens when compared to germ-free non-infected chickens (Figure  6 ). Although there was no significant difference between conventional and germ-free infected chickens due to high data dispersion, these changes in tight junction protein gene expression suggest that the intestinal barrier integrity may be compromised. A B FIGURE 3 Increase in the percentage of CD45 positive cells in the caecal tissues of infected chickens both germ-free and conventional animals compared to non-infected chickens. Caeca were collected at 7 days pi. Cells were isolated, stained with anti-CD45 antibody, assessed for cell viability (ZombieAqua ™ ) and analysed by flow cytometry (A) as described in the materials and methods section. Data are expressed as the median (n ≥ 5/ group for at least 2 experiments).
Statistical analysis was performed using a Kruskall-Wallis test with a Dunn's multiple comparisons post-test. ****P < 0. 0001; ns: non significant. (B) Representative image of CD45 + cells recruitment in E. tenella infected conventional and germ-free chickens compared to non-infected chickens (SP8 confocal laser-scanning Leica microscope; magnification 63X; image: 2048×2048 pixels, Z stacks and mosaic merge; Leica Application Suite X software).
bacteriological analysis was performed for the presence of APEC. After E. tenella infection, there was a conspicuous increase in APEC presence in the spleens of conventionally infected chickens (4 out of 6) compared to conventionally non-infected ones (2 out of 8), pointing to a potential bacterial translocation resulting from the infection (Figure  7B ). These data support the notion that epithelial damage in conventionally infected chickens presents an opportunity for bacterial translocation, ultimately leading to an exaggerated inflammatory response, as opposed to germ-free ones (Figure  8 ).
tenella is dependent on the microbiota. Gene expression was assessed by qPCR. CLDN1 gene expression was increased in conventional chickens at 7 days pi compared to germ-free infected chickens. CLDN2 gene expression was increased while OCLN gene expression was decreased in conventional chickens at 7 days pi compared to conventional non-infected chickens. However, these gene expressions were unchanged in conventional infected chickens compared to germ-free infected chickens due to high variance. Data are expressed as the median (n ≥ 10/group). Statistical analysis was performed using a Kruskall-Wallis test with a Dunn's multiple comparisons post-test. *P < 0. 05 **P < 0. 01 *** P < 0. 001 ****P < 0. 0001; ns: non significant.
A B FIGURE 7 E. tenella infection leads to bacterial translocation. (A) Commensal enterobacteria were followed in the spleen of infected and non-infected chickens at 7 days pi (n ≥ 7/group). (B) APEC were administered to the animals at 1 day old. Chickens were infected with E. tenella at 14 days old. Translocation of bacteria in the spleen was performed at day 7 pi (n = 10). Data are expressed as the number of spleens positive or negative for bacteria. as we observed no differences in the parameters measured in noninfected germ-free and conventional chickens, it is unlikely to observe a different response in non-infected germ-free chickens receiving a microbiota. Previous research revealed that the microbiota plays a role in parasite development, as evidenced by lower parasite loads in germ-free chickens compared to conventional chickens, despite similar invasion rates in host epithelial cells  (Gaboriaud et al.
Immunofluorescence staining Caecal tissues were directly frozen in OCT. Tissues were sliced and fixed in ice cold 50% ethanol, 50% acetone. Leukocytes were stained for 1 h at 37°C using anti-anti-CD45 Alexa Fluor 488 (SouthernBiotech). Cell nuclei were counterstained with DAPI (ThermoFisher Scientific, Waltham, MA, USA) for 5 min at room temperature and mounted using Permafluor ™ (Epredia, Kalamazoo, MI, USA). Stack of tiled images were acquired with SP8 confocal laser-scanning microscope (HC PL APO CS2 63x/1. 20 WATER; 2048 × 2048 pixels; Leica Application Suite X software (3. 7. 4. 23463 version) and a mosaic merge was performed to obtain wide images of the caecal tissue slices.
Gene Accession number Forward sequence (5' to 3') Reverse sequence (5' to 3') Sequences of primers for genes coding for protein junctions are described in  (Gloux et al. , 2019). Tomal et al. 10. 3389/fcimb. 2023. 1250080 Frontiers in Cellular and Infection Microbiology frontiersin. org Rad). Inflammatory, anti-inflammatory and protein junction gene expressions were analysed using Gallus gallus housekeeping genes G10 and GAPDH (Supplementary data: Table  1 , Eurogentec, Seraing, Belgium). The protocol used for qPCR was: 95°C for 5 min and 40 cycles at 95°C for 10 s and 60°C for 15 s followed by 60°C for 5 s. Melting curves were performed at 60°C for 5 s followed by gradual heating (0. 5°C /s) to 95°C. qPCRs were performed in duplicate for each experiment. Gene expression was normalised to Ct values obtained for Gallus gallus G10 and GAPDH using the formula: 2 -(Ct Gallus gallus specific gene -Ct mean of Gallus gallus housekeeping genes).
Gene expression values are expressed as the median. To obtain a global view of inflammatory gene expression, a heatmap analysis was performed using the R package Pheatmap  (Kolde, 2019). Median of gene expression values were normalized with a Z-score approach and scaled by genes. Hierarchical clustering analysis was also performed according to Z-score values. To investigate if gene expression depends on the animal group, a Principal Component Analysis (PCA) was performed. 2 -dCT values were transformed in log10, quality of variables based on cos 2 was established and PCA was generated using the R package Factominer  (Lêet al, 2008).
Statistical analysis For all data except PCA, statistical analysis was performed using GraphPad Prism ® 9 (GraphPad Software Inc. , La Jolla, CA, USA). For two groups, a non-parametric Mann-Whitney test was used. For more than two groups, a Kruskall-Wallis test with a Dunn's multiple comparisons post-test was performed. For PCA, statistical differences between groups were performed using a pairwise PERMANOVA (Permutational multivariate analysis of variance) test  (Anderson, 2001)  with 999 permutations via Vegan  (Jari et al, 2022)  and pairwiseAdonis R packages  (Martinez Arbizu, 2017). All packages were performed through Rstudio software version 4. 1. 0 (RStudio: Integrated Development for R. RStudio, PBC, Boston).
Acknowledgments We are grateful to all the employees of the facility for  Infection of Farm, Model and Wildlife Animals (PFIE) , for the animal care and skills with the animal experiments ( T. Chaumeil ,  M. Renouard ,  S. Lavillatte ,  P. Cousin ,  A. Faurie ,  O. Dubes ,  M. Girault ). The authors are thankful to the society  Boyéaccouvage (La Boissière en Gatine, France ) for providing fertilized eggs.
Selection and use of datasets The analyses presented herein were run on horse-based strongyle community data on 441 horses that were obtained from the original protocols of four previously published studies  [4, 7, 10, 18]. The selection of studies was as follows. In the frame of a wider meta-analysis  [22] , we identified 34 papers presenting cyathostomin abundance or prevalence data. To be able to contact the authors and access individual observations and to sample sufficient diversity from the data, we then restricted the datasets to papers published no more than 25 years ago and studies comprising at least 40 horses. This approach retained studies performed in Europe (Table  1 ), including necropsies performed in Normandy  [18]  and worm collections after the deworming of horses in Poland  [7]  and Ukraine  [10].
Another dataset remained inaccessible  [20]. Our first step was to identify papers associating FEC data and strongyle species counts  [7, 10]  in order to study the relationship between FEC and species diversity (Table  1 ). Second, the unique hierarchical nature of the Ukrainian dataset  [10]  was leveraged to apply a diversity partitioning approach across multiple scales, including horse age groups. (Table  1 ). Lastly, co-occurrence between strongyle species was investigated from the necropsy data collected in Normandy. To cross-validate the co-occurrence pattern found in this dataset, we looked for independent data from a geographically disconnected area. The Australian dataset by Bucknell et al. [4]  was identified from the aforementioned meta-analysis, and the data were obtained from Ian Beveridge (personal communication).
Relationship between strongyle community diversity and corresponding horse FEC To evaluate how strongyle community diversity could relate to measured FEC in their host, we first estimated Spearman's correlations between FEC and species richness, or between FEC and the Gini-Simpson index. This analysis was implemented on the Ukrainian and Polish horses independently but was not performed on the necropsy data as FEC measures were not available for the latter data. To further establish how horse FEC may contribute to species richness and the Gini-Simpson index, both variables were regressed independently upon some effects of interest using the following linear mixed model with the lme4 package  [28] : Where Y i,j,k,l refers to the strongyle community species richness or Gini-Simpson index for horse iof age age i (binned in nine groups), log(FEC + 50) i is the horse transformed FEC (offset by 50 eggs/g, i.
e. the minimal sensitivity of the measure), Sex i,j is the horse sex (female, gelding or stallion) and Farm i,k stands for horse i 's operation (eight levels) and was fitted as a random effect to account for the various drenching strategies. This Y ijk ∼ log(FEC + 50) i + Age i + Sex i,j + Farm i,k , analysis was restricted to the operations with a minimum of 10 horses from 156 and 32 horses from the Ukrainian and Polish datasets, respectively. The horse country of origin was not considered as it was partially confounded with horse age and accounted for by the farm effect. Difference in average horse ages between both populations was tested with a Student's t-test. The model conditional R 2 (accounting for the variance explained by both the random and fixed effects) was estimated with the 'r.
Impact of horse age on strongyle community diversity To investigate how horse age was impacting on strongyle community structure, we re-analysed horse-wise community data gathered in Ukraine from 197 horses across 17 operations  [10]. We restricted our analysis to eight farms with at least five age groups available, involving 112 individual horses (age range: 1-22 years). Due to the paucity of data for some discrete age values, 8-and 9-year-old horses (n = 4 and n = 3 individuals, respectively) were binned together with the category of 7-yearold horses (n = 4 individuals), and horses aged > 10 years (16 horses between 11 and 22 years of age) were defined as a single age group. In all other categories, age groups corresponded to horse age. Ultimately, nine age groups were available for analysis, with at least 12 observations in each age group.
Rare strongyle species (< 5% prevalence across the 112 horses; n = 2 species) were not considered further, leaving 31 strongyle species for inclusion in the analyses. Because of this data curation, it was possible that past conclusions made on this dataset regarding the relationship between strongyle species richness and horse age  [10]  would differ from those in the present study. To ensure that data curation did not modify past conclusions, the species richness of horse i from Farm i,j was modelled using the same analysis of variance as applied in the previous study  [10] : The relative contributions of the within-host diversity (ɑ-diversity) and the differences among communities (β-diversity) were estimated using an additive partitioning approach. In this framework, the γ-diversity (total diversity found across 8 farms in Ukraine) is expressed as the respective contributions of ɑand β-diversity terms  [23].
This framework makes the comparison of ɑand β-diversity terms possible across the considered scales, including farms (which account for the anthelmintic management type), age group and individuals  [24]. This analysis was implemented with the 'adipart()' function of the vegan package, as described in  [30]. Parameter significance was determined following 1000 permutations  [23]. This procedure shuffles species across samples while keeping their respective abundance and aggregation constant to determine the null distribution of ɑand β-diversity estimates at each hierarchical level. Females accounted for 75% of the dataset, and males (14 stallions and 13 geldings) were unequally represented across operations (6 in 2 farms, < 3 otherwise). To ensure that these results were not affected by the sex of the horse, the additive partitioning approach was applied twice: (i) either on the 112 horse datasets without considering their sex; or (ii) restricted to the 85 females.
Because of the hierarchical nature of the analysis, ages were binned into discrete categories. To ensure that this transformation did not affect the conclusions drawn, age was fitted as a continuous predictor or as a categorical variable onto a non-metric multidimensional scaling (NMDS) ordination results (Bray-Curtis distance) applied to species counts using the 'envfit()' function of the vegan package  [27]. Third, we searched for indicator species that would be more often encountered in a given age class. This analysis was achieved with the 'multipatt()' function of the indicspecies R package (v. 1. 7. 9). We considered the indicator value (the relative mean species abundance in a group weighed by its relative frequency of occurrence in that group)  [31]  and the Pearson's phi coefficient of association  [32] , whose significance was determined with 1000 permutations.
Analysis at the anatomical niche level: species co-occurrences from necropsy data To study the interactions between strongyle species within their hosts, we re-analysed individual necropsy report data collected in France  [18]. Abundances of 20 strongyle species and other non-strongyle species (Anoplocephala sp. , Oxyuris sp. ) were quantified within the caecum, ventral colon and dorsal-colon of 36 horses for the French dataset. These horses had not received log (Species richness) i ∼ log(Faecal Egg Count) i + Age i + Farm i,j anthelmintic drug within the last 2 months prior to necropsy despite being regularly treated  [18] , had their age registered and were infected by at least one parasite species. To avoid spurious signals associated with rare observations, we focused on the 15 species reaching at least 10% prevalence.
To support observations made in the French cohort, an independent dataset from Australia was considered. This dataset considered 150 horses and 29 species counted in the caecum, small colon or large colon. The data were restricted to 17 strongyle species with > 5% prevalence and 145 horses with at least one strongyle species identified. Other relevant metadata associated with each horse were not available (I Beveridge, personal communication). To capture the strongyle community assembly pattern across anatomical niches, we applied the hierarchical Bayesian joint species distribution model  [33, 34]  implemented in the HMSC R package v. 3. 0-10 [34]. Joint species distribution models regress the whole vector of species abundances upon environmental predictors, thereby simultaneously accounting for the dependencies of species occurrences in a given sampling unit and for the effect of environmental covariates on these occurrences  [35, 36].
This model offers an integrated framework to decompose species interactions into an environmentally dependent (raw co-occurrences) and residual co-occurrences  [33, 37]. Raw co-occurrences would be compatible with shared response of two species to an environmental feature  [38]. On the contrary, residual co-occurrences may arise from ecological interactions (facilitation or competition) or because of uncaptured variation  [38]. We used the same approach as described elsewhere  [39] , fitting a first model accounting for the overall worm burden and a second full model accounting for the additional effects of the gut compartment (caecum, ventral colon and dorsal colon in the French dataset or caecum, small colon and large colon for the Australian dataset) and horse age for the French dataset as environmental covariates affecting species occurrences.
Contribution of horse age to strongyle community dissimilarity across Ukraine To further establish the relative importance of horse age to the remodelling of strongyle communities, we applied a diversity partitioning framework to the Ukrainian dataset  [10]. Because of the hierarchical nature of the analysis, nine age groups were considered (as described the Methods section), and data curation was applied to ensure a sufficient number of observations were available for each level of the factors of interest. We first checked that the previously reported relationship between strongyle species richness and horse age still held (Additional file 2: Table  S2 ). Worm count was indeed inversely associated with horse age, i. e. worm count was higher in younger horses (F (1, 102) = 10. 2, P = 0. 002; Additional file 2: Table  S2 ).
Significant variation was also found across the farms retained in the analysis (F (7, 102) = 8. 27, P < 0. 0001). Therefore, the considered subset was taken to be a good representation of the original dataset  [10]. Second, the differential effect of using age as a categorical predictor or a continuous predictor was tested. Following an NMDS ordination (Additional file 3: Figure  S1 ), horse age had explanatory powers of 13. 37% (P = 0. 01) and 9. 52% (P = 0. 004) when considered as a categorical or a continuous predictor, respectively. This result suggests that horse age was a significant descriptor of the variation between communities irrespective of the applied binning. Partitioning of overall γ-diversity across Ukraine operations revealed an outstanding contribution of within-horse α-diversity (45.
Co-occurrence of species within different gut compartments After establishing the relative contribution of horse age to the dissimilarity between strongyle communities, we undertook an analysis to model species interactions in the various gut compartments using data collected in France (Fig. 4 ). Observed patterns in this dataset were compared with co-occurrences from an Australian cohort of 141 horses (Additional file 4: Figure  S2 ). The predictive power of the modelling approach was high, with a mean AUC of 0. 90 and R 2 of 0. 79 for models based on presence-absence or positive abundance data, respectively (Additional file 5: Table  S3 ; Additional file 6: Figure  S3 ; Additional file 7: Figure  S4 , Additional file 8: Figure  S5 , Additional file 9: Figure  S6 ). The gut compartment accounted for at least one quarter of the observed variation in species co-occurrences (Table  2 ), while inter-horse variation varied between 11.
3 and 25% (Table  2 ). This host-related variance was higher with species occurrences than with their abundances, suggesting that the host acts as a filter for species establishment but has a more limited impact on their abundances. In line with the aforementioned effect of age on the dissimilarity between communities, horse age accounted for > 10% of species variance in the French cohort (Table  2 ). The higher contribution of the worm burden in that cohort may reflect the fewer number of worms found in the French horses (75% quantile = 2040 worms) than in the Australian horses (75% quantile = 8266 worms). Raw and residual co-occurrences only supported positive interactions between the considered strongyle species in the French and Australian populations (Fig. 4 ; Additional file 4: Figure  S2 ; Additional file 10: Dataset 1), with the exception of a negative interaction (Spearman's correlation coefficient rs = -0.
49; Additional file 10: Dataset 1) between the occurrences Coronocyclus coronatus and Cylicostephanus longibursatus in the Australian populations (Additional file 4: Figure  S2 ). While raw co-occurrence coefficients were high across models and populations, estimates based on species presence/absence in the Australian population were milder between members of the Cylicostephanus genus (C. calicatus, C. goldi and C. longibursatus) and other species (Additional file 4: Figure  S2 ). The correction for environmental covariates however erased about half of observed co-occurrences in both abundance (11 out of 26 in the French data; 35 out of 81 in the Australian data) and presence-absence (21 out of 42 in the French data; 27 out of 98 in the Australian data) models (Fig. 4 ; Additional file 4: Figure  S2 ).
Acknowledgements We are greatly indebted to  Pr. Ian Beveridge  who kindly provided the dataset constructed by Bucknell and collaborators. We are grateful to the  GenoToul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul : https:// doi. org/ 10. 15454/1. 55723 69328 96116 7E12 ) for providing computing resources for some of the analyses.
While in uniform landscapes, the seek for food time corresponds to the time that animals use to move through the feeding landscape as they eat. The seek for food time is zero when the FAD is high (e. g. housed cows with no competition at the feed bunk). The current framework does not account for competition at the feed bunk in housed cattle (which would imply an increase in seek for food time with increasing competition). Finally, the time spent ruminating is an important part of feeding behaviour. However, as discussed later, we assume that the rumination time is defined relative to the level of intake. This assumption seems reasonable given the dataset we compiled (see below).
These distributions describe the range of plausible parameter values and the consequence of these values on the fit of the model to the validation data. We used Geweke's value  (Geweke, 1992)  to check both convergence of the BI procedure, and the sensitivity of the error to the parameter values (i. e. their variability across the Markov chains). We assume that the framework is more sensitive to those parameters with higher Geweke's value. During BI, the DRAM algorithm uses Euclidean distances between observed and predicted data (DMI, ER and ET) for parameter generation, by rejecting those parameters that increase said distances. To compute these distances, a validation database was built using data from 21 papers when adult Holstein cows were used (Supplementary Materials S1-S3). The parameters related to MST, proportion of the mature ER, and the proportion of the TCT used for SDA (parameters in Eq.