dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016)  implemented in R (R Core Team, 2016) using the following parameters: maxN = 0, maxEE = 2, truncQ = 2, trimleft = 17, rm. phix = TRUE, and pool = "pseudo". Chimera were removed with the consensus method to obtain the final amplicon sequence variants (ASVs) abundance table. This step was followed by taxonomic annotation using the assign Taxonomy function of DADA2 with the Silva Dataset v132  (Quast et al. , 2013). The obtained file was rarefied to 10,000 counts per sample with the "Phyloseq" R package (McMurdie and Holmes, 2013) (Supplementary Figure  S1 ). A total of 14,366 ASVs and 231 genera were finally kept in the abundance tables for 1,564 pigs, including 812 pigs fed the CO diet and 752 pigs fed the HF diet. To facilitate biological interpretations, all analyses were performed at the genus level.
X is the incidence matrix relating observations to fixed effects. Z is the incidence matrix of the additive genetic effects. u ~ N(0, A σ² u ) is the vector of additive genetic effects for the considered trait, where A is the pedigree relationship matrix built tracing back five generations of pedigree and σ² u is the additive genetic variance. n ~ N(0, Iσ² n ) is the random effect of the pen nested within batch, applied to all traits. W is an incidence matrix relating performances to the random effect n. Finally, e ~ N(0, Iσ² e ) is the residual random effect. Variance components were estimated by Restricted Maximum Likelihood using the ASREML 4. 0 software  (Gilmour et al. 2009). Due to potential deviations from normality that may lead to underestimated standard errors, an empirical significance heritability threshold was determined using a bootstrap approach.
Variance components estimations for both diets combined. To increase statistical power, heritabilities were estimated by combining data from both diets. The same linear mixed model as model 1 was applied to data from both diets, with the following changes: y = X + Zu + Wn + e (model 2) , where traits and effects were as defined for model 1, except ß, the vector of fixed effects, that contained the fixed effect of the diet in addition to the effects listed for model 1, and n ~ N(0, I σ² n ) that was the random effect of the pen nested within batch within diet. The same bootstrapping approach as for separate analyses was used to estimate empirically the 5% significance threshold under the null hypothesis. Then, keeping only microbiota traits with a heritability significantly different from zero, bivariate analyses were carried out to estimate covariances of microbiota traits with ADG and feed and digestive efficiency traits.
X, Z, and W are the corresponding incidence matrices as defined for the previous models. Finally, e i ~ N(0, IΣ e ) is the residual random effects, with Σ e the residual covariance matrix between traits. Variance components were estimated by Restricted Maximum Likelihood using the ASREML 4. 0 software  (Gilmour et al. 2009). For the genetic correlations between genera abundances and production traits, 68% CI were computed as genetic correlation ± 1. 00 × standard error, to identify the genera that were unlikely to be correlated with the production traits (genetic correlation significantly different from zero) and use this information in the following hierarchical cluster analysis (see next paragraph). The genetic correlations were qualified as low for absolute values between 0. 00 and 0. 20, moderate between 0.
The clustering was carried out with hclust function of the R package "STAT" and represented with as. dendogram  (Bolar, 2019). The dendogram associated with the heatmap was produced with the heatmap. 2 function of the R packages "gplots"  (Warnes et al. , 2020 ). An exact Fisher test was carried out in R (R Core Team, 2016) to identify families overrepresented in the different groups. Finally, bivariate genetic analyses were carried out to estimate genetic correlations between heritable microbiota traits, using the same linear mixed models and the same methodology as previously presented for the univariate analyses.
Variance components of microbiota traits for both diets combined From the previous results, we considered that genetic by diet interactions were limited and microbiota traits could be considered as the same trait in the two diets. Then, heritabilities were estimated for the relative abundances of all genera and diversity indices combining data of both diets to gain statistical power. Resulting heritability estimates, along with their confidence intervals, are presented in Figure  3  and in Supplementary Table  S3. Estimated heritabilities were null to moderate (up to 0. 31 ± 0. 07) and, for most genera, were consistent with those obtained considering the diets separately. Based on the bootstrap approach, all heritabilities higher than 0. 058 were significantly different from zero for a 5% type I error.
Impact of the diet on variance components estimates of microbiota traits Impact of the diet on variance components of the microbiota traits in this study could have been underestimated due to the limited size of the datasets within diet (812 and 752). However, heritability of microbiota traits were low to moderate within diet, and similar to those reported in the literature  (Estelle et al. , 2016; Camarinha-Silva et al. , 2017; Aliakbari et al. , 2021). Heritability estimated for each genus and alpha diversity indices were generally similar between diets. Thus, even though mean abundances were significantly different for 66 out of the 71 genera in this dataset (Supplementary Table  S2 ,  Déru et al. , 2021b) , the proportion of phenotypic variance under genetic control was similar in both diets.
In our study, genetic correlations estimated between microbiota traits across diets were generally high (≥ 0. 70), suggesting that genotype by diet interactions were limited for most of them. Out of the 24 considered genera, only Lachnospiraceae. Genetic correlations between abundances of 60 genera with heritability significantly different from zero, and feed and digestive efficiency traits for data of growing pigs fed with a conventional and high-fiber diet combined 1. Blue, green, and pink colors show limits between groups obtained from the hierarchical analysis of genetic correlations between these genera and production traits. Legend: ADG = average daily gain; DFI = daily feed intake; RFI = residual feed intake; FCR = feed conversion ratio; DC E = digestibility coefficient of energy; DC N = digestibility coefficient of nitrogen; DC OM = digestibility coefficient of organic matter 168% confidence interval was determined by subtracting or adding one standard error to the estimated heritability.
Nevertheless, these comparisons with the literature are complicated by taxonomy nomenclature differences (in recent databases Ruminococcus 1 and Ruminococcus 2 are distinguished), and different experimental conditions (diet, date of feces collection, limited number of data…). Further studies to better understand the metabolic interplay of these microbiota and the host in different dietary conditions would be necessary. However, altogether a diet with increased fiber content seems to have a limited impact on the variance components of the microbiota traits. Consistent conclusions have been drawn for growth rate, feed, and digestive efficiency traits based on this experiment: feeding pigs with the high-fiber diet significantly affects mean performances, but would imply limited genotype by diet interactions that would not affect breeding strategies  (Déru et al.
(2016) , but also with  Le Sciellour et al. (2019). Validation with other studies. So far, only few studies have reported genetic relationships and correlations between microbiota and production traits. In the recently published study of  Aliakbari et al. (2021) , microbiota differences between pig lines divergently selected for RFI were computed. Their dataset was independent from ours (different animals, farms, and diets), but the same molecular procedures and bioinformatic pipelines were used to produce the microbiota data. Among the abundances of six genera most favorably associated with digestive and feed efficiency in our study, five were more abundant in their low RFI line (Candidatus Soleaferrea was too rare to be analyzed), whereas among the abundances of six genera most unfavorably associated with digestive and feed efficiency in our study, five were less abundant in their low RFI line (Alloprevotella, Prevotella 2, Faecalibacterium, Prevotella 9, and Coprococcus 2), and one showed no line difference (Butyricicoccus).
Towards functional hypotheses. The discussion of the role of specific genera and the interpretation of genetic correlations with production traits is generally difficult. For multiple genera, we can find a combination of consistencies and discrepancies between known functions of some microbiota, or previously reported associations with feed efficiency, and the direction of the correlations observed in our dataset. In addition, some of those genera were found associated with efficiency in pigs in previous studies applied to pigs of various ages and feeding conditions, with in some cases opposite associations or no indication of the direction, as already reported in the review of  Gardiner et al. (2020). For instance, some of the top associated genera of the two groups are known to produce short-chain fatty acids (SCFA) from fiber digestion, which was initially claimed to favor efficiency.
Eighty percent of the cost for determining microbiota composition from fecal samples (excluding labor required for fecal collection and material collection) is related to sequencing depth and 20% to microbial DNA extraction. A relatively high sequencing depth (here a minimum of 10,000 sequences per sample) is necessary to accurately quantify genera abundances: in our dataset, 79% of the genera had less than 100 counts on average, 90% had less than 200 counts, which corresponds to only six (CO) and seven (HF) genera with more than 200 counts on average. The 10,000 reads, thus, correspond to a minimum cost of phenotyping for the selection of some genera strongly correlated with digestive efficiency traits, as most are not among the most frequent ones. However, it is possible that variance components of the Shannon index would remain stable with lower sequencing depth, thus reducing sequencing cost, and this option could be considered in the future.
Acknowlegments 1 This study is part of the MicroFeed project, under grant ANR-16-CE20-0003, and the Feed-a-Gene Project, funded from the European Union's H2020 Programme under grant agreement n°633531. The authors would like to acknowledge the breeding companies Axiom and Nucleus for providing animals and co-funding VD´s Ph. D. thesis through France Génétique Porc. Finally, the authors thank the UEPR phenotyping station staff in Le Rheu for animal raising and data recording.
Methodology Data and information were gathered during the third WOAH Animal Welfare Global Forum, organised on this occasion, in collaboration with the Centre of Excellence in Animal Welfare Science and the Swedish Centre for Animal Welfare at the Swedish University of Agricultural Sciences, the latter being a WOAH Collaborating Centre on Animal Welfare. The meeting attendance was by invitation to WOAH Members (National Animal Welfare Focal Points), WOAH Animal Welfare Collaborating Centres and International Organisations with Collaboration Agreements with WOAH. The invitation was sent to 226 potential participants. Background information on the attendees (attendees' professional profile/demographic data) was collected through a questionnaire during registration. The information gathering exercises were spread over the three days of this virtual (via Zoom, a videoconferencing platform) meeting, which was split into two sub-meetings, each in one of two time zones.
Analyses Not everybody attended the first two days and not all people scored all questions, hence the number of data points included in the different analyses varied. An initial descriptive analysis was done with the independent individual scores. The mean scores were determined for each SDG versus AW, taking into account the direction of the assessment. A Wilcoxon-signed-rank test was used to evaluate if there was a difference in the scores given by participants on the impact of achieving a specific SDG on AW versus the impact of improving AW on the achievement of an SDG. Countries were grouped in two different ways: according to average income in that country and according to its geographical location. In the first analysis, countries were classified as low income, low/middle income, upper/middle income and high income as defined by the World Bank (World Bank, 2022), although low income and low/middle income countries were later combined due to the small number of participants from these countries.
In the second analysis, countries were grouped according to the five WOAH geographic regions; Africa, Americas, Europe, Middle East and the combined Asia, Far East and Oceania region  (WOAH, 2022b). Differences were analysed using Kruskal Wallis. Differences in how participants scored on day 1 and day 2 were analysed using Paired sign ranked tests. Differences in scores according to the gender of the participants were analysed using Mann Whitney U tests. All analyses were done using Excel and SAS ® software (version 9. 4). That this analysis involved multiple comparisons should be taken into consideration when interpreting the results. As part of the registration procedure, potential participants were informed and had to agree to their responses in the exercises being analysed. During the meeting, participants were reminded that their comments were being recorded.
Participants were informed that all data would be made anonymous. Qualitative analysis of the discussions was carried out. Transcribing and structuring of the topics taken up by participants was done by the same person. Rationales from the discussions on days 2 and 3, both verbal and in writing, and notes from the slides presented on day 2 were organised by SDG, by direction of the link and on whether it had a positive or negative impact. All authors then contributed to refining the text in the table. Duplicated concepts were removed and rationales edited to improve clarity and to present the points made more succinctly. Subsequently, the recordings of the meetings (days 2 and 3) were listened to again, to ensure concepts were not missed or misinterpreted.
• Summary of intervention measures on day 80. • Effectiveness of 5 potential control strategies compared to the baseline scenario: -Culling all pig herds in the protection zone. -Increase of active search from 1 to 2 km. -Culling all pig herds 3 km around positive WB carcasses. -Increase of surveillance zone from 5 to 10 km. -Culling all herds that traded pigs with infected farms. non-adjacent pixels. We also ignored the possibility of hunting and testing recovered () WB. During each phase of the challenge a two-step approach to numerical calculation with the WB model was adopted, whereby (i) available data were used to obtain approximate Bayesian estimates for parameters, and (ii) those parameter estimates were subsequently used in simulations designed to assess and compare different control measures of epidemic control.
Probabilistic modelling of transmission among domestic pig sites A farm contamination model was developed to estimate, for each pig herd on the island, the probability to become infected within a given time period following each data release date. These probabilities accounted for three contamination pathways: 1. direct contact with infectious WB; 2. reception of infected live pigs from animal trade; 3. fomites from neighbouring pig herds. This model used known locations of pig production facilities as the epidemiological units. We call these pig production sites, or pig sites for short. We also use the term pig herds -although this is only appropriate on sites that have not undergone culling. We rarely refer to pig farms, since a single farm may consist of multiple sites. Infection and detection probabilities are considered at the site/herd level.
𝜋 𝑂 wb 𝑖𝑗 Infection probability due to exposure to infectious WB in the observed period. Zero for indoor herds. [ 1 + exp ( -𝛼 -𝛽𝑒 wb 𝑖𝑗 ) ] -1 Expected probability from a logistic regression on the exposure to infectious WB for outdoor herds only. were at risk of ASF contamination via direct contact with infectious WB. Secondly, herd size was used as a quantitative measure of the expected impact of the alternative control strategies proposed by the challenge organizers. We did not use any other farm site characteristics data in the model, since exploratory analyses showed no particular effect of these variables on the six infection probabilities (Table  4 ). The pig herd model did not require setting any initial conditions, apart from the fixed parameters listed in Table  5 , among which there was the day of disease introduction which represented the beginning of the exposition period.
Probability of transmission through WB exposure The exposure of pig herds to infectious WB,  wb  , was quantified via kernel density smoothing of observed WB case data. This smoothing was performed once for each challenge phase, and was applied to the entire set of available cases -thus temporal fluctuations in the risk of exposure to infectious WB were neglected. We then fitted a logistic regression for the infection status of pig sites given their level of exposure to infectious WB. This allowed the estimation of a probability of infection and detection,   wb  , for each outdoor farm site, pertaining to the entire observation period ranging from the beginning of exposure (day_expo_start) up to the data release date minus the detection_delay. We derived an estimate of the daily probability of infection,   wb  , based on the number of days considered during the observation period, assuming constant risk and independence across time.
Implementation Our primary language was R (R. Core Team, 2020) and we used 44 CRAN packages -the most critical ones being drake for reproducibility  (Landau, 2018) , NIMBLE for the WB model (de Valpine and Turek, 2017; de Valpine and Paciorek, 2020), sf for spatial data  (Pebesma and Bivand, 2005) , knitr for rendering Rmarkdown  (Xie, 2020) , and tmap for mapping  (Tennekes, 2018). Whilst drake provides a pipeline for reproducible research (by ensuring every user generates the same target R objects), the heavy calculations performed with NIMBLE did not fit within the drake framework, thus drake and Rmarkdown were simply given access to the outputs of NIMBLE scripts. The code, data and reports from each challenge phase are available at https: //forgemia. inra. fr/umr-astre/asf-challenge.
Implications for real scenarios and lessons learnt 4. 4. 1. The role of the density of WB populations Despite numerous simplifications, the WB model did appear to provide a robust characterization of the speed and extent of spatial spread. This success was largely due to the availability of very detailed information regarding WB densities. Whilst our linear equation for the expected density of WB per pixel (Eq. (  1 )) might clearly over simplify many real situations, it does appear to have provided a useful approximation to the spatial distribution of WB within the challenge's data generation model. By comparing spread in various directions (e. g. Figs. S10  and S11 ) with maps of WB density (Fig. 3 ), we can clearly see that WB densities affect the speed and extent of dissemination. This relation is largely a result of density dependence in the force of infection (Eq.
(  2 )). This relationship explains why ASF was unable to invade the administrative region north of the epicentre in our simulations. If density dependence is indeed the most pertinent mode of transmission, then clearly the quality and quantity of WB data can greatly affect the reliability of estimates concerning the speed and extent of ASF dispersal. The implication of this density∼speed relationship for real world applications is that WB densities need to be known across entire landscapes, at least up to a constant of proportionality, for predicting spatial heterogeneity in the spread of ASF among sylvatic reservoirs. Thus, one of the most important aspects when developing predictive models in the context of real ASF epidemics is the quality and quantity of available WB abundance data. However, this typically presents a critical knowledge gap in real world scenarios because WB numbers are extremely difficult to estimate with accuracy in the field.
Furthermore, numerous methods exist for estimating WB densities in the field, and these vary in their accuracy and reliability (ENETWILD consortium, 2020), therefore available density figures from various countries may not be directly comparable. As such, teams attempting to map WB densities across large areas must typically make various simplifying hypotheses concerning data quality, thus levels of precision remain unknown. These real world errors and uncertainties have important implications for the robustness of predictions from ASF dissemination models built using current WB density maps. This aspect was greatly simplified during the ASF challenge, where hunting and landscape data alone were sufficient for obtaining reliable estimates of WB densities across Merry Island.
The geographical resolution of the analysis In our simulations, dissemination among WB reached speeds of up to ∼ 27 km/month (Fig. S10 ). This value is much higher than estimates reported in real situations, calculated using data from seven different countries in Western and Central Europe, which range from 2. 9 to 11. 7 km/month  (Podgórski and Śmietanka, 2018; European Food Safety Authority (EFSA) et al. , 2018). It could be tempting to conclude that the 5 km pixels were too coarse to provide ecologically realistic simulations. Indeed, it is possible that a combination of the 5 km pixels, plus the low resolution (just three classes) land cover map, overly homogenized the landscape and therefore smoothed over potential landscape barriers (such as rivers) to ASF dissemination. Whilst a more realistic representation of WB ecology might be expected by adopting smaller pixels and a more detailed land cover map, our chosen approach struck a balance between ecological realism and computation time: smaller pixels risked making the numerical calculations too slow for meeting challenge deadlines.
6 mm thick) of the whole spine was performed for the latex and barium sulfate-injected samples. Helicoidal slices were acquired, images were processed, and results were obtained using a software (Horos version 3. 3. 5; Horos Project) and treated by the authors. The position, anatomical relation, and size of IAB were recorded on CT images. Thereafter, C2-3 dorsal laminectomy was performed layer by layer on each of the 5 spinal columns. After the laminectomy, IABs were delicately dissected. When opening the vertebral canal, the spinal cord in its dura matter was removed to assess the ventral aspect of the vertebral canal. The position, anatomical relation, and size of IAB were assessed. The size of IABs was measured on its widest craniocaudal dimension. Then, the ratio between this data and the length of the dorsal por-tion of the vertebral body of the caudal vertebra was calculated for each dog.
2 The dorsal communication between the right and left IAB observed on the histologic slides was very small compared to the vertical component of the IABs. For IABs running in an oblique direction, their continuity and accurate diameters could not be properly evaluated on transverse cross sections. We collected data only regarding the comparison of their sizes. We failed to describe dorsal communication during the dissection study. We believe that IABs could not be identified during the dissection of the ligamentum flavum owing to their small size or because they were not correctly filled with latex. Barium sulfate was added to the latex in 2 of our samples to create radiopacity for CT evaluation. The volume ratio was determined before the study to create sufficient radiopacity to be observed on the CT, taking care not to overly thicken the latex.