dataesr/scientific-paragraphs-categorization huggingface dataset:
Pedigree Analyses Pedigree analyses were computed using the program ENDOG (v. 4. 8,  [7] ). In order to ascertain the circumstances affecting the genetic history of the CDE horse breed population, some parameters were calculated to determine the amount of available information, the pedigree completeness level, the number of complete generations (t) and the generation interval (GI) as a measure of the speed of genetic transmission due to the physiology of the species and the particular management performed. The first one was assessed as the proportion of ancestors known per generation for each offspring  [8]. The t value was computed as the sum of (1/2) n , where n is the number of generations separating the individual from each known ancestor  [9]. The GI was calculated as the average age of parents at the birth of their offspring kept for reproduction  [10].
The three parameters were computed for the whole population and for a reference population, including only animals born in the last 10 years (between 2011 and 2021), which mostly corresponds to animals from the last generation. These will also be referred to as "active animals". In order to monitor the animal genetic resources for this breed, the effective population size (Ne) was also computed. PopRep 1. 0. software's [11] decision tree was used for picking the best method to calculate Ne for our data. After the analyses, the Ne calculated by individual increase in inbreeding, following Ref  [12] , appeared as the method that best fitted our data. Ne was calculated as  = , where Ft is the inbreeding coefficient of the offspring, and Ft-1 is the inbreeding coefficient of the parents.
The three parameters were computed for the whole population and for a reference population, including only animals born in the last 10 years (between 2011 and 2021), which mostly corresponds to animals from the last generation. These will also be referred to as "active animals". In order to monitor the animal genetic resources for this breed, the effective population size (Ne) was also computed. PopRep 1. 0. software's (PopRep. tzv. fal. de (accessed on 15 June 2022)) [11] decision tree was used for picking the best method to calculate Ne for our data. After the analyses, the Ne calculated by individual increase in inbreeding, following Ref  [12] , appeared as the method that best fitted our data. Ne was calculated as Ne = 1 2 * ∆F , where ∆F = Ft-Ft-1 1-Ft -1 , where F t is the inbreeding coefficient of the offspring, and F t-1 is the inbreeding coefficient of the parents.
Performance Data The EBV of 12,197 horses for Show Jumping, 5253 horses for Eventing and 7790 horses for Dressage disciplines, from the CDE pedigree, were available for this study, including the performance data of animals from 2004 to 2020  [13]. Genetic parameters for the Show Jumping, Eventing and Dressage sport disciplines used in this study were obtained from the 2020 routine genetic evaluation information of the CDE breeding program. Genetic evaluation implies a multivariate BLUP analysis using the following genetic model: y = Xb + Za + Wp + Qr + e, where y was the vector of observations for the analyzed traits of each discipline; b was the vector of fixed effects; a was the vector of additive genetic effects; p the vector of riderhorse interaction effect (for Show Jumping discipline) or the rider effect (for Eventing and Dressage disciplines); r the vector of permanent environmental effects (for Show Jumping and Dressage disciplines); and e the vector of random residual terms, X, Z, W and Q were the incidence matrices assigning observations to the fixed, animal, rider-horse interaction or rider and permanent environmental effects, respectively.
When AR is calculated for a founder, it expresses the proportion of the genetic information arising from it, and thus, its relevance in the development of the pedigree. In order to check the effect that breeders' decisions have had when selecting the breeding stock on the genetic merit of the CDE population, the genetic variability was assessed for the whole population and also according to a hypothetical breeding scenario, where only CDE horses with CDE breed parents were allowed to register as CDE in the CDE official Stud Book. Furthermore, using results from the Pedigree analyses developed before with the program ENDOG (v. 4. 8,  [7] ), the evolution across time of F and AR values for animals with best EBVs for the performance ability for Show Jumping, was also evaluated. The EBVs values used were described previously in Section 2.
Probability of Gene Origin of the CDE Horse Breed Ancestors with both unknown parents in the available database were considered founders, as it is a common practice (unless molecular information exists for those animals). In order to check the genetic representation of the founder individuals in the CDE population and in all the breed groups considered, the genetic contribution of founders to the descendant gene pool of the population  [18]  was assessed, classified by breed of origin and per period. This way the evolution of the relative influence of each breed in the composition of the CDE population could be studied. Additionally, for the 10 most contributing founders on each period (P0 to P4) their mean EBV for Show Jumping performance and that of their descendants on each period was computed to determine the genetic trend with time.
Notwithstanding, despite descendants showing better EBVs than their CDE founders, there is still margin for improvement and the breeding association must make an effort to foster the performance of this breed by coordinating the management actions and encouraging the breeders to follow their recommendations for the selection and mating of the horses. Moreover, for future studies, if the levels of inbreeding increase, genetic models for sport performance in this breed could be improved by including pedigree inbreeding to account for inbreeding depression, as reported previously for other parameters  [40]. However, for future studies, an introgression analysis using genotypic data would help reveal the real contributions of other breeds in the CDE pedigree.
Acknowledgments M. T. S. , D. R. G. , and H. L. conceived the project, convened the expert task force, and led the biweekly teleconferences. L. Zeise and S. E. E. facilitated the meeting funding. The original set of KCs were formulated and subsequently modified by the whole group, and all authors contributed ideas to these discussions and wrote or edited specific sections of the manuscript. Subgroups of authors initially wrote/reviewed the KC descriptions [KC1 (E. C. , A. C. , and A. A. R. ),  KC2  (M. P. , B. P. L. , and  S. E. A. ) ,  KC3  (C. C. M. , E. C. , and D. R. G. ),  KC4  (G. M. L. , A. C. , and E. C. ), KC5 (B. P. L. , C. C. M. , and  L. Zhang ),  KC6  (H. L. , L. P. M. , and  S. E. E. ) ,  KC7  (G. R. B. , H. L. , and M. P. ), KC8 (B. L. F. K. , G. M. L. , and L. P. M. ),  KC9  (S.
Emotional reactivity test At 208 dpf, we assessed fish emotional reactivity by using our established protocol  (Poisson et al. , 2017; Valotaire et al. , 2020). We isolated 12 fish (~ 45 g) per treatment (E and B) into a novel tank (72 L: 55 × 45 × 29 cm) for 40 min. Six fish (3/treatment) could be simultaneously tested since we have 6 dedicated test-tanks of this size, thus 4 sessions were needed to test 24 fish. The first 18 min of the test were video recorded and the following variables were then analyzed by EthoVision® XT software (v. 14. 0. 1234) by a 1-min time step: maximum speed (cm/s), distance traveled (cm), time spent not moving (%), time spent in the periphery (s), angular velocity ( • /s) and number of rotations.
Statistical analysis All analyses were done with the RStudio© software version 1. 4. 1106. The packages used in addition to those natively installed are the following: "car" to calculate the ANOVA tables, "lme4" for the mixed models and "emmeans" for the post-hoc tests. The diagrams and the regression curves were made with the "ggplot2" package. For in situ observations of the group, the percentage of isolated individuals and group activity were analyzed with repeated measures analysis of variance (ANOVA type III). The number of flight behaviors was analyzed with a Generalized Linear Mixed Model (GLMM) since the dataset followed a Poisson distribution. Treatment (E and B) was the fixed explanatory factor and rearing tanks were considered as random factors. Regarding the emotional reactivity test, the fixed factors were treatment (E and B) and time (1-min steps over 18 min), and the random factor was the individual.
Post-hoc tests of multiple comparisons (Tukey's HSD) were then performed with the "emmeans" package. Cortisol data were analyzed using ANOVA (type III) since the dataset followed a normal distribution. For weight data, we ran an ANOVA for repeated measures using treatment and time as fixed factors, followed by Tukey'HSD tests if the interaction treatment x time was significant (P < 0. 05). For complement (ACH50) activity, condition-factor and feedconversion ratio, Student's t-tests were performed (independent explanatory variables, normal distribution). We analyzed fin erosions and lysozyme activity according to treatments with Wilcoxon's tests since data did not meet normal distribution.
Plant Collection and Processing Cladium mariscus (L. ) Pohl aerial parts, including leaves and inflorescences (Figure  1  WeatherSpark based on data obtained in the Faro International Airport). Inflorescences were present during summer and, to a lesser extent, in autumn, while green leaves were collected all year. In addition, only for summer season, samples of leaves (Le) and inflorescences (Inf) were also collected, for separate analysis. Afterward, samples were transported to the laboratory, washed, frozen at -20 • C until freeze-dried (Lyoalfa 15) for 3 days and reduced to powder using a coffee and a ball miller (Retsch PM 100). For plant collection in the wild, mandatory licenses for the Portuguese territory were obtained, and the protocol for collection was followed according to the standard procedures recommended by "Instituto da Conservacãþo da Natureza e das Florestas (ICNF), " the national regulatory body.
Statistical Analysis Results are expressed either as effective concentration that inhibits 50% of the larval exsheathment and/or egg hatching (EC 50 ) values (µg/mL) including 95% confidence intervals (CI). EC 50 values and 95% CI were obtained by Probit analysis, while significant differences among groups were detected by relative median potency estimates, through IBM SPSS Statistics v. 26. 0 software. PVPP results are expressed as average ± standard error of the mean (SEM).
ACKNOWLEDGMENTS The help provided by  Dr. Nicolas Fabre  and  Valérie Cristofolli  ( Faculty of Pharmacy, Université Paul Sabatier Toulouse III, France ) is gratefully acknowledged. In addition, technical and human support provided by  CICT of Universidad de Jaén (UJA, MINECO ,  Junta de Andalucía, FEDER)  and  Setha Ketavong  ( UMR IHAP 1225 INRAE/ENVT Toulouse ) are sincerely recognized and thanked.
FUNDING This work was supported by  Foundation for Science and Technology (FCT) , the  Portuguese National Budget funding  ( GreenVet-ALG-01-0145-FEDER-028876  and  UIDB/04326/2020  projects). This work was also made under the frame of the project  HaloFarMs , which was part of the  Partnership on Research and Innovation in the Mediterranean Area (PRIMA)  Programme supported by the  European Union  and funded by the national funding bodies of Participating States (FCT in Portugal). MO acknowledged FCT for the Ph. D. grant  SFRH/BD/123658/2016. LC was sustained by  FCT Scientific Employment Stimulus  ( CEECIND/00425/2017 ). CL acknowledged CAPES (finance code  001 ) for the scholarship and the  Doctoral Program Sandwich Abroad  (Grant no. 88881. 187940/2018-01 ).
Fecal sample analysis Fecal samples were collected individually from the rectum of each horse every Monday of the experimental period (day 0, day 7, day 14 and day 21). Samples were stored at +4 °C and shipped to the INRAE Centre Val de Loire facilities (Nouzilly, France) for further processing. Individual FEC data were determined using a modified McMaster technique  (Raynaud et al. , 1970)  based on 5 g of fecal matter diluted with a dilution factor of 5. Eggs were then counted using optical microscopy (×150 magnification), with a minimum detection limit of 50 EPG. To evaluate the effect of the diet on larval development, the remaining fecal matter (40-90 g) was incubated individually for each horse for 12 days at +25 °C and 60% relative humidity during 2 weeks as suggested by  Roberts and O'Sullivan (1950).
The resulting PCR products were purified and loaded onto the Illumina MiSeq cartridge according to the manufacturer's instructions. Libraries were further processed for a single run of MiSeq sequencing using 500 cycle reagent kit v3 (2 × 250 output; Illumina, USA), the raw data of which are registered under SRA bioproject number PRJNA840924. The quality of the run was checked internally using PhiX, and then each paired-end sequence was assigned to its sample with the help of the previously integrated index. To assess the predictive ability of the nemabiome approach and determine the best set of parameters for data processing, five mock communities of known species composition were built from worms collected and morphologically identified in Ukraine (T. A. Kuzmina, personal communication)  or Poland (M.
, 2021). Denoising was performed using the pseudo-pool option, and chimaera removal was performed under the default consensus mode. Taxonomic classification was subsequently done using the curated ITS-2 database (https:// www. nemabiome. ca/its2-database. html), last accessed on 2 February 2022  (Workentine et al. , 2020). Two taxonomic assignments were considered and compared, considering mock communities as a ground truth. First, the IdTaxa  (Murali et al. , 2018)  function implemented in the DECIPHER package v2. 18. 1  (Wright, 2016)  was applied with 100 bootstraps and a threshold of 50%. Second, the dada2 assignTaxonomy  (Callahan et al. , 2016)  function was implemented as already described  (Poissant et al. , 2021) , i. e. setting tryRC = TRUE, minBoot = 0, outputBootstraps = TRUE and retaining ASVs with 50% bootstrap support.
Egg reappearance period At the end of the sainfoin-fed diet (day 21), all horses were treated with oral IVM (Eqvalan, 200 μg kg -1 BW, Boehringer Ingelheim, Lyon, France) and were still fed with their respective experimental diets for 4 days. In addition, the ERP was determined from measurements of individual FECR data on 5 occasions over a 42-day window (on day 36, day 50, day 63, day 71 and day 78). The ERP was defined by WAAVP  (Nielsen et al. , 2022)  as the time when the upper confidence interval for the mean fecal egg count reduction (FECR) fell below the mean of FECR determined 2 weeks post-treatment minus 10%. FECR data were calculated according to the WAAVP guidelines  (Coles et al. , 1992 (Coles et al. , , 2006) )  as FECd21 -FECi FECd21 × 100, where FECd21 -FEC data on day 21, FEC i stands for the FEC measured at each timepoint i between day 36 and day 78.
Ivermectin dosage in plasma and pharmacokinetics parameters To quantify horse exposure to IVM, blood samples (9 mL) were taken from the jugular vein of each horse in heparinized tubes at 0, 1, 2, 24, 48, 72 and 96 h after IVM treatment. Blood samples were kept at +4 °C before centrifugation (1500 g for 30 min), and the plasma was frozen at -20 °C until further shipment and processing. Plasmatic IVM concentration was determined by high-performance liquid chromatography (HPLC) with fluorescence detection according to previously described and validated methods  (Alvinerie et al. , 1999). Data were analysed using a noncompartmental approach with version 4. 2 of the Kinetica Tm computer program (innaPhase, Philadelphia, USA). The partial area under the plasma concentration-time curve (AUC) was calculated from 1 to 96 h by the linear trapezoidal rule.
Statistical analysis To test for the effect of the diet on FEC and larval development rate, a generalized estimating equation model was implemented with the geeglm function of the geepack v. 1. 3-1 package  (Højsgaard et al. , 2006). To test for differences in alpha diversity, a t-test was performed between the Shannon index values estimated for the sainfoin and control group on day 0 and day 21. To test for a differential temporal trend in species abundance between both groups during the experimental diet, species counts were regressed upon the experimental group and the day effects and setting horse as a random effect with the nlme package v. 3. 1-155. This analysis was restricted to 12 horses with data on both days (n = 8 in the sainfoin group, n = 4 in the control group) and to the most abundant cyathostomin species (overall abundance of 10 000 counts, including C.
nassatus, Cylicocyclus ashworthi, Cylicostephanus minutus and C. longibursatus). Species counts were 4th-root transformed to better fit normality (Shapiro-Wilk test = 0. 56 and 0. 96 before and after transformation, respectively). FEC measured after IVM treatment was modelled with a linear mixed-effects model using the lme function of the nlme package  (Pinheiro et al. , 2013) , fitting diet, time and diet × time interaction terms as fixed effects and horse as a random effect. The FECR was measured according to the WAAVP guidelines  (Coles et al. , 1992 (Coles et al. , , 2006) )  with Bayesian hierarchical models using the fecrtCI function on the eggCounts package v. 3. 2-3 (Wang et al. , 2022). The average IVM concentration and C max for the sainfoin group were compared to the control group at every time point (+1, +2, +24, +48, +72 and +96 h).
Data are from 12 horses with samples successfully amplified. above 57 days in both groups. The plasmatic IVM concentration was also measured. The drug profiles followed a typical kinetic curve expected after an oral drench with a maximum IVM concentration (C max ) recorded at 24 h in both groups. Importantly, the sainfoin diet negatively impacted the IVM concentration in plasma. These IVM concentrations were twice lower in horses of the sainfoin group when compared with those fed the control diet (Fig. 5B ). This was observed at 24 and 48 h after IVM treatment (P = 0. 001 and 0. 006, respectively, Fig. 5B, Table 3 ). Consequently, the average AUC, reflecting the animal exposure to the IVM drug, was significantly lower in horses fed with the sainfoin diet (P < 0. 001, Table  3 ) than in the control group.
Malsa ,  G. Fleurance ,  G. Sallé ,  E. Courtot ,  T. A. Kuzmina ,  M. Basiaga ,  S. Dhorne-Pollet ,  N. Mach ,  A. Lespine  drafted the paper; P. Gombault prepared the sainfoin pellets. Financial support. This work benefited from the financial support of the  Institut Français du Cheval et de l'Équitation (IFCE)  and  Institut Carnot France Future Élevage  ( F2E ). Joshua Malsa  is a grateful recipient of a joint  Fond Eperon  and  IFCE  fellowship.
Sequence alignment was performed using the MUSCLE procedure  (Edgar 2004 ) imbedded in MEGA v. 7  (Kumar et al. 2016). Sequences were collapsed to haplotypes using software COLLAPSE V1. 2 (D. Posada; unpublished software). We blasted each distinct mitochondrial haplotype against the pig reference genome Sscrofa11. 1 (GCA_000003025. 6) to ensure that they corresponded to genuine mitochondrial sequences, rather than nuclear DNA sequences of mitochondrial origin  (Schiavo et al. 2017). For each haplotype, we performed a NCBI nucleotide BLAST search to identify identical haplotypes reported in previous work. We used the haplotypes identified in this study in association with the sequences generated by  Scandura et al. (2008)  to build a haplotype network based on the median-joining method  (Bandelt et al.
1999)  followed by MP construction  (Polzin and Vahdati Daneshmand 2003)  using the software NETWORK v4. 640 (www. fluxus-engineering. com; unpublished software). Following  Frantz et al. (2012) , we amplified a 345-bp-long fragment of the melanocortin-1 receptor (MC1R) coat colour gene that included the single nucleotide polymorphisms between codon positions 95 and 166 (based on  Fang et al. 2009). A subset of 12 Corsican wild boars and seven domestic pigs from the Nustrale breed, as well as 14 wild boars from Luxembourg (see below), were genotyped using the Porcine SNP60 v2 BeadChip (Illumina Inc. ) following manufacturer's instructions. GenomeStudio 2. 0 software (Illumina Inc. ) was employed to call genotypes using a custom cluster file to improve call rates. Only autosomal SNPs, mapping to chromosomes 1-18 Heredity (2022) 128:279 -290 on the reference genome Sscrofa build 11.
1, were retained for analysis. The resulting 60K genotypes were merged with publicly available data from 44 domestic pigs and 60 wild boars sampled in France, Iberia, Italy and Sardinia  (Iacolina et al. 2016). The 14 Luxembourg 60K genotypes were generated in the context of a different, unpublished study, but included here to increase the sample size of the wild boar reference data set. Genotypes from 11 Bornean bearded pigs (S. barbatus;  Yang et al. 2017)  were included in analyses requiring an outgroup (i. e. TREEMIX, see below). Quality control filtering (call rate >90% and missing genotypes <10%) was carried out in PLINK 1. 9  (Purcell et al. 2007 ). The implementation of the KING  (Manichaikul et al. 2010)  algorithm in PLINK was employed to remove one sample from pairs of closely related (duplicate or 1 st degree) samples.
The resulting dataset (50K SNP panel set hereafter) was pruned for minor allele frequency (MAF > 0. 01) and linkage disequilibrium (LD) in PLINK. SNPs with r 2 > 0. 5 were removed from sliding windows of 50 SNPs and with 10 SNPs of overlap using the indep-pairwise function. The pruned dataset is hereafter referred to as 30K SNP panel set and was used in analyses assuming independent SNP loci. MAFs, observed (H 0 ) and expected heterozygosity (H e ) were estimated from the 50K SNP panel set in PLINK for each domestic pig breed and regional wild boar population. The degree of genetic divergence was estimated using  Weir and Cockerham's θ (1984; hereafter Weir's θ)  as implementing in the StAMPP package (v. 1. 6. 1; Pembleton et al. 2013)  in R (v. 3. 6. 0; R Core Team 2019) using the 30K SNP panel set.
Ninety-five percent confidence intervals (95% CI) were estimated based on 100 bootstraps across loci. A principal component analysis (PCA) was performed to investigate ordinal relationships among groups and individuals using the adegenet R package  (v. 2. 1. 1; Jombart 2008). The Bayesian clustering approach as implemented in  STRUCTURE (v. 2. 3. 4. ; Pritchard et al. 2000)  was employed with the 30K SNP panel set to determine the most likely number of distinct genetic clusters K based on the admixture model with correlated allele frequencies. An alternative ancestry prior of α=1/K as starting value was employed as recommended by  Wang (2017). Each estimation comprised an initial 70,000 iterations as burn-in, followed by an additional 200,000 iterations. Ten replicate estimations were conducted for values of K ranging from one to 15.
The most likely number of K was inferred from Pr  [X|K] , where X denotes the data, as described by  Pritchard et al. (2000)  as well using the ad hoc statistic ΔK developed by  Evanno et al. (2005). For each K, the replicate with the highest Pr[X|K] was plotted with the pophelper R package (v. 2. 3. 0; Francis 2017). The topology of group splits and migration events was inferred from allele frequency variations of the 50K SNP panel set with the TREEMIX algorithm  (v. 1. 13; Pickrell and Pritchard 2012). S. barbatus genotypes were used as outgroup to root the tree. The TREEMIX input file was generated using the gl2treemix conversion function in the dartR package  (v. 1. 8. 3; Gruber et al. 2018). The maximum likelihood tree was estimated assuming a block size of 20 SNPs (to account for possible LD of adjacent SNPs) with up to 10 migration events m over three independent replicate runs.