dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample and performance data collection Samples and performance data were collected throughout the life of the pigs from days 10 to 155. Pigs were weighed on days 10, 28, 29, 31 and 36, to calculate the average daily gains (ADGs) between days 10 and 28  ) and between days 29 and 36  ). Bristles were collected from the rump, using an electric hair clipper (Isis, Aesculape, Germany), on days 66 and 148. Blood samples were collected from the jugular vein of individuals using BD Vacutainer 18G (1. 2 Â 5mm) needles (BD Bioscience, Le Pont de Claix, France). On days 28, 29 and 36, for respectively, 10, 20 and 30 ml blood collection, pigs were gently maintained in a supine position on the knees of one investigator, while on day 66, pigs were nose-snared for a 20 ml blood draw. Blood samples were kept for 1h30 at ambient temperature during transportation.
In the laboratory, samples used for whole blood analyses (white blood cell count, flow cytometry, phagocytosis and whole blood assay) were first homogenised on a wheel for 30 minutes. Plasma and serum samples were then obtained after centrifugation for 10 minutes at room temperature at 1 300 and 2 000g, respectively, and stored at À80 °C until analysis. At slaughter, the hot carcass weight and lean meat percentage were obtained for all pigs that were still ear-tagged (n = 128/155). The lean meat percentage was calculated based on images obtained at the slaughterhouse from the CSB Image-Meater device (CSB-System AG, Geilenkirchen, Deutschland) as previously described  (Blum et al. , 2014). All data were collected on both males and females, except blood samples that were collected on males only for practical reasons.
Phagocytosis test Ex vivo phagocytosis was assessed on day 36 using the Phagotest TM Kit (BD Biosciences) according to the protocol given by the manufacturer. Briefly, 50 ll of heparinised blood samples were incubated for 10 min at 37 °C with opsonised FITC-labelled E. coli bacteria. Ice-incubated negative controls were included in each experiment. Samples were immediately analysed by flow cytometry using FSC/SSC dot plot to discriminate granulocytes and monocytes. Results were computed using the FlowJo software and expressed as a percentage of phagocytes among monocytes and granulocytes and relative Mean Fluorescence Intensity (rMFI, ratio of the mean fluorescence intensity of the positive cells to the negative cells).
Statistical analyses Statistical analyses were performed with R (version 4. 2. 0, RStudio Team (2020)). The main model used the lme4 function of the ''lmer" package and included the early-life management mode (conventional vs alternative) as a fixed effect, and the sow (n = 12), the biological mother (n = 35), and the batch (n = 3) as random effects. For analyses at different time points, the age was entered as a fixed effect and the individual as a random effect. Whenever applicable, the sex was added as a fixed effect. The interaction between early-life management and time and/or sex was evaluated. The ADG was adjusted to the initial weight of the period, i. e. the ADG 10-28 was adjusted to weight on day 10 while the ADG 29-36 was adjusted to weight on day 29. The variables IL-6, insulin, body lesions and tail-biting scores were first analysed as qualitative variables (< or >19 pg/ml for IL-6, < or >2.
001). At the slaughterhouse, we collected data from 128 pigs that were still ear-tagged. Early-life management strategies did not affect the hot carcass weight and lean meat percentage at slaughter (data not shown). As expected, sex had a significant effect on the lean meat percentage (P < 0. 01). Indeed, males displayed a higher percentage of lean meat than females, with respective emmeans of 61. 1%  [60. 4-61. 8 ] vs 60. 4% [59. 7-61. 1].
Acknowledgements We are grateful to P. and  S. Homo  for giving us access to their farm as well as for pig management. We thank  T. Potiron  for her help in the protocol implementation on the farm. We are grateful to  J. C. Lorgère ,  T. Khakhishvili  and  L. Morvan  for their kind assistance at the slaughterhouse. We thank  Dr. S. Bosch  for careful reading of the manuscript. We acknowledge LabOniris (for giving us access to their haematology analyser) as well as the  MSD laboratory  (for PCV2 antibody titration). We also thank the  INRAE , especially the  SANBA Metaprogram , as well as the  Pays de la Loire region  for the PhD funding of S. Gavaud.
Playback was performed through a HMA120 Ecler amplifier and three spherical Ecler loudspeakers (euC106) modified through an embedded module developed by @Excelessens (Soleau 622093 250123) that allowed a non-audible propagation of all experimental frequencies. The loudspeakers were distributed equidistant (9. 5 m) in the 30-m long walkway of the barn, hanging from the roof well above the horses' head (5 m high) (Figure  2 ). Horses were located at a distance of 2. 8 ± 1 m from the closest loudspeaker, i. e. , at homogeneous distances from the sound source. All the equipment was set up on 14 March 2022 so as to leave time for habituation to the equipment and the playback started on 11 April in the evening and lasted until 3 May in the morning. During the playback phase, the broadcast took place (automatically) twice a day for 45 min: 10:00-10:45 p.
Assessment of Behavioral and Physiological States of Horses We were particularly interested in assessing whether the stimulation could influence the behavior of horses as well as their internal state. Therefore, data were collected that included validated behavioral indicators of welfare (i. e. , chronic states), emotional level (i. e. , excitatory components) and time budget. Many studies on horses but also other farm animals have relied upon heart rate variables to assess emotional states. We chose not to use such a system as horses have associative memories of work or other 'invasive' human-related objects with past experiences  [31, 32]  and this seems to be still more so the case for racehorses (Gueguen et al. in prep. ). Moreover, heart rate is more involved in short-lived emotions, whereas the physiological changes observed reflected medium-term effects.
Heart rate measures do not really help with durable states, as their correlation with stereotypic behaviors or pain is controversial  [33] [34] [35]  and changes in welfare states are not necessarily associated with heart rate changes  [36]. Moreover, girthing is a common problem in racehorses due to possible health problems, and most of all, it would have been quite impractical. Physiological data were based on hematological data, rather than measures of the HPA axis-related hormones, which are more ambiguous indicators of chronic stress  [37] , especially in horses  [19, 38, 39]. Hematological measures are clearly influenced by horses' welfare/chronic stress state  [14, 21, 38, 40]  and especially training stress in racehorses  [21, 40]. Behavioral observations were performed by a single observer (MD) during the first and third weeks of each phase in each horse's home stall.
MD received extensive training for ethological observations until agreement reached 96% on a subset of data between LG and her (kappa coefficient of 0. 85). Horse training occurred every morning between 6:00 and 11:00 am, with a break at 8. 00 am, so morning observations were performed during the break or late morning. In order to ensure having enough contexts to assess all possible types of behaviors  [41, 42] , observation sessions were conducted during the three periods: pre-meal period (the 6:00 am hay distribution), in the morning (8:00 am and 11:00 am) and in the afternoon (14:15 pm and 15:50 pm). At that time of year, daylight occurred only after 6:00 am; thus, in order to avoid disturbing the horses during the dark periods, the observer wore an infrared lamp (Brand: Forclaz). The idea was to record the horses during the first pre-meal (hay) period.
In total, we obtained five observation sessions per horse per week (i. e. , 1 before feeding, 2 in the late morning and 2 in the afternoon). Each phase included two weeks of observations, so we performed 10 sessions (5 sessions × 2 weeks) of observation per horse per phase. This corresponds to 160 scans (16 scans per horse per session × 10 sessions) per horse and for each phase (PRE, PL, POST). The data were expressed as a percentage of scans for each activity.
In addition to routine sampling, five milliliters of blood were collected in heparinized EDTA K3 tubes (BD Vacutainer ® , Becton, Dickinson and Company, Franklin Lakes, NJ, USA), then placed in a refrigerator at 4 • C before being sent to a veterinary laboratory for analysis (LABEO Franck Duncombe, Caen, France) within the day. Hematological data analyzed were the number of red blood cells (RBC), white blood cells (WBC), platelets (Pl), hemoglobin (Hb), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), and percentages of each type of leucocyte (neutrophils, eosinophils, basophils, lymphocytes, monocytes). The standards of the laboratory that carried out the analyses are not those for racehorses, so the standards of another laboratory (Rossdale Laboratory  [65] ) specializing in thoroughbreds were used.
Statistical Analyses Data consisted of actual individual numbers for the physiological data, percentage of scans per activity per horse and number of behavioral occurrences over 10 all occurrence sessions (50 min). As the data were not normally distributed (Shapiro-Wilk test, p < 0. 05), non-parametric statistical tests were used. The statistical tests were performed using R 4. 2. 0 software (alpha threshold set at p < 0. 05). Friedman and subsequent Wilcoxon tests were used to evaluate the behavioral and physiological changes in relation to the experimental phase, i. e. , between pre-playback, playback and post-playback phases. A Benjamini-Hochberg (BH) correction was applied for multiple comparisons  [66]. Only statistically significant results are mentioned in the results section. The whole set of results can be found in Appendix E.
0 ± 10%), time spent in fixed stances (15. 0 ± 10%) or stereotypic behaviors (13. 0 ± 10%). Hematological data revealed that all horses were within reference standards for hemoglobin (Hb) and eosinophils. A few horses were, however, outside norms for some parameters: five were slightly below the standard for red blood cells (RBC) (standard: 9-14 × 10 6 /mm 3 , horses outside norms: mean ± standard deviation of horses, X ± ES = 8. 2 × 10 6 ± 6. 1 × 10 5 ), two were below the standard for Mean corpuscular hemoglobin concentration (MCHC) (standard: 32-36 g/dL, horses outside norms: X ± ES = 32 ± 0. 007), five for platelets (standard: 127-206/mm 3 , horses outside norms: X ± ES = 104 ± 26) and three for monocytes (standard: 2-6%, horses outside norms: X ± ES = 1 ± 0. 7), while two were above the norm for neutrophils (standard: 36-68%, horses outside norms: X ± ES = 70 ± 2.
Data collected from pathology reports for each individual included origin, signalment, contraceptive status, weight, nutritional condition (based on fat stores and skeletal muscle mass) and pathology findings. Concurrent lesions were tabulated. In all cases, tissues were routinely processed for histology following fixation in neutral buffered formalin and stained with haematoxylin and eosin (HE) according to standard operating procedures. Apocrine gland origin was confirmed in a subset of tumours (n ¼ 4) by immunohistochemistry using Mouse Monoclonal Antibody to Cytokeratin (CAM5. 2) (CliniSciences, www. clinisciences. com) following standard laboratory protocols. Initial histopathological evaluations were performed by veterinary pathologists (AF, EM, KL, MM). Two cases from the Institute of Veterinary Pathology of the LMU Munich underwent additional review by one of the authors (MH) under supervision (DD, MM).
Cases included in this study were derived from both South African and European facilities. Available data do not allow for reliable comparison of the incidence of neoplasia between South African and European cases, and the role of genetic diversity in the pathogenesis of CATs in AWDs remains uncertain. In the present study, neoplasms occurred predominantly in AWDs 8 years of age or older. One animal was 5 years of age and the average age of affected animals was 10 years. In humans, full maturity of apocrine glands is considered a prerequisite to lesion development, and age of onset in both domestic animals and the AWDs in this case series suggests similar considerations  [23]. No CATs were detected during a survey of 46 free-ranging AWDs in South Africa, although at least 80% of animals were less than 4 years of age  [24].
Acknowledgments The authors thank all contributing institutions for the submission of case material for this study, in particular  Drs Sandra Avril  and  Doroth ee Ordonneau ,  Cerza Zoo ;  Dr Alexis Maillot ,  Amn eville ;  Drs Thierry Petit  and  Thomas Charpentier ,  Palmyre Zoo ; and  Mr. Deon Cornelius ,  UBhetyan-O-Africa. Excellent technical assistance was provided by the pathology laboratory technicians of the  Faculty of Veterinary Science, University of Pretoria, South Africa , the  Institute of Pathology of the LMU, Vet Diagnostics Laboratoire and Vet Agro Sup.
Mitochondrial DNA sequencing Amplification of three fragments of the mitochondrial genome, including cytochrome b (Cyt b), cytochrome oxidase subunit 1 (COI) and NADH dehydrogenase 1 (ND1) genes generated a concatenated dataset with a total length of 1693 bp. All genes were amplified by polymerase chain reaction (PCR; see Supplementary Material, section 1) and sequenced using primers specifically designed for this study or previously published primers (Table  S2 ). Sanger sequences were assembled and edited using Geneious Prime v. 2022. 1 (Gene Codes) and checked for possible stop codons based on the Alternative Flatworm Mitochondrial Code (transl_table 14). ddRAD library preparation and reference genome ddRAD libraries were prepared using 350 ng of DNA per sample following a modified version of the protocol proposed by  Peterson et al.
ddRAD data assembly and SNP calling FastQC  (Andrews, 2010)  was applied for an initial quality check. The process_radtags program in Stacks v. 2. 5. 3 was used to demultiplex and filter the raw reads with low quality and uncalled bases  (Rochette et al. , 2019). Also, cut sites, barcodes and adaptors were removed from ddRAD outputs. Next, paired-end read alignment of each sample to the reference genome was performed using the default mode of Bowtie 2  (Langmead & Salzberg, 2012). SNPs calling and assembling loci were carried out from the alignment using the ref_map. pl wrapper program. Data were then filtered for missingness, minor allele frequency (MAF) and linkage disequilibrium (LD) in Stacks (see Supplementary Material, section 3 for details). In sum, we generated three SNP matrices in variant call format (VCF) comprising (1) 44,298 SNPs with a mean coverage per locus of ca.
, 2021). The first ddRAD dataset (44,298 SNPs) was applied for the analysis as the calculation assumed that the input data were not reduced based on LD filtering. We used Plink v. 1. 90 (Chang et al. , 2015)  to convert the VCF file to the bed format. The default data filtering was applied with the "filterdata" function in the R package (see De  Jong et al. , 2021).
Population structure analyses Using the concatenated COI and Cyt b (801 bp), haplotype networks were constructed to visualize relationships among the parasite populations via the Median-Joining algorithm in PopArt v. 1. 7  (Leigh & Bryant, 2015). Moreover, ddRAD data set was applied to construct a phylogenetic network using a neighbour-net algorithm and 1,000 bootstraps executed in SplitsTree v. 4. 10  (Huson & Bryant, 2006). Several clustering analyses were performed to discover the number of distinct genetic groups. First, the model-based evolutionary clustering method was applied in ADMIXTURE  (Alexander et al. , 2009)  via its parallel processing capabilities (AdmixPiPe:  Mussmann et al. , 2020). Second, a Principal Component Analysis (PCA) was performed using the glPCA function from the adegenet R package  (Jombart & Collins, 2015).
Next, we applied the TESS3R package in R to provide additional estimates of the role of geography in the genetic structure  (Caye et al. , 2018). TESS3R explores the implications of genetic diversity in natural populations using geographic and genotypic data simultaneously. Lastly, we applied the programs fineRADstructure and RADpianter v. 0. 2  (Malinsky et al. , 2018)  to reveal population genetic structure based on the nearest-neighbour haplotype (For details on ADMIXTURE and fineRADstructure analyses, see Supplementary Material, section 5).
The Ligula species complex was divided into five biogeographic realms: (1) Palearctic,  (2)  Afrotropical, (3) Indomalayan, (4) Australasian and (5) Nearctic. Inferred ancestral ranges were allowed to occupy up to five areas, thereby facilitating clear estimations. Likelihoodbased measures of three biogeographic models along with their modified J types were ranked according to the AIC method to evaluate model quality. We applied phylogenetic trees from both mtDNA and ddRAD data to reconstruct ancestral host fish affiliations using a Bayesian framework implemented in sMap v. 1. 0. 7  (Bianchini and Sánchez-Baracaldo, 2021). The software uses stochastic mapping analysis to reconstruct the evolutionary history of discrete characters at the branch and node of the tree. In the present study, Ligula plerocercoids were collected from 41 fish species representing 18 genera in four orders (Cypriniformes, Osmeriformes, Gobiiformes and Galaxiiformes).
Genome-wide analysis of hybridization and introgression Treemix v. 1. 12 (Pickrell & Pritchard, 2012)  was applied to examine gene flow among populations. Utilizing allelic frequency data, the analysis generated an ML tree, followed by inferring the historical migratory events between populations. We assumed n migration events and the calculation of each migration event was done separately. We used the third ddRAD data set and grouped it into 11 populations based on phylogenetic analysis. The R package 'optM'  (Fitak, 2021)  was then used to optimize the number of migration events according to  Evanno et al. (2005)  (Supplementary Material, section 9). Visualization of the graph results was accomplished by the plot function (plotting_funcs. R) in R. We also employed the ABBA-BABA (D-statistics) and F statistics tests to further investigate patterns of gene flow among populations  (Patterson et al.
Phylogenetic relationships and occasional mito-nuclear discordance Ligula phylogenies based on ddRAD and mtDNA data revealed 11 distinct lineages and showed a generally well-supported phylogenetic structure for the Ligula species complex. We characterized several new lineages from previously unexplored areas (CSA, EAR) or lineages that were not analysed in the global context before (L. pavlovskii, New Zealand; see  Lagrue et al. , 2018; Vitál et al. , 2021). In addition, several differences in the phylogeny of the group were recovered compared to previous studies based on fewer loci and less material  (Bouzid et al. , 2008; Štefka et al. , 2009). Ethiopia formed a basal lineage to other populations in our analysis, contrary to the findings of the previous study  (Bouzid et al. , 2008)  where Nearctic samples (from Canada and Mexico) were at the basal position.
Our dataset also allowed for a more fine scale distinction of the lineages previously clustered with Lineage B  (Bouzid et al. , 2008). Only two cases of discordance between mitochondrial and nuclear-ddRAD phylogenies were found, with L. pavlovskii and China showing different topologies in each data set. To exclude a possibility that the incongruence is only due to the lack of signal in the smaller mtDNA dataset, we performed a congruence test between mtDNA and ddRAD topologies and found the topology differences were significant (AU and SH tests). Similar mito-nuclear phylogenetic discordances have also been reported in several other metazoan taxa (e. g. Bouzid et al. , 2008; Shaw, 2002; Weigand et al. , 2017) , often credited to the molecular signature of selection  (Morales et al. , 2015) , incomplete lineage sorting  (Degnan & Rosenberg, 2009)  or introgressive hybridization  (Bisconti et al.
, 2018; Petkevičiūtė, 1992). Here, we used a range of species delimitation analyses to identify the putative species in the Ligula complex. The results based on ddRAD data revealed a slightly lower number of candidate species than the non-recombining mtDNA. Our findings also indicated that although different methods of species delimitation produced highly consistent species boundaries, they were not entirely similar. Carstens et al. (2013)  argued that such incongruence either signifies a difference in the power of one or more of the methods to detect cryptic lineages or that assumptions of the species delimitation approach have been violated. They suggested that species delimitation inferences should be drawn cautiously in order to avoid delimiting entities that fail to accurately represent evolutionary lineages.
Acknowledgements we are grateful to  Anna Mácová  and  Roman Hrdlička  for their help with the fieldwork. The authors also wish to thank  Eva Čisovská ,  Košice, Slovakia  for providing D. latus samples to generate outgroup sequences. The research was supported by a grant from the  Czech Science Agency  (no. GA19-04676S ). Computational resources were supplied by the project " e-Infrastruktura CZ" (e-INFRA CZ LM2018140 ) supported by the  Ministry of Education, Youth and Sports of the Czech Republic.
AOPs are by definition chemical agnostic (i. e. chemical independent), meaning that the biology depicted should hold for any stressor (mostly chemicals) perturbing the biological pathway(s). Information in the AOP-Wiki is therefore limited to "prototypical" stressors (usually those used to provide evidence for AOP development). The AOP-Wiki does not aim at providing a comprehensive database of chemicals perturbing the AOPs, which has the benefit of providing more "universal" mechanistic knowledge but also makes the usability of AOPs for risk assessors more challenging (also discussed later in this paper). The KERs in AOPs can be quantified, thereby offering a formal approach to quantitatively predict an AO from MIEs or KEs, which would greatly support NGRA. The development of quantitative AOPs (qAOPs) will therefore be an important step to consolidating the relationship between toxicokinetics and toxicodynamics within NGRA  (Punt et al.
, 2020; WHO; UNEP, 2012). The Endocrine Disruptor Screening Program (EDSP) of the US EPA focuses on estrogen, androgen, and thyroid hormone signalling pathways, using a variety of NAMs, including in vitro test batteries and computational tools, aimed at identifying and prioritizing EDCs (reviewed in  Browne et al. , 2017). In this US EPA program, the AOP concept has been used to structure and evaluate mechanistic information, establish connections among pathways leading to different adverse outcomes, and design screening strategies by mapping assays to AOPs and AOP networks. Further, the OECD Conceptual Framework (CF) for Testing and Assessment of Endocrine Disrupters (OECD, 2018a) is a pragmatic example of how the use of non-testing information (CF level 1) can be leveraged with mechanistically-informed in vitro data (CF2), mechanistically-informed Fig.
2. Strengths and limitations of AOPs as a tool to translate scientific data into regulatory relevant knowledge to support risk assessment. Five regulatory applications (light grey box) benefit from the curated and chemical-agnostic AOP-knowledge (light yellow box and yellow arrowhead), but the full adoption of AOPs is currently hindered by several limitations (light blue box and blue arrowhead). Some ongoing or proposed initiatives should help overcome the limitations in future (ways forward). Benefits, applications, limitations and ways forward are all commented in greater details in the manuscript. Abbreviations: AOP, adverse outcome pathway; IATAs, integrated approaches to testing and assessment; BoE, biomarkers of effect; KE, key events; KERs, key event relationships; NGRA, next generation risk assessment.