dataesr/scientific-paragraphs-categorization huggingface dataset:
This group also comprised 7 ancient samples that could not be identified as either wild or domestic, including the following: la Grotte du Taï, France (∼7,100 y BP: AA294); Santa Maria in Selva, Italy (Late Neolithic: AA629); and El Portalón, Spain (∼5,400 y BP: AA513; ∼4,500 y BP: AA507; ∼3,600 y BP: AA512, AA511;∼900 y BP: AA513). Lastly, 2 ancient wild boars, 1 from Birsmatten-Basisgrotte, Switzerland (∼7,700 y BP: AA241) and 1 from Siniarzewo, Poland (∼2,900 y BP: LG507) were also found to fall closer to modern European wild boars. All of these samples possessed a European mtDNA signature (Dataset S1). Collectively, these results reveal a fluctuating temporal pattern of Near Eastern genomic ancestry in western Eurasian domestic pigs, and the general trend shows that the samples closer in time and space to the source of the first Near Eastern pigs possessed a greater proportion of Near Eastern ancestry.
Given the bias toward modern European wild boar haplotypes in our dataset, it is possible that our analysis did not possess sufficient power to identify Near Eastern ancestry in those ∼10 regions. Doing so will require additional sequencing of modern and ancient Near Eastern pigs. The Evolution and Dispersal of Black Coat Color. To further assess the potential relevance of Near Eastern ancestry to the genetic and phenotypic makeup of early and modern domestic pigs, we investigated the Melanocortin 1 Receptor (MC1R) gene. This gene has been shown to harbor functional mutations (linked to the loss of camouflage coat color) that are highly correlated with domestic status (SI Appendix). Our analyses of previously published and novel modern and ancient MC1R sequence data (269 domestic pigs and 46 wild boar) demonstrate that a specific nonsynonymously derived mutation [D124N  (40) ], which is associated with black (or black and white spotted) coat color in western Eurasian domestic pigs, is almost absent in both modern and ancient wild boars from the Near East and Europe (1 of 92; SI Appendix, Fig.
S8 ). The only wild boar that possessed 1 copy of the derived allele originated from a population in The Netherlands that is known to have recently interbred with domestic pigs  (41). By characterizing this SNP in ancient domestic pigs (using NGS and PCR assays; Dataset S1), we identified 64 of 76 animals with at least 1 copy of the derived allele (the remaining 12 were homozygous for the wild type). Altogether, this suggests that while the ancestral allele at this locus cannot be used to unequivocally distinguish wild and domestic pigs, the derived allele is highly indicative of domestic status. The earliest pigs that possessed the derived allele were found at Neolithic Ulucak Höyük in western Anatolia (∼8,650 y BP: AL1102; ∼8,250 y BP: Ulu48). The earliest European pigs that possess the derived allele are from Neolithic sites in Bulgaria (∼7,500 y BP: Cav6, Kov19), Romania (∼7,200 y BP: Uiv10), and Germany (∼7,100 y BP: KD033, KD037).
ACKNOWLEDGMENTS. We thank  L. Orlando  and  J. Schraiber  for their valuable comments and  R. Redding ,  A. Dinu ,  A. Devault ,  A. Mahforozi ,  A. Hammon ,  A. McMahon ,  B. Wilkens ,  C. Commenge ,  C. Minniti ,  D. Kennet ,  D. Mol ,  D. Binder ,  E. Vika ,  G. Bayle ,  H. -J. Döhle ,  I. Dubset ,  I. Fiore ,  J. de Grossi Mazzorin ,  J. Enk ,  J. Glimmerveen ,  J. Courtin ,  J. L. Arsuaga ,  J. Lev-Tov ,  K. Aaris-Sørensen ,  K. Post ,  M. Özdo gan ,  M. Özbas ¸aran ,  P. Miracle ,  R. Portela-Miguez ,  R. Meadow ,  S. Davis ,  S. Bréhard ,  S. Hanik ,  U. Albarella ,  T. O'Connor ,  S. Hansen ,  M. Toderas ,  P. Bogucki ,  D. Lo Vetro ,  R. Andreescu ,  P. Mirea ,  D. Popovici ,  C. Micu ,  I. M. Chicideanu ,  P. Damian ,  C. Becker ,  R. Gleser ,  S. Hansen ,  R. Hofmann ,  R. Krauss ,  M.
Study Design and Farming Data Collection This study was carried out between January 2014 and April 2015 and involved 85 organic broiler flocks in France (about 10% of the French organic broiler farms), with 70 farmers under contract with a company (Comp farmers) and 15 independent farmers (Ind farmers). The population was stratified according to commercial outlets in France: 80% of farmers were under contract with a company for production sales (chicks and food supply, poultry slaughter by the company) and 20% were independent farmers selling their production themselves to local markets for direct sale only  [2]. Given the absence of a national register of organic broiler farms in France, the farmers under contract were selected from seven production companies and the independent farmers via organic producer associations.
Data Processing Data collected on farming conditions, and the health and welfare characteristics were described as the mean (with SD: Standard deviation) or frequency for the 85 flocks and for the two strata (n = 15 Ind farms vs. n = 70 Comp farms). The results were also compared between these two strata using χ 2 test for qualitative data and non-parametric Wilcoxon test for quantitative data, considering a significant difference with p < 0. 05. This data processing was performed with statistical software R (R foundation, Vienna, Austria).
9 vs. 2. 1, p = 0. 03). The preventive treatments were mainly phytotherapy products and nutritional supplements (vitamins and minerals). Most of the flocks were vaccinated against coccidiosis (87%), without a significant difference between the independent farms and the farms in companies. The organic broiler flocks on independent farms were commonly slaughtered in an approved slaughter room on the farm (7/15 vs. 0/70, p < 0. 01). Given the slaughtering of small numbers of animals to supply the local market, few slaughtering data were recorded on the independent farms and it was not possible to collect them. Data regarding the slaughter age and weight of organic broilers were obtained for about 60 flocks on the farms in companies (despite contacts with the farmers): The chickens were slaughtered at an average of 86 days (SD = 5.
The survey questionnaire and the protocol of observations were tested and standardized before implementation, and data were collected by only two investigators. Any assessment bias during observations can therefore be considered limited. The assessment of the feather, the footpad dermatitis, and hock skin lesions was based on the Welfare Quality ® protocol. However, a wide range of other welfare indicators are included in Welfare Quality ® , like mortality or litter quality. In our study, the mortality was reported daily by the farmer and the litter quality was scored according to our protocol. The same referential for all the indicators would have given a more standardized picture of the welfare status.
15. 0 (2012-03-30) (http://molpath. charite. de/cutoff/). The methodology relies on the use of a mixture model of two Gaussian distributions fitted to the histogram of the drug susceptibility (IC50). The optimal cutoff was determined as the value where the probability density functions of the mixing distribution coincide  [38]. The correlation analysis on NFX and BZN susceptibilities at distinct parasitic stage was performed using the linear regression function of GraphPad Prism 6. 0 (GraphPad Software, La Jolla, CA, USA).
Patients Patients were selected from the local clinical data base between May 2001 and December 2017. We included all nocardiosis cases defined as "proven", "probable" or "possible" based on the similar invasive fungal infection (IFI) classification  [17]. We defined "possible" nocardiosis by the presence of clinical and radiological features (nodule, condensation, abscess), with or without dissemination  [8] , no alternative diagnosis and improvement after anti-Nocardia treatment in an immunocompromised host. "Probable" nocardia was defined by identification of non-direct bacteriological criteria (direct exam, pathology, positive Nocardia PCR), and "proven" by positive Nocardia culture confirmation, as described recently  [18]  (table  1 ). Pathology arguments to bacterial infection or gram-positive bacillus were considered as non-direct bacteriological criteria.
In purpose to strength our analysis, we selected a matched 2:1 control cohort based on the following matching criteria: on hematological disease, AHCT conditioning (myeloablative, MAC or reduced intensity conditioning regimens, RIC), primary graft-versus-host disease (GHVD) prophylaxis (drugs regimens), HLA matching (MRD, MUD, MMUD and haploidentical), sex, age, and season at the AHCT from retrospective data base. Immune reconstitution data over time were compared between these two groups during the first two years after AHCT. We collected data concerning hematological disease, AHCT management, follow-up of immune recovery, and nocardiosis characteristics directly from the charts.
Through interpreting the high factor loadings of the first two discriminant functions, the DFA indicated that a combination of vocal parameters was implicated in individual distinctiveness of cattle high-frequency calls. Across the putatively positive and negative datasets, the DFA consistently relied on AM var, AM rate, F0 var and F0 max to build the discriminant functions. The MANOVA then confirmed this vocal individuality, with all the parameters selected for this study, bar subharmonics, significantly differing between heifers. While many vocal individuality studies have examined formant frequencies due to their relationship with caller morphology  2, 14, 21  , this was less practical in the present study where calls were characterised by very high F0s and consequently less clear formant frequencies  22, 23.
Nevertheless, our results reveal that a range of source and nonlinear parameters have the potential to encode individuality information, which has similarly been demonstrated in the groans of fallow deer, where formants were less important cues to individuality  29. In the present study, to minimise over-estimation of individual classification percentages, we classified data that was not used to build the original discriminant functions  34. Our study utilised two separate datasets for DFA training and testing, with data including inhomogeneous samples from five farming contexts emitted over different days. This novel and robust approach enabled an increase in confidence around the accuracy of the analysis and subsequent stability of vocal individuality. We found that classification performance slightly declined when using the putatively positive and negative 'training sets' to classify vocal individuality in their respective opposite putatively positive or negative 'test set'.
For cattle, which are a social herd-living prey-species 1 , emitting idiosyncratic calls, especially during times of high arousal, could facilitate altruism directed from conspecifics  37  , with whom they develop stable social relationships  1. In the current dataset, the slight decline in classification percentages across valences could be explained by two reasons. Firstly, due to the rarity of vocalisations emitted by heifers in some contexts and the difficulty in obtaining high-quality vocalisations in commercial farming environments, the calls were unevenly distributed across putatively positive and negative valence. Secondly, this classification decline could relate to the within-heifer vocal variability which likely arose from cattle being exposed to different emotionally loaded contexts.
Additionally, if calls were produced as part of a sequence, we only selected them for analyses if they were more than 10 s apart in order to reduce homogeneity associated with consecutive calling. In total, 53 of the 333 vocalisations were derived from sequences of low and high-frequency vocalisations, with only two vocalisations selected from the same sequence considering they were non-consecutive. Vocalisation analyses. Vocalisations were analysed using Praat DSP package v. 6. 0. 31  50 , through both calculation off the oscillograms and spectrograms; and by using a series of custom-built scripts  31, 51  to automatically extract a range of acoustic features. Vocalisations were visualised as narrow-band spectrograms (FFT method, window length = 0. 1 s, time steps = 1000, frequency steps = 250, Gaussian window shape, dynamic range = 60 dB) and a total of 21 vocal parameters were measured in each of the vocalisations (Table  3 ).
Prior to running the scripts, the full duration (s) of the call was measured directly off the oscillogram. Nonlinear phenomena were widely prevalent in the calls including 80% and 93% of the putatively positive and negative calls, respectively (See Supplementary Methods for further details on prevalence). For this reason, the percentages of nonlinear phenomena relative to the full call duration were calculated off the spectrogram. Nonlinear phenomena criteria were adopted from previous vocal studies in non-human mammalian species  [52] [53] [54] [55]  and included deterministic chaos, biphonation sidebands, subharmonics, and frequency jumps. Example waveforms and spectrograms of the nonlinear phenomena are provided in Fig. 2. Before extracting the vocal parameters, a script was run to add silences of 0.
1 s to each side of the 333 calls. Using custom-built scripts in Praat  31, 51  , we then batch-processed the acoustic analyses, with output data exported to Microsoft Excel for further examination. In the script, pitch floor and ceiling settings were adapted to the  individual heifer voices and these settings were maintained across calls collected during positive and negative valence for a given heifer. Specific Praat procedures are detailed in the Supplementary Methods.
Statistical analyses. Statistical analyses were performed using SPSS v. 24 (IBM Corp. Released 2016). Two separate stepwise discriminant function analysis (DFA) procedures were used to quantify the extent of which individual heifers could be classified based on their calls. The DFAs were conducted both within each putative valence and across the putative positive and negative valences to establish whether individual differences in the high-frequency calls of heifers are maintained. In both DFAs, the grouping variable was heifer (1-13), the discriminant variables were the 21 vocal parameters and the selection variable was valence (positive = 1 or negative = 2). A first DFA was run with the selection variable set to 1 (positive). In this DFA, the 170 putatively positive calls were used as a 'training set' , to firstly classify the 170 putatively positive calls to the correct individual, and secondly classify the 163 putatively negative calls in the 'test set' to the correct individual.
To confirm the accuracy of the DFA classifications, we used two-tail binomial tests to see whether correct classifications were significantly higher than chance expectations  34, 35, 56. Graphical representations of the first two discriminant functions scores for heifer vocal individuality were additionally formulated in R Studio v. 1. 1. 463 using the ggplot2 package  57. We also conducted two multivariate general linear models (MANOVA) using the putatively positive and negative calls separately, to determine whether there were significant differences between heifers in their 21 vocal parameters. In both MANOVAs, heifer was included as the categorical fixed factor and the 21 vocal parameters were used as the independent variables. Descriptive statistics (means ± SE) are provided in the Supplementary Materials for all vocal parameters of heifers during putative positive and negative valence.
Acknowledgements We thank all of the farm staff and undergraduate students who assisted with the field work and cattle husbandry procedures. Author A. G was supported by an  Australian Government Research Training Program (RTP) Scholarship. This research was partially supported by the  University of Lyon IDEXLYON  project as part of the " Programme Investissements d' Avenir"  ( ANR-16-IDEX-0005 ), the  Labex CeLyA , the  Institut universitaire de France (NM) , the  Centre national de la recherche scientifique (CNRS) , and the  University of Lyon/Saint-Étienne.
Measures of animal's body weight Body weight data were collected at birth, then three times before the weaning period (once per week), two times per week from 29 to 50 days, once per week from 50 to 100 days, and then once every 2 weeks until the end of the animal's life. On average, each pig was weighed 20 times. The measures of animal's BW were carried out with two different scales: a SWR3P1 (Balea Group, Saint-Mathieu-de-Tréviers, France) scale (with a BW limit of 30 kg) for the nursery, and a SWR3P-BMC scale (with a BW limit of 150 kg) for the fattening period. Faecal score data Faeces were individually observed at days  0, 2, 6, 8, 12, 15, 20, 27 and 34  with respect to weaning and were scored according to three levels: 0 for normal, 1 for soft faeces but without diarrhoea and 2 for evident diarrhoea (Le  Floc'h et al.
, 2014). The faecal score data were analysed in three different ways: (1) by summing the number of diarrhoea measurements and correcting by the number of observations per animal (FS_sum); (2) by group levels, being level 0 for those animals that have no diarrhoea observations on any of the measures, level 1 for those that have only one diarrhoea record and level 2 for the animals that have shown two or more diarrhoea records (FS_gr); and (3) by the presence or absence of diarrhoea records (FS_p_a) taking into account all the observations made. Blood sampling and haematological traits Blood samples were collected at 28 and 34 days of age from the jugular vein into 4 mL vacutainer tubes containing ethylenediaminetetraacetic acid as anticoagulant.
Model evaluation and calibration We tested the structural identifiability of both the Gompertz and the perturbed models. That is, we determined if it was theoretically possible to determine uniquely the model parameters given the available measurements (see, e. g. Mu ñoz-Tamayo et al. (2018) for a discussion on structural identifiability). Identifiability testing was performed using the freely available software DAISY  (Bellu et al. , 2007). Both models are structurally globally identifiable, implying that the parameter estimation problem is well posed (it has unique solution). The mathematical models (Gompertz and perturbed) were further calibrated for each animal by minimising the least squares error: J E ¼ X n t i¼1 W i À W d;i À Á 2 (7) where W d is the weight data (kg), W the weight predicted by the model and n t the total number of measurements.
Statistical analyses to relate model parameters with haematological traits Correlation analyses were performed to explore the relationships between the perturbed growth model parameters and the faecal score data and haematological indicator data. Pearson and Spearman correlations among them were analysed in R using the 'cor' function in the base package (R Core Team, 2017). When required, data were normalised applying the log 2 or log 10 transformation. Visualisation of correlations was performed with the 'corrplot' R package  (Wei and Simko, 2017). Only the correlations with P-value less than 0. 05 were considered as significant and were thus represented.
Growth curve modelling from weaning To get an overview of the piglets' response to weaning, the BW measurements of the first 75 days after this critical period were analysed. Figure  1  displays the BW dynamic trajectories of four animals exhibiting two extreme patterns. The experimental data were compared to predicted responses given by both the Gompertz and perturbed growth model. Animals were ranked according to the goodness of fit for the Gompertz equation given by the criterion J in Eq. (  8 ). This ranking provided a first indication on the magnitude of the perturbation and animal resilience, i. e. the higher the value of J, the greater the degree of growth perturbation (Supplementary Table  S1 ). Moreover, the analysis of J reflects a difference of one order of magnitude between the most growth-perturbed animal and the least growth-perturbed one.
Acknowledgements We are grateful to the personnel at  GENESI 's farm at  Le Magneraud  for their implication for the generation of animals and samples. Experiments were funded by the  Pigletbiota  project by the  French Agence Nationale de Recherche (ANR ; project:  ANR-14-CE18-0004 ). We are grateful to all members of the  Pigletbiota consortium  that supported this project, and which include DELTAVIT (CCPA group),  InVivo-NSA  ( InVivo group ),  LALLEMAND, SANDERS (AVRIL group ) and TECHNA companies and the  ALLIANCE R&D association (AXIOM, CHOICE GENETICS , NUCLEUS and IFIP). We are also grateful to the  Valorial competitiveness cluster  for its support to the project. Note: a preprint version of this manuscript was previously published at https://doi. org/10. 1101/428920. M. Revilla ,.
, 2010; Lansade et al. , 2018). Concerning rider safety the way a horse perceives humans can influenced the level of risk during handling  (Hall et al. , 2018) , and this is supported by our results. Indeed, regarding the threatening behaviours demonstrated by the horses, nine potentially dangerous incidents were observed (teeth or a hoof passing within 10cm of the rider's body or head). The riders often did not see what had happened, because the horse was not in their field of vision when the incident occurred. Fortunately, the incidents observed in our study did not lead to injury. However, when these data are compared with the rate and severity of accidents with riders on foot, which are much higher than for riders in the saddle, it can be seen that it is not always the case  (Wolyncewicz et al.
Therefore, future studies are needed to investigate the impact of these factors on the footbath contamination. No major differences were recorded in any of the farms when comparing the average OM concentrations before and after a stand time of more than 12 h (between milkings) (difference between means = -0. 91 (SD 2. 06) g/l (data not shown)). Similarly, during the stand time, no reduction on the bath's volume (liquid depth) was recorded. Nevertheless, the number of passages before the stand time, the stand times and the temperatures during the stand time were highly variable between farms, restricting comparative analyses or any inferences with the small n collected. The ML of footbaths increased as well with the number of cow passages (r τ = 0. 59, P < 0. 001), and the number of defecations (r τ = 0.
However, the small sample size of this study enabled to evaluate the variations of these parameters. From the records, slight variations on the pH measures were observed within and between the farms during the cow passages. The baseline pH measure differed between the sampled farms (Table  2 ). The variation on the temperatures recorded during the cow passages was evident and following the ambient temperature (data not shown). Further studies are necessaries to evaluate properly, if the initial pH from water sourced and the small variations on pH during the cow passages could affect the efficacy of specific biocides. From the 1200 cow passages recorded, only one cow urinated and on average over 200 passages 6% of cows defecated in footbaths. These findings are similar to the results of Manning et al.
Copromicroscopical survey in sheep farms The sample size of the farms studied was estimated considering 15,387 Sardinian sheep farms (National Data Bank of the Italian Ministry of Health; https ://www. vetin fo. it) with an expected Dicrocoelium spp. prevalence of 15%, and confidence level of 95% (http://www. raoso ft. com/sampl esize. html). A total of 190 dairy sheep farms in Sardinia (Fig. 1 ) were investigated from 2013 to 2014. Within each flock, 15 individual rectal faecal samples from sheep older than 3 years of age were collected. These samples were then split into three different faecal pools from five animals, which were then analysed using the FLOTAC ® method with a heavy saturated zinc sulphate solution, specific gravity (SG) 1350)  [31, 32]. Data were processed for each farm considering the eggs per gram (EPG) mean of the three faecal pools.