dataesr/scientific-paragraphs-categorization huggingface dataset:
Bootstrapped ADMIXTURE (100 bootstrap pseudo-replicates) found that MV242 contained 4. 15±0. 19 % wild genetic material (Fig. 2C ). We conducted a haplotype-based clustering analysis of all modern and ancient domestic donkeys using fineSTRUCTURE (version 4. 1. 1) (35). We converted the variants in the VCF file and the recombination map present in all individuals (n=2,245,992) to the required input file formats using custom R scripts and the provided perl scripts from the fineSTRUCTURE package. We excluded 58 Chinese and Tibetan donkeys so as to avoid overrepresenting this region. Additionally, we removed modern individuals that were identified in the previous ADMIXTURE analysis as having a high proportion of wild admixture (n=6) and admixture between different populations (n=2), which were found to confound the output, resulting in a final dataset of 172 individuals.
FineSTRUCTURE was run with default parameters to paint the chromosomes and model haplotype sharing between individuals. The maximum likelihood tree and co-ancestry matrix was plotted from the output files using modified versions of the R scripts provided with the fineSTRUCTURE package (Fig. 2A , 2B, S9). To estimate the genetic sharedness between each ancient individual with the modern subpopulations, we calculated outgroup f3-statistics in the form of (modern, ancient; kiang) using ADMIXTOOLS2  (58, 65) , using only variants present in all individuals (n=2,245,992). We used the mean and standard error from the outgroup f3-statistics to plot a heatmap comparing relatedness between the ancient individuals to the modern populations (Fig. 3B ). To further confirm the genetic makeup of our ancient individuals, as inferred by fineSTRUCTURE analysis and outgroup f3-statistics, we constructed Treemix models using the imputed matrix with variants present in all individuals, first pruning the matrix in PLINK using the parameter "--indep-pairwise 50 10 0.
2" (n=175,093 variants after filtering). In accordance with earlier Treemix models (Fig. 1B ), we removed modern donkeys with high proportions of wild admixture or that were hybrids between different regions, and included   S2 , Fig. S10 ). However, in two sites, fineSTRUCTURE analysis showed potentially different genetic makeup in individuals from Yenikapi and Shahr-i-Qumis, so were modelled separately. Each Treemix model was run for 0-10 migration events with 5 replicates and a k value of 1000. The optimal migration edges were inferred using optM, and the 100 bootstraps were preformed using the BITE package as above (Fig. 2C , 2D, 2E 3C, 3D, 3E, S10). A deletion in TBX3 has been found to be responsible to the phenotypic change from a grey dun coat to a coloured coat in donkeys  (13). A single nucleotide deletion in the TBX3 gene (CT>C-) results in derived coat colours in homozygous individuals, which has previously been annotated to JADWZW010000009.
1:42742556 on this version of the assembly  (13). We genotyped all ancient and modern individuals in our dataset. As a confirmation of the validity of this genotyping, we found that all wild individuals were genotyped for the dun coat colour, but the reference individual (a black donkey) was genotyped for a derived coat colour. With a post-imputation filter of GP>=0. 99, 19 out of 31 ancient donkeys were genotyped for the TBX3 locus. However, with a GP>=0. 9, the TBX3 genotype of 25 ancient individuals could be inferred. Coat color phenotypes in ancient donkeys showed that derived coat colors were present across multiple locations, ranging from western Asia (Iran, Shahr-i-Qumis) to Iberia (Portugal, Albufeira) (Fig. 3A ). Colored coats appeared almost simultaneously in our dataset in samples from Shahr-i-Qumis and Boinville-en-Woëvre.
05", with sites covered in at least 75% of individuals. Transitions were removed and the separate chromosome files were merged together, before running NgsRelate (n=473,263 variants). High correlations between the KING coefficient estimated using NgsRelate and the IBD coefficient estimated for the phased and imputed data using the KING software (r=0. 871, r 2 =0. 759) showed that accurate relationship inferences could be inferred using imputed data (Fig. S12 ). We estimated inbreeding as runs of homozygosity (ROH) for all modern and ancient donkeys using three methods. First, using PLINK using the "--homozyg" function for all imputed transversions (n=238 individuals, n= 1,949,850 transversions), with a cut off length of at least 1 MB. Estimating runs of homozygosity requires dense haplotypes, however imputation errors in the low-coverage ancient samples may lead to inaccurate calculations of inbreeding levels.
We estimated ROHs using NgsF-HMM, using the same files as generated for NgsRelate (n= 473,263 variants), and using a minimum epsilon of 1e-8. We then filtered the ROHs to only select those with a total length over 1MB, containing more than 100 SNPs and with at least one SNP per 50KB on average, in line with the parameters defining an ROH in PLINK (Fig. 4A , Fig. 4B ). We also estimated ROHs from the bam files of the modern and ancient donkeys by searching for regions with a low density of heterozygous variants. First, we down sampled the bam file for each modern and ancient donkey to the lowest coverage sample in our dataset (0. 77X) using SAMtools. Next, we generated counts files using ANGSD with the parameters: "-doCounts 1 -dumpCounts 4", conditioning only on sites with a MAF >=0. 05 in modern donkeys.
We added ancient donkeys from each site to the Treemix models separately, then estimating the optimal number of migration edges and performed 100 bootstrap pseudo-replicates for each model. We found that placement on ancient donkeys on the Treemix models constructed using imputed and pseudo-haploidized data was highly similar, confirming the accuracy of our imputation panel (Fig. S10 ). Next, we constructed a neighbour joining tree to further confirm the population structure of the modern and ancient donkeys. We first calculated pairwise genetic distances between all samples using PLINK, then retrieved the tree topology by implementing the bioNJ algorithm in FastME (version 2. 1. 4)(129), with 100 bootstrap pseudo-replicates to assess node supports (Fig. S15 ). The genome of MV242 was found to contain divergent genetic material, as confirmed by ADMIXTURE analysis and Treemix phylogenies using imputed data (Fig.
2E , 3A, S4, S10, S15). However, because there may be errors in the imputed haplotypes of this individual due to the divergent genetic makeup, we used pseudo-haplodized data for further analysis. We modelled f4(E. a. som, MV242; HORN+KEN, x) statistics to determine whether genetic material from this lineage was present in modern donkey subpopulations (x) using qpDstat (version 751) from the Admixtools package  (58, 65). We grouped modern donkeys into the same subpopulations used on the Treemix models (Fig. S10 ). P-values were obtained through multiple test correction of Z-scores with a significance threshold of 0. 05. Positive and significant f4-statistics provided evidence of MV242 ancestry in modern donkeys from eastern Asia, Nubia, central Asia, Turkey, Syria, Tunisia, Iran and western Europe (Fig.
Uniparental markers To construct the mitochondrial phylogeny, we called variants with "-doHaploCall 1 -minMapQ 25 -minQ 30 -doDepth 5" using ANGSD. Additionally, we included the mitochondrial genomes of three Equus hemionus hemippus (accession numbers: ERS7669491, ERS7669492, ERS7669493) (20) (n=2,805 variants, n=256 individuals. We generated a tree with IQ-TREE (version 1. 6. 12) (60), using 100 bootstrap pseudo-replicates for assessing node support (Fig. 5A ). The tree was rooted between the zebras and hemiones+ kiangs, as per Jónsson and colleagues  (24). To construct the Y-chromosome phylogeny, we called variants using ANGSD with the parameters: "-isHap 1 -baq 1 -remove_bads 1 -uniqueOnly 1 -minMapQ 25 -minQ 30 -rmTriallelic 1e-4-SNP_pval 1e-6 -C 50" for all male equids in our dataset (n=125), conditioning on transversions only and including only variants present in more than 90% of individuals, leaving a total of 3,171 variants in the final dataset.
We generated a tree with IQ-TREE (version 1. 6. 12) (60), using the same parameters as those used to generate the mitochondrial tree (Fig. 5B ). To estimate the time to the most recent common ancestor (TMRCA), we constructed Bayesian skyline plots using mitochondrial and Y-chromosome variation of domestic donkeys only (n=238 and 121 individuals, respectively) using BEAST (version 2. 6. 5) (68-70). We estimated the optimal substitution model for both datasets using the BIC scores estimated from IQ-TREE. we converted the multi-alignment fasta files to BEAST input files using BEAUTi (version 2. 5. 26) (68-70) specifying the following parameters: 1) the optimal model for all three datasets was GTR, with an empirical distribution and a gamma category count of 4. 2) Tips of the ancient individuals were dating in years before present using radiocarbon dates, where available, or the mean of the time period estimated from archaeological context.
For ancient donkeys from Shahr-i-Qumis, their age was inferred from the single individual radiocarbon dated at this site (AM805). run for a total of 500,000,000 iterations for Y-chromosomal and 350,000,000 for mitochondrial reconstructions. The posterior distributions of the tree heights were generated using Tracer (version 1. 7. 1) (130) with 20% as burn-in (Fig. 4B, D ). Table  S1 : Sample information for all modern donkeys and wild equids (n=222). The country of origin, short country code, genome depth-of-coverage, the proportion of missing variants after variant calling and accession number are reported. Accessions numbers starting with "SRS" were downloaded from the National Library of Medicine, "ERS" from the European Nucleotide Archive, and those starting with "SAMC" from the Genome Sequence Archive database.
(21,41,50,131)tion and accession numbers for each modern (n=79) and ancient 877 horse (n=75) used for estimating inbreeding levels (Fig. 4D, E, F). Whole-genome sequence 878 data and metadata on the site, country and age (inferred from the radiocarbon dates) for the 879 ancient horses were obtained from(21,41,50,131). Whole-genome sequence data for.
Aren Maeir, for facilitating sampling. The DonkeyBank collection of modern donkey DNA samples is supported by the  European Union 's  Horizon 2020 Research and Innovation Programme  (Grant Agreement Number  857251 ). Lucia Perez-Pardal  is funded by national funds from  FCT -Fundação para a Ciência e a Tecnologia, I. P. Genoscope and GeT-PlaGe  sequencing platforms are partly funded by  France Génomique National  infrastructure, funded as part of  Investissement d 'avenir' program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). This project has received funding from the  CNRS, University Paul Sabatier (AnimalFarm IRP) , and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreements 885729-AncestralWeave, 295729-CodeX, 853272-PALAEOFARM, and;  681605-PEGASUS ).
60. B. Q. Minh, H. A. Schmidt, O. Chernomor, D. Schrempf, M. D. Woodhams, A. von Haeseler et al. , IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530-1534 (2020). Fig. 1: Modern donkey dataset and population evolutionary history. A) Number and geographical distribution of modern donkey samples (n=207). Pie charts show the ADMIXTURE proportion of domestic ancestry (grey), African wild ass ancestry (white) and kiang ancestry (black) averaged across all individuals from each country (56). For visualization, the total surface of each pie chart is scaled to 2%. B) Smartpca (57) of modern donkeys, with the imputed ancient samples in black. C) Treemix phylogeny of modern domesticates (excluding individuals with high wild introgression, n=201) (27).
Node supports are estimated from 100 bootstrap pseudo-replicates (confidence < 90% in red). Percentage values indicate admixture proportions inferred from Treemix (27). D) SMC++ demographic trajectories (colored) and split time estimates (black) for pairs of main geographic regions (28), repeating the analysis on two datasets of three individuals per population (the second dataset is shown in semi-transparency). Modern donkeys are colored and shaped according to geographical location and continents in all panels.
We created two datasets to fully exploit the genetic information of these samples, both pseudo-haploidising genomes at transversion sites (n= 4,833,570), and imputing genomes for the set of variants identified in the modern panel (n=7,161,029) (BEAGLE Evelyn T. Todd, Laure Tonasso-Calvière, Lorelei Chauvey, Stéphanie Schiavinato, Antoine Fages, versions 4. 0 and 5. 1). Those datasets were used to infer the past population dynamics and assess Andaine Seguin-Orlando, Pierre Clavel, Naveed Khan, Lucía Perez Pardal, Laura Patterson Rosa, Pablo breeding management through ADMIXTURE, PLINK, Treemix, fineSTRUCTURE (version Librado, Harald Ringbauer, Marta Verdugo, John Southon, Jean-Marc Aury, Aude Perdereau, 4. 1. 1) (35), KING (version 2. 2. 7) (66), NgsRelate (version 2) (67), NgsF-HMM (version 1) Emmanuelle Vila, Matilde Marzullo, Ornella Prato, Umberto Tecchiati, Giovanna Bagnasco Gianni, Antonio Tagliacozzo, Vincenzo Tinè, Francesca Alhaique, João Luís Cardoso, Maria João Valente, (59) and qpDstat (version: 751) (58, 65).
3. 0) (105) following the methodology from Gaunitz and colleagues (50) for single indexed DNA libraries, and the methodology from Librado and colleagues (21) for triple indexed libraries. AdapterRemoval2 also ensured that paired-end reads showing sufficient sequence overlap were collapsed and trimmed (truncated) if ends showed insufficient qualities. Collapsed, truncated and those paired end reads not collapsed (paired) were then parsed through PALEOMIX version 1. 2. 13. 2 (106) for Bowtie2 mapping against the donkey mitochondrial (CM027722. 1), and nuclear reference sequence (GCA_016077375. 1,https://ftp. ncbi. nlm. nih. gov/genomes/genbank/vertebrate_mammalian/E quus_asinus/all_assembly_versions/GCA_016077325. 1_EquAsi1. 0). Finally, the optimized parameters recommended by Poullet and Orlando.
The main outcomes of interest were perceived speaker height and aggression. Ohala's frequency code  [17]  predicts that gradually lowering formant frequencies should convey large size and an aggressive attitude, while raising them should have the opposite effect, as also reported by Chuenwattanapranithi et al. [15]. To distinguish between aggression and general arousal, we added an additional response scale for emotion intensity; perceived authenticity was also measured to check whether experimental manipulations sounded natural.
Data analysis Unaggregated responses from the tests of English speech, Persian speech and roars (47 993 trials) were pooled and analysed using a single Bayesian multilevel model fit with the R package brms  [30]. Posterior distributions of model parameters and fitted values were summarized by their medians and 95% credible intervals (CIs). We compared credible values of effect sizes: when the credible intervals on estimates are far from the null value (e. g. zero), this indicates a credible effect given the observed data, model structure and prior knowledge. The outcome variable was the rating of a vocalization on a continuous scale (0 to 1), which was modelled with zero-one-inflated beta distribution  [31]. The model predicted the rating in an individual trial as a function of Experiment (three levels: English speech, Persian speech and roars), Condition (four levels: low, high, rising and falling), Manipulation strength (four levels: 0, 1.
Moreover, when formant frequencies are shifted around some average value (Experiment 1), the resulting change in apparent VTL actually has a negative effect on apparent size compared to holding the VTL stable at the same average value, although it does make the caller sound slightly more emotional. For a vocalizer trying to intimidate, the safest bet is therefore to elongate their vocal tract before they start speaking or calling and to keep it extended, never letting the audience hear what the 'short' end of their vocal range sounds like. It is important to emphasize that this finding applies specifically to aggressive vocal signals in humans and needs to be both replicated and extended. We focused on aggression because it maps most directly and uncontroversially on Ohala's frequency code  [17] : if a particular vocal change makes a speaker sound larger or smaller, as VTL changes are known to do, it is highly likely to be relevant to the expression of aggression.
During spring, observations on the same day were interspaced by two, or two and a half hours. During fall, as days shortened over time, observations within the same day were spaced from 3 hours (period 1) to 2 hours (periods 2 and 3). To obtain the behavioral budget of chickens and rate of recorded behaviors, behavioral data were collected via a continuous sampling method. Each focal animal was observed for 30 s, five times a day during the spring, and four times a day during the fall. Behaviors were divided into "states" and "events. " "States" were all behaviors lasting more than 1 s (e. g. , standing, resting, and locomotion), and "events" were those lasting <1 s (e. g. , shaking, wing flapping and pecking). An event could also occur in "bouts" (a short period of intense activity of a specific behavior), but events within bouts were counted separately and individually  (3, (43) (44) (45).
Positive social pecking ATOL_0000846 ATOL_0000817 ATOL_0000826 Pecks on the beak of a flockmate (mainly to collect feed particles) or on another part of the body without damage to the plumage and skin of the targeted chicken *These behaviors were rare (<5% of the observed individuals expressed the behavior) and were discarded from statistical analyses. **Traits in reference to the ontology ATOL: https://www. atol-ontology. com/en/atol-2/, in accordance with the PILLOW project data management plan. tested chickens, individuals were kept in another crate with the same dimensions and stocking density. At the end of each halfday (∼4 h for all individuals to be tested), all tested individuals were released back into the barn.
Chicken behaviors were also correlated to the total percentage of individual range visits (% period 2 + % period 3), to determine which behaviors were linked to range use. Finally, to verify if behaviors were consistent over time, non-parametrical partial correlations, within each behavior and across periods, were run. Through these analyses, we aimed to verify whether behaviors from consecutive periods were similar (period 1 x 2, and period 2 x 3), but also whether chicken behavior after range access could be predicted by early behavior, before range access (period 1 x period 3). Due to the multiple correlations carried out, p-values were corrected using the Holm-Bonferroni method. All statistical analyses were performed using IBM SPSS 21. Statistical significance was accepted at p ≤ 0. 05 (after corrections, when applied).
Accordingly, latent pain sensitization was mostly evidenced in rodents' models of chronic pain, but still need to be established in chronic pain patients. In this case, further studies will be needed to determine more precisely its underlying molecular mechanisms. This would provide new insights on therapeutic strategies design by identifying new targets. Notably, further chronic pain treatment developed should display not only analgesic but also anti-hyperalgesic properties that could prevent the transition from acute to chronic pain such as ZH853  (Feehan and Zadina, 2019)  or nefopam  (Laboureyras et al. , 2009). from the French government managed by "Agence National de la Recherche" under "Programme d'investissement d'avenir" (ANR-10-LABX-0034 and ANR-17-EURE-0022, EURIDOL graduate school of pain).
Blood samples were collected at 24h before and 1h, 3h, 5h and 24h after the injection to characterize the physiological response to the challenge  (Parois et al. , 2022) , which is not reported here. To minimize the effect of human interventions on distance moved, data of the whole pen were removed from 1 minute before until 4 minutes after the start of LPS injection and blood sampling. Videos from 3 days prior to injection starting at 7:00 till 4 days post-injection ending at 18:00 were selected to measure the distance moved (see below).
Frame Rate Based on earlier experience (van der Zande et al, 2021), there was inconsistency from the tracking algorithm on how precise the bounding boxes were placed in a frame, even though the pigs themselves were not moving (e. g. sleeping). For calculating distance moved, this would lead to a considerable overestimation of activity. By not using all the 10 FPS of the video, some of the noise could also be deleted. To find the optimal frame rate, a gold standard dataset was annotated using the computer vision annotation tool CVAT  (Sekachev et al. , 2019). To create the gold standard, every 4 th frame was manually annotated, and CVAT interpolated the frames in between to minimize the noise an annotator could introduce by manually drawing bounding boxes for every frame. All the frames of in total 12 videos of three minutes each were annotated, balanced for enriched and barren housing, and high and low activity levels, where in a low activity video at least 5 pigs were not moving, and high activity had at least 5 pigs moving.
Resilience Parameters Based on the rolling mean using a 24-hour window, resilience parameters could be quantified (Figure  2 ). During the three-day baseline, the mean and maximum error (i. e. maximum deviation observed from the mean) was calculated for each individual. The mean and boundary of the error were applied to post LPS injection observations and represented their original state, i. e. state before injection. Changes in activity were identified by finding the longest consecutive stretch of data points below the boundary starting not later than 24 hours post-injection of LPS. From this change (i. e. , a dip), several resilience parameters could be quantified, namely a binary trait of showing a dip in activity or not, the time from the injection to the start of the dip (referred to as onset of the dip), the recovery time from injection to the end of the dip, the Area Under the Curve (AUC) of the dip to the boundary (Figure  2 ).
Statistical Analysis Statistical analyses were performed with the software R 4. 1. 0. (R Core Team, 2013). Pearson correlations were calculated between the distance moved according to the gold standard and the tracking algorithm at different frame rates, to assess the optimal frame rate for assessing distance moved. Resilience parameters were analyzed using (generalized) linear mixed models with the function 'glmer' and 'lmer' from the R package 'lme4'. All parameters, except the ratio of AUC and maximum percentual decrease, were log-transformed to obtain normality. The effect of housing, sex and their interaction on average distance moved and maximum error during the baseline was analyzed with a linear mixed model. Concerning the response to LPS, first, the effect of the challenge treatment (LPS injected vs.
The time from injection to start of the dip (onset of the dip) and ratio of AUC were analyzed for the pigs with a dip in activity only. Pen and batch were included as random effects in each model. P-values below 0. 05 were considered as significant effects, and Pvalues below 0. 1 as tendencies. When a significant effect was found, comparisons between groups were made with the 'emmeans' function from the R package 'emmeans', including a Tukey correction.