dataesr/scientific-paragraphs-categorization huggingface dataset:
These are the Danish Data Protection Agency (https://www. datatilsynet. dk/english) for phenotypic data from study participants recruited in Denmark, the Federal Commissioner for Data Protection (https://www. bfdi. bund. de/EN/Home/ home_node. html) for phenotypic data from study participants recruited in Germany and the Commission Nationale Informatique & Libertés (https://www. cnil. fr/en/home) for phenotypic data of study participants recruited in France. Application procedures are given on the outlined websites. If such permission is granted, phenotypic data will be then made available by the corresponding authors within 5 weeks. Source data are provided with this paper.
Semi-free ranging Tonkean macaques may face social dilemmas such as displacing a peer to reach their preferred juice reward or avoiding conflict by choosing a less preferred available reward. Nonsocial decision. We extracted N = 8 096 situations where all MALT were unoccupied for more than 30 s before the beginning of the new player session. These data were used to compute individual preferences for juice flavors. Each individual displayed personal preferences to work on a specific MALT that is arguable driven by preference in reward flavor. (B) Socio-economic decision. We considered a total of N = 51 954 social dilemmas where a new player had to decide in which MALT he/she will work on. The hierarchical rank of others monkeys (depicted here from black to light gray for higher to lower ranking, respectively) were considered to model the new player social dimension of these decisions.
Four different flavours of syrups (same sucrose concentration) were available during data collection (exotic fruits, apple, peach and strawberry). MALT allows automatic identification of each subject due to a RFID dual-detection system. 71  For that purpose, subjects were all equipped with two RFID microchips (UNO MICRO ID / 12, ISO Transponder 2. 12 * 12mm), injected into each forearm during the annual veterinary health check under appropriate anaesthesia. When the RFID chip of an animal is detected by the MALT, it resumes his/her personal experimental sessions, which remains open for 30 seconds after the last screen touch or RFID detection. If another animal tries to engage with the cognitive tasks while another individual's session is active, a displacement event (mentioning a 'winner' that managed to displace the 'loser') is recorded in our database.
This method is currently unable to record situations in which the new player failed at displacing the other monkey. Social hierarchy of the group (see Figure  S4 ) can be computed indifferently using these displacement events or more classical observation of spontaneous social conflicts. 26, 74  QUANTIFICATION AND STATISTICAL ANALYSIS Our dataset includes 26 MALT players, 260 days (between 23/03/2017 to 15/10/2018) where all of the four MALT were used for more than 30 minutes on a given day. We extracted 8 096 situations where all MALT were unoccupied for more than 30 seconds before the beginning of the new player session. These data were used to compute individual preferences for juice flavours. We considered in our model the mean subjective preferences for juice reward during the whole duration of data collection and thus approximate that these preferences were stable in time.
We considered 51 954 social dilemmas where a new player had to decide in which MALT he/she will play while at least one of the MALT was already occupied by a peer. We also considered for specific analysis in Figure  2  a filtered dataset where only the preferred MALT was occupied by a peer before the new player decision, this corresponds to 1870 social dilemmas. To assess statistical significance in this analysis, we used Wilcoxon paired (signed-rank test) and unpaired test (rank sum test) when appropriate. Fisher's exact test, a statistical significance test used in the analysis of contingency tables, was used to assess the effect of the presence of another monkey (vs nobody) in the preferred MALT of the new player. d KEY RESOURCES Dominance hierarchy was assessed using Elo-rating  75  and the package 'EloRating' in R,  76  ordinal ranks were used during data analysis and we approximated that hierarchical rank by using the mean elo-rating score over the experimental period.
Elo-rating was used with a starting score of 1000, and the k factor (i. e. , the maximal amount of 'points' an individual can get from an interaction, function: optimizek, package: EloRating) was optimised. Results of the Elo-rating analysis can be seen in Figure  S4. The data used to compute the hierarchy is based on the displacements recorded at the MALT. It has been recently demonstrated that the use of these information provide a reliable assessment of dominance hierarchy, similar to the one obtains using more classical manual observations of behaviours. 26, 74  Analyses were performed using custom scripts written in Matlab (R2022a, the Mathworks). GLM and stepwise analysis were performed using the function fitglm and stepwiseglm, respectively. Confidence interval of estimates were standard errors.
MALT X, Y correspond to the chosen and unchosen MALT respectively. We considered decisions amongst the 4 MALT as pairs of decisions between the chosen (X) and the remaining three unchosen MALT (Y). With a total of 26 subjects and 6 pairs of MALT, we can consider a total of 156 pairs of MALT. We used classical logit link function defined as: Choice MALT X vs Y = 1 À 1 + expðÀ xÞ Á (Equation  1 ) In the model depicted in Figures  3  and S1,  x was equal to: x = b 0 + brank ðRank monkey MALT Y À Rank monkey MALT XÞ (Equation  2 ) From the fitted parameters,  39  we derived measures for the indifference point (exp (-b 0 /brank)) and the sigmoid steepness (brank), used in the analysis depicted in Figure  4. In the model depicted in Figure  5 , x was equal to: x = b 0 + brank ðRank monkey MALT Y À Rank monkey MALT X + bjpref À Juice preference MALT X À Juice preference MALT Y Á (Equation  3 ) During data analysis, we approximated that the hierarchical rank of an unoccupied MALT was equal to the lowest ordinal hierarchical rank of the group plus 1 (here 26 + 1 = 27).
ACKNOWLEDGMENTS The authors are grateful to the  University of Strasbourg and Silabe  (https://silabe. com/) for supporting this research and providing expert animal care. The MALT development was supported by the  University of Strasbourg Institute for Advanced Study (USIAS)  as part of a  USIAS fellowship  to H. M. This work was further funded by  ANR-21-CE37-0016  to S. B. and H. M. We would also like to thank  Adam Rimele  for computer and programming support of the MALT. We also thank  Charlotte Canteloup  and  Romain Ligneul  for their insightful comments on an earlier version of this manuscript.
Here we approached this issue by undertaking extensive screening for the presence and diversity of trypanosomes in natural populations of 28 tick species from Europe (France and the Netherlands) and South America (French Guiana). We further conducted a phylogenetic analysis of the trypanosomes found in ticks to investigate their degree of specialization for ticks. To this aim, we estimated how phylogenetically related are trypanosomes shared among tick species in nature, including 18S rRNA (small subunit [SSU]), EF1-α, hsp60 and hsp85 gene sequences obtained in this study and those already available on public databases. If ticks are vectors of trypanosomes, we predict that they should harbour phylogenetically related trypanosomes clustering in a monophyletic clade distinct from other trypanosome clades associated with transmission by other blood-feeding invertebrates.
Phylogenetic analyses We reconstructed the phylogenetic relationships between trypanosome species and strains using 18S rRNA, EF1-α, hsp60 and hsp85 gene sequences obtained in this study and sequences available in GenBank, including sequences of representative trypanosome species and sequences obtained from previous studies based on DNA extracted from ticks (Table  1 ). The 18S rRNA global dataset used for phylogenetic reconstructions included sequences from 59 trypanosome species, and sequences of three Leishmania species used as outgroups. Because previous studies of trypanosomes in ticks (listed in Table  1 ) have used different 18S rRNA typing protocols, some of the sequences available in GenBank were obtained from distinct positions along the 18S rRNA gene sequences. Indeed, while some studies sequenced fragments located near the 5′ end of the 18S rRNA gene sequence  [12] , other studies sequenced fragments rather located in the middle  [27]  or near the 3′ end  [22].
In addition, most of these sequences are quite short (typically <350 bp) compared to the complete 18S rRNA gene sequence, which is 2100-2300-bp long depending on the trypanosome species  [12, 22, 27]. As a result, most of these short 18S rRNA gene sequences cannot be used in the same phylogenetic analysis because they do not overlap and cannot be aligned with each other. To circumvent this issue and include all trypanosome sequences of ticks in our phylogenetic analyses, we thus created four separate datasets: The first dataset is an alignment of a large 18S rRNA gene fragment (1950 bp) and the three others are alignments based on distinct fragments of the 18S rRNA gene sequence (5′ end region: 240 bp; middle region: 540 bp; 3′ end region: 250 bp). Because 18S rRNA gene sequence has been commonly used as an exclusive taxonomic marker for the trypanosomes detected in ticks (cf.
references in Table  1 ), no EF1-α, hsp60 or hsp85 gene sequences of tick trypanosomes were available on public databases. Phylogenetic investigations were thus based on EF1-α, hsp60 and hsp85 gene sequences produced in this study and those of representative trypanosome species available on GenBank. For each dataset, the gBlocks program  [28]  was used to remove poorly aligned positions and to obtain unambiguous gene sequence alignments. The 18S rRNA phylogenies were based on nucleotide sequences while the EF1-α, hsp60 and hsp85 phylogenies were based on amino acid sequences because of ambiguities in their nucleotide alignments. Phylogenetic reconstructions were based on maximum likelihood (ML) analyses using the MEGA program (https:// www. megas oftwa re. net/). The evolutionary models most closely fitting the sequence data were determined using Akaike information criterion.
A D value of 1 indicates a phylogenetically random distribution, whereas a D value inferior to 1 indicates phylogenetic clustering. We further assessed the significance of the D metric estimates using permutation tests: P (D < 1) indicates whether the D metric is significantly smaller than 1, meaning that the trait is not randomly distributed over the phylogeny. We calculated the D metric implemented by the function 'phylo. d' in the R package caper with the default parameter of 1000 permutations.
pestanai (94. 3% and 93. 5%, respectively; GenBank accession number: AJ009159) and T. copemani (93. 8% and 92. 6%, respectively; GenBank accession number: AJ620558). However, the trypanosome of I. ricinus was 99. 9% identical to the Trypanosoma sp. strain Bratislava 1 (GenBank accession number: MT482752) recently found in I. ricinus from Slovakia  [14]. Although we collected some infected I. ricinus feeding on roe deer, the trypanosome strain detected in our ticks was clearly distinct from the trypanosomes previously identified in this mammal (pairwise nucleotide identities <84%; GenBank accession numbers: KJ397593-KJ397597, KF805972, HM138533); the trypanosomes of roe deer are considered to belong to the species T. theileri  [30]. We further obtained the trypanosome EF1-α, hsp60 and hsp85 gene sequences from the seven infected I.
pestanai clade obtained from blood-feeding invertebrates other than ticks is present in public databases. However, within the T. pestanai clade, the trypanosomes of ticks also cluster with trypanosomes infecting mammals, including T. pestanai infecting European badgers and dogs, and T. copemani and T. gilletti, infecting a diversity of Australian and Tasmanian marsupials (Figs. 1, 2A-C ). Trypanosoma copemani, T. gilletti and T. vegrandis are distinct from the species T. pestanai, but all four have previously been found to be phylogenetically related species belonging to the T. pestanai clade  [2, 10, [31] [32] [33] [34]. Based on the 1950-bp 18S rRNA gene sequence alignment, T. pestanai, T. copemani, T. gilletti and T. vegrandis formed a robust clade, supported by a 99% bootstrap value, together with trypanosomes detected in ticks (Fig.
1 ). Further phylogenetic reconstructions based on three distinct regions of the 18S rRNA gene sequence enabled extension of the analyses to include additional members of the T. pestanai clade (Additional file 1: Figs. S1-S3 ), including T. caninum infecting South American dogs  [35] , and T. vegrandis infecting a range of Australian marsupials  [32]. Although the phylogenetic trees show some polytomies due to insufficient sequence polymorphism, each dataset confirmed the phylogenetic proximity of trypanosomes of A. oblongoguttatum and I. ricinus with other trypanosomes found in ticks. Also included in the T. pestanai clade are the trypanosomes found in 13 other tick species: Hae. hystricis, Hae. falva, Hae. bancrofti, R. microplus, Hya. anatolicum, Amblyomma triguttatum, A. brasiliense, Ixodes canisuga, I.
noyesi was identified in questing ticks  [36] , T. irwini was detected in engorged ticks from Koalas  [22]. Further phylogenetic analyses based on EF1-α, hsp60 and hsp85 gene sequences confirmed the positioning of the trypanosomes of A. oblongoguttatum and I. ricinus within the Trypanosoma genus (Fig. 2A-C ). The hsp60 and hsp85 phylogenetic trees further corroborated the close relatedness of the trypanosomes of A. oblongoguttatum and I. ricinus (no trypanosome EF1-α PCR product was obtained from A. oblongoguttatum; Fig. 2B, C ). Few trypanosome EF1-α, hsp60 and hsp85 gene sequences were available on GenBank, including sequences mostly from trypanosomes of the T. brucei and T. cruzi clades but not from the T. pestanai, T. caninum, T. vegrandis, T. copemani and T. gilletti species. This lack of data made it impossible to compare the trypanosomes of A.
cruzi, which can be transmitted by ca. 20 triatomine species belonging to different genera  [4]. The third line of evidence is the broad geographical distribution of Trypanosoma associated with ticks. While the presence of Trypanosoma is rarely investigated in ticks, they have been detected from distant continents (Europe, Asia, South America and Australia), suggesting a worldwide distribution of the T. pestanai clade. However, the distribution of T. pestanai clade members is more structured geographically and fits with the distribution areas of tick species, as best shown with T. pestanai for European Ixodes species such as I. ricinus and I. canisuga  [9] , or with T. vegrandis, T. copemani and T. gilletti for Australian Ixodes and Amblyomma species  [12, 21, 22]. Finally, phylogenies based on sequences available in public databases showed that no Trypanosoma was detected in blood-feeding invertebrates other than ticks cluster in the T.
10 hen such haplotype clades contain at least 25% of the individuals in our dataset, we refer to them as extended common haplotypes (ECHs) (Figure  2B ). We identified clades of ECHs in sliding windows of 500 kb with a step size of 100 kb using a clique-finding algorithm. 12 The ECHs identified in each individual are shown in Figure  2C  (see Figures  S3  and S4  for the effect of alternative minimum clade sizes). They are almost exclusively found in non-African populations with no apparent geographical differentiation in frequency outside of Africa. The sharing of haplotypes among non-Africans indicates that they rose to high frequency after the main exodus from Africa but before the subsequent diversification of non-African populations. The combined length of all ECHs is 17. 3 Mb, or 11% of the entire X chromosome.
In the simulation, we assume haplotypes of 500 kb, although most ECHs are longer. To cover a range of scenarios, we perform simulations for time spans of 5,000, 10,000, 15,000, 20,000, and 25,000 years. To find an appropriate bottleneck Ne for our simulations, we estimated a population demography, using SMC++, of the non-African samples used in our analysis (STAR Methods). From this, we estimate the lowest autosomal Ne in the bottleneck as 4,125. In our simulations, we conservatively assume an autosomal Ne of 3,000 and include, for comparison, a more unlikely autosomal Ne of 1,500. We perform simulations for two different X/autosome ratios of effective population size: one of 0. 65 corresponds to the median ratio among African individuals in our dataset, and a lower one of 0. 51 corresponds to the median ratio among non-Africans.
This shows that neutral processes alone are very unlikely to have caused the observed ECHs. Second, we performed forward simulations of full X chromosomes using a published population demography and a recombination map. Since the SGDP data results from a very extensive population structure and demographics not well suited for simulation, we chose the homogeneous CEU population (Utah residents with Northern and Western European ancestry) from the 1000 Genomes dataset as our model population. 15 We repeated our inference procedure on the CEU population and then performed forward simulations to assess the probability that the ECHs called in this population were produced by genetic drift. In our inference of ECHs on the 49 CEU male X chromosomes, we need to consider that a large sample from this single homogeneous population will much more often find recent common ancestry than the highly structured SGDP.
To accommodate this contribution to the clade size of such early coalescences, we increase the ECH clade size from 25%, used in our main analysis, to 50%. Inference using this clade size reproduces 15 of the 19 ECH peaks identified among males from the SGDP (STAR Methods). In the CEU population, the ECHs together cover 10% of the entire chromosome, similar to the 11% observed in the SGDP dataset. In the subsequent forward simulations of the past 200,000 generations, performed using SLiM3,  16  we use the previously published population size trajectory for CEU that, to our knowledge, estimates the strongest bottleneck. 17 he simulations also use a fine-scale pedigree-based recombination map of the X chromosome. 14 We perform simulations using the same X/autosome Ne ratios (0. 51 and 0. 65) used in our frequency simulations.
5th percentile of proportions obtained from simulations (Figure  6 ), it is extremely unlikely that the ECHs arose neutrally in the CEU population. It is even less likely that they arose neutrally before the Ust'-Ishim 45,000 years ago, as revealed by the analysis of the SGDP male dataset. The two separate analyses both indicate that positive selection must have driven the ECHs to high frequencies. Selective sweeps on the X chromosome may be recurrent We have previously reported evidence of selective sweeps in the human-chimpanzee ancestor  5  (K. M. , unpublished data; STAR Methods). We therefore tested if these overlap with the independent observation of ECH regions we report here. We find a strong overlap between the ECHs and the sections of the chromosome swept at least once during the 2-4 million years that separated the human-chimpanzee and human-gorilla speciation events,  18, 19    Proportion of non-African haplotypes called ECHs in each 100 kb window across the X chromosome (blue).
A B Figure  5. Estimated probabilities of neutrally evolving 500 kb ECHs (A) Evenly spaced probabilities as the fraction of simulations in which a 500 kb haplotype along the chromosome rises to at least the specified frequency in 500,000 simulations. Each panel show simulations for the duration of the bottleneck, which increases from left (5,000 years) to right (25,000 years) in intervals of 5,000 years. Colors represent X chromosome population sizes listed in the legend, with the assumed autosomal population size and X/autosome ratio in parentheses. Crosses represent the upper bounds on the probability in each case where no haplotypes reached the target frequency in the simulation. A dashed horizontal line marks the 0. 05 value. Vertical dotted lines represent the frequencies of the ECHs we identify in the SGDP male dataset.
Limitations of the study Our inference of ECHs has limited resolution because we require that an ECH spans at least 500 kb. This resolution does not allow fine mapping of any candidate genes responsible for the sweeps and leaves our study unable to identify Gene Ontology enrichments. Another limitation is that the SGDP dataset does not A B Figure 7. Admixture proportions in chromosomal regions of partial sweeps Mean admixture proportions of ECHs (purple) and in remaining haplotypes (black) are computed separately for each 100 kb window. Error bars with wide caps designate the standard 95% confidence intervals obtained from 10,000 bootstrapping iterations. Error bars with narrow caps designate Jeffrey's binomial confidence interval, which better represents confidence when frequencies are zero or very low (in computing this interval, we assume that the sample size equals the number of 100 kb sequence windows).
ECH inference We initially remove the two pseudoautosomal regions of chromosome X because these recombine with the Y chromosome. We then computed the proportion of pairwise differences along the chromosome for each pair of male individuals in non-overlapping windows of 100kb. We discarded windows with fewer than 50,000 called positions (50%) and considered these missing data. We then computed the pairwise distance between all the male X chromosomes in sliding windows of 500kb with a step of 100kb. For each window, we identify sets of haplotypes where the pairwise distance between all pairs is smaller than 5e-5. These sets must include at least 25% of individuals in the dataset (at least 40 males). We do this by constructing a graph where nodes represent individuals, and edges connect individuals with a distance smaller than 5e-5.
We then use a clique-finding algorithm  12  to extract fully connected sets of at least 40 individuals (25% of individuals). We refer to each haplotype in such a set as an Extended Common Haplotype (ECH). An ECH may be longer than 500kb if it spans several overlapping 500kb windows. Figures  S3  and S4  show the distribution of ECHs called for alternative clique sizes of 32 and 49 (20% and 30% of individuals, respectively). The maximal pairwise distance of 5e-5 corresponds to an expected common ancestry of 59,520 years BP and was chosen to postdate the out-of-Africa event. The date is obtained assuming a mutation rate of 4. 2e-10 = 4. 3e-10 * 0. 8 * 1. 221. 4. 3e-10 is the autosomal mutation rate, 13 0. 8 corrects for sex-specific mutation rate and hemizygosity of the X, and 1. 221 corrects for a contribution of false-positive SNPs in the SGDP dataset.
The latter correction is computed as the ratio between divergence to the human reference of sample S_Eskimo_Sireniki-1 at two filtering levels provided in the SGDP dataset: filtering level 9 (used in this study and recommended for population genetic analysis) and the strictest filtering level 1 (used for estimation of mutation rate). 10 As described above, 100kb sequence windows with fewer than 50,000 called positions (50%) are considered missing data. Chromosomal 100kb windows, where this is true for more than 10% of individuals, are masked, leaving 138,5 Mb of analyzed chromosome in the downstream analyses.