dataesr/scientific-paragraphs-categorization huggingface dataset:
The three datasets were finally merged using PLINK v1. 9  (Chang et al. , 2015) , leading to a set of 2 https://samtools. github. io/hts-specs/VCFv4. 3. pdf 35,656 autosomal SNPs with consistent positions and reference and alternative alleles in the three datasets. Genetic diversity at these markers is summarized by principal component analysis, which showed no significant effect of the genotyping technology (Supplementary Figure  S1 ).
Defining Temporal Samples Generation time was set to 4 years based on the comparison between the birth dates of the 25 bulls of the Gene2Farm project used in our dataset (see Samples section) and the birth dates of 25 offspring of these bulls (one per bull) genotyped in the Gene2Farm project but not used in our study. Consequently, we divided the period 1980-2013 into nine consecutive non-overlapping periods of 4 years and defined these periods as the generations of the experiment. Animals were assigned to one generation according to their birth date. In order to satisfy the hypotheses of a Wright-Fisher evolution model, as assumed by the HMM time series approach, we then tried to limit inbreeding and relatedness within each generation by estimating the genetic relationship matrix in each generation using GCTA  (Yang et al.
, 2011) , focusing on SNPs with a minor allele frequency (MAF) greater than 10%. We removed animals with an inbreeding rate above 0. 07 (six animals), and the most inbred animal of each animal pair with relatedness above 0. 1 (30 animals). These two thresholds were based on a visual inspection of the empirical distributions of inbreeding and relatedness (Supplementary Figure  S2 ) and aimed at removing outlier individuals or individual pairs. This led to a final set of 117 animals, with sampling times described in Supplementary Table  S1. Only eight generations were used in the final analyses because generation 1 included no sample after filtering. We also defined an alternative dataset without IMAGE's WGS samples. Indeed, these animals represent a large part of the two most recent generations (8 and 9), so including them implies a focus on SNPs that are polymorphic over these two generations.
However, SNPs that are monomorphic over generations 8 and 9 (removed by the WGS calling procedure described in DNA Sequencing and Bioinformatics Analysis section) might correspond to interesting selection signatures where one positively selected allele was fixed in the population before generation 8. Repeating the procedure described above without WGS samples led to a dataset including 43,951 SNPs and 106 animals belonging to generations 2-8 (Supplementary Table  S1 , last line). In order to evaluate the potential impact of our choice of generation time, we also defined a dataset including all animals but classifying them into seven consecutive non-overlapping generations of 5 years. The resulting sampling times and sizes are shown in Supplementary Table  S2.
Detecting Selection From Time Series Data We detected the loci that have been under selection in the Asturiana de los Valles breed between 1980 and 2013 using a new method that exploits the evolution of allele frequencies in a population along different sampling times  (Paris et al. , 2019). This method is based on a HMM approach, which allows for the modeling of both the stochastic evolution of population allele frequencies over time, as a result of genetic drift and selection (if any), and the additional noise arising from the finite sample size at each time point. Other similar HMM approaches were previously proposed in the literature, but they were either less accurate or limited for computational reasons to very small population sizes; see  Paris et al. (2019)  for more details. We applied the time series approach either with or without WGS samples (the number of individuals and SNPs for the two analyzed are summarized in Table  1 ); for SNPs that were shared by the two datasets, we kept the p-value computed with WGS samples, as this corresponds to the larger sample.
A first look at the results showed four SNPs with extreme p-values, for which one allele was fixed in chip data while the other was almost fixed in NGS data. Such extreme patterns suggest an error (inversion of alleles) while merging the chip and NGS datasets, consistent with the fact that two of these SNPs were G/C SNPs. These were thus removed from the analysis. SNPs with a MAF below 5% over all the sampling times were also removed from this time series analysis. Indeed, some assumptions of the Likelihood Ratio Test used by  Paris et al. (2019)  to detect selection are not satisfied for rare alleles, which may lead to less accurate p-values. Besides, such SNPs correspond to allele frequency trajectories showing little variation over time, which are unlikely to contribute to significant evidence of selection in a time series analysis.
This lead to a final set of 35,913 SNPs, among which 33,509 were segregating within WGS data and 2,404 were absent from these data. In order to exploit linkage disequilibrium information, we detected genomic regions with a local excess of low p-values (i. e. , of selection candidates), using the local score approach proposed in  Fariello et al. (2017). The score function at each SNP was -log 10 (p-value)-1, as recommended by these authors to optimize detection power. As the distribution of p-values obtained from our test was close to uniform, we could evaluate the significance threshold for each chromosome using the closed-form formula provided in equation (  3 ) of their study (p. 3703), implemented in the R code available at https:// forge-dga. jouy. inra. fr/projects/local-score.
Estimating Effective Population Size Allele frequency trajectories not only depend on selection intensity but also on effective population size. Before estimating selection at each locus, we thus estimated this parameter by combining information from all loci. We used the method of  Hui and Burt (2015)  as implemented in the R package NB, 3 and considered the dataset without the WGS samples to avoid bias against allele frequency trajectories where one of the alleles gets fixed before generation 8, which leads to an overestimation of population size.
Detecting Selection From Present Time Data We also applied a method that focuses on present time data and screens the genome for specific patterns kept by positive selection during population history, possibly a long time ago. Among the several methods available for this purpose, we computed the nSL statistic  (Ferrer-Admetlla et al. , 2014)  using the software selscan  (Szpiech and Hernandez, 2014). This statistic looks for long haplotypes segregating at high frequency in the population, measuring haplotype length by the number of SNPs rather than the genetic distance, which makes it more robust to local variations of the recombination rate. To take advantage of the higher detection power derived from a higher SNP density, we computed nSL from WGS data using the following steps: (i) removing all variants with six missing genotypes or more; (ii) phasing the 15 individuals and imputing missing genotypes at the 13,588,815 remaining SNPs using shapeit  (Delaneau et al.
Selection Signatures Detected From the HMM Time Series Approach The maximum likelihood estimation of effective population size in Asturiana de los Valles was equal to 408. 3 animals, with a 95% confidence interval between 350 and 450. Based on this value, we evaluated the evidence for recent selection at 35,913 autosomal and 238 X-linked SNPs with a MAF above 5% over all the sampling times using the HMM time series approach. The smallest p-value was equal to 2. 7e-05, which cannot be considered significant, given the number of tests performed. The p-value distribution was close to uniform (Supplementary Figure  S3 ), as expected for any test under the null hypothesis, though with a deficit of very small p-values. This indicates that our testing procedure is well-calibrated while outlining that the dataset considered here presents little evidence for selection at the SNP level.
Selection Signatures Detected From Present Time Data Among the SNPs included in the HMM time series approach, 30,649 autosomal and 190 X-linked SNPs could be analyzed with the nSL procedure described in the Materials and Methods section. Because the p-values associated with a given nSL score is difficult to evaluate, we used an outlier approach considering all SNPs with an nSL score above 5 as potential candidates (see Supplementary Figure  S5  for the full distribution of nSL scores). This approach provided eight candidate SNPs under selection, which could be grouped into six regions (Table  3 ). To take advantage of the higher SNP density available for this test (all recent samples were sequenced rather than just genotyped on a chip), we also considered the nSL results obtained for 10,556,992 autosomal and 168,022 X-linked SNPs called from WGS data.
As expected, this higher SNP density provided a much higher detection power, retrieving 4,217 SNPs with an nSL score above 5, which could be grouped into 307 regions. Considering that isolated outstanding nSL scores are unlikely for such a high SNP density and might be due to false positives rather than true selection events, we focused on regions with more than 10 candidate SNPs and reduced this first list to 42 candidate regions, as reported in Supplementary Table  S3. Six of them were particularly outstanding, as they exhibited more than 10 SNPs with an nSL score above 6. These six regions, listed in Table  4 , include three of the top regions detected with nSL from the merged SNP chip-WGS dataset (Table  3 ), on chromosomes 2 (two neighboring regions that could be considered as one) and 10.
DISCUSSION The present study, framed within the IMAGE project, aimed at enhancing the use of gene bank collections in animal breeding. To outline the invaluable resources provided by periodic resampling and cryopreservation of germplasm and tissue samples from local less competitive populations, we analyzed existing and new data from the Spanish Asturiana de los Valles beef cattle breed, using a new statistical approach that takes advantage of genomic time series to detect and characterize recent selection signatures in a population  (Paris et al. , 2019). Results from such selection scans will help the livestock industry to exploit the genetic variation fine-tuned by natural and human-driven selection on different breeds to improve productivity. In the specific dataset considered here, only a few significant selection signatures were detected with this approach.
For instance, computer simulations performed in  Paris et al. (2019, Figure 6 ) suggest that for an effective population size of 100 haploids and an evolution time of 10 generations, selected loci can be detected with reasonably high power only if selection intensity is greater than 0. 5 (detection power should be higher for an effective size of 800 haploids as in Asturiana, but this scenario was not considered in the simulations). Second, only 35,913 SNPs were analyzed, which reduces the chance to observe markers in strong linkage disequilibrium with causal selected variants. The effect of SNP density on detection power has been outlined in previous selection scans for selection (e. g. ,  Boitard et al. , 2016). It can also be seen in the present study by the much lower number of candidate regions detected with the nSL score from the merged SNP chip-WGS dataset (30,649 autosomal SNPs) when compared with the WGS dataset alone (10,556,992 autosomal SNPs).
A more exhaustive deciphering of recent selection in the Asturiana breed, potentially revealing loci under weaker selection, could likely be achieved by including more samples and/or increasing SNP density using 800K chips or WGS data. Additional samples for the period considered in this study  are available in the Spanish cryobank. The time series could also be enriched by considering more recent samples, which may also improve detection power. However, no biological material is available before 1980, which corresponds to the creation of the bio-bank, so a retrospective time series analysis will not be possible before this date. Another interesting observation from our study was the low correlation observed between the scores obtained from the time series and the nSL methodologies. While this might be due to the lack of power of the two tests in this specific dataset, this could also reflect a more fundamental complementarity between the two tests: the time series approach focuses on very recent selection events, and the nSL detects a larger variety of events, most of them being older than the period covered by our samples.
The five SNPs considered here could correspond to this situation, although for the four decreasing trajectories, this strongly depends on the shape of the trajectory further in the past. However, these SNPs are most likely not the causal variants under selection, and the allele frequency trajectory at the causal variants might be quite different from the ones observed here, especially with the low SNP density of our dataset. One of the top regions detected by the nSL methodology, when considering either the SNPs shared by the chip and the WGS data or the WGS data alone, was located on Chr2 around 7 Mb (Tables  3  and 4 ). This region includes the myostatin (MSTN) gene, whose allele nt821(del11) associated with the double muscling phenotype, was probably introduced in the north of Spain in the 1940s through Simmental hypertrophic individuals, a trait inherited from Central European Frisian bovines  (Garcia Fierro, 1972; Ménissier, 1982; Dunner et al.
A region on Chr4 included three GTPase genes (GIMAP7, GIMAP4, and GIMAP8), IMAP family members that have been related to the primary immunodeficiency pathway and were shown to play a major role in feed utilization and the metabolism of lipids, sugars, and proteins in Jersey cattle  (Salleh et al. , 2017). In line with these functions, a region on Chr 7 included the TICAM1 gene, involved in native immunity and previously associated with bovine trypanotolerance in some African Bos taurus breeds  (Noyes et al. , 2011). Another region on Chr 10, detected with nSL on both the merged SNP chip-WGS dataset and WGS dataset alone, included the FLRT2 gene, which is related to embryonic development  (Haines et al. , 2006)  and has been associated with calf birth weight by a GWAS in Holstein  (Cole et al.
In conclusion, allele frequency trajectories over the few last generations contain rich information about the selection history of populations, which cannot be obtained from classical selection scan approaches based on present time data only. The HMM time series approach combined with a statistical method allowing for the detection of clusters of small p-values pointed out several candidate regions in the Asturiana de los Valles cattle breed with a clear shift in allele frequencies over the few last generations. It also allowed for annotating historical signatures found by the nSL statistic by showing that the advantageous allele in these regions was already at high frequency in the breed in 1980 and did not further expand over this time. The HMM time series and nSL signatures of selection included several candidate genes related to carcass and meat traits (MSTN, FLRT2, CRABP2, ZNF215, RBPMS2, OAZ2, and ZNF609), immunity (GIMAP7, GIMAP4, GIMAP8, and TICAM1), or olfactory receptors (OR2D2, OR2D3, OR10A4, and 0R6A2), which inform us about the direction of applied active selection in the last few decades in Asturiana de los Valles.
DATA AVAILABILITY STATEMENT The SNP chip and WGS datasets newly generated for this study can be found in the European Nucleotide Archive (ENA, accession number PRJEB38981) repository. The SNP calling pipelines (DNA Sequencing and Bioinformatics Analysis) can be found at https://forgemia. inra. fr/bios4biol/workflows/-/tree/master/ Snakemake. Pipeline IMAGE_calling was used for initial calling and pipeline IMAGE_vqsr for refined calling using the VQSR approach. The scripts implementing the other analyses described in the Materials and Methods section (Merging and Cleaning Genotypes, Defining Temporal Samples, Detecting Selection From Time Series Data, Estimating Effective Population Size,.
To discriminate between these alternatives, a mixed hierarchical logistic regression was carried out to assess the impact of target position (1-6), prosody type (certain/doubt or honesty/lie), and prosody reliability (reliable or unreliable) on the accuracy (correct or incorrect response) of participants' recall. There was a main effect of reliability on performances (X2 = 7. 5, b = -0. 05 +/-0. 02 sem, z = -2. 7, p < 0. 01), but, consistent with Study 1 and 2, no effect of type (X2 = 0. 03, p > 0. 8) and no interaction between type and reliability (X2 = 0. 63, p > 0. 4). This is consistent with our finding that a common prosodic signature supports social perceptions of both certainty and honesty. We therefore collated the data for certain/honest and lie/doubt for the rest of this analysis.
In addition, three participants did not use the confidence scale reliably (i. e. , two participants reported the same rating >75% of the time out of 100 possible values, and one participant used <10% of the scale, oscillating around the midpoint) and were therefore excluded for the analysis concerning confidence judgments (similar results were obtained in the full group regarding most analysis, except for the computation of metacognitive sensitivity since sigmoid curves for high-and low-confidence trials could not be reliably fitted for these outlying participants). Participants signed a consent form and were remunerated for their participation. Ethical approval was obtained, and experimental data were collected at INSEAD/ Sorbonne University Center for Behavioral Science. Stimuli. Sounds were created with CLEESE, a voice transformation toolbox (https:// forum.
The task was coded using Matlab. Reverse correlation analysis. Three reverse correlation kernels were computed for each participant and each task by subtracting the mean pitch/loudness/duration of the spoken words classified as certain (i. e. , selected) or honest (i. e. , not selected) from the mean pitch/loudness/duration of the spoken words classified as doubtful (i. e. , not selected) or lies (i. e. , selected). Note that, because the instructions were counterbalanced in our two tasks (i. e. , in the certainty task participants had to report which prosody was reliable, while in the honesty task participants had to report which prosody was unreliable), we reverse coded the values for the certainty task in order to allow direct comparisons between the two tasks (i. e. , we subtracted the values for unchosen/honest voices from the values for chosen/lying voices).
The impact of segment and task were assessed for each acoustic dimension by running hierarchical linear mixed regressions including participant as a random factor with the lmerTest package in R  76. Percentage of agreement. Agreement across tasks was computed for each participant as the correspondence between judgments given in the honesty task and certainty task (e. g. , agreement = 1 if for one pair of stimuli the participant perceived the same stimuli to be more certain and less dishonest). Agreement within task was computed for each participant by comparing responses given to repeated pairs of stimuli (e. g. , agreement = 1 if for one pair of repeated stimuli the participant selected the same stimulus twice). Note that, although agreement can provide a coarse measure of choice consistency, it is prone to decision biases.
Participants signed a consent form and were remunerated for their participation. Ethical approval was obtained, and experimental data were collected at INSEAD/ Sorbonne University Center for Behavioral Science. Stimuli. Stimuli were constructed from the same spoken pseudo-words used in Study 1, but this time specific acoustic manipulations (as opposed to the random manipulations used in the first experiment) were applied to the original stimuli. Precisely, we modified the pitch, loudness, and duration of the 16 original stimuli (8 pseudo-words and 2 speakers) so that they match the four archetypical dynamic filters derived from the reverse correlation study (certain, honest, doubt, lie). These modifications were applied with three different levels of gains, with the maximum level of gain chosen so that the maximum value of the kernel approximates the original value chosen as a maximum for the first study, ensuring that the manipulated voices would still sound natural to the listeners.
Acknowledgements The authors thank  Louise Vasa ,  Gabriel Vogel ,  Laura Rachman ,  Lou Seropian ,  Mauricio S. Barrientos Alvarez , and  Martin A. Castro Yanez  for their help with collecting the data, as well as  Marco Liuni  and  Pablo Arias  for their input regarding the voice transformation algorithms and acoustic analysis, and  Guillaume Dezecache  for valuable comments on a previous version of the manuscript. This work was funded by  ERC StG CREAM   335536  (to J. -J. A. ), a  H2020-MSCA-IF-2018  grant (845859 -JDIL, to L. G. ),  FrontCog  grant  ANR-17-EURE-0017  (to E. P. ) and a support of the  Fondation pour l'Audition  (J. -J. A. ).
Availability and future directions We invite readers to download installers for Microsoft Windows from www. crimson. software for both CRIMSON GUI and CRIMSON Flowsolver. These are currently time-limited binaries, but users can compile their own versions from the provided source code. Alternatively, users can follow the documentation on the CRIMSON website, describing how to interface the GUI with their own fluid solver. The source code for the GUI is available on GitHub under GNU Affero General Public License v3. 0 (https://github. com/carthurs/CRIMSONGUI), and for the Flowsolver under the GNU General Public License v3. 0 (https://github. com/carthurs/ CRIMSONFlowsolver). Both licenses further include the Commons Clause. Archived versions of the source code are available under DOIs 10. 7302/679b-dw96 and 10.
Guidance for getting started with CRIMSON The version of CRIMSON GUI and CRIMSON Flowsolver current at the time of publication of this article is provided in File 1 at https://doi. org/10. 7302/4vdx-ek59. However, a newer version may be available from www. crimson. software, as suggested in each of the cases below. To install CRIMSON, first run the CRIMSON-2019. 11. 01 exe installer, then run the CRIMSON_Flowsolver exe installer. The order is important. If you use the default installation paths in both, then the GUI will be able to find and run the flowsolver. If you install the GUI to a non-default location, you must change the Flowsolver's install path in the same manner. If you wish to build from source, follow the instructions in the readme. md file in the CRIM-SON Flowsolver repository https://github.
Libraries and frameworks used in CRIMSON The following pre-existing libraries are integral to the CRIMSON software. See S1 Fig for an overview of the structure. The Medical Imaging Interaction Toolkit (MITK)  [10]  was chosen as the foundation for the CRIMSON GUI due to its established position as an open-source package for development of interactive medical image processing and ongoing development by the German Cancer Research Center. It provides functionality for working with medical image datasets, including multiple orthogonal and 3D views of the image volume, data storage and undo/redo functionality, and image processing and segmentation algorithms  [58]  via the Insight Toolkit (ITK), Visualization Toolkit (VTK) and Qt  [59]. Additionally, it includes a Python interface for easy prototyping and MITK release 2016.