dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis We assessed drivers of measures of bat activity and occurrence around wind turbine nacelles, including factors related to landscape composition and heterogeneity, weather conditions, the Julian day, wind turbine functioning and dimensions and recording methods (i. e. the recorder type and the trigger sensitivity). In the first step, since we compiled data produced by different contributors, we expected the existence of multiple combinations between the recorder type and the trigger sensitivity. However, these recording methods deeply affect the number of bat passes recorded  (Adams et al. , 2012). Confounding effects between recording methods and factors of interest (e. g. landscape composition) could prevent modelling them simultaneously. Using all compiled data, we therefore tested for trends in the landscape composition and heterogeneity, weather conditions, and wind turbine functioning and dimensions between recording methods (i.
We chose the combination of the Batmode set to a trigger sensitivity of 37 dBSPL which had the largest dataset resulting in 34 wind turbines, 8619 nights and 65,775 bat passes. Based on this subset, we performed the same GLMMs workflow as presented above (see Supporting information S2 and Table  S6  for more details) to assess the respective effects of explanatory variables on the LRE and MRE activity and SRE occurrence. For each explanatory variable, we checked the potential need for adding a non-linear effect by visual inspection of Generalized Additive Mixed Models (GAMM, R package mgcv;  Wood, 2011;  see Table  1  for variables that required quadratic or cubic effects). We also checked the absence of multicollinearity by calculating the Variance Inflation Factor (VIF) for each explanatory variable (R package performance;  Lüdecke et al.
, 2021). All variables showed a VIF < 2, implying no evidence of multicollinearity  (Chatterjee and Hadi, 2006). It should be noted that wind speed and blade speed were not excessively correlated thanks to maintenance periods that stopped the turbines in all wind conditions (Fig. S3 ). Overall model validation was performed using diagnostic plots (R packages DHARMa and performance;  Lüdecke et al. , 2021). Full models were compared to null ones using the Akaike information criterion (AIC)  (Burnham and Anderson, 2002) , and goodness of fit was assessed using the marginal R 2 (variance explained by the fixed effects) and conditional R 2 (variance explained by both fixed and random factors) values  (Nakagawa and Schielzeth, 2013). All analyses were performed using a significance threshold of 5 % in R statistical software v.
Assessing the effectiveness of using model equations to limit bat exposure compared to conventional curtailments Using the same Batmode dataset, we assessed whether curtailment of wind turbines based on multiple-factor models could be more efficient in limiting bat activity exposure at the scale of all wind turbines than commonly used blanket curtailment methods. For that, we trained full models for each guild on a 50 % fully random subset of the dataset (hereafter referred to as "training dataset") and predicted bat activity on the other 50 % (hereafter referred to as "prediction dataset"), and this 100 times. Then, we computed for each prediction dataset the remaining percentage of bat activity (for LRE and MRE guilds) or occurrence (for SRE guild) (i. e. the real bat activity or occurrence recorded while the blades were moving) and the percentage of lost blade rotations (i.
We computed Area Under Curve (AUC) values for both curtailment methods to evaluate which one was the most effective (i. e. with the highest AUC value) (R package MESS). We also estimated to what extent the effectiveness of curtailment methods was preserved when wind turbines included in the training dataset differed from those in the prediction dataset. For that, we repeated the procedure explained above, but using a training dataset constituted of data from 33 out of 34 wind turbines and a prediction dataset constituted of data from the 34th wind turbine, and we repeated it for each wind turbine to present its results while computing AUC values for both curtailment methods. These turbine-by-turbine assessments were only conducted for LRE and MRE guilds for which we had enough data for each wind turbines.
Bat monitoring A total 98,627 bat passes were recorded at 59 wind turbines and 14,937 nights. However, as described above, in order to avoid confounding effects between recording methods and other explanatory variables, we only selected wind turbines monitored using Batmode recorders which exhibited no trigger sensitivity variation, while including most of the data (i. e. 34 wind turbines out of 59 and 8619 nights out of 14,937). Data from Batmode resulted in a total of 65,775 bat passes recorded, with 43,519 passes of LRE, 22,135 passes of MRE and 121 passes of SRE (see Table  S3  for species composition). At least one pass of LRE, MRE and SRE was recorded in 35 %, 18 % and 1 % of nights, respectively (Table  S3 ).
One hundred iterations were performed to train generalized linear mixed models (GLMM) on a random selection of 50 % of the Batmode dataset and predict bat activity (for LRE and MRE guilds) and occurrence (for SRE guild) as well as computing remaining recorded bat activity and lost blade rotations on the remaining 50 % (see Section 2. 4). The method first links the percentage of recorded bat activity and the percentage of lost blade rotations, respectively, to the wind speed threshold below which the turbine is curtailed when no temperature threshold and various minimum temperature thresholds were applied (blanket curtailment, black) and the predicted bat activity above which the turbine is curtailed (curtailment algorithm, blue). Then the method links the percentage of remaining recorded bat activity and the percentage of lost blade rotations for both curtailment methods to compare their effectiveness presented for the three guilds in panel B.
For the blanket curtailment, panel B shows the effectiveness of the method when no temperature threshold (B1) and a minimum temperature of 10 °C (B2) were applied. importance in the models. All gradients of drivers strongly varied among recorder type/trigger sensitivity combinations, thus preventing any modelling of the effects of drivers on bat activity based on the full dataset due to confounding effects. Indeed, different recorder type/trigger sensitivity combinations can lead to very different levels of bat activity between sites due to the different detection distances generated by the material specificities and settings  (Darras et al. , 2020; Adams et al. , 2012). We opted to compensate for this problem by modelling the effect of the drivers on bat activity after separating the recorder type/trigger sensitivity combinations.
, 2016; Verboom and Huitema, 1997)  or the density of all edge habitats  (Ancillotto et al. , 2017; Mendes et al. , 2017). We also found forest cover to positively affect MRE activity, consistent with  Roemer et al. (2019) , who showed that bat activity at height decreased with the distance to the forest, and with  Boughey et al. (2011)  who found higher bat activity at ground level with an increasing proportion of forest. Unexpectedly, our model revealed a positive effect of impervious habitat proportion on SRE activity, a relationship that is elsewhere described as negative  (Gili et al. , 2020). Impervious habitat in this dataset corresponds to roads and the buffer scale selected is very local (100 m). This is likely an indirect positive effect related to ecotone/hedgerow associated with roads (i.
Limitations and recommendations The study calls for prudence when using data from different recording methods that should be controlled before any modelling as they could strongly bias the algorithm to use for curtailment. This requires regulatory databases (as is the case with the DEPOBIO tool in France; https://depotlegal-biodiversite. naturefrance. fr/) to demand the input of metadata related to the methods used, or ideally to harmonise these methods. Thus, in order to be generalized to all types of material and settings, the algorithm should either be adapted to each type using appropriate data, or a ratio of equivalence in activity between pairs of material/settings should be defined in future studies. To go further in the modelling of bat exposure, the curtailment algorithm method we propose should be adapted on an intra-night scale to account for the variation of bat activity during the night and thus minimise even more production losses  (Behr et al.
Acknowledgments This work was funded by  France Energie Eolienne (FEE)  with the additional support of the  Agence de la transition écologique (ADEME) , the  region Bretagne (SAD) , the  Office Français de la Biodiversité (OFB) , and the  DREAL Pays de la Loire. We thank  France Energie Eolienne  ( FEE ) for involving these members in the data collection process and especially  Jérôme Dumont  and  Frédéric Roch. We thank all participants which provided acoustic and weather/wind turbine functioning data. We thank  CC-IN2P3  and  PCIA-MNHN  for providing computing and storage facilities, and  Didier Bas  for his help in this process. We also thank  Léa Mariton  for her help in computing rainfall data, and  Robin Dixon  on behalf of all  FEE members Véronique de Billy on behalf of OFB  for proofreading the first version of the manuscript.
In contrast, admixed individuals from the Americas have on average 417 copies. Furthermore, we find that on average individuals harboring at least one Denisovan-like haplotype have a significantly elevated number of repeat copies, than individuals harboring two Human-like haplotypes (Denisovan-like mean: 446, Human-like mean: 349, Welch's T-test P-value: 6. 423e-28). We find that Admixed American populations have a significantly greater proportion of individuals with repeat copies greater than the non-African mean, than all other non-African populations (Fisher's exact test, odds ratio: 2. 2, P-value: 4. 976e-11; Table  S21 ). Importantly, these findings were corroborated through analysis of a subset of these samples using long-read sequence data from the Human Pangenome Reference Consortium (HPRC) and Human Genome Structural Variant Consortium (HGSVC), that revealed an extra four copies of a 3,171 bp segment of the MUC19 tandem repeat exclusively in American samples.
Data Availability: The 1,000 Genomes Project Phase III, Simons Genome Diversity Project, highcoverage archaic genomes, Human Pangenome Reference Consortium, and Human Genome Structural Variant Consortium datasets are all publicly available. Ancient American genomes are available after signing data agreements from the original publications. All software used in this study is publicly available and all statistical tests are described in the methods. All the information needed to reproduce the results in this study is described in the methods and supplemental methods, additionally; the original code can be found: https://github. com/David-Peede/MUC19/tree/main.
Combined data set The autosomal VCF files for each modern human dataset (i. e. , TGP and SGDP) and archaic genomes were initially merged using BCFtools v1. 13, after the initial merging the resulting VCF files were filtered to only include sites with mono-allelic or bi-allelic SNPs with an ancestral allele call present and where at least one archaic had a MQ >= 25 and GQ >= 40-archaics that did not meet this threshold were coded as missing data. Since the TGP genotypes were imputed and only include information for variable sites-unlike the archaic data which contains information for all sites-any site that was originally absent in the TGP data but present in archaic data we assumed to be homozygous for the reference allele in the TGP data [Huerta-Sánchez et al 2014]. After the final dataset was curated we annotated coding sites using SnpEff v5.
For each analysis we used an outlier approach to identify if a given SNP exhibits signatures consistent with positive selection by setting the significance threshold at the genome-wide 99th PBS percentile. To calculate the P-value for the 748kb longest introgressed tract region we computed the observed mean PBS(MXL:CHB:CEU) value for the longest introgressed tract region and compare the observed mean to a distribution of mean PBS values from the genomic background of 748kb nonoverlapping windows with comparable effective sequence length density-i. e. , an effective sequence length of at least 400kb amongst the combined dataset-and determined proportion of windows from the genomic background with a mean PBS value greater than or equal to what we observed at the 748kb region.
e. , an sequence length of at least 40kb amongst the combined dataset. To calculate P-values, we determined the proportion of windows from the genomic background with an archaic SNP density greater than or equal to what we observed at the 72kb MUC19 region. After correcting for two multiple comparisons (i. e. , one per allelic state) for both Denisovan-specific and Neanderthal-specific SNPs using the Bonferroni correction, a P-value less than 0. 025 is considered significant.
Identifying the Donor of the Longest Introgressed Tract found in MXL To determine the most likely archaic source of the 748kb longest introgressed tract (Chr12:40787196-40964559) found in an MXL individual (i. e. , NA19789), inferred from Skov et al. [2018]  we computed the sequence divergence between each of the NA19789's chromosomes and the four archaic diplotypes. To identify the archaic donor for the 748kb longest introgressed tract, we compared the observed sequence divergence to a distribution of sequence divergence from the genomic background of 748kb non-overlapping windows with comparable effective sequence length density-i. e. , an effective sequence length of at least 400kb amongst the combined dataset. To calculate P-values, we determined the proportion of windows from the genomic background with a sequence divergence less than or equal to what we observed at the 748kb region.
00252 include all African chromosomes and were considered to be Human-like haplotypes. Additionally, we computed sequence divergence between all chromosomes for the 15 Papuan individuals from the SGDP dataset and each archaic diplotype and using the same sequence divergence threshold as was done for the TGP dataset identified three Denisovan-like haplotypes and 27 Human-like haplotypes amongst the Papuans. divergence from the genomic background of 72kb windows with comparable effective sequence length density-i. e. , an effective sequence length of at least 40kb. To calculate P-values, we determined the proportion of windows from the genomic background with an average sequence divergence less than or equal to what we observed at the 72kb region. After correcting for four multiple comparisons per haplotype group using the Bonferroni correction, a P-value less than 0.
Gene Flow from Denisovans into non-African modern humans We calculated D+ for the 72kb MUC19 region for all non-African TGP populations to test for introgression from the archaic humans. To test hypotheses about introgression on a population level we also calculated D+ for all combinations of P1 = {YRI}, P2 = {all non-African populations}, and P3 = {Denisovan, Altai Neanderthal, Chagyrskaya Neanderthal, Vindija Neanderthal}. To assess significance, we calculated D+ values from the genomic background of 72kb windows with comparable effective sequence length-i. e. , an effective sequence length of at least 40kb amongst the combined dataset. After correcting for 19 multiple comparisons per P3 using the Bonferroni correction, a P-value less than 0. 00263 is considered significant.
Additionally, we were interested in understanding if African individuals and individuals carrying the Denisovan-like haplotype are also outliers for heterozygosity in our focal 72kb MUC19 region. To do so we computed the average number of heterozygous sites amongst all African individuals (n = 504), heterozygous individuals (n = 255) who carry exactly one copy of the Denisovan-like haplotype, and homozygous individuals (n = 16) who carry two copies of the Denisovan-like haplotype for the 72kb MUC19 region, and compared our observed values to distributions of average heterozygous site counts from the genomic background of 72kb windows with comparable effective sequence length density-i. e. , an effective sequence length of at least 40kb amongst the combined dataset. To calculate P-values for the African and heterozygous individuals, we determine the proportion of windows from the genomic background with an average number of heterozygous sites greater than or equal to what is observed at the 72kb MUC19 region, and to calculate P-values for homozygous individuals we determine the proportion of windows from the genomic background with an average number of heterozygous sites less than or equal to what is observed at the 72kb MUC19 region.
Repeat counts from long read data Phased long-read genomes were obtained from the HPRC and HGSVC. A region corresponding to hg38 Chr12:40482543-40491249 was extracted from each of the. fasta files and used to report repeat length for each allele. The repeat length was divided by 30 and then averaged between the two haplotypes for each individual to calculate an estimated repeat length. To ensure that the repeat copies inferred from long read data was comparable to our inferences from short read data we computed both Pearson's and Spearman's correlation coefficients and their corresponding P-values for the TGP individuals who had both type of sequencing data available, using the scipy. stats. pearsonr and scipy. stats. spearmanr functions implemented in scipy v1. 7. 2  [Virtanen et al. , 2020].
Acknowledgements: We would like to thank  Alyssa Funk  for contributing to the development of the PBS analysis,  Ratchanon Pornmongkolsuk  for early visualizations of global frequencies of MUC19, and  Paolo Provero  for his insightful comments and discussion. We would also like to thank the  Crawford and Ramachandran  and laboratories, especially  Ria Vinod ,  Julian Stamp ,  Chibuikem Nwizu ,  Elizabeth Chevy , and  Cole Williams  for their invaluable feedback and support throughout the duration of this project. Part of this research was conducted using computational resources and services at the  Center for Computation and Visualization, Brown University. Funding: This work was supported by  The Leakey Foundation  [to FAV], the  National Institutes of Health  [ 1R35GM128946-01  to EHS], and the  Alfred P.
Sloan Foundation  [to EHS]. DP is also a trainee supported under the  Brown University Predoctoral   Training Program in Biological Data Science  ( NIH T32 GM128596 ). PM was funded by the  National Institutes of Health  ( R35GM142978 ) and  Burroughs Wellcome Fund  ( Career Award  at the Scientific Interface). This work was supported, in part, by  US National Institutes of Health (NIH)  grant  R01NS122766  (to PNV). EHS, FJ, and MAA are supported by the  Human Frontier Science Program.
Processing T1-weighted images were segmented and spatially normalized using the CAT12 toolbox on SPM12 on MATLAB (v2019b)  (27). Reconstructed PET images were corrected for motion before calculation of mean standard uptake value (SUV) parametric images on 50-60 min interval. CT scans were co-registered on T1-weighted images. The transformation thereby derived was applied to SUV PET images to co-register them on T1-weighted images. A binary inclusive mask of pooled gray and white matter segment at p > 0. 5 was applied. The automated anatomical labeling (AAL3) atlas  (28)  was deformed to each subject's MRI native space and mean SUV values were then extracted using the PETPVE12 toolbox (29) on bilateral frontal, temporal, cerebellar cortex (CC), whole brain (WB) and a temporal meta-ROI including the bilateral hippocampus, parahippocampal cortex, amygdala and fusiform gyrus.
Acknowledgments We would like to thank the  Fondation Alzheimer  for the financial backing of this study, the  Toulouse Purpan University Hospital Center , especially  Delphine Vernet  and  Benjamin Cottin  as well as the  French National Agency for Research called "Investissements d'   Avenir" IRON Labex  (number  ANR-11-LABX-0018-01 ) for their support. We are grateful to the  INSERM/UPS Tonic PET  and MRI platforms for their technical assistance, especially to  Laura Guerrier ,  Hélène Gros-Dagnac  and  Mélissa Villatte ,  Maëva Fisher  for help advancing the project. For their technical assistance, support for data acquisition and caring for patients, we also would like to thank the  Center of Clinical Investigation (CIC  1436) especially  Stéphanie Bras ,  Fabienne Calvas ,  Monique Galitzky ,  Laurent Marquine ,  Célie Laplace ,  Béatrice Lagarde ,  Sandrine Rolland ,  Sandrine Bonnet ,  Edith Carneiro ,  Laurent Cales , and  Pascale Gauteul.
Obtaining a multi-technique dataset on SMT chert characterization will fulfil an important need, allowing different researchers to identify this chert type frequently found in the archaeological sites of the NE Iberian Peninsula. In this work, we develop the first multi-scalar approach to the chert of the Sant Genís Formation (Priabonian, Cenozoic) of evaporitic origin with a view to obtaining a detailed characterization of the different lithological units yielding it. From a petrographic perspective, Cenozoic cherts of evaporitic origin are characterized by great heterogeneity, the absence of microfossils, and a high presence of post-genetic alterations such as the development of white patina, which frequently make it difficult to undertake a correct archaeological ascription. SMT chert (Sant Martí de Tous, Barcelona) is found within evaporite lithofacies (sabkha-like), defining a depositional environment comprising shallow saline lakes with low-concentration brines of calcium sulfates, where massive gypsums, gypsiferous marls, carbonate sedimentation, gypsarenites, and red and gray lutites are deposited, depending on the horizontal location (lake center or margin) and stage (dilution, exposure)  (Ortí et al.
We petrographically analyze 46 samples (enclosing rocks and cherts of geological and archaeological origin), of which six chert types representing the main lithologies of the formation are tested by multiple material analyses. Techniques including X-ray diffraction (XRD), magnetic property analysis, portable X-ray fluorescence (p-EDXRF), inductively coupled plasma optical emission spectroscopy (ICP-OES), and neutron activation analysis (NAA) are applied to explore the mineralogical and geochemical variability of the samples. The data obtained are compared with three archaeological chert samples (AR'01-M-Q47-49; AR'02-M-U47-220; and AR'02-M-P53-537) from level M (between 51. 8 ± 1. 4 and 55. 8 ± 2. 3 ka BP) of Abric Romaní, which were previously assigned to this formation (Gómez de  Soler et al.
Survey and sampling The cartographic and geological data available for the area were analyzed to identify the different chert-bearing units. Once located, the chert outcrops were registered in a specifically designed database, where their main characteristics (location, accessibility, geological context, etc. ) were recorded. In addition, the outcrop locations were imported in a vector format onto a digitized map with geological and cartographic data (scale 1: 50,000) to create a specific cartography of the chert availability in the area. To provide a quantitative approximation of the layout of the chert, the chert abundance ratio method (CAR method) was applied to estimate the chert input of the Sant Genís Formation to the territory. This methodology has been broadly Fig. 1  Sant Genís Formation (green and orange) in a DEM map with the aforementioned chert outcrops (black dots).