dataesr/scientific-paragraphs-categorization huggingface dataset:
The model was implemented in stan language using the R package brms (seed = 12345,, thin = 5, nchains = 4, niter = 10000)  (Bürkner, 2017; Bürkner, 2018). The same analysis was carried out by splitting the free-living category into two subcategories, namely eusocial and free-living. A new GLM model was then built (GLM(Number EVEs ~free-living+eusocial + endoparasitoid + ectoparasitoid * Branch_length, family = zero-inflated neg binomial)). Posterior predictive check was done using the package brmsfit in order to check that the model was correctly predicting the proportion of zeros. Indices relevant to describe and characterize the posterior distributions were computed using the R package BayestestR  (Makowski et al. , 2010). Autocorrelation was studied using the effective sample size index (ESS) with a value greater than 1000 being sufficient for stable estimates  (Bürkner, 2017).
05). Overall, free-living species have better assemblies. Because better assembly quality should facilitate the discovery of endogenous viral elements both by sequence homology detection and by a better assessment of the endogenized nature of the EVE (scaffolds A, B, C, and D), we should thus underestimate the number of EVEs in endo and ectoparasitoid species compared to free-living species. Since our analysis led to the opposite conclusion, our results cannot be explained by this feature of the dataset. In our main analysis, we only considered EVEs with scores from A to D as confidently endogenized. To test the impact of this scoring system on our conclusions, we ran the very same analysis on either top-confidence EVEs (A score) (Figure  4 -figure supplement 1), or on a more relaxed dataset also including poorly-scored EVEs (A-F).
Data availability All sequencing data are available at NCBI via the BioProject accession number NCBI: PRJNA826991. All pipelines, statistical as well as figures scripts are available under the GitHub repository : [https://github. com/BenjaminGuinet/Viral-domestication-among-Hymenoptera, (copy archived at swh:1:rev:a96c55309b929044f19c0b267523bd31170b14c2)].
Crosses indicate the expected proportion of events associated with the different lifestyles, based on the respective frequencies in our database (ectoparasitoid = 24/124, endoparasitoid = 37/124, free-living = 63/124). Guinet et al. eLife 2023;12:e85993. DOI: https:// doi. org/ 10. 7554/ eLife. 85993 8 of 30.
Guinet et al. eLife 2023;12:e85993. DOI: https:// doi. org/ 10. 7554/ eLife. 85993 26 of 30The following dataset was generated: Author(s) Year Dataset title Dataset URL Database and Identifier Guinet B, Lepetit 2023 Genome assemblies and https://www. ncbi. nlm. NCBI BioProject, D, Charlat S, Buhl SRA reads of parasitoid nih. gov/ bioproject/ PRJNA826991 PN, Notton DG, wasps PRJNA826991 Cruaud A, Rasplus J-Y, Stigenberg J, de Vienne DM, Bastien B, Varaldi J.
A B Figure  5. Test of the vacillation hypothesis against the stable preparation hypothesis (A) Cross-correlation of grip-uncued single-trial projections along the grip dimension. The result is shown as a heatmap where each pixel represents the correlation between projections computed at two different time points of the trial (t 1 and t 2 ; abscissa and ordinate of the heatmap). We gathered projections computed at t 1 and t 2 into two separate vectors of size [nTrialx1] and calculated the Pearson correlation between the two vectors. For this analysis, to increase statistical power, we normalized the grip-uncued projections within each session by the associated grip-cued projections to allow pooling the data across sessions and animals. The data shown are from the PG-uncued (HF) condition. (B) Log likelihood ratio of single-trial projections in the grip-uncued condition.
Neural recordings Spiking data were recorded during multiple behavioral sessions (n = 8 for monkey N, n = 24 for monkey L). Each session had on average n = 89 simultaneously recorded neurons, and n = 142 successful trials. Spikes were sorted offline using Offline Spike Sorter, version 3, Plexon Inc. , Dallas, TX, USA). Spike clusters which were separated significantly from each other and with less than 1% of inter-spike intervals (ISIs) of 2 ms and less were considered as single units (single-unit activity, SUA), whereas less well separated clusters and/or more than 1% of 2 ms ISIs were considered as multi-unit (multi-unit activity, MUA) recordings. In all analyses, we included both SUA and MUA.
ACKNOWLEDGMENTS N. M. was funded by the  European Molecular Biology Organization  ( EMBO ALTF 329-2021 ). T. B. and A. R. were supported by the  RIKEN-CNRS Collaborative Research Agreement , the  Helmholtz portfolio theme ''Supercomputing  and modeling for the human brain'' (SMHB), and the  ANR-GRASP (France). The authors wish to thank  Manuel Zaepffel  and  Margaux Duret  for participating in the data collection. The authors also thank  Daniel J. O'Shea  as well as the two anonymous reviewers for their constructive feedback on the manuscript. Finally, N. M. wishes to pay a tribute to  Krishna V. Shenoy , whose groundbreaking work over the years largely inspired the present study.
Deposited data Electrophysiology data This paper Zenodo: https://doi. org/10. 5281/ zenodo. 7573890 Software and algorithms MATLAB MathWorks https://www. mathworks. com/ products/matlab. html Code for the neural analyses This paper Zenodo: https://doi. org/10. 5281/ zenodo. 7573890 Other CerePlex Direct Blackrock Microsystems https://blackrockmicro. com/ neuroscience-research-products/ neural-data-acquisition-systems/ cereplex-direct-daq/.
Participants We recruited 114 participants (M age = 20. 6, SD age = 4. 2, 102 identified as wom en, 12 as men) among psychology students directly on the official website of the lab where they could access the web page of the experiment (see https://sites. google. com/view/etude-lip-21-22-220). We ran a Bayesian Sequential analysis (as pre-registered, see Supplementary Materials) but instead of stopping recruitment when the Bayesian Sequential analysis was over, we stopped recruiting when we realized the method did not have the same effect as  Symonds et al. (2012). Before stopping data collection, we conducted a sensitivity analysis to evaluate the minimum effect size our design could have captured: our design could have detected a Cohen's d =. 29, which is smaller than the Cohen's d = 2. 5 we estimated to represent the effect size Symonds et al.
We scraped the average temperature and photoperiod of the day (i. e. , number of hours of sunlight) from the open dataset of the collaborative project OpenstreetMap France to be used as control variables. During the measurement, we used an air-con ditioning unit to maintain room temperature at 19. 5°C, 95%  CI [19. 3, 19. 7]   (Brasil et al. , 2020; Symonds et al. , 2012) , but also measured the temperature of the room via a BlueMaestro TempoDisc to measure any variations. Index finger temperature was meas ured using the ISP131001, a wireless temperature sensor that showed good validity and reliability in both warm and cold conditions  (Sarda et al. , 2021). Following the installation of the sensors, participants then went through the following steps: 1. The experimenters asked them to take off their clothes and bra strap (if applicable) covering the measured areas (i.
Data Analyses Data Preprocessing We first downloaded room temperature data from the TempoDisc Data via an app called Tempo Plus 2. We then downloaded finger skin temperature data from the ISP131001 directly from our lab's OVH server. We cleaned finger and room temperature data with a Python 3. 9 script to merge finger and room temperature at each measure of BAT activity for further analyses. We coded demographic qualitative variables numerically: sex (binary, 0 = female, 1 = male), tobacco use (continuous, number of cigarettes per day), alcohol consumption (continuous, number of day(s) per week the participant consumes alcohol), and shivering (binary, 0 = no shivering, 1 = shivering).
Manipulation Check: Statistical Analysis Analysis scripts are available at https://osf. io/tjc9y/. Statistical analyses were conducted using R-4. 2. 1 for Windows (R Core Team, 2013) with the R packages ggplot2  (Wickham, 2016) , party 1. 3-10 (Hothorn et al. , 2006) , lattice 0. 20-45  (Sarkar, 2008) , and plyr 1. 8. 7  (Wickham, 2011). We (a) validated the experimental setup with descriptive statistics and (b) evaluated the effect of the cooling intervention on relative and absolute supraclavic ular skin temperature. We ran a paired t-test on relative and absolute supraclavicular skin temperature as within-participants between the two conditions (presence or not of the cooling intervention). Doing so allowed us to compare our effect with the effects of  Symonds et al. 's (2012)  study. We then (c) assessed the variations of relative and absolute supraclavicular skin temperature during the experiment through a one-way repeated-measure ANOVA on relative and absolute supraclavicular skin temperature as within-participants between time points.
In our case, we included as many possible predictors as we could to explore all possible explanations. Note that, in contrast to the traditional linear model, conditional random forests benefit from having additional predictors in the dataset for accuracy of prediction as it has fewer problems with collinearity. Conditional random forest evalu ated if independent variables could predict relative supraclavicular skin temperature in cold conditions. Conditional random forest relies on bagging, which involves building a decisional tree to predict the dependent variable based on a random part of the dataset. We set the bagging process to be repeated for 500 iterations (ntree), which should lead to sufficiently stable results  (Latinne et al. , 2001). Given that our total number of variables were 16 and 17, we fixed the number of variables to sample (mtry) at 4 or 5.
We included this participant in the analyses. Note. Reported over 94 participants. n 0 is the number of participants that do not smoke, or take no medication, or do not consume alcohol, or present no health conditions, or have not shivered during the experiment. Reversely, n 1 is the number of participants that smoke, or take medication, or consume alcohol, or present health conditions, or shivered during the experiment. NAs Missing data are not displayed here. "Medication" refers to any use of a medication that is known to influence Brown Adipose Tissue. "Health condition" refers to any cardiovascular, respiratory or neurological condition of the participant.
Investigating Possible Explanations for Non-Replication There are a host of reasonable alternative explanations for why the manipulation failed to achieve the desired effect. For instance, perhaps our sample differed on BMI, tobacco use, alcohol use, or that some of our participants shivered (amongst many other explan ations). Given that we had data to investigate these alternative explanations, we were able to test them within our dataset. Using Null-Hypothesis Significance Testing would be an inefficient way to test such alternative hypotheses, as, given the sheer number of potential tests, it is nearly impossible not to data-dredge towards a significant effect. We therefore investigated if the variation of relative supraclavicular skin temperature during the cooling intervention could be explained by one or several variables of our study by running a set of conditional random forests with the difference between rela tive supraclavicular skin temperature at t = 30 min and at t = 45 min, as dependent variable.
Our predictors were age, gender, average temperature and photoperiod of the day, individual scores of the two questionnaires of particular interest (scores in the two subscales of the ECR and the four subscales of the STRAQ-1), BMI, tobacco use, alcohol consumption, shivering, average finger temperature, and average room temperature. The number of days since the last menstrual onset was added in a second model because it contained missing data for 28 participants. Some of our predictors were known to be correlated with BAT activity (such as age, gender, body mass index, environmental temperature and photoperiod) and some were not (such as tobacco use and alcohol consumption). We ran each model four times (twice with 4 variables to sample and twice with 5). In the first (16 predictors, n = 90) and the second model (17 predictors, n = 62), none of the predictors differed from random noise, given that all variables were to the left of the red lines (see Figure  4 ).
Acknowledgments: The authors have no additional (i. e. , non-financial) support to report. Competing Interests:  Hans IJzerman  has a conflict of interest as he wrote a popular science book about social thermoregulation (Heartwarming; IJzerman, 2021) and is the director of a company to ensure accuracy in applied behavioral science (https://fr. linkedin. com/company/annecy-behavioral-science-lab). The other co-authors report no conflict of interest.
Extragalactic processing modules The modules in the Gaia data processing system that deal explicitly with extragalactic objects are as follows. DSC and Vari classify Gaia objects using supervised machine learning methods. Vari additionally provides characterisations of the light curves. UGC (Unresolved Galaxy Classifier), QSOC (Quasar Classifier), and OA (Outlier Analyser) analyse the results from DSC, the first two computing redshifts. EO analyses the surface brightness profiles of an input source list. We summarize these modules here, leaving more detailed descriptions to the individual processing papers cited below. We also include in our analysis the list of quasars identified for Gaia-CRF3. Some sources, in particular galaxies, are partially resolved by Gaia. Their two-dimensional structure -combined with the fact that Gaia observes sources over a range of position anglescan induce a spurious (non-intrinsic) photometric variability or an apparent astrometric variability, the latter potentially being interpreted by the astrometric processing  (Lindegren et al.
Discrete Source Classifier (CU8-DSC) The Discrete Source Classifier uses the BP/RP spectrum together with the mean G-band magnitude, the variability in this band, the parallax, and the proper motion to classify each Gaia source probabilistically into five classes: quasar; galaxy; anonymous (essentially single star); white dwarf; binary star. DSC is trained empirically on Gaia data with labels for the quasar and galaxy classes coming from Sloan Digital Sky Survey (SDSS) spectroscopic classifications. The distributions of the training data in colour and magnitude are shown in Fig. 1. The training data define the classes (see Sect. 6. 2), so these are not the same class definition adopted by other modules that contribute extragalactic source identifications to Gaia DR3. DSC comprises three classifiers.
Outlier Analysis (CU8-OA) The Outlier Analysis (OA) module was originally intended to analyse those sources that receive low classification probabilities for all DSC classes. As DSC-Combmod tends to give rather extreme probabilities -near to 0. 0 or 1. 0 -we used OA to analyse all sources that have all DSC-Combmod probabilities less than 0. 999. This corresponds to 56 million sources. OA uses a Self-Organizing Map  (Kohonen 1982) , an unsupervised neural network that groups together similar data on a two-dimensional grid of neurons, in our case 30 × 30. The data here are the BP/RP spectra. From this we compute a prototype spectrum of each neuron as the mean of all spectra assigned to that neuron. We further compute various statistics for each neuron, such as the mean G, G BP , G RP , parallax, and Galactic latitude.
Indeed, the apparent variability of galaxies in the Gaia data is mostly an artefact of their extension combined with the Gaia on-board detection algorithm and scanning law (see  Sect. 4. 4. 2)  and so does not justify the release of their time series (which are meant only for genuine variable objects). Nevertheless, the characteristics of these artificial brightness variations made it possible to identify galaxies as if they Further analysis and characterisation of the variable AGN classifications (by the module Vari-AGN) led to a higher purity selection of about 872 000 objects  (Carnerero et al. 2022) , whose AGN-specific metrics are published in the vari_agn table, and repeated in the qso_candidates table (see Sect. 3). The purity of this AGN sample was estimated to be about 95%. The galaxy sample in galaxy_candidates is perhaps even purer, estimated at 99%, although with a lower completeness at around 40%  (Rimoldini et al.
Equation C. 4 is only valid for large values of N. How large N should be is problem dependent. We performed simulations using 65 536 noisy realisations of a problem with N = 64 and eight variables in m, where all matrices, except W i , are uniformly distributed in  [-1, 1]  and W i is an orthogonal transformation of matrices whose eigenvalues are uniformly drawn in [0. 001, 1]. This led to a maximum absolute error in the median correlation coefficients of 0. 007. The Octave/Matlab source code for minimizing Equation C. 1 and for computing the approximate covariance matrix from Equation C. 4 is available at https://github. com/ldelchambre/gls_mean/.
Acknowledgements This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are processed by the Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia and the archive website is https: //archives. esac. esa. int/gaia. Further acknowledgments are given in appendix A. the  Israel Ministry of Science and Technology  through grant  3-18143  and the  Tel Aviv University Center for Artificial Intelligence and Data Science (TAD)  through a grant; the  Agenzia Spaziale Italiana (ASI)  through contracts  I/037/08/0, I/058/10/0 ,  2014-025-R. 0 , 2014-025-R. 1. 2015 ,and  2018-24-HH.
Histone folds are common in several bacterial clades We carried out a systematic homology search for histone-fold proteins in bacteria. Using a phylogenetically balanced database of 18,343 bacterial genomes, we found 416 proteins that contain a predicted histone-fold domain (Supplementary Table  1 ). Of the genomes, 1. 86% encode at least one histone-fold domain, compared with 92. 8% of genomes that encode HU. In agreement with previous work, we identified two major histone size classes, containing either a singlet or a doublet histone fold  26. Both classes are typically devoid of other recognized domains (Fig. 1a  and Supplementary Table  1 ). Like their archaeal homologues, most bacterial singlets also lack the long, disordered N-terminal tails that are characteristic of eukaryotic histones  16, 27.
Histones are abundant in bacteria outside Bdellovibrionota Is B. bacteriovorus unique among bacteria in using histones as a major building block of chromatin? We examined publicly available gene expression data and generated additional transcriptomic and proteomic data. At both the transcript and protein levels, we find high expression of histone-fold proteins in several distantly related bacteria (Fig. 6a ). We also observe notable phylogenetic persistence of histone genes in clades where gene expression profiles are not available, including in the Planctomycetota and Elusimicrobia (Supplementary Figs. 10 11 12 13 ). We became particularly curious about L. interrogans, the causative agent of leptospirosis, whose histone (Uniprot ID: A0A2H1XGH2 gene ID: LA_2458) is exceptionally abundant. A previous study  38  found this histone to be the fourth most abundant protein in the cell (out of 1,502 proteins quantified), something we recapitulate, finding it ranked second out of 2,433 proteins (Fig.
Homology survey of histone-fold proteins PFAM HMM models of known histone-fold domains (Histone (PF00125), CBFD_NFYB_HMF (PF00808) and DUF1931 (PF09123)) were searched against a phylogenetically diverse database of 18,343 bacterial genomes (Supplementary Table  3 ) using hmmsearch from the HMMER package (v. 3. 1b2) with the -cut_ga option to ensure reproducibility. In addition, a list of eight prokaryotic histone-fold seed sequences were obtained from ref. 26 and used to direct homology search with Jackhmmer (HMMER v. 3. 1b2). Jackhmmer results with P < 1 × 10 -3 were kept. Hits were combined and proteins longer than 200 amino acids discarded. All proteomes in the database were obtained from NCBI (accessed on 20 May 2020). When GenBank proteome files were not available, proteomes were predicted using Prodigal (v.
Proteomics data processing Data were processed using the MaxQuant software platform (v. 1. 6. 10. 43)  48  , with database searches carried out by the in-built Andromeda search engine against the GenBank proteome of each organism. A reverse decoy database approach was used at a 1% false discovery rate (FDR) for peptide spectrum matches. Search parameters were as follows: maximum missed cleavages set to 2, fixed modification of cysteine carbamidomethylation and variable modifications of methionine oxidation, protein N-terminal acetylation, asparagine deamidation and cyclization of glutamine to pyro-glutamate. Label-free quantification (LFQ) was enabled, with an LFQ minimum ratio count of 1. The 'match between runs' function was used, with match and alignment time limits of 0. 7 and 20 min, respectively.
Data generated on the Orbitrap Q-Exactive were also processed using an alternative pipeline, pFind (v. 3), which allows unbiased identification of peptide modifications present in a given sample  49. The top five modifications identified by this pipeline were added to the MaxQuant search space and normalized abundance (IBAQ) computed as before. This added step did not affect normalized abundance estimates for our proteins of interest, and abundance values across the entire sample with or without this added step are very well correlated (rho > 0. 99, P = 0).
Nucleoid enrichment analysis Relative nucleoid enrichment analysis was carried out  15  after quantification of proteins by MaxQuant. We compared the protein intensities (LFQ) from the soluble and the nucleoid-enriched fraction using the R package DEP (v. 1. 8. 0). Globally, we observed robust enrichment of proteins with predicted membrane localization, providing validation for the approach (Supplementary Fig. 16 ).
RNA-seq analysis Untrimmed reads were mapped using Bowtie2 (v. 2. 4. 4)  50  for A. aeolicus (single-end reads) and using BWA (v. 0. 7. 17) for B. bacteriovorus Article https://doi. org/10. 1038/s41564-023-01492-x (paired-end). Read counts of all genes were estimated using the Python package HTSeq (v. 0. 6. 1)  51.
Apo protein crystallization Recombinant Bd0055 in storage buffer (1 M NaCl, 1 mM EDTA, 50 mM HEPES pH 7. 5, 10% glycerol) was crystallized through hanging drop diffusion in mother liquor (3. 2 M (NH 4 ) 2 SO 4 , 100 mM BICINE pH 9) by gently mixing 1 µl of protein to 1 µl of mother liquor on a coverslip and sealing it onto a pre-greased well of a 24-well crystal tray containing 1 ml of mother liquor. Crystals formed within 24 h and matured within 3 d. Crystals were looped and frozen in liquid nitrogen. X-ray diffraction data were collected on a Rigaku XtaLAB MM003 X-ray diffractometer and indexed in DIALS (v. 3. 14). Molecular replacement was done using Phenix (v. 1. 20) and structures were refined in Coot (v. 0. 9. 8. 5). Short, idealized alpha helices were used as search models, and most of the remaining structure was built automatically using AutoBuild.
DNA binding (FP) Protein (50 µM) was titrated using an Opentrons OT-2 liquid handling robot into 10 nM Alexa488 end-labelled 147 bp DNA by serial dilution down to a protein concentration less than the DNA probe in reaction buffer (10 mM NaCl, 1 mM EDTA, 50 mM HEPES pH 6). After incubation at r. t. for >15 min (overnight incubation did not change overall results), fluorescence polarization data were collected using a BMG Labtech CLARIOstar microplate reader. Data were analysed in GraphPad Prism (v. 9) and fit to an [Inhibitor] versus response, four-parameter nonlinear regression curve.