dataesr/scientific-paragraphs-categorization huggingface dataset:
prasinos (Figure  5C ) and Ostreococcus lucimarinus  (Leconte et al. , 2020)  are attributed to either differential fitness, or seasonal dynamics among BOC haplotypes, or both. The cosmopolitan distribution of B. prasinos, along with its abundance in metagenomic dataset and the important biological resource available, offer a unique opportunity to monitor the spatio-temporal population dynamics of BOC haplotypes and to further study the role of BOCs in an environmental context  (Vannier et al. , 2016; Lambert et al. , 2019).
Important size variations for the SOC were displayed in our dataset, especially between Arctic strains isolated during the DarkEdge cruise for which we identified the smallest SOCs (Figure  6 ). Structural and transcriptional modifications of the SOC were shown by  Yau et al. (2016 Yau et al. ( , 2018) )  to be induced by prasinovirus infection and linked to increased spectrum of resistance, suggesting a role of the SOC in viral resistance mechanisms in Mamiellophyceae. Moreover, switches between resistant and susceptible phenotypes within isogenic cultures were associated with SOC rearrangements in O. tauri and O. mediterraneus, and proposed as a coexistence mechanism in microalgae-virus interactions  (Yau et al. , 2020). As shown by  Blanc-Mathieu et al. (2017) , smaller SOC sizes are associated with decreased resistance spectrum.
tauri by  Yau et al. (2016) , in which one half of the chromosome is expressed in susceptible strains while the other half is expressed by resistant strains. Therefore suggesting a common regulation of both transcription and structural rearrangement in this region to minimize loss of fitness in favor of genomic diversity for rapid genomic adaptation. Yau et al. (2016)  proposed several theories to explain these observations, including the activation of retrotransposons and epigenetic modifications in response to biotic stress. To comprehensively test these theories, an in-depth characterization of SOC structural variations, including gene-content and structure, is necessary. As such, the extensive dataset described here represents a valuable resource for understanding the intricate interactions between Mamiellophyceae and associated viruses.
Acknowledgements The authors acknowledge the ISO 9001 certified IRD i-Trop HPC (member of the South Green Platform) at IRD Montpellier for providing HPC resources that have contributed to the research results reported within this paper. URL: https://bioinfo. ird. fr/http://www. southgreen. fr. We thank Marie-Hélène Forget, as DarEdge coordinator, as well as all the other participants of this expedition and the members of the Amundsen Vessel for their help in collecting water samples during the DarkEdge Cruise. We thank the Roscoff Culture Collection for providing access to collected strains. We thank Marie-Line Escande and the platform BioPIC at the Oceanological Observatory of Banyuls-sur-Mer for electron microscopy service. This work and the salaries of LD and JR were funded by the french Agence Nationale de la recherche (ANR Clima-Clock, ANR-20-CE20-0024 to FYB, AF and OJ).
We focus here on unsupervised approaches  [50] -  [55]. Most of these methods have been developed for pairs of images. Consequently, changes can be detected only when they represent some modification between the initial and final time instants. This disregards potentially relevant changes which occur in intermediary instants of time and are subsequently reversed. Moreover, simultaneous analysis of many pairs of images can, even for time series of medium length, pose serious statistical burdens such as low power and high false positive ratios  [56] -  [58]. Change detection using SAR images is challenging due to the high dimensionality of multitemporal datasets associated with the speckle noise. In this context, a wavelet transform is a convenient tool, given its robustness to noisy data and the efficient algorithms available to compute it.
C. Actual remote sensing application In this section, we compare the performance of WECS, TAAD, ECS, CVA, PCAKM, IRMAD, and ISFA in a realworld application. Since FMW did not perform so well in the simulations, as we can see in Fig. 5 (b), we decided not to apply this method. We employ for WECS the db2 wavelet basis and resolution level J = 2. We tried other values of J, but J = 2 provides better results. Our data consists of 85 multitemporal images of a forest region at the border of Brazil and the French Guiana, as shown in Figure  7. The images' time instants vary from December 26th, 2015 to December 3rd, 2017. They were obtained from the Sentinel-1 SAR GRD dataset, maintained by the European Space Agency  [68]. Each image contains the amplitude signal backscatters relative to VV and VH polarizations, a spatial resolution of 10 m, and support of 1538 × 1556 pixels wide in ground range projection.
In this modified version, which we call GalBar 1 , the classical GalPot program is used to compute the background axisymmetric component of the potential. Equation (  2 ) is used to compute the contribution of each nonzero mode to the total Galactic potential, as expressed in Eq. (1). Then, the corresponding acceleration field is computed locally by measuring the finite difference of potential in a Cartesian grid where the points are spaced in intervals of 10 -7 kpc ( 20 AU) 2. We verified that this method gives a similar acceleration field than the classical GalPot for different axisymmetric potentials.
Simulation of disc tidal streams: The Hyades The main objective of this paper is to study the impact of the Galactic bar on the morphology and on the dynamics of stellar streams inhabiting the Galactic disc. One of the best candidates for this analysis is the Hyades tidal stream, due to the proximity of its progenitor with the Sun (45. 7 pc, Gaia Collaboration 2018) and for which 6D phase-space information is available for numerous of its stars thanks to the successive Gaia data releases. Using DR2 and eDR3 data, the stream has been measured to be 800 pc long  (Röser et al. 2019; Jerabkova et al. 2021) , a value that is similar to what was predicted in early simulations  (Chumak et al. 2005; Ernst et al. 2011) , in which its progenitor, the Hyades open cluster, had a total mass of 1230 M at the moment of its formation 600-700 Myr ago  (Perryman 1998; Lebreton et al.
6. The LoS velocities used here were measured by the Radial Velocity Spectrometer (RVS) instrument onboard of the Gaia satellite, and they were obtained by cross-matching the J21 sample with the Gaia third Data Release (DR3; Gaia Collaboration 2021) based on the source_id parameters. Over the 862 stars initially present in the J21 sample, 430 have LoS velocities, most of them contained in the central region of the stream and in the Hyades cluster itself, with a typical individual uncertainty of 2. 9 km s -1. Although the majority of the stars beyond 150 pc do not have LoS measurements, the high velocity dispersion of several tens of km s -1 tends to indicate that the J21 sample is dominated by contaminants in that region. Therefore, due to the method used to select the stars, and due to the presence of a high fraction of contaminants in the furthest parts of the stream, we conclude that the J21 sample is not suited to discriminate which stream models (and, hence, which bar models) are in best agreement with the observations.
Therefore, the likelihood, p(u| f H ), of a given star with data u given a fraction of stars present in the Hyades stream f H can be defined as p(u| f H ) = f H p H (u) + (1 -f H ) p MW (u), (5) where p H and p MW are, respectively, the probability distributions of the Hyades stream and of the MW foreground and background. Both p H and p MW can be decomposed as a spatial projection density (p pos ), a kinematic (p kin ) and a distance (p dist ) components, assuming that each of them are independent of each other. Using the LoS velocities from the Gaia DR3 it would be possible to add an additional component to the likelihood, but we prefer to use them as an independent validation check of our method. Moreover, not all stars have an LoS velocity, so that with this other component of the likelihood, the spatial coverage of the stream will not be uniform and may potentially bias our selection.
Results The fraction of stellar members of the Hyades stream ( f H ) was found by exploring the parameter space of the posterior distribution with the emcee Python package  (Foreman-Mackey et al. 2013). According to the Bayes theorem, the posterior probability distribution function for the fraction of stars in the Hyades stream f H , given the filtered data of N stars D = {u 1 ,. , u N }, is p( f H |D) ∝ p(D| f H ) × P( f H ), where p(D| f H ) ≡ Π N i=1 p(u i | f H ) is the total likelihood of the data given f H and P( f H ) is a flat prior on f H between 0 and 1. We found that the posterior distribution of the fraction of stars in the Hyades stream is well approximated near its mode by a Gaussian of mean and standard deviation (µ, σ) = (82. 81, 2. 72)×10 -5 for the barless model, and (µ, σ) = (165.
To push our analysis a bit further, we used the calibrated atmospheric parameters derived from the Gaia RVS spectra by the MatisseGauguin pipeline to check the consistency with the abundances from the Hyades cluster. Following the guidelines from Recio-Blanco et al. (  2023 ), we selected stars having reliable parameters using the flags_gspspec 5  from the Gaia dataset: -VbroadT = 0 -VbroadG = 0 -VbroadM = 0 -KMgiantPar = 0. The first three parameters assume that the impact of rotation is minimal in the temperature, surface gravity, and metallicity estimates, whilst the last one guarantees that the parametrisation for K and M-giant is correct 6  with these criteria, 149 (145) stars with a v los for the barless (slow bar) selection have a metallicity and αabundance estimate. Among these, 117 stars are included in both selections.
Acknowledgements. The authors thank  Alejandra Recio-Blanco  and  Giuseppina Battaglia  for useful discussions, as well as the anonymous referee for comments that increased the scientific quality of the publication. G. F. T. acknowledges support from  Agencia Estatal de Investigación del Ministerio de Ciencia en Innovación (AEI-MICIN)  under grant number  PID2020-118778GB-I00/10. 13039/501100011033 , the  MICIN  under grant number  FJC2018-037323-I , and the  AEI  under grant number  CEX2019-000920-S. B. F. , G. M. and R. I. acknowledge funding from the  European Research Council (ERC)  under the  European Unions Horizon 2020 research and innovation program  (grant agreement No. 834148 ) and from the  Agence Nationale de la Recherche  ( ANR  projects  ANR-18-CE31-0006  and  ANR-19-CE31-0017 ).
C. L. acknowledges funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No. 852839 ) This work has made use of data from the  European Space Agency (ESA)  mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research has made use of the SIMBAD database and of the VizieR catalogue access tool, operated at  CDS, Strasbourg, France. The N-body simulations, and the selected candidate members of the Hyades stream will be shared via private communication upon a reasonable request.
Hi-C analysis Forward and reverse reads were aligned separately with bowtie2 v2. 3. 5. 1  [29]. From these alignments, Hi-C matrices and genomic distance law were generated using hicstuff v3. 0. 3 (https:// github. com/ koszu llab/ hicst uff ). Matrices were balanced using the ICE algorithm  [30]. For comparative analysis, matrices were binned at 10 kbp resolution and were downsampled to the same number of contacts. Reproducibility between replicates was assessed using the hicreppy v0. 0. 6 implementation (https:// github. com/ cmdor et/ hicre ppy) of the HiCrep algorithm  [31]. Comparison between matrices was done using log2 ratio and serpentine v0. 1. 3 60 for flexible binning  [32].
Bacterial genome annotations and genomic features Genomes were annotated using the last version of the NCBI Prokaryotic Genome Annotation Pipeline (PGAP)  [33]. The coverage was computed using tinycov (https:// github. com/ cmdor et/ tinyc ov), v0. 3. 0. The GC content and the GC skew were computed using dnaglider (https:// github. com/ cmdor et/ dnagl ider), v0. 0. 4. The parS sites have been detected using a degenerated parS consensus sequence TGT TTC ACG TGA AACA  [34]  and allowing 2 mismatches. The ori and ter positions were approximated based on the GC skew inversion. The shift closer to the parS cluster was annotated as the ori and the shift as the opposite of the genome as the ter. Figures were generated using pyGenometracks v3. 6  [35].
Prophage annotations Prophage annotation was performed using the OMM 12  genomes  [25]  except for B. caecimuris, B. longum, and F. plautii for which the version re-assembled in this article was used. Both Vibrant  [36] , v1. 2. 1 and Virsorter2  [37] , v2. 2. 3 with their respective databases, were used to annotate the bacterial genomes. The data from mitomycin C-induced prophages was obtained from Zünd et al. [38]. Refinement of the prophage annotation using the HiC contact map was performed using an insulation score method  [13]  to detect the borders of the prophage region. We refine the borders of the in silico annotated prophage boundaries using the two closest detected borders. The code is available at https:// github. com/ abign aud/ oligo mm_ analy sis.
A first annotation was realized using PATRIC  [41] , with the "phage" recipe. Then, each predicted protein was compared to the PHROG  [42]  database v3 using the HHsuite v3. 3. 0 (hhsearch_omp function). For hits with a probability above 88%, the corresponding PHROG annotation was manually added to the corresponding protein. The annotated genomes were then visualized using Clinker  [43]  v1. 1. 0.
Shared-protein network analysis OMM 12 -induced prophages were clustered using vCon-TACT2  [45]  v0. 9. 19 with the following parameters: -rel-mode 'Diamond' -db 'ProkaryoticViralRefSeq201-Merged' -pcs-mode MCL -vcs-mode ClusterONE. Publicly available phage genomes from ViralRef Seq V. 201  [46]  and Cenote Human Virome Database (CHVD)  [47]  were used as reference collections for the analysis. The resulting network was visualized and annotated using Cytoscape v3. 9. 1  [48]. The edge-weighted spring-embedded model was applied to position genomes sharing most protein clusters.
Detection of ssDNA phage in virome samples Cleaned reads from virome 5 and virome 6 (Supplementary Table  1 ) were merged into one single dataset. Reads were assembled using SKESA  [54]  v2. 4. 0 and Metaviral SPAdes  [55]  v3. 15. 2 using k-mer size 21 and resulting contigs (≤ 15 kb ≥ 1 kb) were analyzed with VIBRANT  [36]  v1. 2. 1 and VirSorter2  [37]  v2. 2. 3 with a default option to predict ssDNA sequences. Except for the M13 spiked control, no ssDNA was identified in the sequenced samples. All contigs from the SKESA and Metaviral SPAdes assemblies were joined in a single multifasta file and proteins were predicted and annotated using PROKKA  [56]. The whole set of predicted proteins was then screened with BlastP for Microviridae characteristic MCP and Rep proteins contained in the PHROG database  [42]  (v3, PHROG 514, and 713, respectively).
[38] ) and/or in vivo (n = 10). Out of the 16 prophages regions defined by Hi-C (15 in vitro and/or in vivo + one in vivo only), 11 were found to be among the 13 induced prophages. For the remaining five functional Hi-C-defined prophages, we were unable to detect any significant signal in the virome data suggesting that those prophages are not active enough, or not functional, or produce abortive cycles. Two induced prophages were also not found to be associated with a specific 3D pattern, possibly due to a lack of structuration of the corresponding genomic region or eventually to differences between fecal samples recovered for Hi-C and virome analyses. We then compared the prophages coordinates defined by Hi-C data with virome sequences and found that the boundaries of the prophages often (9/11) agree between the datasets (Table  2 ).
caecimuris I48. To further characterize the virome of OMM 12 mice, we first sequenced additional samples spiked with known concentrations of phages  [69]. This led us to estimate that OMM 12 fecal samples contain around 10 8 VLP/g of feces (Methods). Second, we searched for free viruses from reads that did not match OMM 12 genomes to perform    [49]  and SPAdes  [55]  (~ 2-4% of the reads). However, this resulted in only five contigs of a size superior to 5 kb (Supplementary Table  3 ). BLAST analysis, using RefSeq database, of these contigs revealed that they correspond to a portion of induced prophages of B. caecimuris or C. innocuum and thus likely result from alignment errors (Supplementary Table  4 ). The unmapped reads were also used for taxonomic annotation using Kaiju  [52]  (Supplementary Fig.
In-depth analysis of induced prophages The above 13 induced prophages were named after French castles, and their genomes were analyzed in detail (Fig. 5b  and Table  2 ). First, we looked for a circularization signal, in the virome sample reads (Methods). Detected for 12 out of 13 prophages, this signal led us to precisely map the prophage coordinates. Second, using those exact coordinates, we annotated the phage genomes using the PHROG database  [42]  (Methods). A putative function was assigned to at least 25% and up to 60% of the genes including in all genomes the terminase and/or portal proteins, two hallmark proteins of phage genomes (Fig. 5b ). Integrases were also frequently found, in agreement to the temperate lifestyle of these phages. Third, PhageTer-mVirome  [44]  was successfully used for 10 phages to identify their packaging method and termini (Table  2 ).
Finally, we searched for homologs using BLAST against the nucleotide (nt) database, but only distant homologs were identified, showing that these prophages are largely new. Thus, we used vContact2  [45]  to generate shared protein networks and identify the groups of phages that most closely relate to these 13 phages. While only a few high-quality RefSeq genomes clustered closely with the OMM 12 prophages, many metagenomic phage sequences belonging to the Cenote Human Virome Database (CHVD)  [47]  closely clustered with them (Supplementary Fig. 8 ). This clustering allowed us to predict their possible morphology (Table  2 ). Overall, these 13 induced OMM 12 phages represent newly characterized viruses of the intestinal microbiota, for which there is a sufficient basis for the creation of the new viral clades.
Funding This research was supported by funding to BS from  DFG-STE-1971/11-1  ( PhaStGut  project), to BS from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  865615 ), to LD and  MMa  from  PRCI   ANR-20-CE92-0048  ( PhaStGut  project), and to RK from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  771813 ) and from  JPI-EC-AMR   STARCS ANR-16-JPEC-0003-05. QLB received funding from  École Doctorale FIRE-Program Bettencourt. AB is supported by an  ENS  fellowship from the  French Ministry of Higher Education, Research and Innovation. MMo and JRG were supported by  JCJC   ANR-18-CE35-0011  (project  CDPhages ). MT received funding from  DigestScience. Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , was supported by  France Génomique  ( ANR-10-INBS-09 ) and IBISA.
A dataset of 1,647 ASV was used for subsequent diversity analyses. A Neighbour-joining phylogenetic tree of the 1,647 ASV was constructed using MEGA11  [29]  by first aligning ASV sequences with MUSCLE  [30]  and then building a Neighbour joining-tree based on a distance matrix corrected with the Kimura 2P method. Metadata and ASV tables and the phylogenetic tree were uploaded to the NAMCO server for downstream microbiota diversity analyses (https://exbio. wzw. tum. de/namco/, [31]. NAMCO is a microbiome explorer server based on a set of R packages, including Phyloseq for diversity analyses  [32]  and PICRUSt2 for functional predictions  [18]. Alpha-diversity analyses (observed richness, Shannon and Simpson diversity, statistical test with pairwise post-hoc Dunn test) were performed with Phyloseq and tidyverse, ggpubr, rstatix, multcompView R packages and plotted with ggplot2.
Acknowledgments This study was carried out using the facilities of the  PathoBios International Joint Laboratory (LMI)-Observatory of Plant Pests and Diseases in West Africa-Biodiversity and Biosafety  (www. pathobios. com;twitter. com/PathoBios). We are very grateful to  Issouf Sanga ,  Laurentine Kabre ,  Sylvie S. Guissou ,  Alexandre P. Ouedraogo  and  Manaka Douanio  for their contributions to this research. We also thank  Gilles Bena ,  Pierre Czernic ,  Ludivine Guigard  and  Nicolas Busset  for constructive discussions throughout this project.
Indeed, during the mating season, males-usually the alpha male-mateguard cycling females, favoring high-ranking ones when several females are receptive at the same time. 34  In this study, we first developed an AI-based measure of femininity applicable to field pictures. We benchmarked the method in humans using both standardized and non-standardized photographic portraits scored for femininity and attractiveness by human judges. We then applied this method to 13,000 photographic portraits of adult female mandrills collected in their natural environment during daily monitoring, to obtain an annual femininity score-quantified in two different ways-for each studied female. Finally, we assessed how females' femininity scores influence socio-sexual interactions with their groupmates (male copulations, intra-and inter-sexual spatial proximity, aggression, and grooming), using a large behavioral database collected over 10 years on 39 adult males and 95 adult females.
Learning a face space Estimating femininity requires first to build a face space. DCNNs can yield biologically realistic face spaces when trained to recognize individuals. Here, we used VGGFace,  35  a DCNN  36  previously trained to individually recognize more than two thousands celebrities from 2. 6 million face images, as a base model for all analyses, for both humans and mandrills. Because VGGFace needs to be retrained with a mandrill   37  for humans and the Mandrillus Face Database  40  for mandrills to simultaneously perform identity verification and sex classification (i. e. , multi-task learning). Portrait pictures of the studied individuals were then fed to the newly trained DCNN, and their features extracted. The 128-d penultimate layer of the DCNN was used as a face space. Images of CFD database (for analyses on humans) and Mandrillus Face Database restricted to adults (for analyses on mandrills) were projected into their respective face space.
In this depiction, a highly feminine face i is described by a high value of dcm i and a small value of dcf. portrait dataset to learn a mandrill face space (see in the following), for a comparable evaluation of model performance, we built a human face space by retraining VGGFace with another human face dataset (FaceScrub dataset  37  ). Indeed, the quality of the retraining can be influenced by the size of the new dataset: we thus subsampled the FaceScrub dataset to keep the same number of individuals and mean number of images per individual as in our mandrill image dataset. Following previous studies,  27, 29  we used the penultimate layer of the retrained model as the face space. Indeed, with a DCNN trained to recognize individuals from their face, this layer encodes information about identity but also other characteristics of individuals, such as their sex and ethnic origin,  29, 31  while being relatively invariant to illumination, pose, and facial expression.