dataesr/scientific-paragraphs-categorization huggingface dataset:
Error rates for SNP chip data remained consistently under 1. 3% regardless of the size of the datasets. In contrast, the error rates for WGS datasets uctuated, as depicted in Figure  S5. A notable negative relationship exists between the site read depth and genotype errors. Speci cally, as the read depth decreases, mismatch rates rise. This suggests WGS data is more unreliable in identifying heterozygotes as read depth decreases, not unexpectedly. In the case of SNP chip data, metrics like the Fisher Linear Discriminant (FLD) and the call rate (CR) have signi cant effects on these rates, as illustrated in Figures  S8, S9 , and S10. Importantly, after ltering out SNPs from both datasets based on the identi ed impactful metrics (FLD greater than 6, CR over 98. 5, and a read depth above 20), the multivariate analysis results, speci cally PCA, for both the WGS and chip data from the 18-sample set were strikingly alike (Fig.
The chip's SNP density dropped from an initial 102 SNPs per 1Mb window to 57 SNPs per 1 Mb post-quality control (Table  S21 ). This drop in the density is due mainly to the fact that only 70% of the probes generate the expected uorescence signals. As we had an overrepresentation of variants in coding regions on the SNP chip, compared to the WGS dataset (Figure  S11 ), we tested our ability to correct for this bias using SNPs from the chip data that followed the same functional proportions as the ones in the WGS data by comparing clustering and PCA results between "corrected" and "uncorrected" chip datasets (Table  4 ). Ancestry analyses using sNMF-LEA consistently produced similar results for both datasets (Figure  S23  and S24), and similar clustering of groups were obtained from multivariate methods using PCA (Figure  S25 ).
As more samples are added to the dataset, e ciency in compute time becomes more important. The PCA analyses identi ed the main clusters in the native range (Figs. 1  and 8 ). East Asia formed one group, while South and Southeast Asian samples were grouped together, and Indonesian samples formed their own cluster. Overall, the PCA results indicated a distribution of samples from East Asia along PC1, following a latitudinal order, with Japanese samples on the right and Chinese at the left (Fig. 7 ). The samples from mainland Japan clustered at the right of PC1, matching the clustering pattern from the ancestral analysis ("green" cluster Fig. 6 ). Finally, DAPC results with two discriminant functions correctly identi ed the ve genetic clusters, supporting the differentiation of QNC population (Vietnam), but also indicated that KUN population (Maldives) may be genetically differentiated from the other populations from South Asia (Fig.
Acknowledgements We sincerely thank everyone who contributed to this research by sharing their samples. We also appreciate the hard work of the laboratory staff at  Yale , who ensured that DNA extractions and sampling handling ran smoothly. We extend our gratitude to the numerous undergraduate students who dedicated their time and efforts to this project. Their meticulous work in DNA extractions and the preparation of samples for genotyping was indispensable to the success of our research. Their commitment and enthusiasm greatly enriched the project, and their contributions will always be remembered and valued.
Encephalization and relative mass or number of cells in the telencephalon of teleosts are not correlated Residuals obtained by fitting a log 10 -log 10 regression of brain mass against body mass data from this dataset with previously published data on actinopterygians (Figure  3 ) using a phylogenetic generalized least square (PGLS) model ranged from -0. 142 (D. rerio) to 0. 38 (the wrasse T. hardwicke), with only one other species (C. anchorago) with a residual >0. 30 (Figure  3 , Table  S1 ). Excluding the large C. anchorago from our analysis gave a residual of 0. 488 for C. anchorago, placing it above T. hardwicke (Table  S3 , see Document S1). Overall, these two tool-using species were the most encephalized of our dataset. In order to compare the degree of encephalization with the relative mass and cellular composition of major brain regions, the brains of ten species were dissected into five parts (Figure  4A ): the telencephalon (Tel), the optic tectum (TeO), the rest of the Forebrain/Midbrain (rFM), the cerebellum (Cb) and the rest of the Hindbrain (rH) following the rostro-caudal and dorso-ventral axis (Figures  4B-E , STAR Methods).
50 ± 0. 01 (Adjusted R 2 : 0. 8382, t=65. 978, p<0. 0001). Encephalization was then determined by extracting the residuals of log 10 -log  10  brain and body mass for each species of the dataset to remove allometry in brain size. 64 The 11 species studied were ranked based on the value of their residual (Table  S1 ).
ACKNOWLEDGEMENTS We thank  Jean-Michel Hermel  and  Naomie Pradère  ( NeuroPSI, CNRS/Université Paris-Saclay ) for their help with brain sampling. We thank  Dimitri Rigaudeau  ( INRAE, Jouy-en-Josas ) for providing us trouts and zebrafish, the  DECA team (NeuroPSI)  for Astyanax specimens, and  Joël Attia  ( Université de Saint-Etienne ) for cichlids, as well as the members of TEFOR animal facility ( CNRS UMS2010 ,  INRA   UMS1451 ,  Université Paris-Saclay ), especially  Krystel Saroul  and  Christophe de Medeiros  for fish care. We thank members of  NeuroPSI  for technical support, and the MIMA2 platform (https://doi. org/10. 15454/1. 5572348210007727E12; INRAE, Jouy-en-Josas ) for access to their lightsheet microscope.
7 ± 4. 9 μM and 45. 6 ± 37. 0 μM respectively) compared to the ones for HGSLHKNA and KRHGEWRPS (195. 5 ± 64. 0 μM and 737. 9 ± 230. 0 μM respectively), which means that they are good metal chelators; thus, they reduced the formation of volatiles through metal chelation. However, the correlation between K D or K A and the quantity of primary or secondary oxidation product (Fig. S4 ) was not very visible because of insufficient data and all the 4 peptides studied were selected due to their good affinity to Ni 2+ (low K D , high K A ). Meanwhile, Alc + Flv and Prt + Flv pea protein hydrolysates showed good metal chelating abilities as well  (El Hajj et al. , 2022). Despite that metal chelating activity was investigated with a Ni 2+ -NTA chip, we can extrapolate to the Fe 2+ chelating capacity, Fe 2+ being a borderline HSAB acid as Ni 2+ as well.
Acknowledgements The authors thank the  French ministry government  for the  MESR grant , the  ANR   JCJC MELISSA  ( 2020-2024 ), the " Impact Biomolecules " project of the " Lorraine Université d'Excellence "(Investissements d'avenir-ANR  project number  15-004 ), notably with the project DrEAM 6, that financially supported the doctoral mobility of  Rachel Irankunda  at DTU. The authors would also like to thank  Lis BERNE  for her skillful work in the laboratory.
Database In our study, we employed NCBI virus database  [27]  to investigate the genomic information and sequence gaps of different strains. The NCBI Virus database provides comprehensive information on viral sequences and their characteristics, including genome sequences, SRA data, and detailed annotations. Therefore, we utilized this database to extract various sequencing results that contain genome and SRA information for different strains. To access the SRA data and reference genome sequences of these strains, we used the SRA toolbox, a command-line tool developed by the NCBI. This tool enables users to download raw sequencing data in the form of SRA files, which can be converted into fastq files for further analysis. We combined these data with the corresponding reference genome sequences to obtain a more complete understanding of the genetic makeup of each strain.
Acknowledgments:  Dong-Qing Wei  is supported by grants from the  National Science Foundation of China  (Grant No. 32070662 ,  61832019 ,  32030063 ), and  Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University  ( YG2021ZD02 ). The computations were partially performed at the  Pengcheng Lab. and the  Center for High-Performance Computing, Shanghai Jiao Tong University.
Most of them only estimate an average dispersal distance or a diffusion rate  (Broquet and Petit, 2009; Roques et al. , 2016). The second class enables to infer the entire shape of the dispersal kernel by directly observing dispersal with dedicated experiments tracking the movement of tagged individuals, thanks to particular morphological structures or genetic markers. They often rely on heavy experimental procedures for data acquisition, requiring for example to control for the source strength and location  (Soubeyrand et al. , 2007; Rieux et al. , 2014). The third class of methods make use of specific mathematical modelling to infer dispersal from observations of ongoing dynamics  (Soubeyrand et al. , 2009a; Rieux et al. , 2013; Bousset et al. , 2015; Grosdidier et al. , 2018). If phenomenological models can hardly disentangle the observed data from the dispersal process per se, mechanistic-statistical models do so and enable a proper inference of dispersal using spatio-temporal datasets  (Wikle, 2003a; Soubeyrand et al.
Furthermore, the probabilistic model describing the observation process in the mechanistic-statistical approach can handle such multiple datasets with varying supports  (Wikle, 2003b). However, inference based on multi-type data remains a challenging statistical issue as the observation variables describing each data type follow different distribution laws  (Chagneau et al. , 2011)  and can be correlated or, more generally, dependent because they are governed by the same underlying dynamics  (Bourgeois et al. , 2012; Georgescu et al. , 2014; Soubeyrand et al. , 2018). This requires careful definition of the conditional links between the observed variables and the model parameters (the so-called observation laws) in order to identify and examine complementarity and possible redundancy between the data types.
Dispersal and demographic processes ruling the epidemic wave The inferential framework developed and tested in previous section was used to fit and compare the four models considered to the real dataset gathered in the Durance River valley and to derive confidence intervals of the parameters of interest (Appendix D. 3). Model selection was used to decipher which dispersal process was best supported by the data set, for five initial conditions. The large AIC difference in favour of hypothesis J ExpP indicates that poplar rust propagules assuredly disperse according to an exponential-power dispersal kernel along the Durance River valley (Table  3 ). Note that for all kernels, the five initial conditions lead to similar estimations. Under the R. D. hypothesis, however, initial conditions can lead to different estimations because of local optima, but all AIC resulting from the R.
However, even in this context, observation errors are likely present in our dataset as in any large spatio-temporal study. The latent variables used in hierarchical models are best suited to handle the fact that a tree observed to be healthy can actually be infected. False detection of infection could also be taken into account. This could make sense as a sister species, M. alli-populina, not easily discernible from M. larici-populina in the field, can also infect poplar leaves. This species can predominate locally in the downstream part of the Durance River valley. This could have led to over-estimate the disease severity at some locations. Yet, all infected leaves from twigs were collected and minutely inspected in the lab under a Stereo Microscope (25 magnification) to check for species identification.
the Durance River valley: Audrey Andanson, Béranger Bertin, Olivier Caël, Bénédicte Fabre, Christine Gehin, Claude Husson, Benoît Marçais, and Agathe Vialle. We also thank Benoît Marçais for fruitful discussions on disease monitoring, Bénédicte Fabre for the calculus of the density in uredinia on a poplar leaf, and Fabrice Elegbede for advices on statistical analyses. This work was supported by grants from the French National Research Agency (ANR-09-BLAN-0145, Emile project; ANR-18-CE32-0001, Clonix2D project; ANR-14-CE25-0013, project NONLOCAL, ANR-11-LABX-0002-01, Cluster of Excellence Arbre; 20-PCPA-0002, Beyond project). Constance Xhaard was supported by a PhD fellowship from the French Ministry.
(2015)  (see detailed method below). From the 1276 isolates, 36 strains were selected (and deposited at the CIRM-CFBP strain collection) to recover variable relative abundance and prevalence on seed and seedling microbiota and covering a large phylogenetic diversity (FigS1, Fig1-B). DNA of these bacteria was extracted using the Wizard® Genomic DNA Purification Kit (Promega). Genomes were sequenced at the BGI (China) using DNBSEQ technology and assembled with Spades v3. 15. 3 using the default k-mer parameters  (-k 21, 33, 55, 77, 99, 127)  and the following options: -cov-cutoff auto, --isolate  (Prjibelski et al 2020). Genomes are available on NCBI using the BioProject ID PRJNA1041598.
The raw amplicon sequencing data are available on the European Nucleotide Archive (ENA) with the accession number PRJEB59714. The bioinformatic processing of the amplicons originating from the bacterial strain collection and SynCom experiments was performed in R. In brief, primer sequences were removed with cutadapt 2. 7  (Martin, 2011)  and trimmed fastq files were processed with DADA2 version 1. 22. 0  (Callahan et al. , 2016). Chimeric sequences were identified and removed with the removeBimeraDenovo function of DADA2. Amplicon Sequence Variant (ASV) taxonomic affiliations were performed with a naive Bayesian classifier  (Wang et al. 2007 ) with our inhouse gyrB database (train_set_gyrB_v5. fa. gz) available upon request. Unassigned sequences at the phylum level and parE sequences (a gyrB paralog) were filtered.
Then, only ASVs with a minimum of 20 reads and present in at least 2 different samples were retained for experiment 2 and 3 (3 reads in at least 3 samples for experiment 1). To track our SynCom strains, only ASVs with 100% of identity were considered to be our strains. Some singlenucleotide polymorphisms (SNPs) were identified for some strains and were artificially increasing inoculum richness. They were thus removed before downstream analyses. All R scripts employed in this work are available on GitHub (https://github. com/GontranArnault/BeanSeedSynCom2023).
Gut microbiota metabolism is central to host physiology as its participation in digestion modulates nutrient availability to the host  (5, 6). Moreover, the cross-talk among bacteria shapes the ecosystem  (7) , consequently modulating its function  (8). To investigate mechanistic metabolic interactions between organisms, computational models are useful tools  (9, 10). Researchers can reconstruct metabolic networks based on an organism's complete genome, biochemical databases, and literature knowledge. Leveraging stoichiometry and thermodynamics information, this network can be formalized into a Genome-Scale Metabolic Model (GEM), facilitating the simulation and examination of its metabolic phenotype  (11). Indeed, GEMs predict the metabolic phenotype in a given condition and explain this prediction by revealing important nutrients and activated metabolic pathways  (12).
Interaction score for 331 gut bacterium-enterocyte ecosystems under three different dietary conditions. 331 strain-level gut bacteria models from the EMBL GEMs (16) were selected as they were described as gut microbes based on the Virtual Metabolic Human (VMH) Database  (14). Their pairwise interaction score with the enterocyte was computed under three nutritional conditions (Figure  1A , Table  S1  for complete results). The first condition, an unconstrained diet, represents a synthetic environment where all modeled nutrients are unlimited in the lumen. The other two conditions, the Western Diet (WD) and the protein diet (PD), are biologically representative diets. The WD is high in fat and simple sugars but low in fibers, while the PD is rich in protein but poorer in fats and simple sugars than the WD  (24)  (see Methods).
Interestingly, the increase of S in WD for the mutualistic ecosystem is not explained by reaching a higher objective value but because the interaction partially compensates for the lower objective value of each organism in WD compared to PD (Table  S2 ). More than a beneficial effect of WD, this illustrates how limited resources can increase interactions  (25). While WD enhanced some interactions favoring bacteria, it mostly favored competitive interactions. Integration of metabolic interaction score and type to predict "Host-beneficial" microbes. The VMH database  (14)  provided the ecological type of the 331 bacterial GEMs. Microorganisms were categorized into three ecological types: probiotic, pathogen, commensal, and uncharacterized. Probiotics had a significantly higher score compared to pathogens in PD (Mann-Whitney, p = 0.
Methods Genome-Scale Metabolic Models. The 331 bacteria models used in this study are CarveMe reconstructions extracted from the publicly available EMBL GEMs database  (16). These bacteria were selected based on their description as gut bacteria in the Virtual Metabolic Human (VMH) database  (14). The bacteria described in the VMH database's metadata as "Pathogen," "Opportunistic pathogen," or "Putative Pathogen" were joined in the "Pathogen" ecological type (n = 46). The ones described as "Probiotic" or "Probiotic poten-.
Resource availability. Lead contact. Further information and requests for resources should be directed to and will be fulfilled by the Lead Contacts, Anna Lambert (anna. lambert@univ-nantes. fr) or Damien Eveillard (damien. eveillard@univ-nantes. fr). Materials availability. This study did not generate new materials. Data and Code Availability. The 331 gut bacteria models were CarveMe reconstruction from the EMBL GEMs database available at: EMBL_GEMs The small intestine intestinal cell model is available at: In silico reconstructions -Thiele lab The minimal microbiome models built for this paper are available at: github -MO_GEMs_Score All original code has been deposited at github -MO_GEMs_Score and is publicly available as of the publication date.
ACKNOWLEDGEMENTS AL benefits a doctoral fellowship managed by  Bba Milk Valley , dairy industrial association. We thank  Regional Councils of Bretagne  (grant no. 19008213 ) and  Pays de la Loire  (grant no. 2019-013227 ) for their financial support through the interregional project PROLIFIC. The  bioinformatics core facility of Nantes (BiRD, Biogenouest) ,  University of Nantes, France , provided computational support.
Primer sequences, listed in Table  1 , were designed by using Primer3 program (https://bioinfo. ut. ee/primer3-0. 4. 0/) or previously described in the literature, namely Cs18s, β-LYC, CCD2, ALDH, UGT74AD1  (Gómez-Gómez et al. , 2017) , CCD7  (Rubio-Moraga et al. , 2014a) , and UGT709G1  (Diretto et al. , 2019). The amplification efficacy of the oligonucleotide pairs was tested before performing qRT-PCR analysis  (Livak and Schmittgen, 2001).
Acknowledgments We thank  Paul Devienne  at the  LEEC laboratory  for his help in taking care of termite colonies. We thank  Baptiste Piqueret  at  LEEC laboratory  for inspiring discussions. We thank  Anand Douady  for his inspiring experiments with salt and evaporation. This work was supported by a  Royal Society Newton International  Fellowship  NIF\R1\180238  and by a  Leverhulme Research Project Grant   RPG-2021-196.
Acknowledgments The authors thank  Hanh Tong  and  Sabrine Naimi  for their help with protocol and animal care. The authors thank  Lillian Chau ,  Brittaney Bonhomme , and  Lisa Nessel  for their help in implementing the Functional Research of Emulsifiers in Humans Corrected study. The authors also thank the  Hist'IM and the Genom'IC platforms (Institut Cochin, INSERM U1016, Paris, France)  for their expertise. Finally, the authors acknowledge the  Molecular Evolution  core at the  Parker H. Petit Institute for Bioengineering and Bioscience at Georgia Institute of Technology  for the use of their shared equipment, services, and expertise regarding the RNA sequencing approach.
Funding Supported by a starting grant from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  ( ERC-2018-StG-804135 INVADERS ); a  Chaire d'Excellence  from  IdEx Université de Paris  ( ANR-18-IDEX-0001 ); an award from the  Fondation de l'Avenir  ( AP-RM-21-032 );  Agence Nationale de la Rechercher (ANR)  grants  EMULBIONT  ( ANR-21-CE15-0042-01 ) and  DREAM  ( ANR-20-PAMR-0002 ); and the national program Microbiote from  INSERM  (B. C. ). Also supported by  National Institutes of Health  grants  DK115180  (A. T. G. , J. D. L and G. D. W. ),  5UL1TR001878  (G. D. W. ), and  P30-DK050306  (G. D. W. ); the  Penn Center for Nutritional Science and Medicine ; and the  Max Planck Society.
Acknowledgements This study was carried out in the framework of the  Agricultural Model Intercomparison and Improvement Project (AgMIP). KCK  was supported by  AdAgriF -Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation ( CZ. 02. 01. 01/00/22_008/0004635 ). Partial funding was provided by the  BonaRes project Soil3  ( BOMA 03037514 ,  031B0515C ) of the  Federal Ministry of Education and Research (BMBF), Germany , by  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation)  under Germany's  Excellence Strategy -EXC 2070 -390732324  and by the  European Union  ( EU horizon project   IntercropValuES , grant agreement No  101081973 ).
It is specifically designed to assimilate optical remote sensing datasets at native high resolution into a simple but generic agronomic model (SAFYE-CO2) over large territories. All the processing steps are conceived in a comprehensive manner (Fig. 1 ). A brief point wise description of the data flow and processing steps is presented here and detailed in the following subsections: 1. A pre-processing "Data ingestion" step allows the updating of existing data sets through automated downloading of satellite images and weather forcing. Optical Bottom Of Atmosphere (BOA) reflectances are downloaded for. Satellite data are uncompressed and relevant spectral bands are stacked. The weather data is stored in time series with the associated correspondence matrix to the high resolution grid defined by the user.
, 2021). This enables a harmonised Level-2A database with an efficient cloud masking algorithm  (Baetens et al. , 2019). The data contains quality indicators including cloud coverage. The datasets are presented as granules (tiles) of 110 × 110 km ortho-images in the UTM projection. Prior to the processing, the remote sensing datasets are decompressed, re-sampled at 10 m resolution using nearest neighbour.
Weather forcing data Daily weather data maps covering the simulation period and spatial extents are used to force the crop model. Cumulative daily global incoming solar radiation, Rg in W m -2 and daily average air temperature at 2 m, T a in °C are needed for the vegetation growth module in SAFYE-CO2. Based on previous studies that showed the impact of diffuse radiation on crop development and photosynthesis  (Béziat, 2009; Roderick et al. , 2001) , the diffuse incoming radiation is computed based  on De Jong (1980). Furthermore, when the water budget module of SAFYE-CO2 is activated , daily potential evapotranspiration, ET0 in mm d -1 , and daily cumulative rainfall in mm d -1 are extracted from the weather data. AgriCarbon-EO supports two data sources that provide weather data: the Météo-France SAFRAN dataset  (Vidal et al.