dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets description In order to predict genomic cross values, we used a TP composed of two datasets of lines from two institutes: Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement  (INRAE)  with its subsidiary company Agri-Obtentions (AO), INRAE-AO, and Groupe d'Étude et de contrôle des Variétés Et des Semences, GEVES. The detailed description of the TP for the 4 analyzed traits is provided in Supplementary Tables  S1 to S9. The INRAE-AO dataset was composed of a breeding population of F8-F9 winter bread wheat lines developed by  INRAE-AO and evaluated between 2000 and 2022 in two French geographical area (North and South). Each year, the lines were phenotyped in up to 9 locations in France for several traits of interest including yield, grain protein content, plant height and heading date.
On average, depending on the trait, between 157 and 169, and between 26 and 42 varieties were evaluated each year in North and South areas, respectively. Each variety has been evaluated for at least two years. In each location, some registered varieties were used as controls and have been evaluated for several consecutive years. We used the data base corresponding to high yield crop management (optimized pesticide, fungicide and nitrogen amount). The total number of phenotyped lines in the INRAE-AO dataset was 2,673, 2,369, 2,439 and 2,672 for yield, grain protein content, plant height and heading date, respectively. The GEVES dataset is composed of the VATE (Valeur Agronomique, Technologique et Environnementaleagronomic, technological and environmental value) data on winter bread wheat collected by the GEVES as part of the evaluation of varieties for national registration between 2000 and 2022.
We included in our analyses the varieties evaluated in the North and the South national bread wheat experimentation networks with high yield crop management. On average, depending on the trait, between 44 and 46, and between 26 and 27 varieties were evaluated each year in North and South networks, respectively. In these networks, some registered varieties were also used as controls and have been evaluated for several consecutive years. The total number of phenotyped lines in the GEVES dataset was 650, 644, 650 and 647 for yield, grain protein content, plant height and heading date, respectively.
Correlation of phenotypes between years In order to check the reliability and consistency of observations from one year to the next in the TP, we computed Pearson's correlations between years for each trait and each dataset (Supplementary Tables  S2 to S9 ). For the INRAE-AO dataset, the average number of varieties in common between two consecutive years ranged, depending on the trait, from 54 to 60, and from 11 to 16 in North and South areas, respectively. For the GEVES dataset, the average number of varieties in common between two consecutive years was 25 and 15 for the four phenotypes in North and South networks, respectively. For each year, we used the following mixed model to obtain one Best Linear Unbiased Estimator (BLUE) value per line across trials: 𝑦 𝑖𝑗 = 𝜇 + 𝑔 𝑖 + 𝑙 𝑗 + 𝑟 𝑖𝑗 with 𝑦 𝑖𝑗 the phenotype for line 𝑖 in location 𝑗, 𝜇 the grand mean, 𝑔 𝑖 the fixed effect of line 𝑖, 𝑙 𝑗 the random effect of location 𝑗, and 𝑟 𝑖𝑗 the random effect of residual.
00a2LM toolset. Next, we excluded varieties that did not have a phenotype for the yield trait in the real TP dataset (2,146). We computed euclidean distances on the 10 PC and performed a hierarchical cluster analysis (146 groups) with the Ward's minimum variance method (Murtagh and Legendre 2014) (dist and hclust functions, respectively, from the stats v4. 1. 1 R package (R Core Team 2021)). In each cluster, we picked a line with the minimal value of the maximal relationship (GRM value) across all the remaining varieties, leading to the selection of 146 parents. Finally, we generated 73 crosses by randomly mating this new set of parents. This final procedure of random mating was replicated 10 times. We considered the median values of correlations across the 10 random matings to compare with the crosses from our experimental data.
Quality of the Training Population We used as TP the historical French registration data (GEVES) and the INRAE-AO breeding program, both Southern and Northern France networks. We checked the repeatability of observations in the TP by computing Pearson's correlations between years for each trait and each dataset (Supplementary Tables  S2 to S9 ). are the line, the environment, the interaction between line and environment, and the residual estimated variances using a mixed model with trials as random effect. rep ̂plot and rep ̂design are repeatabilities at the plot and design levels, computed from these estimated variances. ℎ genomic 2 was estimated using RR-BLUP model. The cross-validation accuracy was estimated using 60% of the data as the training population, and 40% as the validation population.
The variance components, phenotypic repeatabilities and genomic prediction accuracies of the TP are provided in Table  1. The repeatabilities at the plot level were relatively low for yield (0. 32), moderate for grain protein content (0. 52) and high for plant height and heading date (0. 73 and 0. 85, respectively). Repeatabilities at the design level were very high for all traits (ranging from 0. 91 to 0. 99). All these values reinforce the good correlations obtained above and suggest a high-quality TP dataset. The genomic heritabilities were moderate for yield (0. 53), grain protein content (0. 51) and plant height (0. 56), and high for heading date (0. 73). Finally, the cross-validation accuracy was quite similar for all traits, with moderate to high values ranging from 0. 52 (plant height) to 0.
A high-quality TP In this study, we took advantage of a historical TP of more than 20 years of data to predict three cross value components (PM, SD and UC) of 73 or 101 crosses depending on the trait using genomic prediction models. We compared these predictions to experimental observations of progenies. All the results concerning the TP (correlations between years, repeatabilities at plot and design levels) suggest a high-quality dataset. The prediction accuracies (cross-validation) ranging from 0. 52 (plant height) to 0. 67 (heading date) were higher than previously obtained with a smaller TP (G. Charmet, personal communication). Moreover, based on preliminary studies (data not shown) inspired from an iterative approach that removes the environments (year x location) one by one and that computes prediction accuracy at each iteration  (Heslot et al.
2013) , we kept all the environments of the TP because we did not detect any environment deteriorating the quality of the prediction. We also checked (data not shown) the prediction accuracy within each sub-dataset of the TP (GEVES, INRAE-AO, North, South) and the prediction abilities of the 73 crosses obtained for the three cross-value components using these sub-datasets as TP. We observed better prediction abilities using all datasets combined. All these preliminary results led us to retain the entire dataset as the TP in this study. Due to the size and complexity of the TP design, for computational reasons (the need of pre-corrected phenotypes in the R package, and time to run), and as done in previous studies  (Tiede et al. 2015 ; Danguy des Déserts et al. 2023), we separated the steps of correction for the fixed effects such as spatial coordinates and environmental effects (location and year) from the estimation of marker effects.
Acknowledgements The authors thank the  Fonds de Soutien à l'Obtention Végétale (FSOV)  for financing the  FSOV 2020 I -PrediCropt  project. The authors acknowledge the partners of the project (Agri-Obtentions and  Florimond-Desprez  veuve & fils) and the INRAE personal responsible for experimental evaluation ( Laurent Falchetto ,  Sandrine Berges -INRAE UE.
Filamentation assay To assess filamentation, the strains were cultured in 3 ml of YPD supplemented with 10% FBS at 37 C with shaking at 180 rpm for 3 h in 24-deep-well plates. Cells were observed using an Olympus IX 83 microscope equipped with a 20Â or 40Â objective. For each strain, five regions were randomly selected, and images were captured with the Hamamatsu ORCA Flash cooled CCD camera, using the Cell Sens software. Filamentation phenotypes were scored quantitatively and qualitatively by eye as "poorly filamentous" to "highly filamentous" (indicated as scores of 1e5 respectively) and as "hyphae", "pseudohyphae" and "both pseudohyphae and hyphae" (indicated as H, P and P/H, respectively). The assessment of filamentation was performed independently by two persons, the results are presented as a consensus of the two analysis.
Statistical analysis Statistical analyses were performed using one-way ANOVA followed by Tukey's multiple comparisons test or unpaired t-test. The data are represented as mean ± standard error of the mean (SEM). A p-value of <0. 05 was considered statistically significant. Significance levels are indicated as follows: *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001. Graphs and statistical analyses were generated using GraphPad Prism 9. 5. 1 (GraphPad Software, La Jolla, CA, USA).
Funding This work was founded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie action, Innovative Training Network: FunHoMic ; grant  N 812969. Work in the laboratory of C. d. E. is supported by the  Agence Nationale de Recherche  ( ANR-10-LABX-62-IBEID ). E. P. was supported by  FAR Mission Oriented 2022  and FAR instrumentation 2022.
8 SR11) (Thermo Fisher Scientific, Bremen, Germany). Samples were desalted and concentrated for 10 min at 5 mL/min on an LCPackings trap column (Acclaim PepMap 100, C18, 75 mm inner diameter £ 2 cm long, 3 mm particles, 100 A pores). The peptide separation was conducted using a LCPackings nano-column (Acclaim PepMap C18, 75 mm inner diameter £ 50 cm long, 2 mm particles, 100 A pores) at 300 nL/min, by applying linear gradient consisted of 4 to 60% B during 90 min. Mobile phases consisted of (A) 0. 1% formic acid, 97. 9% water, 2% acetonitrile (v/v/v), and (B) 0. 1% formic acid, 15. 9% water, 84% acetonitrile (v/v/v). Data were acquired in positive mode in datadependent mode to automatically switch between high resolution full-scan MS1 spectra (R at 60,000) and low resolution CID-MS2, in the 300 to 1,800 m/z range.
The 20 most intense peptide ions with charge states ≥ 2 were sequentially isolated and fragmented in the linear ion trap using CID mode (collision energy 35%, activation time 10 ms, Qz 0. 25). Dynamic exclusion was activated during 30 s with a repeat count of 1. The lock mass was enabled for accurate mass measurements. Polydimethylcyclosiloxane (m/z, 445. 1200025 (Si(CH 3 ) 2 O) 6 ) ion was used for internal recalibration of the mass spectra. MS/ MS ion searches were performed using Mascot search engine version 2. 3. 2 (Matrix Science, London, UK) via Proteome Discoverer 2. 1 software (Thermo Fisher Scientific, Bremen, Germany) against NCBIprot_viridiplantae or NCBIprot_chordata database (January 2017). The search parameters included trypsin as a protease with 2 allowed missed cleavages and carbamidomethylcysteine, methionine oxidation and acetylation of N-term protein as variable modifications.
Molecular Modeling of Chicken Proteases PRSS2 and PRSS3 Complexed to BBI A 3D model of each chicken protease was built by comparative modeling using SWISS-MODEL software  (Waterhouse et al. , 2018)  at Expasy portal (www. expasy. ch) and the mature sequence of the enzyme (Uni-protKB accession codes Q90628 and Q90629 for PRSS2 and PRSS3, respectively). In the automated modeling procedure, porcine trypsin (Protein Data Bank (PDB) accession code: 3MYW) was used as a template to generate chicken PRSS2 model. Similarly, bovine beta trypsin (PDB accession code: 1G3B) was used as a template to model chicken PRSS3 as both proteases share 75% protein sequence identity. Because the sequence identity of chicken proteases with their respective reference template is quite high, the modeling procedure was straightforward and the quality of the models was found to be satisfactory, as confirmed by the quality analysis performed by SWISS-MODEL tool.
The complex of each chicken protease with pea BBI was built using the X-ray coordinates of the bovine trypsin/soybean BBI complex (PDB accession code: 1D6R) and the X-ray structure of pea BBI (PDB accession code: 1PBI). To build each protease/inhibitor complex, PRSS2 or PRSS3 models and pea BBI were superimposed onto the corresponding protease and inhibitor of the trypsin/soybean BBI complex. The superimposition was performed by structural alignment using the minimum root mean square deviation (r. m. s. d) method. Pea BBI structure is a crystallographic dimer in which the carboxy-tail moiety of each BBI molecule adopts an extended conformation that stabilizes the dimer through the formation of a beta sheet-like structure (Li de la  Sierra et al. , 1999). Since it is unlikely that this dimer represents the functional state of BBI (Li de la  Sierra et al.
, 1999) , the carboxy-terminus from Leu67 to Asn73 was removed in the BBI monomer used to build the protease/BBI complex. Values of r. m. s. d after superimposition were 1. 52 A , 0. 48 A , and 0. 33 A for PRSS2-trypsin, PRSS3-trypsin, and pea BBI-soybean BBI pairs, respectively. These results indicate a high structural similarity of the reconstructed complexes with the trypsin/soybean BBI complex. A quick minimization using Swiss-PDBViewer  (Guex and Peitsch, 1997)  was performed to overcome minor steric constraints at the interface between PRSS2 or PRSS3 protease, and pea BBI. Electrostatic calculations were performed using the Pymol software (Schr€ odinger, 2021) and Adaptive Poisson-Boltzmann Solver plugin  (Baker et al. , 2001). The color-coded electrostatic potential molecular surface was represented using Pymol, using a conventional color scheme ranging from red (negatively charged regions) to deep blue (positively charged regions).
3. 4. 4b HPAIV H5N1, one of the carcasses being found in the bear's enclosure: a black-headed gull (Chroicocephalus ridibundus), which the zoo veterinarians also necropsied (mark ed pulmonary and splenic congestion were the most remarkable findings identified at necropsy). In the days following the bear's death, other bears of the same species displayed mild to moderate clinical respiratory signs, but could not be sampled to be analyzed as part of this study. The possibility that the bear might have been infected by an H5N1 virus of clade 2. 3. 4. 4. b was subsequently suspected, and confirmed by molecular analysis of biological samples sent to the French reference laboratory for high pathogenicity avian influenza, which deposited the viral genome sequence on GISAID (isolate ID EPI_ISL_17233426).
Phylogenetic and genetic analyses We performed next generation sequencing on several bear samples (lymph node, lung, and liver) and one gull sample (brain), using an Element AVITI sequencer (Element Biosciences, San Diego, CA) and a 2x150 bp paired-end protocol. We found H5N1 virus reads in all samples, in sufficient numbers to reconstitute whole genome consensus sequences, available under accession numbers OR634756 to OR634763 and OR634764 to OR634771 for the bear and the gull isolates, respectively. Details of the viral (>100 reads) and bacterial (>1000 reads) species identified by metagenomics can be found in Supplementary File 2. Phylogenetic analysis performed on HA gene segment showed that viruses found on both animals belonged to clade 2. 3. 4. 4b. The HA sequence of the bear-derived virus shared 99.
Bioinformatics analysis The metagenomic data analysis was performed with Kraken2  [24]. We set a threshold at 100 and 1000 reads for the abundance of the viral and bacterial species, respectively. The reads were then mapped on a H5N1 reference genome (GISAID isolate ID: EPI_ISL_17233426) with minimap2  [25]  and the consensus sequences were generated using iVar  [26].
Acknowledgment This study was performed in the framework of the " Chaire de Biosécurité et Santé Aviaires ", hosted by the  National Veterinary College of   Toulouse (ENVT)  and funded by the  Direction Générale de l'Alimentation, Ministère de l'Agriculture et de la Souveraineté Alimentaire, France. The authors would like to thank  HELIXIO SAS (Saint Beauzire-France)  for performing the metagenomics analysis.
sequences of each viral gene segment detected in black bear were compared with the most related sequences available in GISAID (https://www. gisaid. org/) and aligned by using MAFFT version 7 (https://mafft. cbrc. jp/alignment/server/index. html). Maximum-likelihood phylogenetic trees were generated using RaxML, version 8. 2. X (https://sco. h-its. org/exelixis/software. html), with a GTR model associated with gamma distribution (https://github. com/tamuri/treesub/blob/master/README. md. Phylogenetic trees were then visualized by using FigTree version 1. 4. 2.
Background Microsatellite markers represent a low-cost and efficient tool for rapid genotyping as compared to single nucleotide polymorphism markers in laboratories with limited resources. For the economically important yam species widely cultivated in developing countries, very few microsatellite markers are available and no marker database has been developed to date. Herein, we conducted a genome-wide microsatellite marker development among four yam species, identified cross-species transferable markers, and designed an easy-to-use web portal for the yam breeder community.
Results The screening of yam genomes resulted in 318,713; 322,501; 307,040 and 253,856 microsatellites in Dioscorea alata, D. rotundata, D. dumetorum, and D. zingiberensis, respectively. Mono-, di-and trinucleotides were the most important types of repeats in the different species and a total of 864,128 primer pairs were designed. Furthermore, we identified 1170 cross-species transferable microsatellite markers. Among them, a subset of 17 markers were experimentally validated with good discriminatory power regarding the species and the ploidy levels. Ultimately, we created and deployed a dynamic Yam Microsatellite Markers Database (Y2MD) available at http://yamdb. 42web. io/. Y2MD is embedded with various useful tools such as JBrowse, Blast, insilicoPCR, and SSR Finder to facilitate the exploitation of microsatellite markers in yams.
In plant breeding, the development of molecular marker databases has been extensively promoted in economically important plants such as Sesamum indicum  (Dossa et al. , 2017) , Vicia faba  (Mokhtar et al. , 2020) , Nicotiana spp. (Wang et al. , 2018) , legumes species (n = 13) (Duan and Kaundal, 2021), horticultural species (n = 112)  (Song et al. , 2021) , and Anemone coronaria  (Martina et al. , 2022). Since the usage is based on a graphical user interface, the database offers the breeders, a userfriendly path for rapid identification of molecular markers without a pre-requisite expertise in command line scripting. A web database concentrates all information in one platform and assists the breeders to design markers related to a trait of interest. Yams (Dioscorea spp. ) represent an important staple food crop, feeding over 300 million people in tropical and subtropical regions  (Mignouna et al.
, 2022) , indicating that SSRs might be highly transferable from one species to another, even likely, to those wild species that have not yet been sequenced. Therefore, having a comprehensive SSR database for the yam breeders is fundamental for pre-breeding and breeding activities. The present study was designed to (i) mine the genome of four yam species for the identification single-species and cross-species transferable SSR markers, (ii) create primer sets for the markers, (iii) evaluate the discriminatory power of selected markers, and (iv) build a digital web resource to support yam breeding and genetic research.
Genomic sequence resources A total of four genome sequences were retrieved from the National Center for Biotechnology Information (NCBI) genome portal, including Dioscorea alata cultivar TDa95/00328 (GenBank assembly accession: GCA_020875875. 1), D. dumetorum (GenBank assembly accession: GCA_902712375. 1), D. rotundata cultivar TDr96_F1 (GenBank assembly accession: GCA_009730915. 2), and D. zingiberensis isolate CJ2019 (GenBank assembly accession: GCA_014060945. 1) (Supplementary Table  1 ). Besides, we added a newly assembled chromosomescale assembly (unpublished data) of another greater yam (D. alata) "Kabusa".
Construction and deployment of Yam Microsatellite Markers Database The Yam Microsatellite Markers Database (Y2MD) database conception relied on three major steps detailed in Figure  1 : (i) data compilation in SQL database, (ii) back and front-end web construction, and (iii) tools integration. In brief, using the generated SSR markers datasets, the Y2MD SQL database was built. Thus, the Y2MD server was implemented using Linux, Apache, MySQL, and PHP (LAMP) web application platform. The user-friendly web interface was then developed with the aid of JavaScript, and the HyperText Preprocessor (PHP). To enable the users to conduct their own analyses conveniently, additional functionalities such as JBrowse  (Skinner et al. , 2009) , Blast  (Altschul et al. , 1990)  The numbers of SSRs per chromosomes are strongly correlated to the chromosome lengths (Figure  3a ).
Development of Yam Microsatellite Markers Database We built up a comprehensive database by integrating SSR markers and whole genome assemblies and annotations datasets (Figure  1 ). The Y2MD page contains data of four Dioscorea species and over 864,128 primer pairs. Also all SSRs data are downloadable in Excel, PDF, text, CSV format. The graphical user interface (GUI) menu has six tabs " Home", " Species", " Tools", " SSRs", " Download " and " About ". The "Species" tab displays information on each species with the possibility to choose the SSR of choice according to the type of patterns, the number of repeats, the position, etc. Information on experimentally validated SSRs and cross-species transferable SSRs are available in the "SSRs" tab. On the "Download" page, the links to the different genomes are available.
To enable the database user to perform some fundamental analyses, several functionalities were embedded in Y2MD among which JBrowse (Figure  6a ), insilicoPCR (Figure  6b ), SSR Finder (Figure  6c ), and Y2MD Blast (Figure  6d ). Thus, Y2MD empowered the user to find an SSR with SSR Finder, and proceed to a quick amplification check with insilicoPCR. Besides, the user can also blast his own sequence of interest onto a genome with Y2MD Blast tool. With JBrowse, the user is able to dive into the genome annotation map to find out potential genes related to a targeted SSR. Altogether, Y2MD offers a dynamic platform to the yam breeder to remotely use the available genomic resources in a GUI user-friendly manner. The website is freely accessible at http://yamdb. 42web. io/.
Deployment of Yam Microsatellite Markers Database for yam breeders Many databases dealing with SSR marker data from various species have been made available to the public. However, no database is available to study yam microsatellites. A yam microsatellites marker database will be a useful platform for advancing genetic evaluation, genomic research, and yam breeding. The Pan Species Microsatellite Database  (Du et al. , 2020)  containing SSR data from 18,408 organisms is limited by the lack of features such as ePCR, Jbrowse  (Buels et al. , 2016)  and Blast  (Altschul et al. , 1990). This is also the case for the Tea Microsatellite Database  (Dubey et al. , 2020)  and SSRome  (Mokhtar and Atia, 2019). In this study, we compiled all generated microsatellite data into Yam Microsatellite Markers Database (Y2MD) (Figure  6 ).