dataesr/scientific-paragraphs-categorization huggingface dataset:
To define the scale in the automatic macro script (Additional file 4), the user has to draw a horizontal line of "x" mm along the horizontal ruler captured in one image using the "straight line" selection tool in the ImageJ tool bar. The length in pixels ("LP") is indicated in the status bar of ImageJ and has to be reported in the macro script, Step 2: Set scale, as the third value of the "make line" command (0,0, "LP",0). The real value ("x") as well as the unit (mm) of the drawn line have to be defined in the next line of the macro script "run("Set Scale…", "known = x unit = mm"). Concerning hue, saturation and brightness, the user has to first select "HSB" as the "color space" and then move the sliders until the desired objects are highlighted in red (when "red" is selected as the "threshold color").
For help in this setting, the user can select a small region of the particles to be identified and click the "Sample" button to predefine the hue, saturation and brightness values. The six values indicated at the end of the sliders have to be reported in step 4 of the macro script in the min[0], max[0] (for hue values), min[1], max  [1]  for saturation values, min  [2]  and max  [2]  for brightness values. For additional information about these commands, the user can refer to the ImageJ website on "Documentation" and "Menu Commands" (https:// imagej. nih. gov/ ij/ docs/ guide/ 146-Part-V. html# toc-Part-V). The macro defined for Fig. 4  was set up with the following parameters: scale = 1100 pixels, 50 mm; cropping image = (400, 426, 2634, 1824), hue = 0-55, saturation = 102-255, brightness = 95-255.
Statistical analysis Data were subjected to statistical analysis using Statgraphics Centurion XV. II professional software (Statpoint technologies Inc. ). The normality of residuals was verified by the Kolmogorov-Smirnov test. One-factor ANOVA was used for the statistical analysis of the effect of the experiment on the number of tubercles using the 4 experiments A, B, C and D mentioned in the experimental design paragraph. Two-factor ANOVA was used to analyze the effect of sunflower genotype and Orobanche race on the number of tubercles counted automatically using experiment E with 44 rhizotrons in total with the 5 sunflower genotypes inoculated with both Orobanche races. A similar two-way ANOVA was performed on the number of tubercles counted manually. The correlation coefficient was calculated using Microsoft Excel (2016).
We obtained the consensus sequence by comparing the de novo analysis with reference sequences from the Influenza Research Database (https://www. fludb. org) (24). We downsampled the reads to fit a coverage of 80× and submitted them to the SPAdes version 3. 1. 1 de novo assembler (http://cab. spbu. ru/software/ spades). We submitted the de novo contigs to BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi) on a local nucleotide database. For each segment, we selected the best matches for Bowtie 2 alignment  (25). Finally, we compared de novo assemblies and alignment on the references and assessed their strict identities. We retained only the sequences with a coverage of >30× for all segments for further analysis. For the following analyses we considered only sequences from nucleotide positions 20-2248 for polymerase basic (PB) 2 protein, 4-2259 for PB 1 protein, 41-2151 for polymerase acidic (PA) protein, 49-1704 for hemagglutinin, 14-1458 for nucleoprotein (NP), 50-1385 for neuraminidase, 38-936 for matrix protein, and 28-815 for nonstructural protein, according to the first ATG.
Phylogenetic Analysis For the phylogenetic analysis, we used only samples with complete sequences for each segment. We aligned the sequences with ClustalW (http://www. clustal. org). We used MEGA version 7. 0 software  (26)  to construct maximum-likelihood phylogenetic trees with 500 bootstrap replicates using the Tamura 3-parameter model. Then, we compared each segment that was representative of a phylogenetic group (i. e. , closed sequences with >98% nucleotide identity) to sequences available in the GISAID database (https://www. gisaid. org). For each sequence, we concatenated 8 AIV gene segments and tested them for reassortment using the RDP4 software  (27)  with the SIScan, Bootscan, RDP, MaxChi, and GENECOV methods. We estimated the time to most recent common ancestor (tMRCA) of the viral sequences by performing Bayesian coalescent phylogenetic analyses in BEAST version 1.
Epizootic Case Situation During winter 2016-17, France declared 539 cases of HPAI H5 infection, the second-highest number of cases in Europe. In total, 488 cases were in domestic or captive birds, primarily ducks, and 51 cases were in wild birds (Figure  1 ). The 488 domestic cases were mainly in southwestern France, whereas H5N8 infection was more common in wild birds in other areas of France (Appendix Table  1 ). Seventeen cases were detected in wild birds, mostly common buzzards, in southwestern France, whereas cases in wild birds from other areas were in waterfowl (mostly swans). During this period in southwestern France, other AIVs were also identified, indicating viral cocirculation within poultry farms (data not shown).
Acknowledgments We thank the veterinarians, farmers, and  Office National de la Chasse de la et de la Faune Sauvage  staff involved for the collection of field samples. We are also grateful to the screening laboratories for the initial screening samples and to the local  Directions Départementales de la Protection des Populations and central Direction Générale de l'Alimentation official veterinary services  for the outbreak management. This study was supported by  Fonds de la Recherche pour l'Influenza Aviaire   2019  from the  French Ministry of Agriculture.
Genomicus is a database and web-server dedicated to comparative genomics in eukaryotes. Its main functionality is to graphically represent the conservation of genomic blocks between multiple genomes, locally around a specific gene of interest or genomewide through karyotype comparisons. Since 2010 and its first release, Genomicus has synchronized with 60 Ensembl releases and seen the addition of functions that have expanded the type of analyses that users can perform. Today, five public instances of Genomicus are supporting a total number of 1029 extant genomes and 621 ancestral reconstructions from all eukaryotes kingdoms available in Ensembl and Ensembl Genomes databases complemented with four additional instances specific to taxonomic groups of interest. New visualization and query tools are described in this manuscript.
INTRODUCTION Initiated in 2010, the Genomicus database and visualization tools aim to navigate genomes in several dimensions: linearly along chromosomal axes, transversely through the comparison of genomes or genomic segments and chronologically along evolution. Over the last 12 years, we have maintained uninterrupted service, updated data regularly, and developed new tools following user suggestions. Initially developed around vertebrate genome data, it has been extended to respond to the needs of a wider community focusing on additional groups now covering all eukaryotic lineages. The present article gives a 12-year perspective on this tool, describes its historical functionalities, the novelties since the previous NAR Database issues  (1) (2) (3) (4)  and plans for the future on new functionalities and data.
From a vertebrate genome evolution browser to multiple dedicated servers The development of Genomicus was initially motivated by the need to visualize vertebrate ancestral genome reconstructions inferred by our laboratory. We first developed a simple web server that stored gene positions, gene trees and enabled navigation through vertebrate genomes and their ancestors  (1) , in order to pinpoint possible errors in reconstructions by AGORA (Algorithm for Gene Order Reconstruction in Ancestors, https://github. com/DyogenIBENS/ Agora). In 2011, Genomicus was the first online database server that allowed comparisons of hundreds of genomic segments in one single interface, and as far as we know, this is still the case. Effectively, users navigate through genomes according to three dimensions: linearly along gene neighbourhoods, transversally between genomes, and evolutionary through ancestral gene content and order reconstruction.
The Ensembl database  (5)  provided extant genome data represented in the first Genomicus server, and further extensions to Genomicus followed the growth of Ensembl, in particular in 2013, when we published 4 additional genome browsers (2) dedicated to groups represented in Ensembl Genomes (6) (Plants, Fungi, Metazoa, Protists). Since 2011, the vertebrate-specific Genomicus browser is updated 4 times a year, following the releases of the Ensembl database, for a total of 50 complete server releases. In addition of these five instances based on Ensembl or Ensembl Genomes data, other servers are available for the study of specific genomes annotation. A tunicate instance is linked to the ANISEED database  (7) , as well as specific servers dedicated to amphioxus or trout comparative genomics  (8, 9)   available for plants since 2014  (10) , and only since the current release for protists, metazoan and fungi.
Evolution of the number of genomes The number of extant genomes represented in Genomicus vertebrates increases with each quarterly update, and has more than quadrupled in ten years, in par with the number of reconstructed ancestors (Figure  1 ). Starting from 54 extant genomes (43 ancestral genomes) in release 53, it reached 247 extant genomes in release 100 (243 ancestral genomes). The use and representation of data as well as the fluidity of queries on the server remains efficient, despite the amount of information requested and transferred. Other Genomicus servers have also seen an increase in the amount of data, with a focus on less frequent but more major releases, including ancestral genome reconstructions in the current one.
Main search and visualization tools available on Genomicus servers Over the last 12 years, the code behind Genomicus servers has evolved in response to user feedbacks and the needs of the comparative genomics community. Interfaces to query the database and visualization modules have also been developed and integrated into the different servers in response to the availability of new data (Conserved Non Coding Elements, ancestral reconstructions). If our first purpose was to visualize multiple genome comparisons, it was quite obvious that pairwise genome studies would be of interest. Therefore, in addition to PhyloView and AlignView described in previous Genomicus article (1), matrix dotplot of homologous genes (MatrixView), karyotype comparisons (KaryoView) and PhylDiagView, a tool to compute conserved gene order between two genomes (modern or ancestral) have been deployed (3,4) (Figure  2 ).
Ancestors for fungi, metazoan and protists Year 2021 saw new releases for Genomicus-Plants (based on Ensembl Genomes 49), Genomicus-Fungi (based on Ensembl Genomes 43), Genomicus-Protists and Genomicus-Metazoa (both based on Ensembl Genomes 51). For the first time for fungi, protists and metazoan, we computed ancestral genome reconstructions with AGORA (https:// github. com/DyogenIBENS/Agora), and made them available in the respective servers (Table  1 ).
Search and visualization of specific evolutionary scenario in gene families In Genomicus, all visualization tools, inference of ancestral gene organization and computation of synteny are based on the availability of a forest of gene trees reconciled with a species tree (downloaded from Ensembl or Ensembl Genomes databases, or computed with the same procedure as in the EnsemblCompara pipeline  (11) ). While each gene family has its own evolutionary history made of gene duplication and gene deletions or absence thereof, some evolutionary patterns exist where multiple gene families will share an identical history in parts of their tree. Being able to query the entire forest of trees to extract all the families that fit a specific pattern is a powerful strategy to isolate at once the extant genes that result from this pattern.
To this end, users can now search Genomicus Vertebrate using the RapGreen software (12) (Figure  5 ). The tree patternmatching part of the software instantly parses all the gene trees available in the Genomicus server and retrieves specific evolutionary scenario defined by the user. These include a given tree topology with optional speciation and duplication nodes, explicit taxa at the leaves and/or on ancestral nodes, constraints on the branches limiting additional speciations or duplications. For example, users can search for all families that retained paralogues in the river trout (Salmo truta) and in the rainbow trout (Oncorhynchus mykiss) after the Salmonidae (4R) whole genome duplication (see Fig- Nucleic Acids Research , 2022, Vol. 50, Database issue D1031 ure 5A for pattern). Families corresponding to the query pattern are listed, can be downloaded (Figure  5B ) and can be visualized in InTreeGreat (Figure  5C ) (part of the Rap-Green package) that highlights in red the pattern in the specific gene tree and links genes to PhyloView in Genomicus (Figure  5D ).
GENOMICUS SOFTWARE IMPLEMENTATION Genomicus is composed of Perl (version 5. 30) scripts and modules, alongside with Python (version 2. 7) libraries from other projects in our laboratory (LibsDyogen, PhylDiag) for PhylDiag computation, executed with mod perl on an Apache2 (version 2. 4) server and querying a MariaDB (version 10. 5. 12) database. The web server is running on an Ubuntu server 20. 04. The pages embed inline-SVG drawings in XHTML while the JavaScript (version 1. 9. 1) usage is limited to an information panel retrieved with AJAX calls. The tree pattern search web interface is implemented as a Javascript/PHP user interface to edit patterns and explore results connected with a Java daemon running on Open-JDK 11. 0. 9. 1 that implements the tree pattern matching algorithm for parsing and exploring the tree forest in a minimal amount of time.
FUNDING Program Investissements d'Avenir launched by the  French Government  and implemented by  ANR  [ ANR-10-LABX-54   MEMOLIFE ,  ANR-10-IDEX-0001-02 PSL* Université Paris ];  RENABI-IFB call Funding  [ ANR-11-INSB-0013 ]. Funding for open access charge: CNRS.
Managed Regarding the model's capability of simulating overgrazed grasslands, and the response of productivity and soil organic carbon to different grazing intensities (Supplementary Discussion 3), we found that ORCHIDEE-GM captured the decreasing leaf area index (LAI) trend due to overgrazing in the western United States, southern Brazil, Argentina and Australia  33  , but underestimated overgrazing effects in central Asia and Sub-Saharan Africa. The historical model simulation also captures the areas classified as overgrazed in surveys from the 1980s in the Global Assessment of Soil Degradation (GLASOD) database  34. The historical simulation showed an increase in overgrazed areas since the 1860s with the largest expansion of overgrazed grassland occurring in Africa. The modelled responses of soil organic carbon to different grazing intensities are comparable with those from meta-analysis based on in situ observations  11, 31, 32.
The grassland GHG balance (unit: Gt CO 2e yr -1 ) was calculated as ), between the grassland ecosystem/farm system and the atmosphere. E LUC-grass (unit: Pg C yr -1 ) is the land-use change emissions from deforestation to pasture. All the GHG flux calculations in this study were made on a grid-cell basis. A detailed description of the estimation of the GHG fluxes can be found in Supplementary Methods 1. The uncertainty in the regional and global GHG fluxes and their components was assessed using a Monte Carlo approach (n = 10,000). Given the scarcity of evaluation data for changes in grassland soil carbon, we estimated the uncertainty of the grassland carbon budget by comparing the simulated carbon balance with a compilation of observations at site and regional level obtained from an extensive literature survey (Supplementary Discussion 1).
Third, CH 4 and N 2 O emissions from sparsely grazed grasslands (i. e. , mainly from wild grazers, and to a small extent from N 2 O emissions from atmospheric nitrogen deposition) were varied, following the reconstructed regional wild grazer population, over the period 1800-1859 (see Supplementary Methods 1 for details), but kept constant, at the 1800 value, over the period 1750-1799. The human population density since 1750 was retrieved from the History Database of the Global Environment (HYDE) compiled by the Netherlands Environmental Assessment Agency  41. The HYDE gridded data are available for the beginning of each decade from 1750 until 1860. Annual data were linearly interpolated within each decade. Attribution of RF induced by anthropogenic GHG fluxes from grasslands. In all the simulations by OSCAR v3.
Acknowledgements J. C. , D. S. G. , T. G. , and P. C. acknowledge support from the  ERC Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. The input of P. S. contributes to the  Belmont Forum/ FACCE-JPI  funded  DEVIL  project ( NE/M021327/1 ), the  UK NERC  funded projects  U-GRASS  ( NE/M016900/1 ) and  Soils-RGRREAT  ( NE/P019455/1 ) and the  EU  funded  H2020-EU. 3. 5. 1. project  VERIFY (No 776810 ). B. G. received support from the  French Agence Nationale de la Recherche (ANR) Convergence Lab Changement climatique et usage des terres (CLAND). This work was performed using HPC resources from  GENCI-TGCC  (Grant  2017-A0030106328 ). We thank  Dr. William Kolby Smith  for sharing NPP data. We thank all people of the TRENDY modelling group who provided TRENDYv6 data used in this study. We thank  John Gash  for his effort on English language editing.
Code availability The source code for ORCHIDEE-GM v3. 2 is available at https://doi. org/10. 14768/ 20190319001. 1. The source code of OSCAR is available at https://github. com/tgasser/ OSCAR (https://doi. org/10. 5281/zenodo. 4282113). The code used to generate all the results of this study and the specific scripts used to execute the attribution with OSCAR are available from the corresponding author upon request.
2  for two examples. In this paper we contribute to this line of work, and propose to replace the heuristics used in previous methods, such as Cosmo et al. (  2019 ), with a purely data-driven approach for the first time. Our key idea is to design a deep neural network, that both constraints the space of solutions based on the set of shapes given at training, and at the same time, allows us to solve the isospectralization problem with a single forward pass, thus avoiding expensive and error-prone optimization. We note that a related idea has been recently proposed in  Huang et al. (2019)  via the so-called OperatorNet architecture. However, that work is based on shape difference operators  (Rustamov et al. 2013 ) and as such requires a fixed source shape and functional maps to each shape in the dataset to properly synthesize a shape.
Shape-from-spectrum recovery To evaluate our method, we trained our model on 1853 3D shapes from the CoMA dataset  (Ranjan et al. 2018 ) of human faces; 100 shapes of an unseen subject are used for the test set. We repeated this test at four different mesh resolutions: ∼4K (full resolution), 1K, 500, and 200 vertices respectively. For each resolution, we independently compute the Laplacian spectrum and use these spectra to recover the shape. Comparison. We compared our method in terms of reconstruction accuracy to the state-of-the-art isospectralization method of Cosmo et al. (  2019 ), as well as to a nearestneighbors baseline, consisting in picking the shape of the training set with the closest spectrum to the target one. In addition, we trained two separate architectures (with and without the ρ block) and compared them.
(2019). Also, the latter approach consists in an expensive optimization which requires hours to run, while our method is instantaneous at test time. We perform further experiments on the human bodies category, by training our model on a set of 3014 shapes (in T-Pose) from the SURREAL dataset  Varol et al. (2017). The quantitative evaluation is reported on different test sets in the   2 , and qualitatively in Figures  7  and 8. In the qualitative examples, the shapes have been remeshed to have a different connectivity from the ones seen at training time. The numbers reported in the legend encode the relative error i (λ gt i -λ i ) 2 /(λ gt i ) 2 , where λ gt i are the ground-truth eigenvalues, while λ i are the eigenvalues of the shapes as labeled in the figures (the smaller, the better). Finally, in Figure  9 , we test our model on shapes that are outside the training distribution.
In the first row, two target human shapes selected from the SHREC19 benchmark  Melzi et al. (2019). In the second row, an example on animals for a shape from SHREC20  Dyke et al. (2020). Even if the input geometry is far from the training distribution, our model is able to provide meaningful results that respect the main semantic features of the target shape. For example, with the hippo shape in the bottom row, several features of the target shape are missing in our result, but we are able to retrieve the global geometry and the correct class among the ones present in the training set. We remark that these shapes are very challenging, since they come from different datasets, represent different subjects, different poses, and are discretized with completely different meshes.
123 -SURREAL: 755 shapes from the SURREAL dataset with the same pose and connectivity as the training shapes, but unseen subject; -SURREAL rem: remeshed version of the former, ranging from 25% to 70% of the original number of vertices (see Fig. 7  for an example); -SURREAL uni: remeshed version with uniform density, causing loss of detail for several thin subparts (see the top left shape of Fig. 16  for an example). In these test sets, all the shapes are in the same pose and the ground truth is available. We measure the mean squared error between the 3D coordinates of the ground truth vertices and those of the shape recovered from the spectrum. 2  confirm that higher order FEM leads to more accurate results on all the test sets. Autoencoder architectures. As mentioned in the previous section, we can build our model on top of any autoencoder.
This result suggests that more complex probabilistic generative models (e. g. exploiting the mesh hierarchy) and additional refinement of our method for applications requiring a high level of accuracy are promising directions for further investigation. Generalization to different data. Finally, we tested on the FAUST dataset  (Bogo et al. 2014) , which is a data distribution outside of the training data SURREAL. Also in this case, we generated three different test sets: FAUST, FAUST rem and FAUST uni (last 3 rows of Table  2 ). These shapes are registrations of real human bodies, and are far from the ones seen at training time in terms of pose and subject (see Fig. 8  for an example). The task here is to evaluate the generalization capabilities of our model; given as input the eigenvalues of a FAUST shape in arbitrary pose, we aim to recover the FAUST shape in T-pose by using our model trained on SUR-REAL data.
Timing and Implementation Details The experiments were run on a i9-9820X 3. 30GHz CPU, with 32GB of RAM and a RTX 2080 Ti GPU. In general, the runtime depends on the number of vertices; for the data we used in our tests, on average we observed that an epoch requires 20 to 30 seconds. We used fewer vertices for the PointNet version of the network to compensate the computational cost of Chamfer distance computation. In our configuration, a full training requires 10 to 12 hours without any ad-hoc optimization (e. g. , early stopping). Our code is publicly available at https://git. io/JGJWE.