dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  H. Ashbury ,  H. Germain ,  F. Halkett ,  S. Kamoun ,  C. Zipfel ,  S. Hacquard ,  M. Saubin ,  R. Sormani , and the members of the  Groupe de Recherche de Biologie Végétale  ( GRBV, Québec, Canada ) and  Unité Mixte de Recherche Interactions Arbres-Microorganismes (UMR IAM, Nancy, France)  for fruitful discussions. We warmly thank  A. Coince  for the fruitful discussions before and during this project. This project was executed during the COVID-19 lockdowns in France during the springs of 2020 and 2021. The data that support the findings of this study as well as the detailed methods used are available in the supplementary files or can be obtained from the corresponding author upon reasonable request.
The authors are supported by grants overseen by the  French PIA programs Lab of Excellence ARBRE  ( ANR-11-LABX-0002-01 ) and  Lorraine Université d'Excellence  ( ANR-15-IDEX-04-LUE ), by the  French PIA  project  Clonix2D  ( ANR-18-CE32-0001 ), by the  Pôle Scientifique   A2F  of the  Université de Lorraine , and by the  Région Grand Est (France). e-Xtra: Supplementary material is available online. The author(s) declare no conflict of interest.
e. , the 2006 review article by Jones and Dangl that presented the zig-zag conceptual model of the plant immune system). The raw data used to build this figure are available in Supplementary Dataset S1 (columns A to H). ). B, Venn diagram showing the overlapping contribution of the three keyword searches ('plant immunity', 'plant pathogen', and 'plant defense') to the HIPPY collection. C, Share of the HIPPYs within the plant immunity literature, in terms of number of publications (upper panel) and total number of citations (lower panel). HIPPYs represent approximately 0. 20% of the publications in the field of plant immunity (170 HIPPYs among 84,248 publications) but account for 4. 22% of the total number of citations (121,255 citations among a total of 2,873,089). The surface of the black triangles is proportional to the indicated percentage.
The Copernicus Marine IBI-MFC (Iberia-Biscay-Ireland Monitoring and Forecasting Centre) has delivered operational wave forecasts since 2017. The operational application is based on a MFWAM model (Meteo-France WAve Model) set-up, running at a 1/20º grid (5-km). The research presented here was conducted to improve the accuracy of the IBI-MFC wave model products, by means of (i) including a new wave data assimilation scheme and (ii) developing a new coupled ocean-wave modelling framework. Evaluation of these set-up upgrades, in terms of improvements in IBI wave model system capabilities, is here presented. All the model sensitivity test runs, performed for the year 2018, are assessed over the whole IBI domain, using the available in-situ (from 49 mooring buoys) and independent satellite wave observation.
The results show that the most relevant improvement is due to the data assimilation, while the impact of surface ocean currents, although less significant, also improves the wave model qualification over the IBI area. The demonstrated benefit, related to the herein proposed upgrades, supported the IBI-MFC decision to evolve its operational wave system, using (since the March 2020 Copernicus Marine Release) the resulting wave model set-up, with data assimilation and currents-wave coupling for operational purposes.
The Copernicus Marine Service high-level strategy includes a roadmap with associated Research and Development (R&D) priorities  [24] , which identifies some developments per thematic area that are key for the future service evolution. Among others, (i) upgrade of data assimilation schemes (to improve the analysis and reanalysis capabilities) and (ii) enhancement of the representations of coupling effects between ocean-wave-sea-ice-atmosphere-land components (to improve forecast model solutions) are seen as prioritized research lines for any Copernicus Marine Monitoring and Forecasting Centres (MFC). Specifically, for the European Atlantic Façade, the Copernicus Marine IBI-MFC (Iberia-Biscay-Ireland Monitoring Forecasting Centre) delivers daily ocean model estimates and forecasts of different physical and biogeochemical parameters, including, since 2016, hourly wave forecasts and multi-year products  [25].
• To analyse the impacts in the IBI wave model solution related to the use of surface current-wave coupling, evaluating the contribution of surface ocean currents in the wave energy balance. To address these questions, different wave model sensitivity tests are performed. Several wave model simulations generated with the IBI-MFC wave model set-up, and only differing from the operational version (available in 2018) for the activation of the new data assimilation scheme and in the degree of the ocean current forcing applied, are run. The assessment of these model simulations is conducted using several local available in-situ and satellite wave observations. The paper is organised as follows: Section 2 provides a description of the Copernicus IBI-MFC wave model system and the different model sensitivity tests performed, together with the model assessment proposed.
The IBI Area and IBI-MFC Wave Model The Copernicus Marine IBI-MFC (Iberia-Biscay-Ireland Monitoring Forecasting Centre) o ffers a comprehensive portfolio of regular and systematic regional information on the state of the ocean for the European Atlantic façade, supporting all kinds of marine applications. As part of this IBI-MFC service, a short term (10-days) high-resolution wave forecast is updated twice a day for the IBI area. Hourly instantaneous data for significant wave height, wave direction, wave period variables, together with wind sea and swell (primary and secondary partitioned wave spectra) parameters are delivered as part of this regional Copernicus Marine IBI wave product. The MFWAM model configuration for IBI MFC is implemented on the IBI domain  • N and -19-5 • E; see geographical domain in Figure  1 ) with a horizontal resolution of 5 km approximately (1/20 • ).
Model Sensitivity Test: The Proposed IBI Wave Model Upgrades The Copernicus Marine IBI-MFC identified, among others, the following two major shortcomings in its wave forecast service: (i) The lack of IBI wave analyses to initialize IBI forecast cycles (ii) The absence of any coupling of the IBI wave model data with ocean currents. Bridging these 2 identified gaps in the Copernicus IBI wave service was considered as a major goal for the IBI-MFC service evolution planned for the last Copernicus-1 Service Phase (2018-2021) and 2 different research working lines were followed to achieve the objective. These specific research lines were fully aligned with the Copernicus Marine Evolution Strategy  [20] , and their scientific research priorities (implementation of data assimilation schemes and enhancement of the model coupling between different earth The IBI-MFC wave model was upgraded (January 2018 Operational release) to improve the drag coefficient variation with the wind speed, resulting in positive impacts in the surface stress characterization.
This improvement of the surface stress is certainly needed for the coupling with the IBI ocean currents here tested. To this end, a new setting on the wave dissipation term, the sheltering parameter, and the use of Phillips spectrum tail for the high-frequency part of the wave spectrum was also implemented. Moreover, the minimum water depth is taken as 5 m (instead of the 1 m value used in former IBI wave model versions). Associated to these upgrades, slight improvements in terms of significant wave height were obtained (reduced scatter index, around 1. 9% when comparing model results with observations from altimeters  [31] ). The bathymetry used is derived from the 1 arc minute ETOPO1 ocean bathymetry by National Geophysical Data (NOAA)  [32]. The wave spectrum is discretized in 24 directions and 30 frequencies ranging from 0.
Model Sensitivity Test: The Proposed IBI Wave Model Upgrades The Copernicus Marine IBI-MFC identified, among others, the following two major shortcomings in its wave forecast service: (i) The lack of IBI wave analyses to initialize IBI forecast cycles (ii) The absence of any coupling of the IBI wave model data with ocean currents. Bridging these 2 identified gaps in the Copernicus IBI wave service was considered as a major goal for the IBI-MFC service evolution planned for the last Copernicus-1 Service Phase (2018-2021) and 2 different research working lines were followed to achieve the objective. These specific research lines were fully aligned with the Copernicus Marine Evolution Strategy  [20] , and their scientific research priorities (implementation of data assimilation schemes and enhancement of the model coupling between different earth system components) were two of the major amelioration axes proposed for the Copernicus Marine products and services.
Conclusions Two major conclusions emerge from these tests. First, the significant improvement on the wave model set-up that was used in the IBI operations in December 2019 (IBI-CO), activating current forcing (IBI-CU), data assimilation (IBI-DA), or both (IBI-OP), with the major upgrade in the performances, including data assimilation, with the scatter error indicator decreasing by more than 1% (Table  5 ). Second, the importance of having realistic high-quality forcings fields. In particular, the use of unrealistic currents inputs may spoil the wave model performance (Figure  8 ). The research performed, combining information from different wave model solutions and several observational data sources, benefited the IBI-MFC operational wave forecast service, supporting the decision-making process, related to their latest major operational upgrade (occurred in the Copernicus Marine Operational Release March 2020).
The extensive assessment performed with the IBI-DA and IBI-CU run, presented here, was useful to demonstrate, for each proposed IBI model set-up novelty, the significance of the associated improvements (in terms of IBI wave forecasting local added value). After verifying the impacts that these two proposed novelties have on the IBI wave solution (and always using, as a referential solution, the IBI wave model configuration in operations), a new IBI wave configuration (combining both the data assimilation and the current-wave coupling) were prepared to be transitioned into operations. The significant improvement achieved by this IBI-OP configuration, with respect to the control one (IBI-CO, the IBI set-up currently in operations), in terms of wave solution over the IBI area, throughout the year 2018, supports the IBI-MFC decision to use this set-up for operational purposes, to be implemented in the IBI Wave, operational from March 2020.
Acknowledgments: This study has been conducted using E. U. Copernicus  Marine Service Information. Specifically, from their in-situ TAC Product (INISITU_IBI_NRT_OBSERVATIONS_013_033) and CMEMS L3 wave altimetric product (WAVE_GLO_WAV_l3_SWH_NTRT_OBSERVATIONS_014_001). Likewise, satellite observations from the HY2A mission, provided by the  CNES , have been used.
Overview of deep-sea hydrothermal vent microbial communities To explore the community composition of microorganisms in the CR hydrothermal vents, metagenomes were sequenced from five plume and background seawater samples collected at the "Wocan" and "Tianxiu" hydrothermal fields (Supplementary Table  1 ). For comparison, metagenomes of 29 other samples derived from seven different hydrothermal vent sites in the Pacific Ocean, Atlantic Ocean, and southwest Indian Ocean were retrieved from the public database (Supplementary Table  1 ). These sites are abbreviated as follows: Axial Seamount (Axial), Southwest Indian Ridge (SWIR), Menez Gwen (Menez), Eastern Lau Spreading Center (Lau), Guaymas Basin (Guaymas), Mid-Cayman Rise (Cayman), and Southern Mariana Trough (Mariana). The final datasets include 34 metagenomes representing four different types of habitats in hydrothermal environments, i.
It should be noted that the plume samples from CR (this study) and Lau  [11]  were processed by multiple displacement amplification (MDA). To assess the bias introduced by the MDA process, we compared the taxonomic composition inferred from CR metagenomic data to those from 16S rRNA gene pyrosequencing (Jiang et al. , unpublished). Similar to previous observations in Lau plume samples  [11] , no significant differences were found in the abundance patterns, suggesting that MDA bias did not obscure the major trends. De novo assembly and binning of these metagenome sequencing data resulted in the reconstruction of 581 high-or medium-quality prokaryotic metagenomeassembled genomes (MAGs)  [39]  with ≥ 50% completeness and ≤ 10% contamination. These MAGs were clustered at 95% average nucleotide identity (ANI), representing species-level groups spanning 54 phyla, including 515 bacterial and 66 archaeal MAGs (Supplementary Fig.
The outer ring represents the phage DNA packaging strategies by color DNA packaging strategies, while the rest of them formed several novel branches, indicating the remarkable diversity of head-tail phages in our datasets. ssDNA viruses accounted for approximately 36% of the total set of vOTUs, with primary assignment to the family Microviridae. Phages in the family Microviridae are among the smallest DNA viruses  [46] , with circular genomes ranging from 4. 2 to 6. 5 kb  [47, 48]. A total of 872 complete or near-complete genomes of Microviridae were recovered from hydrothermal vent metagenomes. The amino acid sequences of the well-conserved major capsid protein VP1 were used as a phylogenetic marker for the classification of these viruses. The majority of the VP1 sequences showed < 70% shared identities relative to their best matches in NCBI's NR database and showed < 70% shared identities compared to each other, reflecting high levels of divergence.
The phylogenetic analysis showed that most (694 genomes) of the hydrothermal vent-derived Microviridae belonged to the subfamily Gokushovirinae, followed by group D (143 genomes). A few sequences were clustered within the clades Pequenovirus (8 genomes), Pichovirinae (4 genomes), Bullavirinae (1 genome), and Alpavirinae (1 genome), and the other 22 genomes were not clustered with any known subfamilies (Fig. 3A ). Interestingly, the new clade seemed to consist of viruses with small genomes (approximately or less than 4 kb). One of these has a genome size of 3559 bp and encodes only three putative ORFs, including a capsid protein, a replication initiator, and a protein of unknown function. It represents the smallest microvirus recovered in our datasets and the smallest ssDNA phage with the least ORFs reported to date.
Viruses in this group were also present in our hydrothermal vent metagenomics datasets (Supplementary Fig. 2B , Supplementary Table  2 ). As shown in Supplementary Table  2 , 27 vOTUs were classified as viruses from the Mimiviridae, Phycodnaviridae, or other NCLDV families. However, the sizes of these vOTUs ranged from 6112 to 24,611 bp and only represented small genome fragments of the viruses. Approximately 19% of the total vOTUs did not show any significant sequence similarity to any known viral families and could not be taxonomically classified for the time (Supplementary Fig. 2B ). It should be noted that all the hydrothermal plume, diffuse fluid, and background seawater samples used in this study were passed through 0. 22 μm filters (Supplementary Table  1  and references therein). Giant viruses, integrated proviruses, and actively infecting viruses within the cells would be retained on the membrane, while most free virus particles of small size would be lost during this step, such as the non-tailed ssDNA viruses.
2%, 7. 8%, and 1. 5% of plume, fluid, and sediment samples, respectively) was the most dominant group of dsDNA viruses in most samples, followed by Siphoviridae (9. 1%, 4. 6%, and 2. 4%) and Podoviridae (7. 6%, 4. 8%, and 0. 7%). The ssDNA viruses also accounted for a large fraction of viral communities. In all of the sediment metagenomes, the relative abundance of Microviridae was higher than that of any other dsDNA virus family. The CRESS-DNA virus group was present in most plume samples and three of the sediment samples, accounting for approximately 2. 2% of the total viral reads (Fig. 4 ). It is possible that the enrichment of ssDNA viruses in these datasets was caused by the MDA process, which used phi29 DNA polymerase and preferentially amplified small circular ssDNA molecules  [50, 51].
However, the quantification of viral DNA without amplification also revealed the dominance of ssDNA viruses in the total DNA viral assemblages of deep-sea sediments  [52]. Thus, we suggest that ssDNA viruses are abundant and play an important role in hydrothermal vent environments. The eukaryotic NCLDVs accounted for 0. 4% of the total viral reads in hydrothermal plume samples, on average. Of these families, Mimiviridae and Phycodnaviridae are the most abundant. In contrast, samples from diffuse fluid and sediment contained fewer NCLDVs. Large DNA viruses in this group have been frequently detected in marine metagenomes, including those derived from hydrothermal vents  [30, [53] [54] [55]. The NCLDV sequences in metagenomic datasets may come from marine unicellular eukaryotes or free giant viruses, but their roles in these ecosystems are largely unknown.
Hydrothermal vent viruses are novel and endemic To gain further insight into this viral diversity and distribution, we then used an extensively validated, network-based method  [56]  to investigate the relationship among hydrothermal vent vOTUs and viral sequences identified from other marine ecosystems. The 4662 vOTUs recovered from hydrothermal vents were compared to NCBI Viral RefSeq v97 and viral contigs in other marine metagenomic datasets: GOV 2. 0 seawater  [57]  and cold seeps  [58]. vConTACT2 was used to de novo predict genus-level groups (viral clusters, VCs) from viral population data  [56]. Lastly, a total of 16,618 clusters were generated, reflecting the huge and unexplored diversity of marine viruses (Fig. 5A ). As the largest marine virus database to date, the GOV 2. 0 datasets contributed the largest number of VCs (14,968 clusters), while taxonomically known viruses from NCBI RefSeq only formed 482 clusters.
Consistent with previous studies  [58] , the viral compositions in different habitats varied considerably, with only 30 clusters shared by all three marine metagenomic datasets (Fig. 5A ). The 4662 hydrothermal vent vOTUs were grouped into 1138 genus-level clusters, of which only 32 VCs contained genomes of known viruses from NCBI Ref-Seq. Moreover, 584 VCs (~51%) were exclusively composed of hydrothermal vent viruses, which may represent completely new genus candidates (Fig. 5A ). These VCs contained 1107 vOTUs, most of which were ssDNA viruses within Microviridae (548 vOTUs) and the CRESS-DNA virus families  (122). Approximately one-third of the vOTUs (314) could be classified as tailed phages, probably of novel genera within Myoviridae (144), Podoviridae  (65) , Siphoviridae  (57) , and unclassified Caudovirales  (48).
AMGs of vent viruses are involved in various metabolic pathways Viral infections can affect host metabolism via the expression of viral AMGs. To better understand the ecological impact of viruses in deep-sea hydrothermal ecosystems, we searched the AMGs in hydrothermal vent viral genomes and calculated their relative abundances. Based on the comprehensive annotation of viral ORFs, a total of 608 genes were considered to be putative AMGs (Fig. 7 , Supplementary Table  4 ). Sequence homology searches against the NCBI NR database showed that a large proportion of these AMGs were probably acquired from Proteobacteria, especially the Gammaproteobacteria and Alphaproteobacteria, while ~21% of the AMGs came from unclassified source species (Fig. 7B ). The origins of AMGs reflect virus-host connections, because phages generally acquire AMGs from their hosts  [76].
Metagenome assembly and annotation The raw reads obtained by Illumina paired-end sequencing were trimmed and quality filtered using fastp software  [90]. Clean reads were then assembled using MEGAHIT  [91]  with default options. Metagene  [92]  was used to predict protein coding sequences (CDS) from the metagenomic assemblies. Nonredundant genes generated by CD-HIT clustering (with 95% shared sequence identity and 90% coverage) were aligned to the NCBI NR database using BLASTp (with an e-value cut-off of 1e-5) for taxonomic classification. Functional annotations were conducted based on comparisons with KEGG  [93] , egg-NOG v5. 0  [94] , and the Carbohydrate-Active enZYmes (CAZy) databases  [95]  using the BLASTp  [96]  program (with an e-value cut-off of 1e-5). Additionally, 29 publicly available metagenomic datasets generated from hydrothermal vent samples (Supplementary Table  1 ) were downloaded from the NCBI Sequence Read Archive (SRA) database and were quality controlled, assembled, and annotated as described above.
Metagenomic binning and metagenome-assembled genome (MAG) classification Contigs larger than 1500 bp from the final assemblies were included for metagenomic binning using Meta-BAT2 v2. 12. 1  [97]  with default parameters. The original bins were then run through MetaWRAP's reassemble_ bins module  [98]  to improve their quality, and the completeness and contamination of the resulting bins were evaluated by CheckM  [99]. The high-and medium-quality bins (completeness ≥ 50% and contamination ≤ 10%) were then dereplicated at 95% average nucleotide identity (ANI) using dRep v2. 3. 2  [100] , resulting in 581 specieslevel MAGs. The taxonomic assignment of the MAGs was performed using the GTDB-Tk package v0. 3. 2  [101]  and was ultimately converted into the corresponding NCBI taxonomy. The phylogenomic relationships of MAGs were inferred using IQ-TREE 2  [102]  based on a concatenation of 120 bacterial or 122 archaeal marker genes identified by GTDB-Tk.
Identification of viral contigs Contigs ≥ 2 kb from metagenome assemblies were used to recover viral sequences. VirSorter analysis  [40]  was run with the parameter "--db 2 (viromes database), " and only the highest confidence contig categories 1, 2, 4, and 5 were included in this study, with categories 4 and 5 being manually curated. Contigs containing at least one of the viral hallmark genes (such as "virion structure, " "capsid, " "portal, " "head, " "tail, " "baseplate, " or "terminase") were retained. VIBRANT v1. 2. 1  [41]  was also used to identify viral contigs using the default parameters, and only the complete circular, high-and medium-quality drafts were kept for further analysis. The contigs identified by VirSorter and VIBRANT were then compiled and clustered at 95% shared nucleotide identity and 80% coverage  [105] , yielding 4662 viral populations, or viral operational taxonomic unit (vOTUs).
Abundance profiling in metagenomics data For taxonomic profiling of prokaryotic communities, 16S miTags were recovered from the metagenomic reads using phyloFlash  [36]. The extracted 16S miTags were mapped against the SILVA SSU Ref. database (v132)  [106]  for taxonomic assignment. To calculate the relative abundances of vOTUs and host microorganisms in each sample, clean reads from metagenomes were mapped to the viral contigs or microbial genomes using the CoverM package (https:// github. com/ wwood/ CoverM) with contig mode and genome mode, respectively. RPKM (reads per kilobase per million mapped reads) values were selected to represent the relative abundances of the viral and host populations. Pearson correlation was used to calculate the distances between samples for hierarchical clustering.