dataesr/scientific-paragraphs-categorization huggingface dataset:
Model development and scenarios Stochastic models were developed in Microsoft Excel with RiskAmp add-in for simulation modelling; they are available on request from the corresponding author. Programme Evaluation and Review Technique (PERT) distribution was assigned to the identi ed uncertain parameters. The information gained from the analysis of the primary data collected, available literature and expert opinion was used to decide on what species to include, and to de ne scenarios for the nancial impact analysis. The data were used to model a representative herd or ock for each species including the number of animals per age group and sex. Further, the information was used to de ne scenarios for the analysis. Integrated production and partial budget analysis models were set up for one year, which is approximately the production cycle of lactating cows in the study populations.
ACKNOWLEDGEMENTS We thank the  Senegalese communities  involved in the project, the local facilitators, especially that of  Mapate Gaye  [resident of Richard Toll]. We are also particularly grateful to  Dr Samba Diop  of the  Institut Supérieur de Formation Agricole et Rurale, Université de Thiès, Bambey, Senegal  for his full support and assistance regarding the economic evaluations in the eld, the interview team who helped with data collection and translation, comprising of  Mr. Cheikh Tidiane Thiam ,  Mr. Farota Souleymane ,  Mr. Ben Abass Faye ,  Mr. Alassane Ndiaye ,  Mr. Sam Moustapha ,  Mr. Alioune Sy  and  Mr. Simon Senghor. We are also extremely grateful to  Dr Chrissy Roberts  from the  London School of Tropical Medicine and Hygiene  for full use and access throughout to their OpenDataKit (ODK) hardware and software.
Statistical Analyses All analyses were performed using R 4. 0. 0  [65]. We applied linear mixed-effect models on percent cover and maximum canopy height, using package 'lme4'  [66] , with temperature, species and their interaction through time as fixed effects, and mesocosm (or block) as a random effect, i. e. , Y ~(Day) * Species * Temperature + (1|Mesocosm). Model assumptions were checked graphically and with Shapiro-Wilk and Levene's tests, and showed no need for data transformation. An analysis of deviance was performed on the output of the models with package 'car'  [67]  to test the impact of fixed effects. For each species, the differences between temperature treatment T0 and T3 were established by comparing the overlapping of 95% confidence intervals from the predicted values of the models.
Tukey's HSD test was applied using package 'agricolae'  [70]  for multiple comparisons when p-values were significant (≤0. 05). Student's t-tests were performed to evaluate the impact of temperature on the number of flowers opened at a time t. We plotted kernel density estimates and used package 'mixtools'  [71]  to determine the number and timing of flowering peaks and duration of flowering period with an expectation-maximization algorithm. Plots were generated using package 'ggplot2'  [72].
Acknowledgments: We are very grateful to  Thierry Fontaine ,  Jean-Luc Foulon  and  Fouad Nassur  for all the help provided to set up mesocosms and monitor the water circulation system. We want to thank  Emeline Chesneau  and  Valérie Gouesbet  for their assistance with water chemistry and plant measurements. Thank you as well to  Maxime Dahirel  and  Benjamin Bergerot  for their statistical advice.
Results: Environmental genomics of abundant marine eukaryotic DNA viruses DNA-dependent RNA polymerase subunits A (RNApolA) and B (RNApolB) are evolutionarily informative gene markers occurring in most of the known DNA viruses infecting marine microbial eukartyotes  5, 17  , which until now only included Nucleocytoviricota. Here, we performed a comprehensive search for RNApolB genes from the euphotic zone of polar, temperate, and tropical oceans using large coassemblies from 798 metagenomes (total of 280 billion reads that produced ~12 million contigs longer than 2,500 nucleotides)  15, 16  derived from the Tara Oceans expeditions  14. These metagenomes encompass eight plankton size fractions ranging from 0. 8 µm to 2000 µm (Table  S1 ), all enriched in microbial eukaryotes  18, 19. We identified RNApolB genes in these contigs using a broad-spectrum hidden Markov model (HMM) profile and subsequently built a database of more than 2,500 nonredundant environmental RNApolB protein sequences (similarity <90%; Table  S2 ).
These MAGs were identified in all the oceanic regions (Table  S3 ). We incorporated marine Nucleocytoviricota MAGs from previous metagenomic surveys  7, 8  and reference genomes from culture and cell sorting to construct a comprehensive database enriched in large and giant marine eukaryotic double-stranded DNA viruses (thereafter called Global Ocean Eukaryotic Viral [GOEV] database, Table  S4 ). The GOEV database contains ~0. 6 million genes and provides contextual information to identify main ecological and evolutionary properties of MAGs containing to the new RNApolB clades.
The presence of these genes in mirusviruses establishes they are bona fide large DNA viruses capable of forming viral particles similar to those of previously known viruses in the realm Duplodnaviria. Phylogenetic inferences of the DNApolB gene using the GOEV database and a wide range of eukaryotic and additional viral lineages  24  supported the evolutionary distance of mirusviruses relative to all other known clades of double-stranded DNA viruses (Figure  S4 ). The mirusvirus DNApolB was positioned at the base of a clade grouping the Herpesviridae and the eukaryotic Zeta-type sequences, and close to the eukaryotic Delta-type sequences and the class Megaviricetes of Nucleocytoviricota (with the closest relative being 'Pandoravirales'). By contrast, they are distant from both Caudoviricetes and the class Pokkesviricetes of Nucleocytoviricota.
Mirusviruses have a unique and complex functional lifestyle The 111 'Mirusviricota' MAGs contain a total of 22,242 genes organized into 35 core gene clusters present in at least 50% of MAGs, 1,825 non-core gene clusters, and finally 9,018 singletons with no close relatives within the GOEV database (Tables  S5 ). Core gene clusters provided a window into critical functional capabilities shared across subclades of mirusviruses. Aside from the capsid and informational modules, they correspond to functions related to DNA stability (H3 histone), DNA replication (DNA replication licensing factor, glutaredoxin/ribonucleotide reductase, Holliday junction resolvase, three-prime repair exonuclease 1), transcription (multisubunit RNA polymerase, TATA-binding protein), gene expression regulation (lysine specific histone demethylase 1A), post-transcriptional modification of RNA (RtcB-like RNAsplicing ligase) and proteins (putative ubiquitin protein ligase), protein degradation (trypsin-like, C1 and M16-family peptidases), cell growth control (Ras-related protein), detection of external signals (sensor histidine kinase) and light sensitive receptor proteins (heliorhodopsins).
Thus, mirusviruses encode an elaborate toolkit which could enable fine-tuning the cell biology and energetic potential of their hosts for optimal virus replication. Finally, ten core gene clusters could not be assigned any function, representing as many hallmark 'Mirusviricota' genes coding for proteins (with confident 3D structure predictions distant from those in reference databases) that await experimental functional characterizations. Clustering of 'Mirusviricota' MAGs and reference viral genomes from culture (including Nucleocytoviricota, Herpesvirales and Caudoviricetes) based on quantitative occurrence of gene clusters highlighted the strong functional differentiation between mirusviruses and herpesviruses and, conversely, a strong functional similarity between mirusviruses and the class Megaviricetes of Nucleocytoviricota that is also widespread at the surface of the oceans (Figure  S5  and Table  S6 ).
Thus, function-wise and aside from the virion morphogenesis module, mirusviruses more closely resemble the marine Nucleocytoviricota viruses as compared to Herpesvirales. To further explore the functional landscape of eukaryoteinfecting marine viruses, we clustered their genomes based on quantitative occurrence of gene clusters using the entire GOEV database (Table  S5 ). The mirusviruses clustered together and were further organized into subclades in line with phylogenomic signals (Figure  S6 ). In contrast, this analysis emphasized the complex functional makeup of Nucleocytoviricota lineages, with some clades (e. g. , the Imitervirales and Algavirales) split into multiple groups. Aside from the informational module, gene clusters connecting a substantial portion of 'Mirusviricota' and Nucleocytoviricota genomes were dominated by functions involved in DNA replication: the glutaredoxin/ribonucleotide reductase, Holliday junction resolvase, proliferating cell nuclear antigen, dUTPase and DNA topoisomerase II.
In addition, heliorhodopsins encoded by Micromonas populations (prominent green algae widespread in the sunlit ocean) might have been acquired from mirusviruses through an ancient horizontal gene transfer (Figure  3 ). For now, precise host assignment for 'Mirusviricota' is lagging far behind genomic recoveries, echoing the knowledge gap for most Nucleocytoviricota viruses. Nonetheless, the two lines of evidence presented above suggest that mirusviruses have a long history of infecting abundant phytoplankton lineages. The 'Mirusviricota' genes recruited 6. 7 million metatranscriptomic reads from Tara Oceans (0. 0016% of 420 billion reads), representing 13% of the overall signal for the GOEV database (Table  S9 ). Thus, in situ transcriptomic signal for the 111 mirusvirus MAGs is already non-negligible as compared to the more numerous Nucleocytoviricota genomes (1,269 of them were detected in the Tara Oceans metagenomes), stressing the relevance of 'Mirusviricota' to eukaryotic virus-host dynamics in marine systems.
The mirusvirus informational module links two major realms of DNA viruses To further validate the genomic content of mirusviruses and to exclude the possibility of artificial chimerism, we created an HMM for the newly identified 'Mirusviricota' MCP and used it as bait to search for complete genomes in additional databases. First, we only found two 'Mirusviricota' MCPs in a comprehensive viral genomic resource from the <0. 2 µm size fraction of the surface oceans (GOV2  12  ), suggesting that most virions in this clade are larger than 0. 2 µm in size. We subsequently screened for the 'Mirusviricota' MCP in a database containing hundreds of metagenomic assemblies from the 0. 2-3 µm size fraction of the surface oceans  25. We found a contiguous 'Mirusviricota' genome (355 genes) in the Mediterranean Sea affiliated to the clade M2 with a length of 431.
1. Co-assemblies yielded 78 million contigs longer than 1,000 nucleotides for a total volume of 150. 7 Gbp. Constrained automatic binning was performed on each co-assembly output, focusing only on the 11. 9 million contigs longer than 2,500 nucleotides. Briefly, (1) anvi'o profiled contigs using Prodigal 32 v2. 6. 3 with default parameters to identify an initial set of genes, (2) we mapped short reads from the metagenomic set to the contig using BWA v0. 7. 15  33  (minimum identity of 95%) and stored the recruited reads as BAM files using samtools 34 , (3) anvi'o profiled each BAM file to estimate the coverage and detection statistics of each contig, and combined mapping profiles into a merged profile database for each metagenomic set. We then clustered contigs with the automatic binning algorithm CONCOCT  35  by constraining the number of clusters per metagenomic set to a number ranging from 50 to 400 depending on the set (total of 2,550 metagenomic blocks from ~12 million contigs).
Diversity of DNA-dependent RNA polymerase B subunit genes. We used HMMER 36 v3. 1b2 to detect genes matching to the DNA-dependent RNA polymerase B subunit (RNApolB) among all 2,550 metagenomic blocks based on a single HMM model. We used CD-HIT  37  to create a non-redundant database of RNApolB genes at the amino acid level with sequence similarity <90% (longest hit was selected for each cluster). Short sequences were excluded. Finally, we included reference RNApolB amino acid sequences from Bacteria, Archaea, Eukarya and giant viruses 5 : the sequences were aligned with MAFFT 38 v7. 464 and the FFT-NS-i algorithm with default parameters and trimmed at >50% gaps with Goalign v0. 3. 5 (https://www. github. com/evolbioinfo/goalign). We performed a phylogenetic reconstruction using the best fitting model according to the Bayesian Information Criterion (BIC) from the ModelFinder  39  Plus option with IQ-TREE 40 v1.
Creation of the GOEV database. In addition to the Nucleocytoviricota and Mirus MAGs characterized in our study, we included marine Nucleocytoviricota MAGs characterized using automatic binning by Schulz et al. 7  (n=743) and Moniruzzaman et al. 8  (n=444), in part using Tara Oceans metagenomes. We also incorporated 235 reference Nucleocytoviricota genomes mostly characterized by means of cultivation but also cell sorting within plankton  42. We determined the average nucleotide identity (ANI) of each pair of Nucleocytoviricota or Mirus MAGs using the dnadiff tool from the MUMmer package 43 v4. 0b2. MAGs were considered redundant when their ANI was >98% (minimum alignment of >25% of the smaller MAG in each comparison). Manually curated MAGs were selected to represent a group of redundant MAGs.
For groups lacking manually curated MAGs, the longest MAG was selected. This analysis provided a non-redundant genomic database of 1,593 marine MAGs plus 224 reference genomes, named the GOEV database. We created a single CONTIGs database for the GOEV database using anvi'o. Prodigal 32 was used to identify genes. Curation of hallmark genes. The amino-acid sequence datasets for RNApolA, RNApolB, DNApolB, and TFIIS were manually curated through BLASTp alignments (BLAST 44 v2. 10. 1) and phylogenetic reconstructions, as previously described for eukaryotic hallmark genes  16. Briefly, multiple sequences for a single hallmark gene within the same MAG were inspected based on their position in a corresponding single-protein phylogenetic tree performed with the same protocol as described above ("Diversity of DNA-dependent RNA polymerase B subunit genes" section).
3. 5 (https://www. github. com/evolbioinfo/goalign). IQ-TREE 40 v1. 6. 2 was used for the phylogenetic reconstructions, with the ModelFinder 39 Plus option to determine the best fitting model according to BIC. Supports were computed from 1,000 replicates for the Shimodaira-Hasegawa (SH)-like approximation likelihood ratio (aLRT)  45  and ultrafast bootstrap approximation (UFBoot 46 ). As per IQ-TREE manual, supports were deemed good when SH-like aLRT >= 80% and UFBoot >= 95%. Anvi'o v7. 1 was used to visualize and root the phylogenetic trees. Resolving hallmark genes occurring multiple times. We manually inspected all the duplicated sequences (hallmark genes detected multiple times in the same genome) that remained after the curation step, in the context of the individual phylogenetic trees (see previous section).
These sequences were not included in subsequent analyses. This step created a set of curated and duplicate-free hallmark genes. Supermatrix phylogenetic analysis of the GOEV database. Concatenations of the four aligned and trimmed curated and duplicated-free hallmark genes (methods as described above) were performed in order to increase the resolution of the phylogenetic tree. Genomes only containing TFIIS out of the four hallmark genes were excluded. For the remaining MAGs and reference genomes, missing sequences were replaced with gaps. Ambiguous genomes determined based on the presence of major and isolated (i. e. , not a clade pattern) incongruences within single and concatenated proteins trees, as well as on frequent long branches and unstable positions in taxon sampling inferences, were removed.
The concatenated phylogenetic trees were reconstructed using IQ-TREE 40 v1. 6. 2 with the best fitting model according to the BIC from the ModelFinder 39 Plus option. The resulting tree was then used as a guide tree for a phylogenetic reconstruction based on the site-specific frequency PMSF mixture model 47 (LG+C30+F+R10). For the concatenated trees, supports were computed from 1,000 replicates for the Shimodaira-Hasegawa (SH)-like approximation likelihood ratio (aLRT)  45  and ultrafast bootstrap approximation (UFBoot 46 ). As per IQ-TREE manual, supports were deemed good when SH-like aLRT >= 80% and UFBoot >= 95%. Anvi'o v7. 1 was used to visualize and root the phylogenetic trees. Taxonomic inference of GOEV database. We determined the taxonomy of Nucleocytoviricota MAGs based on the phylogenetic analysis results, using guidance from the reference genomes within the GOEV database as well as previous taxonomical inferences by Schulz et al.
7  , Moniruzzaman et al. 8  and Aylward et al. 17. Biogeography of the GOEV database. We performed a mapping of all metagenomes to calculate the mean coverage and detection of the GOEV database. Briefly, we used BWA v0. 7. 15 (minimum identity of 90%) and a FASTA file containing the 1,593 MAGs and 224 reference genomes to recruit short reads from all 937 metagenomes. We considered MAGs were detected in a given filter when >25% of their length was covered by reads to minimize non-specific read recruitments  48. The number of recruited reads below this cut-off was set to 0 before determining vertical coverage and percent of recruited reads. Metatranscriptomics of the GOEV database. We performed a mapping of all Tara Oceans metatranscriptomes to calculate the mean coverage and detection of genes found in the GOEV database.
Only MAGs observed in at least three samples were considered. Ecological network was built using FlashWeave 49 v0. 15. 0 sensitive model with Julia v1. 3. 1. A threshold to determine the statistical significance was set to alpha = 0. 01. To compare the performance of FlashWeave to naive correlations, we calculated the spearman rank correlation coefficient using the package Scipy v1. 3. 1. To build a global Mirus-eukaryote interactome, we pooled associations from the five size fractions by keeping the best positive or negative associations of each genome pair (i. e. , the edges with the highest absolute weights). We used a phylogeny-guided filtering approach, Taxon Interaction Mapper (TIM)  18  , to predict the host using the global virus-eukaryote interactome. All the virus-eukaryote associations were mapped on the 'Mirusviricota' informational module phylogenetic tree to calculate the significance of the enrichment of specific associations using TIM.
TIM provided a list of nodes in the viral tree and associated NCBI taxonomies (order, class, and phylum) of eukaryotes that show significant enrichment in the leaves under the nodes. Orthologous groups from Orthofinder. Orthologous groups (OGs) in Mirus MAGs (N = 111), a Mirus near-complete contiguous genome, and reference genomes from the Virus-Host Database (including 1,754 Duplodnaviria, 184 Varidnaviria, and 11 unclassified genomes) were generated. We used Orthofinder 50 v 2. 5. 2 (-S diamond_ultra_sens) to generate OGs. A total of 26,045 OGs were generated and, OGs (N = 9,631) with at least five genome observations were used to cluster genomes. AGNOSTOS functional aggregation inference. AGNOSTOS partitioned protein coding genes from the GOEV database in groups connected by remote homologies and categorized those groups as members of the known or unknown coding sequence space based on the workflow described in Vanni et al.
Identification and modeling of the Mirus major capsid protein. The putative major capsid protein of Mirus as well as the other morphogenetic module proteins were identified with the guidance of AGNOSTOS results, using HHsearch against the publicly available Pfam v35, PDB70, and UniProt/Swiss-Prot viral protein databases  52, 53. The candidate MCP was then modeled using the AlphaFold2 54,55 and RoseTTAFold  56. The resulting 3D models were then compared to the major capsid protein structures of phage HK97 and human cytomegalovirus and visualized using ChimeraX  57. Functional inferences of Nucleocytoviricota genomes. Genes from the GOEV database were BLASTP against Virus-Host DB 58 , RefSeq 59 , UniRef90 60 , NCVOGs 61 , and NCBI nr database using Diamond 62 v2. 0. 6 with a cut-off E-value 1 × 10 -5.
A recently published GVOG database 17 was also used in annotation using hmmer 36 v3. 2. 1 search with E-value 1 × 10 -3 as a significant threshold. In addition, KEGG Orthology (KO) and functional categories were assigned with the Eggnog-Mapper 63 v2. 1. 5. Finally, tRNAscan-SE 64 v2. 0. 7 predicted 7,734 tRNAs. 3D structure prediction of 'Mirusviricota' core gene. Proteins corresponding to 'Mirusviricota' core gene clusters and lacking functional annotation based on sequence similarities were modeled using the AlphaFold2 54,55 (-c full_dbs -t 2022-03-12). DALI server  65  was used to predict their functionality based on protein structure comparisons. Realm assignation of genes from a near complete genome. Two in-house HMM databases were created as follows. First, all coding sequences (CDS) labeled as "Nucleocytoviricota" were removed from the Varidnaviria CDS dataset (N= 53,776) in the Virus-Host Database (VHDB 66 , May 2022).
To this dataset, Tara Ocean Nucleocytoviricota MAGs (all were manually curated) and 235 reference Nucleocytoviricota genomes were integrated. The final Nucleocytoviricota protein database contained 269,523 CDS. Similarly, we replaced all Herpesvirales CDS in the VHDB Duplodnaviria CDS dataset with Herpesvirales protein sequences downloaded from NCBI in April 2022. Additionally, a marine Caudovirales database including jumbo phage environmental genomes  67, 68  was integrated into the Duplodnaviria proteins. The final Duplodnaviria protein database contained 748,546 proteins. Proteins in the two databases were independently clustered at 30% sequence identity (-c 0. 4 --cov-mode 5), using Linclust in MMseqs 69 v13-45111. Gene clusters with fewer than 3 genes were removed, and the remaining gene clusters were aligned using 38 v 7.
487. HMM files (N= 16,689 and 57,259 for Varidnaviria and Duplodnaviria, respectively) were created using the hmmhuild in HMMER3 70 v3. 2. 1. All proteins in the near-complete 'Mirusviricota' genome were searched against the two custom HMM databases using the hmmsearch with a cut-off E-value 1 × 10 -6.
Statistical analyses. A "greater" Fisher's exact test was employed to identify KO functions as well as gene clusters with remote homologies that are differentially occurring between the 111 'Mirusviricota' MAGs on one side, and all other Nucleocytoviricota in the GOEV database on the other side. P-values were corrected using the Benjamini-Hochberg procedure in R, and values <0. 05 were considered significant. Naming of Mirus and Procul. The latin adjective "Mirus" (surprising, strange) was selected to describe the putative new Duplodnaviria phylum: the 'Mirusviricota'. The latin adverb "Procul" (away, at distance, far off) was selected to describe the putative new class of Nucleocytoviricota discovered from the Arctic and Southern Oceans: the 'Proculviricetes'. Data availability. Data our study generated has been made publicly available at https://doi.
org/10. 6084/m9. figshare. 20284713. This link provides access to (1) the RNApolB genes reconstructed from the Tara Oceans assemblies (along with references), (2) individual FASTA files for the 1,593 non-redundant marine Nucleocytoviricota and mirusvirus MAGs (including the 697 manually curated MAGs from our survey) and 224 reference Nucleocytoviricota genomes contained in the GOEV database, (3) the GOEV anvi'o CONTIGS database, (4) genes and proteins found in the GOEV database, (5) manually curated hallmark genes and corresponding phylogenies, (6) HMMs for hallmark genes, (7) a FASTA file for the near-complete contiguous genome (SAMEA2619782_METAG_scaffold_2), (8) and supplemental tables.