dataesr/scientific-paragraphs-categorization huggingface dataset:
PLASTOME ASSEMBLY AND EVALUATION Assembly of the complete plastome of Glandora prostrata subsp. lusitanica isolate BPTPS049 using the GetOrganelle pipeline (v1. 7. 3. 1) ERR10047929 is the SRA link where the reads used to assemble the complete plastome of Glandora prostrata subsp. lusitanica can be found. Forward and reverse reads are named CUE_AAADOSDA_4_1_HKFTKDSXY. 12BA133_clean. fastq. gz and CUE_AAADOSDA_4_2_HKFTKDSXY. 12BA133_clean. fastq. gz, respectively. The typical recipe suggested for Embryophyta plant plastome assembly (https://github. com/Kinggerm/GetOrganelle) using the GetOrganelle pipeline (v1. 7. 3. 1) (Jin et al. , 2020)  was applied as described in the commands below while setting the flags "--max-reads" and "--reduce-reads-for-coverage" to 25 million and one thousand, respectively: get_organelle_from_reads.
PHYLOGENETIC ANALYSIS The phylogenetic analysis was performed using the concatenated nucleotide sequences coding for the shared proteome (75 coding sequences) extracted from a selected dataset. This dataset was composed of all 12 verified and complete chloroplast genomes belonging to the Boraginaceae family available in GenBank (Accession date: 2022-06-01) and the complete chloroplast genome of G. prostrata subsp. lusitanica obtained in this study. Four additional sequences were used as outgroups in the phylogenetic analysis: Ehretia dicksonii Hance (MZ555766. 1) and Tiquilia plicata (Torr. ) A. T. Richardson (MG573056. 1), belonging to the Boraginales order but not from the Boraginaceae family; Salvia rosmarinus Spenn. (NC_027259; Lamiaceae) also belonging to the lamiids clade; and Tilia platyphyllos Scop.
(NC_062378; Malvaceae) from the malvids clade. The following list describes the complete chloroplast genomes used in this phylogenetic analysis (17 verified and complete chloroplast genomes, which comprises the outgroups), including the binomial nomenclature, GenBank accession number, formal publication reference, and the corresponding number of CDS described for each record. The dataset used for the phylogenetic analysis presented in the manuscript was composed of all 12 verified and complete chloroplast genomes belonging to the Boraginaceae family available in GenBank (Accession date: 2022-06-01) and the complete chloroplast genome of G. prostrata subsp. lusitanica obtained in this study: -Arnebia euchroma (NC_053782)  (Park et al. , 2020)  o 88 CDSs -Arnebia guttata (NC_053780)  (Park et al.
, 2020)  o 88 CDSs -Arnebia tibetana (NC_053781)  (Park et al. , 2020)  o o 83 CDSs -Lithospermum erythrorhizon (NC_053783)  (Park et al. , 2020)  o 88 CDSs -Nonea vesicaria (NC_060826) (no formal publication) o 89 CDSs -Onosma fuyunensis (NC_049569)  (He et al. , 2021)  o 87 CDSs -Trigonotis peduncularis (MZ911745)  (Wu et al. , 2022)  o 86 CDSs Four additional sequences were used as outgroups in the phylogenetic analysis: -Ehretia dicksonii (MZ555766)  (Xu et al. , 2022)  o 87 CDSs -Tiquilia plicata (MG573056)  (Schneider et al. , 2018)  o 86 CDSs -Salvia rosmarinus (NC_027259) (no formal publication) o 87 CDSs -Tilia platyphyllos (NC_062378) (no formal publication) o 84 CDSs The phylogenetic analysis was performed using the following 75 concatenated nucleotide sequences, where no repeated genes were used, coding for the shared proteome extracted from the dataset described above: atpA  , atpB, atpE, atpF, atpH, atpI, ccsA, cemA, clpP, infA, matK, ndhA, ndhB, ndhC, ndhD, ndhE, ndhF, ndhG, ndhH, ndhI, ndhJ, ndhK, pafI, pafII, petA, petB, petD, petG, petL, petN, psaA, psaB, psaC, psaI, psaJ, psbA, psbB, psbC, psbD, psbE, psbF, psbH, psbI, psbJ, psbK, psbL, psbM, psbN, psbT, psbZ, rbcL, rpl2, rpl14, rpl16, rpl20, rpl22, rpl32, rpl33, rpl36, rpoA, rpoB, rpoC1, rpoC2, rps2, rps3, rps4, rps7, rps11, rps12, rps14, rps15, rps16, rps18, rps19, ycf1  The concatenated nucleotide sequences described above were aligned locally using the command line and MAFFT v7.
505  (Katoh & Standley, 2013)  using the automatic algorithm (with a 1. 53 gap opening penalty). mafft --auto glandora_prostrata_subsp_lusitanica_shared_CDS_nucleotide. fasta > glandora_prostrata_subsp_lusitanica_shared_CDS_nucleotide_mafft_aligned. fasta The MAFFT alignment-containing file was further analysed with the IQ-TREE 2 software package (Minh et al. , 2020) using the command line. The ultrafast bootstrap with  UFBoot (Hoang et al. , 2018)  within the IQ-TREE 2 software package was performed using 10,000 replicates by running the following command: iqtree2 -s glandora_prostrata_subsp_lusitanica_shared_CDS_nucleotide_mafft_aligned. fastaalrt 10000 -B 10000 (Options description: "-s" -Input alignment file; "-alrt" -Replicates for SH approximate likelihood ratio test; "-B" -Replicates for ultrafast bootstrap) The best-fit substitution model (TVM+F+I+I+R2 chosen according to the Bayesian Information Criterion) was selected according to ModelFinder (Kalyaanamoorthy et al.
, 2017), followed by a tree reconstruction (that is available in Figure  3  of the manuscript) using IQ-TREE  (Nguyen et al. , 2015). The phylogenetic analysis was simultaneously performed using the concatenated amino acid sequences translated from the 75 nucleotide sequences coding for the shared proteome described above. Similarly, the concatenated amino acid sequences were aligned locally and further analysed with the IQ-TREE 2 software package (Minh et al. , 2020) using the command line (ultrafast bootstrap performed using 10,000 replicates). The best-fit substitution model (JTT+F+I+I+R2 chosen according to the Bayesian Information Criterion) was selected according to  ModelFinder (Kalyaanamoorthy et al. , 2017) , followed by a tree reconstruction (Figure  S4 ) using IQ-TREE  (Nguyen et al.
Methodology This study was part of the AfricaRISING project (Africa Research Into Sustainable Intensification for the Next Generation), a Research for Development project that was active in Malawi, Tanzania, Ethiopia, Ghana and Mali. The project aimed at sustainably intensifying smallholder farming systems using a basket of novel technologies and techniques. These were introduced through participatory action research methods and by facilitating co-creation and co-learning between farmers and researchers (http:// africa-rising. net/). The study sites of the AfricaRISING project in Central Malawi were in the Dedza and Ntcheu Extension Planning Areas (EPAs). In each EPA, two districts were chosen, Linthipe and Golomoti in Dedza, and Nsipe and Kandeu in Ntcheu. In April 2014 and January 2015, for a baseline survey, structural and functional farm and household data from 75 farmer households from the four districts, was collected using semi-structured surveys.
After the Q-sorts were performed, a matrix was composed of the 52 statements (rows) and the 38 Q-sorts (columns). This matrix was used in the statistical analysis which was performed in the R environment using the package qmethod  (Zabala, 2014 ). Pearson's Method was used to examine correlations between Q-sorts. The analysis in R Statistics (R Core Team, 2022) included the following statistical results; Q-sort factor loadings, flagged Q-sorts, statement z-scores, statement factor scores, general factor characteristics (average relative coefficients, number of Q-sorts loading per factor, eigenvalues, explained variance, reliability and standard error of the f-scores), correlation between factor z-scores, standard error of differences between factors and finally the distinguishing and consensus statements per factor.
Acknowledgements The research was funded by the  United States Agency for International Development (USAID)  ( AID-BFS-G-11-00002 ) as part of the  US Government 's ' Feed the Future' Initiative. The contents are the responsibility of the producing organizations and do not necessarily reflect the opinion of  USAID  or the  U. S. Government. We would like to thank the following people from the  International Institute for Tropical Agriculture (IITA) ;  Dr. Mateete Bekunda ,  Dr. Irmgard Hoeschle-Zeledon  and  Dr. Regis Chikowo , from the AfricaRISING project, and  Isaac  and  Emanuel Jambo  and all their support staff at the research station in Chitedze, for assisting in logistical arrangements during fieldwork at the Africa RISING sites. We thank  Grace Gangu  for her tireless reading of statements, and finally we would like to thank all the farmers from Dedza and  Ntcheu in Central Malawi  who took part in this study.
Funding LT is supported by a PhD grant from the  UniversitéParis-Saclay and MESRI (Ministère de l'Enseignement Supeŕieur, de la Recherche, et de l'Innovation), France. The GN laboratory is supported by the  French Agence Nationale de la Recherche HIPATH  project ( ANR-17-CE20-0025 ) and by the  Institut Universitaire de France (IUF).
GM = + + + + α β β β β S SCRAP s +. ε [9] The main data of interest were the coefficients β β β prev prevDD MLD lame , , , which measure the cost associated with an additional lame cow, the cost of an additional DD infection and the cost of an additional week of lameness duration, respectively. Interactions within cofactors were systematically tested. Parameters. For each etiology, the base risk was determined through a review of the literature to highlight a wide range of prevalence for each foot disorder leading to a mean prevalence (Supplemental File S2, https: / / doi. org/ 10. 6084/ m9. figshare. 22064786. v3; Robcis, 2023b). All the probabilities of LS change after one given foot disorder based on both disorder-specific clinical presentation and the author's experience. All the milk losses per etiology are summarized in Table  2.
Statistical analyses All statistical analyses were performed using the R software version 4. 2. 1 (R Core Team, 2022). The constitutive amounts of GSLs were analyzed using ANOVAs that included the tissue, the organ maturity and the interaction between these two factors as explanatory variables. The effect of mechanical damage on GSLs amounts was analyzed using ANOVAs, which included the treatment (undamaged vs. damaged), the tissue and the interaction between these two factors as explanatory variables. Response variables were systematically square-root transformed to ensure a better model fitting. Pairwise comparisons of estimated marginal means (EMMeans) were performed using the 'emmeans' R package  (Lenth, 2019)  and p-values adjusted using the false discovery rate correction  (Benjamini and Hochberg, 1995).
Despite being perennially frigid, polar oceans form an ecosystem hosting high and unique biodiversity. Various organisms show different adaptive strategies in this habitat, but how viruses adapt to this environment is largely unknown. Viruses of phyla Nucleocytoviricota and Mirusviricota are groups of eukaryoteinfecting large and giant DNA viruses with genomes encoding a variety of functions. Here, by leveraging the Global Ocean Eukaryotic Viral database, we investigate the biogeography and functional repertoire of these viruses at a global scale. We first confirm the existence of an ecological barrier that clearly separates polar and nonpolar viral communities, and then demonstrate that temperature drives dramatic changes in the virus-host network at the polar-nonpolar boundary. Ancestral niche reconstruction suggests that adaptation of these viruses to polar conditions has occurred repeatedly over the course of evolution, with polar-adapted viruses in the modern ocean being scattered across their phylogeny.
Polar barrier for giant viruses We investigated the biogeography of giant virus genomes from the Global Ocean Eukaryotic Viral (GOEV) database  31. Their abundance profiles across Tara Oceans samples from different size fractions (Fig. 1a ; Supplementary Fig. 1a ; Supplementary Data 1) revealed 1380 viral genomes that showed signals (>25% of the genome length was mapped by reads, see methods in our previous paper  31  ) in at least one sample out of 928 samples (The details of biogeography were in the supplementary text; Supplementary Figs. 1 2 3 ; Supplementary Data 2). The presence/absence distribution of viral genomes across biomes revealed a large number of genomes specific to the Polar biome. Out of 569 genomes detected in polar regions, 262 (46. 05%) were exclusive to the Polar biome (Supplementary Fig.
In the marine eukaryotic genome database  30  , Chaetocerotales genomes exclusively distribute in high latitudinal polar oceans (temperature optima = 0. 72 ± 0. 63 °C). So, the putative virus-Chaetocerotales relationship may account for the diversity of giant viruses in high-latitude regions. However, this in silico prediction is limited by the absence of direct evidence of host-virus interaction and by the current unavailability of genomes of polar Chaetocerotales isolates. Five individual networks inferred using input matrices for the relative frequencies of eukaryotes (five size fractions) and giant viruses (Pico-size fraction). The best positive or negative association (i. e. , the edges with the highest absolute weights between two genomes) were selected to build the integrated interactome.
Polar-specific viral functions and their phylogenetic distributions Genomic adaptation (i. e. , adaptation by alteration of gene repertoire) to polar regions was investigated based on functions encoded in the viral genomes. We first annotated genes in the viral genomes with the KEGG Orthologs (KOs). For KOs (n = 1591) that were observed in more than four genomes, we calculated robust temperature and latitude optima (Supplementary Data 4). The temperature optima ranged from -1. 54 °C to 27. 31 °C, and the latitude optima from 5. 25 °to 78. 96 °. The distribution of these values revealed two major groups of KOs: one distributed in high-latitude/low-temperature regions (n = 314, 19. 74%) and another in lower-latitude/higher-temperature regions (n = 1,277, 80. 26%) (Fig. 3a ). The 314 Polar-specific genes had temperature optima below 10 °C and latitude optima above 50 °.
Additionally, nitrate transporter (K02575) had a high ratio of polar to nonpolar phylogenetic diversity (ratio = 7. 96), thus showing a comparatively wide phylogenetic distribution in Polar genomes. The nitrate transporter pathway has a role in assimilating extracellular nitrate/nitrite, implying a potential role for Polar viruses to reprogram host metabolism to fit the nitrate-deficient polar oceans  56. Some metabolic functions, including CoA biosynthesis (4′-phosphopantetheinyl transferase) and secondary metabolite biosynthesis (hydroxymandelonitrile lyase and 2-polyprenyl-6-hydroxyphenyl methylase), also showed a high phylogenetic diversity for Polar genomes. At the pathway level, three pathways were significantly enriched with Polar-specific KOs (Fig. 3b ; Fisher's exact test, P < 0. 05).
Global Ocean Eukaryotic Viral (GOEV) database Metagenomic datasets and environmental data are provided in Supplementary Data 1. The Global Ocean Eukaryotic Viral (GOEV) database contained 1817 viral genomes  31. The initial version of the GOEV database included 697 genomes reconstructed from 798 Tara Oceans metagenomes, 1187 MAGs from two previous metagenomic surveys  33, 62  , and 235 reference NCLDV genomes. Redundancy in the dataset was reduced based on Average Nucleotide Identity (ANI) of 98% (by always retaining the 697 genomes from Tara Oceans metagenomes), ultimately resulting in a refined database containing 1817 genomes. Taxonomic inference, read mapping, gene call and gene annotation of the GOEV were performed in a previous work  31. 1380 detected viruses were classified into six main taxonomic groups: five orders (i.
Phylogenetic tree construction Phylogenetic trees used in this study were reconstructed using IQ-TREE v. 1. 6. 2  63. The viral species tree was reconstructed with the sitespecific frequency PMSF model following a best-fitting model according to the BIC from the ModelFinder Plus option. Tree visualization and analysis were carried out using ETE3 toolkit v. 3. 1. 1  64. iTOL v. 6 was used to visualize the phylogenetic trees  65. Phylogenetic diversity was calculated using the 'pd' function in the R package 'picante' 66 for polar and nonpolar genome subsets.
Ecological analyses Diversity analyses were performed using R v. 4. 0. 1 67 in Rstudio v. 1. 3. 959  68. To evaluate the diversity of each sample, the richness (number of MAGs), Shannon's index and Pielou's evenness were calculated with the package "vegan"  69. Compositional variation among samples was assessed with a non-metric multidimensional scaling (NMDS) ordination based on Bray-Curtis dissimilarity. Samples with low viral abundance and richness produce outliers that reduce the readability of the NMDS ordination plot. To avoid such a bias, samples for which the sum of cumulative coverage was less than 10 or richness was less than 5 (set as the cutoff threshold) were removed from the compositional variation analyses. Statistical significance of differences among the sample groups (size fractions and biomes) was tested using an ANOSIM (analysis of similarities) with 9999 permutations.
Gene annotation and clustering Genes were predicted using Prodigal v. 2. 6. 3  72  within anvi'o v6. 1  73  with the default parameters. Gene cluster communities were classified through the AGNOSTOS  74  workflow. Those two steps were performed and described in a previous work  28. For functional annotation, genes were assigned to KEGG Orthologs (KOs) using eggNOG-mapper v. 2. 1. 5  75  ("Diamond" with an E-value cut-off of 1. 0 × 10 -5 ). Viral marker genes were searched with in-house HMM profiles from NCVOG (nucleocytoplasmic virus orthologous genes)  76  and GVOG (giant virus orthologous groups)  23  databases using HMMER v. 3. 2. 1 (http://hmmer. org) with an E value of 1 × 10 -3. Antifreeze proteins were detected using InterProScan v. 5. 44-79. 0  77. Low-complexity regions of protein sequences were identified using the option '-qmask seg' in usearch v.
Endogenous viral signals We searched the viral signals in 713 genomes from the eukaryotic environmental genomes database using VirSorter2 v. 2. 2. 3  30, 83. Both NCLDV and Lavidaviridae (virophage) genomic insertions (or co-binning) were searched using --min-score 0. 85 and 0. 95 for NCLDV and virophage, respectively. We next obtained long-read assembled genomes of two Chaetoceros isolates, C. muelleri  84  and C. tenuissimus  85. GEVEs were detected using ViralRecall v. 2. 1 (-s 0 -w 15)  86.
Ancestral states estimation and relative evolution divergency Ancestral states of Nonpolar and Polar viruses were estimated using the function "ace" (Ancestral Character Estimation) in the R package 'ape'  88. The input files were a rooted phylogenetic tree based on the four-hallmark gene set (RNApolA, RNApolB, DNApolB and TFIIS). In the tree, we retained only viruses with biome assignments of Polar or Nonpolar, and excluded viruses with "Unknown" biomes. We used type = "discrete", method = "ML", and model = "ER" (one-parameter equal rates model). The ancestral states were analyzed based on a series of likelihood values for Polar and Nonpolar. Relative Evolutionary Divergence values were calculated using the "get_reds" function in the package "castor"  89.
Acknowledgements This work was supported by  JSPS/KAKENHI  ( 18H02279  and  22H00384 , to H. O. ), and the  Collaborative International Joint Research Program   of the Institute for Chemical Research, Kyoto University  (No. 2021-29 ,  2022-26  to T. O. D. ; No. 2022-27 , to S. C. ), and the  H2020 European Commission project AtlantECO  (award number  862923 , to S. C. ). Computational time was provided by the  SuperComputer System, Institute for Chemical Research,  Kyoto University. We further thank the  Tara Oceans consortium , and the people and sponsors who supported Tara Oceans. Tara Oceans (including both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the  Tara Expeditions Foundation  and the continuous support of 23 institutes (https:// oceans.
Data availability The metagenome data from Tara Oceans are available at the ENA under accession PRJEB402, the metadata of metagenomes used in this study are summarized in Supplementary Data 1. FASTA files for the 1817 giant virus genomes from the Global Ocean Eukaryotic Viral (GOEV) database can be accessed here: https://doi. org/10. 6084/m9. figshare. 20284713. Additionally, the accession numbers of 1593 nonredundant marine Nucleocytoviricota and mirusvirus MAGs and 224 reference genomes in the GOEV database are provided in Supplementary Data 2. Other data used in this study include: Giant Virus Orthologous Groups (GVOGs) database (https://faylward. github. io/ GVDB/); Virus-Host Database (https://www. genome. jp/virushostdb); Tara Oceans Eukaryotic Genomes Database (https://www. genoscope.
DNA yields were then divided by the pellet mass to obtain a rough approximation of DNA concentration. Samples were sent to the Marine Biological Laboratories (Woods Hole, MA, USA) for amplicon library preparation of the V4-5 region of the 16s ribosomal subunit gene (518F and 926R) for amplicon sequencing on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) as described in Newton et al. (17). We used the R package dada2 (using default parameters) to convert raw reads into amplicon sequence variants (ASVs) and taxonomy tables by filtering reads based on the quality, removing primer-binding regions, inferring sequencing error rates, merging paired-end reads, removing chimeras, and assigning taxonomy based on the Silva reference database (version 128)  (17) (18) (19).
ACKNOWLEDGMENTS This work was supported by the  National Science Foundation  grant ( IOS-1444571 ) to C. W. , B. E. , and L. M. and grant ( EPS-1755726 ) to C. W. and B. E. We would like to thank  Alex Buerkle  and  Jenney Moua  for their help with these experiments. Our gratitude goes to  Jonathan Eisen  and anonymous reviewers for comments on an earlier draft of this manuscript.
M. belari has a widely heterozygous genome We analyzed the level and the distribution of heterozygosity in the genome of M. belari females, from our laboratory strain JU2817 and nine other wild strains, which had been sampled in different locations around the world  (17). We sequenced mixed stage animals from each strain and mapped the short reads on the assembled genome of M. belari JU2817. We computed genome-wide heterozygosity by counting the proportion of heterozygous positions relative to the total number of positions using Analysis of Next Generation Sequencing Data (ANGSD)  (18). Each strain being isofemale (see Materials and Methods), the genotype of a strain corresponds to the genotype of a single individual. We found that all 10 strains had approximately the same level of heterozygosity of about 1.
Recombination, if at work, breaks allele associations, leading to a decay of LD as the physical distance between SNPs increases  (25). To assess the extent of LD in M. belari, we analyzed the genomic data previously obtained from the 10 strains. We called SNPs and used the LDhelmet program to estimate the genome-wide distribution of the effective population recombination rate ρ. This analysis indicated that, in both species, the estimated ρ was homogeneous across the genome with no local enrichment or losses (fig. S1 ), with a point estimate of the average ρ of 0. 038 per base pair (bp). This implies that LD is lost as the distance between the considered loci exceeds ~100 bp. The average estimated ρ in M. belari was similar to estimates reported in sexual species of arthropods, such as D. melanogaster  (20) , and indicative of a high effective population recombination rate in this auto-pseudogamous species.