dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Yannis Moreau ,  Hugo Covés ,  Victoria Paterson ,  Francesca Colombo ,  Aurélie Barraud ,  Paulo Chainho ,  Cédric Hennache ,  Jose Luis Acuna ,  Nina Schlapfes , and  Barbara La Porta  for their help with field sampling. Thanks to  Nataliya Stashchuk  for useful discussions about the ocean circulation modelling and to  Barbara Mable  for useful comments on the manuscript. Sampling in Morocco was carried out in collaboration with  Hakima Zidane  and  Chlaida Malika  of the  National Fisheries Research Institute, Casablanca, Morocco.
Funding Fieldwork, laboratory work, and genomic analyses:  A. Muir  and  F. Nunes  (FN) were supported by a grant from the  Regional Council of Brittany , from the  European Regional Development Fund (ERDF)  and supported by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ", to FN. Ocean circulation modelling:  R. Ross  and  A. Knights  (AK), were supported by an internal grant from the  Faculty of Science and Environment, University of Plymouth , to AK. Temperature data collection: F. P. Lima  and  R. Seabra  were supported by  FEDER  funds through  COMPETE  ( POCI-01-0145-FEDER-031088 ) and through  POR Norte  ( NORTE-01-0145-FEDE R-031053 ), and by  Portuguese national funds through FCT  ( PTDC/BIA-BMA/ 31088/2017 ,  PTDC/BIA-BMA/31053/2017 ).
Availability of data and materials DNA sequences are available on NCBI SRA (http://www. ncbi. nlm. nih. gov/ bioproject/645800; BioSample accessions: SAMN15516880-978)  [125]. Sample site locations, temperature data and ocean circulation model outputs are available on FIGSHARE (https://figshare. com/articles/dataset/Seascape_ genomics_reveals_population_isolation_in_the_reef-building_honeycomb_ worm_Sabellaria_alveolata_L_/12645896)  [126]. Galaxy workflow is available on github: https://github. com/annamuir/sabellaria. git.
We present new geochemical and isotopic data for rock samples from two island arc volcanoes, Erromango and Vulcan Seamount, and from a 500 m thick stratigraphic profile of lava flows exposed on the SW flank of Vate Trough back-arc rift of the New Hebrides Island Arc (NHIA). The basalts from the SW rift flank of Vate Trough have ages of ~0. 5 Ma but are geochemically similar to those erupting along the active back-arc rift. The weak subduction component in the back-arc basalts implies formation by decompression melting during early rifting and rifting initiation by tectonic processes rather than by lithosphere weakening by arc magma. Melting beneath Vate Trough is probably caused by chemically heterogeneous and hot mantle that flows in from the North Fiji Basin in the east. The melting zone beneath Vate Trough back-arc is separate from that of the arc front, but a weak slab component suggests fluid transport from the slab.
Arc rifting can occur between the fore-arc and back-arc  (Martinez & Taylor, 2006) , and it has been proposed that the ascent of arc magmas may weaken the lithosphere causing rifting  (Molnar & Atwater, 1978). The melts beneath island arc/back-arc systems possibly rise as diapirs of partially molten mantle rocks that may consist of mixtures between peridotite, sediments, and metasomatized hydrous peridotite  (Gerya & Yuen, 2003; Hall & Kincaid, 2001; Hasenclever et al. , 2011; Sleeper & Martinez, 2014). Thus, the magmatic rocks recovered at subduction zones are highly variable and reflect the geodynamic situation at the time of formation, for example, the location of the magma system relative to the slab and mantle wedge. Here we provide new geochemical and isotopic data on basaltic rocks from two volcanoes (Erromango and Vulcan Seamount) of the New Hebrides Island Arc (NHIA) front and from the rifted flank of the young Vate Trough back-arc basin that yield insights into early stages of island arc rifting and magma genesis.
(2012). The analyses were performed using an acceleration voltage of 15 kV, a beam current of 15 nA and a defocused beam of 10 μm in diameter. The reproducibility of the method was checked by periodic analyses of VG-2, VG-99, and VG-568 standards, and the average concentrations obtained for VG-2 are shown in the supporting information Table  S1. The data for the volcanic glasses (Table  S1 ) represent averages of 10 individual spot analyses of a single glass chip. Fresh cores of whole-rock samples were washed with deionized water, dried at 40°C, coarse crushed and ground in a vibratory agate disc mill. The powders were used to produce fused glass beads. Major element oxides and selected trace elements (Ba, Cr, Ga, Nb, Ni, Rb, Sr, V, Y, Zn, and Zr) were determined on a Spectro Xepos Plus XRF spectrometer at the GeoZentrum Nordbayern.
(2001). The Nd isotopic composition was determined using dynamic multicollection; the data are normalized relative to 146 Nd/ 144 Nd ¼ 0. 7219. The long-term reproducibility of the La Jolla Nd reference material was 0. 511855 ± 0. 000006 (2σ for n ¼ 14 analyses) for 143 Nd/ 144 Nd. Analytical uncertainties of the individual measurements are reported as 2σ m. Total procedural blanks are less than 30 pg Nd. Instrumental fractionation was corrected with 0. 1% amu, as determined from repeated measurement of lead reference-material NIST SRM 981. Accuracy and precision of reported 10. 1029/2020GC008946.
7219. Samarium interference on masses 144, 148, and 150 was corrected by measuring 147 Sm. The average value of the NIST SRM 987 Sr standard during this study was 0. 710259, and an in-house Nd standard solution gave 143 Nd/ 144 Nd of 0. 511840, equivalent to a value of 0. 511850 for the La Jolla standard. Sr isotope data are reported relative to a value of 0. 710250 for the NBS987 standard. Lead isotope measurements were carried out on a Thermo Fisher NEPTUNE MC-ICP-MS using a 207 Pb/ 204 Pb double spike to correct for instrumental mass fractionation. The double spike, with a 207 Pb/ 204 Pb ratio of 0. 813508, was calibrated against a solution of NIST SRM 982 equal atom Pb standard. Samples were diluted with 2% HNO 3 to a concentration of approximately 20 ppb, and an aliquot of this solution was spiked in order to obtain a 208 Pb/ 204 Pb ratio of about 1.
The Composition of the Mantle Wedge Beneath the NHIA Several authors noted the difference in isotopic composition between lavas from the Central NHIA, including Efate and those further south and north  (Briqueu et al. , 1994; Crawford et al. , 1995; Peate et al. , 1997). Whereas the Central NHIA volcanoes formed from a source with 87 Sr/ 86 Sr > 0. 7036 and 206 Pb/ 204 Pb < 18. 6, lavas from the southern volcanoes have 87 Sr/ 86 Sr < 0. 7036 and 206 Pb/ 204 Pb > 18. 6  (Briqueu et al. , 1994; Crawford et al. , 1995; Laporte et al. , 1998; Peate et al. , 1997). Our new data from Vulcan Seamount in the New Hebrides arc front indicate that the boundary between the two mantle domains occurs between Erromango and Vulcan Seamount at ~18. 5°S (Figures  1b  and 2 ). The Central NHIA signature in Vulcan Seamount implies that the collision of the D'Entrecasteaux Ridge had an effect on magmatism further south than previously assumed, that is, the geochemical effect on the arc is some 50 km wider than in earlier estimates.
Mycoviruses are widespread and purportedly common throughout the fungal kingdom, although most are known from hosts in the two most recently diverged phyla, Ascomycota and Basidiomycota, together called Dikarya. To augment our knowledge of mycovirus prevalence and diversity in underexplored fungi, we conducted a large-scale survey of fungi in the earlier-diverging lineages, using both culture-based and transcriptome-mining approaches to search for RNA viruses. In total, 21. 6% of 333 isolates were positive for RNA mycoviruses. This is a greater proportion than expected based on previous taxonomically broad mycovirus surveys and is suggestive of a strong phylogenetic component to mycoviral infection. Our newly found viral sequences are diverse, composed of double-stranded RNA, positive-sense single-stranded RNA (ssRNA), and negative-sense ssRNA genomes and include novel lineages lacking representation in the public databases.
Furthermore, viral RNA is detected visually by gel electrophoresis (see Fig. 2 ), which could result in false negatives in instances of low-titer infections. To our knowledge, estimates of the proportion of false negatives based on chromatography have not been reported. We tested the relative accuracy of cellulose chromatography for identifying fungal isolates with viral infection and compared the results to more indirect sequence-based alternative approaches. In silico approaches to mycovirus discovery have become more common  (15) (16) (17). These approaches search transcriptomic data for sequences similar to RNA-dependent RNA polymerase (RdRp), which is the only conserved gene among RNA viruses and is diagnostic of infection. Conceptually, these approaches should be more sensitive to low titer viral infections because they do not rely on gel visualization and high transcriptome sequencing depth is readily achieved.
From Umbelopsis nana TLT204, which was positive by in-house chromatography but negative in silico, we sequenced purified dsRNA and assembled a toti-like viral contig containing a complete RdRp domain and an L-A virus major coat-protein domain, confirming a viral presence in our culture. Most likely, the virus was lost through subculture of this isolate in the laboratory in which transcriptome analysis was performed. Paired comparison of screening approaches suggests minimal discrepancies, but more viruses overall were detected by the in silico method (33. 3% positive) than in vitro (17% positive). The in silico method is likely to be more sensitive than the in vitro method, but it is possible that the higher virus detection rate in silico was a result of phylogenetic skews of the data sets rather than methodology.
In In silico screening. We obtained unassembled RNA-Seq data from the SRA database (see Table  S2  in the supplemental material), stringently filtered raw reads for quality (minimum [min] phred score 20) using the fastxtoolkit  (31) , and assembled contigs de novo with Trinity assembler  (32). We predicted open reading frames (ORFs) with Transdecoder (33) using default parameters and queried the protein translations with hmmscan (34) against a custom RdRp HMM database. We constructed the RdRp database as in Gilbert 2019: we downloaded entire alignments of Pfam families RdRp_1, RdRp_2, RdRp_3, RdRp_4, and Mito_RdRp and generated HMM profiles from each using hmmbuild (HMMER2; hmmer. org). We further queried each RdRp profile hit found in the transcriptome ORFs with TBLASTN and BLASTP to the NCBI nt and nr databases (downloaded 18 September 2019), respectively, and considered isolates positive for viral infection if the resultant hits were viral sequences with an E value Ͻ e Ϫ10.
In all cases, at least one viral contig was extended by this method, but the RdRp region was most often unchanged. For additional informatics details and code, see https://github. com/jimyers/ Mycoviruses-in-early-diverging-fungal-lineages. Mycovirus sequencing. (i) Pacific Biosciences sequencing. We prepared purified dsRNAs obtained from in vitro screens of Allomyces sp. strain JMM01, Allomyces sp. strain DJ-02, Allomyces sp. strain DJ-07, Zopfochytrium polystomum WB228, and Ustilago maydis (as a control) as described by M. J. Roossinck in 2010  (43)  with slight modifications. Briefly, we purified dsRNAs by cellulose chromatography as described above, except the final elution was performed with 20 l of 2ϫ STE (pH 8. 0). We mixed 1 l of dsRNA with 7 l of H 2 O and 2 l of tagged random 12-mer at 20 M (5=-ACCTTCGGATCCTCC-N12-3=), placed the tube in boiling water for 2 min to melt strands, immediately quenched the sample on ice, used the Omniscript reverse transcription kit according to the manufacturer's protocol, incubated the tubes on ice for 10 min followed by 60 min at 50°C, removed the unreacted template with 2 l of RNase A (5 mg/ml), and then incubated the sample at room temperature for 15 min, followed 3 min at 80°C to denature the enzymes.
We used a Qiagen PCR purification kit according to the manufacturer's protocols to clean up PCR products and amplified them by PCR with individually barcoded primers using GoTaq polymerase, followed by gel extraction using a Qiagen gel extraction kit and the manufacturer's protocol. For each isolate, we sequenced 120 ng of product on a PacBio RSII at the University of Michigan Advanced Genomics Core (UM-AGC). We analyzed reads with SMRT Portal (parameters: minimum barcode score 22, minimum of 5 passes, minimum 90% accuracy) and removed adapters with cutadapt, followed by read correction, trimming, and assembly with Canu (v1. 3) (parameters: minOverlapLength ϭ 100, minRead-Length ϭ 150, errorRate ϭ 0. 035, estimated genome size of 10,000 bp). For quality control, we compared a U. maydis contig to the GenBank U.
polystomum WB228 to the UM-AGC for library preparation using the Illumina TruSeq stranded mRNA protocol and 2 ϫ 50 sequencing using an Illumina HiSeq 4000. All RNA-Seq was processed to remove adapters and low-quality sequences from paired-end data using trimgalore (min quality threshold 5) (https://github. com/FelixKrueger/TrimGalore), followed by assembly de novo with Trinity. We performed ORF prediction, HMM queries for RdRp homologs, and PRICE extension as described above. For transcriptomes sequenced by JGI and not published previously (see Table  S2 ), stranded cDNA libraries were generated using the Illumina Truseq stranded RNA LT kit. mRNA was purified from 1 g of total RNA using magnetic beads containing poly(T) oligonucleotides, fragmented, and reverse transcribed using random hexamers and SSII (Invitrogen), followed by second-strand synthesis.
For a proxy of viral titer, we calculated the abundance of viral transcripts generated in the transcriptome with the align_and_estimate_abundance. pl script of the Trinity package (https://github. com/trinityrnaseq/trinityrnaseq), employing bowtie for alignment and the RSEM estimation method. Mycovirus sequence analysis and phylogenetics. We classified each new mycoviral sequence by top blast hit and grouped them into clusters corresponding to "branches" described in the most recent RNA virus phylogeny  (23). ORFs, including each RdRp gene, were predicted and translated using the universal genetic code except for sequences with top blast hits to mitoviruses, for which we used the standard mitochondrial genetic code. Despite our best attempts to resolve assemblies, some remained fragmented.
ACKNOWLEDGMENTS We thank  Georgianna May  and  Kerry O'Donnell  for providing fungal cultures and  Jesús Peña  at  University of California-Riverside  and  Sabrina Blakenship  and  Jeff Beard  at  Oregon State University  for efforts to generate transcriptomes and genomes for the Zygolife project. We also thank  Andreas Brachman  for depositing RNA-Seq data of Geosiphon pyriforme into GenBank. We thank  Kerrie Barry  and  Chris Daum  and his team for coordination and sequencing of the transcriptomes generated by  JGI  and used in this work. We thank  Melissa Duhaime  and  Alisha Quandt  for helpful conversations. We declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The study was conceived of by J.
Methods (a) Extraction of data from the LIFE project We extracted information on the amount of funding allocated to various species using the LIFE projects database (https://ec. europa. eu; accessed between February and May 2020). Note that we focused here on a species-level conservation approach  [9] , in contrast with other more general conservation measures (such as socio-ecological approaches  [15, 16]  and others  [17] ) that are only indirectly covered by LIFE projects. We first filtered LIFE projects specifically aimed at species conservation, using the query STRAND = 'All'; YEAR = 'All'; COUNTRY = 'All'; THEMES = ' Species'; SUB-THEMES = 'Amphibians'; 'Birds'; 'Fish'; 'Invertebrates'; 'Mammals'; 'Reptiles'. This query resulted in 819 LIFE projects that met the search criteria. A second query focused on THEMES = 'Biodiversity issues'; SUB-THEMES = 'Ecological coherence'; 'Invasive species'; 'Urban biodiversity', which matched an additional 298 LIFE projects.
(b) Calculation of species traits To obtain a deeper understanding of the factors underlying the observed pattern of conservation measures among species, we investigated whether the number of LIFE projects and the budget allocation for each species was driven by its risk of extinction, body size, and/or online popularity. We estimated the extinction risk category of each species in our database using the International Union for Conservation of Nature Red List of Threatened Species  [18]. Using application programming interface (API) keys, we automatically assigned each species to its IUCN extinction risk category, and then we manually checked this matching to correct potential mistakes. Body size is one of the few traits that is fully comparable across distant taxa  [19] , and it is also one of the most conspicuous traits correlating with extinction risk  [20].
We estimated body size for birds using the Collins Bird Guide  [21] , for mammals using Aulagnier et al. [22] , for reptiles and amphibians using Speybroek et al. [23] , and for fish using FishBase  [24]. For invertebrates, we estimated body size using diverse sources, from Wikipedia to original species descriptions and expert opinion and unpublished data by the authors (e. g. molluscs and insects). We characterized the online popularity of each species in our database using a culturomics approach  [25]  based on the volume of Internet searches performed on Google's search engine. We obtained data on the average monthly relative search volume recorded between January 2010 and December 2019 for each species from the Google Trends API. Google Trends returns relative search volume data ranging from 0 to 100; the maximum value is assigned to the highest proportion of total searches observed during any given month of the sampled period and all other values are scaled in relation to it.
(c) Statistical analyses We explored the relationships between the three traits and conservation attention with a generalized linear mixed model that accounted for taxonomic non-independence among species  [29]. We performed the analyses in R  [30] , initially exploring the dataset following a general protocol for data exploration  [31]. We checked the homogeneity of continuous variables and logtransformed non-homogeneous variables, when appropriate. We verified multicollinearity among predictors with pairwise Pearson's r correlations (electronic supplementary material, figure  S1 ), setting the threshold for collinearity at |r| > 0. 7  [31]. We visualized potential associations between continuous and categorical variables with boxplots (electronic supplementary material, figure  S2 ). Given that average online popularity (logarithm) and relative popularity (logarithm) were highly correlated (r = 0.
96), we included only average popularity in the regression analysis. Furthermore, since the number of LIFE projects and total budget were highly correlated (r = 0. 79), we expressed conservation attention only as the number of LIFE projects (dependent variable). We fitted a generalized linear mixed model to the data with the R package lme4  [32]. The mixed part of the model allowed us to take into account taxonomic non-independence of observations, under the assumption that closely related species should share more similar traits than would be expected of a random sample of species. More specifically, the taxonomic relatedness among species was accounted for with a nested random intercept structure (Class/Order/Family). Given that the response variable number of projects are counts, we initially selected a Poisson error and a log link function.
Strains identification Strains identification was realized using MALDI-TOF MS according to the process previously reported  [22] [23]. A Microflex LT spectrometer (Bruker, Daltonics, Bremen, Germany) was used for this purpose. Spectra (Figure  1 ) were output and analyzed using the Biotyper 3. 0 software against the Bruker database that was regularly incremented with the local URMS database (https://www. mediterranee-infection. com/urms-data-base). If any identification was not provided, the 16S rRNA gene was amplified using the primer pair fD1 and rP2 (Eurogentec, Angers, France) and sequenced using the Big Dye® Terminator v1. 1 Cycle Sequencing Kit and 3500xLGenetic Analyzer capillary sequencer (Thermofisher, Saint-Aubin, France), as previously described  [24]. All 16S rRNA nucleotide sequences were assembled and corrected using CodonCode Aligner software (http://www.
Genome characteristics EZ1 biorobot with the EZ1 DNA tissue kit (Qiagen, Hilden, Germany) was used for genomic DNA extraction. Then, sequencing was performed with a MiSeq sequencer (Illumina Inc, San Diego, CA, USA) using the Nextera Mate Pair sample prep kit and Nextera XT Paired End (Illumina), as previously reported  [24]. The assembly was carried out using a set of software such as Velvet  [26] , Spades  [27]  and Soap Denovo  [28]. Sequences are trimmed with MiSeq and Trimmomatic  [29]  software, whereas untrimmed data were processed only using MiSeq software. To decrease assembly gaps, we used GapCloser software  [30]. Scaffolds that have a nucleotide number <800 (bp) and scaffolds that have a depth value lower than 25% of the mean depths were removed. The best assembly was selected using different criteria (number of scaffolds, N50, number of N).
Genomes of these three species were annotated as previously described  [31]. Furthermore, the Genome-to-Genome Distance Calculator (GGDC) web server available at (http://ggdc. dsmz. de) was used to evaluate the overall similarity among the compared genomes and to replace the wet-lab DNA-DNA hybridization (DDH) by a digital DDH (dDDH)  [32]. Average nucleotide identity analysis was also estimated using the OAT software  [33]. Finally, the Clustage software  [34]  is employed to group the genomes of these three strains in order to study distribution of accessory elements within each strain.
Strain identification and phylogenetic analysis Mass spectrometry identification of single colony from each strain first cultivated on blood agar in anaerobic atmosphere at 37°C was failed. This suggested that these isolates were not referenced in the database and may be unknown species. Their MALDI-TOF MS spectra were added to our database to expand its content. 16S rDNA-based similarity analysis of strain Marseille-P4344 T , strain Marseille-P4200 T and strain Marseille-P3545 T against GenBank yielded highest nucleotide sequence similarities of 97. 21% sequence identity with Clostridium uliginosum strain CK55 (GenBank accession no. NR_028920. 1), 97. 02% sequence identity with Clostridium tetanomorphum strain DSM 4474 (GenBank accession no. NR_043671. 1) and 97. 63% sequence identity with Hathewaya histolytica strain JCM 1403 (GenBank accession no.
Funding sources: This study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program « Investissements d'avenir », reference  ANR-10-IAHU-03 , the  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI. This research was supported by a grant from the  Institut Universitaire de France (IUF, Paris, France)  to  Professor Anthony Levasseur.