dataesr/scientific-paragraphs-categorization huggingface dataset:
The convergence of the MCMC chains was inspected with Tracer 1. 7. 1  (Rambaut et al. , 2018). The chains for each dataset were combined with Logcombiner v2. 6. 2  (Bouckaert et al. , 2014). We discarded as burn-in the first 10-30% of generations for each MCMC chain. The trees sampled from the MCMC runs were summarized as maximum clade credibility tree with TreeAnnotator v2. 6. 2  (Bouckaert et al. , 2014). The input xml files for BEAST2 analysis are available as Dataset S3.
Fossil calibrations We used 15 termite fossils as minimal age internal calibrations (Table  S6 ). In all cases, we used the youngest fossil age estimates reported on the Paleobiology Database (https://paleobiodb. org/). Note that fossil age estimates can be controversial and alternative age estimates exist (see e. g. Kasinski et al. (2020)  for hypotheses on Baltic amber age). However, the widths of the 95% HPD intervals produced by our Bayesian time-calibrated phylogeny typically exceed the interval of alternative estimates of fossil ages and we therefore presume that our inference of time-calibrated phylogeny is robust with respect to the use of alternative fossil age estimates. For reproducibility, we adhered here to the ages reported in Paleobiology Database although these might not always reflect the latest consensus on the fossil age.
During the traversal, two types of nodes were detected: 1) youngest nodes that with high probability (>90%) do not share the realm with that of the extant representative of each Kalotermitidae genera in each realm, and 2) the oldest nodes that share with high probability (>90%) their realm with all of their descendants. The upper limit of 95% HPD age interval of node type 1 and the lower limit of 95% HPD age interval delimitate the high probability dispersal age interval. Since the oldest fossils of kalotermitids were discovered in Oriental realm we tested a hypothesis of Oriental distribution of crown Kalotermitidae by constraining the range of the last common ancestor of extant kalotermitids to include Oriental realm. The analysis was performed using R package BioGeoBEARS v1. 1. 2  (Matzke, 2013)  with maximum range size set to 3 and using a dispersal-extinction-cladogenesis (DEC) model  (Ree and Smith, 2008).
Data and script availability Figures, tables, supplementary data      S8. Figure  S1. Time-calibrated summary evidence tree (SE-tree) including all Kalotermitidae samples and non-Kalotermitidae outgroups. The tree tip labels consist of sample collection codes (as present also in Table  S1 ) and the species names. Table  S1. Summary of sample collection and analytical procedure information. were dropped in the summary tree in Figure  1. Internal tree nodes with RASP probabilities are in the table defined as the last common ancestor of tree tips with labels "tip1ID" and "tip2ID". Table columns including mean ancestral realm probabilities and their standard deviations for each realm and each node are denoted as "Means" and "SDs", respectively, along with the name of each realm. Table  S6. Fossil calibration points used in molecular clock analysis.
Our analysis of DCMs relies on BGC-Argo's global data set of vertical measurements of fluorescence of Chla (Fchla) and optical backscattering of particles (b bp ) in addition to hydrological parameters and up to four additional core BGC parameters  (Chai et al. , 2020; Claustre et al. , 2020; Roemmich et al. , 2019). Fchla and b bp are respectively proxies of the concentrations of (Chla)  (Roesler et al. , 2017)  and particulate organic carbon (POC)  (Cetinić et al. , 2012; Loisel & Morel, 1998; Stramski et al. , 1999) , whose combined analysis allows identification of DCMs and discrimination between DAMs and DBMs  (Cornec et al. , 2021). Meanwhile, to probe into eddies, we co-located the global data set of BGC-Argo profiles with the TOEddies database  (Laxenaire et al. , 2018)  providing a global atlas of mesoscale eddies automatically detected from Absolute Dynamical Topography (ADT) obtained via multi-satellite altimetry.
BGC Argo Database Classification and Environmental Parameters Calculation Data from 505 BGC-Argo floats (∼50,000 profiles collected from 2010 to 2020, Argo, 2020) equipped with (Chla) and b bp sensors (in addition to sensors for CTD  [conductivity, temperature, depth] , and up to four additional BGC core parameters) were processed and qualified as described in  Bellacicco et al. (2019)  and  Wong et al. (2020). Vertical profiles were classified as presenting a DCM or not (NO) based on the analysis of bio-optical profiles (see Supporting Information, SI: Text S1, Figure  S1 , and Table  S1 ). DCM profiles were then further segregated into photoacclimation (DAM) or biomass maxima (DBM) following  Cornec et al. (2021). The characterization of DCM profiles was assessed through the depth of the DCM (Z DCM ) as well as (Chla) and b bp at the DCM depth ([Chla] DCM and b bpDCM , proxies of DCM intensity;  Hopkinson & Barbeau, 2008).
Mesoscale Eddy Database and BGC-Argo Profiles Colocation In the geostrophic framework, mesoscale eddies can be characterized by concentric circles of Sea Surface Height (SSH), highlighting the predominance of rotation in their dynamics. This particular property has prompted the development of algorithms for automatic eddy detection from satellite altimetry maps (e. g. ,  Chaigneau et al. , 2008 Chaigneau et al. , , 2009;; Chelton et al. , 2011). In this study, we take advantage of the global eddy atlas obtained by applying the TOEddies algorithm  (Laxenaire et al. , 2018)  to daily 0. 25° × 0. 25° AVISO ADT maps produced by Ssalto/Duacs and distributed by Copernicus-Marine Environment Services, either in delayed time (1993 to early 2019) or near-real time (early 2019 to present). This algorithm identifies, on ADT maps, local maxima (minima) as the possible center of AC (C).
The features are classified as eddies if the outermost closed ADT contour containing only one possible center is characterized by a difference in ADT with the center greater than 1 mm (see  Laxenaire et al. (2018)  for details including validation of their method using surface drifters). This closed contour defines the outer limit of each eddy, which, in general, is affected by the external field  (Laxenaire et al. , 2019). The eddy's core is defined as the surface encompassed by the ADT contours with the highest mean absolute azimuthal velocity. On the basis of the resulting database, we categorized the ocean surface, and thus flagged collocated BGC-Argo profiles, according to their position, either as regions outside eddy influence, within an eddy core (either AC or C) or within an eddy edge (defined as the area between the eddy core and its outermost contour).
To obtain a climatological view of eddy presence, we computed the percentage of time per month from 2010 to 2018 (period where the ADT data were available in delayed mode) for which each pixel of the ADT maps lay in one of the three areas (outside, core and edge of eddies) defined by the TOEddies atlas. From these data, we estimated monthly and yearly climatologies covering the period of the BGC-Argo sampling by computing mean values (respectively monthly and yearly) of the percentage of occurrence per pixel for each eddy polarity and area (i. e. , AC core , AC edge , C core and C edge ).
Database Regionalization and Calculation of the Anomalies of the Parameters To assess the global distribution of DCMs and their typology (DAM or DBM), as well as the occurrence of eddies, we grouped the data of BGC-Argo profile types (SI: Table  S2 ) per seven 20°-latitude bands centered on the Equator and up to 70° in both hemispheres (Figure  1 ). The BGC-Argo database's representativeness of eddy occurrence was verified by comparison with the TOEddies climatologies (SI: Text S3 and Figure  S2 ). For each eddy, we computed the proportions of DAM and DBM profiles, which we then compared to those outside eddy influence (i. e. , EXT). We also defined the difference in percentage of DAMs and DBMs between CORNEC ET AL. 10. 1029/2021GL093470 each eddy types (i. e. , AC core , AC edge , C core and C edge ) and with EXT as a metric of DCM anomalies associated with the presence of mesoscale eddies.
Conclusion In this study, we addressed the effect of mesoscale eddies on Deep Chlorophyll Maxima (DCM) characteristics as well as the environmental context that controls their development and persistence in the global ocean. To our knowledge, this study is the first to address the impact of mesoscale eddies on those deep phytoplanktonic features at the world ocean scale, combining two complementary databases. We found that at a global scale, cyclonic eddies (C) and anticyclonic eddies (AC) have opposite effects on DCM occurrence: the largest proportion of profiles with biomass maxima (DBMs) are found inside C whereas the largest proportion of photoacclimation-related maxima (DAMs) are inside AC. In low-latitude stratified environments dominated by quasi-permanent DCMs (i. e. , subtropical and subequatorial regions), 20%-30% of surfaces are impacted by mesoscale eddies.
Figure1. Map of the BGC-Argo profile database and percentages of distributions of profile types regarding to their biological types (No deep maxima, Deep photoAcclimation Maximum, and Deep Biomass Maximum; inner circles), and mean percentages of eddy polarities and sections regarding to the BGC-Argo database sampling (Cyclonic eddies and Anticyclonic eddies cores and edges, outside of eddy influence; outer circles) per 20°-latitude bands (black lines). The color of the points corresponds to the profile position regarding to eddies presence, polarity and section (same color scale as for the outer circles).
Acknowledgments This study is a contribution to the following projects: remOcean ( European Research Council , Grant agreement 246777); REFINE ( European Research Council , Grant agreement 834177);  SOCLIM (BNP Foundation) ;  AtlantOS (European Union 's  Horizon 2020 research and innovation program , Grant  2014-633211 );  NAOS (Agence Nationale de la Recherche , grant agreement J11R107-F);  BGC-Argo-France (CNES-TOSCA ).
Data Availability Statement All BGC-Argo data are available at https://doi. org/10. 17882/42182#75239 or at ftp://ftp. ifremer. fr/ifremer/ argo/dac/. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo. ucsd. edu, https://www. ocean-ops. org). The Argo Program is part of the Global Ocean Observing System. The gridded altimeter products were produced by SSALTO/DUACS and distributed by the Copernicus Marine Environment Monitoring Service. The database produced for this paper is available at the following: https://doi. org/10. 17882/76733. The authors wish to thank the Argo Data Management team (ADMT) and the BGC-Argo Data Management team (BGC ADMT).
The genome of PkV RF01 is predicted to contain 1,161 genes comprising 1,121 protein-coding DNA sequences (CDSs) and 40 tRNA genes corresponding to 13 amino acids. Most tRNA genes (30 out of 40) are clustered in three genomic regions that lack predicted CDSs, a feature also observed in other Mimiviridae. For example, all tRNAs of TetV (n = 10) and CroV (n = 22) are encoded consecutively on the same strand  (18, 59). The average CDS length is 1,046 bp (minimum: 297; maximum: 1,493). Intergenic regions average 217 bp in length, with a cumulative sum of 244,005 bp, which corresponds to a gene density of 82. 8%. Of the 1,121 predicted CDSs, 641 (57%) exhibited sequence similarities (BLASTP E-value conservative cutoff of 1 × 10 -5 ) to protein sequences in the UniRef90 database (Fig. 2A ). Among them, 165 were most similar to Mimiviridae.
A total of 339 proteins (30%) showed significant sequence similarity with proteins in the Cluster of Orthologous Gene (COG) database (61) (Fig. 4 ). The distribution of COG functions associated with these hits was dominated by "Posttranslational modification, protein turnover, chaperones" (43 proteins) and "Cell wall/membrane/envelope biogenesis" (42 proteins), which is approximately two times more proteins than in other Mimiviridae members except for Tupanvirus. Among other well-represented categories, numbers of proteins in "Replication, recombination and repair" (36 proteins) and "Transcription" (23 proteins) were similar to those of other Mimiviridae, while the categories of "Translation, ribosomal structure and biogenesis" (25 proteins) and "Amino acid transport and metabolism" (20 proteins) were respectively in the same range or higher than those of heterotroph-infecting Mimiviridae (mimiviruses, BsV, and CroV).
Phylogenetic analyses Five core genes, SDHA, and SDHB The phylogenetic position of PkV RF01 was inferred from concatenated protein alignments of five core nucleocytoplasmic virus orthologous genes (NCVOGs) (87): D5-like helicase-primase (NCVOG0023), DNA polymerase elongation subunit family B (NCVOG0038), DNA or RNA helicases of superfamily II (NCVOG0076), packaging ATPase (NCVOG0249), and Poxvirus Late Transcription Factor VLTF3-like (NCVOG0262). Sequences were obtained from the NCVOG database (ftp. ncbi. nlm. nih. gov/pub/wolf/COGs/NCVOG/) (88). Additional sequences were obtained from genomes retrieved from GenBank and annotated with HMMER v3. 12b using the hmmsearch (89) command with hidden Markov models available in Schulz et al. (2017)  (13). Sequences from each NCVOG were aligned independently using MAFFT L-INS-i (  90 ).
Rpb2, IleRS, and AsnRS To reconstruct a phylogenetic tree based on the second largest RNA polymerase subunit, homologs were recruited by comparing Mimivirus Rpb2 against all proteins of viruses and selected organisms in the KEGG database using the GenomeNet BLASTP tool (https://www. genome. jp/). Organisms were manually selected from the KEGG list to ensure broad taxonomic coverage of the tree of life. The retrieved amino acid sequences were aligned using MAFFT-LINSI (90) and then trimmed using trimAl  (91)  with the following parameters: '-resoverlap 0. 5 -seqoverlap 70 -gt 0. 8 -st 0. 001 -cons 50'. The tree was reconstructed using FastTree  (95)  as implemented in the GenomeNet TREE tool (https://www. genome. jp/tools-bin/ete). Isoleucine tRNA synthase and aspartyl tRNA synthetase viral and cellular homologs were retrieved and aligned in the same way.
Gene prediction and functional and taxonomic annotation GeneMarkS with the option 'virus' (98) predicted 1,121 open reading frames (ORFs) in the fully assembled genome sequence of PkV RF01, while tRNAscan-SE  (99)  predicted 41 tRNAs. PkV RF01 CDS amino acid sequences were searched against Virus-Host DB (100), RefSeq (101), UniRef90  (102) , and COG (61) databases using BLASTP with an E-value of 1 × 10 -5 as the significant similarity threshold and against the Conserved Domain Database (103) using RPS-BLAST with an E-value threshold of 1 × 10 -2. The 10 best hits for each database were compiled in a single file and manually inspected to transfer annotations of subject sequences to our query. In ambiguous cases, such as distant homologs (often seen in viral genomes) or unclear or contradictory annotations of subject sequences, the query was searched against KEGG genes  (104)  to allow extensive manual checking using GenomeNet tools (https://www.
genome. jp/; alignment quality, length comparison to canonical genes, and links with KEGG orthology). We automatically annotated glycosyltransferases (GTs) and other carbohydrate-active enzymes (glycoside hydrolases, GHs; polysaccharide lyases, PLs; carbohydrate esterases, CEs; and auxiliary activities, AAs) in PkV RF01 and all viral genomes in Virus-Host DB (as of June 2018) using the hmm option of the dbCAN2 pipeline and its profile database  (73). We retained hits with E-values < 1 × 10 -5 and domain coverage > 35%, which corresponded to default settings.
Taxonomic and functional analysis of vSDHA homologs in OM-RGCv1 We searched PkV RF01 SDHA and SDHB against OM-RGCv1 (105) using the Ocean Gene Atlas (106) BLAST-based tool and kept the top 50 hits with significant E-values for further analysis. We then collected genome fragments (contigs) encoding these 50 SDHAs and 50 SDHBs by searching via BLASTN for identical hits over full SDHA or SDHB lengths against Tara ocean assemblies (downloaded from EBI) used to construct OM-RGCv1. We predicted ORFs in these genome fragments using GeneMarkS. The resulting 1,113 amino acid sequences were functionally annotated by searching against Pfam protein families (107) using profile HMM scan (108) and also taxonomically using a last common ancestor strategy as in  (109) ; in brief, protein sequences were searched against a database composed of UniRef cells, MMETSP  (110)  and Virus-Host DB (100) data using DIAMOND  (111).
Acknowledgements The recording of tilt series was performed with the help of  Sebastian Schultz  at the  Unit of Cellular Electron Microscopy , the  Norwegian Radium Hospital. Initial sequencing (MiSeq and Pacbio ) of PkV RF01 total DNA was performed at the Norwegian Sequencing Center (https://www. sequencing. uio. no/). We thank  Hilde M. K. Stabell  and  Solveig Siqveland ,  Department of Biological Sciences, University of Bergen, Norway , for technical assistance with molecular biology experiments as well as  Christian Rückert ,  Bielefeld University , for support in manual finishing of genome assembly and  Minyue Fan ,  Kyoto University , for assistance in genes analysis. This work was supported by the  Research Council of Norway  project entitled "Uncovering the key players for regulation of phytoplankton function and structure: lesson to be learned from algal virus-haptophyte coexistence" (VirVar, project number  294364  to RAS).
Additional funding was provided by the  European Union   Horizons 2020 research and innovation program , grant agreement no. 685778  ("Virus-X") to RAS and DB. This work was also supported by the  Future Development Funding Program  of the  Kyoto University Research Coordination Alliance. HO was supported by  JSPS/KAKENHI  (No. 18H02279 ), and  Scientific Research on Innovative Areas  from the  Ministry of Education, Culture, Science, Sports and Technology (MEXT) of Japan  (Nos. 16H06429 ,  16K21723 ,  16H06437 ). The Super Computer System,  Institute for Chemical Research, Kyoto University , provided computational time. We thank  Barbara Goodson , from  Edanz Group  (www. edanzediting. com/ac), for editing the English text of a draft of this manuscript.
Data availability Raw sequence reads and PkV RF01 genome sequence were deposited at the European Bioinformatics Institute (EMBL-EBI) (https://www. ebi. ac. uk) under project name PRJEB37450. The complete video records of a cryo-electron tomogram of a PkV RF01 virion and sequence data as well as curated gene annotation table as reported in this study are available at https://github. com/RomainBlancMathieu/PkV-RF01.
Data value chain Data governance Harmonisation Registry biomarker nomenclature, and data formats, limits our capacity to compare and integrate data sets retrospectively (reuse). Registration of HBM studies is common for clinical trials; however, the study designs and resulting data collections cannot be traced easily. We argue that an HBM Global Registry Framework (HBM GRF) could be the solution to several of challenges hampering the (re)use of HBM (meta)data. The aim is to develop a global, hostindependent HBM registry framework based on the use of harmonised open-access protocol templates from designing, undertaking of an HBM study to the use and possible reuse of the resulting HBM (meta)data. This framework should apply FAIR (Findable, Accessible, Interoperable and Reusable) principles as a core data management strategy to enable the (re)use of HBM (meta)data to its full potential through the data value chain.
Moreover, we believe that implementation of FAIR principles is a fundamental enabler for digital transformation within environmental health. The HBM GRF would encompass internationally harmonised and agreed open access templates for HBM study protocols, structured web-based functionalities to deposit, find, and access harmonised protocols of HBM studies. Registration of HBM studies using the HBM GRF is anticipated to increase FAIRness of the resulting (meta)data. It is also considered that harmonisation of existing data sets could be performed retrospectively. As a consequence, data wrangling activities to make data ready for analysis will be minimised. In addition, this framework would enable the HBM (inter)national community to trace new HBM studies already in the planning phase and their results once finalised.
Introduction Human biomonitoring (HBM) is defined as the method for assessing human exposure to chemicals or their effects by measuring chemicals, their metabolites or reaction products (and/or their effects biomarkers) in human specimens  (WHO, 2015a). HBM is a valuable tool to support the environment and health policy-making process because it provides quantitative actual distribution of exposures in a population. Environmental pollutants can then be mapped for emerging pollutants, as well as data regarding their resulting health effects, and/or population susceptibility. HBM has a long history serving health surveys with well-known national programs such as the German Environmental Surveys (GerES), the US National Health and Nutrition Examination Survey (NHANES), the Canadian Health Measures Survey (CHMS), and the Korean National Environmental Health Survey (KoNEHS).
, 2020; Zare Jeddi et al. , 2020). If combined with health surveys and cohorts, HBM (meta)data can also provide opportunities to investigate the relationship between internal exposure and health effects, promote risk-reduction measures, monitor exposure trends, and evaluate the effectiveness of implemented national and global policies (e. g. ,  (Eykelbosh et al. , 2018; Romano et al. , 2020) ). Overall, the use of HBM (meta) data increases the value of exposure information in risk assessment and management context  (Wilhelm, 2020). Given the multiple benefits of using HBM at national and global levels, the use of HBM is a recognized priority in chemical safety. HBM can increase the robustness of regulatory long-term decisions for marketed chemicals, and in particular, the increased importance of mixture risk assessment of chemicals and grouping approaches, as echoed at national, supranational, and intergovernmental level such as UN,  WHO, OECD and EU (EC, 2020a; OECD, 2018; SAICM, 2013; WHO, 2015b).
To serve this role, methodologies should be harmonised and HBM (meta) data should be easily findable, accessible, interoperable, secure, shared and reused by default  (EC, 2020a). Regrettably, HBM has received little regulatory application to date which is partly due to a lack of sufficient, reliable, quality-assured, and well-structured (meta)data. HBM is an important and useful tool, yet quite complicated in terms of its design, application, and interpretation. The existing legislative frameworks on chemicals do not currently provide clear harmonised guidance for developing a comprehensive and integrated assessment of (combined) internal exposures to chemicals from different sources and routes. Any available guidance has evolved separately in different regulatory frameworks  (Bopp et al.
, 2018; Drakvik et al. , 2020; Evans et al. , 2016; Fantke et al. , 2020a; Louro et al. , 2019). This article aims to describe the challenges and needs to drastically increase the use and possible reuse of HBM data. It describes requirements to make HBM study information (metadata as wells as measurement data) more FAIR (Findable, Accessible, Interoperable, and Reusable)  (Wilkinson et al. , 2016). In this article, these four principles are grouped in the coupled cornerstones of findability/accessibility and interoperability/reusability. Findability/accessibility is a system to store HBM (meta)data. Interoperability/reusability is how the (meta)data themselves should be expressed (and stored in HBM data repositories). For both cornerstones, harmonisation is critical. Harmonisation of storage systems for HBM (meta)data helps to find and better access HBM (meta)data (better findability and quicker access) where harmonisation of the HBM (meta)data generation itself enables reuse and interoperability.