dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Here, we provide a comprehensive dataset of zooplankton size distribution, taxonomic composition and total biomass based on individual measurements of body size and document the shape of the size spectrum on a global scale. Previous zooplankton studies have mostly reported NBSS estimates based on nets, or total biomass from UVP5 data  (Forest et al. , 2012; Biard et al. , 2016; Drago et al. , 2022). However, here we report new, complete, zooplankton size distribution estimates across the global ocean, using the combination of both net and UVP approaches. In the following sections, we compare our UVP5 and net datasets to other studies so far restricted to specific oceanic regions, we discuss discrepancies between each approach and finally highlight the novel understanding of the complete NBSS.
Comparison of our global zooplankton dataset to existing case studies We find that zooplankton communities collected with nets exhibit higher NBSS values towards the poles, a pattern that is largely driven by crustaceans. This pattern observed towards the poles, also described in  (Brandão et al. , 2021) , is induced by the higher contributions of the large-size grazing Calanidae relative to the smaller omnivorous-carnivorous Cyclopoida and Poecilostomatoida (i. e. Oithonidae, Oncaeidae and Corycaeidae) that are more dominant in tropical regions  (Brandão et al. , 2021; Soviadan et al. , 2022). It is also well known that zooplankton size decreases with increasing temperature and an increase in the relative contribution of small phytoplankton, while it increases with concentrations of oxygen, macronutrients, total phytoplankton biomass and the relative contribution of large phytoplankton  (Brun et al.
, 2016; Brandão et al. , 2021; Soviadan et al. , 2022). This latitudinal trend is consistent with other observations of high Copepoda abundance or biomass in the Polar oceans  (Hirche and Mumm, 1992; Balazy et al. , 2018; Pinkerton et al. , 2020; Drago et al. , 2022). Our results also agree with recent in situ imaging data compilation and model output which found that polar waters are dominated by Copepoda whereas in the intertropical waters mixotrophic rhizarians represent a substantial part of the biomass  (Biard et al. , 2016; Drago et al. , 2022). The median mesozooplankton biomass found in the present study, which varied from 1. 3 -2. 9 mgC m -3 in the epipelagic (Figure  6  supplementary), is also within the range of biomass reported in the COPEPOD global database  (Moriarty and O'Brien, 2013)  with values of 0.
98 -3. 6 mg C m -3. We calculated zooplankton NBSS slopes derived from UVP5, Multinet samples and the reconstructed NBSS using mode near 1 mm and a linear variation of NBSS with increasing size classes. Despite the significant impact of the previously undersampled rhizarians on NBSS slopes derived from the UVP5 (see next section), especially in tropical and temperate regions, we focus our comparison on global datasets NBSS slopes obtained with nets, as they are the only slopes (for zooplankton-only) reported to our knowledge in previous studies. The NBSS slopes calculated from the Multinet collection (NBSS_Zmtn) were compared with a global compilation. The comparison showed that the reported values are well within the range of observed values and that they show a contrasting spatial distribution around the globe, consistent with the variability observed between low-productivity and high-productivity systems.
In this lower size range, many of the particles were identified as marine snow, larvacean houses, diatom rafts, or fecal pellet strings, which we discarded in our analysis as they did not constitute living zooplankton. However, we note that the majority of our unclassified images were of particles smaller than 1 mm ESD, which lacked any resolvable characteristics to support their identification. Thus, it is possible that a large percentage of the small-unidentified particles in the UVP5 dataset were small zooplankton such as early life stages of copepod. Crustaceans and carnivorous gelatinous were the best groups represented in both UVP and Multinet across all samples. This suggests that UVP5 sampled well crustaceans especially in polar region where their size is bigger, even though the volume imaged was more limited.
These differences suggest that rhizarians, when abundant, have a profound impact on the slope of the NBSS. This biases our ability to use only NBSS calculated from net collected samples as an indicator of the trophic flow of energy, while the high taxonomic resolution of net tows remains important. Lower observed volume and resolution prevents the use of the UVP5 to study mesozooplankton biodiversity. Therefore, with current technologies, both methods should be combined. We recommend the use of only in situ imaging technologies when increased resolution and larger field of view are possible. Until then, imaging technologies together with nets samples analysis provides the complete datasets to study ecosystems functioning, using NBSS as a key planktonic variable.
Acknowledgement Tara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Ocean Foundation and the continuous support of 23 institutes (https://oceans. taraexpeditions. org/). The global sampling effort was enabled by countless scientists and crew who sampled aboard the Tara from 2009-2013, and we thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expeditions. We are also grateful to the countries who graciously granted sampling permission. We thank Agnès b. and Etienne Bourgois, the Prince Albert II de Monaco Foundation, the Veolia Foundation, Region Bretagne, Lorient Agglomeration, Serge Ferrari, Worldcourier, and KAUST for support and commitment. We also thank "Make Our Planet Great Again Team" for the postdoctoral fellow support.
Funding This work was supported by  Centre National de Recherche Scientifique in particular Groupement de Recherche  [ GDR3280 ], The  Research Federation  for the  Study of Global Ocean Systems Ecology and Evolution  [ FR2022 /Tara-Oceans GOSEE ], the  European Molecular Biology Laboratory , the  French Ministry of Research , and the  French Government  '' Investissements d'Avenir'' programs   OCEANOMICS  [ ANR-11-BTBR-0008 ] and the  EMBRC-France  [ ANR-10-INBS-02 ]. DS was supported by a the  Fond Français pour l'Environnement Mondial  and the  Make Our Plat Great Again fellowships. LS was supported for the initial phase by the  Chair VISION  from  CNRS/Sorbonne University. Grants for the collection and processing of the Tara Oceans data set was provided by  NASA   Ocean Biology and Biogeochemistry Program  to the  University of Maine ; the  Canada Excellence  research chair on remote sensing of Canada's new Arctic frontier; and the  Canada Foundation for Innovation.
COPERT model was adapted to local conditions to estimate direct combustion emissions and European Monitoring and Evaluation Program and U. S. Environmental Protection Agency methodologies to estimate wear and dust resuspension emissions. We used the OpenLCA ® Software, the ecoinvent 3. 4 database, and all locally available information to calculate indirect emissions. As far as is known, this is the first Well-to-Wheels emissions inventory considering all passenger vehicles in Bogotá. To evaluate the consistency of this study, we compared results from this inventory with those published in Bogotá and other cities worldwide. This study highlights the importance of including other emission sources than vehicle fuel combustion in emission inventories.
2018). Indirect emissions were estimated using the OpenLCA ® software, the ecoinvent 3. 4 databases, and available information for this case study  (CUE 2012; Cuéllar et al. 2016; Ecoinvent Association 2018; Greendelta GmbH 2018). This inventory were contrasted against the results from Bogotá already published and the literature inventories. Since the indirect emissions are linked to fuel production processes, other systems that require fuel combustion to operate, different from vehicles, take part in these indirect emissions. This approach uses emission factors based on the amount of fuel consumed by the vehicles (i. e. , grams of indirect pollutant emitted by a gram of fuel consumed), which allows scaling the indirect emissions only to the vehicles.
The life cycle inventory (LCI) data for each vehicle energy source is available mainly in ecoinvent 3. 4. database (Ecoinvent Association 2018). Ecoinvent is a database of LCIs validated and recognized worldwide as a suitable tool for developing life cycle assessment studies  (Ecoinvent Association 2018; Greendelta GmbH 2021). This database has LCI information for different industrial sectors with around 17 000 data sets in fields such as energy supply, agriculture, transport, biofuels and biomaterials, chemicals, building materials, wood, and waste treatment, among others (Ecoinvent Association 2018). For diesel, gasoline, and natural gas production processes, the production modules of the ecoinvent 3. 4 database were the input data for the OpenLCA® software (Ecoinvent Association 2018). This study found that the CO 2 -Eq emissions of the reference process created from the database are similar to the official reports from the Colombian Petroleum Company (Ecopetrol)  (Ecopetrol S.
A. 2013 ). The differences between the official data and the results obtained here are around 12% for gasoline and 18% for diesel  (Cuéllar Álvarez 2016). The CUE Consortium (CUE 2012) information for biofuels was used to estimate life cycle emissions from biodiesel and bioethanol production. In Colombia, bioethanol is produced from sugarcane and biodiesel from palm oil. Results from OpenLCA® were used to obtain the emission factors by energy source for the pollutants included in this study (Table  2 ). Appendix C of the supplementary material presents all the emission factors used by fuel type.
Traditional and special transport buses also generate significant CO 2 -Eq, PM 2. 5 , and NOx emissions. Motorcycles generate an essential fraction of VOC. A large part of emissions from these vehicle categories is direct for all pollutants, except SO 2 , mainly due to old vehicle technologies used in the city and poor maintenance. Conversely, BRT buses generate much lower emissions of all contaminants. In 2015, most of this type of bus were Euro III and had the highest VKT of all categories; only 1 460 BRT buses made this fleet (Table  1 ). Data regarding the indirect and exhaust EF is only partially available for TSP and PM 10 ; differently, information is available for PM 2. 5. Therefore, only PM 2. 5 Wear and dust resuspension (W&R) emissions are included in this study. W&R, in terms of PM 2.
Direct emissions from tires, brakes, road surface wear, and dust resuspension (W&R) Table  4  compares results from this study with those reported by  Beltrán et al. (2012) and SDA (2015a). This comparison indicates that PM 2. 5 emissions from wear and paved roads from this study are within the same orders of magnitude as those reported by Beltran et al. Emissions from wear, paved and unpaved roads from this study are 38, 22, and 32% above Beltran's results, respectively. These two emission inventories use emission factors from the same databases but use different vehicular activities and methodologies. Beltran estimated desegregated emissions in all city roads, while we reported aggregated emissions in the city. Comparing the total emissions of each category in this study with Beltran's results is possible.
All contigs >1,000 nts were analyzed using BLASTx  (Altschul et al. , 1990)  against a RefSeq viral protein database (RefSeq release 205). For all contigs that we identified as anellovirus-like, we assembled a dataset and designed sets of abutting primers (Supplementary Table  S1 ) to recover complete genomes from each of the individual samples of the Weddell seal nasal and vaginal swabs and the grizzly bear fecal sample. The RCA products were used as templates with specific primer pairs to amplify the anellovirus genomes using Kapa HiFi Hotstart DNA polymerase with the following thermal cycling conditions: 95 ∘ C for 3 min; 25 cycles of 98 ∘ C for 20 s, 60 ∘ C for 15 s, 72 ∘ C for 2 min, and a final extension of 72 ∘ C for 3 min. The amplicons were resolved on a 0. 7 per cent agarose gel stained with SYBR Safe (ThermoFisher, USA), and ∼2-2.
5 kb size fragments were excised, gel purified, and cloned into pJET1. 2 plasmid vector (ThermoFisher) and transformed into Escherichia coli XL1-blue competent cells. The resulting recombinant plasmids were Sanger sequenced by primer walking at Macrogen Inc. (South Korea). ORFs in the cloned and Sanger sequenced genomes were identified using ORF finder tool in Geneious Prime 2022. 1. 1 (Biomatters Ltd, New Zealand) and manually checked by global alignments. The sequences have been deposited in GenBank under accession numbers OP629190-OP629445. Pairwise identity analysis to determine species classification was performed using SDT v 1. 2  (Muhire, Varsani, and Martin 2014).
Recombination analyses The 246 sequences of the anelloviruses identified from Weddell seal samples in this study represent two anellovirus species Torque teno pinniped virus 8 (n = 160) and Torque teno pinniped virus 9 (n = 86), and thus, these were aligned together with other members of these two species using Multiple Alignment using Fast Fourier Transform (MAFFT) with auto option  (Katoh and Standley 2013). Recombination in the aligned datasets was inferred using Recombination Detection Program 5 (RDP5)  (Martin et al. 2021 ) with default settings. Sequences were auto-masked for optimal recombination detection, and only events detected with more than three different methods implemented in RDP5 coupled with phylogenetic support for recombination and a P-value of <0. 05 were considered credible.
Dataset of anellovirus proteins Sequences representing all anellovirus species (n = 153) listed in the Virus Metadata Resource provided by the International Committee on Taxonomy of Viruses (ICTV) were downloaded from the National Center for Biotechnology Information. This reference dataset was supplemented with the newly sequenced anelloviruses from Weddell seals (n = 246) and grizzly bears (n = 10) (Supplementary Tables  S2  and 3 ). Three main ORFs, ORF1-3, and hypothetical ORFs from Weddell seal anelloviruses (WSAs) were extracted from the annotated sequence files in Geneious Prime 2022. 1. 1 (Biomatters Ltd) and used in the subsequent analyses.
Only the top 20 hits against the Protein Data Bank (PDB50) database were extracted. To build the structure-based dendrogram, structures of ORF1 and their top DALI hits were used in an all-against-all comparison, and an average linkage clustering was performed via DALI. The protein sequences representing each anellovirus genus were used as queries in profile-profile comparisons with HHsearch  (Steinegger et al. 2019 ) against the PDB70 and viral protein (UniProt-SwissProt-viral70_3_Nov_2021) profile databases  (Gabler et al. 2020).
Search for Rep protein motifs To search for the potential anellovirus Rep homologs, all six possible frames were in silico translated from representative anellovirus genomes using Prodigal  (Hyatt et al. 2010 ) in meta mode and five different translation tables: standard code; vertebrate mitochondrial code; yeast mitochondrial code; invertebrate mitochondrial code, and bacterial, archaeal, and plant plastid code. These resulting amino acid sequences were used as input for Inter-ProScan  (Jones et al. 2014 ) against all available databases with default settings.
Anellovirus ORF1 is a homolog of SJR CPs To analyze the relationship between anelloviruses and other viruses, we performed BLASTp searches against the viral RefSeq database queried with ORF1 protein sequences representing each Downloaded from https://academic. oup. com/ve/article/9/1/vead035/7181269 by Institut Pasteur -CeRIS user on 16 June 2023 of the Anelloviridae genera. No significant matches were obtained outside of the Anelloviridae. Thus, we sought to gain insights into the provenance of anelloviruses through more sensitive profileprofile comparisons. Searches queried with the ORF1 sequence of torque teno virus 1 (TTV1; NP_817122) against the PDB and viral protein profile databases using HHsearch yielded a highly significant hit (probability = 95. 8) to the CP of a bat circovirus (PDB id: 6RPO; Supplementary Table  S5 ).
2021). Inspection of the generated models confirmed that all ORF1 protein homologs contained the conserved SJR domain (Fig. 3B, C , Supplementary Fig. 2 ). Indeed, DALI  (Holm 2020 ) searches against the PDB database queried with the predicted jelly-roll domains of different ORF1 protein orthologs consistently yielded best hits, with significant Z-scores (Z = 6. 0-9. 6), to the CPs of eukaryotic ssDNA and ssRNA viruses from the families Circoviridae, Hepeviridae, Astroviridae, Marnaviridae, Nodaviridae, and Dicistroviridae, and the genus Papanivirus (Supplementary Table  S6 ). Collectively, the results of profile-profile and structure comparisons indicate that the anellovirus ORF1 protein is homologous to the jelly-roll CPs of other eukaryote-infecting ssDNA viruses and, in particular, to the CPs of circoviruses.
Structural dissection of the anellovirus ORF1 proteins Anellovirus ORF1 proteins display a remarkable variation in length and sequence. To verify whether these proteins are orthologous, we performed an all-against-all structural comparison of the ORF1 homologs from different Anelloviridae genera and CPs of other viruses identified as the top hits in the DALI searches. This analysis showed that all anelloviruses form a compact, arguably, monophyletic group (Fig. 4A ), suggesting that the dramatic evolution observed among the ORF1 sequences took place following their divergence from a common ancestor. To reconstruct the gradual evolution of the ORF1 structure, we superposed the structures of ORF1 homologs from different genera with the compact CP of a circovirus (PDB id: 6RPK; Fig. 4B ). This comparison allowed further delineating the borders of the anellovirus jelly-roll domain and exposed the differences compared to the SJR CP of circoviruses.
In particular, most ORF1 proteins contain two prominent insertions. One is located between β-strands B and  C (Fig. 4B ) and, depending on the virus, can adopt an extended loop, α-helix or β-hairpin conformation. The other insertion is located within a loop connecting the β-strands H and I. The H-I insertion varies substantially in both size and predicted structure, from an extended loop to an elaborate α + β domain (Fig. 4B, C ). Structural searches queried with the latter domain did not produce significant matches against the PDB database, suggesting that this fold is so far unique to anelloviruses. Notably, the relative orientation of the α + β domain with respect to the jelly-roll domain is similar to the 'projection' (P) domains present in the CPs of a variety of ssDNA and ssRNA viruses, such as bacilladnaviruses, tombusviruses, nodaviruses, caliciviruses, and several other groups of viruses  (Wery et al.
Funding A. B. was supported by a post-doctoral fellowship from  Foundation pour la Recherche Mèdicale  (grant number  SPF202110014092 ). M. K. was supported by a grant from the  l'Agence Nationale de la Recherche  ( ANR-20-CE20-0009-02 ). A. L. K. and R. S. B. were supported by  Institutional Development Awards (IDeA)   Networks of Biomedical Research Excellence Assistantships  (grant number  P20GM103395 ) from the  National Institute of General Medical Sciences of the National Institutes of Health (NIH). E. V. K. was supported by the  Intramural Research Program  of the  NIH (National Library of Medicine). Weddell seal samples were collected under  National Marine Fisheries Service Marine Mammal  permit #17411,  Antarctic Conservation  Act permit # 2014-003 , and the  University of Alaska Anchorage  and University of Alaska Fairbanks's  Institutional Animal Care  and Use Committee approvals # 419971  and # 854089 , with funding from the  National Science Foundation  grant  ANT-1246463  to J.
During wastewater treatment, micropollutants are only partly eliminated and may present a risk for human health and aquatic ecosystems. The potential impacts these substances may have are currently underestimated due to the lack in available concentrations that lie below the limit of quantification (LOQ) for an important set of micropollutants. Here, the potential impacts due to 261 organic micropollutants on human health and aquatic environments were investigated at the scale of France. Even with concentrations below the LOQ, certain micropollutants were found to have a significant potential impact. For unmeasured concentrations, a global concentration distribution built from several datasets with different LOQ was used. By disregarding the unmeasured micropollutants, the potential impacts have been underestimated by >300 % on both human health and aquatic environments.
Among these 261 organic micropollutants, 153 emitted compounds were estimated from at least ten measuring sites and from 90 % of the measured data above the limit of quantification. The potential impacts of these 153 compounds were assessed by  Aemig et al. (2021)  for those present in the USEtox® database  (Rosenbaum et al. , 2007)  and by  Servien et al. (2022a)  for the compounds that are not present in this latter database.
Characterization factors Among the remaining 108 compounds, 58 have a CF for ecotoxicological potential impacts (CF ET ) and 43 a CF for human potential impacts (CF HT ) in the USEtox® database version 2. 12 with a default landscape (to remain consistent with  Servien et al. (2022a Servien et al. ( , 2022b)) ). USEtox® is an international consensus for characterizing the human and ecotoxicological potential impacts of chemicals (UNEP-SETAC, 2019). It was developed by the life cycle initiative under the United Nations Environmental Programme (UNEP) and the Society for Environmental Toxicology and Chemistry (SETAC)  (Henderson et al. , 2011)  to produce a transparent and unanimous characterization model. Within a single characterization factor, this model assembles the chemical fate, exposure and effect for each of the several thousands of organic and inorganic compounds.
While the structure of the USEtox® multimedia model is always the same, the CF of a molecule can be established using numerous physicochemical parameters (such as solubility, hydrophobicity, degradability) and detailed toxicological and ecotoxicological data must be provided. In detail, CFs were calculated in the USEtox database via the following formula: CF = FF × XF × EF. FF was the fate factor indicating the residence time in the environment and was estimated with physicochemical properties for organic micropollutants or speciation for inorganic micropollutants. XF was the exposure factor i. e. the fraction of micropollutants in the environment that was available for organisms. EF was the effect factor corresponding to the effect on the aquatic environment (considering three trophic levels) or the effect on Human health.
These models are based on a comparison between different linear or non-linear (machine learning) models and, according to a cluster-then-predict approach, could be either global or local. The modelled CFs were compared to USEtox® database values. The differences between the modelled CFs and the USEtox® CFs were minor, therefore the models were considered to be applicable. The best approaches were then selected for each cluster and each characterization factor. To apply the cluster-then-predict approaches, the cluster of each new compound needed to be estimated using a supervised clustering approach based on clusters previously obtained by  Servien et al. (2022a). This was applied using the wellknown k-nearest neighbour approach through the knn function of the R package class  (Ripley, 1996).