dataesr/scientific-paragraphs-categorization huggingface dataset:
A peculiar velocity structure in the epicenter area Two weeks after the Mw 4. 9 earthquake, a first shearwave velocity model representative of the epicentral region was produced. This velocity model was inferred from the analysis of 5 days of continuous seismic ambient noise recorded synchronously by 4 telemetred broad band stations (Figure  9a ). Dispersion curves were extracted by using the 3-component RTBF algorithm  [Wathelet et al. , 2018]  implemented in the Geopsy software  [Wathelet et al. , 2020]  which allows to extract phase velocities of Rayleigh and Love waves, as well as the signed ellipticity of Rayleigh waves. Dispersion estimates of both Rayleigh and Love waves are very clear from 0. 1 Hz to 0. 5 Hz (Figures  9b-d ). The strong directionality of the seismic noise wavefield counter-balances the small number of available seismological stations (Figure  9e ).
, left). Another source of complement C3 activation is spontaneous hydrolysis of the labile thioester group. This nucleophilic attack by water to the C3 thioester is known as the "tick-over" mechanism, and it is accompanied by a conformational change in C3 resulting in a C3b-like conformation, generating a molecule called C3(H 2 O). 84 C3(H 2 O) is able to bind to FB and, after FB cleavage by FD, to form a fluid phase convertase. The role of this fluid phase convertase is still debated in the complement field. Some suggest that the labile thioester provides a F I G U R E 2 The conformational change occurring upon C3 activation by a C3 convertase. When C3 (left, PDB ID 2A73) is cleaved by a C3 convertase (C4b2a or C3bBb), the anaphylatoxin domain is released to generate C3a in the fluid phase, and C3b which opsonizes the target surface, thanks to an extensive conformational change exposing the thioester in the TE domain, allowing it to react with a nucleophile on the surface (center, PDB ID 2I07).
The substrate-bound alternative pathway C3 convertase. A model of substrate binding by the alternative pathway C3 convertase based on the crystal structure with PDB ID: 3PVM. C3b in the C3 convertase is shown in gray, Bb in roseate. The substrate C3 is shown in brown, with the highlighted ANA domain in lime green, and the MG4 domain in green. The figure underlines the importance of the MG4 domain interface for efficient convertase activity. In fact, residues in MG4 are part of the interface between the substrate C3 and the C3b in the convertase (left), but also between the C3b in the convertase and the substrate C3 (right). To generate the model, C3 was aligned to C5 in the CVF:C5 complex (PDB ID 3PVM), while C3b bound to Bb was aligned to CVF. F I G U R E 4 The cleavage steps carried out by FI during C3b regulation.
When C3b (left, PDB ID 2I07) is cleaved by FI in the CUB domain (pink), the C3f fragment is released in the fluid phase, and iC3b remains flexibly attached to the target surface (center, PDB ID 7AKK). A last FI cleavage in the CUB domain releases the C3c fragment and leaves C3d bound to the surface (right, PDB ID 7AKK). The thioester glutamine is shown in red, anchoring the TE domain to the activator surface. The location of the remaining iC3b domains is putative, as they are flexibly attached to the TE domain through what remains of the CUB domain (pink). of the molecular mechanism and structural organization of the C5 convertases is a major challenge to be fully resolved in the upcoming years. The C5 convertases are denoted C4b2bC3b and C3bBbC3b, and they cleave C5 to release the anaphylatoxin C5a and the larger.
Fundinginformation Grant/ Award Number :  ANR-21-CE14-0066-02 ;  Danmarks Frie Forskningsfond , Grant/ Award Number:  DFF-1025-00015B ;  Institut National de la Santé et de la Recherche Médicale ;  Institut National Du Cancer , Grant/Award Number:  INCa_16096 ;  Labex Immuno- Oncology Excellence Program ;  Les Entreprises contre le Cancer, Ligue Regionale Contre le Cancer (GEFLUC) IDF ; l' Idex Sorbonne Université (Programmes Investissements d'Avenir Émergences   2021-2022 ), Grant/Award Number: CompLys;  Ligue Contre le Cancer , Grant/Award Number:  RS22/75-37 ;  Sorbonne Université ;  Université de Paris Cité Summary.
F I G U R E 3 3 The substrate-bound alternative pathway C3 convertase. A model of substrate binding by the alternative pathway C3 convertase based on the crystal structure with PDB ID: 3PVM. C3b in the C3 convertase is shown in gray, Bb in roseate. The substrate C3 is shown in brown, with the highlighted ANA domain in lime green, and the MG4 domain in green. The figure underlines the importance of the MG4 domain interface for efficient convertase activity. In fact, residues in MG4 are part of the interface between the substrate C3 and the C3b in the convertase (left), but also between the C3b in the convertase and the substrate C3 (right). To generate the model, C3 was aligned to C5 in the CVF:C5 complex (PDB ID 3PVM), while C3b bound to Bb was aligned to CVF. F I G U R E 4 The cleavage steps carried out by FI during C3b regulation.
(1987). Bacteria were quantified using a flow cytometer (FAC-SCalibur, Becton Dickinson, Oxford, UK). Seawater samples (1. 7 mL) were fixed with 85 µL glutaraldehyde (1 % final concentration) and stored at -80 • C until analysis. Samples were stained with SYBR Green I (molecular probes), enumerated with a laser emitting at 488 nm, and detected by their signature in a plot of side scatter (SSC) versus green fluorescence (FL1). Heterotrophic bacteria were distinguished from photosynthetic bacteria (Prochlorococcus and Synechococcus) by their signature in a plot of red fluorescence (FL2) versus green fluorescence (FL1). Yellow-green latex beads (1 µm, Polysciences) were used as an internal standard  (Gasol and del Giorgio, 2000). Cell counts were determined with the CellQuest software (Becton Dickinson).
For autotrophic pico and nanoplankton <20 µm, 2 mL samples were fixed with formaldehyde (1 % final concentration) and stored frozen (-80 • C) until analysis. Red and orange autofluorescence was used to identify Chl a and phycoerythrin cells. Cell counts were determined with CellQuest software (Becton Dickinson); picoplankton and nanoplankton populations containing Chl a and/or phycoerythrin (i. e. Synechococcus) were identified and enumer-ated. We converted the cell abundance of the different autotrophic pico-and nanoplankton populations into biomass assuming 43 fg C per cell for Prochlorococcus, 120 fg C per cell for Synechococcus, 500 fg C per cell for eukaryotic picoplankton, and 3. 100 fg C per cell for eukaryotic nanoplankton based on  Hernández-Hernández et al. (2020). We report the autotrophic pico-and nanoplankton biomass as the sum of eukaryotic pico-and nanoplankton and cyanobacteria (Prochlorococcus and Synechococcus) biomass.
Primary production (PP) of organic carbon was calculated according to  Gargas (1975) : PP µmol C L -1 d -1 = a2 × DI 12 C × 1. 05 × k 1 × k 2 a1 , (1) where a1 and a2 are the activities (DPM: disintegrations per minute) of the added solution and the sample corrected for the dark sample, respectively, and DI 12 C is the concentration (µmol L -1 ) of dissolved inorganic carbon (DIC) in the sample. DIC concentration was calculated from total alkalinity using the R package seacarb  (Gattuso et al. , 2020). Total alkalinity of the seawater was acquired through the open-cell titration method  (Dickson et al. , 2007). The value 1. 05 is a correction factor for the discrimination between 12 C and 14 C, as the uptake of the 14 C isotope is 5 % slower than the uptake of 12 C, k 1 is a correction factor for sub-sampling (bottle volume / filtered volume), and k 2 is the incubation time (d -1 ).
Data analysis Statistical analyses and calculations were conducted using the software R (v4. 0. 3) in R studio (v1. 1. 414; Ihaka and Gentleman, 1996). Analysis of variances (ANOVA) and Tukey's test were performed on the different parameters by grouping the stations by their position (Table  S1 ). Seawater density was calculated using R package oce v1. 3. 0  (Kelley, 2018) , and the mixed layer maximum depth was determined as the depth at which a change from the surface density of 0. 125 kg m -3 has occurred  (Levitus, 1982). Erroneous estimates of mixed layer maximum depth have been corrected manually on five profiles. Other R packages used in this study include corrplot v0. 84  (Dray, 2008 ) and ggplot2 v3. 3. 3  (Wickham, 2016). Section plots were made using Ocean Data View v5. 6. 2  (Schlitzer, 2020).
Conclusions Our results highlight the ability of a CE to be an autotrophic vector toward the open ocean with organic matter freshly produced by the phytoplankton community inside. Yet, despite the strong autotrophy associated with the CE, phytoplankton exudation of DOM was not always enough to com-pensate for bacterial metabolic needs. Even if BP was enhanced in the CE, the BGE was rather low and varied substantially. Instead, heterotrophic bacteria preferentially used DOM for respiration. Microbial metabolic activity dynamics within eddies are complex and require further investigations to better understand and unravel carbon cycling in these features. Financial support. This research has been supported by the Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie (grant no.
Acknowledgements. We thank the captain and the crew of the  R/V Meteor  for their support during the M156 cruise. We thank  Jon Roa ,  Tania Klüver  and  Lindsay Scheidemann  for sampling on board. We thank the two anonymous reviewers for their constructive comments on earlier versions of the manuscript. We thank  Jon Roa  and  Sandra Golde  additionally for the analysis of dissolved organic matter and  Tania Klüver  for cell counting and bacterial and phytoplankton activity analyses. We thank  Bettina Domeyer  and  Regina Surberg  for the nutrient analyses. This study has been conducted using  EU Copernicus Marine Service information. The results contain modified Copernicus Climate Change Service information for 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.
Acknowledgements The authors of this article wish to thank  Jonathan M. Kenoyer  for his invaluable assistance in the initial stages of the development of this research project and for the many tips he provided. We also thank  Michel Lenoir  and  Michel Brenet  for making the experimental lithic drills. Finally, we thank  Benoît Bérard  for taking elastomer casts of the Vivé bead perforations. The study of the archaeological artifacts would not have been possible without the help of  Christian Stouvenot  and  Gwenola Robert  ( DAC Guadeloupe ),  Susana Guimaraes ,  Katarina Jacobson  ( Musée Edgar Clerc ),  Thierry Dorival  ( DAC Martinique ), and  François Petit  (owner of  Hope Estate ). The authors also want to thank the three reviewers for their highly valuable comments. Preprint version 4 of this article has been peer-reviewed and recommended by  Peer Community In Archaeology  (https://doi.
Diatoms thus participate in the provision of major ecosystem services on which humans depend socially and economically  (Costanza et al. , 1997; Hope et al. , 2019). Diatoms were among the first microscopic organisms to be detected in the sea, and, over the past two centuries, extensive occurrence data have been obtained through expeditions and observations carried in polar, temperate, tropical and equatorial waters (see  Round et al. 1990 , Estrada et al. , 2016; Boltovskoy & Valentin, 2018; Sunagawa et al. , 2020). Until recently, species occurrence and abundance were obtained using microscopy, which has traditionally been used for the description of diatom species whose frustules are highly differentiated. In this century, the use of genetic characters and of high-throughput sequencing (HTS) has shed light on the distribution and dynamics of species that are more difficult to identify with optical microscopy and has highlighted the widespread existence of cryptic species, which are not distinguishable based on morphological features (e.
Obtention of the metabarcoding data The procedures used for DNA extraction and amplification of the 18S V4 region of the ribosomal operon are described respectively in  Caracciolo et al. (2022)  for the SOMLIT-Astan station and in  Piredda et al. (2017)  for the LTER-MC station. The eukaryote-specific primers used were TAReuk454FWD1 (5'-CCAGCASCYGCGGTAATTCC-3', Saccharomyces cerevisiae position 565-584) and TAReukREV3 (5'-ACTTTCGTTCTTGATYRA-3', Saccharomyces cerevisiae position 964-981)  (Stoeck et al. , 2010)  for SOMLIT-Astan and TAReuk454FWD1 and V4 18S Next. Rev (5'-ACTTTCGTTCTTGATYRATGA-3', Saccharomyces cerevisiae position 964-984)  (Piredda et al. , 2017)  for LTER-MC. Sequencing was performed on Illumina MiSeq platforms (2x250bp) for both time series. Overall, 5,100,816 diatom reads were obtained for SOMLIT-Astan station and 12,524,142 for LTER-MC station.
Raw sequences for SOMLIT-Astan are available at the European Nucleotide Archive (ENA) under the project id PRJEB48571. The paired-end fastq files obtained from sequencing were demultiplexed. The sequence portions corresponding to the primers TAReuk454FWD1 and TAReukREV3 were trimmed from both SOMLIT-Astan and LTER-MC using Cutadapt v2. 8 (Martin, 2011) and untrimmed reads were filtered out. This resulted in reverse reads from LTER-MC starting with three artificial nucleotides (TGA), but allows comparisons between the two sites. Then, forward and reverse reads were trimmed at position 220 and 210 respectively and reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R-package dada2  (Callahan et al.
, 2016). For each run, error rates were defined using the function learnErrors(), reads were dereplicated using the function derepFastq() function and denoised using the dada() function with default options before being merged. Remaining chimaeras were removed using the function removeBimeraDenovo(). Mixed orientated reads from the same sample were summed together and, in case of sequencing replicates, the readset with the highest number of reads was retained. Only amplicon sequence variants (ASVs) with at least two reads were retained. To assign taxonomy to ASVs, a native implementation of the naive Bayesian classifier method was used from the assignTaxonomy() function using PR2 v 4. 13. For more details relative to the bioinformatic pipeline used to generate the ASV tables, see https://github.
com/benalric/MetaB_Roscoff_Naples_diatoms. Finally, datasets corresponding to the SOMLIT-Astan samples (size fraction > 3 µm; years 2011, 2012 and 2013) and LTER-MC samples ( size fraction > 1. 2 µm; years 2011, 2012 and 2013) were pooled and ASVs observed in less than two samples were discarded. In order to take into account sequencing depth variability amongst samples, 24,974 reads were randomly sampled 10 times without replacement using the R function rtk() (rtk library). The rounded mean of the 10 values was reported as the number of reads in the rarefied ASV table. ASVs with 0 reads were considered as absent after rarefaction.
Taxonomic reassignment of diatoms ASVs ASVs assigned to Bacillariophyta with bootstrap confidence superior or equal to 99%, during the ASV table construction, were selected and reassigned, using a global pairwise alignment approach (usearch_global VSEARCH's command), with an in-house diatom reference database. The reference database contains all diatom sequences from EukRibo (a manually curated rDNA 18S database, https://doi. org/10. 5281/zenodo. 6327891), as well as additional sequences from species or strains present at our sites but lacking in the database (for more details, see https://gitlab. sb-roscoff. fr/nhenry/rosko-naples-diatoms). ASVs inherited the taxonomy of the best hit or the last common ancestor in case of ties. ASVs with similarity scores below 90 % were discarded.
Different and shared diatom ASV pools in Roscoff and Naples A total of 2,920 eukaryotic ASVs for 5,037,761 reads and 5,805 eukaryotic ASVs for 12,294,614 reads were retrieved for the SOMLIT-Astan station (Roscoff) and the LTER-MC station (Naples), respectively. Among these, 178 ASVs representing 924,969 reads and 235 ASVs representing 1,511,092 reads were assigned to diatoms for Roscoff and Naples, respectively. The Bacillariophyta represented 6. 1 and 4. 0 % of total ASVs richness in Roscoff and Naples, respectively. Altogether, our dataset contained 338 distinct diatom ASVs detected either in Roscoff or Naples and assigned to diatoms. As expected in habitats as different as those represented by the SOMLIT-Astan station and LTER-MC station, a large proportion (78%) of the diatom ASVs detected along the years 2011 to 2013 were unique to either one or the other site (103 and 160 unique ASVs, i.
, 1995; Caracciolo et al. , 2022). The relative high contribution of benthic and tychopelagic species to winter communities is also a distinctive feature of the SOMLIT-Astan station  (Guilloux et al. , 2013 , Caracciolo et al. , 2022) , and more generally of tidally-mixed coastal habitats  (Hernandez-Farinas et al. , 2017). Previous direct comparisons of individual samples (1 sample from SOMLIT-Astan and 2 samples from LTER-MC from the water column, and 1 sediment samples from each site) produced using HTS  (Nanjappa et al. , 2014 , Piredda et al. , 2018)  had also pointed to the marked individuality of these geographically distant sites. Our analysis of genetic datasets collected over 3 annual cycles which takes into account the highly seasonal character of plankton communities in temperate environments probably provides a more accurate image of the shared and unique lists of taxa/genetic types at the 2 sites.
The absence of ASVs in either one or the other site is always extremely difficult to interpret. It is also the case in our study based on datasets obtained from samples collected along several annual cycles. In our case, in addition, size fractions analysed differed between the 2 sites since plankton was collected onto 3 and 1. 2 µm filters at SOMLIT-Astan and LETR-MC, respectively. The dataset from LTER-MC is thus probably enriched in sequences of pico-and nanoplanktonic species compared to that of SOMLIT-Astan. Yet, because cells smaller than the pore size, but also nucleic acids from broken cells of all sizes are always trapped on filters or stick to larger cells or particles, size fractionationation is never perfect  (Chaffron et al. , 2021; Caracciolo et al. 2022). The number of samples studied also differed between the 2 sites (69 and 40 samples examined in total for SOMLIT-Astan and LTER-MC, respectively).
Hence, the fact that the total number of ASVs only found in LTER-MC (160 ASVs) was higher than in SOMLIT-Astan (103 ASVs) is difficult to interpret. One explanation would have been that the number of reads per sample is much higher for LTER-MC. After random subsampling at 24,974 reads, however, this difference is still observed with 152 and 103 ASVs for LTER-MC and SOMLIT-Astan respectively. Also, two different reverse primers were used for the PCR amplification steps for the 2 time-series stations (see material and methods) which probably led to differences in the overall relative abundances between the eukaryotic supergroups. In the future, analyses of datasets that are currently obtained on the long-term in a more concerted manner at time-series stations installed in different biogeographic regions and habitats will probably allow us to progress concerning questions regarding the ubiquity or restricted distribution of microscopic organisms.
In fact, if  Cermeno and Falkowski (2009, 2010)  have elegantly shown that the "Everything is everywhere but the environment select '' famous statement fits with diatoms fossil records, doubts are still remaining. One of the reasons relates to the fact that cryptic species are common among diatom taxa, and in particular for the smallest ones. In this study, one of the most important advantages of using datasets collected over several annual cycles was that it allowed us to determine pools of core ASVs (those present in > 30% of the samples examined) which can be considered as characteristic of the 2 respective habitats. A relatively high percentage of these ASVs (40%) that were core in either one or the other sites, were actually shared between the 2 sites. Pools of species and or genetic variants identified as core fitted with published lists of diatoms in the 2 sites, and included pelagic genera that are most common along the coasts of Europe (such as Thalassiosira, Chaetoceros, Guinardia and Pseudo-nitzschia, for which both shared and non shared ASVs were identified).
The synchronic variations in read contribution to total counts in such different and geographically distant sites could indicate that internal controls of growth rate and sexual reproduction, rather that external environmental parameters are at work  (Cianelli et al. , 2017; D'Alelio et al. , 2010; Jewson et al. , 2016; Longobardi et al. 2022). The 4 other species, identified as core and abundant in the 2 sites, may be more adapted to respond to natural environmental variability, and take advantage, for example, of pulses of nutrients. To conclude, this study illustrates the importance of time-series stations and of the associated datasets produced in the long term for ecological studies. It also illustrates the importance of reference databases such as PR2 and EukRibo. Here, the final assignments of diatom ASVs used the manually curated EukRibo database.
Acknowledgments : The authors would like to thank the captains and crew of the Neomysis research ship for their help during sampling at the  SOMLIT-Astan station. They also thank  A. Passarelli ,  F. Tramontano ,  M. Cannavacciuolo , and  G. Zazo  ( SZN ) and all the LTER-MC team for collaboration in sampling and data production, and the crew of the  R/V Vettoria  for assistance during the work at sea. We are grateful to the  RCC  for the maintenance of phytoplankton strains isolated from this station that served for the assignment of some of the genetic sequences. The Electron Microscopy facility of the SZN that helped with the analysis of diatom frustules is acknowledged. This study was supported by a PhD fellowship from  Sorbonne University  to MC, the  French government research agency programs CALYPSO  ( ANR-15-CE01-0009 ),  BIOMARKS ("Investissement d'avenir ",  ANR-08-BDVA-0003 ) and  OCEANOMICS  ( ANR-11-BTBR-0008 ), the  CNRS-INSU EC2CO CYCLOBS research project grant , the  French Biodiversity Agency   INDIGENE  project (grant number  OFB.
20. 0552 ), the  Gordon and Betty Moore Foundation  through the  UniEuk grants   GBMF5275  and  GBMF8908  and the  European Union program Assemble+ (Horizon 2020 , grant agreement number  287589 ). We are grateful to the  Roscoff Bioinformatics platform ABiMS  (http://abims. sb-roscoff. fr),part of the  Institut Français de Bioinformatique  ( ANR-11-INBS-0013 ) and  BioGenouest network , for providing computing and storage resources.
Little is known about aquatic invertebrate biogeography in the Caribbean. • We present the first database of aquatic insect collections made in the Caribbean archipelago over the last 50 years. • 2/3 of the aquatic insects recorded were endemic. • Continuous one-threshold models fitted the overall, Odonata and Coleoptera species richness • Discontinuous two-threshold models fitted the Trichoptera and Ephemeroptera species richness • Geographical and local climate variables and dispersal abilities combine together for explaining the richness and composition of the Caribbean aquatic entomofauna.