dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization of the IGK Genes Briefly, 144 genes and 214 alleles of the IGK locus have been annotated and integrated into the IMGT ® databases. Among the 144 genes, we have found for the IGKV: 76 F, six ORF, 47 P and nine genes which have alleles with different functionalities (Functional or ORF: IGKV2-86; Functional or pseudogene: IGKV1-25, IGKV1-37, IGKV1-46, IGKV2-7, IGKV2-58, IGKV2-61, IGKV2-62, IGKV7-13) (Table  2 ); for the IGKJ genes, five are F and the only IGKC gene is F. The subgroups IGKV1 and IGKV2 are the most represented (≥50 genes for each subgroup)  [73]. The IGKV1 subgroup has the greatest number of F genes with 39 genes out of 56, followed by the IGKV2 subgroup with 21 F out of 50 (Table  12 ). The IGKV3 subgroup has 11 F genes out of 19. In contrast, the least represented subgroups are IGKV4, IGKV5, IGKV6, and IGKV7 with respectively four, two, four, and one genes.
The two genes of the IGKV5 subgroup are F. The IGKV7 subgroup comprises only one gene: IGKV7-13 which has two alleles with different functionalities (functional or pseudogene). In addition to the seven subgroups, the IGK locus also has one clan IGKV(II) which comprises two truncated pseudogenes. Finally, the IMGT ® databases count 138 IGKV genes and 206 alleles. All the five J genes of the IGK locus are F. For now, one allele of each IGKJ gene has been annotated (Table  13 ). The only IGKC is F and currently two alleles have been described. Moreover, 43 additional (21 F, two ORF, 20 P) IGKV genes have been annotated from the IMGT ® reference sequence IMGT000063 (Mmul_10) compared to the IGK locus sequence NW_001099007 (Mmul_05121). However, four missing IGKV genes on the sequence IMGT000063 (IGKV2-13-1, IGKV2-13-2, IGKV1-25-1, and IGKV3-26-1) were found in the accession number NW_001099007.
This will lead to the description of haplotypes taking into account the localization and the relative order of the genes in each locus in order to highlight new allelic polymorphisms and/or structural variations. This will also lead to more accurate gene assignments and calculations of somatic hypermutation (SHM) in this species. Extreme caution should be taken to correctly identify this genetic diversity. Taking advantage of the IMGT biocuration pipeline, the IG germline repertoire and the IMGT ® reference directories were established according to IMGT ® nomenclature. The annotation of sequences, genes and structural data were integrated in the IMGT ® databases, tools and web resources. As a result of this effort, 597 IG genes and 908 IG alleles have been integrated into IMGT ®. Despite the high similarity of the human and rhesus monkey genome, some differences have also been observed.
Indeed, the genomic organization and characterization of IG genes highlighted that, based on the assembly Mmul_10 of the rhesus monkey and the data available on the IMGT/GENE-DB, the rhesus monkey has more V genes than human (excluding orphons). For example, the IMGT/GENE-DB contains 228 IGHV genes for rhesus monkeys versus 162 IGHV genes for humans; 149 IGLV genes for rhesus monkeys versus 79 IGLV genes for humans; 138 IGKV genes for rhesus monkeys versus 77 IGKV genes for humans. Consequently, this difference has an impact on the number of functional genes found in these species, 86 F IGHV genes for rhesus monkeys versus 57 F IGHV genes for humans; 73 F IGLV genes for rhesus monkeys versus 33 F IGLV genes for humans; 85 F IGKV genes for rhesus monkeys versus 41 F IGKV genes for humans. It would be interesting to study the genomic organization and characterization of IG genes in the other available Macaca mulatta assemblies, as well as in the genome of additional nonhuman primates to determine the proportion of genes for each species and track their evolution.
If not, heptamers with at most one mutation and nonamers with at most two mutations compared with the corresponding consensus sequences in the locus are also considered as canonical. However, there are also exceptions where the heptamer and nonamer do not obey this rule and are found rearranged in cDNAs available in IMGT/LIGM-DB and generalist nucleotide databases (GenBank and ENA). For example, the J-HEPTAMER of the gene IGHJ2 ('ggctgtg' instead of 'cactgtg' or 'caatgtg') has been found rearranged in 159 cDNAs in IMGT/LIGM-DB  [39]  and the V-HEPTAMER of the allele IGLV8-125*01 ('cacggcg' instead of 'cacagtg') has also been found in cDNAs. Interestingly, the allele IGLV3-16*01, which is a pseudogene because there is no INIT-CODON (https: //www. imgt. org/ligmdb/view?id=IMGT000062, accessed on 11 November 2021), is found rearranged in the cDNA accession number KCWN01002936.
It is observed that their sequences, in our dataset, are highly similar to the consensus sequences which were previously identified in experimental studies of eukaryotic species  [80, 81]. Although they are well aligned with these consensuses, in some cases, nucleotide alterations appear in their motifs, which provide uniqueness to the subgroup they are identified in. These two elements seem to remain conserved, to ensure a high rate of transcriptional activation, acting synergistically to the octamer or decamer elements  [78, 79]. However, the precise mechanism underlying this synergism remains to be elucidated for the Macaca mulatta. Our IG promoter analysis revealed additional regulatory elements in the 5 flanking sequences of immunoglobulin V genes, which could increase the rate of transcription  [78].
Acknowledgments: We are grateful to  Gérard Lefranc  for helpful discussions, to the IMGT ® team for their expertise and constant motivation and to  Emilie Carillon ,  Saida Saljoqi  and  Nathalie Bosc  for the previous annotation of the Macaca mulatta IG loci. IMGT ® is a registered trademark of CNRS. IMGT ® is a member of the  International Medical Informatics Association (IMIA) ; the  Global Alliance for Genomics and Health  ( GA4GH ) and the  Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE). IMGT ® is currently supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI) , the  University of Montpellier , and the  French Infrastructure Institut Français de Bioinformatique (IFB).
Funding: IMGT ® was funded in part by the  BIOMED1  ( BIOCT930038 ), Biotechnology  BIOTECH2  ( BIO4CT960037 ), fifth  PCRDT Quality of Life and Management of Living Resources  ( QLG2-2000-01287 ), and sixth  PCRDT Information Science and Technology (ImmunoGrid ,  FP6 IST-028069 ) programmes of the  European Union (EU). IMGT ® received financial support from the  GIS IBiSA ,  BioCampus Montpellier , the  Région Occitanie (Grand Plateau Technique pour la Recherche (GPTR)) , the  Agence Nationale de la recherche (ANR)  and the  Labex MabImprove  ( ANR-10-LABX-53-01 ). IMGT ® is currently supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI)  and the  University of Montpellier (UM). This work was granted access to the  High Performance Computing (HPC) resources of Meso@LR and of Centre Informatique National de l'Enseignement Supérieur (CINES) , to  Très Grand Centre de Calcul (TGCC) of the Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA)  and to Institut du développement et des ressources en informatique scientifique (IDRIS) under the allocation  036029  ( 2010-2022 ) made by  GENCI  ( Grand Equipement National de Calcul Intensif ).
Cat and PEPut mutations do not affect the metabolome in MC To better characterize the effect of PykA mutations on cellular metabolism in MC, the metabolome of cells hosting a PykA catalytic activity ranging from 3 to 160% (the wild-type strain and mutants pykA GD245/6AA , pykA T278A , pykA T>D , pykA L536A , and pykA T>A which have a PykA activity 3, 4, 10, 53, and 160% of wild-type cells, respectively) was analyzed. For each strain, metabolomes were prepared from 4 independent cultures and analyzed by LC/MS. Untargeted analysis was carried out using XCMS and yielded 1960 and 14335 features in the positive and negative ionization modes, respectively, with automatic compound annotation (Kegg database; Table  S2 ). Principal components analysis (PCA) was conducted on the full data matrix. The PCA score plots (Fig.
3A, B ) showed that Dimension 1 capture most of the variance in both Fig. 2  Ori/ter ratios and PykA catalytic activities in Cat and PEPut mutants. Ori/ter ratios were grouped (color code) according to the Mann-Whitney U test at a significance of p < 0. 01 (see Table  S1  for details). Numbers in brackets refer to the number of independent iterations. The horizontal gray bars highlight the wild-type ratio area. Pyruvate kinase activities (bolded numbers expressed in % of wild-type activity) in crude extracts were determined from at least three independent experiments (SD/means < 10%). A Cat mutant analysis. B PEPut mutant analysis. See Fig. 1  for details modes. In the positive mode, this axis clearly separates the wild-type and pykA T>D samples while the remaining data are mixed into each other.
In the negative mode, pykA GD245/6AA , pykA T278A , pykA L536A , and pykA T>A are better segregated along Dimension 2, while wild-type and pykA T>D remain well separated in Dimension 1, with pykA T>D samples clearly away from the others. A closer inspection of PCA loadings plots identified 14 variables having the largest effect on each component. Of these, six have a biased signal due to poor correction of retention times and three are not significantly affected (fold change <3)  (Stuani et al. 2014 ) as compared to the wild-type strain. Remaining variables pointed to molecules that do not correspond to any compound of the Kegg database or to molecules that are not expected to participate in B. subtilis metabolism. Altogether, this analysis did not detect significant changes in metabolite (including detectable nucleotides) concentrations and uncovered no relationship between score plots and PykA activity.
To further characterized the effect of PykA mutation on the metabolome, a study based on a procedure allowing detection of metabolites with short lifetime was carried out on the wild-type and pykA T>D strains (see Experimental Procedures)  [94]. This analysis confirmed the inference of Fig. 3A ,B and the lack of significant changes in NTP pools (Fig. S3 ). A targeted analysis on PykA substrates and products revealed no significant change in ATP, pyruvate, and ADP concentrations and a 4. 9 lower PEP content in the mutant (Fig. 3C ). The data also uncovered an ion that accumulated in the pykA T>D mutant, annotated as pseudaminic acid (Pse) in the Kegg database (Fig. 3D ). Consistently, another accumulating ion annotated as a derivative of Pse (CMP-Pse) was found in the mutant. Putative Pse was observed mainly in the negative mode at m/z 333.
PEPut is an inhibitor of initiation and Cat, a stimulator of elongation Microscopy studies showed that nucleoids are efficiently segregated in pykA mutants grown in MC (Fig. S5 , top raw). Therefore, a cell cycle analysis was carried out in wild-type cells and pykA mutants using flow cytometry (runout experiments) and qPCR (marker frequency analysis), as described previously  [97]. Representative results are given Fig. S5  (middle and bottom rows), Fig. 4  Primarily replication defect in pykA mutants. Ori/ter ratios were measured in cells replicating the chromosome from a plasmid replicon instead of the natural chromosomal replication origin. See Fig. 1  for details and the complete set of data is summarized in Table  S3. Results showed that PykA mutations can affect replication and cell cycle to remarkably high degrees and confirmed the prime importance of PEPut and Cat in initiation and elongation, respectively.
Replication assays with DnaE were carried out at a polymerase concentration (10 nM) that produces a low amount of replication products in order to facilitate the detection of stimulatory effects. In reaction mixtures containing M13 ssDNA annealed to a 20-mer DNA oligonucleotide and DnaE in combination or not with equimolar concentrations of PykA, a substantial stimulation of DnaE polymerase activity by PykA was found in terms of both sizes and amounts of nascent DNA synthesized (Fig. 7A , left panel). A similar stimulation was observed with a 60-mer oligonucleotide annealed onto M13 ssDNA and a 15-mer oligonucleotide annealed onto a 110-mer oligonucleotide (Fig. S9A  and data not shown). The stimulation was specific to PykA as it was not observed with equimolar amounts of BSA and M13 ssDNA primed with a 20-mer (Fig.
Cells treated with paraformaldehyde were incubated 10 min at 4 °C. Samples treated or not with paraformaldehyde were then stained with DAPI, collected, and resuspended in 10 μL MC medium as above. Microscopy was carried out using an epifluorescence microscope (Zeiss, Axio Observer. Z1) with a × 100 magnification oil-immersion objective (Plan-APOCHROMAT Ph3) and a CMOS camera (Orca Flash 4. 0 LT Hamamatsu). Digital images were acquired and analyzed using the Zen 2. 6 (blue edition) software.
Metabolomic data processing LC/MS raw data were converted to mzXML using MSConvert 3. 0. 20338 [117]  and processed with R package XCMS  [118, 119]  with an "in-house" modified KEG-GREST (Tenenbaum and Maintainer, 2021) R function for automatic compound annotation. Multicore processing was made possible through R package BiocParallel  (Morgan et al. , 2021). Multivariate analyses were conducted using muma for data pretreatment (pareto scaling) and FactominR for PCA visualization  [120, 121]. Metabolite identification was aided by MS 2 interpretation and retention time matching to commercial reference standards. Metabolomics raw data are available in MetaboLights (https:// www. ebi. ac. uk/ metab oligh ts/ MTBLS 4415)  [122].
Helicase assays Briefly, DnaC helicase assays were carried out with 633 nM (referring to monomer) each DnaC and DnaI, in 50 mM NaCl, 50 mM Tris-HCl pH 7. 5, 10 mM MgCl 2 , 1 mM DTT, 2. 5 mM ATP, and 2 nM DNA substrate, in the presence and absence of DnaG (313 nM) and/or PykA (633 nM monomer). The DNA substrate was constructed by annealing a 5′-32 P radioactively labelled 100mer oligonucleotide (5′-CAC ACA CAC ACA CAC ACA CAC ACA CAC ACA CAC ACA CAC ACA CAC ACA CAC ACA CAC ACC CCT TTA AAA AAA AAA AAA AAA GCC AAA AGC AGT GCC AAG CTT GCA TGC C-3′) onto M13 ssDNA. The helicase activity was assayed by monitoring the displacement of the radioactive oligonucleotide from the M13 ssDNA through non-denaturing PAGE using 10% w/v polyacrylamide gels. Data were quantified using a Personal Molecular Imager with Quantity One 1-D analysis software (Bio-Rad) and analyzed with GraphPad Prism 4 software.
Polymerase assays Time course polymerase assays were carried out by monitoring the DnaE primer extension activity using a 5′-32 P radioactively labelled 20mer (5′-CAG TGC CAA GCT TGC ATG CC-3′) or 60mer (5′-CAG TGC CAA GCT TGC ATG CCT GCA GGT CGA CTC TAG AGG ATC CCC GGG TAC CGA GCT CGA -3′) annealed to M13 ssDNA substrates (2 nM), in 50 mM Tris-HCl pH 7. 5, 50 mM NaCl, 10 mM MgCl 2 , 1 mM DTT, 1 mM dNTPs, and 10 nM DnaE in the presence or absence of 40 nM PykA tetramer or 40 nM BSA. In some reactions with a lower, suboptimal 2 nM DnaE, 8 nM PykA tetramer was used. Nascent DNA was resolved through alkaline gel electrophoresis, as described before  [43, 49]. Visualization and quantification were carried out using a Personal Molecular Imager with Quantity One 1-D analysis software (Bio-Rad) and data were analyzed with GraphPad Prism 4 software.
05, 0. 1, 0. 25, 0. 5, 0. 75, 1, 2, 4, 8, and 10 mM). Data were analyzed using GraphPad Prism 4 software to plot a Hill plot and a Michaelis-Menten plot from where V max , K m , and n (the Hill coefficient) were calculated using GraphPad Prism 4 software.
Funding This work was supported by the  Biotechnology Biological Sciences Research Council (BBSRC)  grant ( BB/R013357/1 ) to P. S. , a  BBSRC sub-contract  ( RIS 1165589 ) to L. J. and by recurring research funds from  CNRS  ( Centre National de la Recherche Scientifique ) and  UEVE (Université d'Evry Val d'Essonne)  to L. J. S. H. was funded by a PhD fellowship from the  MESR (Ministère de l'Enseignement Supérieur et de la Recherche ; ED SDSV,  Université Paris-Saclay, Université d'Evry Val d'Essonne ). M. P. is a PhD student partially funded by a  Vice Chancellor's Excellence Award  at the  University of Nottingham. L. J. is on the CNRS staff. Funders had no role in the design of the study, the analysis and interpretation of data, and in the manuscript writing.
We use hereafter the International Thermodynamic Equations of Seawater (TEOS-10) framework  (McDougall & Barker, 2011)  with Conservative Temperature Θ (°C) and Absolute Salinity S A (g kg -1 ). Glider data were processed using the SOCIB toolbox (https://github. com/socib/glidertoolbox). Thermal lag issues of the GPCTD probe were corrected by applying the method of  Garau et al. (2011). Although thermal lag corrections were applied, the thermocline and halocline gradients were so large that some differences remained between the up and down profiles in these layers in the salinity and related parameters (dissolved oxygen and chlorophyll a). The density was then despiked and interpolated, and salinity was recalculated from the temperature and the corrected density. For some profiles, the salinity measurements did not pass the quality control, which is why there is no salinity.
Data From a Research Vessel Complementary hydrographic data were collected during the A-TWAIN 2017 research cruise onboard R/V Lance (14 September to 26 September 2017). 13 CTD stations were performed on 24 September 2017 along a section above the southern YP, the southern part of the section (pale pink dots in Figure  1b ) heading northwest and the northern parts (olive dots in Figure  1b ) heading north and joining the glider trajectory. The ship-board CTD was 4 of 13 a Sea-Bird Electronics SBE911+. Pressure, temperature, and practical salinity data are accurate to ±0. 5 dbar, ±2 × 10 -3 °C, and ±3 × 10 -3 , respectively. One 300 kHz RDI Workhorse (Teledyne RD Instrument (RDI) downward oriented) Lowered Acoustic Doppler Current Profiler (LADCP) was attached to the CTD frame to measure the zonal and meridional components of the velocity through the water column at each station with a 8-m vertical bin-size resolution.
, 2021). This model allows for higher spatial resolution north of Svalbard and more accurate wind reanalysis than the ERA5 reanalysis data set  (Haakenstad et al. , 2021). Sea ice concentration estimates are from AMSR-2 (Advanced Microwave Scanning Radiometer), derived from brightness temperatures, with a 3. 125 × 3. 125 km spatial resolution. They are recovered from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF, www. osi-saf. org). The sea ice edge is defined as the 15% sea ice concentration contour.
Conclusion We described the AW circulation and properties north of Svalbard in September 2017 combining ocean glider data and hydrographic observations. Part of the AW pathway over the YP was anomalous and several cold freshwater lenses were observed detaching from the Svalbard Shelf. The sampling was performed after a period of southerly winds, an unusual situation for the region and the season. Over the 2010-2020 period, most of the southerly wind events occurred in winter and were associated with polar lows  (Woods & Caballero, 2016). Strong southerly winds with a meridional component larger than 8 m s -1 represent less than 5% in that 2010-2020 period. However, the year 2017 was peculiar with southerly winds twice more frequent than during the reference 2010-2020 period. Concurrent to these southerly wind conditions, we observed widespread cold freshwater lenses north and west of Svalbard that had detached from the shelf.
Statistical Analysis One-way ANOVAs were performed to determine differences among the three sites studied in the environmental parameters as well as in oxygen and nutrient fluxes measured on each component (i. e. seagrasses, sediment, benthic community) in both light and darkness. The significance level was set at a = 0. 05. Data were transformed (square-root) when necessary to meet parametric conditions. If -parametric conditions were not meet after transformation, a non-parametric Kruskal-Wallis test was applied. To link the biogeochemical descriptors and the oxygen fluxes measured on each component of the lagoon, a Spearman's Rank correlation analysis was conducted considering the normalized mean values at each station (i. e. n=3 ranks, station A, B and C) using the IBM SPSS software. Lineal regression model was run between nutrient fluxes and initial nutrients concentration.
Discussion This study provides the first metabolic dataset for the seagrass Syringodium isoetifolium in the West Indian Ocean. The seagrass community in the Reunion lagoon was net autotrophic and contributed to the lagoon benthic metabolism. Large variability among sites was detected in the metabolic balance of each one of the benthic components assessed (i. e. seagrass, sediment, community) also associated with the natural and/or anthropic-driven environmental variability found in the lagoon. The metabolic balance of the benthic compartment in Reunion was slightly heterotrophic but highly variable. The high productivity and autotrophic metabolic balance of S. isoetifolium helped decreasing the sediment net heterotrophy. No significant nutrient inputs were detected in this study and the benthic community showed potential capacity for maintaining the lagoon oligotrophic condition via nitrogen retention (uptake by microphytobenthos and seagrasses) and removal (denitrification and anammox) but other sources of contamination, such as organic CECs, may potentially affect the benthic community and metabolism in the lagoon and should be considered in the future studies.
Acknowledgments Authors thank the  ENVRIplus  project for providing TransNational Access (TNA) to OSU-Réunion stations -MACRORE project. IO was supported by a postdoctoral grant ( H2020-MSCA-IF-2016-752250 ). EG-R is supported by the  Spanish Ramon y Cajal Program  ( RYC2019-027675-I ). The authors thank  Sophie Bureau , for her valuable help and her technical support in the field.
Funding This research was supported by funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements No  654182  ( ENVRIplus  project,  TNA-MACRORE ) and No  752250  ( Marie Skłodowska-Curie grant  scheme  MSCA-IF-EF-ST ,  SEAMET ). This study received funding from the  Integrative Marine Ecology Department  from  Stazione Zoologica Anton Dohrn ; from the  2014-2020   European Regional Development Fund (ERDF)   Operational Programme ; the  Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (Spain)  ( FEDER-UCA18-107225 ); from the  Portuguese national  funds from  FCT -Foundation for Science and Technology  through projects  UIDB/ 04326/2020 ,  UIDP/04326/2020  and  LA/P/0101/2020.
M. S. P. and S. T. acknowledge funding of a Leibniz-Junior Research Group (project number J94/2020) via the Leibniz Competition, M. S. P. also thanks the Klaus Tschira Stiftung and German Scholars Organization for support via a KT Boost Fund. M. S. P. and B. F. acknowledge support from the Partenariat Hubert Curien (PHC) for PROCOPE project 44677UE and the Deutscher Akademischer Austauschdienst for PPP grant 57512596 funded by the Bundesministerium fur Bildung und Forschung. P. A. O. , B. F. , and R. I. acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 834148) and from the Agence Nationale de la Recherche (ANR projects ANR-18-CE31-0006 and ANR-19-CE31-0017). This research has made use of NASA's Astrophysics Data System.