dataesr/scientific-paragraphs-categorization huggingface dataset:
• Kegg: no result. The IMG-M and MG-RAST servers  [299, 300]  and the commercially available QIAGEN CLC Microbial Genomics Module are convenient tools which integrate data resulting from the interrogation of these different databases. They make it possible to quantify and compare the abundance of the main functional families in the target ecosystems. For more accurate function prediction, especially for substrate specificity or mechanism prediction of an enzyme, sequence analysis can be complemented with local genomic neighborhood analysis. Indeed, the in silico analysis of polysaccharide utilization loci (PUL) composition, and thus, of the combinations of CAZymes involved in glycoside catabolism, can be a good way to predict the substrate targeted by a Bacteroidota PUL  [301] , and by each of its CAZyme components.
This helped to discover the biological function of Uhgb_MP (for 'unknown human gut bacterium_mannoside-phosphorylase'), the first GP issued from an uncultured bacterium  [109] , thanks to the cloning of entire metagenomic PULs in fosmids  [286]. More recently, this strategy permitted elucidating the function of a novel sucrose-6 F -phosphate-phosphorylase from the cultured human gut bacterium Ruminococcus gnavus E1  [44]. Nevertheless, such a strategy is restricted to long DNA sequences, and cannot be used with highly fragmented loci sequences, such as those found in datasets obtained by massive and random sequencing of metagenomes. Moreover, when multiple uncharacterized GPs from the same family are found together on the same PUL, predicting the substrate nature is very difficult, making the results uncomplete.
This structure-sequence-function data integration permitted focusing biochemical characterization efforts onto five cluster representatives, to demonstrate the segregation of hydrolases and phosphorylases at the cluster level, and identify new functions, such as the GH130 Man-GlcA phosphorylase  [112]. Finally, we can report the discovery of a new GP activity among GH94 enzymes using an SSN based strategy, 4-O-β-D-glucosyl-D-galactose phosphorylase  [86]. However, the CAZy database and the publicly available metagenomic sequence datasets remain largely untapped, representing gold mines to be explored with such approaches, with the prospect of novel GP discoveries.
g. , GPs versus GHs or a particular activity. Splitting large datasets into sub-groups of similar sequences with SSNs is also an interesting strategy to decrease computational time and to improve structural alignments, based either on crystallographic protein structures or on tridimensional models. Indeed, SSNs alone cannot be used to predict enzyme mechanism, nor substrate or linkage specificities, which can vary due to differences in a single amino acid. When a sufficient number of enzymes with different mechanisms and linkage specificities have been biochemically characterized, primary sequence analysis can be sufficient to predict the GP or GH mechanism. However, most of the time, SSNs and multiple sequence alignment analysis are insufficient to predict substrate specificities, since discrete differences of the active site topologies between enzymes belonging to the same meta-node can result in different activities.
Funding: This research was funded by the  European Union 's  Horizon 2020 framework  programmes (grant numbers  LEIT-BIO-2015-685474 ,  Metafluidics  and  FNR-2020-SEP-210639118 , RadicalZ) and by the  Agence Nationale de la Recherche  ( ANR-16-CE20-0006 , Oligomet). A. L. was supported by  INSA Toulouse and China Scholarship Council  under the ' CSC-UT/INSA Program '. The APC was funded by the  European Union 's  Horizon 2020 framework programme  (grant number  FNR-2020-SEP-210639118 , RadicalZ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Secondary oxidation volatile compound determination Secondary oxidation volatile compounds were extracted using headspace solid-phase microextraction gas chromatography  (Lebrun et al. , 2008). Each sample was analyzed in triplicate. Samples (1 g) were placed in headspace vials (10 mL) with benzyl butyrate (5 mL/100 mL) as internal standard for semi-quantification. Extraction and analysis were carried out on divinylbenzene/carboxen/polydimethylsiloxane fibers (Supelco, Bellefonte, Pennsylvania, USA), and a tandem gas chromatograph 6890/MSD 5973 system (Agilent Technologies, Palo Alto, USA) with a Gerstel Multipurpose Sampler MPS-2, following the method developed by  Moustiés et al. (2019). Data were analyzed with MassHunter version B. 06. 00 (Agilent Technologies, Palo Alto, USA). Peaks were identified by comparing the obtained mass spectra with those of the NIST 14 database.
Lipid profile determination by thin layer chromatography Neutral lipids were separated by one-dimensional migration using hexane/diethylether/acetic acid (65:35:1, v/v/v). For each sample, 4 and 8 mL of a lipid solution (0. 5 mg/mL) were applied automatically on the thin layer. The equipment used was a Camag automatic TLC sampler 4 (Muttenz, Switzerland) controlled by the Wincats software system (2008). After migration, plates (HPTLC 60F254, 20 Â 10 cm; Merck, Darmstadt, Germany) were dried at 70 °C for 2 min, and then lipids revealed with a solution of 15. 9% copper sulfate in orthophosphoric acid/water (92:8, v/v). Then, plates were scanned by a laser UV detection system (Camag TLC scanner 3, Muttenz, Switzerland). The wavelength of maximum absorption was 500 nm and the spots were quantified by integration of the spot intensities with the Wincats software  (2008).
Diverse classes represent the numerous CAZyme families: glycoside hydrolases (GH) depolymerize carbohydrate substrates by hydrolysing glycosidic linkages; glycosyltransferases (GT) lead the formation of glycosidic bonds; carbohydrate esterases (CE) catalyze the hydrolysis of carbohydrate esters; and polysaccharide lyases (PL) operate the non-hydrolytic cleavage of glycosidic bonds. In addition, enzymes with auxiliary activities, including redox enzymes and carbohydrate-binding modules (CBM), are also involved in carbohydrate metabolism  [56]. Functional prediction based on bacterial genomes revealed that GHs are the most representative CAZymes in the human gut microbiome  [50]. The exploration of GH diversity highlighted the number of families increasing steadily, and as of March 2022, 173 sequence-based families of GHs have been defined in the continuously updated CAZy database  [55].
In the intestinal environment, the products of carbohydrate breakdown can be either slotted into primary metabolic pathways, which could be called selfish behaviour, or act for the public good. These latter show cooperative behaviour, where polymers and hydrolytic enzymes can simply diffuse or can be shared in outer membrane vesicles (OMVs). P, phosphorus; H + , hydrogen ions; SusA-SusD, SusG, SusR, SUS homologs; TonB, ExbB, ExbD, outer membrane receptors. The development of automatic bioinformatics by the CAZy team has fuelled the PUL database, which catalogues all experimentally characterised PUL from Bacteroidetes species  [57, 64]. Along with genomic, transcriptomic, structural biology, and protein biochemistry studies, a tremendous diversity of PUL, targeting specific plant, algal, animal, and microbial glycans, has been identified and functionally characterised.
Satellite salinity shows a contrasted spatial structure with eddies and filaments (Fig. 1a ). In this study, we take advantage of the physical and biogeochemical data collected during the EUREC 4 A-OA/ATOMIC (Elucidating the Role of Clouds-Circulation Coupling in Climate Ocean-Atmosphere and Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign) experiment in January-February 2020, combined with satellite data, to understand how the NBC rings and their related structures impact the air-sea CO 2 flux in winter. The paper is organized as follows. We present the in situ observational data from the EUREC 4 A-OA/ATOMIC experiment, as well as the satellite data, in Sect. 2. We identify the water masses observed in the region and their physical and biogeochemical properties and estimate the CO 2 fluxes at regional scale using empirical relationships in Sect.
Surface water mass identification In an effort to understand how biogeochemistry is forced by physical processes in the ocean, we used surface chl a to complement SST and SSS data in defining surface water masses. We observed that in the northwestern tropical Atlantic in winter in situ f CO 2 strongly depends on these three variables (Figs. 4  and 5 ). There is a strong positive dependence of f CO 2 on SSS, with low f CO 2 for low SSS (Fig. 5c  and d ). Across the whole EUREC 4 A-OA/ATOMIC region, SST did not vary much (mean SST of 27 • C and standard deviation of 0. 5 • C), but warmer waters present higher f CO 2. The dependence on chl a allows for the discrimination of water masses with the same surface T and S properties but not the same f CO 2 (Fig. 5 ). Satellite-based chl a is hard to discriminate from detrital material using ocean colour where both are present as they have close spectral characteristics.
Acknowledgements. This research has been supported by the  European Research Council (ERC)  advanced grant  EUREC 4 A  (grant agreement no. 694768 ) under the  European Union 's  Horizon 2020 research and innovation programme  ( H2020 ), with additional support from  CNES  (the  French National Centre for Space Studies)  through the  TOSCA SMOS-Ocean, TOEddies , and  EUREC 4 A-OA proposals , the  French national programme LEFE INSU ,  IFREMER , the  French research fleet , the  French research infrastructures AERIS  and  ODATIS ,  IPSL , the  Chaire Chanel programme  of the  Geosciences Department at ENS , and the  EUREC 4 A-OA JPI Ocean and Climate programme. Léa Olivier  was supported by a scholarship from  ENS  and  Sorbonne Université. We thank  Jonathan Fin  at the  Service National d'Analyse des paramètres Océaniques du CO 2  ( SNAPO-CO 2  ) at  LOCEAN  for the analysis of DIC and TA samples, as well as  François Baurand  at the  US IMAGO  for the nutrient analysis.
Code availability. Code used in this study can be made available upon reasonable request to the corresponding author. Data availability. We benefited from numerous data sets made freely available and listed here: the ADT produced by Ssalto/Duacs distributed by CMEMS (https://resources. marine. copernicus. eu), the chl a and SST maps produced by CLS (https: //datastore. cls. fr/catalogues/chlorophyll-high-resolution-daily urlhttps://datastore. cls. fr/catalogues/chlorophyll-highresolution-daily and https://datastore. cls. fr/catalogues/ sea-surface-temperature-infra-red-high-resolution-daily), the SMOS L2Q field produced by CATDS  (CATDS, 2019)  (https://doi. org/10. 12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0), the SMAP maps produced by Remote Sensing System (RSS v4 40 km), the CCI+SSS maps produced in the frame of the ESA CCI+SSS project (https://doi.
quality control of data on the Ronald H. Brown. We also warmly thank the captain and crew of RVs Atalante, Maria S. Merian and Ronald H. Brown. The measurements on the Ronald H. Brown were supported by the Global Ocean Monitoring and Observation (GOMO) programme (fund Ref. 100007298). support. This research has been supported by the Centre National d'Etudes Spatiales (grant no. 6146) and the European Research Council, H2020 European Research Council (grant no. EU-REC4A (694768)), and Global Ocean Monitoring and Observation (GOMO) programme (fund ref. 100007298). Fresh plume NBC waters Modified NBC NASW SOCAT f CO 2 316. 2 413. 4 385. 8 349 f CO 2 reconstructed from 310. 7 410. 7 392. 9 358. 7 MERIS, Financial SOCAT SST and SSS Transect date Ship name Expocode 2016/01/05 Colibri (France) 35MJ20151229 35MJ20160107 642B20151209 2016/01/08 2015/12/23 Colibri MSC Marianna Benguela Stream 2013/02/10 (France) (Panama) (the Netherlands) 33RO20130108.
Strengthening health promoting education systems Over the years, countries have invested in generating nationally representative and scientifically credible data on behavioural and social determinants of health and nutrition in school age children, such as the global school-based student health and health behaviour in school aged children surveys. However, comparable data on school health and nutrition policies, programmes, and services are lacking. To address this gap, several initiatives are under way. The School Meals Coalition is developing a measurement framework for school meals programmes and will create a global database. Beyond school meals, harmonisation of indicators and data collection systems is needed to monitor integrated school health and nutrition programmes. WHO and Unesco, in consultation with partners, have recently redesigned the global school health policy and practices survey to align with their global standards.
Assessment of Maternal Diet Maternal diet composition and feeding practices were assessed by analyzing: (i) a 24-h recall questionnaire; (ii) a food consumption questionnaire (including feeding practices); (iii) a food security questionnaire at each follow-up visit. The questionnaires are presented in the supplementary data (Table  S1 ). Briefly, the 24-h recall is an interview that gathers all information about all of the food and portions that were consumed the previous day. The food consumption questionnaire collects information on the different food categories that were eaten by the mother the previous day in a closed, standard questionnaire format following the FAO recommendations  [22]. This questionnaire considers meat, poultry, and fish as a joint category. As the literature reports that fatty acid levels are associated with fish consumption, fish was also analyzed separately in these analyses to improve the accuracy of the statistical associations.
Statistical Analyses The questionnaires' data were gathered on the field using REDCap  [31, 34]  electronic data tools that were hosted at Institut Pasteur online platform. The 24-h recall was completed on paper and then translated into a database by a trained nutritionist. Univariate analyses were performed as follows: Spearman's coefficient was used to evaluate the correlation between the continuous variables (WDDS, HFIAS, number of meals, etc. ,) with the nutrient concentration in HM; the Skillings-Mack test was used to analyze the statistical significance of the evolution of the variables over time; a Mann-Whitney test was used to assess the association of the continuous variables with the bivariate variables (food-group consumption and nutrient concentration in HM, nutrient concentration in HM and undernourishment status of the women at the point of delivery, WDDS calculation depending on the evaluation method, e.
g. , 24-h recall or food-frequency questionnaire, etc. ); a Fisher's exact test was used to analyze the statistical significance of the variables with different categories among the groups (HFIAS groups and consumption of food groups, etc. ). Mixed models with a random intercept at the mother's levels were used to evaluate univariate and multivariate analyses of the maternal diet on the different HM nutrients (retinol, lactose, FA, and AA). Results from the 24-h recall were considered for the final multivariate analyses as they accurately reported the real diet of the women. For the multilevel models, only models with statistically significant results are shown. These statistical analyses were performed using Stata MP Software (Stata Corp, College Station, TX, USA). The statistical significance of the p value was set at p < 0.
Acknowledgments: We thank all of the participating women and infants, the  MITICA  Consortium, the  Henri Izamo Maternity staff in Bangui  and the  Institut Pasteur de Bangui  staff for their continuous support in the MITICA study. We warmly thank  Martin Balvers ,  Mark Becker  and  Geritt Witte  for the FA, FAA, TAA, and retinol analytical assessment of HM. We highly appreciate the excellent quantitative HMO analyses as conducted by  Bernadet Blijenberg  and  John Gonsalves. We thank  Pascale Vonaesch  for her contribution to the MITICA study, and  Maria Carmen Collado  for her essential help with the breastmilk sampling and laboratory techniques.
A final melting curve from 65˚C to 95˚C was added to determine amplification specificity. The amplification kinetics of each product were checked at the end of each cycle by measuring the fluorescence derived from the incorporation of EvaGreen into the double-stranded PCR products using the SsoFast EvaGreen Supermix 2X Kit (Bio-Rad, France). The results were analyzed using Bio-Rad CFX Maestro software, version 1. 1 (Bio-Rad, France). RNA were quantified and normalized to the 16S rRNA housekeeping gene. qRT-PCR for each condition were carried out in triplicate. All biological repeats were selected and reported. Amplification efficiencies for each primer pairs were between 75% and 100%. Primer pairs used for qRT-PCR are listed in Table  2.
The peptides were subjected to NSI source followed by tandem mass spectrometry (MS/MS) in Fusion Lumos coupled online to the UPLC. Intact peptides were detected in the Orbitrap at a resolution of 120,000. Peptides were selected for MS/MS using an HCD setting of 30; ion fragments were detected in the Orbitrap at a resolution of 30,000. The electrospray voltage applied was 2. 1 kV. MS1 spectra were obtained with an AGC target of 4E5 ions and a maximum injection time of 50 ms, and targeted MS2 spectra were acquired with an AGC target of 2E5 ions and a maximum injection time of 60 ms. For MS scans, the m/z scan range was 350 to 1800. The resulting MS/MS data were processed and quantified by LFQ (area under the curve) using Proteome Discoverer 2. 4 against an E. coli K12 protein database obtained from Uniprot.
Acknowledgments We thank the members of the  Ezraty group  for comments on the manuscript, advice and discussions. Thanks to  Pr. Dietrich H. Nies  ( Martin-Luther-Universitat Halle-Wittenberg ) for providing CusF plasmids. We also thank  M. Ilbert  ( BIP-CNRS ),  D. Byrne-Kodjabachian  ( IMM-CNRS ) for the circular dichroism experiment, helpful suggestions, reagents and comments on the manuscript. Special thanks to the former  Marseillaise Barras  team (Team Barras 4 ever) and to  Frederic Barras  (now at the  Institut Pasteur) for lab space , support and discussions.
Funding: BE was supported by  Agence Nationale Recherche (ANR)  (# ANR-16-CE11-0012-02 METOXIC ), (# ANR-21-CE44-0024  MetCop) and  Centre National Recherche Scientifique (CNRS)  (# PICS-PROTOX ). CH was supported by  Fondation pour la Recherche Medicale (FRM). GG was supported by  Aix-Marseille Universite(AMidex-Post-doc). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical analysis All experimental analyses were performed in triplicate and the results were expressed as mean AE standard deviation. Mean differences were analysed by one-way analysis of variance (ANOVA) and Duncan's multiple comparison test using SPSS 20. 0 software (IBM, Armonk, NY, USA) at the significance levels of p < 0. 01. The same software was used to run Pearson's correlation analysis at the significance levels of p < 0. 01 and 0. 05.
Study design This was an observational, exploratory study. Study participants underwent a visit consisting of a research imaging session ( 1 H and 23 Na MRI of either the right or left leg), demographic and clinical data collection and blood biochemistry. HD patients were scanned on the day following their last HD treatment, whenever feasible. Outcomes were assessed retrospectively by clinical chart review. The study received approval from the Western University Human Research Ethics board and was conducted in compliance with the declaration of Helsinki and all applicable regulatory requirements. This study was prospectively registered (ClinicalTrials. gov Identifier: NCT03004547).
3 cm 2 ; and total acquisition time: ∼30 min. During the same imaging session, additional axial 1 H MR images were acquired using a standard (spoiled gradient-recalled echo) pulse sequence to identify and delineate the relevant anatomical structures. 23 Na concentration maps were generated using custom software developed within MATLAB, version 9. 6. 0-R2019a (The MathWorks Inc. , Natick, MA, USA). A region of interest encompassing the whole skin (skin [Na + ]) was manually segmented after superimposing 1 H and 23 Na images, as detailed previously  [10].
Study Population Participants attending the fourth visit of the longitudinal familial cohort STANISLAS (Suivi Temporaire Annuel Non-Invasif de la Santé des Lorrains Assurés Sociaux)  20  were included in this study. The STANISLAS cohort is a population-based study of 1006 families that each comprise 2 parents and at least 2 children (4295 participants) from the Lorraine region (eastern France) recruited during 1993-1995 at the Center for Preventive Medicine. The participants were of French origin and free of acute or chronic disease. 1705 participants underwent the fourth examination from 2011 to 2016. After exclusion of participants with missing information on food intake (n=10), without subclinical organ damage assessment data (n=56), cardiovascular disease history (n=26), and genetic data (n=86) as well as those with a daily energy intake either below 1000 or above 5000 kcal (n=63), the cross-sectional study population who underwent at the fourth examination consisted of 1464 participants.
PUFA and dietary intake assessment Dietary intake was assessed using a validated food frequency questionnaire (FFQ) as previously described  21, 22. In brief, the participants reported their consumption frequency and portion size of 133 food items over the previous 3 months. The consumption frequency was reported using 6 levels in the questionnaire, ranging from "never or rarely" to "twice or more a day. " The portion size of each food item was estimated using standard serving sizes and food models. PUFAs intake, i. e. DHA, EPA, docosapentaenoic acid (DPA), linoleic acid (LA), alpha linolenic acid (ALA) and AA were estimated from all the items in a scale of grams per day by multiplying the consumption frequency of each item by the nutrient content of selected portions. Nutritional data were extracted from the French food composition database established by the French Data Centre on Food Quality (Ciqual, last updated in 2013).
dssp-uliege. be/FFQ). Sources of FundingMagnusBäck is an awardee of a Gutenberg Chair of Excellence from the Région Grand Est and the Eurométropole de Strasbourg (France) and supported by the Swedish Research Council (grant number 2019-01486). The STANISLAS study is sponsored by the Nancy CHRU. This work is supported by the French Ministry of Health "Programme Hospitalier de Recherche Clinique Inter regional 2013", by the Contrat de Plan Etat-Lorraine and FEDER Lorraine, and a public grant overseen by the French National Research Agency (ANR) as part of the second "Investissements d'Avenir" program FIGHT-HF (reference: ANR-15-RHU-0004) and by the French PIA project "Lorraine Université d'Excellence", reference ANR-15-IDEX-04-LUE, by FEDER Lorraine and CPER IT2MP. It is also supported by FOCUS-MR (reference: ANR-15-CE14-0032-01), ERA-CVD EXPERT (reference: ANR-16-ECVD-0002-02), and the Fondation de Recherche en Hypertension Artérielle.