dataesr/scientific-paragraphs-categorization huggingface dataset:
Henrissat  (Lombard et al. 2014). Thirty-two sequences of GH1, GH3, GH5, GH8, GH11, GH16, GH26, GH30, GH31, GH32, GH42, GH43, GH51, GH53, GH68, GH73 were linked to the degradation of hemicellulose, 4 sequences of GH5, GH9, GH12, GH48 for cellulose metabolism, 1 sequence (GH5) either for hemicellulose or cellulose degradation, and 11 sequences of GH (GH28, GH105) and PL (PL1, PL3, PL9, PL11, PL2) were attributed to pectin degradation (Table  2 ). Four sequences corresponded to enzymes (GH1, GH4) involved in glycolytic pathways. The remaining sequences were mainly related to the hydrolysis of starch and peptidoglycan (not shown). The accession numbers correspond to the CDS sequences annotated with Prokka as indicated in the manuscript. The corresponding sequences are provided in Figure  S2  Page 11 of 13 379 These predictions first confirmed the experimental observations in this study for the degradation of xylan, lichenan and CMC as the CCMM B940 genome carries glycoside hydrolases from GH5, GH9, GH11 and GH16 families of CAZYmes known to target these substrates.
Acknowledgements Funding for this research was provided by  Belgian Cooperation   FRAB/CCMM2 , the  National Center for Scientific and Technical Research (CNRST)  and the  National Research Institute for Agriculture, Food and Environment (INRAE). Maski Soufiane  is a PhD student at  Faculty of Science, Mohammed V University, Rabat, Morocco. Serigne Inssa Ngom  is a recipient of a grant for doctoral research allocated by the French ministry of higher education and research (doctoral school  569 ,  University of Paris-Saclay ). The authors would like to thank  Mrs Zehra Esra Ilhan  for English improvement of the manuscript.
Populations InterConnect is a European Commission-funded project, which optimises the use of existing data by enabling cross-cohort analyses within consortia without pooling of data at a central location  [24]. For the current research question, 43 studies with information on fish intake and incident type 2 diabetes were invited to join the consortium. These studies were identified by searching published articles on PubMed containing information on type 2 diabetes incidence and dietary fish intake and by reviewing the methodology used in each of the identified cohorts. 28 prospective cohort studies were included in the final collaborative group. Reasons for non-participation of studies varied, including being unable or unwilling to set up a server to allow federated analyses; low priority for the research question; or lacked funding or resources.
We were also able to investigate the association of different types of fish with type 2 diabetes, unlike prior research. Unlike previous literature-based metaanalyses, we harmonised exposures and outcomes and consistently adjusted for the same confounders, which reduced heterogeneity and enhanced comparability across studies. Furthermore, consortia-based individual-level meta-analyses reduce the risk of publication bias; specifically, we included 11 cohorts that had requisite data but had not previously published on this topic. Our federated meta-analysis approach overcame constraints of the physical pooling of data due to governance or ethical and resource issues. Our study had some limitations. Although we adjusted for a range of potential socio-demographic, lifestyle, and dietary confounders, we cannot exclude the possibility of residual confounding due to unmeasured or imprecisely measured factors, which is inherent to observational studies.
Acknowledgments: We would like to thank the participants, principal investigators, and study teams of the individual cohorts included in this collaboration. The work presented herein was made possible using the OBiBa suite (www. obiba. org,accessed on  16 March 2021 ), a software suite developed by  Maelstrom Research  (www. maelstrom-research. org, accessed on  16 March 2021 ), and DataSHIELD (http://www. datashield. ac. uk, accessed on  16 March 2021 ), a software suite developed by the Data to Knowledge ( D2K ) Research Group. We thank EPIC-InterAct collaborators and  Nicola Kerrison  at the  MRC Epidemiology Unit  for assistance relating to the  EPIC-InterAct dataset. We also thank the  AusDiab Steering Committee  for providing data from the AusDiab study.
Data Availability Statement: The project was undertaken using a federated approach in which analyses are performed centrally while data remain within the governance structure of the original studies. Data are held peripherally across multiple research institutions rather than being transferred and stored at a central location. Researchers seeking the analysis dataset for this work should submit requests to the individual studies detailed in the supplementary information. Studies from the USA were downloaded from the BioLINCC repository on September 2016 (https://biolincc. nhlbi. nih. gov/ home/, accessed on 16 March 2021).
Acknowledgements The authors are thankful to  Quentin Dewulf ,  Fanette Chambonnet  and  Anne Loyens  for their great technical help,  Pr Claude Alain Maurage  (the  University of Lille, Histology lab)  for his advice and the  Region Hauts-de-France  and the  BPI Innovation  for their financial supports. The expertise of semen collector  Asif Rehman  is gratefully acknowledged. The authors would like to thank  Dr EG Crichon  for his help with editing the manuscript. This study was sponsored by  H. H. General Sheikh Mohammed  bin  Rashid Al Maktoum ,  Ruler of Dubai.
Child undernutrition is a global health issue associated with a high burden of infectious disease. Undernourished children display an overabundance of intestinal pathogens and pathobionts, and these bacteria induce enteric dysfunction in undernourished mice; however, the cause of their overgrowth remains poorly defined. Here, we show that disease-inducing human isolates of Enterobacteriaceae and Bacteroidales spp. are capable of multi-species symbiotic cross-feeding, resulting in synergistic growth of a mixed community in vitro. Growth synergy occurs uniquely under malnourished conditions limited in protein and iron: in this context, Bacteroidales spp. liberate diet-and mucin-derived sugars and Enterobacteriaceae spp. enhance the bioavailability of iron. Analysis of human microbiota datasets reveals that Bacteroidaceae and Enterobacteriaceae are strongly correlated in undernourished children, but not in adequately nourished children, consistent with a diet-dependent growth synergy in the human gut.
4l ). Together these data identify a large number of possibilities for carbohydrate-based cross-feeding between Bacteroidales and E. coli, and suggest efficient utilization of shared resources in the mixed community. Bacteroidaceae and Enterobacteriaceae are co-abundant in the fecal microbiota of undernourished children. We hypothesized that if the interbacterial synergism between Bacteroidales and Enterobacteriaceae occurs in the human intestine, members of these taxa should be co-abundant in the microbiota of undernourished children, whose diets are typically rich in carbohydrates but poor in sources of animal protein. To test this prediction, we analyzed 16S rRNA sequencing data from fecal samples of children in low-and middle-income countries. Data were retrieved from four studies spanning five countries (Central African Republic, India, Madagascar, Malawi, and Peru) and included publicly available data as well as new sequences generated through the Afribiota study  11, 12, [26] [27] [28].
Children were included for analysis if they were 2-5 years of age and had linear growth measurements available as an outcome. Linear growth stunting, a reflection of chronic undernutrition, was defined by height-for-age z-score as detailed in "Methods". Consistent with our prediction, the relative abundance of the Bacteroidaceae and Enterobacteriaceae families were significantly positively correlated in the microbiota of stunted children, but not of nonstunted children (Table  1  and Fig. 5a ). Moreover, the association between these taxa became progressively stronger with degree of stunting severity (Fig. 5b ), supporting the importance of nutritional status in modulating this association. The correlation between Bacteroidaceae and Enterobacteriaceae in stunted children was consistent in four out of the five countries individually, as well as in the pooled analysis (Table  1 ); the only exception was the dataset from Dinh et al.
are primarily colonic bacteria; thus, the specificity of this relationship to fecal samples likely reflects growth in their preferred niche. At the genus level, Bacteroides were strongly correlated with Escherichia-Shigella in feces in a nutritional status-dependent manner (Fig. 5c, d ). Bacteroides were also strongly correlated with Salmonella in stunted children from the Afribiota dataset (Fig. 5e-h ). Like E. coli, Salmonella has been previously shown to benefit from the presence of B. thetaiotaomicron. We confirmed that, like our E. coli strains, S. enterica Typhimurium benefited from the presence of the full Bacteroidales mix (Supplementary Fig. 7A ) and provided a reciprocal advantage to Bacteroidales (Supplementary Fig. 7B ) in MAL-M media. Salmonella was not detected in the other human studies that we analyzed, and other Enterobacteriaecae that were sporadically detected across studies (Klebsiella, Citrobacter, Enterobacter, and Providencia) were not significantly associated with Bacteroides in any of the datasets.
5e-06, rho = 0. 31), while the slight trend seen in nonstunted children disappeared entirely in children who were neither stunted nor anemic (n = 112, P = 0. 9925, rho = -0. 001). The modulation of this relationship by anemia is consistent with our in vitro observations that iron limitation contributes to codependencies between Bacteroidaceae and Enterobacteriaceae. To further investigate the relationship between Bacteroidaceae and Enterobacteriaceae members at a species and functional level, we gathered publicly available fecal metagenomics data for stunted and nonstunted children 2-5 years old. We identified and processed datasets meeting these criteria from China 29 and Zimbabwe  30. Sample sizes after exclusion of younger children and of children without either Bacteroidaceae or Enterobacteriaceae present in the sample were modest (N = 7 and 13 children, respectively, from China and Zimbabwe).
Encouragingly, however, correlations between Bacteroidaceae and Enterobacteriaceae abundance in this small metagenomics dataset were consistent with those observed previously (Supplementary Table  3 ) and reached significance in stunted children from Zimbabwe and in the combined metagenomics abundance data (Supplementary Table  3 ; N = 20, rho = 0. 47, P = 0. 036). We, therefore, proceeded with an exploratory analysis of species-and gene-level correlations in the pooled metagenomics dataset. At the species level, the Bacteroides most strongly correlated with E. coli abundance were B. ovatus (rho = 0. 48, P = 0. 031) and B. dorei (rho = 0. 44, P = 0. 049). Indeed, as expected based on the family-and genus-level correlations, the majority of Bacteroides species were positively correlated with E.
coli, and many of these species-level associations were consistent between datasets (Supplementary Table  4 ). These correlations should be interpreted with caution due to the small sample sizes; however, they suggest that in complex human microbiotas, B. ovatus and B. dorei cooperate particularly successfully with E. coli. This is consistent with our in vitro experiments in which both of these species showed cooperative individual growth with E. coli. To explore gene-level associations, we binned all genes from the positively correlated Bacteroides species in Supplementary Table  4 , reasoning there may be functional redundancy within this taxonomic group, and searched for any Bacteroides gene that correlated significantly with the abundance of E. coli or Enterobacteriaceae. Interestingly, an iron storage protein from the Bacteroides group was amongst the five genes correlated (raw P value <0.
In the absence of a selective medium for all Bacteroidales species, Bacteroidales growth was quantified by qPCR. In all, 4-8 individual replicates are typically shown per growth assay, as stated in the figure legends for each panel. Each data point represents a single bacterial culture tube; further, each growth assay was repeated at least twice (i. e. , from different seed cultures on a different day) with pooled results from all replicates shown. All media comparison experiments (Fig. 2  and Supplementary Fig. 2 ) were repeated at least three separate times with pooled results shown. The strain-wise replicates shown in Fig. 6  Schematic of the metabolic network between Bacteroidales and Enterobacteriaceae. In the context of a carbohydrate-rich, protein-deficient malnourished diet, Bacteroidales spp.
After the reaction, 20-µL aliquots were injected onto a Phenomenx PFP UPLC column (2. 1 × 150 mm, 1. 7 µm) to run LC-MS/MS on an Agilent 1290 UHPLC system coupled to an Agilent 6495B QQQ mass spectrometer, which was operated in the negative-ion multiple-reaction monitoring (MRM) mode. For LC separation, 0. 1% formic acid in water/methanol (76/24, v/v) was used as the mobile phase at 0. 25 mL/min and 40 °C for isocratic elution over a period of 15 min. The raw data were acquired using the Agilent MassHunter® 7. 0 software. After data acquisitions, linearly regressed calibration curves of individual compounds were constructed with the analyte-to-internal standard peak area ratios measured from the injection of the calibration curves. For those compounds without their isotopelabeling analogs as the internal standards, 13 C 6 -fructose was used a common internal standard.
, 20-30% B in 1 min. In all, 30-40% B in 7 min. , 40-50% in 2 min. , 50-100% in 2 min. , 100% for 1 min. , 100-20 in 1 min. , and 20% for 2 min. Collision energy of 10 V was used for multiple-reaction monitoring (MRM), and LC-MS/MS data were analyzed by Mass Hunter Qualitative Analysis B. 06. 00 software (Agilent Technologies). The identification and quantification of the SCFAs were carried out based on the retention time and mass fragmentation pattern compared with standards. Six-point calibration curves made by peak area vs concentration of the pure standards were used to quantify the different SCFA. The linearity of the curves was determined by the coefficient of determination (R 2 ), being higher than 0. 99 for all standards. Concentrations of the SCFAs were calculated by interpolating the calibration curves of individual compounds.
Taxonomic assignment was performed using a Naive Bayes classifier trained on the SILVA database (v132, 99% OTU sequences, region 515F/806R)  54. For Bacteroides which were not identified to the species level by the SILVA database, the 16S sequence was further queried using BLAST to rationally assign all five BG community members. Filtering at the amplicon sequence variant (ASV) level was performed in R using the phyloseq package  55  , to remove any ASVs that were not assigned to the initial community members (4. 8% of all reads). Taxonomic barplots were then created using ggplot2  56. To calculate the total abundance of each individual Bacteroidales species over time, relative abundances were multiplied by the total Bacteroidales abundance, as determined by 16S Bacteroidetes-specific qPCR, at each time point.
Bioinformatic analysis of human fecal microbiota: 16S amplicon sequencing. 16S rRNA gene sequencing of the V4 region were retrieved from four studies  11, 12, 28, 57  spanning five countries (Madagascar, Central African Republic, India, Malawi, and Peru) and included both publicly available data as well as new sequences available through the Afribiota study. Children were included for analysis if they were 2-5 years of age and had linear growth measurements (height-or length-for-agez-score) available as an outcome. Where longitudinal samples were available, a single sample at 2 years of age was included per child. For Dihn et al. 2016, height-for-age z-score was calculated from available height, age, and gender data using the public World Health Organization tool  58. In all datasets, stunting was defined as HAZ ≤ -2 and nonstunted as HAZ > -2.
All participants received oral and written information about the study and the legal representatives of the children provided written consent to participate in the study  26. The detailed inclusion and exclusion criteria and recruitment procedures of the Afribiota study are described in the published protocol  26. Part of the dataset was previously published and is available at the European Nucleotide Archive (ENA) accession PRJEB27868. New sequencing data analyzed here are deposited at the ENA accession PRJEB48119. To obtain new sequences through the Afribiota study, DNA was extracted with a QiaAmp cador ® Pathogen Mini kit using an additional bead-beating step and library preparation was performed as recommended by Kozich et al. 41  using standard barcoded primers targeting the 16S V4 region (v4.
SA501-v4. SA508 and v4. SA701-v4. SA712). The amplicon library was sequenced on a MiSeq using the MiSeq 500 Cycle V2 Reagent Kit (250 ×2). OTU table was generated using the Dada2 pipelines developed by the Holmes lab with slight modifications (https:// benjjneb. github. io/dada2/ 62 ) and taxonomy was assigned using the Silva Database (version 132). The OTU table was transferred to Huus KE for further analysis. Data from Desai et al. were obtained directly as an OTU table from their publicly available GitHub page (https://github. com/chandni177/ BacterialViralMicrobiome_of_GrowthVelocity_in_EED)  27. Raw sequencing data from Dinh et al. 28  and Rouhani et al. 11  were downloaded from the NCBI Sequence Read Archive (accession codes PRJNA279828 and PRJEB28159 respectively) using SRAtoolkit (v 2.
10. 8) and analyzed in qiime2 using the Deblur error correction option and SILVA taxonomic assignment (99% OTU sequences, region 515F/806R)  52, 60. Further processing of all studies was performed in R, including exclusion of taxa with low fractional abundance (<0. 001%) and rarefaction to 5000 reads. Samples that were missing either taxon (i. e. , zero values) were excluded for correlation analysis, and the relative abundance of nonzero values was log-transformed. Bioinformatic analysis of human fecal microbiota: shotgun sequencing. Shotgun metagenomics sequencing data of the human fecal microbiome were retrieved from two published studies in Zimbabwe and China  29, 30  that reported linear growth data in children. Children were included for analysis if they were 2-5 years of age and had linear growth measurements (HAZ) available as an outcome.
Raw sequencing data were retrieved from the NCBI Nucleotide Sequence Archive (accession codes PRJNA521455 and PRJNA543967) using SRAtoolkit (v 2. 10. 8) and processed according to the published Biobakery workflow (https:// huttenhower. sph. harvard. edu/biobakery_workflows/)  61. Sequence quality control and removal of human reads was performed using KneadData 0. 7. 4; bacterial abundances were calculated using MetaPhlan3. 0; and bacterial gene abundances were calculated with Humann3 using nucleotide-based alignment against the Chocophlan database 62. Gene abundance tables were grouped by uniref90_level4ec and normalized to copies per million (cpm) according to the standard pipeline. Normalized taxa and gene abundance tables were imported into R. Taxonomic data were processed analogously to the 16S data using Phyloseq 55 , as described above and as detailed in the attached R markdown file.
Also as described above, stunting was defined by HAZ, and samples that were missing either taxon (i. e. , zero values) were excluded for correlation analysis. Species-level analysis was performed by pairwise Spearman's correlation of all detected Bacteroides species against the abundance of E. coli, and associations were reported by raw P value for taxa that were consistent (positively or negatively associated) in both datasets individually. For subsequent gene-level analysis, genes from all Bacteroides species that were consistently positively associated with E. coli were binned together (i. e. , summed per gene) and the gene table was further filtered for genes present in at least 60% (12 out of 20) samples, to try and reduce sparseness of the dataset while maintaining sample size. Within this subset of genes, pairwise Spearman's correlations against the abundance of Enterobacteriaceae and Escherichia were performed, and both raw P value and FDR-corrected P value are reported.
Exact P values and test statistics for all statistical results presented in the main and supplemental figures are listed in Supplementary Table  8. previously published and is available at the European Nucleotide Archive (ENA) accession PRJEB27868. All other sequencing data used in the study were previously published. Data from Desai et al. 27  were obtained directly as an OTU table from their publicly available GitHub page (https://github. com/chandni177/ BacterialViralMicrobiome_of_GrowthVelocity_in_EED). 16S rRNA sequencing data from Dinh et al. 28  were retrieved from the NCBI Sequence Read Archive (SRA) under accession code PRJNA279828. 16S rRNA sequencing data from Rouhani et al. 11  were retrieved from the NCBI SRA under accession code PRJEB28159. Shotgun metagenomics sequencing data from Osakunor et al.
30  were retrieved from the NCBI SRA under accession code PRJNA521455. Shotgun metagenomics sequencing data from Li et al. 29  were retrieved from the NCBI SRA under accession code PRJNA543967. The SILVA database v132 for 16S taxonomic assignment can be accessed from https://www. arbsilva. de/download/archive/qiime/. The Chocophlan database for metagenomics assignment was accessed from https://github. com/biobakery/humann#download-thechocophlan-database. Raw metabolomics data have been deposited at Metabolomics Workbench accession number PR001175. Reagents and bacterial strains are available upon request. Source data are provided with this paper.
Acknowledgements Many thanks to  Dr. Cara Haney  for her feedback on early versions of this story and to  Dr. Jon Briggs  for his helpful input on Bacteroides carbohydrate metabolism. We are also grateful to the authors of Dinh et al. 2016, Desai et al. 2020, Vonaesch et al. 2018, Rouhani et al. 2019, Li et al. 2019, and Osakunor et al. 2020 for making their data and metadata freely available, and to  Dr. Edson Rocha  for generously sending us the B. fragilis ΔfeoAB mutant strain. We would also like to thank our anonymous reviewers for their valuable feedback and suggestions. Work in B. B. F. 's lab was funded by grants from  Canadian Institutes for Health Research. B. B. F. is also a Senior Fellow at the  Canadian Institute for Advanced Research. K. E. H. and M. C. were supported by a  CIHR Vanier Scholarship  and a  UBC Four-Year Fellowship.
Methods We performed a bibliographic research of publications referenced in PubMed database, using the following terms: "indirect calorimetry", "energy expenditure", "resting energy expenditure", "VCO 2 ", "VO 2 ", "nutritional therapy". We included mainly studies published in the last ten years, related to indirect calorimetry principle, innovations, patient-centred benefits, clinical use in practice and medico-economic aspects. Based on authors' clinical experience, we provide few clinical cases.
Financial support was provided by the European Society for Clinical Nutrition and Metabolism and the European Society of Intensive Care Medicine. C Pichard received an unrestricted grant from the Public Foundation Nutrition 2000Plus. Dr N Achamrah and M Delsoglio are research fellows supported by personal grants of the Public Foundation Nutrition 2000Plus. E De Waele and MM. Berger received funds form the Public Foundation Nutrition 2000Plus. Credit author statement Najate Achamrah and Claude Pichard have outlined this manuscript. Marta Delsoglio, Elisabeth De Waele, and Mette M. Berger adapted the pedagogic content and critically reviewed the manuscript.