dataesr/scientific-paragraphs-categorization huggingface dataset:
Supporting our PUL approach, using GH only led to growth predictions that were not meaningful. Several parameters play a part in the model prediction performance. Both annotation quality and discrepancy between datasets and gene and enzyme terminology differences have been a hurdle in designing new PUL models. As shown by the comparison of GH annotation tools, an accurate annotation of CAZymes is key to improve prediction performance. Most automated annotation pipelines for transcriptomic data do not accurately annotate for GH. Then, the Pfam domain used for functional domain characterization may easily provide the right annotation of only one domain of the GH, as we observed for several PUL where we did not capture the GH catalytic domain. Another aspect is that our PUL models do not have a size limit, as long as the distance between neighboring genes annotated as the same PUL family is less than 5 kb.
The growth threshold could be adjusted accordingly for the different fibers, when more experimental data are available. However, the experimental growth medium itself influences growth yield in a strain-to-strain manner that is difficult to predict. The results would need to be reassessed or taken with caution if strains were grown in a culture medium that is different from the reference. The experimental data we used to measure the pipeline performance were obtained in PY medium. However, the transcriptomics data used to build the PULs models were obtained on CM medium, which could account for differential genes being over-expressed, at the time of sampling, compared to growth validation data on PY medium. Indeed, the growth medium may change which genes are prioritized in the model. It remains to be determined whether this bias would be more impactful on complex PUL models with large numbers of genes or complex operon structure where genes, not co-transcribed, might not be captured in a single transcriptome time point.
The extreme modularity of some PULs indicates that the conclusions about growth results from one species to another have to be taken with caution since, like others, we detected strain-to-strain variations within the same species for inulin and levan. The complexity of fiber structures makes the links between CAZyme genes and functional interpretation very uncertain. In order to determine the strain capacity to break down a specific fiber, we linked CAZy genes to a given fiber or prebiotic. However, this representation has limitations because (i) the complexity and substrate diversity hardly fit the CAZyme databases and several families are displaying a wide phylogenetic diversity, such as amylase GH13 (as described by  Stam et al. , 2007)  and (ii) certain families can be dedicated to/involved in different substrates (GH32 targeting inulin and levan as showed by  Sheridan et al.
One way to circumvent these drawbacks or limits is to obtain massive large datasets performed in the same conditions, with strains for which the genomes are available. To improve FiberGrowth pipeline performance, a larger number of strains should be grown in the exact same conditions. This could be done using robots under anaerobic chamber, similar to the work by  Zou et al. (2019) , leading to > 6,000 isolated strains and 1,520 new genomes sequenced. Note that if more experimental data become available, it would also be possible to adopt a machinelearning approach to infer potential PUL rather than having a deterministic approach such as FiberGrowth. Another way to increase our capacity to obtain large growth dataset arises from the recent advances in droplet microfluidic culture, which can increase the capacity and lower the cost of purified substrates, to screen for growth on fiber.
However, an accurate annotation of PUL seems difficult in metagenomics datasets. We already showed, using 40-kb E. coli fosmid libraries built from fecal samples or distal ileum mucosa, that glycosyl hydrolases were modular and subject to recent horizontal gene transfer, not just between phylogenetically close genomes but above the genus level  (Tasse et al. , 2010; Tauzin et al. , 2020). A challenge remains to accurately annotate glycosyl hydrolase loci in metagenomic data, only feasible with long assembly with enough coverage. Predicting individual response to prebiotic or fiber intake, we previously had demonstrated that the diversity of fiber-rich food items correlated with microbiome 16S rDNA diversity in young adults  (Tap et al. , 2015). Others have demonstrated that a 5-day regimen with a plant-based diet compared to animal productbased diet  (David et al.
, 2013)  increased microbial diversity. Mining existing large datasets such as iHMP2  (Proctor et al. , 2019)  can be of importance to find correlations between the breakdown of polymers from human origin, which have been linked to inflammatory bowel diseases. Those include not only human mucus and related compounds, such as heparin, but also human gangliosides. As a growing number of metagenomics datasets are being generated worldwide, correlations can be inferred between dietary intake and microbiome composition. However, most food questionnaires are not designed to provide indication on specific fiber. Interestingly, new AIassisted tools for computing food images on smartphones may provide a more accurate picture of the ingested fiber content. There is still a lack of knowledge between a food element/items and the set of glycosyl hydrolases necessary for its breakdown and the subsequent production of beneficial metabolites through fermentation.
CONCLUSION The diversity of the CAZy gene families involved in the breakdown of glycans and the extreme modularity of the operons that encode them, sometimes at the strain level, offer a challenge to building PUL models that are generic for a given fiber. However, we demonstrated, for the first time, that a pipeline, combining automated genome screening and annotation of PULs, allowed us to build growth prediction models and measure their accuracies on Bacteroidetes available growth data. Some PUL models require optimization, while for levan, heparin, inulin, and arabinoxylan, FiberGrowth pipeline can compute growth estimations with TPR > 0. 8 and FPR < 0. 33 in 1 min on an unannotated genome. This work also demonstrated that, despite advances in PUL bioinformatics screening and computing modeling, the lack of biochemical characterization of glycosyl hydrolases and PUL systems remains a main issue.
The NuAge cohort and subject selection The present study was designed within the 5-year observational Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge). The cohort was constituted of 1793 men and women in good general health, selected from three age groups (68À72, 73À77, 78À82) at recruitment. French or English-speaking communitydwelling participants were committed to give fasting blood, undergo several direct measures annually, and to answer questionnaires related to food and health biannually. The NuAge database comprises large qualitative and quantitative data related to anthropometry/ body composition, nutrition/dietary intakes, numerous markers of physical, clinical and cognitive status, physical activity, functional autonomy and social functioning. Methodological description of measures, questionnaires and blood test, processing and storing have been described in Gaudreau et al.
Ethical approval All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the NuAge study. The NuAge Study has been approved by the Research Ethics Board (REB) of both the Geriatric University Institutes of Montreal and Sherbrooke Research Centers. The management framework of the actual NuAge Database and Biobank has been approved by the REB of the CIUSSS-de-l'Estrie-CHUS (protocol #2019-2832).
The electron energy was 70 eV and mass data were collected in a full scan mode (m/z 55-700) using a resolving power of 7000 (FWHM) to m/z 464 (perfluorotributylamine, PFTBA). Acquisition rate was 5 spectra/sec with acquisition time of 200 msec/ spectrum. Four heptane blanks were injected at the beginning of each sequence, followed by four pool samples, and then one pool sample and one derivatization control sample after each set of 10 samples. Initially tune and calibrate the system were performed using PFTBA with acquisition conditions 2 GHz EDR with N2 (1. 5 mL/ min) and the limits for average PPM error were 3. 0 and maximum error: 8. 0. Also, a calibration was made between each sample.
A water suppression signal was achieved by pre-saturation during the relaxation delay. The spectral width was set to 20 ppm for each spectrum, and 256 scans were collected with 32 K points. Free induction decays were multiplied by an exponential window function before Fourier Transform. The spectra were manually phased and calibrated to the lactate signal (d 1. 33 ppm), and the baseline was corrected using TopSpin 3. 2 software (Bruker, Karlsruhe, Germany).
Data treatment Following metabolomic/lipidomic analyses, some samples were identified as missing, because of problem in sample preparation or missing data (1 for C18Pos, 6 for HILIC, 4 for Lipidomic and 1 for GCMS). All the obtained raw data from metabolic profiles were processed to yield a data matrix containing variables and peak intensities. All the data treatments were performed separately for each analytical method as individual datasets, under the Galaxy web-based platform Worflow4Metabolomics (W4M)  [34]  to ensure the standardization and reproducibility of the data treatment workflows.
Data extraction and pre-processing for MS First, raw data were extracted using XCMS  [35] , followed by quality checks and signal drift correction according to the strategy described by van der Kloet et al. [36]  based on the use of pooled QC samples, to yield a data matrix containing retention times, masses, and peak intensities that have been corrected for batch effects. These steps include noise filtering, automatic peak detection, and chromatographic alignment. In particular, all XCMS extractions used a "minfrac" parameter of 0. 2 to keep variables if present in at least 20% of the samples, since a huge variability of profiles in the selected individuals was expected. Due to a high degree of correlations between the two lipidomic extracted datasets, they were merged for further data processing.
Filtration During the analysis, metabolites produce several analytical features corresponding to signals derived from different adduct ions generated in the ESI process, signals from the presence of isotopes in the molecule, signals from in-source fragmentation processes, and to different peaks from the same molecule in NMR. The data extraction step results in thousands of features present in the final datasets with a high degree of correlation, which is a constraint for the use of various data mining and statistical methods. For example, analytical redundancy highly affects multiple testing correction. Indeed, having non-independent variables (coming from the same metabolite) lead to an over-correction of data that can hide potentially relevant information. Therefore, the analytical redundancy inside each of the 6 datasets was reduced in the present study.
In metabolomics, filtering was technique-specific but with a common characteristic to reduce correlation above 0. 90 and to select one single representative per group, as being the most intense signal for MS data and the purest one for NMR. This procedure was conducted using the Analytic Correlation Filtration (ACorF) tool  [37]  within W4M, with a manual selection of the representative feature only for NMR. In lipidomics, this step was performed according to the workflow previously described  [32]. Briefly, a first automatic feature annotation was achieved through using an in silico database containing the exact masses corresponding to pseudo-molecular ions ( [M + H] + , [M-H] À and [M-2H] 2À ), adducts([M+NH 4 ] + , [M+Na] + ,[M-H+CHO 2 ] À ) , and in source fragments ([M + HÀH 2 O] + ) ions along with their corresponding 13 C and double 13 C isotopes.
Measurement of serum metabolomes in the NuAge MetS subcohort Correlation analyses were performed to give an overview of links between detected metabolites/lipids in serum, both at the level of method datasets and individual variables. First, the RV coefficients  [38]  were used to provide insight into the global association between datasets using the R software (version 3. 4. 1) [39] , with the R-package FactoMineR  [40]. This coefficient  [38]  is a multivariate generalization of the squared Pearson correlation coefficient, defining a scale of similarity between two matrices and measure to what degree the different datasets give the same view on the samples  [41]. Second, to investigate individual correlations between detected features, pairwise Spearman correlation coefficients between variables were calculated using the Between Table Correlation tool available in the W4M and a network analysis was done.
Metabolite and lipid levels modulated with MetS Individuals in this study were selected stable regarding their MetS status. Nonetheless we could expect that part of their metabolism was affected by time. Thus, the impact of time on the metabolomic/ lipidomic datasets was also evaluated. As no interaction effect was observed between status and time, linear mixed models (LMM) were performed to analyze repeated measures, considering fixed effect factors (time, status (case/control), and their interactions) and subject as random effect, using the module available in W4M. In order to verify that the LMM assumptions were met, we considered the different residuals of LMM. The assumption of homogeneity of variance of the residuals was checked for each fixed factor using a Levene test. Then, the normality of the conditional residuals and random effect residuals were verified using quantile-quantile plot.
Identification of a comprehensive molecular MetS signature The objective of the present study was to identify a limited number of metabolites that could together reflect the MetS status. In this context, a variable selection was first performed based on the methodology developed by Rinaudo et al. [44] , using the biosigner module available in the W4M Galaxy instance  [34]  on each individual dataset. The aim was to focus on the variables, which significantly contribute to the performance of the discrimination. As feature selection may be affected by correlations between variables, a Pearson correlation filter on each dataset (over 0. 8) was applied beforehand. All variables selected by biosigner with at least one of three classifiers (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Random Forest (RF)) were first considered.
Metabolite annotation The metabolite annotation was first conducted computationally using W4M and then, all annotations involved manual curation and interpretation of spectra. Metabolites contributing to the discrimination of the MetS phenotype were first identified using in-house databases, containing the reference spectra of more than 2000 authentic standard compounds analyzed in the same analytical conditions, and providing a comprehensive spectral information (i. e. protonated or deprotonated molecules, adducts and in-source fragment ions for LC-HRMS, or molecular ions as well as major fragments for GCMS). Metabolite annotation was first performed by using these spectral databases according to accurately measured masses within MS spectra and chromatographic retention times. Confirmation of metabolite annotation in LC-HRMS was then accomplished by running additional LC-MS/MS experiments using a Dionex Ultimate chromatographic system combined with a Q-Exactive mass spectrometer (Thermo Fisher Scientific) under non-resonant collision-induced dissociation conditions using higher-energy C-trap dissociation (HCD) in both positive and negative ion modes, conducted on the same QC samples, and with the instrument set in the targeted acquisition mode, using inclusion lists.
Resulting MS/MS spectra were then manually matched to those included in the in-house spectral database and acquired using different collision energies. Confirmation of metabolite annotation in GC-MS was done by matching electron impact spectra, as well as using reports from the literature. Then, the remaining unknown compounds were identified on the basis of their exact masses which were compared to those registered in Metlin (https://metlin. scripps. edu; [48] ), in the Human Metabolome Database (HMDB; www. hmdb. ca; [49] ), in Massbank (https:// massbank. eu/MassBank/; [50] ), in Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www. genome. jp/kegg/; [51] ), or in the National Institute of Standard and Technology (NIST; https:// www. nist. gov/srd/nist-special-database-14; [52] ).
Database queries were performed with a mass error of 0. 005 Da, and a retention time difference of 0. 1 min for the in-house databases. Database results were confirmed using appropriate standards when available, isotopic patterns, and mass fragmentation analyses. For unidentified ions, the number of plausible elemental compositions were restricted to a small number (or uniquely identified) with the support of additional chemical information, i. e. the molecular formula of the precursor ions, reports from the literature  [53] , and knowledge of possible metabolic pathways. Metabolites were classified accordingly to Sumner et al. [54]  concerning the levels of confidence in the identification process: identified (confirmed by an authentic chemical standard analyzed under the same conditions, with the match at least two orthogonal criteria among accurate measured mass, retention time and MS/MS or EI(MS) spectrum), putatively annotated (spectral similarity with public/commercial spectral libraries), putatively characterized compound classes or unknown.
It is important to note that only very few standards of lipid species are commercially available compared to the large diversity of endogenous lipid species present in complex biological matrices. Therefore, results of in-house database queries were filtered, according to the workflow described in Seyer et al. [32] , taking into account retention time ranges of each lipid class, as well as isotope patterns, for selection of relevant lipid species, as previously described in the data filtration section. Finally, all HCD mass spectra resulting from the additional MS/MS experiments, were manually inspected to identify specific diagnostic ions and to confirm the structure of lipid species  [55]  (see Supplemental Fig. 2 ), that were named following the Lipid-Maps nomenclature  [56]. Spectral assignments were based on matching 1D and 2D NMR data to reference spectra in a homemade reference database, as well as with other databases (http://www.
Extraction of modulated metabolic network To link metabolites identified in untargeted metabolomics/lipidomics experiments within the context of genome-scale reconstructed metabolic networks, the metabolites described as modulated after LMM, stable over time, and identified or annotated, were mapped into the human genome-scale metabolic network Recon2. 2 [58]. This network contains 7785 reactions and 6047 metabolites. In order to map the modulated metabolites on this network, we first retrieved their ChEBI identifier and then search for their matching identifier in the Recon2. 2 network using the "identifier matcher tool" in MetExplore. This tool allows performing both an exact matching (to find the exact metabolite in the network corresponding to the modulated metabolite from the experimental dataset) and an ontology-based matching (to make the link with a corresponding more generic class metabolite, when the exact same metabolite cannot be retrieved in the network)  [59].
The algorithm computes the lightest path between each pair of metabolites in the dataset. The lightest path is a sequence of reactions and metabolites connecting two metabolites and minimizing a topological criterion in the network  [60, 61]. For one dataset, the related sub-network is thus the union of all the lightest paths between metabolites present in this dataset. Pathway enrichment analyses were performed to assess whether the modulated metabolites were significantly over-represented in a metabolic pathway. Pathway enrichment statistics were performed using the one-tailed exact Fisher test, with a BH correction for multiple tests, using the metabolic pathways defined in Recon2. 2. All computational and visualization tasks were performed within MetExplore web server based on the Recon2.
Overview of study population Fifty-eight quantitative variables in total were considered to precisely describe the selected population: 23 biochemical parameters, 8 clinical variables, 25 nutritional data (essentially related to macronutrient intake and selected nutrients described as being related with MetS), and finally 2 scores related to physical activity and global health (see Materials and Methods). As defined in the subject selection process (see Materials and Methods), MetS status of individuals was stable over the three years follow-up (for the 4 time points considered). Behind the stability of MetS status, the clinical parameters associated with MetS, analyzed at T1 and T4, were found stable over time, with a slight improvement for some of them (i. e. significant reduction of systolic blood pressure, fasting glucose and triglycerides (TG)).
1a  and b ). After data processing using a reproducible online Galaxy workflow  [34] , consisting in automatic peak detection, alignment and integration, quality control steps and removal of redundant spectral features, a total of 2915 metabolite and lipid related robust features (mean CV in QCs = 13%), were detected across at least 20% of the subjects over time (Fig. 1b ). Correlation analyses of still unannotated species were performed to give an overview of links between detected features. The similarity of the various retrieved datasets evaluated using RV coefficients  [38]  between matrices was found to be low (< 0. 18), in accordance with the complementary serum metabolome coverage of the 6 chosen methods (Fig. 1c ). This result was also confirmed by the low level of strong correlations between individual metabolite and lipid features detected in the different platforms, with only 7% of Spearman correlation coefficients found significant (after Benjamini-Hochberg (BH) correction) and > 0.
Metabolite and lipid levels modulated with MetS This 3-year follow-up and the analyses performed at two time points (T1 and T4) enabled us to study the serum metabolome modulations by MetS and robustly delineate the associated metabolic perturbations, in relation with clinical parameters and some potentially interacting extrinsic factors, such as diet and physical activity. In regards to the characteristics of the study population, consistent results were observed with a wide range of metabolites modulated by MetS and stable over time. More precisely, 476 metabolite and lipid features were found significant between cases and controls, and independent of time, which represents around 16% of the detected metabolome/lipidome. Fig. 2 (a  and b , Datasets EV2 and EV3) highlights the magnitude of these changes as well as their respective significance (248 metabolites and 228 lipids).