dataesr/scientific-paragraphs-categorization huggingface dataset:
Among them, 158 were successfully identified or annotated, and 26 were attributed to putatively characterized classes (e. g. by spectral similarity to known compounds of a chemical class), as exemplified in Supplementary Fig. 2. Only nine compounds are shared metabolites detected by several methods (reported as metabolite_Mx in Datasets EV2 and EV3). Moreover, six metabolites are detected still with redundant spectral features (mainly carbohydrates), due to interference of co-eluting compounds (reported as metabolite_Fx in Datasets EV2 and EV3). Globally 50% of these molecular features are positively associated with MetS, with a high confidence (LMM corrected pvalue < 10 À5 ) for the identified carbohydrates, amino acids and some lipids (mainly TGs, corrected p-value < 10 À7 ). Among the negatively c: The similarity of method blocks evaluated using RV coefficients (a matrix correlation coefficient (see method section)) after Multiple Factorial Analysis.
d: Spearman correlation network between the 2915 metabolite and lipid features from the six metabolomic/lipidomic datasets (significant correlation >0. 7). Nodes correspond to features obtained using the different analytical platform (Pink: C18pos; Dark Blue: C18neg; Green: GCMS; Blue: HILICneg; Yellow: Lipidomics; Black: NMR). Two nodes are connected by an edge if correlation is significant between the corresponding features (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ). modulated molecules, metabolites from food (e. g. 1,5-anhydroglucitol), microbial metabolism (e. g. indole-3-propionic acid, pipecolic acid) and several amino acid derivatives and peptides (corrected pvalues ranging from 10 À3 to 10 À5 ) were highlighted.
Identification of a comprehensive molecular MetS signature A full feature selection strategy was developed to build a comprehensive molecular MetS signature, stable over time (Fig. 5a ). After LMM, all the variables stable over time regardless of their link with MetS (i. e. 2176 variables in total for the six datasets) were submitted to a feature selection step. The biosigner recursive feature selection method  [44]  was applied to each of the six datasets (after filtration of intrinsic correlations) to select the smallest feature subsets which significantly contribute to the MetS prediction using 3 binary classifiers with distinct mathematical backgrounds (Partial Least Square-Discriminant Analysis (PLS-DA), Random Forest, and Support Vector Machine), and repeated five times (see Materials and Methods).
For each dataset, the metabolites most contributing to prediction, selected by at least one of the three methods, and being part of the union over the five repetition, were retained. This approach allows selecting the necessary and sufficient variables for MetS prediction, mainly due to its ability to combine large number of data permutations with three different multivariate methods. It resulted in six subsets, including a selection from 2 to 39 variables. These six predictive subsets were then integrated into a PLS-DA model to obtain a comprehensive molecular MetS signature (that includes 102 variables, see Supplementary data) and characterize its discriminant power. The resulting validated model allowed a good discrimination between cases and controls (R 2 Y = 0. 68 and Q 2 Cum = 0. 60).
Results showed, as expected, good complementarity of the 6 methods, allowing to detect a large diversity of metabolites, from very polar molecules such as carbohydrates, or amino acids to very apolar compounds such as TG. This complementarity was also evidenced at each data treatment steps, first by the low correlation level between method datasets, and between individual metabolite and lipid related features, and second by the small number of shared metabolites and lipids detected by several methods. Among them, it is interesting to note that carbohydrates and derivatives, often poorly detected in LC-HRMS methods, are more precisely characterized (because of enhanced separation performance) by GC-HRMS and more quantitatively assessed by NMR. The significantly altered metabolites captured by the multimetabolomic platforms were integrated to reveal the modulated metabolic pathways and get potential information about the involved mechanisms.
The contextualization of metabolites into pathways and diseases is a difficult task that stems from several of the following aspects. Firstly, despite the use of the most recent databases and tools, a large part of the metabolome remains unknown and identifiers of annotated species have to be harmonized, as well as ontologies between different research fields to fully map identified metabolites and nodes in metabolic networks  [59]. This partial mapping, together with some annotation uncertainties (especially for lipids), can lead to limitation of biological interpretation  [59]. Secondly, the complexity of interpretation lies in the fact that metabolites are involved in multiple overlapping pathways. Thirdly, the challenge in metabolic network analysis resides in the quality of the reconstructed networks, as well as the choice of the algorithm to extract sub-networks  [60].
Eventually, although pathway enrichment analysis is useful in identifying the metabolic pathways in which modulated metabolites are overrepresented and which are therefore more likely to be modulated, the results of this analysis must be interpreted with caution as the different state-of-the-art metabolomics methods and databases allow covering only a limited part of the metabolic network, resulting in some uncovered or partially covered metabolic pathways that would never be highlighted because of analytical limits  [67]. In the present study, the mapping of modulated metabolites into the highly curated human metabolic network Recon2. 2, performed with the MetExplore tool  [62] , revealed that among the specific identified modulated metabolic pathways, changes in the global metabolism of amino acids is of key importance in MetS.
In lipidomics dataset, TG were found to be the most modulated family, as expected in the context of MetS and dyslipidemia. Lysophosphatidylcholine species (LPCs) as major bioactive lipid components of oxidized LDL-cholesterol, are known to play a key role in inflammation processes  [85, 86]. In particular, LPC levels were described as decreased with insulin resistance, independently of obesity  [87]. More specifically, two main modulated species in our study (namely LPC(18:2_0:0) and LPC(16:0_0:0), corrected LMM p-value = 2 £ 10 À4 and 3 £ 10 À3 , respectively) were found as being associated with metabolic health in obesity  [88]. Another study deeply investigated the positive associations between LPC levels and whole-body insulin sensitivity and concluded that they were related to muscle insulin sensitivity, rather than hepatic insulin resistance  [89].
Chenodeoxycholic acid (corrected LMM p-value=10 À3 ) in particular, has been shown to be increased in insulin resistance and obesity  [104, 105]. In addition to this biological contextualization, different alternative integration methods have been developed in systems biology to reduce the gap between the high amount of generated data and the knowledge of complex biological systems. Performing large-scale metabolomics requires special attention, in particular because of the high dimensionality of data  [106]. Special attention must therefore be paid to standard operating procedures for data production and data processing  [107]  to be able to extract meaningful information  [108]. In the present strategy, a mid-level data fusion (i. e. selection of variables from each metabolomic/lipidomic dataset before integration) was performed to build the MetS comprehensive signature.
Contributors BC, JAM, ET, CJu, PG and EP-G designed the study. BC, NL, ET, MP, PG, and EP-G performed subject selection. CC, FC, BeC, CJo, BL, CM, and EC-V generated raw data, pre-processed and annotate them (for NMR, HILIC-HRMS, lipidomics, C18-HMRS and GC-HRMS, respectively). SM processed the data and performed statistical analyses. MB, JFM, MP and ET supervised statistical analyses. FJ, NP and FV performed network analyses. FF and EP-G supervised metabolomics analyses. BC and EP-G draft the manuscript. PG and JAM were part of the initial co-principal investigators of the NuAge study and are part of the NuAge Database and Biobank Steering Committee. PG is the scientific director of the NuAge Biobank. All authors contributed to writing, critically revisiting and approved the submitted version of the manuscript.
Acknowledgments The  NuAge Study  was supported by a research grant from the  Canadian Institutes of Health Research (CIHR ;  MOP-62842 ). The actual NuAge Database and Biobank, containing data and biologic samples of 1753 NuAge participants (from the initial 1793 participants), are supported by the  Fonds de recherche du Qu ebec (FRQ ;  2020-VICO-279753 ), the  Quebec Network for Research on Aging , a thematic network funded by the  Fonds de Recherche du Qu ebec -Sant e (FRQS)  and by the  Merck-Frost Chair  funded by  La Fondation de l'Universit e de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the  MetaboHUB French infrastructure  ( ANR-INBS-0010 ).
The  NuAge Study  was supported by a research grant from the  Canadian Institutes of Health Research (CIHR ;  MOP-62842 ). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the  Fonds de recherche du Qu ebec (FRQ ;  2020-VICO-279753 ), the  Quebec Network for Research on Aging , a thematic network funded by the  Fonds de Recherche du Qu ebec -Sant e (FRQS)  and by the  Merck-Frost Chair  funded by  La Fondation de l'Universit e de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB  French infrastructure  ( ANR-INBS-0010 ). All authors had full access to the full data in the study and accept responsibility to submit for publication.
7B ). Such viral loads and cytokine pro les were also associated with a reduction in lung lesions (Fig. 3H  and Extended data Fig. 8 ), as scored by X-ray computerized tomography (CT). We then analyzed the immune responses of all animals following SARS-CoV-2 viral challenge. The naive controls showed the slowest development of anti-S, anti-RBD, and anti-N IgG (Fig. 3F ), of which the levels remained signi cantly lower than for the other two groups at day 20 p. expo. (p = 0. 022). The nonvaccinated convalescent animals raised a rapid and robust anamnestic antibody response (Fig. 3F ), which was associated with a signi cant increase (p = 0. 031) in the serum capacity to neutralize ACE2 binding to RBD (Fig. 3G ) by p. expo. day 9. The anti-S-and anti-RBD-speci c antibody responses and neutralization activity of the serum was maintained in the vaccinated macaques at the high levels already achieved at the time of challenge and remained superior to that of the control macaques (Fig.
RBD (10 µg/mL) or LPS (100 ng/mL, Invivogen). Next, cells were washed in PBS and incubated 15min with LIVE/DEAD xable Blue Dead Cell marker (Life Technologies), anti-CD11b-V450 (ICRF44, BD), anti-CD3-V500 (SP34-2, BD), anti-CD86-BV605 (2331, BD), anti-CD11c-APC (3. 9, BioLegend), anti-CD20-SB702 (2H7, Invitrogen), anti-CD80-BV786 (L307. 4, BD), anti-CD8-BV650 (BW135/80, Miltenyi Biotec), anti-CD14-A700 (M5E2, BioLegend), anti-HLA-DR-APC-H7 (L243, BD), anti-CD4-FITC (L200, BD), anti-CD45-PerCP (D058-1283, BD), anti-CD69-PE-Cy7 (FN50, BD) and anti-CD40-AF594 (42G5). Next, cells were washed twice with PBS and acquired on the ZE5 ow cytometer (Biorad). Analysis was performed on FlowJo v. 10 software. For activation markers, results were expressed as fold change geometric MFI, obtained by dividing the geometric MFI measure in αCD40.
BD GolgiPlug (Becton Dickinson France) was added in all conditions and the culture was continued for additional 18 hours. Next, spleen cells were washed using FACS buffer (PBS, supplemented with 1% FBS) and incubated with tetramer-PE (ProImmune Ltd, Oxford, UK), Live dead xable Aqua Dead marker (Life Technologies) and the following antibodies: anti-hCD3-A700 (UCHT1, Sony), anti-h-CD4-BV605 (RPA-T4, Sony), anti-hCD8-APC-Cy7 (SK1, Sony) for 30 minutes. Following xation and permeabilization, spleen cells were stained with intracellular antibodies: anti-hIFNγ-PerCPCy5. 5 (B27, Sony), anti-hIL-2-BV421 (17H12, Sony), anti-hTNFα-PC7 (Mab11, Sony) for 30 minutes. Stained cells were acquired on the LSRII ow cytometer (BD Biosciences). FlowJo v. 10. 7. 1 software was used for data analysis (TreeStar, Inc.
Then cells were stimulated with S or N sequence overlapping peptide pools at a nal concentration of 2 μg/mL. Brefeldin A was added to each well at a nal concentration of 10µg/mL and the plate was incubated at 37°C, 5% CO2 during 18 h. Next, cells were washed, stained with a viability dye (LIVE/DEAD xable Blue dead cell stain kit, ThermoFisher), and then xed and permeabilized with the BD Cyto x/Cytoperm reagent. Permeabilized cell samples will be stored at -80 °C before the staining procedure. Antibody staining was performed in a single step following permeabilization. After 30 min of incubation at 4°C, in the dark, cells were washed in BD Perm/Wash buffer then acquired on the ZE5 ow cytometer (Biorad). Analysis was performed on FlowJo v. 10 software.
Experimental infection of macaques with SARS-CoV-2 Four weeks after immunization, all animals were exposed to a total dose of 10 6 pfu of SARS-CoV-2 virus (hCoV-19/France/ lDF0372/2020 strain; GISAID EpiCoV platform under accession number EPI_ISL_406596) via the combination of intranasal and intra-tracheal routes (0. 25 mL in each nostril and 4. 5 mL in the trachea, i. e. a total of 5 mL; day 0), using atropine (0. 04 mg/kg) for pre-medication and ketamine (5 mg/kg) with medetomidine (0. 05 mg/kg) for anesthesia. Nasopharyngeal, tracheal and rectal swabs, were collected at 1, 2, 3, 4, 6, 9, 14 and 20 days post exposure (d. p. exp. ) while blood was taken at 2, 4, 6, 9, 14 and 20 d. p. exp. Bronchoalveolar lavages (BAL) were performed using 50 mL sterile saline at 3 d. p. exp in order to be close to the peak of viral replication and to be able to observe a difference between the vaccinated and control groups.
Virus quanti cation in cynomolgus macaque samples Upper respiratory (nasopharyngeal and tracheal) and rectal specimens were collected with swabs (Viral Transport Medium, CDC, DSR-052-01). Tracheal swabs were performed by insertion of the swab above the tip of the epiglottis into the upper trachea at approximately 1. 5 cm of the epiglottis. All specimens implemented in the software Monolix (www. lixoft. com). The duration of the eclipse phase (1/κ) and the clearance of the virus (c) were estimated by pro le likelihood. The production of viral particles by infected cells (ρ) has been xed to 19,000 in trachea and 36,000 in nasopharynx copies per productively infected cell per day according to previous estimations  5. The proportion of infectious virus (m) has been xed to 1/1000 according to previous work  5.
ACKNOWLEDGEMENTS We thank  B. Delache ,  S. Langlois ,  J. Demilly ,  N. Dhooge ,  P. Le Calvez ,  M. Potier ,  J. M. Robert ,  T. Prot  and  C. Dodan  for the NHP experiments;  L. Bossevot ,  M. Leonec ,  L. Moenne-Loccoz ,  M. Calpin-Lebreau  and  J. Morin  for the RT-qPCR, ELISpot and Luminex assays, and for the preparation of reagents; W. Gros for NHP T-cell assays and ow cytometry;  B. Fert  for her help with the CT scans;  S. Tricot  for uorescent labelling of Ab used in ow cytometry;  M. Barendji ,  J. Dinh  and  E. Guyon  for the NHP sample processing;  S. Keyser  for the transports organization;  F. Ducancel  and  Y. Gorin  for their help with the logistics and safety management;  I. Mangeot  for here help with resources management and B. Targat contributed to data management; We thank  S.
Bellil  and  V. Enouf  for contribution to viral stock challenge production and  A. Nougairede  for sharing the plasmid used for the sgRNA assays standardization. Funding: The programme was funded by the  Vaccine Research Institute  via the  ANR-10-LABX-77  grant. Studies in hu-mice model have been supported by  ARN  grant  ANR-20-COV6-0004-01. The  Infectious Disease Models and Innovative Therapies (IDMIT)  research infrastructure is supported by the ' Programme Investissements d'Avenir , managed by the  ANR  under reference  ANR-11-INBS-0008. The  Fondation Bettencourt Schueller  and the  Region Ile-de-France  contributed to the implementation of IDMIT's facilities and imaging technologies. The NHP study received nancial support from REACTing, the  Fondation pour la Recherche Medicale  ( FRM; AM-CoV-Path ) and the  European Infrastructure   TRANSVAC2  ( 730964 ) for implementation of in vivo imaging technologies an NHP immune assays.
the  Fondation Bettencourt Schueller  and the  Region Ile-de-France  for the contribution to the implementation of imaging facilities; and the  Domaine d'Intérêt Majeur (DIM) 'One Health'  for its support. The NHP model of SARS-CoV-2 infection was developed thanks to the help of REACTing (Paris france), the  National Research Agency (ANR, Paris , france;  AM-CoV-Path ) and the  European Union's Horizon 2020  ( H2020 ) research and innovation program Fight-nCov ( 101003555 ),  European Union   IMI2  program  CARE  ( 101005077 ) and the  European Infrastructure   TRANSVAC2  ( 730964 ). The virus stock was obtained through the  EVAg  platform (https://www. european-virus-archive. com/), funded by  H2020  ( 653316 ).
The hu-mice were housed in Mondor Institute of Biomedical Research infrastructure facilities (U955 INSERM-Paris the O ce for Laboratory Animal Welfare (OLAW, assurance number #A5826-01, US). The protocols were approved by the institutional ethical committee "Comité d'Ethique en Expérimentation Animale du Commissariat à l'Energie Atomique et aux Energies Alternatives" (CEtEA #44) under statement number A20-011. The study was authorized by the "Research, Innovation and Education Ministry" under registration number APAFIS#24434-2020030216532863v1. αCD40. RBD vaccine Production and quality assurance of the αCD40. RBD vaccine Vectors and sequences for humanized anti- human CD40 12E12 IgG4 and control human IgG4 antibodies have been described previously 1,2,3. GenBank sequences HQ738666. 1 and KP684037 describe the human IgG4 chimeric forms of the 12E12, anti-human CD40 H and L chains.
Data Analysis MassHunter Qualitative Analysis B. 07. 00 (Agilent Technologies, Santa Clara, CA, USA) was used for data processing and deconvolution with the parameter of 30000 absolute peak height, and NIST14 and Fiehn mass spectrometry databases were used as references for qualitative analysis. Agilent MassHunter Mass Profiler Professional 13. 1. 1 was used for principal component analysis (PCA), cluster analysis and variance analysis. Metabo Analyst online (https://www. metaboanalyst. ca/home. xhtml, accessed on 15 April 2021) analysis software was used to conduct metabolic pathway analysis. Analysis of variance (ANOVA) was performed, and metabolites were compared between probenazole-treated and non-treated groups. Significant difference was determined with the fold change >1 and p < 0. 05.
It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNet-Models includes tissue-and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.
INTRODUCTION During the past decade, the development of highthroughput technologies has dramatically decreased the cost of generating large-scale multi-omics datasets  (1). This has opened up the possibilities to study human wellness and diseases systematically  (2). Although analysis of individual omics methodologies has been proven beneficial in different clinical applications, integrating multi-omics data may offer novel insights and provide a more comprehensive understanding of biological functions in the human body in health and disease  (3). For instance, a recent study integrated time series phenomics, metabolomics and W272 Nucleic Acids Research, 2021, Vol. 49, Web Server issue fluxomics data from the subjects with various degrees of liver fat and revealed that non-alcoholic fatty liver disease (NAFLD) is associated with glycine and serine deficiency  (4).
The data in iNetModels are obtained from recent studies, where large-scale Multi-Omics Biological Networks (MOBNs) analyses have been performed for individuals with different metabolic conditions. Moreover, we retrieved data from The Genotype-Tissue Expression (GTEx) Project and The Cancer Genome Atlas (TCGA), created normal tissue-and cancer-specific Gene Co-expression Networks (GCNs) and presented the networks in the iNetModels (Figure  1A ). The user can simultaneously query for 1-5 features, visualize the selected features and their neighbouring features, download the associated network in both table and figure format, and analyse them using independent network analysis tools, including Cytoscape  (9)  and iGraph  (10). We also encourage users to upload their networks into iNetModels and make those networks accessible to a broader audience for creating an open platform to share and visualize their networks.
PLATFORM DESCRIPTION AND FEATURES iNetModels 2. 0 is a web-based platform that includes two main features: a database and an interactive visualization of multi-omics network analysis (Figure  1B ). It is an updated and improved version of the previous work, TCSBN, released in 2017  (11). First, we reconstructed the GCNs presented in TCSBN based on more recent datasets and expanded it to 85 different tissue-and cancer-specific networks. Second, we broadened the platform by adding 20 MOBNs from multiple independent studies. Finally, we improved the backend and frontend of the platform for a better user experience. To our knowledge, this platform is the only publicly available platform that enables the exploration of MOBNs that were generated based on personalized omics data.
Data sources The tissue-and cancer-specific GCNs were constructed using GTEx (v8) and TCGA (v27. 0-fix) primary tumour data, respectively. We retrieved the gene transcripts per million (TPM) from GTEx Portal and the fragment per kilobase of transcript per million mapped reads (FPKM) files of the TCGA program from the Genomics Data Commons (GDC) portal. MOBNs were generated as the consensus of clinical variables, plasma proteomics, plasma metabolomics and metagenomics data based on from three independent longitudinal wellness profiling studies: (i) SCAPIS-SciLifeLab Wellness Profiling study  (12) : clinical variables, metabolomics, proteomics and gut metagenomics data from 4 visits over a year from 101 individuals (50-65 years old), (ii) P100 study  (13) : clinical variables, metabolomics and proteomics data collected from three visits of 108 individuals (21-89+ years old) in over 9 months, (iii) Integrative Personal Omics (  14 ): clinical variables, metabolomics and proteomics data from three visits over weight gain and loss period (97 days) from 10 insulin-sensitive (IS) and 13 insulin-resistance (IR) overweight individuals.