dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analyses The type of response for each food stimulus (hit or miss) and each nonfood stimulus (correct rejection or false alarm) was recorded. Each participant was assigned a hit score (i. e. , number of food stimuli categorized as food), a miss score (i. e. , number of food stimuli categorized as nonfood), a correct rejection score (i. e. , number of nonfood stimuli categorized as nonfood), and a false alarm score (i. e. , number of nonfood stimuli categorized as food). Hit, miss, correct rejection, and false alarm scores could vary between 0 and 10. These scores were used to calculate a categorization performance score, the sensitivity index A , and a categorization strategy score, the Beta, derived from SDT  (57) , adapting them to experiments based on small numbers of stimuli [see ref.
(70) ]. SDT is used to analyze data derived from tasks where a decision is made regarding the presence or absence of a signal (i. e. , the foods) embedded in noise (i. e. , the perceptually similar nonfoods). The A represents the distance between the mean of the signal distribution and the mean of the noise distribution. The greater the A the better an individual is at discriminating the signal from the noise. A ranged from 0 to 1, with 0. 5 indicating responses at chance level, and 1 maximum discriminability. A = log N H +0. 5 N M +0. 5 -log N FA +0. 5 N CR +0. 5 The decision criterion Beta represents the individual's strategy to categorize stimuli as the signal rather than the noise. Beta ranged from -1 to 1, with negative values indicating a liberal strategy (i. e. , children tending to categorize any stimulus as food), and positive values indicating a conservative strategy (i.
Obesity has been linked to vitamin D (VD) deficiency and low calcium (CAL) status. In the last decade, dietary supplementation of vitamin D and calcium (VD-CAL) have been extensively studied in animal experiments and human studies. However, the physiological mechanisms remain unknown as to whether the VD-CAL axis improves homeostasis and reduces biomarkers in regulating obesity and other metabolic diseases directly or indirectly. This review sought to investigate their connections. This topic was examined in scientific databases such as Web of Science, Scopus, and PubMed from 2011 to 2021, and 87 articles were generated for interpretation. Mechanistically, VD-CAL regulates from the organs to the blood, influencing insulin, lipids, hormone, cell, and inflammatory functions in obesity and its comorbidities, such as non-alcoholic fatty liver disease, cardiovascular disease, and type-2 diabetes mellitus.
Search Strategy and Method Scientific databases, such as Web of Science, Scopus, and PubMed, were utilised to conduct a methodical search for relevant articles by employing the following key phrases: "correlation", "relationship", "link", "association", "vitamin-D", "calcium", "obesity", "rats", "mice", and "human clinical trial". The search generated 87 articles published between 2011 and 2021, which investigated the relationship of VD-CAL in obesity (Figure  1 ). Table  1  shows the inclusion and exclusion criteria for this literature search strategy. Duplicates, review articles, protocol studies, second analysis reports, articles not related to VD-CAL, and articles not published in English were excluded. The interrelationship findings were compiled by summarising them in tables.
Antioxidant Activity, DPPH•Assay The radical scavenging activity of the extracts was evaluated using the stable free radical DPPH• according to the method described by Brand-Williams et al. [47]. The details of the procedure and calculations have already been reported in a previous study  [48]. The bleaching rate of the DPPH• radical was monitored in the presence of chestnut extracts and in a Trolox ® (antioxidant standard) solution, for the sake of comparison, to evaluate the IC50 (the half-maximal concentration of extract necessary to halve the initial concentration of DPPH• at equilibrium). Various concentrations of extracts were analyzed at 515 nm (Cary 60 UV-Vis spectrophotometer, Agilent Technologies, Santa Clara, CA, USA). The collected data were processed with Bobo Least Squares software (ver.
The inhibition of PV-Cu 2+ complex formation is expressed as the % of inhibition against a reference solution, which is prepared the same way as the samples but contains water in place of chestnut peel extract, and the inhibition is calculated using Equation (1): % of inhibition = ABSref -ABSsample ABSref (1) The percent inhibition was calculated for various concentrations of each sample, and the IC 50 was calculated by probit regression (collected data were processed by Bobo Least Squares software (ver. 0. 9. 1. ) [49]. The sample IC 50 values were then compared with the IC 50 values obtained from an EDTA solution to express the results as EDTA equivalents.
LC-MS Analysis and IR Spectra The LC-MS analysis was performed on a Waters FractionLink equipped with a diode array detector (DAD) and a mass spectrometer (single quadrupole) supported by MassLynx V4. 1 software. MS detection was performed in ESI+ mode (capillary: 3. 00 kV; cone: 20. 00 V; source temperature: 110 • C; and desolvation temperature: 220 • C, mass span 100-800). The DAD was monitored at 280 and 335 nm. Chromatographic separation was performed with a Waters Xbridge column (C18 4. 6×150mm, 5 µm), eluents: A = Water/TFA 0. 1%, and B = I/TFA 0. 1%; gradient (min, %B): 0, 5; 9. 98, 5; 42. 38, 15; 57. 34, and 30; 69. 80, 60; 76. 29, 100; 88. 75, and 100. Flow: 1 mL/min; injection volume: 20 µL. IR spectra were recorded using a Spectrum Two ATR (Perkin Elmer, Waltham, MA, United States).
No toxicity was observed in any case (data not shown). In addition to this antioxidant capacity in the adipocyte model, the influence of the chestnut extracts on adipocyte maturation was also preliminarily tested to identify potential antiadipogenic activity. In this way, different extract concentrations were added during the maturation process, from day 0 until day 9, when the triglyceride accumulation was measured as described in the Methods section. All the extracts were able to reduce triglyceride accumulation in a dose-response pattern and with statistical significance from 20 µg/mL (Figure  8 ). Once again, no influence of either the upscaling or the spray-drying was observed. Lipid droplet reduction after different doses of the extracts can also be observed in Figure  9. However, in this case, the longer exposure of cells to the extracts (9 days) also reduced the cell viability, especially during the first 2 days when the cells were still pre-adipocytes, making further studies necessary to increase the safety-activity balance of these extracts for anti-adipogenic purposes.
All three extracts showed a significant reduction in ROS levels, as shown in Figure  7 , with no loss of biological activity during either the upscaling process or the spray-drying process. This reduction is dose dependent in all cases and statistically significant from 75 µ g/mL (100 µ g/mL for the semi-industrial spray-dried sample). No toxicity was observed in any case (data not shown). In addition to this antioxidant capacity in the adipocyte model, the influence of the chestnut extracts on adipocyte maturation was also preliminarily tested to identify potential antiadipogenic activity. In this way, different extract concentrations were added during the maturation process, from day 0 until day 9, when the triglyceride accumulation was measured as described in the Methods section. All the extracts were able to reduce triglyceride accumulation in a dose-response pattern and with statistical significance from 20 µ g/mL (Figure  8 ).
Ion source and transfer line were, respectively, set at 230 • C and 250 • C. Compounds were identified by comparison of the spectra with NIST (Gaithersburg, MD, USA) and Wiley (John Wiley & Sons, Inc. , Hoboken, NJ, USA) databases, and only compounds presenting a match factor > 800 were retained. The internal standard was used to semi-quantify volatile compounds based on peak area ratio. Thirty-eight aroma compounds were analysed, of which ten alcohols (1-hexanol, 1octen-3-ol, 1-pentanol, 2-phenylethanol, 2-methyl-1-butanol, 2-methyl-1-propanol, 3-methyl-1-butanol, 3-nonen-1-ol-(E), benzyl alcohol, ethanol), 9 aldehydes (2-heptenal, 2-hexenal, 2-octenal-(E), 3-methyl-butanal, benzaldehyde, heptanal, hexanal, nonanal, octanal), 3 acids (acetic acid, hexanoic acid, octanoic acid), 4 esters (ethyl_heptanoate, ethyl hexanoate, ethyl octanoate, hexyl acetate), 4 ketones (1-octen-3-one, 2-octanone, 4-nonanone, 6-methyl-5hepten-2-one), 3 furan derivatives (2-pentylfuran, 5-methyl-2-furfural, furfural), 2 lactones (2-nonenoic acid gamma-lactone, gamma-nonalactone), 2 phenol derivatives (vanilline, 4-vinylguaiacol), and 1 terpene (limonene), specifically, were found in yeast and sourdough breads, according to the literature [23,29,41-43].
Acknowledgments: The authors warmly thank the three bakers,  Edith Brissiaud , the Cravirola collective, and  Stéphane Marrou , who provided their sourdoughs and welcomed us into a bakery to make experimental breads. We thank  Kristel Moinet , who connected bakers and researchers and leads one of the funding projects. We are also grateful to  Alexandra Gainon  and  Didier Granger  for the milling,  Marie-Hélène Morel ,  Peggy Rigou , and  Luc de Vuyst  for interesting discussions, and  Sandrine Chay  for minerals analysis. This study was financially supported by the  Fondation de France  participatory research project "Gluten: mythe ou réalité 2?", the  French Occitanie region , and  INRAE.
Histological analyses. Histological features were assessed using hematoxylin-eosin-saffron (HES) staining, Alcian blue (AB) staining, and the periodic acid-Schiff (PAS) method in accordance with standard protocols  [31, 32]  in the Histology facility of aBridge platform of UMR 1313 GABI. Images were analysed with Panoramic Viewer software. Lymphocyte populations in the spleen and mesenteric lymph nodes After euthanasia, cell suspensions of spleen (at weaning and 5 days) and mesenteric lymphoid nods (only at weaning) were obtained by mechanically extrusion through a 40-µm nylon cell strainer (BD, Switzerland). Cells were washed through the strainer using 1 ml of Dulbecco's Modi ed Eagle's Medium (DMEM, Gibco, France) supplemented with 10% fetal bovine serum (FBS, Gibco). Erythrocytes were lysed by incubation with the red blood cell lysing buffer Hybri-Max (Sigma-Aldrich) according with manufacturer instructions.
For each sample, aliquots of 10 6 cells were transferred to two 96-well plates (Grenier, France). Following standard protocols as previously described  [27, 30] , cells were stained with one of the next: (i) anti-CD4-FITC, anti-CD3e-percp, anti-T-bet-PE, (ii) anti-CD4-FITC, anti-CD3e-percp, anti-Gata3-PE, (ii) anti-CD4-FITC, anti-CD3e-PerCP, and anti-rorγ-PE or i) anti-CD4-FITC, anti-CD3e-PerCP, and anti-Foxp3-PE (all from eBioscience, France). All stainings were performed in the presence of CD16/CD32 (eBioscience). Samples were subsequently analysed using an Accuri C6 cytometer (BD). The data obtained from the cyto uorimetric analysis were processed using CFlowSampler software (BD). Short-chain fatty acids (SCFAs) determinations. The day of the sacri ce, caecal samples were collected and put in liquid nitrogen at -80°C.
Samples were extracted with water (wt g/vol) and deproteinized overnight at 4°C with the addition of phosphotungstic acid (10%, Sigma). Concentrations of SCFAs were determined using a gas chromatograph (GC; Agilent 6890N Network) equipped with a split-splitless injector (GC Agilent 7890B), a ame-ionization detector, and a capillary column (15 m x 0. 53 mm x 0. 5 µm) packed with SP 1000 (Nukol; Supelco 25236). The ow rate of hydrogen, the carrier gas, was 10 mL/min; the temperatures of the injector, column, and detector were 200°C, 100°C, and 240°C, respectively. 2-ethylbutyrate was used as the internal standard. Two replicates were performed for each sample. We collected the SCFA data and integrated the peaks using the GC's default software (Agilent). To determine the nal concentrations of SCFAs, the samples were weighed before and after protein precipitation to obtain the appropriate multiplication factor.
The 16S RNA raw reads are available at NCBI's SRA repository (Bioproject PRJNA876103; accessions SAMN30638467 to SAMN30638852). Quality control was performed on the resulting FastQ les using FastQC software (https://www. bioinformatics. babraham. ac. uk/projects/fastqc) and bioinformatics analyses were conducted by using the Quantitative Insights Into Microbial Ecology (QIIME 1. 9. 1) software package  [33]  with the subsampled open-reference OTU picking approach  [34]. and the Greengenes reference database (version 13. 8)  [35]. Singleton OTUs and OTUs representing less than 0. 005% of the total number of sequences were removed from the dataset as suggested by the software developers  [36]. Chimeric sequences were identi ed using the BLAST algorithm and removed using QIIME. Microbiota diversity analyses were conducted in R by using the Phyloseq package (v.
1. 22. 3) [37]. Richness and diversity analyses were performed at the OTU level. Alpha diversity and beta diversity were calculated using the Shannon index and Whittaker's index, respectively. Richness was de ned as the total number of OTUs present in each sample. The vegan (v. 2. 5-3) package was used to perform non-metric multidimensional scaling (NMDS) by using Bray-Curtis dissimilarity values. The env_ t function was used evaluate the statistical signi cance of the study variables within NMDS ordination space. In addition, permutational multivariate analyses of variance were performed using distance matrices and the adonis function (alpha level, p < 0. 05). The OTU differential abundance testing was carried out with the metagenomeSeq package (v1. 20. 1) [38]  with OTU counts normalized using the cumulative sum scaling (CSS) method, and a zero-in ated Gaussian distribution mixture model ( tZig function).
Microbial networks inference. Two microbial networks, one for each of the delivery mode (VD and CSD) were inferred at genus level with SPCIT approach  [45]. In brief, SPCIT is an ensemble approach that rst calculate correlations between features using the Sparse Correlations for Compositional data software  [46] , and, in a second step, estimate the signi cant interactions using a data-driven methodology based on partial correlation and information theory  [47]. In the network, every node represents a genus and every edge (connections between nodes) represents a signi cant interaction. Cytoscape software  [48]  was used to visualize the network and to calculate the node centrality values and network topological parameters using the CentiScaPe plugin  [49].
Metagenome functional predictions. The bioinformatics software package PICRUSt  [40]  was subsequently used to predict the functional content of the inferred synthetic metagenomes. Samples clustering was performed on R (version 3. 6. 1, https://www. r-project. org/) accordingly to the KEGG orthology (KOs) abundance pro les, based on Euclidean distance measure as metric and Ward-linkage method. The principal coordinates analysis (PCoA) of KOs Euclidean distances was plotted using the R vegan package  [41]. After PICRUSt prediction we collected from the 6. 909 KOs the ones mapping into the butyrate syntheses pathways. Three KOs (butyrate kinase (buk, K00929), vinylacetyl-CoA 3,2-isomerase (AbfD, 4Hbt, K14534) and butyryl-CoA:acetate CoA transferase (Ato, K01034)) were recovered and the mean abundance was compared at each point (Wilcoxon rank-sum test) between VD and CSD groups.
Mouse gene expression and microarray analysis. Total RNA was isolated from the colon samples using RNeasy Mini Kit (QIAGEN), according to manufacturer's instructions. The RNA integrity was veri ed in Bioanalyzer 2100 with RNA 6000 Nano chips (Agilent Technologies). Only samples with RNA integrity > 9 were selected for the study. Mouse transcriptomics was performed using SurePrint G3 Mouse GE 8x60K Microarray (Design ID: 028005, Agilent Technologies), according to manufacturer's instructions in the Genomic facility and aBridge platform of UMR 1313 GABI. Microarray data are deposited in GEO under the accession number GSE214311. Probe intensities were corrected, log 2 scaled and quantile normalized using the limma R package version 3. 38  [43]. In order to remove noise from expression patterns associated with very weakly expressed genes, the probes with values higher than the lowest 5%-quantile of each sample, in at least 4 samples, were retained.
Statistical analyses were performed using GraphPad v9 (GraphPad Software, San Diego, CA). Survival curves analyses have been performed by Logrank test (Mantel Cox). For weight curves, a multiple unpaired T-test was performed per day with fewer assumptions corrected for multiple comparison with Holm-Sidak method. Normality and variance analysis were performed using Shapiro-Wilk normality test and one-way ANOVA (Brown-Forythe test) respectively. For normal samples (Gaussian distribution) with equal variances three-way ANOVA has been performed to compare the effect of the delivery, treatment and sex for the DNBS trials; multiple comparisons were carried out using Tukey's test. For non-normal samples or/and with unequal variances nonparametric tests have been performed (Kruskal-Wallis test). Multiple comparisons were carried out using Dunn's test.
AB: alcian blue AbfD: vinylacetyl-CoA 3,2-isomerase ATO: butyryl-CoA:acetate CoA transferase AUC: area under the curve. BUK: butyrate kinase. CD: Crohn's disease CFU: colony forming units. CSD: C-section delivery CSS: cumulative sum scaling DMEM: Dulbecco's Modi ed Eagle's Medium DNBS: 2,4-dinitrobenzene sulphonic acid FBS: fetal bovine serum. FDR: false discovery rate. FSA: uorescein sulphonic acid GC: goblet cells. The 16S RNA raw reads are available at NCBI's SRA repository (Bioproject PRJNA876103; accessions SAMN30638467 to SAMN30638852). Microarray data are deposited in GEO under the accession number GSE214311. Competing interest Authors do not have con ict of interest Author's contributions RM, HS, PB and PL designed the study. MB, RM, KT, SC, FF, CK, FM and JP performed the experimental work.
Acknowledgements The authors would like to thank  Dr. Manuel Revilla  for his advice on mice weight modelling and  Dr. Laura Zingarreti  and  Dr. Marie Laure Michel  for valuable discussions and comments on the manuscript. We are grateful to  INRAE's MIGALE facility  for providing the computational resources needed for our bioinformatics analysis (MIGALE,  INRAE ,  2020. Migale bioinformatics Facility , doi:  10. 15454/1. 5572390655343293E12 ). We thank the histology facility and aBridge platform of  UMR 1313  GABI and the MIMA2 platform for access to the virtual slide scanner (Pannoramic SCAN,  3DHISTECH ). We acknowledge the staff of the  INRAE Infectiology of Fishes and Rodents Facility (IERP-UE907, Jouy-en-Josas Research Center, France ) and Micalis axenic animal facilities (ANAXEM) in which animal experiments have been performed.
Acknowledgments is work was a part of a project funded by the  CNRST  ( Centre National pour la Recherche Scientifique et Technique, Maroc ), the  UMP (Université Mohamed Premier, Oujda, Maroc) , the  ANPMA (Agence Nationale des Plantes Medicinales et Aromatiques, Maroc) ,  Conseil Départemental d'Eure et Loir , and the  Région Centre-Val de Loire.
9] [30]  The present study includes larger numbers of patients with NAFLD etiologies,  31  the milder NAFLD, with no evidence of hepatocellular injury, and the nonalcoholic steatohepatitis (NASH) with the presence of inflammation and hepatocyte injury. It also includes patients with HCC and a control group of 45 adults and 60 teenage individuals. A few patients with less common etiologies, such as primary biliary cirrhosis and primary sclerosing cholangitis, were included, but the data are given for reference only.
Samples The present study focuses on a survey of 293 patients from a French cohort dedicated to HCC investigation and stored at the INSERM CRCL Hépatologie biobank. Controls comprise a group of 45 adult blood donors provided by Lyon Etablissement Français du Sang (EFS) (22 men and 23 women, Table  1 ). Sixty samples were drawn from healthy teenage controls (27 men and 33 women) from the French VITADOS study (NCT01832623) targeted at a cross-sectional evaluation of bone, vessels, nutrition, and vitamin D status in healthy children and teenagers aged 10-18 years. 32 All samples from these 3 cohorts are from individuals living within a close perimeter around Lyon, France. Samples from patients with NAFLD were obtained from Clinique Saint-Michel in Toulon (18-71 year old) with body mass index values of 31-52, that is, in the obesity range.
Statistical Methods A first set of calculations and graphics was generated using MedCalc statistical software, version 18. 6 (MedCalc Software bvba, Ostend, Belgium). For normality tests, quantitative parameters were compared between the different groups using a Student t-test. For non-normal populations, the nonparametric Mann-Whitney rank-sum test was used. The Wilcoxon test was used for the paired resection samples. Coexisting populations of d 65 Cu values were identified in 2 ways. First, the empirical probability density functions (pdf, or histograms) and cumulative density functions (cdf) were established. The cdf were fitted with Gaussian mixtures using the Matlab fitgmdist function which implements the iterative maximum-likelihood Expectation-Maximization (EM) algorithm. Mixtures of 2, 3, and 4 gaussian components were tested using the Pearson's chi-squared test.
Acknowledgments: The authors thank the  Labex Institut des Origines de Lyon , the  Institut National des Sciences de l'Univers , the  Cancéropole Lyon Auvergne Rhône Alpes , and the  Ecole Normale Supérieure de Lyon  for financial and technical support. This study was performed using the  INSU/CNRS MC-ICP -MS national facility at  ENS-Lyon  with the help of  Mélanie Simon  and  Eloise Godfroy  for sample preparation and analysis. The authors are grateful to the 2 reviewers for their time and insightfulness. Janne Blichert-Toft  is gratefully thanked for English editing.
Funding: The authors are grateful to the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) Lyon for its financial support within the program " Investissements d'Avenir " of the French government operated by the  National Research Agency (ANR). Support by the  ONCOSTARTER program  of the  Cancéropole Lyon Auvergne Rhône Alpes (CLARA, ISOLIVER  grant) is gratefully acknowledged. This study was also funded by the  Institut National des Sciences de l'Univers  and the  Ecole Normale Supérieure de Lyon.
First Approach by the Relevant Metabolites Identifying the routes through the degradative intermediates remains possible if growth is slowed down by depleting the C and N sources of the medium. The strain preferentially utilizes the aromatic compounds over glucose, their degradation can be followed along with the growth by RP-HPLC-DAD ( reverse-phase-HPLCdiode array detector ) , RP-HPLC-MS ( mass spectrometry coupling in electrospray ionization [ESI] mode ) , and/or GC-MS ( gas-liquid chromatography-mass spectrometry in electron-impact ionization [EI] mode ) of the derivatized metabolites  ( Liebgott et al. , 2007 ( Liebgott et al. , -2009 ) ). Accurate identification is achieved by their respective retention time, UV-visible spectra, and m/z mass data, over all standards available and spectra from the databank ( NIST or Wiley ).