dataesr/scientific-paragraphs-categorization huggingface dataset:
Maximum respiration potential (VO 2 max) was determined in a metabolic treadmill (Columbus instruments, Columbus, OH, USA) with an incremental speed protocol. During the run, VO 2 and VCO 2 are measured. The experiment is stopped when mice are exhausted, RER is above 1 for more than one  To enable comparisons between the two studies, data were normalized to Na in a, b and total metal concentrations in c, d. In panels c and d, the black line and gray error band represent a linear regression and its 95% confidence interval, respectively. Source data are provided as a Source Data file. interval, or the VO 2 reaches a clear plateau. Liver mitochondrial complex I and IV activity (Mitosciences, Eugene, OR, USA), citrate synthase (Sigma) were determined according to the manufacturer's protocols. Liver and muscle ROS were determined by 4-HNE detection in PBS tissue lysates using an ELISA-based method (Cell Biolabs, San Diego, CA, USA).
P is computed by a moderated ttest (limma), two-sided and FDR-corrected with age as a covariate. In panels b and c, the black line and gray error band represent a simple linear regression and its 95% confidence interval, respectively. Source data are provided as a Source Data file. either analyzed in positive (acidic solvent) or negative (basic solvent) ionization mode. GC-MS was performed on bistrimethyl-silyl-triflouroacetamide derivatized samples in a 5% phenyl GC column. Proteomics. Proteomics was performed as described in Yu et al. 48. Briefly, total proteome was isolated using a RIPA-M buffer (pH 7. 5; nonidet NP-40, 0. 1% sodium doexycholate, 150 mM NaCl, 1 mM EDTA, 50 mM Tris, protease inhibitor cocktail, 10 mM NaF, 5 mM 2-glycerophosphate) and lysed with a 8 M urea buffer (pH 8. 1, 75 mM NaCl, 10 mM NaF, 5 mM 2-glycerophosphate; 8 M urea; protease inhibitor cocktail).
We then removed peptides that are identified in only one sample, as well as proteins identified by a single unique peptide sequence. Finally, peptidelevel intensities were summarized to the protein level with the MsqRobSum R package  50  , (proteins were grouped by gene annotations). Data analysis. Outlier measurements were removed using the 'normal' method (differences between each value and the mean divided by standard deviation) through the R package 'outliers'. Less than 5% of measurements were removed this way. All correlation analyses were performed by parallel linear regression using the 'limma' R package using age as a covariate  51. P-values were corrected for multiple testing through the Benjamini-Hochberg false discovery rate method. Pearson correlation coefficients were computed separately through the 'cor.
test' function. Age-corrected values were computed by estimating the effect of age through a linear model value ~age and extracting the intercept + residuals of the model. Those values were used for visualization only, but statistical analyses were performed using age as a covariate instead. MSEA. In order to use the most complete and up-to-date repository of metabolite information, we based our analysis on the Human Metabolome Database (HMDB). We downloaded the database and parsed information about biological processes and cellular components from the relevant entries in the database, then generated a search file for the ClusterProfiler R package  52. For MSEA, metabolites were sorted based on their degree of correlation with each metal (calculated with limma package, see 'analysis' section) using the following formula: -log 10 (P-value)* Pearson correlation coefficient.
Metabolite Set enrichment analysis (MSEA) was performed using the ClusterProfiler package in R software. GSEA. Gene set databases were downloaded from the Molecular Signatures Database (MSigDB) v7. Fig. 4  Enrichment analyses in the liver metabolome and proteome converge towards common associations between ions and mitochondrial and fatty acid metabolism. a, b Metabolites and proteins were sorted on their correlation with individual metals in the liver, then metabolite or gene -set enrichment analyses (MSEA, (a), GSEA, (b)) were performed. For each metal, the top three gene or metabolite sets are pictured. FDR-corrected p-values. **Adj. P < 0. 01, *Adj. P < 0. 05, 'Adj. P < 0. 1 (c) Network representation, combining the most significant correlations between metals, between metals and phenotypes (Spearman Rho and limma test, both), as well as the metabolite and gene set enrichment analyses.
All tests were two-sided when applicable and p-values are FDR corrected. To limit the number of nodes, only nodes with very significant associations are pictured (Adj. P < 0. 01), but all edges with Adj. P < 0. 05 are drawn. Source data are provided as a Source Data file. proteins were sorted by -log 10 (P-value)* Pearson correlation coefficient and GSEA was performed with the ClusterProfiler R package. Networks. All network representations show the correlation analyzes and MSEA/ GSEA results outlined above. Only correlations with Adj. P-values < 0. 005 were drawn. Network visualizations were generated using the igraph and ggraph R packages. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
14 c Scatterplots of Spearman Rho correlation coefficients, and Spearman test FDR-corrected p-value calculated between metals and age in the data from Zhang et al. vs. those of the present study. The Zhang dataset measured metals at 16 timepoints at 2 months interval, as opposed to our studies' 3 timepoints, yet trends converge towards the same age effects in the liver, muscle, and kidney. The effects of age in the heart do not match, but are very low in both studies. d Scatterplots of Spearman Rho correlation coefficients and Spearman test FDR-corrected p-value calculated between pairs of metals from Zhang et al. vs. those of the present study.
Acknowledgements We thank all members of  J. Auwerx 's laboratory for helpful discussions. This project has received financial support from the  CNRS  through the  MITI interdisciplinary programs , the  Bullukian Foundation , the  École Polytechnique Fédérale de Lausanne  (EPFL),  European Research Council  ( ERC-AdG-787702 ) and  Swiss National Science Foundation  ( 31003A_179435 ) and a  Global Research Laboratory grant  from the  National Research Foundation of Korea  ( 2017K1A1A2013124 ). L. S. was supported by  EPFL  and  ENS de Lyon  convention thanks to  P. Gillet. L. J. E. G. was supported by the  European Union Horizon   2020 Marie Skłodowska-Curie Individual Fellowship "AmyloAge" (896042 ).
Code availability Data analysis was performed in R software version 3. 5. 2. The following open-source packages were used in analysis and figure generation: MsqRobSum: proteomics; limma: differential analyzes and parallel linear regressions; ClusterProfiler: Gene and Metabolite set enrichment analyzes; ggplot2: figure generation; igraph, ggraph: network visualizations. All code used in the paper is available on request.
The study was approved by the French Society of Anesthesiology and Critical Care Medicine Research Ethics Board (HIGHSEPS study, IRB number 00010254). Written informed consent was obtained from participating patients. Patient demographics, diagnosis, Simplified Acute Physiology Score II (SAPSII) and Sepsisrelated Organ Failure Assessment (SOFA) severity scores  [25, 26] , organ supportive therapies, including renal replacement therapy and vasopressor use, and clinical data were prospectively collected. Data regarding the site of infection were gathered. Data on ICU and in-hospital mortality at 28 days (Day 28), duration of mechanical ventilation, number of days alive without mechanical ventilation at Day 28, length of stay in the ICU and hospital stay were collected. At admission, plasma concentrations of total cholesterol, HDL-C, LDL-C and triglycerides were measured.
Statistical analysis Qualitative data are expressed as absolute numbers and proportions and were compared using the chi-square test or Fisher's exact test as appropriate. Quantitative data are expressed as medians and interquartile ranges and were compared using the Mann-Whitney or Kruskal-Wallis tests as appropriate. For Day-28 mortality discrimination, the receiver operating characteristic curve (ROC) analysis tested the best threshold values (using the Youden index) of total cholesterol (TC), HDL-C, TC/HDL-C ratio, LDL-C and triglycerides, and the area under the curve (AUC) was calculated. The Day-28 survival rate was analyzed by the Kaplan-Meier test and compared by a log rank test. To assess whether hepatic dysfunction was an independent factor of mortality, a multivariate analysis was carried out using a binary logistic regression model.
Variables with nominal 2-tailed p values less than 0. 1 were entered into the multivariate model, except for variables with obvious collinearity. The final models was selected using the backward stepwise regression using the AIC and the Tjur's R2 coefficient of discrimination. All statistical analyses were performed using R software (R Core Team, 2014) figures were produced using 'ggplot2 package' and statistics using 'stat package'. A p value < 0. 05 was considered statistically significant.
Identification of a Dol-PP-GlcNAc UDP-GlcNAc GlcNAc GT candidate in S. acidocaldarius To identify the glycosyltransferase that conducts this first committed synthetic step in the Nglycosylation process in S. acidocaldarius, we searched for similar biosynthesis processes to identify potential homologs. The bacterial enzyme MurG transfers a GlcNAc residue from UDP-GlcNAc to the C4 hydroxyl group of MurNAc-(pentapeptide)-PP-undecaprenol to produce the lipid-linked β-(1,4) disaccharide known as lipid II  (Ha et al. , 1999 , Men et al. , 1998). Although MurG does not synthesize chitobiose like S. acidocaldarius, the enzyme uses a similar lipid-linked acceptor and an identical sugar donor (UDP-GlcNAc) and therefore presents a valid candidate for evaluation. MurG has been classified by the Carbohydrate Active enZyme database (http://www.
cazy. org/) as a family 28 GT  (Lombard et al. , 2014). Up to now, only members of the archaeal families Methanobacteriaceae and Methanopyraceae, which synthesize pseudomurein as their cell wall structure  (Albers and Meyer, 2011, Meyer and Albers, 2020) , were found to have a GT28 family homolog in the CAZy database  (Lombard et al. , 2014). Another candidate homolog is the eukaryal protein complex formed by Alg13 and Alg14 that transfers GlcNAc from UDP-GlcNAc to the C4 hydroxyl of GlcNAc-PP-dolichol to produce the β-(1-4) chitobiose core of the eukaryal N-glycan. Alg13 and Alg14 are classified as GT 1 family members but are distantly related to GT28 family enzymes  (Lombard et al. , 2014). Alg13 and Alg14 form the C-and N-terminal domains, respectively, of a functional heterodimeric GT enzyme (Fig 2 ); Alg14 contains the acceptorbinding site and the membrane association domain, while Alg13 possesses the UDP-GlcNAc-binding pocket.
Indeed, in lower Eukarya, e. g. in D. discoideum and E. histolytica, having the exchanged domain orientation, lacking an N-terminal TMD  (Averbeck et al. , 2007). Despite the low sequence identity, a similar structural topology of Agl24 to Agl14/13 and MurG is not surprising, as GTs in general show only a limited number of structural folds. GTs mainly possess a GT-A or GT-B fold, whereas oligosaccharyltransferases and a number of recently characterized GTs display a GT-C, GT-D, or GT-E folds, respectively  (Mestrom et al. , 2019). Crystal structures of the bacterial MurG (PDB:3s2U, 1F0K, 1NLM) and the C-terminal half of the eukariotic Alg13 (PDB:2jzc) show typical GT-B structural characteristics  (Brown et al. , 2013 , Ha et al. , 2000 , Hu et al. , 2003 , Wang et al. , 2008 , Raman et al.
, 2008). In the Alg13 crystal structure this C-terminal helix is associated with itself, as Agl14 is missing. The orientation is predicted to be similar to the linker in MurG and the change is indicated by the black arrow (in Fig  2A )  (Gao et al. , 2008). The structural model of Agl24 is also predicted to contain a C-terminal helix, which interacts with the N-terminal part of the protein (Fig  2A ). We revealed a conserved GGxGGH14 motif (Agl24) within the N-terminal sequences of  MurG and Alg14 (Fig 1D, S2, and 2B). This motif is located in the cavity between the two different structural domains next to the substrate binding pocket. The crystal structure of MurG (PDB: 3s2u) in complex with UDP-GlcNAc shows the close proximity of the sugar donor to the conserved residues (Fig  2B )  (Brown et al.
Next to the Alg13-or Alg14-like cluster, there is a second distinct cluster of EpsF-or EpsE-like sequences, respectively. The function of EpsF and EpsE has been studied in Lactobacillales, where these enzymes are involved in exopolysaccharide production  (De Vuyst et al. , 2001 , Kolkman et al. , 1997). Here, similar to Alg14/13, the combined activity of EpsE and EpsF links either a glucose (Glc) from  UDP-Glc (van Kranenburg et al. , 1999 , Kleerebezem et al. , 1999)  Nevertheless, in the homology searches used to expand the previous set  (Lombard, 2016) , some of the hits formed well-supported branches in preliminary phylogenies (Sup Data 1). By including some of them in expanded Alg13/EpsF and Alg14/EpsE datasets, none of them branch within the Alg13/EpsFlike or Alg14/EpsE-like clades. Instead, they are fused Agl24-like sequences, mainly from Euryarchaeota and various DPANN phyla.
Acknowledgments We thank  Vladimir I. Torgov ,  Vladimir V. Veselovsky , and  Leonid L. Danilov  from the  N. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia  for sharing the synthetic acceptor molecules. We thank  Prof. Alexander J. Probst  from the  University Duisburg-Essen  for fruitful discussions and  Mr. Tom Snelling  for assay assistance.
Physicochemical and morphological characterization of CuO-NPs To determine structural, chemical and morphological characteristics of biosynthesized CuO-NPs various characterization techniques were carried out including XRD, FTIR, SEM and DLS. To conrm the synthesis of CuO-NPs, X-ray diffractometer was used. CuO-NPs were subjected to XRD instrument (Model-D8 Advance, Germany) in range of 10-70 with step time of 0. 55 seconds. Data was analyzed through Cu Ka radiation with wavelength of 1. 5406 Å. Debye-Scherrer equation was used to determine average size of CuO-NPs.
55 AE 0. 08 mg g À1 DW (Table  1 ). These phytochemicals owing to their reduction capacities might have served as a reducing agent for reduction of precursor salt into CuO-NPs. Previously, it has also been reported that silymarin has a strong reducing potential. 33, 50 Our results are in harmony and supported by previous report. 111 ), (  111 ), (  112 ), (  200 ), (  202 ), (  020 ), (  203 ), (  113 ), (  311 ) and (220), conrmed with JCPDS le no. 048-1548. 52 Data indicates crystalline monoclinic phase with average size of 15 nm using the Debye Scherrer equation. These results are supported by the previous reports. 53, 54. 1. 3 Scanning Electron Microscopy (SEM). Morphological characteristics of biosynthesized CuO-NPs were investigated by performing SEM. SEM micrograph shows surface morphology of CuO-NPs exhibiting spherical shape with some extent of agglomeration (Fig.
0 ∓2. 3, M French = 8. 7 ∓2. 5, W = 498, p =. 04). However, this effect may be due to the effect of exposure to odors, as Germans were significantly more exposed to odors in their work compared to French (25 Germans sometimes or often exposed vs 9 French, X2 [2] = 15, p =. 0006). To eliminate the effect of exposure, we filtered the data to extract individuals who were not exposed to odors, which corresponded to French and 12 Germans. In order to have equivalent groups, we then randomly selected 12 Germans in this sample. A Kruskal-Wallis test was conducted to compare verbalizations in the 6 main categories according to language. There were 99 replications in order to get a stable p-value. The mean p-value was used to interpret the test. In this case, there was no longer a difference between Germans and French for the number of references to Characterization (M French = 1.
9 ∓0. 9, M Germans = 1. 9 ∓1. 2, t = 0. 26, p =. 69), suggesting that the interindividual variability that was observed is more likely due to diverse exposure to smells rather than to language per se. Then, the twenty odorants were organized in four different spaces of representation, with the construction of Euclidean distance matrices: Then, a Principal Component Analysis (PCA) was conducted on these matrices and the first extracted principal components were correlated to each other to determine if the different spaces shared similar organization. Finally, to compare the different spaces, we used Dissimilarity Matrix Analysis (Mantel tests, 9999 permutations). All the statistical analysis were performed with R software (version 3. 5. 0), with the packages FactomineR for the PCA, and ade4 for the Mantel tests.
Second, there was no relationship between the e-nose response and both measures of perception when we considered the odorants altogether. However, when separating them into two groups according to the degree of inter-individual variability in the perception they evoked, we found a relationship between the e-nose distance matrix and both ratings and descriptions matrices, only for the odorants eliciting low variability in hedonic ratings among the subjects. If we look at the typical source or associations of these 10 odorants in PubChem (molecular database) and atlases for perfumers, four of them are typically associated with rot or decaying food (valeric acid, butyric acid, propanoic acid, acetic acid), four are typically associated with food (Lcarvone as mint, geraniol as citrus, benzaldehyde as almond, and butyl butyrate as fruit), and one is associated with medicinal use (cineole as eucalyptus).
For the latter, one may collect all available data on deorphanized receptors and use their ligands as stimuli in the analysis. Today, such databases do exist  [38] [39] [40]  but are likely not large enough yet to generate datasets that machine learning techniques can include in their algorithms. In this context, it is important to set up interdisciplinary projects combining chemistry, biology, computational sciences and psychology. In summary, as our study and Poivet and colleagues  [21]  point out, if biology is not accounted for in computational models, one cannot achieve accurate and meaningful understanding of the stimulus percept issue. Artificial noses can be a way to study it by circumventing the difficulty to observe in vivo responses of the human olfactory receptors, but future experiments should tend to include natural systems' biology in their investigation.
freudenreichii and subsp. shermaniidhas been considered irrelevant. The only difference between these subspecies was whether they had the ability to reduce nitrate (subsp. freudenreichii) or ferment lactose (subsp. shermanii). Because these phenotypic traits are not genetically coupled, they had to be classified as separate phenotypes. Table  1  lists important species for food fermentation, along with their previous and new nomenclature. For dairy-based propionibacteria, a new tool based on 16S rDNA sequences is available today: Dairy DB is a custom-made dairy-related, manually cured database taking advantage of the today available powerful bioinformatics tools  (Meola et al. , 2019). Former methods of propionibacteria identification using molecular tools are still valid, but WGS has increased in importance, and it allows multi-locus sequence typing to be used to group bacteria.
Genetic Properties At the time of writing, 36 complete genome assemblies for dairy propionibacteria (specifically, the species P. freudenreichii, A. acidipropionici and A. jensenii) are publicly available in the NCBI's genome database (National Center for Biotechnology Information). The size of the genomes ranges from 2. 5 to 3. 7 Mb (Table  3 ). A. acidipropionici genome contains nearly 1000 more proteincoding genes than those of P. freudenreichii and A. jensenii. All the genes encoding transport proteins have a larger number of units in A. acidipropionici than in P. freudenreichii. As downsizing of the genome is associated with specialization, A. acidipropionici still has extensive metabolic potential. The most commonly reported complete genome sequences are those of P. freudenreichii strains.
Chewing Simulator The chewing simulator connected to the PTR-ToF-MS apparatus was specifically developed for studying the in vitro release of aroma compounds  [15, 31]. The samples used were 5 g pate pieces. The operating conditions included force and shear angle of 25 and 2 • , respectively, unless otherwise stated. The influx of artificial saliva into the chewing simulator was carried out at a setting of 1 mL/min. The data were collected in five replicates (n = 5) and averaged. Nitrogen flow to the simulator was at 70 mL/min. For weak mastication, the chewing simulator operation conditions for force and shear angle were 5 daN and 2 • , respectively. For normal mastication, these were 30 daN and 3 • , respectively. The main ions for each volatile compound were checked using reference single-aroma compound solutions.
0. 3. 3  (Jeraldo et al. , 2014)  and taxonomy assigned according to Silva v128. Archaeal sequences were analysed following the standard QIIME 1 pipeline  (Caporaso et al. , 2010)  and taxonomy assigned with RIM DB  (Seedorf et al. , 2014). Bioinformatic analysis metrics are summarised in Supplementary Table  S1. Final OTU tables containing ! three sequences present in at least three samples were analysed in R using the package 'vegan' version 2. 5-7  (Oksanen et al. , 2020). Diversity indices (Shannon, Simpson, Richness and Evenness) were computed using implemented functions, and statistical differences were tested using the non-parametric Kruskal-Wallis test to evaluate the effect of plant at each incubation time. For b-diversity analysis, OTU tables were rarefied to an even depth based on the lowest count of filtered raw reads.
These were 2 846 and 1 209 reads for bacteria, at time points 3 h and 12 h, respectively; 316 and 848 for Archaea at 3 h and 12 h, respectively, and 1 000 for protozoa for both sampling times. Bray-Curtis method was used for computing dissimilarity indices with the vegdist function. Principal Coordinate Analysis (PCoA) was performed on dissimilarity matrices with prcomp function. Permutational multivariate ANOVA was performed using Adonis function. Correlations were computed using the function 'corr' of vegan, and 'Hmisc' and 'corrplot' packages were used for plotting correlation matrices. Differential abundance analysis to evaluate the effect of plant at each incubation time was done with MicrobiomeAnalyst  (Chong et al. , 2020)  using default data filtering (four minimum count, 20% prevalence, low variance filter: 10% interquantile range), relative log expression (RLE) for normalisation and the metagenomeSeq package  (Paulson et al.
Degradation and fermentation data: Calculations and statistical analyses Rumen degradability (D) of DM and N were calculated using an exponential model with lag time  (Denham et al. , 1989) : D ðtÞ ¼ a þ b ð1 À e À cðt À LÞÞ where D is the degradation after t hours of rumen incubation; t = hours of rumen incubation (0, 3, 6, 12, 24, 48 and 96 h); a = rapidly degradable fraction (%); b = slowly degradable fraction (%); c = degradation rate constant of the b fraction (per hour); L = lag time before the beginning of degradation of the b fraction (h). The non-linear procedure (PROC NLIN) of SAS v9. 4 (SAS Inst. Inc. , Cary, NC, USA) was used to fit degradation data to the model. This model was chosen after a preliminary comparison of models with or without lag time, because lag time was higher than 3 h for four of the six feeds, and because the model with lag time globally led to a lower sum of squares than the model without lag time.