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431 results for “Fermentation”
Database containing Raw Data of Growth and Survival of Pathogens and Microbiota from Fate Studies in Artisanal Fermented Foods
<p>Deliverable 5.5 is a spreadsheet containing metadata and raw data obtained from the fate studies conducted in Workpackage 5. In case of reutilisation of these data, the source should be properly cited.</p>
Supplementary material 1 for Thesis Chapter 2 - An obligate aerobe hybridises hydrogen fermentation and carbon storage to adapt to hypoxia
<p>Supplementary material for paired comparative metabolomics and proteomics on <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 during hypoxia, as part of chapter 2 for the thesis "Biochemistry and physiology of mycobacterial adaptations to energy starvation".</p> <p>Description below is identical to that provided in 'Summary.docx'. </p> <p>Proteomics_analysis.xlsx</p> <p>Includes raw and annotated data for comparative proteomics experiments for chapter 2.</p> <p>The tab ‘Annotated comparisons’ contains fold change and p values for the comparisons for each protein from <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 derived from LFQ-Analyst. Functional annotations are derived from KEGG pathways and modules, which utilise the spreadsheets in ‘MSMEG gene annotation’ ‘Protein ids to KEGG pathway’ and ‘KEGG Pathway and Modules’ to link KEGG annotations to MSMEG_XXXX gene identifiers and MSMEG_XXXX to Uniprot ID. Output from LFQ-Analyst is provided in the ‘Full_dataset’, ‘Imputed_matrix’ and ‘Original_matrix’ tabs.</p> <p>Data provided by the Monash Proteomics and Metabolomics Facility for upload into LFQ-analyst are provided as the ‘combined_protein.tsv’ and ‘LFQ-Analyst_experimental_design.txt’.</p> <p> </p> <p>Metabolism_analysis.xlsx</p> <p>Includes annotated data for comparative metabolomics experiments for chapter 2. Within the spreadsheet, TR refers to transition, ST refers to stationary phase and EXP refers to exponential phase. The tabs ‘TRvsEXP’, ‘STvsTR’ and ‘STvsEXP’ contain fold change and p values for each metabolite detected for each comparison. The remaining tabs categorise the metabolites based on KEGG database and IDEOM annotations. For broader categories (‘Lipid metabolism’,’ Carbohydrate metabolism’, ‘Cofactor metabolism’, ‘Nucleotide metabolism’, ‘Amino acid metabolism’ and ‘Peptides’ tabs), annotations were derived directly from filtering the ‘Map’ column of ‘Comparisons’ tab of the IDEOM worksheet (IDEOM_analysis.xlsb). Screenshots are pasted into each tab to show the filtering settings. The remaining tabs comprise narrower categories which were manually annotated with reference to KEGG pathways and maps, and also include rows corresponding to the proteomics data for these categories, so the proteomics and metabolomics data can be interpreted together. The ‘Proteomics’ tab contains the proteomics data referenced by these tabs, which is a copy of the ‘Annotated comparisons’ tab from the ‘Proteomics_analysis.xlsx’ file. A value of ‘N’ indicates the metabolite or protein (at least according to the name in the same row) was not found in these datasets.</p> <p>The IDEOM worksheet (IDEOM_analysis.xlsb) was provided by the Monash Proteomics and Metabolomics Facility and was used for further analysis and for annotations. ‘Data_for_MA_no_normalization.csv’ was also provided by the Monash Proteomics and Metabolomics Facility for upload into Metaboanalyst (https://www.metaboanalyst.ca/).</p>
Untargeted metabolomic approach using UHPLC-HRMS to unravel the impact of fermentation on color and phenolic composition of Rosé wines
<p>Color is a major quality trait of rosé wines due to their packaging in clear glass bottles. This color is due to the presence of phenolic pigments extracted from grapes to wines and products of reactions taking place during the wine-making process. This study focuses on changes occurring during alcoholic fermentation of Syrah, Grenache and Cinsault musts, conducted at laboratory (250 mL) and pilot (100 L) scales. Color and phenolic composition of the musts and wines were analyzed using UV-visible spectrophotometry and metabolomics fingerprints were acquired by Ultra-High Performance Liquid Chromatography−High Resolution Mass Spectrometry. The acquisition dataset protocol is available in the affiliated publication on Molecules</p>
Evaluating the Efficacy of the Mediterranean Diet to the Low- Fermentable, Oligosaccharides, Disaccharides, Monosaccharides, and Polyols (FODMAP) Diet in Treating Irritable Bowel Syndrome(IBS)
ClinicalTrials.gov study NCT05807919. IPD Sharing: NO. Countries: 1. Publications: 1.
A Human Trial to Evaluate the Efficacy and Safety of Fermented Turmeric in Subjects Showing Mild Hepatic Injury
ClinicalTrials.gov study NCT01634256. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Fermented Vegetables on Gut Microflora and Inflammation in Women
ClinicalTrials.gov study NCT03407794. IPD Sharing: NO. Countries: 1. Publications: 2.
Effect of Fermentable Carbohydrate on Glucose Homeostasis
ClinicalTrials.gov study NCT01841073. IPD Sharing: NO. Countries: 1. Publications: 3.
Effect of Fermented Soybean (FSB) Supplementation on Gas-troesophageal Reflux Disease (GERD)
ClinicalTrials.gov study NCT06524271. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Transient acetaldehyde production by SO2 producing S. cerevisiae promotes survival of Oenococcus oeni during co-fermentation
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Maximizing long-term biohydrogen production with Clostridium thermocellum in high solids lignocellulosic biomass fermentations
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Data from: Fermentation of the sugar kelp (Saccharina latissima) – exploring the potential of the kelp’s native microbiota as starter culture and the microbiological food safety of fermented products
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Data from: Effects of fermented vegetables on the gut microbiota for prevention of cardiovascular disease
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Data from: Enhancing fermentation performance through the reutilisation of wine yeast lees
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Distributed flux balance analysis simulations of serial biomass fermentation by two organisms
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Data from: Microbial composition play the leading role in volatile fatty acid production in the fermentation of different scale of corn stover with rumen fluid
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Bacterial dispersal and drift drive microbiome diversity patterns within a population of feral hindgut fermenters
<p>Studies of microbiome variation in wildlife often emphasize host physiology and diet as proximate selective pressures acting on host-associated microbiota. In contrast, microbial dispersal and ecological drift are more rarely considered. Using amplicon sequencing, we characterized the bacterial microbiome of adult female (<i>n</i> = 86) Sable Island horses (Nova Scotia, Canada) as part of a detailed individual-based study of this feral population. Using data on sampling date, horse location, age, parental status, and local habitat variables, we contrasted the ability of spatiotemporal, life history, and environmental factors to explain microbiome diversity among Sable Island horses. We extended inferences made from these analyses with both phylogeny-informed and phylogeny-independent null modeling approaches to identify deviations from stochastic expectations. Phylogeny-informed diversity measures were correlated with spatial and local habitat variables, but null modelling results suggested that heterogeneity in ecological drift, rather than differential selective pressures acting on the microbiome, was responsible for these correlations. Conversely, phylogeny-independent diversity measures were best explained by host spatial and social structure, suggesting that taxonomic composition of the microbiome was shaped most strongly by bacterial dispersal. Parental status was important but correlated with measures of β-dispersion rather than β-diversity (mares without foals had lower alpha diversity and more variable microbiomes than mares with foals). Our results suggest that between host microbiome variation within the Sable Island horse population is driven more strongly by bacterial dispersal and ecological drift than by differential selective pressures. These results emphasize the need to consider alternative ecological processes in the study of microbiomes.</p>
Data from: Postoperative changes in fecal bacterial communities and fermentation products in obese patients undergoing bilio-intestinal bypass
We assessed the gut microbial ecology of 11 severely obese patients before and after bilio-intestinal bypass (BIB). Fecal samples were evaluated for microbial communities using 16S rDNA Illumina sequencing, real-time PCR targeting functional genes, and gas chromatography of short chain fatty acids (SCFAs). At 6 months after surgery, subjects exhibited significant improvements in metabolic markers (body weight, glucose, and lipid metabolism) compared with baseline. The fecal microbiota of post-surgery individuals was characterized by an overall decrease of bacterial diversity, with a significant reduction in Lachnospiraceae, Clostridiaceae, Ruminococcaceae, Eubacteriaceae, and Coriobacteriaceae. On the contrary, there were significant increases of genera Lactobacillus, Megasphaera, and Acidaminococcus and the family Enterobacteriaceae. The pH was decreased in fecal samples from patients after BIB and SCFA profiles were altered, with lower percentages of acetate and propionate and higher levels of valerate and hexanoate. Some changes in the bacterial populations were associated with variations in the patients' metabolic health parameters, namely Gemmiger and glucose, Lactobacillus and glucose, and Faecalibacterium and triglycerides. The results from this study of BIB patients furthers our understanding of the composition of gut microbiota and the functional changes that may be involved in improving obesity-related conditions following weight-loss surgery.
Data and code: A fast method to distinguish between fermentative and respiratory metabolisms in single yeast cells
<p>If you have questions, please contact the first autor via the email lauraluzia@gmail.com<br>Link to the bioRxiv preprint -> https://www.biorxiv.org/content/10.1101/2023.06.23.546324v1</p>
Fig. 2 in Lactobacillus suantsaii sp. nov., isolated from suan-tsai, a traditional Taiwanese fermented mustard green
Fig. 2. Phylogenetic tree based on the concatenated housekeeping gene sequences (pheS and rpoA) showing the relationship of Lactobacillus suantsaii sp. nov. L88T with strains of closely related species. The tree was reconstructed by the neighbour-joining method on the basis of a comparison of 1138 bp, and Leuconostoc mesenteroides subsp. mesenteroides ATCC 8293T was used as an outgroup. Bootstrap values based on 1000 replicates are shown at branch nodes for neighbour-joining, maximum-likelihood and minimum-evolution methods, respectively. Bar, 5 % sequence divergence.
Fig. 3 in Lactobacillus suantsaii sp. nov., isolated from suan-tsai, a traditional Taiwanese fermented mustard green
Fig. 3. Phylogenomic tree of Lactobacillus suantsaii sp. nov. L88T with strains of closely related species. The tree was reconstructed by the maximum-likelihood method on the basis of a comparison of 660 core genes. Leuconostoc mesenteroides subsp. mesenteroides ATCC 8293T was used as an outgroup. Bar, 10 % sequence divergence.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.