During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.foodchem.2016.05.080 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review.
BMC Med Inform Decis Mak
January 2025
Department of Digital Systems, University of Piraeus, Piraeus, Greece.
Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
View Article and Find Full Text PDFThe microbiota-derived bile acid taurodeoxycholic acid improves hepatic cholesterol levels in mice with cancer cachexia.
Gut Microbes
December 2025
Metabolism and Nutrition Research Group, Louvain Drug Research Institute, UCLouvain, Université catholique de Louvain, Brussels, Belgium.
Alterations in bile acid profile and pathways contribute to hepatic inflammation in cancer cachexia, a syndrome worsening the prognosis of cancer patients. As the gut microbiota impinges on host metabolism through bile acids, the current study aimed to explore the functional contribution of gut microbial dysbiosis to bile acid dysmetabolism and associated disorders in cancer cachexia. Using three mouse models of cancer cachexia (the C26, MC38 and HCT116 models), we evidenced a reduction in the hepatic levels of several secondary bile acids, mainly taurodeoxycholic (TDCA).
View Article and Find Full Text PDFA practical approach to the spatial-domain calculation of nonprewhitening model observers in computed tomography.
Med Phys
January 2025
Department of Nuclear Medicine and Medical Physics, Karolinska University Hospital, Stockholm, Sweden.
Background: Modern reconstruction algorithms for computed tomography (CT) can exhibit nonlinear properties, including non-stationarity of noise and contrast dependence of both noise and spatial resolution. Model observers have been recommended as a tool for the task-based assessment of image quality (Samei E et al., Med Phys.
View Article and Find Full Text PDFAccurate predictions on small data with a tabular foundation model.
Nature
January 2025
Machine Learning Lab, University of Freiburg, Freiburg, Germany.
Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.
View Article and Find Full Text PDFGene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy.
Commun Med (Lond)
January 2025
Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Background: Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions.
Methods: We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!