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Record W1972601427 · doi:10.1139/w04-051

Lipid nutrition of<i>Saccharomyces cerevisiae</i>in winemaking

2004· article· en· W1972601427 on OpenAlexvenueno aff
Simona Belviso, Laura Bardi, Alessandra Biondi Bartolini, M. Marzoná

Bibliographic record

VenueCanadian Journal of Microbiology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFermentationErgosterolBiochemistryYeastWinemakingSaccharomyces cerevisiaeYeast in winemakingFructoseBiologyFood scienceSugarLipid metabolismSucroseSaccharomycesChemistry

Abstract

fetched live from OpenAlex

Biosynthesis of cell membrane lipids is a crucial metabolic pathway for the growth and viability of eucaryotic microorganisms. In Saccharomyces cerevisiae, unsaturated fatty acids and ergosterol synthesis needs molecular oxygen. Stuck and sluggish fermentations are related to this aspect of metabolism and constitute a major problem in the wine industry. Anaerobiosis, when lipids are not available in the growth medium, highly stresses cells. They release lipid biosynthesis metabolites and soon cease to multiply. This paper describes an investigation of the nutritional role of exogenous lipids from inactivated yeast cells (IYCs). Fermentations were carried out in a nitrogen-rich synthetic medium similar to grape juice with glucose and fructose as carbon sources, without lipid sources, and in anaerobiosis. The effect of the addition of IYC was assessed. Cell growth, cell lipid composition, glucose and fructose consumption, and acetic acid production were measured during fermentation. Addition of IYC boosted cell growth and sugar consumption, whereas acetic acid production decreased. Biomass yield was influenced by ergosterol availability and increased when IYCs were added. Fatty acid composition of yeast cells was changed by IYC addition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.206
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations44
Published2004
Admission routes1
Has abstractyes

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