Effects of acetic acid and lactic acid on the growth of Saccharomyces cerevisiae in a minimal medium
Bibliographic record
Abstract
Specific growth rates (mu) of two strains of Saccharomyces cerevisiae decreased exponentially (R2 > 0.9) as the concentrations of acetic acid or lactic acid were increased in minimal media at 30 degrees C. Moreover, the length of the lag phase of each growth curve (h) increased exponentially as increasing concentrations of acetic or lactic acid were added to the media. The minimum inhibitory concentration (MIC) of acetic acid for yeast growth was 0.6% w/v (100 mM) and that of lactic acid was 2.5% w/v (278 mM) for both strains of yeast. However, acetic acid at concentrations as low as 0.05-0.1% w/v and lactic acid at concentrations of 0.2-0.8% w/v begin to stress the yeasts as seen by reduced growth rates and decreased rates of glucose consumption and ethanol production as the concentration of acetic or lactic acid in the media was raised. In the presence of increasing acetic acid, all the glucose in the medium was eventually consumed even though the rates of consumption differed. However, this was not observed in the presence of increasing lactic acid where glucose consumption was extremely protracted even at a concentration of 0.6% w/v (66 mM). A response surface central composite design was used to evaluate the interaction between acetic and lactic acids on the specific growth rate of both yeast strains at 30 degrees C. The data were analysed using the General Linear Models (GLM) procedure. From the analysis, the interaction between acetic acid and lactic acid was statistically significant (P < or = 0.001), i.e., the inhibitory effect of the two acids present together in a medium is highly synergistic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".