Effect of Environmental Conditions on the Flocculation of<i>Saccharomyces Cerevisiae</i>
Bibliographic record
Abstract
The flocculation behavior of two Saccharomyces cerevisiae strains expressing either Flo1 or NewFlo phenotype was examined. The behavior of the two strains was examined while varying ethanol (0–l0.0 ml/100 ml), pH (3.8–5.8), ionic strength (0.01–0.20M), and temperature (5–25°C). The flocculation behavior of Flo1 cells was insensitive to ethanol and pH changes. NewFlo cells exhibited significantly increased flocculation with increases in ethanol concentration (P < 0.05) and pH value (P < 0.01). Increasing ionic strength and decreasing temperature significantly (P < 0.01) retarded flocculation in both strains. The apparent activation energy of flocculation at pH 4.0 and 1 × 108 cells per milliliter was estimated to be 3.2 and 11.0 kcal/mol for Flo1 and NewFlo strains, respectively, indicating distinct sensitivities to temperature. Interestingly, flocculation inhibition by urea was reversed by washing with 100 mM acetate buffer (20°C, pH 4.0, containing 1.0 mM Ca2+), presumably due to the reversible unfolding of zymolectin molecules. A semiempirical model was developed that indicated that the flocculation behavior is affected by the cell volume fraction for both Flo1 (r2 = 0.93) and NewFlo (r2 = 0.97) strains. This semiempirical model allows adjustment of Helms values due to variation in cell volume fraction, thus partially explaining reported variations of Helms values with respect to fermentation time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".