Influence of Cell Surface and Nanomechanical Properties on the Flocculation Ability of Industrial Saccharomyces cerevisiae Strains
Bibliographic record
Abstract
In the past few years, atomic force microscopy (AFM) has provided novel information on the ultrastructural and nanomechanical properties of yeast cell walls that play a major role in determining the flocculation characteristics of the yeasts. In this study, we used AFM to visualize at the nanoscale the cell surface topography and to determine cell wall nanomechanical properties (e.g. elasticity and adhesion) of different strains of S. cerevisiae employed for brewing, winemaking and fuel alcohol production. Cell surface topography was found to correlate with the flocculation behaviour of these strains during their late stationary phase, with the cell surface of flocculent cells being rougher than that of weakly flocculent cells. The elastic modulus of the yeast cell walls showed that weakly flocculent strains had a more rigid cell wall than highly flocculent strains. This difference in elasticity seemed to have an effect on the adhesive properties of the yeast cell walls, with weakly flocculent yeasts displaying lower adhesion energy than the highly flocculent strains. These findings seem to indicate that yeast cell surface nanomechanical properties play an important role in governing flocculation.
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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) | 0.000 | 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) | 0.001 | 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".