The influence of self-cycling fermentation long- and short-cycle schemes on Saccharomyces cerevisiae and Escherichia coli
Bibliographic record
Abstract
Self-cycling fermentation (SCF), a cyclic process in which cells, on average, divide once per cycle, has been shown to lead to whole-culture synchronization and improvements in productivity during bioconversion. Previous studies have shown that the completion of synchronized cell replication sometimes occurs simultaneously with depletion of the limiting nutrient. However, cases in which the end of cell doubling occurred before limiting nutrient exhaustion were also observed. In order to better understand the impact of these patterns on bioprocessing, we investigated the growth of Saccharomyces cerevisiae and Escherichia coli in long- and short-cycle SCF strategies. Three characteristic events were identified during SCF cycles: (1) an optimum in control parameters, (2) the time of completion of synchronized cell division, and (3) the depletion or plateau of the limiting nutrient. Results from this study and literature led to the identification of three potential trends in SCF cycles: (A) co-occurrence of the three key events, (B) cell replication ending prior to the co-occurrence of the other two events, and (C) depletion or plateau of the limiting nutrient occurring later than the co-occurrence of the other two events. Based on these observations, microbial physiological differences were analyzed and a novel definition for SCF is proposed.
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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".