Density-Dependent Sorting of Physiologically Different Cells of <i>Vibrio parahaemolyticus</i>
Bibliographic record
Abstract
A pure bacterial culture is composed of clonal cells in different physiological states. Separation of those subpopulations is critical for further characterization and for understanding various processes in the cultured cells. We used density-dependent cell sorting with Percoll to separate subpopulations from cultures of a marine bacterium, Vibrio parahaemolyticus. Cells from cultures in the exponential and stationary phases were fractionated according to their buoyant density, and their culturability and ability to maintain culturability under low-temperature and low-nutrient stress (stress resistance) were determined. The buoyant density of the major portion of the cells decreased with culture age. The culturability of stationary-phase cells increased with increasing buoyant density, but that of exponential-phase cells did not. Stress resistance decreased with increasing buoyant density regardless of the growth phase. The results indicate that density-dependent cell sorting is useful for separating subpopulations of different culturabilities and stress resistances. We expect that this method will be a powerful tool for analyzing cells in various physiological states, such as the viable but nonculturable state.
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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) | 0.001 | 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".