Quantification of autolysis in<i>Penicillium chrysogenum</i>by semiautomated image analysis
Bibliographic record
Abstract
An image analysis method is described for the characterization of empty (autolyzed and inactive) regions within the mycelia of filamentous fungi. It extends a previous method that characterized only regions filled with cytoplasm or vacuoles (i.e., the active biomass). The method is semiautomatic, requiring some manual editing before automated measurements. When the method was used for samples from a batch fermentation of an industrial strain of Penicillium chrysogenum, the empty regions were observed to constitute up to 15% (by projected area) of the biomass during the growth phase. After nutrient exhaustion, however, the proportion of empty regions rose rapidly, eventually representing more than 50% of the biomass by the end of fermentation. The increase in the percentage of empty regions coincided with a decrease in biomass (as measured by dry cell weight) and a fall in penicillin titre. Further morphological analysis revealed that fragmentation of mycelia, particularly clumps, coincided with increases in the levels of empty regions. This new image analysis method gave additional information on hyphal differentiation and a measure of autolysis. It was also a useful indicator of the processes leading to autolysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".