High‐Throughput Computational Analysis of Biofilm Formation from Time‐Lapse Microscopy
Bibliographic record
Abstract
Candida albicans biofilm formation in the presence of drugs can be examined through time-lapse microscopy. In many cases, the images are used qualitatively, which limits their utility for hypothesis testing. We employed a machine-learning algorithm implemented in the Orbit Image Analysis program to detect the percent area covered by cells from each image. This is combined with custom R scripts to determine the growth rate, growth asymptote, and time to reach the asymptote as quantitative proxies for biofilm formation. We describe step-by-step protocols that go from sample preparation for time-lapse microscopy through image analysis parameterization and visualization of the model fit. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Sample preparation Basic Protocol 2: Time-lapse microscopy: Evos protocol Basic Protocol 3: Batch file renaming Basic Protocol 4: Machine learning analysis of Evos images with Orbit Basic Protocol 5: Parametrization of Orbit output in R Basic Protocol 6: Visualization of logistic fits in R.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".