High‐Throughput Computational Analysis of Biofilm Formation from Time‐Lapse Microscopy
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it