Using Beads as a Focus Fiduciary to Aid Software-based Autofocus Accuracy in Microscopy
Bibliographic record
Abstract
Abstract Brightfield microscopy is an ideal application for studying live cell systems in a minimally invasive manner. This is advantageous in long-term experiments to study dynamic cellular processes such as stress response. Depending on the sample type and preparation, the inherent qualities of brightfield microscopy, being very low contrast, can contribute to technical issues such as focal drift, sequencing lags, and complete failure of software autofocus systems. Here, we describe the use of microbeads as a focus aid for long-term live cell imaging to address these autofocus issues. This protocol is inexpensive to implement, without extensive additional sample preparation and can be used to capture focused images of transparent cells in a label-free manner. To validate this protocol, a widefield inverted microscope was used with software-based autofocus to image overnight in time-lapse format, demonstrating the use of the beads to prevent focal drift in long-term experiments. This improves autofocus accuracy on relatively inexpensive microscopes without using hardware-based focus aids. To validate this protocol, the KNIME logistics software was used to train a random forest model to perform binary image classification. Key features label-free live cell imaging in time-lapse format Troubleshooting software autofocus for brightfield mode Graphical overview
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".