COSMAS: a lightweight toolbox for cardiac optical mapping analysis
Bibliographic record
Abstract
Optical mapping is widely used in experimental cardiology, as it allows visualization of cardiac membrane potential and calcium transients. However, optical mapping measurements from a single heart or cell culture can produce several gigabytes of data, warranting automated computer analysis. Here we present COSMAS, a software toolkit for automated analysis of optical mapping recordings in cardiac preparations. COSMAS generates activation and conduction velocity maps, as well as visualizations of action potential and calcium transient duration, S1-S2 protocol analysis, and alternans mapping. The software is built around our recent 'comb' algorithm for segmentation of action potentials and calcium transients, offering excellent performance and high resistance to noise. A core feature of our software is that it is based on scripting as opposed to relying on a graphical user interface for user input. The central role of scripts in the analysis pipeline enables batch processing and promotes reproducibility and transparency in the interpretation of large cardiac data sets. Finally, the code is designed to be easily extended, allowing researchers to add functionality if needed. COSMAS is provided in two languages, Matlab and Python, and is distributed with a user guide and sample scripts, so that accessibility to researchers is maximized.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.033 |
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".