Graphical User Interface on Analysis of Mechanics and Dynamics of Biopolymers in Living Cells
Bibliographic record
Abstract
The mechanical properties of cells depend on the qualities of filamentous-actin (f-actin for short), among other cytoskeletal polymers. The ability to quickly determine f-actin qualities in a sample will help cellular biologists with cytoskeletal protein research. The goal of this project is to create a Matlab program which would allow researchers to determine, with limited manual input, the average filament length and persistence lengths of selected filaments. Through the graphical user interface, the user loads the image stack and can optimize the tracking result by altering several expectations of the program such as the expected contrast between the filament and background. The interface prompts users to select filaments of interest and gives the option of modifying the tracking solution by shortening it as well as retracking with a different contrast. The details of the filament tracking software can be found on the abstract, The Analysis of Mechanics and Dynamics of Biopolymers in Living Cells. The program will calculate basic statistics, mean and standard deviation, on the elongation and persistence lengths of all selected filaments and save them to a text file. Using this relatively automated program, researchers will be able to analyze a higher percentage of their data at a much lower cost to their time. Additional work would include further increasing how automatic this program is by eliminating the need for the user to select filaments of interest, allowing the program to track over many frames and by increasing the accuracy of the tracking program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.488 | 0.077 |
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".