Understanding the Changes in Mitochondrial Morphology through Dynamic and Three-dimensional Fluorescence Micrographs
Bibliographic record
Abstract
Mitochondria are highly dynamic organelles that are vital to the survival of any animal, undergoing regular fission and fusion events in response to the needs or stresses of the host, leading to the constant remodeling of the mitochondrial network. Because of this, being able to evaluate the mitochondrial network in three dimensions, as well as over time, offers a benefit in understanding how the system responds to factors such as stress or pharmaceutical intervention. Fluorescence imaging of the mitochondrial networks of cells enables the ability to visualize and monitor these changes. However, the mitochondrial network is often described as a two-dimensional and static structure that is defined by unstandardized metrics. Therefore, we set out to describe a pipeline that enables the user to prepare their images for the mitochondrial event localizer (MEL), an ImageJ plugin tool that detects fission and fusion events in the mitochondrial network over time and in a 3-dimensional manner, thus, offering insight into the dynamic changes that this network undergoes. Additionally, we describe the benefits of understanding fission and fusion in light of the changes in the mitochondrial count and morphological changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".