Cluster coarsening on drops exhibits strong and sudden size-selectivity
Bibliographic record
Abstract
Autophagy, an important process for degradation of cellular components, requires the targeting of autophagy receptor proteins to potential substrates. Receptor proteins have been observed to form clusters on membranes. To understand how receptor clusters might affect autophagy selectivity, we model cluster coarsening on a polydisperse collection of spherical drop-like substrates. Our model receptor corresponds to NBR1, which supports peroxisome autophagy. We recover dynamical scaling of cluster sizes, but find that changing the drop size distribution changes the cluster-size scaling distribution. The magnitude of this effect is similar to how changing the spatial-dimension affects scaling in bulk systems. We also observe a sudden onset of size-selection of the remaining drops with clusters, due to clusters evaporating from smaller drops and growing on larger drops. This coarsening-driven size selection provides a physical mechanism for autophagy selectivity, and may explain reports of size selection during peroxisome degradation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".