Reply to: Do actin isoforms have unique functionalities at the protein level?
Bibliographic record
Abstract
The driving force behind our study 1 was to gain molecular and mechanistic insight into observations that different actin isoforms, in particular β- and γ-actin, exhibit markedly different localizations within a single cell 2 , and to interrogate the functions of each isoform in a cellular context where both β- and γ-actin are expressed at endogenous levels. Many labs have postulated that different actin isoform networks interact with a distinct subset of actin-binding proteins, reviewed in ref. 3 . Moreover, recent structural studies suggest that different actin isoforms can interact with the same partners in slightly different ways 4 . As such, the biological roles of each isoform are far from settled. If the β- and γ-actin isoforms perform specialized functions in cells that express both, i.e., the “normal” context, then re-positioning isoform networks by targeting formins to different regions of the cell would also cause actin interactors with an isoform preference to relocalize (mislocalize) along with their preferred network, as seen in Shah et al. 1 . Depending on the actin-binding protein and its precise function, spatiotemporal relocalization would be expected to have consequences for the cell. This is indeed what we found.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".