Bibliographic record
Abstract
Arfs are small Ras-superfamily proteins important for regulating membrane trafficking including the recruitment of vesicular coats as well as a diverse range of other functions. There are five Arfs in humans: two Class I Arfs (Arf1 and Arf3), two Class II Arfs (Arf4 and Arf5) and one Class III Arf (Arf6), with Class I and Class II Arfs present on the Golgi apparatus among other locations. These Golgi Arfs (Arf1, Arf3, Arf4 and Arf5) are highly similar in sequence, and knockout studies have established a complex pattern of redundancy, with Arf4 alone able to support cell survival in tissue culture. Moreover, adding to the complexity, functions of Arfs on distinct membranes can involve non-overlapping sets of effectors (e.g., COPI on cis-Golgi membranes and clathrin adaptors on trans-Golgi network). The three classes of Arfs are found in most metazoans, suggesting biologically important specialization the details of which are beginning to emerge. This review examines recent studies using siRNA and CRISPR/Cas9 knockouts of mammalian Arfs combined with functional assays of the secretory pathway in the context of detailed localization of fluorescently-tagged Arfs by fluorescent and super-resolution microscopy and the existing literature using more conventional techniques. We suggest that specificity of effector recruitment involves additional membrane determinants which need to be considered in future studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".