Reduced Mobility of the Alternate Splicing Factor (Asf) through the Nucleoplasm and Steady State Speckle Compartments
Bibliographic record
Abstract
Compartmentalization of the nucleus is now recognized as an important level of regulation influencing specific nuclear processes. The mechanism of factor organization and the movement of factors in nuclear space have not been fully determined. Splicing factors, for example, have been shown to move in a directed manner as large intact structures from sites of concentration to sites of active transcription, but splicing factors are also thought to exist in a freely diffusible state. In this study, we examined the movement of a splicing factor, ASF, green fluorescent fusion protein (ASF-GFP) using time-lapse microscopy and the technique fluorescence recovery after photobleaching (FRAP). We find that ASF-GFP moves at rates up to 100 times slower than free diffusion when it is associated with speckles and, surprisingly, also when it is dispersed in the nucleoplasm. The mobility of ASF is consistent with frequent but transient interactions with relatively immobile nuclear binding sites. This mobility is slightly increased in the presence of an RNA polymerase II transcription inhibitor and the ASF molecules further enrich in speckles. We propose that the nonrandom organization of splicing factors reflects spatial differences in the concentration of relatively immobile binding sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".