Mathematical model of nuclear speckle morphology
Bibliographic record
Abstract
Nuclear speckles are nuclear bodies consisting of populations of small and irregularly shaped droplet-like molecular condensates that contain various splicing factors. Recent experiments have shown the following morphological features of nuclear speckles: (I) Each molecular condensate contains SON and SRRM2 proteins, and MALAT 1 non-coding RNA surrounds these condensates; (II) In the normal interphase of the cell cycle, these condensates are broadly distributed throughout the nucleus in multicellular organisms. In contrast, the fusion of condensates leads to the formation of strongly condensed spherical droplets when cell transcription is suppressed; (III) SON is dispersed spatially in MALAT1 knocked-down cells, whereas MALAT1 is dispersed in SON knocked-down cells by the collapse of nuclear speckles. However, the detailed interactions among molecules that reveal the mechanisms of this rich variety of morphologies remain unknown. In this study, a coarse-grained molecular dynamics model of the nuclear speckle was developed considering the dynamics of SON, SRRM2 or SRSF2, MALAT1 , and pre-mRNA as representative components of condensates. The simulations reproduced the abovementioned morphological changes, by which the interaction strength among the representative components of the condensates was predicted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".