Bibliographic record
Abstract
Selector proteins are transcription factors that coordinate the formation and identity of organs and appendages. The proper formation of these tissues requires the selector proteins to regulate the expression of a large set of genes. Many selector proteins are involved in regulating multiple developmental processes, yet it is not completely clear how they are able to activate different sets of genes in a tissue-specific manner. An association with cofactors is thought to be one method by which enhancer selectivity is achieved. During wing development the selector protein Scalloped (SD) interacts with the cofactor Vestigial (VG). This interaction leads to the activation of a specific set of downstream wing genes. Herein, data are presented indicating that the switch in binding selectivity is likely achieved by VG altering the general affinity that the SD protein has for DNA. The decreased affinity for DNA is compensated for by the fact that the VG protein forms a complex containing two SD proteins. These two properties ensure that the SD-VG complex is able to bind only to enhancers that have two consecutive binding sites. Furthermore, data are presented that indicate that the function of the two terminal domains of the VG protein is not restricted to activating transcription and promoting the recruitment of two SD proteins.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".