Bibliographic record
Abstract
This chapter discusses the influence a gene's neighbors can have on that gene's expression and looks at the mechanisms by which this can occur. Both the packaging of DNA and its metabolic functioning are greatly facilitated by supercoiling. Supercoiling imparts torsional stress to DNA, which influences its interaction with RNA polymerase and other DNA-binding proteins as well as contributing to its compaction. The importance of supercoiling to cellular functioning is illustrated by the tight control prokaryotes maintain over this property of their genomes. The greatest difference between small plasmids and larger molecules may be that plasmids can rotate the entire molecule around its long axis during transcription, relieving local changes in supercoding levels. One additional finding that further supports the twin-supercoil domain model is that the length of the transcript located upstream from the leu-500 promoter affects the level of supercoiling changes. The large difference in transcription rates between mutant and wild-type strains may here be obscuring the fact that doubling the output of a gene can have significant consequences for a cell, either good or bad, depending on the situation. 4,5',8-trimethylpsoralen (TMP) is a sensitive indicator of supercoiling, and it is claimed to be able to detect changes in supercoil levels of 15% and 12%. Changes of this magnitude are regularly experienced by the genome of Escherichia coli.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".