Bibliographic record
Abstract
Abstract A nonsense mutation occurs when a sense codon, one that codes for an amino acid, is changed to a chain‐termination codon, UAG, UAA or UGA. A nonsense suppressor can result from a second mutation affecting the translational apparatus. This mutation enables the cell to insert an amino acid in response to the nonsense codon, resulting in a wild‐type or near wild‐type phenotype. Some suppressor mutations change the anticodon of a transfer ribonucleic acid (tRNA) so that it can pair with the nonsense codon. Other suppressors increase the readthrough of the nonsense mutation. Readthrough occurs at low levels but changes in the ribosome, tRNA or in translation factors can increase readthrough by altering the initial selection steps, proofreading or quality control in decoding the messenger RNA. Manipulation of the accuracy of translation holds promise as a method for the treatment of genetic diseases, many of which result from nonsense mutations. Key Concepts Suppression results when one mutation counteracts the effect of another mutation to give a wild‐type phenotype. The nonsense codons UAG, UAA and UGA do not code for amino acids, but signal the end of the protein‐coding sequence in the mRNA. Nonsense suppression competes with chain termination. Errors occur during translation and include reading a nonsense codon as sense as well as misreading and frameshifting. Decoding during elongation involves conformational changes in the ribosome, tRNA and EF‐Tu. Changes in the ribosome or other components of the translational apparatus can modify decoding and thus enhance or reduce readthrough of nonsense codons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".