Inferring Somatic Mutation Rates Using the Stop-Enhanced Green Fluorescent Protein Mouse
Bibliographic record
Abstract
A new method is developed for estimating rates of somatic mutation in vivo. The stop-enhanced green fluorescent protein (EGFP) transgenic mouse carries multiple copies of an EGFP gene with a premature stop codon. The gene can revert to a functional form via point mutations. Mice treated with a potent mutagen, N-ethyl-N-nitrosourea (ENU), and mice treated with a vehicle alone are assayed for mutations in liver cells. A stochastic model is developed to model the mutation and gene expression processes and maximum-likelihood estimators of the model parameters are derived. A likelihood-ratio test (LRT) is developed for detecting mutagenicity. Parametric bootstrap simulations are used to obtain confidence intervals of the parameter estimates and to estimate the significance of the LRT. The LRT is highly significant (alpha < 0.01) and the 95% confidence interval for the relative effect of the mutagen (the ratio of the rate of mutation during the interval of mutagen exposure to the rate of background mutation) ranges from a minimum 200-fold effect of the mutagen to a maximum 2000-fold effect.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".