Focus on numbers – characterizing protein accumulation at DNA double-strand breaks
Bibliographic record
Abstract
Unrepaired DNA double-strand breaks can lead to cell death or genomic rearrangements. The DNA damage response (DDR) is a complex signaling cascade in which a plethora of factors act to finely tune repair pathway choice. Several DDR proteins have been shown to accumulate at sites of DNA lesions in characteristic dot-like structures known as DNA repair foci. Changes in foci brightness, commonly expressed in arbitrary intensity units, are often used as readout for DNA repair dynamics. However, due in part to technical challenges, the stoichiometry, absolute number of proteins recruited to DDR foci, and their impact on the resolution of the break remain incompletely characterized. Here, we combine spatial intensity distribution analysis (SpIDA) and a custom foci detection algorithm into an easy-to-use pipeline that, starting from confocal images, allows quantitative description of protein accumulation in DNA repair foci. Moreover, by quantifying foci based on their molecular count, SpIDA overcomes the limitations of ambiguous intensity units, enabling stoichiometric quantification between repair factors and providing a unifying means for experimental comparisons.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".