In vivo and in vitro ageing results in accumulation of de novo copy number variations in bulls
Bibliographic record
Abstract
We have identified de novo copy number variations (CNVs) generated in bulls as they age. Blood samples from eight bulls were collected and SNP arrayed in a prospective design over 30 months allowing us to differentiate de novo CNVs from constant CNVs that are present throughout the sampling period. Quite remarkably, the total number of CNVs doubled over the 30-month period, as we observed an almost equal number of de novo and constant CNVs (107 and 111, respectively, i.e. 49% and 51%). Twice as many de novo CNVs emerged during the second half of the sampling schedule as in the first part. It suggests a dynamic generation of de novo CNVs in the bovine genome that becomes more frequent as the age of the animal progresses. In a second experiment de novo CNVs were detected through in vitro ageing of bovine fibroblasts by sampling passage #5, #15 and #25. De novo CNVs also became more frequent, but the proportion of them was only ~25% of the total number of CNVs (21 out of 85). Temporal generation of de novo CNVs resulted in increasing genome coverage. Genes and quantitative trait loci overlapping de novo CNVs were further investigated for ageing related functions.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".