Short communication: Relationship of blood DNA methylation rate and milk performance in dairy cows
Bibliographic record
Abstract
The objective of this study was to investigate the global methylation rate in blood DNA and its relationship with lactation performance. A total of 196 mid-lactation dairy cows were fed the same diet under the same management. Milk yield was recorded and blood samples were collected from the jugular vein before morning feeding. The blood global DNA methylation rates were quantified using a methylation quantification kit. Overall, the average blood global DNA methylation rate of all cows was 12.4%. When DNA methylation rates were compared between cows with high (n = 40; 37.0 to 42.0 kg/d) and low (n = 33; 24.0 to 30.0 kg/d) milk yield, DNA methylation rates in the lower-yield cows (14.1 ± 0.7%) were significantly higher than those in the higher-yield animals (11.6 ± 0.7%). Our results indicated an association of milk and protein yields with global DNA methylation rates in lactating dairy cows. However, further research is needed to determine whether this association reflects the true influence of epigenetic mechanisms on yield or whether other factors, such as different proportions of blood cell types in high- and low-yielding cows, affect apparent global DNA methylation levels.
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 | 0.000 | 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".