Heat stress and immune response phenotype affect DNA methylation in blood mononuclear cells from Holstein dairy cows
Bibliographic record
Abstract
Heat stress negatively affects health and production in cows. Examining the cellular response to heat stress could reveal underlying protective molecular mechanisms associated with superior resilience and ultimately enable selection for more resilient cattle. This type of investigation is increasingly important as future predictions for the patterns of heat waves point to increases in frequency, severity, and duration. Cows identified as high immune responders based on High Immune Response technology (HIR) have lower disease occurrence compared to their average and low immune responder herd-mates. In this study, our goal was to identify epigenetic differences between high and low immune responder cows in response to heat stress. We examined genome-wide DNA methylation of blood mononuclear cells (BMCs) isolated from high and low cows, before and after in vitro heat stress. We identified differential methylation of promoter regions associated with a variety of biological processes including immune function, stress response, apoptosis, and cell signalling. The specific differentially methylated promoter regions differed between samples from high and low cows, and results revealed pathways associated with cellular protection during heat stress.
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 | 0.000 |
| 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) | 0.001 | 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".