Tissues, Metabolic Pathways and Genes of Key Importance in Lactating Dairy Cattle
Bibliographic record
Abstract
Milk and dairy products are valuable sources of food for humans. Increased milk yield and changes in milk composition in dairy cows have been achieved through a variety of means including better nutrition, management and genetic selection. This selection can be performed without consideration of the specific genes and genetic variation involved. However, association analysis using dense SNP genotyping panels provides an approach for identifying genomic regions affecting milk production. Coupling physiological and metabolic information with association analysis results can provide greater insight into the specific genetic variants and molecular mechanisms affecting production traits as well as the potential effects of these variants on fertility in dairy cattle. To this end, this review highlights key tissues, metabolic pathways and genes of importance in lactating dairy cattle, particularly early in lactation. Physiological and metabolic adaptations in three key tissues (adipose, mammary gland and liver) are discussed, followed by the important endocrine adaptations during negative energy balance. Key genes mediating metabolic and endocrine adaptations are also highlighted. Finally, genes that account for variation in production traits are presented in relation to the tissues and processes described. Knowledge of the genes and pathways involved will be important for ongoing efforts aimed at finding other genes and variants that contribute to milk production and fertility traits. Also, a better understanding of the molecular basis of these traits may lead to more accurate genomic predictions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".