Differential allele-specific accumulation of bovine kappa-casein mRNA throughout lactation
Bibliographic record
Abstract
A differential allele-specific accumulation of kappa-casein mRNA that is not linked to the kappa-casein protein variants is described in Holstein cows. Actually, cows genotyped kappa-casein AB were a mixed population. For the first group of kappa-casein AB cows, allele A-specific kappa-casein mRNA contents within mammary epithelial cells were lower than the allele B-specific ones (cows LH), suggesting that the allele A-specific kappa-casein gene was expressed with lower efficiency in mRNA. For the other group of kappa-casein AB cows, allele A- and B-specific kappa-casein mRNA accumulated to a similar level within mammary epithelial cells (cows HH). The objective of this study was to determine whether the accumulation of allele-specific kappa-casein mRNA remained constant throughout lactation for the two groups of cows. Quantitative RT-PCR was used to monitor Holstein cows kappa-casein AB genotyped HH and LH throughout lactation for the proportion of allele B-specific mRNA accumulation relative to the total kappa-casein encoded mRNA within mammary epithelial cells: RNA was extracted from milk somatic cells known to contain a small proportion of mammary epithelial cells. Mean values of allele B-specific mRNA content were 50.6+/-0.5 and 54.0+/-0.9%, for cows HH and cows LH, respectively, and did not vary during lactation (P> 0.10). This suggests that the phenotypic expression of the genetic mutation that causes the differential allele-specific accumulation of kappa-casein mRNA was not affected by physiological and environmental factors, which tend to vary considerably throughout lactation.
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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 | 0.001 | 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".