Effects of dietary energy intake and cold exposure on kinetics of plasma phenylalanine, tyrosine and protein synthesis in sheep
Bibliographic record
Abstract
An isotope dilution method of [2H5]phenylalanine (Phe) and [2H2]tyrosine (Tyr) was used to determine the effects of metabolisable energy (ME) intake and cold exposure on plasma Phe and Tyr turnover rates in sheep. Whole body protein synthesis (WBPS) was calculated with the [2H5]Phe model. Eight adult sheep were assigned to two dietary treatments receiving the same amount of crude protein and either 515 or 828 kJ x kg BW(-0.75) x d(-1) of ME (Me-ME diet and Hi-ME diet, respectively) with a crossover design for two 28 d periods. The sheep were exposed from a thermoneutral environment (23 +/- 1 degrees C) to a cold environment (2 +/- 1 to 4 +/- 1 degrees C) for 6 d for each dietary treatment. The primed-continuous infusion method of isotope dilution was conducted in both environmental temperatures. Plasma Phe turnover rate (PheTR) tended to be greater and plasma Tyr turnover rate (TyrTR) was greater (p = 0.03) for the Hi-ME diet compared with the Me-ME diet. Plasma PheTR increased (p = 0.04) and plasma TyrTR tended to increase during cold exposure. Whole body protein synthesis tended to be greater for the Hi-ME diet compared with the Me-ME diet and increased (p = 0.03) during cold exposure compared to the thermoneutral environment, but no interaction was detected. It was concluded that in sheep, plasma PheTR and WBPS (as determined by the [2H5]Phe model) tended to be influenced by and plasma TyrTR was influenced by ME intake. Further, plasma PheTR and WBPS increased and plasma TyrTR tended to increase during cold exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".