Inferring the Skeletal Muscle Developmental Changes of Grazing and Barn-Fed Goats from Gene Expression Data
Bibliographic record
Abstract
Thirty-six Xiangdong black goats were used to investigate age-related mRNA and protein expression levels of some genes related to skeletal muscle structural proteins, MRFs and MEF2 family, and skeletal muscle fiber type and composition during skeletal muscle growth under grazing (G) and barn-fed (BF) feeding systems. Goats were slaughtered at six time points selected to reflect developmental changes of skeletal muscle during nonrumination (days 0, 7, and 14), transition (day 42), and rumination phases (days 56 and 70). It was observed that the number of type IIx in the longissimus dorsi was increased quickly while numbers of type IIa and IIb decreased slightly, indicating that these genes were coordinated during the rapid growth and development stages of skeletal muscle. No gene expression was affected (P > 0.05) by feeding system except Myf5 and Myf6. Protein expressions of MYOZ3 and MEF2C were affected (P < 0.05) by age, whereas PGC-1α was linearly decreased in the G group, and only MYOZ3 protein was affected (P < 0.001) by feeding system. Moreover, it was found that PGC-1α and MEF2C proteins may interact with each other in promoting muscle growth. The current results indicate that (1) skeletal muscle growth during days 0-70 after birth is mainly myofiber hypertrophy and differentiation, (2) weaning affects the expression of relevant genes of skeletal muscle structural proteins, skeletal muscle growth, and skeletal muscle fiber type and composition, and (3) nutrition or feeding regimen mainly influences the expression of skeletal muscle growth genes.
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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 | 0.000 |
| 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) | 0.000 | 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".