Identification and characterization of long non-coding RNAs in mammary gland tissues of Chinese Holstein cows
Bibliographic record
Abstract
LncRNAs (Long non-coding RNA) is an RNA molecule with a length of more than 200 bp. LncRNAs can directly act on mRNA, thus affecting the expression of downstream target genes and proteins, and widely participate in many important physiological and pathological regulation processes of the body. In this study, RNA-Seq was performed to detect lncRNAs from mammary gland tissues of three Chinese Holstein cows, including three cows at 7 d before calving and the same three cows at 30 d postpartum (early lactation stage). A total of 1,905 novel lncRNAs were detected, 57.3% of the predicted lncRNAs are ≥ 500 bp and 612 lncRNAs are intronic lncRNAs. The exon number of lncRNAs ranged from 2 to 10. A total of 96 lncRNAs were significantly differentially expressed between two stages, of which 47 were upregulated and 49 were downregulated. Pathway analysis found that target genes were mainly concentrated on the ECM-receptor interaction, Jak-STAT signaling pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway. This study revealed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows at non-lactation and early lactation periods, and provided a basis for studying the functions of lncRNAs in Holstein cows during different lactation periods.
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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) | 0.000 | 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".