114 Non-coding RNAs in bovine mammary glands.
Bibliographic record
Abstract
Non-coding RNAs (ncRNAs) are a class of untranslated RNA molecules that have emerged as new regulators of gene expression. The roles of ncRNAs including small interfering RNA, microRNA (miRNA), PIWI-interacting RNA, small nucleolar RNA, long non-coding RNA (lncRNA) and circular RNA, in the regulation of traits of economic importance in livestock are gaining more importance. MiRNA, the most studied class of ncRNA in bovine, is known to control the activities of about 60% of all protein-coding genes and regulate almost every cellular process investigated in mammals. The recent release of miRbase v22 (2018) and NONECODE database (2017) listed about 1,100 miRNAs and 22,386 lncRNAs transcripts identified in the bovine genome. Aided by advances in deep sequencing technologies and bioinformatics tools, the roles of ncRNAs, in particular miRNAs and lncRNAs, in mammary gland (MG) functions and disease are beginning to emerge. This talk will focus on the roles of miRNA and lncRNA in MG health and productivity. The evidence of miRNA roles in lactation signalling, nutritional and disease regulation will be highlighted based on our own findings and relevant literature. For instance, miR-29b/miR-363 and miR-874/miR-6254 are important mediators of the transition signals between lactogenesis and galactopoiesis, and galactopoiesis and involution, respectively, while miR-199c, miR-199a-3p, miR-98, miR-378, miR-148b, miR-21-5p and miR-200a are crucial for the MG response to diets rich in unsaturated fatty acids. Different approaches (in vivo versus in vitro) for exploring miRNA roles in response to mastitis, an important disease of the MG will be highlighted. Furthermore, possible roles of lncRNA in MG functions, such as responses to diets and the implication of the colocation of lncRNA genes with QTL for milk traits and mastitis, will be discussed. Finally, potential application of emerging genome editing technologies for ncRNA functional studies and implication for MG health and productivity will be presented.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".