Expression and Role of PIWI Proteins and piRNAs in Reproduction of Water Buffalo (Bubalus bubalis, Linn.)
Bibliographic record
Abstract
High-fertile and productive dairy animals are important to satisfy the growing population's demand. Sire fertility is one of the essential factors that regulate the overall pregnancy rate of dairy herds. However, sire fertility varies from 10 to 90%, suggesting that male fertility largely accounts for varying fertility levels across the herd. Sub-fertile bulls and females should be identified and discarded promptly to improve the dairy herd's productivity. The most dominant factors implicated in culling are poor semen quality, poor semen freezability (<35% post-thaw motility), and poor libido for the bulls and hard breeders for females that cause huge economic loss to the raisers. Understanding the basic mechanism of male and female fertility has undergone tremendous change in recent times owing to the advancement of molecular tools judging the essential molecules responsible for fertility. Presently, a new molecular niche has surfaced in testes, strongly influencing the fertilization potential of spermatozoa. Over the last decade, there has arrived a conclusion that out of several factors, piRNA and PIWI proteins are largely implicated in regulating the vital aspects of fertility and embryogenesis. While this development is advancing in other animals, very limited information is available on PIWI protein and piRNAs in large animals. Except for a few sporadic information on PIWI protein in cattle, very limited information is available on piRNAs and PIWI protein in regulation with buffalo bull fertility and growth of embryos of buffaloes, posting a huge demand for research.
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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) | 0.001 | 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".