Understanding the role of rumen epithelial host-microbe interactions in cattle feed efficiency
Bibliographic record
Abstract
Feed efficiency is one of the economically important traits for the cattle industry that affects profit (feed costs) and the environment (production of manure and methane). Due to that feed efficiency is driven by multi-factors, mechanisms contributing to the animal to animal variation in this trait have not been well defined, limiting the development of precision feeding strategies to improve the herd production efficiency. Rumen microbial fermentation and volatile fatty acids (VFA) production have been recently reported to be associated with cattle feed efficiency, however the roles of rumen epithelial function in feed efficiency are less studied although the rumen epithelium has an important function in VFA absorption and metabolism which can affect host feed efficiency. Rumen epithelium is colonized with a diverse microbial population, termed epimural microbiota, which has proposed functions in tissue development, barrier and inflammation, urea transport, and oxygen scavenging, suggesting that they can affect rumen epithelial functions and subsequently cattle feed efficiency. Especially, prospective functions of epimural microbiota, enhanced rumen immunity and increased rumen epithelial thickness, might contribute to less nutritional requirement for tissue recuperation. Thus, the understanding of the functions of rumen epithelium, epimural microbiota, and rumen epithelial host-microbe interactions is essential to identify their roles in contributing to feed efficiency. In this review, we will focus on to date research findings on the structure of rumen epithelium, epimural microbiota, and epithelial host-microbe interactions together with their functions and how these are associated with feed efficiency, aiming to provide insights on future directions to study rumen epithelial host-microbe interactions and improve the rumen functions in cattle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".