Investigation of ruminal bacterial diversity in dairy cattle fed supplementary monensin alone and in combination with fat, using pyrosequencing analysis
Bibliographic record
Abstract
The objective of this study was to examine and compare the effects of monensin, both alone and together with dietary fat, on ruminal bacterial communities in dairy cattle fed the following 3 diets: a control diet, the control diet supplemented with monensin, and the control diet supplemented with both monensin and fat. Bacterial communities in the liquid and the adherent fractions of rumen content were analyzed using 454 pyrosequencing analysis of 16S rRNA gene amplicons. Most sequences were assigned to phyla Firmicutes and Bacteroidetes, irrespective of diets and fractions. Prevotella was the most dominant genus, but most sequences could not be classified at the genus level. The proportion of Gram-positive Firmicutes was reduced by 4.5% in response to monensin but increased by 12.8% by combination of monensin and fat, compared with the control diet. Some of the operational taxonomic units in Firmicutes and Bacteroidetes were also affected by monensin or by the combination of monensin with fat. The proportion of numerous bacteria potentially involved in lipolysis and (or) biohydrogenation was increased by both monensin and fat. The Shannon diversity index was decreased in the control diet supplemented with both monensin and fat, compared with the other 2 diet groups. Supplementary fats hinder bacterial attachment to plant particles and then result in decreased bacterial diversity in the rumen. The finding of this study may help in understanding the effect of monensin and fat on ruminant nutrition and the adverse effect of monensin and fat, such as milk fat depression and decreased feed digestibility.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".