73 Effect of subacute ruminal acidosis (SARA) and Saccharomyces cerevisiae fermentation products on gastrointestinal microbiome of dairy cows.
Bibliographic record
Abstract
Subacute ruminal acidosis caused by high-grain feeding can cause dysbiosis of the rumen microbiome, which is associated with the pathogenesis of the rumen disorders. Saccharomyces cerevisiae fermentation products (SCFP) have been widely used as rumen fermentation modifiers to stabilize the rumen conditions. The objective of this study was to investigate if SCFP can attenuate the impact of SARA on microbial communities in the rumen. Thirty two Holstein lactating dairy cows were assigned into four treatment groups (n=8/trt) that received a base TMR (34.9 DM NDF, 18.6 % DM starch) supplemented daily with 1) 140 g of ground corn (Control), 2) 14 g Diamond V Original XPCTM mixed in 126 g of ground corn (XPC), 3) 19 g Diamond V NutriTek® mixed in 121 g of ground corn (NTL), and 4) 38 g Diamond V NutriTek® mixed in 102 g of ground corn (NTH) in a randomized complete block design. The experiment lasted from 4 weeks before until 12 weeks after calving. The SARA challenges were conducted on week 5 (SARA1) and 8 (SARA2) after calving by replacing 20% of the base TMR with pellets containing 50% barley and 50% wheat. Rumen liquid and solid samples were collected weekly. DNA was extracted from each sample and subjected to Illumina sequencing of V1–V2 regions of 16S rRNA gene and analyzed by QIIME2. Differential abundance analysis with gneiss was used to analyze the microbial composition. Alpha- and beta-diversities were analyzed using MIXED procedure of SAS and PERMANOVA, respectively. Both SARA challenges decreased (P < 0.05) richness and evenness of rumen liquid and solid microbial communities in the control, XPC and NTL groups. NTH treatment however prevented these reductions in the rumen liquid microbiota during SARA challenges. Supplementation with NTH was able to prevent rumen microbiota from losing their diversity during the SARA challenges.
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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".