Conventional solid fermentation alters mycotoxin contents and microbial diversity analyzed by high-throughput sequencing of a <i>Fusarium</i> mycotoxin-contaminated diet
Bibliographic record
Abstract
Fusarium mycotoxins, commonly present in corn and its derived products for animals, has caused significant economic impact on swine reproduction in the People’s Republic of China. The objective of the present study, therefore, was to evaluate changes in mycotoxin contents and microbial diversity by conventional solid fermentation of a contaminated diet. Three diets were evaluated, as follows: control group, basal diet; test group 1 (25 Toxin), basal diet in which corn and corn gluten meal were replaced with 25% mycotoxin-contaminated corn and corn gluten meal, respectively; test group 2 (50 Toxin), basal diet in which corn and corn gluten meal were replaced with 50% mycotoxin-contaminated corn and corn gluten meal, respectively. Compound strains were used for solid-state fermentation of the compound feed. Under the present experimental conditions, conventional solid fermentation altered microbial diversity, as demonstrated by high-throughput sequencing of a Fusarium mycotoxin-contaminated diet, and the content of zearalenone (ZEA) was reduced significantly. However, deleterious effects were also observed with regard to the contents of deoxynivalenol (DON) and fumonisin B1 (FUMB1). These results may have implications for animals consuming Fusarium mycotoxin-contaminated food or feed by conventional solid fermentation.
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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) | 0.000 | 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".