<i>Fusarium</i>mycotoxins: a trans-disciplinary overview
Bibliographic record
Abstract
Due to health risks and economic losses associated with mycotoxins produced by Fusarium species, there is a compelling need for an improved understanding of these fungi from across diverse perspectives and disciplinary approaches. In this article, we provide a transdisciplinary overview of: (i) Fusarium phylogenetics; (ii) linkages between mycotoxin biosynthetic gene clusters and chemical structures; (iii) biotransformation of mycotoxins to reduce toxicity; (iv) Fusarium population biology; (v) genomics of secondary metabolite production; and (vi) mycotoxigenic fusaria in a phytobiomes context. Phylogenetic studies have made tremendous progress in delineating the species that comprise the genus Fusarium, many of which are morphologically cryptic. Accurate species identification and a thorough understanding of the distribution of mycotoxin biosynthetic genes among those species will facilitate control of mycotoxin contamination. The biochemical pathways leading to the formation of several Fusarium mycotoxins have been elegantly linked with the genes responsible for each chemical transformation during synthesis, and for most structural differences among chemotypes. Screens for the biotransformation of mycotoxins have led to the description of chemical modifications that impact bioactivity and have implications for monitoring and testing of the food supply. Population biology studies have revealed the potential for introductions of foreign genotypes to alter regional populations of mycotoxigenic fusaria. Genomic analyses have begun to reveal the complex evolutionary history of the genes responsible for mycotoxin production, both across and within lineages. Improved understanding of how climate variability impacts plant–Fusarium interactions and mycotoxin accumulation is necessary for effective plant resistance. Additionally, improved understanding of interactions between Fusarium and other members of crop microbiomes is expected to produce novel strategies for limiting disease and mycotoxin accumulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".