Deoxynivalenol accumulation and detoxification in cereals and its potential role in wheat–Fusarium graminearum interactions
Bibliographic record
Abstract
Deoxynivalenol (DON) is a prominent mycotoxin showing significant accumulation in cereal plants during infection by the phytopathogen Fusarium graminearum . It is a virulence factor that is important in the spread of F. graminearum within cereal heads, and it causes serious yield losses and significant contamination of cereal grains. In recent decades, genetic and genomic studies have facilitated the characterization of the molecular pathways of DON biosynthesis in F. graminearum and the environmental factors that influence DON accumulation. In addition, diverse scab resistance traits related to the repression of DON accumulation in plants have been identified, and experimental studies of wheat–pathogen interactions have contributed to understanding detoxification mechanisms in host plants. The present review illustrates and summarizes the molecular networks of DON mycotoxin production in F. graminearum and the methods of DON detoxification in plants based on the current literature, which provides molecular targets for crop improvement programs. This review also comprehensively discusses recent advances and challenges related to genetic engineering-mediated cultivar improvements to strengthen scab resistance. Furthermore, ongoing advancements in genetic engineering will enable the application of these molecular targets to develop more scab-resistant wheat cultivars with DON detoxification traits.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".