Assessment of deoxynivalenol and deoxynivalenol derivatives in <i>Fusarium graminearum</i>-inoculated Canadian maize inbreds
Bibliographic record
Abstract
The prevalence of maize, wheat and other cereal crops in Ontario, Canada, has led to frequent epidemics of ear rot and head blight disease caused by the broad host pathogen Fusarium graminearum. This fungus produces the trichothecene deoxynivalenol (DON) which is one of the most common mycotoxins contaminating food and feed globally and poses a risk to human and animal health. DON is commonly glycosylated by plant hosts into deoxynivalenol-3-glucoside (DON-3G) as part of a defence mechanism to detoxify and sequester mycotoxins. However, it has been documented that DON-3G can be hydrolyzed in the digestive system of animals, increasing the bioavailability of DON. There is a need for additional documentation of the incidence and ratio of DON-3G to DON in Canadian cereal crops. We investigated the occurrence and relative proportion of DON and modified DON compounds in Canadian-adapted maize germplasm with a wide range of ear rot tolerance. Forty-nine maize inbreds were manually challenged (kernel inoculation) with F. graminearum (15-ADON chemotype) as well as phenotyped for tolerance to Gibberella ear rot (GER) under field conditions. LC/MS was used to analyse 581 maize samples for DON, DON-3G, 15-ADON, and 15-ADON-3G. The DON-3G to DON mean molar ratio ranged from 3.1 to 19.7%. DON content was positively correlated with GER disease severity (r = 0.66, p < 0.01), while the DON-3G:DON molar ratio and disease severity were negatively correlated (r = −0.64, p < 0.001). Thus, GER-tolerant maize genotypes had relatively lower total DON content and higher DON-3G:DON ratios.
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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 | 0.001 | 0.001 |
| 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".