A plant biostimulant made from the marine brown algae Ascophyllum nodosum and chitosan reduce Fusarium head blight and mycotoxin contamination in wheat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fusarium head blight (FHB) caused by Fusarium graminearum is a disease that results in yield loss and mycotoxin contamination in wheat globally. This study assessed the effect of a plant biostimulant prepared from a brown macroalga Ascophyllum nodosum (Liquid Seaweed Extract; LSE) alone and in combination with chitosan in controlling Fusarium. Wheat seedlings drenched with LSE and chitosan in combination showed reduced severity of F. graminearum infection on leaves as evidenced by a significant reduction in necrotic area and fewer number of conidia produced in the necrotic area. Gene expression studies showed that the combination of LSE and chitosan amplified the response of pathogenesis-related genes (TaPR1.1, TaPR2, TaPR3, TaGlu2) in wheat seedlings infected with Fusarium spores above that observed for the individual treatments. The combination treatments were more effective in enhancing the activity of various defense related enzymes such as peroxidase and polyphenol oxidase. FHB studies on adult plants showed a reduction of bleached spikes in wheat heads treated with the combination of LSE and chitosan. Mycotoxin content appeared to be correlated with FHB severity. Combination treatments of LSE and chitosan reduced the levels of mycotoxins deoxynivalenol and sambucinol in wheat grains. Systemic disease resistance appears to be induced by LSE and chitosan in response to F. graminearum in wheat by inducing defense genes and enzymes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it