Investigating the potential of unsaturated fatty acids as antifungal crop protective agents
Bibliographic record
Abstract
Pathogenic fungi cause significant yield losses and quality reductions to many crops including canola, wheat, and barley. Toxic metabolites produced by fungal pathogens, along with excessive application of synthetic fungicides, can also pose risks to human and livestock health. Hydroxy unsaturated fatty acids (HUFAs) are novel alternatives to commonly used fungicides. Here, the antifungal activities of two HUFAs, coriolic acid (CA) and ricinoleic acid (RA), were assessed in vitro and in planta for their activity against the important phytopathogens Fusarium graminearum Schwabe, Pyrenophora tritici-repentis (Died.) Drechsler, Pyrenophora teres f. teres Drechsler, Sclerotinia sclerotiorum (Lib.) de Bary, Leptosphaeria maculans Ces. & De Not., and Aspergillus niger Tiegh. on selected media, monocots or dicots. The results in vitro indicated that both CA and RA showed the strongest inhibitory activity against L. maculans and A. niger, but their activities varied with different fungi. On wheat and barley, CA but not RA reduced disease severity caused by Pyrenophora spp.; on canola, treatment with high levels of CA and RA led to oxidative damage of the plant tissues, and treatment with low concentrations of CA and RA did not reduce disease severity caused by L. maculans or S. sclerotiorum on canola. Our findings suggest that the utility of HUFAs in reducing disease severity caused by pathogenic fungi depends on the dosage and the plant and fungus targeted. High concentrations of HUFA can be phytotoxic on certain plants. In addition to their direct antifungal properties, additional mechanisms may be involved in the disease reduction of CA, suggesting the need for further evaluation of its potential use in crop protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".