Recent advances in bio‐chemical, molecular and physiological aspects of membrane lipid derivatives in plant pathology
Bibliographic record
Abstract
Plant pathogens pose a significant threat to the food industry and food security accounting for 10-40% crop losses annually on a global scale. Economic losses from plant diseases are estimated at $300B for major food crops and are associated with reduced food availability and accessibility and also high food costs. Although strategies exist to reduce the impact of diseases in plants, many of these introduce harmful chemicals to our food chain. Therefore, it is important to understand and utilize plants' immune systems to control plant pathogens to enable more sustainable agriculture. Lipids are core components of cell membranes and as such are part of the first line of defense against pathogen attack. Recent developments in omics technologies have advanced our understanding of how plant membrane lipid biosynthesis, remodelling and/or signalling modulate plant responses to infection. Currently, there is limited information available in the scientific literature concerning lipid signalling targets and their biochemical and physiological consequences in response to plant pathogens. This review focusses on the functions of membrane lipid derivatives and their involvement in plant responses to pathogens as biotic stressors. We describe major plant defense systems including systemic-acquired resistance, basal resistance, hypersensitivity and the gene-for-gene concept in this context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".