AWARD LECTURE / CONFÉRENCE D'HONNEUR Prospects for controlling plant fungal diseases Alternatives based on chemical ecology and biotechnology
Bibliographic record
Abstract
Plants produce a diverse array of secondary metabolites associated with important defence and resistance mechanisms. In general, within the same plant family the chemical structures of these metabolites are related and suggest common biogenetic precursors. Crucifers are known to produce constitutive and induced tryptophan derived metabolites. Pathogenic fungi can resist such defences utilizing a variety of processes, as for example, enzymatic detoxification. On the other hand, to facilitate their penetration and colonization of the plant tissues, fungal phytopathogens can produce phytotoxic metabolites, some of which are selectively toxic to host-plants. However, plants may be resistant to these pathogens partly due to their ability to detoxify these selective phytotoxins. Nonetheless, the outcome of these interactions frequently favours the pathogen and can lead to enormous yield losses or even major plant epidemics. An overview of some of the most recent studies of metabolic pathways and stress responses in crucifers and several of their pathogens is presented. Potential strategies to prevent and control plant microbial diseases based on chemical ecology studies and biotechnology will be discussed. Key words: phytoalexin, phytotoxin, chemical defence, metabolic detoxification, destruxin B.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".