Toxin-based in-vitro selection and its potential application to date palm for resistance to the bayoud Fusarium wilt
Bibliographic record
Abstract
Date palm (Phoenix dactylifera L.) is qualified as a 'tree' of great ecological and socio-economical importance in desert oases. Unfortunately, it is being decimated, especially in Morocco and Algeria, by a fusariosis wilt called bayoud and caused by Fusarium oxysporum f. sp. albedinis (Fao). Controlling this disease requires the implementation of an integrated management program. Breeding for resistance is one of the most promising component strategies of this program. Few naturally resistant cultivars with a mediocre fruit quality (dates) are known. Conventional and non-conventional methods are under development and have to use the simplest and easiest methods to screen for resistant individuals. The use of pathogen toxins as selective agents at the tissue culture step might be a source of variability that can lead to the selection of individuals with suitable levels of resistance to the toxin and/or to the pathogen among the genetic material available. Foa produces toxins such as fusaric, succinic, 3-phenyl lactic acids and their derivatives, marasmins and peptidic toxins. These toxins can be used bulked or separately as selective agents. The aim of this contribution was to give a brief overview on toxins and their use as a mean to select resistant lines and to initiate a discussion about the potential use of this approach for the date palm-Foa pathosystem. This review does not pretend to be comprehensive or exhaustive and was prepared mainly to highlight the potential use of Foa toxins for selecting date palm individuals with a suitable resistance level to bayoud using toxin-based selective media.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".