Banana defense response against pathogens: breeding disease-resistant cultivars
Bibliographic record
Abstract
Banana ( Musa spp.), being a globally significant fruit crop, faces a myriad of threats from various diseases, such as Fusarium wilt, Xanthomonas wilt, bunchy top disease, and weevils disease. This review provides an overview of recent advancements in molecular mechanisms and immune signaling pathways underlying disease resistance in banana. First, the review discusses the latest research advances on banana pests and diseases. Subsequently, this review explores the immune responses and signaling pathways, pattern recognition receptor-triggered immunity, effector-triggered immunity, cell death, reactive oxygen species, autophagy, hormonal pathways, and other players involved in banana–disease interactions. Finally, the review discusses the current understanding of the genetic architecture of disease resistance in banana, focusing on the identification of defense-related genes and quantitative trait loci associated with resistance to major pathogens and offering recommendations for genetic research. The conclusion underscores the significance of research on banana immunity, specifically highlighting the crucial need to identify endogenous resistance genes and elucidate immune signaling pathways for future efforts aimed at breeding disease-resistant banana. This review offers a comprehensive perspective on the molecular mechanisms underlying disease resistance in banana and serves as a valuable reference for breeding efforts aimed at enhancing banana's resistance to pathogens.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".