Signal pathways involved in microbe–nematode interactions provide new insights into the biocontrol of plant-parasitic nematodes
Bibliographic record
Abstract
Plant-parasitic nematodes (PPNs) cause severe damage to agricultural crops worldwide. As most chemical nematicides have negative environmental side effects, there is a pressing need for developing efficient biocontrol methods. Nematophagous microbes, the natural enemies of nematodes, are potential biocontrol agents against PPNs. These natural enemies include both bacteria and fungi and they use diverse methods to infect and kill nematodes. For instance, nematode-trapping fungi can sense host signals and produce special trapping devices to capture nematodes, whereas endo-parasitic fungi can kill nematodes by spore adhesion and invasive growth to break the nematode cuticle. By contrast, nematophagous bacteria can secrete virulence factors to kill nematodes. In addition, some bacteria can mobilize nematode-trapping fungi to kill nematodes. In response, nematodes can also sense and defend against the microbial pathogens using strategies such as producing anti-microbial peptides regulated by the innate immunity system. Recent progresses in our understanding of the signal pathways involved in microbe-nematode interactions are providing new insights in developing efficient biological control strategies against PPNs. This article is part of the theme issue 'Biotic signalling sheds light on smart pest management'.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".