Leveraging RNA Interference to Impact Insecticide Resistance in the Colorado Potato Beetle, Leptinotarsa decemlineata
Bibliographic record
Abstract
The Colorado potato beetle, Leptinotarsa decemlineata Say, is a potato pest that can cause important economic losses to the potato industry worldwide. Diverse strategies have been deployed to target this insect such as biological control, crop rotation, and a variety of insecticides. Regarding the latter, this pest has demonstrated impressive abilities to develop resistance against the compounds used to regulate its spread. Substantial work has been conducted to better characterize the molecular signatures underlying this resistance, with the overarching objective of leveraging this information for the development of novel approaches, including RNAi-based techniques, to limit the damage associated with this insect. This review first describes the various strategies utilized to control L. decemlineata and highlights different examples of reported cases of resistances against insecticides for this insect. The molecular leads identified as potential players modulating insecticide resistance as well as the growing interest towards the use of RNAi aimed at these leads as part of novel means to control the impact of L. decemlineata are described subsequently. Finally, select advantages and limitations of RNAi are addressed to better assess the potential of this technology in the broader context of insecticide resistance for 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".