Evaluating different double-stranded RNA structures for their ability to control pest flea beetles
Bibliographic record
Abstract
Canola is an economically important Canadian crop that suffers significant annual losses by damage from feeding flea beetles (Phyllotreta cruciferae and P. striolata). Increasing incidences of resistance and concerns about off-target effects with current insecticides demands new methods of control. Recently, environmentally safer approaches to pest control have been investigated using RNA interference (RNAi), a sequence-specific gene silencing mechanism triggered by exogenous double-stranded RNA (dsRNA), to selectively induce the mortality of targeted species. This study examined three different dsRNA structures for their ability to kill P. striolata flea beetles and thereby reduce feeding damage on canola leaves. Long linear dsRNAs (212-214 bp), short hairpin RNAs (hpRNAs) (21-24 bp), and short paperclip RNAs (pcRNAs) (21-24 bp) targeting mRNAs of three essential genes, Ras opposite (Rop), Sec23, and Snf7 in P. striolata were investigated. P. striolata adults were fed dsRNA-treated canola leaf disks, and impacts on insect survivorship and leaf material consumption were recorded over an eight-day period. pcRNAs targeting Sec23 and Snf7, and long dsRNAs targeting Sec23 and Rop effectively killed flea beetles, resulting in the reduced consumption of treated canola leaf tissues. Consumption of both the Sec23-specific long dsRNA and pcRNAs caused similar levels of flea beetle mortality (68% and 76%, respectively), whereas only the Rop-specific long dsRNA and the Snf7 pcRNA were effective at killing the beetles (76% and 84%, respectively). hpRNAs proved the least effective across all gene targets, killing at most, between 40-52% of flea beetles, depending on the gene target, and in general, these values were not significantly different relative to the negative controls. While this study examined dsRNAs specific for only three target genes, it provides evidence that RNAi-based pesticides have the potential to control these economically important pests and that short pcRNAs can be as effective as conventional long linear dsRNAs.
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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 | 0.000 | 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) | 0.001 | 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 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".