SIGS vs. HIGS: opportunities and challenges of RNAi pest and pathogen control strategies
Bibliographic record
Abstract
Insect pests and fungal pathogens are estimated to cause 20–40% yield reduction to crops annually, causing $290 billion of economic loss every season worldwide. Pest and pathogen impacts are a persistent and ever-increasing problem for global food production, especially due to climate change and growing populations. Frequent use of chemical pesticides has resulted in increased resistance among pests and pathogens due to the strong selection pressure that the pesticides exert, resulting in the rapid accumulation of mutations that confer behavioural, mechanical and/or biochemical resistance within the pest populations. Due to rising resistance and increasing interest in control measures with low environmental impact, there is an immediate need to find alternative pest and pathogen management strategies. RNA interference (RNAi) has been developed as a control strategy by exploiting inherent cellular defence processes, providing a species-specific biological approach to crop management. Delivery of double-stranded RNA (dsRNA) can be accomplished non-transgenically by spray-induced gene silencing (SIGS), virus-mediated host-induced gene silencing (VmHIGS) or transgenically through host-induced gene silencing (HIGS), specifically targeting pest and pathogen messenger RNAs with sequence homology. Accomplishing effective RNAi strategies requires consideration into how SIGS, VmHIGS, and HIGS approaches intersect with the crop species and pest or pathogen being targeted. Additional technical advancements for the delivery and uptake of dsRNAs, messenger RNA target identification and the possibility of insect or fungal dsRNA resistance are currently being explored. These considerations will enhance the utility, ease of use and implementation of both spray-based and transgenic applications of RNAi technology for improved food security.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".