Localized efficacy of environmental RNAi in Tetranychus urticae
Bibliographic record
Abstract
Environmental RNAi has been developed as a tool for reverse genetics studies and is an emerging pest control strategy. The ability of environmental RNAi to efficiently down-regulate the expression of endogenous gene targets assumes efficient uptake of dsRNA and its processing. In addition, its efficiency can be augmented by the systemic spread of RNAi signals. Environmental RNAi is now a well-established tool for the manipulation of gene expression in the chelicerate acari, including the two-spotted spider mite, Tetranychus urticae. Here, we focused on eight single and ubiquitously-expressed genes encoding proteins with essential cellular functions. Application of dsRNAs that specifically target these genes led to whole mite body phenotypes-dark or spotless. These phenotypes were associated with a significant reduction of target gene expression, ranging from 20 to 50%, when assessed at the whole mite level. Histological analysis of mites treated with orally-delivered dsRNAs was used to investigate the spatial range of the effectiveness of environmental RNAi. Although macroscopic changes led to two groups of body phenotypes, silencing of target genes was associated with the distinct cellular phenotypes. We show that regardless of the target gene tested, cells that displayed histological changes were those that are in direct contact with the dsRNA-containing gut lumen, suggesting that the greatest efficiency of the orally-delivered dsRNAs is localized to gut tissues in T. urticae.
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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".