Identification and application of exogenous dsRNA confers plant protection against Sclerotinia sclerotiorum and Botrytis cinerea
Bibliographic record
Abstract
Sclerotinia sclerotiorum, the causal agent of white stem rot, is responsible for significant losses in crop yields around the globe. While our understanding of S. sclerotiorum infection is becoming clearer, genetic control of the pathogen has been elusive and effective control of pathogen colonization using traditional broad-spectrum agro-chemical protocols are less effective than desired. In the current study, we developed species-specific RNA interference-based control treatments capable of reducing fungal infection. Development of a target identification pipeline using global RNA sequencing data for selection and application of double stranded RNA (dsRNA) molecules identified single gene targets of the fungus. Using this approach, we demonstrate the utility of this technology through foliar applications of dsRNAs to the leaf surface that significantly decreased fungal infection and S. sclerotiorum disease symptoms. Select target gene homologs were also tested in the closely related species, Botrytis cinerea, reducing lesion size and providing compelling evidence of the adaptability and flexibility of this technology in protecting plants against devastating fungal pathogens.
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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 | 0.001 |
| 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".