Resistance and tolerance to viroid infection: status and prospects
Bibliographic record
Abstract
Abstract Viroids are the smallest known plant pathogens. Although their genome sequences are not known to encode any peptides, they can nonetheless replicate and cause disease in several plant species. One of the goals of research into viroids is to safeguard crops from viroid diseases and hence limit the economic losses to acceptable levels. In order to control viroid disease epidemics, and to mitigate the damage they cause, various different approaches have been used. These approaches include the development of viroid-resilient plants through breeding programs and the grafting of high-yield varieties onto viroid-resistant rootstocks. Furthermore, biotechnological approaches—such as the transgenic expression of ribonuclease, ribozymes, antisense RNA, and hairpin RNA—have been tested against various viroid-host combinations. In this review, we summarize the current understanding of both the natural genetic resources available with either viroid resistance or disease tolerance and the gene-editing technologies that is available for enhancing viroid resistance/tolerance. Additionally, we discuss currently available technologies with which plant viral diseases can be managed (e.g., spray-induced gene silencing and genome editing) in the context of their application to future viroid research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".