Bibliographic record
Abstract
Plant viruses recruit cellular translation factors not only to translate their viral RNAs but also to regulate their replication and potentiate their local and systemic movement. Because of the virus dependence on cellular translation factors, it is perhaps not surprising that many natural plant recessive resistance genes have been mapped to mutations of translation initiation factors eIF4E and eIF4G or their isoforms, eIFiso4E and eIFiso4G. The partial functional redundancy of these isoforms allows specific mutation or knock-down of one isoform to provide virus resistance without hindering the general health of the plant. New possible targets for antiviral strategies have also been identified following the characterization of other plant translation factors (eIF4A-like helicases, eIF3, eEF1A and eEF1B) that specifically interact with viral RNAs and proteins and regulate various aspects of the infection cycle. Emerging evidence that translation repression operates as an alternative antiviral RNA silencing mechanism is also discussed. Understanding the mechanisms that control the development of natural viral resistance and the emergence of virulent isolates in response to these plant defense responses will provide the basis for the selection of new sources of resistance and for the intelligent design of engineered resistance that is broad-spectrum and durable.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".