Sweet and sour cherry trees growing at new cultivar testing orchard and certified stock collection in Hungary are highly infected with CVA and PrVF
Bibliographic record
Abstract
• Viromes of sour cherry and sweet cherry trees were determined using small RNA-HTS. • Prunus virus F was described for the first time in Hungary. • Cherry virus A and Prunus virus F occured frequently in the sampled collections. • Infection with Prunus necrotic ringspot virus increased during the testing period Hungary has a long tradition of sour cherry breeding. Clone selection for new cultivars with desirable traits recently replaced by classical breeding, and new cultivar candidates waiting for evaluations of their phenotypes are maintained at a testing orchard. The viral status of these new sour cherry cultivar candidates was tested using small-RNA high-throughput sequencing (HTS) as an unbiased diagnostic method. Moreover, the viromes of certified stock collections of sour cherry and sweet cherry growing in the vicinity of the testing orchard were also determined. Bioinformatic analysis of the sRNA HTS was validated using unbiased methods, revealing that the trees had very high rates of cherry virus A (CVA) and Prunus virus F (PrVF) infections. The latter virus was described in Hungary for the first time. While the stock collection was free from Prunus necrotic ringspot virus (PNRSV), this regulated virus was present at the testing orchard. As a follow-up study, the survey of the testing orchard was repeated after two years using RT-PCR with a detailed survey of the stock collection. The infection pattern of the viruses suggests the spread of PNRSV via pollen at the testing orchard, where the trees can bloom. Notably, the strains of both CVA and PrVF varied to a large extent. A low concentration of virus-derived small RNAs suggests that they do not induce strong antiviral RNAi, which could explain why their infections can be latent, without visible symptoms. However, to determine the impacts of frequent coinfections on fruit yield and tree health, which could be further altered by the changing climate, further investigations will be needed in the future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".