The recombinogenic history of turnip mosaic potyvirus reveals its introduction to Japan in the 19th century
Bibliographic record
Abstract
Abstract Characterizing the detailed spatial and temporal dynamics of plant pathogens can provide valuable information for crop protection strategies. However, the epidemiological characteristics and evolutionary trajectories of pathogens can differ markedly from one country to another. The most widespread and important virus of brassica vegetables, turnip mosaic virus (TuMV), causes serious plant diseases in Japan. We collected 317 isolates of TuMV from Raphanus and Brassica plants throughout Japan over nearly five decades. Genomic sequences from these isolates were combined with published sequences. We identified a total of eighty-eight independent recombination events in Japanese TuMV genomes and found eighty-two recombination-type patterns in Japan. We assessed the evolution of TuMV through space and time using whole and partial genome sequences of both nonrecombinants and recombinants. Our results suggest that TuMV was introduced into Japan after the country emerged from its isolationist policy (1639–1854) in the Edo period and then dispersed to other parts of Japan in the 20th century. The results of our analyses reveal the complex structure of the TuMV population in Japan and emphasize the importance of identifying recombination events in the genome. Our study also provides an example of surveying the epidemiology of a virus that is highly recombinogenic.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".