Genetic Diversity and Pathogenicity of <i>Ralstonia solanacearum</i> Causing Tobacco Bacterial Wilt in China
Bibliographic record
Abstract
Bacterial wilt caused by Ralstonia solanacearum is the most serious soilborne disease of tobacco (Nicotiana tabacum) in China. In this study, 89 strains were collected in 2012 to 2014 from across the four major tobacco-growing areas in China. The strains were identified as phylotype I by multiplex polymerase chain reaction and further divided into seven sequevars based on polymorphisms in the endoglucanase (egl) gene. Among the seven sequevars, four (15, 17, 34, and 44) have been previously described as pathogens of tobacco and two (13 and 14), which are reported here on tobacco, were previously found only on other plants. In addition, a new sequevar named 54 was identified. Strains from tobacco from different regions showed different levels of genetic diversity based on partial egl gene sequences. The farther north the distribution, the lower the gene diversity found. Pathogenicity of 27 representative strains was assessed by inoculation onto three tobacco cultivars of varying susceptibility. Through cluster analysis of area under the disease progress curve values, the 27 strains were classified into different pathotypes based on virulence; however, no obvious associations were found between sequevar and pathotype. These results will assist in determining geographical distribution of strains, and provide the foundation for breeding and integrated management programs in China.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".