Virulence structure of<i>Magnaporthe oryzae</i>populations from Fujian Province, China
Bibliographic record
Abstract
Rice blast, caused by Magnaporthe oryzae, is one of the most devastating diseases of rice worldwide. The aim of the present study was to elucidate the virulence structure of M. oryzae populations in Fujian Province, China, over the period 2006–2015. For this purpose, 456 M. oryzae isolates collected from diverse cultivars from eight rice cropping regions in Fujian were screened for the presence of 11 known avirulence genes: Avr-Pik, Avr-Pita1, Avr-Pita2, Avr-Pita3, PWL2, Avr1-CO39, ACE1, Avr-Piz-t, Avr-Pia, Avr-Pii and Avr-Pikm, with gene-specific molecular markers. The results showed that Avr-Pik and Avr-Pita3 occurred at the highest frequency (94.5% and 91.7%, respectively), while Avr1-CO39 and Avr-Pii were not detected. Further, the remaining avirulence genes occurred at frequencies ranging from 5.9% to 89.0%. Temporal population dynamics revealed that Avr-Pik was uniformly distributed in all 10 years, at frequencies of more than 87.5%. Spatial distribution analysis showed avirulence genes were present at different frequencies among the geographic regions. In addition, 24 rice monogenic lines of IRRI-Japan with known blast resistance genes were inoculated to assess the virulence of 60 isolates. The results revealed that the resistance gene Pik showed the broadest resistance spectrum to the isolates tested and therefore would be the most useful in rice blast resistance breeding. The resistance genes Pi-z5, Pi-1(1), Pi-kp, Pi-9(t), Pi-ta(1) and Pi-kh also were effective and would therefore also be of value to resistance breeding programmes. The present study provides information that should be useful for the development and deployment of rice blast resistant cultivars in Fujian Province.
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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 | 0.000 | 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".