Evaluation of Agronomic Performance of Mutant Rice Lines of Mentik Wangi Variety (Oryza sativa L.) Resulting from OSSWEET11 Gene Editing
Bibliographic record
Abstract
Bacterial leaf blight, caused by Xanthomonas oryzae pv.oryzae (Xoo), is a major disease that significantly reduces rice yields.A local variety of Central Java, Mentik Wangi, has several unique characteristics that are favored by consumers, such as a soft texture and a distinctive fragrant aroma.Despite its advantages, Mentik Wangi is susceptible to bacterial leaf blight because Xoo targets the susceptibility gene OsSWEET11.CRISPR/Cas9 technology can induce resistance by mutating the gene's promoter, preventing its recognition by Xoo.This study aimed to observe the phenotypic traits of T1 mutant lines of Mentik Wangi rice edited using CRISPR/Cas9.T1 seeds derived from T0 parents were analyzed for agronomic traits, including plant height, tiller number, flowering time, and grain weight.Dunnett's test showed no significant differences between mutant lines and their wild-type parents, suggesting no pleiotropic effects from the OsSWEET11 mutation.These results indicate that mutating susceptibility genes can be a viable approach for developing bacterial leaf blight-tolerant rice without compromising agronomic performance.However, molecular analyses are needed to confirm the inheritance of mutations and correlate them with agronomic traits in the T1 generation.This study demonstrates the potential of CRISPR/Cas9 technology in breeding disease-resistant rice varieties.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".