PCR Markers Derived from Comparative Genomics for Detection and Identification of the Rice Pathogen <i>Ustilaginoidea virens</i> in Plant Tissues
Bibliographic record
Abstract
Ustilaginoidea virens is a fungal pathogen that causes the false smut disease in rice and reduces the yield and quantity of the grains. A nested polymerase chain reaction (PCR)-based assay was developed to detect U. virens using genes of U. virens as specific targets. Ninety-six candidate genes of U. virens were found through first-round homology screening against a local database comprising 46 genomes of fungi, bacteria, and plants, with a second-round comparison with the GenBank NR database to further identify genes unique to U. virens. Among 96 remaining candidate genes, 20 of them (GenBank accessions KY617806 to KY617825) were randomly selected for further testing and, eventually, six sets of nested PCR primers were developed after further sensitivity, specificity, and detection tests. All six sets could detect DNA of U. virens at as little as 1 to 10 fg/μl from field or lab samples. These primers may be used to detect infection by U. virens at early stages, for use in research toward mitigating disease spread, as well as for studying the ecology of U. virens. This study also serves to illustrate that a comparative genomics method may allow for selection and development of highly specific primers once draft or complete genomes are available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".