Comparative genomics-guided loop-mediated isothermal amplification for characterization of<i>Pseudomonas syringae</i>pv. phaseolicola
Bibliographic record
Abstract
AIMS: To design and evaluate a loop-mediated isothermal amplification (LAMP) protocol by combining comparative genomics and bioinformatics for characterization of Pseudomonas syringae pv. phaseolicola (PSP), the causal agent of halo blight disease of bean (Phaseolus vulgaris L.). METHODS AND RESULTS: Genomic sequences of Pseudomonas syringae pathovars, P. fluorescens and P. aeruginosa were analysed using multiple sequence alignment. A pathovar-specific region encoding pathogenicity-related secondary metabolites in the PSP genome was targeted for developing a LAMP assay. The final assay targeted a polyketide synthase gene, and readily differentiated PSP strains from other Pseudomonas syringae pathovars and other Pseudomonas species, as well as other plant pathogenic bacteria, e.g. species of Pectobacterium, Erwinia and Pantoea. CONCLUSION: A LAMP assay has been developed for rapid and specific characterization and identification of PSP from other pathovars of P. syringae and other plant-associated bacteria. SIGNIFICANCE AND IMPACT OF THE STUDY: This paper describes an approach combining a bioinformatic data mining strategy and comparative genomics with the LAMP technology for characterization and identification of a plant pathogenic bacterium. The LAMP assay could serve as a rapid protocol for microbial identification and detection with significant applications in agriculture and environmental sciences.
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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) | 0.000 | 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".