Recombinase Polymerase Amplification Assay for Field Detection of Tomato Bacterial Spot Pathogens
Bibliographic record
Abstract
Bacterial spot of tomato is caused by Xanthomonas gardneri, X. euvesicatoria, X. perforans, and X. vesicatoria. Current diagnostic methods for the pathogens are not in-field assays. Recombinase polymerase amplification (RPA) is ideal for in-field detection assays, because it is an isothermal technique that is rapid and more tolerant to inhibitors compared with polymerase chain reaction. Hence, novel RPA probes and primers were designed to amplify regions of the hrcN gene of X. gardneri, X. euvesicatoria, and X. perforans. The X. gardneri RPA is specific to X. gardneri with a detection limit of 10 6 CFU/ml and detected X. gardneri in lesions from naturally (n = 6) or artificially (n = 18) infected plants. The X. euvesicatoria RPA detects both X. euvesicatoria and X. perforans with a detection limit of 10 6 CFU/ml and detected both pathogens in plants artificially infected (n = 36) or naturally infected (n = 85) with either X. euvesicatoria or X. perforans. The X. perforans RPA is specific to X. perforans with a detection limit of 10 7 CFU/ml. Although the X. perforans RPA assay was unable to detect X. perforans from lesions, the X. euvesicatoria RPA was successfully used in field to detect X. perforans from symptomatic field samples (n = 31). The X. perforans RPA was then used to confirm the pathogen in the laboratory. The X. euvesicatoria and X. gardneri RPA is promising for rapid, real-time in-field detection of bacterial spot and one of the first developed among plant pathogenic bacteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".