Detection and Identification of Six <i>Monilinia</i> spp. Causing Brown Rot Using TaqMan Real-Time PCR from Pure Cultures and Infected Apple Fruit
Bibliographic record
Abstract
Brown rot is a severe disease affecting stone and pome fruit. This disease was recently confirmed to be caused by the following six closely related species: Monilinia fructicola, M. laxa, M. fructigena, Monilia polystroma, M. mumecola, and M. yunnanensis. Because of differences in geographic distributions, some of these species are important quarantine pathogens in certain countries. In this study, we developed TaqMan real-time polymerase chain reaction (PCR) assays to detect and identify the six species. Primer pairs and probes were designed for Monilinia fructicola, M. fructigena, M. laxa, and Monilia polystroma based on sequence differences in the laccase-2 genes. Additionally, based on sequence differences in the elongation factor genes, primer pairs and probes were designed for Monilia mumecola and M. yunnanensis. The real-time PCR assays were able to specifically identify the target pathogens, with detection limits of 10 to 100 fg of DNA, which is equivalent to one to seven conidia. The assays were also able to detect the target pathogens in a mixed DNA sample comprising all six Monilinia spp. and related species. The real-time PCR assays accurately detected target fungi from infected apple fruit. Furthermore, the identification results were consistent with those of traditional morphological methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".