Development of a TaqMan Real-Time PCR Assay for Quantification of Airborne Conidia of <i>Botrytis squamosa</i> and Management of Botrytis Leaf Blight of Onion
Bibliographic record
Abstract
The use of a DNA-based method for quantifying airborne inoculum of Botrytis squamosa, a damaging pathogen of onion, was investigated. A method for purifying DNA from conidia collected using rotating-arm samplers and quantifying it using a TaqMan real-time quantitative polymerase chain reaction (qPCR) assay is described. The sensitivity of the qPCR assay was high, with a detection limit of 2 conidia/rod. A linear relationship between numbers of conidia counted with a compound microscope and those determined with the qPCR assay was obtained. Receiver operating characteristic curve analysis was used to evaluate the reliability of the two methods of conidia quantification (microscope examination and qPCR assay) to predict the risk of disease being below or above a damage threshold (D(th)). In total, 142 field samples from commercial onion fields were analyzed. At damage thresholds of 5 or 10 lesions/leaf, conidia quantification with the qPCR assay was more reliable at predicting disease risk than conidia quantification based on microscope counts. The proportion of decisions where the disease was present and predicted was higher for the qPCR assay than for the microscope counts, with values of 0.95 and 0.89 compared with 0.79 and 0.81 for D(th) of 5 and 10 lesions/leaf, respectively. The proportion of decisions where the disease was present but not predicted was lower for the qPCR assay than for microscope counts, with values of 0.05 and 0.11 compared with 0.20 and 0.19 for D(th) of 5 and 10 lesions/leaf, respectively. The results demonstrated that this new qPCR assay was reliable for quantifying B. squamosa airborne inoculum in commercial onion fields and that molecular conidia quantification could be used as a component of a risk management system for Botrytis leaf blight.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".