A Multiplex qPCR Assay for Detection and Quantification of <i>Plasmodiophora brassicae</i> in Soil
Bibliographic record
Abstract
Various physical and chemical factors in soil can inhibit the detection and quantification of soilborne plant pathogens using quantitative polymerase chain reaction (qPCR) assays. A multiplexed TaqMan qPCR assay, including a competitive internal positive control (CIPC), was developed to identify and (where necessary) compensate for inhibition in the quantification of resting spores of Plasmodiophora brassicae from soil. The CIPC amplicon was developed by modifying a sequence coding for green fluorescent protein so that it could be amplified with P. brassicae-specific primers. Addition of CIPC at 5 fg/μl to the singleplex qPCR assay designed to quantify P. brassicae genomic DNA did not reduce the sensitivity, specificity, or reproducibility of the assay. Each of the soil samples, either artificially inoculated or naturally infested with P. brassicae, exhibited no amplification of the CIPC. When the samples were diluted and reassessed, the quantification cycle of the CIPC relative to the control (water only) was delayed in each sample. The magnitude of the delay was used to adjust the estimate of resting spore concentration. The corrected concentration estimates were significantly higher than the unadjusted estimate, which indicated the presence of DNA inhibitors in samples even after dilution. The only exception was a mineral soil sample inoculated with a low concentration (103 spores/g) of resting spores. The assay was optimized for use on a range of soil types. A sample of 0.25 g for mineral soil and 0.10 g for high-organic-matter soil was optimum for recovery of DNA of P. brassicae. The assay represents an improvement over existing assays for estimating resting spore concentration in infested fields.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".