Developing PCR-Based Assays for Detecting Pea Seed-borne Mosaic Virus (PSbMV) in Plants, Seeds, and Its Aphid Vector, <i>Acyrthosiphon pisum</i>
Bibliographic record
Abstract
), in a nonpersistent manner. To mitigate the risks associated with PSbMV, it is crucial to plant virus-free seeds, detect the virus at an early stage, and implement effective control measures for the vectors, given that most commercial pulse cultivars are vulnerable to the virus. This study designed and assessed multiple primers for PCR-based virus detection and demonstrated their capability to identify PSbMV isolates in infected plant tissues. The primers successfully detected PSbMV in dried plant tissues and in aphids collected from infected plants, even after being stored at room temperature for up to 3 months. Furthermore, a hydrolysis probe-based assay was developed, and its effectiveness for quantitative PCR (qPCR), digital PCR (dPCR), and droplet digital PCR (ddPCR) was evaluated. Our results showed high sensitivity and linearity of the assay, capable of detecting PSbMV at concentrations as low as 22 copies per reaction mix using digital PCRs. Our findings underscore the effectiveness of the developed primers and assay for the rapid and sensitive detection of PSbMV isolates in a variety of plant tissues, aphids, and seed samples, offering improved tools for disease monitoring and management in agricultural settings.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".