Molecular diagnostic assays based on cpn60 UT sequences reveal the geographic distribution of subgroup 16SrXIII-(A/I)I phytoplasma in Mexico
Bibliographic record
Abstract
Geographically diverse samples from strawberry exhibiting symptoms of Strawberry Green Petal (SbGP), periwinkle plants with virescence, and blackberry, blueberry, and raspberry plants displaying yellowing and inedible fruits, were assayed for the presence of phytoplasma DNA. PCR targeting the 16S rRNA-encoding gene and chaperonin-60 (cpn60) showed that the plants were infected with phytoplasma subgroup16SrXIII-(A/I)I (SbGP/MPV). To examine the geographic distribution of this pathogen in Mexico, we designed an array of cpn60-targeted molecular diagnostic assays for SbGP/MPV phytoplasma. A fluorescent microsphere hybridization assay was designed that was capable of detecting SbGP/MPV phytoplasma in infected plant tissues, successfully differentiating it from other known phytoplasma cpn60 UT sequences, while identifying a double infection with SbGP/MPV and aster yellows (16SrI) phytoplasma. Two quantitative assays, quantitative real-time PCR (qRT-PCR) and droplet digital PCR (ddPCR), gave similar results in infected samples. Finally, a loop-mediated isothermal amplification (LAMP) assay provided rapid detection of SbGP/MPV phytoplasma DNA. Application of these assays revealed that SbGP/MPV phytoplasma is widely distributed in Central Mexico, with positive samples identified from eleven localities within three states separated by hundreds of kilometres. These results also provide tools for determining the presence and geographic distribution of this pathogen in plant and insect samples in other localities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".