Development and validation of a rapid loop-mediated isothermal amplification assay for the detection of Chrysomyxa and characterization of Chrysomyxa woroninii overwintering on Picea in China
Bibliographic record
Abstract
Chrysomyxa rusts cause significant damage to spruce in both natural forests and plantations. Particularly, Three Chrysomyxa species, Chrysomyxa deformans, Chrysomyxa qilianensis, and Chrysomyxa rhododendri, listed as National Forest Dangerous Pests in China, have severely affected many economically and ecologically important spruce native species in China. Also, Chrysomyxa arctostaphyli, an important plant quarantine fungus, causes a damaging broom rust disease on spruce. Therefore, rapid, and efficient detection tools are urgently needed for proper rust disease detection and management. In this study, a sensitive, genus-specific loop-mediated isothermal amplification (LAMP) assay targeting the ITS-28S rRNA region was developed to detect the presence of Chrysomyxa in spruce needle and bud samples. After optimization and validation, the LAMP assay was found to be sensitive to detect as low as 5.2 fg/µL DNA, making it suitable for rapid on-site testing for rust infection. The assay was also specific to Chrysomyxa species, with no positive signals from other rust genus/species. The application of LAMP in the early detection of rust infections in spruce needles and buds was investigated, and spatial colonization profiles as well as the means of overwintering of Chrysomyxa woroninii in infected buds and branches were verified using the LAMP assay. This LAMP detection method will facilitate further studies on the characteristics of the life cycle and inoculation of other systemic rusts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".