A Molecular Method to Assess Viability of <i>Phytophthora</i> in Infected Wood Following Phytosanitary Heat Treatment
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Bibliographic record
Abstract
International trade in wood products is an important component of the global economy. However, wood and wood products may have pests associated with them that could be introduced into importing countries, posing phytosanitary risks and leading to the implementation of regulatory restrictions that affect wood trade. The application of heat to kill wood-associated pests has been a successful phytosanitary method to reduce their spread. To evaluate the efficacy of wood heat treatment to kill fungal and fungus-like pathogens, the method of choice has been to grow organisms in cultures for subsequent identification. However, some plant pathogens can be difficult or impossible to grow in axenic cultures, and a molecular method can still be useful for assessing pathogen viability after heat treatment. RNA is a single-stranded molecule that is responsible for the transcription of genes. Since it becomes rapidly unstable after cell death, it provides a measure of viability. We therefore designed and tested RNA-based molecular diagnostic assays targeting essential genes and assessed their presence after heat treatment in wood colonized by four Phytophthora species of phytosanitary concern ( P. × multiformis, P. cinnamomi, P. lateralis, and P. ramorum) through reverse transcription and real-time polymerase chain reaction (RT-qPCR). Our assays differentiate between genomic and mRNA as the TaqMan probes span exon–intron junctions. We validated these RT-qPCR assays to assess heat treatment efficacy of Phytophthora-inoculated wood. These assays can be very useful tools to assess the effectiveness of current and emerging phytosanitary wood treatments. [Formula: see text] Copyright © 2024 His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources Canada. This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it