A Molecular Method to Assess Viability of <i>Phytophthora</i> in Infected Wood Following Phytosanitary Heat Treatment
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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".