Molecular Diagnostics and Detection of Oomycetes on Fiber Crops
Bibliographic record
Abstract
Fiber crops are an important group of economic plants. Traditionally cultivated for fiber, fiber crops have also become sources of other materials such as food, animal feed, cosmetics and medicine. Asia and America are the two main production areas of fiber crops in the world. However, oomycete diseases have become an important factor limiting their yield and quality, causing devastating consequences for the production of fiber crops in many regions. To effectively control oomycete pathogens and reduce their negative impacts on these crops, it is very important to have fast and accurate detection systems, especially in the early stages of infection. With the rapid development of molecular biology, the diagnosis of plant pathogens has progressed from relying on traditional morphological features to the increasing use of molecular methods. The objective of this paper was to review the current status of research on molecular diagnosis of oomycete pathogens on fiber crops. Our search of PubMed identified nearly 30 species or subspecies of oomycetes on fiber crops, among which the top three species were Phytophthora boehmeriae, Phytophthora nicotianae and Pythium ultimum. The gene regions that have been used for molecular identifications of these pathogens include the internal transcribed spacer (ITS) regions of the nuclear ribosomal RNA gene cluster, and genes coding for translation elongation factor 1α (EF-1α) and mitochondrial cytochrome c oxidase subunits I and II (Cox 1, Cox 2), etc. We summarize the molecular assays that have been used to identify these pathogens and discuss potential areas of future development for fast, specific, and accurate diagnosis of oomycetes on fiber crops.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".