Membrane-Based Oligonucleotide Array Developed from Multiple Markers for the Detection of Many <i>Phytophthora</i> Species
Bibliographic record
Abstract
Most Phytophthora spp. are destructive plant pathogens; therefore, effective monitoring and accurate early detection are important means of preventing potential epidemics and outbreaks of diseases. In the current study, a membrane-based oligonucleotide array was developed that can detect Phytophthora spp. reliably using three DNA regions; namely, the internal transcribed spacer (ITS), the 5' end of cytochrome c oxidase 1 gene (cox1), and the intergenic region between cytochrome c oxidase 2 gene (cox2) and cox1 (cox2-1 spacer). Each sequence data set contained ≈250 sequences representing 98 described and 15 undescribed species of Phytophthora. The array was validated with 143 pure cultures and 35 field samples. Together, nonrejected oligonucleotides from all three markers have the ability to reliably detect 82 described and 8 undescribed Phytophthora spp., including several quarantine or regulated pathogens such as Phytophthora ramorum. Our results showed that a DNA array containing signature oligonucleotides designed from multiple genomic regions provided robustness and redundancy for the detection and differentiation of closely related taxon groups. This array has the potential to be used as a routine diagnostic tool for Phytophthora spp. from complex environmental samples without the need for extensive growth of cultures.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".