Development of a DNA Macroarray for the Detection and Identification of Fungal Pathogens Causing Decline of Young Grapevines
Bibliographic record
Abstract
Young vine decline (YVD) is a complex disease caused by at least 51 different fungi and responsible for important economic losses to the grapevine industry worldwide. YVD fungi are known to occur in planting material. Hence, detection prior to planting is critical to assure longevity of newly established vineyards. A DNA macroarray based on reverse dot-blot hybridization containing 102 oligonucleotides complementary to portions of the β-tubulin region was developed for detection of YVD fungi. Specificity of the array was first evaluated against 138 pure fungal cultures representing 72 different taxa from nine genera, including 37 YVD species. In total, 61 species, including 34 YVD pathogens, were detected and identified by the array. The detection limit of the array was below 0.1 pg of genomic DNA. The array was validated against artificially inoculated canes and soil and commercial planting material, with the latter showing a high incidence of YVD fungi in nursery plants otherwise not detected by traditional plating and culturing. This DNA array proved to be a rapid and specific tool to simultaneously detect and identify most YVD fungi in a single test, which has the potential to be used in commercial diagnostics or by the grapevine nursery industry to determine the health status of the planting material.
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 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 | 0.000 |
| 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 teacher head, 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".