Advances in Diagnostics of Downy Mildews: Lessons Learned from Other Oomycetes and Future Challenges
Bibliographic record
Abstract
Downy mildews are plant pathogens that damage crop quality and yield worldwide. Among the most severe and notorious crop epidemics of downy mildew occurred on grapes in the mid-1880s, which almost destroyed the wine industry in France. Since then, there have been multiple outbreaks on sorghum and millet in Africa, tobacco in Europe, and recent widespread epidemics on lettuce, basil, cucurbits, and spinach throughout North America. In the mid-1970s, loss of corn to downy mildew in the Philippines was estimated at US$23 million. Today, crops that are susceptible to downy mildews are worth at least $7.5 billion of the United States' economy. Although downy mildews cause devastating economic losses in the United States and globally, this pathogen group remains understudied because they are difficult to culture and accurately identify. Early detection of downy mildews in the environment is critical to establish pathogen presence and identity, determine fungicide resistance, and understand how pathogen populations disperse. Knowing when and where pathogens emerge is also important for identifying critical control points to restrict movement and to contain populations. Reducing the spread of pathogens also decreases the likelihood of sexual recombination events and discourages the emergence of novel virulent strains. A major challenge in detecting downy mildews is that they are obligate pathogens and thus cannot be cultured in artificial media to identify and maintain specimens. However, advances in molecular detection techniques hold promise for rapid and in some cases, relatively inexpensive diagnosis. In this article, we discuss recent advances in diagnostic tools that can be used to detect downy mildews. First, we briefly describe downy mildew taxonomy and genetic loci used for detection. Next, we review issues encountered when identifying loci and compare various traditional and novel platforms for diagnostics. We discuss diagnosis of downy mildew traits and issues to consider when detecting this group of organisms in different environments. We conclude with challenges and future directions for successful downy mildew detection.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".