A new and effective method to induce infection of Phyllachora maydis into corn for tar spot studies in controlled environments
Bibliographic record
Abstract
BACKGROUND: Tar spot of corn is a significant and spreading disease in the continental U.S. and Canada caused by the obligate biotrophic fungus Phyllachora maydis. As of 2023, tar spot had been reported in 18 U.S. states and one Canadian Province. The symptoms of tar spot include chlorotic flecking followed by the formation of black stromata where conidia and ascospores are produced. Advancements in research and management for tar spot have been limited by a need for a reliable method to inoculate plants to enable the study of the disease. The goal of this study was to develop a reliable method to induce tar spot in controlled conditions. RESULTS: We induced infection of corn by P. maydis in 100% of inoculated plants with a new inoculation method. This method includes the use of vacuum-collection tools to extract ascospores from field-infected corn leaves, application of spores to leaves, and induction of the disease in the dark at high humidity and moderate temperatures. Infection and disease development were consistently achieved in four independent experiments on different corn hybrids and under different environmental conditions in a greenhouse and growth chamber. Disease induction was impacted by the source and storage conditions of spores, as tar spot was not induced with ascospores from leaves stored dry at 25 ºC for 5 months but was induced using ascospores from infected leaves stored at -20 ºC for 5 months. The time from inoculation to stromata formation was 10 to 12 days and ascospores were present 19 days after inoculation throughout our experiments. In addition to providing techniques that enable in-vitro experimentation, our research also provides fundamental insights into the conditions that favor tar spot epidemics. CONCLUSIONS: We developed a method to reliably inoculate corn with P. maydis. The method was validated by multiple independent experiments in which infection was induced in 100% of the plants, demonstrating its consistency in controlled conditions. This new method facilitates research on tar spot and provides opportunities to study the biology of P. maydis, the epidemiology of tar spot, and for identifying host resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".