Implementation of a new method to identify Verticillium isolates affecting potato cultivars in Alberta
Bibliographic record
Abstract
Potato (Solanum tuberosum L.) is the most important non-cereal crop in the world, with 300 million tons produced every year. Despite its economic importance, potato plants are susceptible to numerous diseases caused by fungi, bacteria, and viruses, that lead to tremendous financial losses for the industry. A common potato disease called Verticillium wilt, or potato early dying, is caused by fungi from the genus Verticillium. The two species that affect potato cultivars in Alberta are V. dahliae and V. albo-atrum. These soil-borne fungi affect xylem tissues preventing water intake causing dehydration in plants. This results in necrosis of the tissues and thus, leads to early plant mortality. Unfortunately, once a crop has developed disease symptoms, there is little that can be done to prevent harvest losses that year. This is why a fast and reliable method for early detection of the fungi is necessary. Traditionally, fungal identification is performed by observing morphological characteristics of mycelia on solid media with a microscope; however, it is time consuming, requires specialized skill set, and species identification can only be achieved by genome sequencing. We are currently implementing a new method developed by Inderbitzin et al. (2013) to identify the species of Verticillium isolates by detecting the presence of species-specific nucleotide sequences1. After extracting fungal DNA, a polymerization chain reaction (PCR) is conducted with species-specific primers for both V. dahlia and V. albo-atrum. By visualizing the presence of an amplified fragment on agarose gel, it is possible to determine the Verticillium species. We are testing and optimizing this method on eight non-identified pure Verticillium cultures isolated from fields in Alberta to determine their species. We are also working on the isolation of new Vertcillium cultures from seven potato plant samples collected during the past growing season across Southern Alberta that showed symptoms of Verticillium wilt. This will allow us to build a collection of Verticillium isolates for future genetic analysis. Overall, this new method will be a very powerful tool to detect the presence of Verticillium fungi in soil and plants before the emergence of any disease symptoms, which allows potato grower to control this disease in a timely manner preventing harvest losses. 1Inderbitzin P, Davis RM, Bostock RM, Subbarao KV (2013) Identification and Differentiation of Verticillium Species and V. longisporum Lineages by Simplex and Multiplex PCR Assays. PLoS ONE 8:e65990 *Indicates presenter
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".