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Disease prediction tools improve management efforts for many plant diseases. Prediction and downstream prevention demand information about disease etiology, which can be complicated for some diseases, like those caused by soilborne microorganisms. Fortunately, the availability of machine learning methods has enabled researchers to elucidate complex relationships between hosts and pathogens without invoking difficult-to-satisfy assumptions. The etiology of a destructive plant disease, Verticillium wilt of mint, caused by the fungus Verticillium dahliae was reevaluated with several supervised machine learning methods. Specifically, the objective of this research was to identify drivers of wilt in commercial mint fields, describe the relationships between these drivers, and predict wilt. Soil samples were collected from commercial mint fields. Wilt foci, V. dahliae, and plant-parasitic nematodes that can exacerbate wilt were quantified. Multiple linear regression, a generalized additive model, random forest, and an artificial neural network were fit to the data, validated with 10-fold cross-validation, and measures of explanatory and predictive performance were compared. All models selected nematodes within the genus Pratylenchus as the most important predictor of wilt. The fungus after which this disease is named, V. dahliae, was the fourth most important predictor of wilt, after crop age and cultivar. All models explained around 50% of the total variation (R2 ≤ 0.46), and exhibited comparable predictive error (RMSE ≤ 1.21). Collectively, these models revealed that the quantitative relationships between two pathogens, mint cultivars and age are required to explain wilt. The ascendance of Pratylenchus spp. in predicting symptoms of a disease assumed to primarily be caused by V. dahliae exposes the underestimated contribution of these nematodes to wilt. This research provides a foundation on which predictive forecasting tools can be developed for mint growers and reminds us of the lessons that can be learned by revisiting assumptions about disease etiology.
This article was published in the following journal.
Name: PloS one
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The joining of RNA from two different genes. One type of trans-splicing is the "spliced leader" type (primarily found in protozoans such as trypanosomes and in lower invertebrates such as nematodes) which results in the addition of a capped, noncoding, spliced leader sequence to the 5' end of mRNAs. Another type of trans-splicing is the "discontinuous group II introns" type (found in plant/algal chloroplasts and plant mitochondria) which results in the joining of two independently transcribed coding sequences. Both are mechanistically similar to conventional nuclear pre-mRNA cis-splicing. Mammalian cells are also capable of trans-splicing.
Symbiotic combination (dual organism) of the MYCELIUM of FUNGI with the roots of plants (PLANT ROOTS). The roots of almost all higher plants exhibit this mutually beneficial relationship, whereby the fungus supplies water and mineral salts to the plant, and the plant supplies CARBOHYDRATES to the fungus. There are two major types of mycorrhizae: ectomycorrhizae and endomycorrhizae.
A plant genus of the family ULMACEAE that is susceptible to Dutch elm disease which is caused by the ASCOMYCOTA fungus, Ophiostoma.
An approach of practicing medicine with the goal to improve and evaluate patient care. It requires the judicious integration of best research evidence with the patient's values to make decisions about medical care. This method is to help physicians make proper diagnosis, devise best testing plan, choose best treatment and methods of disease prevention, as well as develop guidelines for large groups of patients with the same disease. (from JAMA 296 (9), 2006)
A class of plant growth hormone isolated from cultures of Gibberella fujikuroi, a fungus causing Bakanae disease in rice. There are many different members of the family as well as mixtures of multiple members; all are diterpenoid acids based on the gibberellane skeleton.