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Camellia sinensis is one of the major crops grown in Taiwan and has been widely cultivated around the island. Tea leaves are prone to various fungal infections, and leaf spot is considered one of the major diseases in Taiwan tea fields. As part of a survey on fungal species causing leaf spots on tea leaves in Taiwan, 19 fungal strains morphologically similar to the genus Diaporthe were collected. ITS (internal transcribed spacer), tef1-α (translation elongation factor 1-α), tub2 (beta-tubulin), and cal (calmodulin) gene regions were used to construct phylogenetic trees and determine the evolutionary relationships among the collected strains. In total, six Diaporthe species, including one new species, Diaporthe hsinchuensis, were identified as linked with leaf spot of C. sinensis in Taiwan based on both phenotypic characters and phylogeny. These species were further characterized in terms of their pathogenicity, temperature, and pH requirements under laboratory conditions. Diaporthe tulliensis, D. passiflorae, and D. perseae were isolated from C. sinensis for the first time. Furthermore, pathogenicity tests revealed that, with wound inoculation, only D. hongkongensis was pathogenic on tea leaves. This investigation delivers the first assessment of Diaporthe taxa related to leaf spots on tea in Taiwan.
Bacteria that are capable of organizing themselves as biofilms are an important public health issue. Knowledge discovery focusing on the ability to swarm and conquer the surroundings to form persistent colonies is therefore very important for microbiological research communities that focus on a clinical perspective. Here, we demonstrate how a machine learning workflow can be used to create useful models that are capable of discriminating distinct associated growth behaviors along distinct phenotypes. Based on basic gray-scale images, we provide a processing pipeline for binary image generation, making the workflow accessible for imaging data from a wide range of devices and conditions. The workflow includes a locally estimated regression model that easily applies to growth-related data and a shape analysis using identified principal components. Finally, we apply a density-based clustering application with noise (DBSCAN) to extract and analyze characteristic, general features explained by colony shapes and areas to discriminate distinct Bacillus subtilis phenotypes. Our results suggest that the differences regarding their ability to swarm and subsequently conquer the medium that surrounds them result in characteristic features. The differences along the time scales of the distinct latency for the colony formation give insights into the ability to invade the surroundings and therefore could serve as a useful monitoring tool.