• Mycobiology
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• v.44 no.4
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• pp.319-324
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• 2016

Dieback in strawberry (Seolhyang cultivar) was first observed during the nursery season (June to September) in the Nonsan area of Korea in the years 2012 and 2013. Initial disease symptoms included dieback on runners, as well as black rot on roots, followed by wilting and eventually blackened, necrotic discoloration in the crowns of daughter plants. A fungus isolated from the diseased roots, runners, and crowns is close to Lasiodiplodia theobromae based on morphological characteristics. Analysis of a combined dataset assembled from sequences of the internal transcribed spacer and translation elongation factor 1- alpha genes grouped nine fungal isolates with the type strain of L. theobromae. The isolates showed strong pathogenicity on strawberry cultivars Kumhyang, Seolhyang, and Akihimae, fulfilling Koch's postulates. Based on these results, the pathogen responsible for dieback on strawberry plants in Korea was identified as L. theobromae.

• Molecules and Cells
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• v.42 no.7
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• pp.557-567
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• 2019

TSPAN12, a member of the tetraspanin family, has been highly connected with the pathogenesis of cancer. Its biological function, however, especially in ovarian cancer (OC), has not been well elucidated. In this study, The Cancer Genome Atlas (TCGA) dataset analysis revealed that upregulation of TSPAN12 gene expression was significantly correlated with patient survival, suggesting that TSPAN12 might be a potential prognostic marker for OC. Further exploration showed that TSPAN12 overexpression accelerated proliferation and colony formation of OVCAR3 and SKOV3 OC cells. Knockdown of TSPAN12 expression in A2780 and SKOV3 cells decreased both proliferation and colony formation. Western blot analysis showed that several cyclins and cyclin-dependent kinases (CDK) (e.g., Cyclin A2, Cyclin D1, Cyclin E2, CDK2, and CDK4) were significantly involved in the regulation of cell cycle downstream of TSPAN12. Moreover, TSPAN12 accelerated mitotic progression by controlling cell cycle. Thus, our data demonstrated that TSPAN12 could be a novel molecular target for the treatment of OC.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.48-54
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• 2019

This paper proposed a method of validation data augmentation for improving the grading accuracy of diabetic macular edema (DME) using deep learning. The data augmentation technique is basically applied in order to secure diversity of data by transforming one image to several images through random translation, rotation, scaling and reflection in preparation of input data of the deep neural network (DNN). In this paper, we apply this technique in the validation process of the trained DNN, and improve the grading accuracy by combining the classification results of the augmented images. To verify the effectiveness, 1,200 retinal images of Messidor dataset was divided into training and validation data at the ratio 7:3. By applying random augmentation to 359 validation data, $1.61{\pm}0.55%$ accuracy improvement was achieved in the case of six times augmentation (N=6). This simple method has shown that the accuracy can be improved in the N range from 2 to 6 with the correlation coefficient of 0.5667. Therefore, it is expected to help improve the diagnostic accuracy of DME with the grading information provided by the proposed DNN.

• Biomolecules & Therapeutics
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• v.30 no.1
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• pp.98-116
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• 2022

The small GTPase RhoA has been studied extensively for its role in actin dynamics. In this study, multiple bioinformatics tools were applied cooperatively to the microarray dataset GSE64714 to explore previously unidentified functions of RhoA. Comparative gene expression analysis revealed 545 differentially expressed genes in RhoA-null cells versus controls. Gene set enrichment analysis (GSEA) was conducted with three gene set collections: (1) the hallmark, (2) the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and (3) the Gene Ontology Biological Process. GSEA results showed that RhoA is related strongly to diverse pathways: cell cycle/growth, DNA repair, metabolism, keratinization, response to fungus, and vesicular transport. These functions were verified by heatmap analysis, KEGG pathway diagramming, and direct acyclic graphing. The use of multiple gene set collections restricted the leakage of information extracted. However, gene sets from individual collections are heterogenous in gene element composition, number, and the contextual meaning embraced in names. Indeed, there was a limit to deriving functions with high accuracy and reliability simply from gene set names. The comparison of multiple gene set collections showed that although the gene sets had similar names, the gene elements were extremely heterogeneous. Thus, the type of collection chosen and the analytical context influence the interpretation of GSEA results. Nonetheless, the analyses of multiple collections made it possible to derive robust and consistent function identifications. This study confirmed several well-described roles of RhoA and revealed less explored functions, suggesting future research directions.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• Journal of Biomedical Engineering Research
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• v.43 no.2
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• pp.102-108
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• 2022

Purpose: To propose a deep learning-based clinical decision support technique for laryngeal disease on epiglottis, tongue and vocal cords. Materials and Methods: A total of 873 laryngeal endoscopic images were acquired from the PACS database of Pusan N ational University Yangsan Hospital. and VGG16 model was applied with transfer learning and fine-tuning. Results: The values of precision, recall, accuracy and F1-score for test dataset were 0.94, 0.97, 0.95 and 0.95 for epiglottis images, 0.91, 1.00, 0.95 and 0.95 for tongue images, and 0.90, 0.64, 0.73 and 0.75 for vocal cord images, respectively. Conclusion: Experimental results demonstrated that the proposed model have a potential as a tool for decision-supporting of otolaryngologist during manual inspection of laryngeal endoscopic images.

• Mycobiology
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• v.49 no.6
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• pp.559-581
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• 2021

The coastal marine ecosystems of Vietnam are one of the global biodiversity hotspots, but the biodiversity of marine fungi is not well known. To fill this major gap of knowledge, we assessed the genetic diversity (ITS sequence) of 75 fungal strains isolated from 11 surface coastal marine and deeper waters in Nha Trang Bay and Van Phong Bay using a culture-dependent approach and 5 OTUs (Operational Taxonomic Units) of fungi in three representative sampling sites using next-generation sequencing. The results from both approaches shared similar fungal taxonomy to the most abundant phylum (Ascomycota), genera (Candida and Aspergillus) and species (Candida blankii) but were different at less common taxa. Culturable fungal strains in this study belong to 3 phyla, 5 subdivisions, 7 classes, 12 orders, 17 families, 22 genera and at least 40 species, of which 29 species have been identified and several species are likely novel. Among identified species, 12 and 28 are new records in global and Vietnamese marine areas, respectively. The analysis of enzyme activity and the checklist of trophic mode and guild assignment provided valuable additional biological information and suggested the ecological function of planktonic fungi in the marine food web. This is the largest dataset of marine fungal biodiversity on morphology, phylogeny and enzyme activity in the tropical coastal ecosystems of Vietnam and Southeast Asia. Biogeographic aspects, ecological factors and human impact may structure mycoplankton communities in such aquatic habitats.

• Genomics & Informatics
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• v.21 no.1
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• pp.6.1-6.8
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• 2023

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

• Journal of Biomedical Engineering Research
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• v.45 no.1
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• pp.1-9
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• 2024

Communication with others plays an important role in human social interaction and information exchange in modern society. However, some individuals have difficulty in communicating due to dysarthria. Therefore, it is necessary to develop effective diagnostic techniques for early treatment of the dysarthria. In the present study, we propose a mobile device-based methodology that enables to automatically classify dysarthria type. The light-weight CNN model was trained by using the open audio dataset of Korean patients with dysarthria. The trained CNN model can successfully classify dysarthria into related subtype disease with 78.8%~96.6% accuracy. In addition, the user-friendly mobile application was also developed based on the trained CNN model. Users can easily record their voices according to the selected inspection type (e.g. word, sentence, paragraph, and semi-free speech) and evaluate the recorded voice data through their mobile device and the developed mobile application. This proposed technique would be helpful for personal management of dysarthria and decision making in clinic.

• Korean Journal of Ecology and Environment
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• v.43 no.3
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• pp.385-399
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• 2010

The objective of this study was to analyze long term temporal trends of water chemistry and spatial heterogeneity for 83 sampling sites of Geum-River watershed using water quality dataset during 2003~2007 (obtained from the Ministry of Environment, Korea). The water quality, based on multi-parameters of temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (TN), total phosphorus (TP), and electric conductivity (EC), largely varied depending on the landuse patterns, years and seasons. The watershed was classified into three different landuse types: forest stream (Fo), agricultural stream (Ag), and urban stream (Ur). Largest seasonal variabilities in most parameters occurred during the two months of July to August and these were closely associated with large spate of summer monsoon rain. Conductivity, used as a key indicator for an ionic dilution during rainy season, and nutrients of TN and TP had inverse functions of precipitation. BOD, COD decrease during the rainy season. Minimum values in the conductivity, TN, and TP were observed during the summer monsoon, indicating an ionic and nutrient dilution of river water by the rainwater. In contrast, major inputs of suspended solids (SS) occurred during the period of summer monsoon. The landuse patterns analyses, based on the variables of BOD, COD, TN, TP and SS, showed that the values were greater in the agricultural stream (Ag) than in the forest stream (Fo) and urban stream (Ur) and that water quality was worst in the urban stream (Ur). The overall dataset suggest that efficient water quality management, especially in Gap-Stream and Miho-Stream, which showed worst water quality is required along with some of urban stream (Ur), based on the analysis of landuse patterns.