• The Plant Pathology Journal
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• v.40 no.1
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• pp.30-39
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• 2024

The conservation of the endangered Korean fir, Abies koreana, is of critical ecological importance. In our previous study, a yeast-like fungus identified as Aureobasidium pullulans AK10, was isolated and shown to enhance drought tolerance in A. koreana seedlings. In this study, the effectiveness of Au. pullulans AK10 treatment in enhancing drought tolerance in A. koreana was confirmed. Furthermore, using transcriptome analysis, we compared A. koreana seedlings treated with Au. pullulans AK10 to untreated controls under drought conditions to elucidate the molecular responses involved in increased drought tolerance. Our findings revealed a predominance of downregulated genes in the treated seedlings, suggesting a strategic reallocation of resources to enhance stress defense. Further exploration of enriched Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction networks revealed significant alterations in functional systems known to fortify drought tolerance, including the terpenoid backbone biosynthesis, calcium signaling pathway, pyruvate metabolism, brassinosteroid biosynthesis, and, crucially, flavonoid biosynthesis, renowned for enhancing plant drought resistance. These findings deepen our comprehension of how AK10 biostimulation enhances the resilience of A. koreana to drought stress, marking a substantial advancement in the effort to conserve this endangered tree species through environmentally sustainable treatment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5276-5298
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• 2019

Internet of Things (IoT) based sensor networks have gained unprecedented popularity in recent years. With the exponential explosion of the objects (sensors and mobiles), the bandwidth and the speed of data transmission are dwarfed by the anticipated emergence of IoT. In this paper, we propose a novel heterogeneous IoT model integrated the power line communication (PLC) and WiFi network to increase the network capacity and cope with the rapid growth of the objects. We firstly propose the mean transmission delay calculation algorithm based the 3D Markov chain according to the multi-priority of the objects. Then, the attractor selection algorithm, which is based on the adaptive behavior of the biological system, is exploited. The combined the 3D Markov chain and the attractor selection model, named MASM, can select the optimal path adaptively in the heterogeneous IoT according to the environment. Furthermore, we verify that the MASM improves the transmission efficiency and reduce the transmission delay effectively. The simulation results show that the MASM is stable to changes in the environment and more applicable for the heterogeneous IoT, compared with the other algorithms.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.85-96
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• 2003

The Revised Universal Soil Loss Equation (RUSLE) has been used in over 100 countries to estimate potential long-term soil erosion from the field. However, the RUSLE estimated soil erosion cannot be used to estimate the sediment delivered to the stream networks. For an effective erosion control, it is necessary to compute sediment delivery ratio (SDR) for watershed and sediment yield at watershed outlet. Thus, the Sediment Assessment Tool for Effective Erosion Control (SATEEC) was developed in this study to compute the sediment yield at any point in the watershed. To compute spatially distributed sediment yield map, the RUSLE was first integrated with the ArcView GIS and three area based sediment delivery ratio methods were incorporated in the SATEEC. The SATEEC was applied to the Bangdong watershed, Chuncheon, Gangwon Province to demonstrate how it can be used to estimate soil loss and sediment yield for a watershed. The sediment yield using USDA SDR method is 8,544 ton/year and 4,949 ton/year with the method by Boyce. Thus, use of watershed specific SDR is highly recommended when comparing the estimated sediment yield with the measured sediment data. The SATEEC was applied with hypothetical cropping scenario and it was found that the SATEEC can be used to assess the impacts of different management on the sediment delivered to the stream networks and to find the sediment source areas for a reach of interest. The SATEEC is an efficient tool to find the best erosion control practices with its easy-to-use interface.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.165-170
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• 2020

The spiking neural network operates in a different mechanism than the existing neural network. The existing neural network transfers the output value to the next neuron via an activation function that does not take into account the biological mechanism for the input value to the neuron that makes up the neural network. In addition, there have been good results using deep structures such as VGGNet, ResNet, SSD and YOLO. spiking neural networks, on the other hand, operate more like the biological mechanism of real neurons than the existing activation function, but studies of deep structures using spiking neurons have not been actively conducted compared to in-depth neural networks using conventional neurons. This paper proposes the method of loading an deep neural network model made from existing neurons into a conversion tool and converting it into a spiking deep neural network through the method of replacing an existing neuron with a spiking neuron.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5325-5330
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• 2014

Background: Chronic myelocytic leukemia is a disease that threatens both adults and children. Great progress has been achieved in treatment but protein-protein interaction networks underlining chronic myelocytic leukemia are less known. Objective: To develop a protein-protein interaction network for chronic myelocytic leukemia based on gene expression and to predict biological pathways underlying molecular complexes in the network. Materials and Methods: Genes involved in chronic myelocytic leukemia were selected from OMIM database. Literature mining was performed by Agilent Literature Search plugin and a protein-protein interaction network of chronic myelocytic leukemia was established by Cytoscape. The molecular complexes in the network were detected by Clusterviz plugin and pathway enrichment of molecular complexes were performed by DAVID online. Results and Discussion: There are seventy-nine chronic myelocytic leukemia genes in the Mendelian Inheritance In Man Database. The protein-protein interaction network of chronic myelocytic leukemia contained 638 nodes, 1830 edges and perhaps 5 molecular complexes. Among them, complex 1 is involved in pathways that are related to cytokine secretion, cytokine-receptor binding, cytokine receptor signaling, while complex 3 is related to biological behavior of tumors which can provide the bioinformatic foundation for further understanding the mechanisms of chronic myelocytic leukemia.

• Genomics & Informatics
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• v.2 no.4
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• pp.191-194
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• 2004

Exponentially increasing biopathway data in recent years provide us with means to elucidate the large-scale modular organization of the cell. Given the existing information on metabolic and regulatory networks, inferring biopathway information through scientific reasoning or data mining of large scale array data or proteomics data get great attention. Naturally, there is a need for a user-friendly system allowing the user to combine large and diverse pathway data sets from different resources. We built a data warehouse - BIOWAY - for analyzing and visualizing biological pathways, by integrating and customizing resources. We have collected many different types of data in regards to pathway information, including metabolic pathway data from KEGG/LIGAND, signaling pathway data from BIND, and protein information data from SWISS-PROT. In addition to providing general data retrieval mechanism, a successful user interface should provide convenient visualization mechanism since biological pathway data is difficult to conceptualize without graphical representations. Still, the visual interface in the previous systems, at best, uses static images only for the specific categorized pathways. Thus, it is difficult to cope with more complex pathways. In the BIOWAY system, all the pathway data can be displayed in computer generated graphical networks, rather than manually drawn image data. Furthermore, it is designed in such a way that all the pathway maps can be expanded or shrinked, by introducing the concept of super node. A subtle graphic layout algorithm has been applied to best display the pathway data.

• Genomics & Informatics
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• v.6 no.3
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• pp.153-156
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• 2008

Complex diseases such as stroke and cancer have two or more genetic loci and are affected by environmental factors that contribute to the diseases. Due to the complex characteristics of these diseases, identifying candidate genes requires a system-level analysis of the following: gene ontology, pathway, and interactions. A database and user interface, termed StrokeBase, was developed; StrokeBase provides queries that search for pathways, candidate genes, candidate SNPs, and gene networks. The database was developed by using in silico data mining of HGNC, ENSEMBL, STRING, RefSeq, UCSC, GO, HPRD, KEGG, GAD, and OMIM. Forty candidate genes that are associated with cerebrovascular disease were selected by human experts and public databases. The networked cerebrovascular disease gene maps also were developed; these maps describe genegene interactions and biological pathways. We identified 1127 genes, related indirectly to cerebrovascular disease but directly to the etiology of cerebrovascular disease. We found that a protein-protein interaction (PPI) network that was associated with cerebrovascular disease follows the power-law degree distribution that is evident in other biological networks. Not only was in silico data mining utilized, but also 250K Affymetrix SNP chips were utilized in the 320 control/disease association study to generate associated markers that were pertinent to the cerebrovascular disease as a genome-wide search. The associated genes and the genes that were retrieved from the in silico data mining system were compared and analyzed. We developed a well-curated cerebrovascular disease-associated gene network and provided bioinformatic resources to cerebrovascular disease researchers. This cerebrovascular disease network can be used as a frame of systematic genomic research, applicable to other complex diseases. Therefore, the ongoing database efficiently supports medical and genetic research in order to overcome cerebrovascular disease.

• Carbon letters
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• v.22
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• pp.1-13
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• 2017

Porous materials play a vital role in science and technology. The ability to control their pore structures at the atomic, molecular, and nanometer scales enable interactions with atoms, ions and molecules to occur throughout the bulk of the material, for practical applications. Three-dimensional (3D) porous carbon-based materials (e.g., graphene aerogels/hydrogels, sponges and foams) made of graphene or graphene oxide-based networks have attracted considerable attention because they offer low density, high porosity, large surface area, excellent electrical conductivity and stable mechanical properties. Water pollution and associated environmental issues have become a hot topic in recent years. Rapid industrialization has led to a massive increase in the amount of wastewater that industries discharge into the environment. Water pollution is caused by oil spills, heavy metals, dyes, and organic compounds released by industry, as well as via unpredictable accidents. In addition, water pollution is also caused by radionuclides released by nuclear disasters or leakage. This review presents an overview of the state-of-the-art synthesis methodologies of 3D porous graphene materials and highlights their synthesis for environmental applications. The various synthetic methods used to prepare these 3D materials are discussed, particularly template-free self-assembly methods, and template-directed methods. Some key results are summarized, where 3D graphene materials have been used for the adsorption of dyes, heavy metals, and radioactive materials from polluted environments.

• Journal of Life Science
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• v.10 no.4
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• pp.403-407
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• 2000

For the biological control of the root-lesion nematodes, Pratylenchus spp., which damage directly and indirectly to the leaf perilla, the nematical effect of three nematode-trapping fungi, Arthrobotrys oligospora, A. conoides and A. dactyloides was evaluated in the field. Three species of Arthrobotrys were isolated from the culture soil of leaf perilla in 1998 and were observed the capture of the root-lesion nematodes, Pratylenchus spp. by adhesive hyphal networks or constricting rings on agar. At 40 days after treatment, the plant-parasitic nematodes and root-lesion nematode populations were approximately increased 3.5 fold in untreated control plot, while the nematode population in fungi treatment plots was similar to initial population. In the A. dactyloides plot, however, the population of plant-parasitic nematodes and Pratylenchus spp. was approximately reduced 65% and 53%, respectively. Thus, the fungus A. dachyloides should provide as biological agent for the control of Pratylenchus spp.

• Genomics & Informatics
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• v.8 no.3
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• pp.150-158
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• 2010

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.