• Korean Journal of Plant Taxonomy
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• v.46 no.2
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• pp.247-255
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• 2016

As an attempt to determine the identity of the old trees of flowering cherries planted in the yard of the Catholic Archdiocese of Daegu, we conducted comparative phylogenetic analyses between wild and cultivated Prunus yedoensis Matsum. We generated the phylogeny (MP) and haplotype network (TCS) of 25 individuals, including wild P. yedoensis, from Jeju Island, cultivated P. ${\times}$yedoensis 'Somei-yoshino' from Korea and Japan, and P. spachiana f. ascendens (Makino) Kitam. from Jeju Island and Japan based on highly informative sequences of two cpDNA regions (rpl16 gene and trnS-trnG intergenic spacer). The wild and cultivated P. yedoensis were distinguished from each other in both the phylogeny and haplotype networks, and the old flowering cherry trees in Daegu had a cpDNA haplotype identical to that of the cultivated P. ${\times}$yedoensis 'Someiyoshino'. Compared to the cultivated P. ${\times}$yedoensis 'Somei-yoshino', wild P. yedoensis appears to have greater haplotype diversity, presumably originating from the genetic diversity of P. spachiana f. ascendens that functioned as a maternal parent in the hybrid origin of wild P. yedoensis. A future detailed study requires extensive sampling of P. spachiana f. ascendens from Japan and Korea to determine their precise phylogenetic relationships relative to wild and cultivated P. yedoensis. We concluded that the old flowering cherry trees planted in the yard of the Catholic Archdiocese of Daegu are highly likely to be of cultivated origin rather than wild types from Jeju Island, as previously speculated.

• Progress in Medical Physics
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• v.26 no.2
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• pp.72-78
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• 2015

Organic solvents are known toxic effects like vertigo, behavioral obstacle, distracting, and peripheral neuropathy in neuron areas. However, there have been few studies how neurotoxic solvents-exposed workers are affected by the cognitive load of preceding working memory tasks. Therefore, we used fMRI as to measure the neural correlates of working memory impairment in occupational workers who had from chronic exposure to organic solvent. Twenty-nine solvent-exposed workers were included in this study. Each participant concluded the verbal N-back tasks (1- and 2-back) during the fMRI acquisition. Within-group analyses showed fronto-parietal networks were active in each condition. Direct comparisons between 1- and 2-back showed higher activation during the 2-back than 1-back. We found that increased activation of these regions at lower task demand is associated with increased cost of implementing.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Proceedings of the Microbiological Society of Korea Conference
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• 2007.05a
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• pp.120-122
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• 2007

All living organisms use numerous signal-transduction pathways to sense and respond to their environments and thereby survive and proliferate in a range of biological niches. Molecular dissection of these signalling networks has increased our understanding of these communication processes and provides a platform for therapeutic intervention when these pathways malfunction in disease states, including infection. Owing to the expanding availability of sequenced genomes, a wealth of genetic and molecular tools and the conservation of signalling networks, members of the fungal kingdom serve as excellent model systems for more complex, multicellular organisms. Here, we employed Cryptococcus neoformans as a model system to understand how fungal-signalling circuits operate at the molecular level to sense and respond to a plethora of environmental stresses, including osmoticshock, UV, high temperature, oxidative stress and toxic drugs/metabolites. The stress-activated p38/Hog1 MAPK pathway is structurally conserved in many organisms as diverse as yeast and mammals, but its regulation is uniquely specialized in a majority of clinical Cryptococcus neoformans serotype A and D strains to control differentiation and virulence factor regulation. C. neoformans Hog1 MAPK is controlled by Pbs2 MAPK kinase (MAPKK). The Pbs2-Hog1 MAPK cascade is controlled by the fungal "two-component" system that is composed of a response regulator, Ssk1, and multiple sensor kinases, including two-component.like (Tco) 1 and Tco2. Tco1 and Tco2 play shared and distinct roles in stress responses and drug sensitivity through the Hog1 MAPK system. Furthermore, each sensor kinase mediates unique cellular functions for virulence and morphological differentiation. We also identified and characterized the Ssk2 MAPKKK upstream of the MAPKK Pbs2 and the MAPK Hog1 in C. neoformans. The SSK2 gene was identified as a potential component responsible for differential Hog1 regulation between the serotype D sibling f1 strains B3501 and B3502 through comparative analysis of their meiotic map with the meiotic segregation of Hog1-dependent sensitivity to the fungicide fludioxonil. Ssk2 is the only polymorphic component in the Hog1 MAPK module, including two coding sequence changes between the SSK2 alleles in B3501 and B3502 strains. To further support this finding, the SSK2 allele exchange completely swapped Hog1-related phenotypes between B3501 and B3502 strains. In the serotype A strain H99, disruption of the SSK2 gene dramatically enhanced capsule biosynthesis and mating efficiency, similar to pbs2 and hog1 mutations. Furthermore, ssk2, pbs2, and hog1 mutants are all hypersensitive to a variety of stresses and completely resistant to fludioxonil. Taken together, these findings indicate that Ssk2 is the critical interface protein connecting the two-component system and the Pbs2-Hog1 pathway in C. neoformans.

• Journal of Life Science
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• v.28 no.10
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• pp.1233-1243
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• 2018

Sterol regulatory-element binding proteins (SREBPs) are a family of transcription factors that regulate lipid homeostasis and metabolism by controlling the expression of enzymes required for endogenous cholesterol, fatty acid (FA), triacylglycerol, and phospholipid synthesis. The three SREBPs are encoded by two different genes. The SREBP1 gene gives rise to SREBP-1a and SREBP-1c, which are derived from utilization of alternate promoters that yield transcripts in which distinct first exons are spliced to a common second exon. SREBP-2 is derived from a separate gene. Additionally, SREBPs are implicated in numerous pathogenic processes, such as endoplasmic reticulum stress, inflammation, autophagy, and apoptosis. They also contribute to obesity, dyslipidemia, diabetes mellitus, and nonalcoholic fatty liver diseases. Genome-wide analyses have revealed that these versatile transcription factors act as important nodes of biological signaling networks. Changes in cell metabolism and growth are reciprocally linked through SREBPs. Anabolic and growth signaling pathways branch off and connect to multiple steps of SREBP activation and form complex regulatory networks. SREBPs are activated through the PI3K-Akt-mTOR pathway in these processes, but the molecular mechanism remains to be understood. This review aims to provide a comprehensive understanding of the role of SREBPs in physiology and pathophysiology at the cell, organ, and organism levels.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1053-1060
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• 2021

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Korean Journal of Biological Psychiatry
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• v.12 no.1
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• pp.32-41
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• 2005

Objective:Impaired processing of working memory is one of the cognitive deficits seen in patients with schizophrenia. This aimed at corroborating the differences in the brain activities involved in the process of working memory between patients with schizophrenia and the control subjects. Method:Fourteen patients with schizophrenia and 12 healthy volunteers were recruited in this study. Functional magnetic resonance imaging(fMRI) was used to assess cortical activities during the performance of a 2-back visual working memory paradigm using the Korean alphabet as mnemonic content. Results:Group analysis revealed that left lateral prefrontal cortex and right parietal lobule showed decreased cortical activities in the patient group. On the other hand, an increased activation in left superior and middle frontal gyrus, left middle temporal gyrus, right cuneus, both occipital lobes, right fusiform gyrus and right cingulate gyrus. The activation in left anterior lobe and both declive of cerebellum was also increased. Conclusions:This study showed a decreased activation in left lateral prefrontal and right parietal neural networks from the patient group and confirmed the earlier findings on the impaired working memory of patients with schizophrenia using fMRI investigation. The regions implicated in our study suggest an abnormal functioning of the fronto-parietal cortical areas that are critical to the information processing stream, which might be correspondent to common pathophysiology rather than a common etiology in schizophrenia.

• Journal of The Korean Association For Science Education
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• v.35 no.6
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• pp.997-1006
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• 2015

Perception of the experiment is one of the most important factors of students' understanding of scientific inquiry and the nature of science. This study examined the perception of middle and high school students of the meaning of 'experiment' in the biological sciences. Semantic network analysis (SNA) was especially used to visualize students' perception structure in this study. One hundred and ninety middle school students and 200 high school students participated in this study. Students responded to two questions on the meaning of 'experiment' in science and biology. This study constructed four semantic networks based on the collected response. As a result, middle school students about the 'experiment' in science are 'we', 'direct', 'principle' of such words was aware of the experiments from the center to the active side. The high school students' 'theory', 'true', 'information' were recognized as an experiment that explores the process of creating a knowledge center including the word. In addition, middle school students relative to 'experiment' of the creature around the 'dissection', 'body', high school students were recognized as 'life', 'observation' observation activities dealing with the living organisms and recognized as a core. The results of this study will be used as important evidence in the future to map out an experiment in biological science curriculum.

• Korean Journal of Ecology and Environment
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• v.33 no.3 s.91
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• pp.230-243
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• 2000

A large-scale community data were patterned by utilizing an unsupervised learning algorithm in artificial neural networks. Data for benthic macroinvertebrates in streams of South Korea reported in publications for 12 years from 1984 to 1995 were provided as inputs for training with the Kohonen network. Taxa included for the training were 5 phylum, 10 class, 26 order, 108 family and 571 species in 27 streams. Abundant groups were Diptera, Ephemeroptera, Trichoptera, Plecoptera, Coleoptera, Odonata, Oligochaeta, and Physidae. A wide spectrum of community compositions was observed: a few tolerant taxa were collected at polluted sites while a high species richness was observed at relatively clean sites. The trained mapping by the Kohonen network effectively showed patterns of communities from different river systems, followed by patterns of communities from different environmental disturbances. The training by the proposed artificial neural network could be an alternative for organizing community data in a large-scale ecological survey.