• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.602-605
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• 2009

This study is intended to analyze unscientific concepts shared by high school students regarding planet movement; produce a learning program to address these concepts; and investigate what impact the application of the program to planet observation and classroom lessons may have on their grasp of planet movement and their attitudes toward science at large. Application of the learning program developed in this study to teaching and learning courses led to the discovery that the program is a useful tool to enhance students' understanding of planet movement. These results suggest that a variety of programs including planet movement activities that may keep students interested in science should be continued. The above study results may be utilized in geoscience teaching and learning. It is deemed necessary to develop better learning programs and study teaching and learning methods regarding not only planet movement but also other spheres.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.22 no.1
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• pp.41-49
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• 2019

Recent studies on pediatric inflammatory bowel disease (IBD) have revealed that early-onset IBD has distinct phenotypic differences compared with adult-onset IBD. In particular, very early-onset IBD (VEO-IBD) differs in many aspects, including the disease type, location of the lesions, disease behavior, and genetically attributable risks. Several genetic defects that disturb intestinal epithelial barrier function or affect immune function have been noted in these patients from the young age groups. In incidence of pediatric IBD in Korea has been increasing since the early 2000s. Neonatal or infantile-onset IBD develops in less than 1% of pediatric patients. Children with "neonatal IBD" or "infantile-onset IBD" have higher rates of affected first-degree relatives, severe disease course, and a high rate of resistance to immunosuppressive treatment. The suspicion of a monogenic cause of VEO-IBD was first confirmed by the discovery of mutations in the genes encoding the interleukin 10 (IL-10) receptors that cause impaired IL-10 signaling. Patients with such mutations typically presented with perianal fistulae, shows a poor response to medical management, and require early surgical interventions in the first year of life. To date, 60 monogenic defects have been identified in children with IBD-like phenotypes. The majority of monogenic defects presents before 6 years of age, and many present before 1 year of age. Next generation sequencing could become an important diagnostic tool in children with suspected genetic defects especially in children with VEO-IBD with severe disease phenotypes. VEO-IBD is a phenotypically and genetically distinct disease entity from adult-onset or older pediatric IBD.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.5
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• pp.295-303
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• 2019

A heart simulator, UT-Heart, is a finite element model of the human heart that can reproduce all the fundamental activities of the working heart, including propagation of excitation, contraction, and relaxation and generation of blood pressure and blood flow, based on the molecular aspects of the cardiac electrophysiology and excitation-contraction coupling. In this paper, we present a brief review of the practical use of UT-Heart. As an example, we focus on its application for predicting the effect of cardiac resynchronization therapy (CRT) and evaluating the proarrhythmic risk of drugs. Patient-specific, multiscale heart simulation successfully predicted the response to CRT by reproducing the complex pathophysiology of the heart. A proarrhythmic risk assessment system combining in vitro channel assays and in silico simulation of cardiac electrophysiology using UT-Heart successfully predicted drug-induced arrhythmogenic risk. The assessment system was found to be reliable and efficient. We also developed a comprehensive hazard map on the various combinations of ion channel inhibitors. This in silico electrocardiogram database (now freely available at http://ut-heart.com/) can facilitate proarrhythmic risk assessment without the need to perform computationally expensive heart simulation. Based on these results, we conclude that the heart simulator, UT-Heart, could be a useful tool in clinical medicine and drug discovery.

• Mass Spectrometry Letters
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• v.12 no.3
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• pp.93-105
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• 2021

Exosomes have gained the attention of the scientific community because of their role in facilitating intercellular communication, which is critical in disease monitoring and drug delivery research. Exosome research has grown significantly in recent decades, with a focus on the development of various technologies for isolating and characterizing exosomes. Among these efforts is the use of matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS), which offers high-throughput direct analysis while also being cost and time effective. MALDI is used less frequently in exosome research than electrospray ionization due to the diverse population of extracellular vesicles and the impurity of isolated products, both of which necessitate chromatographic separation prior to MS analysis. However, MALDI-MS is a more appropriate instrument for the analytical approach to patient therapy, given it allows for fast and label-free analysis. There is a huge drive to explore MALDI-MS in exosome research because the technology holds great potential, most notably in biomarker discovery. With methods such as fingerprint analysis, OMICs profiling, and statistical analysis, the search for biomarkers could be much more efficient. In this review, we highlight the potential of MALDI-MS as a tool for investigating exosomes and some of the possible strategies that can be implemented based on prior research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1689-1705
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• 2023

To deal with the potential XSS vulnerabilities in the source code of the power communication network, an XSS vulnerability detection method combining the static analysis method with the dynamic testing method is proposed. The static analysis method aims to analyze the structure and content of the source code. We construct a set of feature expressions to match malignant content and set a "variable conversion" method to analyze the data flow of the code that implements interactive functions. The static analysis method explores the vulnerabilities existing in the source code structure and code content. Dynamic testing aims to simulate network attacks to reflect whether there are vulnerabilities in web pages. We construct many attack vectors and implemented the test in the Selenium tool. Due to the combination of the two analysis methods, XSS vulnerability discovery research could be conducted from two aspects: "white-box testing" and "black-box testing". Tests show that this method can effectively detect XSS vulnerabilities in the source code of the power communication network.

• Genomics & Informatics
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• v.21 no.3
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.1-22
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• 2009

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction and integration both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of techniques for Web service discovery have been proposed, but the discovery challenge has not been satisfactorily addressed. Unfortunately, most existing solutions are either too rudimentary to be useful or too domain dependent to be generalizable. In this paper, we propose a Web service organizing framework that combines clustering techniques with string matching and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. Our proposed approach has several appealing features : (1) It minimizes the requirement of prior knowledge from both service consumers and publishers; (2) It avoids exploiting domain dependent ontologies; and (3) It is able to visualize the semantic relationships among Web services. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service registries. We report on some preliminary results demonstrating the efficacy of the proposed approach.

• Journal of Life Science
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• v.22 no.10
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• pp.1428-1433
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• 2012

Longevity is an exciting but difficult subject to study because it is determined by complex processes that require the coordinated action of several genetic factors as well as physiological and environmental influences. Genetic approaches have been applied to animal models to identify the molecular mechanism responsible for longevity. Several experimental model organisms obtained over the last decades suggest that the complete deletion of a single gene by gene targeting has proven to be an invaluable tool for the discovery of the mechanisms underlying longevity. The first discovery of long-lived mutants came from Caenorhabditis elegans research, which identified the insulin/IGF-1 pathway as responsible for longevity in this worm. IGF-1 is a multifunctional polypeptide that has sequence similarity to insulin and is involved in normal growth and development of cells. Several factors in the IGF-1 system have since been studied by gene targeting in the control of longevity in lower species, including nematode and fruit fly. In addition, significant progress has been made using mice models to extend the lifespan by targeted mutations that interfere with growth hormone/IGF-1 and IGF-1 signaling cascades. A recent finding that IGF-1 is involved in aging in mice was achieved by using liver-specific knockout mutant mice, and this clearly demonstrated that the IGF-1 signal pathway can extend the lifespan in both invertebrates and vertebrate models. Although the underlying molecular mechanisms for the control of longevity are not fully understood, it is widely accepted that reduced IGF-1 signaling plays an important role in the control of aging and longevity. Several genes involved in the IGF-1 signaling system are reviewed in relation to longevity in genetically modified mice models.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.33-54
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• 2010

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of public Web service repositories have been proposed, but the Web service taxonomy generation has not been satisfactorily addressed. Unfortunately, most existing Web service taxonomies are either too rudimentary to be useful or too hard to be maintained. In this paper, we propose a Web service taxonomy generation framework that combines an artificial neural network based clustering techniques with descriptive label generating and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service repositories. We report on some preliminary results demonstrating the efficacy of the proposed approach.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.