• IE interfaces
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• v.23 no.1
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• pp.1-11
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• 2010

There are various ways to manage the patient flow of the hospital outpatient system. However, it is difficult to apply many implementation solutions to the real outpatient system at once. Because first, the expected effects of each different solution are very much depend on the real situation of the system and applied other solutions, and second, owing to the limited resources, each solution should be implemented according to the priority. In order to overcome these difficulties, this paper focuses on proposing the comprehensive subset of implementation solutions, which is one of the most effective among various kinds of subsets, and verifying the effects of it. The comprehensive subset of solutions is derived from conducting design of experiments and simulation which determine the optimum set of different solutions and analyze the particular interactions and priority order among them. This implementation strategy can solve the difficulties of applying different kinds of various solutions to the hospital outpatient system.

• IMMUNE NETWORK
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• v.9 no.2
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• pp.41-45
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• 2009

Activating and inhibitory cell surface receptors play important roles in regulation of immune responses. Recent progress has demonstrated that many inhibitory receptors pair with activating, as well as inhibitory, isoforms, both of whose genes are located in small clusters on a chromosome. We and others identified paired activating and inhibitory immunoglobulin-like receptors, designated myeloid-associated immunoglobulin-like receptors (MAIR) (CD300). MAIR is a multigene family consisting of nine genes on a small segment of mouse chromosome 11. MAIR family receptors are preferentially expressed on myeloid cells, including macrophages, dendritic cells, granulocytes, and bone-marrow-derived cultured mast cells, and a subset of B cells and regulate activation of these cells. Thus, MAIR plays an important role in innate immunity mediated by myeloid cells.

• BMB Reports
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• v.47 no.6
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• pp.299-310
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• 2014

Cohesin is a multi-protein complex composed of four core subunits (SMC1A, SMC3, RAD21, and either STAG1 or STAG2) that is responsible for the cohesion of sister chromatids following DNA replication until its cleavage during mitosis thereby enabling faithful segregation of sister chromatids into two daughter cells. Recent cancer genomics analyses have discovered a high frequency of somatic mutations in the genes encoding the core cohesin subunits as well as cohesin regulatory factors (e.g. NIPBL, PDS5B, ESPL1) in a select subset of human tumors including glioblastoma, Ewing sarcoma, urothelial carcinoma, acute myeloid leukemia, and acute megakaryoblastic leukemia. Herein we review these studies including discussion of the functional significance of cohesin inactivation in tumorigenesis and potential therapeutic mechanisms to selectively target cancers harboring cohesin mutations.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.651-654
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• 2005

Ontology developing process has aroused a lot of controversy among knowledge engineers and knowledge users. The recent surges on ontology building methodologies and practical ontology applications have explored a broad spectrum of knowledge management challenges. On the one hand, the abundant methodology theories provide us with a set of useful heuristic rules, from which we get the overview of ontology building process. But on the other hand, every research groups would like to justify their theories by listing their specific characteristics and unique method when approaching the right way. However, there is still no one “correct” way or methodology for developing ontologies. In this case, the methods used to evaluate only a subset of specific domain do not make any sense to the commonsense users. As a result, a comprehensive understanding of domain ontology is urgent and necessary.

• Biomolecules & Therapeutics
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• v.32 no.1
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• pp.1-12
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• 2024

Adverse drug reactions (ADRs) are an inherent aspect of drug use. While approximately 80% of ADRs are predictable, immune system-mediated ADRs, often unpredictable, are a noteworthy subset. Skin-related ADRs, in particular, are frequently unpredictable. However, the wide spectrum of skin manifestations poses a formidable diagnostic challenge. Comprehending the pathomechanisms underlying ADRs is essential for accurate diagnosis and effective management. The skin, being an active immune organ, plays a pivotal role in ADRs, although the precise cutaneous immunological mechanisms remain elusive. Fortunately, clinical manifestations of skin-related ADRs, irrespective of their severity, are frequently rooted in immunological processes. A comprehensive grasp of ADR morphology can aid in diagnosis. With the continuous development of new pharmaceuticals, it is noteworthy that certain drugs including immune checkpoint inhibitors have gained notoriety for their association with ADRs. This paper offers an overview of immunological mechanisms involved in cutaneous ADRs with a focus on clinical features and frequently implicated drugs.

• Fisheries and Aquatic Sciences
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• v.19 no.4
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• pp.21.1-21.11
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• 2016

Background: The evaluation of suitable reference genes as normalization controls is a prerequisite requirement for launching quantitative reverse transcription-PCR (RT-qPCR)-based expression study. In order to select the stable reference genes in abalone Haliotis discus hannai tissues (gill and hepatopancreas) under heavy metal exposure conditions (Cu, Zn, and Cd), 12 potential candidate housekeeping genes were subjected to expression stability based on the comprehensive ranking while integrating four different statistical algorithms (geNorm, NormFinder, BestKeeper, and ${\Delta}CT$ method). Results: Expression stability in the gill subset was determined as RPL7 > RPL8 > ACTB > RPL3 > PPIB > RPL7A > EF1A > RPL4 > GAPDH > RPL5 > UBE2 > B-TU. On the other hand, the ranking in the subset for hepatopancreas was RPL7 > RPL3 > RPL8 > ACTB > RPL4 > EF1A > RPL5 > RPL7A > B-TU > UBE2 > PPIB > GAPDH. The pairwise variation assessed by the geNorm program indicates that two reference genes could be sufficient for accurate normalization in both gill and hepatopancreas subsets. Overall, both gill and hepatopancreas subsets recommended ribosomal protein genes (particularly RPL7) as stable references, whereas traditional housekeepers such as ${\beta}-tubulin$ (B-TU) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) genes were ranked as unstable genes. The validation of reference gene selection was confirmed with the quantitative assay of MT transcripts. Conclusions: The present analysis showed the importance of validating reference genes with multiple algorithmic approaches to select genes that are truly stable. Our results indicate that expression stability of a given reference gene could not always have consensus across tissue types. The data from this study could be a good guide for the future design of RT-qPCR studies with respect to metal regulation/detoxification and other related physiologies in this abalone species.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.335-346
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• 2010

The present study investigates the difficulty of solving the mathematical problem, namely the DLP (Discrete Logarithm Problem) for ephemeral keys. The DLP is the basis for many public key cryptosystems. The ephemeral keys are used in such systems to ensure security. The DLP defined on a prime field $Z^*_p of random prime is considered in the present study. The most effective method to solve the DLP is the ICM (Index Calculus Method). In the present study, an efficient way of computing the DLP for ephemeral keys by using a new variant of the ICM when the factors of p-1 are known and small is proposed. The ICM has two steps, a pre-computation and an individual logarithm computation. The pre-computation step is to compute the logarithms of a subset of a group and the individual logarithm step is to find the DLP using the precomputed logarithms. Since the ephemeral keys are dynamic and change for every session, once the logarithms of a subset of a group are known, the DLP for the ephemeral key can be obtained using the individual logarithm step. Therefore, an efficient way of solving the individual logarithm step based on the newly proposed precomputation method is presented and the performance is analyzed using a comprehensive set of experiments. The ephemeral keys are also solved by using other methods, which are efficient on random primes, such as the Pohlig-Hellman method, the Van Oorschot method and the traditional individual logarithm step. The results are compared with the newly proposed individual logarithm step of the ICM. Also, the DLP of ephemeral keys used in a popular password key exchange protocol known as Chang and Chang are computed and reported to launch key recovery attack.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.12-14
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• 2005

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) by means of genetically optimized fuzzy polynomial neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms(GAs). The conventional SOFPNNs hinges on an extended Group Method of Data Handling(GMDH) and exploits a fixed fuzzy inference type in each FPN of the SOFPNN as well as considers a fixed number of input nodes located in each layer. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, a collection of the specific subset of input variables, and the number of membership function) and addresses specific aspects of parametric optimization. Therefore, the proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series).