• Genomics & Informatics
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• v.16 no.2
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• pp.30-35
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• 2018

Patient-derived xenograft (PDX) models are useful tools for tumor biology research and testing the efficacy of candidate anticancer drugs targeting the druggable mutations identified in tumor tissue. However, it is still unknown how much of the genetic alterations identified in primary tumors are consistently detected in tumor tissues in the PDX model. In this study, we analyzed the genetic alterations of three primary colorectal cancers (CRCs) and matched xenograft tissues in PDX models using a next-generation sequencing cancer panel. Of the 17 somatic mutations identified from the three CRCs, 14 (82.4%) were consistently identified in both primary and xenograft tumors. The other three mutations identified in the primary tumor were not detected in the xenograft tumor tissue. There was no newly identified mutation in the xenograft tumor tissues. In addition to the somatic mutations, the copy number alteration profiles were also largely consistent between the primary tumor and xenograft tissue. All of these data suggest that the PDX tumor model preserves the majority of the key mutations detected in the primary tumor site. This study provides evidence that the PDX model is useful for testing targeted therapies in the clinical field and research on precision medicine.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.471-477
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• 2005

This paper proposes an efficient constraint-based optimization model for the design of 3G mobile networks, such as universal mobile telecommunications system (UMTS). The model concerns about finding a set of sites for locating radio network controllers (RNCs) from a set of pre-defined candidate sites, and at the same time optimally assigning node Bs to the selected RNCs. All these choices must satisfy a set of constraints and optimize an objective function. This problem is NP-hard and consequently cannot be practically solved by exact methods for real size networks. Thus, this paper proposes a hybrid search strategy for tackling this complex and combinatorial optimization problem. The proposed hybrid search strategy is composed of three phases: A constraint satisfaction method with an embedded problem-specific goal which guides the search for a good initial solution, an optimization phase using local search algorithms, such as tabu algorithm, and a post­optimization phase to improve solutions from the second phase by using a constraint optimization procedure. Computational results show that the proposed search strategy and the model are highly efficient. Optimal solutions are always obtained for small or medium sized problems. For large sized problems, the final results are on average within $5.77\%$ to $7.48\%$ of the lower bounds.

• Korean Journal of Construction Engineering and Management
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• v.6 no.5 s.27
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• pp.78-88
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• 2005

Given the trend of increased apartment buildings and high-rise buildings, the maintenance of apartment buildings has been set a higher value. For this reason, a total performance evaluation model of the existing buildings has recently been developed. But, it has a lack of the management work analysis. The purpose of this study, therefore, is the management work analysis for items selection of maintenance performance evaluation of apartment buildings. Candidate items of Maintenance performance evaluation was made with the existing literature and business system analysis. Easy systemicity of performance evaluation supplemented doing question to academic experts and housing managers. Finally decided maintenance performance evaluation items are to classify 14 administrative and 15 technical items.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.879-886
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• 2009

The purpose of this article is to present the application of Least Squares Support Vector Machine in analyzing the existing structure of brand. We estimate the parameters of the Market Share Attraction Model using a non-parametric technique for function estimation called Least Squares Support Vector Machine, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. Estimation by Least Squares Support Vector Machine technique makes it a good candidate for solving the Market Share Attraction Model. To illustrate the performance of the proposed method, we use the car sales data in South Korea's car market.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.491-501
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• 2002

Case-based reasoning is emerging as a leading methodology for the application of artificial intelligence. CBR is a reasoning methodology that exploits similar experienced solutions, in the form of past cases, to solve new problems. Hybrid model achieves some convergence of the wide proliferation of credit evaluation modeling. As a result, Hybrid model showed that proposed methodology classify more accurately than any of techniques individually do. It is confirmed that proposed methodology predicts significantly better than individual techniques and the other combining methodologies. The objective of the proposed approach is to determines a set of weighting values that can best formalize the match between the input case and the previously stored cases and integrates fuzzy sit concepts into the case indexing and retrieval process. The GA is used to search for the best set of weighting values that are able to promote the association consistency among the cases. The fitness value in this study is defined as the number of old cases whose solutions match the input cases solution. In order to obtain the fitness value, many procedures have to be executed beforehand. Also this study tries to transform financial values into category ones using fuzzy logic approach fur performance of credit evaluation. Fuzzy set theory allows numerical features to be converted into fuzzy terms to simplify the matching process, and allows greater flexibility in the retrieval of candidate cases. Our proposed model is to apply an intelligent system for bankruptcy prediction.

• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.51-60
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• 2020

Optical character recognition (OCR) has been studied for decades because it is very useful in a variety of places. Nowadays, OCR's performance has improved significantly due to outstanding deep learning technology. Thus, there is an increasing demand for commercial-grade but affordable OCR systems. We have developed a low-cost, high-performance OCR system for the industry with the cheapest embedded developer kit that supports GPU acceleration. To achieve high accuracy for industrial use on limited computing resources, we chose a state-of-the-art text recognition algorithm that uses an end-to-end deep learning network as a baseline model. The model was then improved by replacing the feature extraction network with the best one suited to our conditions. Among the various candidate networks, EfficientNet-B3 has shown the best performance: excellent recognition accuracy with relatively low memory consumption. Besides, we have optimized the model written in TensorFlow's Python API using TensorFlow-TensorRT integration and TensorFlow's C++ API, respectively.

• The Korean Journal of Physiology and Pharmacology
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• v.14 no.4
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• pp.199-204
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• 2010

The potential therapeutic action of shikonin in an experimental model of rheumatoid arthritis (RA) was investigated. As a RA animal model, DBA/1J mice were immunized two times with type II collagen. After the second collagen immunization, mice were orally administered shikonin (2 mg/kg) once a day for 35 days, and the incidence, clinical score, bone mineral density (BMD), bone mineral content (BMC) and joint histopathology were evaluated. BMD in the proximal regions of the tibia largely increased in the shikonin treatment group compared with the control group. We also examined the effect of shikonin on inflammatory cytokines and cartilage protection. Shikonin treatment significantly reduced the incidence and severity of collagen-induced arthritis (CIA), markedly abrogating joint swelling and cartilage destruction. Shikonin also significantly inhibited the production of matrix metalloproteinase (MMP)-1 and up-regulated tissue inhibitors of metalloproteinase (TIMP)-1 in mice with CIA. In conclusion, shikonin exerted therapeutic effects through regulation of MMP/TIMP; these results suggest that shikonin is an outstanding candidate as a cartilage protective medicine for RA.

• IMMUNE NETWORK
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• v.13 no.6
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• pp.257-263
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• 2013

Although pathogenesis of human rheumatoid arthritis (RA) remains unclear, arthritogenic T cells and downstream signaling mediators have been shown to play critical roles. An increasing numbers of therapeutic options have been added for the effective control of RA. Nevertheless, there is still a category of patients that fails treatment and suffers from progressive disease. The recently developed immunosuppressant CP-690550, a small molecule JAK kinase inhibitor, has been implicated as an important candidate treatment modality for autoimmune arthritis. In this study, we evaluated the therapeutic effect of CP-690550 on established arthritis using an SKG arthritis model, a pathophysiologically relevant animal model for human RA. CP-690550 treatment revealed remarkable long-term suppressive effects on SKG arthritis when administered to the well-advanced disease (clinical score 3.5~4.0). The treatment effect lasted at least 3 more weeks after cessation of drug infusion, and suppression of disease was correlated with the reduced pro-inflammatory cytokines, including IL-17, IFN-${\gamma}$, and IL-6 and increased level of immunoregulatory IL-10.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.12
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• pp.775-780
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• 2008

A dynamic model to simulate LNG reliquefaction process has been developed. The model was applied to two candidate cycles for LNG reliquefaction process, which are Reverse Brayton and Claude cycles. The simulation was intended to simulate the pilot plant under construction for operation of the two cycles and evaluate their feasibility. According to the simulation results, both satisfy control requirements for safe operation of brazed aluminum plate-fin type heat exchangers. In view of energy consumption, the Reverse Brayton cycle is more efficient than the Claude cycle. The latter has an expansion valve in addition to the common facilities sharing with the Reverse Brayton cycle. The expansion valve is a main cause to the efficiency loss. It generates a significant amount of entropy associated with its throttling and increases circulation flow rates of the refrigerant and power consumption caused by its leaking resulting in lowered pressure ratio. It is concluded that the Reverse Brayton cycle is more efficient and simpler in control and construction than the Claude cycle.