• IMMUNE NETWORK
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• v.10 no.6
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• pp.198-205
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• 2010

Background: A crucial limitation of DNA vaccines is its weak immunogenicity, especially in terms of eliciting antibody responses in non-human primates or humans; therefore, it is essential to enhance immune responses to vaccination for the development of successful DNA vaccines for humans. Methods: Here, we approached this issue by evaluating interleukin-7 (IL-7) as a genetic adjuvant in cynomolgus monkeys immunized with multigenic HCV DNA vaccine. Results: Codelivery of human IL-7 (hIL-7)-encoding DNA appeared to increase DNA vaccine-induced antibody responses specific for HCV E2 protein, which plays a critical role in protecting from HCV infection. HCV-specific T cell responses were also significantly enhanced by codelivery of hIL-7 DNA. Interestingly, the augmentation of T cell responses by codelivery of hIL-7 DNA was shown to be due to the enhancement of both the breadth and magnitude of immune responses against dominant and subdominant epitopes. Conclusion: Taken together, these findings suggest that the hIL-7-expressing plasmid serves as a promising vaccine adjuvant capable of eliciting enhanced vaccine-induced antibody and broad T cell responses.

• Reproductive and Developmental Biology
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• v.32 no.1
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• pp.65-70
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• 2008

Members of the Vps (Vacuolar protein sorting) protein family involved in the formation of the retromer complex have been discovered in a variety of species such as yeast, mouse, and human. A mammalian retromer complex is composed of Vps26, Vps29, and Vps35 proteins and plays and important role in cation-independent mannose-6-phosphate receptor retrieval from the endosome to the trans-Golgi network. In this study, we have identified the full-length sequences of the retromer components of Vps26, Vps29, and Vps35 in micro pigs. The cDNA sequences of these retromer components have been determined and the result showed there is 99% homology among the component counterparts from mouse, micro pigs, and humans. In addition, the retromer complexes formed with hetero-components were found in the brain of micro pigs. Based on above results, we suggest mammalian Vps components are well conserved in micro pigs.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.438-441
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• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• IMMUNE NETWORK
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• v.11 no.1
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• pp.1-10
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• 2011

Interleukin-7 (IL-7) is an essential cytokine for T cells. However, IL-7 is not produced by T cells themselves such that T cells are dependent on extrinsic IL-7. In fact, in the absence of IL-7, T cell development in the thymus as well as survival of naive T cells in the periphery is severely impaired. Furthermore, modulating IL-7 availability in vivo either by genetic means or other experimental approaches determines the size, composition and function of the T cell pool. Consequently, understanding IL-7 expression is critical for understanding T cell immunity. Until most recently, however, the spatiotemporal expression of in vivo IL-7 has remained obscured. Shortage of such information was partly due to scarce expression of IL-7 itself but mainly due to the lack of adequate reagents to monitor IL-7 expression in vivo. This situation dramatically changed with a recent rush of four independent studies that describe the generation and characterization of IL-7 reporter mice, all utilizing bacterial artificial chromosome transgene technology. The emerging consensus of these studies confirmed thymic stromal cells as the major producers of IL-7 but also identified IL-7 reporter activities in various peripheral tissues including skin, intestine and lymph nodes. Strikingly, developmental and environmental cues actively modulated IL-7 reporter activities in vivo suggesting that IL-7 regulation might be a new mechanism of shaping T cell development and homeostasis. Collectively, the availability of these new tools opens up new venues to assess unanswered questions in IL-7 biology in T cells and beyond.

• IMMUNE NETWORK
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• v.11 no.1
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• pp.11-41
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• 2011

The discovery of microRNA (miRNA) is one of the major scientific breakthroughs in recent years and has revolutionized current cell biology and medical science. miRNAs are small (19~25nt) noncoding RNA molecules that post-transcriptionally regulate gene expression by targeting the 3' untranslated region (3'UTR) of specific messenger RNAs (mRNAs) for degradation of translation repression. Genetic ablation of the miRNA machinery, as well as loss or degradation of certain individual miRNAs, severely compromises immune development and response, and can lead to immune disorders. Several sophisticated regulatory mechanisms are used to maintain immune homeostasis. Regulatory T (Treg) cells are essential for maintaining peripheral tolerance, preventing autoimmune diseases and limiting chronic inflammatory diseases. Recent publications have provided compelling evidence that miRNAs are highly expressed in Treg cells, that the expression of Foxp3 is controlled by miRNAs and that a range of miRNAs are involved in the regulation of immunity. A large number of studies have reported links between alterations of miRNA homeostasis and pathological conditions such as cancer, cardiovascular disease and diabetes, as well as psychiatric and neurological diseases. Although it is still unclear how miRNA controls Treg cell development and function, recent studies certainly indicate that this topic will be the subject of further research. The specific circulating miRNA species may also be useful for the diagnosis, classification, prognosis of diseases and prediction of the therapeutic response. An explosive literature has focussed on the role of miRNA. In this review, I briefly summarize the current studies about the role of miRNAs in Treg cells and in the regulation of the innate and adaptive immune response. I also review the explosive current studies about clinical application of miRNA.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.6
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• pp.47-52
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• 2002

The wavelet functions are originated from scaling functions and can be used as activation function in the hidden node of the network by deciding two parameters such as scale and center. In this paper, we would like to propose the mixed structure. When we compose the WNN using wavelet functions, we propose to set a single scale function as a node function together. The properties of the proposed structure is that while one scale function approximates the target function roughly, the other wavelet functions approximate it finely. During the determination of the parameters, the wavelet functions can be determined by the global search algorithm such as genetic algorithm to be suitable for the suggested problem. Finally, we use the back-propagation algorithm in the learning of the weights.

• Journal of the Korean Geographical Society
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• v.44 no.1
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• pp.31-55
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• 2009

This research aims 1) to characterize the spatial distribution of low relief landforms (plains) via analyses of a Digital Elevation Model (DEM), 2) to classify plains according to morphological and genetic similarity, and 3) to develop a model to explain forming processes of plains in the Korean peninsula. Plains can easily be separated from high relief mountaneous areas by analyzing the DEM. The overall morphological and locational characteristics of plains can be categorized into lava plains, fluvial-marine plains, erosional plains, intermontane basins, and higher ground plains. It is concluded that the characteristic of each plain type is decided by base-level changes caused by tectonic uplift and sea-level changes, and topological relationship of different rock types. Different plain types do not exist independently, but connected with each others along stream networks. The model developed is able to combine the morphological characteristics of plains with the channel network to conceptualize characteristics and development pathways of plains in the Korean Peninsula.

• Genomics & Informatics
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• v.12 no.4
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• pp.181-186
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• 2014

Genome-wide association studies have proven the highly polygenic architecture of complex diseases or traits; therefore, single-locus-based methods are usually unable to detect all involved loci, especially when individual loci exert small effects. Moreover, the majority of associated single-nucleotide polymorphisms resides in non-coding regions, making it difficult to understand their phenotypic contribution. In this work, we studied epistatic interactions associated with three common diseases using Korea Association Resource (KARE) data: type 2 diabetes mellitus (DM), hypertension (HT), and coronary artery disease (CAD). We showed that epistatic single-nucleotide polymorphisms (SNPs) were enriched in enhancers, as well as in DNase I footprints (the Encyclopedia of DNA Elements [ENCODE] Project Consortium 2012), which suggested that the disruption of the regulatory regions where transcription factors bind may be involved in the disease mechanism. Accordingly, to identify the genes affected by the SNPs, we employed whole-genome multiple-cell-type enhancer data which discovered using DNase I profiles and Cap Analysis Gene Expression (CAGE). Assigned genes were significantly enriched in known disease associated gene sets, which were explored based on the literature, suggesting that this approach is useful for detecting relevant affected genes. In our knowledge-based epistatic network, the three diseases share many associated genes and are also closely related with each other through many epistatic interactions. These findings elucidate the genetic basis of the close relationship between DM, HT, and CAD.

• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.