• IMMUNE NETWORK
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• v.6 no.1
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• pp.33-42
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• 2006

Background: Calcineurin plays a crucial role in T cell activation, cell growth, apoptosis, and angiogenesis, and its over-expression has been implicated in the pathogenesis of cardiomyopathy and stroke. However, the expression and function of calcineurin in the pathologic lesion of chronic inflammatory diseases, like rheumatoid synovium, remain to be defined. This study was aimed to determine the role of calcineurin in inflammatory arthritis and investigate the expression and function of calcineurin in the rheumatoid synovium and synoviocytes, the actual site of chronic inflammation. Methods: Immuno-histochemical staining using specific antibody to calcineurin was perfomed in the synovium of rheumatoid arthritis (RA). Fibroblast-like synoviocytes (FLS) from RA and osteoarthritis (OA) patients were isolated from RA and OA patients, and cultured with IL-1${\beta}$ and TNF-${\alpha}$ in the presence or absence of cyclosporin A, a calcineurin inhibitor. The calcineurin expression was assessed by phosphatase assay and Western blotting analysis. IL-6, -10, -17, matrix metalloproteinase (MMP)-1, -2, -3, and -9 released into the culture supernatants were measured by ELISA. After transfection with GFP-Cabin 1 gene into synoviocytes, the levels of IL-6 and MMPs were measured by ELISA. Results: Calcineurin was highly expressed in the lining layer of synovium and cultured synoviocytes of RA patients. The elevated calcineurin activity in the rheumatoid synoviocytes was triggered by proin flammatory cytokines such as IL-1${\beta}$ and TNF-${\alpha}$. In contrast, IL-10, an anti-inflammatory cytokine, failed to increase the calcineurin activity. The targeted inhibition of calcineurin by the over-expression of Cabin 1, a natural calcineurin antagonist, inhibited the production of IL-6 and MMP-2 by rheumatoid synoviocytes in a similar manner to the calcineurin inhibitor, cyclosporin A. Conclusion: These data suggest that abnormal activation of calcineurin in the synoviocytes may contribute to the pathogenesis of chronic arthritis, and thus provide a potential target for controlling inflammatory arthritis.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.135-145
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• 2008

As a fundamental input to the travel demand forecasting, OD has been always a concern in obtaining the accurate link traffic volume. Numerous methods were applied thus far without a complete success. Some existing OD estimation techniques generally extract regular samples and expand those sample into population. These methods, however, leaves some to be desired in terms of accuracy. To complement such problems, research on estimating OD using additional information such as link traffic volume as well as sample link use rate have been accomplished. In this paper, a new approach for estimating static origin-destination (OD) using probe vehicle has been proposed. More specifically, this paper tried to search an effective sample rate which varies over time and space. In a sample test network study, the traffic volume error rate of each link was set as objective function in solving the problem. As a key result the MAE (mean absolute error) between expanded OD and actual OD was identified as about 5.28%. The developed methodology could be applied with similar cases. Some limitations and future research agenda have also been discussed.

• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Macromolecular Research
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• v.17 no.2
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• pp.110-115
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• 2009

The electrical, morphological and rheological properties of melt and dry mixed composites of poly ethylene (PE)/graphite (Gr), polypropylene (PP)/Gr and PP/nickel-coated carbon fiber (NCCF) were investigated as a function of filler type, filler content and processing temperature. The electrical conductivities of dry mixed PP/NCCF composites were increased with decreasing processing temperature. For the melt mixed PP/NCCF composites, the electrical conductivities were higher than those of the melt mixed PE/Gr and PP/Gr composites, which was attributed to the effect of the higher NCCF aspect ratio in allowing the composites to form a more conductive network in the polymer matrix than the graphite does. From the results of morphological studies, the fillers in the dry mixed PP/NCCF composites were more randomly dispersed compared to those in the melt mixed PP/NCCF composites. The increased electrical conductivities of the dry mixed composites were attributed to the more random dispersion of NCCF compared to that of the melt mixed PP/NCCF composites. The complex viscosities of the PP/Gr composites were higher than those of the PP/NCCF composites, which was attributed to the larger diameter of the graphite particles than that of the NCCF. Furthermore, the fiber orientation in the 'along the flow' direction during melt mixing was attributed to the decreased complex viscosities of the melt mixed PP/NCCF composites compared those of the melt mixed PP/Gr composites.

• IMMUNE NETWORK
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• v.9 no.3
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• pp.90-97
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• 2009

Background: CD147, as a cellular receptor for cyclophilin A (CypA), is a multifunctional protein involved in tumor invasion, inflammation, tissue remodeling, neural function, and reproduction. Recent observations showing the expression of CD147 in leukocytes indicate that this molecule may have roles in inflammation. Methods: In order to investigate the role of CD147 and its ligand in the pathogenesis of atherosclerosis, human atherosclerotic plaques were analyzed for the expression pattern of CD147 and CypA. The cellular responses and signaling molecules activated by the stimulation of CD147 were then investigated in the human macrophage cell line, THP-1, which expresses high basal level of CD147 on the cell surface. Results: Staining of both CD147 and CypA was detected in endothelial cell layers facing the lumen and macrophage-rich areas. Stimulation of CD147 with its specific monoclonal antibody induced the expression of matrix metalloproteinase (MMP)-9 in THP-1 cells and it was suppressed by inhibitors of both ERK and NF-${\kappa}B$. Accordingly, the stimulation of CD147 was observed to induce phosphorylation of ERK, phosphorylation-associated degradation of $I{\kappa}B$, and nuclear translocation of NF-${\kappa}B$ p65 and p50 subunits. Conclusion: These results suggest that CD147 mediates the inflammatory activation of macrophages that leads to the induction of MMP-9 expression, which could play a role in the pathogenesis of inflammatory diseases such as atherosclerosis.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.401-411
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• 2023

Many studies have been conducted to improve technology for semen cryopreservation in pigs. However, computer-assisted analysis of sperm motility and morphology is insufficient to predict the molecular function of frozen-thawed semen. More accurate expression patterns of boar sperm proteins may be derived using the isobaric tags for relative and absolute quantification (iTRAQ) technique. In this study, the iTRAQ-labeling system was coupled with liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis to identify differentially expressed CM10-fractionated proteins between fresh and frozen-thawed boar semen. A total of 76 protein types were identified to be differentially expressed, among which 9 and 67 proteins showed higher and lower expression in frozen-thawed than in fresh sperm samples, respectively. The classified functions of these proteins included oxidative phosphorylation, mitochondrial inner membrane and matrix, and pyruvate metabolic processes, which are involved in adenosine triphosphate (ATP) synthesis; and sperm flagellum and motile cilium, which are involved in sperm tail structure. These results suggest a possible network of biomarkers associated with survival after the cryopreservation of Duroc boar semen.

• Journal of Life Science
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• v.26 no.9
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• pp.1027-1032
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• 2016

Antigen is substance causing disease derived from pathogen. Living organism has the immune system in terms of defense mechanism against antigen. Antigen is processed through several pathways such as phagocytosis, antibody action, complement activation, and cytotoxins by NK or cytotoxic T lymphocyte via MHC molecule. Lymph node (LN) is comprised of the complicated 3 dimensional network and several stromal cells. Fibroblastic reticular cells (FRC) are distributed in T zone for interaction with T cells. FRC produces the extra cellular matrix (ECM) into LN for ECM reorganization against pathogen infections and secretes homing chemokines. However, it has not so much been known about the involvement of the antigen process of FRC. The present report is for the function of FRC on antigen process. For this, FRC was positioned with several infected situations such as co-culture with macrophage, T cell, lipopolysaccharide (LPS) and TNFα stimulation. When co-culture between FRC with macrophage and T cells was performed, morphological change of FRC was observed and empty space between FRCs was made by morphological change. The matrix metallo-proteinase (MMP) activity was up-regulated by Y27632 and T cells onto FRC. Furthermore, inflammatory cytokine, TNFα regulated the expression of adhesion molecules and MHC I antigen transporter in FRC by gene chip assay. NO production was elevated by FRC monolayer co-cultured with macrophage stimulated by LPS. GFP antigen was up-taken by macrophage co-cultured with FRC. Collectively, it suggests that FRC assists of the facilitation of antigen process and LN stroma is implicated into antigen process pathway.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.99-106
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• 2004

This paper presents some comparisons between Cournot-Nash and Stackelberg game in bi-level program, composed of both upper level program and lower level one. The upper level can be formulated to optimize a specific objective function, while the lower formulated to express travelers' behavior patterns corresponding to the design parameter of upper level problem. This kind of hi-level program is to determine a design parameter, which leads the road network to an optimal state. Bi-level program includes traffic signal control, traffic information provision, congestion charge and new transportation mode introduction as well as road expansion. From the view point of game theory, many existing algorithms for bi-level program such as IOA (Iterative Optimization Assignment) or IEA (Iterative Estimation Assignment) belong to Cournot-Nash game. But sensitivity-based algorithms belongs to Stackelberg one because they consider the reaction of the lower level program. These two game models would be compared by using an example network and show some results that there is no superiority between the models in deterministic case, but in stochastic case Stackelberg approach is better than that of Cournot-Nash one as we expect.