Search results for: Byung Seok Lee

Authors: Seunghwan Seo, Woo-Seok Lee, Ji-Sun Shin, Young Kyoung Rhee, Chang-Won Cho, Hee-Do Hong, Kyung-Tae Lee

Abstract:

Doenjang, known as traditional Korean food, is product of a natural mixed fermentation process carried out by lactic acid bacteria (LAB). Lactobacillus sakei K040706 (K040706) has been accepted as the most populous LAB in over ripened doenjang. Recently, we reported the immunostimulatory effects of K040706 in RAW 264.7 macrophages and in a cyclophosphamide-induced mouse model. In this study, we investigated the ameliorative effects of K040706 in a dextran sulfate sodium (DSS)-induced colitis mouse model. We induced colitis using DSS in 5-week-ICR mice over 14 days with or without 0.1, 1 g/kg/day K040706 orally. The body weight, stool consistency, and gross bleeding were recorded for determination of the disease activity index (DAI). At the end of treatment, animals were sacrificed and colonic tissues were collected and subjected to histological experiments and myeloperoxidase (MPO) accumulation, cytokine determination, qRT-PCR and Western blot analysis. Results showed that K040706 significantly attenuated DSS-induced DAI score, shortening of colon length, enlargement of spleen and immune cell infiltrations into colonic tissues. Histological examinations indicated that K040706 suppressed edema, mucosal damage, and the loss of crypts induced by DSS. These results were correlated with the restoration of tight junction protein expression, such as, ZO-1 and occludin in K040706-treated mice. Moreover, K040706 reduced the abnormal secretions and mRNA expressions of pro-inflammatory mediators, such as nitric oxide (NO), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). DSS-induced mRNA expression of intercellular adhesion molecule (ICAM) and vascular cell adhesion molecule (VCAM) in colonic tissues was also downregulated by K040706 treatment. Furthermore, K040706 suppressed the protein and mRNA expression of toll-like receptor 4 (TLR4) and phosphorylation of NF-κB and signal transducer and activator of transcription 3 (STAT3). These results suggest that K040706 has an anti-colitic effect by inhibition of intestinal inflammatory responses in DSS-induced colitic mice.

Keywords: Lactobacillus sakei, NF-κB, STAT3, ulcerative colitis

Procedia PDF Downloads 301

Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

Abstract:

The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

Procedia PDF Downloads 206

Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

Abstract:

The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 257

Authors: Hyeon-Seung Kim, Sang-Mi Park, Sun-Ju Han, Leen-Seok Kang

Abstract:

As BIM (Building Information Modeling) application continually expands, the visual simulation techniques used for facility design and construction process information are becoming increasingly advanced and diverse. For building structures, BIM application is design - oriented to utilize 3D objects for conflict management, whereas for civil engineering structures, the usability of nD object - oriented construction stage simulation is important in construction management. Simulations of 5D and 6D objects, for which cost and resources are linked along with process simulation in 4D objects, are commonly used, but they do not provide a decision - making function for process management problems that occur on site because they mostly focus on the visual representation of current status for process information. In this study, an nD CAD system is constructed that facilitates an optimized schedule simulation that minimizes process conflict, a construction duration reduction simulation according to execution progress status, optimized process plan simulation according to project cost change by year, and optimized resource simulation for field resource mobilization capability. Through this system, the usability of conventional simple simulation objects is expanded to the usability of active simulation objects with which decision - making is possible. Furthermore, to close the gap between field process situations and planned 4D process objects, a technique is developed to facilitate a comparative simulation through the coordinated synchronization of an actual video object acquired by an on - site web camera and VR concept 4D object. This synchronization and simulation technique can also be applied to smartphone video objects captured in the field in order to increase the usability of the 4D object. Because yearly project costs change frequently for civil engineering construction, an annual process plan should be recomposed appropriately according to project cost decreases/increases compared with the plan. In the 5D CAD system provided in this study, an active 5D object utilization concept is introduced to perform a simulation in an optimized process planning state by finding a process optimized for the changed project cost without changing the construction duration through a technique such as genetic algorithm. Furthermore, in resource management, an active 6D object utilization function is introduced that can analyze and simulate an optimized process plan within a possible scope of moving resources by considering those resources that can be moved under a given field condition, instead of using a simple resource change simulation by schedule. The introduction of an active BIM function is expected to increase the field utilization of conventional nD objects.

Keywords: 4D, 5D, 6D, active BIM

Procedia PDF Downloads 249

Authors: Farah Amalina Mohamed Affandi, Redhwan Ahmad Al-Naggar, Seok Mui Wang, Thanikasalam Kathiresan

Abstract:

Gestational choriocarcinoma is a condition in which there is an abnormal growth or a tumor inside the women’s uterus after conception. It is a type of gestational trophoblastic disease which is relatively rare and malignant. The current epidemiological data of this disease are inadequate. The purposes of this study are to examine the epidemiology of choriocarcinoma and their risk factors based on all available population-based and hospital-based data of the disease. In this study, we searched The MEDLINE and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases using the keywords ‘choriocarcinoma’, ‘gestational’, ‘gestational choriocarcinoma’ and ‘epidemiology’. We included only human studies published in English between 1995 and 2015 to ensure up to date evidence. Case studies, case reports, animal studies, letters to the editor, news, and review articles were excluded. Retrieved articles were screened in three phases. In the first phase, any articles that did not match the inclusion criteria based solely on titles were excluded. In the second phase, the abstracts of remaining articles were screened thoroughly; any articles that did not meet our inclusion criteria were excluded. In the final phase, full texts of the remaining articles were read and assessed to exclude articles that did not meet the inclusion criteria or any articles that fulfilled the exclusion criteria. Duplicates articles were also removed. Systematic reviews and meta-analysis were excluded. Extracted data were summarized in table and figures descriptively. The reference lists of included studies were thoroughly reviewed in search for other relevant studies. A total of ten studies met all the selection criteria. Nine were retrospective studies and one was cohort study. Total numbers of 4563 cases of choriocarcinoma were reviewed from several countries which are Korea, Japan, South Africa, USA, New Mexico, Finland, Turkey, China, Brazil and The Netherlands. Different studies included different range of age with their mean age of 28.5 to 30.0 years. All studies investigated on the disease’s incidence rate, only two studies examined on the risk factors or associations of the disease. Approximately 20% of the studies showed a reduction in the incidence of choriocarcinoma while the other 80% showed inconsistencies in rate. Associations of age, fertility age, occupations and socio-demographic with the status remains unclear. There is limited information on the epidemiological aspects of gestational choriocarcinoma. The observed results indicated there was a decrease in the incidence rate of gestational choriocarcinoma globally. These could be due to the reduction in the incidence of molar pregnancy and the efficacy of the treatment, mainly by chemotherapy.

Keywords: epidemiology, gestational choriocarcinoma, incidence, prevalence, risk factor

Procedia PDF Downloads 300

Authors: Jeongyeon Park, Yeo Jun Yoon, Jiyoung Seo, In Seok Moon, Hae Jun Lee, Kiwon Song

Abstract:

Cold Atmospheric Pressure Plasma (CAPP) is defined as a partially ionized gas with electrically charged particles at room temperature and atmospheric pressure. CAPP generates reactive oxygen species (ROS) and reactive nitrogen species (RNS), and has potential as a new apoptosis-promoting cancer therapy. With an annular type dielectric barrier discharge (DBD) CAPP-generating device combined with a helium (He) gas feeding system, we showed that CAPP selectively induced apoptosis in various cancer cells while it promoted proliferation of the adipose tissue-derived stem cell (ASC). The apoptotic effect of CAPP was highly selective toward p53-mutated cancer cells. The intracellular ROS was mainly responsible for apoptotic cell death in CAPP-treated cancer cells. CAPP induced apoptosis even in doxorubicin-resistant cancer cell lines, demonstrating the feasibility of CAPP as a potent cancer therapy. With the same device and exposure conditions to cancer cells, CAPP stimulated proliferation of the ASC, a kind of mesenchymal stem cell that is capable of self-renewing and differentiating into adipocytes, chondrocytes, osteoblasts and neurons. CAPP-treated ASCs expressed the stem cell markers and differentiated into adipocytes as untreated ASCs. The increase of proliferation by CAPP in ASCs was offset by a NO scavenger but was not affected by ROS scavengers, suggesting that NO generated by CAPP is responsible for the activated proliferation in ASCs. Usually, cancer stem cells are reported to be resistant to known cancer therapies. When we applied CAPP of the same device and exposure conditions to cancer cells to liver cancer stem cells (CSCs) that express CD133 and epithelial cell adhesion molecule (EpCAM) cancer stem cell markers, apoptotic cell death was not examined. Apoptotic cell death of liver CSCs was induced by the CAPP generated from a device with an air-based flatten type DBD. An exposure of liver CSCs to CAPP decreased the viability of liver CSCs to a great extent, suggesting plasma be used as a promising anti-cancer treatment. To validate whether CAPP can be a promising anti-cancer treatment or an adjuvant modality to eliminate remnant tumor in cancer surgery of vestibular schwannoma, we applied CAPP to mouse schwannoma cell line SC4 Nf2 ‑/‑ and human schwannoma cell line HEI-193. A CAPP treatment leads to anti-proliferative effect in both cell lines. We are currently studying the molecular mechanisms of differential physiological effect of CAPP; the proliferation of ASCs and apoptosis of various cancer cells and CSCs.

Keywords: cold atmospheric pressure plasma, apoptosis, proliferation, cancer cells, adult stem cells

Procedia PDF Downloads 250

Authors: Soo-Yong Cho, Chong-Hyun Cho, Chae-Whan Rim, Sang-Kyu Choi, Jin-Gyun Kim, Ju-Seok Nam

Abstract:

Recently, vertical axis wind turbines (VAWT) have been widely used to produce electricity even in urban. They have several merits such as low sound noise, easy installation of the generator and simple structure without yaw-control mechanism and so on. However, their blades are operated under the influence of the trailing vortices generated by the preceding blades. This phenomenon deteriorates its output power and makes difficulty predicting correctly its performance. In order to improve the performance of VAWT, wind power towers can be applied. Usually, the wind power tower can be constructed as a multi-story building to increase the frontal area of the wind stream. Hence, multiple sets of the VAWT can be installed within the wind power tower, and they can be operated at high elevation. Many different types of wind power tower can be used in the field. In this study, a wind power tower with circular column shape was applied, and the VAWT was installed at the center of the wind power tower. Seven guide walls were used as a strut between the floors of the wind power tower. These guide walls were utilized not only to increase the wind velocity within the wind power tower but also to adjust the wind direction for making a better working condition on the VAWT. Hence, some important design variables, such as the distance between the wind turbine and the guide wall, the outer diameter of the wind power tower, the direction of the guide wall against the wind direction, should be considered to enhance the output power on the VAWT. A numerical analysis was conducted to find the optimum dimension on design variables by using the computational fluid dynamics (CFD) among many prediction methods. The CFD could be an accurate prediction method compared with the stream-tube methods. In order to obtain the accurate results in the CFD, it needs the transient analysis and the full three-dimensional (3-D) computation. However, this full 3-D CFD could be hard to be a practical tool because it requires huge computation time. Therefore, the reduced computational domain is applied as a practical method. In this study, the computations were conducted in the reduced computational domain and they were compared with the experimental results in the literature. It was examined the mechanism of the difference between the experimental results and the computational results. The computed results showed this computational method could be an effective method in the design methodology using the optimization algorithm. After validation of the numerical method, the CFD on the wind power tower was conducted with the important design variables affecting the performance of VAWT. The results showed that the output power of the VAWT obtained using the wind power tower was increased compared to them obtained without the wind power tower. In addition, they showed that the increased output power on the wind turbine depended greatly on the dimension of the guide wall.

Keywords: CFD, performance, VAWT, wind power tower

Procedia PDF Downloads 353

Authors: Jin-Hee Ahn, Seok-Hyeon Jeon, Young-Bin Lee, Min-Gyun Ha, Yu-Chan Hong

Abstract:

Corrosion durability of steel bridges can be generally affected by atmospheric environments of bridge installation, since corrosion problem is related to environmental factors such as humidity, temperature, airborne salt, chemical components as SO₂, chlorides, etc. Thus, atmospheric environment condition should be measured to estimate corrosion condition of steel bridges as well as measurement of actual corrosion damage of structural members of steel bridge. Even in the same atmospheric environment, the corrosion environment may be different depending on the installation direction of structural members. In this study, therefore, atmospheric corrosion monitoring was conducted using atmospheric corrosion monitoring sensor, hygrometer, thermometer and airborne salt collection device to examine the corrosion durability of steel bridges. As a target steel bridge for corrosion durability monitoring, a cable-stayed bridge with truss steel members was selected. This cable-stayed bridge was located on the coast to connect the islands with the islands. Especially, atmospheric corrosion monitoring was carried out depending on structural direction of a cable-stayed bridge with truss type girders since it consists of structural members with various directions. For atmospheric corrosion monitoring, daily average electricity (corrosion current) was measured at each monitoring members to evaluate corrosion environments and corrosion level depending on structural members with various direction which have different corrosion environment in the same installed area. To compare corrosion durability connected with monitoring data depending on corrosion monitoring members, monitoring steel plate was additionally installed in same monitoring members. Monitoring steel plates of carbon steel was fabricated with dimension of 60mm width and 3mm thickness. And its surface was cleaned for removing rust on the surface by blasting, and its weight was measured before its installation on each structural members. After a 3 month exposure period on real atmospheric corrosion environment at bridge, surface condition of atmospheric corrosion monitoring sensors and monitoring steel plates were observed for corrosion damage. When severe deterioration of atmospheric corrosion monitoring sensors or corrosion damage of monitoring steel plates were found, they were replaced or collected. From 3month exposure tests in the actual steel bridge with various structural member with various direction, the rust on the surface of monitoring steel plate was found, and the difference in the corrosion rate was found depending on the direction of structural member from their visual inspection. And daily average electricity (corrosion current) was changed depending on the direction of structural member. However, it is difficult to identify the relative differences in corrosion durability of steel structural members using short-term monitoring results. After long exposure tests in this corrosion environments, it can be clearly evaluated the difference in corrosion durability depending on installed conditions of steel bridges. Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03028755).

Keywords: corrosion, atmospheric environments, steel bridge, monitoring

Procedia PDF Downloads 323

Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

Abstract:

Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

Procedia PDF Downloads 165