• Proceedings of the Plant Resources Society of Korea Conference
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• 2023.04a
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• pp.51-51
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• 2023

The world-wide rate of obesity is increasing continuously, representing a serious medical threat since it is associated with a variety of diseases including type 2 diabetes, cardiovascular disease, and numerous cancers. Sophora japonicais used as a traditional herb for medicinal purposes in eastern Asia. However, the anti-obesity effects of S. japonicafruit have not been explored. The aim of this study is to investigate the inhibition of adipocyte differentiation and adipogenesis by an ethanol extract of S. japonicafruit (EESF) in 3T3-L1 pre-adipocytes. Our results demonstrate that EESF suppressed the terminal differentiation of 3T3-L1 pre-adipocytes in a dose-dependent manner, as confirmed by a decrease in lipid droplet number and lipid content through Oil Red O staining. EESF significantly reduced the accumulation of cellular triglyceride, which was associated with a significant inhibition of the levels of pro-adipogenic transcription factors, including PPARγ, C/EBPα and C/EBPβ. In addition, EESF potentially down regulated the expression levels of adipocyte-specific proteins, including aP2 and leptin. In particular, EESF treatment effectively enhanced the activation of the AMPK signaling pathway; however, the co-treatment with compound C, an inhibitor of AMPK, significantly restored the EESF-induced inhibition of pro-adipogenic transcription factors and adipocyte-specific genes. These results indicate that EESF may exert an anti-obesity effect by controlling the AMPK signaling pathway, suggesting that the fruit extract of S. japonica may be a potential anti-obesity agent.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.949-958
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• 2022

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.568-580
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• 2006

The purpose of this study was to investigate the brain activities by 4-types of Generating Process of Scientific Emotion (GPSE) in the hypothesis-generating biological phenomena by using fMRI. Four-types of GPSE were involved in the Basic Generating Process (BGP), Retrospective Generating Process (RGP), Cognitive Generating Process (CGP) and Attributive Generating Process (AGP). For this study, we made an experimental design capable of validating the 4-types of generating process (e.g. BGP, RGP, CGP and AGP), and then measured BOLD signals of 10 pre-service teachers' brain activities by 3.0T fMRI system. Subjects were 10 healthy females majoring in biology education. As a result, there were clear differences among 4-types of GPSE. Brain areas activated by BGP were at right occipital lobe (BA 17), at left thalamus and left parahippocampal gyrus, while in the case of RGP, at left superior parietal lobe (BA 8, 9), at left pulvinar and left globus pallidus were activated. Brain areas activated by CGP were the right posterior cingulate and left medial frontal gyrus (BA 6). In the case of AGP, the most distinctively activated brain areas were the right medial frontal gyrus (BA 8) and left inferior parietal lobule (BA 40). These results would mean that each of the 4-types of GPSE has a specific neural networks in the brain, respectively. Furthermore, it would provide the basis of brain-based learning in science education.

• Korean Journal of Construction Engineering and Management
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• v.22 no.4
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• pp.59-65
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• 2021

Since the 2016 "Housing Act Partial Amendment" and the "2018 Housing Comprehensive Amendment Plan", interest in the pre sale system and post sale system of apartment houses has been on the rise. In order to compare the advantages and disadvantages of the pre sale system and the post sale system of apartment houses, and to establish the basis for the institutional policy of the post sale system, a questionnaire survey method was used for tenants of the apartment house from the public side, and issues of time and cost. The time series analysis method is intended to suggest an appropriate time for payment of contributions. Accordingly, through a review of existing theories and literature, the post sale system of public and private institutions was organized, and through a questionnaire survey, the path to securing pre sale money, product information of the model house, and the degree of awareness of the effect of the post sale system were investigated. For the post sale fund support and payment method, it is necessary to increase the commercial line for existing financiers from the user's point of view, and it is necessary to operate in consideration of the economic power of the pre sale market by region. Both 60% post sale and 80% post sale have a price range of up to KRW 10 million, and the total interest rate is 5.0%, and the annual interest rate is about 2.8% for 60% post sale, and about 2.1% for 80% post sale, which is lower than the current 3.1%. I need an interest rate. The research is a perception survey targeting a total of 5,213 households in a sample of after sale apartments in public institutions. As the actual values are analyzed using a time series on the effects of market supply and demand and market prices, there is a limit to applying them to prospective residents of private apartments. In addition, to respond to first time tenants, a questionnaire survey was conducted on five complexes that have moved in within the last five years.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.672-677
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• 2014

Torrefaction is a thermal treatment process to pre-treat biomass at temperature of $200{\sim}300^{\circ}C$ under an inert atmosphere. It was known that torrefaction process strongly depended on the decomposition temperature of the lignocellulosic constituents in biomass. In this work, the torrefaction characteristics of baggase has been studied. This study focuses on the relation between the energy yields, heating values, gas emission, volatile and ash constituents with torrefaction temperatures and times. The activation energies of baggase torrefaction has been studied by using TGA (Thermogravimetric Analyzer). From this work, it was seen that ash constituents and heating values were increased with torrefaction temperature, while volatile constituents and energy yields decreased. It was also found that carbon monoxide containing oxygen were decomposed at a lower temperature than those of hydrocarbon compounds, $C_xH_y$.

• Applied Chemistry for Engineering
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• v.21 no.2
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• pp.183-187
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• 2010

Activated carbons (ACs) have been prepared from pitch by the combination of a chemical oxidation with different $H_2O_2$ concentrations i.e., 5, 15, and 25 wt% and a chemical activation with KOH at a constant KOH/pitch ratio of 3/1. The influence of $H_2O_2$ solution on the microporous properties of the pitch and the final activated carbons were invested using XRD, FT-IR, XPS, $N_2$-adsorption, and SEM. XRD indicated that the value of interplanar distance $d_{002}$ increased by chemical oxidation. FT-IR and XPS results showed that the chemical oxidation promoted the formation of surface oxygen functionalities. Also, the specific surface area of the resulting ACs was increased with increasing the concentration of $H_2O_2$ chemical oxidation and showed a maximum value of $2111m^2/g$ at 25 wt% $H_2O_2$ concentration.

• Bulletin of the Korean Chemical Society
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• v.31 no.5
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• pp.1295-1300
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• 2010

The $CO_2-CH_4$ reaction catalyzed by Ni/silicon wafers was kinetically studied by using a photoacoustic technique. The catalytic reaction was performed at various partial pressures of $CO_2$ and $CH_4$ (50 Torr total pressure of $CO_2/CH_4/N_2$) in the temperature range of 500 - $650^{\circ}C$ in a static reactor system. The photoacoustic signal that varied with the $CO_2$ concentration during the catalytic reaction was recorded as a function of time. Under the reaction conditions, the $CO_2$ photoacoustic measurements showed the as-prepared Ni thin film sample to be inactive for the reaction, while the $CO_2/CH_4$ reactions carried out in the presence of the sample pre-treated in $H_2$ at $600^{\circ}C$ were associated with significant time-dependent changes in the $CO_2$ photoacoustic signal. The rate of $CO_2$ disappearance was measured from the $CO_2$ photoacoustic signal data in the early reaction period of 50 - 150 sec to obtain precise kinetic data. The apparent activation energy for $CO_2$ consumption was determined to be 6.9 kcal/mol from the $CO_2$ disappearance rates. The partial reaction orders, determined from the $CO_2$ disappearance rates measured at various $PCO{_2}'S$ and $PCH{_4}'S$ at $600^{\circ}C$, were determined to be 0.33 for $CH_4$ and 0.63 for $CO_2$, respectively. Kinetic data obtained in these measurements were compared with previous works and were discussed to construct a catalytic reaction mechanism for the $CO_2-CH_4$ reaction over Ni/silicon wafer at low pressures.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.12
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• pp.961-968
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• 2012

In this study, we have investigated the effects of Mn and Co co-doping on defects, J-E curves and grain boundary characteristics of ZnO-$Bi_2O_3$ (ZB) varistor. Admittance spectra and dielectric functions show two bulk defects of $Zn_i^{{\cdot}{\cdot}}$ (0.17~0.18 eV) and $V_o^{\cdot}$ (0.30~0.33 eV). From J-E characteristics the nonlinear coefficient (${\alpha}$) and resistivity (${\rho}_{gb}$) of pre-breakdown region decreased as 30 to 24 and 5.1 to 0.08 $G{\Omega}cm$ with sintering temperature, respectively. The double Schottky barrier of grain boundaries in ZB(MCo) ($ZnO-Bi_2O_3-Mn_3O_4-Co_3O_4$) could be electrochemically single type. However, its thermal stability was slightly disturbed by ambient oxygen because the apparent activation energy of grain boundaries was changed from 0.64 eV at lower temperature to 1.06 eV at higher temperature. It was revealed that a co-doping of Mn and Co in ZB reduced the heterogeneity of the barrier in grain boundaries and stabilized the barrier against an ambient temperature (${\alpha}$-factor= 0.136).

• Bulletin of the Korean Chemical Society
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• v.32 no.11
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• pp.3893-3898
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• 2011

Cellular uptake of double-stranded DNA (dsDNA) triggers strong innate immune responses via activation of NF-${\kappa}B$ transcription factor. However, the detailed mechanism of dsDNA-mediated innate immune response remains yet to be elucidated. Here, we show that the expression of tazarotene-induced gene 3 (TIG3) is dramatically induced by dsDNA stimulation, and the siRNA-mediated down-regulation of TIG3 mRNA results in significant suppression of dsDNA-triggered cytokine expression. Because TIG3 has been previously shown to physically interact with transglutaminase (TG) 1 to activate TG activity, and TG2 has been shown to induce NF-${\kappa}B$ activity by inducing $I{\kappa}B{\alpha}$ polymerization, we tested whether TG also plays a role in dsDNA-mediated innate immune response. Pre-treatment of TG inhibitors dramatically reduces dsDNA-triggered cytokine induction. We also show that, in HeLa cells, TG2 is the major TG, and TIG3 physically interacts with TG2. Combined together, our results suggest a novel mechanism of dsDNA-triggered innate immune response which is critically dependent on TIG3 and TG2.