• Proceedings of the Korean Society of Applied Pharmacology
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• 1995.10a
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• pp.115-124
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• 1995

The purpose of the present study was to address whether the in vivo noradrenergic neural activities in the locus coeruleus are involved in the regulation of blood pressure. Two groups of the animals were prepared, 1) SHR and 2) age-matched normotensive control, WKY. At the age of 6 and 16 weeks, blood pressure and the releases of NE from the locus coeruleus in SHR and KWY were measured by in vivo microdialysis at three different conditions: 1) normal, 2) elevated state of blood pressure by systemic injected phenylephrine and 3) increased state of neural activity by perfused phenylephrine into the locus coeruleus. The basal release of NE of SHR were significantly higher than that of WKY, Phenylephrine treatment caused elevation of blood pressure in both SHR and WKY in dose-dependent manner. Following phenylephrine injection, the releases of NE from the locus coeruleus of SHR were significantly decreased, whereas the significant change of NE in WKY was observed in the highest dose of phenylephrine. Phenylephrine perfusion into the locus coeruleus through microdialysis probe caused pressor responses and the pressor response in SHR was greater compared with that in WKY. The results from the present study suggests that the noradrenergic nervous system in the locus coeruleus may contribute as one of the development and maintenance factors for hypertension in SHR.

• Molecules and Cells
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• v.38 no.11
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• pp.975-981
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• 2015

Precise 3D spatial mapping of cells and their connections within living tissues is required to fully understand developmental processes and neural activities. Zebrafish embryos are relatively small and optically transparent, making them the vertebrate model of choice for live in vivo imaging. However, embryonic brains cannot be imaged in their entirety by confocal or two-photon microscopy due to limitations in optical range and scanning speed. Here, we use light-sheet fluorescence microscopy to overcome these limitations and image the entire head of live transgenic zebrafish embryos. We simultaneously imaged cranial neurons and blood vessels during embryogenesis, generating comprehensive 3D maps that provide insight into the coordinated morphogenesis of the nervous system and vasculature during early development. In addition, blood cells circulating through the entire head, vagal and cardiac vasculature were also visualized at high resolution in a 3D movie. These data provide the foundation for the construction of a complete 4D atlas of zebrafish embryogenesis and neural activity.

• The Journal of Korean Medicine
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• v.29 no.5
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• pp.1-13
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• 2008

Objective : This study is investigate the effects of Sagunjatang-Ga-Nokyong(SGJ-NY) treatment on regenerative responses of corticospinal tract(CST) axons in the injured spinal cord. Methods :Using rats, we damaged their spinal cord, and then applied SGJ-NY extract to the lesion. Then we observed GAP-43 and NGF protein, astrcyte, axonal regeneration responses and axonal elongation. Result :Determination of GAP-43 and NGF protein levels were increased. And increased proliferation of astrocyte and enhanced processes in astrocytes were observed by SGJ-NY treatment. Higher number of astrocytes within the injury cavity in SGJ-NY treated group were showed, yet CSPG proteins were a weaker staining in the cavity in SGJ-NY. CST axons extended into the cavity and to the caudal area in SGJ-NY treated group were increased. Conclusion : SGJ-NY treatment might increase neural activity in the injured spinal cord tissue, and improved axonal regeneration responses. In this process, activation of astrocytes may play a role in promoting enhanced axonal elongation. the current study show that SGJ-NY exerts positive activity on inducing nerve regeneration responses by elevating neural tissue migration activities.

• Convergence Security Journal
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• v.21 no.3
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• pp.31-38
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• 2021

Tor, known as Onion Router, guarantees strong anonymity. For this reason, Tor is actively used not only for criminal activities but also for hacking attempts such as rapid port scan and the ex-filtration of stolen credentials. Therefore, fast and accurate detection of Tor traffic is critical to prevent the crime attempts in advance and secure the organization's information system. This paper proposes a novel classification model that can detect Tor traffic and classify the traffic types based on CNN(Convolutional Neural Network). We use UNB Tor 2016 Dataset to evaluate the performance of our model. The experimental results show that the accuracy is 99.98% and 97.27% in binary classification and multiclass classification respectively.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3567-3582
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• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• The Korean Journal of Physiology and Pharmacology
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• v.20 no.1
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• pp.41-51
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• 2016

Adult hippocampal dentate granule neurons are generated from neural stem cells (NSCs) in the mammalian brain, and the fate specification of adult NSCs is precisely controlled by the local niches and environment, such as the subventricular zone (SVZ), dentate gyrus (DG), and Toll-like receptors (TLRs). Epigallocatechin-3-gallate (EGCG) is the main polyphenolic flavonoid in green tea that has neuroprotective activities, but there is no clear understanding of the role of EGCG in adult neurogenesis in the DG after neuroinflammation. Here, we investigate the effect and the mechanism of EGCG on adult neurogenesis impaired by lipopolysaccharides (LPS). LPS-induced neuroinflammation inhibited adult neurogenesis by suppressing the proliferation and differentiation of neural stem cells in the DG, which was indicated by the decreased number of Bromodeoxyuridine (BrdU)-, Doublecortin (DCX)- and Neuronal Nuclei (NeuN)-positive cells. In addition, microglia were recruited with activating TLR4-NF-${\kappa}B$ signaling in the adult hippocampus by LPS injection. Treating LPS-injured mice with EGCG restored the proliferation and differentiation of NSCs in the DG, which were decreased by LPS, and EGCG treatment also ameliorated the apoptosis of NSCs. Moreover, pro-inflammatory cytokine production induced by LPS was attenuated by EGCG treatment through modulating the TLR4-NF-${\kappa}B$ pathway. These results illustrate that EGCG has a beneficial effect on impaired adult neurogenesis caused by LPS-induced neuroinflammation, and it may be applicable as a therapeutic agent against neurodegenerative disorders caused by inflammation.

• Biomolecules & Therapeutics
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• v.24 no.1
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• pp.99-107
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• 2016

Triclosan is an antimicrobial or sanitizing agent used in personal care and household products such as toothpaste, soaps, mouthwashes and kitchen utensils. There are increasing evidence of the potentially harmful effects of triclosan in many systemic and cellular processes of the body. In this study, we investigated the effects of triclosan in the survivability of cultured rat neural stem cells (NSCs). Cortical cells from embryonic day 14 rat embryos were isolated and cultured in vitro. After stabilizing the culture, triclosan was introduced to the cells with concentrations ranging from $1{\mu}M$ to $50{\mu}M$ and in varied time periods. Thereafter, cell viability parameters were measured using MTT assay and PI staining. TCS decreased the cell viability of treated NSC in a concentration-dependent manner along with increased expressions of apoptotic markers, cleaved caspase-3 and Bax, while reduced expression of Bcl2. To explore the mechanisms underlying the effects of TCS in NSC, we measured the activation of MAPKs and intracellular ROS. TCS at $50{\mu}M$ induced the activations of both p38 and JNK, which may adversely affect cell survival. In contrast, the activities of ERK, Akt and PI3K, which are positively correlated with cell survival, were inhibited. Moreover, TCS at this concentration augmented the ROS generation in treated NSC and depleted the glutathione activity. Taken together, these results suggest that TCS can induce neurodegenerative effects in developing rat brains through mechanisms involving ROS activation and apoptosis initiation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.123-132
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• 2021

Malicious activities of Botnets are responsible for huge financial losses to Internet Service Providers, companies, governments and even home users. In this paper, we try to confirm the possibility of detecting botnet traffic by applying the deep learning model Convolutional Neural Network (CNN) using the CTU-13 botnet traffic dataset. In particular, we classify three classes, such as the C&C traffic between bots and C&C servers to detect C&C servers, traffic generated by bots other than C&C communication to detect bots, and normal traffic. Performance metrics were presented by accuracy, precision, recall, and F1 score on classifying both known and unknown botnet traffic. Moreover, we propose a stackable botnet detection system that can load modules for each botnet type considering scalability and operability on the real field.