• Journal of Ginseng Research
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• v.46 no.3
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• pp.435-443
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• 2022

Background: Post-traumatic stress disorder (PTSD) is a psychiatric disease that develops following exposure to a traumatic event and is a stress-associated mental disorder characterized by an imbalance of neuroinflammation. Korean Red Ginseng (KRG) is the herbal supplement that is known to be involved in a variety of pharmacological activities. We aimed to investigate the effects of KRG on neuroinflammation as a potential mechanism involved in single prolonged stress (SPS) that negatively influences memory formation and consolidation and leads to cognitive and spatial impairment by regulating BDNF signaling, synaptic proteins, and the activation of NF-κB. Methods: We analyzed the cognitive and spatial memory, and inflammatory cytokine levels during the SPS procedure. SPS model rats were injected intraperitoneally with 20, 50, or 100 mg/kg/day KRG for 14 days. Results: KRG administration significantly attenuated the cognitive and spatial memory deficits, as well as the inflammatory reaction in the hippocampus associated with activation of NF-κB in the hippocampus induced by SPS. Moreover, the effects of KRG were equivalent to those exerted by paroxetine. In addition, KRG improved the expression of BDNF mRNA and the synaptic protein PSD-95 in the hippocampus. Taken together, these findings demonstrate that KRG exerts memory-improving actions by regulating anti-inflammatory activities and the NF-κB and neurotrophic pathway. Conclusion: Our findings suggest that KRG is a potential functional ingredient for protecting against memory deficits in mental diseases, such as PTSD.

• Nutrition Research and Practice
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• v.9 no.5
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• pp.480-488
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• 2015

BACKGROUND/OBJECTIVES: Alzheimer's disease (AD) is characterized by deficits in memory and cognitive functions. The accumulation of amyloid beta peptide ($A{\beta}$) and oxidative stress in the brain are the most common causes of AD. MATERIALS/METHODS: Caffeic acid (CA) is an active phenolic compound that has a variety of pharmacological actions. We studied the protective abilities of CA in an $A{\beta}_{25-35}$-injected AD mouse model. CA was administered at an oral dose of 10 or 50 mg/kg/day for 2 weeks. Behavioral tests including T-maze, object recognition, and Morris water maze were carried out to assess cognitive abilities. In addition, lipid peroxidation and nitric oxide (NO) production in the brain were measured to investigate the protective effect of CA in oxidative stress. RESULTS: In the T-maze and object recognition tests, novel route awareness and novel object recognition were improved by oral administration of CA compared with the $A{\beta}_{25-35}$-injected control group. These results indicate that administration of CA improved spatial cognitive and memory functions. The Morris water maze test showed that memory function was enhanced by administration of CA. In addition, CA inhibited lipid peroxidation and NO formation in the liver, kidney, and brain compared with the $A{\beta}_{25-35}$-injected control group. In particular, CA 50 mg/kg/day showed the stronger protective effect from cognitive impairment than CA 10 mg/kg/day. CONCLUSIONS: The present results suggest that CA improves $A{\beta}_{25-35}$-induced memory deficits and cognitive impairment through inhibition of lipid peroxidation and NO production.

• Korean Journal of Medicinal Crop Science
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• v.28 no.2
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• pp.85-94
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• 2020

Background: Alzheimer's disease (AD) is caused by various factors, such as cholinergic dysfunction, regulation of neurotrophic factor expression, and accumulation of amyloid-beta. We investigated whether or not a combination of Carthamus tinctorius L. seed and Taraxacum coreanum (CT) has a protective effect on scopolamine-induced memory impairment in a mouse model. Methods and Results: Mice were orally pretreated with CT (50, 100 and 200 mg/kg/day) for 14 days, and scopolamine (1 mg/kg/day) was injected intraperitoneally before subjecting them to behavior tests. CT-administered mice showed better novel object recognition and working memory ability than scopolamine-treated control mice. In T-maze and Morris water maze tests, CT (100 and 200 mg/kg/day) significantly increased space perceptive ability and occupancy to the target quadrant, respectively. In addition, 100 and 200 mg/kg/day of CT attenuated cholinergic dysfunction through inhibition of butyryl cholinesterase in brain tissue. Furthermore, CT-administered mice showed higher cyclic adenosine monophosphate-response element-binding protein (CREB) levels and lower amyloid precursor protein (APP) levels compared to scopolamine-treated control mice. Conclusions: CT improved scopolamine-induced memory impairment through inhibition of cholinergic dysfunction, up-regulation of CREB, and down-regulation of APP. Therefore, CT could be a useful therapeutic agent for AD with protective effects on cognitive impairment.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1210-1212
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• 1998

The complexity of newly developed protection algorithm and higher performance requested by the user makes the software embedded in the protective relay much harder to develop and maintain. The versatility of 32bit microprocessor and the availability of cheaper memory semiconductors introduced the fertile developing background for the protective relay developers. The use of component object model(COM) in the software developing process enables the developer to write much complex code in the easy and safe way and to maintain the code easily, too. And the aid of the COM library, the distributed computing environment will be expected to appear by the use of the COM programming model in the protective relay firmware program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2141-2155
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• 2020

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

• Journal of The Korean Association For Science Education
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• v.25 no.6
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• pp.698-709
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• 2005

When modeling students' conceptual understanding, there are several different frameworks, among which are the alternative conception framework and the mental model framework, which converge to suggest a form of knowledge representation. However, little research has explained how they are different from each other and from memory. The purpose of this study was to develop a new mental model theory that integrates the different terminologies and their background theories, which refer to students' ideas not only in science education, but also in other research areas. For this purpose, at first, we compared different terminologies including alternative conception, p-prim, and mental models, and the underlying theories used for representing students' ideas in learning science. Through such comparison, we tried to find the relationship among them. We reviewed related literature and synthesized the results from both cognitive science (related research areas) and science education approaches, especially, Vosniadou's mental model theory. Based on reviewing previous studies, we have developed a preliminary mental model theory 'an integrated theoretical structure of mental models'. We applied the new mental model theory to interpret data on students' ideas about circular motion from our previous research. We expect our new mental model theory will help us understand how students form their own ideas in science from an integrated perspective.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.39-47
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• 2006

The evaluation of MDx family hash functions such as MD5 is difficult because the design background or a generalized model is unknown. In this paper, an approximated model is proposed to generalize hash functions. The diffusion of a input difference is tested by an approximated model for MD5. The results show that MD5 does not provide perfect diffusion, so MD5 is weak against some attacks. We propose a multiple chaining pattern which provides perfect diffusion in approximated model of hash function without extra calculation or memory. And We show the probability of differential characteristics of our proposal.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Journal of Ginseng Research
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• v.45 no.1
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• pp.108-118
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• 2021

Background: Korean ginseng (Panax ginseng Meyer) contains a variety of ginsenosides that can be metabolized to a biologically active substance, compound K. Previous research showed that compound K could be enriched in the red ginseng extract (RGE) after hydrolysis by pectinase. The current study investigated whether the enzymatically hydrolyzed red ginseng extract (HRGE) containing a notable level of compound K has cognitive improving and neuroprotective effects. Methods: A scopolamine-induced hypomnesic mouse model was subjected to behavioral tasks, such as the Y-maze, passive avoidance, and the Morris water maze tests. After sacrificing the mice, the brains were collected, histologically examined (hematoxylin and eosin staining), and the expressions of antioxidant proteins analyzed by western blot. Results: Behavioral assessment indicated that the oral administration of HRGE at a dosage of 300 mg/kg body weight reversed scopolamine-induced learning and memory deficits. Histological examination demonstrated that the hippocampal damage observed in scopolamine-treated mouse brains was reduced by HRGE administration. In addition, HRGE administration increased the expression of nuclear-factor-E2-related factor 2 and its downstream antioxidant enzymes NAD(P)H:quinone oxidoreductase and heme oxygenase-1 in hippocampal tissue homogenates. An in vitro assay using HT22 mouse hippocampal neuronal cells demonstrated that HRGE treatment attenuated glutamate-induced cytotoxicity by decreasing the intracellular levels of reactive oxygen species. Conclusion: These findings suggest that HRGE administration can effectively alleviate hippocampus-mediated cognitive impairment, possibly through cytoprotective mechanisms, preventing oxidative-stress-induced neuronal cell death via the upregulation of phase 2 antioxidant molecules.