• Biomolecules & Therapeutics
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• v.31 no.3
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• pp.285-297
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• 2023

Alzheimer's disease (AD) is a neurodegenerative disease with progressive memory loss and the cognitive decline. AD is mainly caused by abnormal accumulation of misfolded amyloid β (Aβ), which leads to neurodegeneration via a number of possible mechanisms such as down-regulation of brain-derived neurotrophic factor-tropomyosin-related kinase B (BDNF-TRKB) signaling pathway. 7,8-Dihydroxyflavone (7,8-DHF), a TRKB agonist, has demonstrated potential to enhance BDNF-TRKB pathway in various neurodegenerative diseases. To expand the capacity of flavones as TRKB agonists, two natural flavones quercetin and apigenin, were evaluated. With tryptophan fluorescence quenching assay, we illustrated the direct interaction between quercetin/apigenin and TRKB extracellular domain. Employing Aβ folding reporter SH-SY5Y cells, we showed that quercetin and apigenin reduced Aβ-aggregation, oxidative stress, caspase-1 and acetylcholinesterase activities, as well as improved the neurite outgrowth. Treatments with quercetin and apigenin increased TRKB Tyr516 and Tyr817 and downstream cAMP-response-element binding protein (CREB) Ser133 to activate transcription of BDNF and BCL2 apoptosis regulator (BCL2), as well as reduced the expression of pro-apoptotic BCL2 associated X protein (BAX). Knockdown of TRKB counteracted the improvement of neurite outgrowth by quercetin and apigenin. Our results demonstrate that quercetin and apigenin are to work likely as a direct agonist on TRKB for their neuroprotective action, strengthening the therapeutic potential of quercetin and apigenin in treating AD.

• Korean Journal of Schizophrenia Research
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• v.21 no.2
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• pp.74-80
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• 2018

Objectives : This study investigated whether long-acting injectable (LAI) paliperidone is different from its oral form in terms of the effect on cognitive function in schizophrenia spectrum and other psychotic disorders. Methods : We reviewed the medical records of patients in Seoul National University Bundang Hospital who were diagnosed as having schizophrenia and/or other psychotic disorders based on DSM-5 from 2016 to 2017. Seven patients were treated with oral paliperidone and 11 were treated with paliperidone palmitate. All patients underwent clinical and neuropsychological assessment, including the Korean version of the MATRICS Consensus Cognitive Battery (MCCB) at their first visit or within one month of their initial treatment. MCCB was repeated within three to 12 months after the initial assessment. Results : There was no significant difference between the two groups in most cognitive domains including speed of processing, attention and vigilance, working memory, verbal learning, visual learning and reasoning and problem solving domain. However, patients treated with paliperidone palmitate showed better improvement in social cognition domain than those taking oral paliperidone. The standardized values of social cognition domain scores had significantly improved over time in patients under paliperidone palmitate, demonstrating a significant time-by-group interaction. Conclusion : Our results show that long-acting injectable paliperidone could be helpful in some aspects of improving cognitive function in schizophrenia spectrum and other psychotic disorders. Further studies with other antipsychotics are necessary to generalize the results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.17-24
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• 2004

In this paper, the wavelet video coding using Low-Band-Shift(LBS) method and multiresolution motion estimation(MRME) is proposed. To overcome shift- variant property on wavelet coefficients, the LBS was proposed. LBS method previously has superior performance in terms of rate-distortion characteristic. However, this method needs more memory and computational complexity. Therefore to reduce computational complexity of video coding using LBS, we combine MRME with LBS. When mm is applied only, it has 7 times as much as existing method's motion vector because each subband has different motion vector using property of LBS, number of motion vector decreases. Proposed method decreases motion vector, and it decreases motion compensated Prediction error by detailed motion estimation. And then it shows better coding performance. Also this method reduces computational amount by smaller search area in higher resolution. The computational complexity of the proposed method is 12.1% of that of existing method at 3-level wavelet transform. The experimental results with the proposed method show about 0.2∼9.7% improvement of MAD performance in case of lossless coding, and 0.1∼2.0㏈ improvement of PSNR performance at 4he same bit rate in case of lossy coding.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.316-324
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• 2005

e-Learning has been mushrooming with wide range of teaming groups from pedagogy to andragogy As e-teaming opportunities increase, many people raise question about whether e-teaming show positive teaming effects. The related research emphasized that e-learning would be a failure in terms of understanding of e-Learners and activating intuitive teaming activities from learner's long-term memory span. The e-teaming strategies based on the traditional classroom and resulted boring and ineffective teaming outcomes, should be changed to provide authentic and effective teaming results. This paper analyzed that how learners have received e-Learning for the last few years from the research and explained what could be the failing aspects in e-Learning. To be successful, e-loaming should consider the e-learner's individualized teaming style and thinking patterns. When considering of various e-Learning components, the quality of e-teaming should not be focused on any specific single factor, but develop every individual factor to be integrated into high level of quality. In conclusion, this paper suggest that it is needed new understandings of e-Loaming and e-Learner. Also the e-Learning strategies should be examined throughly whether they are on the side of learners and realized how they learn from e-Learning. Finally, we should add enormous imagination into e-loaming for next generation because new generation's teaming patterns significantly differ from their parent's generation.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.692-704
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• 2005

With the improvement of computer hardware, GPUs(Graphics Processor Units) have tremendous memory bandwidth and computation power. This leads GPUs to use in general purpose computation. Especially, GPU implementation of compute-intensive physics based simulations is actively studied. In the solution of differential equations which are base of physics simulations, tridiagonal matrix systems occur repeatedly by finite-difference approximation. From the point of view of physics based simulations, fast solution of tridiagonal matrix system is important research field. We propose a fast GPU implementation for the solution of tridiagonal matrix systems. In this paper, we implement the cyclic reduction(also known as odd-even reduction) algorithm which is a popular choice for vector processors. We obtained a considerable performance improvement for solving tridiagonal matrix systems over Thomas method and conjugate gradient method. Thomas method is well known as a method for solving tridiagonal matrix systems on CPU and conjugate gradient method has shown good results on GPU. We experimented our proposed method by applying it to heat conduction, advection-diffusion, and shallow water simulations. The results of these simulations have shown a remarkable performance of over 35 frame-per-second on the 1024x1024 grid.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.13-21
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• 2010

One of the most distinguished features of the DHP association rules mining algorithm is that it counts the support of hash key combinations composed of k items at phase k-1, and uses the counted support for pruning candidate large itemsets to improve performance. At this time, it is desirable for each hash key combination to have a separate count variable, where it is impossible to allocate the variables owing to memory shortage. So, the algorithm uses a direct hashing mechanism in which several hash key combinations conflict and are counted in a same hash bucket. But the direct hashing mechanism is not efficient because the distribution of hash key combinations is unvalanced by the characteristics sourced from the mining process. This paper proposes a mapped perfect hashing function which maps the region of hash key combinations into a continuous integer space for phase 3 and maximizes the efficiency of direct hashing mechanism. The results of a performance test experimented on 42 test data sets shows that the average performance improvement of the proposed hashing mechanism is 7.3% compared to the existing method, and the highest performance improvement is 16.9%. Also, it shows that the proposed method is more efficient in case the length of transactions or large itemsets are long or the number of total items is large.

• Journal of Applied Biological Chemistry
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• v.64 no.3
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• pp.299-307
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• 2021

Accumulation of amyloid beta (Aβ) and oxidative stress are the most common reason of Alzheimer's disease (AD). In the present study, we investigated the cognitive improvement effects of butanol (BuOH) fraction from Momordica charantia in Aβ_25-35-induced AD mouse model. To develop an AD mouse model, mice were received injection of Aβ_25-35, and then orally administered BuOH fraction from M. charantia at doses of 100 and 200 mg/kg/day during 14 days. In the T-maze and novel object recognition test, administration of BuOH fraction from M. charantia L. at doses of 100 and 200 mg/kg/day improved spatial ability and novel object recognition by increased explorations of novel route and new object. In addition, BuOH fraction of M. charantia-administered groups improved learning and memory abilities by decreased time to reach hidden platform in Morris water maze test. Oral administration of BuOH fraction from M. charantia significantly inhibited lipid peroxidation and nitric oxide levels in the brain, liver, and kidney compared with Aβ_25-35-induced control group. These results indicated that BuOH fraction of M. charantia improved Aβ_25-35-induced cognitive impairment by attenuating oxidative stress. Therefore, M. charantia could be useful for protection from Aβ_25-35-induced cognitive impairment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.