• Journal of Communications and Networks
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• v.17 no.6
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• pp.549-557
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• 2015

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platformand the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4Gbyte of storage size (87%of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• Biomolecules & Therapeutics
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• v.27 no.2
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• pp.160-167
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• 2019

Depression is a major mood disorder. Abnormal expression of glial glutamate transporter-1 (GLT-1) is associated with depression. Schisantherin B (STB) is one bioactive of lignans isolated from Schisandra chinensis (Turcz.) Baill which has been commonly used as a traditional herbal medicine for thousands of years. This paper was designed to investigate the effects of STB on depressive mice induced by forced swimming test (FST). Additionally, we also assessed the impairment of FST on cognitive function in mice with different ages. FST and open field test (OFT) were used for assessing depressive symptoms, and Y-maze was used for evaluating cognition processes. Our study showed that STB acting as an antidepressant, which increased GLT-1 levels by promoting PI3K/AKT/mTOR pathway. Although the damage is reversible, short-term learning and memory impairment caused by FST test is more serious in the aged mice, and STB also exerts cognition improvement ability in the meanwhile. Our findings suggested that STB might be a promising therapeutic agent of depression by regulating the GLT-1 restoration as well as activating PI3K/AKT/mTOR pathway.

• Biomedical Science Letters
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• v.21 no.2
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• pp.92-102
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• 2015

This work aimed to study whether rice bran extract fermented with Lactobacillus plantarum (LW) promotes functional recovery and reduces cognitive impairment after ischemic brain injury. Ischemic brain injury was induced by middle cerebral artery occlusion (MCAO) in rats. Four groups were studied, namely the (1) sham, (2) vehicle, (3) donepezil, and (4) LW groups. Animals were injected with LW once a day for 7 days after middle cerebral artery occlusion. LW group showed significantly improved neurological function as compared to the vehicle group, as well as enhanced learning and memory in the Morris water maze. The LW group showed the greatest functional recovery. Moreover, the LW group showed an enhanced more survival cells anti-apoptotic effect in the cortex and neural cell densities in the hippocampal DG and CA1. In addition, this group showed enhanced expression of neurotrophic factors, antioxidant genes, and the acetylcholine receptor gene, as well as synaptophysin (SYP), Fox-3 (NeuN), doublecortin (DCX), and choline acetyltransferase (ChAT) proteins. Our findings indicate that LW treatment showed the largest effects in functional recovery and cognitive improvement after ischemic brain injury through stimulation of the acetylcholine receptor, antioxidant genes, neurotrophic factors, and expression of NeuN, SYP, DCX, and ChAT.

• Journal of Computing Science and Engineering
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• v.12 no.1
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• pp.24-35
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• 2018

Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.

• The Korean Journal of Food And Nutrition
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• v.33 no.3
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• pp.347-355
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• 2020

Cognitive impairment is considered to be key research topics in the field of neurodegenerative diseases and in understanding of learning and memory. In the present study, we investigated neuroprotective effects of Schisandra chinensis (SC) and Ribes fasciculatum (RF) extracts in hydrogen peroxide-induced neuronal cell death in vitro and scopolamine-induced cognitive impairment in Sprague Dawley^® (SD) rat in vivo. Apoptotic cell death in neuroblastic PC12 cell line was induced by hydrogen peroxide for 1 hour at 100 μM. However, mixture of SC and RF treatment prevented peroxide induced PC12 cell death with no neurotoxic effects. For in vivo experiment, the effect of SC and RF extracts on scopolamine-induced cognitive impairment in SD rat was evaluated by spontaneous alternation behavior in Y-Maze test. After 30 min scopolamine injection, the scopolamine-induced rats presented significantly decreased % spontaneous alteration and acetylcholine level, compared to non-induced group. However, treatment of SC+RF extracts rescued the reduced % spontaneous alteration with acetylcholine concentration from hippocampus in scopolamine-induced rats. These results suggested that mixture of SC and RF extract may be a potential natural therapeutic agent for the prevention of cognitive impairment.

• Journal of Microbiology and Biotechnology
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• v.9 no.6
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• pp.798-803
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• 1999

Prolyl endopeptidase [PEP; EC 3.4.21.26], a serine protease which is known to cleave peptide bonds on the carboxy side of a proline residue, plays an important role in the degradation of proline-containing neuropeptides that have been suggested to participate in learning and memory processes. An abnormal increase in the level of PEP, which can lead to generation of $A{\beta}$, is also suggested to be involved in Alzheimer's type senile dementia. In the course of screening PEP inhibitors from Basidiomycetes, the mushroom Polyozellus multiplex exhibited a high inhibitory activity against PEP. Two active compounds were isolated from the ethyl acetate soluble fraction by consecutive purification, using silica gel, Sephadex LH-20, and Lobar RP-18 chromatography. The chemical structures of these compounds were identified as thelephoric acid and 12-acety1-2,3,7,8-tetrahydroxy-[12H]-12-hydroxymethylbenzobis[I.2b,3.4b'] benzofuran-11-one (kynapcin-9) by spectral data including UV, IR, MS, HR-MS, $^1H-,{\;}^{13}C-$, and 2D-NMR. The $IC_{50}$ values of the thelephoric acid and kynapcin-9 were 0.157 ppm (446nM) and 0.087 ppm (212nM) and their inhibitor constants ($K_i$) were 0.73ppm ($2.09{\;}\mu\textrm{m}$) and 0.060 ppm (146 nM), respectively. Furthermore, they were non-competitive with a substrate in Dixon plots.

• Journal of Veterinary Science
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• v.22 no.1
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• pp.7.1-7.13
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• 2021

Background: Niemann-Pick disease type C (NPC) is caused by the mutation of NPC genes, which leads to the abnormal accumulation of unesterified cholesterol and glycolipids in lysosomes. This autosomal recessive disease is characterized by liver dysfunction, hepatosplenomegaly, and progressive neurodegeneration. Recently, the application of induced neural stem cells (iNSCs), converted from fibroblasts using specific transcription factors, to repair degenerated lesions has been considered a novel therapy. Objectives: The therapeutic effects on NPC by human iNSCs generated by our research group have not yet been studied in vivo; in this study, we investigate those effects. Methods: We used an NPC mouse model to efficiently evaluate the therapeutic effect of iNSCs, because neurodegeneration progress is rapid in NPC. In addition, application of human iNSCs from NPC patient-derived fibroblasts in an NPC model in vivo can give insight into the clinical usefulness of iNSC treatment. The iNSCs, generated from NPC patientderived fibroblasts using the SOX2 and HMGA2 reprogramming factors, were transplanted by intracerebral injection into NPC mice. Results: Transplantation of iNSCs showed positive results in survival and body weight change in vivo. Additionally, iNSC-treated mice showed improved learning and memory in behavior test results. Furthermore, through magnetic resonance imaging and histopathological assessments, we observed delayed neurodegeneration in NPC mouse brains. Conclusions: iNSCs converted from patient-derived fibroblasts can become another choice of treatment for neurodegenerative diseases such as NPC.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.