• Journal of Life Science
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• v.21 no.5
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• pp.647-655
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• 2011

The neurotoxicity of aggregated amyloid ${\beta}$ ($A{\beta}$) has been implicated as a critical cause in the pathogenesis of Alzheimer's disease (AD). It can cause neurotoxicity in AD by evoking a cascade of apoptosis to neuron. Here, we investigated the neuroprotective effects of cilostazol, which acts as a phosphodiesterase III inhibitor, on $A{\beta}_{25-35}$-induced cytotoxicity in mouse neuronal cells and cognitive decline in the C57BL/6J AD mouse model via peroxisome proliferator-activated receptor (PPAR)-${\gamma}$ activation. $A{\beta}_{25-35}$ significantly reduced cell viability and increased the number of apoptotic-like cells. Cilostazol treatment recovered cells from $A{\beta}$-induced cell death as well as rosiglitazone, a PPAR-${\gamma}$ activator. These effects were suppressed by GW9662, an antagonist of PPAR-${\gamma}$ activity, indicative of a PPAR-${\gamma}$-mediated signaling. In addition, cilostazol and rosiglitazone also restored PPAR-${\gamma}$ activity levels that had been altered as a result of $A{\beta}_{25-35}$ treatment, which were antagonized by GW9662. Furthermore, cilostazol also markedly decreased the number of apoptotic-like cells and decreased the Bax/Bcl-2 ratio. Intracerebroventricular injection of $A{\beta}_{25-35}$ in C57BL/6J mice resulted in impaired cognitive function. Oral administration of cilostazol (20 mg/kg) for 2 weeks before $A{\beta}_{25-35}$ injection and once a day for 4 weeks post-surgery almost completely prevented the $A{\beta}_{25-35}$-induced cognitive deficits, as did rosiglitazone. Taken together, our findings suggest that cilostazol could attenuate $A{\beta}_{25-35}$-induced neuronal cell injury and apoptosis as well as promote the survival of neuronal cells, subsequently improving cognitive decline in AD, partly because of PPAR-${\gamma}$ activation. The phosphodiesterase III inhibitor cilostazol may be the basis of a novel strategy for the therapy of AD.

• Journal of Food Hygiene and Safety
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• v.33 no.6
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• pp.438-446
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• 2018

The purpose of this study was to investigate food safety awareness, knowledge, and behavior in college students, to provide basic data for the increase in food safety awareness. Data were collected from 252 college students in Gyeonggi region, using a self-administered questionnaire. In results of concern about food safety, subjects responded 3.48 of 5.00 and have knowledge about food safety education revealing significantly higher awareness and concern than subjects without knowledge about food safety education. Food safety awareness of distributed food was 2.55, considered unsafe. Among reasons in perceiving food as unsafe, 62.3% of subjects expressed distrust about safety relative food production. As for risk factors relative to food safety, subjects responded that the highest risk factor was food additives (2.35), followed by heavy metal (2.38) and endocrine disrupters (2.38). Correlation analysis resulting in risk factors for food had positive correlation with each other, heavy metal revealed highest correlation with pesticide residue (r = 0.674), than with endocrine disrupters (r = 0.672). Also, genetically modified food revealed high correlation with radiation irradiated food. Regression analysis demonstrated that concern about food safety significantly influenced pro-actively engaging in food safety education. Meanwhile, 63.5% of subjects correctly responded to food safety knowledge items. The item 'the heavy metals are contaminated the most, in the roots of vegetables' revealed the lowest correct answer rate (38.1%). In food safety behavior, the item 'always wash hands before handling food and meal's revealed 3.85, and subjects with awareness and concern about food safety education, responded in significantly higher numbers than subject without awareness and concern about food safety. The most neglected concern was relative to frozen food thawed at room temperature. Together, students recognize that distributed foods are unsafe, and students with awareness and concern about food safety education showed higher knowledge compared to without awareness and concern experience about food safety eduction. So, systematic education using accurate and objective data is required to reduce anxiety and raise the level of awareness and concern about food safety.

• Journal of Life Science
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• v.24 no.1
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• pp.86-91
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• 2014

Trichoplusia ni cells are used as a host permissive cell line in the baculovirus expression system, which is useful for large-scale production of human sugar transport proteins. However, the activity of endogenous sugar transport systems in insect cells is extremely high. Therefore, the transport activity resulting from the expression of exogenous transporters is difficult to detect. Furthermore, very little is known about the nature of endogenous insect transporters. To exploit the expression system further, the effect of D-fructose on 2-deoxy-D-glucose (2dGlc) transport by T. ni cells was investigated, and T. ni cell-expressed human transporters were photolabeled with [$^3H$] cytochalasin B to develop a convenient method for measuring the biological activity of insect cell-expressed transporters. The uptake of 1 mM 2dGlc by uninfected- and recombinant AcMPV-GTL infected cells was examined in the presence and absence of 300 mM of D-fructose, with and without $20{\mu}M$ of cytochalasin B. The sugar uptake in the uninfected cells was strongly inhibited by fructose but only poorly inhibited by cytochalasin B. Interestingly, the AcMPV-GTL-infected cells showed an essentially identical pattern of transport inhibition, and the rate of 2dGlc uptake was somewhat less than that seen in the non-infected cells. In addition, a sharply labeled peak was produced only in the AcMPV-GTL-infected membranes labeled with [$^3H$] cytochalasin B in the presence of L-glucose. No peak of labeling was seen in the membranes prepared from the uninfected cells. Furthermore, photolabeling of the expressed protein was completely inhibited by the presence of D-glucose, demonstrating the stereoselectivity of labeling.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.