• Journal of Pharmaceutical Investigation
• /
• v.40 no.2
• /
• pp.117-123
• /
• 2010

Ibudilast, 3-isobutyryl-2-isopropyrazolo[1,5-a]pyridine, is a nonselective inhibitor of cyclic nucleotide phosphodiesterase (PDE). It preferentially inhibits PDE 3A, PDE4, PDE10 and PDE11 as well as a number of the other PDE families, albeit to a lesser extent. Ibudilast is used clinically to treat bronchial asthma and cerebrovascular disorders. Thes e clinical uses are based on the ability of ibudilast to inhibit platelet aggregation, improve cerebral blood flow and attenuate allergic reactions. The purpose of the present study was to evaluate the bioequivalence of two ibudilast capsules, Ketas capsule (Handok Pharmaceuticals Co., Ltd.) and Pinatos capsule (Sam Chun Dang Pharm. Co., Ltd.), according to the guidelines of the Korea Food and Drug Administration (KFDA). The in vitro release of ibudilast from the two ibudilast formulations was tested using KP Apparatus method with various dissolution media. Twenty six healthy male subjects, 23.31${\pm}$1.09 years in age and 70.45${\pm}$8.51 kg in body weight, were divided into two groups and a randomized $2{\times}2$ cross-over study was employed. After a single capsule containing 10 mg as ibudilast was orally administered, blood samples were taken at predetermined time intervals and the concentrations of ibudilast in serum were determined using HPLC/UV detector. The dissolution profiles of two formulations were similar in all tested dissolution media. The pharmacokinetic parameters such as $AUC_t$, $C_{max}$ and $T_{max}$ were calculated, and computer programs (Equiv Test and K-BE Test 2002) were utilized for the statistical analysis of the parameters using logarithmically transformed $AUC_t$, $C_{max}$ and untransformed $T_{max}$. The results showed that the differences between two formulations based on the reference drug, Ketas, were 6.99%, -2.48% and 9.93% for $AUC_t$, $C_{max}$ and $T_{max}$, respectively. There were no sequence effects between two formulations in these parameters. The 90% confidence intervals using logarithmically transformed data were within the acceptance range of log 0.8 to log 1.25 (e.g., log 0.8791~log 1.1861 and log 0.8347~log 1.1199 for $AUC_t$ and $C_{max}$, respectively). Thus, the criteria of the KFDA bioequivalence guideline were satisfied, indicating Pinatos capsule was bioequivalent to Ketas capsule.

• Korean Journal of Clinical Laboratory Science
• /
• v.50 no.2
• /
• pp.118-125
• /
• 2018

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB), but the genes associated with the host immune system can be attributed to the development of TB. The ITGB2 gene encodes the integrin beta 2 chain CD18 protein and is present on chromosome 21. The integrin beta 2 chain is an integrin expressed in leukocytes and plays a very important role in leukocyte maturation and attachment. ITGB2 plays an important role in the phagocytosis of MTB and the aggregation of leukocytes in MTB infections. This study examined the genetic polymorphisms of the ITGB2 gene between the TB case and normal control using Korean genomic and epidemiologic data. As a result, a statistically significant correlation was confirmed in 10 SNPs. The most significant SNP was rs113421921 (OR=0.69, CI: 0.53~0.90, $P=5.8{\times}10^{-3}$). In addition, rs173098, one of the significant 10 SNPs, is possibly located in a binding motif with the transcription factor cofactor p300, and can affect ITGB2 gene expression. These findings suggest that the pathogenesis of TB may be influenced by a range of genetic factors related to the immune function of the host, e.g., the reactions associated with the recruitment and attachment of leukocytes. The results of this study could be used to predict the infection control for tuberculosis in a patient-tailored manner.

• Journal of Korea Port Economic Association
• /
• v.35 no.4
• /
• pp.85-106
• /
• 2019

Offshore wind power is a representative renewable energy source and a rapidly growing industry. In Gyeongsangnam-do, offshore wind farms of 461.9MW are being pushed for in the Yokji island and are expected to expand further to over 1GW in the future. Accordingly, ports supporting the storage, assembly, transportation, and installation of offshore wind power equipment are expected to play an important role in the smooth progress of the offshore wind farm development project. Based on previous research and cases in major countries,this study prepared criteria for assessment of ports supporting offshore wind farmsand evaluated ports in Gyeongsangnam-do, which are linked to Yokji island offshore wind farms. The assessment criteria have been subdivided into distance from the offshore wind farm, port entry and exit restrictions, navigational areas, fishery rights factors, additional costs, berth length, depth of berth, size of the port yard, port berth bearing pressure, interference with other cargo, a civil appeal, and relevant industrial aggregation. The ports of Tongyeong, Samcheonpo, Kohyun, Masan, and Jinhae in Gyeongsangnam-do were selected and evaluated. As a result, the port of Tongyoung was superior in terms of distance from the Yokji island offshore wind farm. The ports of Samcheonpo, Masan, and Jinhae were evaluated as excellent in other criteria such as berth length, depth of berth, and so on. This study is expected to be used as a source of basic data for offshore wind power companies and policymakers to select and evaluate the supporting ports of offshore wind farms.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.145-165
• /
• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.139-157
• /
• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Environmental Policy
• /
• v.13 no.1
• /
• pp.69-93
• /
• 2014

The aim of this research was to develop a climate change vulnerability index at the district level (Si, Gun, Gu) with respect to the health care sector in Korea. The climate change vulnerability index was esimated based on the four major causes of climate-related illnesses : vector, flood, heat waves, and air pollution/allergies. The vulnerability assessment framework consists of six layers, all of which are based on the IPCC vulnerability concepts (exposure, sensitivity, and adaptive capacity) and the pathway of direct and indirect impacts of climate change modulators on health. We collected proxy variables based on the conceptual framework of climate change vulnerability. Data were standardized using the min-max normalization method. We applied the analytic hierarchy process (AHP) weight and aggregated the variables using the non-compensatory multi-criteria approach. To verify the index, sensitivity analysis was conducted by using another aggregation method (geometric transformation method, which was applied to the index of multiple deprivation in the UK) and weight, calculated by the Budget Allocation method. The results showed that it would be possible to identify the vulnerable areas by applying the developed climate change vulnerability assessment index. The climate change vulnerability index could then be used as a valuable tool in setting climate change adaptation policies in the health care sector.

• Journal of Plant Biotechnology
• /
• v.43 no.3
• /
• pp.359-366
• /
• 2016

Tradescantia is a perennial plant in the family of Commelinaceae. It is known to be sensitive to radiation. In this study, Tradescantia BNL 4430 was irradiated with gamma radiation at doses of 50 to 1,000 mGy in a phytotron equipped with a $^{60}Co$ radiation source at Korea Atomic Energy Research Institute, Korea. At 13 days after irradiation, we extracted RNA from irradiated floral tissues for RNA-seq. Transcriptome assembly produced a total of 77, 326 unique transcripts. In plantlets exposed to 50, 250, 500, and 1000 mGy, the numbers of up-regulated genes with more than 2-fold of expression compared that in the control were 116, 222, 246, and 308, respectively. Most of the up-regulated genes induced by 50 mGy were heat shock proteins (HSPs) such as HSP 70, indicating that protein misfolding, aggregation, and translocation might have occurred during radiation stress. Similarly, highly up-regulated transcripts of the IQ-domain 6 were induced by 250 mGy, KAR-UP oxidoreductase 1 was induced by 500 mGy, and zinc transporter 1 precursor was induced by 1000 mGy. Reverse transcriptase (RT) PCR and quantitative real time PCR (qRT-PCR) further validated the increased mRNA expression levels of selected genes, consistent with DEG analysis results. However, 2.3 to 97- fold higher expression activities were induced by different doses of radiation based on qRT-PCR results. Results on the transcriptome of Tradescantia in response to radiation might provide unique identifiers to develop in situ monitoring kit for measuring radiation exposure around radiation facilities.

• Journal of Life Science
• /
• v.21 no.6
• /
• pp.796-804
• /
• 2011

Microglia are central nervous system (CNS)-resident professional macrophages that function as the principal immune cells responding to pathological stimulations in the CNS. Activation of microglia, induced by various pathogens, protects neurons and maintains homeostasis in the CNS, but severe activation causes inflammatory responses secreting various neurotoxic molecules such as nitric oxide (NO), prostaglandin $E_2$ ($PGE_2$) and pro-inflammatory cytokines. Allium fistulosum, a member of the onion family, is mainly cultivated for consumption, as well as medicinal use in Oriental medicine. It has been reported that A. fistulosum has various biological effects such as anti-oxidant, anti-platelet aggregation, anti-fungus and anti-cholesterol synthesis, however there has been no research about the anti-inflammatory effects of A. fistulosum extracts. In this study, it was undertaken to explore the functions of A. fistulosum as a suppressor of neuronal inflammation by using BV2 microglia cells. As a result, it was found that four kinds of extracts of A. fistulosum effectively reduced the expressions of inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) at both mRNA and protein levels, and also attenuated pro-inflammatory cytokines such as tumor necrosis alpha (TNF-${\alpha}$), interleukin-$1{\beta}$ (IL-$1{\beta}$) and interleukin-6 (IL-6) at the mRNA level in BV2 stimulated by lipopolysaccharide (LPS). In addition, the extracts of A. fistulosum attenuated the release of NO markedly, as well as resulting in slight decreases of TNF-${\alpha}$ and IL-6 production, the effects of which were most significant when treated with ethyl alcohol extract from the whole A. fistulosum. In conclusion, the data indicated that the anti-inflammatory actions of A. fistulosum against BV2 microglia cells is through the down-regulation of iNOS, COX2 and pro-inflammatory cytokines such as TNF-${\alpha}$ and IL-6, and these effects are expected to help in the protection of nerve tissues by suppressions of neuronal inflammation in various neurodegenerative diseases.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.8 no.2
• /
• pp.86-96
• /
• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• Korean Journal of Soil Science and Fertilizer
• /
• v.40 no.2
• /
• pp.118-130
• /
• 2007

The steep-sloped agricultural land was severely damaged by rainfall events during July and August every year. The objective of this study was to investigate an effects of intensive rainfall to the soil properties of the steep-sloped agricultural land. Survey sites including the Sacheon myeon area were located in Gangneung, those were severely damaged from a forest fire in April 2000. Surveys were taken at these sites after two years of forest fire and severe rainfall events in August 2002, which included an event that poured with 870 mm of rainfall in a day. After this storm, soil erosion, burying, and flooding were observed. Severe soil loss was found at lower soil-depths, greater slopes, longer slope lengths, and concave landscapes. Soil loss and land slides were often found at areas with having a coarser textures, higher bulk densities, lower water holding capacity, and lower rates of soil aggregation. Crop growth stagnation was found at the site of crop restoration because of low soil fertility and poor drainage caused by the abrupt textural changes. In conclusion, it is necessary to manage the steep-slope agricultural land based on environmental impact assessment data of macro morphological and physical characteristics by intensive rainfall.