• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.438-444
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• 2005

Purpose: Anhedonia has been proposed to be the result of a basic neurophysiologic dysfunction and a vulnerability marker that precede and contribute to the liability of developing schizophrenia. We hypothesized that anhedonia, as a construct reflecting the decreased capacity to experience pleasure, should be associated with decreased positive hedonic affect trait. This study examined the relationship between anhedonia and positive hedonic affect trait and searched for the brain legions which correlate with anhedonia in normal subjects. Materials and Methods: Using $^{18}F$-FDG PET scan, we investigated the brain activity of twenty one subjects during resting state. Questionnaires were administrated after the scan in order to assess the self-rated individual differences in physical/social anhedonia and positive/negative affect traits. Results: Negative correlation between physical anhedonia score and positive affect trait score was significant (Pearson coefficient =-0.440, p<0.05). The subjects physical and social anhedonia scores showed positive correlation with metabolic rates in the cerebellum and negative correlation with metabolic rates in the inferior temporal gyrus and middie frontal gyrus. In addition, the positive affect trait score positively correlated with various areas, most prominent with the inferior temporal gyrus. Conclusion: These results suggest that neural substrates, such as the inferior temporal gyrus and prefrontal-cerebellar circuit, which dysfunction has been proposed to be involved with the cognitive deficits of schizophrenia, may also play a significant role in the liability of affective deficits like anhedonia.

• Development and Reproduction
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• v.6 no.2
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• pp.117-122
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• 2002

Dopamine(DA), norepinephrine(NE), and epinephrine(E) belong to a class of neurotransmitters known as catecholamine (CA) which are synthesized and secreted by mammalian brain and adrenal medulla. CA regulate several behavior patterns connected with breeding, and regulate GnRH-gonadotropin hormone axis' vitality between hypothalamus-pituitary gland linking with reproduction freeze. The present study examined effects of sex steroid hormone on the transcriptional activities of CA biosynthesis enzymes, tyrosine hydroxylase(TH), dopamine $\beta$ -hydroxylase(DBH), and phenylethaolamine-N-methyl transferase(PNMT). Mature female rats were ovariectomized(OVX) and implanted with 17 $\beta$-estradiol(E$_2$: 500 $\mu\textrm{g}$/ml) or sesame oil. Forty-eight hours after implantation all the animals were sacrificed. Total RNAs were extracted immediately and were applied to semi-quantitative reverse transcription-polymerase chain reaction(RT-PCR). The expression level of TH was appeared by hypothalamus > SNc> adrenal medulla orders in OVX＋Oil group, and by SNc > hypothalamus) adrenal medulla orders in OVX+E$_2$ group. Treatment with E$_2$ significantly increased TH expression in SNc and adrenal medulla but in hypothalamus, the reduced TH expression was observed. The expression level of DBH was appeared by adrenal medulla > SNc > hypothalamus orders in OVX＋Oil group and in OVX＋E$_2$ group. Administration of E$_2$ significantly reduced DBH expression in SNc, and increased in adrenal medulla. Two cDNA products, large(PNMT1) and small(PMNTs) species of 110bp difference, were amplified in SNc and hypothalamus, but only PNMTs was observed in adenal medulla. The PNMTs expression level was in the order of adrenal medulla > hypothalamus > SNc in both OVX＋Oil and OVX＋E$_2$ group. The PNMTs expression in SNc and adrenal medulla was significantly increased byE$_2$. The present report demonstrated that estrogen effects on transcriptional activities for CA biosynthethic enzymes were tissue specific in adrenal medulla as well as different region of brain. These results suggest that it might be crucial relationship between the type of estrogen receptor and CA enzyme gene expression.

• Journal of Life Science
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• v.26 no.3
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• pp.282-288
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• 2016

Protein-protein interactions regulate the subcellular localization and function of receptors, enzymes, and cytoskeletal proteins. Proteins containing the postsynaptic density-95/disks large/zonula occludens-1 (PDZ) domain have potential to act as scaffolding proteins and play a pivotal role in various processes, such as synaptic plasticity, neural guidance, and development, as well as in the pathophysiology of many diseases. Multi-PDZ domain protein 1 (MUPP1), which has 13 PDZ domains, has a scaffolding function in the clustering of surface receptors, organization of signaling complexes, and coordination of cytoskeletal dynamics. However, the cellular function of MUPP1 has not been fully elucidated. In the present study, a yeast two-hybrid system was used to identify proteins that interacted with the N-terminal PDZ domain of MUPP1. The results revealed an interaction between MUPP1 and Wdpcp (formerly known as Fritz). Wdpcp was identified as a planar cell polarity (PCP) effector, which is known to have a role in collective cell migration and cilia formation. Wdpcp bound to the PDZ1 domain but not to other PDZ domains of MUPP1. The C-terminal end of Wdpcp was essential for the interaction with MUPP1 in the yeast two-hybrid assay. This interaction was further confirmed in a glutathione S-transferase (GST) pull-down assay. When coexpressed in HEK-293T cells, Wdpcp was coimmunoprecipitated with MUPP1. In addition, MUPP1 colocalized with Wdpcp at the same subcellular region in cells. Collectively, these results suggest that the MUPP1-Wdpcp interaction could modulate actin cytoskeleton dynamics and polarized cell migration.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Radiation Oncology Journal
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• v.24 no.4
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• pp.255-262
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• 2006

$\underline{Purpose}$: To evaluate the pathological prognostic factors related to local recurrence after radical surgery and adjuvant radiation therapy in advanced rectal cancer. $\underline{Materials\;and\;Methods}$: Fifty-four patients with advanced rectal cancer who were treated with radical surgery followed by adjuvant radiotherapy and chemotherapy between February 1993 and December 2001 were enrolled in this study. Among these patients, 14 patients experienced local recurrence. Tissue specimens of the patients were obtained to determine pathologic parameters such as histological grade, depth of invasion, venous invasion, lymphatic invasion, neural invasion and immunohistopathological analysis for expression of p53, Ki-67, c-erb, ezrin, c-met, phosphorylated S6 kinase, S100A4, and HIF-1 alpha. The correlation of these parameters with the tumor response to radiotherapy was statistically analyzed using the chi-square test, multivariate analysis, and the hierarchical clustering method. $\underline{Results}$: In univariate analysis, the histological tumor grade, venous invasion, invasion depth of the tumor and the over expression of c-met and HIF-1 alpha were accompanied with radioresistance that was found to be statistically significant. In multivariate analysis, venous invasion, invasion depth of tumor and over expression of c-met were also accompanied with radioresistance that was found to be statistically significant. By analysis with hierarchical clustering, the invasion depth of the tumor, and the over expression of c-met and HIF-1 alpha were factors found to be related to local recurrence. Whereas 71.4% of patients with local recurrence had 2 or more these factors, only 27.5% of patients without local recurrence had 2 or more of these factors. $\underline{Conclusion}$: In advanced rectal cancer patients treated by radical surgery and adjuvant chemo-radiation therapy, the poor prognostic factors found to be related to local recurrence were HIF-1 alpha positive, c-met positive, and an invasion depth more than 5.5 mm. A prospective study is necessary to confirm whether these factors would be useful clinical parameters to measure and predict a radio-resistance group of patients.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Clinical and Experimental Pediatrics
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• v.46 no.7
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• pp.695-701
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• 2003

Purpose : It had been suggested that pain arising from deep somatic body regions influences neural activity within periaqueductal gray(PAG) of midbrain via distinct spinal pathways. Aspirin is one of the popular non-steroidal anti-inflammatory drugs used in the management of pain. Fos expression was used as a marker for neuronal activity throughout central neurons following painful peripheral stimulation. This study was prepared to investigate changes of c-Fos immunoreactivity in midbrain by deep pain and effects of aspirin. Methods : Male Sprague-Dawley rats were injected with 0.1 mL of 5% formalin in the plantar muscle of the right hindpaw. For experimental group II, aspirin was injected intravenously before injection of formalin. An aspirin-untreated group was utilized as group I. Rats were sacrificed at 0.5, 1, 2, 6 and 24 hours after formalin injection. Rat's brains were removed and sliced in rat brain matrix. Brain slices were coronally sectioned at interaural 1.00-1.36 mm. Serial sections were immunohistochemically reacted with polyclonal c-Fos antibody. The numbers of c-Fos protein immunoreactive neurons in ventrolateral periaqueductal gray(VLPAG) and dorsomedial periaqueductal gray(DMPAG) were counted and analyzed statistically with Mann-Whitney U tests. Results : Higher numbers of c-Fos protein immunoreactive neurons were found in VLPAG. In both VLPAG and DMPAG of formalin-treated group, the numbers of c-Fos protein immunoreactive neurons were significantly higher at all time points than the formalin-untreated group, which peaked at two hours. The numbers of c-Fos immunoreactive neuron of the aspirin-treated group were less compared to the aspirin-untreated group at each time point. Conclusion : These results provide some basic knowledge in understanding the mechanism of formalin-induced deep somatic pain and the effects of aspirin.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.109-113
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• 1997

Purpose: To evaluate the differences of functional imaging patterns between conventional spoiled gradient echo (SPGR) and echo planar imaging (EPI) methods in cerebral motor cortex activation. Materials and Methods: Functional MR imaging of cerebral motor cortex activation was examined on a 1.5T MR unit with SPGR (TRfrE/flip angle=50ms/4Oms/$30^{\circ}$, FOV=300mm, matrix $size=256{\times}256$, slice thickness=5mm) and an interleaved single shot gradient echo EPI (TRfrE/flip angle = 3000ms/40ms/$90^{\circ}$, FOV=300mm, matrix $size=128{\times}128$, slice thickness=5mm) techniques in five male healthy volunteers. A total of 160 images in one slice and 960 images in 6 slices were obtained with SPGR and EPI, respectively. A right finger movement was accomplished with a paradigm of an 8 activation/ 8 rest periods. The cross-correlation was used for a statistical mapping algorithm. We evaluated any differences of the time series and the signal intensity changes between the rest and activation periods obtained with two techniques. Also, the locations and areas of the activation sites were compared between two techniques. Results: The activation sites in the motor cortex were accurately localized with both methods. In the signal intensity changes between the rest and activation periods at the activation regions, no significant differences were found between EPI and SPGR. Signal to noise ratio (SNR) of the time series data was higher in EPI than in SPGR by two folds. Also, larger pixels were distributed over small p-values at the activation sites in EPI. Conclusions: Good quality functional MR imaging of the cerebral motor cortex activation could be obtained with both SPGR and EPI. However, EPI is preferable because it provides more precise information on hemodynamics related to neural activities than SPGR due to high sensitivity.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.