• Development and Reproduction
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• v.10 no.3
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• pp.159-168
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• 2006

Rodents and many other mammals have two chemosensory systems that mediate responses to pheromones, the main and accessory olfactory system, MOS and AOS, respectively. The chemosensory neurons associated with the MOS are located in the main olfactory epithelium, while those associated with the AOS are located in the vomeronasal organ(VNO). Pheromonal odorants access the lumen of the VNO via canals in the roof of the mouth, and are largely thought to be nonvolatile. The main pheromone receptor proteins consist of two superfamilies, V1Rs and V2Rs, that are structurally distinct and unrelated to the olfactory receptors expressed in the main olfactory epithelium. These two type of receptors are seven transmembrane domain G-protein coupled proteins(V1R with $G_{{\alpha}i2}$, V2R with $G_{0\;{\alpha}}$). V2Rs are co-expressed with nonclassical MHC Ib genes(M10 and other 8 M1 family proteins). Other important molecular component of VNO neuron is a TrpC2, a cation channel protein of transient receptor potential(TRP) family and thought to have a crucial role in signal transduction. There are four types of pheromones in mammalian chemical communication - primers, signalers, modulators and releasers. Responses to these chemosignals can vary substantially within and between individuals. This variability can stem from the modulating effects of steroid hormones and/or non-steroid factors such as neurotransmitters on olfactory processing. Such modulation frequently augments or facilitates the effects that prevailing social and environmental conditions have on the reproductive axis. The best example is the pregnancy block effect(Bruce effect), caused by testosterone-dependent major urinary proteins(MUPs) in male mouse urine. Intriguingly, mouse GnRH neurons receive pheromone signals from both odor and pheromone relays in the brain and may also receive common odor signals. Though it is quite controversial, recent studies reveal a complex interplay between reproduction and other functions in which GnRH neurons appear to integrate information from multiple sources and modulate a variety of brain functions.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Acupuncture Research
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• v.17 no.1
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• pp.43-54
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• 2000

Previously two papers dealing with YoungSu(against the meridian course and following the course of the meridian) Acupuncture were published by our group. Here we are reporting the further analysis of YoungSu and WonBang(by twisting and rotating the needle) acupuncture methods. It is very important to understand objectively the Qi variation induced by the reinforcing-reducing manipulation method in the acupuncture therapy. We decided the medical treatment by utilizing the PyongChi Method (a kind of method to figure out the way of treatment by observing the unbalanced state of five phases). The Qi variation in the meridian treated by YoungSu and WonBang, the recovery of five phases deviation were measured by choosing single acupoint instead of complex acupoints. By using Youngsu and WonBang, we increased or decreased the Qi of the phase which caused the unbalanced state. We observed whether the Qi of the treated meridian can be increased and if the state of unbalance can be recovered. To achieve the effect of reinforcing-reducing, we needed a correct choice of treating method and a selection of a proper meridian in advance. This study was carried out by adding another way of acupuncture from the previous paper. We discovered that the effects of reinforcing-reducing by each manipulation method could be superposed each other when two counteracting Youngsu and WonBang methods were treated at the left and the right side of human body which was correspondent with our previous paper. We found that the Qi variation of the treated meridian, which was induced by Youngsu and WonBang, was linearly proportional to the reduction of five phase deviations. The slope of Qi variation was almost similar (y = -0.413x - 0.138) as that of previous paper (y = -0.266x - 0.038, Y = -0.446x - 0.079). It is assumed that the addition of other basic methods on the top of reinforcing-reducing manipulation method would magnify the effect of acupuncture.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of Life Science
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• v.28 no.8
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• pp.992-998
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• 2018

Cancer cells grow in an environment composed of various components that supports tumor growth. Major cell types in the tumor microenvironment are fibroblast, endothelial cells and immune cells. All of these cells communicate with cancer cells. Among infiltrating immune cells as an abundant component of solid tumors, macrophages are a major component of the tumor microenvironment and orchestrates various aspects of immunity. The complex balance between pro-tumoral and anti-tumoral effects of immune cell infiltration can create a chronic inflammatory microenvironment essential for tumor growth and progression. Macrophages express different functional programs in response to microenvironmental signals, defined as M1 and M2 polarization. Tumor-associated macrophages (TAM) secret many cytokines, chemokines and proteases, which also promote tumor angiogenesis, growth, metastasis and immunosuppression. TAM have multifaceted roles in the development of many tumor types. TAM also interact with cancer stem cells. This interaction leads to tumorigenesis, metastasis, and drug resistance. TAM obtain various immunosuppressive functions to maintain the tumor microenvironment. TAM are characterized by their heterogeneity and plasticity, as they can be functionally reprogrammed to polarized phenotypes by exposure to cancer-related factors, stromal factors, infections, or even drug interventions. Because TAMs produce tumor-specific chemokines by the stimulation of stromal factors, chemokines might serve as biomarkers that reflect disease activity. The evidence has shown that cancer tissues with high infiltration of TAM are associated with poor patient prognosis and resistance to therapies. Targeting of TAM in tumors is considered a promising therapeutic strategy for anti-cancer treatment.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Korean Journal of Biological Psychiatry
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• v.4 no.1
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• pp.67-73
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• 1997

The changes of electroencephalogram(EEG) in patients with dementia are most commonly studied by analyzing power or magnitude in certain traditionally defined frequency bands. However because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to chaos theory, irregular signals of EEG can also result from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the correlation dimension. The authors have analyzed EEG epochs from three patients with dementia of Alzheimer type and three matched control subjects. The multichannel correlation dimension is calculated from EEG epochs consisting of 15 channels with 16,384 data points per channel. The results showed that patients with dementia of Alzheimer type had significantly lower correlation dimension than non-demented controls on 12 channels. Topographic analysis showed that the correlation dimensions were significantly lower in patients with Alzheimer's disease on frontal, temporal, central, and occipital head regions. These results show that brains of patients with dementia of Alzheimer type have a decreased complexity of electrophysiological behavior. We conclude that the nonlinear analysis such as calculating correlation dimension can be a promising tool for detecting relative changes in the complexity of brain dynamics.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Development and Reproduction
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• v.9 no.1
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• pp.1-5
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• 2005

The hypothalamo-pituitary-gonadal hormone axis is centrally controlled by a complex regulatory network of excitatory and inhibitory signals, that is dormant during infantile and juvenile periods and activated at puberty. The kisspeptins are the peptide products of the KiSS-1 gene and the endogenous agonists for the G protein-coupled receptor 54(GPR54). Although KiSS-1 was initially discovered as a metastasis suppressor gene, a recent evidence suggests the KiSS-1/GPR54 system is a key regulator of the reproductive system. Yet the actual role of the KiSS-1/GPR54 system in the neuroendocrine control of gonadotropin secretion remains largely unexplored, the system could be the first missing link in the reproductive hormonal axis. Central or peripheral administration of kisspeptin stimulates the hypothalamic-pituitary-gonadal axis, increasing circulating gonadotropin levels in rodents, sheep, monkey and human models. These effects appear likely to be mediated via the hypothalamic GnRH neuron system, although kisspeptins may have direct effects on the anterior pituitary gland. The loss of function mutations of the GPR54(GPR54-/-) have been associated with lack of puberty onset and idiopathic hypogonadotropic hypogonadism(IHH). So kisspeptin infusion may provide a novel mechanism for HPG axis manipulation in disorders of the reproductive system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.5647-5654
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• 2012

In this paper, we developed an electro impedance spectroscopy (EIS) device, which are primarily used for the analysis of fuel cells or batteries, to widen its coverage to the next generation super capacitor EDLC characterization. The developed system was composed of a signal generator that can generate various signal patterns, a potentiostatic generator, and a high speed digital filter for signal processing and measurement program. The developed system is portable, which is not only suitable laboratory use but also for mass production line. The special features of the system include a patterned output signal from 0.01 to 20 kHz, and a fast Fourier transform (FFT) analysis of current signals, both of which are acquired simultaneously. Our tests showed similar results after comparing the analysis from our newly-developed device showing the characteristics of EDLC complex impedance and the analysis from an equivalent impedance which was applied to an equivalent circuit. Now, we can expect a fast inspection time from the application of the present system to the super capacitor production line, based on time-varying changes in electrochemical impedance.