• Journal of Sasang Constitutional Medicine
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• v.27 no.2
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• pp.270-287
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• 2015

Objectives To evaluate the neuroprotective effects of modified Yuldahanso-tang (MYH) in a Parkinson's disease mouse model. Methods 1) Four groups (each of 8 rats per group) were used in this study. 2) The neuroprotective effect of MYH was examined in a Parkinson's disease mouse model. C57BL/6 mice treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, 30 mg/kg/day), intraperitoneal (i.p.) for 5 days. 3) The brains of 2 mice per group were removed and frozen at $-20^{\circ}C$, and the striatum-substantia nigra part was seperated. The protein volume was measured by Bradford method following Bio-Rad protein analyzing kit. Using mouse/Rat Dopamine ELISA Assay Kit. 4) The brains of 2 mice per group were separated and removed. TH-immunohistochemical was examined in the MPTP-induced Parkinson's disease mice to evaluate the neuroprotective effects of MYH on ST and SNpc. 5) Two mice out of each group were anesthetized and skulls were opened from occipital to frontal direction to take out the brains. The brains added TTC solution for 20 minutes for staining. 6) The water tank used for morris water maze test was filled with $28^{\circ}C$ water, and a round platform of 10cm in diameter was installed for mice to step on. The study was carried out once a day within 30 seconds, keep exercising to step on the platform in the pool. 7) The brains of two mice out of each group were fixed in 10% formaldehyde solution and paraphillin substance was infiltrated. They were fragmented by microtome, and observed under an optical microscope after Hematoxylin & Eosin staining. 8) A round acrylic cylinder with its upper side open was filled with clean water and depressive mouse models were forced to swim for 15 minutes. After 24 hours the animals were put in the same equipment for 5 minutes and were forced to swim. 9) The convenient, simple, and accurate high-performance liquid chromatography (HPLC) method was established for simultaneous determination of Neurotransmitters in MPTP-MYH group. Results 1) MYH possess Dopamine cell protective effect on MPTP-induced injury in striatum and substantia nigra pars compacta. 2) MYH inhibits the loss of tyrosine hydroxylase-immunoreacitive (TH-IR) cells in the striatum and substantia nigra pars compacta on MPTP-induced injury in C57BL/6 mice. 3) MYH possesses improvement effect on MPTP-induced memory deterioration in C57BL/6 mice through the reduction of prolongated Sort of lost time by MPTP injection using the Morris water maze test. 4) MYH possesses hippocampal neuron protective effect on MPTP-induced injury in C57BL/6 mice. 5) MYH possesses improvement effect on MPTP-induced motor behaviour deficits and depression in C57BL/6 mice through the reduction of prolongated losing motion by MPTP injection using the Forced swimming test. 6) MYH increases serotonin product amount on MPTP-induced injury in C57BL/6 mice. Conclusions This experiment suggests that the neuroprotective effect of MYH is mediated by the increase in Dopamin, TH-ir cell, Hippocampus and Serotonin. Furthermore, MYH essential oil may serve as a potential preventive or therapeutic agent regarding Parkinson's disease.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.