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Shuichi Shimada,Hideo Saito,Yoshihide Kawasaki,Shinichi Yamashita,Hisanobu Adachi,Narihiko Kakoi,Takashige Namima,Masahiko Sato,Atsushi Kyan,Koji Mitsuzuka,Akihiro Ito,Takuhiro Yamaguchi,Yoichi Arai 대한비뇨의학회 2017 Investigative and Clinical Urology Vol.58 No.4
Purpose: To evaluate renal function 1 year after radical nephrectomy (RN) for renal cell carcinoma, the preoperative predictors of postnephrectomy renal function were investigated by sex, and equations to predict the estimated glomerular filtration rate (eGFR) 1 year after RN were developed. Materials and Methods: A total of 525 patients who underwent RN between May 2007 and August 2011 at Tohoku University Hospital and its affiliated hospitals were prospectively evaluated. Overall, 422 patients were analyzed in this study. Results: Independent preoperative factors associated with postnephrectomy renal function were different in males and females. Preoperative eGFR, age, tumor size, and body mass index (BMI) were independent factors in males, while tumor size and BMI were not independent factors in females. The equations developed to predict eGFR 1 year after RN were: Predicted eGFR in males (mL/min/1.73 m2)=27.99−(0.196×age)+(0.497×eGFR)+(0.744×tumor size)−(0.339×BMI); and predicted eGFR in females=44.57−(0.275×age)+(0.298×eGFR). The equations were validated in the validation dataset (R2=0.63, p<0.0001 and R2=0.31, p<0.0001, respectively). Conclusions: The developed equations by sex enable better prediction of eGFR 1 year after RN. The equations will be useful for preoperative patient counseling and selection of the type of surgical procedure in elective partial or RN cases.
Asymptotic System Indentification Method Based on Particle Swarm Optimization
Hideo Muroi,Shuichi Adachi 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In general, structure of a system to be identified is unknown for users a priori. This makes the model complex and high order structure. In this paper, we introduce the asymptotic method. (ASYM) to deal with the problem. ASYM calculates a high-order model using the well-known least squares method, then reduces the identified model to a simple one. For this model reduction, various model reduction techniques such as balanced realization and output error reduction were developed. In this paper, a new method to reduce the high-order model using the particle swarm optimization in the frequency domain is proposed. Effectiveness of the proposed method is examined through numerical examples.
Resonance Frequency Estimation of Time-Series Data by Subspace Method
Tomoko Hirao,Shuichi Adachi 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper studies an estimation problem of a dominant resonance frequency from time-series data. We proposed an estimatio method which incorporates system identification technique into time-series analysis. However, this method has a problem that the estimated resonance frequency is biased. In this paper, a new method which uses subspace method is proposed based on time-series data. The key idea of this method is to use an auto-covariance function of the time-series data instead of impulse response or ordinary input-output data. Hankel matrix of the time-series is consturcted by the auto-convariance function. Then, subspace method is applied to the Hankel matrix, and the resonance frequency can be calculated. Effectiveness of the method is examined through numerical examples.
Estimation of Protein Networks for Cell Cycle in Yeast Based on Least–Squares Method
Noriko Takahashi,Takehito Azuma,Shuichi Adachi 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, a new approach to estimation problems of protein networks is proposed, based on an idea of systems biology. Generally, it is difficult to estimate complicated networks by molecular biology. However, it will be possible to solve the difficulty by using the proposed approach. This approach is based on system identification using least?quares method for state?pace models. Moreover, the proposed approach is applied to an estimation problem of protein networks for cell cycle in yeast. Nine proteins are selected from 48 proteins concerned with cell cycle in yeast, then 9?imensional protein networks are estimated.