修正を加えたImPACTソフトウェアとICRP publication110標準ファントムを利用した臓器吸収線量、実効線量の評価

藤田保健衛生大

Mental Task Recognition by EEG Signals: A Novel Approach with ROC Analysis

Electroencephalogram or electroencephalography (EEG) has been widely used in medical fields and recently in cognitive science and brain-computer interface (BCI) research. To distinguish metal tasks such as reading, calculation, motor imagery, etc., it is generally to extract features of EEG signals by dimensionality reduction methods such as principle component analysis (PCA), linear determinant analysis (LDA), common spatial pattern (CSP), and so on for classifiers, for example, k-nearest neighbor method (kNN), kernel support vector machine (SVM), and artificial neural networks (ANN). In this chapter, a novel approach of feature extraction of EEG signals with receiver operating characteristic (ROC) analysis is introduced

Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting

As a kind of efficient nonlinear function approximators, artificial neural networks (ANN) have been popularly applied to time series forecasting. The training method of ANN usually utilizes error back-propagation (BP) which is a supervised learning algorithm proposed by Rumelhart et al. in 1986; meanwhile, authors proposed to improve the robustness of the ANN for unknown time series prediction using a reinforcement learning algorithm named stochastic gradient ascent (SGA) originally proposed by Kimura and Kobayashi for control problems in 1998. We also successfully use a deep belief net (DBN) stacked by multiple restricted Boltzmann machines (RBMs) to realized time series forecasting in 2012. In this chapter, a state-of-the-art time series forecasting system that combines RBMs and multilayer perceptron (MLP) and uses SGA training algorithm is introduced. Experiment results showed the high prediction precision of the novel system not only for benchmark data but also for real phenomenon time series data

Enrichment of Spermatogonial Stem Cells using Side Population in Teleost

Spermatogenesis originates from a small population of spermatogonial stem cells; this population can maintain continuous sperm production throughout the life of fish via self-renewal and differentiation. Despite their biological importance, spermatogonial stem cells are not thoroughly characterized because they are difficult to distinguish from their progeny 5 cells that become committed to differentiation. We previously established a novel technique for germ cell transplantation to identify spermatogonial stem cells based on their colonizing activity and their ability to initiate donor-derived gametogenesis in the rainbow trout (Oncorhynchus mykiss). Although spermatogonial stem cells can be retrospectively identified after transplantation, there is currently no technique to prospectively enrich for or purify spermatogonial stem cells. Here, we describe a method for spermatogonial stem-cell enrichment using side-population. With optimized Hoechst 33342 staining conditions, we successfully identified side-population cells among type A spermatogonia. Side-population cells were transcriptomically and morphologically distinct from non-side-population cells. To functionally determine whether the transplantable spermatogonial stem cells were enriched in the side-population fraction, we compared the colonization activity of side-population cells with that of non-side-population cells. Colonization efficiency was significantly higher with side-population cells than with non-side-population cells or with total type A spermatogonia. In addition, side-population cells could produce billions of sperm in recipient. These results indicated that transplantable spermatogonial stem cells were enriched in the side-population fraction. This method will provide biological information that may advance our understanding of spermatogonial stem 20 cells in teleosts. Additionally, this technique will increase the efficiency of germ-cell transplantation used in surrogate broodstock technology