Energy landscape analysis of neuroimaging data

Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analyzed, and the data length.Comment: 22 pages, 4 figures, 1 tabl

Study on Mechanism of Caisson Type Sea Wall Movement During Earthquakes

Model vibration tests under gravity and centrifuge model vibration tests in 50G were performed to investigate the behavior of caisson type sea mill with reclaimed ground below and behind the caisson. In the tests, sliding of caisson occurred only during excitation, which indicates that it is impossible to predict the displacement of caisson and the deformation of back-fill ground without taking account of both inertia force of caisson and dynamic earth pressure. As for the dynamic earth pressure acts on the caisson, it was found that when input acceleration is small, the dynamic earth pressure seems to restrain the movement of caisson and the excess pore water pressure hardly occurs. On the other hand, when input acceleration is large enough to cause liquefaction, the dynamic earth pressure seems to promotes the movement of caisson

CLDN15 is a novel diagnostic marker for malignant pleural mesothelioma

博士（医学）福島県立医科大

Experimental and numerical analysis of developer flow in a developing machine

Behaviors of developers flowing in a simplified developing machine was analyzed by evaluating torques of mixers in both experiments and numerical simulations in this research. In spherical and non-cohesive particles, the simulation result is in good agreement in the experimental result by adjusting the coefficient of friction. In nonspherical or cohesive particles, the simulation result can coincide with the experimental result by adjusting the coefficient of friction and Hamaker constant