核モーメントを通じた不安定核の構造の研究

Tohoku University酒見 泰寛論

Five-year plan against cerebrovascular and cardiovascular diseases

Cerebrovascular diseases including stroke and cardiovascular diseases are the leading causes of death among people more than 75 years old in Japan. The major causes of the need for long-term care in Japan are also cerebrovascular disease and cardiovascular disease, which together account for more than one-fifth of the total. Medical expenses for both cerebrovascular and cardiovascular diseases account for 20% of the total, which is the highest by injury/illness classification. Five-year plan against cerebrovascular and cardiovascular diseases were published for the purpose of notifying the importance of overcoming these diseases. The 2 main goals of the five-year plan against cerebrovascular disease and cardiovascular disease are to extend healthy life expectancy and to decrease age-adjusted mortality of cerebrovascular and cardiovascular diseases. The five-year plan against cerebrovascular disease and cardiovascular disease includes 5 major measures : developing human resources engaged in cerebrovascular and cardiovascular diseases ; enhancing service provision systems related to health, medical care, and welfare services ; promoting registration project regarding cerebrovascular and cardiovascular diseases ; spreading awareness of prevention measures and accurate information on cerebrovascular and cardiovascular diseases ; and promoting research on cerebrovascular and cardiovascular diseases. The measures are important for all patients, and all the promotions are needed to achieve the plan’s goals. Here, we describe the outline of the five-year plan against cerebrovascular and cardiovascular diseases

Shortcut of the photosynthetic electron transfer in a mutant lacking the reaction center-bound cytochrome subunit by gene disruption in a purple bacterium, Rubrivivax gelatinosus

AbstractA mutant lacking the reaction center-bound cytochrome subunit was constructed in a purple photosynthetic bacterium, Rubrivivax gelatinosus IL144, by inactivation of the cytochrome gene. Photosynthetic growth of the C244 mutant strain occurred at approximately half the rate of the wild-type strain. Although mutagenesis resulted in a greatly reduced amount of membrane-bound cytochromes c, illumination induced cyclic electron transfer and the generation of membrane potential in the mutant as observed in the wild-type strain. These findings are consistent with previous observations that the cytochrome subunit is absent in the reaction center complex in some species of purple bacteria and that the biochemical removal of the subunit did not significantly affect the in vitro electron transfer from the soluble cytochrome c to the photosynthetic reaction center. These results suggest that the cytochrome subunit in purple bacteria is not essential for photosynthetic electron transfer and growth, even in those species generally containing the subunit

Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization

The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space. Usually, such design relies on the prior definition of design parameters and places restrictions on synthesizing novel shapes. In this work, we propose a data-driven shape encoding and generating method, which automatically learns representations from existing airfoils and uses the learned representations to generate new airfoils. The representations are then used in the optimization of synthesized airfoil shapes based on their aerodynamic performance. Our model is built upon VAEGAN, a neural network that combines Variational Autoencoder with Generative Adversarial Network and is trained by the gradient-based technique. Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm. Our experiments show that the learned features encode shape information thoroughly and comprehensively without predefined design parameters. By interpolating/extrapolating feature vectors or sampling from Gaussian noises, the model can automatically synthesize novel airfoil shapes, some of which possess competitive or even better aerodynamic properties comparing with training airfoils. By optimizing shape on learned features via a genetic algorithm, synthesized airfoils can evolve to have specific aerodynamic properties, which can guide designing aerodynamic products effectively and efficiently

Heuristic-based Incremental Probabilistic Roadmap for Efficient UAV Exploration in Dynamic Environments

Autonomous exploration in dynamic environments necessitates a planner that can proactively respond to changes and make efficient and safe decisions for robots. Although plenty of sampling-based works have shown success in exploring static environments, their inherent sampling randomness and limited utilization of previous samples often result in sub-optimal exploration efficiency. Additionally, most of these methods struggle with efficient replanning and collision avoidance in dynamic settings. To overcome these limitations, we propose the Heuristic-based Incremental Probabilistic Roadmap Exploration (HIRE) planner for UAVs exploring dynamic environments. The proposed planner adopts an incremental sampling strategy based on the probabilistic roadmap constructed by heuristic sampling toward the unexplored region next to the free space, defined as the heuristic frontier regions. The heuristic frontier regions are detected by applying a lightweight vision-based method to the different levels of the occupancy map. Moreover, our dynamic module ensures that the planner dynamically updates roadmap information based on the environment changes and avoids dynamic obstacles. Simulation and physical experiments prove that our planner can efficiently and safely explore dynamic environments