Ecology of willow in the Arctic for reconstruction of Indigirka river condition and its tributaries.

GRENE北極気候変動研究事業研究成果報告会日時：2016年3月4日（金）　14：30-16：30 (Core time 14：.30-15：40)会場：国立国語研究所　2Fホワイ

Cell Dynamics in Three-dimensional (3D) Culture Environments

A three-dimensional (3D) cell culture system provides an effective platform to study cell dynamics in in vivo-mimicking conditions and thus plays an important role in understanding cell biology, organ function, and disease model. This dissertation investigates cell dynamics in a variety of 3D environments including solid and liquid matrix. We study cell dynamics in 3D hydrogel microparticles and show that cells exhibit significant differences with that from 2D monolayer culture, including cell cycle, survival, morphology and the sensitivity to inflammation. We further develop a 3D printed cell-laden hybrid hydrogel construct to investigate colon cancer cell dynamics in physiologically relevant bowel environment. Such system enables in vivo-mimicking cell environment and offers an effective platform to uncover inflammation mechanisms in bowel area. Long-term cell culture in 3D solid matrix, however, is challenged by nutrient delivering problems. We thus engineer a novel leaf-inspired artificial microvascular network to support the long-term cell growth. Apart from the 3D solid environment, we also investigate cell dynamics cultured in 3D fluidic environment and study the regulatory roles of shear stress in circulating cancer cells. Cancer cells are circulated in suspension for mimicking cancer metastasis through blood stream and a previously unrecognized role of circulatory shear stress in regulating cancer cell dynamics is revealed. The research presented in this dissertation introduces a comprehensive study of cell dynamics in 3D environments and paves a new avenue to establish physiologically relevant model systems for tissue engineering and artificial functional organs

Computational fluid dynamics simulation of fluidized bed polymerization reactors

In this research, a CFD algorithm for simulation of fluidized bed polymerization reactors is described. In order to properly model the evolution of a polydisperse solid phase, population balance equation (PBE) must be solved along with other transport equations. A novel approach---DQMOM is applied to polydisperse fluidized bed to simulate particle aggregation and breakage in the reactors. Two different aggregation and breakage kernels are tested and the performance of the DQMOM approximation with different numbers of nodes are compared. Results show that the approach is very effective in modeling solid segregation and elutriation and in tracking the evolution of the PSD, even though it requires only a small number of scalars. After successfully developed DQMOM-multi-fluid CFD model, the multi-fluid model is validated with available experiments and discrete particle simulation (DPS). The results show good agreements with experiment data for binary system and DPS reults, and the simulations can describe segregation and mixing behavior in the fluidized bed;After the model development and validation, 2D and 3D simulations are conducted for a pilot-scale polymerization fluidized bed at operating conditions. Significant differences are observed between 2D and 3D simulations. The results shows that, for an industrial-scale fluidized bed, only 3D simulations are able to match the statics (bed height and pressure drop) and the dynamics (pressure power spectra) properties of the bed. The residence time for a polyethylene pilot reactor is on the order of hours, and the time scale for the fluid dynamics in the bed is on the seconds. It is impossible to run a three-dimensional simulation for hours using current CFD codes. Due to the time scale problem, a chemical reaction engineering model based on the age of particles is combined with multi-fluid model to initialize the fluidized bed to a steady state. Direct quadrature method of moments (DQMOM) is used to simulate the particle size distribution in the bed. The hot spots in the fluidized bed are also investigated using CFD simulations

Support vector machines for classification.

Introduction: A classifier is a hyperplane that separates data into different categories or classes. A trainable classifier is a classifier that may make its performance better in response to information it receives and tasks it takes. Training is the process by which the parameters of a function are adjusted in response to categories or classes. A training procedure is a training algorithm that implements the training process