高齢マウスにおける心筋ペリリピン2過剰発現は脂肪心筋をきたし、コネキシン43のリモデリングから心房細動を誘発する

福井大学博士(医学)thesi

Tar and soot generation behaviors from ABS, PC and PE pyrolysis

In this study we performed a non-isothermal thermogravimetric analysis on three thermoplastics—ABS, PC and PE. The Coats and Redfern method (Nature 201:68–69, 1964) was then used to approximate the kinetic parameters of each material. In addition, we performed a series of pyrolysis experiments in a batch reactor, for each plastic. The experiments were performed over the temperature range of 600–1000 °C at a constant residence time. The liquid and solid products of the pyrolysis, were collected, separated and weighted. Those products were categorized as soot, tar and char (PC only), and their relative weight to initial sample weight (DAF) was plotted against the temperature. The tar measured was exclusively medium to high molecular weight (>80 g/mol). Results revealed that relative tar and soot production, for all three materials, first increases and then decreases with temperature increase. The maximum achieved tar yields for ABS, PC and PE were at 700, 650 and 800 °C, respectively; and the maximum soot yields were at 1000, 1000, 950 °C, respectively.journal articl

Modeling of Physical Phenomena by Probabilistic Cellular Automaton

A new approach of modeling of physica1 phenomena by using a probabilistic cellular automaton is proposed. Our model aims to reproduce a spatio-temporal black and white pattern (e.g., patterns obtained by Ising model, Diffusion Limited Aggregation model). Our model has three model parameters that characterize the nearest neighbor interaction. Optimization of the parameters is carried out by genetic algorithms. Two kinds of genetic algorithms are tested here: a classical one and more sophisticated one. Although convergence of the model parameters by sophisticated one is faster than classical one, the same optimum prameters is obtained by both algorithms.departmental bulletin pape

Gravier (2010) data set: Breast cancer study with 2 classes.

<p>Gravier (2010) data set: Breast cancer study with 2 classes.</p

Effect of data normalization on fuzzy clustering of DNA microarray data-3

<p><b>Copyright information:</b></p><p>Taken from "Effect of data normalization on fuzzy clustering of DNA microarray data"</p><p>BMC Bioinformatics 2006;7():134-134.</p><p>Published online 14 Mar 2006</p><p>PMCID:PMC1431564.</p><p>Copyright © 2006 Kim et al; licensee BioMed Central Ltd.</p> and each cluster contains 50 genes; (b) SD3/90 contains nine overlapped patterns over 10 time points, and each cluster contains 10 genes

R source code.

This file contains the R functions that implement ALT-NSC and GEN-NSC. (ZIP)</p

Effect of data normalization on fuzzy clustering of DNA microarray data-0

<p><b>Copyright information:</b></p><p>Taken from "Effect of data normalization on fuzzy clustering of DNA microarray data"</p><p>BMC Bioinformatics 2006;7():134-134.</p><p>Published online 14 Mar 2006</p><p>PMCID:PMC1431564.</p><p>Copyright © 2006 Kim et al; licensee BioMed Central Ltd.</p>by the Z, R or Lowess method, after FCM clustering using an value of 2 or the optimal value. The -axis indicates the sorted membership values for each gene (highest, second highest, etc.) and the -axis indicates the membership value. (a) serum data, (b) sporulation data and (c) yeast data

Three groups with class-imbalance (<i>π</i>₁ = 0.4, <i>π</i>₂ = 0.2, <i>π</i>₃ = 0.4), sparse block diagonal structure (<i>ρ</i> = 0.5) and moderate mean difference (<i>γ</i> = 0.5).

<p>Three groups with class-imbalance (<i>π</i>₁ = 0.4, <i>π</i>₂ = 0.2, <i>π</i>₃ = 0.4), sparse block diagonal structure (<i>ρ</i> = 0.5) and moderate mean difference (<i>γ</i> = 0.5).</p

Pomeroy (2002) data set: CNS study with 4 classes.

<p>Pomeroy (2002) data set: CNS study with 4 classes.</p

Two groups with sparse block diagonal structure (<i>ρ</i> = 0.5) and class-balance (<i>π</i>₁ = 0.5).

<p>Two groups with sparse block diagonal structure (<i>ρ</i> = 0.5) and class-balance (<i>π</i>₁ = 0.5).</p