A Case Study of Growth of Community Revitalization Movement in Chizu, Tottori Prefecture, Japan

Kaso is the deterioration of community infrastructure resulting from the migration of young people from rural areas to urban centers. It occurred in Japan during the period of rapid economic growth after World War II. Due to the conservative nature of remaining rural residents, community revitalization is often difficult. This paper uses the theory of social norms to analyze a case of successful community revitalization resulting from community empowerment

Kaplan-Meier curves for overall survival for MDS patients according to the HSC-CMP score(a, b), which was made by CCAM, and the well-established classifications (c–g).

<p>Patients were stratified into 2 to 6 groups by the followings: (a) HSC-CMP score, two groups (1: and 2: ). (b) HSC-CMP score, three groups (1: , 2: , and 3: ). (c) Cytopenia score. (d) Blast score. (e) Karyotype score. (f) IPSS score. (g) Disease classification. P values are by log-rank test.</p

Kaplan-Meier curves for time to AML transformation for MDS patients according to the HSC-CMP score(a, b), which was made by CCAM, and the well-established classifications (c–g).

<p>Patients were stratified into 2 to 6 groups by the followings: (a) HSC-CMP score, two groups (1: and 2: ). (b) HSC-CMP score, three groups (1: , 2: , and 3: ). (c) Cytopenia score. (d) Blast score. (e) Karyotype score. (f) IPSS score. (g) Disease classification. P values are by log-rank test.</p

The features of CCAM and other univariate and multivariate/multidimensional methods for microarray analysis.

<p>The features of CCAM and other univariate and multivariate/multidimensional methods for microarray analysis.</p

Summary of microarray datasets used in this study.

<p>Summary of microarray datasets used in this study.</p

Schematic representation of CCAM and instructions for its practical usage.

<p>(a) Overview of CCAM. See Methods for the full instructions. (b) Schematic representation of the decomposition of variation (<i>inertia</i>). (1) Total inertia is divided into constrained and unconstrained inertias by regression of main data on explanatory variables. (2) Constrained inertia is distributed to different axes by singular value decomposition.</p