Language, respect and the power of protest: Linguistic stereotyping and people with disabilities in Japan

Discriminatory language became an important social issue in the west in the late twentieth century, when debates on political correctness and minority rights focused largely on the issue of respect in language. Japan is often criticized for having made only token attempts to address this issue. This paper investigates how one marginalized group—people with disabilities—has dealt with discriminatory and disrespectful language. The debate has been played out in four public spaces: the media, the law, literature, and the Internet. The paper discusses the kind of language, which has generated protest, the empowering strategies of direct action employed to combat its use, and the response of the media, the bureaucracy, and the literati. Government policy has not kept pace with social change in this area; where it exists at all, it is often contradictory and far from clear. I argue that while the laws were rewritten primarily as a result of external international trends, disability support groups achieved domestic media compliance by exploiting the keen desire of media organizations to avoid public embarrassment. In the absence of language policy formulated at the government level, the media effectively instituted a policy of self-censorship through strict guidelines on language use, thereby becoming its own best watchdog. Disability support groups have recently enlisted the Internet as an agent of further empowerment in the ongoing discussion of the issue.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (