Selective loss of dopaminergic nigro-striatal neurons in brains of Atm-deficient mice

Ataxia-telangiectasia (AT) is a human disease caused by mutations in the ATM gene. The neural phenotype of AT includes progressive cerebellar neurodegeneration, which results in ataxia and eventual motor dysfunction. Surprisingly, mice in which the Atm gene has been inactivated lack distinct behavioral ataxia or pronounced cerebellar degeneration, the hallmarks of the human disease. To determine whether lack of the Atm protein can nonetheless lead to structural abnormalities in the brain, we compared brains from male Atm-deficient mice with male, age-matched controls. Atm-deficient mice exhibited severe degeneration of tyrosine hydroxylase-positive, dopaminergic nigro-striatal neurons, and their terminals in the striatum. This cell loss was accompanied by a large reduction in immunoreactivity for the dopamine transporter in the striatum. A reduction in dopaminergic neurons also was evident in the ventral tegmental area. This effect was selective in that the noradrenergic nucleus locus coeruleus was normal in these mice. Behaviorally, Atm-deficient mice expressed locomotor abnormalities manifested as stride-length asymmetry, which could be corrected by peripheral application of the dopaminergic precursor l-dopa. In addition, these mice were hypersensitive to the dopamine releasing drug d-amphetamine. These results indicate that ATM deficiency can severely affect dopaminergic neurons in the central nervous system and suggest possible strategies for treating this aspect of the disease.

Computer speech synthesis: its status and prospects.

Computer speech synthesis has reached a high level of performance, with increasingly sophisticated models of linguistic structure, low error rates in text analysis, and high intelligibility in synthesis from phonemic input. Mass market applications are beginning to appear. However, the results are still not good enough for the ubiquitous application that such technology will eventually have. A number of alternative directions of current research aim at the ultimate goal of fully natural synthetic speech. One especially promising trend is the systematic optimization of large synthesis systems with respect to formal criteria of evaluation. Speech recognition has progressed rapidly in the past decade through such approaches, and it seems likely that their application in synthesis will produce similar improvements.