Analyses of the BASIS-24 supported its reliability and validity for assessing mental health status from the patient's perspective.

Increasing racial and ethnic diversity calls for mental health assessment instruments that are appropriate, reliable, and valid for the wide range of cultures that comprise the current US population. However, most assessment instruments have not been tested on diverse samples. This study assessed psychometric properties and sensitivity to change of the revised Behavior and Symptom Identification Scale (BASIS-24) among the three largest race/ethnicity groups in the USA: Whites, African-Americans, and Latinos. BASIS-24 assessments were obtained for 2436 inpatients and 2975 outpatients treated at one of 27 mental health and/or substance abuse programs. Confirmatory factor analysis and several psychometric tests supported the factor structure, reliability, concurrent validity, and sensitivity of the instrument within each race/ethnicity group, although discriminant validity may be weaker for African-Americans and Latinos than for Whites. Further research is needed to test and validate assessment instruments with other race/ethnicity groups.

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Image restoration including deconvolution techniques offers a powerful tool to improve resolution in images and to extract information on the multiscale structure stored in astronomical observations. We present a new method for statistical deconvolution, which we call expectation through Markov Chain Monte Carlo (EMC2). This method is designed to remedy several shortfalls of currently used deconvolution and restoration techniques for Poisson data. We use a wavelet-like multiscale representation of the true image to achieve smoothing at all scales of resolution simultaneously, thus capturing detailed features in the image at the same time as larger scale extended features. Thus, this method smooths the image, while maintaining the ability to effectively reconstruct point sources and sharp features in the image. We use a principled, fully Bayesian model-based analysis, which produces extensive information about the uncertainty in the fitted smooth image, allowing assessment of the errors in the resulting reconstruction. Our method also includes automatic fitting of the multiscale smoothing parameters. We show several examples of application of EMC2 to both simulated data and a real astronomical X-ray source.

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