A county-level analysis of persons living with HIV in the southern United States.

This study uses county-level surveillance data to systematically analyze geographic variation and clustering of persons living with diagnosed HIV (PLWH) in the southern United States in 2011. Clusters corresponding to large metropolitan areas - including Miami, Atlanta, and Baltimore - had HIV prevalence rates higher (p < .001) than the regional rate. Regression analysis within the counties included in these clusters determined that race was a significant indicator for PLWH. These results provide a general picture of the distribution of PLWH in the southern United States at the county level and provide insights for identifying local geographic areas with a high number of PLWH, as well as subpopulations that may have an increased risk of infection.

Higher-order wide-angle split-step spectral method for non-paraxial beam propagation.

We develop a higher-order method for non-paraxial beam propagation based on the wide-angle split-step spectral (WASSS) method previously reported [Clark and Thomas, Opt. Quantum. Electron., 41, 849 (2010)]. The higher-order WASSS (HOWASSS) method approximates the Helmholtz equation by keeping terms up to third-order in the propagation step size, in the Magnus expansion. A symmetric exponential operator splitting technique is used to simplify the resulting exponential operators. The HOWASSS method is applied to the problem of waveguide propagation, where an analytical solution is known, to demonstrate the performance and accuracy of the method. The performance enhancement gained by implementing the HOWASSS method on a graphics processing unit (GPU) is demonstrated. When highly accurate results are required the HOWASSS method is shown to be substantially faster than the WASSS method.

Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity.

Diffuse optical tomography (DOT) is an important functional imaging modality in clinical diagnosis and treatment. As the number of wavelengths in the acquired DOT data grows, it becomes very challenging to reconstruct diffusion and absorption coefficients of tissue, i.e., a DOT image. In this paper, we consider the hyperspectral DOT (hyDOT) inverse problem as a multiple-measurement vector (MMV) problem by exploiting the joint sparsity of the images to be reconstructed. Then we propose a fast stochastic greedy algorithm based on the MMV stochastic gradient matching pursuit (MStoGradMP) and the mini-batching technique. Numerical results show that the proposed algorithm can achieve higher reconstruction accuracy with significantly reduced running time than the related gradient descent method with sparsity regularization.