Participation in Self-Collection of Maternal and Infant DNA in a Case-Control Study on Clubfoot

National Institute of Child Health and Human Development (HD051804

Symptomatic central nervous system tuberculoma, a case report in the United States and literature review

Intracranial tuberculoma is one of the rare central nervous system manifestations of Mycobacterium tuberculosis (MTB), seen in only 1% of tuberculosis patients. It can manifest as single or multiple lesions, most commonly located in the frontal and parietal lobes. Clinical features are similar to any space-occupying lesion in the brain and can present in the absence of MTB symptoms in other parts of the body. In this article, a 69-year-old immunocompetent man, with history of treated latent tuberculosis infection (LTBI) was reported. He presented with multiple joint arthralgias, weight loss, odd behavior, forgetfulness, intermittent fevers and syncope. Brain imaging revealed numerous enhancing intra-parenchymal lesions in cerebral and cerebellar hemispheres. Patient was successfully treated with anti-tuberculosis medications and corticosteroids, with clinical improvement on future follow ups. High clinical suspicion for tuberculoma as a differential diagnosis of any brain lesion, even in immunocompetent patients in low MTB prevalence countries, can result in early diagnosis and successful clinical outcomes

Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions

In this paper, we study the problem of compressed sensing using binary measurement matrices and $\ell_1$-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices. We establish sufficient conditions for a column-regular binary matrix to satisfy the robust null space property (RNSP) and show that the associated sufficient conditions % sparsity bounds for robust sparse recovery obtained using the RNSP are better by a factor of $(3 \sqrt{3})/2 \approx 2.6$ compared to the sufficient conditions obtained using the restricted isometry property (RIP). Next we derive universal \textit{lower} bounds on the number of measurements that any binary matrix needs to have in order to satisfy the weaker sufficient condition based on the RNSP and show that bipartite graphs of girth six are optimal. Then we display two classes of binary matrices, namely parity check matrices of array codes and Euler squares, which have girth six and are nearly optimal in the sense of almost satisfying the lower bound. In principle, randomly generated Gaussian measurement matrices are "order-optimal". So we compare the phase transition behavior of the basis pursuit formulation using binary array codes and Gaussian matrices and show that (i) there is essentially no difference between the phase transition boundaries in the two cases and (ii) the CPU time of basis pursuit with binary matrices is hundreds of times faster than with Gaussian matrices and the storage requirements are less. Therefore it is suggested that binary matrices are a viable alternative to Gaussian matrices for compressed sensing using basis pursuit. \end{abstract}Comment: 28 pages, 3 figures, 5 table