This study demonstrates a link of gout, not hyperuricaemia, with a higher risk of death from all causes and cardiovascular diseases.

A new algorithm is proposed for robust principal component analysis with predefined sparsity patterns. The algorithm is then applied to separate the singing voice from the instrumen tal accompaniment using vocal activity information. To eval uate its performance, we construct a new publicly available iKala dataset that features longer durations and higher quality than the existing MIR-IK dataset for singing voice separation.Part of it will be used in the MIREX Singing Voice Separa tion task. Experimental results on both the MIR-IK dataset and the new iKala dataset confirmed that the more informed the algorithm is, the better the separation results are.Index Terms-Low-rank and sparse decomposition, singing voice separation, informed source separation

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We address an important issue of fully low-cost and low-complex video compression for use in resource-extremely limited sensors/devices. Conventional motion estimation-based video compression or distributed video coding (DVC) techniques all rely on the high-cost mechanism, namely, sensing/sampling and compression are disjointedly performed, resulting in unnecessary consumption of resources. That is, most acquired raw video data will be discarded in the (possibly) complex compression stage. In this paper, we propose a dictionary learning-based distributed compressive video sensing (DCVS) framework to "directly" acquire compressed video data. Embedded in the compressive sensing (CS)-based single-pixel camera architecture, DCVS can compressively sense each video frame in a distributed manner. At DCVS decoder, video reconstruction can be formulated as an l 1minimization problem via solving the sparse coefficients with respect to some basis functions. We investigate adaptive dictionary/basis learning for each frame based on the training samples extracted from previous reconstructed neighboring frames and argue that much better basis can be obtained to represent the frame, compared to fixed basis-based representation and recent popular "CS-based DVC" approaches without relying on dictionary learning.