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Sorting is a very important task in computer science and becomes a critical operation for programs making heavy use of sorting algorithms. General-purpose computing has been successfully used on Graphics Processing Units GPUs to parallelize some sorting algorithms. Two GPU-based implementations of the quicksort were presented in literature: the GPU-quicksort, a compute-unified device architecture CUDA iterative implementation, and the CUDA dynamic parallel CDP quicksort, a recursive implementation provided by NVIDIA Corporation. We propose CUDA-quicksort an iterative GPU-based implementation of the sorting algorithm. CUDA-quicksort has been designed starting from GPU-quicksort. Unlike GPU-quicksort, it uses atomic primitives to perform inter-block communications while ensuring an optimized access to the GPU memory. Experiments performed on six sorting benchmark distributions show that CUDA-quicksort is up to four times faster than GPU-quicksort and up to three times faster than CDP-quicksort. An in-depth analysis of the performance between CUDA-quicksort and GPU-quicksort shows that the main improvement is related to the optimized GPU memory access rather than to the use of atomic primitives. Moreover, in order to assess the advantages of using the CUDA dynamic parallelism, we implemented a recursive version of the CUDA-quicksort. Experimental results show that CUDA-quicksort is faster than the CDP-quicksort provided by NVIDIA, with better performance achieved using the iterative implementation. Copyright © 2015 John Wiley & Sons, Ltd.

Phosphodiesterase 11 (PDE11) is the latest isoform of the PDEs family to be identified, acting on both cyclic adenosine monophosphate and cyclic guanosine monophosphate. The initial reports of PDE11 found evidence for PDE11 expression in skeletal muscle, prostate, testis, and salivary glands; however, the tissue distribution of PDE11 still remains a topic of active study and some controversy. Given the sequence similarity between PDE11 and PDE5, several PDE5 inhibitors have been shown to cross-react with PDE11. Accordingly, many non-selective inhibitors, such as IBMX, zaprinast, sildenafil, and dipyridamole, have been documented to inhibit PDE11. Only recently, a series of dihydrothieno[3,2-d]pyrimidin-4(3H)-one derivatives proved to be selective toward the PDE11 isoform. In the absence of experimental data about PDE11 X-ray structures, we found interesting to gain a better understanding of the enzyme-inhibitor interactions using in silico simulations. In this work, we describe a computational approach based on homology modeling, docking, and molecular dynamics simulation to derive a predictive 3D model of PDE11. Using a Graphical Processing Unit architecture, it is possible to perform long simulations, find stable interactions involved in the complex, and finally to suggest guideline for the identification and synthesis of potent and selective inhibitors.

In previous studies, we identified a locus for schizophrenia on 6q23.3 and proposed the Abelson helper integration site 1 (AHI1) as the candidate gene. AHI1 is expressed in the brain and plays a key role in neurodevelopment, is involved in Joubert syndrome, and has been recently associated with autism. The neurodevelopmental role of AHI1 fits with etiological hypotheses of schizophrenia. To definitively confirm our hypothesis, we searched for associations using a dense map of the region. Our strongest findings lay within the AHI1 gene: single-nucleotide polymorphisms rs11154801 and rs7759971 showed significant associations (P=6.23E-06; P=0.84E-06) and haplotypes gave P values in the 10E-8 to 10E-10 range. The second highest significant region maps close to AHI1 and includes the intergenic region between BC040979 and PDE7B (rs2038549 at P=9.70E-06 and rs1475069 at P=6.97E-06), and PDE7B and MAP7. Using a sample of Palestinian Arab families to confirm these findings, we found isolated signals. While these results did not retain their significance after correction for multiple testing, the joint analysis across the 2 samples supports the role of AHI1, despite the presence of heterogeneity. Given the hypothesis of positive selection of schizophrenia genes, we resequenced a 11 kb region within AHI1 in ethnically defined populations and found evidence for a selective sweep. Network analysis indicates 2 haplotype clades, with schizophrenia-susceptibility haplotypes clustering within the major clade. In conclusion, our data support the role of AHI1 as a susceptibility gene for schizophrenia and confirm it has been subjected to positive selection, also shedding light on new possible candidate genes, MAP7 and PDE7B.-Torri, F., Akelai, A., Lupoli, S., Sironi, M., Amann-Zalcenstein, D., Fumagalli, M., Dal Fiume, C., BenAsher, E., Kanyas, K., Cagliani, R., Cozzi, P., Trombetti, G., Lievers, L. S., Salvi, E., Orro, A., Beckmann, J. S., Lancet, D., Kohn, Y., Milanesi, L., Ebstein, R. B., Lerer, B., Macciardi, F. Fine mapping of AHI1 as a schizophrenia susceptibility gene: from association to evolutionary evidence.