Full UPF3B function is critical for neuronal differentiation of neural stem cells

Acknowledgments We thank Fred H Gage (Salk Institute, La Jolla, CA, USA) for HCN-A94 cells and Niels Gehring (University of Cologne, Germany) for constructs. We gratefully acknowledge Tenovus Scotland (Project Grant G11-06), Moonlight Prowl (FS) and the Saudi Arabian Ministry of Higher Education via King Abdullah Program for Scholarships for support (TA). JA is supported by a PhD studentship from Medical Research Scotland (PhD-654-2012) and Dundee Cell Products.Peer reviewedPublisher PD

High-Throughput Characterization of Porous Materials Using Graphics Processing Units

We have developed a high-throughput graphics processing units (GPU) code that can characterize a large database of crystalline porous materials. In our algorithm, the GPU is utilized to accelerate energy grid calculations where the grid values represent interactions (i.e., Lennard-Jones + Coulomb potentials) between gas molecules (i.e., CH$_{4}$ and CO$_{2}$) and material's framework atoms. Using a parallel flood fill CPU algorithm, inaccessible regions inside the framework structures are identified and blocked based on their energy profiles. Finally, we compute the Henry coefficients and heats of adsorption through statistical Widom insertion Monte Carlo moves in the domain restricted to the accessible space. The code offers significant speedup over a single core CPU code and allows us to characterize a set of porous materials at least an order of magnitude larger than ones considered in earlier studies. For structures selected from such a prescreening algorithm, full adsorption isotherms can be calculated by conducting multiple grand canonical Monte Carlo simulations concurrently within the GPU