ABSTRACT
We model, via classical molecular dynamics simulations, the plastic phase of ice VII across a wide range of the phase diagram of interest for planetary investigations. Although structural and dynamical properties of plastic ice VII are mostly independent on the thermodynamic conditions, the hydrogen bond network (HBN) acquires a diverse spectrum of topologies distinctly different from that of liquid water and of ice VII simulated at the same pressure. We observe that the HBN topology of plastic ice carries some degree of similarity with the crystal phase, stronger at thermodynamic conditions proximal to ice VII, and gradually lessening when approaching the liquid state. Our results enrich our understanding of the properties of water at high pressure and high temperature and may help in rationalizing the geology of water-rich planets.
ABSTRACT
Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of mutational changes in genes encoding for proteins modulating the membrane of excitable cells, whose biological correlates are assessed at electrophysiological level, could provide useful predictive clues. We apply this concept to the analysis of variants in sodium channel NaV1.7 subunit found in patients with chronic painful syndromes, by the implementation of a dedicated computational pipeline empowering different and complementary techniques including homology modeling, network theory, and machine learning. By testing three templates of different origin and sequence identities, we provide an optimal condition for its use. Our findings reveal the usefulness of our computational pipeline in supporting the selection of candidates for cell electrophysiology assay and with potential clinical applications.
