Numerical Scaling Studies of Kinetically-Limited Electrochemical Nucleation and Growth with Accelerated Stochastic Simulations

A stochastic atomic-scale lattice-based numerical method based on the Exact Lattice First Passage Time method was developed for the simulation of the early stages of kinetically controlled electrochemical nucleation and growth. Electrochemical reaction and surface diffusion on a hexagonal lattice was accounted for in a pristine physical model system that included edge diffusion along steps, and movement over step edges with Ehrlich-Schwöbel barrier. Five cases were investigated: homoexpitaxy, heteroepitaxy, multi-layer growth, terraces, and confined regions. For each, the influence of the physical parameters, deposition conditions, and system geometry on growth morphology was investigated. Simulation based studies of multilayer surface morphology were able to distinguish between layer-by-layer and island growth modes. On stepped terraces, parameter regions associated with he surface diffusion to deposition flux ratio (D/F) and the Ehrlich-Schwöbel barrier were identified under which deposition occurred either at the step edge or by nucleation and growth of islands on the terraces. The probability of growing single crystals in a small confined region was found to scale with D/F and the radius squared. © 2014 The Electrochemical Society

A method for studying decision-making by guideline development groups

Background: Multidisciplinary guideline development groups (GDGs) have considerable influence on UK healthcare policy and practice, but previous research suggests that research evidence is a variable influence on GDG recommendations. The Evidence into Recommendations (EiR) study has been set up to document social-psychological influences on GDG decision-making. In this paper we aim to evaluate the relevance of existing qualitative methodologies to the EiR study, and to develop a method best-suited to capturing influences on GDG decision-making.Methods: A research team comprised of three postdoctoral research fellows and a multidisciplinary steering group assessed the utility of extant qualitative methodologies for coding verbatim GDG meeting transcripts and semi-structured interviews with GDG members. A unique configuration of techniques was developed to permit data reduction and analysis.Results: Our method incorporates techniques from thematic analysis, grounded theory analysis, content analysis, and framework analysis. Thematic analysis of individual interviews conducted with group members at the start and end of the GDG process defines discrete problem areas to guide data extraction from GDG meeting transcripts. Data excerpts are coded both inductively and deductively, using concepts taken from theories of decision-making, social influence and group processes. These codes inform a framework analysis to describe and explain incidents within GDG meetings. We illustrate the application of the method by discussing some preliminary findings of a study of a National Institute for Health and Clinical Excellence (NICE) acute physical health GDG.Conclusion: This method is currently being applied to study the meetings of three of NICE GDGs. These cover topics in acute physical health, mental health and public health, and comprise a total of 45 full-day meetings. The method offers potential for application to other health care and decision-making groups

Functional network inference of the suprachiasmatic nucleus

In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure