A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data

In this paper, we develop a computationally efficient discrete approximation to log‐Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data. Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely, that each such model is tied to a specific partition of the study area, and allows for spatially continuous prediction. We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle upon Tyne, UK. Our results suggest that, when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales. The proposed methodology is implemented in the open‐source R package SDALGCP

CRANKITE: a fast polypeptide backbone conformation sampler

Background: CRANKITE is a suite of programs for simulating backbone conformations of polypeptides and proteins. The core of the suite is an efficient Metropolis Monte Carlo sampler of backbone conformations in continuous three-dimensional space in atomic details. Methods: In contrast to other programs relying on local Metropolis moves in the space of dihedral angles, our sampler utilizes local crankshaft rotations of rigid peptide bonds in Cartesian space. Results: The sampler allows fast simulation and analysis of secondary structure formation and conformational changes for proteins of average length

Geometry of Goodness-of-Fit Testing in High Dimensional Low Sample Size Modelling

We introduce a new approach to goodness-of-fit testing in the high dimensional, sparse extended multinomial context. The paper takes a computational information geometric approach, extending classical higher order asymptotic theory. We show why the Wald – equivalently, the Pearson X2 and score statistics – are unworkable in this context, but that the deviance has a simple, accurate and tractable sampling distribution even for moderate sample sizes. Issues of uniformity of asymptotic approximations across model space are discussed. A variety of important applications and extensions are noted

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

Statistical analysis of modal gating in ion channels

Ion channels regulate the concentrations of ions within cells. By stochastically opening and closing its pore, they enable or prevent ions from crossing the cell membrane. However, rather than opening with a constant probability, many ion channels switch between several different levels of activity even if the experimental conditions are unchanged. This phenomenon is known as modal gating: instead of directly adapting its activity, the channel seems to mix sojourns in active and inactive modes in order to exhibit intermediate open probabilities. Evidence is accumulating that modal gating rather than modulation of opening and closing at a faster time scale is the primary regulatory mechanism of ion channels. However, currently, no method is available for reliably calculating sojourns in different modes. In order to address this challenge, we develop a statistical framework for segmenting single-channel datasets into segments that are characteristic for particular modes. The algorithm finds the number of mode changes, detects their locations and infers the open probabilities of the modes. We apply our approach to data from the inositol-trisphosphate receptor. Based upon these results, we propose that mode changes originate from alternative conformational states of the channel protein that determine a certain level of channel activity

CHY representations for gauge theory and gravity amplitudes with up to three massive particles

We show that a wide class of tree-level scattering amplitudes involving scalars, gauge bosons, and gravitons, up to three of which may be massive, can be expressed in terms of a Cachazo-He-Yuan representation as a sum over solutions of the scattering equations. These amplitudes, when expressed in terms of the appropriate kinematic invariants, are independent of the masses and therefore identical to the corresponding massless amplitudes.Comment: 20 pages, 1 figure; v2: minor typos corrected, published versio

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Emotional support, education and self-rated health in 22 European countries

<p>Abstract</p> <p>Background</p> <p>The analyses focus on three aims: (1) to explore the associations between education and emotional support in 22 European countries, (2) to explore the associations between emotional support and self-rated health in the European countries, and (3) to analyse whether the association between education and self-rated health can be partly explained by emotional support.</p> <p>Methods</p> <p>The study uses data from the European Social Survey 2003. Probability sampling from all private residents aged 15 years and older was applied in all countries. The European Social Survey includes 42,359 cases. Persons under age 25 were excluded to minimise the number of respondents whose education was not complete. Education was coded according to the International Standard Classification of Education. Perceived emotional support was assessed by the availability of a confidant with whom one can discuss intimate and personal matters with. Self-rated health was used as health indicator.</p> <p>Results</p> <p>Results of multiple logistic regression analyses show that emotional support is positively associated with education among women and men in most European countries. However, the magnitude of the association varies according to country and gender. Emotional support is positively associated with self-rated health. Again, gender and country differences in the association were observed. Emotional support explains little of the educational differences in self-rated health among women and men in most European countries.</p> <p>Conclusion</p> <p>Results indicate that it is important to consider socio-economic factors like education and country-specific contexts in studies on health effects of emotional support.</p

Formation of unique nanocrystalline Cu-In-Se bulk pn homojunctions for opto-electronic devices

Semiconductor pn junctions, integrated in optoelectronic devices require high quality crystals, made by expensive, technically difficult processes. Bulk heterojunction (BHJ) structures offer practical alternatives to circumvent the cost, flexibility and scale-up challenges of crystalline planar pn junctions. Fabrication methods for the current organic or inorganic BHJ structures invariably create interface mismatch and low doping issues. To overcome such issues, we devised an innovative approach, founded on novel inorganic material system that ensued from single-step electrodeposited copper-indium-selenide compounds. Surface analytical microscopies and spectroscopies reveal unusual phenomena, electro-optical properties and quantum effects. They support the formation of highly-ordered, sharp, abrupt 3-dimensional nanoscale pn BHJs that facilitate efficient charge carrier separation and transport, and essentially perform the same functions as crystalline planar pn junctions. This approach offers a low-cost processing platform to create nanocrystalline films, with the attributes necessary for efficient BHJ operation. It allows roll-to-roll processing of flexible devices in simple thin-film form factor.Partial funding for this work is provided by customers of Xcel Energy through a grant from the Renewable Development Fund. The authors gratefully acknowledge sample preparation, analytical contributions and useful discussions with Sharmila Menezes and Yan Li (InterPhases Solar); Senli Guo (Brucker Nano); Terrence McGuckin (Ephemeron Labs); and Nassim Rahimi (HORIBA Scientific). A. Samantilleke acknowledges Prof. L. M. Peter (Bath University, UK) for introducing EER technique

GBR 12909 administration as a mouse model of bipolar disorder mania: mimicking quantitative assessment of manic behavior

Mania is a core feature of bipolar disorder (BD) that traditionally is assessed using rating scales. Studies using a new human behavioral pattern monitor (BPM) recently demonstrated that manic BD patients exhibit a specific profile of behavior that differs from schizophrenia and is characterized by increased motor activity, increased specific exploration, and perseverative locomotor patterns as assessed by spatial d. It was hypothesized that disrupting dopaminergic homeostasis by inhibiting dopamine transporter (DAT) function would produce a BD mania-like phenotype in mice as assessed by the mouse BPM. We compared the spontaneous locomotor and exploratory behavior of C57BL/6J mice treated with the catecholamine transporter inhibitor amphetamine or the selective DAT inhibitor GBR 12909 in the mouse BPM. We also assessed the duration of the effect of GBR 12909 by testing mice in the BPM for 3 h and its potential strain dependency by testing 129/SvJ mice. Amphetamine produced hyperactivity and increased perseverative patterns of locomotion as reflected in reduced spatial d values but reduced exploratory activity in contrast to the increased exploration observed in BD patients. GBR 12909 increased activity and reduced spatial d in combination with increased exploratory behavior, irrespective of inbred strain. These effects persisted for at least 3 h. Thus, selectively inhibiting the DAT produced a long-lasting cross-strain behavioral profile in mice that was consistent with that observed in manic BD patients. These findings support the use of selective DAT inhibition in animal models of the impaired dopaminergic homeostasis putatively involved in the pathophysiology of BD mania