Spatiotemporal Variations in Coexisting Multiple Causes of Death and the Associated Factors

The study and practice of epidemiology and public health benefit from the use of mortality statistics, such as mortality rates, which are frequently used as key health indicators. Furthermore, multiple causes of death (MCOD) data offer important information that could not possibly be gathered from other mortality data. This study aimed to describe the interrelationships between various causes of death in the United States in order to improve the understanding of the coexistence of MCOD and thereby improve public health and enhance longevity. The social support theory was used as a framework, and multivariate linear regression analyses were conducted to examine the coexistence of MCOD in approximately 80 million death cases across the United States from 1959 to 2005. The findings showed that in the United States, there is a statistically significant relationship between the number of coexisting MCOD, race, education, and the state of residence. Furthermore, age, gender, and marital status statistically influence the average number of coexisting MCOD. The results offer insights into how the number of coexisting MCOD vary across the United States, races, education levels, gender, age, and marital status and lay a foundation for further investigation into what people are dying from. The results have the long-term potential of helping public health practitioners identify individuals or communities that are at higher risks of death from a number of coexisting MCOD such that actions could be taken to lower the risks to improve people\u27s wellbeing, enhance longevity, and contribute to positive social change

Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data

This document is the Accepted Manuscript version of the following article: Emmanuel Oluwatobi Salawu, Evelyn Hesse, Chris Stopford, Neil Davey, and Yi Sun, 'Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data', Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 201, pp. 115-127, first published online 5 July 2017. Under embargo. Embargo end date: 5 July 2019. The Version of Record is available online at doi: https://doi.org/10.1016/j.jqsrt.2017.07.001. © 2017 Elsevier Ltd. All rights reserved.Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 μm), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 μm in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles’ orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle’s size and size PADs.Peer reviewe

RaFoSA: Random forests secondary structure assignment for coarse-grained and all-atom protein systems

<p>Secondary structures (SS) of proteins are of great importance to structural, molecular, and computational biology and chemistry. Accurate and reliable method for automatic SS assignment when only coarse-grained (CG) information is available is needed. RaFoSA, a novel, accurate, and reliable method for automatic SS assignment based on coordinates of alpha carbon (CAC) atoms alone is presented here. Results from RaFoSa have been rigorously compared to those from Dictionary of Protein SS (DSSP, the acclaimed gold-standard for automatic SS assignment) and STRIDE. Requiring only CAC, RaFoSA achieves an agreement of 96% (and 94%) with DSSP (and STRIDE) that require all-atom and hydrogen-bonding information. No known automatic SS assignment method based on CG system has ever achieved such agreement with DSSP and STRIDE. Furthermore, RaFoSA has been applied to a real-life problem and its possible use for ranking proteins in their order of SS-based stability is shown in this paper. Overall, RaFoSA’s abilities to accurately and reliably assign SS to CG or all-atom protein systems make this work important. Furthermore, it must be emphasized that SS assignment by RaFoSA is different from (and is more rigorous than) SS prediction from amino acids sequence. Indeed, SS assignment by RaFoSA can differentiate between frames from molecular dynamics simulations trajectories, while existing methods for SS prediction from amino acid sequence cannot. Source codes and a webserver implementation of RaFoSA are available at <a href="http://bioinformatics.center/RaFoSA" target="_blank">http://bioinformatics.center/RaFoSA</a>.</p