Discovering and interpreting transcriptomic drivers of imaging traits using neural networks

Motivation. Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these "radiogenomic" studies often use linear, shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These current studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical traits, such as prognosis. Results. We present an approach based on neural networks that takes high-dimensional gene expressions as input and performs nonlinear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperform comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that imaging traits had specific transcription patterns, e.g., edema and genes related to cellular invasion, and 15 radiogenomic associations were predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic associations with clinical value

Methods to test for association between a disease and a multi-allelic marker applied to a candidate region

We report the analysis results of the Genetic Analysis Workshop 14 simulated microsatellite marker dataset, using replicate 50 from the Danacaa population. We applied several methods for association analysis of multi-allelic markers to case-control data to study the association between Kofendrerd Personality Disorder and multi-allelic markers in a candidate region previously identified by the linkage analysis. Evidence for association was found for marker D03S0127 (p < 0.01). The analyses were done without any prior knowledge of the answers

Temperature-Dependent Anomalies in the Structure of the (001) Surface of LiCu2O2

Surface corrugation functions, derived from elastic helium atom scattering (HAS) diffraction patterns at different temperatures, reveal that the Cu2+ rows in the (001) surface of LiCu2O2 undergo an outward displacement of about 0.15 {\AA} as the surface was cooled down to 140 K. This is probably the first time that isolated one-dimensional magnetic ion arrays were realized, which qualifies the Li1+Cu2+O2-2 surface as a candidate to study one-dimensional magnetism. The rising Cu2+ rows induce a surface incommensurate structural transition along the a-direction. Surface equilibrium analysis showed that the surface Cu2+ ions at bulk-like positions experience a net outward force along the surface normal which is relieved by the displacement. Temperature-dependent changes of the surface phonon dispersions obtained with the aid of inelastic HAS measurements combined with surface lattice dynamical calculations are also reported.Comment: 4 pages, 7 figure

Clouds + Games: A multifaceted approach

The computer game landscape is changing: people play games on multiple computing devices with heterogeneous form-factors, capability, and connectivity. Providing high playability on such devices concurrently is difficult. To enhance the gaming experience, designers could leverage abundant and elastic cloud resources, but current cloud platforms aren't optimized for highly interactive games. Existing studies focus on streaming-based cloud gaming, which is a special case for the more general cloud game architecture. The authors explain how to integrate techniques from the cloud and game research communities into a complete architecture for enhanced online gaming quality. They examine several open issues that appear only when clouds and games are put together. © 2014 IEEE

CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation, we introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that by using CNAViz, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling

Introduction to the Special Section on Social Computing and Social Internet of Things

The papers in this special section focus on social computing and the social Internet of Things (SIoT). SIoT is a new and latest paradigm that extends Internet of Things. This provides an ideal platform for interconnected devices and objects to effectively interact across social platforms for the betterment of the community on a whole. Any Social Internet of things based system means that the data is distributed in nature and focuses on the interest of a larger group of people than a particular individual. Thus social Internet of things have a wide scope and can be used to develop a wide range of applications that involves a group of people or community working towards accomplishing a common objective such as joint ventures, office setup, co-ownerships and so on. Social Computing may be defined as the study of the collaborative behavior of a group of computer users working on some common objectives

Enhancing in vitro biocompatibility and corrosion protection of organic-inorganic hybrid sol-gel films with nanocrystalline hydroxyapatite

Application of novel organic-inorganic hybrid sol-gel coatings containing dispersed hydroxyapatite (HAp) particles improves the biocompatibility, normal human osteoblast (NHOst) response in terms of osteoblast viability and adhesion of a Ti6Al4V alloy routinely used in medical implants. The incorporation of HAp particles additionally results in more effective barrier proprieties and improved corrosion protection of the Ti6Al4V alloy through higher degree of cross-linking in the organopolysiloxane matrix and enhanced film thickness

Locally weighted transmission/disequilibrium test for genetic association analysis

The transmission/disequilibrium test statistic has been used for assessing genetic association in affected-parent trios. In the presence of multiple tightly linked marker loci where local dependency may exist, haplotypes are reconstructed statistically to estimate the joint effects of these markers. In this manuscript, we propose an alternative to the haplotype approach by taking a weighted average of multiple loci, where the weight is proportional to the product of (1-2X recombination fraction) and the linkage disequilibrium between markers. As an illustration, we applied the method to the simulated Aipotu data