University of Virginia

About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.

LUMPY: a probabilistic framework for structural variant discovery

Abstract: Comprehensive discovery of structural variation (SV) from whole genome sequencing data requires multiple detection signals including read-pair, split-read, read-depth and prior knowledge. Owing to technical challenges, extant SV discovery algorithms either use one signal in isolation, or at best use two sequentially. We present LUMPY, a novel SV discovery framework that naturally integrates multiple SV signals jointly across multiple samples. We show that LUMPY yields improved sensitivity, especially when SV signal is reduced owing to either low coverage data or low intra-sample variant allele frequency. We also report a set of 4,564 validated breakpoints from the NA12878 human genome. https://github.com/arq5x/lumpy-sv.

miR-9, a MYC/MYCN-activated microRNA, regulates E-cadherin and cancer metastasis

Abstract: β-catenin signalling, which contributes to upregulated expression of the gene encoding vascular endothelial growth factor (VEGF); this leads, in turn, to increased tumour angiogenesis. Overexpression of miR-9 in otherwise non-metastatic breast tumour cells enables these cells to form pulmonary micrometastases in mice. Conversely, inhibiting miR-9 by using a ‘miRNA sponge’ in highly malignant cells inhibits metastasis formation. Expression of miR-9 is activated by MYC and MYCN, both of which directly bind to the mir-9-3 locus. Significantly, in human cancers, miR-9 levels correlate with MYCN amplification, tumour grade and metastatic status. These findings uncover a regulatory and signalling pathway involving a metastasis-promoting miRNA that is predicted to directly target expression of the key metastasis-suppressing protein E-cadherin. Metastases are responsible for more than 90% of cancer-related mortality. These secondary growths arise through a multistep process that begins when cancer cells within primary tumours break away from neighbouring cells and invade the basement membrane 1 . This local invasion may frequently be triggered by contextual signals that carcinoma cells receive from the nearby stroma, causing them to undergo an epithelial–mesenchymal transition (EMT) 2 . Subsequently, metastasizing cells enter the circulation either directly or through lymphatics. Size constraints in the microvasculature cause many of these cells to be arrested at distant sites, where they may extravasate and enter the foreign tissue parenchyma. There they may remain dormant or, with low efficiency, proliferate from occult micrometastases to form angiogenic, clinically detectable metastases. The absence of EMT-inducing signals in the foreign microenvironment may cause such disseminated cells to revert to an epithelial phenotype by means of a mesenchymal–epithelial transition. Critical regulators of the metastatic process include both proteins and miRNAs 3,4

Verbal and Nonverbal Communication of Deception

Abstract: Publisher Summary Lying and lie detection are the two components that, together, make up the exchange called as the “communication of deception.” Deception is an act that is intended to foster in another person a belief or understanding that the deceiver considers false. This chapter presents a primarily psychological point of view and a relatively microanalysis of the verbal and nonverbal exchange between the deceiver and the lie detector. The chapter discusses the definition of deception. It describes the deceiver's perspective in lie-detection, including the strategies of deception and behaviors associated with lie-telling. The lie-detector's perspective is also discussed in the chapter, and it has described behaviors associated with the judgments of deception and strategies of lie detection. The chapter discusses the outcomes of the deceptive communication process—that is, the accuracy of lie detection—and explores methodological issues, channel effects in the detection of deception, and other factors affecting the accuracy of lie detection.

Localization for mobile sensor networks

Abstract: Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.