A Variant in a MicroRNA complementary site in the 3' UTR of the KIT oncogene increases risk of acral melanoma.

MicroRNAs (miRNAs) are small ∼22nt single stranded RNAs that negatively regulate protein expression by binding to partially complementary sequences in the 3' untranslated region (3' UTRs) of target gene messenger RNAs (mRNA). Recently, mutations have been identified in both miRNAs and target genes that disrupt regulatory relationships, contribute to oncogenesis and serve as biomarkers for cancer risk. KIT, an established oncogene with a multifaceted role in melanogenesis and melanoma pathogenesis, has recently been shown to be upregulated in some melanomas, and is also a target of the miRNA miR-221. Here, we describe a genetic variant in the 3' UTR of the KIT oncogene that correlates with a greater than fourfold increased risk of acral melanoma. This KIT variant results in a mismatch in the seed region of a miR-221 complementary site and reporter data suggests that this mismatch can result in increased expression of the KIT oncogene. Consistent with the hypothesis that this is a functional variant, KIT mRNA and protein levels are both increased in the majority of samples harboring the KIT variant. This work identifies a novel genetic marker for increased heritable risk of melanoma

A pneumococcal MerR-like regulator and S-nitrosoglutathione reductase are required for systemic virulence

Copyright © 2007 by the Infectious Diseases Society of America. All rights reserved.A transcriptional regulator, NmlR(sp), has been identified in Streptococcus pneumoniae that is required for defense against nitric oxide (NO) stress. The nmlR(sp) gene is cotranscribed with adhC, which encodes an alcohol dehydrogenase that is able to reduce S-nitrosoglutathione (GSNO) with NADH as reductant. nmlR(sp) and adhC mutants exhibited a reduced level of NADH-GSNO oxidoreductase activity and were more susceptible to killing by NO than were wild-type cells. Comparison of the virulence of wild-type and mutant strains by use of a mouse model system showed that NmlR(sp) and AdhC do not play a key role in the adherence of pneumococci to the nasopharynx in vivo. An intraperitoneal challenge experiment revealed that both NmlR(sp) and AdhC were required for survival in blood. These data identify novel components of a NO defense system in pneumococci that are required for systemic infection.Uwe H. Stroeher, Robert S. Kidd, Sian L. Stafford, Michael P. Jennings, James C. Paton and Alastair G. McEwa

Neurology Case Reporting: a call for all

From antiquity to present day, the act of recording and publishing our observations with patients remains essential to the art of medicine and the care of patients. Neurology is rich with case reports over the centuries. They contribute to our understanding and knowledge of disease entities, and are a cornerstone of our professional development as physicians and the care of our patients. This editorial seeks to enthuse and invigorate house staff and practicing physicians everywhere to continue the long and time-honored tradition of neurology case reporting

Different atmospheric moisture divergence responses to extreme and moderate El Niños

On seasonal and inter-annual time scales, vertically integrated moisture divergence provides a useful measure of the tropical atmospheric hydrological cycle. It reflects the combined dynamical and thermodynamical effects, and is not subject to the limitations that afflict observations of evaporation minus precipitation. An empirical orthogonal function (EOF) analysis of the tropical Pacific moisture divergence fields calculated from the ERA-Interim reanalysis reveals the dominant effects of the El Niño-Southern Oscillation (ENSO) on inter-annual time scales. Two EOFs are necessary to capture the ENSO signature, and regression relationships between their Principal Components and indices of equatorial Pacific sea surface temperature (SST) demonstrate that the transition from strong La Niña through to extreme El Niño events is not a linear one. The largest deviation from linearity is for the strongest El Niños, and we interpret that this arises at least partly because the EOF analysis cannot easily separate different patterns of responses that are not orthogonal to each other. To overcome the orthogonality constraints, a self-organizing map (SOM) analysis of the same moisture divergence fields was performed. The SOM analysis captures the range of responses to ENSO, including the distinction between the moderate and strong El Niños identified by the EOF analysis. The work demonstrates the potential for the application of SOM to large scale climatic analysis, by virtue of its easier interpretation, relaxation of orthogonality constraints and its versatility for serving as an alternative classification method. Both the EOF and SOM analyses suggest a classification of “moderate” and “extreme” El Niños by their differences in the magnitudes of the hydrological cycle responses, spatial patterns and evolutionary paths. Classification from the moisture divergence point of view shows consistency with results based on other physical variables such as SST

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Knowledge based improvement:simulation and artificial intelligence for identifying and improving human decision-making in an operations systems

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant

Results of the MAJORANA DEMONSTRATOR's Search for Double-Beta Decay of 76Ge to Excited States of 76Se

The MAJORANA DEMONSTRATOR is searching for double-beta decay of 76Ge to excited states (E.S.) in 76Se using a modular array of high purity Germanium detectors. 76Ge can decay into three E.S.s of 76Se. The E.S. decays have a clear event signature consisting of a ββ-decay with the prompt emission of one or two γ-rays, resulting in with high probability in a multi-site event. The granularity of the DEMONSTRATOR detector array enables powerful discrimination of this event signature from backgrounds. Using 21.3 kg-y of isotopic exposure, the DEMONSTRATOR has set world leading limits for each E.S. decay, with 90% CL lower half-life limits in the range of (0.56 2.1) ⋅ 1024 y. In particular, for the 2v transition to the first 0+ E.S. of 76Se, a lower half-life limit of 0.68 ⋅ 1024 at 90% CL was achieved

Personality judgments from everyday images of faces

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: an ESRC studentship [ES/I900748/1] and postdoctoral research support from the Australian Research Council Centre of Excellence in Cognition and its Disorders, University of Western Australia (CE110001021), to the first author. The work was completed while the first author was at the University of York, UK. We thank Richard Vernon for calculating the attributes used in Study 2.Peer reviewedPublisher PD

ADC Nonlinearity Correction for the Majorana Demonstrator

Imperfections in analog-to-digital conversion (ADC) cannot be ignored when signal digitization requirements demand both wide dynamic range and high resolution, as is the case for the Majorana Demonstrator 76Ge neutrinoless double-beta decay search. Enabling the experiment's high-resolution spectral analysis and efficient pulse shape discrimination required careful measurement and correction of ADC nonlinearities. A simple measurement protocol was developed that did not require sophisticated equipment or lengthy data-taking campaigns. A slope-dependent hysteresis was observed and characterized. A correction applied to digitized waveforms prior to signal processing reduced the differential and integral nonlinearities by an order of magnitude, eliminating these as dominant contributions to the systematic energy uncertainty at the double-beta decay Q value

On the power and the systematic biases of the detection of chromosomal inversions by paired-end genome sequencing

One of the most used techniques to study structural variation at a genome level is paired-end mapping (PEM). PEM has the advantage of being able to detect balanced events, such as inversions and translocations. However, inversions are still quite difficult to predict reliably, especially from high-throughput sequencing data. We simulated realistic PEM experiments with different combinations of read and library fragment lengths, including sequencing errors and meaningful base-qualities, to quantify and track down the origin of false positives and negatives along sequencing, mapping, and downstream analysis. We show that PEM is very appropriate to detect a wide range of inversions, even with low coverage data. However, % of inversions located between segmental duplications are expected to go undetected by the most common sequencing strategies. In general, longer DNA libraries improve the detectability of inversions far better than increments of the coverage depth or the read length. Finally, we review the performance of three algorithms to detect inversions -SVDetect, GRIAL, and VariationHunter-, identify common pitfalls, and reveal important differences in their breakpoint precisions. These results stress the importance of the sequencing strategy for the detection of structural variants, especially inversions, and offer guidelines for the design of future genome sequencing projects