Bidirectional ventricular tachycardia in cardiac sarcoidosis.

A 73-year-old man with history of pulmonary sarcoidosis was found to have runs of non-sustained bidirectional ventricular tachycardia (BVT) with two different QRS morphologies on a Holter monitor. Cardiac magnetic resonance delayed gadolinium imaging revealed a region of patchy mid-myocardial enhancement within the left ventricular basal inferolateral myocardium. An 18-fluorodeoxyglucose positron emission tomography (FDG-PET) showed increased uptake in the same area, consistent with active sarcoid, with no septal involvement. Follow-up FDG-PET one year later showed disease progression with new septal involvement. Cardiac sarcoidosis, characterized by myocardial inflammation and interstitial fibrosis that can lead to conduction system disturbance and macro re-entrant arrhythmias, should be considered in differential diagnosis of BVT. BVT may indicate septal involvement with sarcoidosis before the lesions are large enough to be detected radiologically

Contextual Anomaly Detection in Big Sensor Data

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada

State Drought Programs and Plans: Survey of the Western United States

Drought preparedness programs are considered a primary defense against drought hazards. This article investigates state drought programs in the western United States, including a review of drought plans and interviews with state drought officials. While nearly all states have developed drought plans and larger drought programs, the scope and depth of these programs vary widely. State programs and plans typically address monitoring, declaration and response, and communication and coordination. Yet few states conduct postdrought assessments or impact and risk assessments. Resources tend to be allocated more for drought response than mitigation. Officials emphasized not only the importance of available monitoring data, but also the need for improved information for monitoring and predicting drought. State drought officials recommended the following: (1) clear and relevant drought indicators and triggers; (2) frequent communication and coordination among state agencies, local governments, and stakeholders; (3) regularly updated drought plans; and (4) strong leadership that includes a full-time state drought coordinator

Beyond Flux-Limited Diffusion: Parallel Algorithms for Multidimensional Radiation Hydrodynamics

This paper presents a new code for performing multidimensional radiation hydrodynamic (RHD) simulations on parallel computers involving anisotropic radiation fields and nonequilibrium effects. The radiation evolution modules described here encapsulate the physics provided by the serial algorithm of Stone et. al (ApJSupp, vol 80, pp. 819-845), but add new functionality with regard to physics and numerics. Physics enhancments include the addition of time dependence to the computation of the variable tensor Eddington factor (VTEF) closure term, and a matter-radiation coupling scheme which is particularly robust for nonequilibrium problems. Numerical highlights include a discussion of how our code is implemented for parallel execution and a description of our scalable linear solver module. We present a suite of numerical tests from which the virtues and vices of our method may be gleaned; these include nonequilibrium Marshak waves, 2-D "shadow" tests showing the one-sided illumination of an opaque cloud, and full RHD+VTEF simulations of radiating shocks. We conclude that radiation moment solutions closed with variable tensor Eddington factors show a dramatic qualitative improvement over results obtained with flux-limited diffusion, and further that this approach has a bright future in the context of parallel RHD simulations in astrophysics.Comment: 57 pages (including 18 eps figures); submitted to the ApJ Supplement; prepared with AASTEX 5.

Service Evolution Patterns

Service evolution is the process of maintaining and evolving existing Web services to cater for new requirements and technological changes. In this paper, a service evolution model is proposed to analyze service dependencies, identify changes on services and estimate impact on consumers that will use new versions of these services. Based on the proposed service evolution model, four service evolution patterns are described: compatibility, transition, split-map, and merge-map. These proposed patterns provide reusable templates to encourage well-defined service evolution while minimizing issues that arise otherwise. They can be applied in the service evolution scenario where a single service is used by many, possibly unknown, consumers’ applications. In such a scenario, providers evolve their services independently from consumers, which might cause unexpected errors and incur unpredicted impact on the dependent consumers\u27 applications. Therefore, providers can use these patterns to estimate the impact that changes to be introduced to their services may cause on their consumers, and to allow consumers smoothly migrate to the newest version of the service

The Impact of Human Papillomavirus Educational Intervention Study on the Knowledge, Health Beliefs, Health Behaviors and Increasing the Use of Gardasil in Women of Color

Lack of human papillomavirus (HPV) knowledge and cervical cancer awareness are factors contributing to a disproportion in African American (AA) women with cervical cancer. The purpose of this intervention study was to use gender specific and culturally appropriate HPV educational materials to increase HPV knowledge and cervical cancer awareness, to increase health beliefs, and the intent for AA women to use the HPV vaccine. Convenience sampling was used to describe a sample of 98 AA women recruited from an Ambulatory Women’s health clinic between 2015 and 2017. HPV educational videos and pamphlets materials were used to collect baseline and post intervention knowledge using a self-administered questionnaire, video, and pamphlet. Results revealed an increase in HPV and cervical cancer knowledge, and recommended use of HPV vaccine with family members. HPV educational materials increased women’s knowledge of HPV and cervical cancer, increased healthy behaviors, and the intent to use HPV vaccine with family members, without personal intent to take the HPV vaccine. Future research is needed to examine the decrease in AA women’s’ intent to receive the HPV vaccine

An Iterative Association Rule Mining Framework to K-Anonymize a Dataset

Preserving and maintaining client privacy and anonymity is of utmost importance in any domain and specially so in healthcare, as loss of either of these can result in legal and ethical implications. Further, it is sometimes important to extract meaningful and useful information from existing data for research or management purposes. In this case it is necessary for the organization who manages the dataset to be certain that no attributes can identify individuals or group of individuals. This paper proposes an extendable and generalized framework to anonymize a dataset using an iterative association rule mining approach. The proposed framework also makes use of optional domain rules and filter rules to help customize the filtering process. The outcome of the proposed framework is a preprocessed dataset which can be used in further research with confidence that anonymity of individuals is conserved. Evaluation of this research will also be described in the form of a case study using a test dataset provided by the Lawson Health Research Institute in London, Ontario, Canada as a part of their Mental Health Engagement Network (MHEN) study