In this study, we propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA algorithm by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares (SoS) reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial

Cloud computing provides a promising solution to the big data problem associated with next generation sequencing applications. The increasing number of cloud service providers, who compete in terms of performance and price, is a clear indication of a growing market with high demand. However, current cloud computing based applications in bioinformatics do not profit from this progress, because they are still limited to just one cloud service provider. In this paper, we present different use case scenarios using hybrid services and resources from multiple cloud providers for bioinformatics

The strain encoding (SENC) technique directly encodes regional strain of the heart into the acquired MR images and produces two images with two different tunings so that longitudinal strain, on the short-axis view, or circumferential strain on the long-axis view, are measured. Interleaving acquisition is used to shorten the acquisition time of the two tuned images by 50%, but it suffers from errors in the strain calculations due to inter-tunings motion of the heart. In this work, we propose a method to correct for the inter-tunings motion by estimating the motion-induced shift in the spatial

Cloud computing is enabling proper, on-demand network access to a shared pool of computing resources that is elastic in reserve and release with minimal interaction from cloud service provider. As cloud gains maturity, cloud service providers are becoming more competitive, which increase the percentage of cloud adoption. But security remains the most cited challenge in Cloud. So, while we are progressing in cloud adoption, we have to define key elements of our cloud strategy and governance. Governance is about applying policies relating to used services. Therefore, it has to include the

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of "resources-on-demand" and "pay-as-you-go", scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem

The rise in Arabic usage within various socialmedia platforms, and notably in Twitter, has led to a growing interest in building ArabicNatural Language Processing (NLP) applications capable of dealing with informal colloquialArabic, as it is the most commonly used form of Arabic in social media. The uniquecharacteristics of the Arabic language make the extraction of Arabic named entities achallenging task, to which, the nature of tweets adds new dimensions. The majority ofprevious research done on Arabic NER focused on extracting entities from the formallanguage, namely Modern Standard Arabic

Wireless sensor networks open up a new realm of ubiquitous computing applications based on distributed large-scale data collection by embedded sensor nodes that are wirelessly connected and seamlessly integrated within the environment. 3D visualization of sensory data is a challenging issue, however, due to the large number of sensors used in typical deployments, continuous data streams, and constantly varying network topology. This paper describes a practical approach for interactive 3D visualization of wireless sensor network data. A regular 3D grid is reconstructed using scattered sensor

Despite the important role of object tracking using the Optical Flow (OF) in computer graphics applications, it has a limited role in segmenting speckle-free medical images such as magnetic resonance images of the heart. In this work, we propose a novel solution of the OF equation that allows incorporating additional constraints of the shape of the segmented object. We formulate a cost function that include the OF constraint in addition to myocardial contour properties such as smoothness and elasticity. The method is totally different from the common naïve combination of OF estimation within

Evaluation of cardiac functions using Strain Encoded (SENC) magnetic resonance (MR) imaging is a powerful tool for imaging the deformation of left and right ventricles. However, automated analysis of SENC images is hindered due to the low signal-to-noise ratio SENC images. In this work, the authors propose a method to segment the left and right ventricles myocardium simultaneously in SENC-MR short-axis images. In addition, myocardium seed points are automatically selected using skeletonisation algorithm and used as hard constraints for the graph-cut optimization algorithm. The method is based

RNA interference (RNAi) is a post-transcriptional gene silencing mechanism that mediates the sequence-specific degradation of targeted RNA and thus provides a tremendous opportunity for development of oligonucleotide-based drugs. Here, we report on the design and validation of small interfering RNAs (siRNAs) targeting highly conserved regions of the hepatitis C virus (HCV) genome. To aim for therapeutic applications by optimizing the RNAi efficacy and reducing potential side effects, we considered different factors such as target RNA variations, thermodynamics and accessibility of the siRNA