Khalifa University

About: Khalifa University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Computer science & Adsorption. The organization has 3752 authors who have published 10909 publications receiving 141629 citations.

Inherited human IFN- γ deficiency underlies mycobacterial disease

Abstract: Mendelian susceptibility to mycobacterial disease (MSMD) is characterized by a selective predisposition to clinical disease caused by the Bacille Calmette-Guerin (BCG) vaccine and environmental mycobacteria. The known genetic etiologies of MSMD are inborn errors of IFN-γ immunity due to mutations of 15 genes controlling the production of or response to IFN-γ. Since the first MSMD-causing mutations were reported in 1996, biallelic mutations in the genes encoding IFN-γ receptor 1 (IFN-γR1) and IFN-γR2 have been reported in many patients of diverse ancestries. Surprisingly, mutations of the gene encoding the IFN-γ cytokine itself have not been reported, raising the remote possibility that there might be other agonists of the IFN-γ receptor. We describe 2 Lebanese cousins with MSMD, living in Kuwait, who are both homozygous for a small deletion within the IFNG gene (c.354_357del), causing a frameshift that generates a premature stop codon (p.T119Ifs4*). The mutant allele is loss of expression and loss of function. We also show that the patients' herpesvirus Saimiri-immortalized T lymphocytes did not produce IFN-γ, a phenotype that can be rescued by retrotransduction with WT IFNG cDNA. The blood T and NK lymphocytes from these patients also failed to produce and secrete detectable amounts of IFN-γ. Finally, we show that human IFNG has evolved under stronger negative selection than IFNGR1 or IFNGR2, suggesting that it is less tolerant to heterozygous deleterious mutations than IFNGR1 or IFNGR2. This may account for the rarity of patients with autosomal-recessive, complete IFN-γ deficiency relative to patients with complete IFN-γR1 and IFN-γR2 deficiencies.

MEdge-Chain: Leveraging Edge Computing and Blockchain for Efficient Medical Data Exchange

Abstract: Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system.

Indoor Air Quality in Buildings: A Comprehensive Review on the Factors Influencing Air Pollution in Residential and Commercial Structure

Abstract: Worldwide people tend to spend approximately 90% of their time in different indoor environments. Along with the penetration of outside air pollutants, contaminants are produced in indoor environments due to different activities such as heating, cooling, cooking, and emissions from building products and the materials used. As people spend most of their lives in indoor environments, this has a significant influence on human health and productivity. Despite the two decades of indoor air quality (IAQ) research from different perspectives, there is still a lack of comprehensive evaluation of peer-reviewed IAQ studies that specifically covers the relationship between the internal characteristics of different types of building environments with IAQ to help understand the progress and limitations of IAQ research worldwide. Therefore, this review of scientific studies presents a broad spectrum of pollutants identified in both residential and commercial indoor environments, highlighting the trends and gaps in IAQ research. Moreover, analysis of literature data enabled us to assess the different IAQs in buildings located in different countries/regions, thus reflecting the current global scientific understanding of IAQ. This review has the potential to benefit building professionals by establishing indoor air regulations that account for all indoor contaminant sources to create healthy and sustainable building environments.

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images.

Abstract: We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.

Optimal Load Distribution for the Detection of VM-Based DDoS Attacks in the Cloud

Abstract: Distributed Denial of Service (DDoS) constitutes a major threat against cloud systems owing to the large financial losses it incurs. This motivated the security research community to investigate numerous detection techniques to limit such attack's effects. Yet, the existing solutions are still not mature enough to satisfy a cloud-dedicated detection system's requirements since they overlook the attacker's wily strategies that exploit the cloud's elastic and multi-tenant properties, and ignore the cloud system's resources constraints. Motivated by this fact, we propose a two-fold solution that allows, first, the hypervisor to establish credible trust relationships toward guest Virtual Machines (VMs) by considering objective and subjective trust sources and employing Bayesian inference to aggregate them. On top of the trust model, we design a trust-based maximin game between DDoS attackers trying to minimize the cloud system's detection and hypervisor trying to maximize this minimization under limited budget of resources. The game solution guides the hypervisor to determine the optimal detection load distribution among VMs in real-time that maximizes DDoS attacks’ detection. Experimental results reveal that our solution maximizes attacks’ detection, decreases false positives and negatives, and minimizes CPU, memory and bandwidth consumption during DDoS attacks compared to the existing detection load distribution techniques.