98 research outputs found

    A spectrally-accurate FVTD technique for complicated amplification and reconfigurable filtering EMC devices

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    The consistent and computationally economical analysis of demanding amplification and filtering structures is introduced in this paper via a new spectrally-precise finite-volume time-domain algorithm. Combining a family of spatial derivative approximators with controllable accuracy in general curvilinear coordinates, the proposed method employs a fully conservative field flux formulation to derive electromagnetic quantities in areas with fine structural details. Moreover, the resulting 3-D operators assign the appropriate weight to each spatial stencil at arbitrary media interfaces, while for periodic components the domain is systematically divided to a number of nonoverlapping subdomains. Numerical results from various real-world configurations verify our technique and reveal its universality

    Cross-Layer Theoretical Analysis of NC-aided Cooperative ARQ Protocols in Correlated Shadowed Environments

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    In this paper, we propose a cross-layer analytical model for the study of network coding (NC)-based Automatic Repeat reQuest (ARQ) medium access control (MAC) protocols in correlated slow-faded (shadowed) environments, where two end nodes are assisted by a cluster of relays to exchange data packets. The goal of our work is threefold: 1) to provide general physical-layer theoretical expressions for estimating crucial network parameters (i.e., network outage probability and expected size of the active relay set), applicable in two-way communications; 2) to demonstrate how these expressions are incorporated into theoretical models of the upper layers (i.e., MAC); and 3) to study the performance of a recently proposed NC-aided cooperative ARQ (NCCARQ) MAC protocol under correlated shadowing conditions. Extensive Monte Carlo experiments have been carried out to validate the efficiency of the developed analytical model and to investigate the realistic performance of NCCARQ. Our results indicate that the number of active relays is independent of the shadowing correlation in the wireless links and reveal intriguing tradeoffs between throughput and energy efficiency, highlighting the importance of cross-layer approaches for the assessment of cooperative MAC protocols

    Counter-Regulation of Interleukin-1α (IL-1α) and IL-1 Receptor Antagonist in Murine Keratinocytes

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    Interleukin-1α (IL-1α) is a potent proinflammatory cytokine constitutively expressed by keratinocytes, which also synthesize a specific inhibitor of IL-1 activity, intracellular IL-1 receptor antagonist (IL-1ra). Although homeostatic regulation of the IL-1 system in keratinocytes has long been suspected, there is currently little evidence for this. To explore this issue, the PAM212 murine keratinocyte cell line was exposed to increasing concentrations of either IL-1α or IL-1ra and the opposing ligand was assessed by ELISA. Release of IL-1ra was induced following stimulation by murine IL-1α in a concentration-dependent manner and, conversely, IL-1ra stimulation increased IL-1α release. To determine whether a similar homeostatic circuit operates in vivo, epidermis from transgenic mice in which overexpression of IL-1α or IL-1ra was targeted to keratinocytes was analyzed. Epidermal sheets derived from IL-1α transgenic mice released eight times more IL-1ra than those from wild-type mice following ex vivo culture and similarly, IL-1α release was increased 3–4-fold in epidermal sheets derived from IL-1ra transgenic epidermis, Use of specific neutralizing antibodies against type I and type II IL-1 receptors indicated that the counter-regulation mechanism is mediated extracellularly through the type I IL-1 receptor alone. Taken together, these observations provide the first demonstration of mutual counter-regulation of IL-1 receptor ligands in keratinocytes

    Multi-tenant slicing for spectrum management on the road to 5G

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    ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The explosive data traffic demand in the context of the 5G revolution has stressed the need for network capacity increase. As the network densification has almost reached its limits, mobile network operators are motivated to share their network infrastructure and the available resources through dynamic spectrum management. Although some initial efforts have been made in this direction by concluding sharing agreements at a coarse granularity (i.e., months or years), the 5G developments require fine timescale agreements, mainly enabled by network slicing. In this article, taking into account the radical changes foreseen for next generation networks, we provide a thorough discussion of the challenges that network slicing brings in the different network parts, while introducing a new entity capable of managing the end-to-end slicing in a coherent manner. In addition, according to the paradigm shift of operators sharing their resources in a common centralized pool, we design a cooperative game to study the potential cooperation aspects among the participants. The experimental results highlight the performance and financial gains achievable by operators through multi-tenant slicing, providing them with the necessary incentives for network upgrade toward 5G.Peer ReviewedPostprint (author's final draft

    Role of human epicardial adipose tissue–derived miR-92a-3p in myocardial redox state

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    Background Visceral obesity is directly linked to increased cardiovascular risk, including heart failure. Objectives This study explored the ability of human epicardial adipose tissue (EAT)-derived microRNAs (miRNAs) to regulate the myocardial redox state and clinical outcomes. Methods This study screened for miRNAs expressed and released from human EAT and tested for correlations with the redox state in the adjacent myocardium in paired EAT/atrial biopsy specimens from patients undergoing cardiac surgery. Three miRNAs were then tested for causality in an in vitro model of cardiomyocytes. At a clinical level, causality/directionality were tested using genome-wide association screening, and the underlying mechanisms were explored using human biopsy specimens, as well as overexpression of the candidate miRNAs and their targets in vitro and in vivo using a transgenic mouse model. The final prognostic value of the discovered targets was tested in patients undergoing cardiac surgery, followed up for a median of 8 years. Results EAT miR-92a-3p was related to lower oxidative stress in human myocardium, a finding confirmed by using genetic regulators of miR-92a-3p in the human heart and EAT. miR-92a-3p reduced nicotinamide adenine dinucleotide phosphate (NADPH)-oxidase–derived superoxide (O2.–) by targeting myocardial expression of WNT5A, which regulated Rac1-dependent activation of NADPH oxidases. Finally, high miR-92a-3p levels in EAT were independently related with lower risk of adverse cardiovascular events. Conclusions EAT-derived miRNAs exert paracrine effects on the human heart. Indeed miR-92a-3p suppresses the wingless-type MMTV integration site family, member 5a/Rac1/NADPH oxidase axis and improves the myocardial redox state. EAT-derived miR-92a-3p is related to improved clinical outcomes and is a rational therapeutic target for the prevention and treatment of obesity-related heart disease

    A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

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    Background: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. Methods and results: We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P  Conclusion: The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art. </p

    Adipose tissue-derived WNT5A regulates vascular redox signaling in obesity via USP17//RAC1-mediated activation of NADPH oxidases

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    Obesity is associated with changes in the secretome of adipose tissue (AT), which affects the vasculature through endocrine and paracrine mechanisms. Wingless-related integration site 5A (WNT5A) and secreted frizzled-related protein 5 (SFRP5), adipokines that regulate noncanonical Wnt signaling, are dysregulated in obesity. We hypothesized that WNT5A released from AT exerts endocrine and paracrine effects on the arterial wall through noncanonical RAC1-mediated Wnt signaling. In a cohort of 1004 humans with atherosclerosis, obesity was associated with increased WNT5A bioavailability in the circulation and the AT, higher expression of WNT5A receptors Frizzled 2 and Frizzled 5 in the human arterial wall, and increased vascular oxidative stress due to activation of NADPH oxidases. Plasma concentration of WNT5A was elevated in patients with coronary artery disease compared to matched controls and was independently associated with calcified coronary plaque progression. We further demonstrated that WNT5A induces arterial oxidative stress and redox-sensitive migration of vascular smooth muscle cells via Frizzled 2–mediated activation of a previously uncharacterized pathway involving the deubiquitinating enzyme ubiquitin-specific protease 17 (USP17) and the GTPase RAC1. Our study identifies WNT5A and its downstream vascular signaling as a link between obesity and vascular disease pathogenesis, with translational implications in humans

    A Data Compression Hardware Accelerator Enabling Long-Term Biosignal Monitoring Based on Ultra-Low Power IoT Platforms

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    For highly demanding scenarios such as continuous bio-signal monitoring, transmitting excessive volumes of data wirelessly comprises one of the most critical challenges. This is due to the resource limitations posed by typical hardware and communication technologies. Driven by such shortcomings, this paper aims at addressing the respective deficiencies. The main axes of this work include (a) data compression, and (b) the presentation of a complete, efficient and practical hardware accelerator design able to be integrated in any Internet of Things (IoT) platform for addressing critical challenges of data compression. On one hand, the developed algorithm is presented and evaluated on software, exhibiting significant benefits compared to respective competition. On the other hand, the algorithm is fully implemented on hardware providing a further proof of concept regarding the implementation feasibility with respect to state-of-the art hardware design approaches. Finally, system-level performance benefits, regarding data transmission delay and energy saving, are highlighted, taking into consideration the characteristics of prominent IoT platforms. Concluding, this paper presents a holistic approach based on data compression that is able to drastically enhance an IoT platform’s performance and tackle efficiently a notorious challenge of highly demanding IoT applications such as real-time bio-signal monitoring
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