Estimation of shear stress by using a myocardial bridge-mural coronary artery simulating device

Background: This study was aimed at developing a myocardial bridge-mural coronary artery simulative device and analyzing the relationship between shear stress on the mural coronary artery and atherosclerosis. Methods: A myocardial bridge-mural coronary artery simulative device was used to simulate experiments in vitro. In the condition of maintaining any related parameters such as system temperature, average flow rate, and heart rate, we calculated and observed changes in proximal and distal mean values, and oscillatory value of shear stress on the mural coronary artery by regulating the compression level of the myocardial bridge to the mural coronary artery. Results: Under 0% compression, no significant differences were observed in distal and proximal mean values and oscillatory value of the shear stress on the mural coronary artery. With the increase in the degree of compression, the mean shear stress at the distal end was greater than that at the proximal end, but the oscillatory value of the shear stress at the proximal end was greater than that at the distal end. Conclusions: The experimental results of this study indicate that myocardial bridge compression leads to abnormal hemodynamics at the proximal end of the mural coronary artery. This abnormal phenomenon is of great significance in the study of atherosclerosis hemodynamic pathogenesis, which has potential clinical value for pathological effects and treatments of myocardial bridg

High speed self-testing quantum random number generation without detection loophole

Quantum mechanics provides means of generating genuine randomness that is impossible with deterministic classical processes. Remarkably, the unpredictability of randomness can be certified in a self-testing manner that is independent of implementation devices. Here, we present an experimental demonstration of self-testing quantum random number generation based on an detection-loophole free Bell test with entangled photons. In the randomness analysis, without the assumption of independent identical distribution, we consider the worst case scenario that the adversary launches the most powerful attacks against quantum adversary. After considering statistical fluctuations and applying an 80 Gb $\times$ 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than $10^{-5}$. Such self-testing random number generators mark a critical step towards realistic applications in cryptography and fundamental physics tests.Comment: 34 pages, 10 figure

High-speed measurement-device-independent quantum key distribution with integrated silicon photonics

Measurement-device-independent quantum key distribution (MDI-QKD) removes all detector side channels and enables secure QKD with an untrusted relay. It is suitable for building a star-type quantum access network, where the complicated and expensive measurement devices are placed in the central untrusted relay and each user requires only a low-cost transmitter, such as an integrated photonic chip. Here, we experimentally demonstrate a 1.25 GHz silicon photonic chip-based MDI-QKD system using polarization encoding. The photonic chip transmitters integrate the necessary encoding components for a standard QKD source. We implement random modulations of polarization states and decoy intensities, and demonstrate a finite-key secret rate of 31 bps over 36 dB channel loss (or 180 km standard fiber). This key rate is higher than state-of-the-art MDI-QKD experiments. The results show that silicon photonic chip-based MDI-QKD, benefiting from miniaturization, low-cost manufacture and compatibility with CMOS microelectronics, is a promising solution for future quantum secure networks.Comment: 30 pages, 12 figure

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Energy efficient mobile edge computing in dense cellular networks

Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet the increasing computation demands from mobile devices, with the dense deployment of Base Stations (BSs), is foreseen as a key step towards the next generation mobile networks. However, new challenges arise for designing energy efficient networks since radio access resources and computing resources of BSs have to be jointly managed, and yet they are complexly coupled with traffic in both spatial and temporal domains. In this paper, we address the challenge of incorporating MEC into dense cellular networks, and propose an efficient online algorithm, called ENGINE (ENerGy constrained offloadINg and slEeping) which makes joint computation offloading and BS sleeping decisions in order to maximize the quality of service while keeping the energy consumption low. Our algorithm leverages Lyapunov optimization technique, works online and achieves a close-to-optimal performance without using future information. Our simulation results show that our algorithm can effectively reduce energy consumption while guaranteeing quality of service for users

Computation Peer Offloading in Mobile Edge Computing with Energy Budgets

The dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing capabilities paves the way for pervasive mobile edge computing (MEC), enabling ultra-low latency and location-awareness for emerging mobile applications. To handle spatially imbalanced computation workloads in the network, cooperation among SBSs via peer offloading is essential to avoid large latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many challenges due to uncertainties of the system dynamics, limited energy budget committed by SBS owners and co- provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called OPEN, by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term energy budget. OPEN works online without requiring future information of system dynamics, yet provides provably near-optimal performance compared to the oracle solution with complete future information. Extensive simulations are carried out and show that proposed algorithm dramatically improves the performance of edge computing system

Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks

The (ultra-)dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing functionalities paves the way for pervasive mobile edge computing (MEC), enabling ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. To handle spatially uneven computation workloads in the network, cooperation among SBSs via workload peer offloading is essential to avoid large computation latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many unique challenges in small cell networks due to limited energy resources committed by self-interested SBS owners, uncertainties in the system dynamics and co-provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called OPEN, by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term constraints. OPEN works online without requiring information about future system dynamics, yet provides provably near-optimal performance compared to the oracle solution that has the complete future information. In addition, this paper formulates a novel peer offloading game among SBSs, analyzes its equilibrium and efficiency loss in terms of the price of anarchy in order to thoroughly understand SBSs' strategic behaviors, thereby enabling decentralized and autonomous peer offloading decision making. Extensive simulations are carried out and show that peer offloading among SBSs dramatically improves the edge computing performance