Left ventricular diastolic function in relation to the urinary proteome: a proof-of-concept study in a general population

Background: In previous studies, we identified two urinary proteomic classifiers, termed HF1 and HF2, which discriminated subclinical diastolic left ventricular (LV) dysfunction from normal. HF1 and HF2 combine information from 85 and 671 urinary peptides, mainly up- or down-regulated collagen fragments. We sought to validate these classifiers in a population study. Methods: In 745 people randomly recruited from a Flemish population (49.8 years; 51.3% women), we measured early and late diastolic peak velocities of mitral inflow (E and A) and mitral annular velocities (e' and a') by conventional and tissue Doppler echocardiography, and the urinary proteome by capillary electrophoresis coupled with mass spectrometry. Results: In the analyses adjusted for sex, age, body mass index, blood pressure, heart rate, LV mass index and intake of medications, we expressed effect sizes per 1-SD increment in the classifiers. HF1 was associated with 0.204 cm/s lower e' peak velocity (95% confidence interval, 0.057–0.351; p = 0.007) and 0.145 higher E/e' ratio (0.023–0.268; p = 0.020), while HF2 was associated with a 0.174 higher E/e' ratio (0.046–0.302; p = 0.008). According to published definitions, 67 (9.0%) participants had impaired LV relaxation and 96 (12.9%) had elevated LV filling pressure. The odds of impaired relaxation associated with HF1 was 1.38 (1.01–1.88; p = 0.043) and that of increased LV filling pressure associated with HF2 was 1.38 (1.00–1.90; p = 0.052). Conclusions: In a general population, the urinary proteome correlated with diastolic LV dysfunction, proving its utility for early diagnosis of this condition

Time-Dependent Pricing for Bandwidth Slicing under Information Asymmetry and Price Discrimination

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP's revenue and reducing the peak-to-average traffic ratio (PATR).This work was supported in part by the National Natural Science Foundation of China under Grant Number 61971189, the Science and Technology Project of State Grid Corporation of China under Grant Number SGSDDK00KJJS1900405, the Exploration Project of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) under Grant Number LAPS2019-12, the Fundamental Research Funds for the Central Universities under Grant Number 2020MS001, and the National Key R&D Program of China under Grant Number 2019YFB1704702. This article was presented in part at the International Wireless Communications and Mobile Computing Conference (IWCMC’18), Limassol, Cyprus, 2018. The associate editor coordinating the review of this article and approving it for publication was T. He. (Corresponding author: Bo Gu.) Zhenyu Zhou is with the State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 10220

Combined Centralized and Distributed Resource Allocation for Green D2D Communications

When integrating device-to-device (D2D) communications with densely deployed cellular networks, both energy efficiency (EE) and quality of service (QoS) will be severely degraded by strong intracell and intercell interference. To optimize EE while guaranteeing QoS provisioning, a three-stage energy-efficient resource allocation algorithm is proposed, which combines centralized interference mitigation and distributed power allocation algorithms by exploiting multi-cell cooperations, nonco-operative game, nonlinear fractional programming, and Lagrange dual decomposition. Simulation results have demonstrated that the proposed algorithm achieves a nearly zero infeasibility ratio, and improves EE performance significantly for both cellular and D2D user equipments (UEs) compared to the previous distributed scheme.Location : Shenzhen, CHINADate : NOV 02-04, 201