Pericardial Fat and Echocardiographic Measures of Cardiac Abnormalities

Objective: Pericardial adipose tissue (PAT), a regional fat depot adjacent to the myocardium, may mediate the complex relation between obesity and cardiac left ventricular (LV) abnormalities. We sought to evaluate the association of PAT with echocardiographic measures of LV abnormalities in the Jackson Heart Study (JHS). Research Design and Methods: A total of 1,414 African Americans (35% men; mean age 58 years) from the JHS underwent computed tomographic assessment of PAT and abdominal visceral adipose tissue (VAT) from 2007 to 2009 and echocardiography examination between 2000 and 2004. Echocardiographic measures of left atrial (LA) internal diameter, LV mass, LV ejection fraction (LVEF), and E-wave velocity-to-A-wave velocity ratio (E/A ratio) were examined in relation to PAT, VAT, BMI, and waist circumference (WC). Results: All adiposity measures were positively correlated with LA diameter and LV mass and negatively correlated with E/A ratio (P = 0.02 to 0.0001) and were not with LVEF (P = 0.36–0.61). In women, per 1-SD increment of PAT, we observed association with higher LV mass (9.0 $\pm$ 1.7 gm, P = 0.0001) and LA diameter (1.0 $\pm$ 0.1 mm, P = 0.0001). However, the magnitude of the association between PAT and cardiac measures was similar compared with VAT (P = 0.65 [LV mass]; P = 0.26 [LA diameter]) and was smallercompared with BMI (P = 0.002 [LV mass]; P = 0.01 [LA diameter]) and WC (P = 0.009 [LA diameter]). Conclusions: PAT is correlated with echocardiographic measures of cardiac LV abnormalities, but the association is not stronger than other adiposity measures

Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

As one of the key technologies in the fifth generation of mobile communications, massive multi-input multi-output (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radio-frequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users, and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm

Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay. Specifically, a deep Q-network (DQN) is conceived for capturing the relationship between the optimal routing decision and the local geographic information observed by the forwarding node. The DQN is trained in an offline manner based on historical flight data and then stored by each airplane for assisting their routing decisions during flight. To boost the learning efficiency and the online adaptability of the proposed DQN-routing, we further exploit the knowledge concerning the system’s dynamics by using a deep value network (DVN) conceived with a feedback mechanism. Our simulation results show that both DQN-routing and DVN-routing achieve lower E2E delay than the benchmark protocol, and DVN-routing performs similarly to the optimal routing that relies on perfect global information

Intelligent Predictive Beamforming for Integrated Sensing and Communication Based Vehicular-to-Infrastructure Systems

Integrated Sensing and Communication (ISAC) has become a promising paradigm for next-generation wireless communications, which are capable of jointly performing sensing and communication operations. In ISAC systems, sensing accuracy and transmission rate are two major metrics to be targeted. In this paper, we propose a predictive beamforming approach based on the multi-dimensional feature extraction network (MDFEN) for vehicle-to-infrastructure (V2I) systems. In particular, in order to achieve high precision and low latency beamforming, the roadside unit (RSU) will perform angle parameter estimation and prediction based on the ISAC signal echoes. Furthermore, our predictive beamforming approach based on the multidimensional feature extraction network (MDFEN) is capable of improving the efficient beam alignment by exploiting the joint spatio-temporal characteristics of the received signals at the RSU side. Simulation results demonstrate that the proposed approach achieves a higher accuracy in angle tracking compared to convolutional neural network and long short-term memory models. At the same time, the system is capable of obtaining a higher transmission rate

Dietary Patterns, Abdominal Visceral Adipose Tissue and Cardiometabolic Risk Factors in African Americans: the Jackson Heart Study

Dietary behavior is an important lifestyle factor to impact an individual’s risk of developing cardiovascular disease (CVD). However, the influence of specific dietary factors on CVD risk for African Americans remains unclear. We conducted a cross-sectional study of 1775 participants from Jackson Heart Study (JHS) Exam 2 (between 2006 and 2009) who were free of hypertension, diabetes and CVD at the baseline (between 2001 and 2004). Dietary intakes were documented using a validated food-frequency questionnaire (FFQ) and dietary patterns were generated by factor analysis. Three major dietary patterns were identified: a “southern”, a “fast food” and a “prudent” pattern. After adjustment for age, sex, smoking and alcohol status, education level and physical activity, high “southern” pattern score was associated with an increased odds ratio (OR) for high abdominal visceral adipose tissue (VAT) (OR:1.80, 95%CI:1.1–3.0, p=0.02), hypertension (OR:1.42, 95%CI:1.1–1.9, p=0.02), diabetes (OR:2.03, 95%CI:1.1–3.9, p=0.03) and metabolic syndrome (OR:2.16, 95%CI:1.3–3.6, p=0.004). Similar associations were also observed in the “fast food” pattern (p ranges 0.03–0.0001). The “prudent” pattern was significantly associated, in a protective direction, with hypertension (OR 0.69, 95%CI 0.5–0.9, p=0.02). In conclusion, dietary patterns, especially the “southern” pattern, identified from a regional specific FFQ in this Deep South African Americans, are correlated with abdominal VAT and cardiometabolic risk factors

Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data

Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-toground and multi-hop air-to-air links. In this article, we conceive space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earthorbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-, groundand sea-layer. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of SAGINs, we propose a deep learning (DL) aided multi-objective routing algorithm, which exploits the quasi-predictable network topology and operates in a distributed manner. Our simulation results based on real satellite, flight, and shipping data in the North Atlantic region show that the integrated network enhances the coverage quality by reducing the end-to-end (E2E) delay and by boosting the E2E throughput as well as improving the path-lifetime. The results demonstrate that our DL-aided multiobjective routing algorithm is capable of achieving near Paretooptimal performance

Multiobjective Optimization for Integrated Ground-Air-Space Networks: Current Research and Future Challenges

With space and aerial platforms deployed at different altitudes, integrated ground-air-space (IGAS) networks will have multiple vertical layers, hence forming a 3D structure. These 3D IGAS networks integrating both aerial and space platforms into terrestrial communications constitute a promising architecture for building fully connected global next-generation networks (NGNs)

Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information

Priority-Aware Secure Precoding Based on Multi-Objective Symbol Error Ratio Optimization

The secrecy capacity based on the assumption of having continuous distributions for the input signals constitutes one of the fundamental metrics for the existing physical layer security (PHYS) solutions. However, the input signals of real-world communication systems obey discrete distributions. Furthermore, apart from the capacity, another ultimate performance metric of a communication system is its symbol error ratio (SER). In this paper, we pursue a radically new approach to PHYS by considering rigorous direct SER optimization exploiting the discrete nature of practical modulated signals. Specifically, we propose a secure precoding technique based on a multi-objective SER criterion, which aims for minimizing the confidential messages’ SER at their legitimate user, while maximizing the SER of the confidential messages leaked to the illegitimate user. The key to this challenging multi-objective optimization problem is to introduce a priority factor that controls the priority of directly minimizing the SER of the legitimate user against directly maximizing the SER of the leaked confidential messages. Furthermore, we define a new metric termed as the security-level, which is related to the conditional symbol error probability of the confidential messages leaked to the illegitimate user. Additionally, we also introduce the secure discrete-input continuous-output memoryless channel (DCMC) capacity referred to as secure-DCMC-capacity, which serves as a classical security metric of the confidential messages, given a specific discrete modulation scheme. The impacts of both the channel’s Rician factor and the correlation factor of antennas on the security-level and the secure-DCMC-capacity are investigated. Our simulation results demonstrate that the proposed priority-aware secure precoding based on the direct SER metric is capable of securing transmissions, even in the challenging scenario, where the eavesdropper has three receive antennas, while the legitimate user only has a single one

Biomimetic three-dimensional glioma model printed in vitro for the studies of glioma cells and neurons interactions

The interactions between glioma cells and neurons are important for glioma progression but are rarely mimicked and recapitulated in in vitro three-dimensional (3D) models, which may affect the success rate of relevant drug research and development. In this study, an in vitro bioprinted 3D glioma model consisting of an outer hemispherical shell with neurons and an inner hemisphere with glioma cells is proposed to simulate the natural glioma. This model was produced by extrusion-based 3D bioprinting technology. The cells survival rate, morphology, and intercellular Ca2+ concentration studies were carried out up to 5 days of culturing. It was found that neurons could promote the proliferation of glioma cells around them, associate the morphological changes of glioma cells to be neuron-like, and increase the expression of intracellular Ca2+ of glioma cells. Conversely, the presence of glioma cells could maintain the neuronal survival rate and promote the neurite outgrowth. The results indicated that glioma cells and neurons facilitated each other implying a symbiotic pattern established between two types of cells during the early stage of glioma development, which were seldom found in the present artificial glioma models. The proposed bioprinted glioma model can mimic the natural microenvironment of glioma tissue, provide an in-depth understanding of cellâ cell interactions, and enable pathological and pharmacological studies of glioma.The work was supported by the Program of the National Natural Science Foundation of China [52275291], [51675411], [81972359], the Fundamental Research Funds for the Central Universities, and the Youth Innovation Team of Shaanxi Universities