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Key message: We identified a dosage-dependent dominant negative form of Sar1c, which confirms the essential role of COPII system in mediating ER export of storage proteins in rice endosperm. Abstract: Higher plants accumlate large amounts of seed storage proteins (SSPs). However, mechanisms underlying SSP trafficking are largely unknown, especially the ER-Golgi anterograde process. Here, we showed that a rice glutelin precursor accumulation13 (gpa13) mutant exhibited floury endosperm and overaccumulated glutelin precursors, which phenocopied the reported RNAi-Sar1abc line. Molecular cloning revealed that the gpa13 allele encodes a mutated Sar1c (mSar1c) with a deletion of two conserved amino acids Pro134 and Try135. Knockdown or knockout of Sar1c alone caused no obvious phenotype, while overexpression of mSar1c resulted in seedling lethality similar to the gpa13 mutant. Transient expression experiment in tobacco combined with subcellular fractionation experiment in gpa13 demonstrated that the expression of mSar1c affects the subcellular distribution of all Sar1 isoforms and Sec23c. In addition, mSar1c failed to interact with COPII component Sec23. Conversely, mSar1c competed with Sar1a/b/d to interact with guanine nucleotide exchange factor Sec12. Together, we identified a dosage-dependent dominant negative form of Sar1c, which confirms the essential role of COPII system in mediating ER export of storage proteins in rice endosperm.

The reconfigurable intelligent surface (RIS) can proactively modify the wireless communication environment and further improve the service quality of the wireless networks. Motivated by this vision, in this paper, we propose to introduce the RIS into the unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) systems. Considering both the amount of completed task bits and the energy consumption, the energy efficiency of the RIS-assisted UAV-enabled MEC systems is maximized by jointly optimizing the bit allocation, phase shift, and UAV trajectory via an iterative algorithm with a double-loop structure. Simulation results show that: 1) the UAV tends to fly closer to the RIS rather than the IoT devices; 2) the energy efficiency first increases and then decreases with the increase of the total amount of task-input bits of IoT devices; 3) higher energy efficiency can be achieved by our proposed algorithm.

The reconfigurable intelligent surface (RIS) can proactively modify the wireless communication environment and further improve the service quality of the wireless networks. Motivated by this vision, in this paper, we propose to introduce the RIS into the unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) systems. Considering both the amount of completed task bits and the energy consumption, the energy efficiency of the RIS-assisted UAV-enabled MEC systems is maximized by jointly optimizing the bit allocation, phase shift, and UAV trajectory via an iterative algorithm with a double-loop structure. Simulation results show that: 1) the UAV tends to fly closer to the RIS rather than the IoT devices; 2) the energy efficiency first increases and then decreases with the increase of the total amount of task-input bits of IoT devices; 3) higher energy efficiency can be achieved by our proposed algorithm.

Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for computation resources of IoT users to support diverse applications. Mobile edge computing (MEC) has been deemed a promising solution to settle the conflict between the resource-hungry mobile applications and the resource-constrained IoT users. On the other hand, in order to provide ubiquitous and reliable connectivity in wireless networks, unmanned aerial vehicles (UAVs) can be leveraged as efficient aerial platforms by exploiting their inherent attributes, such as the on-demand deployment, high cruising altitude, and controllable maneuverability in three-dimensional (3D) space. Thus, the UAV-enabled aerial MEC is believed as a win-win solution to facilitate cost-effective and energy-saving communication and computation services in various environments. In this paper, we provide a comprehensive survey on the UAV-enabled aerial MEC. Firstly, the related advantages and research challenges for aerial MEC are discussed. Then, we provide a comprehensive review of the recent research advances, which is categorized by different domains, including the joint optimization of UAV trajectory, computation offloading and resource allocation, UAV deployment, task scheduling and load balancing, interplay between aerial MEC and other technologies, as well as the machine-learning (ML)-driven optimization. Finally, some important research directions deserved more efforts in future work are summarized.

In this paper, we investigate the energy-efficient multi-user mobile-edge computing offloading problem in massive MIMO enabled HetNets, where the CPU-cycle frequency of mobile devices, uplink power control, computational task offloading ratio and uplink transmission duration are jointly optimized. The problem is formulated as minimizing the energy consumption of all mobile devices while satisfying the maximum latency requirement. Specifically, to address this non-convex problem, a low-complexity algorithm is proposed relied on alternating optimization, where we address the joint computational task offloading ratio and uplink transmission duration optimization problem and the uplink power control problem iteratively. Besides, the effectiveness and convergence of the proposed iterative algorithm are analytically studied. Numerical results demonstrate that our proposed algorithm consumes less energy compared to local computing and full uploading schemes, and the application of massive MIMO in HetNets helps to reduce energy consumption of mobile devices.

In this paper, we investigate the energy-efficient multi-user mobile-edge computing offloading problem in massive MIMO enabled HetNets, where the CPU-cycle frequency of mobile devices, uplink power control, computational task offloading ratio and uplink transmission duration are jointly optimized. The problem is formulated as minimizing the energy consumption of all mobile devices while satisfying the maximum latency requirement. Specifically, to address this non-convex problem, a low-complexity algorithm is proposed relied on alternating optimization, where we address the joint computational task offloading ratio and uplink transmission duration optimization problem and the uplink power control problem iteratively. Besides, the effectiveness and convergence of the proposed iterative algorithm are analytically studied. Numerical results demonstrate that our proposed algorithm consumes less energy compared to local computing and full uploading schemes, and the application of massive MIMO in HetNets helps to reduce energy consumption of mobile devices.

In this paper, we investigate the resource optimization problem for device-to-device (D2D) communications in the fifth-generation cellular networks, where multiple D2D links and cellular links share the same spectrum. A general framework is proposed to jointly optimize the energy efficiency (EE), spectral efficiency (SE), and queuing delay. Specifically, we formulate the problem as a stochastic optimization model aiming at maximizing the EE and SE concurrently under the network stability constraint, where subchannel allocation and power control are jointly optimized. Afterwards, with the help of Lyapunov techniques and weighted sum method, it is then transformed into a single-objective optimization problem, which is a mixedinteger and non-convex problem. Therefore, to solve this challenging subchannel allocation and power control problem with low computational complexity, we separate it into two levels of problems, and a twostage iterative algorithm is proposed, which only requires polynomial computational complexity. Through theoretical analysis and numerical results, the effectiveness, convergence, and optimality of the proposed algorithm are validated.

In this paper, we concentrate on the robust multiobjective optimization (MOO) for the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-to-device (D2D) communications underlaying heterogeneous networks (HetNets). Different from traditional resource optimization, we focus on finding robust Pareto optimal solutions for spectrum allocation and power coordination in D2D communications underlaying HetNets with the consideration of interference channel uncertainties. The problem is formulated as an uncertain MOO problem to maximize EE and SE of cellular users (CUs) simultaneously while guaranteeing the minimum rate requirements of both CUs and D2D pairs.With the aid of "-constraint method and strict robustness, we propose a general framework to transform the uncertain MOO problem into a deterministic single-objective optimization problem. As exponential computational complexity is required to solve this highly non-convex problem, the power coordination and the spectrum allocation problems are solved separately, and an effective two-stage iterative algorithm is developed. Finally, simulation results validate that our proposed robust scheme converges fast and significantly outperforms the non-robust scheme in terms of the effective EE-SE tradeoff and the quality of service satisfying probability of D2D pairs.

In this paper, we investigate the resource optimization problem for device-to-device (D2D) communications in the fifth-generation cellular networks, where multiple D2D links and cellular links share the same spectrum. A general framework is proposed to jointly optimize the energy efficiency (EE), spectral efficiency (SE), and queuing delay. Specifically, we formulate the problem as a stochastic optimization model aiming at maximizing the EE and SE concurrently under the network stability constraint, where subchannel allocation and power control are jointly optimized. Afterwards, with the help of Lyapunov techniques and weighted sum method, it is then transformed into a single-objective optimization problem, which is a mixedinteger and non-convex problem. Therefore, to solve this challenging subchannel allocation and power control problem with low computational complexity, we separate it into two levels of problems, and a twostage iterative algorithm is proposed, which only requires polynomial computational complexity. Through theoretical analysis and numerical results, the effectiveness, convergence, and optimality of the proposed algorithm are validated.

In this paper, we concentrate on the robust multiobjective optimization (MOO) for the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-to-device (D2D) communications underlaying heterogeneous networks (HetNets). Different from traditional resource optimization, we focus on finding robust Pareto optimal solutions for spectrum allocation and power coordination in D2D communications underlaying HetNets with the consideration of interference channel uncertainties. The problem is formulated as an uncertain MOO problem to maximize EE and SE of cellular users (CUs) simultaneously while guaranteeing the minimum rate requirements of both CUs and D2D pairs.With the aid of "-constraint method and strict robustness, we propose a general framework to transform the uncertain MOO problem into a deterministic single-objective optimization problem. As exponential computational complexity is required to solve this highly non-convex problem, the power coordination and the spectrum allocation problems are solved separately, and an effective two-stage iterative algorithm is developed. Finally, simulation results validate that our proposed robust scheme converges fast and significantly outperforms the non-robust scheme in terms of the effective EE-SE tradeoff and the quality of service satisfying probability of D2D pairs.

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive MIMO enabled HetNets while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously.With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared to other algorithms.

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive MIMO enabled HetNets while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously.With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared to other algorithms.

In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) while ensuring proportional rate fairness in massive multiple-input multiple-output enabled heterogenous networks, where user association and power coordination are jointly considered. It is first formulated as a multi-objective optimization problem, and then transformed into a single-objective optimization problem. To solve this mixed-integer non-convex problem, an effective algorithm is developed, where the original problem is separated into lower level power coordination problem and master user association problem. Simulation results verify that our proposed algorithm can significantly improve the performance of EE-SE tradeoff and obtain higher rate fairness compared with other algorithms.

In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) while ensuring proportional rate fairness in massive multiple-input multiple-output enabled heterogenous networks, where user association and power coordination are jointly considered. It is first formulated as a multi-objective optimization problem, and then transformed into a single-objective optimization problem. To solve this mixed-integer non-convex problem, an effective algorithm is developed, where the original problem is separated into lower level power coordination problem and master user association problem. Simulation results verify that our proposed algorithm can significantly improve the performance of EE-SE tradeoff and obtain higher rate fairness compared with other algorithms.

The regulation of macrophage orientation pathological conditions is important but still incompletely understood. Here, we show that IL-10 and Rag1 double knockout mice spontaneously develop colitis with dominant M1 macrophage phenotype, suggesting that IL-10 regulates macrophage orientation in inflammation. We demonstrate that IL-10 stimulation induced miR-146b expression, and that the expression of miR-146b was impaired in IL-10 deficient macrophages. Our data show that miR-146b targets IRF5, resulting in the regulation of macrophage activation. Furthermore, miR-146b deficient mice developed intestinal inflammation with enhanced M1 macrophage polarization. Finally, miR-146b mimic treatment significantly suppresses M1 macrophage activation and ameliorates colitis development in vivo. Collectively, the results suggest that IL-10 dependent miR-146b plays an important role in the modulation of M1 macrophage orientation. (C) 2016 The Authors. Published by Elsevier B.V.