Cognitive function is an important ability of the brain, but cognitive dysfunction can easily develop once the brain is injured in various neuropathological conditions or diseases. Photobiomodulation ...therapy is a type of noninvasive physical therapy that is gradually emerging in the field of neuroscience. Transcranial photobiomodulation has been commonly used to regulate neural activity in the superficial cortex. To stimulate deeper brain activity, advanced photobiomodulation techniques in conjunction with photosensitive nanoparticles have been developed. This review addresses the mechanisms of photobiomodulation on neurons and neural networks and discusses the advantages, disadvantages and potential applications of photobiomodulation alone or in combination with photosensitive nanoparticles. Photobiomodulation and its associated strategies may provide new breakthrough treatments for cognitive improvement.
Federated learning is a newly emerged distributed machine learning paradigm, where the clients are allowed to individually train local deep neural network (DNN) models with local data and then ...jointly aggregate a global DNN model at the central server. Vehicular edge computing (VEC) aims at exploiting the computation and communication resources at the edge of vehicular networks. Federated learning in VEC is promising to meet the ever-increasing demands of artificial intelligence (AI) applications in intelligent connected vehicles (ICV). Considering image classification as a typical AI application in VEC, the diversity of image quality and computation capability in vehicular clients potentially affects the accuracy and efficiency of federated learning. Accordingly, we propose a selective model aggregation approach, where "fine" local DNN models are selected and sent to the central server by evaluating the local image quality and computation capability. Regarding the implementation of model selection, the central server is not aware of the image quality and computation capability in the vehicular clients, whose privacy is protected under such a federated learning framework. To overcome this information asymmetry, we employ two-dimension contract theory as a distributed framework to facilitate the interactions between the central server and vehicular clients. The formulated problem is then transformed into a tractable problem through successively relaxing and simplifying the constraints, and eventually solved by a greedy algorithm. Using two datasets, i.e., MNIST and BelgiumTSC, our selective model aggregation approach is demonstrated to outperform the original federated averaging (FedAvg) approach in terms of accuracy and efficiency. Meanwhile, our approach also achieves higher utility at the central server compared with the baseline approaches.
Inflammatory depression is closely related to neuroinflammation. However, current anti-inflammatory drugs have low permeability to cross blood-brain barrier with difficulties reaching the central ...nervous system to provide therapeutic effectiveness. To overcome this limitation, the nano-based drug delivery technology was used to synthesize melanin-like polydopamine nanoparticles (PDA NPs) (~ 250 nm) which can cross the blood-brain barrier. Importantly, PDA NPs with abundant phenolic hydroxyl groups function as excellent free radical scavengers to attenuate cell damage caused by reactive oxygen species or acute inflammation. In vitro experiments revealed that PDA NPs exhibited excellent antioxidative properties. Next, we aimed to investigate the therapeutic effect of PDA NPs on inflammatory depression through intraperitoneal injection to the lipopolysaccharide-induced inflammatory depression model in mice. PDA NPs significantly reversed the depression-like behavior. PDA NPs was also found to reduce the peripheral and central inflammation induced by LPS, showing that alleviated splenomegaly, reduced serum inflammatory cytokines, inhibited microglial activation and restored synaptic loss. Various experiments also showed that PDA NPs had good biocompatibility both in vivo and in vitro. Our work suggested that PDA NPs may be biocompatible nano-drugs in treating inflammatory depression but their clinical application requires further study.
The current cloud-based Internet-of-Things (IoT) model has revealed great potential in offering storage and computing services to the IoT users. Fog computing, as an emerging paradigm to complement ...the cloud computing platform, has been proposed to extend the IoT role to the edge of the network. With fog computing, service providers can exchange the control signals with the users for specific task requirements, and offload users' delay-sensitive tasks directly to the widely distributed fog nodes at the network edge, and thus improving user experience. So far, most existing works have focused on either the radio or computational resource allocation in the fog computing. In this work, we investigate a joint radio and computational resource allocation problem to optimize the system performance and improve user satisfaction. Important factors, such as service delay, link quality, mandatory benefit, and so on, are taken into consideration. Instead of the conventional centralized optimization, we propose to use a matching game framework, in particular, student project allocation (SPA) game, to provide a distributed solution for the formulated joint resource allocation problem. The efficient SPA-(S,P) algorithm is implemented to find a stable result for the SPA problem. In addition, the instability caused by the external effect, i.e., the interindependence between matching players, is removed by the proposed user-oriented cooperation (UOC) strategy. The system performance is also further improved by adopting the UOC strategy.
In device-to-device (D2D) communication, mobile users communicate directly without going through the base station. D2D commutation has the advantage of improving spectrum efficiency. But the ...interference introduced by resource sharing of D2D has become a significant challenge. In this paper, we try to optimize the system throughput while simultaneously meeting the quality of service (QoS) requirements for both D2D users and cellular users (CUs). We implement matching theory to solve the resource allocation problem. We utilize two efficient stable matching algorithms to optimize the social welfare while ensuring the network stability. More importantly, we introduce the idea of cheating in matching to further improve D2D users' throughput. It is proven that the cheating mechanism benefits a subset of D2D users without hurting the performance of the rest. Through the simulation results, we demonstrate the effectiveness of both the stable matching and cheating algorithms in terms of improving both D2D users and the overall throughput in D2D communications.
Recently intensive efforts have been made on the transformation of the world's largest physical system, the power grid, into a "smart grid" by incorporating extensive information and communication ...infrastructures. Key features in such a "smart grid" include high penetration of renewable and distributed energy sources, large-scale energy storage, market-based online electricity pricing, and widespread demand response programs. From the perspective of residential customers, we can investigate how to minimize the expected electricity cost with real-time electricity pricing, which is the focus of this paper. By jointly considering energy storage, local distributed generation such as photovoltaic (PV) modules or small wind turbines, and inelastic or elastic energy demands, we mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach. From the theoretical analysis, we have also found a good tradeoff between cost saving and storage capacity. A salient feature of our proposed approach is that it can operate without any future knowledge on the related stochastic models (e.g., the distribution) and is easy to implement in real time. We have also evaluated our proposed solution with practical data sets and validated its effectiveness.
The prevalence of high performance mobile devices such as smartphones and tablets has brought fundamental changes to existing wireless networks. The growth of multimedia and location-based mobile ...services has exponentially increased network congestion and the demands for more wireless access. This has led to the development of advanced techniques to address the resulting challenges based on the concept of cooperation in various heterogeneous network scenarios. Thus, innovative incentive mechanisms in wireless networks are needed to ensure the participation of third party nodes, such as access points, small cells, and users. In this tutorial, we demonstrate the effectiveness of contract theory to design incentive mechanisms for a wide range of application scenarios in wireless networks. In contract theory, participants are offered properly designed rewards based on their performances to encourage better participation. First, we present an overview of basic concepts and models of contract theory, with comparisons to other related methods from economics. We then discuss incentive mechanisms, with a focus on the design of rewards in a contract. We demonstrate how contract theory can be utilized for developing effective incentive mechanisms for emerging wireless network scenarios such as traffic offloading, mobile crowdsourcing, and spectrum trading.
Summary Background Acute kidney injury (AKI) has become a worldwide public health problem, but little information is available about the disease burden in China. We aimed to evaluate the burden of ...AKI and assess the availability of diagnosis and treatment in China. Methods We launched a nationwide, cross-sectional survey of adult patients who were admitted to hospital in 2013 in academic or local hospitals from 22 provinces in mainland China. Patients with suspected AKI were screened out on the basis of changes in serum creatinine by the Laboratory Information System, and we reviewed medical records for 2 months (January and July) to confirm diagnoses. We assessed rates of AKI according to two identification criteria: the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) AKI definition and an increase or decrease in serum creatinine by 50% during hospital stay (expanded criteria). We estimated national rates with data from the 2013 report by the Chinese National Health and Family Planning Commission and National Bureau of Statistics. Findings Of 2 223 230 patients admitted to the 44 hospitals screened in 2013, 154 950 (7·0%) were suspected of having AKI by electronic screening, of whom 26 086 patients (from 374 286 total admissions) were reviewed with medical records to confirm the diagnosis of AKI. The detection rate of AKI was 0·99% (3687 of 374 286) by KDIGO criteria and 2·03% (7604 of 374 286) by expanded criteria, from which we estimate that 1·4–2·9 million people with AKI were admitted to hospital in China in 2013. The non-recognition rate of AKI was 74·2% (5608 of 7555 with available data). Renal referral was done in 21·4% (1625 of 7604) of the AKI cases, and renal replacement therapy was done in 59·3% (531 of 896) of those who had the indications. Delayed AKI recognition was an independent risk factor for in-hospital mortality, and renal referral was an independent protective factor for AKI under-recognition and mortality Interpretation AKI has become a huge medical burden in China, with substantial underdiagnosis and undertreatment. Nephrologists should take the responsibility for leading the battle against AKI. Funding National 985 Project of China, National Natural Science Foundation of China, Beijing Training Program for Talents, International Society of Nephrology Research Committee, and Bethune Fund Management Committee.
In this paper, we investigate the minimization of the total energy cost of multiple residential households in a smart grid neighborhood sharing a load serving entity. Specifically, each household may ...have renewable generation, energy storage as well as inelastic and elastic energy loads, and the load serving entity attempts to coordinate the energy consumption of these households in order to minimize the total energy cost within this neighborhood. The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. Numerical results based on real-world trace data show that our control algorithm can effectively reduce the total energy cost in the neighborhood.
Recent developments on DFWS have shown that wireless signals can be utilized not only as a communication medium to transmit data, but also as an enabling tool for realizing non-intrusive device-free ...sensing. DFWS has many potential applications, for example, human detection and localization, human activity and gesture recognition, surveillance, elder or patient monitoring, emergency rescue, and so on. With the development and maturity of DFWS, we believe it will eventually empower traditional wireless networks with the augmented ability to sense the surrounding environment, and evolve wireless communication networks into intelligent sensing networks that could sense human-scale context information within the deployment area of the network. The research field of DFWS has emerged quickly recently. This article tries to provide an integrated picture of this emerging field and hopefully inspire future research. Specifically, we present the working principle and system architecture of the DFWS system, review its potential applications, and discuss research challenges and opportunities.
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