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This article presents a wideband dB-linear variable-gain amplifier (VGA) with a compensated negative pseudo-exponential generation (C-NPEG) technique for high-speed applications. By shifting the concave and the convex function of an original negative pseudo-exponential generator (NPEG), two additional control signals are produced to manipulate two variable-gain Gilbert-cell-based amplifiers, respectively. Cascading these two stages with another variable gain stage controlled by the original NPEG, the multiplication of three different but mutually compensated control signals is realized. As a result, the dB-linear gain range of the overall VGA is extended, and the gain error is suppressed with the proposed C-NPEG technique. To further improve the VGA’s gain while maintaining a wide bandwidth, a fixed-gain stage utilizing five common-source amplifiers with RC -degeneration and active feedback is implemented. Fabricated in 40-nm CMOS technology, the core circuit consumes 24.6-mA current (excluding the output buffer) under a power supply of 1.1 V and occupies an active die area of 0.038 mm2. The measured gain of the overall VGA varies from −34 to 17 dB, while the dB-linear gain error over the 51-dB gain range is within ±1 dB. Meanwhile, the −3-dB bandwidth is relatively constant from 7 to 8 GHz under different gain settings.

Psoriasis is a chronic inflammatory disease that affects approximately 1%–5% of the population worldwide. Considering frequent relapse, adverse drug reactions, and large costs of treatment, it is urgent to identify new medications for psoriasis. Keratinocytes play an essential role during psoriasis development, and they express high levels of β2-Adrenergic receptor (β2-AR), which increases intracellular cAMP levels when activated. Increased level of cAMP is associated with the inhibition of epidermal cell proliferation. In the present study, we observed the effect of salmeterol, a long-acting β2-AR agonist, on the proliferation and apoptosis of keratinocytes as well as imiquimod-induced psoriasis-like skin lesions in mice. As phosphodiesterase 4 (PDE4) inhibitors increases intracellular cAMP concentration by inhibiting its inactivation, we further explored the synergetic effect of a PDE4 inhibitor and salmeterol on psoriasis-like skin lesions in mice. Our results indicated that salmeterol effectively inhibited the proliferation of HaCaT cells induced by TNF-α and serum, and this effect was accompanied by significantly increased apoptosis and CREB phosphorylation, which were reversed by the PKA inhibitor, H89. Salmeterol ameliorated imiquimod-induced psoriasis-like skin lesions in mice, but salmeterol combined with a PDE4 inhibitor had no synergetic effect in improving skin lesions in mice. Of note, the synergistic effects of anti-proliferation and induction of apoptosis in HaCaT cells appeared by inhibiting ERK signaling. In summary, salmeterol, a long-acting β2-AR agonist, alleviates the severity of psoriasis via inhibiting the proliferation and promoting apoptosis of keratinocytes, partially by activating the cAMP/PKA signaling pathway.

Carbon dots have been widely focused on the field of metal ion detection due to their excellent optical property. Herein, novel orange fluorescent nitrogen and boron co-doped carbon dots (NB-CDs) are obtained by one-pot solvothermal using p-phenylenediamine and boric acid as raw materials. The NB-CDs exhibit excitation-independent emissions and the maximum emission wavelength is 597 nm at 420 nm excitation. The fluorescence can be quenched by Ce

As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.

Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost : they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Low retrieval accuracy : they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix ${{\mathbf P}}$ P . Then, it uses the matrix ${{\mathbf P}}$ P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix ${{\mathbf P}}$ P to the edge server and uses it to multiply the image ${{\mathbf x}}$ x . The result ${{\mathbf P}}^T{{\mathbf x}}$ P T x , i.e. , image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix ${{\mathbf P}}$ P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.

This paper presents a supply-insensitive low-power quadrature local oscillation (LO) generator based on the current-reused voltage-controlled oscillator (VCO) and divide-by-two frequency divider (FD). By utilizing the mutual power-supply rejection (M-PSR) technique, a voltage regulator is embedded into the FD to provide a supply-insensitive voltage for VCO. Meanwhile, the feedback loop provides a supply-insensitive current and time constant compensation for FD. The M-PSR technique does not require extra voltage headroom, thus the proposed quadrature LO generator can work under the supply of 0.9 V. A prototype of the proposed quadrature LO generator is implemented in a 28-nm CMOS process. Measurement results indicate that the LO generator achieves a phase noise of -110.5 dBc/Hz@1MHz at the carrier of 5.9 GHz and the power consumption is 1.8 mW. This work has a competitive figure of merit in supply sensitivity or in area (FOMss or FOMa respectively) among the state-of-the-art works. Ministry of Education (MOE) Published version This work was supported in part by Singapore Ministry of Education Academic Research Fund Tier 2 under Grant MOE2019-T2-1-114, and in part by China Scholarship Council.

Background Secretory carrier membrane proteins 3 (SCAMP3) is an endocytosis-associated protein involved in regulating endosomal pathways and the trafficking of vital signaling receptors. This study aimed to comprehensively assess the role of SCAMP3 in hepatocellular carcinoma (HCC) by integrated bioinformatics analysis. Methods In this study, bioinformatics databases were used to explore the differential expression status and prognostic value of SCAMP3 gene in HCC, and bioinformatics analyses of survival data and interactors of SCAMP3 were conducted to predict the prognostic value of SCAMP3 in HCC. Results Using the TCGA data, our data shows that SCAMP3 mRNA expression is most significantly different between liver and hepatocellular carcinoma tissues and higher expression of SCAMP3 has unfavorable prognostic significance in HCC. Tumor grade, stage, and gender also showed a significant relevance with SCAMP3 expression. High SCAMP3 expression of males revealed significantly poorer survival and progression compared with low SCAMP3 expression of males. BioGRID statistics explores 79 unique interactions with SCAMP3 and multiple post translational modifications. Further analysis finds that SOCS2 may negatively correlate with SCAMP3, while GBA, MX1, and DDOST positively correlate with SCAMP3. Moreover, ncRNA analysis shows that SCAMP3 gene expression is positively associated with lncRNA SBF2-AS1 and negatively related with Has-miR-145. The expressions of SBF2-AS1 and Has-miR-145 are also significantly related with survival in HCC. Discussion SCAMP3 expression can be affected by multiple genes or ncRNAs expression that are associated with survival, thus suggesting that SCAMP3 can be used as a clinical diagnosis and prognostic biomarker in HCC.

Recently, Low Earth Orbit (LEO) satellites experience rapid development and satellite edge computing emerges to address the limitation of bent-pipe architecture in existing satellite systems. Introducing energy-consuming computing components in satellite edge computing increases the depth of battery discharge. This will shorten batteries' life and influences the satellites' operation in orbit. In this paper, we aim to extend batteries' life by minimizing the depth of discharge for Earth observation missions. Facing the challenges of wireless uncertainty and energy harvesting dynamics, our work develops an online energy scheduling algorithm within an online convex optimization framework. Our algorithm achieves sub-linear regret and the constraint violation asymptotically approaches zero. Simulation results show that our algorithm can reduce the depth of discharge significantly.

Mobile-edge computing provisions computing and storage resources by deploying edge servers (ESs) at the edge of the network to support ultralow delay and high bandwidth services. To ensure QoS of latency-sensitive services in vehicular networks, service migration is required to migrate data of the ongoing services to the closest ES seamlessly when users move across different ESs. To achieve seamless service migration, path selection is proposed to obtain one or more paths (consisting of several switches and ESs) to transfer service data. We focus on the following problems about path selection: 1) where to implement path selection? 2) how to coordinate interests of mobile users (i.e., vehicles) and network providers since they have conflicting interests during path selection? and 3) how to ensure seamless service migration during the migration of vehicles? To address the above problems, this article investigates path selection for seamless service migration. We propose a path-selection algorithm to jointly optimize both interests of the network plane (i.e., the cost for network providers) and service plane (i.e., QoE of users). We first formulate it as a multiobjective optimization problem and further prove theoretically that the proposed algorithm can give a weakly Pareto-optimal solution . Moreover, to improve the scalability of the proposed algorithm, a distance-based filter strategy is designed to eliminate undesired switches in advance. We conduct experiments on two synthesized data sets and the results validate the effectiveness of the proposed algorithm.

Vehicular edge computing (VEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this article, we study the problem of task scheduling in VEC, where multiple computation-intensive vehicular applications can be offloaded to roadside units (RSUs) and each application can be further divided into multiple tasks with task dependency. The tasks can be scheduled to different mobile-edge computing servers on RSUs for execution to minimize the average completion time of multiple applications. Considering the completion time constraint of each application and the processing dependency of multiple tasks belonging to the same application, we formulate the multiple tasks scheduling problem as an optimization problem that is NP-hard. To solve the optimization problem, we develop an efficient task scheduling algorithm. The basic idea is to prioritize multiple applications and prioritize multiple tasks so as to guarantee the completion time constraints of applications and the processing dependency requirements of tasks. The numerical results demonstrate that our proposed algorithm can significantly reduce the average completion time of multiple applications compared with benchmark algorithms.

As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure probability, a VC may not be executed when it meets with any PM/switch fault. Although the VMs of a VC can be spread out across different fault domains to minimize the risk of violating the availability requirement of the VC, this increases the network core traffic. Therefore, avoiding the decrease in availability caused by the heterogeneous PM/switch failure probabilities and bandwidth-related bottlenecks has been a constant challenge. In this paper, we first introduce a joint optimization function to measure the overall risk cost and overall bandwidth usage in the network core to allocate the same set of data-intensive applications. We then introduce an approach to maximize the value of the joint optimization function. Finally, we performed a side-by-side comparison with prior algorithms, and the experimental results show that our approach outperforms the other existing algorithms.

Nowadays scalable IoT management is a bottleneck of IoT development due to the geographically dispersed distribution, fragmented ownerships, and ever-growing population of IoT devices. To intelligently manage massive decentralized applications (dApps) in IoT usecases, Edgence (EDGe + intelligENCE) is proposed to use edge clouds to access IoT devices and users, and then use its in-built blockchain to realize self-governing and self-supervision of the edge clouds. Edgence proposes to use masternode technology to introduce IoT devices and users into a closed blockchain system, which can extend the range of blockchain to IoT-based dApps. Further, masternodes do good to scalability by raising the TPS (transactions per second) of the blockchain network. To support various dApps, a three-tier validation is proposed, namely script validation, smartcontract validation, and masternode validation. To avoid energy consumption resulted by blockchain consensus, Edgence proposes a random but verifiable way to elect a masternode to generate each new block. The potential of the tailored Edgence is shown by examples of decentralized crowdsourcing and AI training.

Dual enhancement on electronic & ionic conductivity of Ti2Nb10O29 (TNO) electrodes is the key point to achieve high performance by promoting reaction kinetics with accelerated ion/electron transfer when applied in Li-ion batteries (LIBs). Herein, a powerful synergistic trinity strategy is proposed to construct integrated N-doped TNO@TiC/C-NC hierarchical electrodes, in which novel designed TiC/C-NC core-branch skeleton is used to load N-doped TNO (N-TNO) nanoparticles with symbiotic oxygen vacancy. The conductive hierarchical TiC/C-NC core-branch skeleton can not only enhance the electronic conductivity of the whole electrode, but also provide more active loading sites and ensure the structure stability. Moreover, the oxygen defect and N-doping in TNO could narrow the band-gap and enlarge the lattice, both improving the intrinsic electronic and ionic conductivity. The above internal & external synergistic strategies work together to accelerate reaction kinetics of N-TNO@TiC/C-NC to realize a long cycle life (up to 10000 cycles) and preeminent high-rate capability both in liquid batteries (165 mAh g−1 at 100C) and solid-state batteries (108 mAh g−1 at 40C). Furthermore, the excellent high-rate performance is also verified in TNO//LFP (LiFePO4) batteries. Our strategy provides valid routes to achieve fast reaction kinetics in LIBs for advanced high-power energy storage.

Cyber-physical-social systems comprise physical, cyber, and social worlds with various resources as services. The operation and configuration of these services require service composition approaches that can be used to integrate essential components in these worlds, and organize and share relevant information as needed. Although the present service-oriented architecture can effectively support service composition, there still exist many challenges such as high time cost and low reliability for cyber-physical-social systems. In this paper, we propose a fast and reliable service composition approach to integrate the physical network, cyberspace, and social network. In this approach, first, skyline component computation is performed to reduce the solution space; then, the coefficient of variation is employed to filter the components with high quality of service (QoS) fluctuation and finally the best optimal components are selected by maximizing the fitness function based on users’ end-to-end QoS requirements. We applied our approach to real-world dataset and the results showed that our proposed approach has better reliability as well as lower time cost than other approaches.

Influenza A virus (IAV) is an infectious pathogen, threatening the population and public safety with its epidemics. Therefore, it is essential to better understand influenza virus biology to develop efficient strategies against its pathogenicity. Autophagy is an important cellular process to maintain cellular homeostasis by cleaning up the hazardous substrates in lysosome. Accumulating research has also suggested that autophagy is a critical mechanism in host defense responses against IAV infection by degrading viral particles and activating innate or acquired immunity to induce viral clearance. However, IAV has conversely hijacked autophagy to strengthen virus infection by blocking autophagy maturation and further interfering host antiviral signalling to promote viral replication. Therefore, how the battle for autophagy between host and IAV is carried out need to be known. In this review, we describe the role of autophagy in host defence and IAV survival, and summarize the role of influenza proteins in subverting the autophagic process as well as then concentrate on how host utilize antiviral function of autophagy to prevent IAV infection.

With the increasing production of municipal sewage sludge (MSS) in China every year, the co-firing of MSS and pulverized coal is getting more and more widely applied in large coal-fired power plants. The co-firing of MSS and pulverized coal will produce a large amount of particulate matter (PM) emissions, especially submicron particles. In this paper, the formation characteristics of submicron particles in the co-firing process of coal and MSS were studied in a drop tube furnace. The influence of the furnace temperature and the addition ratio of sludge on the particle size distribution and element composition of submicron particles in MSS, pulverized coal combustion and co-firing was mainly studied. The experimental results show that the furnace temperature has an influence on the formation of PM

Avian and human influenza viruses bind to porcine sialic acid receptors to generate novel viruses that pose a potential pandemic threat to public health. Evidence suggests that the host factors regulating the influenza virus life cycle and viral reassortment are potential broad-spectrum antiviral drug targets, compared to the ineffective seasonal vaccines against highly pathogenic viruses, leading to drug resistance. After performing a genome-wide CRISPR-Cas9 screen targeting 13,735 genes in porcine cell lines, we identified several host factors critical for influenza virus infection-notably, a conserved oligomeric Golgi complex protein, COG8, which regulates viral protein transport and immune factor expression. Viral titers indicated that the loss of

Porcine reproductive and respiratory syndrome (PRRS), a viral infection caused by PRRS virus (PRRSV) can result in severe reproductive failure, and respiratory disease in the pigs thus causing enormous economic losses to the global swine industry. Although the cellular receptors for PRRSV have been identified, but mechanisms underlying PPRSV replication remain obscure. Here, we have performed a genome-scale CRISPR/Cas9 knockout screen in the pig kidney cells with PRRSV. Several genes were found to be highly enriched post-PRRSV selection, just like KxDL Motif Containing 1(KXD1), Proteasome 26S Subunit, Non-ATPase 3 (PSMD3) and Galectin 2 (LGALS2) and soon on. Importantly, we have identified that loss of KXD1 resulted in the restricted autophagy and inhibited replication of PRRSV. Therefore, our study demonstrates that CRISPR/Cas9 system can be effectively used for the screening of pig factors responsible for PRRSV replication.

With the popularization of the cloud, an increasing number of users and developers need to combine multiple different services to satisfy their requirements in the cloud. Consequently, the question of how to combine these services that are published by different service providers in the cloud has become an active focus of research in service-oriented cloud systems. Service composition is the core technology of service-oriented cloud systems.

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. Conventional therapies involving surgery or pharmacological strategies have shown limited therapeutic effects due to a lack of cardiac tissue repair. Gene therapy has opened an avenue for the treatment of cardiac diseases through manipulating the underlying gene mechanics. Several gene therapies for cardiac diseases have been assessed in clinical trials, while the clinical translation greatly depends on the delivery technologies. Non-viral vectors are attracting much attention due to their safety and facile production compared to viral vectors. In this review, we discuss the recent progress of non-viral gene therapies for the treatment of cardiovascular diseases, with a particular focus on myocardial infarction (MI). Through a summary of delivery strategies with which to target cardiac tissue and different cardiac cells for MI treatment, this review aims to inspire new insights into the design/exploitation of non-viral delivery systems for gene cargos to promote cardiac repair/regeneration.

A growing number of mobile services demand intensive computation resources and high energy consumption. Traditional cloud-based task offloading incurs excessive transmission delay. With mobile edge computing, we can offload tasks to the nearby edge servers, thus mitigating long data transmission latency. Current task offloading and resource allocation approaches ignore the extra overhead brought by container instance startup. This naturally leads to a long overall latency and high energy consumption. Motivated by the limitations of existing literature, we address task offloading and resource allocation problem considering container instance startup time. We first examine the startup latency of containers started from different types of images, and present its impact on overall performance. Then, we formulate the problem as optimization problem. The problem is hard to address because mutual effects of offloading decision, resource allocation decision and container instance startup contribute to the overall performance. We resort to Gibbs sampling to decouple offloading decision and resource allocation. Finally, taking into account all potential conditions brought up by container instance startup, an optimal computation resource allocation and uplink power assignment scheme are derived through bisection, and Karush-Kuhn-Tucher (KKT) conditions. Comprehensive evaluations are conducted to verify the performance of the proposed approach, and demonstrate its superiority over current notable solutions.

For a long time, the system of scientific methodology has been composed of logic, empirical (falsification), qualitative, quantitative and deterministic, and corresponding thinking tools. However, under the background of complexity science, the category of methodology should be changed, that is, on the basis of traditional methodology, non-classical logic, hierarchy, stereotype (topological invariant) and uncertainty should be added. This is also the main idea behind the “Thoery of Tri-state” in the first part of this paper. The core idea in the theory of “Tri-state” is “Tri-state Logic” (“positive | negative | uncertain state”). The ontology of “Tri-state Logic” aims to reveal the meta space-time movement law of things transforming from one form to another, that is, the coupling of time and space in the development of things, and the orientation and evolution of the continuity of things. The mathematical basis of “Tri-state Logic” is knot theory and dynamics theory. The second part of this paper designs a machine-consciousness model framework based on the “Theory of Tri-state” (Tri-state Logic). Its research starting point is the perspective of cognitive dynamics (cognitive psychology + dynamics), which is very different from the research ideas proposed by Minsky's “The Emotion Machine”. At the same time, this paper also tries to answer Turing's questions from different space-time dimensions, and gives an experimental idea of “kindergarten game” by comparing Turing's “imitation game”.

This paper reports a cross-coupled pair regeneration based programmable gain amplifier (CCPR- PGA) with an improved total harmonic distortion (THD) performance. By utilizing the ping-pong technique, two identical conventional CCPR-PGAs are operating alternatively. These two CCPR-PGAs connect to the output of the overall PGA only at their hold state, as a result, the ripples generated during the processes of reset, sample and regeneration are effectively suppressed. Simulated in 28-nm CMOS technology, the proposed PGA achieves a gain range of 45.4 dB with a gain error of ± 0.3 dB. The bandwidth of the proposed PGA reaches 40 MHz with a clock frequency of 100 MHz. Moreover, the THD performance is improved by 25.8 dB at 1 MHz and 17.6 dB at 10 MHz when compared to the conventional structure. The overall PGA consumes 460 μΑ current from a supply voltage of 0.9 V.