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Abstract Programmable RNA editing is harnessed for modifying mRNA. Besides mRNA, miRNA also regulates numerous biological activities, but current RNA editors have yet to be exploited for miRNA manipulation. To engineer primary miRNA (pri-miRNA), the miRNA precursor, we present a customizable editor REPRESS (RNA Editing of Pri-miRNA for Efficient Suppression of miRNA) and characterize critical parameters. The optimized REPRESS is distinct from other mRNA editing tools in design rationale, hence enabling editing of pri-miRNAs that are not editable by other RNA editing systems. We edit various pri-miRNAs in different cells including adipose-derived stem cells (ASCs), hence attenuating mature miRNA levels without disturbing host gene expression. We further develop an improved REPRESS (iREPRESS) that enhances and prolongs pri-miR-21 editing for at least 10 days, with minimal perturbation of transcriptome and miRNAome. iREPRESS reprograms ASCs differentiation, promotes in vitro cartilage formation and augments calvarial bone regeneration in rats, thus implicating its potentials for engineering miRNA and applications such as stem cell reprogramming and tissue regeneration.

Low-grade body inflammation is a major cause of osteoarthritis (OA), a common joint disease. Gut dysbiosis may lead to systemic inflammation which can be prevented by probiotic administration. The Lactobacillus delbrueckii subsp. lactis 557 (LDL557) has been demonstrated to have beneficial effects for anti-inflammation. This study investigated the effects of LDL557 on OA progress using monosodium iodoacetate (MIA)-induced OA of rats. Live or heat-killed (HK)-LDL557 of a low or high dose was administrated for two weeks before MIA-induced OA, and then continuously administrated for another six weeks. After taking supplements for eight weeks, OA progress was analyzed. Results showed that MIA induced knee joint swelling, chondrocyte damage, and cartilage degradation, and supplementation with a high dose of LDL557 reduced MIA-induced knee joint swelling, chondrocyte damage, and cartilage degradation. Additionally, MIA increased serum levels of the matrix-degrading enzyme MMP-13, while a high dose of HK-LDL557 decreased it for the controls. Simultaneously, bone turnover markers and inflammatory cytokines of serum were assayed, but no significant differences were found except for a TNF-α decrease from a low dose of live LDL557. These results demonstrated that supplementation with high doses of live LDL557 or HK-LDL557 can reduce the progression of MIA-induced OA in rats.

This special issue is a collection of emerging trends and challenges in applying learning-driven approaches to data fabric architectures within the cloud-to-thing continuum. As data generation and processing increasingly occur at the edge, there is a growing need for intelligent, adaptive data management solutions that seamlessly operate across distributed environments. In this special issue, we received research contributions from various groups around the world. We chose the eight most appropriate and novel contributions to include in this special issue. These eight contributions were further categorized into three themes: Data Handling approaches, resource optimization and management, and security and attacks. Additionally, this editorial suggests future research directions that will potentially lead to groundbreaking insights, which could pave the way for a new era of learning techniques in Data Fabric and the Cloud-to-Thing Continuum.

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city’s efficient operation. The idea behind “smart cities” is that information and communication technology (ICTs) need to be included in a city’s routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The “drone” or “uncrewed aerial vehicle” (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

Nickel-titanium (NiTi) alloys are widely used in orthopedic implants for their good biocompatibility and mechanical properties. They are also applied for hydrogen storage capacity in energy application. Here we evaluated the use of NiTi alloys as carriers for delivering hydrogen molecules to wound sites, where these hydrogen molecules could be released to reduce the free radicals and inhibit the inflammatory reactions at these sites. The results of the XRD characterization, cyclic voltammetry, and thermal desorption analyses showed that the NiTi alloys used in this study could effectively carry hydrogen molecules after treating with cathodic hydrogen charging method. After hydrogen charging, the results of nano-indentation test revealed that the Young's modulus for these materials decreased from 81.45 GPa to 62.15 GPa, and its hardness decreased from 5.33 GPa to 4.50 GPa, which could help to improve the biocompatibility; and the water contact angle decreased from 75.04° to 63.83°, which is beneficial for cellular attachment. The results of the in vitro and in vivo study show that the use of hydrogen charged NiTi alloys could lead to higher cellular viability in human osteosarcoma cells (MG63), mouse fibroblasts (L929), and rat bone mesenchymal stem cells (BMSCs) and would significantly reduce inflammation.

E-learning is an evolutionary concept, not a revolutionary concept since it has been introduced and practiced in previous decades as well. The current research paper contributes to educational research based on the investigation of holistic perspectives of the ongoing online teaching activities during the lockdown period. The article is built on a literature review. The methodologies are described and the results are presented. There have been research on distant learning and digitalization, but mostly in terms of potential, obstacles, and student assessments, as well as their influence on education. There are no empirical research on the worldwide trend of how employees utilize e-learning resources to determine instructors’ interest in and attitudes about using them. This chasm is also regarded a new angle of empirical research that uses the Chi-square test of independence to find patterns. The present research aims to build a realistic instrument that can assess instructors’ attitudes about e-learning during COVID-19 with this purpose in mind.The paper consists of two parts: a literature review and statistical analysis. The research was conducted by means of a diagnostic opinion CAWI (Computer Assisted Web Interview) questionnaire and statistical analysis. This study was conducted among 342 teachers working in various universities located in the following countries: Poland, Pakistan, Iraq, USA, UK, Germany, and Austria. The questionnaire consisted of 20 questions. The researchers provided a detailed overview of e-learning practices during the pandemic and its future prospects from the teacher’s point of view. The goals of this article and the research problems are to learn about the worldwide trend of how employees utilize e-learning resources, to identify teachers’ interests in and attitudes towards using e-learning resources throughout the globe, and to recommend opportunities for workers to use e-learning resources around the world, which is a very important issue, especially in the era of globalization, society 5.0, and industry 4.0. The authors also proposed a Chi-square statistical analysis to check whether there exists a relationship between two variables based on which the hypotheses were formulated. The results of the study clearly show that the respondents approach to e-learning was very responsible because they realized that it was up to them to properly convey knowledge. The results of empirical research show that the digitalization of education is no longer a future trend, but today’s trend or even a university standard in the education improvement direction.

Internet of Things (IoT) and artificial intelligence (AI) can realize the concept of “smart city.” Video surveillance in smart cities is, usually, based on a centralized framework in which large amounts of real-time media data are transmitted to and processed in the cloud. However, the cloud relies on network connectivity of the Internet that is sometimes limited or unavailable; thus, the centralized framework is not sufficient for real-time processing of media data needed for smart video surveillance. To tackle this problem, edge computing - a technique for accelerating the development of AIoT (AI across IoT) in smart cities - can be conducted. In this paper, a distributed real-time object detection framework based on edge-cloud collaboration for smart video surveillance is proposed. When collaborating with the cloud, edge computing can serve as converged computing through which media data from distributed edge devices of the network are consolidated by AI in the cloud. After AI discovers global knowledge in the cloud, it to be shared at the edge is deployed remotely on distributed edge devices for real-time smart video surveillance. First, the proposed framework and its preliminary implementation are described. Then, the performance evaluation is provided regarding potential benefits, real-time responsiveness and low-throughput media data transmission.

Basically, it is hard for endeavors to recognize plant leaf images by a layman due to the varieties in some plant leaves and the extensive information collected for investigation. It is hard to build an automated recognition framework that can handle massive data and give an intermediate analysis. Image examination and order and pattern recognition are some issues that are effectively connected to the existing methods. This paper focuses on designing an automated plant recognition system based on the best recognition algorithm and the Google platform to locate all plant locations on a map. A case study of India, which has huge biodiversity, is illustrated. The proposed system can show the detailed location of that particular species, where they can be found, and the shortest distance from the current location.

Abstract To assess the 3-year outcome of half-dose verteporfin photodynamic therapy (PDT) in central serous chorioretinopathy (CSC) with optical coherence tomography angiography (OCT-A) detected choroidal neovascularization (CNV), we performed a retrospective, interventional study. Patients were divided into 2 groups according to the fluorescein angiography: point source leakage in group 1 and diffuse oozing in group 2. Data were collected from patients including changes of best-corrected visual acuity (BCVA), size of CNV, central macular thickness (CMT), choroidal thickness (CT), reabsorption of subretinal fluid (SRF), sessions of half-dose PDT, and the number of intravitreal injections (IVI) of anti-vascular endothelial growth factor (anti-VEGF). There was a total of 34 eyes in 32 patients included. The mean sessions of half-dose PDT was 1.50 ± 0.75. The mean number of IVI of anti-VEGF was 1.38 ± 3.34. BCVA improved from 0.38 ± 0.33 to 0.20 ± 0.22 (p

Abstract Various eHealth applications based on the Internet of Things (IoT) contain a considerable number of medical images and visual electronic health records, which are transmitted through the Internet everyday. Information forensics thus becomes a critical issue. This paper presents a data hiding algorithm for absolute moment block truncation coding (AMBTC) images, wherein secret data, or the authentication code, can be embedded in images to enhance security. Moreover, in view of the importance of transmission efficiency in IoT, image compression is widely used in Internet-based applications. To cope with this challenge, we present a novel compression method named gradient-based (GB) compression, which is compatible with AMBTC compression. Therefore, after applying the block classification scheme, GB compression and data hiding can be performed jointly for blocks with strong gradient effects, and AMBTC compression and data hiding can be performed jointly for the remaining blocks. From the experimental results, we demonstrate that the proposed method outperforms other state-of-the-art methods.

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greedy, and random algorithms, respectively, in comparison in the simulated environment.

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.

Cyanobacteria can convert CO2 to chemicals such as 2,3-butanediol (2,3-BDO), rendering them promising for renewable production and carbon neutralization, but their applications are limited by low titers. To enhance cyanobacterial 2,3-BDO production, we developed a combinatorial CRISPR interference (CRISPRi) library strategy. We integrated the 2,3-BDO pathway genes and a CRISPRi library into the cyanobacterium PCC7942 using the orthogonal CRISPR system to overexpress pathway genes and attenuate genes that inhibit 2,3-BDO formation. The combinatorial CRISPRi library strategy allowed us to inhibit fbp, pdh, ppc, and sps (which catalyzes the synthesis of fructose-6-phosphate, acetyl-coenzyme A, oxaloacetate, and sucrose, respectively) at different levels, thereby allowing for rapid screening of a strain that enhances 2,3-BDO production by almost 2-fold to 1583.8 mg/L. Coupled with a statistical model, we elucidated that differentially inhibiting all the four genes enhances 2,3-BDO synthesis to varying degrees. fbp and pdh suppression exerted more profound effects on 2,3-BDO production than ppc and sps suppression, and these four genes can be repressed simultaneously without mutual interference. The CRISPRi library approach paves a new avenue to combinatorial metabolic engineering of cyanobacteria.

The aim of 5G wireless networks to provide Mbps and Gbps data rates to end users is expected to be fulfilled by the advanced technologies such as multi-input multi-output (MIMO), carrier aggregation (CA), inter/intra-cell communication, and adaptive modulation and coding techniques, which would be all realized in the Long Term Evolution-Advanced (LTE-A) heterogeneous network constituted by macrocells (MCs) and small cells (SCs) adopting these 5G advanced techniques. Given the potential of significantly increasing the network performance, the resource allocation (RA) problem involved becomes harder than ever especially when MIMO and CA are included in the RA problem involving multiple types of resources to be concurrently determined for the global optimization. Facing this challenge, we develop a framework to jointly optimize energy efficiency (EE), spectrum efficiency (SE), and queue length for downlink transmissions with an overall and comprehensive consideration of dynamically allocating resource blocks (RBs), component carriers (CCs), modulation and coding schemes (MCSs), and deciding user association (UA) with a power control (PC) mechanism on discrete power levels (PLs) in the heterogeneous LTE-based MIMO wireless networks. Specially, for the complex joint RA, UA, and PC problem, we conduct a mixed integer programming model to accommodate the stochastic optimization problem involved with the drift-plus-penalty (DPP) approach for Lyapunov opportunistic optimization. In particular, although it involves a nondeterministic polynomial time (NP) problem, we can still show a reduced problem to be solved easily through linear relaxation when its coefficient matrix is totally unimodular (TUM), and to be solved efficiently as well even when the TUM property is not guaranteed. Based on the reduction, we further develop a distributed or semi-distributed algorithm operated on two levels to approach the optimal results with lower complexity if the UA requirement can be relaxed. Finally, apart from exhibiting its performance on the weighting parameters, the numerical experiments also show our approach to make a good tradeoff among SE, EE, and queue length, and outperform the greedy-based state-of-the-art algorithms.

Background: Combined non-viral gene therapy (GT) of ischemia and cardiovascular disease is a promising tool for potential clinical translation. In previous studies our group has developed combined gene therapy by vascular endothelial growth factor 165 (VEGF165) + hepatocyte growth factor (HGF). Our recent works have demonstrated that a bicistronic pDNA that carries both human HGF and VEGF165 coding sequences has a potential for clinical application in peripheral artery disease (PAD). The present study aimed to test HGF/VEGF combined plasmid efficacy in ischemic skeletal muscle comorbid with predominant complications of PAD-impaired glucose tolerance and type 2 diabetes mellitus (T2DM). Methods: Male C57BL mice were housed on low-fat (LFD) or high-fat diet (HFD) for 10 weeks and metabolic parameters including FBG level, ITT, and GTT were evaluated. Hindlimb ischemia induction and plasmid administration were performed at 10 weeks with 3 weeks for post-surgical follow-up. Limb blood flow was assessed by laser Doppler scanning at 7, 14, and 21 days after ischemia induction. The necrotic area of m.tibialis anterior, macrophage infiltration, angio- and neuritogenesis were evaluated in tissue sections. The mitochondrial status of skeletal muscle (total mitochondria content, ETC proteins content) was assessed by Western blotting of muscle lysates. Results: At 10 weeks, the HFD group demonstrated impaired glucose tolerance in comparison with the LFD group. HGF/VEGF plasmid injection aggravated glucose intolerance in HFD conditions. Blood flow recovery was not changed by HGF/VEGF plasmid injection either in LFD or HFD conditions. GT in LFD, but not in HFD conditions, enlarged the necrotic area and CD68+ cells infiltration. However, HGF/VEGF plasmid enhanced neuritogenesis and enlarged NF200+ area on muscle sections. In HFD conditions, HGF/VEGF plasmid injection significantly increased mitochondria content and ETC proteins content. Conclusions: The current study demonstrated a significant role of dietary conditions in pre-clinical testing of non-viral GT drugs. HGF/VEGF combined plasmid demonstrated a novel aspect of potential participation in ischemic skeletal muscle regeneration, through regulation of innervation and bioenergetics of muscle. The obtained results made HGF/VEGF combined plasmid a very promising tool for PAD therapy in impaired glucose tolerance conditions.

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.

In a network architecture, an intrusion detection system (IDS) is one of the most commonly used approaches to secure the integrity and availability of critical assets in protected systems. Many existing network intrusion detection systems (NIDS) utilize stand-alone classifier models to classify network traffic as an attack or as normal. Due to the vast data volume, these stand-alone models struggle to reach higher intrusion detection rates with low false alarm rates( FAR). Additionally, irrelevant features in datasets can also increase the running time required to develop a model. However, data can be reduced effectively to an optimal feature set without information loss by employing a dimensionality reduction method, which a classification model then uses for accurate predictions of the various network intrusions. In this study, we propose a novel feature-driven intrusion detection system, namely χ2-BidLSTM, that integrates a χ2 statistical model and bidirectional long short-term memory (BidLSTM). The NSL-KDD dataset is used to train and evaluate the proposed approach. In the first phase, the χ2-BidLSTM system uses a χ2 model to rank all the features, then searches an optimal subset using a forward best search algorithm. In next phase, the optimal set is fed to the BidLSTM model for classification purposes. The experimental results indicate that our proposed χ2-BidLSTM approach achieves a detection accuracy of 95.62% and an F-score of 95.65%, with a low FAR of 2.11% on NSL-KDDTest+. Furthermore, our model obtains an accuracy of 89.55%, an F-score of 89.77%, and an FAR of 2.71% on NSL-KDDTest−21, indicating the superiority of the proposed approach over the standard LSTM method and other existing feature-selection-based NIDS methods.

With the development of the ability to crack encrypted images, conventional image encryption techniques are no longer safe enough. Disguising a to-be-encrypted image into another visually-different image that is similar to the prepared target image is a solution that can be used to transmit an image securely. Inspired by the existing reversible image transformation technique, in this paper, we propose an improved method for camouflaging images that further decrease the distortion between the final camouflaged image and the target image. First, the G channel of the secret image is transformed and further refined to create a tentative camouflaged G channel with the reference of the G channel of the target image. Then, the color difference channels of the secret image are transformed with the reference of subtraction of the R channel (or B channel) and the tentatively camouflaged G channel. After shifting the color difference channels back to the R channel (or B channel), the sub-blocks are refined by a 16-candidate-pattern optimization strategy to generate the tentative camouflaged R channel (or B channel). After the combination of the RGB channels to generate the tentative camouflaged image, the final camouflaged version is generated by embedding all of the auxiliary information, which is collected for lossless recovery of the secret image, into the tentative image in a reversible manner. The experimental results demonstrated the efficacy of the proposed method, and our average gain in the color image peak signal-to-noise ratio (CPSNR) was more than 0.35 dB, whereas the state-of-the-art method is around 32.28 dB.

A novel bonding approach for flexible substrate vertical stacking is proposed in this paper. Unlike the traditional anisotropic conductive/non-conductive paste/film used in flexible substrate bonding, the metal thin film is adopted as bonding material in this approach. Experiment results show that the novel bonding approach has good electrical properties and reliability performances. The innovative bonding approach is considered a breakthrough of the traditional restrictions in flexible substrates, including I/O pitch, electrical reliability, and bonding conditions. This development is expected to contribute to the fabrication of future flexible substrate stacking.

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

The modern world has no available drugs for the treatment of enteroviruses (EV), which affect millions of people worldwide each year. The EV71 is a major causative disease for hand, foot, and mouth disease; sometimes it is associated with severe central nervous system diseases. Treatment for enteroviral infection is mainly supportive; treatment for aseptic meningitis caused by enteroviruses is also generally symptomatic. Upon the urgent request of new anti-enterovirus drugs, a series of hinged aromatic compounds with polynulei were synthesized through two different chemical pathways. Among these morpholine–furan/thiophene/pyrrole–benzene–pyrazole conjugates, three new agents exhibited inhibitory activity with EC50 = 2.29–6.16 μM toward EV71 strain BrCr in RD cells. Their selectivity index values were reached as high as 33.4. Their structure–activity relationship was deduced that a thiophene derivative with morpholine and trifluorobenzene rings showed the greatest antiviral activity, with EC50 = 2.29 μM.

This paper proposes a reversible data hiding technique based on the residual histogram shifting technique. To improve the hiding capacity, this study proposes a multiple-round hierarchical prediction mechanism that generates the prediction errors of each image block. The prediction errors of each block are collected to produce the residual histogram and the secret data are then embedded into the residual histogram to obtain the embedded image. Experimental results demonstrate that the proposed technique not only provides good hiding capacity, but also maintains good image quality of the embedded image. In addition, this technique can be easily extended for image integrity protection as it is capable of resisting error propagation.

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

Enterovirus 71 (EV71) causes seasonal epidemics of hand-foot-and-mouth disease and has a high mortality rate among young children. We recently demonstrated potent induction of the humoral and cell-mediated immune response in monkeys immunized with EV71 virus-like particles (VLPs), with a morphology resembling that of infectious EV71 virions but not containing a viral genome, which could potentially be safe as a vaccine for EV71. To elucidate the mechanisms through which EV71 VLPs induce cell-mediated immunity, we studied the immunomodulatory effects of EV71 VLPs on human monocyte-derived dendritic cells (DCs), which bind to and incorporate EV71 VLPs. DC treatment with EV71 VLPs enhanced the expression of CD80, CD86, CD83, CD40, CD54, and HLA-DR on the cell surface; increased the production of interleukin (IL)-12 p40, IL-12 p70, and IL-10 by DCs; and suppressed the capacity of DCs for endocytosis. Treatment with EV71 VLPs also enhanced the ability of DCs to stimulate naïve T cells and induced secretion of interferon (IFN)-γ by T cells and Th1 cell responses. Neutralization with antibodies against Toll-like receptor (TLR) 4 suppressed the capacity of EV71 VLPs to induce the production of IL-12 p40, IL-12 p70, and IL-10 by DCs and inhibited EV71 VLPs binding to DCs. Our study findings clarified the important role for TLR4 signaling in DCs in response to EV71 VLPs and showed that EV71 VLPs induced inhibitor of kappaB alpha (IκBα) degradation and nuclear factor of kappaB (NF-κB) activation.