Search

The collection and sharing of electronic health records (EHRs) via the Internet of Things (IoT) can enhance the accuracy of disease diagnosis. However, it is challenging to guarantee the secure search of EHR during the sharing process. The advent of blockchain is a promising solution to address the issues, owing to its remarkable features such as immutability and anonymity. In this paper, we propose a novel blockchain-based secure sharing system over searchable encryption and hidden data structure via IoT devices. EHR ciphertexts of data owners are stored in the interplanetary file system (IPFS). A user with proper access permissions can search for the desired data with the data owner’s time-bound authorization and verify the authenticity of the search result. After that, the data user can access the relevant EHR ciphertext from IPFS using a symmetric key. The scheme jointly uses searchable encryption and smart contract to realize secure search, time control, verifiable keyword search, fast search, and forward privacy in IoT scenarios. Performance analysis and proof demonstrate that the proposed protocol can satisfy the design goals. In addition, performance evaluation shows the high scalability and feasibility of the proposed scheme.

The advanced metering infrastructure (AMI) in modern networked smart homes brings various advantages. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of ML algorithms. In this paper, we present a covert electricity theft strategy through mimicking normal consumption patterns. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we propose a feature extraction method and develop a novel detection model based on deep learning. Extensive experiments show that the presented attack could evade existing mainstream detectors and the proposed countermeasure outperforms existing leading methods.

The fast proliferation of digital twin (DT) establishes a direct connection between the physical entity and its deployed digital representation. As markets shift toward mass customization and new service delivery models, the digital representation has become more adaptive and agile by forming digital twin networks (DTN). DTN institutes a real-time single source of truth everywhere. However, there are several issues preventing DTN from further application, which are centralized processing, data falsification, privacy leakage, lack of incentive mechanism, etc. To make DTN better meet the ever-changing demands, we propose a novel blockchain-enabled adaptive asynchronous federated learning (FedTwin) paradigm for privacy-preserving and decentralized DTN. We design Proof-of-Federalism (PoF), which is a tailor-made consensus algorithm for autonomous DTN. In each DT's local training phase, generative adversarial network enhanced differential privacy is used to protect the privacy of local model parameters while a modified Isolation Forest is deployed to filter out the falsified DTs. In the global aggregation phase, an improved Markov decision process is leveraged to select optimal DTs to achieve adaptive asynchronous aggregation while providing a roll-back mechanism to redact the falsified global models. With this paper, we aim to provide insights to the forthcoming researchers and readers in this under-explored domain.

Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.

With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully Dynamic Self-Attention Spatio-Temporal Graph Networks (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF).

With the fast boom of Internet of Medical Things (IoMT) devices and an increasing focus on personal health, personal health data are extensively collected by IoMT and stored as personal health records (PHRs). PHRs are frequently shared for accurate diagnosis, prognosis prediction, health advice consulting, etc. Since PHRs are highly private, the data sharing process leads to wide-ranging concerns on privacy leakage and security compromise. Existing research has shown that the centralized systems, as the mainstream mode, are under the great risks. Motivated by this, we propose a consortium blockchain based PHR management and sharing scheme, which is both security-aware and privacy-preserving. We adopt the interplanetary file system (IPFS) to store PHR ciphertext of IoMT. Then, Zero-knowledge proof can provide evidence for verifying keyword index authentication on blockchain. Moreover, the scheme jointly leverages modified attribute-based cryptographic primitives and tailor-made smart contracts to achieve secure search, privacy preservation, and personalized access control in IoMT scenarios. Security analysis is conducted to show the designed protocols attain the expected design goals. This is followed by extensive evaluation results derived from real-world datasets, which demonstrate the superiority of the proposed scheme over current leading ones.

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target denial of service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new Committee Members Auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir’s Secret Sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB’s resistance against double-spend attacks, an improved Nakamoto’s attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

Stem cell transplantation technology provides the cell reconstruction of damaged heart a completely new therapy approach. Due to the inappropriate microenvironment such as reactive oxygen radicals caused by ischemic infarct, the survival and retention rates of cell transplantation are not desirable. A thermo sensitive chitosan-vitamin C (CSVC) hydrogel scaffold was developed to reduce oxidative stress injury after myocardial infarction, thereby increasing the cell survival rate of cell transplantation. Vitamin C was conjugated on the chitosan chain by electrostatic adsorption. Compared to chitosan, CSVC complex had a higher solubility and stronger antioxidant property. CSVC hydrogel has suitable gelation time and injectable properties. Scanning electron microscopy showed that chitosan hydrogels had three-dimensional porous structure with irregular pores interconnected throughout the construct. Live/dead and HE staining results showed that CSVC hydrogel can support the survival and adhesion of cardiomyocytes. Compared with chitosan hydrogel, CSVC hydrogel can clearly improve the survival of cardiomyocytes and reduce the ROS level under H

In order to evaluate the importance of deep learning techniques in stroke diseases, this paper systematically reviews the relevant literature. Deep learning techniques have a significant impact on the diagnosis, treatment, and prediction of stroke. In addition, this study also discusses the current bottlenecks and the future development prospects of deep learning technology.

Driverless parking, an influential application of Mobility as a Service (MaaS) model, is one of the clear early benefits for autonomous vehicles, given often narrow spaces and multiple potential hazards (such as pedestrians stepping out from in between other vehicles). In recent years, real momentum has been building up for designing automated parking models for vehicles. However, in such an autonomous parking design, location privacy and identity privacy issues are always overlapping due to the improper sharing of data. Most existing studies barely investigate and poorly address such privacy issues. Motivated by this, we develop (and evaluate) an experience-driven, secure and privacy-aware framework of parking reservations for automated cars. Our idea of using differential privacy with zero-knowledge proof provides both security and privacy guarantees to users. Furthermore, the performance of the developed model is enhanced by exploiting reinforcement learning approach such that the utility of the system and the parking reservation rate can be maximized. Extensive evaluation demonstrates the superiority of the proposed model.

Cognitive computing, a revolutionary AI concept emulating human brain's reasoning process, is progressively flourishing in the Industry 4.0 automation. With the advancement of various AI and machine learning technologies the evolution toward improved decision making as well as data-driven intelligent manufacturing has already been evident. However, several emerging issues, including the poisoning attacks, performance, and inadequate data resources, etc., have to be resolved. Recent research works studied the problem lightly, which often leads to unreliable performance, inefficiency, and privacy leakage. In this article, we developed a decentralized paradigm for big data-driven cognitive computing (D2C), using federated learning and blockchain jointly. Federated learning can solve the problem of “data island” with privacy protection and efficient processing while blockchain provides incentive mechanism, fully decentralized fashion, and robust against poisoning attacks. Using blockchain-enabled federated learning help quick convergence with advanced verifications and member selections. Extensive evaluation and assessment findings demonstrate D2C's effectiveness relative to existing leading designs and models.

Status epilepticus (SE) leads to irreversible neuronal damage and consists of a complex pathogenesis that involves oxidative stress and subsequent autophagy. Rosiglitazone has recently been considered as a potential neuroprotective factor in epilepsy because of its antioxidative function. The aim of this study was to assess the effects of rosiglitazone in SE rat models and investigate whether its mechanisms of action involve autophagy via the antioxidant factor, nuclear factor erythroid 2-related factor 2 (Nrf2). The male Sprague-Dawley rats (200–220 g) were used to establish lithium-pilocarpine-induced SE model. We found that rosiglitazone markedly improved neuronal survival at 24-h post-SE as indicated via Hematoxylin-Eosin and Nissl staining. Furthermore, along with a reduction in reactive oxygen species, rosiglitazone pretreatment enhanced the antioxidative activity of superoxide dismutase and the expression level of Nrf2, as detected via chemical assay kits and Western blotting, respectively. In addition, the microtubule-associated protein light chain 3II (LC3II)/LC3I ratio was increased and peaked at 24 h after SE, whereas p62 mRNA levels were sharply elevated at 72 h after SE, both SE-induced increases of which were reversed via rosiglitazone pretreatment. To further test our hypothesis of the key role of Nrf2 in this process, small-interfering RNA for Nrf2 (siNrf2) was then transfected into SE rats to knockdown Nrf2 expression. We found that siNrf2 partially blocked the above effects of rosiglitazone on autophagy-related proteins in SE rats. Taken together, our findings suggest that rosiglitazone attenuates oxidative-stress-induced autophagy via increasing Nrf2 in SE rats and may be used as a promising therapeutic strategy for SE treatment.

Privacy leakage becomes increasingly serious because massive volumes of data are constantly shared in diverse booming cyber-physical social networks (CPSN). Differential privacy is one of the dominating privacy-preserving methods, but most of its extensions assume all data users share the same privacy requirement, which fails to satisfy various privacy expectations in practice. To address this issue, customizable privacy preservation based on differential privacy is a potentially promising countermeasure. However, we found that customizable protection will trigger the composition mechanism of differential privacy and leads to unexpected correlations among injected noises that weakens privacy protection and reveal more sensitive inforamtion. As a result, customizable privacy protection is vulnerable to two primary attacks: background knowledge attack and collusion attack. To optimize the tradeoff between customizable privacy preservation and data utility, we propose a customizable reliable differential privacy model (CRDP), which provides customizable protection on each individual while being attack-proof. We define social distance as the shortest path between two nodes, which is used as an index to customize the privacy protection levels, followed by quantitatively modeling the attacks under the framework of differential privacy. We develop a modified Laplacian mechanism in which the noise generation complies with a Markov stochastic process.Consequently, the correlations of noises are properly de-coupled so that CRDP can simultaneously minimize background knowledge attacks and eliminate collusion attacks in this particular scenario. The evaluation results from real-world datasets show the feasibility and superiority of CRDP in terms of customizable privacy preservation and reliable attack resistance.

© 2013 IEEE. The booming of edge computing enables and reshapes this big data era. However, privacy issues arise because increasing volume of data are published per second while the edge devices can only provide limited computing and storage resources. In addition, this has been aggravated by new emerging features of edge computing, such as decentralized and hierarchical infrastructure, mobility, and content-Aware applications. Although some existing privacy preserving methods are extended to this domain, the privacy issues of data dissemination between multiple edge nodes and end users is barely studied. Motivated by this, we propose a dynamic customizable privacy-preserving model based on Markov decision process to obtain the optimized trade-off between customizable privacy protection and data utility. We start with establishing a game model between users and adversaries based on a QoS-based payoff function. A modified reinforcement learning algorithm is deployed to derive the exclusive Nash Equilibrium. Furthermore, the model can achieve fast convergence by the reduction of cardinality from n to 2. Extensive experimental results confirm the significance of the proposed model comparing to the existing work both in terms of effectiveness and feasibility.

The cyber-physical social system (CPSS) enables human social interaction from cyberspace to the physical world by sharing an increasing volume of spatial-temporal data. The sensitive information in the shared data is appealing to the adversaries and various attacks. However, most existing research assumes that the privacy protection level is identical regardless of various requirements, which is not practical. Subsequently, this results in either over-protection or failing to resist the leading attacks like collusion attacks. Motivated by this, a personalized model is proposed by using generative adversarial nets (GAN) to achieve differential privacy and thereby enhancing spatial-temporal private data sharing. A Differential Privacy Identifier is added to the classic GAN with a Generator and a Discriminator . The deployment of the differentially Private GAN (P-GAN) enables the generation of the sanitized data that can perfectly approximate the spatial-temporal trajectory while providing high-level privacy protection. P-GAN optimizes the trade-off between personalized privacy protection and improved data utility while maintaining fast convergence. Simultaneously, the random noise generation guaranteed by GAN breaks the correlation of injected noises and make P-GAN attack-proof against the collusion attacks. Extensive evaluation results on real-world datasets show the superiority of P-GAN from the aspects of optimized trade-off and efficiency.

With the ensuing surge in data communication volume and the growing need for privacy protection, limiting centralized data collection to the minimum required for specific tasks has been mandatory in industries. This is now guided by the modern privacy legislation, namely, the General Data Protection Regulation and the California Consumer Protection Act. Privacy leakage has become increasingly serious because of massive volume and a variety of data transmission and Quality-of-Service (QoS) requirements in the Industrial Internet-of-Things (IIoT) networks. Although differential privacy is the core privacy protection paradigm, most of its extensions assume all parties share the same level of privacy requirements, which cannot meet varying needs and QoS of IIoT devices in practice. In addition, with multiple paths access to the cloud server (often operated by the trusted third party in IIoT) for higher reliability and performance, satisfying both the privacy and QoS is nontrivial during the data transmission. The usual transmission over both the cellular and WiFi interfaces simultaneously for continuous connectivity among devices, edge networks, and the server is crucial. As a result, we observe that IIoT data privacy is highly vulnerable to collusion attacks. Motivated by this observation, we develop a detailed QoS modeling for multipath TCP over IIoT and propose a QoS-aware personalized privacy protection model. Our model works in two different layers: one at the cloud server and another at the network edges (access points/base station). The aim is not only to balance the load but also to achieve the required QoS and optimize the tradeoff between privacy protection and efficiency. The extensive experimental results based on the real-world data sets illustrate the superiority of the proposed model in terms of privacy protection and efficiency.

As the extension of cloud computing and a foundation of IoT, fog computing is experiencing fast prosperity because of its potential to mitigate some troublesome issues, such as network congestion, latency, and local autonomy. However, privacy issues and the subsequent inefficiency are dragging down the performances of fog computing. The majority of existing works hardly consider a reasonable balance between them while suffering from poisoning attacks. To address the aforementioned issues, we propose a novel blockchain-enabled federated learning (FL-Block) scheme to close the gap. FL-Block allows local learning updates of end devices exchanges with a blockchain-based global learning model, which is verified by miners. Built upon this, FL-Block enables the autonomous machine learning without any centralized authority to maintain the global model and coordinates by using a Proof-of-Work consensus mechanism of the blockchain. Furthermore, we analyze the latency performance of FL-Block and further derive the optimal block generation rate by taking communication, consensus delays, and computation cost into consideration. Extensive evaluation results show the superior performances of FL-Block from the aspects of privacy protection, efficiency, and resistance to the poisoning attack.

Tremors have been reported even with a low dose of tacrolimus in patients with nephrotic syndrome and are responsible for hampering the day-to-day work of young active patients with nephrotic syndrome. This study proposes a neural network model based on seven variables to predict the development of tremors following tacrolimus. The sensitivity and specificity of this algorithm are high. A total of 252 patients were included in this study, out of which 39 (15.5%) experienced tremors, 181 patients (including 32 patients who experienced tremors) were randomly assigned to a training dataset, and the remaining were assigned to an external validation set. We used a recursive feature elimination algorithm to train the training dataset, in turn, through 10-fold cross-validation. The classification performance of the classifer was then used as the evaluation criterion for these subsets to find the subset of optimal features. A neural network was used as a classification algorithm to accurately predict tremors using the subset of optimal features. This model was subsequently tested in the validation dataset. The subset of optimal features contained seven variables (creatinine, D-dimer, total protein, calcium ion, platelet distribution width, serum kalium, and fibrinogen), and the highest accuracy obtained was 0.8288. The neural network model based on these seven variables obtained an area under the curve (AUC) value of 0.9726, an accuracy of 0.9345, a sensitivity of 0.9712, and a specificity of 0.7586 in the training set. Meanwhile, the external validation achieved an accuracy of 0.8214, a sensitivity of 0.8378, and a specificity of 0.7000 in the validation dataset. This model was capable of predicting tremors caused by tacrolimus with an excellent degree of accuracy, which can be beneficial in the treatment of nephrotic syndrome patients.

Neurovascular system plays a vital role in controlling the blood flow into brain parenchymal tissues. Additionally, it also facilitates the metabolism in neuronal biological activities. Cerebral microvascular endothelial cells (MECs) are involved in mediating progression of the diseases related to cerebral vessels, including stroke. Arachidonic acid can be transformed into epoxyeicosatrienoic acids (EETs) under the catalysis by cytochrome P450 epoxygenase. We have reported that EETs could protect neuronal function. In our research, the further role of 14,15-EET in the protective effects of cerebral MECs and the potential mechanisms involved in oxygen glucose deprivation and reperfusion (OGD/R) were elucidated. In our study, we intervened the SIRT1/FOXO3a pathway and established a TSPO knock down model by using RNA interference technique to explore the cytoprotective role of 14,15-EET in OGD/R injury. Cerebral MECs viability was remarkably reduced after OGD/R treatment, however, 14,15-EET could reverse this effect. To further confirm whether 14,15-EET was mediated by SIRT1/FOXO3a signaling pathway and translocator protein (TSPO) protein, we also detected autophagy-related proteins, mitochondrial membrane potential, apoptosis indicators, oxygen free radicals, etc. It was found that 14,15-EET could regulate the mitophagy induced by OGD/R. SIRT1/FOXO3a signaling pathway and TSPO regulation were related to the protective role of 14,15-EET in cerebral MECs. Moreover, we also explored the potential relationship between SIRT1/FOXO3a signaling pathway and TSPO protein. Our study revealed the protective role and the potential mechanisms of 14,15-EET in cerebral MECs under OGD/R condition.

Neurovascular system plays a vital role in controlling the blood flow into brain parenchymal tissues. Additionally, it also facilitates the metabolism in neuronal biological activities. Cerebral microvascular endothelial cells (MECs) are involved in mediating progression of the diseases related to cerebral vessels, including stroke. Arachidonic acid can be transformed into epoxyeicosatrienoic acids (EETs) under the catalysis by cytochrome P450 epoxygenase. We have reported that EETs could protect neuronal function. In our research, the further role of 14,15-EET in the protective effects of cerebral MECs and the potential mechanisms involved in oxygen glucose deprivation and reperfusion (OGD/R) were elucidated. In our study, we intervened the SIRT1/FOXO3a pathway and established a TSPO knock down model by using RNA interference technique to explore the cytoprotective role of 14,15-EET in OGD/R injury. Cerebral MECs viability was remarkably reduced after OGD/R treatment, however, 14,15-EET could reverse this effect. To further confirm whether 14,15-EET was mediated by SIRT1/FOXO3a signaling pathway and translocator protein (TSPO) protein, we also detected autophagy-related proteins, mitochondrial membrane potential, apoptosis indicators, oxygen free radicals, etc. It was found that 14,15-EET could regulate the mitophagy induced by OGD/R. SIRT1/FOXO3a signaling pathway and TSPO regulation were related to the protective role of 14,15-EET in cerebral MECs. Moreover, we also explored the potential relationship between SIRT1/FOXO3a signaling pathway and TSPO protein. Our study revealed the protective role and the potential mechanisms of 14,15-EET in cerebral MECs under OGD/R condition.

One of the critical problems for flexible vehicles is how to simultaneously address the multiple uncertainty compensation and flexible vibration suppression. This paper focuses on the smooth adaptive fault-tolerant control design problem of a two-layer framework for flexible air-breathing hypersonic vehicles subject to contingent actuator failures and multiple model uncertainties. The first layer provides a disturbance observer–based neural adaptive fault-tolerant controller overcoming actuator failures and multiple model uncertainties. The second layer relies on the tracking differentiator and filter combined with the controller seamlessly, generating smooth reference information, which is highly desirable for flexible vibration suppression. Then, the analysis by the Lyapunov theory strictly proves the uniform ultimately boundedness of all the control and filter state variables. Finally, the simulation results demonstrate the dominant tracking control performance of the proposed control method.

In this chapter, we present the mainstream research of current privacy preservation in IoTs built upon the literature review we have done in recent years [1–3].

Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models. To address the above challenges, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weights to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL.

Background: Curcumin has shown considerable pharmacological activity, including antiinflammatory activity. Nevertheless, the pharmacological effect of curcumin may be limited because of poor water solubility, metabolizing rapidly and systemic elimination. Objective: In the current research, a novel curcumin nanoemulsion (Cur-NE) was developed for improving in vitro permeability and bioavailability via pulmonary administration. Methods: The Cur-NE was prepared by a modified emulsification-evaporation method and its surfac morphology, particles size and distribution, and encapsulation efficiencies of drug in NE were characterized. In vitro transmembrane transport experiment was performed to investigate the transport profile of curcumin across Xenopus alveolar membrane. The pharmacokinetics of Cur-NE in rabbits was evaluated. Results: The average particles size, zeta potential, polydispersity index of Cur-NE were 234.8±1.08 nm, -19.5±0.2 mV and 0.10, respectively. Xenopus alveolar membrane was used in the transmembrane transport study, the cumulative amount of curcumin was 6.6% for curcumin suspensions, but nearly 50% for Cur-NE at the time of 8 h (P Conclusion: Thus, a novel Cur-NE for pulmonary drug delivery was developed for improving in vitro permeability and bioavailability, which can be an alternate to the oral administration.

Due to dramatically increasing information published in social networks, privacy issues have given rise to public concerns. Although the presence of differential privacy provides privacy protection with theoretical foundations, the trade-off between privacy and data utility still demands further improvement. However, most existing studies do not consider the quantitative impact of the adversary when measuring data utility. In this paper, we firstly propose a personalized differential privacy method based on social distance. Then, we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each other. We formalize all the payoff functions in the differential privacy sense, which is followed by the establishment of a static Bayesian game. The trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning algorithm. The proposed method achieves fast convergence by reducing the cardinality from n to 2. In addition, the in-place trade-off can maximize the user’s data utility if the action sets of the user and the adversary are public while the strategy sets are unrevealed. Our extensive experiments on the real-world dataset prove the proposed model is effective and feasible.

Long non‐coding RNAs (lncRNAs) have been identified as playing critical roles in multiple diseases. However, little is known regarding their roles and mechanisms in post‐stroke angiogenesis. Our studies focused on deciphering the functional roles and the underlying mechanisms of the lncRNA metastasis‐associated lung adenocarcinoma transcript 1 (MALAT1) in the process of angiogenesis following oxygen‐glucose deprivation/reoxygenation (OGD/R). We characterized the up‐regulation of MALAT1 expression in the process of angiogenesis after hypoxic injury in vivo and in vitro. We further showed that compared with the empty vector, MALAT1 knockdown had significantly reduced the capacity for angiogenesis, which was measured by 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium (MTT), scratching, cell cycle and immunofluorescent staining. Thus, our findings suggest that MALAT1 may mediate proangiogenic function in OGD/R. To further explore the potential mechanisms, we used lentiviruses expressing shMALAT1 and empty vector; the results revealed that shMALAT1 reduced the expression of 15‐lipoxygenase 1 (15‐LOX1), vascular endothelial growth factor (VEGF) and the phosphorylation of signal transducers and activators of transcription 3 (pSTAT3). Taken together, our results are the first to propose that MALAT1 may regulate angiogenesis through the 15‐LOX1/STAT3 signalling pathway, and they may provide a critical target for the treatment of hypoxic injury and an avenue for therapeutic angiogenesis.

In edge computing, edge nodes are hierarchically arranged, while data transmission is allowed among edge nodes. Because end devices are the closest to raw data sources, they usually submit requests with sensitive information to the edge nodes. Privacy issues occur during the transmission process. In addition, when an end user sends a request to edge nodes, the end user connects to the closest edge node initially, and the request is passed to the upper-layer edge nodes for further processing when the resources of the initial nodes are exhausted. The resource limitation includes computation, storage capabilities, and the number of users. For example, if a user generates one request to the connected edge node exceeding its computation power, the upper-layer edge node will be involved in the operation. Additionally, the information will be transmitted to the other edge nodes, which might be malicious. Thus, privacy preservation is necessary to prevent privacy leakage from data transition among multiple edge nodes in hierarchical structures.