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Abstract Background Ischemic stroke and transient ischemic attack (TIA) are the most prevalent cerebrovascular diseases. The conventional antiplatelet drugs are associated with an inherent bleeding risk, while indobufen is a new antiplatelet drug and has the similar mechanism of antiplatelet aggregation as aspirin with more safety profile. However, there have been no studies evaluating the combination therapy of indobufen and clopidogrel for antiplatelet therapy in cerebrovascular diseases. Objective The CARMIA study aims to investigate the effectiveness and safety of a new dual antiplatelet therapy consisting of indobufen and clopidogrel comparing with the conventional dual antiplatelet therapy consisting of aspirin and clopidogrel in patients with minor ischemic stroke or high-risk TIA. Methods An open-label randomized controlled clinical trial was conducted at a clinical center. We randomly assigned patients who had experienced a minor stroke or transient ischemic attack (TIA) within 72 h of onset, or within 1 month if they had intracranial stenosis (IS), to receive either indobufen 100 mg twice daily or aspirin 100 mg once daily for 21 days. For patients with IS, the treatment duration was extended to 3 months. All patients received a loading dose of 300 mg clopidogrel orally on the first day, followed by 75 mg once daily from the second day to 1 year. We collected prospective data using paper-based case report forms, and followed up on enrolled patients was conducted to assess the incidence of recurrent ischemic stroke or TIA, mRS score, NIHSS (National Institutes of Health Stroke Scale) score, and any bleeding events occurring within 3 month after onset. Results We enrolled 202 patients diagnosed with ischemic stroke or transient ischemic attack. After applying the criteria, 182 patients were eligible for data analysis. Endpoint events (recurrence of ischemic stroke/TIA, myocardial infarction, or death) were observed in 6 patients (6.5%) receiving aspirin and clopidogrel, including 4 (4.3%) with stroke recurrence, 1 (1.1%) with TIA recurrence, and 1 (1%) with death. In contrast, no endpoint events were reported in the indobufen and clopidogrel group (P = 0.029). The group of patients receiving indobufen and clopidogrel exhibited significantly lower modified Rankin Scale (mRS) score. (scores range from 0 to 6, with higher scores indicating more severe disability) compared to the aspirin and clopidogrel group (common odds ratio 3.629, 95% CI 1.874–7.036, P 0.05). Bleeding events were observed in 8 patients (8.6%) receiving aspirin and clopidogrel, including 4 (4.3%) with skin bleeding, 2 (2.2%) with gingival bleeding, 1 (1.1%) with gastrointestinal bleeding, and 1 (1.1%) with urinary system bleeding. On the other hand, only 1 patient (1.1%) in the indobufen and clopidogrel group experienced skin bleeding (P = 0.035). Conclusion The combination of indobufen and clopidogrel has shown non-inferior and potentially superior effectiveness and safety compared to aspirin combined with clopidogrel in patients with minor ischemic stroke and high-risk TIA in the CARMIA study (registered under chictr.org.cn with registration number ChiCTR2100043087 in 01/02/2021).

Fragile X Syndrome (FXS) is a genetic neurodevelopmental disorder closely associated with intellectual disability and autism spectrum disorders. The core of the disease lies in the abnormal expansion of the CGG trinucleotide repeat sequence at the 5′end of the FMR1 gene. When the repetition exceeds 200 times, it causes the silencing of the FMR1 gene, leading to the absence of the encoded Fragile X mental retardation protein 1 (FMRP). Although the detailed mechanism by which the CGG repeat expansion triggers gene silencing is yet to be fully elucidated, it is known that this process does not alter the promoter region or the coding sequence of the FMR1 gene. This discovery provides a scientific basis for the potential reversal of FMR1 gene silencing through interventional approaches, thereby improving the symptoms of FXS. Epigenetics, a mechanism of genetic regulation that does not depend on changes in the DNA sequence, has become a new focus in FXS research by modulating gene expression in a reversible manner. The latest progress in molecular genetics has revealed that epigenetics plays a key role in the pathogenesis and pathophysiological processes of FXS. This article compiles the existing research findings on the role of epigenetics in Fragile X Syndrome (FXS) with the aim of deepening the understanding of the pathogenesis of FXS to identify potential targets for new therapeutic strategies.

Abstract Cross-domain recommendation (CDR) has become an important research direction in the field of recommender systems due to the increasing demand for personalized recommendations across different domains. However, CDR faces multiple challenges, including data sparsity, popularity bias, and long-tail problems. To address these challenges, we propose a novel framework that combines graph contrastive embedding and multi-head cross-attention transfer for cross-domain recommendation, called GCE-MCAT. Specifically, in the pre-training process, we generate more uniform user and item embeddings through contrastive learning, effectively solving the problem of inconsistent data embedding space distribution and recommendation popularity bias. Moreover, we propose a multi-head cross-attention transfer mechanism that allows the model to extract user common and specific domain features from multiple perspectives and perform cross-domain bidirectional knowledge transfer. Finally, we propose a cross-domain feature fusion mechanism that dynamically assigns weights to common user features and specific domain features. This enables the model to more effectively learn common user interests. We evaluate the proposed framework on three real-world CDR datasets and show that GCE-MCAT consistently and significantly improves recommendation performance compared to state-of-the-art methods. In particular, the proposed framework has demonstrated remarkable effectiveness in addressing long-tail distribution and enhancing recommendation novelty, providing users with more diversified recommendations and reducing popularity bias.

Abstract Vehicular fog computing (VFC) provisions computing services at the edge of networks by fully exploiting the idle resources of vehicle loaded computer systems. Task scheduling and resource allocation revolved around VFC have gained tremendous attention recently. Currently, most of these works in VFC have focused on response time optimization or energy reduction. Computing services are provisioned in a pay-as-you-go model and vehicles as resource contributors are stimulated by the benefits obtained by leasing these resources. How to maximize their own benefits is one of big concerns but few of current works have recognized its importance in VFC. We in this paper introduce the notion of resource pooling into VFC where the computing resources of vehicles are pooled together to jointly provision computational services in a community. A genetic algorithm based strategy is proposed to solve the optimization problem for the sake of benefit maximization. Extensive experiments have been carried out to evaluate the approach and the numeric results have demonstrated that our strategy outstands other approaches with regards to the optimization objective.

Intelligent transportation system (ITS) has attracted extensive attention in both academia and industry for its potential benefits. For example, ITS is dedicated to convenient, economical and environmentally friendly service provisioning for the drivers and passengers in vehicles via advanced technologies including artificial intelligence (AI), knowledge mining, depth fusion, etc. Besides, several newly emerging computing paradigms revolved around ITS such as vehicular cloud and vehicular fog computing are proposed to fully exploit idle computing and communication resources within connected vehicles. As the number of vehicular applications is explosively increasing, it has posed great challenges to the limited capabilities of vehicle loaded computer systems and communication facility. Accordingly, more intelligent resource allocation strategies are needed for computationally intensive and time sensitive vehicular applications. In this paper we propose a road side unit (RSU) empowered vehicular network that consists of three hierarchical layers-vehicular cloud, RSU-enabled cloudlet, and central cloud, respectively. RSU is enhanced with edge servers such that it can intelligently respond to the resource requests in a real time fashion. To this end, an approximate but efficient resource allocation strategy is proposed that can intelligently optimize the utility value from the perspective of RSU-enabled cloudlet. Extensive experiments are carried out to evaluate the performance of the strategy. The results reveal that the proposed algorithm DbHA shows great advantages over other approaches such as the genetic algorithm (GA) and particle swarm optimization (PSO) in both respects (i.e., performance and response latency).

In the traditional CNN design, the hyperparameters, such as the size of the convolutional kernel and stride, are difficult to determine. In this paper, a new convolutional network architecture, named multi-branch fuzzy architecture network (MBFAN), was proposed for this problem. In MBFAN, some branches with a certain convolutional neural network architecture are connected in parallel. In each branch, a different-sized convolutional kernel is applied. By data training and normalization, a weight is given to each branch. By these weights, the important features in the final output are strengthened. By normalization, the branches were interconnected together, making the training process more efficient. Due to overfitting, with the increase of branches, the MBFAN accuracy increases, and then decreases. The number of branches is optimized when the MBFAN accuracy is highest. On the other hand, the location of the convolutional kernel center in an image has a great influence on the convolutional results. This is also discussed in MBFAN. For the experiments, the proposed MBFAN was adopted and tested in a simple convolutional network and a VGG16 network.

With the rapid development of the Internet of Things (IOT) and increasing research on tracking control of the affine IOT systems with uncertainty, a nonlinear control method of command filtering backstepping with self-organizing approximation is proposed. A command filter with a saturation structure is used to eliminate the analytic computation of command derivatives, and tracking errors are redefined to compensate the discrepancy between the command filtered signals and the analytic computation of command derivatives. Command filtered backstepping is used to decouple the higher IOT system and simplify the structure of the IOT system. Self-organizing approximation is used to online approximate the uncertain and eliminate the effect of the uncertain in the IOT system. The approximation for the uncertain is finished by a linear product between a set of basis functions and weighted functions on the local region. The weighted sum of compensated tracking errors is used as the prespecified tracking accuracy performance. A new local approximator is added to the control system according to the criterion based on the Lyapunov theory to enhance the capacity of the approximator when the prespecified tracking accuracy performance cannot be achieved. Finally, an application for a simple IOT system is analyzed by simulations. The simulation results show that the control method for the IOT system is effective.

The explosive growth of motor vehicles in urban areas has heavily burdened the existing transportation systems. Consequently, there are recently emerging smart transportation paradigms that aim to ease urban transportation pressure. Smart traffic signal control, which is considered as one of the breakthrough technologies in smart transportation paradigm, has ushered significant academic and industry efforts for its considerable benefits. The state-of-the-art proposals usually rely on a centralized infrastructure with powerful computing abilities to deal with a large amount of different traffic data. However, the centralized processing approach is often hindered by a long and even unbearable response latency, restricting its wide deployment and applications in the real world. To overcome this latency-related issue and achieve near-optimal traffic signal control in nearly real time, we have proposed a non-centralized approach as well as a fog computing-based architecture, and thus traffic data can be handled in a smarter way. To be specific, the traffic data are processed right at where it was generated, i.e., at the edge rather than at a centralized facility. In this paper, the phase timing of a single intersection is to be handled real-time by a local fog node with a genetic optimization algorithm, and the task for the regional optimization is offloaded to the centralized cloud and executed. The simulation experiments are conducted on the simulator to evaluate the performance of our proposal, and the results confirm that the proposed architecture and algorithm have significant improvement on the average duration time compared to existing approaches.

BackgroundThe NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome has been identified as an important mediator of blood–brain-barrier disruption in sepsis-associated encephalopathy (SAE). However, no information is available concerning the critical upstream regulators of SAE.MethodsLipopolysaccharide (LPS) was used to establish an in vitro model of blood–brain barrier (BBB) disruption and an in vivo model of SAE. Disruption of BBB integrity was assessed by measuring the expression levels of tight-junction proteins. NLRP3 inflammasome activation, pro-inflammatory cytokines levels, and neuroapoptosis were measured using biochemical assays. Finally, the FITC-dextran Transwell assay and Evan’s blue dye assay were used to assess the effect of Maf1 on LPS-induced endothelial permeability in vitro and in vivo.ResultsWe found that Maf1 significantly suppressed the brain inflammatory response and neuroapoptosis induced by LPS in vivo and in vitro. Notably, Maf1 downregulated activation of the NF-κB/p65-induced NLRP3 inflammasome and the expression of pro-inflammatory cytokines. In addition, we found that Maf1 and p65 directly bound to the NLRP3 gene promoter region and competitively regulated the function of NLRP3 in inflammations. Moreover, overexpression of NLRP3 reversed the effects of p65 on BBB integrity, apoptosis, and inflammation in response to LPS. Our study revealed novel role for Maf1 in regulating NF-κB-mediated inflammasome formation, which plays a prominent role in SAE.ConclusionsRegulation of Maf1 might be a therapeutic strategy for SAE and other neurodegenerative diseases associated with inflammation.

Interactive genetic algorithms have been used in a wide variety of applications and extensively developed to facilitate the personalization and customization of products for users. However, the ambiguity effect or cognitive ambiguity of users during the product customization process will affect the effects of the final customized product. Here, we first deconstructed the ambiguity effect into cognitive ambiguity during early decision-making and that during the decision-making process. A spatial mapping strategy that involves “text-image-symbol” and a clustering strategy were then proposed to mitigate cognitive ambiguity in the two different stages, respectively. Then, a specific application example—“Chinese vase design” was studied. Based on the proposed strategy and interactive genetic algorithm, a prototype of computer-aided design system for product modeling was developed by MATLAB software. Then, 10 users were invited to experiment at three different cases (not using the proposed strategy, using spatial mapping strategy, using spatial mapping strategy and scheme clustering strategy). The experiment results have verified that the spatial mapping strategy was efficient to solve cognitive ambiguity during early decision-making, and clustering strategy was efficient to solve cognitive ambiguity during the decision-making process.

The Industrial Internet of Things is cited as the latest means for making manufacturing more flexible, cost effective, and responsive to changes in customer demands. In this paper, we present a mobile cloud based scheduling strategy for the industrial Internet of Things. Several computing paradigms, such as mobile cloud computing, fog computing, and edge computing can be integrated to the industrial Internet of Things, which allow to offload tasks to the cloud for execution. We model the task scheduling problem as an energy consumption optimization problem, while taking into account task dependency, data transmission, and some constraint conditions, such as response time deadline and cost, and further solve it by genetic algorithms. A series of simulation experiments are conducted to evaluate the performance of the algorithm and the results have shown that our proposal is more efficient than the baseline approach.

In virtue of the rapid development of the Internet of Things (IoT), Organizations have grown to rely on their cyber systems and networks. However, this phenomenon also creates many new information security issues. In this paper, we propose an evolutionary algorithm improved cuckoo search (ICS) to pretrain a backpropagation neural network (BPNN) for the sake of improving the accuracy and stability. Using this pre-training process, the BPNN can surmount the defect of falling into the local minima and greatly improve its efficiency. Then, this neural network is used as a part of information security risk assessment (ISRA) processes for a miniature IoT system. An illustration example is introduced to demonstrate that the ICS-BPNN outperforms other neural networks in this ISRA process.

Device-to-device (D2D) communications enable direct communications among mobile entities, which brings new revolutions to existing cellular networks. Many use cases which can benefit from D2D are introduced such as vehicles-to-vehicles communication, vehicles-to-infrastructure communication, machine-to-machine communication, and so on. With the help of these information communication techniques, we propose a real-time traffic signal control approach to relieve traffic problems in this paper. Currently, a series of traffic problems, such as traffic congestion, traffic accidents, and vehicle exhaust emission, are increasingly inconveniencing city residents, especially in rush hours. One of the most dominating approaches to relieve the traffic congest is to determine the phase timing of traffic signals. However, a major shortcoming of the existing phase timing related control strategies is of highly computational complexity, which causes, to some extent, a response delay. The approach based on D2D communication, in this paper, on one hand can collect data of various types via sensors and actuators and on the other hand can reduce the response time as much as possible. Specifically, considering an intersection with four legs, we encoded the corresponding set of signal lights of each leg using a genetic algorithm. To evaluate the efficiency of phase timing plan in this paper, we have conducted extensive simulations, and the results show that our approach can respond to the considered traffic flow within one second. Compared with other traffic signal control systems, the performance is improved almost by 67% with regards to the queue length waiting at the intersections during traffic signal light cycle(s).

An experience of finding a vacant parking slot can be very stressful in densely populated areas, especially in peak hours. Such parking process takes a long time, wastes significant gasoline, and emits extra vehicle exhaust that harms the environment. Smart parking, aiming to assist drivers in finding desirable parking slots more efficiently through information and communication technologies such as vehicle ad hoc networks (VANETs), has received extensive attention recently. Current VANETs-based parking slot allocations cannot provide a fully satisfactory solution, because vehicle communication devices-onboard units-and roadside units lack computational capabilities to perform humanized and accurate service provisioning, such as real-time parking slots information and probabilistic prediction on future parking slots. Therefore, we, in this paper, propose a fog computing-based smart parking architecture to improve smart parking in real time. Fog nodes deployed at parking lots, cooperating with each other, enable realtime parking slot information provisioning as well as parking requests processing. The cloud center can further enhance smart parking capability by enforcing global optimization on parking requests allocation. The experimental results of our approaches show higher efficiency compared with other parking strategies. The proposed fog computing-based smart parking can lower the average parking cost and minimize gasoline wastes and vehicle exhaust emission.

Objective This study was performed to explore the correlation between cognitive impairment and renal microangiopathy in patients with type 2 diabetic nephropathy (T2DN) by detecting changes in cognitive function and cerebral metabolism in these patients with different stages of T2DN. Methods Prospectively maintained databases were reviewed from 2006 to 2017. Blood biochemical indexes and the urinary albumin excretion rate (UAER) were measured in all participants. Cognitive function was assessed by the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA). Cognitive impairment was the primary endpoint. Renal microangiopathy was the secondary endpoint. Pearson correlation analysis was used to assess correlations. Results Two hundred sixteen patients with type 2 diabetes mellitus (T2DM) were divided into three groups according to their UAER: T2DM without nephropathy (n=72), early T2DM with nephropathy (n=74), and the clinical stage of early T2DM with nephropathy (n=70). Healthy participants were selected as the normal control group (n=70). Pearson correlation analysis demonstrated that the total MMSE and MoCA score was negatively correlated with the UAER (r=−0.327) and positively correlated with the estimated glomerular filtration rate (r=0.428) in patients with T2DN. Conclusions The present study showed a positive correlation between cognitive impairment and renal microangiopathy in patients with T2DN.

In wireless sensor networks, node placement plays a significant role in terms of meeting design goals, such as cost effectiveness, connectivity, lifetime, and data latency. In tunnel environments, the energy consumption of sensor nodes and relay nodes is imbalanced because of the long and narrow shape of tunnels. In this article, to reduce the use of extra batteries and the number of forwarding times for a message, we propose a new approach to assist in the placement of relay nodes for two-tier wireless sensor networks in a tunnel environment. The approach proposed is based on the principle that working relay nodes can change approximately at the same time during network operation. It seems the relay nodes move to a new place when they are instead by dormant relay nodes. The approach provides a novel method that has been proved to find optimal places to deploy relay nodes in tunnel environment. Moreover, the approach balances the energy consumption of entire sensor nodes and prolongs the lifetime of entire networks significantly, which is shown in simulations.