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The phenomenal popularity of smart mobile computing hardware is enabling pervasive edge intelligence and ushering us into a digital twin era. However, the natural barrier between edge equipment owned by different interested parties poses unique challenges for cross-domain trust management. In addition, the openness of radio access and the accessibility of edge services render edge intelligence systems vulnerable and put sensitive user data in jeopardy. This paper presents an intrusion protection mechanism for edge trust transfer to address the inter-edge trust management issue and the conundrum of detecting indistinguishable malevolent nodes launching weak attacks. First, an inter-edge reputation transfer framework is established to leverage the trust quality of different edges to retain the accumulated trust histories of users when they roam in multi-edge environments structurally. Second, a fine-grained intrusion protection system is proposed to reduce the negative impact of attacks on user interactions and improve the overall trust quality and system security of edge intelligence services. The experimental results validate the effectiveness and superior performance of the proposed intrusion protection for edge trust transfer in securing, enhancing, and consolidating edge intelligence services.

Abstract Metastasis is the major cause of treatment failure in patients with prostate adenocarcinoma (PRAD). Diverse programmed cell death (PCD) patterns play an important role in tumor metastasis and hold promise as predictive indicators for PRAD metastasis. Using the LASSO Cox regression method, we developed PCD score (PCDS) based on differentially expressed genes (DEGs) associated with PCD. Clinical correlation, external validation, functional enrichment analysis, mutation landscape analysis, tumor immune environment analysis, and immunotherapy analysis were conducted. The role of Prostaglandin D2 Synthase (PTGDS) in PRAD was examined through in vitro experiments, single-cell, and Mendelian randomization (MR) analysis. PCDS is elevated in patients with higher Gleason scores, higher T stage, biochemical recurrence (BCR), and higher prostate-specific antigen (PSA) levels. Individuals with higher PCDS are prone to metastasis, metastasis after BCR, BCR, and castration resistance. Moreover, PRAD patients with low PCDS responded positively to immunotherapy. Random forest analysis and Mendelian randomization analysis identified PTGDS as the top gene associated with PRAD metastasis and in vitro experiments revealed that PTGDS was considerably downregulated in PRAD cells against normal prostate cells. Furthermore, the overexpression of PTGDS was found to suppress the migration, invasion, proliferationof DU145 and LNCaP cells. To sum up, PCDS may be a useful biomarker for forecasting the possibility of metastasis, recurrence, castration resistance, and the efficacy of immunotherapy in PRAD patients. Additionally, PTGDS was identified as a viable therapeutic target for the management of PRAD.

PD-1 blockade therapy has made great breakthroughs in treatment of multiple solid tumors. However, patients with microsatellite-stable (MSS) colorectal cancer (CRC) respond poorly to anti-PD-1 immunotherapy. Although CRC patients with microstatellite instability (MSI) or microsatellite instability-high (MSI-H) can benefit from PD-1 blockade therapy, there are still some problems such as tumor recurrence. Tumor-associated macrophages (TAMs), most abundant immune components in tumor microenvironment (TME), largely limit the therapeutic efficacy of anti-PD-1 against CRC. The CSF1/CSF1R pathway plays a key role in regulating macrophage polarization, and blocking CSF1R signaling transduction may be a potential strategy to effectively reprogram macrophages and remodel TME. Here, we found that increasing expression of CSF1R in macrophages predicted poor prognosis in CRC cohort. Furthermore, we discovered a novel potent CSF1R inhibitor, PXB17, which significantly reprogramed M2 macrophages to M1 phenotype. Mechanically, PXB17 significantly blocked activation of PI3K/AKT/mTORC1 signaling, resulting in inhibition of cholesterol biosynthesis. Results from 3D co-culture system suggested that PXB17-repolarized macrophages could induce infiltration of CD8+ T lymphocytes in tumors and improve the immunosuppressive microenvironment. In vivo, PXB17 significantly halted CRC growth, with a stronger effect than PLX3397. In particular, PXB17 potently enhanced therapeutic activity of PD-1 mAb in CT-26 (MSS) model and prevented tumor recurrence in MC-38 (MSI-H) model by promoting formation of long-term memory immunity. Our study opens a new avenue for CSF1R in tumor innate and adaptive anti-tumor immunomodulatory activity and suggests that PXB17 is a promising immunotherapy molecule for enhancing the efficacy of PD-1 mAb or reducing tumor recurrence of CRC.

LEO satellite constellation (LSC) offers seamless and fast connectivity for non-terrestrial network (NTN).However, the lack of QoS guarantees for users significantly impacts the performance of the NTN system.Considering multi-QoS metrics, a capacity and downlink budget model for LSC was established, and subsequently, the establishment of inter-satellite links was framed as a link budget issue for the QoS guarantee.The non-dominated sorting genetic algorithm (INSGA-II) with improved crossover and mutation operators was proposed to optimize LSC, aiming to maximize coverage and system capacity while minimizing constellation costs.The numerical results demonstrate that the designed LSC exhibits comparable or superior performance to Telesat and Kepler, and its scale is only 64% of the Kepler system.

Offloading Mobile Devices (MDs) computation tasks to Edge Nodes (ENs) is a promising solution to overcome computation and energy resources limitations of MDs. However, there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit. In this paper, we propose an auction-based computation offloading algorithm, inspiring ENs to provide high-quality service by maximizing the profit of ENs. Firstly, a novel cooperation auction framework is designed to avoid overall profit damage of ENs, which is derived from the high computation delay at the overloaded ENs. Thereafter, the bidding willingness of each MD in every round of auction is determined to ensure MD rationality. Furthermore, we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness. Finally, the auction-based profit maximization offloading algorithm is proposed, and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction. Numerical results verify the performance of the proposed algorithm. Compared with the VA algorithm, the ENs profit is increased by 23.8%, and the task discard ratio is decreased by 7.5%.

Abstract Recently, several in‐loop filtering algorithms based on convolutional neural network (CNN) have been proposed to improve the efficiency of HEVC (High Efficiency Video Coding). Conventional CNN‐based filters only apply a single model to the whole image, which cannot adapt well to all local features from the image. To solve this problem, an in‐loop filtering algorithm based on a dynamic convolutional capsule network (DCC‐net) is proposed, which embeds localized dynamic routing and dynamic segmentation algorithms into capsule network, and integrate them into the HEVC hybrid video coding framework as a new in‐loop filter. The proposed method brings average 7.9% and 5.9% BD‐BR reductions under all intra (AI) and random access (RA) configurations, respectively, as well as, 0.4 dB and 0.2 dB BD‐PSNR gains, respectively. In addition, the proposed algorithm has an outstanding performance in terms of time efficiency.

The damage of cement slurry to the reservoir is directly related to the production of oil and gas wells, and is a problem that must be solved for the efficient and economic development of oil and gas reservoirs. According to the characteristics and field conditions of reservoir damage caused by cement slurry, this paper analyzes the main factors affecting the degree of reservoir damage, puts forward the technical index requirements of the evaluation device, and establishes the evaluation method of reservoir damage caused by cement slurry in case of loss of injection. The results show that the influence of temperature on the damage of the cement slurry layer is small, and the influence of pressure difference on the damage to the cement slurry layer is large; When the pressure difference is 22 Mpa, the penetration damage rate caused by cement slurry to the pore center without mud cake attachment is about 43%. After a day of core maintenance, the permeability damage rate will reach 61.2%; The damage of cement slurry to the pore micro-fractured core is very serious. After 1 day of maintenance, the damage to the core permeability after cement slurry solidification is as high as 83%, with an average of 76.5%; When the risk of leakage is low, the cement slurry system with low density and low water loss can be used for cementing operation. The filtrate of cement slurry should be better matched with the formation clay minerals, formation water, and drilling fluid filtrate. When the risk of leakage is high, high-density cement slurry, balanced pressure cementing, pressure control cementing, and other technologies to avoid leakage can be used for cementing operations. When the cementing conditions are more difficult, the cementing slurry system can be used to dissolve the cementing slurry system at a later stage. The research results provide theoretical data support for the study of reducing the damage of cement slurry to the reservoir.

Background: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. Methods: We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. Results: The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. Conclusions: CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients.

Abstract Background Retroperitoneal robotic partial nephrectomy is markedly restricted by limited space and visual field. We introduced a novel Gerota-edge-sling (GES) technique with self-designed traction devices to overcome these defects by attaching Gerota fascia to abdominal wall, and comparatively evaluated its utilization with routine technique. Methods A retrospective analysis was performed for consecutive patients who underwent routine (control group) or GES assisted (GES group) retroperitoneal robotic partial nephrectomy for localized renal tumors in our hospital between March 2018 and June 2020. Clinical data of perioperative outcomes and complications were collected and compared. Comparison of outcomes between anterior versus posterior tumor subgroups was also conducted. Linear regression analysis was used to define the relationship between dissection time and perinephric fat status in each group. Results Totally 103 patients were included, 48 in control and 55 in GES group respectively. All the procedures were completed successfully without conversion or positive surgical margin. GES group had significantly decreased console time (91 ± 36 min vs. 117 ± 41 min, p

To guarantee the heterogeneous delay requirements of the diverse vehicular services, it is necessary to design a full cooperative policy for both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) links. This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links. Specifically, a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user, respectively. A multi-agent reinforcement learning framework is designed to solve these two problems, where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework. Thereafter, a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward. The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.

Biomass-derived carbon-based nanomaterials represent a group of green and high-quality materials which can be potentially employed in the fields of environmental protection, energy conversion and clean energy storage [...]

Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I.

A huge amount of sensitive personal data is being collected by various online health monitoring applications. Although the data is anonymous, the personal trajectories (e.g., the chronological access records of small cells) could become the anchor of linkage attacks to re-identify the users. Focusing on trajectory privacy in online health monitoring, we propose the User Trajectory Model (UTM), a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data (e.g., number of individuals covered by each small cell), and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness (i.e., the expectation and the variance). Eventually, exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation, confirm our theoretical deductions and present counter-intuitive insights.

Radiotherapy for rectal cancer has received increasing research attention in recent years; however, no bibliometric assessment has been conducted on the progress of research in this field. This study aimed to visualize the research evolution and emerging research hotspots in the field of rectal cancer radiotherapy using bibliometric methods. Data were collected from the Web of Science Core Collection database, including countries, institutions, authors, keywords, and co-citations of references, and the CiteSpace software was used for bibliometric analysis. A total of 5,372 publications on radiotherapy for rectal cancer, published between January 2000 and January 2022, were included. An increasing trend in the number of published articles was observed. There is an overall upward trend in the number of publications published, with the US publishing the most in this field, followed by China and the Netherlands. Italian writer Vincenzo Valentini and German writer R. Sauer ranked first in terms of published articles and co-cited authors, respectively. Literature co-citation and keyword co-occurrence analyses showed that early studies focused on topics such as preoperative radiotherapy, combined radiotherapy and chemotherapy, and total mesorectal excision. In recent years, gradually increasing attention has been paid to short-course radiotherapy, x-ray brachytherapy, and stereotactic systemic radiotherapy. Burst analysis suggested that magnetic resonance (MR)-guided neoadjuvant radiotherapy studies, mechanistic studies, and clinical trials may emerge as new research hotspots. Rectal cancer radiotherapy has been widely studied and the research hotspots have considerably changed in recent years. Future research hotspots may include MR-guided neoadjuvant radiotherapy studies, mechanistic studies, and clinical trials.

Lithium–sulfur batteries (LSBs) with a high energy density have been regarded as a promising energy storage device to harness unstable but clean energy from wind, tide, solar cells, and so on. However, LSBs still suffer from the disadvantages of the notorious shuttle effect of polysulfides and low sulfur utilization, which greatly hider their final commercialization. Biomasses represent green, abundant and renewable resources for the production of carbon materials to address the aforementioned issues by taking advantages of their intrinsic hierarchical porous structures and heteroatom-doping sites, which could attribute to the strong physical and chemical adsorptions as well as excellent catalytic performances of LSBs. Therefore, many efforts have been devoted to improving the performances of biomass-derived carbons from the aspects of exploring new biomass resources, optimizing the pyrolysis method, developing effective modification strategies, or achieving further understanding about their working principles in LSBs. This review firstly introduces the structures and working principles of LSBs and then summarizes recent developments in research on carbon materials employed in LSBs. Particularly, this review focuses on recent progresses in the design, preparation and application of biomass-derived carbons as host or interlayer materials in LSBs. Moreover, outlooks on the future research of LSBs based on biomass-derived carbons are discussed.

Graph Neural Networks (GNNs) have been applied in many fields of semi-supervised node classification for non-Euclidean data. However, some GNNs cannot make good use of positive information brought by nodes which are far away from each central node for aggregation operations. These remote nodes with positive information can enhance the representation of the central node. Some GNNs also ignore rich structure information around each central node’s surroundings or entire network. Besides, most of GNNs have a fixed architecture and cannot change their components to adapt to different tasks. In this article, we propose a semi-supervised learning platform ATPGNN with three variable components to overcome the above shortcomings. This novel model can fully adapt to different tasks by changing its components and support inductive learning. The key idea is that we first create a high-order topology graph, which is from similarity of node structure information. Specifically, we reconstruct the relationships between nodes in a potential space obtained by network embedding in graph. Second, we introduce graph representation learning methods to extract representation information of remote nodes on the high-order topology graph. Third, we use some network embedding methods to get graph structure information of each node. Finally, we combine the representation information of remote nodes, graph structure information and feature for each node by attention mechanism, and apply them to learning node representation in graph. Extensive experiments on real attributed networks demonstrate the superiority of the proposed model against traditional GNNs.

Multidrug resistance (MDR) remains a critical bottleneck in successful treatment of osteosarcoma (OS). Luteolin is a flavonoid compound that has been verified to increase the sensitivity to antineoplastic drugs in many tumors. However, its roles in reversing MDR of OS and the potential underlying mechanisms remain largely unknown. In this study, we demonstrated that luteolin enhances cellular chemosensitivity to doxorubicin and cisplatin both in OS cells and xenograft models, and it could increase the miR-384 level and downregulate the PTN expression. Additionally, target analysis confirmed that miR-384 directly modulates PTN expression, and subsequent mechanistic analysis verified that miR-384 could inhibit the MDR of OS cells through suppressing the PTN/β-catenin/MDR1 signaling axis. Further analysis revealed treatment of sensitive MG63 cells with luteolin effectively packaged miR-384 into secreted exosomes and the exosomes could improve doxorubicin response in doxorubicin-resistant MG63/DOX cells. Our study confirmed that luteolin exerts MDR reversal effect against OS cells by regulating PTN expression via miR-384 and it may be a promising therapeutic agent for chemoresistant OS via its targeting of the PTN/β-catenin/MDR1 axis.

α-Fe2O3 has high potential energy storage capacity and can serve as a green and low-cost anode material for lithium-ion batteries. However, α-Fe2O3 suffers large volume expansion and pulverization. Based on DFT calculations, TiO2 can effectively maintain the integrity of the crystal structure during the discharge/charge process. Well-defined cubic α-Fe2O3 is coated with a TiO2 layer using the hydrothermal method with the assistance of oxalic acid surface treatment, and then α-Fe2O3@TiO2 with tunable buffer chambers is obtained by altering the hydrochloric acid etching time. With the joint efforts of the buffer chamber and the robust structure of the TiO2 layer, α-Fe2O3@TiO2 alleviates the expansion of α-Fe2O3 during the discharge/charge process. The optimized sample (FT-1h) achieves good cycling performance. The reversible specific capacity remains at 893.7 mA h g-1, and the Coulombic efficiency still reaches up to 98.47% after 150 cycles at a current density of 100 mA g−1. Furthermore, the reversible specific capacity can return to 555.5 mA h g−1 at 100 mA g−1 after cycling at a high current density. Hence, the buffer chamber and the robust TiO2 layer can effectively improve the cycling stability and rate performance of α-Fe2O3.

Abstract Background Recently, a novel surgical procedure, named as laparoscopic proximal gastrectomy (LPG) with double-tract reconstruction (DTR), has been reported to provide surgical benefits in the treatment of proximal early gastric cancer (EGC) over traditional laparoscopic total gastrectomy (LTG). These benefits include a lower incidence of some surgical complications and better postoperative nutritional status. However, the number of relevant studies is still too low to validate such benefits. Therefore, this systematic review and meta-analysis aimed to assess the surgical features, complications, and postoperative nutritional status of LPG with DTR in comparison to those of LTG. Methods Online databases (PubMed, Web of Science, Cochrane Library, and EMBASE) were scoured for relevant studies published by April 2020. The quality assessment of the included articles was evaluated using the Newcastle-Ottawa scale. Egger’s test was utilized to assess publication bias. Results Nine studies (687 patients) were enrolled for this meta-analysis, and we found that LPG with DTR and LTG had similar surgical features. However, LPG with DTR was superior to LTG in the incidence of reflux syndrome [OR = 0.185; 95%CI 0.083, 0.414; P = 0.000], postoperative nutritional status (hemoglobin [WMD = − 2.326; 95%CI − 4.491, − 0.160; P = 0.035], vitamin B12 [WMD = − 13.072; 95%CI − 22.850, − 3.294; P = 0.009], and body weight [WMD = − 3.514; 95%CI − 5.579, − 1.449; P = 0.001]). Conclusions LPG with DTR has better performance in the incidence of reflux syndrome and postoperative nutritional status compared with LTG. This surgical procedure may therefore have more benefits for patients with proximal EGC.

To address the problem of load imbalance among edge servers and quality of service degradation caused by dynamic changes of user locations in mobile edge computing networks,a mobility aware edge service migration algorithm was proposed.Firstly,the optimization problem was formulated as a mix integer nonlinear programming problem,with the goal of minimizing the perceived delay of user service request.Then,the delay optimization problem was decoupled into the edge service migration and edge node selection sub-problems based on the Lyapunov optimization approach.Thereafter,the fast edge decision algorithm was proposed to optimize the resource allocation and edge service migration under a given radio access strategy.Finally,the asynchronous optimal response algorithm was proposed to iterate out the optimal radio access strategy.Simulation results validate the proposed algorithm can reduce the perceived delay under the service migration cost constraint while comparing with other existing algorithms.

Reversible data hiding (RDH) methods are widely used in many privacy-sensitive real-time applications for digital images. As an efficient RDH method, prediction-error histogram (PEH) shifting technique has found wide application for its high efficiency in increasing embedding capacity. Nowadays, the performance of most PEH-based methods is evaluated only by Peak Signal to Noise Ratio (PSNR) or Structural Similarity (SSIM) Index. However, more and more applications require high quality in both PSNR and SSIM. Therefore, in this paper, we propose a new RDH method to concentrate on both PSNR and SSIM performance, which involves two key techniques: 1) an SSIM-based block selection technique, 2) a PEH-based optimal expansion bins selection technique. Through block selection, the original image is divided into two parts: smooth blocks and rough blocks. We select pixels of smooth block for double embedding and leave pixels of rough block unchanged. According to the optimal expansion bins, reasonable pixels are chosen for embedding such that the embedding distortion can be reduced. With these improvements, our proposed method has higher SSIM and PSNR after embedding compared to other PEH-based methods. The experimental results demonstrate its superiority over some state-of-the-art counterpart and other conventional PEH-based works.

To meet some real-time mobile crowd sensing (MCS) scenarios, there is a tendency to enhance the MCS system with mobile edge computing (MEC). One of the key challenges is how to select some satisfied participants in such an edge-cloud collaboration MCS system to effectively and real-timely handle dynamic and heterogeneous sensing tasks. In this paper, we propose a bilateral satisfaction aware participant selection mechanism in the edge-cloud collaboration MCS system. The participant selection process is coordinated by the cloud service platform and the MEC server. The cloud service platform sends the required data types to the MEC server and evaluates the user reputation through the user history task records. The MEC server generates a set of tasks and evaluates user fitness based on the user's real-time location. Then the MEC server obtains the user sensing cost based on the user status, and develops the task price model based on the user supply index and data demand index. Finally, the participant selection process is transformed into a game between users and the MEC server about the task reward, and the user who accepts the optimal task price is selected as the participant. The results show that the proposed participant selection strategy can effectively reduce the amount of data processed by the cloud platform, shorten the task completion time, and increase bilateral satisfaction.

N, S co-doped bio-carbons with a hierarchical porous structure and high surface area were prepared using a molten salt method and by adopting Entermorpha prolifera (EP) as a precursor. The structure and composition of the bio-carbons could be manipulated by the salt types adopted in the molten salt assisted pyrolysis. When the carbons were used as an activating agent for peroxydisulfate (PDS) in SMX degradation in the advanced oxidation process (AOP), the removal performance in the case of KCl derived bio-carbon (EPB-K) was significantly enhanced compared with that derived from NaCl (EPB-Na). In addition, the optimized EPB-K also demonstrated a high removal rate of 99.6% in the system that used local running water in the background, which proved its excellent application potential in real water treatment. The degradation mechanism study indicated that the N, S doping sites could enhance the surface affinity with the PDS, which could then facilitate 1O2 generation and the oxidation of the SMX. Moreover, a detailed techno-economic assessment suggested that the price of the salt reaction medium was of great significance as it influenced the cost of the bio-carbons. In addition, although the cost of EPB-K was higher (USD 2.34 kg−1) compared with that of EPB-Na (USD 1.72 kg−1), it was still economically competitive with the commercial active carbons for AOP water treatment.

The quality of user experience suffers from performance deterioration dramatically due to the explosively growing data traffic.To improve the poor performance of cell-edge users and heavy-load cell users,which caused by dense network and load imbalance respectively,an QoE-aware video cooperative caching and transmission mechanism in cloud radio access network was proposed.Cooperative gain-aware virtual passive optical network was established to provide cooperative caching and transmission for video streaming by adopting collaborative approach in optical domain and wireless domain.Furthermore,user experience for video streaming,bandwidth provisioning and caching strategy were jointly optimized to improve QoE,which utilized the methods of dynamic caching in optical domain and buffer level-aware bandwidth configuration in wireless domain.The results show that the proposed mechanism enhances the quality of user experience and effectively improves the cache hit rate.

Fog radio access networks has emerged as a promising evolution path for 5G network architecture to satisfy the explosively increasing demands of high-speed data services and massive access requirements of various devices. As a key complement to this path, proximity-based device-to-device (D2D) communications with high transmission rate can better support the above services. For resource allocation in D2D communications, there are two fundamental challenges that should be addressed: 1) how to utilize the mobile characteristics and social relationships among users to enhance the user throughput and 2) how to efficiently allocate the limited resources to guarantee each user’s service satisfaction. In this paper, a D2D dual-link enhanced communication model is developed to improve the throughput of D2D and cellular users (CUs) simultaneously. Then, based on the mobility characteristics and social relationships among users, an optimization problem about resource allocation is formulated and solved with the objective to maximize the user satisfaction. The simulation results verify that our proposed scheme can improve user satisfaction and throughput under limited spectrum resources simultaneously.

Prospective randomized trials and observational studies have revealed that early detection, classification, and removal of neoplastic colorectal polyp (CP) significantly improve the prevention of colorectal cancer (CRC). The current effectiveness of the diagnostic performance of colonoscopy remains unsatisfactory with unstable accuracy. The convolutional neural networks (CNN) system based on artificial intelligence (AI) technology has demonstrated its potential to help endoscopists in increasing diagnostic accuracy. Nonetheless, several limitations of the CNN system and controversies exist on whether it provides a better diagnostic performance compared to human endoscopists. Therefore, this study sought to address this issue. Online databases (PubMed, Web of Science, Cochrane Library, and EMBASE) were used to search for studies conducted up to April 2020. Besides, the quality assessment of diagnostic accuracy scale-2 (QUADAS-2) was used to evaluate the quality of the enrolled studies. Moreover, publication bias was determined using the Deeks' funnel plot. In total, 13 studies were enrolled for this meta-analysis (ranged between 2016 and 2020). Consequently, the CNN system had a satisfactory diagnostic performance in the field of CP detection (sensitivity: 0.848 [95% CI: 0.692-0.932]; specificity: 0.965 [95% CI: 0.946-0.977]; and AUC: 0.98 [95% CI: 0.96-0.99]) and CP classification (sensitivity: 0.943 [95% CI: 0.927-0.955]; specificity: 0.894 [95% CI: 0.631-0.977]; and AUC: 0.95 [95% CI: 0.93-0.97]). In comparison with human endoscopists, the CNN system was comparable to the expert but significantly better than the non-expert in the field of CP classification (CNN vs. expert: RDOR: 1.03, P = 0.9654; non-expert vs. expert: RDOR: 0.29, P = 0.0559; non-expert vs. CNN: 0.18, P = 0.0342). Therefore, the CNN system exhibited a satisfactory diagnostic performance for CP and could be used as a potential clinical diagnostic tool during colonoscopy.

In order to improve the ride comfort and handling stability of the vehicle and realize the recovery of vibration energy, an electromagnetic linear hybrid suspension actuator composed of linear motor and solenoid valve shock absorber is proposed. At the same time, a fault diagnosis and fault-tolerant control strategy is designed to solve the system instability caused by the fault of electromagnetic hybrid active suspension. The 1/4 vehicle two-degree-of freedom suspension model, the linear motor mathematical model, and the solenoid valve shock absorber test model are established. In this paper, the fuzzy sliding mode controller is used as the controller and the unknown input observer is used to estimate the state of the suspension. According to the residual obtained from the unknown input observer and compared with the residual threshold, the suspension fault is determined. In the case of fault, the fuzzy sliding mode controller is used to compensate the force and realize the suspension fault-tolerant control. The performance of the suspension is simulated on random road and bumped road, respectively. The simulation results show that the fault-tolerant control effect of the three performance indexes of the suspension is good, and the ride comfort and safety of the suspension are improved. Finally, the bench test is carried out, and the test results show that in the fault-tolerant control state, the root mean square value of the sprung mass acceleration is reduced by 31.69% compared with the fault state and the dynamic performance of the suspension is improved.

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) accounts for about 20% to 30% of all BC subtypes and is characterized by invasive disease and poor prognosis. With the emergence of anti-HER2 target drugs, HER2-positive BC patient outcomes have changed dramatically. However, treatment failure is mostly due to drug resistance and the special treatment needs of different subgroups. Small molecule tyrosine kinase inhibitors can inhibit multiple targets of the human epidermal growth factor receptor family and activate PI3K/AKT, MAPK, PLC γ, ERK1/2, JAK/STAT, and other pathways affecting the expression of MDM2, mTOR, p27, and other transcription factors. This can help regulate the differentiation, apoptosis, migration, growth, and adhesion of normal cells and reverse drug resistance to a certain extent. These inhibitors can cross the blood-brain barrier and be administered orally. They have a good synergistic effect with effective drugs such as trastuzumab, pertuzumab, t-dm1, and cyclin-dependent kinase 4 and 6 inhibitors. These advantages have resulted in small-molecule tyrosine kinase inhibitors attracting attention. The new small-molecule tyrosine kinase inhibitor was investigated in multi-target anti-HER2 therapy, showed a good effect in preclinical and clinical trials, and to some extent, improved the prognosis of HER2-positive BC patients. Its use could lead to a de-escalation of treatment in some patients, possibly preventing unnecessary procedures along with the associated side effects and costs.

Superconducting single crystal of Ca _10 (Pt _4 As _8 )((Fe _0.92 Pt _0.08 ) _2 As _2 ) _5 has been prepared using flux method, and the physical properties of which are careful examined. Resistivity anisotropy between ab plane and c -axis is observed, T ^−0.5 term originated from the interlayer Josephson coupling is essential to be added to the formula used to describe the out-of-plane resistivity. The density of state (DOS) value at Fermi level derived from the fitting of specific heat data is consistent with the calculation results. Both direct and indirect platinum doping effect have influences on the superconducting transition temperature (T _c ) of Ca 10-3(4)-8 system, the T _c of our sample falls well into the trend strip formed by the data reported previously.

The further expansion of the Internet of things (IoT) traffic and the explosive growth of the terminal data require higher delay performance and computing ability of the access network.The traditional wireless network architecture has been unable to support the rapid development of IoT.Thus,the edge-driven smart IoT architecture was proposed.Firstly,the main factors that restrict the development of IoT were analyzed,and then the edge computing technology was introduced into the IoT to form a “terminal-edge-cloud” cooperative computing architecture to deal with the traffic data efficiently and timely.Finally,the challenges of the edge-driven smart IoT were analyzed and the future work of this system was proposed.

Abstract Background Colorectal cancer (CRC) is a commonly diagnosed digestive malignancy worldwide. Ras-related protein 1A (RAP1A) is a member of the Ras superfamily of small GTPases and has been recently identified as a novel oncoprotein in several human malignancies. However, its specific role in CRC remains unclear. Method In this study, we firstly analyzed its expression and clinical significance in a retrospective cohort of 144 CRC patients. Then, cellular assays in vitro and in vivo were performed to clarify its biological role in CRC cells. Finally, microarray analysis was utilized to investigate the molecular mechanisms regulated by RAP1A in CRC progression. Results Firstly, RAP1A expression was abnormally higher in CRC tissues as compared with adjacent normal tissues, and significantly correlated tumor invasion. High RAP1A expression was an independent unfavourable prognostic factor for CRC patients. Combining RAP1A expression and preoperative CEA level contributed to a more accurate prognostic stratification in CRC patients. Secondly, knockdown of RAP1A dramatically inhibited the growth of CRC cells, while it was opposite for RAP1A overexpression. Finally, the microarray analysis revealed RAP1A promoted CRC growth partly through phosphatase and tensin homolog (PTEN)/forkhead box O3(FOXO3)/cyclin D1(CCND1) signaling pathway. FOXO3 overexpression could partly mimic the inhibitory effect of RAP1A knockdown in CRC growth. Moreover, FOXO3 overexpression inhibited CCND1 expression, but had no impact on RAP1A and PTEN expression. Conclusion RAP1A promotes CRC development partly through PTEN/FOXO3 /CCND1 signaling pathway. It has a great potential to be an effective clinical biomarker and therapeutic target for CRC patients.

Abstract To satisfy the rapidly growing requirements of wireless data services recently, fog computing which is as an extension of cloud computing is proposed for offloading the Internet of Things (IoT) data services and applications. It depends more on the collaboration of near-located devices, instead of sending the information to remote servers. In this paper, we conceive the idea of utilizing device-to-device (D2D) communications as the infrastructures for computation and communication. In particular, D2D communication technology is one of the most important and innovative revolutions in the development of future cellular networks, which greatly enlarges the system capacity and extensively enriches the service category and application diversity. In computation part, by analyzing the interest difference of users, a Virtual Fog Community (VFC) is established in this paper, which exploits the coalition game based on the transfer effectiveness to adaptively divide users into multiple fog communities. In communication part, by perceiving the importance degrees of users and accordingly employing multidimensional network status parameters such as the local centrality and closeness degree, the best cooperative user can be reasonably selected based on the physical and social attributes of users. Therefore, the corresponding inter-community and intra-community D2D cooperative transmission mechanisms are devised to dramatically optimize the network resource utilization. Simulation results show that the proposed mechanism fully exploits the social relationship between users and effectively enhances the network operational efficiency at the cost of relatively low delay.

The mismatch of transmission ability causes the discontinuous transmission of video services in fiber-wireless access network,aimed at this problem,an QoE-aware video distribution mechanism was proposed.According to the structural characteristics of fiber-wireless access network,link state information and scalable video coding structure was considered,a QoE evaluation mode was established,and the particle swarm optimization algorithm was used to select optimal video transmission rate.Then the node transmission capacity and the node matching degree was analyzed,transmission path for the services was selected,so as to ensure the reliable transmission of services.The results show that the proposed mechanism can effectively improve the utilization rate of network resources and reduce network congestion while improving the quality of the user experience.

Mobile crowd sensing (MCS) is becoming a popular paradigm to collect information, which has the potential to change people's life. However, MCS is vulnerable to security threats due to the increasing reliance on communication and computing. The challenges of unique security and privacy caused by MCS include privacy protection, integrity, confidentiality, and availability. To tackle these issues concurrently, we present the design of a dynamical credibility assessment of privacy-preserving (CAPP) strategy, a novel credibility assessment-based solution to protect privacy in opportunistic MCS, which is able to cope with malicious attacks and privacy protection even against intelligent MCS entities. In CAPP, the sensing data are dynamically split into slices and the number of slices is based on the trust of encountered nodes. Specially, node trust is assessed in two dimensions including the quality of contribution trust and social trust, which indicates how likely a node can fulfill its functionality and how trustworthy the relationship between a node and other nodes will be, respectively. Moreover, the secret sharing scheme and an anonymous strategy ensure the data integrity and the anonymity of participants. The effectiveness in privacy protection and efficiency of the proposed scheme are validated through theoretical analysis and numerical results.

Enabling users conveniently accessible to their most interesting jobs or candidates is an important and challenging task for professional network recommenders in professional social networks (PSNs). Nowadays, PSNs accommodate tremendous and diverse users and face continuous uploading and high data freshness. In this paper, an online mining and predicting system is proposed for personalized job or candidate recommendation with big-data support. It considers the users' explicit information as context to achieve personalized recommendation. In addition, the system utilizes the reward extracted from online implicit information of the previous users with similar context information to relieve the cold start problem. Furthermore, a tree-based method is introduced to address large-volume items by effectively analyzing them in cluster level and thus reduce the computational load. Considering the dynamic property of PSNs, our model is adaptive for the expanding dataset, enabling it to real-timely make accurate recommendations for the incessant new arrivals. Moreover, theoretical analysis shows that our algorithm achieves sublinear regret over time. Finally, extensive experiments are conducted to test our algorithm based on the real dataset from ACM RecSys Challenge1 and validate the outstanding performance of our algorithm when compared with other existing algorithms.

Humulus scandens was first adopted as a biomass precursor to prepare biochars by means of a facile molten salt method. The optimized biochar exhibits a high specific surface area of ~450 m2/g, a rich porous structure and abundant oxygen functional groups, which demonstrate excellent adsorption performance for heavy metal ions. The isotherm curves fit well with the Langmuir models, indicating that the process is governed by the chemical adsorption, and that the maximum adsorption capacity can reach 748 and 221 mg/g for Pb2+ and Cu2+, respectively. In addition, the optimized biochar demonstrates good anti-interference ability and outstanding removal efficiency for Cu2+ and Pb2+ in simulated wastewater. The mechanism investigation and DFT calculation suggest that the oxygen functional groups play dominant roles in the adsorption process by enhancing the binding energy towards the heavy metal ions. Meanwhile, ion exchange also serves as the main reason for the effective removal.

The field of enzyme cascades in limited microscale or nanoscale environments has undergone a quick growth and attracted increasing interests in the field of rapid development of systems chemistry. In this study, alcohol dehydrogenase (ADH), lactate dehydrogenase (LDH), and mesoporous silica nanoparticles (MSN) immobilized nicotinamide adenine dinucleotide (NAD+) were successfully immobilized on the zeolitic imidazolate frameworks (ZIFs). This immobilized product was named ZIF@ADH/NAD-MSN/LDH, and the effect of the multi-enzyme cascade was studied by measuring the catalytic synthesis of lactic acid. The loading efficiency of the enzyme in the in-situ co-immobilization method reached 92.65%. The synthesis rate of lactic acid was increased to 70.10%, which was about 2.82 times that of the free enzyme under the optimal conditions (40 °C, pH = 8). Additionally, ZIF@ADH/NAD-MSN/LDH had experimental stability (71.67% relative activity after four experiments) and storage stability (93.45% relative activity after three weeks of storage at 4 °C; 76.89% relative activity after incubation in acetonitrile-aqueous solution for 1 h; 27.42% relative activity after incubation in 15% N, N-Dimethylformamide (DMF) solution for 1 h). In summary, in this paper, the cyclic regeneration of coenzymes was achieved, and the reaction efficiency of the multi-enzyme biocatalytic cascade was improved due to the reduction of substrate diffusion.

Background/Aims: Plasmacytoma variant translocation 1 (PVT1) exerts an oncogenic role in many tumors, including lung cancer. However, the roles of PVT1 in regulating radiosensitivity of NSCLC and its underlying mechanism are still unclear. Methods: Expression levels of PVT1 and miR-195 in NSCLC tissues and cells were examined by qRT-PCR. Effects of PVT1 and miR-195 on cell proliferation, apoptosis and colony formation abilities were assessed by MTT assay, flow cytometry and colony formation assay. Luciferase reporter assay was performed to confirm the relationship between PVT1 and miR-195. Tumor xenograft experiments were conducted to observe the effect of PVT1 on radiosensitivity of NSCLC in vivo. Results: PVT1 was negatively correlated with miR-195 expression in NSCLC tissues and associated with poor prognosis of NSCLC patients. Expression of PVT1 and miR-195 varied inversely after irradiation in NSCLC cells. PVT1 knockdown or miR-195 overexpression enhanced radiosensitivity of NSCLC in vitro by inhibiting proliferation and inducing apoptosis. PVT1 directly interacted with miR-195 and regulated its expression. Moreover, PVT1 knockdown improved radiosensitivity of NSCLC cells in vitro and in vivo by sponging miR-195. Conclusion: Knockdown of PVT1 enhances radiosensitivity of NSCLC by sponging miR-195, providing a novel therapeutic target to improve radiotherapy efficiency in NSCLC.

To meet the rapid growing requirement of data services and offload the heavy base station load, device-to-device (D2D) communication is proposed as one of the key technologies for future cellular network, which can enable users to communicate directly and thus effectively improve the bearing capacity of the network. However, when D2D users transmit packets in the manner of cooperative communication, the cooperative users with non-cooperative behavior, such as the selfishness of user, may lead to a sharp decline in network performance. For this reason, in our paper, a trust-oriented partner selection mechanism (TPSM) is proposed to avoid choosing those users with non-cooperative behavior. In particular, the related psychological researches show that the psychology structure of users can be divided into three aspects: cognition, emotion, and behavior. Considering the psychology structure of users, we build multi-dimensional trust relationships between sending users and cooperative users by evaluating the cognition trust, emotion trust, and behavior trust. Furthermore, aiming at the multi-dimensional trust relationships between sending users and cooperative users, we classify the cooperative users into three categories, namely reliable users, observed users, and unreliable users, by combining the decision-theoretic rough sets based on Naive Bayes. Finally, based on different cooperative transmission scenarios, an optimal partner selection mechanism is proposed in this paper. The optimal partner can be chosen from the reliable cooperative users, according to the different scenarios and factors. The numerical results show that the proposed TPSM can effectively identify the selfish users and notably enhance the packet delivery rate.

An important goal of smart grid is to leverage modern digital communication infrastructure to help control power systems more effectively. As more and more Internet of Things (IoT) devices with measurement and/or control capability are designed and deployed for a more stable and efficient power system, the role of communication network has become more important. To evaluate the performance of control algorithms for inter-dependent power grid and communication network, a test bed that could simulate inter-dependent power grid and communication network is desirable. In this paper, we demonstrate the design and implementation of a novel co-simulator, which would effectively evaluate IoT-aided algorithms for scheduling the jobs of electrical appliances. There are three major features of our co-simulator: 1) large-scale test is achieved by distributed modules that are designed based on a Turing-indistinguishable approach; 2) remote servers or test devices are controlled by local graphical user interface (we only need to configure the simulator on a local server); 3) a software virtual network approach is employed to emulate real networks, which significantly reduces the cost of real-world test beds. To evaluate our co-simulator, two energy consumption scheduling algorithms are implemented. Experimental results show that our co-simulator could effectively evaluate these methods. Thus our co-simulator is a powerful tool for utility companies and policy makers to commission novel IoT devices or methods in future smart grid infrastructure.

Device-to-device (D2D) communications can effectively offload the traffic of cellular system in a distributed way. However, during the data forwarding process, malicious D2D users can intermittently discard data of other users, which seriously affects the data forwarding efficiency. Therefore, a malicious-forwarding-behavior-aware link selection mechanism (MBLS) is proposed in this paper to alleviate the influence of malicious attacks. User behaviors are analyzed according to the correlation between the social relationship and forwarding behavior of users, and the identification of malicious behavior is obtained by Elman neural network. Thus, malicious users can be detected, and then the optimal link can be selected. The simulation results show that the proposed mechanism can effectively detect the malicious behaviors of D2D users, notably improve the reliability of data transmission and significantly enhance the network performance.

Bacillus sp. H16v8 and Bacillus sp. HGD9229 were identified as Aflatoxin B1 (AFB1) degrader in nutrient broth after a 12 h incubation at 37 °C. The degradation efficiency of the two-strain supernatant on 100 μg/L AFB1 was higher than the bacterial cells and cell lysate. Moreover, degradations of AFB1 were strongly affected by the metal ions in which Cu2+ stimulated the degradation and Zn2+ inhibited the degradation. The extracellular detoxifying enzymes produced by co-cultivation of two strains were isolated and purified by ultrafiltration. The molecular weight range of the detoxifying enzymes was 20–25 kDa by SDS-PAGE. The co-culture of two strains improved the total cell growth with the enhancement of the total protein content and detoxifying enzyme production. The degradation efficiency of the supernatant from mixed cultures increased by 87.7% and 55.3% compared to Bacillus sp. H16v8 and HGD9229, individually. Moreover, after the degradation of AFB1, the four products of the lower toxicity were identified by LC-Triple TOF-MS with the two proposed hypothetical degradation pathways.

As the massive sensor data generated by large-scale Wireless Sensor Networks (WSNs) recently become an indispensable part of ‘Big Data’, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation (Sca-PBDA) method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.

The development and construction of smart cities largely depend on the communication network infrastructure，and the latest 5G mobile communication system can sufficiently facilitate the revolution of communication network infrastructure for smart cities.The 3-layer network architecture for smart cities was introduced and the key issues of converged networks，such as the relationship evaluation，trust management and reliability assurance were holistically analyzed.Lastly，the corresponding solutions were given，which could enhance the application bearing capabilities of smart city communication networks.

Thyroid cancer 1 (TC-1, C8ofr4) is widely expressed in vertebrates and associated with many kinds of tumors. Previous studies indicated that TC-1 functions as a positive regulator in the Wnt/β-catenin signaling pathway in non-small cell lung cancer (NSCLC). However, its exact role and regulation mechanism in radiosensitivity of NSCLC are still unclear. The expression level of TC-1 was measured by qRT-PCR and western blot in NSCLC cell lines. Proliferation and apoptosis of NSCLC cells in response to TC-1 knockdown or/and radiation were determined by MTT assay and flow cytometry, respectively. The activation of the Wnt/β-catenin signaling pathway was further examined by western blot in vitro and in vivo. Compared to TC-1 siRNA or radiotherapy alone, TC-1 silencing combined with radiation inhibited cell proliferation and induced apoptosis in NSCLC cell lines by inactivating of the Wnt/β-catenin signaling pathway. Furthermore, inhibition of the Wnt/β-catenin signaling pathway by XAV939, a Wnt/β-catenin signaling inhibitor, contributed to proliferation inhibition and apoptosis induction in NSCLC A549 cells. Combinative treatment of A549 xenografts with TC-1 siRNA and radiation caused significant tumor regression and inactivation of the Wnt/β-catenin signaling pathway relative to TC-1 siRNA or radiotherapy alone. The results from in vitro and in vivo studies indicated that TC-1 silencing sensitized NSCLC cell lines to radiotherapy through the Wnt/β-catenin signaling pathway.

Monitoring human bodies and collecting physiological information are the major healthcare applications for wireless body area networks (WBANs). Due to the limited energy resource of body sensors, their energy depletions will cause severe network performance degradation, such as latency and energy efficiency. The heterogeneity of body sensors can result in different sensor energy consumption rates. Besides, the important degrees of monitored physiological data could vary hugely. Targeting at the above-mentioned problems, an energy-efficient data forwarding strategy (EDFS) is proposed in this paper to balance sensor energy consumption and improve network lifetime and collaborative operations of heterogeneous WBANs. Our major contributions include: 1) the original physiological data are processed by compressed sensing to reduce the data size to be transmitted and 2) the remaining energy levels, sampling frequency, and sensor importance are jointly considered by EDFS for the optimal relay sensor selection. With the EDFS, the energy-efficiency and reliability of WBAN data transmission can be improved. In addition, our simulation results demonstrate that the proposed EDFS can effectively deal with the frequently changing WBAN topologies while providing balanced energy consumption and energy efficiency.

In LTE Advanced system, it is critical to carry on various types of services for different user applications. Obviously, the system resource requirements are quite different for each individual service. In order to allocate and schedule limited resources reasonably, the cooperative game model is established and studied in our paper; furthermore, a novel scheme with dynamical system resource allocating and scheduling is also proposed, where the corresponding resources for each kind of service can be dynamically allocated according to the current traffic status. Consequently, the priorities of users are determined based on the service attribute and channel status. Results show that the spectral efficiency is enhanced effectively by our proposed scheme; in the mean time, the performances of multimedia services are also improved.

With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm—node reliability-aware backup survivable embedding algorithm (NCS)—is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.