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In this study, a general network model called multi-progressive image deblurring network is proposed to correct blurring artifacts and local imaging details in underwater images. As a solution to nonuniform image distortion, a deformable convolution module was designed to enrich the encoded information of the image representation. Using a stepwise feature refinement module, multi-progressive image deblurring network can reduce the loss of contextual information to produce a more realistic underwater image for subsequent applications. Constructing a loss function based on multi-scale content can help the model improve image perception quality. We conducted experimental evaluations on large-scale image deblurring benchmark datasets, such as GoPro and HIDE, achieving excellent results with 32.84 dB and 31.03 dB peak signal-to-noise ratio, respectively, using the proposed method. Subsequently, a detailed optimization comparison was conducted on the in-house underwater image deblurring dataset. Multi-progressive image deblurring network obtained higher-quality, clearer images. Compared with the current state-of-the-art image deblurring algorithms, the proposed model achieved significant results with a 6.6% increase in deblur performance in peak signal-to-noise ratio. Finally, we conducted ablation experiments to evaluate the effectiveness of all the modules in the proposed framework. • Multi-progressive structure can hierarchically fuse global context information. • MPDNet outperforms almost all the SOTA methods on the benchmark datasets. • MPDNet is enough lightweight for deblurring tasks on mobile computing platforms. [ABSTRACT FROM AUTHOR]

• The viral infection induced a systemic antiviral response of C. medinalis. • The expression level of oxidative stress related genes of host is significantly changed after infection. • The enhancement of PiggyBac -like transposon activity is related to host immunity. • Inhibition of peroxidation degradation or increase of oxidative stress level may enhance the virulence of baculovirus. Cnaphalocrocis medinalis granulovirus (CnmeGV) is a potential microbial agent against the rice leaffolder. Innate immunity is essential for insects to survive pathogenic infection. Therefore, to clarify the immune response of Cnaphalocrocis medinalis to the viral colonization, the gene expression profile of C. medinalis infected with CnmeGV was constructed by RNA-seq. A total of 8,503 differentially expressed genes (DEGs) were found including 5,304 up-regulated and 3,199 down-regulated unigenes. Gene enrichment analysis indicated that these DEGs were mainly linked to protein synthesis and metabolic process as well as ribosome and virus-infection pathways. Specifically, a significantly up-regulated PiggyBac-like transposon gene was identified suggested that the enhancement of transposon activity is related to host immunity. Further, the DEGs encoding oxidative stress related genes were identified and validated by RT-qPCR. Overall, 9 antioxidant enzyme genes and 4 antioxidant protein genes were up-regulated, and the extensive glutathione S-transferase genes were down-regulated. Our results provide a basis for understanding the molecular mechanisms of baculovirus action and oxidative stress response in C. medinalis and other insects. [ABSTRACT FROM AUTHOR]

Localization is one of the fundamental issues in Wireless Sensor Networks (WSNs). Recently, various localization methods have been proposed. However, using single anchor node introduces quite long localization delay. Therefore, the path planning problem of a Group of Mobile Anchor Nodes (GMAN) is studied in this paper. First, two path planning schemes named GSCAN and GTURN are proposed. Both GSCAN and GTURN use three mobile anchor nodes which traverse the entire network following the designed trajectory to help nearby unknown nodes with localization. Furthermore, in order to improve localization ratio of unknown nodes, two boundary strategies are proposed to guarantee that the movement trajectories of anchor nodes can cover the whole WSN. Simulation results show that GSCAN and GTURN can achieve better localization performance than LMAT, RPGM and Ou’s in terms of higher localization ratio and lower energy consumption. [ABSTRACT FROM AUTHOR]

Regional triplication waveforms of five intermediate-depth events are modeled to simultaneously obtain the compressional (P) and shear (SH) wave velocity structure beneath northwestern Pacific subduction zone. Both the P- and SH-wave velocity models for three different sub-regions show a low-velocity layer (LVL) with a thickness of ∼55-80 km lying above the 410-km discontinuity with a ∼900 km lateral extent from the Japan Sea to the northeastern Asian continental margin. With the dihedral angle approaching to zero around 400 km, a minute amount of melt atop the 410-km discontinuity caused by the hydrous slab might completely wet olivine grain boundaries and result in a low seismic velocity layer in this specific subduction zone. This mechanism suggests that the 410-LVL is a low viscosity zone that would partially decouple the upper mantle from the transition zone. We infer that the widespread 410-LVL provides evidence for a water-bearing mantle transition zone beneath the western Pacific subduction zone. • We explore P and SH velocity at depth ∼410 km beneath NW Pacific subduction zone. • We detect a pervasive low velocity layer with thickness ∼55-80 km atop the 410. • Melts caused by hydrous stagnant slab completely wet grain boundary result in LVL. • The LVL could be a low viscosity layer, which decouples the upper mantle from MTZ. • The MTZ is water-bearing and the ∼900 km LVL may continue westward further. [ABSTRACT FROM AUTHOR]

Recent studies have shown that the hyperactive Notch pathway is involved in cirrhosis and hepatocellular carcinoma (HCC) development by regulating differentiation of hepatic oval cells (HOCs) into cancer cells. The aim of this study was to investigate whether matrine can alleviate liver injury and promote HOC differentiation into hepatocytes by suppression of Notch pathway. We evaluated the expression of Notch-1, Jagged-1, and Hes-1 in HCC tissue by immunohistochemistry. Stem cell characteristics of HOCs were evaluated by CCK-8, cell cycle, and apoptosis. The expression of Notch pathway, HOC markers and albumin (ALB) was detected by immunohistochemistry, QRT-PCR and western blotting. The effects of matrine in protecting liver in vivo were investigated in a rat Solt-Farber precancerous model. We found an abnormal activated Notch pathway in HCC tissue, and the hyperactive Notch pathway was strongly associated with poor liver function in patients with cirrhosis with HCC. Using siNotch-1 to inhibit Notch pathway confirmed that Notch pathway could maintain stem cell characteristics of HOCs. Matrine inhibited stem cell characteristics of HOCs, the expression of Notch pathway and HOC markers but upregulated ALB. Matrine in combined with siNotch-1 RNA decreased the more potently inhibited HOC markers and Notch pathway. In rat Solt-Farber precancerous model, prophylactic application of matrine alleviated liver injury, downregulated Notch pathway and HOC markers, and upregulated ALB in a dose-dependent manner. Matrine could promote the differentiation of HOCs into hepatocytes by inhibiting the Notch signalling pathway and alleviate liver injury. [ABSTRACT FROM AUTHOR]

In this paper, a novel intrusion detection method combining a deep learning-based method and a feature-based method is proposed for smart grid. Specifically, long short-term memory and extreme gradient boosting are adopted for intrusion detection, and the results are fused based on the accuracies of these two models. As the XGBoost method is sensitive to its parameters and unsuitable selections greatly degrade its performance, in this paper, a Bayesian method is proposed to optimize these parameters. Moreover, a crossover scheme in a genetic algorithm is introduced to reduce the impact of falling into a local optimum of Bayesian optimization. Extensive experimental results show the effectiveness of the proposed algorithm. [Display omitted] • An intrusion detection method is proposed combining two classifiers adaptively for smart grid. • A Bayesian based method is proposed to optimize the parameters of eXtreme Gradient Boosting. • A method is proposed to reduce the impact of falling into local optimum of Bayesian optimization. [ABSTRACT FROM AUTHOR]

As the significant component in Industrial Internet of Things (IIoT), sensor networks have been applied widely in many fields. However, concept drift in data stream produced in sensor networks always brings great difficulty for the robustness of data processing. To solve the problem, we propose a novel concept drift detection method based on angle optimized global embedding (AOGE) and principal component analysis (PCA) for data stream learning in sensors networks. AOGE and PCA analyze the principal components through the projection variance and the projection angle in the subspace, respectively. And then the occurrence of concept drift is determined by observing the change of subspace for each data stream patch. The experiments in synthetic datasets and Intel Lab data demonstrate witness the effectiveness of our method. [ABSTRACT FROM AUTHOR]