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We first introduce a similarity assumption to describe the similarity phenomenon in natural images, and establish a similarity principle which supplies a simple mathematical justification for the non-local means filter in removing Gaussian noises. Using the similarity principe in an adapted way, we then propose a new algorithm, called mixed noise filter (MNF) to remove simultaneously a mixture of Gaussian and random impulse noises. Our experiments show that our new filter improves significantly the trilateral filter in removing mixed noises, and that it is as efficient as the non-local means filter in removing Gaussian noises, and as good as the trilateral filter in removing random impulse noises.

Background: There is growing evidence of the role of long non-coding RNAs (lncRNAs) in cervical cancer (CC). The objective was to discuss whether exosomal lncRNA HNF1A-AS1 impacted drug resistance in CC via binding to microRNA-34b (miR-34b) and regulating TUFT1 expression. Methods: The expression of HNF1A-AS1 in normal cervical epithelial cells, cisplatin (DDP)-sensitive cell line (HeLa/S) and DDP-resistant cell line (HeLa/DDP) cells were detected. HeLa/S and HeLa/DDP cells were interfered with HNF1A-AS1 to determine IC 50 , proliferation, colony formation and apoptosis of CC cells. The exosomes were isolated and identified. Subcellular localization of HNF1A-AS1, expression of miR-34b and TUFT1 in receptor cells were also verified. The binding site between HNF1A-AS1 and miR-34b, together with miR-34b and TUFT1 were confirmed. Tumorigenic ability of cells in nude mice was also detected. Results: HNF1A-AS1 was upregulated in DDP-resistant cell line HeLa/DDP. Silencing HNF1A-AS1 suppressed CC cell proliferation and promoted its apoptosis. HNF1A-AS1 was found to act as a competing endogenous RNA (ceRNA) of miR-34b to promote the expression of TUFT1. Exosomes shuttled HNF1A-AS1 promoted the proliferation and drug resistance of CC cells and inhibited their apoptosis by upregulating the expression of TUFT1 and downregulating miR-34b. Furthermore, suppressed exosomal HNF1A-AS1 in combination with DDP inhibited tumor growth in nude mice. Conclusion: Our study provides evidence that CC-secreted exosomes carrying HNF1A-AS1 as a ceRNA of miR-34b to promote the expression of TUFT1, thereby promoting the DDP resistance in CC cells.

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Existing studies have shown that satellite synthetic aperture radar (SAR) interferometry has two apparent drawbacks, i.e., temporal decorrelation and atmospheric contamination, in the application of deformation mapping. It is however possible to improve deformation analysis by tracking some natural or man-made objects with steady radar reflectivity, i.e., permanent scatterers (PS), in the frame of time series of SAR images acquired over the same area. For detecting land subsidence in Shanghai, China, this paper presents an attempt to explore an approach of PS-neighborhood networking SAR interferometry. With use of 26 ERS-1/2 SAR images acquired 1992 through 2002 over Shanghai, the analysis of subsiding process in time and space is performed on the basis of a strong network which is formed by connecting neighboring PSs according to a distance threshold. The linear and nonlinear subsidence, atmospheric effects as well as topographic errors can be separated effectively in this way. The subsidence velocity field in 10 years over Shanghai is also derived. It was found that the annual subsidence rates in the study area range from -2.1 to -0.6 cm/yr, and the averaged subsidence rate reaches -1.1 cm/yr.

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Accurate forecast of energy consumption is essential in building energy management. Owing to the variation of outdoor weather condition among different seasons, year-round historical weather profile is needed to investigate its feature thoroughly. Daily weather profiles in the historical database contain various features, while different architecture of deep neural network (DNN) models may be identified suitable for specific featuring training datasets. In this study, an integrated artificial intelligence-based approach, consisting of feature extraction, evolutionary optimization and adaptive DNN model, is proposed to forecast week-ahead hourly building energy consumption. The DNN is the fundamental forecasting engine of the proposed model. Feature extraction of daily weather profile is accomplished through clustering techniques. Genetic algorithm is adopted to determine the optimal architecture of each DNN sub-model. Namely, each featuring cluster of weather profile, along with corresponding time signature and building energy consumption, is adopted to train one DNN sub-model. Therefore, the structure, activation function and training approach of DNN sub-models are adaptive to diverse featuring datasets in each cluster. To evaluate the effectiveness of the proposed predictive model, it is implemented on a real office building in the United Kingdom. Mean absolute percentage error of the training and testing cases of the proposed predictive model is 2.87% and 6.12%, which has a 24.6% and 11.9% decrease compared to DNN model with a fixed architecture. With the latest weather forecast, the devised adaptive DNN model can provide accurate week-ahead hourly energy consumption prediction for building energy management system.

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