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Multispectral image (MSI) and hyperspectral image (HSI) fusion is a popular method for HSI super-resolution reconstruction (HSI-SR). MSI-HSI fusion problem is ill-posed and demands several image priors or regularization terms to solve accurately, which is a challenging issue. In this paper, we propose an MSI-HSI fusion model via subspace representation and nonlocal low-rank regularization (SRNLRR). SRNLRR model incorporates the global spectral correlations and spatial nonlocal similarities of HSI to improve the fusion results, where the priors complement each other. First, we use the mode-n tensor-matrix product to project latent high spatial resolution HSI (HR-HSI) into spectral subspace, which can capture spectral low-rankness and reduce computational complexity. Then, based on low-rank representation (LRR) and nonlocal processing strategy, we design a spatial nonlocal LRR regularization (spa-NLRR) and a spectral global LRR regularization (spe-GLRR). These two regularizations analyze the spatial nonlocal similarities and spectral global correlations from intermediate-level vision. Finally, we use the residual regularization program to obtain more image information and input it into the fusion model. We use alternate minimization (AM) methods to optimize the SRNLRR model and employ the alternating direction method (ADM) on spatial/spectral LRR learning. Comparison experiments between the SRNLRR method and six advanced methods on three HSI datasets indicate the superiority of our method. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) has been employed to compress and store hyperspectral images (HSI) to transfer extensive data efficiently, and obtaining high-quality reconstructed images is crucial for subsequent applications. Model-based methods usually capture the priors of HSI, including sparsity, global and nonlocal low-rankness, nonlocal self-similarity and global correlation. However, it is challenging to reasonably integrate different regularizations in a united framework and exploit various priors mentioned to reconstruct high-quality images. In this paper, a tensor optimization model based on the low-rank coefficient tensor and global prior (LRCTGP) is proposed for HSI reconstruction. Complementary regularization terms are integrated into a united framework and effectively promote the reconstruction results. First, we apply the mode- n tensor-matrix product to decompose the original HSI rather than employing the regularization to capture the spectral low-rankness and simultaneously constrain the original and feature HSI. Then, tensor ring decomposition is employed to constrain the coefficient tensor with fewer bands, which is more efficient in the low-rank approximation and has smaller calculation costs than applying it to the original HSI. Moreover, integrating BM3D as a regularizer is more efficient than other patch-based models. Finally, considering the smoothness and global correlation, spatial-spectral total variation (SSTV) is applied to compensate for the shortcomings after decomposing the original and feature HSI, which complements the overall structure and details of reconstructed images and improves the reconstruction quality. Alternating direction method of multipliers (ADMM) is used to optimize the proposed model. Experimental results of the LRCTGP model on different HSI datasets are better than existing state-of-the-art approaches, which proves the superiority. [ABSTRACT FROM AUTHOR]

The availability of spectral library makes hyperspectral sparse unmixing an attractive unmixing scheme, and the powerful feature extraction capability of deep learning meets the requirements of estimating abundances with hundreds of channels in sparse unmixing. However, few related researches have been carried out. In this letter, we propose a window transformer convolutional autoencoder (WiTCAE) to address the sparse unmixing problem. In our method, a well-designed transformer encoder for hyperspectral images (HIS) is applied before convolutional neural network (CNN), aiming at exploring nonlocal information by a new attention mechanism called window-based pixel-level multihead self-attention (WP-MSA). Three consecutive CNN blocks focus on further joint spatial–spectral feature extraction and adjust the number of channels to the number of endmembers contained in the spectral library. Moreover, CNN establishes the connections among windows and smooths out the discontinuities caused by window partition. The decoder is a convolutional layer with a kernel size of 1, and its weights are fixed to a known spectral library. Comparative experiments on both simulated and real datasets confirm the superiority of our proposed network. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the relationship between occupants' emotional attitude, decision behavior, and environmental cognition toward window seats and learning efficiency and the mechanism of this relationship in public spaces (represented by academic libraries). Surveys were delivered to the academic library of Shanghai Jiao Tong University. A total of 280 valid face-to-face interview questionnaires was collected and analyzed for correlation and validation of theoretical models. The results show that learning experience, as a mediator of learning efficiency, has a significant impact on the model of occupants' attitude toward window seat consumption. The impact mechanism was determined, and it indicated that in order to improve the learning efficiency of occupants, indoor re-planning should be carried out to improve the seat satisfaction and occupancy rate. This study introduces the concepts of service design and architectural consumption and constructs an occupant emotional consumption context with the window seat as the consumption product. In addition, it also has guiding value for seat reallocation in public buildings in the COVID-19 era. This theoretical framework provides a direction for the simulation of future construction consumption behavior. [ABSTRACT FROM AUTHOR]

This study explored the application value of 320-slice CT angiography (CTA) combined with transthoracic echocardiography (TTE) in the diagnosis and treatment of complex congenital heart disease. A total of 66 patients with ventricular septal defect (VSD) and atrial septal defect (ASD) repaired were selected for study. All children underwent TTE and CTA examinations, and the difference in the defect diameters of VSD and ASD detected by TTE and CTA were compared. A total of 118 congenital heart diseases such as ASD and VSD were found after surgical treatment and operation. The correct detection rate of CTA in 118 congenital heart disease types was 78.81%, which was higher than 64.41% of TTE, and the difference was statistically significant (P<0.05). The correct detection rate of CTA combined with TTE in 118 congenital heart disease types was 94.07%, which was higher than 78.81% of CTA alone, and the difference was statistically significant (P<0.05). CTA combined with TTE can significantly improve the diagnostic accuracy in the diagnosis and treatment of complex congenital heart disease compared with the two alone, which is of great value for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

CD73, an ectonucleotidase, participates in the regulation of immune responses by controlling the conversion of extracellular AMP to adenosine. In this study, we investigated whether any type of brain cells, especially neuroglia cells, exhibit altered CD73 expression, localization or activity upon experimental autoimmune uveitis (EAU) induction and whether altered CD73 manipulates the activation of effector T cells that interact with such cell types. First, the amount of cell membrane-exposed CD73 was detected by flow cytometry in various types of brain cells collected from either naïve or EAE mice. Compared to that in astrocytes from naïve control mice, the amount of membrane-bound CD73 was significantly decreased in astrocytes from EAE mice, while no significant differences were detected in other cell types. Thereafter, wild-type and CD73-/- astrocytes were used to study whether CD73 influences the function of inflammatory astrocytes, such as the production of cytokines/chemokines and the activation of effector T cells that interact with astrocytes. The results indicated that the addition of exogenous AMP significantly inhibited cytokine/chemokine production by wild type astrocytes but had no effect on CD73-/- astrocytes and that the effect of AMP was almost completely blocked by the addition of either a CD73 inhibitor (APCP) or an adenosine receptor A1 subtype (ARA1) antagonist (DPCPX). Although the addition of AMP did not affect CD73-/- astrocytes, the addition of adenosine successfully inhibited their cytokine/chemokine production. The antigen-specific interaction of astrocytes with invading CD4 cells caused CD73 downregulation in astrocytes from mice that underwent EAE induction. Collectively, our findings support the conclusion that, upon EAE induction, likely due to an interaction with invading CD4+ cells, astrocytes lose most of their membrane-localized CD73; this inhibits the generation of adenosine in the local microenvironment. As adenosine has anti-inflammatory effects on astrocytes and CNS-infiltrating effector T cells in EAE, the downregulation of CD73 in astrocytes may be considered a pro-inflammatory process for facilitating the pathogenesis of EAE. [ABSTRACT FROM AUTHOR]

Purpose Mechanism study of why astrocytes isolated from experimental autoimmune encephalomyelitis (EAE)-induced B6 mice or after being exposed to inflammatory factors had the highest transfection efficiency to larger-sized, but not compacted, pspCS/pDNA particles. Methods Phosphorylatable short peptide conjugated chitosan (pspCS) was compounded with plasmid DNA (pDNA) at different N:P ratios to form pspCS/pDNA particles of different size and zeta potentials. These pspCS/pDNA particles were used for the transfection of astrocytes isolated from either EAE induced or healthy B6 mice. Transfection efficiency and cell permeability of the particles were determined by the internalization of radio [H3]-labeled plasmid and the expression of a luciferase reporter gene respectively. Phagocytosis of EAE-astrocytes was determined by the internalization of FITC labeled dextran beads. By comparing the transfection efficiency of differently-sized pspCS/pDNA particles to normal and phagocytic astrocytes, with or without cytochalasin D, a phagocytosis inhibitor, in the presence, the contribution of phagocytosis to cell permeability and transfection efficiency was evaluated. Results In vivo EAE-induction or in vitro inflammatory factors treatment transferred normal astrocytes to be phagocytic astrocytes which underwent phagocytosis, had the highest cell permeability and transfection efficiency to larger-sized pspCS/pDNA particles formed at lower N:P ratios. When phagocytosis was inhibited by cytochalasin D, both cell permeability and transfection efficiency of phagocytic astrocytes to larger were significantly decreased. Thereafter, particle size, not zeta potential, was verified as the key factor for determining whether the particles could be phagocytosed. In addition phagocytosis was successfully induced in ARPE-19 cells as well, which also improved the transfection efficiency of larger pspCS/pDNA particles. Conclusion A generally accepted concept is that the internalization of cationic polymer/pDNA particles, chitosan-DNA complex for instance, is mainly through the procedure of endocytosis of the transfected cells. More compacted particles with higher zeta potential were used to be considered had higher cell permeability and transfection efficiency. However, here we reported that phagocytosis is another important procedure for determining internalization and transfection efficiency of cationic polymer/pDNA nanoparticles, especially for advanced transfection efficiency of large pspCS/pDNA particles. Thus, for gene delivery applications, the environmental condition of the target cells should be seriously considered for selecting an appropriate gene transfer strategies. [ABSTRACT FROM AUTHOR]

Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed signatures of a hyperspectral image can be expressed in the form of linear combination of only a few spectral signatures (endmembers) in an available spectral library. Simultaneous orthogonal matching pursuit (SOMP) algorithm is a typical simultaneous greedy algorithm for sparse unmixing, which involves finding the optimal subset of signatures for the observed data from a spectral library. But the numbers of endmembers selected by SOMP are still larger than the actual number, and the nonexisting endmembers will have a negative effect on the estimation of the abundances corresponding to the actual endmembers. This paper presents a variant of SOMP, termed backtracking-based SOMP (BSOMP), for sparse unmixing of hyperspectral data. As an extension of SOMP, BSOMP incorporates a backtracking technique to detect the previous chosen endmembers’ reliability and then deletes the unreliable endmembers. Through this modification, BSOMP can select the true endmembers more accurately than SOMP. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is proposed in this paper. Firstly, two inputs with different spectral dimensions and spatial sizes are constructed, which can not only reduce the redundancy of the original dataset but also accurately explore the spectral and spatial features. Then, we design two parallel 3DCNN branches with attention modules, in which one focuses on extracting spectral features and adaptively learning the more discriminative spectral channels, and the other focuses on exploring spatial features and adaptively learning the more discriminative spatial structures. Next, the feature attention module is constructed to automatically adjust the weights of different features based on their contributions for classification to remarkably improve the classification performance. Finally, we design the hybrid architecture of 3D–2DCNN to acquire the final classification result, which can significantly decrease the sophistication of the network. Experimental results on three HSI datasets indicate that our presented TSSFAN method outperforms several of the most advanced classification methods. [ABSTRACT FROM AUTHOR]

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate. [ABSTRACT FROM AUTHOR]

Multispectral image compression can considerably reduce the volume of data and promote their application. However, conventional single-scale compression schemes, such as JPEG2000 and three-dimensional set partitioning in hierarchical tree (3D-SPIHT), do not accurately preserve the features of images due to the complex features of multispectral images. A compression framework based on adaptive multiscale feature extraction with a convolutional neural network is proposed. First, an adaptive multiscale feature extraction module, which is the basic component of the compression framework, is designed to extract the multiscale spatial–spectral features of the multispectral images and adaptively adjust the weights of the features according to the content of the images. Second, the encoder, which is composed of multiscale feature extraction modules, extracts the multiscale spatial–spectral features of the multispectral images, and the extracted features are quantized and encoded by the quantizer and the entropy coder to generate a compressed bitstream. Third, the decoder, which is structurally similar to the encoder, is utilized to recover the images. The rate-distortion optimizer is embedded in the encoder to control the trade-off between the rate loss and the distortion. The results of these experiments on multispectral images of the Landsat 8 satellite and the WorldView-3 satellite validate the better performance of our compression framework compared with the performances of conventional schemes, including JPEG2000 and 3D-SPIHT. In order to further verify the effectiveness of multiscale features, the framework is compared with a single-scale compression algorithm based on deep learning, the experimental results validate that the performance of the single-scale compression algorithm superior to the conventional schemes but inferior to our multiscale algorithm, which indicates that the multiscale features can significantly improve the performance of the compression algorithm. [ABSTRACT FROM AUTHOR]

3D motion estimation via feature point matching is an important issue in machine vision. However, most existing methods are not good enough due to various defects, such as being sensitive to matching error, and hard to give an appropriate threshold for feature point selection. To solve these problems, a mathematical model is obtained from massive experiments to describe how the quantity and quality of matched feature points influence the motion estimation accuracy. This model can be used to select the most appropriate feature points for motion parameter calculation, hence threshold setting is avoided while estimation performance is optimized. Experimental results show that the proposed algorithm can improve the accuracy of 3D motion estimation by up to 50% with little effect on computation time. Furthermore, it is especially suitable for real time applications as there is no need to set threshold manually. [ABSTRACT FROM AUTHOR]

Highlights • Astrocytes express all adenosine receptor subtypes. • ARA1 has superior adenosine binding ability. • Function of immune suppressive in CNS. Abstract The four kinds of adenosine receptor subtypes (ARs), named as ARA1, ARA2A, ARA2B and ARA3, have multiple biological functions. ARs are differently distributed across the body and have distinguished ability of binding adenosine. We try to figure out how these ARs were expressed in astrocytes and which one has the first priority of utilizing adenosine. Firstly, mRNA expressions and membrane localization of all ARs were evaluated by qPCR and western blot. After the membrane localization of all ARs in astrocytes was being confirmed their individual adenosine binding ability was determined by radio-active ligand binding assay respectively. It was revealed that ARA1 had much superior adenosine binding ability than other AR subtypes. Functional study demonstrated that ARA1 potentially mediated an immune suppressive effect in astrocytes. The activation of ARA1 signaling lead to decreased IL-12 and IL-23 production, and decreased chemokine production, including CCL2, CXCL8 and IP-10. When interacted with CD4 cells ARA1 agonist pre-treated astrocytes showed hindered ability of stimulating CD4 cells to secret IL-17 and IFN-γ and inducing CD4 cells' chemo taxi. Finally, in vivo experiment confirmed that local administration of ARA1agonist ameliorated EAE in wild type B6 recipients, but not Ara1 -/- recipients. As a conclusion, this paper suggested that adenosine receptor A1 subtype predominantly binds adenosine in astrocytes and mediates an immunosuppressive effect. [ABSTRACT FROM AUTHOR]