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To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using attention mechanism with feature map to facilitate reconstruction from original low-resolution images to multi-scale super-resolution images. The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.

All existing image inpainting methods based on neural network models are affected by structural distortions and blurred textures on visible connectivity, such that overfitting and overlearning phenomena can easily emerge in the image inpainting processing procedure. Accordingly, in an attempt to address the defects of image inpainting algorithm, such as long iteration time, poor adaptability and unsatisfactory repairing effects, the image inpainting algorithm of improved Generative Adversarial Networks based on deep learning method of Two-Discriminations Network has been proposed in the paper. The proposed method uses image inpainting network, global discrimination network and local discrimination network to create a fusion network to apply computational images. In the training procedure of proposed algorithm, the network of image inpainting algorithm uses similar patching method to fill the broken area in image and set it as input training objects, which greatly improves the speed and quality of image inpainting. The global discrimination network uses global structure with marginal information and feature information to judge the completed image, meaning that it comprehensively achieves visible connectivity. As local discrimination network can judge the computational images, it has also been trained with assisted feature patches found on multiple images. Furthermore, the proposed method can enhance the discriminant capability and solve the problem that the image inpainting network has easily been overfitting when the features are too concentrated and limited in number to process. Our results of designed experiments demonstrate that proposed algorithm has better adaptive capability on several image categories than those state-of-the-arts.

In order to solve the issue mismatching and structure disconnecting in exemplar-based image inpainting, an image completion algorithm based on improved total variation minimization method had been proposed in the paper, refer as ETVM. The structure of image had been extracted using improved total variation minimization method, and the known information of image is sufficiently used by existing methods. The robust filling mechanism can be achieved according to the direction of image structure and it has less noise than original image. The priority term had been redefined to eliminate the product effect and ensure data term had always effective. The priority of repairing patch and the best matching patch are determined by the similarity of the known information and the consistency of the unknown information in the repairing patch. The comparisons with cognitive computing image algorithms had been shown that the proposed method can ensure better selection of candidate image pixel to fill with, and it is achieved better global coherence of image completion than others. The inpainting results of noisy images show that the proposed method has good robustness and can also get good inpainting results for noisy images.

The automatic image annotation is an effective computer operation that predicts the annotation of an unknown image by automatically learning potential relationships between the semantic concept space and the visual feature space in the annotation image dataset. Usually, the auto-labeling image includes the processing: learning processing and labeling processing. Existing image annotation methods that employ convolutional features of deep learning methods have a number of limitations, including complex training and high space/time expenses associated with the image annotation procedure. Accordingly, this paper proposes an innovative method in which the visual features of the image are presented by the intermediate layer features of deep learning, while semantic concepts are represented by mean vectors of positive samples. Firstly, the convolutional result is directly output in the form of low-level visual features through the mid-level of the pre-trained deep learning model, with the image being represented by sparse coding. Secondly, the positive mean vector method is used to construct visual feature vectors for each text vocabulary item, so that a visual feature vector database is created. Finally, the visual feature vector similarity between the testing image and all text vocabulary is calculated, and the vocabulary with the largest similarity used for annotation. Experiments on the datasets demonstrate the effectiveness of the proposed method; in terms of F1 score, the proposed method’s performance on the Corel5k dataset and IAPR TC-12 dataset is superior to that of MBRM, JEC-AF, JEC-DF, and 2PKNN with end-to-end deep features.

Existing image inpainting algorithms based on neural network models are affected by structural distortions and blurred textures on visible connectivity. As a result, overfitting and overlearning phenomena can easily emerge during the image inpainting procedure. Image inpainting refers to the repairing of missing parts of an image, given an image that is broken or incomplete. After the repairing operation is complete, there are obvious signs of repair in damaged areas, semantic discontinuities, and unclearness. This paper proposes an improved image inpainting method based on a new encoder combined with a context loss function. In order to obtain clear repaired images and ensure that the semantic features of images are fully learned, a generative network based on the fusion model of squeeze-and-excitation networks deep residual learning has been proposed to improve the application of network features in order to obtain clear images and reduce network parameters. At the same time, a discriminative network based on the squeeze-and-excitation residual Network has been proposed to strengthen the capability of the discriminative network. In order to make the generated image more realistic, so that the restored image will be more similar to the original image, a joint context-awareness loss training method (contextual perception loss network) has also been proposed to generate the similarity of the local features of the network constraint, with the result that the repaired image is closer to the original picture and more realistic. The experimental results can demonstrate that the proposed algorithm demonstrates better adaptive capability than the comparison algorithms on a number of image categories. In addition, the processing results of the image inpainting procedure were also superior to those of five state-of-the-art algorithms.

The context information of images had been lost due to the low resolution of features, and due to repeated combinations of max-pooling layer and down-sampling layer. When the feature extraction process had been performed using a convolutional network, the result of semantic image segmentation loses sensitivity to the location of the object. The semantic image segmentation based on a feature fusion model with context features layer-by-layer had been proposed. Firstly, the original images had been pre-processed by the Gaussian Kernel Function to generate a series of images with different resolutions to form an image pyramid. Secondly, inputting an image pyramid into the network structure in which the plurality of fully convolutional network was been combined in parallel to obtain a set of initial features with different granularities by expanding receptive fields using Atrous Convolutions, and the initialization of feature fusion with different layer-by-layer granularities in a top-down method. Finally, the score map of feature fusion model had been calculated and sent to the conditional random field, modeling the class correlations between image pixels of the original image by the fully connected conditional random field, and the spatial position information and color vector information of image pixels were jointed to optimize and obtain results. The experiments on the PASCAL VOC 2012 and PASCAL Context datasets had achieved better mean Intersection Over Union than the state-of-the-art works. The proposed method has about 6.3% improved to the conventional methods.

Lycium barbarum L. (abbreviated to L. barbarum), a traditional dual-use plant as food and medicine, contains polysaccharides from Lycium barbarum L. (LBPs) as its key bioactive component. This study aimed to examine the phenotypic characteristics, polysaccharide content, and their correlation with activity across various commercial grades of L. barbarum. Five commercial grades of L. barbarum were selected for analysis to determine their phenotypic characteristics and polysaccharide content. High-performance liquid chromatogram-diode array detection (HPLC-DAD) and 1H NMR were employed to analyze the monosaccharide composition of LBPs, of which their hypoglycemic activity was further valuated. Results revealed significant differences in fruit weight and diameter among different grades (p < 0.05), while floating rate and bulk density remained unaffected by grades. Variations were observed in the chromaticity coordinates, with the c values showing notable differences (p < 0.01). Polysaccharide content tended to increase with higher grades and smaller fruit sizes, ranging from 1.94% to 5.69%. The polysaccharides in different contained monosaccharides of Man, Rha, Ara, Gal, Glc, GalA, GlcA and Xyl, with Ara and Gal being predominant. Identified through 1H NMR spectra, the peak intensity of Ara increased from lower to higher grades, and the arrangement of the chemical shifts reflected distinct commercial grade characteristics. The inhibitory concentration (IC50) against α-amylase and α-glucosidase ranged from 0.418 to 1.345 mg/mL, and 0.474 to 1.052 mg/mL, respectively, indicating good hypoglycemic activity within this range. The main monosaccharide groups Ara, Gal, and GalA were identified as key contributors to enzyme inhibition. Collectively interpreting the phenotypic features, polysaccharide content, monosaccharide composition, NMR data and activity profiles, Ara, Gal and GalA emerge as signature monosaccharide components of LBPs. These results provide novel theoretical insights for L. barbarum quality assessment.

ObjectivesTo assess differences in reperfusion treatment and outcomes between secondary and tertiary hospitals in predominantly rural central China.DesignMulticentre, prospective and observational study.SettingSixty-six (50 secondary and 16 tertiary) hospitals in Henan province, central China.ParticipantsPatients with ST elevation myocardial infarction (STEMI) within 30 days of symptom onset during 2016–2018.Primary outcome measuresIn-hospital mortality, and in-hospital death or treatment withdrawal.ResultsAmong 5063 patients of STEMI, 2553 were treated at secondary hospitals. Reperfusion (82.0% vs 73.0%, p<0.001) including fibrinolytic therapy (70.3% vs 4.4%, p<0.001) were more preformed, whereas primary percutaneous coronary intervention (11.7% vs 68.6%, p<0.001) were less frequent at secondary hospitals. In secondary hospitals, 53% received fibrinolytic therapy 3 hours after onset, and 5.8% underwent coronary angiography 2–24 hours after fibrinolysis. Secondary hospitals had a shorter onset-to-first-medical-contact time (176 min vs 270 min, p<0.001). Adjusted in-hospital mortality (adjusted OR 1.23, 95% CI 0.89 to 1.70, p=0.210) and in-hospital death or treatment withdrawal (adjusted OR 1.18, 95% CI 0.82 to 1.70, p=0.361) were similar between secondary and tertiary hospitals.ConclusionsWith fibrinolytic therapy as the main reperfusion strategy, the reperfusion rate was higher in secondary hospitals, whereas in-hospital outcomes were similar compared with tertiary hospitals. Public awareness, capacity of primary and secondary care institutes to treat STEMI, and establishment of deeper cooperation among different-level healthcare institutes need to further improve.Trial registration numberNCT02641262.

High levels of plasma high-density lipoprotein-cholesterol (HDL-C) are inversely associated with the risk of atherosclerosis and other cardiovascular diseases; thus, pharmacological inhibition of cholesteryl ester transfer protein (CETP) is considered to be a therapeutic method of raising HDL-C levels. However, many CETP inhibitors have failed to achieve a clinical benefit despite raising HDL-C. In the study, we generated transgenic (Tg) rabbits that overexpressed the human CETP gene to examine the influence of CETP on the development of atherosclerosis. Both Tg rabbits and their non-Tg littermates were fed a high cholesterol diet for 16 weeks. Plasma lipids and body weight were measured every 4 weeks. Gross lesion areas of the aortic atherosclerosis along with lesional cellular components were quantitatively analyzed. Overexpression of human CETP did not significantly alter the gross atherosclerotic lesion area, but the number of macrophages in lesions was significantly increased. Overexpression of human CETP did not change the plasma levels of total cholesterol or low-density lipoprotein cholesterol but lowered plasma HDL-C and increased triglycerides. These data revealed that human CETP may play an important role in the development of atherosclerosis mainly by decreasing HDL-C levels and increasing the accumulation of macrophage-derived foam cells.

It was reported in 1995 that T7 and Taq DNA polymerases possess 3′-esterase activity, but without follow-up studies. Here we report that the 3′-esterase activity is intrinsic to the Thermococcus sp . 9°N DNA polymerase, and that it can be developed into a continuous method for DNA sequencing with dNTP analogs carrying a 3′-ester with a fluorophore. We first show that 3′-esterified dNTP can be incorporated into a template-primer DNA, and solve the crystal structures of the reaction intermediates and products. Then we show that the reaction can occur continuously, modulated by active site residues Tyr409 and Asp542. Finally, we use 5′-FAM-labeled primer and esterified dNTP with a dye to show that the reaction can proceed to ca. 450 base pairs, and that the intermediates of many individual steps can be identified. The results demonstrate the feasibility of a 3′-editing based DNA sequencing method that could find practical applications after further optimization. Shiuan-Woei LinWu et al. presents a DNA sequencing technology with high accuracy, using 9°N DNA polymerase. This study expands the current portfolio of enzymes equipped with 3′-esterase activity and shows the feasibility of a 3′-editing-based DNA sequencing.