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With the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. Various Transformer-based networks have shown significant performance advantages over mainstream neural networks in different visual tasks, demonstrating the huge potential of Transformers in the field of image segmentation. However, Transformers were originally designed for NLP and ignore the multidimensional nature of images. In the process of operation, they may destroy the 2D structure of the image and cannot effectively capture low-level features. Therefore, we propose a new multi-scale cross-attention method called M-VAN Unet, which is designed based on the Visual Attention Network (VAN) and can effectively learn local and global features. We propose two attention mechanisms, namely MSC-Attention and LKA-Cross-Attention, for capturing low-level features and promoting global information interaction. MSC-Attention is designed for multi-scale channel attention, while LKA-Cross-Attention is a cross-attention mechanism based on the large kernel attention (LKA). Extensive experiments show that our method outperforms current mainstream methods in evaluation metrics such as Dice coefficient and Hausdorff 95 coefficient.

In this paper, the viewing-angle-switching (VAS) films based on polymer dispersed liquid crystals (PDLCs) were fabricated and the optical properties of the VAS films were studied. The VAS films based on PDLC films can exhibit electrically switchable viewing-angle properties, and the films showed a narrow viewing angle under no electric field and presented a wide viewing angle under electric field (24 V). Furthermore, the smart anti-peeping liquid crystal display (LCD) containing the VAS film based on PDLC film also displayed the electrically switchable viewing-angle performance and the corresponding mechanism was discussed. The results demonstrated both the anti-peeping effect and electrically switchable view-angle effect of the as-fabricated VAS film, promising potential applications in intelligent optical films and novel information displays. [ABSTRACT FROM AUTHOR]

WordNets organize words into synonymous word sets, and the connections between words present the semantic relationships between them, which have become an indispensable source for natural language processing (NLP) tasks. With the development and evolution of languages, WordNets need to be constantly updated manually. To address the problem of inadequate word semantic knowledge of "new words", this study explores a novel method to automatically update the WordNet knowledge base by incorporating word-embedding techniques with sememe knowledge from HowNet. The model first characterizes the relationships among words and sememes with a graph structure and jointly learns the embedding vectors of words and sememes; finally, it synthesizes word similarities to predict concepts (synonym sets) of new words. To examine the performance of the proposed model, a new dataset connected to sememe knowledge and WordNet is constructed. Experimental results show that the proposed model outperforms the existing baseline models. [ABSTRACT FROM AUTHOR]

Iris recognition (IR) is widely adopted for human identification in the biometric fields. However, results using handcrafted features designed with the domain knowledge are obtained by conventional IR algorithms which are complex. Convolutional neural networks (convnets) have been proved to be robust with superior learning capability in many computer vision fields. The deep convnets perform better than shallower convnets. However, training the deep convnets for IR is difficult. IrisConvDeeper, an adaptive architecture based on convnets combining Softmax classifier that is used to alleviate the problems of training deep convnets for IR, is proposed. In addition, a better performance is obtained. Furthermore, a shallower architecture termed as IrisConvShallower is designed for comparison. IrisConvDeeper consists of structures called dense block and has 12 or 14 convolutional layers. Meanwhile, IrisConvShallower consists of six or seven standard convolutional layers. The performance of the proposed architectures is tested on two public iris databases, which are collected under different conditions: CASIA-Iris-V3 and IITD iris image databases. The experimental results demonstrate that the proposed IrisConvDeeper outperforms most of state-of-the-art approaches in terms of correct recognition rate, except some approaches that adopted specific methods and identified fewer subjects compared with our methods. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to investigate the efficacy and safety of endoscopic ultrasound (EUS)-guided injection of cyanoacrylate (CYA) and transjugular intrahepatic portal shunts (TIPSs) in the treatment of patients with cirrhosis with ruptured gastric varices., Methods: In this retrospective study, 105 patients with liver cirrhosis and gastric varicose veins who were admitted to the First Affiliated Hospital of Anhui Medical University between April 2018 and April 2023 without nonselective β-blockers treatment and no portal vein thrombosis were evaluated. The patients were divided into the transjugular intrahepatic portal shunt (TIPS) group (n = 60) and the EUS-CYA group (n = 45) for the purpose of evaluating postoperative rebleeding rates, complications, survival rates, and other factors., Results: During the follow-up, there was no significant difference in the rebleeding rates between the TIPS group and EUS-CYA group within 3 months (5% vs 2.2%; P= 0.825; 10% vs 20%, P= 0.147). However, the TIPS group had significantly lower rebleeding rates than the EUS-CYA group at 6 months (10% vs 33.3%; P= 0.030) and 1 year or longer (11.7% vs 42.2%; P < 0.01). In terms of hepatic encephalopathy, the incidence rate of the TIPS group was significantly higher than that of the EUS-CYA group (20% vs 2.2%; P= 0.006). In addition, there was no difference in the survival rates between the two groups (93.3% vs 97.8%; P= 0.552)., Conclusions: TIPS is superior to EUS in preventing rebleeding in patients with ruptured varices of the fundus, but it has a higher incidence of hepatic encephalopathy, and there is no difference in long-term survival between the two groups., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)