FV-MViT: Mobile Vision Transformer for Finger Vein Recognition.

In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer's self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT.

SPOT-Fold: Fragment-Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map.

Protein structure determination has long been one of the most challenging problems in molecular biology for the past 60 years. Here we present an ab initio protein tertiary-structure prediction method assisted by predicted contact maps from SPOT-Contact and predicted dihedral angles from SPIDER 3. These predicted properties were then fed to the crystallography and NMR system (CNS) for restrained structure modeling. The resulted structures are first evaluated by the potential energy calculated by CNS, followed by dDFIRE energy function for model selections. The method called SPOT-Fold has been tested on 241 CASP targets between 67 and 670 amino acid residues, 60 randomly selected globular proteins under 100 amino acids. The method has a comparable accuracy to other contact-map-based modeling techniques. © 2019 Wiley Periodicals, Inc.

Highly Uniform Perfluoropropane-Loaded Cerasomal Microbubbles As a Novel Ultrasound Contrast Agent.

Microbubbles are widely used as ultrasound contrast agents owing to their excellent echoing characteristics under ultrasound radiation. However, their short sonographic duration and wide size distribution still hinder their application. Herein, we present a hard-template approach to produce perfluoropropane-loaded cerasomal microbubbles (PLCMs) with uniform size and long sonographic duration. The preparation of PLCMs includes deposition of Si-lipids onto functionalized CaCO3 microspheres, removal of their CaCO3 cores and mild infusion of perfluoropropane. In vitro and in vivo experiments showed that PLCMs had excellent echoing characteristics under different ultrasound conditions. More importantly, PLCMs could be imaged for much longer than SonoVue (commercially used microbubbles) under the same ultrasound parameters and concentrations. Our results demonstrated that PLCMs have great potential for use as a novel contrast agent in ultrasound imaging.