Combining the Silhouette and Skeleton Data for Gait Recognition

Gait recognition, a promising long-distance biometric technology, has aroused intense interest in computer vision. Existing works on gait recognition can be divided into appearance-based methods and model-based methods, which extract features from silhouettes and skeleton data, respectively. However, since appearance-based methods are greatly affected by clothing changing and carrying condition, and model-based methods are limited by the accuracy of pose estimation approaches, gait recognition remains challenging in practical applications. In order to integrate the advantages of such two approaches, a two-branch neural network (NN) is proposed in this paper. Our method contains two branches, namely a CNN-based branch taking silhouettes as input and a GCN-based branch taking skeletons as input. In addition, two new modules are proposed in the GCN-based branch for better gait representation. First, we present a simple yet effective fully connected graph convolution operator to integrate the multi-scale graph convolutions and alleviate the dependence on natural human joint connections. Second, we deploy a multi-dimension attention module named STC-Att to learn spatial, temporal and channel-wise attention simultaneously. We evaluated the proposed two-branch neural network on the CASIA-B dataset. The experimental results show that our method achieves state-of-the-art performance in various conditions.Comment: The paper is under consideration at Computer Vision and Image Understandin

A Reduction-Based Approach Towards Scaling Up Formal Analysis of Internet Configurations

The Border Gateway Protocol (BGP) is the single inter-domain routing protocol that enables network operators within each autonomous system (AS) to influence routing decisions by independently setting local policies on route filtering and selection. This independence leads to fragile networking and makes analysis of policy configurations very complex. To aid the systematic and efficient study of the policy configuration space, this paper presents network reduction, a scalability technique for policy-based routing systems. In network reduction, we provide two types of reduction rules that transform policy configurations by merging duplicate and complementary router configurations to simplify analysis. We show that the reductions are sound, dual of each other and are locally complete. The reductions are also computationally attractive, requiring only local configuration information and modification. We have developed a prototype of network reduction and demonstrated that it is applicable on various BGP systems and enables significant savings in analysis time. In addition to making possible safety analysis on large networks that would otherwise not complete within reasonable time, network reduction is also a useful tool for discovering possible redundancies in BGP systems

An integrated software for virus community sequencing data analysis

BACKGROUND: A virus community is the spectrum of viral strains populating an infected host, which plays a key role in pathogenesis and therapy response in viral infectious diseases. However automatic and dedicated pipeline for interpreting virus community sequencing data has not been developed yet.RESULTS: We developed Quasispecies Analysis Package (QAP), an integrated software platform to address the problems associated with making biological interpretations from massive viral population sequencing data. QAP provides quantitative insight into virus ecology by first introducing the definition "virus OTU" and supports a wide range of viral community analyses and results visualizations. Various forms of QAP were developed in consideration of broader users, including a command line, a graphical user interface and a web server. Utilities of QAP were thoroughly evaluated with high-throughput sequencing data from hepatitis B virus, hepatitis C virus, influenza virus and human immunodeficiency virus, and the results showed highly accurate viral quasispecies characteristics related to biological phenotypes.CONCLUSIONS: QAP provides a complete solution for virus community high throughput sequencing data analysis, and it would facilitate the easy analysis of virus quasispecies in clinical applications.</p

Intermittent theta burst stimulation vs. high-frequency repetitive transcranial magnetic stimulation for post-stroke cognitive impairment: Protocol of a pilot randomized controlled double-blind trial

IntroductionIntermittent theta burst stimulation (iTBS), a novel mode of transcranial magnetic stimulation (TMS), has curative effects on patients with post-stroke cognitive impairment (PSCI). However, whether iTBS will be more applicable in clinical use than conventional high-frequency repetitive transcranial magnetic stimulation (rTMS) is unknown. Our study aims to compare the difference in effect between iTBS and rTMS in treating PSCI based on a randomized controlled trial, as well as to determine its safety and tolerability, and to further explore the underlying neural mechanism.MethodsThe study protocol is designed as a single-center, double-blind, randomized controlled trial. Forty patients with PSCI will be randomly assigned to two different TMS groups, one with iTBS and the other with 5 Hz rTMS. Neuropsychological evaluation, activities of daily living, and resting electroencephalography will be conducted before treatment, immediately post-treatment, and 1 month after iTBS/rTMS stimulation. The primary outcome is the change in the Montreal Cognitive Assessment Beijing Version (MoCA-BJ) score from baseline to the end of the intervention (D11). The secondary outcomes comprise changes in resting electroencephalogram (EEG) indexes from baseline to the end of the intervention (D11) as well as the Auditory Verbal Learning Test, the symbol digit modality test, the Digital Span Test findings, and the MoCA-BJ scores from baseline to endpoint (W6).DiscussionIn this study, the effects of iTBS and rTMS will be evaluated using cognitive function scales in patients with PSCI as well as data from resting EEG, which allows for an in-depth exploration of underlying neural oscillations. In the future, these results may contribute to the application of iTBS for cognitive rehabilitation of patients with PSCI

Characterization of gene expression profiles in HBV-related liver fibrosis patients and identification of ITGBL1 as a key regulator of fibrogenesis

Although hepatitis B virus (HBV) infection is the leading cause of liver fibrosis (LF), the mechanisms underlying liver fibrotic progression remain unclear. Here, we investigated the gene expression profiles of HBV-related LF patients. Whole genome expression arrays were used to detect gene expression in liver biopsy samples from chronically HBV infected patients. Through integrative data analysis, we identified several pathways and key genes involved in the initiation and exacerbation of liver fibrosis. Weight gene co-expression analysis revealed that integrin subunit β-like 1 (ITGBL1) was a key regulator of fibrogenesis. Functional experiments demonstrated that ITGBL1 was an upstream regulator of LF via interactions with transforming growth factor β1. In summary, we investigated the gene expression profiles of HBV-related LF patients and identified a key regulator ITGBL1. Our findings provide a foundation for future studies of gene functions and promote the development of novel antifibrotic therapies