Metagenomic Analysis of Flaviviridae in Mosquito Viromes Isolated From Yunnan Province in China Reveals Genes From Dengue and Zika Viruses

More than 6,000 mosquitoes of six species from six sites were collected and tested for their virome using metagenomics sequencing and bioinformatic analysis. The identified viral sequences belonged to more than 50 viral families. The results were verified by PCR of selected viruses in all mosquitoes, followed by phylogenetic analysis. In the present study, we identified the partial dengue virus (DENV), Zika virus (ZIKV), and Japanese encephalitis virus (JEV) sequences in mosquitoes. Metagenomic analysis and the PCR amplification revealed three DENV sequences, one of which encodes a partial envelope protein. Two ZIKV sequences both encoding partial nonstructural protein 3 and one JEV sequence encoding the complete envelope protein were identified. There was variability in the viral titers of the newly isolated virus JEV-China/YN2016-1 of different passage viruses. The newly identified Zika virus gene from ZIKV-China/YN2016-1 was an Asian genotype and shared the highest nucleotide sequence identity (97.1%) with a ZIKV sequence from Thailand isolated in 2004. Phylogenetic analysis of ZIKV-China/YN2016-1 and ZIKV-China/YN2016-2 with known Flavivirus genes indicated that ZIKV has propagated in Yunnan province, China

Metagenomic Sequencing From Mosquitoes in China Reveals a Variety of Insect and Human Viruses

We collected 8,700 mosquitoes in three sites in China, which belonged to seven species. Their viromes were tested using metagenomic sequencing and bioinformatic analysis. The abundant viral sequences were detected and annotated belonging to more than 50 viral taxonomic families. The results were verified by PCR, followed by phylogenetic analysis. In the present study, we identified partial viral genes of dengue virus (DENV), a novel circovirus (CCV), densovirus (DNV), Japanese encephalitis virus (JEV), and Wuhan mosquito virus (WMV) in mosquitoes. Metagenomic analysis and PCR amplification revealed three DENV sequences, which were as homologous to the NS3 gene of DENV from Singapore isolated in 2005, with at least 91% nucleotide (nt) identity. Seven fragments of JEV encoding structural proteins were identified belonging to genotype I. They all shared high homology with structural protein genes of JEV isolated from Laos in 2009. The production of infectious virus particles of the newly isolated virus YunnanJEV2017-4 increased after passage from the BHK-21 cell line to the Vero cell line. Novel circovirus-related genes were identified and as being related to an unnamed gene of a mosquito circovirus (MCCV) sequence from the USA isolated in 2011, with at least 41% nt identity: this distant relationship suggests that the parent virus might belong to a novel circovirus genus. Additionally, numerous known viruses and some unknown viruses were also detected in mosquitoes from Yunnan province, China, which will be tested for propagation

An Improved Antiwindup Design Using an Anticipatory Loop and an Immediate Loop

We present an improved antiwindup design for linear invariant continuous-time systems with actuator saturation nonlinearities. In the improved approach, two antiwindup compensators are simultaneously designed: one activated immediately at the occurrence of actuator saturation and the other activated in anticipatory of actuator saturation. Both the static and dynamic antiwindup compensators are considered. Sufficient conditions for global stability and minimizing the induced L2 gain are established, in terms of linear matrix inequalities (LMIs). We also show that the feasibility of the improved antiwindup is similar to the traditional antiwindup. Benefits of the proposed approach over the traditional antiwindup and a recent innovative antiwindup are illustrated with well-known examples

Effectiveness of an online management platform (Joint Cloud) versus standard process for patients undergoing total knee arthroplasty: study protocol for a prospective randomised controlled trial

Introduction Osteoarthritis (OA) is one of the main causes of mobility impairment in the elderly worldwide. Therefore, total knee arthroplasty (TKA) is often performed and is one of the most successful surgery and has resulted in substantial quality-of-life gains for people with end-stage arthritis. There is still room for improvement in the standard treatment process in the preoperative, intraoperative and postoperative period of TKA. Telerehabilitation has the potential to become a positive alternative to face-to-face rehabilitation nowadays. But it remains unclear how well telemedicine interventions cover the entire surgical pathway (preoperation, intraoperation, postoperation). This study aims to explore the effectiveness of Joint Cloud (JC, an online management platform) compared with existing standard process in regulating functional recovery, pain management, muscle strength changes and other health-related outcomes in patients undergoing total knee arthroplasty preoperation, intraoperation and postoperation.Methods and analysis A randomised controlled trial was designed to compare the online management platform (JC) with standard process (SP) in patients undergoing TKA. A total of 186 TKA patients will be randomly assigned to the intervention (n=93) or control (n=93) group. Patients in the intervention group will receive access to the ‘JC’ mini-program. This mini-program provides popular science information (eg, information about OA and TKA), functional exercise information and communication channels. Patients evaluate their condition and functional level through standardised digital questionnaires. The control group of patients will not accept any functions of this mini-program. The primary outcome is knee functional recovery, and the secondary outcomes are pain management, isometric knee extensor muscle strength, patient satisfaction and cost-benefit analysis. Assessments will be performed 1 month and 3 days before surgery (T0) and 1 month and 3 months after surgery. Data analysis will be performed according to the intent-to-treat (ITT) principle. Repeated measures of linear mixed models and parametric and non-parametric testing will be used for statistical analysis.Ethics and dissemination The study was reviewed and approved by the Tianjin Hospital Medical Ethics Review Committee on 10 February 2023 (2022YLS155). Test data are considered highly sensitive but are available upon request. The findings will be disseminated in peer-reviewed publications.Trial registration number ChiCTR2300068486

Active disturbance rejection control for uncertain time-delay nonlinear systems

This paper addresses the problems of design and analysis of active disturbance rejection control (ADRC) for uncertain nonlinear systems subject to input time-delay. A novel extended state observer (ESO) is first designed as a predictor to realize the predictive state, and the total uncertainty which represents the effects of the unknown nonlinear dynamics and external disturbance. Based on the output of the ESO, a predictive ADRC law is presented. Conditions for the resulting closed-loop system to achieve convergence are obtained. It is shown that the upper bounds of the ESO gains are limited by the time-delay. Numerical simulations are given to illustrate the theoretical results.Accepted versionThis work was supported by the National Natural Science Foundation of China (61873295, 61833016)

A behavior-based mobile robot navigation method with deep reinforcement learning

Deep reinforcement learning-based mobile robot navigation has attracted some recent interest. In the single-agent case, a robot can learn to navigate autonomously without a map of the environment. In the multi-agent case, robots can learn to avoid collisions with each other. In this work, we propose a behavior-based mobile robot navigation method which directly maps the raw sensor data and goal information to the control command. The learned navigation policy can be applied in both single-agent and multi-agent scenarios. Two basic navigation behaviors are considered in our method, which are goal reaching and collision avoidance. The two behaviors are fused based on the risklevel estimation of the current state. The navigation task is decomposed using the behavior-based framework, which is capable of reducing the complexity of the learning process. The simulations and real-world experiments demonstrate that the proposed method can enable the collision-free autonomous navigation of multiple mobile robots in unknown environments

Fast loop closure detection via binary content

Loop closure detection plays an important rolein reducing localization drift in Simultaneous LocalizationAnd Mapping (SLAM). It aims to find repetitive scenes fromhistorical data to reset localization. To tackle the loop closureproblem, existing methods often leverage on the matching ofvisual features, which achieve good accuracy but require highcomputational resources. However, feature point based methodsignore the patterns of image, i.e., the shape of the objects aswell as the distribution of objects in an image. It is believedthat this information is usually unique for a scene and can beutilized to improve the performance of traditional loop closuredetection methods. In this paper we leverage and compressthe information into a binary image to accelerate an existingfast loop closure detection method via binary content. Theproposed method can greatly reduce the computational costwithout sacrificing recall rate. It consists of three parts: binarycontent construction, fast image retrieval and precise loopclosure detection. No offline training is required. Our methodis compared with the state-of-the-art loop closure detectionmethods and the results show that it outperforms the traditionalmethods at both recall rate and speed.NRF (Natl Research Foundation, S’pore)Published versio