Exponential synchronization of general chaotic delayed neural networks via hybrid feedback

This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model,which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator,and covers several well-known neural networks,such as Hopfield neural networks,cellular neural networks(CNNs),bidirectional associative memory(BAM)networks,recurrent multilayer perceptrons(RMLPs).By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality(LMI)technique,some exponential synchronization criteria are derived.Using the drive-response concept,hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria.Finally,detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.

Nasal vaccination of triple-RBD scaffold protein with flagellin elicits long-term protection against SARS-CoV-2 variants including JN.1

Developing a mucosal vaccine against SARS-CoV-2 is critical for combatting the epidemic.Here,we investigated long-term immune responses and protection against SARS-CoV-2 for the intranasal vaccination of a triple receptor-binding domain(RBD)scaffold protein(3R-NC)adjuvanted with a flagellin protein(KFD)(3R-NC+KFDi.n).In mice,the vaccination elicited RBD-specific broad-neutralizing antibody responses in both serum and mucosal sites sustained at high level over a year.This long-lasting humoral immunity was correlated with the presence of long-lived RBD-specific IgG-and IgA-producing plasma cells,alongside the Th17 and Tfh17-biased T-cell responses driven by the KFD adjuvant.Based upon these preclinical findings,an open labeled clinical trial was conducted in individuals who had been primed with the inactivated SARS-CoV-2(IAV)vaccine.With a favorable safety profile,the 3R-NC+KFDi.n boost elicited enduring broad-neutralizing IgG in plasma and IgA in salivary secretions.To meet the challenge of frequently emerged variants,we further designed an updated triple-RBD scaffold protein with mutated RBD combinations,which can induce adaptable antibody responses to neutralize the newly emerging variants,including JN.1.Our findings highlight the potential of the KFD-adjuvanted triple-RBD scaffold protein is a promising prototype for the development of a mucosal vaccine against SARS-CoV-2 infection.

A triple-RBD-based mucosal vaccine provides broad protectionagainst SARS-CoV-2 variants of concern

The rapid mutation and spread of SARS-CoV-2 variants urge the development of effective mucosal vaccines to provide broadspectrum protection against the initial infection and thereby curb the transmission potential.Here,we designed a chimeric tripleRBD immunogen,3Ro-NC,harboring one Delta RBD and two Omicron RBDs within a novel protein scaffold.3Ro-NC elicits potent and broad RBD-specific neutralizing immunity against SARS-CoV-2 variants of concern.Notably,intranasal immunization with 3RoNC plus the mucosal adjuvant KFD(3Ro-NC+KFDi.n)elicits coordinated mucosal IgA and higher neutralizing antibody specificity(closer antigenic distance)against the Omicron variant.In Omicron-challenged human ACE2 transgenic mice,3Ro-NC+KFDi.n immunization significantly reduces the tissue pathology in the lung and lowers the viral RNA copy numbers in both the lung(85.7-fold)and the nasal turbinate(13.6-fold).Nasal virologic control is highly correlated with RBD-specific secretory IgA antibodies.Our data show that 3Ro-NC plus KFD is a promising mucosal vaccine candidate for protection against SARS-CoV-2 Omicron infection,pathology and transmission potential.

Protective Efficacy of Inactivated Vaccine against SARS-CoV-2 Infection in Mice and Non-Human Primates

The ongoing coronavirus disease 2019(COVID-19)pandemic caused more than 96 million infections and over 2 million deaths worldwide so far.However,there is no approved vaccine available for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the disease causative agent.Vaccine is the most effective approach to eradicate a pathogen.The tests of safety and efficacy in animals are pivotal for developing a vaccine and before the vaccine is applied to human populations.Here we evaluated the safety,immunogenicity,and efficacy of an inactivated vaccine based on the whole viral particles in human ACE2 transgenic mouse and in non-human primates.Our data showed that the inactivated vaccine successfully induced SARS-CoV-2-specific neutralizing antibodies in mice and non-human primates,and subsequently provided partial(in low dose)or full(in high dose)protection of challenge in the tested animals.In addition,passive serum transferred from vaccine-immunized mice could also provide full protection from SARS-CoV-2 infection in mice.These results warranted positive outcomes in future clinical trials in humans.

Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks

Underwater wireless sensor networks(UWSNs)can provide a promising solution to underwater target tracking.Due to limited energy and bandwidth resources,only a small number of nodes are selected to track a target at each interval.Because all measurements are fused together to provide information in a fusion center,fusion weights of all selected nodes may affect the performance of target tracking.As far as we know,almost all existing tracking schemes neglect this problem.We study a weighted fusion scheme for target tracking in UWSNs.First,because the mutual information(MI)between a node’s measurement and the target state can quantify target information provided by the node,it is calculated to determine proper fusion weights.Second,we design a novel multi-sensor weighted particle filter(MSWPF)using fusion weights determined by MI.Third,we present a local node selection scheme based on posterior Cramer-Rao lower bound(PCRLB)to improve tracking efficiency.Finally,simulation results are presented to verify the performance improvement of our scheme with proper fusion weights.

Detecting slowly moving infrared targets using temporal filtering and association strategy

The special characteristics of slowly moving infrared targets, such as containing only a few pixels,shapeless edge, low signal-to-clutter ratio, and low speed, make their detection rather difficult, especially when immersed in complex backgrounds. To cope with this problem, we propose an effective infrared target detection algorithm based on temporal target detection and association strategy. First, a temporal target detection model is developed to segment the interested targets. This model contains mainly three stages, i.e., temporal filtering,temporal target fusion, and cross-product filtering. Then a graph matching model is presented to associate the targets obtained at different times. The association relies on the motion characteristics and appearance of targets,and the association operation is performed many times to form continuous trajectories which can be used to help disambiguate targets from false alarms caused by random noise or clutter. Experimental results show that the proposed method can detect slowly moving infrared targets in complex backgrounds accurately and robustly, and has superior detection performance in comparison with several recent methods.

ACE2-independent infection of T lymphocytes by SARS-CoV-2

SARS-CoV-2 induced marked lymphopenia in severe patients with COVID-19.However,whether lymphocytes are targets of viral infection is yet to be determined,although SARS-CoV-2 RNA or antigen has been identified in T cells from patients.Here,we confirmed that SARS-CoV-2 viral antigen could be detected in patient peripheral blood cells(PBCs)or postmortem lung T cells,and the infectious virus could also be detected from viral antigen-positive PBCs.We next prove that SARS-CoV-2 infects T lymphocytes,preferably activated CD4+T cells in vitro.Upon infection,viral RNA,subgenomic RNA,viral protein or viral particle can be detected in the T cells.Furthermore,we show that the infection is spike-ACE2/TMPRSS2-independent through using ACE2 knockdown or receptor blocking experiments.Next,we demonstrate that viral antigen-positive T cells from patient undergone pronounced apoptosis.In vitro infection of T cells induced cell death that is likely in mitochondria ROS-HIF-1a-dependent pathways.Finally,we demonstrated that LFA-1,the protein exclusively expresses in multiple leukocytes,is more likely the entry molecule that mediated SARS-CoV-2 infection in T cells,compared to a list of other known receptors.Collectively,this work confirmed a SARS-CoV-2 infection of T cells,in a spike-ACE2-independent manner,which shed novel insights into the underlying mechanisms of SARS-CoV2-induced lymphopenia in COVID-19 patients.

Stability of SARS-CoV-2 on the Surfaces of Three Meats in the Setting That Simulates the Cold Chain Transportation

Dear Editor,The coronavirus disease 2019(COVID-19)pandemic presents a severe threat to public health and the global economy and the direct origin of the causative agent severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)remains unresolved(WHO 2020).The outbreak in China was successfully contained by the implementation of a strict quarantine strategy,however sporadic outbreaks still occurred and were associated with trade markets in Beijing and Dalian cities.The COVID-19 outbreak in Beijing was traced to contaminated salmon meat sold at the Xinfadi market,Beijing,China(Chinese CDC 2020)。

A slotted floor acquisition multiple access based MAC protocol for underwater acoustic networks with RTS competition

Long propagation delay, limited bandwidth, and high bit error rate pose great challenges in media access control （MAC） protocol design for underwater acoustic networks. A MAC protocol called slotted floor acquisition multiple access （slotted-FAMA） suitable for underwater acoustic networks is proposed and analyzed. This FAMA based protocol adds a time slot mechanism to avoid DATA packet collisions. However, slotted-FAMA is not suitable for dense networks since the multiple request-to-send （RTS） attempts problem in dense networks is serious and greatly limits the network throughput. To overcome this drawback, this paper proposes a slotted-FAMA based MAC protocol for underwater acoustic networks, called RC-SFAMA. RC-SFAMA introduces an RTS competition mechanism to keep the network from high frequency of backoff caused by the multiple RTS attempts problem. Via the RTS competition mechanism, useful data transmission can be completed successfully when the situation of multiple RTS attempts occurs. Simulation results show that RC-SFAMA increases the network throughput efficiency as compared with slotted-FAMA, and minimizes the energy consumption.

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

The protective nasal boosting of a triple-RBD subunit vaccine against SARS-CoV-2 following inactivated virus vaccination

Dear Editor,Though COVID-19 vaccines have been developed and clinically deployed rapidly,new variants of concern(VOCs)are still emerging frequently and escalating around the world.More breakthrough infections occurred even vaccination rates are high.For possible ending of the pandemic,curbing infection and stopping transmission are priority.

A novel approach of noise statistics estimate using H_∞ filter in target tracking

Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox.First, we apply the H_∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H_∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost,which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

H_∞ reference tracking control design for a class of nonlinear systems with time-varying delays

This paper investigates the H∞ trajectory tracking control for a class of nonlinear systems with time- varying delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality （LMI） technique. A unified model consisting of a linear delayed dynamic system and a bounded static nonlinear operator is introduced, which covers most of the nonlinear systems with bounded nonlinear terms, such as the one-link robotic manipulator, chaotic systems, complex networks, the continuous stirred tank reactor （CSTR）, and the standard genetic regulatory network （SCRN）. First, the definition of the tracking control is given. Second, the H∞ performance analysis of the closed-loop system including this unified model, reference model, and state feedback controller is presented. Then criteria on the tracking controller design are derived in terms of LMIs such that the output of the closed-loop system tracks the given reference signal in the H∞ sense. The reference model adopted here is modified to be more flexible. A scaling factor is introduced to deal with the disturbance such that the control precision is improved. Finally, a CSTR system is provided to demonstrate the effectiveness of the established control laws.

Finding misplaced items using a mobile robot in a smart home environment

Smart homes can provide complementary information to assist home service robots.We present a robotic misplaced item finding(MIF)system,which uses human historical trajectory data obtained in a smart home environment.First,a multi-sensor fusion method is developed to localize and track a resident.Second,a path-planning method is developed to generate the robot movement plan,which considers the knowledge of the human historical trajectory.Third,a real-time object detector based on a convolutional neural network is applied to detect the misplaced item.We present MIF experiments in a smart home testbed and the experimental results verify the accuracy and efficiency of our solution.

Energy-efficient localization and target tracking via underwater mobile sensor networks

Underwater mobile sensor networks(UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces,so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction(HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multistep Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking(SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.

Genetic Mutation of SARS-CoV-2 during Consecutive Passages in Permissive Cells

Dear Editor,The ongoing COVID-19 disease,caused by severe acute respiratory syndrome coronavirus 2(SARS-Co V-2),has led to over 112 million confirmed cases and 2.4 million deaths in more than 220 countries as of 25 February 2021(WHO 2021).Hospital-admitted patients show clinical features including fever,dry cough,fatigue,dyspnea,lymphopenia,and pneumonia with radiological groundglass lung opacities(Huang et al.2020;Guan et al.2020).SARS-Co V-2 was quickly isolated and could be detected in clinical samples,such as nasopharyngeal swabs,sputum,alveolar lavage fluid,and feces,as well as occasionally in seminal fluid and tears among other sources(Bhat et al.2020;Li et al.2020),which means that it can infect a variety of human tissues and organs.

Fish ACE2 is not susceptible to SARS-CoV-2

Dear Editor,The ongoing pandemic of coronavirus disease 2019(COVID-19)has reshaped our daily life and caused>4 million deaths worldwide(https://covid19.who.int/).Although lockdown and vaccination have improved the situation in many countries,imported cases and sporadic outbreaks pose a constant stress to the prevention and control of COVID-19.