Antibodies elicited by Newcastle disease virus-vectored H7N9 avian influenza vaccine are functional in activating the complement system

H7N9 subtype avian influenza virus poses a great challenge for poultry industry.Newcastle disease virus(NDV)-vectored H7N9 avian influenza vaccines(NDV_(vec)H7N9)are effective in disease control because they are protective and allow mass administration.Of note,these vaccines elicit undetectable H7N9-specific hemagglutination-inhibition(HI)but high IgG antibodies in chickens.However,the molecular basis and protective mechanism underlying this particular antibody immunity remain unclear.Herein,immunization with an NDV_(vec)H7N9 induced low anti-H7N9 HI and virus neutralization titers but high levels of hemagglutinin(HA)-binding IgG antibodies in chickens.Three residues(S150,G151 and S152)in HA of H7N9 virus were identified as the dominant epitopes recognized by the NDV_(vec)H7N9 immune serum.Passively transferred NDV_(vec)H7N9 immune serum conferred complete protection against H7N9 virus infection in chickens.The NDV_(vec)H7N9 immune serum can mediate a potent lysis of HA-expressing and H7N9 virus-infected cells and significantly suppress H7N9 virus infectivity.These activities of the serum were significantly impaired after heat-inactivation or treatment with complement inhibitor,suggesting the engagement of the complement system.Moreover,mutations in the 150-SGS-152 sites in HA resulted in significant reductions in cell lysis and virus neutralization mediated by the NDV_(vec)H7N9 immune serum,indicating the requirement of antibody-antigen binding for complement activity.Therefore,antibodies induced by the NDV_(vec)H7N9 can activate antibody-dependent complement-mediated lysis of H7N9 virus-infected cells and complement-mediated neutralization of H7N9 virus.Our findings unveiled a novel role of the complement in protection conferred by the NDV_(vec)H7N9,highlighting a potential benefit of engaging the complement system in H7N9 vaccine design.

Nonlinear Dynamic Characteristics of the Vectored Thruster AUV in Complex Sea Conditions

The mobility of the vectored thruster AUV in different environment is the important premise of control system design. The new type of autonomous underwater vehicle (AUV) equipped with rudders and vectored thrusters which are combined to control the course is studied. Firstly, Euler angles representation and quaternion method are applied to establish six-DOF kinematic model respectively, then Newton second law and Lagrangian approach are used to deduce the vectored thruster AUV’s nonlinear dynamic equations with six degrees of freedom (DOF) respectively in complex sea conditions based on the random wave theory according to the structural and kinetic characteristics of the vectored thruster AUV in this paper. The kinematic models and dynamic models based on different theories have the same expression and conclusion, which shows that the kinematic models and dynamic models of the vectored thruster AUV are accurate. The Runge-Kutta arithmetic is used to solve the dynamic equations, which not only can simulate the motions such as cruise and hover but also can describe the vehicle’s low-frequency and high-frequency motion. The results of computation show that the mobility of the vectored thruster AUV in interference-free environment and the integrated signals including low-frequency motion signal and high-frequency motion signal in environmental disturbance accord with practical situation, which not only solve the problem of especial singularities when the pitch angle θ = ±90° but also clears up the difficulties of computation and display of the coupled nonlinear motion equations in complex sea conditions. Moreover, the high maneuverability of the vectored thruster AUV equipped with rudders and vectored thrusters is validated, which lays a foundation for the control system design.

Kinetic Analysis of Vectored Electric Propulsion System for Stratosphere Airship

To enhance the controllability of stratosphere airship,a vectored electric propulsion system is used.By using the Lagrangian method,a kinetic model of the vectored electric propulsion system is established and validated through ground tests.The fake gyroscopic torque is first proposed,which the vector mechanism should overcome besides the inertial torque and the gravitational torque.The fake gyroscopic torque is caused by the difference between inertial moments about two principal inertial axes of the propeller in the rotating plane,appears only when the propeller is rotating and is proportional with the rotation speed.It is a sinusoidal pulse,with a frequency that is twice of the rotation speed.Considering the fake gyroscope torque pulse and aerodynamic efficiency,three blade propeller is recommended for the vectored propulsion system used for stratosphere airship.

Modulating vectored non-covalent interactions for layered assembly with engineerable properties

Vectored non-covalent interactions—mainly hydrogen bonding and aromatic interactions—extensively contribute to(bio)-organic self-assembling processes and significantly impact the physicochemical properties of the associated superstructures.However,vectored non-covalent interaction-driven assembly occursmainly along one-dimensional(1D)or three-dimensional(3D)directions,and a two-dimensional(2D)orientation,especially that of multilayered,graphene-like assembly,has been reported less.In this present research,by introducing amino,hydroxyl,and phenyl moieties to the triazine skeleton,supramolecular layered assembly is achieved by vectored non-covalent interactions.The planar hydrogen bonding network results in high stability,with a thermal sustainability of up to about 330°C and a Young’s modulus of up to about 40 GPa.Upon introducing wrinkles by biased hydrogen bonding or aromatic interactions to disturb the planar organization,the stability attenuates.However,the intertwined aromatic interactions prompt a red edge excitation shift effect inside the assemblies,inducing broad-spectrum fluorescence covering nearly the entire visible light region(400–650 nm).We show that bionic,superhydrophobic,pillar-like arrays with contact angles of up to about 170°can be engineered by aromatic interactions using a physical vapor deposition approach,which cannot be realized through hydrogen bonding.Our findings show the feasibility of 2D assembly with engineerable properties by modulating vectored non-covalent interactions.

Challenges on Induction of Broadly Neutralizing Antibodies for Optimization of HIV Vaccines Development and Vectored Immunoprophylaxis

Despite extensive research efforts, a preventive human immunodeficiency virus (HIV) vaccine remains one of the major challenges in the field of AIDS research. Experimental strategies which have been proven successful for other viral vaccines are not enough to tackle HIV-1 and new approaches to design effective preventive AIDS vaccines are of utmost importance. Due to enormous diversity among global circulating HIV strains, an effective HIV vaccine must elicit broadly protective antibodies based responses;therefore discovering new broadly neutralizing antibodies (bNAbs) against HIV has become major focus in HIV vaccine research. However further understanding of the viral targets of such antibodies and mechanisms of action of bNAbs is required for advancement of HIV vaccine research. This technical note discusses our current knowledge on the bNAbs and immunoprophylaxis using viral vectors with their relevance in designing of new candidates to HIV-1 vaccines.

Single-shot AAV-vectored vaccine against SARS-CoV-2 with fast and long-lasting immunity

Due to the insufficient long-term protection and significant efficacy reduction to new variants of current COVID-19 vaccines,the epidemic prevention and control are still challenging.Here,we employ a capsid and antigen structure engineering(CASE)strategy to manufacture an adenoassociated viral serotype 6-based vaccine(S663V-RBD),which expresses trimeric receptor binding domain(RBD)of spike protein fused with a biological adjuvant RS09.Impressively,the engineered S663V-RBD could rapidly induce a satisfactory RBD-specific IgG titer within 2 weeks and maintain the titer for more than 4 months.Compared to the licensed BBIBP-CorV(Sinopharm,China),a single-dose S663V-RBD induced more endurable and robust immune responses in mice and elicited superior neutralizing antibodies against three typical SARS-CoV-2 pseudoviruses including wild type,C.37(Lambda)and B.1.617.2(Delta).More interestingly,the intramuscular injection of S663V-RBD could overcome pre-existing immunity against the capsid.Given its effectiveness,the CASE-based S663VRBD may provide a new solution for the current and next pandemic.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

基于降维变换的低复杂度双基地EMVS-MIMO雷达高分辨多维参数估计

针对当前算法在实现双基地电磁矢量传感器多输入多输出(electromagnetic vector sensors multiple input multiple output,EMVS-MIMO)雷达的多维参数估计时计算代价较高的问题,通过利用降维变换技术来实现低复杂度的角度参数和极化参数求解。针对阵列接收数据维度较大问题,通过设计相应的波束空间变换矩阵来实现对阵列接收数据的降维处理。针对算法本身的较高计算复杂度问题,采用低计算复杂度的平行因子分解算法。所提算法能够精确地实现对发射因子矩阵和接收因子矩阵的求解。同时,通过新的旋转不变关系构建新的估计信号参数,可以实现对发射/接收俯仰角的求解。进一步,发射/接收方位角、发射/接收极化角和发射/接收极化相位差的估计可以通过发射/接收空间响应矩阵的重构来实现。仿真实验结果表明,所提算法在降低计算复杂度的同时能够保持优越的多维参数估计性能。

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

A solution method for decomposing vector fields in Hamilton energy

Hamilton energy,which reflects the energy variation of systems,is one of the crucial instruments used to analyze the characteristics of dynamical systems.Here we propose a method to deduce Hamilton energy based on the existing systems.This derivation process consists of three steps:step 1,decomposing the vector field;step 2,solving the Hamilton energy function;and step 3,verifying uniqueness.In order to easily choose an appropriate decomposition method,we propose a classification criterion based on the form of system state variables,i.e.,type-I vector fields that can be directly decomposed and type-II vector fields decomposed via exterior differentiation.Moreover,exterior differentiation is used to represent the curl of low-high dimension vector fields in the process of decomposition.Finally,we exemplify the Hamilton energy function of six classical systems and analyze the relationship between Hamilton energy and dynamic behavior.This solution provides a new approach for deducing the Hamilton energy function,especially in high-dimensional systems.

Biotin-modified Galactosylated Chitosan-gene Carrier in Hepatoma Cells Targeting Delivery

Our previous studies have successfully grafted biotin and galactose onto chitosan(CS)and synthesized biotin modified galactosylated chitosan(Bio-GC).The optimum N/P ratio of Bio-GC and plasmid DNA was 3:1.At this N/P ratio,the transfection efficiency in the hepatoma cells was the highest with a slow release effect.Bio-GC nanomaterials exhibit the protective effect of preventing the gene from nuclease degradation,and can target the transfection into hepatoma cells by combination with galactose and biotin receptors.The transfection rate was inhibited by the competition of galactose and biotin.Bio-GC nanomaterials were imported into cells’cytoplasm by their receptors,followed by the imported exogenous gene transfected into the cells.Bio-GC nanomaterials can also cause inhibitory activity in the hepatoma cells in the model of orthotopic liver transplantation in mice,by carrying the gene through the blood to the hepatoma tissue.Taken together,bio-GC nanomaterials act as gene vectors with the activity of protecting the gene from DNase degradation,improving the rate of transfection in hepatoma cells,and transporting the gene into the cytoplasm in vitro and in vivo.Therefore,they are efficient hepatoma-targeting gene carriers.

Tri-axial accelerometry allows to determine parental food provisioning behaviour in a marine bird

The study of parental food provisioning is essential for understanding the breeding ecology of birds.We conducted the first study using accelerometry to detect food provisioning in birds,using Support Vector Machine(SVM)models to identify when adults feed chicks of three different age classes.Accelerometers were attached to the head of adult female Imperial Shags(Leucocarbo atriceps),and various attributes derived from the acceleration signals were used to train SVM models for each chick age class.Model performance improved with chick age class,with SVM models achieving high overall accuracy(>88%)and highest sensitivity in older chick categories(>91%).However,precision values,especially for younger chicks,remained relatively low(between 26%and 45%).The application of a time filter based on the minimum duration of the observed food provisioning behaviours for each chick age category,improved model performance by reducing false provisioning behaviours,particularly in the model for older chicks,which showed the highest precision(72.4%).This study highlights the effectiveness of accelerometry and machine learning in studying parental food provisioning in birds,providing a rapid and accurate data collection method to complement traditional techniques.The described methodology can be applied to any bird species that exhibits distinctive movements while feeding its offspring and has suitable characteristics for attaching an accelerometer to the body part that best captures this movement.Finally,it is hoped that the results of this study will contribute to future research on key questions in parental investment theory and reproductive strategies in birds.

An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

Forward modelling of the Cotton-Mouton effect polarimetry on EAST tokamak

Measurement of plasma electron density by far-infrared laser polarimetry has become a routine and indispensable tool in magnetic confinement fusion research.This article presents the design of a Cotton-Mouton polarimeter interferometer,which provides a reliable density measurement without fringe jumps.Cotton-Mouton effect on Experimental Advanced Superconducting Tokamak(EAST)is studied by Stokes equation with three parameters(s_(1),s_(2),s_(3)).It demonstrates that under the condition of a small Cotton-Mouton effect,parameter s_(2)contains information about Cotton-Mouton effect which is proportional to the line-integrated density.For a typical EAST plasma,the magnitude of Cotton-Mouton effects is less than 2πfor laser wavelength of 432μm.Refractive effect due to density gradient is calculated to be negligible.Time modulation of Stokes parameters(s_(2),s_(3))provides heterodyne measurement.Due to the instabilities arising from laser oscillation and beam refraction in plasmas,it is necessary for the system to be insensitive to variations in the amplitude of the detection signal.Furthermore,it is shown that non-equal amplitude of X-mode and O-mode within a certain range only affects the DC offset of Stokes parameters(s_(2),s_(3))but does not greatly influence the phase measurements of Cotton-Mouton effects.

Compression and stretching of ring vortex in a bulk nonlinear medium

We explore the nonlinear gain coupled Schrödinger system through the utilization of the variables separation method and ansatz technique.By employing these approaches,we generate hierarchies of explicit dissipative vector vortices(DVVs)that possess diverse vorticity values.Numerous fundamental characteristics of the DVVs are examined,encompassing amplitude profiles,energy fluxes,parameter effects,as well as linear and dynamic stability.

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Diffraction deep neural network-based classification for vector vortex beams

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications.However,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the receiver.In this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN.The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks.The energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of turbulence.It has a faster convergence and better accuracy than that based on a convolutional neural network.

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.