Swine acute diarrhea syndrome coronavirus(SADS-CoV),an emerging bat-origin Alphacoronavirus with demonstrated zoonotic potential,poses a significant threat to swine health and has considerable economic implications.Cu...Swine acute diarrhea syndrome coronavirus(SADS-CoV),an emerging bat-origin Alphacoronavirus with demonstrated zoonotic potential,poses a significant threat to swine health and has considerable economic implications.Currently,no licensed vaccines are available.We constructed a replication-deficient human adenovirus type 5(Ad5)vectored vaccine candidate,rAd5-SADS-S,which ex-presses the SADS-CoV spike(S)glycoprotein.The rAd5-SADS-S vaccine elicited robust SADS-CoV-specific humoral immunity and potent cellular responses in both mice and pigs.Notably,rAd5-SADS-S conferred passive protection to neonatal mice against lethal SADS-CoV challenge.These findings establish a preclinical foundation for the development of SADS-CoV vaccines.展开更多
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 wh...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.展开更多
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 validat...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.展开更多
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 as...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.展开更多
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 ...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.展开更多
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 prote...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.展开更多
SARS-CoV-2 has caused more than 3.8 million deaths worldwide,and several types of COVID-19 vaccines are urgently approved for use,including adenovirus vectored vaccines.However,the thermal instability and pre-existing...SARS-CoV-2 has caused more than 3.8 million deaths worldwide,and several types of COVID-19 vaccines are urgently approved for use,including adenovirus vectored vaccines.However,the thermal instability and pre-existing immunity have limited its wide applications.To circumvent these obstacles,we constructed a self-biomineralized adenovirus vectored COVID-19 vaccine(Sad23L-n Co V-S-Ca P)by generating a calcium phosphate mineral exterior(Ca P)based on Sad23L vector carrying the full-length gene of SARS-Co V-2 spike protein(S)under physiological condition.This Sad23L-n Co V-S-Ca P vaccine was examined for its characteristics of structure,thermostability,immunogenicity and avoiding the problem of preexisting immunity.In thermostability test,Sad23L-n Co V-S-Ca P could be stored at 4°C for over 45 days,26°C for more than 8 days and 37°C for approximately 2 days.Furthermore,Sad23L-n Co V-S-Ca P induced higher level of S-specific antibody and T cell responses,and was not affected by the pre-existing anti-Sad23L immunity,suggesting it could be used as boosting immunization on Sad23L-n Co V-S priming vaccination.The boosting with Sad23L-n Co V-S-Ca P vaccine induced high titers of 10^(5.01)anti-S1,10^(4.77)anti-S2 binding antibody,10^(3.04) pseudovirus neutralizing antibody(IC_(50)),and robust T-cell response of IFN-c(1466.16 SFCs/10^(6)cells)to S peptides,respectively.In summary,the selfbiomineralization of the COVID-19 vaccine Sad23L-n Co V-S-Ca P improved vaccine efficacy,which could be used in prime-boost regimen for prevention of SARS-Co V-2 infection in humans.展开更多
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme ...Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.展开更多
In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative...In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
Dengue fever is an acute infectious disease caused by the dengue virus and transmitted by mosquito vectors[1].Its clinical manifestations include high fever,headache,muscle and joint pain,and rash.It holds a significa...Dengue fever is an acute infectious disease caused by the dengue virus and transmitted by mosquito vectors[1].Its clinical manifestations include high fever,headache,muscle and joint pain,and rash.It holds a significant position in global public health.In recent years,its incidence has continued to rise worldwide[2],making it one of the major diseases threatening human health.The disease course of dengue fever is divided into three typical phases:the acute febrile phase,the critical phase,and the recovery phase.While most patients experience mild symptoms,some may progress to severe dengue and potentially fatal outcomes if not promptly and effectively treated during the critical phase.展开更多
Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates dise...Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.展开更多
Disruption of host physiological processes,leading to symptom expression,is a common hallmark during plant virus infections.The concept of“symptoms as strategy”is rapidly reshaping our understanding of plant virolog...Disruption of host physiological processes,leading to symptom expression,is a common hallmark during plant virus infections.The concept of“symptoms as strategy”is rapidly reshaping our understanding of plant virology.An emerging theme is that symptom expressions—such as stunting,curling,and yellowing,which devastate yield—may themselves be evolved viral adaptation strategies rather than collateral damage.展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
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 antige...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.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtain...Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtaining their"fingerprint information"remains a huge obstacle.Herein,we developed a tip-enhanced Raman scattering(TERS)technique to address this challenge.Adopting an optical fiber radial vector mode internally illuminates the plasmonic fiber tip to effectively suppress the background noise while generating a strong electric-field enhanced tip hotspot.Furthermore,the tip hotspot approaching the glucose molecules was manipulated via the shear-force feedback to provide more freedom for selecting substrates.Consequently,our TERS technique achieves the visualization of all Raman modes of glucose molecules within spectral window of 400-3200 cm^(-1),which is not achievable through the far-field/surface-enhanced Raman,or the existing TERS techniques.Our TERS technique offers a powerful tool for accurately identifying Raman scattering of molecules,paving the way for biomolecular analysis.展开更多
基金supported by grants from the National Natural Science Foundation of China(32473022)awarded to Q.W.the Major Science and Technology Project of Gansu Province(23ZDNA007)awarded to Q.W.+3 种基金the State Key Laboratory for Animal Disease Control and Prevention(SKLADCPKFKT202410)awarded to Y.Z.the Distinguished Young Scholars of Chinese Academy of Agricultural Sciences(CAAS)awarded to Q.WBasic Research Center,the Innovation Program of Chinese Academy of Agricultural Sciences(CAAS BRC-LPDC-2025-01)awarded to Q.Wthe Central Public-interest Scientific Institution Basal Research Fund(S2023002)awarded to Y.Z.
文摘Swine acute diarrhea syndrome coronavirus(SADS-CoV),an emerging bat-origin Alphacoronavirus with demonstrated zoonotic potential,poses a significant threat to swine health and has considerable economic implications.Currently,no licensed vaccines are available.We constructed a replication-deficient human adenovirus type 5(Ad5)vectored vaccine candidate,rAd5-SADS-S,which ex-presses the SADS-CoV spike(S)glycoprotein.The rAd5-SADS-S vaccine elicited robust SADS-CoV-specific humoral immunity and potent cellular responses in both mice and pigs.Notably,rAd5-SADS-S conferred passive protection to neonatal mice against lethal SADS-CoV challenge.These findings establish a preclinical foundation for the development of SADS-CoV vaccines.
基金supported by National Hi-tech Research and Development Program of China(863 Program, Grant No. 2006AA09Z235)Hunan Provincial Innovation Foundation For Postgraduate of China(Grant No. CX2009B003)
文摘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.
文摘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.
基金supported by the Fund for Creative Research Groups of National Natural Science Foundation of China (No. 51821093)the National Natural Science Foundation of China (Nos. 52175551, 52075484)(KT and DM)+2 种基金the National Key Research and Development Program (SQ2021YFE010405)(KT)Science Foundation Ireland (SFI) through awards Nos. 15/CDA/3491and 12/RC/2275_P2 (DT)computing resources at the SFI/Higher Education Authority Irish Center for High-End Computing (ICHEC)(SG and DT)
文摘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.
文摘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.
基金supported by the earmarked fund for China Agriculture Research System(CARS-40)the Key Research and Development Project of Yangzhou(Modern Agriculture),China(YZ2022052)the‘‘High-end Talent Support Program’’of Yangzhou University,China。
文摘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.
基金supported by the grants from the National Natural Science Foundation of China(No.32070929,81871655 and 31770185)the Special Funding for COVID-19 Prevention and Control of China(2020M670013ZX)+1 种基金the China Postdoctoral Science Foundation(2021M691474)Guangzhou Bai Rui Kang(BRK)Biological Science and Technology Limited Company,China。
文摘SARS-CoV-2 has caused more than 3.8 million deaths worldwide,and several types of COVID-19 vaccines are urgently approved for use,including adenovirus vectored vaccines.However,the thermal instability and pre-existing immunity have limited its wide applications.To circumvent these obstacles,we constructed a self-biomineralized adenovirus vectored COVID-19 vaccine(Sad23L-n Co V-S-Ca P)by generating a calcium phosphate mineral exterior(Ca P)based on Sad23L vector carrying the full-length gene of SARS-Co V-2 spike protein(S)under physiological condition.This Sad23L-n Co V-S-Ca P vaccine was examined for its characteristics of structure,thermostability,immunogenicity and avoiding the problem of preexisting immunity.In thermostability test,Sad23L-n Co V-S-Ca P could be stored at 4°C for over 45 days,26°C for more than 8 days and 37°C for approximately 2 days.Furthermore,Sad23L-n Co V-S-Ca P induced higher level of S-specific antibody and T cell responses,and was not affected by the pre-existing anti-Sad23L immunity,suggesting it could be used as boosting immunization on Sad23L-n Co V-S priming vaccination.The boosting with Sad23L-n Co V-S-Ca P vaccine induced high titers of 10^(5.01)anti-S1,10^(4.77)anti-S2 binding antibody,10^(3.04) pseudovirus neutralizing antibody(IC_(50)),and robust T-cell response of IFN-c(1466.16 SFCs/10^(6)cells)to S peptides,respectively.In summary,the selfbiomineralization of the COVID-19 vaccine Sad23L-n Co V-S-Ca P improved vaccine efficacy,which could be used in prime-boost regimen for prevention of SARS-Co V-2 infection in humans.
基金supported by grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
文摘Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.
文摘In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
文摘Dengue fever is an acute infectious disease caused by the dengue virus and transmitted by mosquito vectors[1].Its clinical manifestations include high fever,headache,muscle and joint pain,and rash.It holds a significant position in global public health.In recent years,its incidence has continued to rise worldwide[2],making it one of the major diseases threatening human health.The disease course of dengue fever is divided into three typical phases:the acute febrile phase,the critical phase,and the recovery phase.While most patients experience mild symptoms,some may progress to severe dengue and potentially fatal outcomes if not promptly and effectively treated during the critical phase.
基金the Begum Rokeya University,Rangpur,and the United Arab Emirates University,UAE for partially supporting this work。
文摘Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.
基金supported by the National Natural Science Foundation of China(32272482)the Innovation Research 2035 Pilot Plan of Southwest University(SWU-XDZD22002).
文摘Disruption of host physiological processes,leading to symptom expression,is a common hallmark during plant virus infections.The concept of“symptoms as strategy”is rapidly reshaping our understanding of plant virology.An emerging theme is that symptom expressions—such as stunting,curling,and yellowing,which devastate yield—may themselves be evolved viral adaptation strategies rather than collateral damage.
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金the National Natural Science Foundation of China,China(Grant Nos.81925036 and 81872814)the Key Research and Development Program of Science and Technology Department of Sichuan Province,China(Grant No.2020YFS0570)+1 种基金111 project,China(Grant No.B18035)the Fundamental Research Funds for the Central Universities,China。
文摘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.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by National Natural Science Foundation of China(12374358,91950207)Guangdong Basic and Applied Basic Research Foundation(2024A1515010420).
文摘Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtaining their"fingerprint information"remains a huge obstacle.Herein,we developed a tip-enhanced Raman scattering(TERS)technique to address this challenge.Adopting an optical fiber radial vector mode internally illuminates the plasmonic fiber tip to effectively suppress the background noise while generating a strong electric-field enhanced tip hotspot.Furthermore,the tip hotspot approaching the glucose molecules was manipulated via the shear-force feedback to provide more freedom for selecting substrates.Consequently,our TERS technique achieves the visualization of all Raman modes of glucose molecules within spectral window of 400-3200 cm^(-1),which is not achievable through the far-field/surface-enhanced Raman,or the existing TERS techniques.Our TERS technique offers a powerful tool for accurately identifying Raman scattering of molecules,paving the way for biomolecular analysis.
