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
Chimeric antigen receptor natural killer(CAR-NK)cell therapy is an alternative immunotherapy that provides robust tumor-eliminating effects without inducing life-threatening toxicities and graft-versus-host disease.CA...Chimeric antigen receptor natural killer(CAR-NK)cell therapy is an alternative immunotherapy that provides robust tumor-eliminating effects without inducing life-threatening toxicities and graft-versus-host disease.CAR-NK cell therapy has enabled the development of“off-the-shelf”products that bypass the lengthy and expensive cell manufacturing process1.展开更多
Objective:To collate and summarize phenotypic insecticide susceptibility data of Indian malaria vectors from 2017 to 2024,focusing on insecticides used in adult vector control,dichloro diphenyl trichloroethane,malathi...Objective:To collate and summarize phenotypic insecticide susceptibility data of Indian malaria vectors from 2017 to 2024,focusing on insecticides used in adult vector control,dichloro diphenyl trichloroethane,malathion,deltamethrin,alpha-cypermethrin,and permethrin to identify resistance patterns to different classes of insecticides.Methods:The data included information on vector species,location of the study(state/district),insecticide tested,mortality percentage,and susceptibility classification based on the World Health Organization interpretation criteria.Retrospective data were collected from peer-reviewed publications(2017-2024)and up to June 2025.The data were collated for five major malaria vector species,namely Anopheles(An.)culicifacies,An.fluviatilis,An.stephensi,An.baimaii,and An.minimus.Results:Insecticide susceptibility data were available from 86 districts across 16 Indian states for 40615 mosquitoes.The majority of the data was on An.culicifacies(n=28308),followed by An.stephensi(n=5611),An.fluviatilis(n=5967),An.baimaii(n=365),and An.minimus(n=364).Intensity bioassays revealed low to moderate resistance levels in An.culicifacies populations from selected districts in 3 states,Odisha,Madhya Pradesh,and Chhattisgarh against deltamethrin and alpha-cypermethrin.Conclusions:This review highlights spatial and species-level variations in insecticide susceptibility among Indian malaria vectors.The low to moderate intensity suggested that it may not yet be severe enough to cause operational failure with current vector control interventions.Continued monitoring of insecticide resistance,as well as the use of new-generation insecticides and interventions,is suggested to sustain vector control efficacy and manage insecticide resistance in malaria vectors to support India’s malaria elimination.展开更多
Although the Chinese new-generation Fengyun-4B(FY-4B) geostationary satellite Atmospheric Motion Vector(AMV) products became operational in June 2022, their accuracy and utility remain largely unexamined. This study c...Although the Chinese new-generation Fengyun-4B(FY-4B) geostationary satellite Atmospheric Motion Vector(AMV) products became operational in June 2022, their accuracy and utility remain largely unexamined. This study comprehensively evaluates FY-4B AMV products for August and October 2023, as well as January and April 2024,exploring their application in monitoring the South China Sea Summer Monsoon(SCSSM) onset. The results indicate that AMV products derived from the upper-level water vapor absorption channel(AMV_WV) and the infrared channel(AMV_IR) demonstrate high accuracy when compared with ERA5 reanalysis data. The root mean square error(RMSE) is mostly between 4.5 m s^(–1)and 6.4 m s^(–1), with coefficients of determination(R2) values ranging from 0.7 to 0.8, indicating the overall reliability of FY-4B AMVs. The observation errors of AMVs exhibit significant vertical structure characteristics. Specifically, the AMV_WV products demonstrate superior accuracy above 350 h Pa, while the AMV_IR products exhibit reduced errors in the layers between 200–500 h Pa and 700–950 h Pa. Spatially, most areas exhibit low observation errors for AMVs, while clear-sky weather and deep convective cloud systems can increase errors. A lack of clouds or water vapor may reduce the number of observation samples in some areas, leading to unstable RMSE performance, which is particularly evident for AMV_WV RMSE around 25°–30°N in January and near 25°S in August. Deep convective cloud systems can influence AMV retrieval results, leading to systematic observation errors, especially for the infrared channel.Additionally, AMV_WV is more reliable during the daytime, with a lower RMSE compared to nighttime, while AMV_IR exhibits a diverging diurnal variation pattern. Finally, the FY-4B AMV_WV products were applied to monitor the SCSSM event in 2024. Significant zonal wind direction reversal characteristics were observed in key regions around the onset date,indicating that AMVs can serve as effective indicators for monitoring the SCSSM onset.展开更多
In this paper,we shall study structures of even lattice vertex operator algebras by using the geometry of the varieties of their semi-conformal vectors.We first give the varieties of semi-conformal vectors of a family...In this paper,we shall study structures of even lattice vertex operator algebras by using the geometry of the varieties of their semi-conformal vectors.We first give the varieties of semi-conformal vectors of a family of vertex operator algebras V_(√kA_(1)) associated to rank-one positive definite even lattices √kA_(1) for arbitrary positive integers k to characterize these even lattice vertex operator algebras.In such a family of lattice vertex operator algebras V_(√kA_(1)),the vertex operator algebra V_(√2A_(1)) is different from others.Hence we describe the varieties of semi-conformal vectors of V_(√2A_(1)) and the fixed vertex operator subalgebra V^(+)√2A_(1).Moreover,as applications,we study the relations between vertex operator algebras V_(√kA_(1) )and L_(sl_(2))(k,0)for arbitrary positive integers k by the viewpoint of semi-conformal homomorphisms of vertex operator algebras.For case k=2,in the series of rational simple affine vertex operator algebras L_(sl_(2))(k,0)for positive integers k,we show that L_(sl_(2))(2,0)is a unique frame vertex operator algebra with rank 3.展开更多
Intervertebral disc degeneration is a leading cause of lower back pain and is characterized by pathological processes such as nucleus pulposus cell apoptosis,extracellular matrix imbalance,and annulus fibrosus rupture...Intervertebral disc degeneration is a leading cause of lower back pain and is characterized by pathological processes such as nucleus pulposus cell apoptosis,extracellular matrix imbalance,and annulus fibrosus rupture.These pathological changes result in disc height loss and functional decline,potentially leading to disc herniation.This comprehensive review aimed to address the current challenges in intervertebral disc degeneration treatment by evaluating the regenerative potential of stem cell-based therapies,with a particular focus on emerging technologies such as exosomes and gene vector systems.Through mechanisms such as differentiation,paracrine effects,and immunomodulation,stem cells facilitate extracellular matrix repair and reduce nucleus pulposus cell apoptosis.Despite recent advancements,clinical applications are hindered by challenges such as hypoxic disc environments and immune rejection.By analyzing recent preclinical and clinical findings,this review provided insights into optimizing stem cell therapy to overcome these obstacles and highlighted future directions in the field.展开更多
This paper presents an improved,energy-efficient Model Predictive Current Control(MPCC)strategy based on centroid-based virtual voltage vector synthesis for three-phase inverter-fed induction motor drives in electric ...This paper presents an improved,energy-efficient Model Predictive Current Control(MPCC)strategy based on centroid-based virtual voltage vector synthesis for three-phase inverter-fed induction motor drives in electric vehicle(EV)applications.Unlike conventional finite-set MPCC methods that rely on cost function evaluation over discrete switching states,the proposed approach eliminates the need for look-up tables by employing a pre-defined set of virtual vectors.These centroid-based virtual voltage vectors are synthesized by combining two adjacent active vectors and two nonzero voltage vectors in opposite directions adjacent to the sector replacing the traditional switching set.They approximate the reference voltage vector in both magnitude and phase angle,thereby reducing current tracking error through a simplified cost function.The number of candidate vectors is reduced,preserving computational efficiency.Furthermore,the scheme ensures zero average common-mode voltage(CMV)per sampling interval by completely avoiding zero-voltage vectors(ZVVs).The proposed method reduces torque ripple by up to 17%compared to the conventional approach and lowers stator current total harmonic distortion(THD)by 37%,while ensuring evenly distributed switching transitions among inverter legs.This results in reduced switching losses and enhanced drive efficiency-particularly advantageous in EV applications.Experimental validation under the high-speed extra urban driving cycle(EUDC)and low-speed ECE-R15 cycle,including torque ripple and energy consumption analysis,confirms the effectiveness of the approach,achieving an overall efficiency of 83.3%.展开更多
[Objective] The aim was to study the construction and expression of eukaryotic expression vectors of antibacterial peptides （mytilin and myticin） from Mytilus coruscus.[Method] By the screening of antibacterial pept...[Objective] The aim was to study the construction and expression of eukaryotic expression vectors of antibacterial peptides （mytilin and myticin） from Mytilus coruscus.[Method] By the screening of antibacterial peptide genes of mytilin and myticin of Mytilus coruscus,five antibacterial peptide genes were selected.Then,the relative eukaryotic expressing vectors were constructed by the use of PCR technique and DNA recombinant technology.Subsequently,they were transferred in to S78 Saccharomyces cerevisia by using LiAC transformation method,and then preliminary expressing analysis was carried out.[Result] Five eukaryotic expressing vectors of antibacterial peptides from Mytilus coruscus were successfully constructed,and the results of mRNA detection revealed that the five antibacterial peptides from Mytilus coruscus were successfully transcribed.[Conclusion] The results provide a basis for using genetic engineering to express antibacterial peptides of mytilin and myticin from Mytilus coruscus,and for developing the further study of antibacterial peptides from Mytilus coruscus based on this.展开更多
Delta-12 oleate desaturase gene （FAD2-1） which converts oleic acid into linoleic acid, is the key enzyme determining the fatty acid composition of cottonseed oil. By employing RT-PCR method, full length cDNA of cott...Delta-12 oleate desaturase gene （FAD2-1） which converts oleic acid into linoleic acid, is the key enzyme determining the fatty acid composition of cottonseed oil. By employing RT-PCR method, full length cDNA of cotton delta-12 oleate desat- urase gene GhFAD2-1 containing an open reading frame of 1 158 bp was cloned for constructing RNAi vector. A 515 bp long specific fragment of this gene was se- lected for constructing ihpRNA vector under the control of a seed-specific promoter NAPIN, named pFGC1008-NAPIN-FAD2-1; meanwhile miRNA gene-silencing vector pCAMBIA1302-amiRNA-FAD2-1 targeting GhFAD2-1 was also constructed.展开更多
[Objective] To construct prokaryotic expression vectors encoding gene Erb3binding protein （EBP1）, which plays important roles in regulating plant organ size from Nervilia fordii （Hance） Schltr. [Methods] PCR produ...[Objective] To construct prokaryotic expression vectors encoding gene Erb3binding protein （EBP1）, which plays important roles in regulating plant organ size from Nervilia fordii （Hance） Schltr. [Methods] PCR products of NfEBP1 with particular restriction sites and expression vectors, pET-28 and pET-16b were digested. Ligation, transformation and selection were performed to construct the recombinant plasmids pET-28-NfEBP1 and pET-16-NfEBP1. The recombinant plasmids were transformed into E. coli BL21 using heat -shock transformation. [Results] Recombinant plasmids pET-28-NfEBP1-1188 and pET-16-NfEBP1-1188 were constructed and transformed into expressional host cells, E. coli BL21, and validated by colony PCR, sequencing and double digestion. [Conclusion] Prokaryotic expression vectors of EBP1 gene from N. fordii were successfully constructed, which laid the foundation for characterization of the gene function.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
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
The implementation of ICT （information and communication technologies） into the educational process is becoming a reality in the 21st century. Today＇s students grow up with technology. To keep their attention, scie...The implementation of ICT （information and communication technologies） into the educational process is becoming a reality in the 21st century. Today＇s students grow up with technology. To keep their attention, scientific problems should be solved through visualization, which is made possible using ICT in the educational process. In the modem educational process, students still have difficulties in learning science concepts. Also, it is a very common problem that students cannot apply mathematical language and concepts into other science areas such as physics, engineering, etc. For example, students start learning about vectors in mathematics in secondary school. Vectors are very important because they have a wide area of applications especially in physics, engineering and navigation to represent forces, tension, velocity, etc.. Using the free mathematical software GeoGebra, a simulation of using vectors in these areas is made. It will be shown that such simulations increase students＇ interest, keep their attention, and make this knowledge more real and more understandable and connected to the physical world and thus more applicable to their other studies.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
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.
基金supported by grants from the Noncommunicable Chronic Diseases-National Science and Technology Major Project(Grant No.2023ZD0501300)Science Technology Department of Zhejiang Province(Grant No.2021C03117)+2 种基金National Natural Science Foundation of China(Grant No.82350104 and 82170219)Natural Science Foundation of Zhejiang Province,China(Grant No.LY23H080004 and LY24H080001)Medical Health Science and Technology Project of Zhejiang Provincial Health Commission(Grant No.2021KY199)。
文摘Chimeric antigen receptor natural killer(CAR-NK)cell therapy is an alternative immunotherapy that provides robust tumor-eliminating effects without inducing life-threatening toxicities and graft-versus-host disease.CAR-NK cell therapy has enabled the development of“off-the-shelf”products that bypass the lengthy and expensive cell manufacturing process1.
文摘Objective:To collate and summarize phenotypic insecticide susceptibility data of Indian malaria vectors from 2017 to 2024,focusing on insecticides used in adult vector control,dichloro diphenyl trichloroethane,malathion,deltamethrin,alpha-cypermethrin,and permethrin to identify resistance patterns to different classes of insecticides.Methods:The data included information on vector species,location of the study(state/district),insecticide tested,mortality percentage,and susceptibility classification based on the World Health Organization interpretation criteria.Retrospective data were collected from peer-reviewed publications(2017-2024)and up to June 2025.The data were collated for five major malaria vector species,namely Anopheles(An.)culicifacies,An.fluviatilis,An.stephensi,An.baimaii,and An.minimus.Results:Insecticide susceptibility data were available from 86 districts across 16 Indian states for 40615 mosquitoes.The majority of the data was on An.culicifacies(n=28308),followed by An.stephensi(n=5611),An.fluviatilis(n=5967),An.baimaii(n=365),and An.minimus(n=364).Intensity bioassays revealed low to moderate resistance levels in An.culicifacies populations from selected districts in 3 states,Odisha,Madhya Pradesh,and Chhattisgarh against deltamethrin and alpha-cypermethrin.Conclusions:This review highlights spatial and species-level variations in insecticide susceptibility among Indian malaria vectors.The low to moderate intensity suggested that it may not yet be severe enough to cause operational failure with current vector control interventions.Continued monitoring of insecticide resistance,as well as the use of new-generation insecticides and interventions,is suggested to sustain vector control efficacy and manage insecticide resistance in malaria vectors to support India’s malaria elimination.
基金Science and Technology Planning Project of Guangdong Province (2023B1212060019)Natural Science Foundation of China (42175086)Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)(SML2023SP208)。
文摘Although the Chinese new-generation Fengyun-4B(FY-4B) geostationary satellite Atmospheric Motion Vector(AMV) products became operational in June 2022, their accuracy and utility remain largely unexamined. This study comprehensively evaluates FY-4B AMV products for August and October 2023, as well as January and April 2024,exploring their application in monitoring the South China Sea Summer Monsoon(SCSSM) onset. The results indicate that AMV products derived from the upper-level water vapor absorption channel(AMV_WV) and the infrared channel(AMV_IR) demonstrate high accuracy when compared with ERA5 reanalysis data. The root mean square error(RMSE) is mostly between 4.5 m s^(–1)and 6.4 m s^(–1), with coefficients of determination(R2) values ranging from 0.7 to 0.8, indicating the overall reliability of FY-4B AMVs. The observation errors of AMVs exhibit significant vertical structure characteristics. Specifically, the AMV_WV products demonstrate superior accuracy above 350 h Pa, while the AMV_IR products exhibit reduced errors in the layers between 200–500 h Pa and 700–950 h Pa. Spatially, most areas exhibit low observation errors for AMVs, while clear-sky weather and deep convective cloud systems can increase errors. A lack of clouds or water vapor may reduce the number of observation samples in some areas, leading to unstable RMSE performance, which is particularly evident for AMV_WV RMSE around 25°–30°N in January and near 25°S in August. Deep convective cloud systems can influence AMV retrieval results, leading to systematic observation errors, especially for the infrared channel.Additionally, AMV_WV is more reliable during the daytime, with a lower RMSE compared to nighttime, while AMV_IR exhibits a diverging diurnal variation pattern. Finally, the FY-4B AMV_WV products were applied to monitor the SCSSM event in 2024. Significant zonal wind direction reversal characteristics were observed in key regions around the onset date,indicating that AMVs can serve as effective indicators for monitoring the SCSSM onset.
基金Supported by National Natural Science Foundation of China(Grant No.12475002).
文摘In this paper,we shall study structures of even lattice vertex operator algebras by using the geometry of the varieties of their semi-conformal vectors.We first give the varieties of semi-conformal vectors of a family of vertex operator algebras V_(√kA_(1)) associated to rank-one positive definite even lattices √kA_(1) for arbitrary positive integers k to characterize these even lattice vertex operator algebras.In such a family of lattice vertex operator algebras V_(√kA_(1)),the vertex operator algebra V_(√2A_(1)) is different from others.Hence we describe the varieties of semi-conformal vectors of V_(√2A_(1)) and the fixed vertex operator subalgebra V^(+)√2A_(1).Moreover,as applications,we study the relations between vertex operator algebras V_(√kA_(1) )and L_(sl_(2))(k,0)for arbitrary positive integers k by the viewpoint of semi-conformal homomorphisms of vertex operator algebras.For case k=2,in the series of rational simple affine vertex operator algebras L_(sl_(2))(k,0)for positive integers k,we show that L_(sl_(2))(2,0)is a unique frame vertex operator algebra with rank 3.
基金Supported by Henan Province Key Research and Development Program,No.231111311000Henan Provincial Science and Technology Research Project,No.232102310411+2 种基金Henan Province Medical Science and Technology Key Project,No.LHGJ20220566 and No.LHGJ20240365Henan Province Medical Education Research Project,No.WJLX2023079Zhengzhou Medical and Health Technology Innovation Guidance Program,No.2024YLZDJH022.
文摘Intervertebral disc degeneration is a leading cause of lower back pain and is characterized by pathological processes such as nucleus pulposus cell apoptosis,extracellular matrix imbalance,and annulus fibrosus rupture.These pathological changes result in disc height loss and functional decline,potentially leading to disc herniation.This comprehensive review aimed to address the current challenges in intervertebral disc degeneration treatment by evaluating the regenerative potential of stem cell-based therapies,with a particular focus on emerging technologies such as exosomes and gene vector systems.Through mechanisms such as differentiation,paracrine effects,and immunomodulation,stem cells facilitate extracellular matrix repair and reduce nucleus pulposus cell apoptosis.Despite recent advancements,clinical applications are hindered by challenges such as hypoxic disc environments and immune rejection.By analyzing recent preclinical and clinical findings,this review provided insights into optimizing stem cell therapy to overcome these obstacles and highlighted future directions in the field.
文摘This paper presents an improved,energy-efficient Model Predictive Current Control(MPCC)strategy based on centroid-based virtual voltage vector synthesis for three-phase inverter-fed induction motor drives in electric vehicle(EV)applications.Unlike conventional finite-set MPCC methods that rely on cost function evaluation over discrete switching states,the proposed approach eliminates the need for look-up tables by employing a pre-defined set of virtual vectors.These centroid-based virtual voltage vectors are synthesized by combining two adjacent active vectors and two nonzero voltage vectors in opposite directions adjacent to the sector replacing the traditional switching set.They approximate the reference voltage vector in both magnitude and phase angle,thereby reducing current tracking error through a simplified cost function.The number of candidate vectors is reduced,preserving computational efficiency.Furthermore,the scheme ensures zero average common-mode voltage(CMV)per sampling interval by completely avoiding zero-voltage vectors(ZVVs).The proposed method reduces torque ripple by up to 17%compared to the conventional approach and lowers stator current total harmonic distortion(THD)by 37%,while ensuring evenly distributed switching transitions among inverter legs.This results in reduced switching losses and enhanced drive efficiency-particularly advantageous in EV applications.Experimental validation under the high-speed extra urban driving cycle(EUDC)and low-speed ECE-R15 cycle,including torque ripple and energy consumption analysis,confirms the effectiveness of the approach,achieving an overall efficiency of 83.3%.
基金Supported by Agricultural Research Project of Science and Technology Department of Zhejiang Province(2008C22026,2009C32016)Open Topic of Key Open Laboratory of Marine and Estuarine Fishery Resources and Ecology, Ministry of Agriculture ( Open-09-10 )+1 种基金Technology Bureau Project of Zhoushan City (Y20082080)Innovative Business Incubation Program of College Students~~
文摘[Objective] The aim was to study the construction and expression of eukaryotic expression vectors of antibacterial peptides （mytilin and myticin） from Mytilus coruscus.[Method] By the screening of antibacterial peptide genes of mytilin and myticin of Mytilus coruscus,five antibacterial peptide genes were selected.Then,the relative eukaryotic expressing vectors were constructed by the use of PCR technique and DNA recombinant technology.Subsequently,they were transferred in to S78 Saccharomyces cerevisia by using LiAC transformation method,and then preliminary expressing analysis was carried out.[Result] Five eukaryotic expressing vectors of antibacterial peptides from Mytilus coruscus were successfully constructed,and the results of mRNA detection revealed that the five antibacterial peptides from Mytilus coruscus were successfully transcribed.[Conclusion] The results provide a basis for using genetic engineering to express antibacterial peptides of mytilin and myticin from Mytilus coruscus,and for developing the further study of antibacterial peptides from Mytilus coruscus based on this.
文摘Delta-12 oleate desaturase gene （FAD2-1） which converts oleic acid into linoleic acid, is the key enzyme determining the fatty acid composition of cottonseed oil. By employing RT-PCR method, full length cDNA of cotton delta-12 oleate desat- urase gene GhFAD2-1 containing an open reading frame of 1 158 bp was cloned for constructing RNAi vector. A 515 bp long specific fragment of this gene was se- lected for constructing ihpRNA vector under the control of a seed-specific promoter NAPIN, named pFGC1008-NAPIN-FAD2-1; meanwhile miRNA gene-silencing vector pCAMBIA1302-amiRNA-FAD2-1 targeting GhFAD2-1 was also constructed.
基金Supported by Research Fund of the Doctoral Program of Higher Education (200805720004)Scientific Research Foundation for Returned Scholars, Ministry of Education of China ([2009]1001)~~
文摘[Objective] To construct prokaryotic expression vectors encoding gene Erb3binding protein （EBP1）, which plays important roles in regulating plant organ size from Nervilia fordii （Hance） Schltr. [Methods] PCR products of NfEBP1 with particular restriction sites and expression vectors, pET-28 and pET-16b were digested. Ligation, transformation and selection were performed to construct the recombinant plasmids pET-28-NfEBP1 and pET-16-NfEBP1. The recombinant plasmids were transformed into E. coli BL21 using heat -shock transformation. [Results] Recombinant plasmids pET-28-NfEBP1-1188 and pET-16-NfEBP1-1188 were constructed and transformed into expressional host cells, E. coli BL21, and validated by colony PCR, sequencing and double digestion. [Conclusion] Prokaryotic expression vectors of EBP1 gene from N. fordii were successfully constructed, which laid the foundation for characterization of the gene function.
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.
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The implementation of ICT （information and communication technologies） into the educational process is becoming a reality in the 21st century. Today＇s students grow up with technology. To keep their attention, scientific problems should be solved through visualization, which is made possible using ICT in the educational process. In the modem educational process, students still have difficulties in learning science concepts. Also, it is a very common problem that students cannot apply mathematical language and concepts into other science areas such as physics, engineering, etc. For example, students start learning about vectors in mathematics in secondary school. Vectors are very important because they have a wide area of applications especially in physics, engineering and navigation to represent forces, tension, velocity, etc.. Using the free mathematical software GeoGebra, a simulation of using vectors in these areas is made. It will be shown that such simulations increase students＇ interest, keep their attention, and make this knowledge more real and more understandable and connected to the physical world and thus more applicable to their other studies.
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.
