Avian metapneumovirus(aMPV),a paramyxovirus,causes acute respiratory diseases in turkeys and swollen head syndrome in chickens.This study established a reverse genetics system for aMPV subtype B LN16-A strain based on...Avian metapneumovirus(aMPV),a paramyxovirus,causes acute respiratory diseases in turkeys and swollen head syndrome in chickens.This study established a reverse genetics system for aMPV subtype B LN16-A strain based on T7 RNA polymerase.Full-length cDNA of the LN16-A strain was constructed by assembling 5 cDNA fragments between the T7 promoter and hepatitis delta virus ribozyme.Transfection of this plasmid,along with the supporting plasmids encoding the N,P,M2-1,and L proteins of LN16-A into BSR-T7/5 cells,resulted in the recovery of aMPV subtype B.To identify an effective insertion site,the enhanced green fluorescent protein(EGFP)gene was inserted into different sites of the LN16-A genome to generate recombinant LN16-As.The results showed that the expression levels of EGFP at the site between the G and L genes of LN16-A were significantly higher than those at the other two sites(between the leader and N genes or replacing the SH gene).To verify the availability of the site between G and L for foreign gene expression,the VP2 gene of very virulent infectious bursal disease virus(vvIBDV)was inserted into this site,and recombinant LN16-A(rLN16A-vvVP2)was successfully rescued.Single immunization of specificpathogen-free chickens with rLN16A-vvVP2 induced high levels of neutralizing antibodies and provided 100%protection against the virulent aMPV subtype B and vvIBDV.Establishing a reverse genetics system here provides an important foundation for understanding aMPV pathogenesis and developing novel vector vaccines.展开更多
On-chip devices for generating pre-designed vectorial optical fields(VOFs)under surface wave(SW)excitations are highly desired in integrated photonics.However,conventional devices are usually of large footprints,low e...On-chip devices for generating pre-designed vectorial optical fields(VOFs)under surface wave(SW)excitations are highly desired in integrated photonics.However,conventional devices are usually of large footprints,low efficiencies,and limited wave-control capabilities.Here,we present a generic approach to design ultra-compact on-chip devices that can efficiently generate pre-designed VOFs under SW excitations,and experimentally verify the concept in terahertz(THz)regime.We first describe how to design SW-excitation metasurfaces for generating circularly polarized complex beams,and experimentally demonstrate two meta-devices to realize directional emission and focusing of THz waves with oppo-site circular polarizations,respectively.We then establish a systematic approach to construct an integrated device via merging two carefully designed metasurfaces,which,under SW excitations,can separately produce pre-designed far-field patterns with different circular polarizations and generate target VOF based on their interference.As a proof of con-cept,we demonstrate experimentally a meta-device that can generate a radially polarized Bessel beam under SW excita-tion at~0.4 THz.Experimental results agree well with full-wave simulations,collectively verifying the performance of our device.Our study paves the road to realizing highly integrated on-chip functional THz devices,which may find many ap-plications in biological sensing,communications,displays,image multiplexing,and beyond.展开更多
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
To the Editor:We read with interest the article by Wang et al.,titled"Modeling the spread risk of dengue vector Aedes albopictus caused by environmental factors in Shanghai,China"[1].The use of ensemble ecol...To the Editor:We read with interest the article by Wang et al.,titled"Modeling the spread risk of dengue vector Aedes albopictus caused by environmental factors in Shanghai,China"[1].The use of ensemble ecological niche models to map Aedes albopictus distribution in urban Shanghai is both timely and methodologically sound.The identified drivers-vegetation index,temperature,and proximity to water-are well-known contributors to vector proliferation.However,one dimension remains notably underrepresented:human behavioral factors.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 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
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
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
When patients initially present with atrial fibrillation along with an enlarged heart and heart failure, followed by atrioventricular block, it's essential to consider genetic factors.^([1])Genetic testing can off...When patients initially present with atrial fibrillation along with an enlarged heart and heart failure, followed by atrioventricular block, it's essential to consider genetic factors.^([1])Genetic testing can offer crucial diagnostic evidence, aiding in prognosis assessment and the adoption of appropriate treatment strategies.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
Prestack reverse time migration（PSTM） is a common imaging method; however low-frequency noises reduce the structural imaging precision. Thus, the suppression of migration noises must be considered. The generation me...Prestack reverse time migration（PSTM） is a common imaging method; however low-frequency noises reduce the structural imaging precision. Thus, the suppression of migration noises must be considered. The generation mechanism of low-frequency noises is analyzed and the up-, down-, left-, and right-going waves are separated using the Poynting vector of the acoustic wave equation. The computational complexity and memory capacitance of the proposed method are far smaller than that required when using the conventional separation algorithm of 2D Fourier transform. The normalized wavefield separation crosscorrelation imaging condition is used to suppress low-frequency noises in reverse time migration and improve the imaging precision. Numerical experiments using the Marmousi model are performed and the results show that the up-, down-, left-, and right-going waves are well separated in the continuation of the wavefield using the Poynting vector. We compared the imaging results with the conventional method, Laplacian filtering, and wavefield separation with the 2D Fourier transform. The comparison shows that the migration noises are well suppressed using the normalized wavefield separation cross-correlation imaging condition and higher precision imaging results are obtained.展开更多
The laser gyro is most su it able for building the strap down inertial navigation system (SINS), and its acc uracy of attitude algorithm can enormously affect that of the laser SINS. This p aper develops three improv...The laser gyro is most su it able for building the strap down inertial navigation system (SINS), and its acc uracy of attitude algorithm can enormously affect that of the laser SINS. This p aper develops three improved algorithmal expressions for strap down attitude ut ilizing the angular increment output by the laser gyro from the last two and cur rent updating periods according to the number of gyro samples, and analyses the algorithm error in the classical coning motion. Compared with the conventional algorithms, simulational results show that this improved algorithm has higher precision. A new way to improve the rotation vector algorithms is provided.展开更多
In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature...In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.展开更多
A heavy rain process of the Changjiang-Huaihe Meiyu front (MYF) is diagnosed by the agency of the traditional Q vector partitioning (QVP) method to decompose the wet Q vector (Q) in a natural coordinate system that fo...A heavy rain process of the Changjiang-Huaihe Meiyu front (MYF) is diagnosed by the agency of the traditional Q vector partitioning (QVP) method to decompose the wet Q vector (Q) in a natural coordinate system that follows the isoentropes and by using the numerical simulation results of the revised MM4 meso-scale model. The technique shows that the partitioned wet Q vectors can lead to a significant scale separation of vertical motion related to the torrential rain. The results not only verify the existing conclusion that different scales interact throughout the rainstorm but also indicate the largely different roles of these scales during differing phases of the heavy ramfall on a quantitative basis. Specifically, during the developing stage, the large-scale plays a predominant role in forcing vertical motion, while frontal-scale forcing is secondary; during the intense stage, the frontal-scale evolves into the primary factor of forcing vertical motion, whereas the large-scale forcing is minor and plays a diminishing role and can even be ignored; and during the decaying stage, the large-scale once again serves as the main forcing of vertical motion in such a way that the forcing of the frontal-scale decays quickly and is of secondary importance. Furthermore, the partitioned wet Q vectors are suggested to be more suitable than the total wet Q vector for evaluating the potential physical mechanism of rainstorm genesis. The first step is that the forcing of large-scale $2?bla cdot {? Q}_s^*$ gives rise to the genesis of meso-scale $2?bla cdot {? Q}_n^*$ forcing; and then, accordingly as $2?bla cdot {? Q}_n^*$ forcing increases, whereby the secondary circulation is reinforced, the intensity of the rainfall is strengthened; and at last, the secondary circulation caused by $2?bla cdot {? Q}_n^*$ forcing is directly responsible for generation of the MYF heavy rainfall.展开更多
基金supported by the grants from the National Key Research and Development Program of China(2022YFD1800604)the China Agriculture Research System(CARS-41)the Heilongjiang Touyan Innovation Team Program,China。
文摘Avian metapneumovirus(aMPV),a paramyxovirus,causes acute respiratory diseases in turkeys and swollen head syndrome in chickens.This study established a reverse genetics system for aMPV subtype B LN16-A strain based on T7 RNA polymerase.Full-length cDNA of the LN16-A strain was constructed by assembling 5 cDNA fragments between the T7 promoter and hepatitis delta virus ribozyme.Transfection of this plasmid,along with the supporting plasmids encoding the N,P,M2-1,and L proteins of LN16-A into BSR-T7/5 cells,resulted in the recovery of aMPV subtype B.To identify an effective insertion site,the enhanced green fluorescent protein(EGFP)gene was inserted into different sites of the LN16-A genome to generate recombinant LN16-As.The results showed that the expression levels of EGFP at the site between the G and L genes of LN16-A were significantly higher than those at the other two sites(between the leader and N genes or replacing the SH gene).To verify the availability of the site between G and L for foreign gene expression,the VP2 gene of very virulent infectious bursal disease virus(vvIBDV)was inserted into this site,and recombinant LN16-A(rLN16A-vvVP2)was successfully rescued.Single immunization of specificpathogen-free chickens with rLN16A-vvVP2 induced high levels of neutralizing antibodies and provided 100%protection against the virulent aMPV subtype B and vvIBDV.Establishing a reverse genetics system here provides an important foundation for understanding aMPV pathogenesis and developing novel vector vaccines.
基金the financial support from National Natural Science Foundation of China (Nos. 62192771, 12374344, 12221004)National Key Research and Development Program of China (2022YFA1204700, 2020YFA0710100)+1 种基金Natural Science Foundation of Shanghai (Grant No. 23dz2260100)China Postdoctoral Science Foundation 2021TQ0077
文摘On-chip devices for generating pre-designed vectorial optical fields(VOFs)under surface wave(SW)excitations are highly desired in integrated photonics.However,conventional devices are usually of large footprints,low efficiencies,and limited wave-control capabilities.Here,we present a generic approach to design ultra-compact on-chip devices that can efficiently generate pre-designed VOFs under SW excitations,and experimentally verify the concept in terahertz(THz)regime.We first describe how to design SW-excitation metasurfaces for generating circularly polarized complex beams,and experimentally demonstrate two meta-devices to realize directional emission and focusing of THz waves with oppo-site circular polarizations,respectively.We then establish a systematic approach to construct an integrated device via merging two carefully designed metasurfaces,which,under SW excitations,can separately produce pre-designed far-field patterns with different circular polarizations and generate target VOF based on their interference.As a proof of con-cept,we demonstrate experimentally a meta-device that can generate a radially polarized Bessel beam under SW excita-tion at~0.4 THz.Experimental results agree well with full-wave simulations,collectively verifying the performance of our device.Our study paves the road to realizing highly integrated on-chip functional THz devices,which may find many ap-plications in biological sensing,communications,displays,image multiplexing,and beyond.
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 Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai(2023-2025)Key Discipline Project(No.GWVI-11.1-12).
文摘To the Editor:We read with interest the article by Wang et al.,titled"Modeling the spread risk of dengue vector Aedes albopictus caused by environmental factors in Shanghai,China"[1].The use of ensemble ecological niche models to map Aedes albopictus distribution in urban Shanghai is both timely and methodologically sound.The identified drivers-vegetation index,temperature,and proximity to water-are well-known contributors to vector proliferation.However,one dimension remains notably underrepresented:human behavioral factors.
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 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.
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.
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.
基金Military Healthcare Special Scientific Research Project(25BJZ31, awarded to SHI XM)。
文摘When patients initially present with atrial fibrillation along with an enlarged heart and heart failure, followed by atrioventricular block, it's essential to consider genetic factors.^([1])Genetic testing can offer crucial diagnostic evidence, aiding in prognosis assessment and the adoption of appropriate treatment strategies.
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supported by the National Natural Science Foundation of China(No.41174087,41204089)the National Oil and Gas Major Project(No.2011ZX05005-005)
文摘Prestack reverse time migration（PSTM） is a common imaging method; however low-frequency noises reduce the structural imaging precision. Thus, the suppression of migration noises must be considered. The generation mechanism of low-frequency noises is analyzed and the up-, down-, left-, and right-going waves are separated using the Poynting vector of the acoustic wave equation. The computational complexity and memory capacitance of the proposed method are far smaller than that required when using the conventional separation algorithm of 2D Fourier transform. The normalized wavefield separation crosscorrelation imaging condition is used to suppress low-frequency noises in reverse time migration and improve the imaging precision. Numerical experiments using the Marmousi model are performed and the results show that the up-, down-, left-, and right-going waves are well separated in the continuation of the wavefield using the Poynting vector. We compared the imaging results with the conventional method, Laplacian filtering, and wavefield separation with the 2D Fourier transform. The comparison shows that the migration noises are well suppressed using the normalized wavefield separation cross-correlation imaging condition and higher precision imaging results are obtained.
文摘The laser gyro is most su it able for building the strap down inertial navigation system (SINS), and its acc uracy of attitude algorithm can enormously affect that of the laser SINS. This p aper develops three improved algorithmal expressions for strap down attitude ut ilizing the angular increment output by the laser gyro from the last two and cur rent updating periods according to the number of gyro samples, and analyses the algorithm error in the classical coning motion. Compared with the conventional algorithms, simulational results show that this improved algorithm has higher precision. A new way to improve the rotation vector algorithms is provided.
基金The Natural Science Foundation of Heilongjiang Province ( No. F201018)the National Natural Science Foundation of China( No. 60901042)
文摘In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.40075009 and 40205008,and by Project 37020 of the Social Public Special Research Grant of the Ministry of Science and Technology of China.
文摘A heavy rain process of the Changjiang-Huaihe Meiyu front (MYF) is diagnosed by the agency of the traditional Q vector partitioning (QVP) method to decompose the wet Q vector (Q) in a natural coordinate system that follows the isoentropes and by using the numerical simulation results of the revised MM4 meso-scale model. The technique shows that the partitioned wet Q vectors can lead to a significant scale separation of vertical motion related to the torrential rain. The results not only verify the existing conclusion that different scales interact throughout the rainstorm but also indicate the largely different roles of these scales during differing phases of the heavy ramfall on a quantitative basis. Specifically, during the developing stage, the large-scale plays a predominant role in forcing vertical motion, while frontal-scale forcing is secondary; during the intense stage, the frontal-scale evolves into the primary factor of forcing vertical motion, whereas the large-scale forcing is minor and plays a diminishing role and can even be ignored; and during the decaying stage, the large-scale once again serves as the main forcing of vertical motion in such a way that the forcing of the frontal-scale decays quickly and is of secondary importance. Furthermore, the partitioned wet Q vectors are suggested to be more suitable than the total wet Q vector for evaluating the potential physical mechanism of rainstorm genesis. The first step is that the forcing of large-scale $2?bla cdot {? Q}_s^*$ gives rise to the genesis of meso-scale $2?bla cdot {? Q}_n^*$ forcing; and then, accordingly as $2?bla cdot {? Q}_n^*$ forcing increases, whereby the secondary circulation is reinforced, the intensity of the rainfall is strengthened; and at last, the secondary circulation caused by $2?bla cdot {? Q}_n^*$ forcing is directly responsible for generation of the MYF heavy rainfall.
