Prodrugs need to be converted to active drugs to exert their pharmacological activities.Identifying the direct targets of active drugs is essential to elucidate the pharmacological mechanisms of prodrugs,but remains c...Prodrugs need to be converted to active drugs to exert their pharmacological activities.Identifying the direct targets of active drugs is essential to elucidate the pharmacological mechanisms of prodrugs,but remains challenging,especially for active drugs with low stability.展开更多
Low valence vanadium oxide(VO2-x) thin films were prepared on SiO2/Si substrates at room temperature by direct current facing targets reactive magnetron sputtering, and then proc- essed through rapid thermal annealing...Low valence vanadium oxide(VO2-x) thin films were prepared on SiO2/Si substrates at room temperature by direct current facing targets reactive magnetron sputtering, and then proc- essed through rapid thermal annealing. The effects of the annealing on the structure and phase transition property of VO2 were discussed. X-ray photoelectron spectroscopy, X-ray diffraction tech- nique and Fourier transform infrared spectroscopy were employed to study the phase composition and structure of the thin films. The resistance-temperature property was measured. The results show that VO2 thin film is obtained after annealed at 320 ℃ for 3 h, its phase transition tempera- ture is 56 ℃, and the resistance changes by more than 2 orders. The vanadium oxide thin films are applicable in thermochromic smart windows, and the deposition and annealing process is compatible with micro electromechanical system process.展开更多
Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It...Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.展开更多
To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this pap...To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this paper proposes a real-domain out-of-state sparse Bayesian learning algorithm that combines dictionary learning and unitary transformation for direction estimation.The algorithm employs the K-means singular value decomposition dictionary learning method to represent the actual received signal of a uniform linear array using a small number of linear combinations of basic received signals,thereby achieving noise reduction for the original signal.The denoised signal matrix is then constructed into a processing matrix that satisfies the central Hermitian property.By applying a unitary transformation,the signal data is converted from complex-domain operations to real-domain operations,which reduces computational complexity.Finally,singular value decomposition and outlier sparse Bayesian learning algorithms are used for iterative processing to achieve target direction estimation.Simulation analysis and sea trial data results demonstrate that compared with the off-grid sparse Bayesian learning algorithm,under conditions of low signal-to-noise ratio and low frame rate,the proposed algorithm has improved azimuth estimation accuracy and algorithm robustness,and is less complex.展开更多
The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing s...The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.展开更多
In the bistatic case, theoretical analysis and experimental researches on underwater acoustic scattering properties of some submarine model are made in this paper. When sourcet target and receiver have complicated tri...In the bistatic case, theoretical analysis and experimental researches on underwater acoustic scattering properties of some submarine model are made in this paper. When sourcet target and receiver have complicated triangular configuration, the relationships among target strength, incidence angle and bistatic angle are obtained. The validity of this theory is verified by theoretical calculations and tank experiments. The research results can be directly used in bistatic or multistatic underwater acoustic detection systems.展开更多
TNNI3K(troponin-I interacting kinase)encodes a duo tyrosine and serine/threonine kinase implicated in cardiomyopathy,arrhythmias,and cardiac conduction disease(CCD).1 However,no direct downstream phosphorylation targe...TNNI3K(troponin-I interacting kinase)encodes a duo tyrosine and serine/threonine kinase implicated in cardiomyopathy,arrhythmias,and cardiac conduction disease(CCD).1 However,no direct downstream phosphorylation targets of TNNI3K have been identified yet.2 Here,we employed the CRISPR/Cas9 gene-editing technique to generate a splicing mutation in the 4th exon of zebrafish tnni3k ortholog gene that mimics a TNNI3K splicing variant identified from a patient family with cardiomyopathy and CCD.展开更多
Plants are sessile organisms that evolve with a flexible signal transduction system in order to rapidly respond to environmental changes.Drought,a common abiotic stress,affects multiple plant developmental processes e...Plants are sessile organisms that evolve with a flexible signal transduction system in order to rapidly respond to environmental changes.Drought,a common abiotic stress,affects multiple plant developmental processes especially growth.In response to drought stress,an intricate hierarchical regulatory network is established in plant to survive from the extreme environment.The transcriptional regulation carried out by transcription factors(TFs)is the most important step for the establishment of the network.In this review,we summarized almost all the TFs that have been reported to participate in drought tolerance(DT)in plant.Totally 466 TFs from 86 plant species that mostly belong to 11 families are collected here.This demonstrates that TFs in these 11 families are the main transcriptional regulators of plant DT.The regulatory network is built by direct protein-protein interaction or mutual regulation of TFs.TFs receive upstream signals possibly via post-transcriptional regulation and output signals to downstream targets via direct binding to their promoters to regulate gene expression.展开更多
基金support from the National Natural Science Foundation of China(Grant Nos.:U21A20407 and 81973467).
文摘Prodrugs need to be converted to active drugs to exert their pharmacological activities.Identifying the direct targets of active drugs is essential to elucidate the pharmacological mechanisms of prodrugs,but remains challenging,especially for active drugs with low stability.
基金Natural Science Foundation of Tianjin(No.043100811)the Key Program of Natural Science Foundation of Tianjin(No.08JCZDJC17500)
文摘Low valence vanadium oxide(VO2-x) thin films were prepared on SiO2/Si substrates at room temperature by direct current facing targets reactive magnetron sputtering, and then proc- essed through rapid thermal annealing. The effects of the annealing on the structure and phase transition property of VO2 were discussed. X-ray photoelectron spectroscopy, X-ray diffraction tech- nique and Fourier transform infrared spectroscopy were employed to study the phase composition and structure of the thin films. The resistance-temperature property was measured. The results show that VO2 thin film is obtained after annealed at 320 ℃ for 3 h, its phase transition tempera- ture is 56 ℃, and the resistance changes by more than 2 orders. The vanadium oxide thin films are applicable in thermochromic smart windows, and the deposition and annealing process is compatible with micro electromechanical system process.
基金supported in part by the National Science and Technology Council,Taiwan:NSTC 113-2410-H-030-077-MY2.
文摘Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.
基金supported by the National Natural Science Foundation of China(61761048)the Basic Research Special General project of Yunnan Province(202101AT070132)the Yunnan Minzu University Graduate Research Innovation Fund Project(2024SKY122).
文摘To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this paper proposes a real-domain out-of-state sparse Bayesian learning algorithm that combines dictionary learning and unitary transformation for direction estimation.The algorithm employs the K-means singular value decomposition dictionary learning method to represent the actual received signal of a uniform linear array using a small number of linear combinations of basic received signals,thereby achieving noise reduction for the original signal.The denoised signal matrix is then constructed into a processing matrix that satisfies the central Hermitian property.By applying a unitary transformation,the signal data is converted from complex-domain operations to real-domain operations,which reduces computational complexity.Finally,singular value decomposition and outlier sparse Bayesian learning algorithms are used for iterative processing to achieve target direction estimation.Simulation analysis and sea trial data results demonstrate that compared with the off-grid sparse Bayesian learning algorithm,under conditions of low signal-to-noise ratio and low frame rate,the proposed algorithm has improved azimuth estimation accuracy and algorithm robustness,and is less complex.
基金supported by the National Natural Science Foundation of China(11574120,U1636117)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing,Ministry of Education,China(UASP1503)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20161359)Foundation of Key Laboratory of Underwater Acoustic Warfare Technology of China and Qing Lan Project
文摘The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.
文摘In the bistatic case, theoretical analysis and experimental researches on underwater acoustic scattering properties of some submarine model are made in this paper. When sourcet target and receiver have complicated triangular configuration, the relationships among target strength, incidence angle and bistatic angle are obtained. The validity of this theory is verified by theoretical calculations and tank experiments. The research results can be directly used in bistatic or multistatic underwater acoustic detection systems.
基金supported in part by the National Natural Science Foundation of China(No.31970504,31772548,82371863,82070394)the NHC Key Laboratory of Birth Defect for Research and Prevention(Hunan Provincial Maternal and Child Health Care Hospital)(No.KF2021003)the Postgraduate Scientific Innovation Fund of Hunan Province,China(No.CX2018B302).
文摘TNNI3K(troponin-I interacting kinase)encodes a duo tyrosine and serine/threonine kinase implicated in cardiomyopathy,arrhythmias,and cardiac conduction disease(CCD).1 However,no direct downstream phosphorylation targets of TNNI3K have been identified yet.2 Here,we employed the CRISPR/Cas9 gene-editing technique to generate a splicing mutation in the 4th exon of zebrafish tnni3k ortholog gene that mimics a TNNI3K splicing variant identified from a patient family with cardiomyopathy and CCD.
基金supported by Research Initiation Fund for High-level Talents of China Three Gorges University.
文摘Plants are sessile organisms that evolve with a flexible signal transduction system in order to rapidly respond to environmental changes.Drought,a common abiotic stress,affects multiple plant developmental processes especially growth.In response to drought stress,an intricate hierarchical regulatory network is established in plant to survive from the extreme environment.The transcriptional regulation carried out by transcription factors(TFs)is the most important step for the establishment of the network.In this review,we summarized almost all the TFs that have been reported to participate in drought tolerance(DT)in plant.Totally 466 TFs from 86 plant species that mostly belong to 11 families are collected here.This demonstrates that TFs in these 11 families are the main transcriptional regulators of plant DT.The regulatory network is built by direct protein-protein interaction or mutual regulation of TFs.TFs receive upstream signals possibly via post-transcriptional regulation and output signals to downstream targets via direct binding to their promoters to regulate gene expression.
