Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience...Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.展开更多
Passive image interferometry （PII） is becoming a powerful tool for detecting the temporal variations in the Earth＇s structure, which applies coda wave interferometry to the waveforrns from the cross-correlation of ...Passive image interferometry （PII） is becoming a powerful tool for detecting the temporal variations in the Earth＇s structure, which applies coda wave interferometry to the waveforrns from the cross-correlation of seismic ambient noise. There are four techniques for estimating temporal change of seismic velocity with PII： moving-window cross-correlation technique （MWCCT）, moving-window cross-spectrum technique （MWCST）, stretching technique （ST） and moving-window stretching technique （MWST）. In this paper, we use the continuous seismic records from a typical station pair near the Wenchuan Ms8.0 earthquake fault zone and generate three sets of waveforms by stacking cross-correlation function of ambient noise with different numbers of days, and then apply four techniques to processing the three sets of waveforms and compare their results. Our results indicate that the techniques based on moving-window （MWCCT, MWCST and MWST） are superior in detecting the change of seismic velocity, and the MWCST can give a better estimate of velocity change than the other moving-window techniques due to measurement error. We also investigate the clock errors and their influences on measuring velocity change. We find that when the clock errors are not very large, they have limited impact on the estimate of the velocity change with the moving-window techniques.展开更多
In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers a...In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.展开更多
Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the mo...Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the most common types of image forgeries.Thus,an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper.These methods are classified into three types:block-based methods,keypoint-based methods,and deep learning-based methods.In addition,the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost.Finally,further research directions are discussed.展开更多
Image steganography is one of the prominent technologies in data hiding standards.Steganographic system performance mostly depends on the embedding strategy.Its goal is to embed strictly confidential information into ...Image steganography is one of the prominent technologies in data hiding standards.Steganographic system performance mostly depends on the embedding strategy.Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image.The randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an attacker.The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this study.This review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego images.The fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and difficulties.Additionally,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.展开更多
The Indian shield comprises a number of Archean–Paleoproterozoic cratonic blocks and predominantly Meso–Neoproterozoic mobile belts with Archean protoliths.All these ancient cratons were thought to be integral parts of
Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method...Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.展开更多
The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatia...The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.展开更多
This paper proposes a subpixel transformation method to correct Keystone and Smile distortions in fiber spectral images from the Fiber Arrayed Solar Optical Telescope.These distortions affect the spatial and spectral ...This paper proposes a subpixel transformation method to correct Keystone and Smile distortions in fiber spectral images from the Fiber Arrayed Solar Optical Telescope.These distortions affect the spatial and spectral positions,degrading resolution and accuracy.To correct Keystone distortion,we use a local summation and peak-finding method to locate central horizontal coordinates,calculate shifting values,and straighten the curves.For Smile distortion,we use quartic polynomial fitting based on absorption lines at different wavelengths.This technique preserves subpixel components,redistributes pixel values,and interpolates non-fiber portions,rectifying the spectra for accurate analysis.The method can also be applied to other astronomical projects like Large Sky Area Multi-Object Fiber Spectroscopic Telescope,enhancing the accuracy and reliability of spectral data in various astronomical studies.展开更多
Accurate medical image segmentation plays a crucial role in improving the precision of computer-aided diagnosis.However,complex boundary shapes,low contrast and blurred anatomical structures make fine-grained segmenta...Accurate medical image segmentation plays a crucial role in improving the precision of computer-aided diagnosis.However,complex boundary shapes,low contrast and blurred anatomical structures make fine-grained segmentation a challenging task.Variational Bayesian inference quantifies uncertainty through probability distributions and can construct robust probabilistic models for the boundaries of ambiguous organs and tissues.In this paper,we apply variational Bayesian inference to medical image segmentation and propose variational attention to model the uncertainty of low-contrast and blurry tissue and organ boundaries.This enhances the model's ability to perceive segmentation boundaries,improving robustness and segmentation accuracy.Variational attention first estimates the parameters of the probability distribution of latent representations based on input features.Then,it samples latent representations from the learnt distribution to generate attention weights that optimise the interaction between global features and ambiguous boundaries.We integrate variational attention into the U-Net model by replacing its skip connections,constructing a multi-scale variational attention segmentation model(V-UNet).Experiments on the ISBI 2012 and MoNuSeg 2018 datasets show that our method achieves Dice scores of 95.89%and 82.18%,respectively.Moreover,we integrate V-UNet into the Mask R-CNN framework by replacing the FPN feature extraction head and propose a two-stage segmentation method.Compared to the original Mask R-CNN,our method improves the Dice score by 0.81%,mAP by 8.06%and F1 score by 0.51%.展开更多
Deconvolution in radio interferometry faces challenges due to incomplete sampling of the visibilities in the spatial frequency domain caused by a limited number of antenna baselines,resulting in an ill-posed inverse p...Deconvolution in radio interferometry faces challenges due to incomplete sampling of the visibilities in the spatial frequency domain caused by a limited number of antenna baselines,resulting in an ill-posed inverse problem.Reconstructing dirty images into clean ones is crucial for subsequent scientific analysis.To address these challenges,we propose a U-Net based method that extracts high-level information from the dirty image and reconstructs a clean image by effectively reducing artifacts and sidelobes.The U-Net architecture,consisting of an encoder-decoder structure and skip connections,facilitates the flow of information and preserves spatial details.Using simulated data of radio galaxies,we train our model and evaluate its performance on the testing set.Compared with the CLEAN method and the visibility and image conditioned denoising diffusion probabilistic model,our proposed model can effectively reconstruct both extended sources and faint point sources with higher values in the structural similarity index measure and the peak signal-to-noise ratio.Furthermore,we investigate the impact of noise on the model performance,demonstrating its robustness under varying noise levels.展开更多
In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical...In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.展开更多
Antibody drugs,such as monoclonal antibodies and antibody-drug conjugates,have shown significant potential in treating diseases due to their high specificity and affinity.In vivo analysis of antibody drugs with non-in...Antibody drugs,such as monoclonal antibodies and antibody-drug conjugates,have shown significant potential in treating diseases due to their high specificity and affinity.In vivo analysis of antibody drugs with non-invasive and real-time techniques is of importance to understand dynamic behavior of drugs within living organisms,and help evaluate their pharmacokinetics and efficacies.This review summarizes the advances and in vivo analysis methods of antibody drugs,including the techniques of radiolabeled imaging,near-infrared fluorescence imaging and surface-enhanced Raman spectroscopy.The principles,applications,and challenges of each technique are discussed,which provides insights for the development of antibody drugs and in vivo analytical methods.展开更多
This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert ...This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.展开更多
The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals f...The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals from the previous stage of the image stabilization system.However,a new type of image stabilization residual caused by image rotation and projection distortion is introduced when the FSM performs tip-tilt adjustments,reducing both the image stabilization accuracy and the absolute pointing accuracy of the CSST.In this paper,we propose a scheme to compute the image stabilization residuals across the full field of view(FOV)by using a reference star as the target for stabilization control,which can be utilized for subsequent image position correction.To achieve this,we developed a linear optical model for image point displacement by simplifying an existing image point displacement model and incorporating more readily available parameters.The computational accuracy of the new model is equivalent to that of the original,with computational differences of less than 0.03μm.Based on this linear model,we established a calculation model for image stabilization residuals,including those due to image rotation and projection distortion caused by FSM tip-tilt adjustments.This model provides a theoretical foundation for quantifying such residuals during the image stabilization process.Finally,the results of testing using this scheme are provided.Experimental results demonstrate that within the observation FOV of the CSST,when the FSM tilts by(1″,1″),the maximum absolute value of the image stabilization residuals accounts for 20%of the total image stabilization accuracy requirement.This finding underscores the necessity of computing and correcting these residuals to meet performance requirements.展开更多
BACKGROUND Congenital renal arteriovenous fistula(RAVF)is a clinically rare condition and frequently missed and misdiagnosed.Multimodal imaging techniques can pro-vide more detailed diagnostic information to help phys...BACKGROUND Congenital renal arteriovenous fistula(RAVF)is a clinically rare condition and frequently missed and misdiagnosed.Multimodal imaging techniques can pro-vide more detailed diagnostic information to help physicians more accurately diagnose and treat diseases.Combining imaging methods to diagnose RAVF has rarely been reported.CASE SUMMARY A 69-year-old female patient presented with gross hematuria that had persisted for 10 days.The patient underwent ultrasound examinations of the kidneys and renal blood vessels,enhanced computed tomography,three-dimensional com-puted tomography angiography,and digital subtraction angiography of the renal arteries.These revealed dilatation of the left renal vein and abnormal shunting between the left renal artery and vein.The patient was diagnosed with a left RAVF using combined multimodal imaging techniques.The patient was treated with left renal artery embolization immediately after renal arteriography.Hema-turia resolved following the left renal artery embolization without serious bleeding or other complications.The patient made a full recovery after one year of postoperative follow-up.CONCLUSION Multimodal imaging techniques complement each other when diagnosing RAVF,providing detailed diagnostic information that can aid in accurate diagnosis and treatment.In addition,this case reminds the sonographer to pay more attention to the color doppler flow imaging and blood flow spectrum when examining the kidney,so as to avoid misdiagnosis of renal cystic lesions as renal cysts and missed diagnosis of RAVF.展开更多
Against the backdrop of massive sky survey data,the automated detection,classification,and parameter computation of targets have emerged as critical areas demanding urgent breakthroughs.However,in detection and classi...Against the backdrop of massive sky survey data,the automated detection,classification,and parameter computation of targets have emerged as critical areas demanding urgent breakthroughs.However,in detection and classification tasks,model accuracy is often constrained by issues such as small target sizes and insufficient feature information.To address this challenge,we innovatively constructs a fully automated astronomical image analysis pipeline that combines point source detection and classification,galaxy morphological classification,and parameter computation,forming an end-to-end solution.This pipeline achieves automated detection and morphological classification of both point sources and extended sources,and it is also able to compute the basic parameters of galaxy targets.The pipeline first accomplishes the detection and localization of target sources using the YOLOv9 model,and then leverages the optimized ResNet-AE model to initially categorize the detected targets into three major classes:stars,quasars,and galaxies.To tackle the problem of small sizes in some galaxy targets,we filtered out samples with larger sizes and distinct contours.Drawing on morphological characteristics,these samples were further classified into six categories via the DenseNet-SE4 model:barred spiral galaxies,cigar galaxies,elliptical galaxies,intermediate galaxies,spiral galaxies,and irregular galaxies.Following this classification,parameter computation was conducted on the targets.Experimental results show that the detection model has achieved better performance than previous studies,with a mean average precision of 85.20%at Intersection over Union values ranging from 0.5 to 0.95.Both classification models also reached an accuracy of over 85%on the test set.Compared with classical CNN networks,these two classification models boast higher precision,and the computation of target parameters has also yielded reliable outcomes.Experiments verify that this pipeline can act as a supplementary tool for astronomical image processing and be applied to data mining and analysis work in sky surveys.展开更多
基金supported by the National Natural Science Foundation of China,No.31760290,82160688the Key Development Areas Project of Ganzhou Science and Technology,No.2022B-SF9554(all to XL)。
文摘Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.
基金supported by National Natural Science Foundation of China (No. 41074061)Basic Research Plan of the Institute of Earthquake Science, China Earthquake Administration (No. 2007-13)
文摘Passive image interferometry （PII） is becoming a powerful tool for detecting the temporal variations in the Earth＇s structure, which applies coda wave interferometry to the waveforrns from the cross-correlation of seismic ambient noise. There are four techniques for estimating temporal change of seismic velocity with PII： moving-window cross-correlation technique （MWCCT）, moving-window cross-spectrum technique （MWCST）, stretching technique （ST） and moving-window stretching technique （MWST）. In this paper, we use the continuous seismic records from a typical station pair near the Wenchuan Ms8.0 earthquake fault zone and generate three sets of waveforms by stacking cross-correlation function of ambient noise with different numbers of days, and then apply four techniques to processing the three sets of waveforms and compare their results. Our results indicate that the techniques based on moving-window （MWCCT, MWCST and MWST） are superior in detecting the change of seismic velocity, and the MWCST can give a better estimate of velocity change than the other moving-window techniques due to measurement error. We also investigate the clock errors and their influences on measuring velocity change. We find that when the clock errors are not very large, they have limited impact on the estimate of the velocity change with the moving-window techniques.
基金supported by the National Key R&D Program of China(2017YFF0205600)the International Research Cooperation Seed Fund of Beijing University of Technology(2018A08)+1 种基金Science and Technology Project of Beijing Municipal Commission of Transport(2018-kjc-01-213)the Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds(Scientific Research Categories)of Beijing City(PXM2019_014204_500032).
文摘In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.
文摘Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the most common types of image forgeries.Thus,an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper.These methods are classified into three types:block-based methods,keypoint-based methods,and deep learning-based methods.In addition,the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost.Finally,further research directions are discussed.
基金This research was funded by the Ministry of Higher Education(MOHE)through Fundamental Research Grant Scheme(FRGS)under the Grand Number FRGS/1/2020/ICT01/UK M/02/4,and University Kebangsaan Malaysia for open access publication.
文摘Image steganography is one of the prominent technologies in data hiding standards.Steganographic system performance mostly depends on the embedding strategy.Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image.The randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an attacker.The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this study.This review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego images.The fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and difficulties.Additionally,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.
文摘The Indian shield comprises a number of Archean–Paleoproterozoic cratonic blocks and predominantly Meso–Neoproterozoic mobile belts with Archean protoliths.All these ancient cratons were thought to be integral parts of
基金supported by the National Key R&D Program of China 2022YFF0503002the National Natural Science Foundation of China(NSFC,Grant Nos.12333010 and 12233012)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant No.XDB0560000)supported by the Prominent Postdoctoral Project of Jiangsu Province(2023ZB304)supported by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences,grant No.XDA15320000.
文摘Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.12233012,the Strategic Priority Research Program of the Chinese Academy of Sciences,grant No.XDB0560102the National Key R&D Program of China 2022YFF0503003(2022YFF0503000)。
文摘The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.
基金Astronomy Joint Research Fund supported this work under cooperative agreements between the National Natural Science Foundation of China(NSFC)and the Chinese Academy of Sciences(CAS)(project numbers:U2031132 and U1931206).
文摘This paper proposes a subpixel transformation method to correct Keystone and Smile distortions in fiber spectral images from the Fiber Arrayed Solar Optical Telescope.These distortions affect the spatial and spectral positions,degrading resolution and accuracy.To correct Keystone distortion,we use a local summation and peak-finding method to locate central horizontal coordinates,calculate shifting values,and straighten the curves.For Smile distortion,we use quartic polynomial fitting based on absorption lines at different wavelengths.This technique preserves subpixel components,redistributes pixel values,and interpolates non-fiber portions,rectifying the spectra for accurate analysis.The method can also be applied to other astronomical projects like Large Sky Area Multi-Object Fiber Spectroscopic Telescope,enhancing the accuracy and reliability of spectral data in various astronomical studies.
基金supported by the China Chongqing Municipal Education Commission(Grant KJZDM202500505)China Chongqing Municipal Science and Technology Bureau(Grants CSTB2024TIADCYKJCXX0009,CSTB2024NSCQ-LZX0043)+1 种基金Chongqing University of Technology graduate education high-quality development project(Grants gzlsz202304,gzlkc202401,gzltd202502)Chongqing University of Technology-Chongqing LINGLUE Technology Co.Ltd.Electronic Information(artificial intelligence)graduate joint training base.
文摘Accurate medical image segmentation plays a crucial role in improving the precision of computer-aided diagnosis.However,complex boundary shapes,low contrast and blurred anatomical structures make fine-grained segmentation a challenging task.Variational Bayesian inference quantifies uncertainty through probability distributions and can construct robust probabilistic models for the boundaries of ambiguous organs and tissues.In this paper,we apply variational Bayesian inference to medical image segmentation and propose variational attention to model the uncertainty of low-contrast and blurry tissue and organ boundaries.This enhances the model's ability to perceive segmentation boundaries,improving robustness and segmentation accuracy.Variational attention first estimates the parameters of the probability distribution of latent representations based on input features.Then,it samples latent representations from the learnt distribution to generate attention weights that optimise the interaction between global features and ambiguous boundaries.We integrate variational attention into the U-Net model by replacing its skip connections,constructing a multi-scale variational attention segmentation model(V-UNet).Experiments on the ISBI 2012 and MoNuSeg 2018 datasets show that our method achieves Dice scores of 95.89%and 82.18%,respectively.Moreover,we integrate V-UNet into the Mask R-CNN framework by replacing the FPN feature extraction head and propose a two-stage segmentation method.Compared to the original Mask R-CNN,our method improves the Dice score by 0.81%,mAP by 8.06%and F1 score by 0.51%.
基金supported by the National SKA Program of China(2020SKA0110300,2020SKA0110201)the National Natural Science Foundation of China(NSFC,grant Nos.12433012 and 12373097)+1 种基金the Guangdong Province Project of the Basic and Applied Basic Research Foundation(2024A1515011503)the Guangzhou Science and Technology Funds(2023A03J0016).
文摘Deconvolution in radio interferometry faces challenges due to incomplete sampling of the visibilities in the spatial frequency domain caused by a limited number of antenna baselines,resulting in an ill-posed inverse problem.Reconstructing dirty images into clean ones is crucial for subsequent scientific analysis.To address these challenges,we propose a U-Net based method that extracts high-level information from the dirty image and reconstructs a clean image by effectively reducing artifacts and sidelobes.The U-Net architecture,consisting of an encoder-decoder structure and skip connections,facilitates the flow of information and preserves spatial details.Using simulated data of radio galaxies,we train our model and evaluate its performance on the testing set.Compared with the CLEAN method and the visibility and image conditioned denoising diffusion probabilistic model,our proposed model can effectively reconstruct both extended sources and faint point sources with higher values in the structural similarity index measure and the peak signal-to-noise ratio.Furthermore,we investigate the impact of noise on the model performance,demonstrating its robustness under varying noise levels.
基金supported by the National Natural Science Foundation of China (NSFC, Grant No. U1731128)
文摘In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.
基金support from the National Natural Science Foundation of China(Grant Nos.:22322403 and 22074006)the Beijing Natural Science Foundation(Grant No.:2222029)the Beijing Institute of Technology Research Fund Program for Young Scholars.
文摘Antibody drugs,such as monoclonal antibodies and antibody-drug conjugates,have shown significant potential in treating diseases due to their high specificity and affinity.In vivo analysis of antibody drugs with non-invasive and real-time techniques is of importance to understand dynamic behavior of drugs within living organisms,and help evaluate their pharmacokinetics and efficacies.This review summarizes the advances and in vivo analysis methods of antibody drugs,including the techniques of radiolabeled imaging,near-infrared fluorescence imaging and surface-enhanced Raman spectroscopy.The principles,applications,and challenges of each technique are discussed,which provides insights for the development of antibody drugs and in vivo analytical methods.
基金supported from the Strategic Pioneer Program of the Astronomy Large-Scale Scientific FacilityChinese Academy of Sciences and the Science and Education Integration Funding of University of Chinese Academy of Sciences+9 种基金the supports from the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the supports from the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No.XDB0550000the National Natural Science Foundation of China(NSFC,grant Nos.12422303 and12261141690)the supports from the NSFC(grant No.12403024)supports from the NSFC through grant Nos.11988101 and 11933004the Postdoctoral Fellowship Program of CPSF under grant No.GZB20240731the Young Data Scientist Project of the National Astronomical Data Centerthe China Post-doctoral Science Foundation(No.2023M743447)supports from the New Cornerstone Science Foundation through the New Cornerstone Investigator Program and the XPLORER PRIZE。
文摘This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.
基金financially supported by the National Key R&D Program of China(2022YFB3806300)。
文摘The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals from the previous stage of the image stabilization system.However,a new type of image stabilization residual caused by image rotation and projection distortion is introduced when the FSM performs tip-tilt adjustments,reducing both the image stabilization accuracy and the absolute pointing accuracy of the CSST.In this paper,we propose a scheme to compute the image stabilization residuals across the full field of view(FOV)by using a reference star as the target for stabilization control,which can be utilized for subsequent image position correction.To achieve this,we developed a linear optical model for image point displacement by simplifying an existing image point displacement model and incorporating more readily available parameters.The computational accuracy of the new model is equivalent to that of the original,with computational differences of less than 0.03μm.Based on this linear model,we established a calculation model for image stabilization residuals,including those due to image rotation and projection distortion caused by FSM tip-tilt adjustments.This model provides a theoretical foundation for quantifying such residuals during the image stabilization process.Finally,the results of testing using this scheme are provided.Experimental results demonstrate that within the observation FOV of the CSST,when the FSM tilts by(1″,1″),the maximum absolute value of the image stabilization residuals accounts for 20%of the total image stabilization accuracy requirement.This finding underscores the necessity of computing and correcting these residuals to meet performance requirements.
文摘BACKGROUND Congenital renal arteriovenous fistula(RAVF)is a clinically rare condition and frequently missed and misdiagnosed.Multimodal imaging techniques can pro-vide more detailed diagnostic information to help physicians more accurately diagnose and treat diseases.Combining imaging methods to diagnose RAVF has rarely been reported.CASE SUMMARY A 69-year-old female patient presented with gross hematuria that had persisted for 10 days.The patient underwent ultrasound examinations of the kidneys and renal blood vessels,enhanced computed tomography,three-dimensional com-puted tomography angiography,and digital subtraction angiography of the renal arteries.These revealed dilatation of the left renal vein and abnormal shunting between the left renal artery and vein.The patient was diagnosed with a left RAVF using combined multimodal imaging techniques.The patient was treated with left renal artery embolization immediately after renal arteriography.Hema-turia resolved following the left renal artery embolization without serious bleeding or other complications.The patient made a full recovery after one year of postoperative follow-up.CONCLUSION Multimodal imaging techniques complement each other when diagnosing RAVF,providing detailed diagnostic information that can aid in accurate diagnosis and treatment.In addition,this case reminds the sonographer to pay more attention to the color doppler flow imaging and blood flow spectrum when examining the kidney,so as to avoid misdiagnosis of renal cystic lesions as renal cysts and missed diagnosis of RAVF.
基金supported by the National Natural Science Foundation of China(NSFC,grant No.U1731128)。
文摘Against the backdrop of massive sky survey data,the automated detection,classification,and parameter computation of targets have emerged as critical areas demanding urgent breakthroughs.However,in detection and classification tasks,model accuracy is often constrained by issues such as small target sizes and insufficient feature information.To address this challenge,we innovatively constructs a fully automated astronomical image analysis pipeline that combines point source detection and classification,galaxy morphological classification,and parameter computation,forming an end-to-end solution.This pipeline achieves automated detection and morphological classification of both point sources and extended sources,and it is also able to compute the basic parameters of galaxy targets.The pipeline first accomplishes the detection and localization of target sources using the YOLOv9 model,and then leverages the optimized ResNet-AE model to initially categorize the detected targets into three major classes:stars,quasars,and galaxies.To tackle the problem of small sizes in some galaxy targets,we filtered out samples with larger sizes and distinct contours.Drawing on morphological characteristics,these samples were further classified into six categories via the DenseNet-SE4 model:barred spiral galaxies,cigar galaxies,elliptical galaxies,intermediate galaxies,spiral galaxies,and irregular galaxies.Following this classification,parameter computation was conducted on the targets.Experimental results show that the detection model has achieved better performance than previous studies,with a mean average precision of 85.20%at Intersection over Union values ranging from 0.5 to 0.95.Both classification models also reached an accuracy of over 85%on the test set.Compared with classical CNN networks,these two classification models boast higher precision,and the computation of target parameters has also yielded reliable outcomes.Experiments verify that this pipeline can act as a supplementary tool for astronomical image processing and be applied to data mining and analysis work in sky surveys.
