Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological me...Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological mechanisms,preventing and treating cerebral small vessel vasculopathy is challenging.Recent studies have shown that the glymphatic system plays a crucial role in interstitial solute clearance and the maintenance of brain homeostasis.Increasing evidence also suggests that dysfunction in glymphatic clearance is a key factor in the progression of cerebral small vessel disease.This review begins with a comprehensive introduction to the structure,function,and driving factors of the glymphatic system,highlighting its essential role in brain waste clearance.Afterwards,cerebral small vessel disease was reviewed from the perspective of the glymphatic system,after which the mechanisms underlying their correlation were summarized.Glymphatic dysfunction may lead to the accumulation of metabolic waste in the brain,thereby exacerbating the pathological processes associated with cerebral small vessel disease.The review also discussed the direct evidence of glymphatic dysfunction in patients and animal models exhibiting two subtypes of cerebral small vessel disease:arteriolosclerosis-related cerebral small vessel disease and amyloid-related cerebral small vessel disease.Diffusion tensor image analysis along the perivascular space is an important non-invasive tool for assessing the clearance function of the glymphatic system.However,the effectiveness of its parameters needs to be enhanced.Among various nervous system diseases,including cerebral small vessel disease,glymphatic failure may be a common final pathway toward dementia.Overall,this review summarizes prevention and treatment strategies that target glymphatic drainage and will offer valuable insight for developing novel treatments for cerebral small vessel disease.展开更多
A detailed understanding of seismicity originating from the Nanga Parbat syntaxis in the northwestern Himalaya is crucial for characterizing the active fault systems and associated neotectonic processes in the region....A detailed understanding of seismicity originating from the Nanga Parbat syntaxis in the northwestern Himalaya is crucial for characterizing the active fault systems and associated neotectonic processes in the region.Continuous earthquake monitoring through local seismic stations enables high-precision results by constraining the velocity structure.In this study,seismogram data from 244 small-magnitude earthquakes are analyzed to delineate the crustal thickness and investigate the source mechanisms beneath the Nanga Parbat syntaxis.The results are achieved with the application of Coupled Hypocenter Velocity Inversion(CHVI)analysis and Time Domain Moment Tensor(TDMT)analysis.The velocity inversion suggests that the Moho discontinuity lies at 60 km depth with an average vP/vS ratio of 1.735±0.017.The minimum 1D velocity model obtained through velocity inversion with least RMS error is further utilized in determining the source mechanism solution.In contrast to earlier studies,which highlighted strike-slip displacement accompanied by reverse dip-slip components,the present research provides a revised interpretation.The moment tensor analysis conducted in this study provides evidence of transtensional deformation associated with neotectonics,attributed to the presence of multiple shear zones.The results of the source mechanism for the selected earthquakes unveiled that the oblique-slip deformation is significantly controlled by the shear stresses coupled with the normal component of dip-slip movement.This is further supported by the higher values of the doublecouple moment tensor(85%),which indicate shear deformation,while the positive value of the compensated linear vector dipole(15%)confirms the presence of a normal component.The coexistence of transpressive and transtensive stresses,together with shallow hypocentral depths and high-amplitude tangential waveforms,can potentially cause devastating impacts in the surroundings of the Nanga Parbat syntaxis.展开更多
The Wilczek–Zee connection(WZC)is a key concept in the study of topology of quantum systems.Here,we introduce the double Wilczek–Zee connection(DWZC)which naturally appears in the pure-state quantum geometric tensor...The Wilczek–Zee connection(WZC)is a key concept in the study of topology of quantum systems.Here,we introduce the double Wilczek–Zee connection(DWZC)which naturally appears in the pure-state quantum geometric tensor(QGT),another important concept in the field of quantum geometry.The DWZC is Hermitian with respect to the two integer indices,just like the original Hermitian WZC.Based on the symmetric logarithmic derivative operator,we propose a mixed-state quantum geometric tensor.Using the symmetric properties of the DWZC,we find that the real part of the QGT is connected to the real part of the DWZC and the square of eigenvalue differences of the density matrix,whereas the imaginary part can be given in terms of the imaginary part of the DWZC and the cube of the eigenvalue differences.For density matrices with full rank or no full rank,the QGT can be given in terms of real and imaginary parts of the DWZC.展开更多
Abstract:Accurate three-dimensional(3D)velocity models are essential for fitting high-frequency seismic waveform records.This process usually requires regional-scale 3D numerical simulations that are computationally e...Abstract:Accurate three-dimensional(3D)velocity models are essential for fitting high-frequency seismic waveform records.This process usually requires regional-scale 3D numerical simulations that are computationally expensive,especially with sparse seismic networks.Because of the significance of source domain modeling,we propose a hybrid waveform simulation approach that combines the 3D spectral-element method(SEM)with the displacement representation theorem.By separating near-source wavefield excitation from long-distance wave propagation to stations,only the source domain wavefield needs to be recomputed when the local velocity and source models change.We apply the method to the 2019 M_(w)5.0 Changning shallow earthquake to verify its flexibility and effectiveness.We compare high-frequency waveforms computed with different regional velocity models against observations.Results show that the hybrid method achieves accuracy comparable to full SEM 3D simulations while reducing computation costs by more than two orders of magnitude when the structure of the source region updates.Our results further indicate that high-frequency waveforms are highly sensitive to shallow structures.Introducing low-velocity shallow layers into the source region improves near-field waveform fits,indicating pronounced low-velocity sediments in the Changning area.Large surface-wave time delays suggest that shallow velocities within the Sichuan Basin are lower than those in existing published models.In addition,an Interferometric Synthetic Aperture Radar(InSAR)-derived finite-fault model outperforms the point-source model in near-field waveform fitting and better reproduces rupture directivity.The proposed method is practical for high-frequency waveform modeling in areas with complex subsurface structures and rupture processes.展开更多
Multi-dimensional arrays are referred to as tensors.Tensor-valued predictors are commonly encountered in modern biomedical applications,such as electroencephalogram(EEG),magnetic resonance imaging(MRI),functional MRI(...Multi-dimensional arrays are referred to as tensors.Tensor-valued predictors are commonly encountered in modern biomedical applications,such as electroencephalogram(EEG),magnetic resonance imaging(MRI),functional MRI(fMRI),diffusion-weighted MRI,and longitudinal health data.In survival analysis,it is both important and challenging to integrate clinically relevant information,such as gender,age,and disease state along with medical imaging tensor data or longitudinal health data to predict disease outcomes.Most existing higher-order sufficient dimension reduction regressions for matrix-or array-valued data focus solely on tensor data,often neglecting established clinical covariates that are readily available and known to have predictive value.Based on the idea of Folded-Minimum Average Variance Estimation(Folded-MAVE:Xue and Yin,2014),the authors propose a new method,Partial Dimension Folded-MAVE(PF-MAVE),to address regression mean functions with tensor-valued covariates while simultaneously incorporating clinical covariates,which are typically categorical variables.Theorems and simulation studies demonstrate the importance of incorporating these categorical clinical predictors.A survival analysis of a longitudinal study of primary biliary cirrhosis(PBC)data is included for illustration of the proposed method.展开更多
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa...A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.展开更多
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression...Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.展开更多
The triple transgenic mouse model of Alzheimer’s disease(3×Tg-AD)is a widely used model that exhibits region-dependent patterns of progressive amyloid-βand tau pathology.Although structural brain abnormalities ...The triple transgenic mouse model of Alzheimer’s disease(3×Tg-AD)is a widely used model that exhibits region-dependent patterns of progressive amyloid-βand tau pathology.Although structural brain abnormalities on magnetic resonance imaging have been observed in 3×Tg-AD mice at later disease stages(>12 months)and as early as 2 months,few studies have investigated changes in these mice during the stage with extensive amyloid-βdeposition and onset of tau pathology(around 9 months).This study aimed to assess brain morphometry and microstructure alterations in 9 month-old 3×Tg-AD mice to better understand the neural mechanisms underlying these specific pathological features.Voxel-based analyses were employed on T2-weighted and diffusion tensor imaging to identify differences between 3×Tg-AD and control mice.Compared with controls,3×Tg-AD mice exhibited lower gray matter volume in several regions including both hippocampal regions,the right thalamus,the left caudoputamen,and the cortex.Reduced white matter volume was observed in fiber tracts including the corpus callosum,internal capsule,stria terminalis,and olfactory tract.Whole-brain diffusion tensor imaging analysis revealed a significant decrease in fractional anisotropy and an increase in both radial and mean diffusivity within the left dentate gyrus of the hippocampal region and right striatum-like amygdala nuclei,with no significant difference in axial diffusivity.Correlation analyses demonstrated significant associations between behavioral performance measures,with both gray and white matter volumes within regions showing significant morphometric differences.Notably,behavioral performance also exhibited significant correlations with diffusion tensor imaging measures particularly within the left dentate gyrus of the hippocampal region and right striatum-like amygdala nuclei.Immunofluorescence analysis confirmed increased amyloid-βplaques and p-Tau protein expression in the hippocampal regions of 3×Tg-AD mice,which corroborated the magnetic resonance imaging findings.Transcriptome analysis in hippocampus tissue identified 1389 differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that numerous differentially expressed genes were enriched in biological processes relevant to synapse structure,cognition,learning,and memory,with particular emphasis on Wnt and mitogen-activated protein kinase signaling pathways.Collectively,these findings suggest that intricate anatomical and microstructural alterations occur in 3×Tg-AD model mice at the onset of pathology around 9 months,potentially driven by gene expression alterations.Moreover,our results support the potential utility of brain volume and diffusion metrics as biomarkers for Alzheimer’s disease pathology,which could have significant implications for clinical diagnosis of Alzheimer’s disease patients.展开更多
Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in hu...Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.展开更多
Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle...Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle to accurately capture microstructural changes.Various diffusion models have been used to study white matter in systemic lupus erythematosus;however,comparative analyses of their sensitivity and specificity for detecting microstructural changes remain insufficient.To address this,our team designed a diagnostic trial that used multimodal diffusion imaging techniques to observe white matter microstructural changes in patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with an aim to identify key diagnostic biomarkers for these patients.Patients with active lupus who received treatment at the Department of Rheumatology and Immunology,The First Affiliated Hospital of China Medical University,from September 2023 to March 2024 were recruited.According to the standards of the American College of Rheumatology,patients with systemic lupus erythematosus who had neuropsychiatric symptoms were assigned to the systemic lupus erythematosus group,whereas those without neuropsychiatric symptoms were assigned to the non-systemic lupus erythematosus group.Additionally,healthy volunteers matched by region,sex,and age were recruited as controls.All three groups underwent the same diffusion magnetic resonance imaging examination protocol to compare differences in diffusion parameters.Advanced diffusion imaging models were able to sensitively detect microstructural changes in the white matter fibers of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with specific diffusion parameters showing significant abnormalities in key brain regions.In the left superior longitudinal fasciculus subregion and the right thalamic radiations of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,we also identified abnormal diffusion characteristics that were clearly correlated with disease activity,suggesting that microstructural changes in these areas may reflect the dynamic process of neuroinflammatory damage.The present study addresses critical challenges in the diagnosis of systemic lupus erythematosus by identifying specific white matter imaging biomarkers and elucidating the association between microstructural damage and clinical manifestations.The main contributions of our study include:1)establishing axial regression probability parameters from mean apparent propagator magnetic resonance imaging as sensitive biomarkers for systemic lupus erythematosus,particularly in the third subregion of the left superior longitudinal fasciculus;2)demonstrating that multimodal diffusion imaging may be superior to conventional diffusion tensor imaging for detecting white matter microstructural abnormalities in patients with systemic lupus erythematosus;and 3)integrating tract-based spatial statistics with clinically relevant analyses to link imaging findings to pathological mechanisms.展开更多
Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation fram...Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation framework on a DG via the tensor product for capturing the complex cohesive spatio-temporal interdependencies precisely and 2)Acquiring the alliance attention scores by node features and favorable high-order structural correlations.展开更多
Background:Platinum can cause chemotherapy-related cognitive impairment.Low-intensity focused ultrasound(LIFUS)is a promising noninvasive physical stimulation method with a unique advantage in neurological rehabilitat...Background:Platinum can cause chemotherapy-related cognitive impairment.Low-intensity focused ultrasound(LIFUS)is a promising noninvasive physical stimulation method with a unique advantage in neurological rehabilitation.We aimed to investigate whether LIFUS can alleviate cisplatin-induced cognitive impairment in rats and explore the related neuropatho-logical mechanisms.Methods:After confirming the target position for LIFUS treatment in 18 rats,64 rats were randomly divided into four groups:control,model,sham,and LIFUS groups.Before and after LIFUS treatment,detailed biological behavioral assessments and magnetic resonance imaging were performed.Finally,the rats were euthanized,and relevant histopathological and molecular biological experiments were conducted and analyzed.Results:In the Morris water maze,the model group showed fewer platform crossings(1.250.93 vs.5.691.58),a longer escape latency(41.6536.55 s vs.6.382.11 s),and a lower novel object recognition index(29.7711.83 vs.83.695.67)than the control group.LIFUS treatment improved these metrics,with more platform crossings(3.130.34),a higher recognition index(65.588.71),and a shorter escape latency(6.452.27 s).Longitudinal analysis of the LIFUS group further confirmed these improvements.Neuroimaging revealed significant differences in diffusion tensor imaging metrics of specific brain regions pre-and post-LIFUS.Moreover,neuropathology showed higher dendritic spine density,less myelin loss,fewer apoptotic cells,more synapses,and less mitochondrial autophagy after LIFUS treatment.The neuroimaging indicators were correlated with behavioral improvements,highlighting the potential of LIFUS for alleviating cognitive impairment(as demonstrated through imaging and analysis).Our investigation of the molecular biological mechanisms revealed distinct protein expression patterns in the hippocampus and its subregions.In the model group,glial fibrillary acidic protein(GFAP)and ionized calcium-binding adaptor molecule 1(IBA1)expression levels were elevated across the hippocampus,whereas neuronal nuclei(NeuN)expression was reduced.Subregional analysis revealed higher GFAP and IBA1 and lower NeuN,especially in the dentate gyrus subregion.Moreover,positive cell areas were larger in the cornu ammonis(CA)1,CA2,CA3,and dentate gyrus regions.In the CA2 and CA3,significant differences among the groups were observed in GFAP-positive cell counts and areas,and there were variations in NeuN expression.Conclusions:Our results suggest that LIFUS can reverse cisplatin-induced cognitive impairments.The neuroimaging findings were consistent with the behavioral and histological results and suggest a neuropathological basis that supports further research into the clinical applications of LIFUS.Furthermore,LIFUS appeared to enhance the plasticity of neuronal synapses in the rat hippocampus and reduce hippocampal inflammation.These findings highlight the clinical potential of LIFUS as an effective,noninvasive therapeutic strategy and monitoring tool for chemotherapy-induced cognitive deficits.展开更多
As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion ...As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion method that considers anisotropy to improve the effectiveness of inversion accuracy and interpretation accuracy of data. This study is based on the 3D fi nite-diff erence forward modeling of axis anisotropy using the reciprocity theorem to calculate the Jacobian matrix by applying the search method to automatically search for the Lagrange operator. The aim is to establish inversion iteration equations to achieve the axis anisotropic Occam's 3D inversion of tensor CSAMT in data space. Further, we obtain an underground axis anisotropic 3D geoelectric model by inverting the impedance data of tensor CSAMT. Two synthetic data examples show that using the isotropic tensor CSAMT algorithm to directly invert data in anisotropic media can generate false anomalies, leading to incorrect geological interpretations. Meanwhile, the proposed anisotropic inversion algorithm can eff ectively improve the accuracy of data inversion in anisotropic media. Further, the inversion examples verify the eff ectiveness and stability of the algorithm.展开更多
In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice ...In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.展开更多
Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
Large-scale and heavily jointed rocks have inherent planes of anisotropy and secondary structural planes,such as dominant joint sets and random fractures,which result in significant differences in their failure mechan...Large-scale and heavily jointed rocks have inherent planes of anisotropy and secondary structural planes,such as dominant joint sets and random fractures,which result in significant differences in their failure mechanism and deformation behavior compared to other rock types.To address this issue,inherent anisotropic rocks with large-scale and dense joints are considered to be composed of the rock matrix,inherent planes of anisotropy,and secondary structural planes.Then a new implicit continuum model called LayerDFN is developed based on the crack tensor and damage tensor theories to characterize the mechanical properties of inherent anisotropic rocks.Furthermore,the LayerDFN model is implemented in the FLAC3D software,and a series of numerical results for typical example problems is compared with those obtained from the 3DEC,the analytical solutions,similar classical models,laboratory uniaxial compression tests,and field rigid bearing plate tests.The results demonstrate that the LayerDFN model can effectively capture the anisotropic mechanical properties of inherent anisotropic rocks,and can quantitatively characterize the damaging effect of the secondary structural planes.Overall,the numerical method based on the LayerDFN model provides a comprehensive and reliable approach for describing and analyzing the behavior of inherent anisotropic rocks,which will provide valuable insights for engineering design and decision-making processes.展开更多
Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-...Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.展开更多
In this paper,we investigate the method of compensating LTS SQUID Gradiometer Systems data.By matching the attitude changes of the pod in fl ight to the anomalies of the magnetic measurement data,we find that the yaw ...In this paper,we investigate the method of compensating LTS SQUID Gradiometer Systems data.By matching the attitude changes of the pod in fl ight to the anomalies of the magnetic measurement data,we find that the yaw attitude changes most dramatically and corresponds best to the magnetic data anomaly interval.Based on this finding,we solved the compensation model using least squares fitting and Huber's parametric fitting.By comparison,we found that the Huber parametric fit not only eliminates the interference introduced by attitude changes but also retains richer anomaly source information and therefore obtains a higher signal-to-noise ratio.The experimental results show that the quality of the magnetometry data obtained by using the compensation method proposed in this paper has been significantly improved,and the mean value of its improvement ratio can reach 118.93.展开更多
基金supported by the National Natural Science Foundation of China,No.82274304(to YH)the Major Clinical Study Projects of Shanghai Shenkang Hospital Development Center,No.SHDC2020CR2046B(to YH)Shanghai Municipal Health Commission Talent Plan,No.2022LJ010(to YH).
文摘Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological mechanisms,preventing and treating cerebral small vessel vasculopathy is challenging.Recent studies have shown that the glymphatic system plays a crucial role in interstitial solute clearance and the maintenance of brain homeostasis.Increasing evidence also suggests that dysfunction in glymphatic clearance is a key factor in the progression of cerebral small vessel disease.This review begins with a comprehensive introduction to the structure,function,and driving factors of the glymphatic system,highlighting its essential role in brain waste clearance.Afterwards,cerebral small vessel disease was reviewed from the perspective of the glymphatic system,after which the mechanisms underlying their correlation were summarized.Glymphatic dysfunction may lead to the accumulation of metabolic waste in the brain,thereby exacerbating the pathological processes associated with cerebral small vessel disease.The review also discussed the direct evidence of glymphatic dysfunction in patients and animal models exhibiting two subtypes of cerebral small vessel disease:arteriolosclerosis-related cerebral small vessel disease and amyloid-related cerebral small vessel disease.Diffusion tensor image analysis along the perivascular space is an important non-invasive tool for assessing the clearance function of the glymphatic system.However,the effectiveness of its parameters needs to be enhanced.Among various nervous system diseases,including cerebral small vessel disease,glymphatic failure may be a common final pathway toward dementia.Overall,this review summarizes prevention and treatment strategies that target glymphatic drainage and will offer valuable insight for developing novel treatments for cerebral small vessel disease.
文摘A detailed understanding of seismicity originating from the Nanga Parbat syntaxis in the northwestern Himalaya is crucial for characterizing the active fault systems and associated neotectonic processes in the region.Continuous earthquake monitoring through local seismic stations enables high-precision results by constraining the velocity structure.In this study,seismogram data from 244 small-magnitude earthquakes are analyzed to delineate the crustal thickness and investigate the source mechanisms beneath the Nanga Parbat syntaxis.The results are achieved with the application of Coupled Hypocenter Velocity Inversion(CHVI)analysis and Time Domain Moment Tensor(TDMT)analysis.The velocity inversion suggests that the Moho discontinuity lies at 60 km depth with an average vP/vS ratio of 1.735±0.017.The minimum 1D velocity model obtained through velocity inversion with least RMS error is further utilized in determining the source mechanism solution.In contrast to earlier studies,which highlighted strike-slip displacement accompanied by reverse dip-slip components,the present research provides a revised interpretation.The moment tensor analysis conducted in this study provides evidence of transtensional deformation associated with neotectonics,attributed to the presence of multiple shear zones.The results of the source mechanism for the selected earthquakes unveiled that the oblique-slip deformation is significantly controlled by the shear stresses coupled with the normal component of dip-slip movement.This is further supported by the higher values of the doublecouple moment tensor(85%),which indicate shear deformation,while the positive value of the compensated linear vector dipole(15%)confirms the presence of a normal component.The coexistence of transpressive and transtensive stresses,together with shallow hypocentral depths and high-amplitude tangential waveforms,can potentially cause devastating impacts in the surroundings of the Nanga Parbat syntaxis.
基金Project supported by Quantum Science and Technology–National Science and Technology Major Project(Grant No.2024ZD0301000)the National Natural Science Foundation of China(Grant No.12305031)+1 种基金the Hangzhou Joint Fund of the Natural Science Foundation of Zhejiang Province,China(Grant No.LHZSD24A050001)the Science Foundation of Zhejiang Sci-Tech University(Grant Nos.23062088Y and 23062153-Y)。
文摘The Wilczek–Zee connection(WZC)is a key concept in the study of topology of quantum systems.Here,we introduce the double Wilczek–Zee connection(DWZC)which naturally appears in the pure-state quantum geometric tensor(QGT),another important concept in the field of quantum geometry.The DWZC is Hermitian with respect to the two integer indices,just like the original Hermitian WZC.Based on the symmetric logarithmic derivative operator,we propose a mixed-state quantum geometric tensor.Using the symmetric properties of the DWZC,we find that the real part of the QGT is connected to the real part of the DWZC and the square of eigenvalue differences of the density matrix,whereas the imaginary part can be given in terms of the imaginary part of the DWZC and the cube of the eigenvalue differences.For density matrices with full rank or no full rank,the QGT can be given in terms of real and imaginary parts of the DWZC.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0710600)the National Natural Science Foundation of China(Grant Nos.42325401 and U1939202).
文摘Abstract:Accurate three-dimensional(3D)velocity models are essential for fitting high-frequency seismic waveform records.This process usually requires regional-scale 3D numerical simulations that are computationally expensive,especially with sparse seismic networks.Because of the significance of source domain modeling,we propose a hybrid waveform simulation approach that combines the 3D spectral-element method(SEM)with the displacement representation theorem.By separating near-source wavefield excitation from long-distance wave propagation to stations,only the source domain wavefield needs to be recomputed when the local velocity and source models change.We apply the method to the 2019 M_(w)5.0 Changning shallow earthquake to verify its flexibility and effectiveness.We compare high-frequency waveforms computed with different regional velocity models against observations.Results show that the hybrid method achieves accuracy comparable to full SEM 3D simulations while reducing computation costs by more than two orders of magnitude when the structure of the source region updates.Our results further indicate that high-frequency waveforms are highly sensitive to shallow structures.Introducing low-velocity shallow layers into the source region improves near-field waveform fits,indicating pronounced low-velocity sediments in the Changning area.Large surface-wave time delays suggest that shallow velocities within the Sichuan Basin are lower than those in existing published models.In addition,an Interferometric Synthetic Aperture Radar(InSAR)-derived finite-fault model outperforms the point-source model in near-field waveform fitting and better reproduces rupture directivity.The proposed method is practical for high-frequency waveform modeling in areas with complex subsurface structures and rupture processes.
文摘Multi-dimensional arrays are referred to as tensors.Tensor-valued predictors are commonly encountered in modern biomedical applications,such as electroencephalogram(EEG),magnetic resonance imaging(MRI),functional MRI(fMRI),diffusion-weighted MRI,and longitudinal health data.In survival analysis,it is both important and challenging to integrate clinically relevant information,such as gender,age,and disease state along with medical imaging tensor data or longitudinal health data to predict disease outcomes.Most existing higher-order sufficient dimension reduction regressions for matrix-or array-valued data focus solely on tensor data,often neglecting established clinical covariates that are readily available and known to have predictive value.Based on the idea of Folded-Minimum Average Variance Estimation(Folded-MAVE:Xue and Yin,2014),the authors propose a new method,Partial Dimension Folded-MAVE(PF-MAVE),to address regression mean functions with tensor-valued covariates while simultaneously incorporating clinical covariates,which are typically categorical variables.Theorems and simulation studies demonstrate the importance of incorporating these categorical clinical predictors.A survival analysis of a longitudinal study of primary biliary cirrhosis(PBC)data is included for illustration of the proposed method.
基金supported in part by the National Natural Science Foundation of China(62372385,62272078,62002337)Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX1486,CSTB2023NSCQ-LZX0069)。
文摘A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.
基金supported by the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2025TIAD-STX0032)National Key Research and Development Program of China(2024YFF0908200)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2024TIAD-KPX0018)the Southwest University Graduate Student Research Innovation(SWUB24051)。
文摘Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.
基金supported by the National Key R&D Program of China,No.2023YFE0209500(to ZQ)the Natural Science Foundation of Guangdong Province,China,Nos.2023A1515010772(to YL),2025A1515011720(to YL)+1 种基金the Medical Science and Technology Research Foundation of Guangdong Province,No.A2024120(to YL)the National Natural Science Foundation of China,No.U22A20371(to ZQ).
文摘The triple transgenic mouse model of Alzheimer’s disease(3×Tg-AD)is a widely used model that exhibits region-dependent patterns of progressive amyloid-βand tau pathology.Although structural brain abnormalities on magnetic resonance imaging have been observed in 3×Tg-AD mice at later disease stages(>12 months)and as early as 2 months,few studies have investigated changes in these mice during the stage with extensive amyloid-βdeposition and onset of tau pathology(around 9 months).This study aimed to assess brain morphometry and microstructure alterations in 9 month-old 3×Tg-AD mice to better understand the neural mechanisms underlying these specific pathological features.Voxel-based analyses were employed on T2-weighted and diffusion tensor imaging to identify differences between 3×Tg-AD and control mice.Compared with controls,3×Tg-AD mice exhibited lower gray matter volume in several regions including both hippocampal regions,the right thalamus,the left caudoputamen,and the cortex.Reduced white matter volume was observed in fiber tracts including the corpus callosum,internal capsule,stria terminalis,and olfactory tract.Whole-brain diffusion tensor imaging analysis revealed a significant decrease in fractional anisotropy and an increase in both radial and mean diffusivity within the left dentate gyrus of the hippocampal region and right striatum-like amygdala nuclei,with no significant difference in axial diffusivity.Correlation analyses demonstrated significant associations between behavioral performance measures,with both gray and white matter volumes within regions showing significant morphometric differences.Notably,behavioral performance also exhibited significant correlations with diffusion tensor imaging measures particularly within the left dentate gyrus of the hippocampal region and right striatum-like amygdala nuclei.Immunofluorescence analysis confirmed increased amyloid-βplaques and p-Tau protein expression in the hippocampal regions of 3×Tg-AD mice,which corroborated the magnetic resonance imaging findings.Transcriptome analysis in hippocampus tissue identified 1389 differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that numerous differentially expressed genes were enriched in biological processes relevant to synapse structure,cognition,learning,and memory,with particular emphasis on Wnt and mitogen-activated protein kinase signaling pathways.Collectively,these findings suggest that intricate anatomical and microstructural alterations occur in 3×Tg-AD model mice at the onset of pathology around 9 months,potentially driven by gene expression alterations.Moreover,our results support the potential utility of brain volume and diffusion metrics as biomarkers for Alzheimer’s disease pathology,which could have significant implications for clinical diagnosis of Alzheimer’s disease patients.
基金supported by the National Key R&D Program of China,Nos.2017YFA0104302(to NG and XM)and 2017YFA0104304(to BW and ZZ)
文摘Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.
基金supported by the National Natural Science Foundation Joint Fund,No.U22A20309(to PY)the Natural Science Foundation of LiaoningProvince,No.2023-MS-07(to HuL)the Unveiling Key Scientific and Technological Projects of Liaoning Province,No.2021JH1/10400051(to HuL).
文摘Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle to accurately capture microstructural changes.Various diffusion models have been used to study white matter in systemic lupus erythematosus;however,comparative analyses of their sensitivity and specificity for detecting microstructural changes remain insufficient.To address this,our team designed a diagnostic trial that used multimodal diffusion imaging techniques to observe white matter microstructural changes in patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with an aim to identify key diagnostic biomarkers for these patients.Patients with active lupus who received treatment at the Department of Rheumatology and Immunology,The First Affiliated Hospital of China Medical University,from September 2023 to March 2024 were recruited.According to the standards of the American College of Rheumatology,patients with systemic lupus erythematosus who had neuropsychiatric symptoms were assigned to the systemic lupus erythematosus group,whereas those without neuropsychiatric symptoms were assigned to the non-systemic lupus erythematosus group.Additionally,healthy volunteers matched by region,sex,and age were recruited as controls.All three groups underwent the same diffusion magnetic resonance imaging examination protocol to compare differences in diffusion parameters.Advanced diffusion imaging models were able to sensitively detect microstructural changes in the white matter fibers of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with specific diffusion parameters showing significant abnormalities in key brain regions.In the left superior longitudinal fasciculus subregion and the right thalamic radiations of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,we also identified abnormal diffusion characteristics that were clearly correlated with disease activity,suggesting that microstructural changes in these areas may reflect the dynamic process of neuroinflammatory damage.The present study addresses critical challenges in the diagnosis of systemic lupus erythematosus by identifying specific white matter imaging biomarkers and elucidating the association between microstructural damage and clinical manifestations.The main contributions of our study include:1)establishing axial regression probability parameters from mean apparent propagator magnetic resonance imaging as sensitive biomarkers for systemic lupus erythematosus,particularly in the third subregion of the left superior longitudinal fasciculus;2)demonstrating that multimodal diffusion imaging may be superior to conventional diffusion tensor imaging for detecting white matter microstructural abnormalities in patients with systemic lupus erythematosus;and 3)integrating tract-based spatial statistics with clinically relevant analyses to link imaging findings to pathological mechanisms.
基金supported in part by the National Natural Science Foundation of China(62372385).
文摘Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation framework on a DG via the tensor product for capturing the complex cohesive spatio-temporal interdependencies precisely and 2)Acquiring the alliance attention scores by node features and favorable high-order structural correlations.
基金supported by the National Natural Science Foundation of China(82171908 and 82102015)the General Project of the Nanjing Medical Science and Technology Development Program(YKK21075)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515140030).
文摘Background:Platinum can cause chemotherapy-related cognitive impairment.Low-intensity focused ultrasound(LIFUS)is a promising noninvasive physical stimulation method with a unique advantage in neurological rehabilitation.We aimed to investigate whether LIFUS can alleviate cisplatin-induced cognitive impairment in rats and explore the related neuropatho-logical mechanisms.Methods:After confirming the target position for LIFUS treatment in 18 rats,64 rats were randomly divided into four groups:control,model,sham,and LIFUS groups.Before and after LIFUS treatment,detailed biological behavioral assessments and magnetic resonance imaging were performed.Finally,the rats were euthanized,and relevant histopathological and molecular biological experiments were conducted and analyzed.Results:In the Morris water maze,the model group showed fewer platform crossings(1.250.93 vs.5.691.58),a longer escape latency(41.6536.55 s vs.6.382.11 s),and a lower novel object recognition index(29.7711.83 vs.83.695.67)than the control group.LIFUS treatment improved these metrics,with more platform crossings(3.130.34),a higher recognition index(65.588.71),and a shorter escape latency(6.452.27 s).Longitudinal analysis of the LIFUS group further confirmed these improvements.Neuroimaging revealed significant differences in diffusion tensor imaging metrics of specific brain regions pre-and post-LIFUS.Moreover,neuropathology showed higher dendritic spine density,less myelin loss,fewer apoptotic cells,more synapses,and less mitochondrial autophagy after LIFUS treatment.The neuroimaging indicators were correlated with behavioral improvements,highlighting the potential of LIFUS for alleviating cognitive impairment(as demonstrated through imaging and analysis).Our investigation of the molecular biological mechanisms revealed distinct protein expression patterns in the hippocampus and its subregions.In the model group,glial fibrillary acidic protein(GFAP)and ionized calcium-binding adaptor molecule 1(IBA1)expression levels were elevated across the hippocampus,whereas neuronal nuclei(NeuN)expression was reduced.Subregional analysis revealed higher GFAP and IBA1 and lower NeuN,especially in the dentate gyrus subregion.Moreover,positive cell areas were larger in the cornu ammonis(CA)1,CA2,CA3,and dentate gyrus regions.In the CA2 and CA3,significant differences among the groups were observed in GFAP-positive cell counts and areas,and there were variations in NeuN expression.Conclusions:Our results suggest that LIFUS can reverse cisplatin-induced cognitive impairments.The neuroimaging findings were consistent with the behavioral and histological results and suggest a neuropathological basis that supports further research into the clinical applications of LIFUS.Furthermore,LIFUS appeared to enhance the plasticity of neuronal synapses in the rat hippocampus and reduce hippocampal inflammation.These findings highlight the clinical potential of LIFUS as an effective,noninvasive therapeutic strategy and monitoring tool for chemotherapy-induced cognitive deficits.
基金supported by Heilongjiang Province Basic Research Business Expenses for Universities Heilongjiang University Special Fund Project (Grant No. 2023-KYYWF-1494)the Natural Science Foundation of Jiangxi Province (Grant No. 20212BAB213023)。
文摘As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion method that considers anisotropy to improve the effectiveness of inversion accuracy and interpretation accuracy of data. This study is based on the 3D fi nite-diff erence forward modeling of axis anisotropy using the reciprocity theorem to calculate the Jacobian matrix by applying the search method to automatically search for the Lagrange operator. The aim is to establish inversion iteration equations to achieve the axis anisotropic Occam's 3D inversion of tensor CSAMT in data space. Further, we obtain an underground axis anisotropic 3D geoelectric model by inverting the impedance data of tensor CSAMT. Two synthetic data examples show that using the isotropic tensor CSAMT algorithm to directly invert data in anisotropic media can generate false anomalies, leading to incorrect geological interpretations. Meanwhile, the proposed anisotropic inversion algorithm can eff ectively improve the accuracy of data inversion in anisotropic media. Further, the inversion examples verify the eff ectiveness and stability of the algorithm.
基金Supported by National Nature Science Foundation(12371381)Nature Science Foundation of Shanxi(202403021222270)。
文摘In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
基金supported by financial support from the National Natural Science Foundation of China(Grant Nos.52309122 and U2340229)the Innovation Team of Changjiang River Scientific Research Institute(Grant No.CKSF2024329/YT).
文摘Large-scale and heavily jointed rocks have inherent planes of anisotropy and secondary structural planes,such as dominant joint sets and random fractures,which result in significant differences in their failure mechanism and deformation behavior compared to other rock types.To address this issue,inherent anisotropic rocks with large-scale and dense joints are considered to be composed of the rock matrix,inherent planes of anisotropy,and secondary structural planes.Then a new implicit continuum model called LayerDFN is developed based on the crack tensor and damage tensor theories to characterize the mechanical properties of inherent anisotropic rocks.Furthermore,the LayerDFN model is implemented in the FLAC3D software,and a series of numerical results for typical example problems is compared with those obtained from the 3DEC,the analytical solutions,similar classical models,laboratory uniaxial compression tests,and field rigid bearing plate tests.The results demonstrate that the LayerDFN model can effectively capture the anisotropic mechanical properties of inherent anisotropic rocks,and can quantitatively characterize the damaging effect of the secondary structural planes.Overall,the numerical method based on the LayerDFN model provides a comprehensive and reliable approach for describing and analyzing the behavior of inherent anisotropic rocks,which will provide valuable insights for engineering design and decision-making processes.
基金supported by the National Natural Science Foundation of China(62272078)Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300210)
文摘Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.
基金Earth Observation and Navigation Special,Research on Low Temperature Superconducting Aeromagnetic Vector Gradient Observation Technology(2021YFB3900201)projectState Key Laboratory of Remote Sensing Science project.
文摘In this paper,we investigate the method of compensating LTS SQUID Gradiometer Systems data.By matching the attitude changes of the pod in fl ight to the anomalies of the magnetic measurement data,we find that the yaw attitude changes most dramatically and corresponds best to the magnetic data anomaly interval.Based on this finding,we solved the compensation model using least squares fitting and Huber's parametric fitting.By comparison,we found that the Huber parametric fit not only eliminates the interference introduced by attitude changes but also retains richer anomaly source information and therefore obtains a higher signal-to-noise ratio.The experimental results show that the quality of the magnetometry data obtained by using the compensation method proposed in this paper has been significantly improved,and the mean value of its improvement ratio can reach 118.93.
