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
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
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
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
The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelli...The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.展开更多
The quantum geometric tensor(QGT)is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena.The traditional QGT,defned...The quantum geometric tensor(QGT)is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena.The traditional QGT,defned only for pure states,has limited applicability in realistic scenarios where mixed states are common.To address this limitation,we generalize the defnition of the QGT to mixed states using the purifcation bundle and the covariant derivative.Notably,our proposed defnition reduces to the traditional QGT when mixed states approach pure states.In our framework,the real and imaginary parts of this generalized QGT correspond to the Bures metric and the mean gauge curvature,respectively,endowing it with a broad range of potential applications.Additionally,using our proposed mixed-state QGT,we derive the geodesic equation applicable to mixed states.This work establishes a unifed framework for the geometric analysis of both pure and mixed states,thereby deepening our understanding of the geometric properties of quantum states.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
This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great...This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained.This ResNet-based moment tensor prediction technology,whose input is raw recordings,does not require the extraction of data features in advance.First,we tested the network using synthetic data and performed a quantitative assessment of the errors.The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase.Next,we tested the network using real microseismic data and compared the results with those from traditional inversion methods.The error in the results was relatively small compared to traditional methods.However,the network operates more efficiently without requiring manual intervention,making it highly valuable for near-real-time monitoring applications.展开更多
Absorption compensation is a process involving the exponential amplification of reflection amplitudes.This process amplifies the seismic signal and noise,thereby substantially reducing the signal-tonoise ratio of seis...Absorption compensation is a process involving the exponential amplification of reflection amplitudes.This process amplifies the seismic signal and noise,thereby substantially reducing the signal-tonoise ratio of seismic data.Therefore,this paper proposes a multichannel inversion absorption compensation method based on structure tensor regularization.First,the structure tensor is utilized to extract the spatial inclination of seismic signals,and the spatial prediction filter is designed along the inclination direction.The spatial prediction filter is then introduced into the regularization condition of multichannel inversion absorption compensation,and the absorption compensation is realized under the framework of multichannel inversion theory.The spatial predictability of seismic signals is also introduced into the objective function of absorption compensation inversion.Thus,the inversion system can effectively suppress the noise amplification effect during absorption compensation and improve the recovery accuracy of high-frequency signals.Synthetic and field data tests are conducted to demonstrate the accuracy and effectiveness of the proposed method.展开更多
Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors,which exceedingly has gained significant interest from both academic and industrial fields.However,te...Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors,which exceedingly has gained significant interest from both academic and industrial fields.However,temporary-sudden activities caused by accidental behavior pose a major challenge to motion recognition and have been largely overlooked in existing works.To address this problem,the multi-dimensional time series of motion data is modeled as a Time-Frequency(TF)tensor,and the original challenge is transformed into a problem of outlier-corrupted tensor pattern recognition,where transient sudden activity data are considered as outliers.Since the TF tensor can capture the latent spatio-temporal correlations of the motion data,the tensor MPCA is used to derive the principal spatio-temporal pattern of the motion data.However,traditional MPCA uses the squared F-norm as the projection distance measure,which makes it sensitive to the presence of outlier motion data.Therefore,in the proposed outlier-robust MPCA scheme,the F-norm with the desirable geometric properties is used as the distance measure to simultaneously mitigate the interference of outlier motion data while preserving rotational invariance.Moreover,to reduce the complexity of outlier-robust motion recognition,we impose the proposed outlier-robust MPCA scheme on the traditional MPCANet which is a low-complexity deep learning network.The experimental results show that our proposed outlier-robust MPCANet can simultaneously improve motion recognition performance and reduce the complexity,especially in practical scenarios where the real-time data is corrupted by temporary-sudden activities.展开更多
BACKGROUND Subarachnoid hemorrhage(SAH)is associated with high incidence of anxiety and depression disorders(27%-54%and 20%-42%,respectively),significantly affecting patient quality of life.However,the pathophysiologi...BACKGROUND Subarachnoid hemorrhage(SAH)is associated with high incidence of anxiety and depression disorders(27%-54%and 20%-42%,respectively),significantly affecting patient quality of life.However,the pathophysiological mechanisms underlying post-SAH emotional disorders remain poorly understood,limiting targeted therapeutic interventions.AIM To identify potential biomarkers and therapeutic targets through comprehensive analysis of behavioral,neuroimaging,and inflammatory parameters in a rat SAH model.METHODS We established a rat SAH model using cisternal injection of autologous blood and conducted comprehensive assessments including behavioral tests(elevated plus maze,forced swimming test,sucrose preference test),diffusion tensor imaging(DTI),and inflammatory factor detection.Seventy-two male SD rats were randomly divided into sham and SAH groups,with evaluations performed at multiple time points(1 hour to 72 hours post-hemorrhage).DTI parameters including fractional anisotropy(FA)and apparent diffusion coefficient were measured in limbic-prefrontal circuits.Serum and cerebrospinal fluid inflammatory markers[interleukin-6(IL-6),IL-1β,tumor necrosis factor-α]were quantified using enzyme-linked immunosorbent assay.RESULTS SAH rats exhibited significant anxiety-like and depression-like behaviors at 12 hours,which further deteriorated at 24 hours(open arm time:30.3±4.7 seconds vs 82.1±8.3 seconds in controls,P<0.01;immobility time:136.5±12.7 seconds vs 78.3±9.2 seconds in controls,P<0.01).DTI analysis revealed progressive white matter microstructural damage,with hippocampus-prefrontal FA values decreasing by 21.8%and amygdala-prefrontal FA values by 20.3%at 24 hours(P<0.001).Apparent diffusion coefficient values significantly decreased at 12 hours,indicating cellular edema.Inflammatory markers showed marked elevation,with stronger correlations between cerebrospinal fluid IL-1βand behavioral changes(r=0.72-0.81,P<0.001).CONCLUSION This study demonstrates that post-SAH emotional disorders result from a temporal cascade involving early neuroinflammation and progressive limbic-prefrontal circuit microstructural damage.展开更多
Spinal cord injury and non-traumatic myelopathies are major causes of lifelong disability,yet conventional magnetic resonance imaging(MRI)can underestimate microstructural damage.Diffusion tensor imaging(DTI)and tract...Spinal cord injury and non-traumatic myelopathies are major causes of lifelong disability,yet conventional magnetic resonance imaging(MRI)can underestimate microstructural damage.Diffusion tensor imaging(DTI)and tractography map white-matter integrity by measuring fractional anisotropy(FA)and mean diffusivity(MD),but their adoption in spine imaging has been limited by long scan times and complex post-processing.Supsupin et al report a two-minute cervical DTI sequence integrated into routine MRI and applied to four representative pathologies–spinal cord contusion,metastatic compression,degenerative myelopathy,and multiple sclerosis–compared with five controls.Each lesion showed distinctive tractographic and quantitative patterns:For example,reduced FA with preserved MD in contusion and combined FA decrease and MD elevation in metastatic compression.These findings highlight the potential of tractography to improve diagnosis,guide surgical planning,and monitor treatment,while maintaining clinical feasibility.Remaining challenges include limited angular resolution,motion artifacts,and the need for multicenter validation and advanced reconstruction methods.This manuscript places the study in the context of current spinal diffusion imaging and outlines future directions toward routine,precision care.展开更多
The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-d...The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-driven intrusion detection system for Distributed Denial of Service(DDoS)attack detection.The system focuses on intrusion detection from a big data perceptive.As intelligent information processing methods,big data and artificial intelligence have been widely used in information systems.The INS system is an important information system in cyberspace.In advanced INS systems,the network architectures have become more complex.And the smart devices in INS systems collect a large scale of network data.How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge.To address the problem,we design a novel intrusion detection system(IDS)from a big data perspective.The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor.Then,a novel tensor decomposition(TD)method is developed to complete big data mining.The TD method seamlessly collaborates with the XGBoost(eXtreme Gradient Boosting)method to complete the intrusion detection.To verify the proposed IDS system,a series of experiments is conducted on two real network datasets.The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%.Additionally,by altering the scale of the datasets,the proposed IDS system still maintains excellent detection performance,which demonstrates the proposed IDS system’s robustness.展开更多
The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a ...The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions.In the past,the“1-phase,1-partition”rule was usually adopted in numerical analysis,where one constituent phase at the mesoscale formed one partition.The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the same constituent,while it also introduces numerical inaccuracy.This study proposes a new partitioning method for fibrous unitcells under a reduced-order homogenization methodology.In this method,the fiber phase remains 1 partition,but the matrix phase is divided into 2 partitions,which refers to the“12”partitioning scheme.Analytical elastic influence+functions are derived by introducing the elastic strain energy equivalence(Hill-Mandel condition).This research also obtains the analytical eigenstrain influence functions by alleviating the so-called“inclusion-locking”phenomenon.In addition,a numerical approach to minimize the error of strain energy density is introduced to determine the partitioning of the matrix phase.Several numerical examples are presented to compare the differences among direct numerical simulation(DNS),“11”,and“12”partitioning schemes.The numerical simulations show improved++numerical accuracy by the“12”partitioning scheme.展开更多
Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either dire...Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either directly or by reducing it to other problems.This paper introduces the Julia ecosystem for solving and analyzing CSPs with a focus on the programming practices.We introduce some important CSPs and show how these problems are reduced to each other.We also show how to transform CSPs into tensor networks,how to optimize the tensor network contraction orders,and how to extract the solution space properties by contracting the tensor networks with generic element types.Examples are given,which include computing the entropy constant,analyzing the overlap gap property,and the reduction between CSPs.展开更多
When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and ne...When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.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.
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 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 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.
基金supported by the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant Nos.12347104,U24A2017,12461160276,and 12175075)the National Key Research and Development Program of China(Grant No.2023YFC2205802)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20243060 and BK20233001)in part by the State Key Laboratory of Advanced Optical Communication Systems and Networks,China。
文摘The quantum geometric tensor(QGT)is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena.The traditional QGT,defned only for pure states,has limited applicability in realistic scenarios where mixed states are common.To address this limitation,we generalize the defnition of the QGT to mixed states using the purifcation bundle and the covariant derivative.Notably,our proposed defnition reduces to the traditional QGT when mixed states approach pure states.In our framework,the real and imaginary parts of this generalized QGT correspond to the Bures metric and the mean gauge curvature,respectively,endowing it with a broad range of potential applications.Additionally,using our proposed mixed-state QGT,we derive the geodesic equation applicable to mixed states.This work establishes a unifed framework for the geometric analysis of both pure and mixed states,thereby deepening our understanding of the geometric properties of quantum states.
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 dation Foun-of China(Grant Number 42272204)Key Laboratory of Coal sources Re-Exploration and Comprehensive Utilization,Ministry of Natural Resources,Canada(SMDZ-KF2024-4)+1 种基金the Fundamental Research Funds for the Central Universities,China(Grant No.2024JCCXDC06)supported in part by open fund project of State Key Laboratory for Fine Exploration and Intelligent Development of Coal Research(SKLCRSM23KFA04)。
文摘This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained.This ResNet-based moment tensor prediction technology,whose input is raw recordings,does not require the extraction of data features in advance.First,we tested the network using synthetic data and performed a quantitative assessment of the errors.The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase.Next,we tested the network using real microseismic data and compared the results with those from traditional inversion methods.The error in the results was relatively small compared to traditional methods.However,the network operates more efficiently without requiring manual intervention,making it highly valuable for near-real-time monitoring applications.
基金funded by the National Key R&D Program of China(Grant no.2018YFA0702504)the Sinopec research project(P22162).
文摘Absorption compensation is a process involving the exponential amplification of reflection amplitudes.This process amplifies the seismic signal and noise,thereby substantially reducing the signal-tonoise ratio of seismic data.Therefore,this paper proposes a multichannel inversion absorption compensation method based on structure tensor regularization.First,the structure tensor is utilized to extract the spatial inclination of seismic signals,and the spatial prediction filter is designed along the inclination direction.The spatial prediction filter is then introduced into the regularization condition of multichannel inversion absorption compensation,and the absorption compensation is realized under the framework of multichannel inversion theory.The spatial predictability of seismic signals is also introduced into the objective function of absorption compensation inversion.Thus,the inversion system can effectively suppress the noise amplification effect during absorption compensation and improve the recovery accuracy of high-frequency signals.Synthetic and field data tests are conducted to demonstrate the accuracy and effectiveness of the proposed method.
基金supported by the National Science Foundation of China under Grant No.62101467。
文摘Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors,which exceedingly has gained significant interest from both academic and industrial fields.However,temporary-sudden activities caused by accidental behavior pose a major challenge to motion recognition and have been largely overlooked in existing works.To address this problem,the multi-dimensional time series of motion data is modeled as a Time-Frequency(TF)tensor,and the original challenge is transformed into a problem of outlier-corrupted tensor pattern recognition,where transient sudden activity data are considered as outliers.Since the TF tensor can capture the latent spatio-temporal correlations of the motion data,the tensor MPCA is used to derive the principal spatio-temporal pattern of the motion data.However,traditional MPCA uses the squared F-norm as the projection distance measure,which makes it sensitive to the presence of outlier motion data.Therefore,in the proposed outlier-robust MPCA scheme,the F-norm with the desirable geometric properties is used as the distance measure to simultaneously mitigate the interference of outlier motion data while preserving rotational invariance.Moreover,to reduce the complexity of outlier-robust motion recognition,we impose the proposed outlier-robust MPCA scheme on the traditional MPCANet which is a low-complexity deep learning network.The experimental results show that our proposed outlier-robust MPCANet can simultaneously improve motion recognition performance and reduce the complexity,especially in practical scenarios where the real-time data is corrupted by temporary-sudden activities.
基金Supported by Clinical Medicine Research and Translational Project of Anhui Province,No.202204295107020036 and No.202304295107020076the Science and Technology Innovation Guidance Project of Bengbu City,No.20200338.
文摘BACKGROUND Subarachnoid hemorrhage(SAH)is associated with high incidence of anxiety and depression disorders(27%-54%and 20%-42%,respectively),significantly affecting patient quality of life.However,the pathophysiological mechanisms underlying post-SAH emotional disorders remain poorly understood,limiting targeted therapeutic interventions.AIM To identify potential biomarkers and therapeutic targets through comprehensive analysis of behavioral,neuroimaging,and inflammatory parameters in a rat SAH model.METHODS We established a rat SAH model using cisternal injection of autologous blood and conducted comprehensive assessments including behavioral tests(elevated plus maze,forced swimming test,sucrose preference test),diffusion tensor imaging(DTI),and inflammatory factor detection.Seventy-two male SD rats were randomly divided into sham and SAH groups,with evaluations performed at multiple time points(1 hour to 72 hours post-hemorrhage).DTI parameters including fractional anisotropy(FA)and apparent diffusion coefficient were measured in limbic-prefrontal circuits.Serum and cerebrospinal fluid inflammatory markers[interleukin-6(IL-6),IL-1β,tumor necrosis factor-α]were quantified using enzyme-linked immunosorbent assay.RESULTS SAH rats exhibited significant anxiety-like and depression-like behaviors at 12 hours,which further deteriorated at 24 hours(open arm time:30.3±4.7 seconds vs 82.1±8.3 seconds in controls,P<0.01;immobility time:136.5±12.7 seconds vs 78.3±9.2 seconds in controls,P<0.01).DTI analysis revealed progressive white matter microstructural damage,with hippocampus-prefrontal FA values decreasing by 21.8%and amygdala-prefrontal FA values by 20.3%at 24 hours(P<0.001).Apparent diffusion coefficient values significantly decreased at 12 hours,indicating cellular edema.Inflammatory markers showed marked elevation,with stronger correlations between cerebrospinal fluid IL-1βand behavioral changes(r=0.72-0.81,P<0.001).CONCLUSION This study demonstrates that post-SAH emotional disorders result from a temporal cascade involving early neuroinflammation and progressive limbic-prefrontal circuit microstructural damage.
文摘Spinal cord injury and non-traumatic myelopathies are major causes of lifelong disability,yet conventional magnetic resonance imaging(MRI)can underestimate microstructural damage.Diffusion tensor imaging(DTI)and tractography map white-matter integrity by measuring fractional anisotropy(FA)and mean diffusivity(MD),but their adoption in spine imaging has been limited by long scan times and complex post-processing.Supsupin et al report a two-minute cervical DTI sequence integrated into routine MRI and applied to four representative pathologies–spinal cord contusion,metastatic compression,degenerative myelopathy,and multiple sclerosis–compared with five controls.Each lesion showed distinctive tractographic and quantitative patterns:For example,reduced FA with preserved MD in contusion and combined FA decrease and MD elevation in metastatic compression.These findings highlight the potential of tractography to improve diagnosis,guide surgical planning,and monitor treatment,while maintaining clinical feasibility.Remaining challenges include limited angular resolution,motion artifacts,and the need for multicenter validation and advanced reconstruction methods.This manuscript places the study in the context of current spinal diffusion imaging and outlines future directions toward routine,precision care.
基金supported in part by the National Nature Science Foundation of China under Project 62166047in part by the Yunnan International Joint Laboratory of Natural Rubber Intelligent Monitor and Digital Applications under Grant 202403AP140001in part by the Xingdian Talent Support Program under Grant YNWR-QNBJ-2019-270.
文摘The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-driven intrusion detection system for Distributed Denial of Service(DDoS)attack detection.The system focuses on intrusion detection from a big data perceptive.As intelligent information processing methods,big data and artificial intelligence have been widely used in information systems.The INS system is an important information system in cyberspace.In advanced INS systems,the network architectures have become more complex.And the smart devices in INS systems collect a large scale of network data.How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge.To address the problem,we design a novel intrusion detection system(IDS)from a big data perspective.The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor.Then,a novel tensor decomposition(TD)method is developed to complete big data mining.The TD method seamlessly collaborates with the XGBoost(eXtreme Gradient Boosting)method to complete the intrusion detection.To verify the proposed IDS system,a series of experiments is conducted on two real network datasets.The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%.Additionally,by altering the scale of the datasets,the proposed IDS system still maintains excellent detection performance,which demonstrates the proposed IDS system’s robustness.
基金funded by the National Key R&D Program of China(Grant No.2023YFA1008901)the National Natural Science Foundation of China(Grant Nos.11988102,12172009)“The Fundamental Research Funds for the Central Universities,Peking University”.
文摘The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions.In the past,the“1-phase,1-partition”rule was usually adopted in numerical analysis,where one constituent phase at the mesoscale formed one partition.The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the same constituent,while it also introduces numerical inaccuracy.This study proposes a new partitioning method for fibrous unitcells under a reduced-order homogenization methodology.In this method,the fiber phase remains 1 partition,but the matrix phase is divided into 2 partitions,which refers to the“12”partitioning scheme.Analytical elastic influence+functions are derived by introducing the elastic strain energy equivalence(Hill-Mandel condition).This research also obtains the analytical eigenstrain influence functions by alleviating the so-called“inclusion-locking”phenomenon.In addition,a numerical approach to minimize the error of strain energy density is introduced to determine the partitioning of the matrix phase.Several numerical examples are presented to compare the differences among direct numerical simulation(DNS),“11”,and“12”partitioning schemes.The numerical simulations show improved++numerical accuracy by the“12”partitioning scheme.
基金funded by the National Key R&D Program of China(Grant No.2024YFE0102500)the National Natural Science Foundation of China(Grant No.12404568)+1 种基金the Guangzhou Municipal Science and Technology Project(Grant No.2023A03J00904)the Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area,China and the Undergraduate Research Project from HKUST(Guangzhou).
文摘Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either directly or by reducing it to other problems.This paper introduces the Julia ecosystem for solving and analyzing CSPs with a focus on the programming practices.We introduce some important CSPs and show how these problems are reduced to each other.We also show how to transform CSPs into tensor networks,how to optimize the tensor network contraction orders,and how to extract the solution space properties by contracting the tensor networks with generic element types.Examples are given,which include computing the entropy constant,analyzing the overlap gap property,and the reduction between CSPs.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the Guangxi Key Research and Development Projects of China(GuikeAB21238004)the Agricultural Science and Technology Innovation Program.
文摘When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.
