Neuropathic pain is frequently comorbidity with cognitive deficits.Neuralized1(Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory.However,the role of Neurl1 in the cognitive...Neuropathic pain is frequently comorbidity with cognitive deficits.Neuralized1(Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory.However,the role of Neurl1 in the cognitive impairment in neuropathic pain remains elusive.Herein,we found that lumbar 5 spinal nerve ligation(SNL)in male rat-induced neuropathic pain was followed by learning and memory deficits and LTP impairment in the hippocampus.The Neurl1 expression in the hippocampal CA1 was decreased after SNL.And this decrease paralleled the reduction of ubiquitinated-CPEB3 level and reduced production of GluA1 and GluA2.Overexpression of Neurl1 in the CA1 rescued cognitive deficits and LTP impairment,and reversed the reduction of ubiquitinated-CPEB3 level and the decrease of GluA1 and GluA2 production following SNL.Specific knockdown of Neurl1 or CPEB3 in bilateral hippocampal CA1 in naïve rats resulted in cognitive deficits and impairment of synaptic plasticity.The rescued cognitive function and synaptic plasticity by the treatment of overexpression of Neurl1 before SNL were counteracted by the knockdown of CPEB3 in the CA1.Collectively,the above results suggest that the downregulation of Neurl1 through reducing CPEB3 ubiquitination and,in turn,repressing GluA1 and GluA2 production and mediating synaptic plasticity impairment in hippocampal CA1 leads to the genesis of cognitive deficits in neuropathic pain.展开更多
After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the tim...After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the timing of interventions,combined with the limitations of current methods.To address these challenges,various techniques have been developed to aid in the repair and reconstruction of neural circuits at different stages of injury.Notably,neuromodulation has garnered considerable attention for its potential to enhance nerve regeneration,provide neuroprotection,restore neurons,and regulate the neural reorganization of circuits within the cerebral cortex and corticospinal tract.To improve the effectiveness of these interventions,the implementation of multitarget early interventional neuromodulation strategies,such as electrical and magnetic stimulation,is recommended to enhance functional recovery across different phases of nerve injury.This review concisely outlines the challenges encountered following spinal cord injury,synthesizes existing neurostimulation techniques while emphasizing neuroprotection,repair,and regeneration of impaired connections,and advocates for multi-targeted,task-oriented,and timely interventions.展开更多
Regulatory T cells,a subset of CD4^(+)T cells,play a critical role in maintaining immune tolerance and tissue homeostasis due to their potent immunosuppressive properties.Recent advances in research have highlighted t...Regulatory T cells,a subset of CD4^(+)T cells,play a critical role in maintaining immune tolerance and tissue homeostasis due to their potent immunosuppressive properties.Recent advances in research have highlighted the important therapeutic potential of Tregs in neurological diseases and tissue repair,emphasizing their multifaceted roles in immune regulation.This review aims to summarize and analyze the mechanisms of action and therapeutic potential of Tregs in relation to neurological diseases and neural regeneration.Beyond their classical immune-regulatory functions,emerging evidence points to non-immune mechanisms of regulatory T cells,particularly their interactions with stem cells and other non-immune cells.These interactions contribute to optimizing the repair microenvironment and promoting tissue repair and nerve regeneration,positioning non-immune pathways as a promising direction for future research.By modulating immune and non-immune cells,including neurons and glia within neural tissues,Tregs have demonstrated remarkable efficacy in enhancing regeneration in the central and peripheral nervous systems.Preclinical studies have revealed that Treg cells interact with neurons,glial cells,and other neural components to mitigate inflammatory damage and support functional recovery.Current mechanistic studies show that Tregs can significantly promote neural repair and functional recovery by regulating inflammatory responses and the local immune microenvironment.However,research on the mechanistic roles of regulatory T cells in other diseases remains limited,highlighting substantial gaps and opportunities for exploration in this field.Laboratory and clinical studies have further advanced the application of regulatory T cells.Technical advances have enabled efficient isolation,ex vivo expansion and functionalization,and adoptive transfer of regulatory T cells,with efficacy validated in animal models.Innovative strategies,including gene editing,cell-free technologies,biomaterial-based recruitment,and in situ delivery have expanded the therapeutic potential of regulatory T cells.Gene editing enables precise functional optimization,while biomaterial and in situ delivery technologies enhance their accumulation and efficacy at target sites.These advancements not only improve the immune-regulatory capacity of regulatory T cells but also significantly enhance their role in tissue repair.By leveraging the pivotal and diverse functions of Tregs in immune modulation and tissue repair,regulatory T cells–based therapies may lead to transformative breakthroughs in the treatment of neurological diseases.展开更多
The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It ha...The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).展开更多
Adult neurogenesis is a highly dynamic process that leads to the production of new neurons from a population of quiescent neural stem cells(NSCs).In response to specific endogenous and/or external stimuli,NSCs enter a...Adult neurogenesis is a highly dynamic process that leads to the production of new neurons from a population of quiescent neural stem cells(NSCs).In response to specific endogenous and/or external stimuli,NSCs enter a state of mitotic activation,initiating proliferation and differentiation pathways.Throughout this process,NSCs give rise to neural progenitors,which undergo multiple replicative and differentiative steps,each governed by precise molecular pathways that coordinate cellular changes and signals from the surrounding neurogenic niche.展开更多
Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence archite...Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.展开更多
The hippocampus is part of the brain limbic system and plays an important role in learning and memory.Moreover,its ability to form,consolidate,and retrieve different types of memories makes it a central component in t...The hippocampus is part of the brain limbic system and plays an important role in learning and memory.Moreover,its ability to form,consolidate,and retrieve different types of memories makes it a central component in the cognitive functions necessary for everyday life.Understanding the role of the hippocampus helps comprehend how memories are created,stored,and recalled and sheds light on the impact of hippocampal damage in conditions such as Alzheimer’s disease and other forms of dementia.展开更多
Epilepsy,a common neurological disorder,is characterized by recurrent seizures that can lead to cognitive,psychological,and neurobiological consequences.The pathogenesis of epilepsy involves neuronal dysfunction at th...Epilepsy,a common neurological disorder,is characterized by recurrent seizures that can lead to cognitive,psychological,and neurobiological consequences.The pathogenesis of epilepsy involves neuronal dysfunction at the molecular,cellular,and neural circuit levels.Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits.The continuous emergence of new technologies and the rapid advancement of existing ones have facilitated the discovery and comprehensive understanding of the neural circuit mechanisms underlying epilepsy.Therefore,this review aims to investigate the current understanding of the neural circuit mechanisms in epilepsy based on various technologies,including electroencephalography,magnetic resonance imaging,optogenetics,chemogenetics,deep brain stimulation,and brain-computer interfaces.Additionally,this review discusses these mechanisms from three perspectives:structural,synaptic,and transmitter circuits.The findings reveal that the neural circuit mechanisms of epilepsy encompass information transmission among different structures,interactions within the same structure,and the maintenance of homeostasis at the cellular,synaptic,and neurotransmitter levels.These findings offer new insights for investigating the pathophysiological mechanisms of epilepsy and enhancing its clinical diagnosis and treatment.展开更多
Epilepsy is a leading cause of disability and mortality worldwide. However, despite the availability of more than 20 antiseizure medications, more than one-third of patients continue to experience seizures. Given the ...Epilepsy is a leading cause of disability and mortality worldwide. However, despite the availability of more than 20 antiseizure medications, more than one-third of patients continue to experience seizures. Given the urgent need to explore new treatment strategies for epilepsy, recent research has highlighted the potential of targeting gliosis, metabolic disturbances, and neural circuit abnormalities as therapeutic strategies. Astrocytes, the largest group of nonneuronal cells in the central nervous system, play several crucial roles in maintaining ionic and energy metabolic homeostasis in neurons, regulating neurotransmitter levels, and modulating synaptic plasticity. This article briefly reviews the critical role of astrocytes in maintaining balance within the central nervous system. Building on previous research, we discuss how astrocyte dysfunction contributes to the onset and progression of epilepsy through four key aspects: the imbalance between excitatory and inhibitory neuronal signaling, dysregulation of metabolic homeostasis in the neuronal microenvironment, neuroinflammation, and the formation of abnormal neural circuits. We summarize relevant basic research conducted over the past 5 years that has focused on modulating astrocytes as a therapeutic approach for epilepsy. We categorize the therapeutic targets proposed by these studies into four areas: restoration of the excitation–inhibition balance, reestablishment of metabolic homeostasis, modulation of immune and inflammatory responses, and reconstruction of abnormal neural circuits. These targets correspond to the pathophysiological mechanisms by which astrocytes contribute to epilepsy. Additionally, we need to consider the potential challenges and limitations of translating these identified therapeutic targets into clinical treatments. These limitations arise from interspecies differences between humans and animal models, as well as the complex comorbidities associated with epilepsy in humans. We also highlight valuable future research directions worth exploring in the treatment of epilepsy and the regulation of astrocytes, such as gene therapy and imaging strategies. The findings presented in this review may help open new therapeutic avenues for patients with drugresistant epilepsy and for those suffering from other central nervous system disorders associated with astrocytic dysfunction.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
The nervous system function requires a precise but plastic neural architecture.The neuronal shape dictates how neurons interact with each other and with other cells,being the morphology of dendrites and axons the cent...The nervous system function requires a precise but plastic neural architecture.The neuronal shape dictates how neurons interact with each other and with other cells,being the morphology of dendrites and axons the central determinant of the functional properties of neurons and neural circuits.The topological and structural morphology of axons and dendrites defines and determines how synapses are conformed.The morphological diversity of axon and dendrite arborization governs the neuron’s inputs,synaptic integration,neuronal computation,signal transmission,and network circuitry,hence defining the particular connectivity and function of the different brain areas.展开更多
Optogenetics has revolutionized the field of neuroscience by enabling precise control of neural activity through light-sensitive proteins known as opsins.This review article discusses the fundamental principles of opt...Optogenetics has revolutionized the field of neuroscience by enabling precise control of neural activity through light-sensitive proteins known as opsins.This review article discusses the fundamental principles of optogenetics,including the activation of both excitatory and inhibitory opsins,as well as the development of optogenetic models that utilize recombinant viral vectors.A considerable portion of the article addresses the limitations of optogenetic tools and explores strategies to overcome these challenges.These strategies include the use of adeno-associated viruses,cell-specific promoters,modified opsins,and methodologies such as bioluminescent optogenetics.The application of viral recombinant vectors,particularly adeno-associated viruses,is emerging as a promising avenue for clinical use in delivering opsins to target cells.This trend indicates the potential for creating tools that offer greater flexibility and accuracy in opsin delivery.The adaptations of these viral vectors provide advantages in optogenetic studies by allowing for the restricted expression of opsins through cell-specific promoters and various viral serotypes.The article also examines different cellular targets for optogenetics,including neurons,astrocytes,microglia,and Schwann cells.Utilizing specific promoters for opsin expression in these cells is essential for achieving precise and efficient stimulation.Research has demonstrated that optogenetic stimulation of both neurons and glial cells-particularly the distinct phenotypes of microglia,astrocytes,and Schwann cells-can have therapeutic effects in neurological diseases.Glial cells are increasingly recognized as important targets for the treatment of these disorders.Furthermore,the article emphasizes the emerging field of bioluminescent optogenetics,which combines optogenetic principles with bioluminescent proteins to visualize and manipulate neural activity in real time.By integrating molecular genetics techniques with bioluminescence,researchers have developed methods to monitor neuronal activity efficiently and less invasively,enhancing our understanding of central nervous system function and the mechanisms of plasticity in neurological disorders beyond traditional neurobiological methods.Evidence has shown that optogenetic modulation can enhance motor axon regeneration,achieve complete sensory reinnervation,and accelerate the recovery of neuromuscular function.This approach also induces complex patterns of coordinated motor neuron activity and promotes neural reorganization.Optogenetic approaches hold immense potential for therapeutic interventions in the central nervous system.They enable precise control of neural circuits and may offer new treatments for neurological disorders,particularly spinal cord injuries,peripheral nerve injuries,and other neurodegenerative diseases.展开更多
Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postn...Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postnatal neurogenesis remains unclear.In this study,to define the precise role of transforming growth factor-βsignaling in postnatal neurogenesis at distinct stages of the neurogenic cascade both in vitro and in vivo,we developed two novel inducible and cell type-specific mouse models to specifically silence transforming growth factor-βsignaling in neural stem cells in(mGFAPcre-ALK5fl/fl-Ai9)or immature neuroblasts in(DCXcreERT2-ALK5fl/fl-Ai9).Our data showed that exogenous transforming growth factor-βtreatment led to inhibition of the proliferation of primary neural stem cells while stimulating their migration.These effects were abolished in activin-like kinase 5(ALK5)knockout primary neural stem cells.Consistent with this,inhibition of transforming growth factor-βsignaling with SB-431542 in wild-type neural stem cells stimulated proliferation while inhibited the migration of neural stem cells.Interestingly,deletion of transforming growth factor-βreceptor in neural stem cells in vivo inhibited the migration of postnatal born neurons in mGFAPcre-ALK5fl/fl-Ai9 mice,while abolishment of transforming growth factor-βsignaling in immature neuroblasts in DCXcreERT2-ALK5fl/fl-Ai9 mice did not affect the migration of these cells in the hippocampus.In summary,our data supports a dual role of transforming growth factor-βsignaling in the proliferation and migration of neural stem cells in vitro.Moreover,our data provides novel insights on cell type-specific-dependent requirements of transforming growth factor-βsignaling on neural stem cell proliferation and migration in vivo.展开更多
Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to ...Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.展开更多
Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ...Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.展开更多
基金supported by the National Natural Science Foundation of China(82171237,82471246,and 82401462)the Natural Science Foundation of Henan Province,China(242300420381).
文摘Neuropathic pain is frequently comorbidity with cognitive deficits.Neuralized1(Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory.However,the role of Neurl1 in the cognitive impairment in neuropathic pain remains elusive.Herein,we found that lumbar 5 spinal nerve ligation(SNL)in male rat-induced neuropathic pain was followed by learning and memory deficits and LTP impairment in the hippocampus.The Neurl1 expression in the hippocampal CA1 was decreased after SNL.And this decrease paralleled the reduction of ubiquitinated-CPEB3 level and reduced production of GluA1 and GluA2.Overexpression of Neurl1 in the CA1 rescued cognitive deficits and LTP impairment,and reversed the reduction of ubiquitinated-CPEB3 level and the decrease of GluA1 and GluA2 production following SNL.Specific knockdown of Neurl1 or CPEB3 in bilateral hippocampal CA1 in naïve rats resulted in cognitive deficits and impairment of synaptic plasticity.The rescued cognitive function and synaptic plasticity by the treatment of overexpression of Neurl1 before SNL were counteracted by the knockdown of CPEB3 in the CA1.Collectively,the above results suggest that the downregulation of Neurl1 through reducing CPEB3 ubiquitination and,in turn,repressing GluA1 and GluA2 production and mediating synaptic plasticity impairment in hippocampal CA1 leads to the genesis of cognitive deficits in neuropathic pain.
基金supported by the National Key Research and Development Program of China,No.2023YFC3603705(to DX)the National Natural Science Foundation of China,No.82302866(to YZ).
文摘After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the timing of interventions,combined with the limitations of current methods.To address these challenges,various techniques have been developed to aid in the repair and reconstruction of neural circuits at different stages of injury.Notably,neuromodulation has garnered considerable attention for its potential to enhance nerve regeneration,provide neuroprotection,restore neurons,and regulate the neural reorganization of circuits within the cerebral cortex and corticospinal tract.To improve the effectiveness of these interventions,the implementation of multitarget early interventional neuromodulation strategies,such as electrical and magnetic stimulation,is recommended to enhance functional recovery across different phases of nerve injury.This review concisely outlines the challenges encountered following spinal cord injury,synthesizes existing neurostimulation techniques while emphasizing neuroprotection,repair,and regeneration of impaired connections,and advocates for multi-targeted,task-oriented,and timely interventions.
基金supported by the National Natural Science Foundation of China,Nos.32271389,31900987(both to PY)the Natural Science Foundation of Jiangsu Province,No.BK20230608(to JJ)。
文摘Regulatory T cells,a subset of CD4^(+)T cells,play a critical role in maintaining immune tolerance and tissue homeostasis due to their potent immunosuppressive properties.Recent advances in research have highlighted the important therapeutic potential of Tregs in neurological diseases and tissue repair,emphasizing their multifaceted roles in immune regulation.This review aims to summarize and analyze the mechanisms of action and therapeutic potential of Tregs in relation to neurological diseases and neural regeneration.Beyond their classical immune-regulatory functions,emerging evidence points to non-immune mechanisms of regulatory T cells,particularly their interactions with stem cells and other non-immune cells.These interactions contribute to optimizing the repair microenvironment and promoting tissue repair and nerve regeneration,positioning non-immune pathways as a promising direction for future research.By modulating immune and non-immune cells,including neurons and glia within neural tissues,Tregs have demonstrated remarkable efficacy in enhancing regeneration in the central and peripheral nervous systems.Preclinical studies have revealed that Treg cells interact with neurons,glial cells,and other neural components to mitigate inflammatory damage and support functional recovery.Current mechanistic studies show that Tregs can significantly promote neural repair and functional recovery by regulating inflammatory responses and the local immune microenvironment.However,research on the mechanistic roles of regulatory T cells in other diseases remains limited,highlighting substantial gaps and opportunities for exploration in this field.Laboratory and clinical studies have further advanced the application of regulatory T cells.Technical advances have enabled efficient isolation,ex vivo expansion and functionalization,and adoptive transfer of regulatory T cells,with efficacy validated in animal models.Innovative strategies,including gene editing,cell-free technologies,biomaterial-based recruitment,and in situ delivery have expanded the therapeutic potential of regulatory T cells.Gene editing enables precise functional optimization,while biomaterial and in situ delivery technologies enhance their accumulation and efficacy at target sites.These advancements not only improve the immune-regulatory capacity of regulatory T cells but also significantly enhance their role in tissue repair.By leveraging the pivotal and diverse functions of Tregs in immune modulation and tissue repair,regulatory T cells–based therapies may lead to transformative breakthroughs in the treatment of neurological diseases.
文摘The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).
文摘Adult neurogenesis is a highly dynamic process that leads to the production of new neurons from a population of quiescent neural stem cells(NSCs).In response to specific endogenous and/or external stimuli,NSCs enter a state of mitotic activation,initiating proliferation and differentiation pathways.Throughout this process,NSCs give rise to neural progenitors,which undergo multiple replicative and differentiative steps,each governed by precise molecular pathways that coordinate cellular changes and signals from the surrounding neurogenic niche.
基金supported by the National Natural Science Foundation of China (Grant Nos.12192251,12334014,62335019,12134001,1230441812474378)+1 种基金the Quantum Science and Technology National Science and Technology Major Project(Grant No.2021ZD0301403)the Shanghai Municipal Science and Technology Major Project (Grant No.2019SHZDZX01)。
文摘Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.
基金supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (2020M3E5D9079764, RS-2024-00408736)(to KS)supported by Korea Drug Development Fund funded by Ministry of Science and ICT,Ministry of Trade,Industry,and Energy,and Ministry of Health and Welfare (RS-2024-00335752)(to KS)。
文摘The hippocampus is part of the brain limbic system and plays an important role in learning and memory.Moreover,its ability to form,consolidate,and retrieve different types of memories makes it a central component in the cognitive functions necessary for everyday life.Understanding the role of the hippocampus helps comprehend how memories are created,stored,and recalled and sheds light on the impact of hippocampal damage in conditions such as Alzheimer’s disease and other forms of dementia.
基金supported by Basic Research Programs of Science and Technology Commission Foundation of Shanxi Province,No.20210302123486(to WJ).
文摘Epilepsy,a common neurological disorder,is characterized by recurrent seizures that can lead to cognitive,psychological,and neurobiological consequences.The pathogenesis of epilepsy involves neuronal dysfunction at the molecular,cellular,and neural circuit levels.Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits.The continuous emergence of new technologies and the rapid advancement of existing ones have facilitated the discovery and comprehensive understanding of the neural circuit mechanisms underlying epilepsy.Therefore,this review aims to investigate the current understanding of the neural circuit mechanisms in epilepsy based on various technologies,including electroencephalography,magnetic resonance imaging,optogenetics,chemogenetics,deep brain stimulation,and brain-computer interfaces.Additionally,this review discusses these mechanisms from three perspectives:structural,synaptic,and transmitter circuits.The findings reveal that the neural circuit mechanisms of epilepsy encompass information transmission among different structures,interactions within the same structure,and the maintenance of homeostasis at the cellular,synaptic,and neurotransmitter levels.These findings offer new insights for investigating the pathophysiological mechanisms of epilepsy and enhancing its clinical diagnosis and treatment.
基金supported by the National Key Research and Development Program of China,No. 2023YFF0714200 (to CW)the National Natural Science Foundation of China,Nos. 82472038 and 82202224 (both to CW)+3 种基金the Shanghai Rising-Star Program,No. 23QA1407700 (to CW)the Construction Project of Shanghai Key Laboratory of Molecular Imaging,No. 18DZ2260400 (to CW)the National Science Foundation for Distinguished Young Scholars,No. 82025019 (to CL)the Greater Bay Area Institute of Precision Medicine (Guangzhou)(to CW)。
文摘Epilepsy is a leading cause of disability and mortality worldwide. However, despite the availability of more than 20 antiseizure medications, more than one-third of patients continue to experience seizures. Given the urgent need to explore new treatment strategies for epilepsy, recent research has highlighted the potential of targeting gliosis, metabolic disturbances, and neural circuit abnormalities as therapeutic strategies. Astrocytes, the largest group of nonneuronal cells in the central nervous system, play several crucial roles in maintaining ionic and energy metabolic homeostasis in neurons, regulating neurotransmitter levels, and modulating synaptic plasticity. This article briefly reviews the critical role of astrocytes in maintaining balance within the central nervous system. Building on previous research, we discuss how astrocyte dysfunction contributes to the onset and progression of epilepsy through four key aspects: the imbalance between excitatory and inhibitory neuronal signaling, dysregulation of metabolic homeostasis in the neuronal microenvironment, neuroinflammation, and the formation of abnormal neural circuits. We summarize relevant basic research conducted over the past 5 years that has focused on modulating astrocytes as a therapeutic approach for epilepsy. We categorize the therapeutic targets proposed by these studies into four areas: restoration of the excitation–inhibition balance, reestablishment of metabolic homeostasis, modulation of immune and inflammatory responses, and reconstruction of abnormal neural circuits. These targets correspond to the pathophysiological mechanisms by which astrocytes contribute to epilepsy. Additionally, we need to consider the potential challenges and limitations of translating these identified therapeutic targets into clinical treatments. These limitations arise from interspecies differences between humans and animal models, as well as the complex comorbidities associated with epilepsy in humans. We also highlight valuable future research directions worth exploring in the treatment of epilepsy and the regulation of astrocytes, such as gene therapy and imaging strategies. The findings presented in this review may help open new therapeutic avenues for patients with drugresistant epilepsy and for those suffering from other central nervous system disorders associated with astrocytic dysfunction.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
基金supported by the Wellcome Trust(grant No.103852).
文摘The nervous system function requires a precise but plastic neural architecture.The neuronal shape dictates how neurons interact with each other and with other cells,being the morphology of dendrites and axons the central determinant of the functional properties of neurons and neural circuits.The topological and structural morphology of axons and dendrites defines and determines how synapses are conformed.The morphological diversity of axon and dendrite arborization governs the neuron’s inputs,synaptic integration,neuronal computation,signal transmission,and network circuitry,hence defining the particular connectivity and function of the different brain areas.
基金supported by a grant from the Russian Science Foundation,No.23-75-10041(to MY)。
文摘Optogenetics has revolutionized the field of neuroscience by enabling precise control of neural activity through light-sensitive proteins known as opsins.This review article discusses the fundamental principles of optogenetics,including the activation of both excitatory and inhibitory opsins,as well as the development of optogenetic models that utilize recombinant viral vectors.A considerable portion of the article addresses the limitations of optogenetic tools and explores strategies to overcome these challenges.These strategies include the use of adeno-associated viruses,cell-specific promoters,modified opsins,and methodologies such as bioluminescent optogenetics.The application of viral recombinant vectors,particularly adeno-associated viruses,is emerging as a promising avenue for clinical use in delivering opsins to target cells.This trend indicates the potential for creating tools that offer greater flexibility and accuracy in opsin delivery.The adaptations of these viral vectors provide advantages in optogenetic studies by allowing for the restricted expression of opsins through cell-specific promoters and various viral serotypes.The article also examines different cellular targets for optogenetics,including neurons,astrocytes,microglia,and Schwann cells.Utilizing specific promoters for opsin expression in these cells is essential for achieving precise and efficient stimulation.Research has demonstrated that optogenetic stimulation of both neurons and glial cells-particularly the distinct phenotypes of microglia,astrocytes,and Schwann cells-can have therapeutic effects in neurological diseases.Glial cells are increasingly recognized as important targets for the treatment of these disorders.Furthermore,the article emphasizes the emerging field of bioluminescent optogenetics,which combines optogenetic principles with bioluminescent proteins to visualize and manipulate neural activity in real time.By integrating molecular genetics techniques with bioluminescence,researchers have developed methods to monitor neuronal activity efficiently and less invasively,enhancing our understanding of central nervous system function and the mechanisms of plasticity in neurological disorders beyond traditional neurobiological methods.Evidence has shown that optogenetic modulation can enhance motor axon regeneration,achieve complete sensory reinnervation,and accelerate the recovery of neuromuscular function.This approach also induces complex patterns of coordinated motor neuron activity and promotes neural reorganization.Optogenetic approaches hold immense potential for therapeutic interventions in the central nervous system.They enable precise control of neural circuits and may offer new treatments for neurological disorders,particularly spinal cord injuries,peripheral nerve injuries,and other neurodegenerative diseases.
基金supported by NIH grants,Nos.R01NS125074,R01AG083164,R01NS107365,and R21NS127177(to YL),1F31NS129204-01A1(to KW)and Albert Ryan Fellowship(to KW).
文摘Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postnatal neurogenesis remains unclear.In this study,to define the precise role of transforming growth factor-βsignaling in postnatal neurogenesis at distinct stages of the neurogenic cascade both in vitro and in vivo,we developed two novel inducible and cell type-specific mouse models to specifically silence transforming growth factor-βsignaling in neural stem cells in(mGFAPcre-ALK5fl/fl-Ai9)or immature neuroblasts in(DCXcreERT2-ALK5fl/fl-Ai9).Our data showed that exogenous transforming growth factor-βtreatment led to inhibition of the proliferation of primary neural stem cells while stimulating their migration.These effects were abolished in activin-like kinase 5(ALK5)knockout primary neural stem cells.Consistent with this,inhibition of transforming growth factor-βsignaling with SB-431542 in wild-type neural stem cells stimulated proliferation while inhibited the migration of neural stem cells.Interestingly,deletion of transforming growth factor-βreceptor in neural stem cells in vivo inhibited the migration of postnatal born neurons in mGFAPcre-ALK5fl/fl-Ai9 mice,while abolishment of transforming growth factor-βsignaling in immature neuroblasts in DCXcreERT2-ALK5fl/fl-Ai9 mice did not affect the migration of these cells in the hippocampus.In summary,our data supports a dual role of transforming growth factor-βsignaling in the proliferation and migration of neural stem cells in vitro.Moreover,our data provides novel insights on cell type-specific-dependent requirements of transforming growth factor-βsignaling on neural stem cell proliferation and migration in vivo.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)support program(IITP-2025-RS-2023-00259497)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Republic of Korea government(MSIT)(No.IITP-2025-RS-2023-00254129+1 种基金Graduate School of Metaverse Convergence(Sungkyunkwan University))was supported by the Basic Science Research Program of the National Research Foundation(NRF)funded by the Republic of Korean government(MSIT)(No.RS-2024-00346737).
文摘Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01296).
文摘Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.
