The mechanistic target of rapamycin(m TOR) is a serine/threonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regul...The mechanistic target of rapamycin(m TOR) is a serine/threonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regulates diverse aspects of neural development and function. Genetic mutations within the m TOR pathway lead to severe neurodevelopmental disorders, collectively known as “mTORopathies”(Crino, 2020). Dysfunctions of m TOR, including both its hyperactivation and hypoactivation, have also been implicated in a wide spectrum of other neurodevelopmental and neurodegenerative conditions, highlighting its importance in CNS health.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
Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse cha...Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.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
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
Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is...Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.展开更多
In the era of big data and artificial intelligence,optical neural networks(ONNs)have emerged as a promising alternative to conventional electronic approaches,offering superior parallelism,ultrafast processing speeds,a...In the era of big data and artificial intelligence,optical neural networks(ONNs)have emerged as a promising alternative to conventional electronic approaches,offering superior parallelism,ultrafast processing speeds,and high energy efficiency[1-3].However,a major bottleneck in the practical implementation of ONNs is the absence of effective nonlinear activation functions.Self-driven photodetectors have emerged as versatile optical to electrical converters,opening innovative avenues for energy-effective and flexibly integrated activation functions in ONNs through their reconfigurable optoelectronic nonlinearity.展开更多
The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen cl...The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.展开更多
The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Min...The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Ming and Song,2011).Adult quiescent hippocampal neural stem cells(NSCs)are bona fide stem cells and,when activated,give rise to newborn granule neurons in the adult brain,which play vital roles in learning,memory,mood,and affective cognition(Bonaguidi et al.,2011;Ming and Song,2011).展开更多
Objective:To investigate the spatial gradient of intraoperative impedance across the cochlear electrode array in pediatric cochlear implant recipients and assess its potential as a physiological indicator for the elec...Objective:To investigate the spatial gradient of intraoperative impedance across the cochlear electrode array in pediatric cochlear implant recipients and assess its potential as a physiological indicator for the electrode-neural interface.Methods:A prospective observational study involving 56 pediatric patients underwent cochlear implantation with Cochlear Nucleus devices.Intraoperative polarized impedance and electrically evoked compound action potential(ECAP)threshold were recorded across all 1232 electrodes using AutoNRT software.Eight electrodes with open-or short-circuit were excluded,leaving 1,224 for analysis.Impedance values were categorized by cochlear region(basal,middle,apical),and electrodes with elevated impedance(10-20 kΩ)were analyzed for regional distribution and clinical relevance.Data were analyzed for spatial patterns and correlation with the ECAP threshold profiles.Results:A consistent basal-to-apical increase in impedance was observed(7.7±1.9,9.2±1.4,10.8±1.5 kΩ;p<0.001).Impedance and ECAP threshold were weakly correlated(ρ=-0.20,p<0.001;β=-1.26,p<0.001),with a positive association in the apical region(ρ=0.12,p=0.048).Electrodes with higher impedance(1020 kΩ)were less likely to show elevated or absent TNRT(OR=0.175,p=0.02).The impedance gradient persisted across age groups and was significantly correlated with ECAP threshold patterns.Conclusion:Intraoperative impedance monitoring reveals a strong and physiologically consistent gradient,with higher values in apical electrodes.This gradient reflects anatomical and tissue interface variations,which may offer a valuable physiological indicator for intraoperative electrode positioning and neural interface integrity.展开更多
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di...Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.展开更多
The adjoint method is widely used in gradient-based optimization with high-dimensional design variables.However,the cost of solving the adjoint equations in each iteration is comparable to that of solving the flow fie...The adjoint method is widely used in gradient-based optimization with high-dimensional design variables.However,the cost of solving the adjoint equations in each iteration is comparable to that of solving the flow field,resulting in expensive computational costs.To improve the efficiency of solving adjoint equations,we propose a physics-constrained graph neural networks for solving adjoint equations,named ADJ-PCGN.ADJ-PCGN establishes a mapping relationship between flow characteristics and adjoint vector based on data,serving as a replacement for the computationally expensive numerical solution of adjoint equations.A physics-based graph structure and message-passing mechanism are designed to endow its strong fitting and generalization capabilities.Taking transonic drag reduction and maximum lift-drag ratio of the airfoil as examples,results indicate that ADJ-PCGN attains a similar optimal shape as the classical direct adjoint loop method.In addition,ADJ-PCGN demonstrates strong generalization capabilities across different mesh topologies,mesh densities,and out-of-distribution conditions.It holds the potential to become a universal model for aerodynamic shape optimization involving states,geometries,and meshes.展开更多
Red chilli powder(RCP)is a versatile spice accepted globally in diverse culinary products due to its distinct pungent characteristics and red colour.The higher market demand makes the spice vulnerable to unethical mix...Red chilli powder(RCP)is a versatile spice accepted globally in diverse culinary products due to its distinct pungent characteristics and red colour.The higher market demand makes the spice vulnerable to unethical mixing,so its quality assessment is crucial.The non-destructive application of computer vision for measuring food adulteration has always attracted researchers and industry due to its robustness and feasibility.Following the current era of Food Quality 4.0 and artificial intelligence,this study follows an approach based on 1D-convolutional neural networks(CNN)and 2D-CNN models for detecting RCP adulteration.The performance evaluation metrics are used to analyse the efficiency of these models.The histogram features from the Lab colour space trained on the 1D-CNN model(BS-40 and Epoch 100)show an accuracy of 84.56%.On the other hand,the 2D-CNN model DenseNet-121(AdamW and BS-30)also shows a test accuracy of 84.62%.From the observations of this study,it is concluded that CNN models can be a promising tool for solving the adulteration detection problem in food quality evaluation.Further,internet of things-based systems can be developed to aid the industry and government agencies in monitoring the quality of RCP to harness the unethical practices of food adulteration.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.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
In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper propo...In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper proposes a Transformer-based multi-domain-aware mixture of experts model.To address the problem of domain feature differentiation,a mixture of experts(MoE)is introduced into attention to enhance the domain perception ability of the model,thereby improving the domain feature differentiation.To address the trade-off between domain feature distinction and cross-domain parameter sharing,we propose a domain-aware mixture of experts(DMoE).A domain-aware gating mechanism is introduced within the MoE module,simultaneously activating all domain experts to effectively blend domain feature distinction and cross-domain parameter sharing.A loss balancing function is then added to dynamically adjust the impact of the loss function on the expert distribution,enabling fine-tuning of the expert activation distribution to achieve a balance between domains.Experimental results on multiple Chinese-to-English and English-to-French datasets demonstrate that our proposed method significantly outperforms baseline models in both BLEU,chrF,and COMET metrics,validating its effectiveness in multi-domain neural machine translation.Further analysis of the probability distribution of expert activations shows that our method achieves remarkable results in both domain differentiation and cross-domain parameter sharing.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
This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge compon...This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.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
基金supported by grants from Simons Foundation (SFARI 479754),CIHR (PJT-180565)the Scottish Rite Charitable Foundation of Canada (to YL)funding from the Canada Research Chairs program。
文摘The mechanistic target of rapamycin(m TOR) is a serine/threonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regulates diverse aspects of neural development and function. Genetic mutations within the m TOR pathway lead to severe neurodevelopmental disorders, collectively known as “mTORopathies”(Crino, 2020). Dysfunctions of m TOR, including both its hyperactivation and hypoactivation, have also been implicated in a wide spectrum of other neurodevelopmental and neurodegenerative conditions, highlighting its importance in CNS health.
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funded by Guangdong Basic and Applied Basic Research Foundation(2023B1515120064)National Natural Science Foundation of China(62273097).
文摘Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.
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 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 National Natural Science Foundation of China (Grant No.52188102)。
文摘Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.
基金supported by the National Natural Science Foundation of China(52422107,T2394471,and 62571319)Beijing Nova Program(20240484531)+1 种基金China Postdoctoral Science Foundation(2022M710074)and Open Research Fund Program of Beijing National Research Center for Information Science and Technology(04410304023).
文摘In the era of big data and artificial intelligence,optical neural networks(ONNs)have emerged as a promising alternative to conventional electronic approaches,offering superior parallelism,ultrafast processing speeds,and high energy efficiency[1-3].However,a major bottleneck in the practical implementation of ONNs is the absence of effective nonlinear activation functions.Self-driven photodetectors have emerged as versatile optical to electrical converters,opening innovative avenues for energy-effective and flexibly integrated activation functions in ONNs through their reconfigurable optoelectronic nonlinearity.
基金supported by the General Program of the National Natural Science Foundation of China (Grant No.62575116)the National Natural Science Foundation of China (Grant No.62262005)+1 种基金the High-level Innovative Talents in Guizhou Province (Grant No.GCC[2023]033)the Open Project of the Text Computing and Cognitive Intelligence Ministry of Education Engineering Research Center(Grant No.TCCI250208)。
文摘The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.
基金supported by National Institutes of Health(R35NS137480,R35NS116843,and RF1AG079557)by Dr.Miriam and Sheldon G.Adelson Medical Research Foundation.
文摘The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Ming and Song,2011).Adult quiescent hippocampal neural stem cells(NSCs)are bona fide stem cells and,when activated,give rise to newborn granule neurons in the adult brain,which play vital roles in learning,memory,mood,and affective cognition(Bonaguidi et al.,2011;Ming and Song,2011).
文摘Objective:To investigate the spatial gradient of intraoperative impedance across the cochlear electrode array in pediatric cochlear implant recipients and assess its potential as a physiological indicator for the electrode-neural interface.Methods:A prospective observational study involving 56 pediatric patients underwent cochlear implantation with Cochlear Nucleus devices.Intraoperative polarized impedance and electrically evoked compound action potential(ECAP)threshold were recorded across all 1232 electrodes using AutoNRT software.Eight electrodes with open-or short-circuit were excluded,leaving 1,224 for analysis.Impedance values were categorized by cochlear region(basal,middle,apical),and electrodes with elevated impedance(10-20 kΩ)were analyzed for regional distribution and clinical relevance.Data were analyzed for spatial patterns and correlation with the ECAP threshold profiles.Results:A consistent basal-to-apical increase in impedance was observed(7.7±1.9,9.2±1.4,10.8±1.5 kΩ;p<0.001).Impedance and ECAP threshold were weakly correlated(ρ=-0.20,p<0.001;β=-1.26,p<0.001),with a positive association in the apical region(ρ=0.12,p=0.048).Electrodes with higher impedance(1020 kΩ)were less likely to show elevated or absent TNRT(OR=0.175,p=0.02).The impedance gradient persisted across age groups and was significantly correlated with ECAP threshold patterns.Conclusion:Intraoperative impedance monitoring reveals a strong and physiologically consistent gradient,with higher values in apical electrodes.This gradient reflects anatomical and tissue interface variations,which may offer a valuable physiological indicator for intraoperative electrode positioning and neural interface integrity.
基金supported by the Shanghai Pilot Program for Basic Research(22T01400100-18)the National Natural Science Foundation of China(22278127 and 12447149)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Postdoctoral Fellowship Program of CPSF(GZB20250159).
文摘Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.
基金supported by the National Natural Science Foundation of China(Grant No.12272316).
文摘The adjoint method is widely used in gradient-based optimization with high-dimensional design variables.However,the cost of solving the adjoint equations in each iteration is comparable to that of solving the flow field,resulting in expensive computational costs.To improve the efficiency of solving adjoint equations,we propose a physics-constrained graph neural networks for solving adjoint equations,named ADJ-PCGN.ADJ-PCGN establishes a mapping relationship between flow characteristics and adjoint vector based on data,serving as a replacement for the computationally expensive numerical solution of adjoint equations.A physics-based graph structure and message-passing mechanism are designed to endow its strong fitting and generalization capabilities.Taking transonic drag reduction and maximum lift-drag ratio of the airfoil as examples,results indicate that ADJ-PCGN attains a similar optimal shape as the classical direct adjoint loop method.In addition,ADJ-PCGN demonstrates strong generalization capabilities across different mesh topologies,mesh densities,and out-of-distribution conditions.It holds the potential to become a universal model for aerodynamic shape optimization involving states,geometries,and meshes.
文摘Red chilli powder(RCP)is a versatile spice accepted globally in diverse culinary products due to its distinct pungent characteristics and red colour.The higher market demand makes the spice vulnerable to unethical mixing,so its quality assessment is crucial.The non-destructive application of computer vision for measuring food adulteration has always attracted researchers and industry due to its robustness and feasibility.Following the current era of Food Quality 4.0 and artificial intelligence,this study follows an approach based on 1D-convolutional neural networks(CNN)and 2D-CNN models for detecting RCP adulteration.The performance evaluation metrics are used to analyse the efficiency of these models.The histogram features from the Lab colour space trained on the 1D-CNN model(BS-40 and Epoch 100)show an accuracy of 84.56%.On the other hand,the 2D-CNN model DenseNet-121(AdamW and BS-30)also shows a test accuracy of 84.62%.From the observations of this study,it is concluded that CNN models can be a promising tool for solving the adulteration detection problem in food quality evaluation.Further,internet of things-based systems can be developed to aid the industry and government agencies in monitoring the quality of RCP to harness the unethical practices of food adulteration.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
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 NationalNatural Science Foundation of China(U2004163)Key Research and Development Program of Henan Province(No.251111211200).
文摘In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper proposes a Transformer-based multi-domain-aware mixture of experts model.To address the problem of domain feature differentiation,a mixture of experts(MoE)is introduced into attention to enhance the domain perception ability of the model,thereby improving the domain feature differentiation.To address the trade-off between domain feature distinction and cross-domain parameter sharing,we propose a domain-aware mixture of experts(DMoE).A domain-aware gating mechanism is introduced within the MoE module,simultaneously activating all domain experts to effectively blend domain feature distinction and cross-domain parameter sharing.A loss balancing function is then added to dynamically adjust the impact of the loss function on the expert distribution,enabling fine-tuning of the expert activation distribution to achieve a balance between domains.Experimental results on multiple Chinese-to-English and English-to-French datasets demonstrate that our proposed method significantly outperforms baseline models in both BLEU,chrF,and COMET metrics,validating its effectiveness in multi-domain neural machine translation.Further analysis of the probability distribution of expert activations shows that our method achieves remarkable results in both domain differentiation and cross-domain parameter sharing.
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 Natural Science Foundation of China(No.52308332)the General Scientific Research Project of the Education Department of Zhejiang Province(No.Y202455824).
文摘This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.
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.
