An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.

Metabolic and proteostatic differences in quiescent and active neural stem cells

Adult neural stem cells are neurogenesis progenitor cells that play an important role in neurogenesis.Therefore,neural regeneration may be a promising target for treatment of many neurological illnesses.The regenerative capacity of adult neural stem cells can be chara cterized by two states:quiescent and active.Quiescent adult neural stem cells are more stable and guarantee the quantity and quality of the adult neural stem cell pool.Active adult neural stem cells are chara cterized by rapid proliferation and differentiation into neurons which allow for integration into neural circuits.This review focuses on diffe rences between quiescent and active adult neural stem cells in nutrition metabolism and protein homeostasis.Furthermore,we discuss the physiological significance and underlying advantages of these diffe rences.Due to the limited number of adult neural stem cells studies,we refe rred to studies of embryonic adult neural stem cells or non-mammalian adult neural stem cells to evaluate specific mechanisms.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Recent advances in the application of MXenes for neural tissue engineering and regeneration

Transition metal carbides and nitrides(MXenes)are crystal nanomaterials with a number of surface functional groups such as fluorine,hydroxyl,and oxygen,which can be used as carriers for proteins and drugs.MXenes have excellent biocompatibility,electrical conductivity,surface hydrophilicity,mechanical properties and easy surface modification.However,at present,the stability of most MXenes needs to be improved,and more synthesis methods need to be explored.MXenes are good substrates for nerve cell regeneration and nerve reconstruction,which have broad application prospects in the repair of nervous system injury.Regarding the application of MXenes in neuroscience,mainly at the cellular level,the long-term in vivo biosafety and effects also need to be further explored.This review focuses on the progress of using MXenes in nerve regeneration over the last few years;discussing preparation of MXenes and their biocompatibility with different cells as well as the regulation by MXenes of nerve cell regeneration in two-dimensional and three-dimensional environments in vitro.MXenes have great potential in regulating the proliferation,differentiation,and maturation of nerve cells and in promoting regeneration and recovery after nerve injury.In addition,this review also presents the main challenges during optimization processes,such as the preparation of stable MXenes and long-term in vivo biosafety,and further discusses future directions in neural tissue engineering.

Emerging strategies for nerve repair and regeneration in ischemic stroke:neural stem cell therapy

Ischemic stroke is a major cause of mortality and disability worldwide,with limited treatment options available in clinical practice.The emergence of stem cell therapy has provided new hope to the field of stroke treatment via the restoration of brain neuron function.Exogenous neural stem cells are beneficial not only in cell replacement but also through the bystander effect.Neural stem cells regulate multiple physiological responses,including nerve repair,endogenous regeneration,immune function,and blood-brain barrier permeability,through the secretion of bioactive substances,including extracellular vesicles/exosomes.However,due to the complex microenvironment of ischemic cerebrovascular events and the low survival rate of neural stem cells following transplantation,limitations in the treatment effect remain unresolved.In this paper,we provide a detailed summary of the potential mechanisms of neural stem cell therapy for the treatment of ischemic stroke,review current neural stem cell therapeutic strategies and clinical trial results,and summarize the latest advancements in neural stem cell engineering to improve the survival rate of neural stem cells.We hope that this review could help provide insight into the therapeutic potential of neural stem cells and guide future scientific endeavors on neural stem cells.

A Review of Computing with Spiking Neural Networks

Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing.

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages.This approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency regularization.The NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel.In addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity field.The velocity uncertainties are also evaluated through ensemble learning.The effectiveness of the proposed algorithm is demonstrated through numerical examples.The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.

Application of Convolutional Neural Networks in Classification of GBM for Enhanced Prognosis

The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging, to improve prognosis precision and effectiveness. This research study will demonstrate a suggested methodology that makes use of the CNN architecture and is trained using a database of MRI pictures with this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It will be crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can have a big impact on treatment planning and patient outcomes. The paramount objective is to not only address the classification challenge but also to outline a clear pathway towards enhancing prognosis precision and treatment effectiveness.

2015年3月特大磁暴期间中国区域电离层TEC NeuralProphet预报模型研究

延迟是全球卫星导航定位中重要的误差源之一,提高电离层TEC建模和预报精度对改善卫星导航定位精度至关重要.本文构建了以太阳辐射通量指数F_(10.7)、地磁活动指数Dst、地理坐标和中国科学院(Chinese Academy of Sciences,CAS)GIM数据为输入参数的NeuralProphet神经网络模型(NP模型),实现在2015年3月特大磁暴期中国区域电离层TEC短期预报.为验证NP模型的预报精度,本文同时构建了长短期记忆神经网络(Long Short-term Memory Neural Network,LSTM)模型进行对比分析.结果统计分析表明,NP模型在磁暴期(2015年DOY076-078)TEC预报值RMSE和RD分别为0.83 TECU和3.13%,绝对和相对精度较LSTM模型分别提高1.49 TECU和10.25%;且NP模型RMSE优于1.5 TECU的比例达97.24%,远高于LSTM模型.NP模型预报值与CAS具有较好一致性和无偏性,偏差均值仅为-0.01 TECU,而LSTM模型预报值的均值偏大,偏差均值为1.49 TECU.从低纬到中纬度的三个纬度带内,NP模型RMSE分别为1.12、0.83和0.44 TECU,精度比LSTM模型提高1.94、1.56和1.23 TECU.整体上,在磁暴期NP模型预报性能明显优于LSTM模型,能够精细描述中国区域电离层TEC时空变化.

Long non-coding RNA H19 regulates neurogenesis of induced neural stem cells in a mouse model of closed head injury

Stem cell-based therapies have been proposed as a potential treatment for neural regeneration following closed head injury.We previously reported that induced neural stem cells exert beneficial effects on neural regeneration via cell replacement.However,the neural regeneration efficiency of induced neural stem cells remains limited.In this study,we explored differentially expressed genes and long non-coding RNAs to clarify the mechanism underlying the neurogenesis of induced neural stem cells.We found that H19 was the most downregulated neurogenesis-associated lnc RNA in induced neural stem cells compared with induced pluripotent stem cells.Additionally,we demonstrated that H19 levels in induced neural stem cells were markedly lower than those in induced pluripotent stem cells and were substantially higher than those in induced neural stem cell-derived neurons.We predicted the target genes of H19 and discovered that H19 directly interacts with mi R-325-3p,which directly interacts with Ctbp2 in induced pluripotent stem cells and induced neural stem cells.Silencing H19 or Ctbp2 impaired induced neural stem cell proliferation,and mi R-325-3p suppression restored the effect of H19 inhibition but not the effect of Ctbp2 inhibition.Furthermore,H19 silencing substantially promoted the neural differentiation of induced neural stem cells and did not induce apoptosis of induced neural stem cells.Notably,silencing H19 in induced neural stem cell grafts markedly accelerated the neurological recovery of closed head injury mice.Our results reveal that H19 regulates the neurogenesis of induced neural stem cells.H19 inhibition may promote the neural differentiation of induced neural stem cells,which is closely associated with neurological recovery following closed head injury.

Breaking the brain barrier:cell competition in neural development and disease

General information on cell competition:Social behaviors are the basis of biological life.Like species and populations,cell communities experience Darwinian ecological interactions,and in case space and nutrient availability are not uniform throughout the tissue,they begin to compete for ground occupancy.

Implications of regional identity for neural stem and progenitor cell transplantation in the injured or diseased nervous system

Neural stem and progenitor cell(NSPC)transpla ntation has emerged as a promising therapeutic strategy for replacing lost neuronal populations and repairing damaged neural circuits following nervous system injury and disease.A great deal of experimental work has investigated the biology of NSPC grafting in preclinical animal models;more recently.

Epigenetic memory of drug exposure history controls neural stem cell quiescence in the adult brain

Neural stem cells(NSCs)are the source of all neurons and glial cells(astrocytes and oligodendrocytes)in the central nervous system.The adult mammalian brain retains NSCs in the subgranular zone of the dentate gyrus in the hippocampus and ventricular subventricular zone lining the lateral ventricle(Olpe and Jessberger,2022).Adult NSCs in rodents are preserved throughout life and continuously produce new neurons that integrate into the pre-existing neuronal network.

Autophagy in neural stem cells and glia for brain health and diseases

Autophagy is a multifaceted cellular process that not only maintains the homeostatic and adaptive responses of the brain but is also dynamically involved in the regulation of neural cell generation,maturation,and survival.Autophagy facilities the utilization of energy and the microenvironment for developing neural stem cells.Autophagy arbitrates structural and functional remodeling during the cell differentiation process.Autophagy also plays an indispensable role in the maintenance of stemness and homeostasis in neural stem cells during essential brain physiology and also in the instigation and progression of diseases.Only recently,studies have begun to shed light on autophagy regulation in glia(microglia,astrocyte,and oligodendrocyte)in the brain.Glial cells have attained relatively less consideration despite their unquestioned influence on various aspects of neural development,synaptic function,brain metabolism,cellular debris clearing,and restoration of damaged or injured tissues.Thus,this review composes pertinent information regarding the involvement of autophagy in neural stem cells and glial regulation and the role of this connexion in normal brain functions,neurodevelopmental disorders,and neurodegenerative diseases.This review will provide insight into establishing a concrete strategic approach for investigating pathological mechanisms and developing therapies for brain diseases.

Predicting microseismic,acoustic emission and electromagnetic radiation data using neural networks

Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

Unveiling the brain’s symphony:exploring the necessity and sufficiency of neural networks in behavior control

Since the pioneering work by Broca and Wernicke in the 19th century,who examined individuals with brain lesions to associate them with specific behaviors,it was evident that behaviors are complex and cannot be fully attributable to specific brain areas alone.Instead,they involve connectivity among brain areas,whether close or distant.At that time,this approach was considered the optimal way to dissect brain circuitry and function.These pioneering efforts opened the field to explore the necessity or sufficiency of brain areas in controlling behavior and hence dissecting brain function.However,the connectivity of the brain and the mechanisms through which various brain regions regulate specific behaviors,either individually or collaboratively,remain largely elusive.Utilizing animal models,researchers have endeavored to unravel the necessity or sufficiency of specific brain areas in influencing behavior;however,no clear associations have been firmly established.

Women in visual neural regeneration research

The year 2024 marks the 60^(th)anniversary of Title IX and 25 years since the New York Times revealed bias against female faculty members at the Massachusetts Institute of Technology.We take an opportunity here to examine the state of gender bias in a relatively new yet already prominent field,neural regeneration in the visual system,for which there is a well-defined context useful for this purpose.The National Eye Institute(NEI)provided the first round of research funding for its Audacious Goals Initiative(AGI)on visual neural regeneration in 2013 and the last round in 2021.Therefore,we focus on this timespan.Data sources included PubMed,the National Science Foundation(NSF),the NEI,the Blue Ridge Institute for Medical Research and data from the major professional organization for eye and vision research,the Association for Research in Vision and Ophthalmology(ARVO).

A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.