Protein biomarkers of neural system

The utilization of biomarkers for in vivo and in vitro research is growing rapidly. This is mainly due to the enormous potential of biomarkers in evaluating molecular and cellular abnormalities in cell models and in tissue, and evaluating drug responses and the effectiveness of therapeutic intervention strategies. An important way to analyze the development of the human body is to assess molecular markers in embryonic specialized cells, which include the ectoderm, mesoderm, and endoderm. Neuronal development is controlled through the gene networks in the neural crest and neural tube, both components of the ectoderm. The neural crest differentiates into several different tissues including, but not limited to, the peripheral nervous system, enteric nervous system, melanocyte, and the dental pulp. The neural tube eventually converts to the central nervous system. This review provides an overview of the differentiation of the ectoderm to a fully functioning nervous system, focusing on molecular biomarkers that emerge at each stage of the cellular specialization from multipotent stem cells to completely differentiated cells. Particularly, the otic placode is the origin of most of the inner ear cell types such as neurons, sensory hair cells, and supporting cells. During the development, different auditory cell types can be distinguished by the expression of the neurogenin differentiation factor1 (Neuro D1), Brn3a, and transcription factor GATA3. However, the mature auditory neurons express other markers including bIII tubulin, the vesicular glutamate transporter (VGLUT1), the tyrosine receptor kinase B and C (Trk B, C), BDNF, neurotrophin 3 (NT3), Calretinin, etc.

Study of a Bionic Pattern Classifier Based on Olfactory Neural System

Simulating biological olfactory neural system, KⅢnetwork, which is a high-dimensional chaotic neural network, is designed in this paper. Different from conventional artificial neural network, the KⅢnetwork works in its chaotic trajectory. It can simulate not only the output EEG waveform observed in electrophysiological experiments, but also the biological intelligence for pattern classification. The simulation analysis and application to the recognition of handwriting numerals are presented here. The classification performance of the KⅢnetwork at different noise levels was also investigated.

Asymptotic Properties of a Dynamic Neural System with Asymmetric Connection Weights

In this paper, based on new Lyapunov function, the asymptotic properties of the dynamic neural system with asymmetric connection weights are investigated. Since the dynamic neural system with asymmetric connection weights is more general than that with symmetric ones, the new results are significant in both theory and applications. Specially the new result can cover the asymptotic stability results of linear systems as special cases.

Hybrid Designing of a Neural System by Combining Fuzzy Logical Framework and PSVM for Visual Haze-Free Task

Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 106 neurons) to simulate complex process of our brain. But such kind of task is not easy to achieve only based on the analysis of partial differential equations, especially for those complex neural models, e.g. Rose-Hindmarsh (RH) model. So in this paper, we develop a novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifiers (PSVM) learning in the designing of large scale neural networks. Particularly, our approach can effectively simplify the designing process, which is crucial for both cognition science and neural science. At last, we conduct our approach on an artificial neural system with more than 108 neurons for haze-free task, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.

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.

A graph neural network approach to the inverse design for thermal transparency with periodic interparticle system

Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport behaviors.Among the various thermal transport behaviors,achieving thermal transparency stands out as particularly desirable and intriguing.Our earlier work demonstrated the use of a thermal metamaterial-based periodic interparticle system as the underlying structure for manipulating thermal transport behavior and achieving thermal transparency.In this paper,we introduce an approach based on graph neural network to address the complex inverse design problem of determining the design parameters for a thermal metamaterial-based periodic interparticle system with the desired thermal transport behavior.Our work demonstrates that combining graph neural network modeling and inference is an effective approach for solving inverse design problems associated with attaining desirable thermal transport behaviors using thermal metamaterials.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.

Variational Neural Inference Enhanced Text Semantic Communication System

Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,etc.While efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and decoder.However,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart devices.Furthermore,existing work tends to prioritize explicit semantics,neglecting the potential of implicit semantics.This paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder structures.We propose a novel semantic communication system with variational neural inference for text transmission.Specifically,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received text.This information is then utilized to assist in the decoding process.The simulation results show a significant enhancement in system performance and improved robustness.

Establishment of human cerebral organoid systems to model early neural development and assess the central neurotoxicity of environmental toxins

Human brain development is a complex process,and animal models often have significant limitations.To address this,researchers have developed pluripotent stem cell-derived three-dimensional structures,known as brain-like organoids,to more accurately model early human brain development and disease.To enable more consistent and intuitive reproduction of early brain development,in this study,we incorporated forebrain organoid culture technology into the traditional unguided method of brain organoid culture.This involved embedding organoids in matrigel for only 7 days during the rapid expansion phase of the neural epithelium and then removing them from the matrigel for further cultivation,resulting in a new type of human brain organoid system.This cerebral organoid system replicated the temporospatial characteristics of early human brain development,including neuroepithelium derivation,neural progenitor cell production and maintenance,neuron differentiation and migration,and cortical layer patterning and formation,providing more consistent and reproducible organoids for developmental modeling and toxicology testing.As a proof of concept,we applied the heavy metal cadmium to this newly improved organoid system to test whether it could be used to evaluate the neurotoxicity of environmental toxins.Brain organoids exposed to cadmium for 7 or 14 days manifested severe damage and abnormalities in their neurodevelopmental patterns,including bursts of cortical cell death and premature differentiation.Cadmium exposure caused progressive depletion of neural progenitor cells and loss of organoid integrity,accompanied by compensatory cell proliferation at ectopic locations.The convenience,flexibility,and controllability of this newly developed organoid platform make it a powerful and affordable alternative to animal models for use in neurodevelopmental,neurological,and neurotoxicological studies.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

Advanced synaptic devices and their applications in biomimetic sensory neural system

Human nervous system,which is composed of neuron and synapse networks,is capable of processing information in a plastic,dataparallel,fault-tolerant,and energy-efficient approach.Inspired by the ingenious working mechanism of this miraculous biological data processing system,scientists have been devoting great efforts to ar-tificial neural systems based on synaptic devices in recent decades.The continuous development of bioinspired sensors and synaptic devices in recent years have made it possible that artificial sensory neural systems are capable of capturing and processing stimuli informa-tion in real time.The progress of biomimetic sensory neural systems could provide new methods for next-generation humanoid robotics,human-machine interfaces,and other frontier applications.Herein,this review summarized the recent progress of synaptic devices and biomimetic sensory neural systems.Additionally,the opportunities and remaining challenges in the further development of biomimetic sensory neural systems were also outlined.

New insights into the biological roles of immune cells in neural stem cells in post-traumatic injury of the central nervous system

Traumatic injuries in the central nervous system,such as traumatic brain injury and spinal cord injury,are associated with tissue inflammation and the infiltration of immune cells,which simultaneously affect the self-renewal and differentiation of neural stem cells.Howeve r,the tissue repair process instigated by endogenous neural stem cells is incapable of restoring central nervous system injuries without external intervention.Recently,resident/peripheral immune cells have been demonstrated to exert significant effects on neural stem cells.Thus,the resto ration of traumatic injuries in the central nervous system by the immune intervention in neural stem cells represents a potential therapeutic method.In this review,we discuss the roles and possible mechanisms of immune cells on the selfrenewal and differentiation of neural stem cells along with the prognosis of central nervous system injuries based on immune intervention.Finally,we discuss remaining research challenges that need to be considered in the future.Further elucidation of these challenges will fa cilitate the successful application of neural stem cells in central nervous system injuries.

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.

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.

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.

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.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.