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Congenital hydrocephalus:a review of recent advances in genetic etiology and molecular mechanisms

Military Medical Research 2024年第6期11卷 942-958页

作者： Xiu-Yun Liu Xin Song Marek Czosnyka Chiara Robba Zofia Czosnyka Jennifer Lee Summers Hui-Jie Yu Guo-Yi Gao Peter Smielewski Fang Guo Mei-Jun Pang Dong Ming Medical School Tianjin UniversityTianjin 300072China State Key Laboratory of Advanced Medical Materials and Devices Tianjin UniversityTianjin 300072China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300380China School of Pharmaceutical Science and Technology Tianjin University300072 TianjinChina Department of Clinical Neurosciences Addenbrooke’s HospitalUniversity of CambridgeCambridge CB20QQUK San Martino Policlinico Hospital IRCCS for Oncology and Neuroscience16132 GenoaItaly Department of Anesthesiology and Critical Care Medicine Johns Hopkins UniversityBaltimoreMD 21287USA Department of Neurosurgery Tianjin Medical University General HospitalTianjin 300052China Department of Neurosurgery Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China Department of Neurosurgery Tianjin Huanhu HospitalTianjin 300350China

The global prevalence rate for congenital hydrocephalus(CH)is approximately one out of every five hundred births with multifaceted predisposing factors at *** influences stand as a major contributor to CH pathogenesis,and epidemiological evidence suggests their involvement in up to 40%of all cases observed *** about an individual’s genetic susceptibility can significantly improve prognostic precision while aiding clinical decision-making ***,the precise genetic etiology has only been pinpointed in fewer than 5%of human *** occurrences of CH cases are required for comprehensive gene sequencing aimed at uncovering additional potential genetic loci.A deeper comprehension of its underlying genetics may offer invaluable insights into the molecular and cellular basis of this brain *** review provides a summary of pertinent genes identified through gene sequencing technologies in humans,in addition to the 4 genes currently associated with CH(2 X-linked genes L1CAM and AP1S2,2 autosomal recessive MPDZ and CCDC88C).Others predominantly participate in aqueduct abnormalities,ciliary movement,and nervous system *** prospective CH-related genes revealed through animal model gene-editing techniques are further outlined,focusing mainly on 4 pathways,namely cilia synthesis and movement,ion channels and transportation,Reissner’s fiber(RF)synthesis,cell apoptosis,and ***,the proper functioning of motile cilia provides significant impulsion for cerebrospinal fluid(CSF)circulation within the brain ventricles while mutations in cilia-related genes constitute a primary cause underlying this *** far,only a limited number of CH-associated genes have been identified in *** integration of genotype and phenotype for disease diagnosis represents a new trend in the medical *** models provide insights into the pathogenesis of CH and contribute to our understanding of its association w

关键词： Congenital hydrocephalus Genetic disease Central system Cilia Ventricle

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A Memory-Augmented Conditional Neural Process Model for Traffic Prediction

SSRN

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SSRN 2024年

作者： Wei, Ye He, Haitao Yuan, Kunhao Schaefer, Gerald Ji, Zhigang Fang, Hui Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom Centre for Clinical Brain Sciences University of Edinburgh Edinburgh United Kingdom National Key Laboratory of Science and Technology on Micro/Nano Fabrication Shanghai Jiao Tong University Shanghai China

This paper presents the first neural process-based model for traffic prediction, the Memory-augmented Conditional Neural Process (MemCNP). Our model effectively addresses key challenges in capturing complex spatio-temporal traffic patterns in data-sparse situations and introduces a novel framework for uncertainty estimation. We further incorporate a novel memory network module designed to acquire a representative contextual reference, thereby improving our model performance under complex data distributions. By integrating the conditional neural process and the memory network, MemCNP enables the learning of the most representative contexts through iterative updates, thus enhancing generalisation capabilities and providing a more adaptable model for a broad range of real-world traffic scenarios, including highway traffic, urban traffic, and non-motorised traffic. This is demonstrated by comprehensive experimental results on six benchmark datasets, PeMS04, PeMS07, PeMS08, NYCTaxi, CHIBike and T-Drive over existing state-of-the-art traffic prediction models, where MemCNP shows superior performance. Additionally, through ablation and reliability studies, we provide a comprehensive analysis of the model's effectiveness. © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Markov Chain-Guided Graph Construction and Sampling Depth Optimization for EEG-Based Mental Disorder Detection

arXiv

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arXiv 2023年

作者： Wu, Yihan Chang, Tao Xu, Peng Zhang, Yangsong School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang621010 China National Clinical Research Center for Geriatrics China Hospital Sichuan University Chengdu China Yangsong Key Laboratory of Testing Technology for Manufacturing Process Ministry of Education Southwest University of Science and Technology Mianyang621010 China Department of Neurosurgery West China Hospital Sichuan University Chengdu China MOE Key Laboratory for Neuroinformation University of Electronic Science and Technology of China Chengdu610054 China

Graph Neural Networks (GNNs) have received considerable attention since its introduction. It has been widely applied in various fields due to its ability to represent graph structured data. However, the application of GNNs is constrained by two main issues. Firstly, the"over-smoothing" problem restricts the use of deeper network structures. Secondly, GNNs’ applicability is greatly limited when nodes and edges are not clearly defined and expressed, as is the case with EEG *** this study, we proposed an innovative approach that harnesses the distinctive properties of the graph structure’s Markov Chain to optimize the sampling depth of deep graph convolution networks. We introduced a tailored method for constructing graph structures specifically designed for analyzing EEG data, alongside the development of a vertex-level GNN classification model for precise detection of mental disorders. In order to verify the method’s performance, we conduct experiments on two disease datasets using a subject-independent experiment scenario. For the Schizophrenia (SZ) data, our method achieves an average accuracy of 100% using only the first 300 seconds of data from each subject. Similarly, for Major Depressive Disorder (MDD) data, the method yields average accuracies of over 99%. These experiments demonstrate the method’s ability to effectively distinguish between healthy control (HC) subjects and patients with mental disorders. We believe this method shows great promise for clinical diagnosis. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Reliability-Aware Network Slicing based on Multi-Objective Optimization

Reliability-Aware Network Slicing based on Multi-Objective O...

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Congress on Evolutionary Computation

作者： Qiqi Xia Jin Wang Chengqiang Huang Xin Yao Research Institute of Trustworthy Autonomous Systems (RITAS) Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Reliability Technology Lab Huawei Technologies Co. Ltd. Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong SAR China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric.

关键词： Measurement 6G mobile communication Costs Network topology Network slicing Evolutionary computation Approximation algorithms

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Consistent brain Age Difference in Childhood Autism Spectrum Disorder and its Subtypes 4th

Consistent Brain Age Difference in Childhood Autism Spectrum...

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4th International Workshop on Human brain and Artificial Intelligence, HBAI 2024

作者： Sun, Fangling Liang, Chuang Shao, Wei Fu, Zening Zhang, Daoqiang Jiang, Rongtao Qi, Shile Calhoun, Vince D. Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Georgia State University Institute of Technology Emory University AtlantaGA United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States

ISBN: (纸本)9789819640003

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and behavior as well as structural abnormalities regarding brain development. The difference between neuroimaging-predicted brain age and the chronological age (predicted age difference;PAD) is a potential biomarker reflecting individual differences in brain developmental trajectories. However, current findings of PAD in ASD are inconsistent due to the biases introduced by different prediction models. Here, 6 classic 3-dimensional convolutional neural network (3D-CNN) models were used to assess brain age to identify consistent and reliable PAD and its interpretable features for ASD. No significant PAD differences between ASD and typically developing controls (TDC) were observed, but PAD differences in younger age and subtypes were found. Occlusion sensitivity analysis showed that default mode network (DMN) and salience network (SAN) drove the atypical brain developmental trajectories in ASD. Our findings were replicable across 6 CNN models, showing the promise of neuroimaging targets for exploring atypical developmental patterns in ASD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Development of wearable miniature neural signal recording system for birds

Development of wearable miniature neural signal recording sy...

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Chinese Automation Congress (CAC)

作者： Liaofeng Wang Lifang Yang Mengmeng Li Zhongliang Yang Zhigang Shang School of Electrical Engineering Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Birds have a wide range of activities in the natural environment, and there are a lot of uncontrollable factors. Therefore, recording the neural signals and behavior data of birds in natural state transition is a major difficulty in the progress of neuroscience. At present, most neuroscience fields usually use wired EEG acquisition equipment, but due to its large size and heavy weight, it is not suitable for small animals, especially birds, to record neural and behavior data in natural state. In order to solve the above problems, we designed a wearable neuro-signal recording system in this study, which can synchronously record the neuro-signal and attitude data of birds in flight. The core processor of this system uses STM32 series high performance single-chip computer, and its peripheral components are described as follows: (1) 8-channel neural signal acquisition module based on AD1299; (2) GPS global positioning module; (3) Attitude measurement unit; (4) TF card storage module. The total weight of the system is about 20 g and the volume is about 27 mm x 25 mm x 15 mm. Finally, the neural signals and attitude data of the pigeons moving freely on the ground and in the sky were collected using the system we designed. The results showed that the system can not only stably record neural signals, attitude information and position information at the same time, but also does not affect their normal flight. The system has a good application prospect. Researchers can use it to record and analyze the neural signals of experimental animals, especially birds in free movement and even flight. Our work provides a portable and feasible experimental tool for further study of the neural mechanism of the brain in more complex environment.

关键词： Birds Global Positioning System Wireless communication Trajectory Memory management Measurement units Acceleration

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Computational neuroscience:a frontier of the 21st century

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National science Review 2020年第9期7卷 1418-1422页

作者： Xiao-Jing Wang Hailan Hu Chengcheng Huang Henry Kennedy Chengyu Tony Li Nikos Logothetis Zhong-Lin Lu Qingming Luo Mu-ming Poo Doris Tsao Si Wu Zhaohui Wu Xu Zhang Douglas Zhou Center for Neural Science New York University Center for Neuroscience Key Laboratory of Medical Neurobiology of the Ministry of Health of China School of Medicine Zhejiang University Department of Neuroscience and Department of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh Université Claude Bernard Lyon 1 Inserm Stem Cell and Brain Research Institute U1208 Institute of Neuroscience State Key Laboratory of Neuroscience Chinese Academy of SciencesCAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Center for Brain Science and Brain-Inspired Technology Division of Arts and Sciences and NYU-ECNU Institute of Cognitive Neuroscience NYU Shanghai School of Biomedical Engineering Hainan University Huazhong University of Science and Technology-Suzhou Institute for Brainsmatics Division of Biology and Biological Engineering California Institute of Technology Howard Hughes Medical Institute School of Electronics Engineering and Computer Science IDG/Mc Govern Institute for Brain Research PKU-Tsinghua Center for Life SciencesPeking University College of Computer Science and Technology Zhejiang University Institute of Brain-Intelligence Science and Technology Zhangjiang Lab School of Mathematical Sciences MOE-LSCand Institute of Natural Sciences Shanghai Jiao Tong University

THEORY IN NEUROscience The human brain is a biological organ,weighing about three pounds or 1.4 kg,that determines our behaviors, thoughts,emotions and consciousness. Although comprising only 2%of the total body weigh...

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Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization

arXiv

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arXiv 2023年

作者： Chen, Wenjie Liu, Shengcai Ong, Yew-Soon Tang, Ke The School of Information Management Central China Normal University Wuhan430079 China Singapore138632 Singapore Singapore Singapore The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Real-time solutions to the influence blocking maximization (IBM) problems are crucial for promptly containing the spread of misinformation. However, achieving this goal is non-trivial, mainly because assessing the blocked influence of an IBM problem solution typically requires plenty of expensive Monte Carlo simulations (MCSs). Although several approaches have been proposed to enhance efficiency, they still fail to achieve real-time solutions to IBM problems of practical scales. This work presents a novel approach that enables solving IBM problems with hundreds of thousands of nodes and edges in seconds. The key idea is to construct a fast-to-evaluate surrogate model, called neural influence estimator (NIE), as a substitute for the time-intensive MCSs. To this end, a learning problem is formulated to build the NIE that takes the false-and-true information instance as input, extracts features describing the topology and interrelationship between two seed sets, and predicts the blocked influence. A well-trained NIE can generalize across different IBM problems defined on a social network, and can be readily combined with existing IBM optimization algorithms such as the greedy algorithm. The experiments on 25 IBM problems with up to millions of edges show that the NIE-based optimization method can be up to four orders of magnitude faster than MCSs-based optimization method to achieve the same solution quality. Moreover, given a real-time constraint of one minute, the NIE-based method can solve IBM problems with up to hundreds of thousands of nodes, which is at least one order of magnitude larger than what can be solved by existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

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SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

arXiv

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arXiv 2022年

作者： Hu, Yan Qiu, Zhongxi Zeng, Dan Jiang, Li Lin, Chen Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high-resolution segmentation results for analysis, which leads to super-computational load by most existing methods. If based on low-resolution input, they easily ignore tiny vessels or cause discontinuity of segmented vessels. To solve these problems, the paper proposes an algorithm named SuperVessel, which gives out high-resolution and accurate vessel segmentation using low-resolution images as input. We first take super-resolution as our auxiliary branch to provide potential high-resolution detail features, which can be deleted in the test phase. Secondly, we propose two modules to enhance the features of the interested segmentation region, including an upsampling with feature decomposition (UFD) module and a feature interaction module (FIM) with a constraining loss to focus on the interested features. Extensive experiments on three publicly available datasets demonstrate that our proposed SuperVessel can segment more tiny vessels with higher segmentation accuracy IoU over 6%, compared with other state-of-the-art algorithms. Besides, the stability of SuperVessel is also stronger than other algorithms. We will release the code after the paper is published. Copyright © 2022, The Authors. All rights reserved.

关键词： Blood vessels

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Constrained Reinforcement Learning for Dynamic Material Handling

Constrained Reinforcement Learning for Dynamic Material Hand...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chengpeng Hu Ziming Wang Jialin Liu Junyi Wen Bifei Mao Xin Yao Research Institute of Trustworthy Autonomous Systems (RITAS) Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Trustworthiness Theory Research Center Huawei Technologies Co. Ltd Shenzhen China

As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The improvement in material handling can impulse the overall efficiency of the manufacturing system. However, the occurrence of dynamic events during the optimisation of task arrangements poses a challenge that requires adaptability and effectiveness. In this paper, we aim at the scheduling of automated guided vehicles for dynamic material handling. Motivated by some real-world scenarios, unknown new tasks and unexpected vehicle breakdowns are regarded as dynamic events in our problem. We formulate the problem as a constrained Markov decision process which takes into account tardiness and available vehicles as cumulative and instantaneous constraints, respectively. An adaptive constrained reinforcement learning algorithm that combines Lagrangian relaxation and invalid action masking, named RCPOM, is proposed to address the problem with two hybrid constraints. Moreover, a gym-like dynamic material handling simulator, named DMH-GYM, is developed and equipped with diverse problem instances, which can be used as benchmarks for dynamic material handling. Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling.

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