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Mechanism of inflammatory response and therapeutic effects of stem cells in ischemic stroke:current evidence and future perspectives

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Neural Regeneration research 2025年第1期20卷 67-81页

作者： Yubo Wang Tingli Yuan Tianjie Lyu Ling Zhang Meng Wang Zhiying He Yongjun Wang Zixiao Li Vascular Neurology Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina China National Clinical research center for Neurological Diseases Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Shanghai Engineering research center of Stem Cells Translational Medicine ShanghaiChina National center for Healthcare Quality Management in Neurological Diseases Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Institute for Regenerative Medicine Shanghai East HospitalSchool of Life Sciences and TechnologyTongji UniversityShanghaiChina Chinese Institute for Brain research BeijingChina research Unit of Artificial intelligence in Cerebrovascular Disease Chinese Academy of Medical SciencesBeijingChina Beijing Engineering research center of Digital Healthcare for Neurological Diseases BeijingChina

Ischemic stroke is a leading cause of death and disability worldwide,with an increasing trend and tendency for onset at a younger ***,in particular,bears a high burden of stroke *** recent years,the inflammatory response after stroke has become a research hotspot:understanding the role of inflammatory response in tissue damage and repair following ischemic stroke is an important direction for its *** review summarizes several major cells involved in the inflammatory response following ischemic stroke,including microglia,neutrophils,monocytes,lymphocytes,and ***,we have also highlighted the recent progress in various treatments for ischemic stroke,particularly in the field of stem cell ***,understanding the complex interactions between inflammation and ischemic stroke can provide valuable insights for developing treatment strategies and improving patient *** cell therapy may potentially become an important component of ischemic stroke treatment.

关键词： cell therapy immune cell inflammatory ischemic stroke stem cell

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Oxidation behavior of Ni-based superalloy GH4738 under tensile stress

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Rare Metals 2024年第8期43卷 3913-3920页

作者： Ji-Chong Hu Hai-Liang Huang Chong-Chong Wu Xiao-Yu Sun Jie Wang Yan-Hong Yang Jing-Long Qu Liang Jiang Jin-He Dou Yang Chen Institute for Advanced Studies in Precision Materials Yantai UniversityYantai 264005China Department of Management and Engineering Linkoping University58183LinkopingSweden Shi-Changxu Innovation center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang110016China Gaona Aero Material Co. LtdBeijing 100081China College of Artificial intelligence and Big Data Shandong First Medical University and Shandong Academy of Medical SciencesJinan250117China

Revealing the oxidation behavior of superalloys is crucial for optimizing material properties and extending service *** study investigated the oxidation behavior of superalloy GH4738 under stress states at 850℃.High-throughput specimens were fabricated to withstand different stresses at the same *** oxidation s amples were analyzed using the mass gain method to obtain oxidation kinetic *** results show that the external stress below 200 MPa could improve the oxidation resistance of the *** tensile stress increasing,the oxide layer becomes thinner,denser and more complete,while internal oxidation *** tensile stress alters the structure of the external oxide layer from a two-layer to a threelayer *** Cr_(2)O_(3) oxide layer inhibits the outward diffusion of Ti,leading to Ti enrichment at the oxide-matrix interface and altering the oxidation mechanism of GH4738.

关键词： Ni-based Superalloy Oxidation behavior Tensile stress Oxide layer

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Hepatocardiorenal syndrome in liver cirrhosis:recognition of a new entity?

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World Journal of Gastroenterology 2024年第2期30卷 128-136页

作者： Henry H L Wu Amina Rakisheva Arvind Ponnusamy Rajkumar Chinnadurai Renal research Kolling Institute of Medical ResearchRoyal North Shore Hospital&The University of SydneySt.Leonards(Sydney)2065New South WalesAustralia Department of Cardiology City Cardiological CenterAlmaty 050000Kazakhstan Department of Renal Medicine Royal Preston HospitalPreston PR29HTUnited Kingdom Donal O’Donoghue Renal research Centre&Department of Renal Medicine Northern Care Alliance National Health Service Foundation TrustSalford M68HDUnited Kingdom

Emerging evidence and perspectives have pointed towards the heart playing an important role in hepatorenal syndrome(HRS),outside of conventional understanding that liver cirrhosis is traditionally considered the sole origin of a cascade of pathophysiological mechanisms directly affecting the kidneys in this *** the absence of established heart disease,cirrhotic cardiomyopathy may occur more frequently in those with liver cirrhosis and kidney *** is a specific form of cardiac dysfunction characterized by blunted contractile responsiveness to stress stimuli and altered diastolic relaxation with electrophysiological *** the clinical description of these potential cardiac-related complications of the liver,the role of the heart has traditionally been an overlooked aspect of circulatory dysfunction in *** from a physiological sense,temporality(prior onset)of cardiorenal interactions in HRS and positive effects stemming from portosystemic shunting demonstrated an important role of the heart in the development and progression of kidney dysfunction in cirrhotic *** this review,we discuss current concepts surrounding how the heart may influence the development and progression of HRS,and the role of systemic inflammation and endothelial dysfunction causing circulatory dysfunction within this *** temporality of heart and kidney dysfunction in HRS will be *** a subgroup of patients who receive portosystemic shunting,the dynamics of cardiorenal interactions following treatment is *** research to determine the unknowns in this topic is anticipated,hopefully to further clarify the intricacies surrounding the liver-heart-kidney connection and improve strategies for management.

关键词： Hepatorenal syndrome Cardiorenal syndrome Cirrhosis Cardiac dysfunction Circulatory dysfunction

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Real-Time Object Detection and Face recognition Application for the Visually Impaired

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Computers, Materials & Continua 2024年第6期79卷 3569-3583页

作者： Karshiev Sanjar Soyoun Bang SookheeRyue Heechul Jung Department of Artificial intelligence Kyungpook National UniversityDaegu41466Republic of Korea Haga Co. Ltd.Daegu38428Republic of Korea

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable *** approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall ***,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing *** study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application *** introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,*** results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their ***,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition *** comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

关键词： Artificial intelligence deep learning real-time object detection application

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Photonic neuromorphic architecture for tens-of-task lifelong learning

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Light(Science & Applications) 2024年第3期13卷 519-530页

作者： Yuan Cheng Jianing Zhang Tiankuang Zhou Yuyan Wang Zhihao Xu Xiaoyun Yuan Lu Fang Sigma Laboratory Department of Electronic EngineeringTsinghua UniversityBeijing 100084China Beijing National research center for Information Science and Technology(BNRist) Beijing 100084China Institute for Brain and Cognitive Science Tsinghua University(THUIBCS)Beijing 100084China

Scalable,high-capacity,and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning *** electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls,hindering them from the sustainable performance improvement and iterative multi-task *** to another modality of light,photonic computing has been progressively applied in high-efficient neuromorphic ***,we innovate a reconfigurable lifelong-learning optical neural network(L2 ONN),for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware *** from the inherent sparsity and parallelism in massive photonic connections,L2 ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field,while incrementally acquiring expertise on various tasks by gradually enlarging the *** multi-task optical features are parallelly processed by multi-spectrum representations allocated with different *** evaluations on freespace and on-chip architectures confirm that for the first time,L2 ONN avoided the catastrophic forgetting issue of photonic computing,owning versatile skills on challenging tens-of-tasks(vision classification,voice recognition,medical diagnosis,etc.)with a single ***,L2 ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks,and 14×larger capacity than existing optical neural networks while maintaining competitive performance on each individual *** proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme,permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

关键词： life neural versatile

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使用深度学习在CTA扫描卷下实现主动脉夹层分类和直径测量的双功能系统

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Engineering 2024年第3期34卷 83-91页

作者： Zhihui Huang Rui Wang Hui Yu Yifan Xu Cheng Cheng Guangwei Wang Haosen Cao Xiang Wei Hai-Tao Zhang School of Artificial intelligence and Automation&the MOE Engineering research center of Autonomous Intelligent Unmanned Systems&the State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Division of Cardiovascular Surgery Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Wuhan Children’s Hospital:Wuhan Women and Children Medical Care center Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430010China

Acute aortic dissection is one of the most life-threatening cardiovascular diseases,with a high mortality *** prevalence ranges from 0.2%to 0.8%in humans,resulting in a significant number of deaths due to being missed in manual *** importantly,the aortic diameter—a critical indicator for surgical selection—significantly influences the outcomes of surgeries ***,it is an urgent yet challenging mission to develop an automatic aortic dissection diagnostic system that can recognize and classify the aortic dissection type and measure the aortic *** paper offers a dual-functional deep learning system called aortic dissections diagnosis-aiding system(DDAsys)that enables both accurate classification of aortic dissection and precise diameter measurement of the *** this end,we created a dataset containing 61190 computed tomography angiography(CTA)images from 279 patients from the Division of Cardiovascular Surgery at Tongji Hospital,Wuhan,*** dataset provides a slice-level summary of difficult-to-identify features,which helps to improve the accuracy of both recognition and *** system achieves a recognition F1 score of 0.984,an average classification F1 score of 0.935,and the respective measurement precisions for ascending and descending aortic diameters are 0.994 mm and 0.767 mm root mean square error(RMSE).The high consistency(88.6%)between the recommended surgical treatments and the actual corresponding surgeries verifies the capability of our system to aid clinicians in developing a more prompt,precise,and consistent treatment strategy.

关键词： Aortic dissections Computed tomography angiography Classification Deep learning

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New Layered SOA-Based Architecture for Multi-robots Cooperative Online SLAM

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Chinese Journal of Electronics 2014年第1期23卷 25-30页

作者： CAI Yunfei TANG Zhenmin ZHAO Chunxia Institute of Pattern recognition and Artificial intelligence Nanjing University of Science and Technology

A new layered multi-robots architecture for cooperative online simultaneous localization and mapping based on service-oriented architecture(SOA) is proposed. This architecture is constructed by the concepts of service-oriented and loosely coupled center *** methods make robots' underlying function realization be transparent in cooperation and avoid the impact affected by robot heterogeneous characteristics, which will be beneficial to the system construction, expansion, restructuring and maintenance. All data packages follow the SOA unified data format, which can avoid the impact affected by sensor heterogeneous characteristics. The point to point serving model reduces the path of data transmission which can improve the timeliness of the system. All of these features can satisfy the requirements of multi-robots cooperative online SLAM. The simulation results show the feasibility, applicability and practicability of this architecture.

关键词： service-oriented architecture(SOA) Multi-robot cooperative online SLAM(MRCO-SLAM) Loosely coupled

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Exploring the Brain-like Properties of Deep Neural Networks:A Neural Encoding Perspective

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Machine intelligence research 2022年第5期19卷 439-455页

作者： Qiongyi Zhou Changde Du Huiguang He research center for Brain-inspired intelligence and National Laboratory of Pattern recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial intelligence University of Chinese Academy of SciencesBeijing 100190China center for Excellence in Brain Science and intelligence Technology Chinese Academy of SciencesBeijing 100190China

Nowadays,deep neural networks(DNNs)have been equipped with powerful representation *** deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate early visual cortex have outperformed humans on object categorization and have been found to possess many brain-like ***,vision transformers(ViTs)have been striking paradigms of DNNs and have achieved remarkable improvements on many vision tasks compared to *** is natural to ask how the brain-like properties of ViTs *** the model paradigm,we are also interested in the effects of factors,such as model size,multimodality,and temporality,on the ability of networks to model the human visual pathway,especially when considering that existing research has been limited to *** this paper,we systematically evaluate the brain-like properties of 30 kinds of computer vision models varying from CNNs and ViTs to their hybrids from the perspective of explaining brain activities of the human visual cortex triggered by dynamic *** on two neural datasets demonstrate that neither CNN nor transformer is the optimal model paradigm for modelling the human visual *** reveal hierarchical correspondences to the visual pathway as CNNs ***,we find that multi-modal and temporal networks can better explain the neural activities of large parts of the visual cortex,whereas a larger model size is not a sufficient condition for bridging the gap between human vision and artificial *** study sheds light on the design principles for more brain-like *** code is available at https://***/QYiZhou/LWNeuralEncoding.

关键词： Convolutional neural network(CNN) vision transformer(Vi T) multi-modal networks spatial-temporal networks visual neural encoding brain-like neural networks

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Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

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Engineering 2019年第5期5卷 948-953页

作者： Changde Du Jinpeng Li Lijie Huang Huiguang He research center for Brain-Inspired intelligence and National Laboratory of Pattern recognition Institute of AutomationChinese Academy of Sciences(CAS)Beijing 100190China School of Artificial intelligence University of Chinese Academy of SciencesBeijing 100049China center for Excellence in Brain Science and intelligence Technology Chinese Academy of SciencesShanghai 200031China

Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception *** previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning *** example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small *** fact,effectively unifying the encoding and decoding procedures may allow for more accurate *** this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling ***,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational ***,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and ***,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.

关键词： Brain encoding and decoding Functional magnetic resonance imaging Deep neural networks Deep generative models Dual learning

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