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Genes & Diseases 2024年第2期11卷 874-889页

作者： Yancheng Lai Xiaole Lu Yankai Liao Pei Ouyang Hai Wang Xian Zhang Guanglong Huang Songtao Qi Yaomin Li Department of Neurosurgery Institute of Brain DiseaseNanfang HospitalSouthern Medical UniversityGuangzhouGuangdong 510515China Laboratory for Precision Neurosurgery Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdong 510515China

Glioblastoma(GBM)is the most common intrinsic and aggressive primary brain tumor in adults,with a median survival of approximately 15 *** heterogeneity is considered responsible for the treatment resistance and unfavorable ***-mesenchymal transition(PMT)represents GBM malignant progression and recurrence,which might be a breakthrough to understand GBM heterogeneity and overcome treatment *** is a complicated process influenced by crosstalk between GBM and tumor microenvironment,depending on intricate ligand-receptor *** this review,we summarize the autocrine and paracrine pathways in the GBM microenvironment and related ligand-receptor interactions inducing *** also discuss the current therapies targeting the PMT-related autocrine and paracrine ***,this review offers a comprehensive understanding of the failure of GBM-targeted therapy and ideas for future tendencies of GBM treatment.

关键词： Autocrine Glioblastoma Ligand-receptor interaction Microenvironment Paracrine Proneural-mesenchymal transition

Deep learning-based lung sound analysis for intelligent stethoscope

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Military Medical Research 2024年第4期11卷 567-588页

作者： Dong-Min Huang Jia Huang Kun Qiao Nan-Shan Zhong Hong-Zhou Lu Wen-Jin Wang Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055GuangdongChina The Third People’s Hospital of Shenzhen Shenzhen 518112GuangdongChina Guangzhou institute of Respiratory Health China State Key Laboratory of Respiratory DiseaseNational Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou 510120China

Auscultation is crucial for the diagnosis of respiratory system ***,traditional stethoscopes have inherent limitations,such as inter-listener variability and subjectivity,and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in *** emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and *** this basis,machine learning,particularly deep learning,enables the fully-automatic analysis of lung sounds that may pave the way for intelligent *** review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence(AI)in this *** focus on each component of deep learning-based lung sound analysis systems,including the task categories,public datasets,denoising methods,and,most importantly,existing deep learning methods,i.e.,the state-of-the-art approaches to convert lung sounds into two-dimensional(2D)spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung ***,this review highlights current challenges in this field,including the variety of devices,noise sensitivity,and poor interpretability of deep *** address the poor reproducibility and variety of deep learning in this field,this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension:https://***/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.

关键词： Deep learning Lung sound analysis Respiratory sounds

GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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Computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global institute of future technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National institute of Standards and technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

Progress on the antiferromagnetic topological insulator MnBi2Te4

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National science Review 2024年第2期11卷 33-49页

作者： Shuai Li Tianyu Liu Chang Liu Yayu Wang Hai-Zhou Lu X.C.Xie Department of Physics Harbin Institute of Technology Shenzhen institute for Quantum science and Engineering and Department of Physics Southern University of Science and Technology(SUSTech) Quantum science Center of Guangdong-Hong Kong-Macao Greater Bay Area(Guangdong) Shenzhen Key Laboratory of Quantum science and Engineering International Quantum Academy Beijing Academy of Quantum Information sciences Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of Physics Renmin University of China State Key Laboratory of Low Dimensional Quantum Physics Department of PhysicsTsinghua University Frontier science Center for Quantum Information Hefei National Laboratory International Center for Quantum Materials School of Physics Peking University institute for Nanoelectronic Devices and Quantum Computing Fudan University

Topological materials, which feature robust surface and/or edge states, have now been a research focus in condensed maTher physics. They represent a new class of materials exhibiting nontrivial topological phases,and provide a platform for exploring exotic transport phenomena, such as the quantum anomalous Hall effect and the quantum spin Hall effect. Recently, magnetic topological materials have attracted considerable interests due to the possibility to study the interplay between topological and magnetic *** particular, the quantum anomalous Hall and axion insulator phases can be realized in topological insulators with magnetic order. MnBi2Te4, as the first intrinsic antiferromagnetic topological insulator discovered, allows the examination of existing theoretical predictions; it has been extensively studied, and many new discoveries have been made. Here we review the progress made on MnBi2Te4from both experimental and theoretical aspects. The bulk crystal and magnetic structures are surveyed first, followed by a review of theoretical calculations and experimental probes on the band structure and surface states, and a discussion of various exotic phases that can be realized in MnBi2Te4. The properties of MnBi2Te4thin films and the corresponding transport studies are then reviewed, with an emphasis on the edge state transport. Possible future research directions in this field are also discussed.

关键词： MnBi2Te4 magnetic topological insulator antiferromagnetic quantum anomalous Hall effect axion insulator

MART(Splitting-Merging Assisted Reliable)Independent Component Analysis for Extracting Accurate Brain Functional networks

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Neuroscience Bulletin 2024年第7期40卷 905-920页

作者： Xingyu He Vince D.Calhoun Yuhui Du School of Computer and Information technology Shanxi UniversityTaiyuan 030006China Tri-Institutional Center for Translational Research in Neuroimaging and Data science Georgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlanta 30303USA

Functional networks(FNs)hold significant promise in understanding brain *** component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN ***,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model *** extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI *** on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to *** two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age ***,both small-scale and large-scale brain FN templates are provided as benchmarks for future *** together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.

关键词： Independent component analysis Functional magnetic resonance imaging-Brain functional networks Clustering Multi-model-order

Secure and Reliable Routing in the Internet of Vehicles Network:AODV-RL with BHA Attack Defense

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Computer Modeling in Engineering & sciences 2024年第4期139卷 633-659页

作者： Nadeem Ahmed Khalid Mohammadani Ali Kashif Bashir Marwan Omar Angel Jones Fayaz Hassan School of Electronic science Beijing University of Post and TelecommunicationBeijing100086China Department of Computer science Huanggang Normal UniversityHuanggangChina Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabad502345India Department of Computer science and Mathematics Lebanese American UniversityBeirutLebanon Information technology and Management Illinois Institute of TechnologyChicagoUSA College of Computing Capital Technology UniversityLaurelUSA

Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes *** the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against *** additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network *** testing of our solution has revealed its remarkable ability to enhance communication in ***,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and *** results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.

关键词： Black hole attack IoV vehicular ad hoc network AODV routing protocol

Data Driven Vibration Control:A Review

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1898-1917页

作者： Weiyi Yang Shuai Li Xin Luo Chongqing institute of Green and Intelligent technology Chinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Faculty of Information technology and Electrical Engineering University of OuluOulu 90570 VTT-technology Research Center of Finland Oulu 90570Finlan College of Computer and Information science Southwest UniversityChongqing 400715China

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic *** shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC *** convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration *** on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of *** doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

关键词： Data driven vibration control(DDVC) data science designing method feedforward control industrial robot input shaping optimizing method residual vibration

Structure, variation and assembly processes of bacterial communities in different root-associated niches of tomato under periodic drought and nitrogen addition

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Pedosphere 2024年第5期34卷 892-904页

作者： Dan HE Ruifeng GAO Honghong DONG Xiaodi LIU Lijuan REN Qinglong WU Qing YAO Honghui ZHU southern Marine science and Engineering Guangdong Laboratory (Guangzhou) Key Laboratory of Agricultural Microbiomics and Precision Application (MARA) State Key Laboratory of Applied Microbiology Southern China Institute of Microbiology Guangdong Academy of Sciences College of Plant Protection South China Agricultural University Department of Ecology Institute of Hydrobiology Jinan University College of Horticulture South China Agricultural University

Root-associated bacteria play a vital role in the growth and adaptation of host plants to drought stress. These bacteria can be classified as rhizoplane and rhizosphere bacteria based on their distance from the root surface. Tomato plants are often exposed to periodic drought and nitrogen (N) addition throughout their life cycle, but the impacts of these factors on the plant and root-associated bacteria are not well understood. To gain insight into this relationship, we conducted an experiment to monitor the effects of periodic drought and N addition on rhizoplane and rhizosphere bacteria of tomato plants. Drought and N addition had interactive effects on plant and soil properties, which varied with the timing of drought. There were clear divergences in community traits such as alpha diversity, beta diversity, and network topological features between the two types of bacteria. The rhizoplane bacteria showed lower alpha diversity but higher beta diversity and were more sensitive to drought and N addition than the rhizosphere bacteria. Nitrogen addition could downsize the effects of drought on rhizoplane bacterial community compositions. The higher proximity to the root might induce a community to develop more cooperation between different members to cope with plant metabolites, as revealed by the more connected and modularized community network of the rhizoplane bacteria. Drought at the seedling stage had great legacy effects on plant and soil properties. It may enhance selection, cause the dominance of deterministic processes in the assembly of rhizoplane bacteria, and reduce bacterial community network complexity. In conclusion, N addition could interact with drought in affecting tomato plants and their root-associated bacteria, depending on the timing of drought and the fineness of root niches. The higher sensitivity of rhizoplane bacteria to drought and N addition calls for more research due to their higher proximity and importance to plants in future environment

关键词： alpha diversity beta diversity co-occurrence network community composition drought stress node-level network feature rhizoplane bacteria rhizosphere bacteria

Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

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Advances in Atmospheric sciences 2024年第8期41卷 1511-1526页

作者： Shengping HE Helge DRANGE Tore FUREVIK Huijun WANG Ke FAN Lise Seland GRAFF Yvan J.ORSOLINI Geophysical institute University of Bergen and Bjerknes Centre for Climate ResearchBergen 5020Norway Nansen Environmental and Remote Sensing Center Bergen 5007Norway Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 211544China Nansen-Zhu International Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China southern Marine science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 511458China School of Atmospheric science Sun Yat-Sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Norwegian Meteorological institute Oslo 0313Norway Norwegian institute for Air Research Kjeller 2007Norway

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

关键词： Arctic sea ice loss warm Arctic–cold East Asia atmospheric internal variability large-ensemble simulation NorESM2 PAMIP

ResoNet:Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer networks

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Advances in Atmospheric sciences 2024年第7期41卷 1289-1298页

作者： Pumeng LYU Tao TANG Fenghua LING Jing-Jia LUO Niklas BOERS Wanli OUYANG Lei BAI Shanghai Artificial Intelligence Laboratory Shanghai 200072China Zhejiang Meteorological Observatory Hangzhou 310002China institute for Climate and Application Research(ICAR) Nanjing University of Information Science and TechnologyNanjing 210044China Technical university of Munich Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–southern Oscillation(ENSO)events more than 1.5 years in ***,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of ***,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer *** hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across *** show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast *** to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor ***,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by *** results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.

关键词： deep learning ENSO CNN transformer