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Single-cell technologies:From research to application

The innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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A Camera–Radar Fusion Method Based on Edge Computing

A Camera–Radar Fusion Method Based on Edge Computing

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IEEE International Conference on Edge Computing (EDGE)

作者： Yanjin Fu Daxin Tian Xunting Duan Jianshan Zhou Ping Lang Chunmian Lin Xin You Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transportation Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728182544

ISBN: (纸本)9781728182551

Multi-access edge computing provides a low-latency and high-performance network environment for the Internet of Vehicle services by migrating computing and storage resources to the edge of networks and supports the deployment of more regional-specific vehicle-to-everything services. In this paper, we propose a fusion perception method for roadside cameras and millimeter wave radar as an application of edge computing. First, we used YOLOv3 to process camera data and DBSCAN clustering method to process radar data to obtain the position, speed, and category of detections. Next, we used joint calibration to spatially synchronize camera and radar data and a direct update method to temporally synchronize camera and radar data. Furthermore, we used the Munkres algorithm to associate camera and radar detections, and Kalman filter to track the proposed fusion method's perception results. Finally, we conducted simulation experiments to evaluate the proposed method. The simulation results demonstrate the effectiveness of our algorithm.

关键词： Simulation Image edge detection Radar detection Millimeter wave radar Cameras Synchronization Edge computing

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NuSegDG: Integration of heterogeneous space and Gaussian kernel for domain-generalized nuclei segmentation

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Knowledge-Based Systems 2025年 322卷

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Li, Chenxin Chen, Zhen Wang, Yi He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science Computer Vision and Intellifent Perception Laboratory and Municipal Digital Port Technology Key Laboratory University of Nottingham Ningbo Zhejiang China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labour-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei semantic and instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Ne...

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IEEE International Conference on data Mining (ICDM)

作者： Xingcheng Fu Jianxin Li Jia Wu Qingyun Sun Cheng Ji Senzhang Wang Jiajun Tan Hao Peng Philip S. Yu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Macquarie University Sydney Australia School of Computer Science and Engineering Central South University Changsha China University of Illinois at Chicago Chicago IL USA

ISBN: (纸本)9781665423991

Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existing GNNs embed graph data into Euclidean space and thus are less effective to capture the ubiquitous hierarchical structures in real-world networks. Hyperbolic Graph Neural Networks (HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning. In hyperbolic geometry, the graph hierarchical structure can be reflected by the curvatures of the hyperbolic space, and different curvatures can model different hierarchical structures of a graph. However, most existing HGNNs manually set the curvature to a fixed value for simplicity, which achieves a suboptimal performance of graph learning due to the complex and diverse hierarchical structures of the graphs. To resolve this problem, we propose an Adaptive Curvature Exploration Hyperbolic Graph Neural Network named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Specifically, ACE-HGNN exploits a multi-agent reinforcement learning framework and contains two agents, ACE-Agent and HGNN-Agent for learning the curvature and node representations, respectively. The two agents are updated by a Nash Q-leaning algorithm collaboratively, seeking the optimal hyperbolic space indexed by the curvature. Extensive experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.

关键词： Representation learning Geometry Adaptation models Adaptive systems Conferences Reinforcement learning Graph neural networks

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Automatic detection of single-electron regime of quantum dots and definition of virtual gates using U-Net and clustering

arXiv

引用

arXiv 2025年

作者： Muto, Yui Zielewski, Michael R. Shinozaki, Motoya Noro, Kosuke Otsuka, Tomohiro Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Department of Electronic Engineering Graduate School of Engineering Tohoku University Aoba 6-6-05 Aramaki Aoba-Ku Sendai980-8579 Japan Graduate School of Information Sciences Tohoku University 6-3-09 Aramaki-aza-Aoba Aoba-ku Sendai980-8579 Japan Unprecedented-scale Data Analytics Center Tohoku University 468-1 Aramaki-aza-Aoba Aoba-ku Sendai980-8572 Japan WPI Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Center for Science and Innovation in Spintronics Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan

To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Hough transforms

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An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

arXiv

引用

arXiv 2025年

作者： Lin, Meijin Guo, Lin Chen, Dicheng Chen, Jianshu Tu, Zhangren Huang, Xu Wang, Jianhua Qi, Ji Long, Yuan Huang, Zhiguo Guo, Di Qu, Xiaobo Han, Haiwei College of Ocean & Earth Sciences Xiamen University Xiamen361102 China Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China National Integrated Circuit Industry Education Integration Innovation Platform Xiamen University Xiamen361100 China Department of Electronic Science Xiamen University Xiamen361102 China Department of Radiology the First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361100 China Software Technology Company Limited Suzhou215163 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age ± standard deviation = 27.4 ± 3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively;Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel. © 2025, CC BY.

关键词： Metabolites

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Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation

arXiv

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arXiv 2020年

作者： Schutski, Roman Khakhulin, Taras Oseledets, Ivan Kolmakov, Dmitry Center for Computational and Data-Intensive Science and Engineering Skoltech Skolkovo Innovation Center Moscow Region121205 Russia Central Research Institute Huawei Technologies

Tensor networks are the main building blocks in a wide variety of computational sciences, ranging from many-body theory and quantum computing to probability and machine learning. Here we propose a parallel algorithm for the contraction of tensor networks using probabilistic graphical models. Our approach is based on the heuristic solution of the μ -treewidth deletion problem in graph theory. We apply the resulting algorithm to the simulation of random quantum circuits and discuss the extensions for general tensor network contractions. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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An X-and Ku-band multifunctional radar receiver based on photonic parametric sampling

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Chinese Optics Letters 2020年第4期18卷 71-75页

作者： Lei Yu Weiwen Zou Xinwan Li Jianping Chen State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(iMLic)Shanghai Institute for Advanced Communication and Data ScienceDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China

We demonstrate a novel multifunctional radar receiver scheme based on photonic parametric *** working principle of photonic parametric sampling based on four-wave mixing(FWM)process is *** experimentally verify the multifulctional feasibility,the scheme is individually implemented to carry out a four-channel phased array radar reception and a dual-band radar reception.

关键词： microwave photonics radar receiver photonic parametric sampling four-wave mixing

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Spaceborne Lidar and Stereogrammetry data Fusion to Predict Aboveground Biomass in Tropical Forests

Spaceborne Lidar and Stereogrammetry Data Fusion to Predict ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rodrigo V. Leite William Wagner Margaret Wooten Monique B. Schlickmann Carlos A. Silva Cibele H. Do Amaral Diogo N. Cosenza Carlos M.M.E. Torres Ameni Mkaouar Shashank Bhushan David Shean Paul Montesano Douglas C. Morton Christopher S.R. Neigh Goddard Space Flight Center NASA Postdoctoral Program Fellow Greenbelt MD Biospheric Sciences Laboratory Code 618 NASA Goddard Space Flight Center Greenbelt MD USA Forest Biometrics Artificial Intelligence and Remote Sensing Laboratory (Silva lab) School of Forest Fisheries and Geomatics Sciences University of Florida Gainesville FL USA Earth Lab Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder CO USA Environmental Data Science Innovation & Inclusion Lab (ESIIL) University of Colorado Boulder Boulder CO USA Department of Forest Engineering Federal University of Viçosa Viçosa MG Brazil Department of Civil and Environmental Engineering University of Washington Seattle WA USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Quantifying aboveground biomass (AGB) in tropical forests is a challenging but necessary task to support actions to preserve and restore these ecosystems. Recent methods have leveraged the integration of NASA’s spaceborne lidar GEDI with imaging sensors to improve AGB predictions. However, the use of high-resolution (<1 m) stereo images from spaceborne sensors still needs to be explored within these frameworks. The objective of this study was to predict AGB in a tropical forest patch by combining stereo images and GEDI data. A digital surface model (Stereo-DSM) was generated by stereophotogrammetric processing of high-resolution stereo pairs collected from a spaceborne sensor. A canopy height model (Stereo-CHM) was then derived by subtracting an airborne lidar-derived digital terrain model from the Stereo-DSM. Descriptive statistics were calculated from the Stereo-CHM to be used as predictors in the model to predict AGB. We also calculated vegetation indices from the Harmonized Landsat-Sentinel and Sentinel-1 images to compare their relative importance in the model and assess their relationship to the Stereo-CHM metrics. Finally, we trained a Random Forest model using stereogrammetry - derived, multispectral and SAR metrics as features and GEDI’s footprint-level AGB product as a reference. The model to predict AGB yielded performance metrics of r = 0.63, RMSE = 25.38 Mg/ha, and MD = 2.1 Mg/ha. The metrics from the Stereo-CHM were ranked as the most important to the model. This is an indication that these metrics can add important information related to canopy structure to inform GEDI-based models to predict AGB. It is still necessary to evaluate these results considering a variation in canopy cover, topography, and understory vegetation. The findings are important to support advances on the integration of large footprint spaceborne lidar and images with sub-meter spatial resolution to characterize vegetation in tropical forests.

关键词： Measurement Image sensors Forests Laser radar Atmospheric modeling Vegetation mapping Predictive models

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Preparation of sulfur-containing functional group-modified resin and its tungsten‑molybdenum adsorption and separation performance with application to waste-alloy leachate

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Hydrometallurgy 2025年

作者： Tiantian Shi Liwen Ma Na Chen Xiaoli Xi Zuoren Nie Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and Engineering Beijing University of Technology Beijing 100124 China National Engineering Laboratory for Industrial Big-data Application Technology College of Materials Science and Engineering Beijing University of Technology Beijing 100124 China Key Laboratory of Advanced Functional Materials Ministry of Education College of Materials Science and Engineering Beijing University of Technology Beijing 100124 China

Since W and Mo are strategic metals in China, the efficient, clean, and value-added utilization of secondary W and Mo resources is important. Ion-exchange methods using resins for the adsorption of Mo after sulfidation can effectively separate W and Mo, but these methods cause sulfide pollution. In this study, the macroporous anion exchange resin D301, which consists of a styrene-divinylbenzene copolymer matrix with tertiary amine groups, was modified by ethyl (methylthio) acetate (EA) of molecular formula C 5 H 10 O 2 S to form resin EA-D301. This resin has the following properties: (i) enhanced the efficiency of W and Mo adsorption and separation, (ii) exhibited a dense structure and well-developed pores, (iii) maximum adsorption capacity (Q W ) of 1062 mg/g for W, and (iv) maximum separation factor ( β Mo W ) of 6.95 at pH 7.5 and 25 °C in a W Mo solution with equal metal concentrations of 0.05 mol/L. The adsorption of both W and Mo by EA-D301 was hardly affected by other anions, and Q W remained as high as 908 mg/g after ten adsorption-desorption cycles, indicating good stability. The adsorption mechanism was an ion-exchange reaction followed by coordination reactions. When EA-D301 was used to treat actual W Mo waste alloy leachate, a Q W of 647 mg/g and a β Mo W of 4.03 were achieved, demonstrating good performance in industrial applications. The EA-D301 resin is a promising W adsorbent with advantages such as simple synthesis, high adsorption capacity, and environmental friendliness. This resin provides an effective way to extract W and separate W and Mo from various resources.

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