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Fli1 acts in parallel with Pu.1 to control macrophage and neutrophil fate in zebrafish

Journal of Genetics and Genomics 2024年第3期51卷 359-362页

作者： Ao Zhang Jingao Lu Shachuan Feng Huapeng Yu Tao Yu Shizheng Zhao Kemin Chen Zhibin Huang Jin Xu Jianan Y.Qu Zilong Wen Division of Life Science Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Division of Cell Developmental and Integrative BiologySchool of MedicineSouth China University of TechnologyGuangzhouGuangdong 510006China Department of Immunology and Microbiology School of Life ScienceSouthern University of Science and TechnologyShenzhenGuangdong 518055China Biomedical Research Institute Shenzhen Peking University-Hong Kong University of Science and Technology Medical CenterShenzhenGuangdong 518036China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Greater Bay Biomedical Innocenter Shenzhen Bay LaboratoryShenzhenGuangdong 518067China

Macrophages and neutrophils are key components of myeloid cells and play critical roles in innate immune responses,organ formation,and tissue *** integrity of their functions heavily relies on the generation of a proper number of mature macrophages and neutrophils through embryonic and adult *** mammalian adult myelopoiesis,oligopotent common myeloid progenitors(CMPs)are known to be the earliest myeloid progenitors,which give rise to granulocyte-macrophage progenitors(GMPs),subsequently differentiate into unipotent neutrophil and macrophage precursors,and finally,mature macrophages and neutrophils(Orkin and Zon,2008).In contrast,the ontogeny of embryonic myelopoiesis and the mechanism underlying the formation of macrophages and neutrophils remainless understood.

关键词： fish macrophage homeostasis

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A Generalized Outage Prediction Model for Various Types of Extreme Climate Events in Texas

A Generalized Outage Prediction Model for Various Types of E...

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IEEE International Conference on Big Data

作者： Jangjae Lee Sangkeun Matthew Lee Stephanie German Paal Supriya Chinthavali Zachry Department of Civil and Environmental Engineering Texas A&M University College Station TX USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA Geospatial Science and Human Security Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study proposes a generalized model for predicting power outages for various types of extreme weather events. To accomplish the objective of this research, diverse features (e.g., weather data, geographical features, socio-demographic data, and infrastructure information) were leveraged as independent features, while the target variable was the number of customers who had problems with their electricity at the county level. Using the time of occurrence of extreme weather as defined by the National Weather Service, the top ten influential events were selected using the impact index based on cumulative power outages due to each type of extreme weather event. Additionally, a generalized model was created to predict power outages using weather data from one hour before the outage, outage data from one hour prior, as well as socio-demographic, geographic, and infrastructure information and this model was evaluated. The model was developed in two ways: first, as an Ensemble model trained using individual extreme weather events, and second, as a Unified model using all types of extreme weather conditions. As a result of evaluating the model using mean directional accuracy (MDA), one of the evaluation metrics, the ensemble model showed an accuracy of over 0.4 for weather event types such as Winter Storms, Cold/Wind Chill, Frost/Freeze, and Ice Storms. Although this study focused on creating a model specific to Texas, it is possible to expand the data to develop a nationwide model.

关键词： Measurement Accuracy Storms Electricity Predictive models Data models Ice Power system reliability Indexes Optimization

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Research on informatization forecasting particulate matter using spatio-temporal data 7

Research on informatization forecasting particulate matter u...

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7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Wu, Qide Xu, Hongli Huang, Liusheng Guan, Shixuan Lu, Chunchen University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China Suzhou University of Science and Technology School of Environmental Science and Engineering Suzhou215009 China

ISBN: (纸本)9781665442527

In this paper, we study a large number of domestic and international forecasting theories and information construction models on multi-source atmospheric particulate monitoring. It also describes the current research progress of spatial and temporal prediction of atmospheric particulate matter concentration from both measurement point estimation and remote sensing monitoring of atmospheric particulate matter. Besides, it introduces the research progress of the information of atmospheric environment monitoring and forecasting under the traditional stand-alone model and the cloud-based cloud collaboration model. The prediction based on a single point of information is upgraded to a collaborative prediction through the cloud model in cooperation with information from neighboring ends. On the one hand, we compare the methodological theory, original data requirements and technical points of two techniques for atmospheric particulate matter concentration estimation. On the other hand, we summarize the trend of spatio-temporal prediction and information construction of multi-source atmospheric particulate matter based on end-cloud synergy out of the current actual demand, and provide scientific basis and technical guarantee for future research of atmospheric particulate matter. © 2021 IEEE.

关键词： Informatization

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Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI 22

Adaptive Aggregation Weights for Federated Segmentation of P...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Pan, Hongyi Durak, Gorkem Zhang, Zheyuan Taktak, Yavuz Keles, Elif Aktas, Halil Ertugrul Medetalibeyoglu, Alpay Velichko, Yury Spampinato, Concetto Schoots, Ivo Bruno, Marco J. Keswani, Rajesh N. Tiwari, Pallavi Bolan, Candice Gonda, Tamas Gorrins, Michael G. Wallace, Michael B. Xu, Ziyue Bagci, UIas Northwestern University Department of Radiology ChicagoIL United States Istanbul University Istanbul Faculty of Medicine Department of Radiology Istanbul Turkey Istanbul University Istanbul Faculty of Medicine Department of Internal Medicine Istanbul Turkey University of Catania Department of Electrical Electronic and Computer Engineering Catania Italy Erasmus Medical Center Department of Radiology and Nuclear Medicine Rotterdam Netherlands Erasmus Medical Center Department of Gastroenterology and Hepatology Rotterdam Netherlands Northwestern University Department of Gastroenterology and Henatology ChicagoIL United States University of Wisconsin Department of Biomedical Engineering and-Radiology MadisonWI United States Mayo Clinic Division of Gastroenterology and Hepatology JacksonvilleFL United States New York University Division of Gastroenterology and Hepatology New YorkNY United States Johns Hopkins University Division of Gastroenterology and Henatology BaltimoreMD United States Nvidia BethesdaMD United States

ISBN: (纸本)9798331520526

Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in general-izing across domains due to variations in imaging protocols and pa-tient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that in-corporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers). © 2025 IEEE.

关键词： Image segmentation

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Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

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Machine Intelligence Research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

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Modeling and Analysis the Impact of Wastewater Sludge Co-Digestion with Thermo-Alkaline Pretreated Wheat Straw on Biogas Production and Digestate Characteristics at Al-Jouf Region, Saudi Arabia

SSRN

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SSRN 2023年

作者： Alrowais, Raid Said, Noha Mahmoud-Aly, Mohamed Helmi, Ahmed M. Nasef, Basheer M. Mohareb, Mohamed Abdel daiem, Mahmoud M. Department of Civil Engineering College of Engineering Jouf University Sakakah72388 Saudi Arabia Environmental Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt Plant Physiology Division Department of Agricultural Botany Faculty of Agriculture Cairo University Giza12613 Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Buraydah51418 Saudi Arabia Computer and Systems Engineering Dept. Faculty of Engineering Zagazig University Zagazig44519 Egypt Civil Engineering Department College of Engineering Shaqra University Al-Duwadmi Ar Riyadh11911 Saudi Arabia Alwatania Contracting Company Operation and Maintenance of Sewage Stations and Networks Sakakah Saudi Arabia

Anaerobic digestion is one of the best options for producing valuable end products (biogas and bio-fertilizer). In this study, the impact of co-digestion of waste activated sludge with pretreated wheat straw on biogas production and digestate characteristics was investigated. Thermo-alkaline pretreatment of straw has been conducted using NaOH, KOH, and Na2CO3 at different durations (1, 3, and 5h). A feedforward neural network (FFNN) and Logistic models, and SPSS analysis were applied on the experimental data to predict biogas and investigate the significance and the relationship among the variables. NaOH pretreatment for 5h showed the best treatment conditions;the biogas yield reached to 6.59 folds compared to without treatment, and removal ratio of total solids, total volatile solids, chemical oxygen demand, and microbial count reached 63.52 %, 74.60 %, 78.15 %, and 82.22 %, respectively. Methane content reached 67.50 %, indicating a high quality of the biogas. The thermo-alkaline pretreatment induced significant effects on biogas production and digestate characteristics that can be used as bio-fertilizers. Experimental data were successfully modeled for predicting biogas production using the applied models;R2 reached 0.9846 and 0.999 for Logistic and FFNN models, respectively. © 2023, The Authors. All rights reserved.

关键词： Biogas

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Knowledge System,Ontology,and Knowledge Graph of the Deep-Time Digital Earth(DDE): Progress and Perspective

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Journal of Earth Science 2023年第5期34卷 1323-1327页

作者： Xiumian Hu Yiwei Xu Xiaogang Ma Yunqiang Zhu Chao Ma Chao Li Hairong Lü Xinbing Wang Chenghu Zhou Chengshan Wang School of Earth Sciences and Engineering Nanjing UniversityNanjing 210023China State Key Laboratory of Palaeobiology and Stratigraphy Nanjing Institute of Geology and Palaeontology and Center for Excellence in Life and PaleoenvironmentChinese Academy of SciencesNanjing 210008China Department of Computer Science University of IdahoMoscow ID 83844USA Institute of Geographical Science and Natural Resources Chinese Academy of SciencesBeijing 100101China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation&Institute of Sedimentary Geology&College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu 610059China IUGS Deep-Time Digital Earth Research Center of Excellence Suzhou 215101China Department of Automation Tsinghua UniversityBeijing 100084China School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesBeijing 100083China

THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).

关键词： DDE Google Web Semantic

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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

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European Control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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FD-SOS: Vision-Language Open-Set Detectors for Bone Fenestration and Dehiscence Detection from Intraoral Images

arXiv

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arXiv 2024年

作者： Elbatel, Marawan Liu, Keyuan Yang, Yanqi Li, Xiaomeng Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Division of Paediatric Dentistry and Orthodontics Faculty of Dentistry The University of Hong Kong Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

Accurate detection of bone fenestration and dehiscence (FD) is crucial for effective treatment planning in dentistry. While cone-beam computed tomography (CBCT) is the gold standard for evaluating FD, it comes with limitations such as radiation exposure, limited accessibility, and higher cost compared to intraoral images. In intraoral images, dentists face challenges in the differential diagnosis of FD. This paper presents a novel and clinically significant application of FD detection solely from intraoral images. To achieve this, we propose FD-SOS, a novel open-set object detector for FD detection from intraoral images. FD-SOS has two novel components: conditional contrastive denoising (CCDN) and teeth-specific matching assignment (TMA). These modules enable FD-SOS to effectively leverage external dental semantics. Experimental results showed that our method outperformed existing detection methods and surpassed dental professionals by 35% recall under the same level of precision. Code is available at: https: //***/xmed-lab/FD-SOS. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and Control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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