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Taking the pulse of COVID-19:a spatiotemporal perspective

International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big data Earth system emergency geospatial sciences epidemics applications

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Lack of association between vitiligo and major adverse cardiovascular events: A population-based cohort study

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Journal of the European Academy of Dermatology and Venereology : JEADV 2023年第6期37卷 e773-e775页

作者： Po-Chien Wu Sheng-Hsiang Ma Chun-Ying Wu Tzu-Yun Pan Yun-Ting Chang Chen-Yi Wu Department of Dermatology Chang Gung Memorial Hospital Linkou Taoyuan Taiwan. Research Center of Big Data and Meta-Analysis Wan Fang Hospital Taipei Medical University Taipei Taiwan. Department of Dermatology Taipei Veterans General Hospital Taipei Taiwan. College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan. Division of Translational Research Department of Medical Research Taipei Veterans General Hospital Taipei Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan. College of Public Health China Medical University Taichung Taiwan. Department of Dermatology National Yang Ming Chiao Tung University Taipei Taiwan. Institute of Public Health and Department of Public Health National Yang Ming Chiao Tung University Taipei Taiwan.

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Real-time Application of SLAM based-line for Unmanned ground vehicle

Real-time Application of SLAM based-line for Unmanned ground...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Fethi Demim Abdelkrim Nemra Mehdia Hedir Abdenebi Rouigueb Abdelouahab Bazoula Mustapha Hamerlain Laboratoire des Véhicules Autonomes & Intelligents Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Image Processing and Radiation Laboratory Faculty of Electronic and Informatics Bab Ezzouar Algiers Algeria Data Fusion and Analysis Laboratory Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Centre de Developpement des Technologies Avancées CDTA Baba Hassen Algiers Algeria

ISBN: (纸本)9781728131573

This paper addresses the simultaneous localization and mapping (SLAM) of a robot in an unknown environment. Several techniques were proposed in the researches to solve this problem employ the different representations of the environment either by points or lines with their information on the extreme geometric positions. The objective of this paper has proposed a solution based on Extended Kalman Filter (EKF) for a single robot. The algorithm developed in our work is the EKF-SLAM which is implemented experimentally using a Pioneer 3-AT mobile robot equipped with a 2D laser telemeter. The obtained results using EKF-SLAM based lines shows the improvements in terms of accuracy and smoothness compared to the EKF-SLAM based points and odometer.

关键词： Autonomous navigation Mobile robot Localization Map building data fusion introduction Mobile robots autonomous navigation Unmanned Ground Vehicle supersonic low altitude missile simultaneous localization and mapping Robot Smoothness

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Bounding moments of sojourn time in M/G/1 FCFS queue with inaccurate job size information and additive error: Some observations from numerical experiments 1

Bounding moments of sojourn time in M/G/1 FCFS queue with in...

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1st Workshop (Summer Session) at the Conference on Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems, ITTMM-WSS 2018

作者： Milovanova, Tatiana A. Meykhanadzhyan, Lusine A. Razumchik, Rostislav V. 6 Miklukho-Maklaya str. Moscow117198 Russia Department of Data Analysis Decision-Making and Financial Technology Financial University Government of the Russian Federation 49 Leningradsky Prospekt Moscow125993 Russia Institute of Informatics Problems of the Federal Research Center "Computer Science and Control Russian Academy of Sciences 44-2 Vavilova Str. Moscow119333 Russia

In this paper consideration is given to the M/G/1 FCFS system in which the service time distribution is not fully known. There is an unknown "theoretical distribution" of the actual service times. But those service times which are known, follow a different distribution, obtained by adding theoretical services times with the error term drawn from a left-truncated normal distribution. The goal is to derive bounds on the response time in the M/G/1 FCFS queue with the unknown "theoretical distribution" that are better than simply using the known service time distribution. In [1] it is shown that in the case when theoretical service times are multiplied by a log-normally distributed error the M/G/1 LCFS queue with resampling gives better upper bounds on the mean response time in the M/G/1 FCFS queue with the "theoretical distribution" of the service times. Here we present some numerical results, which show that once the error becomes additive the result of [1] is not valid any more. In the calculations it was assumed that the unknown "theoretical distribution" is left-truncated Weibull. The new modification of the LCFS resampling policy is suggested, which leads to the lower bounds for the unknown first and second moments of the response time in the M/G/1 FCFS system with the "theoretical distribution" of the service times. Behaviour of mean response times under other service policies is briefly discussed. Copyright © 2018 for the individual papers by the papers’ authors.

关键词： Weibull distribution

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Author Correction: Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

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Nature communications 2025年第1期16卷 1507页

作者： Jakob Steinfeldt Benjamin Wild Thore Buergel Maik Pietzner Julius Upmeier Zu Belzen Andre Vauvelle Stefan Hegselmann Spiros Denaxas Harry Hemingway Claudia Langenberg Ulf Landmesser John Deanfield Roland Eils Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany. Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany. Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany. Institute of Cardiovascular Sciences University College London London UK. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK. Institute of Health Informatics University College London London UK. Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA. Pattern Recognition and Image Analysis Lab University of Münster Münster Germany. British Heart Foundation Data Science Centre London UK. Health Data Research UK London UK. National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK. Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. roland.eils@bih-charite.de. Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany. roland.eils@bih-charite.de.

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H2O2-Responsive NIR-II AIE Nanobomb for Carbon Monoxide Boosting Low-Temperature Photothermal Therapy

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Angewandte Chemie 2022年第36期134卷

作者： Gongcheng Ma Zhongke Liu Chunguang Zhu Prof. Huajie Chen Prof. Ryan T. K. Kwok Prof. Pengfei Zhang Ben Zhong Tang Prof. Lintao Cai Prof. Ping Gong Guangdong Key Laboratory of Nanomedicine Shenzhen Engineering Laboratory of Nanomedicine and Nanoformulations CAS-HK Joint Lab for Biomaterials Research Laboratory for Biomedical Optics and Molecular Imaging Shenzhen Key Laboratory for Molecular Imaging CAS Key Lab for Health Informatics Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China Contribution: Data curation (lead) Validation (lead) Writing - original draft (lead) Nano Science and Technology Institute University of Science & Technology of China Suzhou 215123 China Contribution: Data curation (equal) Investigation (equal) Key Laboratory of Environmentally Friendly Chemistry and Application of the Ministry of Education College of Chemistry Xiangtan University Xiangtan 411105 China Contribution: Data curation (supporting) Methodology (supporting) Contribution: Project administration (supporting) Resources (supporting) Supervision (supporting) Writing - review & editing (equal) Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction and Institute for Advanced Study The Hong Kong University of Science and Technology (HKUST) Clear Water Bay Kowloon Hong Kong China Contribution: Funding acquisition (equal) Supervision (equal) Writing - review & editing (equal) Contribution: Conceptualization (lead) Formal analysis (lead) Methodology (equal) Project administration (lead) Resources (equal) Supervision (equal) Writing - original draft (equal) Writing - review & editing (lead) School of Science and Engineering Shenzhen Institute of Aggregate Science and Technology The Chinese University of Hong Kong Shenzhen Guangdong 518172 China Contribution: Formal analysis (supporting) Funding acquisition (equal) Writing - review & editing (supporting) Contribution: Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - revi

Low-temperature photothermal therapy (PTT), which circumvents the limitations of conventional PTT (e.g., thermotolerance and adverse effects), is an emerging therapeutic strategy which shows great potential for future clinical applications. The expression of heat shock proteins (HSPs) can dramatically impair the therapeutic efficacy of PTT. Thus, inhibition of HSPs repair and reducing the damage of nearby normal cells is crucial for improving the efficiency of low-temperature PTT. Herein, we developed a nanobomb based on the self-assembly of NIRII AIE polymer PBPTV and carbon monoxide (CO) carrier polymer mPEG(CO). This smart nanobomb can be exploded in a tumor microenvironment in which hydrogen peroxide is overexpressed and release CO into cancer cells to significantly inhibit the expression of HSPs and hence improve the antitumor efficiency of the low-temperature PTT.

关键词： Aggregation Induced Emission Gas Therapy Heat Shock Protein NIR-II Photothermal Therapy

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Amyloid Against Amyloid: Dimeric Amyloid Fragment Ameliorates Cognitive Impairments by Direct Clearance of Oligomers and Plaques

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Angewandte Chemie 2022年第7期135卷

作者： Hee Yang Lee Dr. Seungyeop Baek Minhae Cha Prof. Seung-Hoon Yang Illhwan Cho Heewon Shin Dr. Sejin Lee Dr. Hye Yun Kim Songmin Lee Dr. Jisu Shin Dr. Donghee Lee Kyeonghwan Kim InWook Park Soljee Yoon Dr. Jiyoon Kim Seong Jeong Park Dr. Seong Muk Kim Ko Eun Kim Hye Ju Kim Min-Seok Oh Dr. Gwan-Ho Lee Dr. Byung-Yong Yu Priyadharshini Kannan Dr. Keunwan Park Prof. YoungSoo Kim Department of Pharmacy and Yonsei Institute of Pharmaceutical Sciences Yonsei University Yeonsu-gu Incheon 21983 South Korea Department of Medical Biotechnology Dongguk University Jung-gu Seoul 04620 South Korea Department of Integrative Biotechnology and Translational Medicine Yonsei University Yeonsu-gu Incheon 21983 South Korea Brain Science Institute Korea Institute of Science and Technology Seongbuk-gu Seoul 02792 South Korea Amyloid Solution Bundang-gu Seongnam-si Gyeonggi-do 13486 South Korea Advanced Analysis and data Center Korea Institute of Science and Technology Seongbuk-gu Seoul 02792 South Korea Department of Stem Cell Biology Konkuk University Gwangjin-Gu Seoul 05029 South Korea Department of Biochemical Engineering Gangneung-Wonju National University Gangneung 25457 South Korea Natural Product Informatics Research Center Korea Institute of Science and Technology Gangwon-do 25451 South Korea

Amyloid-β (Aβ) in the form of neurotoxic aggregates is regarded as the main pathological initiator and key therapeutic target of Alzheimer's disease. However, anti-Aβ drug development has been impeded by the lack of a target needed for structure-based drug design and low permeability of the blood–brain barrier (BBB). An attractive therapeutic strategy is the development of amyloid-based anti-Aβ peptidomimetics that exploit the self-assembling nature of Aβ and penetrate the BBB. Herein, we designed a dimeric peptide drug candidate based on the N-terminal fragment of Aβ, DAB, found to cross the BBB and solubilize Aβ oligomers and fibrils. Administration of DAB reduced amyloid burden in 5XFAD mice, and downregulated neuroinflammation and prevented memory impairment in the Y-maze test. Peptide mapping assays and molecular docking studies were utilized to elucidate DAB-Aβ interaction. To further understand the active regions of DAB, we assessed the dissociative activity of DAB with sequence modifications.

关键词： Alzheimer's Disease (AD) Amyloid-β (Aβ) Peptide Drug

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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