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Publisher Correction: Ageing limits stemness and tumorigenesis by reprogramming iron homeostasis

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Nature 2025年 2025 May 22页

作者： Xueqian Zhuang Qing Wang Simon Joost Alexander Ferrena David T Humphreys Zhuxuan Li Melissa Blum Klavdija Krause Selena Ding Yuna Landais Yingqian Zhan Yang Zhao Ronan Chaligne Joo-Hyeon Lee Sebastian E Carrasco Umeshkumar K Bhanot Richard P Koche Matthew J Bott Pekka Katajisto Yadira M Soto-Feliciano Thomas Pisanic Tiffany Thomas Deyou Zheng Emily S Wong Tuomas Tammela Cancer Biology and Genetics Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA. Victor Chang Cardiac Research Institute Sydney New South Wales Australia. Institute for Clinical and Translational Research Albert Einstein College of Medicine New York NY USA. Weill Cornell Graduate School of Medical Science Weill Cornell Medicine New York NY USA. Center for Epigenetics Research Memorial Sloan Kettering Cancer Center New York NY USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA. Wellcome-MRC Cambridge Stem Cell Institute Jeffrey Cheah Biomedical Centre University of Cambridge Cambridge UK. Department of Physiology Development and Neuroscience University of Cambridge Cambridge UK. Laboratory of Comparative Pathology Weill Cornell Medicine Memorial Sloan Kettering Cancer Center and Rockefeller University New York NY USA. Pathology Core Facility Department of Pathology Memorial Sloan Kettering Cancer Center New York NY USA. Institute of Biotechnology HiLIFE University of Helsinki Helsinki Finland. Department of Cell and Molecular Biology Karolinska Institute Stockholm Sweden. Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland. David H. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA USA. Department of Biology Massachusetts Institute of Technology Cambridge MA USA. Institute for NanoBioTechnology Department of Oncology-Cancer Genetics and Epigenetics Johns Hopkins University Baltimore MD USA. Department of Pathology and Cell Biology Columbia University College of Physicians and Surgeons New York NY USA. Departments of Genetics Neurology and Neuroscience Albert Einstein College of Medicine New York NY USA. School of Biotechnology and Biomolecular Sciences UNSW Sy

Analyses of protein cores reveal fundamental differences between solution and crystal structures

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arXiv 2019年

作者： Mei, Zhe Treado, John D. Grigas, Alex T. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures|in the root-mean-square deviations of the Cα atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography. Copyright © 2019, The Authors. All rights reserved.

关键词： X ray crystallography

A Tale of Three: Magnetic Fields along the Orion Integral-shaped Filament as Revealed by the JCMT BISTRO Survey

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Astrophysical Journal Letters 2024年第2期977卷 L31-L31页

作者： Wu, Jintai Qiu, Keping Poidevin, Frédérick Bastien, Pierre Liu, Junhao Ching, Tao-Chung Bourke, Tyler L. Ward-Thompson, Derek Pattle, Kate Johnstone, Doug Koch, Patrick M. Arzoumanian, Doris Lee, Chang Won Fanciullo, Lapo Onaka, Takashi Hwang, Jihye Le Gouellec, Valentin J.M. Soam, Archana Tamura, Motohide Tahani, Mehrnoosh Eswaraiah, Chakali Li, Hua-Bai Berry, David Furuya, Ray S. Coudé, Simon Kwon, Woojin Lin, Sheng-Jun Wang, Jia-Wei Hasegawa, Tetsuo Lai, Shih-Ping Byun, Do-Young Chen, Zhiwei Chen, Huei-Ru Vivien Chen, Wen Ping Chen, Mike Cho, Jungyeon Choi, Youngwoo Choi, Yunhee Choi, Minho Chrysostomou, Antonio Chung, Eun Jung Dai, Sophia Di Francesco, James Diep, Pham Ngoc Doi, Yasuo Duan, Hao-Yuan Duan, Yan Eden, David Fiege, Jason Fissel, Laura M. Franzmann, Erica Friberg, Per Friesen, Rachel Fuller, Gary Gledhill, Tim Graves, Sarah Greaves, Jane Griffin, Matt Gu, Qilao Han, Ilseung Hayashi, Saeko Hoang, Thiem Houde, Martin Inoue, Tsuyoshi Inutsuka, Shu-Ichiro Iwasaki, Kazunari Jeong, Il-Gyo Könyves, Vera Kang, Ji-Hyun Kang, Miju Karoly, Janik Kataoka, Akimasa Kawabata, Koji Kim, Shinyoung Kim, Mi-Ryang Kim, Kyoung Hee Kim, Kee-Tae Kim, Jongsoo Kim, Hyosung Kim, Gwanjeong Kirchschlager, Florian Kirk, Jason Kobayashi, Masato I.N. Kusune, Takayoshi Kwon, Jungmi Lacaille, Kevin Law, Chi-Yan Lee, Hyeseung Lee, Chin-Fei Lee, Sang-Sung Lee, Jeong-Eun Li, Dalei Li, Di Li, Guangxing Liu, Sheng-Yuan Liu, Tie Liu, Hong-Li Lu, Xing Lyo, A-Ran Mairs, Steve Matsumura, Masafumi Matthews, Brenda Moriarty-Schieven, Gerald Nagata, Tetsuya Nakamura, Fumitaka Nakanishi, Hiroyuki Ngoc, Nguyen Bich Ohashi, Nagayoshi Park, Geumsook Parsons, Harriet Peretto, Nicolas Priestley, Felix Pyo, Tae-Soo Qian, Lei Rao, Ramprasad Rawlings, Jonathan Rawlings, Mark Retter, Brendan Richer, John Rigby, Andrew Sadavoy, Sarah Saito, Hiro Savini, Giorgio Seta, Masumichi Sharma, Ekta Shimajiri, Yoshito Shinnaga, Hiroko Tang, Ya-Wen Tang, Xindi Thuong, Hoang Duc Tomisaka, Kohji Tram, Le Ngoc Tsukamoto, Yusuke Viti, Serena Wang, Hongchi Whitworth, School of Astronomy and Space Science Nanjing University 163 Xianlin Avenue Nanjing 210023 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Nanjing 210023 China Instituto de Astrofisíca de Canarias La Laguna Canary Islands Tenerife E-38205 Spain Universidad de La Laguna Dpto. Astrofísica La Laguna Tenerife E-38206 Spain Centre de Recherche en Astrophysique du Québec Département de Physique Université de Montréal 1375 Avenue Thérèse-Lavoie-Roux Montréal H2V OB3 QC Canada Division of ALMA National Astronomical Observatory of Japan Mitaka Tokyo 181-8588 Japan National Radio Astronomy Observatory 1003 Lopezville Road Socorro 87801 NM United States SKA Observatory Jodrell Bank Lower Withington Macclesfield SK11 9FT United Kingdom Jodrell Bank Centre for Astrophysics School of Physics and Astronomy University of Manchester Oxford Road Manchester M13 9PL United Kingdom Jeremiah Horrocks Institute University of Central Lancashire Preston PR1 2HE United Kingdom Department of Physics and Astronomy University College London Gower Street London WC1E 6BT United Kingdom NRC Herzberg Astronomy and Astrophysics 5071 West Saanich Road Victoria V9E 2E7 BC Canada Department of Physics and Astronomy University of Victoria Victoria V8W 2Y2 BC Canada Academia Sinica Institute of Astronomy and Astrophysics No.1 Sec. 4. Roosevelt Road Taipei 10617 Taiwan Division of Science National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Korea Astronomy and Space Science Institute 776 Daedeokdae-ro Yuseong-gu Daejeon 34055 South Korea University of Science and Technology 217 Gajeong-ro Yuseong-gu Daejeon 34113 South Korea National Chung Hsing University 145 Xingda Road South District Taichung City 402 Taiwan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan NASA Ames Research Center Space Science and

As part of the B-fields In Star-forming Region Observations survey, we present James Clerk Maxwell Telescope (JCMT) 850 μm polarimetric observations toward the Orion integral-shaped filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multiscale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90^o for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and nonthermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds. © 2024. The Author(s). Published by the American Astronomical Society.

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Corrigendum to "Multiband rabi antenna using nest microstrip add-drop filter (NMADF) for relativistic sensing applications" [Heliyon 9(2) (February 2023) e13611]

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Heliyon 2023年第7期9卷 e16846页

作者： Somchat Sonasang M Jamsai M A Jalil N T Pham K Ray Niwat Angkawisittpan Preecha Yupapin Sarawoot Boonkirdram Martha Alicia Palomino-Ovando Miller Toledo-Solano Khashayar Misaghian J E Lugo Electronic Technology Faculty of Industrial Technology Nakon Phanom University Nakon Phanom 48000 Thailand. Department of Electrical Engineering Faculty of Industry and Technology Rajamagala University of Technology Isan Sakon Nakhon Campus Sakon Nakhon 47160 Thailand. Department of Physics Universiti Teknologi Malaysia 81310 Skuda Johor Malaysia. Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam. Amity School of Applied Sciences Amity University Rajasthan Jaipur India. Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla Av. San Claudio y AV. 18 sur Col. San Manuel Ciudad Universitaria Pueble Pue 72570 Mexico. Faubert Lab Ecole d'optométrie Université de Montréal Montréal QC H3T1P1 Canada. Research Unit for Computational Electromagnetics and Optical Systems Faculty of Engineering Mahasarakham University Maha Sarakham 44150 Thailand. Department of Electrical Technology School of Industrial Technology Sakonnakhon Technical College Institute of Vocational Education Northeastern 2 Sakonnakhon 47000 Thailand. Program of Electrical and Electronics Faculty of Industrial Technology Sakon Nakhon Rajabhat University 680 Nittayo Mueang Sakon Nakhon 47000 Thailand. CONACYT-Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Pueble Av. San Claudio y Av. 18 sur Col. San Manuel Ciudad Universitaria Puebla Pue 72570 Mexico. Faubert Lab School of Optometry Université de Montréal Montréal QC H3T1P1 Canada. Sage-Sentinel Smart Solutions 1919-1 Tancha Onna-son Kunigamigun Okinawa 904-0495 Japan.

[This corrects the article DOI: 10.1016/***.2023.e13611.].

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

Analysis and computation of some tumor growth models with nutrient: From cell density models to free boundary dynamics

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arXiv 2018年

作者： Liu, Jian-Guo Tang, Min Wang, Li Zhou, Zhennan Department of Mathematics Department of Physics Duke University School of Mathematics and Institute of Natural Sciences MOE-LSC Shanghai JiaoTong University Department of Mathematics Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States Beijing International Center for Mathematical Research Peking University

In this paper, we study the tumor growth equation along with various models for the nutrient component, including the in vitro model and the in vivo model. At the cell density level, the spatial availability of the tumor density n is governed by the Darcy law via the pressure p\(n\) = nγ. For finite γ, we prove some a priori estimates of the tumor growth model, such as boundedness of the nutrient density, and non-negativity and growth estimate of the tumor density. As γ → ∞, the cell density models formally converge to Hele-Shaw flow models, which determine the free boundary dynamics of the tumor tissue in the incompressible limit. We derive several analytical solutions to the Hele-Shaw flow models, which serve as benchmark solutions to the geometric motion of tumor front propagation. Finally, we apply a conservative and positivity preserving numerical scheme to the cell density models, with numerical results verifying the link between cell density models and the free boundary dynamical *** Codes 35K55, 35B25, 76D27, 92C50 Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems

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arXiv 2018年

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry and Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. Copyright © 2018, The Authors. All rights reserved.

关键词： Potential energy

A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems

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A Neuro-Inspired Approach to Solve a Simultaneous Location a...

International Conference on Control, Automation, Robotics and Vision

作者： Matheus Chaves Menezes Edison Pignaton de Freitas Sen Cheng Alexandre Cesar Muniz de Oliveira Paulo Rogerio de Almeida Ribeiro Graduate Program on Computer Science Federal University of Maranhão São Luís Brazil Graduate Program on Computer Science Federal University of Rio Grande do Sul Porto Alegre Brazil Computational Neuroscience Ruhr University Bochum Bochum Germany Department of Informatics Federal University of Maranhão São Luís Maranhão Department of Computer Engineering Federal University of Maranhão São Luís Maranhão

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.

关键词： Navigation Simultaneous localization and mapping Feature extraction Robot vision systems Task analysis Hippocampus