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Correction: Gut microbiome in endometriosis: a cohort study on 1000 individuals

BMC medicine 2024年第1期22卷 448页

作者： Inmaculada Pérez-Prieto Eva Vargas Eduardo Salas-Espejo Kreete Lüll Analuce Canha-Gouveia Laura Antequera Pérez Juan Fontes Andres Salumets Reidar Andreson Oliver Aasmets Katrine Whiteson Elin Org Signe Altmäe Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. inmapprieto@ugr.es. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. inmapprieto@ugr.es. Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. Systems Biology Unit Department of Experimental Biology Faculty of Experimental Sciences University of Jaen Jaen Spain. Institute of Genomics Estonian Genome Centre University of Tartu Tartu Estonia. Department of Physiology Faculty of Veterinary University of Murcia Campus Mare Nostrum Murcia Spain. Department of Computer Engineering Automation and Robotics University of Granada Granada Spain. U. Reproducción UGC Laboratorio Clínico y UGC Obstetricia y Ginecología. HU Virgen de Las Nieves Granada Spain. Competence Centre On Health Technologies Tartu Estonia. Division of Obstetrics and Gynaecology Department of Clinical Science Intervention and Technology Karolinska Institutet Huddinge Stockholm Sweden. Department of Gynaecology and Reproductive Medicine Karolinska University Hospital Huddinge Stockholm Sweden. Department of Obstetrics and Gynaecology Institute of Clinical Medicine University of Tartu Tartu Estonia. Institute of Molecular and Cell Biology University of Tartu Tartu Estonia. School of Biological Sciences University of California Irvine CA USA.

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

引用

Asian Journal of Control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Studying bioluminescence flashes with the ANTARES deep Sea neutrino telescope

arXiv

引用

arXiv 2021年

作者： Reeb, Nico Hutschenreuter, S. Zehetner, P. Ensslin, T. Albert, A. Alves, S. André, M. Anghinolfi, M. Anton, G. Ardid, M. Aubert, J.-J. Aublin, J. Baret, B. Basa, S. Belhorma, B. Bendahman, M. Bertin, V. Biagi, S. Bissinger, M. Boumaaza, J. Bouta, M. Bouwhuis, M.C. Brânzaş, H. Bruijn, R. Brunner, J. Busto, J. Caiffi, B. Capone, A. Caramete, L. Carr, J. Carretero, V. Celli, S. Chabab, M. Chau, T.N. Chiarusi, T. Circella, M. Coleiro, A. Colomer-Molla, M. Coniglione, R. Coyle, P. Creusot, A. Díaz, A.F. de Wasseige, G. Deschamps, A. Distefano, C. Di Palma, I. Domi, A. Donzaud, C. Dornic, D. Drouhin, D. van Eeden, T. Enzenhöfer, A. Fermani, P. Ferrara, G. Filippini, F. Fusco, L. Gatelet, Y. Gay, P. Glotin, H. Gozzini, R. Ruiz, R. Gracia Graf, K. Guidi, C. Hallmann, S. van Haren, H. Heijboer, A.J. Hello, Y. Hernández-Rey, J.J. Hößl, J. Hofestädt, J. Huang, F. Illuminati, G. James, C.W. Jisse-Jung, B. de Jong, M. de Jong, P. Jongen, M. Kadler, M. Kalekin, O. Katz, U. Khan-Chowdhury, N.R. Kouchner, A. Kreykenbohm, I. Kulikovskiy, V. Lahmann, R. Le Breton, R. Lefèvre, D. Leonora, E. Levi, G. Lincetto, M. Lopez-Coto, D. Loucatos, S. Maderer, L. Manczak, J. Marcelin, M. Margiotta, A. Marinelli, A. Martínez-Mora, J.A. Melis, K. Migliozzi, P. Moussa, A. Muller, R. Nauta, L. Navas, S. Nezri, E. Fearraigh, B.Ó. Organokov, M. Pavalaş, G.E. Pellegrino, C. Perrin-Terrin, M. Piattelli, P. Pieterse, C. Poirè, C. Popa, V. Pradier, T. Randazzo, N. Reck, S. Riccobene, G. Romanov, A. Sánchez-Losa, A. Greus, F. Salesa Samtleben, D.F.E. Sanguineti, M. Sapienza, P. Schnabel, J. Schumann, J. Schüssler, F. Spurio, M. Stolarczyk, Th. Taiuti, M. Tingay, S.J. Vallage, B. van Elewyck, V. Versari, F. Viola, S. Vivolo, D. Wilms, J. Zavatarelli, S. Zegarelli, A. Zornoza, J.D. Zúñiga, J. Information Field Theory Group Max Planck Institute for Astrophysics Garching Germany Informatics 6 Chair of Robotics Artificial Intelligence and Real time Systems Technical University of Munich Garching Germany Department of Astrophysics IMAPP Radboud University Nijmegen Nijmegen Netherlands Department of Physics Ludwig-Maximilians University Munich Germany CERN Geneva Switzerland Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace MulhouseF-68200 France IFIC - Instituto de Física Corpuscular CSIC Universitat de València c/ Catedrático José Beltrán 2 Paterna ValenciaE-46980 Spain Technical University of Catalonia Laboratory of Applied Bioacoustics Rambla Exposició Vilanova i la Geltrú Barcelona08800 Spain INFN - Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Centre for Astroparticle Physics Erwin-Rommel-Str. 1 Erlangen91058 Germany Universitat Politècnica de València C/ Paranimf 1 Gandia46730 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université de Paris CNRS Astroparticule et Cosmologie ParisF-75013 France Aix Marseille Univ CNRS CNES LAM Marseille France National Center for Energy Sciences and Nuclear Techniques B.P.1382 RabatR. P. 10001 Morocco University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 R.P. Rabat10000 Morocco Via S. Sofia 62 Catania95123 Italy University Mohammed I Laboratory of Physics of Matter and Radiations B.P.717 Oujda6000 Morocco Nikhef Science Park Amsterdam Netherlands Institute of Space Science Bucharest MagureleRO-077125 Romania Universiteit van Amsterdam Instituut voor Hoge-Energie Fysica Science Park 105 Amsterdam1098 XG Netherlands INFN - Sezione di Roma P.le Aldo Moro 2 Roma00185 Italy Dipartimento di Fisica Università La Sapienza P.le Aldo Moro 2 Roma00185 Italy LPHEA Faculty of Science - Semlali Cadi Ayyad University P.O

We develop a novel technique to exploit the extensive data sets provided by underwater neutrino telescopes to gain information on bioluminescence in the deep sea. The passive nature of the telescopes gives us the unique opportunity to infer information on bioluminescent organisms without actively interfering with them. We propose a statistical method that allows us to reconstruct the light emission of individual organisms, as well as their location and movement. A mathematical model is built to describe the measurement process of underwater neutrino telescopes and the signal generation of the biological organisms. The Metric Gaussian Variational Inference algorithm is used to reconstruct the model parameters using photon counts recorded by the neutrino detectors. We apply this method to synthetic data sets and data collected by the ANTARES neutrino telescope. The telescope is located 40 km off the French coast and fixed to the sea floor at a depth of 2475 m. The runs with synthetic data reveal that we can reliably model the emitted bioluminescent flashes of the organisms. Furthermore, we find that the spatial resolution of the localization of light sources highly depends on the configuration of the telescope. Precise measurements of the efficiencies of the detectors and the attenuation length of the water are crucial to reconstruct the light emission. Finally, the application to ANTARES data reveals the first precise localizations of bioluminescent organisms using neutrino telescope data. © 2021, CC BY.

关键词： Bioluminescence

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

引用

arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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