Zobrazeno 1 - 10
of 1 557
pro vyhledávání: '"Sedlmeier, A."'
Autor:
Pfister, Lena, Gohm, Alexander, Kossmann, Meinolf, Wieser, Andreas, Babić, Nevio, Handwerker, Jan, Wildmann, Norman, Vogelmann, Hannes, Baumann-Stanzer, Kathrin, Alexa, Almut, Lapo, Karl, Paunović, Ivan, Leinweber, Ronny, Sedlmeier, Katrin, Lehner, Manuela, Hieden, Alexander, Speidel, Johannes, Federer, Maria, Rotach, Mathias W.
The multi-scale transport and exchange processes in the atmosphere over mountains -- programme and experiment (TEAMx) wants to advance the understanding of transport and exchange processes over mountainous terrain as well as to collect unique multi-s
Externí odkaz:
http://arxiv.org/abs/2401.06500
Autor:
Raphael Lutz, Florian Grünschläger, Malte Simon, Mohamed H. S. Awwad, Marcus Bauer, Schayan Yousefian, Niklas Beumer, Lea Jopp-Saile, Anastasia Sedlmeier, Llorenç Solé-Boldo, Bogdan Avanesyan, Dominik Vonficht, Patrick Stelmach, Georg Steinbuss, Tobias Boch, Simon Steiger, Marc-Andrea Baertsch, Nina Prokoph, Karsten Rippe, Brian G. M. Durie, Claudia Wickenhauser, Andreas Trumpp, Carsten Müller-Tidow, Daniel Hübschmann, Niels Weinhold, Marc S. Raab, Benedikt Brors, Hartmut Goldschmidt, Charles D. Imbusch, Michael Hundemer, Simon Haas
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-18 (2024)
Abstract The long-term consequences of cancer and its therapy on the patients’ immune system years after cancer-free survival remain poorly understood. Here, we present an in-depth characterization of the bone marrow immune ecosystem of multiple my
Externí odkaz:
https://doaj.org/article/c688665694f5455b9274a4237fb53c77
Autor:
Michael J. Stein, Hansjörg Baurecht, Patricia Bohmann, Béatrice Fervers, Emma Fontvieille, Heinz Freisling, Christine M. Friedenreich, Julian Konzok, Laia Peruchet-Noray, Anja M. Sedlmeier, Michael F. Leitzmann, Andrea Weber
Publikováno v:
BMC Medicine, Vol 22, Iss 1, Pp 1-11 (2024)
Abstract Background Physical activity reduces colorectal cancer risk, yet the diurnal timing of physical activity in colorectal cancer etiology remains unclear. Methods This study used 24-h accelerometry time series from UK Biobank participants aged
Externí odkaz:
https://doaj.org/article/c515ea2265cb493a92b7df6208550d03
Autor:
Esther M. González-Gil, Laia Peruchet-Noray, Anja M. Sedlmeier, Sofia Christakoudi, Carine Biessy, Anne-Sophie Navionis, Yahya Mahamat-Saleh, Rola F. Jaafar, Hansjörg Baurecht, Marcela Guevara, Pilar Amiano Etxezarreta, W. M. Monique Verschuren, Jolanda M. A. Boer, Anja Olsen, Anne Tjønneland, Vittorio Simeon, Carlota Castro-Espin, Dagfinn Aune, Alicia K. Heath, Marc Gunter, Sandra M. Colorado-Yohar, Nuno R. Zilhão, Christina C. Dahm, Erand Llanaj, Matthias B. Schulze, Dafina Petrova, Sabina Sieri, Fulvio Ricceri, Giovanna Masala, Tim Key, Vivian Viallon, Sabina Rinaldi, Heinz Freisling, Laure Dossus
Publikováno v:
BMC Medicine, Vol 22, Iss 1, Pp 1-14 (2024)
Abstract Background The allometric body shape index (ABSI) and hip index (HI), as well as multi-trait body shape phenotypes, have not yet been compared in their associations with inflammatory markers. The aim of this study was to examine the relation
Externí odkaz:
https://doaj.org/article/d6244af08a404787a1fa8b47215766de
We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and sample-weighting, we achieve compa
Externí odkaz:
http://arxiv.org/abs/2212.10093
Autor:
Amina Amadou, Heinz Freisling, Anja M. Sedlmeier, Patricia Bohmann, Emma Fontvieille, Andrea Weber, Julian Konzok, Michael J. Stein, Laia Peruchet-Noray, Anna Jansana, Hwayoung Noh, Mathilde His, Quan Gan, Hansjörg Baurecht, Béatrice Fervers
Publikováno v:
Journal of Epidemiology and Global Health, Vol 14, Iss 2, Pp 420-432 (2024)
Abstract Body shape phenotypes combining multiple anthropometric traits have been linked to postmenopausal breast cancer (BC). However, underlying biological pathways remain poorly understood. This study investigated to what extent the associations o
Externí odkaz:
https://doaj.org/article/8384b4928ebe48c9a09a4b50905aa03a
Autor:
Andrea Weber, Vincent T. van Hees, Michael J. Stein, Sylvia Gastell, Karen Steindorf, Florian Herbolsheimer, Stefan Ostrzinski, Tobias Pischon, Mirko Brandes, Lilian Krist, Michael Marschollek, Karin Halina Greiser, Katharina Nimptsch, Berit Brandes, Carmen Jochem, Anja M. Sedlmeier, Klaus Berger, Hermann Brenner, Christoph Buck, Stefanie Castell, Marcus Dörr, Carina Emmel, Beate Fischer, Claudia Flexeder, Volker Harth, Antje Hebestreit, Jana-Kristin Heise, Bernd Holleczek, Thomas Keil, Lena Koch-Gallenkamp, Wolfgang Lieb, Claudia Meinke-Franze, Karin B. Michels, Rafael Mikolajczyk, Alexander Kluttig, Nadia Obi, Annette Peters, Börge Schmidt, Sabine Schipf, Matthias B. Schulze, Henning Teismann, Sabina Waniek, Stefan N. Willich, Michael F. Leitzmann, Hansjörg Baurecht
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study de
Externí odkaz:
https://doaj.org/article/dee20163ca0f46aa9a4e6758800f77ea
Autor:
Ritz, Fabian, Phan, Thomy, Sedlmeier, Andreas, Altmann, Philipp, Wieghardt, Jan, Schmid, Reiner, Sauer, Horst, Klein, Cornel, Linnhoff-Popien, Claudia, Gabor, Thomas
Publikováno v:
ISoLA 2022: Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. pp 249-265
The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonethele
Externí odkaz:
http://arxiv.org/abs/2208.05219
Autor:
Aljani, Bassam, Garbe, Annette I., Sedlmeier, Eva-Maria, Lickert, Ramona, Rost, Fabian, Ziegler, Anette-Gabriele, Bonifacio, Ezio, Eugster, Anne
Publikováno v:
In Placenta December 2024 158:126-135
Model-based Deep Reinforcement Learning (RL) assumes the availability of a model of an environment's underlying transition dynamics. This model can be used to predict future effects of an agent's possible actions. When no such model is available, it
Externí odkaz:
http://arxiv.org/abs/2112.07263