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Human spaceflight is entering a new era of sustainable human space exploration. By 2030 humans will regularly fly to the Moon's orbit, return to the Moon's surface and preparations for crewed Mars missions will intensify. In planning these undertakings, several challenges will need to be addressed in order to ensure the safety of astronauts during their space travels. One of the important challenges to overcome, that could be a major showstopper of the space endeavor, is the exposure to the space radiation environment. There is an urgent need for quantifying, managing and limiting the detrimental health risks and electronics damage induced by space radiation exposure. Such risks raise key priority topics for space research programs. Risk limitation involves obtaining a better understanding of space weather phenomena and the complex radiation environment in spaceflight, as well as developing and applying accurate dosimetric instruments, understanding related short- and long-term health risks, and strategies for effective countermeasures to minimize both exposure to space radiation and the remaining effects post exposure. The ESA/SciSpacE Space Radiation White Paper identifies those topics and underlines priorities for future research and development, to enable safe human and robotic exploration of space beyond Low Earth Orbit.
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http://dx.doi.org/10.1038/s41526-023-00262-7 | DOI Listing |
J Chem Phys
September 2025
National Synchrotron Radiation Laboratory, State Key Laboratory of Advanced Glass Materials, Anhui Provincial Engineering Research Center for Advanced Functional Polymer Films, University of Science and Technology of China, Hefei, Anhui 230029, China.
Polymer density is a critical factor influencing material performance and industrial applications, and it can be tailored by modifying the chemical structure of repeating units. Traditional polymer density characterization methods rely heavily on domain expertise; however, the vast chemical space comprising over one million potential polymer structures makes conventional experimental screening inefficient and costly. In this study, we proposed a machine learning framework for polymer density prediction, rigorously evaluating four models: neural networks (NNs), random forest (RF), XGBoost, and graph convolutional neural networks (GCNNs).
View Article and Find Full Text PDFElife
September 2025
Department of General Psychology, University of Padua, Padova, Italy.
Humans order numerosity along a left-to-right mental number line (MNL), traditionally considered culturally rooted. Yet, some species at birth show spatial-numerical associations (SNA), suggesting neural origins. Various accounts link SNA to brain lateralization but lack evidence.
View Article and Find Full Text PDFEnviron Mol Mutagen
September 2025
Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
Long-duration spaceflight exposes astronauts to various stressors that can alter human physiology, potentially causing immediate and long-term health effects. These stressors can damage biomolecules, cells, tissues, and organs, leading to adverse outcomes. Developing adverse outcome pathways (AOPs) relevant to radiation exposure can guide research priorities and inform risk assessments of future space exploration activities.
View Article and Find Full Text PDFLancet Oncol
September 2025
Department of Radiation Oncology, Leiden University Medical Centre, Leiden, Netherlands.
Background: The PORTEC-3 trial investigated the benefit of chemoradiotherapy versus pelvic radiotherapy alone for women with high-risk endometrial cancer. We present the preplanned long-term analysis of the randomised PORTEC-3 trial with a post-hoc analysis including molecular classification of the tumours.
Methods: PORTEC-3 was an open-label, multicentre, randomised, international phase 3 trial.
Int J Radiat Oncol Biol Phys
September 2025
Radiation Oncology, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143. Electronic address:
Purpose: Accelerating MR acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize. Convolutional neural networks (CNNs)/Transformers-based deep learning (DL) methods emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition.
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