Non-communicable diseases (NCDs) are chronic diseases that include most ageing-related diseases, representing the main cause of death and disability in the general population. Inflammation and oxidative stress are common features in NCDs, responsible for the cell, tissue, and organ damage that contributes to the progression of these diseases. They may be also key targets for the development of novel preventive and therapeutic strategies. This Special Issue includes 14 peer-reviewed papers, including 12 original research papers and 2 reviews. Together, they represent the most recent progress in the field of several degenerative disorders, aiming to establish specific biomarkers, detailing the pathogenesis and the evolution of these diseases, making a correct diagnosis, and opening up new therapeutic strategies. Of relevance, many studies report the beneficial effects of natural compounds, derived from several plants, leaves, and fruits; their antioxidant and anti-inflammatory properties suggest their use as a dietary supplement for prevention and/or complement to standard therapies. Special thanks to all authors and reviewers for their valuable contributions and constructive suggestions and to the publishing team of Antioxidants for their help in compiling this Special Issue.
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This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.The EditorsProf. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.Dr. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. Prof. Oliver Niggemann got his doctorate in 2001 at the University of Paderborn with the topic "Visual Data Mining of Graph-Based Data". He then worked for almost 8 years in leading positions in the industry. From 2008-2019 he held a professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo/Germany. Until 2019 Prof. Niggemann was also deputy head of the Fraunhofer IOSB-INA, which works in industrial automation. On April 1, 2019 Prof. Niggemann took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut-Schmidt-University in Hamburg / Germany. There he does research at the Institute for Automation Technology IfA in the field of artificial intelligence and machine learning for cyber-physical systems.
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First published in Latin, with Rapin's eclogues, under the title: Eclogae, cum dissertatione de carmine pastorali. Parisiis, apud S. Cramoisy, 1659. The English translation by Thomas Creech, prefixed to his translation of the Idylliums of Theocritus, appeared in 1684.