@article{AngladaTortMastersSteffensetal.2022, author = {Anglada-Tort, Manuel and Masters, Nikhil and Steffens, Jochen and North, Adrian and M{\"u}llensiefen, Daniel}, title = {The Behavioural Economics of Music: Systematic review and future directions}, series = {Quarterly Journal of Experimental Psychology}, volume = {76}, journal = {Quarterly Journal of Experimental Psychology}, number = {5}, publisher = {SAGE}, isbn = {1747-0218}, issn = {1747-0226}, doi = {10.1177/17470218221113761}, year = {2022}, abstract = {Music-related decision-making encompasses a wide range of behaviours including those associated with listening choices, composition and performance, and decisions involving music education and therapy. Although research programmes in psychology and economics have contributed to an improved understanding of music-related behaviour, historically, these disciplines have been unconnected. Recently, however, researchers have begun to bridge this gap by employing tools from behavioural economics. This article contributes to the literature by providing a discussion about the benefits of using behavioural economics in music-decision research. We achieve this in two ways. First, through a systematic review, we identify the current state of the literature within four key areas of behavioural economics-heuristics and biases, social decision-making, behavioural time preferences, and dual-process theory. Second, taking findings of the literature as a starting point, we demonstrate how behavioural economics can inform future research. Based on this, we propose the Behavioural Economics of Music (BEM), an integrated research programme that aims to break new ground by stimulating interdisciplinary research in the intersection between music, psychology, and economics.}, language = {en} } @inproceedings{SteffensHimmelein2022, author = {Steffens, Jochen and Himmelein, Hendrik}, title = {Induced cognitive load influences unpleasantness judgments of modulated noise}, series = {Proceedings of the 24th International Congress on Acoustics}, booktitle = {Proceedings of the 24th International Congress on Acoustics}, address = {Gyeoungju, S{\"u}dkorea}, year = {2022}, language = {en} } @inproceedings{GaudryHuberFlueckigeretal.2022, author = {Gaudry, Arnaud and Huber, Florian and Fl{\"u}ckiger, Julien and Quir{\´o}s, L and Rutz, Adriano and Kaiser, M and Grondin, A and Marcourt, Laurence and Ferreira Queiroz, E and Wolfender, Jean-Luc and Allard, Pierre-Marie}, title = {Short Lecture "Mass spectrometry-based sample vectorization for exploration of large chemodiverse datasets and efficient identification of new antiparasitic compounds"}, series = {Planta Medica}, volume = {88}, booktitle = {Planta Medica}, number = {15}, publisher = {Thieme}, issn = {1439-0221}, doi = {10.1055/s-0042-1758983}, year = {2022}, subject = {Massenspektrometrie}, language = {en} } @article{deJongeMildauMeijeretal.2022, author = {de Jonge, Niek F. and Mildau, Kevin and Meijer, David and Louwen, Joris J. R. and Bueschl, Christoph and Huber, Florian and van der Hooft, Justin J. J.}, title = {Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools}, series = {Metabolomics}, volume = {18}, journal = {Metabolomics}, number = {12}, publisher = {Springer Nature}, issn = {1573-3890}, doi = {10.1007/s11306-022-01963-y}, pages = {22}, year = {2022}, abstract = {Background Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10\% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. Aim of review We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. Key scientific concepts of review This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.}, subject = {Metabolomik}, language = {en} }