UiT The Arctic University of Norway 2 articles published in JoVE Biology Analyzing Mitochondrial Morphology Through Simulation Supervised Learning Abhinanda Ranjit Punnakkal1, Gustav Godtliebsen2, Ayush Somani1, Sebastian Andres Acuna Maldonado3, Åsa Birna Birgisdottir2,4, Dilip K. Prasad1, Alexander Horsch1, Krishna Agarwal3 1Department of Computer Science, UiT The Arctic University of Norway, 2Department of Clinical Medicine, UiT The Arctic University of Norway, 3Department of Physics and Technology, UiT The Arctic University of Norway, 4Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway This article explains how to use simulation-supervised machine learning for analyzing mitochondria morphology in fluorescence microscopy images of fixed cells. Biochemistry High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip David André Coucheron1, Øystein Ivar Helle1, Cristina Ionica Øie2, Jean-Claude Tinguely1, Balpreet Singh Ahluwalia1 1Department of Physics and Technology, UiT The Arctic University of Norway, 2Vascular Biology Research Group, Department of Medical Biology, UiT The Arctic University of Norway Chip-based super-resolution optical microscopy is a novel approach to fluorescence microscopy and offers advantages in cost effectiveness and throughput. Here, the protocols for chip preparation and imaging are shown for TIRF microscopy and localization-based super-resolution microscopy.
