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4 results for “correlative light and electron microscopy”
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</p>
Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"
<p><strong>This folder contains the training dataset used for the paper</strong></p> <p>"DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"</p> <p><em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em></p> <p>F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275</p> <p>------------------------------------------------------------</p> <p>These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above.</p> <p>Contents:</p> <ul> <li>"fluo_training": Fluorescence microscopic channel of the 117 training images </li> <li>"sem_training": Scanning electron microscopic channel of the 117 training images</li> <li>"fluo_validation": Fluorescence microscopic channel of the 4 validation images </li> <li>"sem_validation": Scanning electron microscopic channel of the 4 validation images</li> </ul> <p>License: CC-BY 4.0</p>
Supplementary movies and datasets of the paper: Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP
<p>Imaging data supporting the paper: </p> <p>Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP, containing raw and processed data from fluorescent and electron microscopy.</p> <p> </p>
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