For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data.
View Article and Find Full Text PDFFor investigations into fate specification and cell rearrangements in live images of preimplantation embryos, automated and accurate 3D instance segmentation of nuclei is invaluable; however, the performance of segmentation methods is limited by the images' low signal-to-noise ratio and high voxel anisotropy and the nuclei's dense packing and variable shapes. Supervised machine learning approaches have the potential to radically improve segmentation accuracy but are hampered by a lack of fully annotated 3D data. In this work, we first establish a novel mouse line expressing near-infrared nuclear reporter H2B-miRFP720.
View Article and Find Full Text PDFOrganoids, which are multicellular clusters with similar physiological functions to living organs, have gained increasing attention in bioengineering. As organoids become more advanced, methods to form complex structures continue to develop. There is evidence that the extracellular microenvironment can regulate organoid quality.
View Article and Find Full Text PDFHydrogels have been used extensively to deliver functional molecular cargos in response to external mechanical force. However, the intrinsic brittleness of gels restricts the applicable range of strain to 0.1, thus limiting the range of molecular release rate that may be controlled.
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