Lucas von Chamier

TL;DR: ZeroCostDL4Mic as discussed by the authors is an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab, which allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation, object detection, denoising, and image-to-image translation.

TL;DR: How DL shows an outstanding potential to push the limits of microscopy, enhancing resolution, signal and information content in acquired data is discussed, along with the future directions expected in this field.

TL;DR: A platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab is presented, which allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement and image-to-image translation.

TL;DR: Overall, this work provides the first cellular evidence that mammalian LC3/GABARAP post-translationally modifies proteins akin to ubiquitination, with ATG4 proteases acting like deubiquitinating enzymes to counteract this modification (“deLC3ylation”).

TL;DR: A detailed analysis pipeline is provided illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts.