Matching of EM Map Segments to StructurallyRelevant Biomolecular Regions Electron microscopy is a technique used to determine the structure of biomolecular machines via threedimensional images (called maps). The stateoftheart is able to determine structures at resolutions that allow us to identify up to secondary structural features, in some cases, but it is not widespread. Furthermore, because molecular interactions often require atomiclevel details to be understood, it is still necessary to complement current maps with techniques that provide finergrain structural details. We applied segmentation techniques to maps in the Electron Microscopy Data Bank (EMDB), the standard community repository for these data. We assessed the potential of these algorithms to match functionally relevant regions in their atomicresolution image counterparts by comparing against three protein systems, each with multiple atomicdetailed domains. We found that at least 80% of amino acid residues in 7 out of 12 domains were assigned to single segments, suggesting there is potential to match the lower resolution segmented regions to the atomic counterparts. We also qualitatively analyzed the potential on other EMDB structures, as well as generating the raw segmentation information for the complete EMDB, for interested researchers to use. Results can be accessed online and the library developed is provided as part of an opensource project.

Domain Randomization for Macromolecule Structure Classification and Segmentation in Electron Cyrotomograms It is crucial to study and understand cellular processes. In recent years, Cellular Electron CryoTomography (CECT) serves as a powerful 3D imaging tool to visualize spatial structure of macromolecules inside the cell. However, it is challenging to analyze the macromolecular structures in a systematic way due to nature of the structural complexity of subcellular components. Existing computational and deep learning based approaches suffer from limited scalability, discrimination ability and lack of accurate annotated CECT data. Training with cheap simulated data can alleviate this problem while facing new challenges of bridging the xe2x80x9creality gapxe2x80x9d between synthetic training data and real testing data. In this paper, we tackle the tasks of macromolecule structure classification and segmentation in CECT images by adapting a simple but effective technique, domain randomization. We show that by combining deep neural models and domain randomization, we are able to achieve significant improvements of 35.21% and 46.34% in tasks of classification and semantic segmentation for real CECT data, comparing to the model trained only on syhthetic data that aims to faithfully reproduce realworld data distribution.