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Abstract MotivationCryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms. ResultsIn this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with ‘warp’ modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data. Availabilityand implementationhttps://github.com/xulabs/aitom. Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract Ultrawide‐bandgap semiconductors such as AlN, BN, and diamond hold tremendous promise for high‐efficiency deep‐ultraviolet optoelectronics and high‐power/frequency electronics, but their practical application has been limited by poor current conduction. Through a combined theoretical and experimental study, it is shown that a critical challenge can be addressed for AlN nanostructures by using N‐rich epitaxy. Under N‐rich conditions, the p‐type Al‐substitutional Mg‐dopant formation energy is significantly reduced by 2 eV, whereas the formation energy for N‐vacancy related compensating defects is increased by ≈3 eV, both of which are essential to achieve high hole concentrations of AlN. Detailed analysis of the current−voltage characteristics of AlN p‐i‐n diodes suggests that current conduction is dominated by hole‐carrier tunneling at room temperature, which is directly related to the activation energy of Mg dopants. At high Mg concentrations, the dispersion of Mg acceptor energy levels leads to drastically reduced activation energy for a portion of Mg dopants, evidenced by the small tunneling energy of 67 meV, which explains the efficient current conduction and the very small turn‐on voltage (≈5 V) for the diodes made of nanoscale AlN. This work shows that nanostructures can overcome the dopability challenges of ultrawide‐bandgap semiconductors and significantly increase the efficiency of devices.