Isotropic multi-scale neuronal reconstruction from high-ratio expansion microscopy with contrastive unsupervised deep generative models.

Background And Objective: Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures.

Methods: We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision.

Lateral Two-Dimensional Material Heterojunction Photodetectors with Ultrahigh Speed and Detectivity.

Lateral heterojunctions in two-dimensional (2D) materials have demonstrated potential for high-performance sensors because of the unique electrostatic conditions at the interface. The increased complexity of producing such structures, however, has prevented their widespread use. We here demonstrate the simple and scalable fabrication of heterojunctions by a one-step synthesis process that yields photodetectors with superior device performance.