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. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. RESULTS: The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. CONCLUSION: With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures.

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. Catalytic conversion of a solid precursor at optimized conditions was found to produce lateral nanostructured junctions between graphene domains and 3 nm thin amorphous carbon films. Carrier transport in these heterojunctions was found to proceed by minimizing the path through the amorphous carbon barriers, which results in a self-selective Schottky emission process with high uniformity and low emission barriers. We demonstrate the potential of thus produced heterojunctions by realizing a photodetector that combines an ultrahigh detectivity of 1013 Jones with microsecond response time, which represents the highest performance of 2D material heterojunction devices. These attractive features are retained even for millimeter-scale devices, and the demonstrated ability to produce transparent, patterned, and flexible sensors extends lateral heterojunction sensors toward wearable and large-scale electronics.