Synthesizing realistic microstructure images conditioned on processing parameters is crucial for understanding process-structure relationships in materials design. However, this task remains challenging due to limited training micrographs and the continuous nature of processing variables. To overcome these challenges, we present a novel process-aware generative modeling approach based on Stable Diffusion 3.5 Large (SD3.5-Large), a state-of-the-art text-to-image diffusion model adapted for microstructure generation. Our method introduces numeric-aware embeddings that encode continuous variables (annealing temperature, time, and magnification) directly into the model's conditioning, enabling controlled image generation under specified process conditions and capturing process-driven microstructural variations. To address data scarcity and computational constraints, we fine-tune only a small fraction of the model's weights via DreamBooth and Low-Rank Adaptation (LoRA), efficiently transferring the pre-trained model to the materials domain. We validate realism using a semantic segmentation model based on a fine-tuned U-Net with a VGG16 encoder on 24 labeled micrographs. It achieves 97.1% accuracy and 85.7% mean IoU, outperforming previous methods. Quantitative analyses using physical descriptors and spatial statistics show strong agreement between synthetic and real microstructures. Specifically, two-point correlation and lineal-path errors remain below 2.1% and 0.6%, respectively. Our method represents the first adaptation of SD3.5-Large for process-aware microstructure generation, offering a scalable approach for data-driven materials design.

Table 6 provides summary statistics of domain coverage.

Autoencoder for CryO-EM, inspired by the Noise2Noise (N2N) approach. We mask both images to create denoising and reconstruction tasks.

However, its performance is still limited as it lacks high-quality annotated datasets for training.

Table 6 provides summary statistics of domain coverage.

Autoencoder for CryO-EM, inspired by the Noise2Noise (N2N) approach. We mask both images to create denoising and reconstruction tasks.

Single-particle cryo-electron microscopy (cryo-EM) has become a cornerstone of structural biology, enabling near-atomic resolution analysis of macromolecules through advanced computational methods. However, the development of cryo-EM processing tools is constrained by the scarcity of high-quality annotated datasets. Synthetic data generation offers a promising alternative, but existing approaches lack thorough biophysical modeling of heterogeneity and fail to reproduce the complex noise observed in real imaging. To address these limitations, we present CryoCCD, a synthesis framework that unifies versatile biophysical modeling with the first conditional cycle-consistent diffusion model tailored for cryo-EM. The biophysical engine provides multi-functional generation capabilities to capture authentic biological organization, and the diffusion model is enhanced with cycle consistency and mask-guided contrastive learning to ensure realistic noise while preserving structural fidelity. Extensive experiments demonstrate that CryoCCD generates structurally faithful micrographs, enhances particle picking and pose estimation, as well as achieves superior performance over state-of-the-art baselines, while also generalizing effectively to held-out protein families.

The field of optical microscopy spans across numerous industries and research domains, ranging from education to healthcare, quality inspection and analysis. Nonetheless, a key limitation often cited by optical microscopists refers to the limit of its lateral resolution (typically defined as ~200nm), with potential circumventions involving either costly external modules (e.g. confocal scan heads, etc) and/or specialized techniques [e.g. super-resolution (SR) fluorescent microscopy]. Addressing these challenges in a normal (non-specialist) context thus remains an aspect outside the scope of most microscope users & facilities. This study thus seeks to evaluate an alternative & economical approach to achieving SR optical microscopy, involving non-fluorescent phase-modulated microscopical modalities such as Zernike phase contrast (PCM) and differential interference contrast (DIC) microscopy. Two in silico deep neural network (DNN) architectures which we developed previously (termed O-Net and Theta-Net) are assessed on their abilities to resolve a custom-fabricated test target containing nanoscale features calibrated via atomic force microscopy (AFM). The results of our study demonstrate that although both O-Net and Theta-Net seemingly performed well when super-resolving these images, they were complementary (rather than competing) approaches to be considered for image SR, particularly under different image signal-to-noise ratios (SNRs). High image SNRs favoured the application of O-Net models, while low SNRs inclined preferentially towards Theta-Net models. These findings demonstrate the importance of model architectures (in conjunction with the source image SNR) on model performance and the SR quality of the generated images where DNN models are utilized for non-fluorescent optical nanoscopy, even where the same training dataset & number of epochs are being used.

The past decade has witnessed a transformative "cryoEM revolution" characterized by exponential growth in high - resolution structural data, driven by advances in cryogenic electron microscopy (cryoEM) and cryogenic electron t omography (cryoET). The integration of deep learning technologies into structural proteomics workflows has emerged as a pivotal force in addressing longstanding challenges, including low signal - to - noise ratios, preferred orientation artifacts, and missing - wedge problems th at have historically limited efficiency and scalability. This review article examines the application of Artificial Intelligence (AI) across the entire cryoEM pipeline, from automated particle picking using convolutional neural networks (Topaz, crYOLO, Cry oSegNet) to computational solutions for preferred orientation bias (spIsoNet, cryoPROS) and advanced denoising algorithms (Topaz - Denoise). In cryoET, tools such as IsoNet employ U - Net architectures for simultaneous missing - wedge correction and noise reduct ion, while TomoNet streamlines subtomogram averaging through AI - driven particle detection. The workflow culminates with automated atomic model building using sophisticated tools like ModelAngelo, DeepTracer, and CryoREAD that translate density maps into in terpretable biological structures. These AI - enhanced approaches have demonstrated remarkable achievements, including near - atomic resolution reconstructions with minimal manual intervention, resolution of previously intractable datasets suffering from sever e orientation bias, and successful application to diverse biological systems from HIV virus - like particles to in situ ribosomal complexes. As deep learning continues to evolve, particularly with the emergence of large language models and vision transformer s, the future promises even more sophisticated automation and accessibility in structural biology, potentially revolutionizing our understanding of macromolecular architecture and function.