Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework for 2D-to-3D reconstruction of microstructures called Micro3Diff using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a new concept referred to as multi-plane denoising diffusion, which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic and experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs

Microstructure reconstruction using diffusion-based generative models

Microstructure reconstruction has been an essential part of computational material engineering to reveal the relationship between the microstructures and the material properties. However, it is still challenging to find a general solution for microstructure characterization and reconstruction (MCR) tasks although there have been many attempts such as the descriptor-based reconstruction methods. To address this generality problem, the denoising diffusion probabilistic models are first employed for the microstructure reconstruction task which can be applied to various types of material systems. Several microstructures (e.g., carbonate, ceramics, copolymer, etc.) are considered to be reproduced for validating the proposed models while addressing the quality of the generated images with the quantitative evaluation metrics (FID score, precision and recall). The results show that the proposed diffusion model based approach is applicable for reproducing various types of microstructures with different spatial distributions of morphological features. The present approach also provides a stable training procedure with simple implementation for generating visually similar microstructures (and also statistically equivalent) without requiring expert knowledge and some time-consuming parametric studies. The proposed approach has the potential of being a universal microstructure reconstruction method for handling complex microstructures for materials science

Microstructure Design of Multifunctional Particulate Composite Materials using Conditional Diffusion Models

This paper presents a novel modeling framework to generate an optimal microstructure having ultimate multifunctionality using a diffusion-based generative model. In computational material science, generating microstructure is a crucial step in understanding the relationship between the microstructure and properties. However, using finite element (FE)-based direct numerical simulation (DNS) of microstructure for multiscale analysis is extremely resource-intensive, particularly in iterative calculations. To address this time-consuming issue, this study employs a diffusion-based generative model as a replacement for computational analysis in design optimization. The model learns the geometry of microstructure and corresponding stress contours, allowing for the prediction of microstructural behavior based solely on geometry, without the need for additional analysis. The focus on this work is on mechanoluminescence (ML) particulate composites made with europium ions and dysprosium ions. Multi-objective optimization is conducted based on the generative diffusion model to improve light sensitivity and fracture toughness. The results show multiple candidates of microstructure that meet the design requirements. Furthermore, the designed microstructure is not present in the training data but generates new morphology following the characteristics of particulate composites. The proposed approach provides a new way to characterize a performance-based microstructure of composite materials

A Study on Virtual Reality Storytelling by Story Authoring Tool Algorithm

The objective of this study was to examine the storytelling principles of virtual reality contents, which are recently grabbing much attention, and the patterns of their generation rules and, based on the results, to analyze the elements and structure of a storytelling method suitable for virtual reality contents. In virtual reality environment, a story is usually being generated between choices made by a user who behaves autonomously under simulated environmental factors and the environmental constraints. This corresponds to a mutually complementary role of representation and simulation, which has been hotly discussed in the field of interactive storytelling. This study was conducted based on the assumption that such a mutually complementary realization is ideal for virtual reality storytelling. A simulation-based story authoring tool is a good example that shows this mutual complementation, in that it develops a story through various algorithms which involves the interaction of agents which occur within the strata of a virtual environment. Therefore, it can be a methodology for virtual reality storytelling. The structures and elements of narratives used in virtual reality storytelling which achieve balance of representation and simulation are much similar to an algorithm strategy of a simulation-based story authoring tool. The virtual reality contents released up to now can be classified into four categories based on the two axes of representation and simulation. The study focused on contents which are layered in higher strata of both representation and simulation. In the perspective of representation strata, these contents are actively using such elements as goal, event, action, perception, internal element, outcome, and setting element, which are constituents of ‘Fabula model’, to generate time relations and cause-effect relations. And in the perspective of simulation strata, the use of the ‘Late commitment’ strategy allowed users to understand the meanings of their actions taken during the process of experimenting with various dynamic principles within the environment

The involvement of Eph–Ephrin signaling in tissue separation and convergence during Xenopus gastrulation movements

AbstractIn Xenopus gastrulation, the involuting mesodermal and non-involuting ectodermal cells remain separated from each other, undergoing convergent extension. Here, we show that Eph–ephrin signaling is crucial for the tissue separation and convergence during gastrulation. The loss of EphA4 function results in aberrant gastrulation movements, which are due to selective inhibition of tissue constriction and separation. At the cellular levels, knockdown of EphA4 impairs polarization and migratory activity of gastrulating cells but not specification of their fates. Importantly, rescue experiments demonstrate that EphA4 controls tissue separation via RhoA GTPase in parallel to Fz7 and PAPC signaling. In addition, we show that EphA4 and its putative ligand, ephrin-A1 are expressed in a complementary manner in the involuting mesodermal and non-involuting ectodermal layers of early gastrulae, respectively. Depletion of ephrin-A1 also abrogates tissue separation behaviors. Therefore, these results suggest that Eph receptor and its ephrin ligand might mediate repulsive interaction for tissue separation and convergence during early Xenopus gastrulation movements

Impaired hydrogen sulfide protein expression in patients with peripheral artery disease

INTRODUCTION: Hydrogen sulfide (H2S) is a gaseous signaling molecule that serves various roles in the vasculature, such as upregulating angiogenesis, vascular smooth muscle relaxation, protecting endothelial function, and regulating redox balance. Despite H2S’s positive impacts on vascular homeostasis, it is important to note that its actions depend on its concentrations. At high concentrations, H2S has been reported to increase oxidative stress damage, such as oxidation of cysteine residues and lipid peroxidation. This may indicate that H2S may act as a ‘double-edged sword’ in the field of vascular physiology. Peripheral artery disease (PAD) is an atherosclerotic disease which manifested by claudication (leg pain during walking). Growing evidence suggests that abnormal H2S level may present with vascular diseases, however, only a few animal studies investigated the H2S and H2S -mediated oxidative stress damage in vascular disease models, and there are currently no available studies for human vascular disease patients, such as patients with PAD. Therefore, the purpose of this study was to examine the H2S and oxidative stress damage in peripheral blood mononuclear cells (PBMCs) and skeletal muscle tissues from patients with PAD. METHODS: Western blot was performed using skeletal muscle tissues and PBMCs to examine protein expression of cystathionase (CTH), which catalyzes production of H2S, and glutathione peroxidase-4 (GPx-4) and catalase (CAT), which are antioxidant markers, from healthy adults (CON) and patients with PAD (PAD). RESULTS: Patients with PAD show a lower expression of CTH compared to CON (P \u3c 0.01, PAD: 1.61 ± 0.44, CON: 8.53 ± 0.46). However, CAT expression was not different between groups (P = 0.429, PAD: 0.03 ± 0.02, CON: 0.01 ± 0.01). In addition, CAT and GPx-4 expression was assessed in CON PBMCs (CAT: 5.07 ± 1.14, GPx-4: 0.63 ± 0.3). CONCLUSION: CTH protein expression in the skeletal muscle is attenuated in PAD compared to CON. However, CAT protein expression in the skeletal muscle is not different between groups. These data suggest an impairment is present in the H2S signaling system in the skeletal muscle of patients with PAD