A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes

Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases. Standard nonlinear regression in constitutive modeling requires considerable high-quality data and an explicit form of the constitutive model as prior knowledge. By contrast, we propose a novel approach that combines generative deep learning with Bayesian inference to efficiently infer families of constitutive relationships in data-sparse regimes. Inspired by the concept of functional priors, we develop a generative adversarial network (GAN) that incorporates a neural operator as the generator and a fully-connected neural network as the discriminator. The generator takes a vector of noise conditioned on measurement data as input and yields the predicted constitutive relationship, which is scrutinized by the discriminator in the following step. We demonstrate that this framework can accurately estimate means and standard deviations of the constitutive relationships of the murine aorta using data collected either from model-generated synthetic data or ex vivo experiments for mice with genetic deficiencies. In addition, the framework learns priors of constitutive models without explicitly knowing their functional form, providing a new model-agnostic approach to learning hidden constitutive behaviors from data

Cryoprotective Roles of Carboxymethyl Chitosan during the Frozen Storage of Surimi: Protein Structures, Gel Behaviors and Edible Qualities

Carboxymethyl chitosan (CMCh) is an ampholytic chitosan derivative that manifests versatile applications in food industry, such as antibacterial ingredients and nutritional additives. However, its use as a cryoprotectant remains under-researched. In this study, the cryoprotective effect of CMCh oligosaccharide (CMCO) on frozen surimi (silver carp) was systematically investigated in terms of protein structures, gelling behaviors, and sensory qualities. CMCO (0.6%) was incorporated in the surimi before frozen storage (−18 °C for 60 days) while the commercial cryoprotectant (4% sucrose, 4% sorbitol) was used as a positive control. Results indicated that CMCO could inhibit the freezing-induced denaturation of myofibrillar protein, whose values of solubility, Ca2+-ATPase and sulfhydryl content were 24.8%, 64.7%, and 17.1% higher than the nonprotected sample, respectively, while the surface hydrophobicity was 21.6% lower. Accordingly, CMCO stabilized microstructure of the surimi gels associated with improved gel strength, viscoelasticity, water-holding capacities, and whiteness. Moreover, the cryoprotective effect of CMCO with higher degree of carboxymethyl substitution (DS: 1.2) was more pronounced than that of low-DS-CMCO (DS: 0.8). Frozen surimi treated with high-DS-CMCO achieved competitive gelling properties and sensory acceptability to those with the commercial counterpart. This study provided scientific insights into the development of ampholytic oligosaccharides as food cryoprotectants

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge has 1 track aiming at the stereo image super-resolution problem under a standard bicubic degradation. In total, 238 participants were successfully registered, and 21 teams competed in the final testing phase. Among those participants, 20 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR