Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying the full-field heterogeneous elastic properties of soft materials using traditional computational and engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in using data-driven models to learn full-field mechanical responses such as displacement and strain from experimental or synthetic data. However, research studies on inferring the full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, we propose a physics-informed machine learning approach to identify the elastic modulus distribution in nonlinear, large deformation hyperelastic materials. We evaluate the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) on inferring the heterogeneous material parameter maps across three nonlinear materials with structural complexity that closely resemble real tissue patterns, such as brain tissue and tricuspid valve tissue. Our improved PINN architecture accurately estimates the full-field elastic properties of three hyperelastic constitutive models, with relative errors of less than 5% across all examples. This research has significant potential for advancing our understanding of micromechanical behaviors in biological materials, impacting future innovations in engineering and medicine.

Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks.

The dynamics of systems biological processes are usually modeled by a system of ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from noisy and sparse measurements. Here, we introduce systems-biology-informed neural networks for parameter estimation by incorporating the system of ODEs into the neural networks. To complete the workflow of system identification, we also describe structural and practical identifiability analysis to analyze the identifiability of parameters. We use the ultradian endocrine model for glucose-insulin interaction as the example to demonstrate all these methods and their implementation.

Engaging Alkenes and Alkynes in Deaminative Alkyl-Alkyl and Alkyl-Vinyl Cross-Couplings of Alkylpyridinium Salts.

An alkyl-alkyl cross-coupling of Katritzky alkylpyridinium salts and organoboranes, formed in situ via hydroboration of alkenes, has been developed. This method utilizes the abundance of both alkyl amine precursors and alkenes to form C(sp3)-C(sp3) bonds. This strategy is also effective with alkynes, enabling a C(sp3)-C(sp2) cross-coupling. Under these mild conditions, a broad range of functional groups, including protic groups, is tolerated. As seen with previous alkylpyridinium cross-couplings, mechanistic studies support an alkyl radical intermediate.