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Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

 

Active inference proposes a unifying principle for perception and action as jointly minimizing the free energy of an agent’s internal world model. In the active inference literature, world models are typically pre-specified or learned through interacting with an environment. This paper explores the possibility of learning world models of active inference agents from recorded demonstrations, with an application to human driving behavior modeling. The results show that the presented method can create models that generate human-like driving behavior but the approach is sensitive to input features. 

In conventional theories, topological band properties are intrinsic characteristics of the bulk material and do not depend on the choice of the reference frame. In this scenario, the principle of bulk‐edge correspondence can be used to predict the existence of edge states between topologically distinct materials. In this study, a 2D elastic phononic plate is proposed with a Kekulé‐distorted honeycomb pattern engraved on it. It is found that the pseudospin and the pseudospin‐dependent Chern numbers are not invariant properties, and the number is no longer a sufficient indicator to examine the existence of the edge state. The distinctive pseudospin texture and the pseudomagnetic field are also revealed. Finally, the synthetic helical edge states are successfully devised and experimentally implemented on a dislocation interface connecting two subdomains with bulk pattern identical up to a relative translation. The edge state is also imaged via laser vibrometry.