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Mina Kamao

Publications and source records attributed to Mina Kamao.

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Internal geometries regulate the symmetry of defect configurations in cell populations confined to domains with a negative Euler characteristic

Nematic order of confined cell populations plays an important role in determining cell alignment and stable configurations of topological defects, which are related to various biomechanical phenomena. Topological charges (or winding numbers) of topological defects strictly depend on the Euler characteristic of the confining domain, which has typically been non-negative in studies focused on domains without internal obstacles. However, biological tissues often surround two or more internal obstacles or holes, which inherently generate defects with negative charges. To understand the mechanical interaction between cellular tissue and obstacles, it is necessary to elucidate the geometrical effects of obstacles on cell alignment and defects with negative charges. Here, we investigate how cell populations achieve stable defect configurations of two -1/2 defects in a triply connected domain. First, we present experimental observations of C2C12 myoblasts confined by two circular obstacles of varying diameter, demonstrating that two $-1/2$ defects are the most frequent configuration when the obstacles are sufficiently large. Second, to theoretically validate these experimental observations, we perform systematic stability analyses of defect configurations using an explicit expression of cell alignment and numerical minimization of the Frank elastic energy. Our numerical calculations reveal that the most stable configuration shifts continuously from a horizontal, through off-axis, to a vertical configuration as the obstacle size increases. In addition, the experimentally observed defect positions agreed with these theoretical predictions to within 60 $μ$m. These findings suggest that obstacle sizes control the symmetry of cell alignment, providing insights into how geometric and topological constraints can generate complex force patterns during morphogenesis or organ movements.

physics.bio-ph

MAME: Multidimensional Adaptive Metamer Exploration with Human Perceptual Feedback

Alignment between human brain networks and artificial models has become an active research area in vision science and machine learning. A widely adopted approach is identifying "metamers," stimuli physically different yet perceptually equivalent within a system. However, conventional methods lack a direct approach to searching for the human metameric space. Instead, researchers first develop biologically inspired models and then infer about human metamers indirectly by testing whether model metamers also appear as metamers to humans. Here, we propose the Multidimensional Adaptive Metamer Exploration (MAME) framework, enabling direct, high-dimensional exploration of human metameric spaces through online image generation guided by human perceptual feedback. MAME modulates reference images across multiple dimensions based on hierarchical neural network responses, adaptively updating generation parameters according to participants' perceptual discriminability. Using MAME, we successfully measured multidimensional human metameric spaces within a single psychophysical experiment. Experimental results using a biologically plausible CNN model showed that human discrimination sensitivity was lower for metameric images based on Gram-matrix representations derived from low-level CNN features than for those derived from high-level CNN features. The finding suggests a relatively worse alignment between the metameric spaces of humans and the CNN model for low-level processing compared to high-level processing. Counterintuitively, given recent discussions on alignment at higher representational levels, our results highlight the importance of early visual computations in shaping biologically plausible models. Our MAME framework can serve as a future scientific tool for directly investigating the functional organization of human vision.

cs.LG