Accurate modulation of photoprinting under stiffness imaging feedback for engineering ECMs with high-fidelity mechanical properties

Engineered extracellular matrices(ECMs)that replicate complex in-vivo features have shown great potential in tissue engineering.Biocompatible hydrogel microstructures have been widely used to replace these native ECMs for physiologically relevant research.However,accurate reproduction of the 3D hierarchical and nonuniform mechanical stffness inside one integrated microstructure to mimic the complex mechanical properties of native ECMs presents a major challenge.Here,by using digital holographic microscopy(DHM)-based stffness imaging feedback,we propose a novel closed-loop control algorithm to achieve high-accuracy control of mechanical properties for hydrogel microstructures that recapitulate the physiological properties of native ECMs with high fidelity.During photoprinting,the photocuring area of the hydrogel is divided into microscale grid areas to locally control the photocuring process.With the assistance of a motorized microfluidic channel,the curing thickness is controlled with layer-by-layer stacking.The DHM-based stiffness imaging feedback allows accurate adjustment of the photocuring degree in every grid area to change the crosslinking network density of the hydrogel,thus enabling large-span and high-resolution modulation of mechanical properties.Finally,the gelatin methacrylate was used as a typical biomaterial to construct the highfidelity biomimetic ECMs.The Young's modulus could be flexibly modulated in the 10 kPa to 50 kPa range.Additionally,the modulus gradient was accurately controlled to within 2.9 kPa.By engineering ECM with locally different mechanical properties,cell spreading along the stff areas was observed successfully.We believe that this method can regenerate complex biomimetic ECMs that closely recapitulate in-vivo mechanical properties for further applications in tissue engineering and biomedical research.

Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat

In this paper,we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning.Specifically,we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions.The novelty of our method lies in using the temporal difference(TD)algorithm to optimize the state decision process,which enables the robots to make informed decisions about their behavior choices.To assess the similarity between robot and rat behavior,we use Pearson correlation.We then use TD-λto update the state value function and make state decisions based on probability.The robots execute these decisions using our dynamics-based controller.Our results demonstrate that our method can generate rat-like behaviors on both short-and long-term timescales,with interaction information entropy comparable to that between real rats.Overall,our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.