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Synthetic neural scaffolds hold promise to eventually replace nerve autografts for tissue repair following peripheral nerve injury. Despite substantial evidence for the influence of scaffold geometry and dimensions on the rate of axonal growth, systematic evaluation of these parameters remains a challenge due to limitations in materials processing. We have employed fiber drawing to engineer a wide spectrum of polymer-based neural scaffolds with varied geometries and core sizes. Using isolated whole dorsal root ganglia as an in vitro model system we have identified key features enhancing nerve growth within these fiber scaffolds. Our approach enabled straightforward integration of microscopic topography at the scale of nerve fascicles within the scaffold cores, which led to accelerated Schwann cell migration, as well as neurite growth and alignment. Our findings indicate that fiber drawing provides a scalable and versatile strategy for producing nerve guidance channels capable of controlling direction and accelerating the rate of axonal growth. Keywords: Peripheral nerve repair; Neural scaffold; Fiber drawing; Tissue engineering, National Science Foundation (U.S.) (Award CBET-1253890), National Institute of Neurological Diseases and Stroke (U.S.) (Grant R01 NS086804-01A1)

Computational thinking is an important part of a modern education, and robotics provides a powerful tool for teaching programming logic in an interactive and engaging way. The robot garden presented in this paper is a distributed multi-robot system capable of running autonomously or under user control from a simple graphical interface. Over 100 origami flowers are actuated with LEDs and printed pouch motors, and are deployed in a modular array around additional swimming and crawling folded robots. The garden integrates state-of-the-art rapid design and fabrication technologies with distributed systems software techniques to create a scalable swarm in which robots can be controlled individually or as a group. The garden can be used to teach basic algorithmic concepts through its distributed algorithm demonstration capabilities and can teach programming concepts through its education-oriented user interface., National Science Foundation (U.S.) (grant 1240383), National Science Foundation (U.S.) (grant 1138967), National Science Foundation (U.S.). Graduate Research Fellowship (1122374)