The ability for cells to sense and respond to microenvironmental signals is influenced by their three dimensional (3D) surroundings, which includes the extracellular matrix (ECM). In the 3D environment, vascular structures supply cells with nutrients and oxygen thus affecting cell responses such as motility. Interpretation of cell motility studies though is often restricted by the applied approaches such as 2D conventional soft lithography methods that have rectangular channel cross-sectional morphology. To better simulate cell responses to vascular supply in 3D, we developed a cell on a chip system with microfluidic channels with curved cross-sections embedded within a 3D collagen matrix that emulates anatomical vasculature more closely than inorganic polymers, thus to mimic a more physiologically relevant 3D cellular environment. To accomplish this, we constructed perfusable microfluidic channels by embedding sacrificial circular gelatin vascular templates in collagen, which were removed through temperature control. Motile breast cancer cells were pre-seeded into the collagen matrix and when presented with a controlled chemical stimulation from the artificial vasculature, they migrated towards the vasculature structure. We believe this innovative vascular 3D ECM system can be used to provide novel insights into cellular dynamics during multidirectional chemokineses and chemotaxis that exist in cancer and other diseases.

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The ability to track human operators' hand usage when working in production plants and factories is critically important for developing realistic digital factory simulators as well as manufacturing process control. We propose a proof-of-concept instrumented glove with only a few strain gage sensors and a microcontroller that continuously tracks and records the hand configuration during actual use. At the heart of our approach is a trainable system that can predict the fourteen joint angles in the hand using only a small set of strain sensors. First, ten strain gages are placed at various joints in the hand to optimize the sensor layout using the English letters in the American Sign Language (ASL) as a benchmark for assessment. Next, the best sensor configurations for three through ten strain gages are computed using a support vector machine (SVM) classifier. Following the layout optimization, our approach learns a mapping between the sensor readouts to the actual joint angles optically captured using a Leap Motion system. Five regression methods including linear, quadratic, and neural regression are then used to train the mapping between the strain gage data and the corresponding joint angles. The final proposed model involves four strain gages mapped to the fourteen joint angles using a two-layer feed-forward neural network (NN).

The ability to track human operators’ hand usage when working in production plants and factories is critically important for developing realistic digital factory simulators as well as manufacturing process control. We propose an instrumented glove with only a few strain gauge sensors and a micro-controller that continuously tracks and records the hand configuration during actual use. At the heart of our approach is a trainable system that can predict the fourteen joint angles in the hand using only a small set of strain sensors. First, ten strain gauges are placed at the various joints in the hand to optimize the sensor layout using the English letters in the American Sign Language as a benchmark for assessment. Next, the best sensor configurations for three through ten strain gauges are computed using a support vector machine classifier. Following the layout optimization, our approach learns a mapping between the sensor readouts to the actual joint angles optically captured using a Leap Motion system. Three regression methods including linear, quadratic and neural regression are then used to train the mapping between the strain gauge data and the corresponding joint angles. The final proposed model involves four strain gauges mapped to the fourteen joint angles using a two-layer feed-forward neural network.

conceived and designed the project. L. Wan conducted the experiments and analyzed the results. J. Skoko prepared the cells. O. B. Ozdoganlar and J. Yu prepared the acrylic mastermold. All authors reviewed/edited the manuscript.