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The effect of visible light irradiation on the expression of pluripotent genes (Oct-4, Sox2, and Nanog) in amniotic fluid-derived stem cells (AFSCs) and on the osteogenic differentiation ability of AFSCs was investigated using light-emitting diodes (LEDs) at 0-2 mW/cm(2) in various wavelengths: [blue (470 nm), green (525 nm), yellow (600 nm), and red (630 nm)]. Pluripotent gene expression in AFSCs was up-regulated by visible light irradiation from a LED for more than 6 h. Green light irradiation of AFSCs up-regulated the expression of pluripotent genes more significantly than irradiation with other light. The osteogenic differentiation of AFSCs was facilitated by green and blue light irradiation. Facilitated differentiation into osteogenic cells by visible light irradiation was not mediated by reactive oxygen species (ROS); alkaline phosphatase activity (a marker of early osteogenic differentiation) and gene expression of osteopontin (a marker of late osteogenic differentiation) did not change significantly between AFSCs in differentiation medium with or without a ROS scavenger (vitamin C). The mitogen-activated protein kinase/extracellular signal-regulated protein kinase pathway, as well as other unknown signaling pathways, may be responsible for the activation of signaling pathways that facilitate the differentiation of AFSCs into osteogenic cells on light irradiation.

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An image-based control strategy along with estimation of target motion is developed to track dynamic targets without motion constraints. To the best of our knowledge, this is the first work that utilizes a bounding box as image features for tracking control and estimation of dynamic target without motion constraint. The features generated from a You-Only-Look-Once (YOLO) deep neural network can relax the assumption of continuous availability of the feature points in most literature and minimize the gap for applications. The challenges are that the motion pattern of the target is unknown and modeling its dynamics is infeasible. To resolve these issues, the dynamics of the target is modeled by a constant-velocity model and is employed as a process model in the Unscented Kalman Filter (UKF), but process noise is uncertain and sensitive to system instability. To ensure convergence of the estimate error, the noise covariance matrix is estimated according to history data within a moving window. The estimated motion from the UKF is implemented as a feedforward term in the developed controller, so that tracking performance is enhanced. Simulations are demonstrated to verify the efficacy of the developed estimator and controller.