FOREVER YOUNG FLOWER (FYF) has been reported to play an important role in regulating flower senescence/abscission. Here, we functionally analyzed five Arabidopsis FYF-like genes, two in the FYF subgroup (FYL1/AGL71 and FYL2/AGL72) and three in the SOC1 subgroup (SOC1/AGL20, AGL19, and AGL14/XAL2), and showed their involvement in the regulation of flower senescence and/or abscission. We demonstrated that in FYF subgroup, FYF has both functions in suppressing flower senescence and abscission, FYL1 only suppresses flower abscission and FYL2 has been converted as an activator to promote flower senescence. In SOC1 subgroup, AGL19/AGL14/SOC1 have only one function in suppressing flower senescence. We also found that FYF-like proteins can form heterotetrameric complexes with different combinations of A/E functional proteins (such as AGL6 and SEP1) and AGL15/18-like proteins to perform their functions. These findings greatly expand the current knowledge behind the multifunctional evolution of FYF-like genes and uncover their regulatory network in plants.
Mobile robots frequently replace humans in handling and transporting wafer carriers in semiconductor production lines. A mobile robot is constructed in this paper. The developed mobile robot is primarily composed of a mobile base, a robot manipulator, and a vision system. Since the guidance control system of the mobile base inevitably causes positioning e n o n of the mobile base, this study employs the eye-in-hand vision system to provide visual information for controlling the manipulator of the mobile robot to grasp accurately stationaty material during pick-and-place operations between a predefined Station and the mobile robot. This work further proposes a position-based look-and-move task encoding control strategy for eye-in-hand vision architecture, that maintains all target features in the camera's field of view throughout the visual guiding. Moreover, the manipulator can quickly approach the material and precisely position the end-effector in the desired pose. Numerous techniques are required for implementing such a task, including image enhancement, edge detection, comer and centroid detection, camera model calibration method, robotic handleye calibration method, using a camera with controlled zoom and focus, and task encoding scheme. Finally, the theoretical results for the proposed control strategy are experimentally verified on the constructed mobile robot. Specific experimental demonstrations include grasping the target object with different locations on the station and grasping the target object tilted by different angles to the station
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