The collective behavior of the nuclear array in Drosophila embryos during nuclear cycle (NC) 11 to NC14 is crucial in controlling cell size, establishing developmental patterns, and coordinating morphogenesis. After live imaging on Drosophila embryos with light sheet microscopy, we extract the nuclear trajectory, speed, and internuclear distance with an automatic nuclear tracing method. We find that the nuclear speed shows a period of standing waves along the anterior-posterior (AP) axis after each metaphase as the nuclei collectively migrate towards the embryo poles and partially move back. And the maximum nuclear speed dampens by 28-45% in the second half of the standing wave. Moreover, the nuclear density is 22–42% lower in the pole region than the middle of the embryo during the interphase of NC12-14. To find mechanical rules controlling the collective motion and packing patterns of the nuclear array, we use a deep neural network (DNN) to learn the underlying force field from data. We apply the learned spatiotemporal attractive force field in the simulations with a particle-based model. And the simulations recapitulate nearly all the observed characteristic collective behaviors of nuclear arrays in Drosophila embryos.
Due to the weak rigidity of an industrial robot, its end effector usually has poor absolute positioning accuracy, especially under varying payloads. Such situation is common in scenarios of handling, machining and tool changing. Conventional off-line calibration or compensation methods can only eliminate systematic errors, while such methods are invalid to the dynamic errors brought by varying payloads. This paper proposes a deep reinforcement learning(DRL) approach to solve the problem of dynamic errors, in consideration of external payloads changed manually. An online full closed loop system is established to verify the proposed method, which consists of a KUKA robot KR6, a Leica laser tracker, and a BECKHOFF PLC controller. The robot and the laser tracker work as the slavers of the master PLC controller, in between the communication is accomplished using EtherCAT. Logically, the robot is controlled by mxAutomation and the laser tracker is connected to an embedded EtherCAT slave card. Experiments on the robot demonstrate the effectiveness of the proposed DRL methods. The changed payloads range from 1.177Kg to 4.179 Kg, while the position accuracy of the robot can be maintained no more than 0.4mm by the DRL algorithm.
The emerging collective behaviors during embryogenesis play an important role in precise and reproducible morphogenesis. An important question in the study of collective behavior is what rule underlies the emerging pattern. Here we use the Drosophila embryo as a test tube to study this question. We focus on the nuclear array without membrane separation on the embryo periphery from the nuclear cycle (NC) 11 to NC14. After live imaging with light sheet microscopy, we extract the nuclear trajectory, speed, and internuclear distance with an automatic nuclear tracing method. We find that the nuclear speed shows a period of standing waves along the anterior-posterior (AP) axis after each metaphase as the nuclei collectively migrate towards the embryo poles and partially move back. And the maximum nuclear speed dampens by 38% in the second half of the standing wave. Moreover, the nuclear density is 35% higher in the middle than the pole region of the embryo during the S phase of NC11-NC14. To find mechanical rules controlling the collective motion and packing patterns of the nuclear array, we use the deep neural network (DNN) to learn the force field from data. We find two potential strong nuclear-age-dependent force fields, i.e., the repulsive or attractive force field. Simulations with the particle-based model indicate that only if the net internuclear force is attractive and increases with distance, the pseudo-synchronous mitotic wave in a nuclear array with lower nuclear density in embryo poles can drive the collective motion with the damped standing wave of the nuclear speed, and the collective nuclear motion, in turn, maintains the non-uniform nuclear density.
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