SUMMARY
Radial glial progenitors (RGPs) represent the major neural progenitors for generating neurons and glia in the developing mammalian cerebral cortex
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. They position their centrosomes away from the nucleus at the ventricular zone surface
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. However, the molecular basis and precise function of this highly unique and characteristic subcellular organization of the centrosome remain largely unknown. Here we show that apical membrane anchoring of the centrosome controls the mechanical properties of mouse cortical RGPs and consequently their mitotic behaviour and cortical size and formation. Mother centriole in RGPs specifically develops distal appendages to anchor to the apical membrane. Selective removal of Centrosomal protein 83 (CEP83) eliminates mother centriole distal appendages and disrupts centrosome apical membrane anchorage, resulting in microtubule disorganization and apical membrane stretching and stiffening. It activates mechanically-sensitive Yes-associated protein (YAP) and promotes excessive RGP proliferation and subsequent intermediate progenitor overproduction, leading to the formation of an enlarged cortex with abnormal folding. Simultaneous elimination of YAP suppresses cortical enlargement and folding caused by CEP83 removal. Together, these results uncover a previously unknown role of the centrosome in regulating the mechanical features of neural progenitors, and the size and configuration of the mammalian cerebral cortex.
In wireless sensor networks (WSN), sensor deployment is one of the main topics for enhancing the sensor's coverage rate. In this paper, by modifying updating equation of onlooker bee and scout bee of original artificial bee colony (ABC) algorithm, a sensor deployment algorithm based on the modified ABC algorithm is proposed. Some new parameters such as forgetting and neighbor factor for accelerating the convergence speed and probability of mutant for maximizing the coverage rate are introduced. Simulation results showed that comparing with the deployment method based on original ABC and particle swarm optimization (PSO) algorithm, the proposed approach can achieve a better performance in coverage rate and convergence speed while needing a less total moving distance of sensors.
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