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SUMMARYConversion from fibroblasts to neurons has recently been successfully induced. However, the underlying mechanisms are poorly understood. Here, we find that depletion of p53 alone converts fibroblasts into all three major neural lineages. The induced neuronal cells express multiple neuron-specific proteins and generate action potentials and transmitter-receptor-mediated currents. Surprisingly, depletion does not affect the well-known tumorigenic p53 target, p21. Instead, knockdown of p53 upregulates neurogenic transcription factors, which in turn boosts fibroblast-neuron conversion. p53 binds the promoter of the neurogenic transcription factor Neurod2 and regulates its expression during fibroblast-neuron conversion. Furthermore, our method provides a high efficiency of conversion in late-passage fibroblasts. Genome-wide transcriptional analysis shows that the p53-deficiency-induced neurons exhibit an expression profile different from parental fibroblasts and similar to control-induced neurons. The results may help to understand and improve neural conversion mechanisms to develop robust neuron-replacement therapy strategies.

Electric vehicle (EV) is a promising, environmental friendly technique for its potential to reduce the using of fossil fuels. Massive EVs pose both opportunities and challenges for power systems, especially with the growing amount of wind-power integration. This paper investigates the problem of collaborative optimization scheduling of generators, EVs and wind power. A novel bi-layer optimization of transmission and distribution system is proposed to solve the scheduling problem of EVs charging and discharging load from respective time and space domain in the presence of wind-power. The upper layer optimization in transmission grid coordinates EVs with thermal generators, base load, with the consideration of wind power, to optimize load periods of EVs in the time domain. The lower layer optimization in distribution grid then spatially schedules the location of EVs load. Based on a power system benchmark with a 10-unit transmission grid and an IEEE 33-bus distribution grid, the performance of the proposed bi-layer optimization strategy is evaluated. The impacts of electricity price profile, EVs penetration and EVs load location are analyzed. Simulation results show that the proposed bi-layer optimization strategy can accommodate wind power and improve both the economics of grid operation and benefits of EV users by scheduling EVs charging and discharging temporally and spatially. Also, the results have shown that the location of EVs charging and discharging load is critical for the distribution network planning.

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