BackgroundComplex mechanisms participate in microglial activation after a traumatic brain injury (TBI). TBI can induce autophagy and apoptosis in neurons and glial cells, and moderate hypothermia plays a protective role in the acute phase of TBI. In the present study, we evaluated the effect of TBI and moderate hypothermia on microglial activation and investigated the possible roles of autophagy/apoptosis and toll-like receptor 4 (TLR4).MethodsThe TBI model was induced with a fluid percussion TBI device. Moderate hypothermia was achieved under general anesthesia by partial immersion in a water bath for 4 h. All rats were killed 24 h after the TBI.ResultsOur results showed downregulation of the microglial activation and autophagy, but upregulation of microglial apoptosis, upon post-TBI hypothermia treatment. The expression of TLR4 and downstream myeloid differentiation primary response 88 (MyD88) was attenuated. Moderate hypothermia reduced neural cell death post-TBI.ConclusionsModerate hypothermia can reduce the number of activated microglia by inhibiting autophagy and promoting apoptosis, probably through a negative modulation between autophagy and apoptosis. Moderate hypothermia may attenuate the pro-inflammatory function of microglia by inhibiting the MyD88-dependent TLR4 signaling pathway.Electronic supplementary materialThe online version of this article (10.1186/s12974-018-1315-1) contains supplementary material, which is available to authorized users.
The underwater wireless sensor network is an important component of the underwater three-dimensional monitoring system. Due to the high bit error rate, high delay, low bandwidth, limited energy, and high dynamic of underwater networks, it is very difficult to realize efficient and reliable data transmission. Therefore, this paper posits that it is not enough to design the routing algorithm only from the perspective of the transmission environment; the comprehensive design of the data transmission algorithm should also be combined with the application. An edge prediction-based adaptive data transmission algorithm (EP-ADTA) is proposed that can dynamically adapt to the needs of underwater monitoring applications and the changes in the transmission environment. EP-ADTA uses the end–edge–cloud architecture to define the underwater wireless sensor networks. The algorithm uses communication nodes as the agents, realizes the monitoring data prediction and compression according to the edge prediction, dynamically selects the transmission route, and controls the data transmission accuracy based on reinforcement learning. The simulation results show that EP-ADTA can meet the accuracy requirements of underwater monitoring applications, dynamically adapt to the changes in the transmission environment, and ensure efficient and reliable data transmission in underwater wireless sensor networks.
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