It was identified that microRNAs were involved in the regulation of chronic neuropathic pain. However, the role of miR-206-3p in neuropathic pain was still unclear. In the current study, the role of miR-206-3p, a type of mature miR-206, in neuropathic pain was investigated. The potential mechanisms were also explored. We found that the expression of miR-206-3p decreased in the dorsal root ganglion (DRG) of chronic constriction sciatic nerve injury (CCI) rats, whereas the While histone deacetylase 4 (HDAC4) level increased. Further exploration showed that administration of a miR-206-3p mimic alleviated neuropathic pain and reduced the level of HDAC4, a predicted target of miR-206-3p. Overexpression of HDAC4 attenuated the effects of miR-206-3p on neuropathic pain. Our data revealed a miR-206-3p-HDAC4 signal that played a potentially important role in CCI-induced neuropathic pain.
To extend the lifetime of a wireless sensor network and improve the energy efficiency of its nodes, it is necessary to use node collaborative sleep algorithm to reduce the number of redundant nodes in the network. This paper proposes a particle swarm optimization sleep scheduling mechanism for use in wireless sensor networks based on sleep scheduling algorithm. The mechanism adopts the approach of density control and finds the redundant nodes based on the computation results of the network coverage. Experimental results show that the proposed algorithm can ensure adequate coverage under the premise of the ability to close off the redundant nodes, while reducing the total energy consumption of the network.
Subject reviewThe developmental tendencies of cumulative displacement time series associated with reservoir landslides influenced by large water reservoirs must be effectively predicted. However, traditional methods do not encompass the dynamic response relationships between landslide deformation and its influencing factors. Therefore, a new approach based on the exponential smoothing (ES) and multivariate extreme learning machine methods was introduced to reveal the influencing factors of landslide deformation and to forecast landslide displacement values. First, the influencing factors of reservoir landslide deformation were analysed. Second, the ES method was used to predict the trend term displacement and obtain the periodic term displacement by determining the trend term from the cumulative displacement. Next, multivariate influencing factors were analysed to explain the periodic term displacement. Then, an extreme learning machine (ELM) model was established to predict the periodic term displacement based on the multivariable analysis of influencing factors. Finally, cumulative displacement prediction values were obtained by adding the trend and periodic displacement prediction values. The Bazimen and Baishuihe landslides in Three Gorges Reservoir Area (TGRA) were selected as case studies. The proposed ES-multivariate ELM (ES-MELM) model was compared to the ES-univariate ELM (ES-ELM) model. The results show that reservoir landslide deformation is mainly influenced by periodic reservoir water level fluctuations and heavy rainfall. Additionally, the proposed model yields more accurate predictions than the ES-ELM model. Keywords: displacement prediction; exponential smoothing; Extreme Learning Machine; multivariate influencing factors; reservoir landslide; Three Gorges ReservoirAnaliza faktora utjecaja i predviđanje pomaka klizišta akumulacije -studija slučaja Three Gorges Reservoir (Kina)Pregledni članak Potrebno je učinkovito predviđati kako će se vremenski odvijati kumulativni pomaci povezani s klizištima akumulacija. Međutim, tradicionalne metode ne obuhvaćaju dinamički uspostavljene odnose između deformacije klizišta i faktora koji na to utječu. Dakle, uveden je novi pristup na temelju eksponencijalnog izglađivanja (EI) i multivarijatnih metoda ekstremno učećeg stroja kako bi se otkrili čimbenici od utjecaja na deformacije klizišta i predvidjele vrijednosti pomaka klizišta. Prvo su analizirani faktori koji utječu na deformacije klizišta akumulacije. Zatim je EI postupak rabljen za predviđanje trajanja trenda pomaka i dobivanje trajanja periodičnog pomaka određivanjem trajanja trenda iz kumulativnog pomaka. Dalje, analizirani su multivarijatni utjecajni faktori kako bi objasnili trajanje periodičnog pomaka. Nakon toga, postavljen je model ekstremno učećeg stroja kako bi se predvidjelo trajanje periodičnog pomaka na temelju multivarijatne analize utjecajnih čimbenika. Konačno, dobivene su vrijednosti predviđanja kumulativnog pomaka dodavanjem vrijednosti predviđanja trenda i periodičnog pomaka. Bazimen i B...
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