Kcnk3, Ggta1, and Gpr84 are involved in hyperbaric oxygenation preconditioning protection on cerebral ischemia-reperfusion injury.

The present study aimed to explore the potential mechanism of the effect of hyperbaric oxygenation (HBO) preconditioning on cerebral ischemia and reperfusion injury (CIRI). GSE23160 dataset was used to identify differentially expressed genes (DEGs) from striatum between the middle cerebral artery occlusion (MCAO)/reperfusion and sham rats. The gene clusters with continuous increase and decrease were identified by soft clustering analysis in Mfuzz, and functional enrichment analysis of these genes was performed using clusterProfiler package. The intersection set of the genes with significantly altered expression at post-reperfusion 2, 8, and 24 h were screened in comparison to 0 h (sham group), and the expression of these genes was detected in the MCAO/reperfusion model and HBO preconditioning groups by real-time PCR (RT-PCR) and western blotting. A total of 41 upregulated DEGs, and 7 downregulated DEGs were detected, among which the expression of Gpr84 and Ggta1 was significantly upregulated at each reperfusion phase as compared to the sham group, while the expression of Kcnk3 was significantly downregulated except in the postreperfusion 8 h in the striatum group. RT-PCR and western blotting analyses showed that the expression of Ggta1, Gpr84, and Kcnk3 genes between the MCAO/reperfusion and sham rats were consistent with the bioinformatics analysis. In addition, the HBO preconditioning reduced the expression of Ggta1 and Gpr84 and increased the expression of Kcnk3 in MCAO/reperfusion rats. Kcnk3, Ggta1, and Gpr84 may play a major role in HBO-mediated protection of the brain against CIRI.

Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images.

Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0-4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.