Evidence indicates that after brain injury, neurogenesis is enhanced in regions such as hippocampus, striatum, and cortex. To study the role of hypoxia-inducible factor-1 (HIF−1α) and Wnt signaling in cerebral ischemia/hypoxia-induced proliferation of neural stem cells (NSCs), we investigated the proliferation of NSCs, expression of HIF−1α, and activation of Wnt signaling under conditions of pathologic hypoxia in vitro. NSCs were isolated from 30-day-old Sprague–Dawley rats and subjected to 0.3% oxygen in a microaerophilic incubation system. Cell proliferation was evaluated by measuring the diameter of neurospheres and by bromodeoxyuridine incorporation assays. Real-time quantitative PCR and Western blotting were used to detect mRNA and protein levels of HIF-1α, β-catenin, and cyclin D1 in the NSCs. The results showed that hypoxia increased NSC proliferation and the levels of HIF-1α, β−catenin, and cyclin D1 (p < 0.05). Blockade of the Wnt signaling pathway decreased hypoxia-induced NSC proliferation, whereas activation of this pathway increased hypoxia-induced NSC proliferation (p < 0.05). Knockdown of HIF-1α with HIF-1α siRNA decreased β−catenin nuclear translocation and cyclin D1 expression, and inhibited proliferation of NSCs (p < 0.05). These findings indicate that pathologic hypoxia stimulates NSC proliferation by increasing expression of HIF-1α and activating the Wnt/β-catenin signaling pathway. The data suggest that Wnt/β-catenin signaling may play a key role in NSC proliferation under conditions of pathologic hypoxia.
The results indicate that NSC transplantation has anti-apoptotic activity and can improve the neurological function; these effects are mediated by the up-regulation of Bcl-2 expression in the penumbra.
Online partial discharge (PD) analysis of power transformers is sometimes complex due to the presence of large disturbance and noise as well as to uncertainty in PD source identification. In this paper, a novel approach to PD detection and analysis in online power transformers is presented. The proposed approach provides hardware and software tools to remove disturbance, noise, separate contributions from different sources and, eventually, identify PD sources. Besides, a description of the hardware tools used to assemble the diagnostic system, the tests (in the lab and on real transformers) carried out to build the reference database needed for identification system training will be discussed, providing rules for PD source identification. A number of working cases will be presented to show the effectiveness of the proposed method.
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