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Transcription factor Sp1 is necessary and functional in regulating expression of oncogene ZNF703
Xiaolin Liao,Yongjie Lu,Junbao Yang,Tao Kuang,Lilin Jiang,Yanjun Wang,Huiqun Kang,Bo Jiang,Xiaoli Zhou,Sheng He 한국유전학회 2017 Genes & Genomics Vol.39 No.10
Zinc finger protein 703 (ZNF703) is a putative oncogene in patients with the luminal B molecular subtype of breast cancer. Although the exact function of ZNF703 protein remains largely unknown, its expression and regulation have been implicated in several physiological and pathological processes. In the current study, for the first time, we identified and characterized the human ZNF703 gene promoter region. As a means of characterizing the transcription elements required for expression of ZNF703 protein at different stages, we cloned the promoter region of ZNF703 then created chimeric reporter plasmids for use in luciferase assays. A progressive deletion analysis of the ZNF703 gene’s 5′ and 3′ -flanking regions revealed that the core promoter is located in a 256-bp region ranging from nt-539 to nt-283. Next, we examined the effects of sitespecific mutations and treatment with mithramycin A to identify the functional Sp1 binding site, which was found to be located in a 447 bp region that ranged from nt-509 to nt-76, displayed the characteristics of a CpG island, and overlapped with the promoter region. In conclusion, our data suggest that ZNF703 transcription is regulated by transcription factor Sp1. This finding should facilitate future studies of the mechanism which regulates expression of this important gene.
Qiu Zhibin,Wang Haixiang,Liao Caibo,Lu Zuwen,Kuang Yanjun 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species.