ZNF322A encoding a classical Cys2His2 zinc finger transcription factor was previously revealed as a potential oncogene in lung cancer patients. However, the oncogenic role of ZNF322A and its underlying mechanism in lung tumorigenesis remain elusive. Here we show ZNF322A protein overexpression in 123 Asian and 74 Caucasian lung cancer patients. Multivariate Cox regression analysis indicated that ZNF322A was an independent risk factor for a poor outcome in lung cancer, corroborating the Kaplan–Meier results that patients with ZNF322A protein overexpression had significantly poorer overall survival than other patients. Overexpression of ZNF322A promoted cell proliferation and soft agar growth by prolonging cell cycle in S phase in multiple lung cell lines, including the immortalized lung cell BEAS-2B. In addition, ZNF322A overexpression enhanced cell migration and invasion, whereas knockdown of ZNF322A reduced cell growth, invasion and metastasis abilities in vitro and in vivo. Quantitative proteomic analysis revealed potential ZNF322A-regulated downstream targets, including alpha-adducin (ADD1), cyclin D1 (CCND1), and p53. Using luciferase promoter activity assay combined with site-directed mutagenesis and sequential chromatin immunoprecipitation-PCR assay, we found that ZNF322A could form a complex with c-Jun and cooperatively activate ADD1 and CCND1 but repress p53 gene transcription by recruiting differential chromatin modifiers, such as histone deacetylase 3, in an AP-1 element dependent manner. Reconstitution experiments indicated that CCND1 and p53 were important to ZNF322A-mediated promotion of cell proliferation, whereas ADD1 was necessary for ZNF322A-mediated cell migration and invasion. Our results provide compelling evidence that ZNF322A overexpression transcriptionally dysregulates genes involved in cell growth and motility therefore contributes to lung tumorigenesis and poor prognosis.
Empirical mode decomposition (EMD) is a signal processing method used to extract intrinsic mode functions (IMFs) from a complicated signal. For a measurement with two or more correlated inputs, finding and capturing the correlated IMFs is a critical challenge that must be confronted. In this paper, a new correlated EMD method is proposed. The cross-correlation method was employed to determine dependence between the IMFs. To verify feasibility, an analysis was performed on simulated test signals and practically measured partial discharge (PD) signals collected from several acoustic emission sensors. At the surface of the gas-insulated transmission line, the PD signal arrived at the AE sensors with varying time delays and unique mechanism vibrations. Following an abnormal detection using the standard-deviation variation, the PD signal and the background signal of each sensor were applied using the correlated-EMD method. A twice correlated-EMD calculation was applied to the signals for the purpose of noise elimination. In addition, the unwanted low-frequency IMFs induced from the EMD calculations were excluded. The experimental results reveal that the correlated-EMD method performs well on both selecting and denoising the correlated IMFs. The results further provide analysis on correlated-input applications with a precise signal completely induced from the disturbance.
Recently, near infrared spectroscopy (NIRS) is widely applied on brain activation energy monitoring and adopted in clinical experiments. The advantages of NIRS are its non-invasive measurement and lower electrical disturbances. To verify the covariance and correlation of electroencephalography (EEG) and NIRS, an EEG-NIRS measurement system is implemented for data collection. The EEG and NIRS signals are recorded by common EEG electrodes and specific wavelength optical sensors. The EEG and NIRS sensors are placed with staggered placement to keep the same space resolution. EEG signal is processed with an instrument amplify, a band-pass filter circuit and variable gain amplifier, sequentially. The red light and near infrared light generated by LEDs are projected onto the tissues. Base on the blood oxygen-level dependence, the reflected photons can transfer the information of brain oxygen concentration. The NIRS signal acquired by two optical sensors is converted to voltage and filtered by a band-pass filter. In this measurement system, time-division multiplexing technique is applied on NIRS data collection to get the concentration of oxygenated hemoglobin and deoxy-hemoglobin. So, light sources and optical detectors are controlled by a signal processor. The post process data is digitalized and transferred to personal computer for brain activity estimation. The brain activity is extracted from the EEG and NIRS signal, individually. In this experiment, the EEG and NIRS signals are checked and analyzed. Although two kinds of brain activation indexes are resulted by different signal transfer path, the consistence response approves that the NIRS can be considered to monitor the brain activity. The easily setup of NIRS measurement can bring us more freedom of brain information extraction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.