CCAAT/Enhancer-Binding Protein γ Is a Critical Regulator of IL-1β-Induced IL-6 Production in Alveolar Epithelial Cells

CCAAT/enhancer binding protein γ (C/EBPγ) is a member of the C/EBP family of transcription factors, which lacks known activation domains. C/EBPγ was originally described as an inhibitor of C/EBP transactivation potential. However, previous study demonstrates that C/EBPγ augments the C/EBPβ stimulatory activity in lipopolysaccharide induction of IL-6 promoter in a B lymphoblast cell line. These data indicate a complexing functional role for C/EBPγ in regulating gene expression. Furthermore, the expression and function of C/EBPγ during inflammation are still largely unknown. In this study, we demonstrate that C/EBPγ activation was induced by IL-1β treatment in lung epithelial cells. Importantly, we demonstrate for the first time that C/EBPγ plays a critical role in regulating IL-1β-induced IL-6 expression in both mouse primary alveolar type II epithelial cells and a lung epithelial cell line, MLE12. We further provide the evidence that C/EBPγ inhibits IL-6 expression by inhibiting C/EBPβ but not NF-κB stimulatory activity in MLE12 cells. These findings suggest that C/EBPγ is a key transcription factor that regulates the IL-6 expression in alveolar epithelial cells, and may play an important regulatory role in lung inflammatory responses

Antigen-specific B-cell receptor sensitizes B cells to infection by influenza virus

Influenza A virus-specific B lymphocytes and the antibodies they produce protect against infection. However, the outcome of interactions between an influenza haemagglutinin-specific B cell via its receptor (BCR) and virus is unclear. Through somatic cell nuclear transfer we generated mice that harbour B cells with a BCR specific for the haemagglutinin of influenza A/WSN/33 virus (FluBI mice). Their B cells secrete an immunoglobulin gamma 2b that neutralizes infectious virus. Whereas B cells from FluBI and control mice bind equivalent amounts of virus through interaction of haemagglutinin with surface-disposed sialic acids, the A/WSN/33 virus infects only the haemagglutinin-specific B cells. Mere binding of virus is not sufficient for infection of B cells: this requires interactions of the BCR with haemagglutinin, causing both disruption of antibody secretion and FluBI B-cell death within 18 h. In mice infected with A/WSN/33, lung-resident FluBI B cells are infected by the virus, thus delaying the onset of protective antibody release into the lungs, whereas FluBI cells in the draining lymph node are not infected and proliferate. We propose that influenza targets and kills influenza-specific B cells in the lung, thus allowing the virus to gain purchase before the initiation of an effective adaptive response.National Institutes of Health (U.S.

Research on On-Line Detection Method of Transformer Winding Deformation Based on VFTO

At present, the detection of transformer winding deformation faults is carried out in an offline state, which requires the transformer to cooperate with the implementation of planned power outages, or it takes place after the sudden failure of the transformer when it is out of operation. It is difficult to obtain the status information of the windings online in time. Since the transformer will suffer very fast transient overvoltage (VFTO) impact during operation, combined with the principle of the frequency response method, an online detection method of transformer winding deformation based on VFTO is proposed. In order to study the frequency response characteristics of transformer winding under the impact of VFTO, the generation process of VFTO is simulated by simulation software, and the equivalent circuit model of transformer winding before and after deformation is established. The VFTO signal is injected into the transformer circuit model as an excitation source, and the changes of resonant frequencies of frequency response curve under different deformation types and different deformation degrees of winding are analyzed. The simulation results show that the frequency response curves of different winding deformation types are different. Different deformation degrees are simulated by increasing the radial capacitance by 4%, 13%, and 23%, series inductance by 2%, 4%, and 6%, and longitudinal capacitance by 3%, 6%, and 9%, and the change of resonance frequencies can comprehensively reflect the deformation information of winding. At the same time, the tests of different deformation types and deformation degrees of the simulated winding are carried out. The results show that with the deepening of the change degree of the simulated fault inductance value, the frequency response curve shifts to the low-frequency direction, confirming the feasibility of the online detection method of transformer winding deformation based on VFTO

Temporal and spatial variability of dynamic microstate brain network in early Parkinson’s disease

Abstract Changes of brain network dynamics reveal variations in macroscopic neural activity patterns in behavioral and cognitive aspects. Quantification and application of changed dynamics in brain functional connectivity networks may contribute to a better understanding of brain diseases, and ultimately provide better prognostic indicators or auxiliary diagnostic tools. At present, most studies are focused on the properties of brain functional connectivity network constructed by sliding window method. However, few studies have explored evidence-based brain network construction algorithms that reflect disease specificity. In this work, we first proposed a novel approach to characterize the spatiotemporal variability of dynamic functional connectivity networks based on electroencephalography (EEG) microstate, and then developed a classification framework for integrating spatiotemporal variability of brain networks to improve early Parkinson’s disease (PD) diagnostic performance. The experimental results indicated that compared with the brain network construction method based on conventional sliding window, the proposed method significantly improved the performance of early PD recognition, demonstrating that the dynamic spatiotemporal variability of microstate-based brain networks can reflect the pathological changes in the early PD brain. Furthermore, we observed that the spatiotemporal variability of early PD brain network has a specific distribution pattern in brain regions, which can be quantified as the degree of motor and cognitive impairment, respectively. Our work offers innovative methodological support for future research on brain network, and provides deeper insights into the spatiotemporal interaction patterns of brain activity and their variabilities in early PD