Correlation between electrical characteristics and biomarkers in breast cancer cells

Both electrical properties and biomarkers of biological tissues can be used to distinguish between normal and diseased tissues, and the correlations between them are critical for clinical applications of conductivity (σ) and permittivity (ε); however, these correlations remain unknown. This study aimed to investigate potential correlations between electrical characteristics and biomarkers of breast cancer cells (BCC). Changes in σ and ε of different components in suspensions of normal cells and BCC were analyzed in the range of 200 kHz–5 MHz. Pearson's correlation coefficient heatmap was used to investigate the correlation between σ and ε of the cell suspensions at different stages and biomarkers of cell growth and microenvironment. σ and ε of the cell suspensions closely resembled those of tissues. Further, the correlations between Na+/H+ exchanger 1 and ε and σ of cell suspensions were extremely significant among all biomarkers (pε < 0.001; pσ < 0.001). There were significant positive correlations between cell proliferation biomarkers and ε and σ of cell suspensions (pε/σ < 0.05). The microenvironment may be crucial in the testing of cellular electrical properties. ε and σ are potential parameters to characterize the development of breast cancer

LmCYP4G102: An oenocyte-specific cytochrome P450 gene required for cuticular waterproofing in the migratory locust, Locusta migratoria

Citation: Yu, Z. T., Zhang, X. Y., Wang, Y. W., Moussian, B., Zhu, K. Y., Li, S., . . . Zhang, J. Z. (2016). LmCYP4G102: An oenocyte-specific cytochrome P450 gene required for cuticular waterproofing in the migratory locust, Locusta migratoria. Scientific Reports, 6, 11. doi:10.1038/srep29980Cytochrome P450 superfamily proteins play important roles in detoxification of xenobiotics and during physiological and developmental processes. To contribute to our understanding of this large gene family in insects, we have investigated the function of the cytochrome P450 gene LmCYP4G102 in the migratory locust Locusta migratoria. Suppression of LmCYP4G102 expression by RNA interference (RNAi) does not interfere with moulting but causes rapid loss of body weight - probably due to massive loss of water, and death soon after moulting. Accordingly, maintaining these animals at 90% relative humidity prevented lethality. Consistently, RNAi against LmCYP4G102 provoked a decrease in the content of cuticular alkanes, which as an important fraction of cuticular hydrocarbons have been shown to confer desiccation resistance. In addition, the cuticle of LmCYP4G102- knockdown locusts was fragile and easier deformable than in control animals. Presumably, this phenotype is due to decreased amounts of cuticular water that is reported to modulate cuticle mechanics. Interestingly, LmCYP4G102 was not expressed in the epidermis that produces the cuticle but in the sub-epdiermal hepatocyte-like oenocytes. Together, our results suggest that the oenocyte-specific LmCYP4G102 plays a critical role in the synthesis of cuticular hydrocarbons, which are important for cuticle waterproofing and mechanical stability in L. migratori

Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network

Velocity picking, a critical step in seismic data processing, has been studied for decades. Although manual picking can produce accurate normal moveout (NMO) velocities from the velocity spectra of prestack gathers, it is time-consuming and becomes infeasible with the emergence of large amount of seismic data. Numerous automatic velocity picking methods have thus been developed. In recent years, deep learning (DL) methods have produced good results on the seismic data with medium and high signal-to-noise ratios (SNR). Unfortunately, it still lacks a picking method to automatically generate accurate velocities in the situations of low SNR. In this paper, we propose a multi-information fusion network (MIFN) to estimate stacking velocity from the fusion information of velocity spectra and stack gather segments (SGS). In particular, we transform the velocity picking problem into a semantic segmentation problem based on the velocity spectrum images. Meanwhile, the information provided by SGS is used as a prior in the network to assist segmentation. The experimental results on two field datasets show that the picking results of MIFN are stable and accurate for the scenarios with medium and high SNR, and it also performs well in low SNR scenarios