Growth Differentiation Factor 15 Is Induced by Hepatitis C Virus Infection and Regulates Hepatocellular Carcinoma-Related Genes

Liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC) are commonly induced by chronic hepatitis C virus (HCV) infection. We aimed to identify and characterize the involvement of previously screened cytokine GDF15 in HCV pathogenesis. We examined the GDF15 expression after HCV infection both in vitro and in vivo. Cultured JFH-1 HCV was used to determine the GDF15 function on virus propagation. GDF15 overexpression and RNA interference were employed to profile the GDF15-regulated genes, signaling pathways and cell biology phenotypes. The mRNA expression and protein secretion of GDF15 was dramatically increased in HCV-infected hepatoma cells, which maybe a host response to viral proteins or infection-induced cell stress. Patients infected with HCV had an average 15-fold higher blood GDF15 level than that of healthy volunteers. Three HCC individuals in the HCV cohort showed extremely high GDF15 concentrations. Transfection or exogenously supplied GDF15 enhanced HCV propagation, whereas knockdown of endogenous GDF15 resulted in inhibition of virus replication. Overexpressed GDF15 led to Akt activation and the phosphorylation of Akt downstream targeted GSK-3β and Raf. Several HCC-related molecules, such as E-cadherin, β-catenin, Cyclin A2/B1/D1, were up-regulated by GDF15 stimulation in vitro. Overexpression of GDF15 in hepatoma cells resulted in increased DNA synthesis, promoted cell proliferation, and importantly enhanced invasiveness of the cells. In conclusion, these results suggest that an elevated serum GDF15 level is a potential diagnostic marker for viral hepatitis, and GDF15 may contribute to HCV pathogenesis by altering the signaling and growth of host cells

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

How to Evaluate Smart Cities’ Construction? A Comparison of Chinese Smart City Evaluation Methods Based on PSF

With the rapid development of smart cities in the world, research relating to smart city evaluation has become a new research hotspot in academia. However, there are general problems of cognitive deprivation, lack of planning experience, and low level of coordination in smart cities construction. It is necessary for us to develop a set of scientific, reasonable, and effective evaluation index systems and evaluation models to analyze the development degree of urban wisdom. Based on the theory of the urban system, we established a comprehensive evaluation index system for urban intelligent development based on the people-oriented, city-system, and resources-flow (PSF) evaluation model. According to the characteristics of the comprehensive evaluation index system of urban intelligent development, the analytic hierarchy process (AHP) combined with the experts’ opinions determine the index weight of this system. We adopted the neural network model to construct the corresponding comprehensive evaluation model to characterize the non-linear characteristics of the comprehensive evaluation indexes system, thus to quantitatively quantify the comprehensive evaluation indexes of urban intelligent development. Finally, we used the AHP, AHP-BP (Back Propagation), and AHP-ELM (Extreme Learning Machine) models to evaluate the intelligent development level of 151 cities in China, and compared them from the perspective of model accuracy and time cost. The final simulation results show that the AHP-ELM model is the best evaluation model

Detection of Unresolved Targets for Wideband Monopulse Radar

Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms

Surface Plasmon Resonance (SPR) Combined Technology: A Powerful Tool for Investigating Interface Phenomena

Abstract Interface phenomena refer to the phenomena caused by various physical and chemical processes occurring at phase interfaces. These phenomena can include adhesion, friction, lubrication, evaporation, condensation, adsorption, monolayer formation, and other phenomena and are applied in many fields, such as food, papermaking, rubber, material science, energy, and biomedicine. However, traditional detection equipment cannot meet the interface phenomena observation needs because the information detected by techniques such as electrochemistry, spectroscopy, chromatography, and mass spectrometry is limited, as are the sensitivity and minimum detection limit. Surface plasmon resonance (SPR) combined technology is a powerful tool for investigating interface phenomena. This paper reviews all combination technologies of SPR with various conventional detection systems, emphasizing the combination with electrochemistry, Raman spectroscopy, and mass spectrometry. These technologies can observe interface phenomena through SPR and provide redox, molecular structure, functional group change, and other information. Through this information, the reactions on the interface can be made clearer and more controllable, which plays an important role in practical detection. In the future, SPR combined technology will mainly develop toward signal enhancement, light source coupling mode, and sensor chip design, and SPR will be combined with various detection technologies to achieve real‐time in situ detection of interface phenomena