Alkyne-Tagged Apigenin, a Chemical Tool to Navigate Potential Targets of Flavonoid Anti-Dengue Leads

A flavonoid is a versatile core structure with various cellular, immunological, and pharmacological effects. Recently, flavones have shown anti-dengue activities by interfering with viral translation and replication. However, the molecular target is still elusive. Here we chemically modified apigenin by adding an alkyne moiety into the B-ring hydroxyl group. The alkyne serves as a chemical tag for the alkyne-azide cycloaddition reaction for subcellular visualization. The compound located at the perinuclear region at 1 and 6 h after infection. Interestingly, the compound signal started shifting to vesicle-like structures at 6 h and accumulated at 24 and 48 h after infection. Moreover, the compound treatment in dengue-infected cells showed that the compound restricted the viral protein inside the vesicles, especially at 48 h. As a result, the dengue envelope proteins spread throughout the cells. The alkyne-tagged apigenin showed a more potent efficacy at the EC50 of 2.36 ± 0.22, and 10.55 ± 3.37 µM, respectively, while the cytotoxicities were similar to the original apigenin at the CC50 of 70.34 ± 11.79, and 82.82 ± 11.68 µM, respectively. Molecular docking confirmed the apigenin binding to the previously reported target, ribosomal protein S9, at two binding sites. The network analysis, homopharma, and molecular docking revealed that the estrogen receptor 1 and viral NS1 were potential targets at the late infection stage. The interactions could attenuate dengue productivity by interfering with viral translation and suppressing the viral proteins from trafficking to the cell surface.

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

Adapting the Effect of Asynchronous Impulsive Noise using Interleaving Based on Compound Signal Classification in Broad Plc

Power Line Communication (PLC) is a technology which transforms the power line into pathways for the conveyance of broadband data. It has the advantage for it can avoid new installation since the current installation used for electrical power can also be used for data transmission. However, this power line channel presents a harsh environment for data transmission owing to the challenges of impulsive noise, high attenuation, selective fading and etc. In this paper to provide special structure to signal in frequency domain we have applied interleaving of signal in time domain. Based on the special structure, IN support is determined by using compound signal classification (CSC) and its amplitude is estimated by Least Squares (LS) method in frequency domain, exploiting the noise information on null subcarriers in Discrete Multitone Transform (DMT) system.

Numerical Analysis of Compound ECT Signals due to Various Tube Defects near Support Plate in Steam Generator

In this paper, eddy current signals from various anomalous defects in the steam generator tube are numerically predicted and their signal slope characteristics are investigated. The signal variations due to increased frequencies are also observed. Based on the accumulated knowledge, the analysis of compound signal is attempted which includes the effect of support plate. Both differential and absolute bobbin probe signals are analyzed. For the signal prediction, axisymmetric finite element modeling is used and this leads us to the utilization of slope angle analysis of the signal. Results show that differential signals are useful for locating the position of defect under the support plate, while absolute signals are easy to presume and interpret even though the effect of support plate is mixed. Combined use of these two types of signals will help us accomplish a more reliable inspection.