A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease

Among a group of 310 natural antiviral natural metabolites, our team identified three compounds as the most potent natural inhibitors against the SARS-CoV-2 main protease (PDB ID: 5R84), M^pro. The identified compounds are sattazolin and caprolactin A and B. A validated multistage in silico study was conducted using several techniques. First, the molecular structures of the selected metabolites were compared with that of GWS, the co-crystallized ligand of M^pro, in a structural similarity study. The aim of this study was to determine the thirty most similar metabolites (10%) that may bind to the M^pro similar to GWS. Then, molecular docking against M^pro and pharmacophore studies led to the choice of five metabolites that exhibited good binding modes against the M^pro and good fit values against the generated pharmacophore model. Among them, three metabolites were chosen according to ADMET studies. The most promising M^pro inhibitor was determined by toxicity and DFT studies to be caprolactin A (292). Finally, molecular dynamics (MD) simulation studies were performed for caprolactin A to confirm the obtained results and understand the thermodynamic characteristics of the binding. It is hoped that the accomplished results could represent a positive step in the battle against COVID-19 through further in vitro and in vivo studies on the selected compounds.     

EEG rhythm/channel selection for fuzzy rule-based alertness state characterization

The aim of the paper is to automatically select the optimal EEG rhythm/channel combinations capable of classifying human alertness states. Four alertness states were considered, namely ‘engaged’, ‘calm’, ‘drowsy’ and ‘asleep’. The features used in the automatic selection are the energies associated with the conventional rhythms, \(\delta , \theta , \alpha , \beta\) and \(\gamma\), extracted from overlapping windows of the different EEG channels. The selection process consists of two stages. In the first stage, the optimal brain regions, represented by sets of EEG channels, are selected using a simple search technique based on support vector machine (SVM), extreme learning machine (ELM) and LDA classifiers. In the second stage, a fuzzy rule-based alertness classification system (FRBACS) is used to identify, from the previously selected EEG channels, the optimal features and their supports. The IF–THEN rules used in FRBACS are constructed using a novel differential evolution-based search algorithm particularly designed for this task. Each alertness state is represented by a set of IF–THEN rules whose antecedent parts contain EEG rhythm/channel combination. The selected spatio-frequency features were found to be good indicators of the different alertness states, as judged by the classification performance of the FRBACS that was found to be comparable to those of the SVM, ELM and LDA classifiers. Moreover, the proposed classification system has the advantage of revealing simple and easy to interpret decision rules associated with each of the alertness states.

     

The microRNA-202 as a Diagnostic Biomarker and a Potential Tumor Suppressor

MicroRNA-202 (miR-202) is a member of the highly conserved let-7 family that was discovered in Caenorhabditis elegans and recently reported to be involved in cell differentiation and tumor biology. In humans, miR-202 was initially identified in the testis where it was suggested to play a role in spermatogenesis. Subsequent research showed that miR-202 is one of the micro-RNAs that are dysregulated in different types of cancer. During the last decade, a large number of investigations has fortified a role for miR-202 in cancer. However, its functions can be double-edged, depending on context they may be tumor suppressive or oncogenic. In this review, we highlight miR-202 as a potential diagnostic biomarker and as a suppressor of tumorigenesis and metastasis in several types of tumors. We link miR-202 expression levels in tumor types to its involved upstream and downstream signaling molecules and highlight its potential roles in carcinogenesis. Three well-known upstream long non-coding-RNAs (lncRNAs); MALAT1, NORAD, and NEAT1 target miR-202 and inhibit its tumor suppressive function thus fueling cancer progression. Studies on the downstream targets of miR-202 revealed PTEN, AKT, and various oncogenes such as metadherin (MTDH), MYCN, Forkhead box protein R2 (FOXR2) and Kirsten rat sarcoma virus (KRAS). Interestingly, an upregulated level of miR-202 was shown by most of the studies that estimated its expression level in blood or serum of cancer patients, especially in breast cancer. Reduced expression levels of miR-202 in tumor tissues were found to be associated with progression of different types of cancer. It seems likely that miR-202 is embedded in a complex regulatory network related to the nature and the sensitivity of the tumor type and therapeutic (pre)treatments. Its variable roles in tumorigenesis are mediated in part thought its oncogene effectors. However, the currently available data suggest that the involved signaling pathways determine the anti- or pro-tumorigenic outcomes of miR-202’s dysregulation and its value as a diagnostic biomarker.     

Theoretical bounds for the bit error rate for SAC OCDMA balanced detectors with multiple photodiodes

Photonic Network Communications - In this paper, we derive the theoretical lower and upper bounds for bit error rate (BER) for spectral amplitude coding OCDMA balanced detector (BD) employing...     

Discrete-time implementation of high-gain observers for numerical differentiation

High-gain observers have been used in non-linear control to estimate derivatives of the output. In this paper, we study discrete-time implementation of high-gain observers and their use as numerical differentiators, in noise-free as well as noisy measurements. We show that discretization using the bilinear transformation method gives better results than other discretization methods. We also show that many of the available numerical differentiators are special cases of the bilinear discrete-time equivalents of full-order or reduced-order high-gain observers.     

Theoretical calculations of the surface tension of Ag(1−x)-Cu(x) liquid alloys

The surface tension of silver-copper binary liquid alloys is calculated, in the frame work of Eyring theory. The calculations were made for different compositions (mole fraction, x_Cu = 0, 0.2, 0.4, 0.6, 0.8 and 1), in the temperature range 1100-1800 K. The surface tension decreases with temperature increase, at a fixed copper fraction x_Cu, and increases with increasing copper content. The calculated results are appropriately compared with existing literature data.     

Choice of a pertinent color space for color texture characterization using parametric spectral analysis

This article presents a comparison of different color spaces including RGB, IHLS and L?a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.     

Desorption kinetics of trichloroethylene and perchloroethylene from aged dense nonaqueous phase liquid-contaminated soil

Batch experiments were conducted to investigate the desorption kinetics of a contaminated soil collected from a field site at Hill Air Force Base (AFB), Utah. The site has been contaminated with many dense nonaqueous phase liquids (DNAPLs), primarily trichloroethylene (TCE) and perchloroethylene (PCE), for more than 60 years. Similar desorptive behaviour of both TCE and PCE was observed: a stage of fast release of the contaminant at the start followed by a very slow stage. Moreover, it is suggested that not all the contaminant in the solid phase may be released to the aqueous phase, apparently due to the age factor. Local variations were observed in the effective diffusivity and other sorption-related parameters across the site due to aquifer heterogeneity. Experimental results were then simulated using a numerical code based on the intraparticle diffusion model. Good agreement was found between both results.