Online multiclass EEG feature extraction and recognition using modified convolutional neural network method

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accurac

Clinical, Radiological, and Molecular Findings of Acute Encephalitis in a COVID-19 Patient: A Rare Case Report.

We report a case of encephalitis in a young male patient with severe coronavirus disease 2019 (COVID-19) who initially presented with typical symptoms of fever, dry cough, and shortness of breath but later on developed acute respiratory distress syndrome and required mechanical ventilation. Two days post-extubation, the patient developed new-onset generalized tonic-clonic seizures and confusion. MRI of the brain was done and it showed an abnormal signal in the bilateral medial cortical frontal region. His cerebral spinal fluid (CSF) analysis revealed a characteristic picture of a viral infection with a high white blood cell count and normal glucose and protein levels. After ruling out all common causes of viral encephalitis such as herpes simplex virus (HSV) and based on the review of available literature regarding the neurological manifestations of COVID-19, this case was labeled as acute viral encephalitis secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection

An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, and the SARS-CoV-2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue toward therapeutic intervention. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. Totally 288 potential hits were identified from a half-million bioactive chemicals via a protein-ligand docking protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS– CoV– 1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS– CoV– 2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1′-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50’s of 19 ± 3 μM and 38 ± 3 μM, respectively. The compounds contain two basic structures, pyrimidinetrione and quinoxaline, which were newly found in 3CLpro inhibitor structures and are of high interest for lead optimization. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro

A Comparative Analysis on the Innate Immune Responses of <i>Cirrhinus mrigala</i> Challenged with <i>Pseudomonas aeruginosa</i> and <i>Fusarium oxysporum</i>

Microbes are the most significant ubiquitous pathogens that cause serious infections in freshwater fish, leading to tremendous economic losses. The present study was designed to investigate the extent of changes in cytokine expression, hemato-biochemical parameters, and tissue histology of Cirrhinus mrigala (C. mrigala) challenged with Pseudomonas aeruginosa (P. aeruginosa) and Fusarium oxysporum (F. oxysporum). Fish were divided into three major groups: control, P. aeruginosa-challenged, and F. oxysporum-challenged. The infection in both challenge assays was allowed to progress until 7 days post infection. Upregulated expression of TNF-α and IL-1β was found in blood, gills, livers, and kidneys of the challenged fish. Significant differences were noted in hematological parameters of challenged fish. Alanine aminotransferase, aspartate aminotransferase, and alkaline aminotransferase levels also showed significant differences in infected and control groups. An increase in serum albumin and globulin and a decrease in total protein were noted in infected groups as compared to the control group. Severe histological alterations were noted in gill, liver, and kidney tissues of the infected groups as compared to control. The order of histological alteration index for P. aeruginosa challenge was liver &gt; kidney &gt; gills, and for F. oxysporum challenge it was kidney &gt; liver &gt; gills. These changes in fish infected by P. aeruginosa and F. oxysporum can be used as an effective and subtle index to monitor the physiological and pathological conditions of fish

Comparative Analysis of Eight Numerical Methods Using Weibull Distribution to Estimate Wind Power Density for Coastal Areas in Pakistan

Currently, Pakistan is facing severe energy crises and global warming effects. Hence, there is an urgent need to utilize renewable energy generation. In this context, Pakistan possesses massive wind energy potential across the coastal areas. This paper investigates and numerically analyzes coastal areas' wind power density potential. Eight different state-of-the-art numerical methods, namely an (a) empirical method, (b) graphical method, (c) wasp algorithm, (d) energy pattern method, (e) moment method, (f) maximum likelihood method, (g) energy trend method, and (h) least-squares regression method, were analyzed to calculate Weibull parameters. We computed Weibull shape parameters (WSP) and Weibull scale parameters (WCP) for four regions: Jiwani, Gwadar, Pasni, and Ormara in Pakistan. These Weibull parameters from the above-mentioned numerical methods were analyzed and compared to find an optimal numerical method for the coastal areas of Pakistan. Further, the following statistical indicators were used to compare the efficiency of the above numerical methods: (i) analysis of variance (R-2), (ii) chi-square (X-2), and (iii) root mean square error (RMSE). The performance validation showed that the energy trend and graphical method provided weak performance for the observed period for four coastal regions of Pakistan. Further, we observed that Ormara is the best and Jiwani is the worst area for wind power generation using comparative analyses for actual and estimated data of wind power density from four regions of Pakistan

Efficiency of biologically and locally manufactured silver nanoparticles from Aspergillus niger in preventing Aspergillus flavus to produce aflatoxin B1 on the stored maize grains

This study was conducted in the mycotoxins laboratory, College of Agricultural Engineering Sciences, University of Baghdad, Iraq to evaluate the efficiency of silver nanoparticles manufactured locally and biologically by Aspergillus niger in preventing A. flavus to produce aflatoxin B1 (AFB1). The results of laboratory isolation showed that the companion of fungi genera were Aspergillus spp., Fusarium spp., Penicillum spp. and Rhizopus spp. at rates of 5.66, 14.91, 21.18 and 38.86% respectively. The highest frequency of A. flavus was 19.32%. The results of the TLC test showed that all isolates produced AFB1 in varying rates (%), and the Baghdad / Al-Youssifia isolate was the most productive of AFB1, since it has a largest spot area and most intense fluorescence under the chromatographic plate, hence given a symbolic name AFBY7. The results of HPLC showed that the toxin concentration in the AFBY7 isolate was 124.167 ppb. Also, the results showed the high efficiency of A. niger in the manufacture of silver nanoparticles, as the colour of solution changed from yellow to dark brown. On the other hand, the results of using locally and biologically manufactured silver nanoparticles in the storage experiment to prevent the fungus from producing toxin showed superiority of treatments (T) 0.4, 0.6 and 0.8 mg L-1, since AFB1 was 0.0 ppb compared to T0.2 (3.990 ppb). In addition, the results showed the efficiency of locally and biologically manufactured silver nanoparticles used in reducing AFB1 in the storage experiment of maize grains stored. So that, T0.6 and T0.8 were superior in reducing the AFB1 to 0.0, compared to T0.2 and T0.4, leading AFB1 to reach 10.230 and 5.180 ppb respectively