Berries anthocyanins as potential SARS-CoV–2 inhibitors targeting the viral attachment and replication; molecular docking simulation

The viral respiratory disease, severe acute respiratory syndrome (SARS), has turned into a global health concern. Till now, there is no drug or vaccine has yet been specifically approved for SARS-CoV-2. One of the urgent solutions against the recent COVID-19 disease is the use of dietary molecules, which can be found abundantly in functional food. In the current study, we have conducted a molecular docking approach for eighteen dietary molecules belong to the subclass of anthocyanins, as potential inhibitors of the main protease and spike glycoprotein of SARS-CoV-2. Both selected targets, playing a vital role in attachment and replication of the virus. The results indicated that cyanidin-3-arabinoside exhibited the lowest binding energy and located onto the pocket through a sufficient number of hydrogen bonds with the main protease virus. However, pelargonidin-3-glucoside and pelargonidin 3-rhamnoside display significant binding energy with the spike glycoprotein of SARS-CoV-2. All compounds mentioned above shown high drug-likeness and fulfils the Lipinski’s rule of five, as well as confer favorable toxicity parameters, in addition to ADME values. Considering the obtained results, regular consumption of berry fruits, which are rich in anthocyanin compounds, should be supportive to inhibit viral infectious by reducing of propagation and pathogenicity of SARS-CoV–2.     

PRKAR1B-AS2 Long Noncoding RNA Promotes Tumorigenesis,Survival, and Chemoresistance via the PI3K/AKT/mTOR Pathway

Many long noncoding RNAs have been implicated in tumorigenesis and chemoresistance; however, the underlying mechanisms are not well understood. We investigated the role of PRKAR1B-AS2 long noncoding RNA in ovarian cancer (OC) and chemoresistance and identified potential downstream molecular circuitry underlying its action. Analysis of The Cancer Genome Atlas OC dataset, in vitro experiments, proteomic analysis, and a xenograft OC mouse model were implemented. Our findings indicated that overexpression of PRKAR1B-AS2 is negatively correlated with overall survival in OC patients. Furthermore, PRKAR1B-AS2 knockdown-attenuated proliferation, migration, and invasion of OC cells and ameliorated cisplatin and alpelisib resistance in vitro. In proteomic analysis, silencing PRKAR1B-AS2 markedly inhibited protein expression of PI3K-110α and abrogated the phosphorylation of PDK1, AKT, and mTOR, with no significant effect on PTEN. The RNA immunoprecipitation detected a physical interaction between PRKAR1B-AS2 and PI3K-110α. Moreover, PRKAR1B-AS2 knockdown by systemic administration of 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine nanoparticles loaded with PRKAR1B-AS2–specific small interfering RNA enhanced cisplatin sensitivity in a xenograft OC mouse model. In conclusion, PRKAR1B-AS2 promotes tumor growth and confers chemoresistance by modulating the PI3K/AKT/mTOR pathway. Thus, targeting PRKAR1B-AS2 may represent a novel therapeutic approach for the treatment of OC patients.     

Structural,optical and electrical properties of Ag doped PbS thin films: role of Ag concentration

The present work reports on the chemical synthesizes of (0–8 at.%) silver (Ag)-doped PbS thin films with tunable opto-electrical properties. From the X-ray diffraction analyses, it was understood that the preferred growth orientation of Ag:PbS films was dependent on the Ag doping concentration. The variation in the Ag:PbS films orientation was reflected in the film morphology as observed by scanning electron microscopy (SEM) and atomic force microscopy (AFM). SEM studies revealed that the variation in Ag concentration lead to different grain shapes for different grain orientations. The AFM study showed that the RMS roughness of the undoped PbS film has been reduced considerably due to silver doping. From the optical studies, a widening in the optical band gap was revealed after Ag-doping due to the quantum confinement effect. It was obtained that 4 at.% Ag-doped PbS thin films display an optimum band gap value of 1.45 eV. As for electrical characterization result, the resistivity reduces and the carrier density improved with 4 at.% Ag concentration. Based on all the data, it was concluded that the 4 at.% Ag-doped PbS thin film showed the best morphological, optical and electrical behavior, which recommend it as an active layer for solar cell devices.     

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Networking

Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.     

Triangular traffic signs detection based on RSLD algorithm

This paper describes an efficient method for the detection of triangular traffic signs on grey-scale images. This method is based on the proposed RANSAC symmetric lines detection (RSLD) algorithm which transforms triangle detection into a simple segment detection. A multi-scale approach allows the detection of any warning and yield traffic signs, whatever their distance to the vehicle. This algorithm is applied to a set of selected corners obtained with a coding gradient method. Baseline detection uses the scale of selected triangles to confirm the presence of traffic signs. The study demonstrates that RSLD is a low computation method as compared to standard triangle detection. The performance of the method proposed is compared with recently published methods on road sign databases, which use colour information. An equivalent detection rate is obtained with this algorithm, working on grey-scale images. This algorithm is implemented and runs in real-time at 30 frames per second.     

Stepanov-Like Pseudo Almost Periodic Solution of Quaternion-Valued for Fuzzy Recurrent Neural Networks with Mixed Delays

Neural Processing Letters - Real-valued neural networks or complex-valued neural networks are sometimes inappropriate for some engineering and research problems for instance where the data is...     

Metaheuristic Optimization of Time Series Models for Predicting NetworksTraffic

Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm is employed to optimize the hyper-parameters of long short-term memory (LSTM) network as an efficient time series modeling approach which is widely used in sequence prediction tasks. To prove the superiority of the proposed algorithm, four other optimization algorithms were employed to optimize LSTM, and the results were compared. The evaluation results confirmed the effectiveness of the proposed approach in predicting the traffic of wireless networks accurately. On the other hand, a statistical analysis is performed to emphasize the stability of the proposed approach.     

Design of a perfect black absorber at visible frequencies using plasmonic metamaterials

The design and fabrication of a plasmonic black absorber with almost 100% absorbance spanning a broad range of frequencies from ultraviolet (UV) to the near infrared (NIR) is demonstrated. The perfect plasmonic absorber is achieved by a combination of a metal film with suitable metal/dielectric nanocomposites. Our fabrication technique is simple, versatile, cost-effective, and compatible with current industrial methods for solar absorber production.     

Some optimal traffic regulation schemes for ATM networks: a Markovdecision approach

Some new traffic regulation schemes are defined in terms of a relief-spacing (or spacing of the allowance for cell delivery to the network) function. The class of open-loop traffic regulators (TR's) is defined in terms of relief-spacing functions which depend on some user-state; this class may be viewed as an extension of the Spacer-Controller defined in terms of some constant (user-state independent) relief-function. The optimal open-loop TR's are derived by formulating proper optimization problems and applying a Markov decision approach. Numerical results illustrate the improved performance of the optimal open-loop TR over that of the (constant relief-spacing) Spacer-Controller. Finally, the class of closed-loop TR's is defined in terms of relief-spacing functions which depend on both some user- and some network-state information and its optimal element is derived. The improved performance under the optimal closed-loop TR over that of the optimal open-loop TR is illustrated and their difference determines the performance gain if feedback information can become available on time     

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.