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  • Open Access

    ARTICLE

    Investigation of SARS-CoV-2 Main Protease Potential Inhibitory Activities of Some Natural Antiviral Compounds Via Molecular Docking and Dynamics Approaches

    Nada M. Mostafa1,5,#, Muhammad I. Ismail2,#, Amr M. El-Araby3, Dina M. Bahgat1, Ahmed M. Elissawy1,5, Ahmed M. Mostafa4, Omayma A. Eldahshan1,5,*, Abdel Nasser B. Singab1,5,*

    Phyton-International Journal of Experimental Botany, Vol.91, No.5, pp. 1089-1104, 2022, DOI:10.32604/phyton.2022.018239 - 24 January 2022

    Abstract Coronaviruses caused an outbreak pandemic disease characterized by a severe acute respiratory distress syndrome leading to the infection of more than 200 million patients and the death of more than 4 million individuals. The primary treatment is either supportive or symptomatic. Natural products have an important role in the development of various drugs. Thus, screening of natural compounds with reported antiviral activities can lead to the discovery of potential inhibitory entities against coronaviruses. In the current study, an in-silico molecular docking experiment was conducted on the effects of some of these natural antiviral phytoconstituents, (e.g.,… More >

  • Open Access

    VIEWPOINT

    Synergy of single-cell sequencing analyses and in vivo lineage-tracing approaches: A new opportunity for stem cell biology

    YUKI MATSUSHITA, WANIDA ONO, NORIAKI ONO*

    BIOCELL, Vol.46, No.5, pp. 1157-1162, 2022, DOI:10.32604/biocell.2022.018960 - 06 January 2022

    Abstract Single-cell sequencing technologies have rapidly progressed in recent years, and been applied to characterize stem cells in a number of organs. Somatic (postnatal) stem cells are generally identified using combinations of cell surface markers and transcription factors. However, it has been challenging to define micro-heterogeneity within “stem cell” populations, each of which stands at a different level of differentiation. As stem cells become defined at a single-cell level, their differentiation path becomes clearly defined. Here, this viewpoint discusses the potential synergy of single-cell sequencing analyses with in vivo lineage-tracing approaches, with an emphasis on practical considerations More >

  • Open Access

    VIEWPOINT

    AAV-based gene therapy approaches for genetic forms of tauopathies and related neurogenetic disorders

    MOHAMED AGHYAD AL KABBANI1,2, GILBERT WUNDERLICH3,4, CHRISTOPH KöHLER5, HANS ZEMPEL1,2,*

    BIOCELL, Vol.46, No.4, pp. 847-853, 2022, DOI:10.32604/biocell.2022.018144 - 15 December 2021

    Abstract Tauopathies comprise a spectrum of genetic and sporadic neurodegenerative diseases mainly characterized by the presence of hyperphosphorylated TAU protein aggregations in neurons or glia. Gene therapy, in particular adeno-associated virus (AAV)-based, is an effective medical approach for difficult-to-treat genetic diseases for which there are no convincing traditional therapies, such as tauopathies. Employing AAV-based gene therapy to treat, in particular, genetic tauopathies has many potential therapeutic benefits, but also drawbacks which need to be addressed in order to successfully and efficiently adapt this still unconventional therapy for the various types of tauopathies. In this Viewpoint, we More >

  • Open Access

    ARTICLE

    Efficient Forgery Detection Approaches for Digital Color Images

    Amira Baumy1, Abeer D. Algarni2,*, Mahmoud Abdalla3, Walid El-Shafai4,5, Fathi E. Abd El-Samie3,4, Naglaa F. Soliman2,3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3257-3276, 2022, DOI:10.32604/cmc.2022.021047 - 07 December 2021

    Abstract This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position… More >

  • Open Access

    ARTICLE

    Exploring the Approaches to Data Flow Computing

    Mohammad B. Khan1, Abdul R. Khan2,*, Hasan Alkahtani2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2333-2346, 2022, DOI:10.32604/cmc.2022.020623 - 07 December 2021

    Abstract Architectures based on the data flow computing model provide an alternative to the conventional Von-Neumann architecture that are widely used for general purpose computing. Processors based on the data flow architecture employ fine-grain data-driven parallelism. These architectures have the potential to exploit the inherent parallelism in compute intensive applications like signal processing, image and video processing and so on and can thus achieve faster throughputs and higher power efficiency. In this paper, several data flow computing architectures are explored, and their main architectural features are studied. Furthermore, a classification of the processors is presented based… More >

  • Open Access

    ARTICLE

    Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

    Ming Zhang, Hongbo Li, Xing Deng*

    Energy Engineering, Vol.119, No.1, pp. 237-252, 2022, DOI:10.32604/EE.2022.017916 - 22 November 2021

    Abstract The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To More >

  • Open Access

    ARTICLE

    Piezoresistive Prediction of CNTs-Embedded Cement Composites via Machine Learning Approaches

    Jinho Bang1, SongEe Park2, Haemin Jeon2,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1503-1519, 2022, DOI:10.32604/cmc.2022.020485 - 03 November 2021

    Abstract Conductive cementitious composites are innovated materials that have improved electrical conductivity compared to general types of cement, and are expected to be used in a variety of future infrastructures with unique functionalities such as self-heating, electromagnetic shielding, and piezoelectricity. In the present study, machine learning methods that have been recently applied in various fields were proposed for the prediction of piezoelectric characteristics of carbon nanotubes (CNTs)-incorporated cement composites. Data on the resistivity change of CNTs/cement composites according to various water/binder ratios, loading types, and CNT content were considered as training values. These data were applied More >

  • Open Access

    ARTICLE

    Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

    Meenakshi Malik1, Rainu Nandal1,*, Surjeet Dalal2, Vivek Jalglan3, Dac-Nhuong Le4,5

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 87-99, 2022, DOI:10.32604/iasc.2022.020249 - 26 October 2021

    Abstract It has been observed that driver behavior has a direct and considerable impact upon factors like fuel consumption, environmentally harmful emissions, and public safety, making it a key consideration of further research in order to monitor and control such related hazards. This has fueled our decision to conduct a study in order to arrive at an efficient way of analyzing the various parameters of driver behavior and find ways and means of positively impacting such behavior. It has been ascertained that such behavioral patterns can significantly impact the analysis of traffic-related conditions and outcomes. In… More >

  • Open Access

    ARTICLE

    Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

    Raniyah Wazirali1, Rami Ahmad2,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4922-4946, 2022, DOI:10.32604/cmc.2022.020044 - 11 October 2021

    Abstract Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.… More >

  • Open Access

    ARTICLE

    A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

    Jhonn Pablo Rodríguez1,*, David Camilo Corrales1,2, David Griol3, Zoraida Callejas3, Juan Carlos Corrales1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4725-4743, 2022, DOI:10.32604/cmc.2022.019135 - 11 October 2021

    Abstract Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In… More >

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