Epstein-Barr virus: clinical and epidemiological revisits and genetic basis of oncogenesis

Epstein-Barr virus (EBV) is classified as a member in the order herpesvirales, family herpesviridae, subfamily gammaherpesvirinae and the genus lymphocytovirus. The virus is an exclusively human pathogen and thus also termed as human herpesvirus 4 (HHV4). It was the first oncogenic virus recognized and has been incriminated in the causation of tumors of both lymphatic and epithelial nature. It was reported in some previous studies that 95% of the population worldwide are serologically positive to the virus. Clinically, EBV primary infection is almost silent, persisting as a life-long asymptomatic latent infection in B cells although it may be responsible for a transient clinical syndrome called infectious mononucleosis. Following reactivation of the virus from latency due to immunocompromised status, EBV was found to be associated with several tumors. EBV linked to oncogenesis as detected in lymphoid tumors such as Burkitt's lymphoma (BL), Hodgkin's disease (HD), post-transplant lymphoproliferative disorders (PTLD) and T-cell lymphomas (e.g. Peripheral T-cell lymphomas; PTCL and Anaplastic large cell lymphomas; ALCL). It is also linked to epithelial tumors such as nasopharyngeal carcinoma (NPC), gastric carcinomas and oral hairy leukoplakia (OHL). In vitro, EBV many studies have demonstrated its ability to transform B cells into lymphoblastoid cell lines (LCLs). Despite these malignancies showing different clinical and epidemiological patterns when studied, genetic studies have suggested that these EBV- associated transformations were characterized generally by low level of virus gene expression with only the latent virus proteins (LVPs) upregulated in both tumors and LCLs. In this review, we summarize some clinical and epidemiological features of EBV- associated tumors. We also discuss how EBV latent genes may lead to oncogenesis in the different clinical malignancie

Development of an Approach to Evaluate Website Effectiveness

The internet has been used by individuals, organizations, and governments for business, sports, health, banking, advertisement, education, and other services. Many websites have been developed and designed in the last several decades. However, most have not been developed and designed according to a shared set of design standards. Consequently, there is a need for an approach to evaluate the effectiveness of a website. A literature review was conducted to develop such an approach. Four experts were then consulted to inspect and evaluate the approach, and a questionnaire was completed by three categories: Internet users, website developers, and others to determine its final version. This research resulted in the development of an approach to evaluate website effectiveness, composed of three major criteria: design, content, and functionality, and 17 sub-criteria. The significance of this new approach is that it allows stakeholders to evaluate their websites and determine how to improve them in order to achieve their vision and mission

Development of an Approach to Evaluate Website Effectiveness

The internet has been used by individuals, organizations, and governments for business, sports, health, banking, advertisement, education, and other services. Many websites have been developed and designed in the last several decades. However, most have not been developed and designed according to a shared set of design standards. Consequently, there is a need for an approach to evaluate the effectiveness of a website. A literature review was conducted to develop such an approach. Four experts were then consulted to inspect and evaluate the approach, and a questionnaire was completed by three categories: Internet users, website developers, and others to determine its final version. This research resulted in the development of an approach to evaluate website effectiveness, composed of three major criteria: design, content, and functionality, and 17 sub-criteria. The significance of this new approach is that it allows stakeholders to evaluate their websites and determine how to improve them in order to achieve their vision and mission

Multimodal feature-assisted continuous driver behavior analysis and solving for edge-enabled internet of connected vehicles using deep learning

The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addi-tion, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MOD-AL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works. © 2021 by the author. Licensee MDPI, Basel, Switzerland