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The continuous and rapid spread of the COVID-19 pandemic has emphasized the need to seek new therapeutic and prophylactic treatments. Peptide inhibitors are a valid alternative approach for the treatment of emerging viral infections, mainly due to their low toxicity and high efficiency. Recently, two small nucleotide signatures were identified in the genome of some members of the Coronaviridae family and many other human pathogens. In this study, we investigated whether the corresponding amino acid sequences of such nucleotide sequences could have effects on the viral infection of two representative human coronaviruses: HCoV-OC43 and SARS-CoV-2. Our results showed that the synthetic peptides analyzed inhibit the infection of both coronaviruses in a dose-dependent manner by binding the RBD of the Spike protein, as suggested by molecular docking and validated by biochemical studies. The peptides tested do not provide toxicity on cultured cells or human erythrocytes and are resistant to human serum proteases, indicating that they may be very promising antiviral peptides.

Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones

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