Sana Hamdi

Characterization of two polyvalent phages infecting Enterobacteriaceae.

Bacteriophages display remarkable genetic diversity and host specificity. In this study, we explore phages infecting bacterial strains of the Enterobacteriaceae family because of their ability to infect related but distinct hosts. We isolated and characterized two novel virulent phages, SH6 and SH7, using a strain of Shigella flexneri as host bacterium. Morphological and genomic analyses revealed that phage SH6 belongs to the T1virus genus of the Siphoviridae family. Conversely, phage SH7 was classified in the T4virus genus of the Myoviridae family. Phage SH6 had a short latent period of 16 min and a burst size of 103 ± 16 PFU/infected cell while the phage SH7 latent period was 23 min with a much lower burst size of 26 ± 5 PFU/infected cell. Moreover, phage SH6 was sensitive to acidic conditions (pH < 5) while phage SH7 was stable from pH 3 to 11 for 1 hour. Of the 35 bacterial strains tested, SH6 infected its S. flexneri host strain and 8 strains of E. coli. Phage SH7 lysed additionally strains of E. coli O157:H7, Salmonella Paratyphi, and Shigella dysenteriae. The broader host ranges of these two phages as well as their microbiological properties suggest that they may be useful for controlling bacterial populations.

Characterization of Five Podoviridae Phages Infecting Citrobacter freundii

Citrobacter freundii causes opportunistic infections in humans and animals, which are becoming difficult to treat due to increased antibiotic resistance. The aim of this study was to explore phages as potential antimicrobial agents against this opportunistic pathogen. We isolated and characterized five new virulent phages, SH1, SH2, SH3, SH4, and SH5 from sewage samples in Tunisia. Morphological and genomic analyses revealed that the five C. freundii phages belong to the Caudovirales order, Podoviridae family, and Autographivirinae subfamily. Their linear double-stranded DNA genomes range from 39,158 to 39,832 bp and are terminally redundant with direct repeats between 183 and 242 bp. The five genomes share the same organization as coliphage T7. Based on genomic comparisons and on the phylogeny of the DNA polymerases, we assigned the five phages to the T7virus genus but separated them into two different groups. Phages SH1 and SH2 are very similar to previously characterized phages phiYeO3-12 and phiSG-JL2, infecting, respectively, Yersinia enterocolitica and Salmonella enterica, as well as sharing more than 80% identity with most genes of coliphage T7. Phages SH3, SH4, and SH5 are very similar to phages K1F and Dev2, infecting, respectively, Escherichia coli and Cronobacter turicensis. Several structural proteins of phages SH1, SH3, and SH4 were detected by mass spectrometry. The five phages were also stable from pH 5 to 10. No genes coding for known virulence factors or integrases were found, suggesting that the five isolated phages could be good candidates for therapeutic applications to prevent or treat C. freundii infections. In addition, this study increases our knowledge about the evolutionary relationships within the T7virus genus.

IRIS: A Novel Method of Direct Trust Computation for Generating Trusted Social Networks

Improving trust in social networks appears as the first step toward addressing the existing confidence and privacy concerns related to online social networks. Direct trust is used to develop different trust-based methods such as transitivity and access control, however how to compute direct trust levels is rarely discussed in the literature. To address some of the current limitations, we introduce a novel approach for generating trusted social networks and we compute trust levels between users having direct relationships. Experimental results with data extracted from FOAF files show that our work presents high accuracy.

A New Trust Management Model in P2P Systems

The Peer-to-Peer systems (P2P) are a center of interest in the community of distributed systems, since their advantages in the adaptability and the robustness with regard to the traditional architectures client / server. Unfortunately, the anonymous and open nature of these systems involves an almost impossible surveillance of the network and offers nearly an ideal environment for the spread of the inauthentic files. To offer a more extensive vision of the network and to limit the malicious interactions between different peers of the network, we present a new model of trust management that considers the direct trust based on the peer’s history of uploads and the indirect trust which is calculated after constructing trust paths.

TISoN: Trust Inference in Trust-Oriented Social Networks

Trust systems represent a significant trend in decision support for social networks’ service provision. The basic idea is to allow users to rate each other even without being direct neighbours. In this case, the purpose is to derive a trust score for a given user, which could be of help to decide whether to trust other users or not. In this article, we investigate the properties of trust propagation within social networks, based on the notion of <i>transitivity</i>, and we introduce the <b><i>TISoN</i></b> model to generate and evaluate <b>T</b>rust <b>I</b>nference within online <b>So</b>cial <b>N</b>etworks. To do so, (<i>i</i>) we develop a novel <b><i>TPS</i></b> algorithm for <b>T</b>rust <b>P</b>ath <b>S</b>earching where we define neighbours’ priority based on their direct trust degrees, and then select trusted paths while controlling the path length; and, (<i>ii</i>) we develop different <b><i>TIM</i></b> algorithms for <b>T</b>rust <b>I</b>nference <b>M</b>easuring and build a trust network. In addition, we analyse existing algorithms and we demonstrate that our proposed model better computes transitive trust values than do the existing models. We conduct extensive experiments on a real online social network dataset, Advogato. Experimental results show that our work is scalable and generates better results than do the pioneering approaches of the literature.

Reputation Management in Online Social Networks - A New Clustering-based Approach.

Trust and reputation management stands as a corner stone within the Online Social Networks (OSNs) since they ensure a healthy collaboration relationship among participants. Currently, most trust and reputation systems focus on evaluating the credibility of the users. The reputation systems in OSNs have as objective to help users to make difference between trustworthy and untrustworthy, and encourage honest users by rewarding them with high trust values. Computing reputation of one user within a network requires knowledge of trust degrees between the users. In this paper, we propose a new Clustering Reputation algorithm, called RepC, based on trusted network. This algorithm classifies the users of OSNs by their trust similarity such that most trustworthy users belong to the same cluster. We conduct extensive experiments on a real online social network dataset from Twitter. Experimental results show that our algorithm generates better results than do the pioneering approaches of the literature.

Semantic clustering of users based on shared conceptualizations in folksonomies

Folksonomy systems and social bookmarking tools are rapidly spreading on the Web. Their steady increase reveals a trend towards a more dynamic and interactive space, in which users can individually assign terms to online resources. Even though users find tagging easy to use and understand, they need to see, closely, users sharing same interest as them for many reasons (facilitating the information retrieval and confidentiality management, insuring privacy respect, etc.). In this paper, we introduce a new method for automatically creating a new social network, across a popular folksonomy site, where users are clustered according to their domain of interest. We semantically represent these interests by using DBPEDIA ontology as a multi-domain ontology and we propose a methodology for enriching this ontology.