Antibiotic susceptibility and high prevalence of extended spectrum beta-lactamase producing Escherichia coli in iranian broilers

Extended-spectrum β-lactamase (ESBL) producing Escherichia coli have rapidly spread worldwide and cause serious threats for public health. The study was conducted to determine the antibiotic resistance and characterization of ESBL producing E. coli strains isolated from broilers in Northern Iran. Antibiotic susceptibility test was done for a total of 100 isolates of E. coli, recovered from 240 broiler fecal samples at the slaughterhouse stage. ESBL production was screened using double-disc synergy test (DDST) and presence of four ESBL genes including blaPER, blaVEB, blaTEM and blaCTX-M was tested using PCR. Among 100 strains isolated from broilers, 53 were identified as ESBL-producing E. coli. All (100) ESBL positive isolates were typed according to the presence of one or two ESBL-associated genes. The most prevalent gene among ESBLs was CTX-M (60.3) and the PER gene was not present among isolates. All isolates in this study were resistant to colistin and nalidixic acid but were 100 sensitive to cefalexin and furazolidone. The results demonstrated the high prevalence of antibiotic resistant and ESBL producing E. coli among broilers which representing the risk of increasing these strains in human infections associated with food animals

A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network

Phylogenetic relationships of Iranian Infectious Pancreatic Necrosis Virus (IPNV) based on deduced amino acid sequences of genome segment A and B cDNA

Infectious Pancreatic Necrosis Virus (IPNV) is the causal agent of a highly contagious disease that affects many species of fish and shellfish. This virus causes economically important diseases of farmed rainbow trout, Oncorhynchus mykiss, in Iran which is often associated with the transmission of pathogens from European resources. In this study, moribund rainbow trout fry were collected during an outbreak of IPNV in three different fish farms in one northern province (Mazandaran), and two west provinces (Chaharmahal and Bakhtiari, and Kohgiluyeh and Boyer Ahmad) of Iran. We investigated full genome sequence of Iranian IPNV and compared it with previously identified IPNV sequences. The sequences of different structural and non-structural protein genes were compared with other aquatic birnaviruses sequenced to date. Our results showed that the Iranian isolate fall within genogroup 5, serotype A2 strain SP, having 99 % identity with the strain 1146 from Spain. These results suggest that the Iranian isolate may have originated from Europe

Isolation and expression of recombinant viral protein (VP2) from Iranian isolates of Infectious Pancreatic Necrosis Virus (IPNV) in Escherichia coli

Infectious Pancreatic Necrosis Virus (IPNV) is a member of the family Birnaviridae that has been linked to high mortalities in salmonids. Bacterial based systems as live vectors for the delivery of heterologous antigens offer a number of advantages as vaccination strategies. VP2 is a structural viral protein of IPNV with immunogenicity effects. In this study IPNV was isolated from diseased fry of rainbow trout Oncorhynchus mykiss (Walbaum) using CHSE-214. Then an expression vector was constructed for expression of viral protein VP2. The designed vector was constructed based upon pET-26b (+) with T7 promoter. A fragment containing the full length of the VP2 gene of Iranian Sp strain was amplified by PCR using genomic RNA of IPNV as template and cloned inpET-26b(+) plasmid. Recombinant structural viral protein VP2 was expressed as a soluble, N-terminal PelB fusion protein and secreted into the periplasmic space of Escherichia coli BL21(DE3) and Rosetta (DE3). The glucose, Isopropyl-β-D-thiogalactopyranoside (IPTG) was used as a chemical inducer for rVP2 production in 37º C. The rVP2 was extracted from the periplasm by osmotic shock treatment. The presence of gene in bacterial system of E. coli was confirmed by gel electrophoresis technique. The constructed vector could efficiently express the rVP2 into the periplasmic space of E. coli. The successful cloning and expression of the structural viral protein gene into E. coli can be used for developing a useful and safe vaccine to control IPNV infection in Iranian fish industry

MIR-206 target prediction in breast cancer subtypes by bioinformatics tools

Background: MicroRNAs (miRNAs) are small endogenous non-coding RNAs with fundamental roles in the regulation of protein expression that is involved in the pathogenesis of many cancers including breast cancer. Among them is miR-206, whose role as a tumor suppressor gene has been demonstrated in breast cancer. Consequently, the identification of its putative target in breast cancer is of practical value. Methods: In the present study, we have suggested a new approach for the identification of miR-206 target genes with possible role in breast cancer pathogenesis. We used 15 online tools for the prediction of miR-206 target genes as well as gene expression data produced by DNA microarray technology. Results: By combining these two sets of data, we suggested a list of miR-206 target genes with possible involvement in breast cancer. In addition, we depicted an interaction network including miR-206 and its putative targets. Conclusions: Considering the complexity of miR-206 interactions with several targets, such in silico analyses would considerably lessen the work load of laboratory experiments. © 2018, Author(s)

Analysis of enriched rare variants in JPH2-encoded junctophilin-2 among Greater Middle Eastern individuals reveals a novel homozygous variant associated with neonatal dilated cardiomyopathy.

Junctophilin-2 (JPH2) is a part of the junctional membrane complex that facilitates calcium-handling in the cardiomyocyte. Previously, missense variants in JPH2 have been linked to hypertrophic cardiomyopathy; however, pathogenic "loss of function" (LOF) variants have not been described. Family-based genetic analysis of GME individuals with cardiomyopathic disease identified an Iranian patient with dilated cardiomyopathy (DCM) as a carrier of a novel, homozygous single nucleotide insertion in JPH2 resulting in a stop codon (JPH2-p.E641*). A second Iranian family with consanguineous parents hosting an identical heterozygous variant had 2 children die in childhood from cardiac failure. To characterize ethnicity-dependent genetic variability in JPH2 and to identify homozygous JPH2 variants associated with cardiac disease, we identified variants in JPH2 in a worldwide control cohort (gnomAD) and 2 similar cohorts from the Greater Middle East (GME Variome, Iranome). These were compared against ethnicity-matched clinical whole exome sequencing (WES) referral tests and a case cohort of individuals with hypertrophic cardiomyopathy (HCM) based on comprehensive review of the literature. Worldwide, 1.45% of healthy individuals hosted a rare JPH2 variant with a significantly higher proportion among GME individuals (4.45%); LOF variants were rare overall (0.04%) yet were most prevalent in GME (0.21%). The increased prevalence of LOF variants in GME individuals was corroborated among region-specific, clinical WES cohorts. In conclusion, we report ethnic-specific differences in JPH2 rare variants, with GME individuals being at higher risk of hosting homozygous LOF variants. This conclusion is supported by the identification of a novel JPH2 LOF variant confirmed by segregation analysis resulting in autosomal recessive pediatric DCM due to presumptive JPH2 truncation

Temporal networks of face-to-face human interactions

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.