Prevalence and antibiotic resistance pattern of Campylobacter species in foods of animal origin

Aim: The aim was to determine the prevalence and evaluation of antibiotic resistance pattern and minimum inhibitory concentration (MIC) of Campylobacter species isolated from foods of animal origin. Materials and Methods: A total of 280 samples (comprising 150 chicken meat, 50 chevon and 80 milk) were collected from retail meat markets, slaughter houses and dairy farms and analyzed for isolation of Campylobacter species. A total of 29 isolates comprising 23 Campylobacter jejuni and 6 Campylobacter coli were recovered, characterized biochemically and confirmed by polymerase chain reaction. These isolates were then tested for antibiotic resistance pattern through disc diffusion method, and MIC was assessed by MIC strips. The antibiotic resistance assessment was performed against 8 antibiotics viz. ampicillin, co-trimoxazole, erythromycin, levofloxacin, gentamicin, ciprofloxacin, ceftriaxone, and norfloxacin. Results: The prevalence of Campylobacter spp. in chicken meat, chevon and milk samples were observed 17.33%, 6% and 0%, respectively. All the isolates were resistant to co-trimoxazole but sensitive to erythromycin. All the isolates showed different resistance pattern for the rest of the antibiotics. MIC results revealed that all the isolates were within prescribed concentrations for sensitivity for the antibiotics tested. Conclusions: The foods of animal origin are source of Campylobacter infections to human beings. Thus, the development of antibiotic-resistant strains emphasizes the requirement of better surveillance and monitoring of the foods of animal origin and the use of antimicrobials in veterinary and human medicine require careful regulation

Manycore high-performance computing in bioinformatics

Mining the increasing amount of genomic data requires having very efficient tools. Increasing the efficiency can be obtained with better algorithms, but one could also take advantage of the hardware itself to reduce the application runtimes. Since a few years, issues with heat dissipation prevent the processors from having higher frequencies. One of the answers to maintain Moore's Law is parallel processing. Grid environments provide tools for effective implementation of coarse grain parallelization. Recently, another kind of hardware has attracted interest: multicore processors. Graphic processing units (GPUs) are a first step towards massively multicore processors. They allow everyone to have some teraflops of cheap computing power in its personal computer. The CUDA library (released in 2007) and the new standard OpenCL (specified in 2008) make programming of such devices very convenient. OpenCL is likely to gain a wide industrial support and to become a standard of choice for parallel programming. In all cases, the best speedups are obtained when combining precise algorithmic studies with a knowledge of the computing architectures. This is especially true with the memory hierarchy: the algorithms have to find a good balance between using large (and slow) global memories and some fast (but small) local memories. In this chapter, we will show how those manycore devices enable more efficient bioinformatics applications. We will first give some insights into architectures and parallelism. Then we will describe recent implementations specifically designed for manycore architectures, including algorithms on sequence alignment and RNA structure prediction. We will conclude with some thoughts about the dissemination of those algorithms and implementations: are they today available on the bookshelf for everyone

On participatory service provision at the network edge with community home gateways

Edge computing is considered as a technology to enable new types of services which operate at the network edge. There are important use cases in ambient intelligence and the Internet of Things (IoT) for edge computing driven by huge business potentials. Most of today's edge computing platforms, however, consist of proprietary gateways, which are either closed or fairly restricted to deploy any third-party services. In this paper we discuss a participatory edge computing system running on home gateways to serve as an open environment to deploy local services. We present first motivating use cases and review existing approaches and design considerations for the proposed system. Then we show our platform which materializes the principles of an open and participatory edge environment, to lower the entry barriers for service deployment at the network edge. By using containers, our platform can flexibly enable third-party services, and may serve as an infrastructure to support several application domains of ambient intelligence.Peer ReviewedPostprint (author's final draft

Characterisation of antibiotic-resistant psychrotrophic bacteria in raw milk

Dynamics of raw milk associated bacteria during cold storage of raw milk and their antibiotic resistance was reviewed, with focus on psychrotrophic bacteria. This study aimed to investigate the significance of cold storage of raw milk on antibiotic-resistant bacterial population and analyse the antibiotic resistance of the Gram-negative antibiotic-resistant psychrotrophic bacteria isolated from the cold-stored raw milk samples. Twenty-four raw milk samples, six at a time, were obtained from lorries that collected milk from Finnish farms and were stored at 4°C/4 d, 6°C/3 d and 6°C/4 d. Antibiotics representing four classes of antibiotics (gentamicin, ceftazidime, levofloxacin and trimethoprim-sulfamethoxazole) were used to determine the antibiotic resistance of mesophilic and psychrotrophic bacteria during the storage period. A representative number of antibiotic-resistant Gram-negative isolates retrieved from the cold-stored raw milk samples were identified by the phenotypic API 20 NE system and a few isolates by the 16S rDNA gene sequencing. Some of the isolates were further evaluated for their antibiotic resistance by the ATB PSE 5 and HiComb system. The initial average mesophilic counts were found below 105 CFU/mL, suggesting that the raw milk samples were of good quality. However, the mesophilic and psychrotrophic population increased when stored at 4°C/4 d, 6°C/3 d and 6°C/4 d. Gentamicin- and levofloxacin-resistant bacteria increased moderately (P < 0.05) while there was a considerable rise (P < 0.05) of ceftazidime- and trimethoprim-sulfamethoxazole-resistant population during the cold storage. Of the 50.9 % (28) of resistant isolates (total 55) identified by API 20 NE, the majority were Sphingomonas paucimobilis (8), Pseudomonas putida (5), Sphingobacterium spiritivorum (3) and Acinetobacter baumanii (2). The analysis by ATB PSE 5 system suggested that 57.1% of the isolates (total 49) were multiresistant. This study showed that the dairy environment harbours multidrug-resistant Gramnegative psychrotrophic bacteria and the cold chain of raw milk storage amplifies the antibioticresistant psychrotrophic bacterial population

Speciation of Enterococcus species: better way to deal with clinical infections at resource limited settings

Background: Enterococcus species are well known for its intrinsic resistance pattern to several antibiotics. Hence, appropriate management and prevention is essential in any healthcare facility. Present study was conducted to establish an accessible biochemical tests to differentiate Enterococcus species at resource limited settings.Methods: Enterococci isolated from various clinical specimens were speciated using an array of biochemical reactions and antimicrobial susceptibility testing was performed by Kirby-Bauer disc diffusion method. Results were interpreted as per Clinical and Laboratory Standards Institute (CLSI) guidelines.Results: Out of 107 enterococcal isolates, 63(59%) were E. faecium, 40(37%) were E. fecalis, 2(2%) were E. hirae, 1(0.9%) was E. raffinosus and 1(0.9%) was E. gallinarum. E. faecium and E. fecalis showed 23% and 7% vancomycin resistance respectively, while E. gallinarum showed low level vancomycin resistance.Conclusions: Enterococcus speciation can be done using simple biochemical reactions and its susceptibility pattern enables to distinguish Van phenotypes too. Hence, it is helpful for management of infections in resource limited settings to a greater extent

QR Factorization of Tall and Skinny Matrices in a Grid Computing Environment

Previous studies have reported that common dense linear algebra operations do not achieve speed up by using multiple geographical sites of a computational grid. Because such operations are the building blocks of most scientific applications, conventional supercomputers are still strongly predominant in high-performance computing and the use of grids for speeding up large-scale scientific problems is limited to applications exhibiting parallelism at a higher level. We have identified two performance bottlenecks in the distributed memory algorithms implemented in ScaLAPACK, a state-of-the-art dense linear algebra library. First, because ScaLAPACK assumes a homogeneous communication network, the implementations of ScaLAPACK algorithms lack locality in their communication pattern. Second, the number of messages sent in the ScaLAPACK algorithms is significantly greater than other algorithms that trade flops for communication. In this paper, we present a new approach for computing a QR factorization -- one of the main dense linear algebra kernels -- of tall and skinny matrices in a grid computing environment that overcomes these two bottlenecks. Our contribution is to articulate a recently proposed algorithm (Communication Avoiding QR) with a topology-aware middleware (QCG-OMPI) in order to confine intensive communications (ScaLAPACK calls) within the different geographical sites. An experimental study conducted on the Grid'5000 platform shows that the resulting performance increases linearly with the number of geographical sites on large-scale problems (and is in particular consistently higher than ScaLAPACK's).Comment: Accepted at IPDPS10. (IEEE International Parallel & Distributed Processing Symposium 2010 in Atlanta, GA, USA.

On the design of advanced filters for biological networks using graph theoretic properties

Network modeling of biological systems is a powerful tool for analysis of high-throughput datasets by computational systems biologists. Integration of networks to form a heterogeneous model requires that each network be as noise-free as possible while still containing relevant biological information. In earlier work, we have shown that the graph theoretic properties of gene correlation networks can be used to highlight and maintain important structures such as high degree nodes, clusters, and critical links between sparse network branches while reducing noise. In this paper, we propose the design of advanced network filters using structurally related graph theoretic properties. While spanning trees and chordal subgraphs provide filters with special advantages, we hypothesize that a hybrid subgraph sampling method will allow for the design of a more effective filter preserving key properties in biological networks. That the proposed approach allows us to optimize a number of parameters associated with the filtering process which in turn improves upon the identification of essential genes in mouse aging networks

Detection of Vancomycin Resistant Enterococci with Van A genotype in Clinical Isolates from a Tertiary Care Centre

Enterococcal infections may of at least 12 species including Enterococcus faecalis, E. faecium,E. durans, E. avium, E. casseliflavus, E. gallinarum, E. hirae, E.malodoratus, E. mundtii, E. pseudoavium, E. raffinosus, and E. solitarius. Among enterococcal species, E.faecalis and E. faecium are the two major human pathogens accounting for 85-89% and 10-15% of all enterococcal infections, respectively. Common to all variants of Vancomycin resistance in enterococci is the ability to cause a change in the structure of the pentapeptide incorporated in the 3 dimensional web of peptidoglycans composing the bacterial cell wall: from the original D-Ala-D-Ala to either D-Ala-D-Lactate (D-Ala-DLac) or D-Ala-D-Serine (D-Ala-D-Ser). Enterococcus species have been recognised as a pathogen causing dieases like bacteremia, endocarditis, complicated urinary tract infections, intra abdominal infections, pelvic infections, wound and soft tissue infections etc. VRE has become an important nosocomial pathogen because of its rapid spread, high mortality rates associated with infections, limited option for treatment, and the possibility of transferring vancomycin resistance genes to other more virulent and more prevalent pathogens such as Staphylococcus aureus. The VRE isolates study could be harbouring other Van genes. PCR remains the gold standard for diagnosis of Vancomycin resistance. Emerging Vancomycin resistance among Enterococcus is a cause for concern as this leads to a great difficulty in treating serious infections caused by them