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Coagulase-negative staphylococci (CNS) are opportunistic pathogens that are currently emerging as causative agents of human disease. Though CNS are widespread in the clinic and food, their precise identification at species level is important. Here, using 16S rRNA sequencing, 55 staphylococcal isolates were identified as S. capitis, S. caprae, S. epidermidis, S. haemolyticus, S. pasteuri, S. saprophyticus, S. warneri, and S. xylosus. Although 16S rRNA sequencing is universally accepted as a standard for bacterial identification, the method did not effectively discriminate closely related species, and additional DNA sequencing was required. The divergence of the sodA gene sequence is higher than that of 16S rRNA. To devise a rapid and accurate identification method, sodA-specific primers were designed to demonstrate that species-specific multiplex polymerase chain reaction (PCR) can be used for the identification of CNS species. The accuracy of this method was higher than that of phenotypic identification; the method is simple and less time-consuming than 16S rRNA sequencing. Of the 55 CNS isolates, 92.72% were resistant to at least one antibiotic, and 60% were resistant to three or more antibiotics. CNS isolates produced diverse virulence-associated enzymes, including hemolysin (produced by 69.09% of the isolates), protease (65.45%), lipase (54.54%), lecithinase (36.36%), and DNase (29.09%); all isolates could form a biofilm. Because of the increasing pathogenic significance of CNS, the efficient multiplex PCR detection method developed in this study may contribute to studies for human health. [ABSTRACT FROM AUTHOR]

Pointwise intensity-based algorithms are the most popular algorithms in dynamic laser speckle measurement of physical or biological activity. The output of this measurement is a two-dimensional map which qualitatively separates regions of higher or lower activity. In the paper, we have proposed filtering of activity maps to enhance visualization and to enable quantitative determination of activity time scales. As a first step, we have proved that the severe spatial fluctuations within the map resemble a signal-dependent noise. As a second step, we have illustrated implementation of the proposed idea by applying filters to non-normalized and normalized activity estimates derived from synthetic and experimental data. Statistical behavior of the estimates has been analyzed to choose the filter parameters, and substantial narrowing of the probability density functions of the estimates has been achieved after the filtering. The filtered maps exhibit an improved contrast and allowed for quantitative description of activity. [ABSTRACT FROM AUTHOR]