247,199 research outputs found

    Microbiological applications of high-resolution melting analysis

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    High-resolution melting (HRM) analysis uses real-time PCR instrumentation to interrogate DNA sequence variation and is a low-cost, single-step, closed-tube method. Here we describe HRM technology and provide examples of varied clinical microbiological applications to highlight the strengths and limitations of HRM analysis

    Advances in Microfluidics and Lab-on-a-Chip Technologies

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    Advances in molecular biology are enabling rapid and efficient analyses for effective intervention in domains such as biology research, infectious disease management, food safety, and biodefense. The emergence of microfluidics and nanotechnologies has enabled both new capabilities and instrument sizes practical for point-of-care. It has also introduced new functionality, enhanced sensitivity, and reduced the time and cost involved in conventional molecular diagnostic techniques. This chapter reviews the application of microfluidics for molecular diagnostics methods such as nucleic acid amplification, next-generation sequencing, high resolution melting analysis, cytogenetics, protein detection and analysis, and cell sorting. We also review microfluidic sample preparation platforms applied to molecular diagnostics and targeted to sample-in, answer-out capabilities

    Differentiating between monozygotic twins through methylation specific high resolution melt curve analysis

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    Although short tandem repeat profiling is extremely powerful in identifying individuals from crime scene stains, it is unable to differentiate between monozygotic (MZ) twins. Efforts to address this include mutation analysis through whole genome sequencing and through DNA methylation studies. Methylation of DNA is affected by environmental factors; thus, as MZ twins age, their DNA methylation patterns change. This can be characterized by bisulfite treatment followed by pyrosequencing. However, this can be time-consuming and expensive; thus, it is unlikely to be widely used by investigators. If the sequences are different, then in theory the melting temperature should be different. Thus, the aim of this study was to assess whether high-resolution melt curve analysis can be used to differentiate between MZ twins. Five sets of MZ twins provided buccal swabs that underwent extraction, quantification, bisulfite treatment, polymerase chain reaction amplification and high-resolution melting curve analysis targeting two markers, Alu-E2F3 and Alu-SP. Significant differences were observed between all MZ twins targeting Alu-E2F3 and in four of five MZ twins targeting Alu-SP (P<0.05). Thus, it has been demonstrated that bisulfite treatment followed by high-resolution melting curve analysis could be used to differentiate between MZ twins

    Mixture models for analysis of melting temperature data

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    BackgroundIn addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.ResultsMixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.ConclusionMixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.</p

    Inelastic X-ray scattering study of the collective dynamics in liquid sodium

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    Inelastic X-ray scattering data have been collected for liquid sodium at T=390 K, i.e. slightly above the melting point. Owing to the very high instrumental resolution, pushed up to 1.5 meV, it has been possible to determine accurately the dynamic structure factor, S(Q,ω)S(Q,\omega), in a wide wavevector range, 1.5÷151.5 \div 15 nm1^{-1}, and to investigate on the dynamical processes underlying the collective dynamics. A detailed analysis of the lineshape of S(Q,ω)S(Q,\omega), similarly to other liquid metals, reveals the co-existence of two different relaxation processes with slow and fast characteristic timescales respectively. The present data lead to the conclusion that: i) the picture of the relaxation mechanism based on a simple viscoelastic model fails; ii) although the comparison with other liquid metals reveals similar behavior, the data do not exhibit an exact scaling law as the principle of corresponding state would predict.Comment: RevTex, 7 pages, 6 eps figures. Accepted by Phys. Rev.

    Seasonal variability of the warm Atlantic Water layer in the vicinity of the Greenland shelf break

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    The warmest water reaching the east and west coast of Greenland is found between 200?m and 600?m. Whilst important for melting Greenland's outlet glaciers, limited winter observations of this layer prohibit determination of its seasonality. To address this, temperature data from Argo profiling floats, a range of sources within the World Ocean Database and unprecedented coverage from marine-mammal borne sensors have been analysed for the period 2002-2011. A significant seasonal range in temperature (~1-2?°C) is found in the warm layer, in contrast to most of the surrounding ocean. The phase of the seasonal cycle exhibits considerable spatial variability, with the warmest water found near the eastern and southwestern shelf-break towards the end of the calendar year. High-resolution ocean model trajectory analysis suggest the timing of the arrival of the year's warmest water is a function of advection time from the subduction site in the Irminger Basin
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