Rapid single-nucleotide polymorphism-based identification of clonal Pseudomonas aeruginosa isolates from patients with cystic fibrosis by the use of real-time PCR and high-resolution melting curve analysis

Pseudomonas aeruginosa genotyping relies mainly upon DNA fingerprinting methods, which can be subjective, expensive and time-consuming. The detection of at least three different clonal P. aeruginosa strains in patients attending two cystic fibrosis (CF) centres in a single Australian city prompted the design of a non-gel-based PCR method to enable clinical microbiology laboratories to readily identify these clonal strains. We designed a detection method utilizing heat-denatured P. aeruginosa isolates and a ten-single-nucleotide polymorphism (SNP) profile. Strain differences were detected by SYBR Green-based real-time PCR and high-resolution melting curve analysis (HRM10SNP assay). Overall, 106 P. aeruginosa sputum isolates collected from 74 patients with CF, as well as five reference strains, were analysed with the HRM10SNP assay, and the results were compared with those obtained by pulsed-field gel electrophoresis (PFGE). The HRM10SNP assay accurately identified all 45 isolates as members of one of the three major clonal strains characterized by PFGE in two Brisbane CF centres (Australian epidemic strain-1, Australian epidemic strain-2 and P42) from 61 other P. aeruginosa strains from Australian CF patients and two representative overseas epidemic strain isolates. The HRM10SNP method is simple, is relatively inexpensive and can be completed in <3 h. In our setting, it could be made easily available for clinical microbiology laboratories to screen for local P. aeruginosa strains and to guide infection control policies. Further studies are needed to determine whether the HRM10SNP assay can also be modified to detect additional clonal strains that are prevalent in other CF centres.

Accelerating Large Scale Centroid-based Clustering with Locality Sensitive Hashing

Most traditional data mining algorithms struggle to cope with the sheer scale of data efficiently. In this paper, we propose a general framework to accelerate existing clustering algorithms to cluster large-scale datasets which contain large numbers of attributes, items, and clusters. Our framework makes use of locality sensitive hashing (LSH) to significantly reduce the cluster search space. We also theoretically prove that our framework has a guaranteed error bound in terms of the clustering quality. This framework can be applied to a set of centroid-based clustering algorithms that assign an object to the most similar cluster, and we adopt the popular K-Modes categorical clustering algorithm to present how the framework can be applied. We validated our framework with five synthetic datasets and a real world Yahoo! Answers dataset. The experimental results demonstrate that our framework is able to speed up the existing clustering algorithm between factors of 2 and 6, while maintaining comparable cluster purity.

Prediction of scoliosis curve type based on the analysis of trunk surface topography

Scoliosis treatment strategy is generally chosen according to the severity and type of the spinal curve. Currently, the curve type is determined from X-rays whose acquisition can be harmful for the patient. We propose in this paper a system that can predict the scoliosis curve type based on the analysis of the surface of the trunk. The latter is acquired and reconstructed in 3D using a non invasive multi-head digitizing system. The deformity is described by the back surface rotation, measured on several cross-sections of the trunk. A classifier composed of three support vector machines was trained and tested using the data of 97 patients with scoliosis. A prediction rate of 72.2% was obtained, showing that the use of the trunk surface for a high-level scoliosis classification is feasible and promising.

First-order-reversal-curve analysis of Pr-Fe-B-based exchange spring magnets

Ribbons of nominal composition (Pr(9.5)Fe(84.5)B(6))(0.96)Cr(0.01)(TiC)(0.03) were produced by arc-melting and melt-spinning the alloys on a Cu wheel. X-ray diffraction (XRD) reveals two main phases, one based upon alpha-Fe and the other upon Pr(2)Fe(14)B. The ribbons show exchange spring behavior with H (c) = 12.5 kOe and (BH)(max) = 13.6 MGOe when these two phases are well coupled. Transmission electron microscopy revealed the coupled behavior is observed when the microstructure consists predominantly of alpha-Fe grains (diameter similar to 100 nm.) surrounded by hard material containing Pr(2)Fe(14)B. The microstructure is discussed in terms of a calculation by Skomski and Coey. A first-order-reversal-curve (FORC) analysis was performed for both a well-coupled sample and a poorly coupled sample. The FORC diagrams show two strong peaks for both the poorly coupled sample and for the well-coupled material. In both cases, the localization of the FORC probability suggests magnetizing interactions between particles. Switching field distributions were calculated and are consistent with the sample microstructure.

First-order-reversal-curve analysis of Pr-Fe-B-based nanocomposites

Ribbons of nominal composition (Pr(9.5)Fe(84.5)B(6))(0.96)Cr(0.01)(TiC)(0.03) were produced by arc-melting and melt-spinning the alloys on a Cu wheel. X-ray diffraction reveals two main phases, one based upon alpha-Fe and the other upon Pr(2)Fe(14)B. The ribbons show exchange spring behavior with H(c)=12.5 kOe and (BH)(max)= 13.6 MGOe when these two phases are well coupled. Transmission electron microscopy revealed that the coupled behavior is observed when the microstructure consists predominantly of alpha-Fe grains(diameter similar to 100 nm.) surrounded by hard material containing Pr(2)Fe(14)B. A first-order-reversal-curve (FORC) analysis was performed for both a well-coupled sample and a partially-coupled sample. The FORC diagrams show two strong peaks for both the partially-coupled sample and for the well coupled material. In both cases, the localization of the FORC probability suggests demagnetizing interactions between particles. Switching field distributions were calculated and are consistent with the sample microstructure. (C) 2009 Elsevier B.V. All rights reserved.

A shape-based decomposition of the yield adjustment term in the arbitrage-free Nelson and Siegel (AFNS) model of the yield curve

The appealing feature of the arbitrage-free Nelson-Siegel model of the yield curve is the ability to capture movements in the yield curve through readily interpretable shifts in its level, slope or curvature, all within a dynamic arbitrage-free framework. To ensure that the level, slope and curvature factors evolve so as not to admit arbitrage, the model introduces a yield-adjustment term. This paper shows how the yield-adjustment term can also be decomposed into the familiar level, slope and curvature elements plus some additional readily interpretable shape adjustments. This means that, even in an arbitrage-free setting, it continues to be possible to interpret movements in the yield curve in terms of level, slope and curvature influences. © 2014 © 2014 Taylor & Francis.