The purpose of this quasi-

Mycobacterium neoaurum, a member of the Mycobacterium parafortuitum complex, has only rarely been reported as a pathogen of human infections. We report a case of catheter-related bloodstream infection (CRBSI) due to M. neoaurum in a patient on hemodialysis. The isolate was identified by conventional methods as well as by 16S rRNA gene analysis. The patient was successfully treated with intravenous antibiotics (meropenem and amikacin) for three weeks and the catheter was removed. M. neoaurum should be considered as a possible cause of CRBSI in patients with renal failure. Combination antimicrobial therapy and catheter removal can lead to a favorable clinical outcome.

Radial basis function network (RBFN), commonly used in the classification applications, has two parameters, kernel center and radius that can be determined by unsupervised or supervised learning. But it has a disadvantage that it considers that all the independent variables have the equal weights. In that case, the contour lines of the kernel function are circular, but in fact, the influence of each independent variable on the model is so different that it is more reasonable if the contour lines are oval. To overcome this disadvantage, this paper presents an adaptive radial basis function network (ARBFN) with kernel shape parameters and derives the learning rules from supervised learning. To verify that this architecture is superior to that of the traditional RBFN, we make a comparison between three artificial and fifteen real examples in this study. The results show that ARBFN is much more accurate than the traditional RBFN, illustrating that the shape parameters can actually improve the accuracy of RBFN.

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It is easy for a multi-layered perception (MLP) to form open plane classification borders, and for a radial basis function network (RBFN) to form closed circular or elliptic classification borders. In contrast, it is difficult for a MLP to form closed circular or elliptic classification borders, and for RBFN to form open plane classification borders. Hence, MLP and RBFN have their own advantages and disadvantages in dealing with various classification problems.To combine their advantages, in this paper, we proposed a novel neural network, Hybrid Transfer Function Network (HTFN), whose hidden layer contains sigmoid and Gaussian units at the same time. Although there are two kinds of processing units in HTFN, in this study, we used the principle of minimizing error sum of squares to derive the supervised learning rules for all the network parameters. When HTFN contains only either sigmoid units or Gaussian units in its hidden layer, HTFN can be transferred into MLP and RBFN, respectively. Hence, MLP and RBFN can be considered as a special case of HTFN. To verify that HTFN is superior to MLP and RBFN, this study employed three man-made examples to test the three networks. The results showed that HTFN is more accurate than MLP and RBFN, confirming that combining sigmoid and Gaussian units into hidden layer can combine advantages of MLP and RBFN.