A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

Effects of aluminium chloride and aluminium chlorohydrate on DNA repair in MCF10A immortalised non-transformed human breast epithelial cells

Use of underarm aluminium (Al)-based antiperspirant salts may be a contributory factor in breast cancer development. At the 10th Keele meeting, Al was reported to cause anchorage-independent growth and double strand DNA breaks in MCF10A immortalised non-transformed human breast epithelial cells. We now report that exposure of MCF10A cells to Al chloride or Al chlorohydrate also compromised DNA repair systems. Longterm (19–21 weeks) exposure to Al chloride or Al chlorohydrate at a 10−4 M concentration resulted in reduced levels of BRCA1 mRNA as determined by real-time RT-PCR and BRCA1 protein as determined by Western immunoblotting. Reduced levels of mRNA for other DNA repair genes (BRCA2, CHK1, CHK2, Rad51, ATR) were also observed using real-time RT-PCR. Loss of BRCA1 or BRCA2 gene function has long been associated with inherited susceptibility to breast cancer but these results suggest that exposure to aluminium-based antiperspirant salts may also reduce levels of these key components of DNA repair in breast epithelial cells. If Al can not only damage DNA but also compromise DNA repair systems, then there is the potential for Al to impact on breast carcinogenesis.