BRCA1, a potential biomarker in the treatment of breast cancer

To date, estrogen receptor, progestogen receptor, and HER2/neu represent molecular biomarkers currently used in routine clinical practice to aid treatment decisions. Over the last few years, a large body of preclinical and retrospective clinical data has accumulated that suggests that BRCA1 mutation functions as a novel predictive marker of response to chemotherapy. This article reviews the role of BRCA1 as a predictive marker of chemotherapy response in breast cancer and examines the link between BRCA1 deficiency and the basal-like phenotype. Search strategy. Data for this article were identified through MEDLINE and PubMed searches for published reports using the terms BRCA1, breast cancer, basal-like, chemotherapy, prognosis, and predictive markers. In some cases, due to the restriction of space, readers are referred to review articles to allow further reading. Only articles published in English were included.

WEBCOM-G: A candidate middleware for Grid-Ireland

WebCom-G is a fledgling Grid Operating System, designed to provide independent service access through interoperability with existing middlewares. It offers an expressive programming model that automatically handles task synchronisation – load balancing, fault tolerance, and task allocation are handled at the WebCom-G system level – without burdening the application writer. These characteristics, together with the ability of its computing model to mix evaluation strategies to match the characteristics of the geographically dispersed facilities and the overall problem- solving environment, make WebCom-G a promising grid middleware candidate.

Australia Telescope Compact Array 1.2cm Observations of the Massive Star Forming Region G305.2+0.2

We report on Australia Telescope Compact Array observations of the massive star-forming region G305.2+0.2 at 1.2 cm. We detected emission in five molecules towards G305A, confirming its hot core nature. We determined a rotational temperature of 26 K for methanol. A non-local thermodynamic equilibrium excitation calculation suggests a kinematic temperature of the order of 200 K. A time-dependent chemical model is also used to model the gas-phase chemistry of the hot core associated with G305A. A comparison with the observations suggest an age of between 2 × 104 and 1.5 × 105 yr. We also report on a feature to the south-east of G305A which may show weak Class I methanol maser emission in the line at 24.933 GHz. The more evolved source G305B does not show emission in any of the line tracers, but strong Class I methanol maser emission at 24.933 GHz is found 3 arcsec to the east. Radio continuum emission at 18.496 GHz is detected towards two H ii regions. The implications of the non-detection of radio continuum emission towards G305A and G305B are also discussed.

Erythropoietin receptor expression in non-small cell lung carcinoma: a question of antibody specificity

Immunohistochemical studies on formalin-fixed, paraffin-embedded (FFPE) tissue utilizing polyclonal antibodies form the cornerstone of many reports claiming to demonstrate erythropoietin receptor (EPOR) expression in malignant tissue. Recently, Elliott et al. (Blood 2006;107:1892-1895) reported that the antibodies commonly used to detect EPOR expression also detect non-EPOR proteins, and that their binding to EPOR was severely abrogated by two synthetic peptides based on the sequence of heat shock protein (HSP) 70, HSP70-2, and HSP70-5. We have investigated the specificity of the C20 antibody for detecting EPOR expression in non-small cell lung carcinoma (NSCLC) utilizing tissue microarrays. A total of 34 cases were available for study. Antibody absorbed with peptide resulted in marked suppression of cytoplasmic staining compared with nonabsorbed antibody. Four tumors that initially showed a membranous pattern of staining retained this pattern with absorbed antibody. Positive membranous immunoreactivity was also observed in 6 of 30 tumors that originally showed a predominantly cytoplasmic pattern of staining. Using the C20 antibody for Western blots, we detected three main bands, at 100, 66, and 59 kDa. Preincubation with either peptide caused abolition of the 66-kDa band, which contains non-EPOR sequences including heat shock peptides. These results call into question the significance of previous immunohistochemical studies of EPOR expression in malignancy and emphasize the need for more specific anti-EPOR antibodies to define the true extent of EPOR expression in neoplastic tissue

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

A taxonomy for wavelet neural networks applied to nonlinear modelling

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.