Recovering Social Networks from Individual Attributes

One of the most important challenges of network analysis remains the scarcity of reliable information on existing connection structures. This work explores theoretical and empirical methods of inferring directed networks from nodes attributes and from functions of these attributes that are computed for connected nodes. We discuss the conditions, under which an underlying connection structure can be (probabilistically) recovered, and propose a Bayesian recovery algorithm. In an empirical application, we test the algorithm on the data from the European School Survey Project on Alcohol and Other Drugs.

Non-uptake of predictive genetic testing for BRCA1/2 among relatives of known carriers: Attributes, cancer worry, and barriers to testing in a multicenter clinical cohort

BRCA1/2 test decliners/deferrers have received almost no attention in the literature and this is the first study of this population in the United Kingdom. The aim of this multicenter study is to examine the attributes of a group of individuals offered predictive genetic testing for breast/ovarian cancer predisposition who did not wish to proceed with testing at the time of entry into this study. This forms part of a larger study involving 9 U.K. centers investigating the psychosocial impact of predictive genetic testing for BRCA1/2. Cancer worry and reasons for declining or deferring BRCA1/2 predictive genetic testing were evaluated by questionnaire following genetic counseling. A total of 34 individuals declined the offer of predictive genetic testing. Compared to the national cohort of test acceptors, test decliners are significantly younger. Female test decliners have lower levels of cancer worry than female test acceptors. Barriers to testing include apprehension about the result, traveling to the genetics clinic, and taking time away from work/family. Women are more likely than men to worry about receiving less screening if found not to be a carrier. The findings do not indicate that healthy BRCA1/2 test decliners are a more vulnerable group in terms of cancer worry. However, barriers to testing need to be discussed in genetic counseling.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.

An expression vector containing a rhamnose-inducible promoter provides tightly regulated gene expression in Burkholderia cenocepacia

Infection of the respiratory tract caused by Burkholderia cepacia complex poses a serious risk for cystic fibrosis (CF) patients due to the high morbidity and mortality associated with the chronic infection and the lack of efficacious antimicrobial treatments. A detailed understanding of the pathogenicity of B. cepacia complex infections is hampered in part by the limited availability of genetic tools and the inherent resistance of these isolates to the most common antibiotics used for genetic selection. In this study, we report the construction of an expression vector which uses the rhamnose-regulated P(rhaB) promoter of Escherichia coli. The functionality of the vector was assessed by expressing the enhanced green fluorescent protein (eGFP) gene (e-gfp) and determining the levels of fluorescence emission. These experiments demonstrated that P(rhaB) is responsive to low concentrations of rhamnose and it can be effectively repressed with 0.2% glucose. We also demonstrate that the tight regulation of gene expression by P(rhaB) promoter allows us to extend the capabilities of this vector to the identification of essential genes.