Quantifying BTEX in aqueous solutions with potentially interfering hydrocarbons using a partially selective sensor array.

Partially selective gold nanoparticle sensors have the sensitivity and selectivity to discriminate and quantify benzene, toluene, ethylbenzene, p-xylene and naphthalene (BTEXN) at concentrations relevant to the US Environmental Protection Agency. In this paper we demonstrate that gold nanoparticle chemiresistors can do so in the presence of 16 other hydrocarbons and that they did not reduce the discriminating power of the array. A two-level full factorial designed experiment was performed on unary, binary, ternary, quaternary, quinary combinations of BTEXN analytes with and without the possibly interfering hydrocarbons.

High-throughput fabrication and screening improves gold nanoparticle chemiresistor sensor performance.

Chemiresistor sensor arrays are a promising technology to replace current laboratory-based analysis instrumentation, with the advantage of facile integration into portable, low-cost devices for in-field use. To increase the performance of chemiresistor sensor arrays a high-throughput fabrication and screening methodology was developed to assess different organothiol-functionalized gold nanoparticle chemiresistors. This high-throughput fabrication and testing methodology was implemented to screen a library consisting of 132 different organothiol compounds as capping agents for functionalized gold nanoparticle chemiresistor sensors.

A blood-based predictor for neocortical Aβ burden in Alzheimer's disease: results from the AIBL study.

Dementia is a global epidemic with Alzheimer's disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aβ (extracellular β-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification.

Multivariate analysis of microarray data: differential expression and differential connection.

Background: Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.

NetRaVE: constructing dependency networks using sparse linear regression.

Unlabelled: NetRaVE is a small suite of R functions for generating dependency networks using sparse regression methods. Such networks provide an alternative to interpreting 'top n lists' of genes arising out of an analysis of microarray data, and they provide a means of organizing and visualizing the resulting information in a manner that may suggest relationships between genes.

Availability: NetRaVE is freely available for academic use and has been tested in R 2.