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. The nominal component concentration of the mixtures was 100 µg L(-1), equivalent to approximately 100 parts per billion (ppb). Concentrations predicted with the random forests method had an average root mean square error of 10-20% of the component concentrations. This level of accuracy was achieved regardless of whether or not the 16 possibly interfering hydrocarbons were present. This work shows that the sensitivity and selectivity of gold nanoparticles chemiresistor sensors towards BTEXN analytes are not unduly affected by the other hydrocarbons that are expected to be present at a petroleum remediation site.

A blood-based predictor for neocortical Aß 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 Aß (extracellular ß-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aß accumulation, therefore, providing a platform for developing a population-based screen.

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 2.10.1 under Windows XP, Linux and Mac OS X. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Risk factors for cardiovascular disease in a diabetic population.

A study of the associations with cardiovascular disease (CVD) was made in subjects attending the Diabetic Clinic at Royal Perth Hospital. The variables examined were sex, age at time of study, age of onset of diabetes, duration of diabetes, mode of treatment, control (as assessed by fasting and post-prandial plasma glucose concentrations and glycosylated hemoglobin concentration), insulin levels in subjects not on insulin, obesity, blood pressure, total- and high-density lipoprotein and triglyceride concentrations, and smoking habit. CVD was diagnosed on the basis of (a) past history of myocardial infarction, (b) definite angina, (c) diagnostic ECG abnormality, and (d) cardiomegaly. A multiple logistic regression model identified the variables showing independent, significant associations with CVD as age, high-density lipoprotein cholesterol, diastolic blood pressure, an interaction between smoking and age and an interaction between treatment mode and blood pressure. As in the population generally, high-density lipoprotein cholesterol is the lipid variable showing the most significant association with prevalence of cardiovascular disease. Smoking is associated with a substantially increased risk of CVD in diabetics up to the age of about 70 yr. The use of oral hypoglycemic agents is associated with a lower prevalence of CVD in normotensive subjects, but with an increased risk in those who have systolic hypertension.