Short-read fastA files dataset from complexity-reduced genotyping by sequencing data of bacterial isolates from a public hospital in Australia.

This data article contains short-read sequences (length 30-69 bp) obtained from complexity-reduced genotyping by sequencing (GBS) of 165 samples bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015. These samples represented 14 bacterial species. Data format is shown as filtered fastA files obtained from an Illumina HiSeq2500 sequencer.

Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data.

Background: Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases.

Sponges as sentinels: patterns of spatial and intra-individual variation in trace metal concentration.

If sponges are to be effective biomonitors we require a better understanding of the spatial scales over which metals vary in these organisms. We determined how concentration of Cd, Zn, Cu, Pb, Hg and Se varied over four spatial scales for two common estuarine sponge species in the Sydney region. We examined variability with a fully nested sampling design; between coastal lakes, within coastal lakes, between sponges and within sponges.