Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

Fungal spore fragmentation as a function of airflow rates and fungal generation methods

The aim of this study was to characterise and quantify the fungal fragment propagules derived and released from several fungal species (Penicillium, Aspergillus niger and Cladosporium cladosporioides) using different generation methods and different air velocities over the colonies. Real time fungal spore fragmentation was investigated using an Ultraviolet Aerodynamic Particle Sizer (UVASP) and a Scanning Mobility Particle Sizer (SMPS). The study showed that there were significant differences (p < 0.01) in the fragmentation percentage between different air velocities for the three generation methods, namely the direct, the fan and the fungal spore source strength tester (FSSST) methods. The percentage of fragmentation also proved to be dependant on fungal species. The study found that there was no fragmentation for any of the fungal species at an air velocity ≤ 0.4 m/s for any method of generation. Fluorescent signals, as well as mathematical determination also showed that the fungal fragments were derived from spores. Correlation analysis showed that the number of released fragments measured by the UVAPS under controlled conditions can be predicted on the basis of the number of spores, for Penicillium and Aspergillus niger, but not for Cladosporium cladosporioides. The fluorescence percentage of fragment samples was found to be significantly different to that of non-fragment samples (p < 0.0001) and the fragment sample fluorescence was always less than that of the non-fragment samples. Size distribution and concentration of fungal fragment particles were investigated qualitatively and quantitatively, by both UVAPS and SMPS, and it was found that the UVAPS was more sensitive than the SMPS for measuring small sample concentrations, and the results obtained from the UVAPS and SMAS were not identical for the same samples.

Combining ganglion cell topology and data of patients with glaucoma to determine a structure-function map

PURPOSE: To introduce techniques for deriving a map that relates visual field locations to optic nerve head (ONH) sectors and to use the techniques to derive a map relating Medmont perimetric data to data from the Heidelberg Retinal Tomograph. METHODS: Spearman correlation coefficients were calculated relating each visual field location (Medmont M700) to rim area and volume measures for 10 degrees ONH sectors (HRT III software) for 57 participants: 34 with glaucoma, 18 with suspected glaucoma, and 5 with ocular hypertension. Correlations were constrained to be anatomically plausible with a computational model of the axon growth of retinal ganglion cells (Algorithm GROW). GROW generated a map relating field locations to sectors of the ONH. The sector with the maximum statistically significant (P < 0.05) correlation coefficient within 40 degrees of the angle predicted by GROW for each location was computed. Before correlation, both functional and structural data were normalized by either normative data or the fellow eye in each participant. RESULTS: The model of axon growth produced a 24-2 map that is qualitatively similar to existing maps derived from empiric data. When GROW was used in conjunction with normative data, 31% of field locations exhibited a statistically significant relationship. This significance increased to 67% (z-test, z = 4.84; P < 0.001) when both field and rim area data were normalized with the fellow eye. CONCLUSIONS: A computational model of axon growth and normalizing data by the fellow eye can assist in constructing an anatomically plausible map connecting visual field data and sectoral ONH data.