Spider webs were shown to be effective collectors of bioaerosols and airborne microorganisms. Spider webs were
collected and analyzed for microbial content using two general microbial culture mediums. To be considered suitable
passive collectors, webs had to satisfy three basic conditions; (1) collection of microorganisms without discrimination
based on species or size, (2) collection under variable environmental conditions, and (3) saturation avoidance in the
presence of strong microbial launching sources. Samples were collected from four locations near Wright-Patterson Air
Force Base, OH, a waste water treatment facility, a commercial garden center, a secluded state park area, and a parking
garage located within a medium size metropolitan area. These four locations provided appropriately varied
environmental and physical conditions to test the collection parameters previously stated. A simple collection
methodology was devised; microscopy cover glass slides were used as collection instruments. The methodology assured
sterility during collection and permitted in situ microbial growth, observation, and enumeration. Microbial growth, both
bacteria and fungi, were recovered from all collected spider web samples.
Most microorganisms evolve a suite of volatile metabolites. Some microorganism cultures evolve distinctive odors suggesting that the volatile compounds produced by microorganisms might be used to quickly distinguish microorganism types. We have measured infrared spectra of volatiles from common soil microorganisms. FTIR measurements were performed using the Bomem MB157 Fourier transform infrared spectrophotometer with ZnSe optics, using a MCT detector (500 cm-1 cut off). Spectral signatures of cultures dominated by coccus microorganisms differed from those with bacillus microorganisms. With improved infrared detection, IR signatures of microbial volatiles may be useful to characterize microorganism consortia and the predominant metabolite.
This research applies statistical and artificial neural network analysis to data obtained from measurement of organic compounds in the breath of Fisher-344 rats. The Research Triangle Institute (RTI) developed a breath collection system for use with rates in order to collect and measure volatile organic compounds (VOCs) exhaled. The RTI study tested the hypothesis that VOCs, including endogenous compounds, in breath can serve as markers to exposure to various chemicals such as drugs, pesticides, or carcinogens. A comparative analysis of chromatograms showed that the administration of carbon tetrachloride dramatically altered the VOCs measured in breath; both the compounds detected and their concentrations were greatly impacted. This research demonstrated that neural network analysis and classification discriminates between exposure to carbon tetrachloride versus no exposure. It also identified the chemical compounds in rat breath that best discriminate between carbon tetrachloride exposure and either a vehicle control or no dose. For the data set analyzed, 100 percent classification accuracy was achieved in classifying cases of exposure versus no exposure. The top three marker compounds were identified. The results obtained show that neural networks can be effectively used to analyze complex chromatographic data.
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