SAMPLING METHODOLOGY FOR AIRBORNE SEMIVOLATILE ORGANIC POLLUTANTS USING POLYURETHANE FOAMS (COLLECTION, PESTICIDES, VOLATILE)

Indoor and ambient air organic pollutants have been gaining attention because they have been measured at levels with possible health effects. Studies have shown that most airborne polychlorinated biphenyls (PCBs), pesticides and many polycyclic aromatic hydrocarbons (PAHs) are present in the free vapor state. The purpose of this research was to extend recent investigative work with polyurethane foam (PUF) as a collection medium for semivolatile compounds. Open-porous flexible PUFs with different chemical makeup and physical properties were evaluated as to their collection affinities/efficiencies for various classes of compounds and the degree of sample recovery. Filtered air samples were pulled through plugs of PUF spiked with various semivolatiles under different simulated environmental conditions (temperature and humidity), and sampling parameters (flow rate and sample volume) in order to measure their effects on sample breakthrough volume (V(,B)). PUF was also evaluated in the passive mode using organo-phosphorus pesticides. Another major goal was to improve the overall analytical methodology; PUF is inexpensive, easy to handle in the field and has excellent airflow characteristics (low pressure drop). It was confirmed that the PUF collection apparatus behaves as if it were a gas-solid chromatographic system, in that, (V(,B)) was related to temperature and sample volume. Breakthrough volumes were essentially the same using both polyether and polyester type PUF. Also, little change was observed in the V(,B)s after coating PUF with common chromatographic liquid phases. Open cell (reticulated) foams gave better recoveries than closed cell foams. There was a slight increase in (V(,B)) with an increase in the number of cells/pores per inch. The high-density polyester PUF was found to be an excellent passive and active collection adsorbent. Good recoveries could be obtained using just solvent elution. A gas chromatograph equipped with a photoionization detector gave excellent sensitivities and selectivities for the various classes of compounds investigated. ^

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^