Occupational exposure to perchloroethylene in Portuguese dry-cleaning stores

Perchloroethylene (also known as tetrachloroethylene) is a solvent that has been a mainstay of the dry cleaning industry for decades. Since 1995 the International Agency for Research on Cancer considers that dry cleaning entails exposures that are possibly carcinogenic to humans (Group 2B). Meanwhile, the same institution classified perchloroethylene as probably carcinogenic to humans (Group 2A). Some industries have begun using alternative cleaning methods that do not require the use of perchloroethylene. However, in Portugal this solvent is still the most common dry-cleaning agent. An exploratory study was developed that aimed to find the occupational exposure to perchloroethylene in four Portuguese dry-cleaning stores. Activities involving higher exposure and variables that promote exposure were also investigated. Real-time measurements of volatile organic compounds concentrations were performed using portable equipment (MultiRAE, RAE Systems model – calibrated by isobutylene).

Arrow plot and CA maps on microarray preprocessing methods

Microarray allow to monitoring simultaneously thousands of genes, where the abundance of the transcripts under a same experimental condition at the same time can be quantified. Among various available array technologies, double channel cDNA microarray experiments have arisen in numerous technical protocols associated to genomic studies, which is the focus of this work. Microarray experiments involve many steps and each one can affect the quality of raw data. Background correction and normalization are preprocessing techniques to clean and correct the raw data when undesirable fluctuations arise from technical factors. Several recent studies showed that there is no preprocessing strategy that outperforms others in all circumstances and thus it seems difficult to provide general recommendations. In this work, it is proposed to use exploratory techniques to visualize the effects of preprocessing methods on statistical analysis of cancer two-channel microarray data sets, where the cancer types (classes) are known. For selecting differential expressed genes the arrow plot was used and the graph of profiles resultant from the correspondence analysis for visualizing the results. It was used 6 background methods and 6 normalization methods, performing 36 pre-processing methods and it was analyzed in a published cDNA microarray database (Liver) available at http://genome-www5.stanford.edu/ which microarrays were already classified by cancer type. All statistical analyses were performed using the R statistical software.