Modeling for contamination at sand face for 3D radial probe and dual packer is done with precision, however due to the interval volume of dual packer the contamination at tool inlet is not a simple modeling exercise.
Surface material was statistically significant in the univariable analysis, but not in the final model for MRSA contamination.
Although algorithms such as qpure (Song et al., 2012), genoCN (Sun et al., 2009), ASCAT (Van Loo et al., 2010) and ABSOLUTE (Carter et al., 2012) model for stromal contamination and ploidy estimation on SNP array data, they do not function for genome sequencing data.
We then conducted a sensitivity analysis of the vulnerability of genomic assays to contamination bias by applying median normal tissue gene expression levels in a linear model to adjust for contamination bias, demonstrating that adjustment for contamination improves sensitivity and negative predictive value.
This work has the dual objectives to develop and enhance methods to detect any contamination in the network and to get reliable real-time transport models for source contamination identification and proper countermeasures.
In addition to modeling for basic coverage and MAF, we also model for aneuploidy, normal/diploid contamination of primary tumor samples, mouse contamination of human tumors grown in xenograft and we perform auto-correction of systematic sequencing biases using matched normal/control samples.
In constructing the multilevel models for MRSA contamination, the size of the variance components for ward and surface were extremely small (e.g., <10-6).
An additional limitation is that our models for water contamination did not take into account some geologic factors such as soil quality, groundwater depth, and direction of groundwater flow that could influence the likelihood that a pesticide reaches the water drawn from private wells.
This strategy has become the world's principal model for handling severe radioactive contamination at the landscape level.
In an attempt to validate our model for well water pesticide contamination, we obtained domestic well water sample data from the CADPR; the agency analyzed these samples for multiple chemicals on a nonroutine basis for nearly three decades (1980 to present).
