Analysis of atmospheric transport and the effects of seasonal and interannual transport variability on measurements made at the Pico Mountain observatory

A non-hierarchical K-means algorithm is used to cluster 47 years (1960–2006) of 10-day HYSPLIT backward trajectories to the Pico Mountain (PM) observatory on a seasonal basis. The resulting cluster centers identify the major transport pathways and collectively comprise a long-term climatology of transport to the observatory. The transport climatology improves our ability to interpret the observations made there and our understanding of pollution source regions to the station and the central North Atlantic region. I determine which pathways dominate transport to the observatory and examine the impacts of these transport patterns on the O3, NOy, NOx, and CO measurements made there during 2001–2006. Transport from the U.S., Canada, and the Atlantic most frequently reaches the station, but Europe, east Africa, and the Pacific can also contribute significantly depending on the season. Transport from Canada was correlated with the North Atlantic Oscillation (NAO) in spring and winter, and transport from the Pacific was uncorrelated with the NAO. The highest CO and O3 are observed during spring. Summer is also characterized by high CO and O3 and the highest NOy and NOx of any season. Previous studies at the station attributed the summer time high CO and O3 to transport of boreal wildfire emissions (for 2002–2004), and boreal fires continued to affect the station during 2005 and 2006. The particle dispersion model FLEXPART was used to calculate anthropogenic and biomass-burning CO tracer values at the station in an attempt to identify the regions responsible for the high CO and O3 observations during spring and biomass-burning impacts in summer.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.