Thermodynamics of Forming Water Clusters at Various Temperatures and Pressures by Gaussian-2, Gaussian-3, Complete Basis Set-QB3, and Complete Basis Set-APNO Model Chemistries; Implications for Atmospheric Chemistry

The Gaussian-2, Gaussian-3, complete basis set- (CBS-) QB3, and CBS-APNO methods have been used to calculate ΔH° and ΔG° values for neutral clusters of water, (H2O)n, where n = 2−6. The structures are similar to those determined from experiment and from previous high-level calculations. The thermodynamic calculations by the G2, G3, and CBS-APNO methods compare well against the estimated MP2(CBS) limit. The cyclic pentamer and hexamer structures release the most heat per hydrogen bond formed of any of the clusters. While the cage and prism forms of the hexamer are the lowest energy structures at very low temperatures, as temperature is increased the cyclic structure is favored. The free energies of cluster formation at different temperatures reveal interesting insights, the most striking being that the cyclic trimer, cyclic tetramer, and cyclic pentamer, like the dimer, should be detectable in the lower troposphere. We predict water dimer concentrations of 9 × 1014 molecules/cm3, water trimer concentrations of 2.6 × 1012 molecules/cm3, tetramer concentrations of approximately 5.8 × 1011 molecules/cm3, and pentamer concentrations of approximately 3.5 × 1010 molecules/cm3 in saturated air at 298 K. These results have important implications for understanding the gas-phase chemistry of the lower troposphere.

The ability of the Gaussian-2, Gaussian-3, Complete Basis Set–QB3, and Complete Basis Set–APNO model chemistries to model the geometries of small water clusters

The Gaussian-2, Gaussian-3, Complete Basis Set-QB3, and Complete Basis Set-APNO methods have been used to calculate geometries of neutral clusters of water, (H2O)n, where n = 2–6. The structures are in excellent agreement with those determined from experiment and those predicted from previous high-level calculations. These methods also provide excellent thermochemical predictions for water clusters, and thus can be used with confidence in evaluating the structures and thermochemistry of water clusters.

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.

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.

CaCO3, foraminifer fragmentation, benthic carbon isotopes, coarse lithic counts and radiogenic isotopes (Sr, Nd, Pb) of the Pliocene and earliest Pleistocene terrigenous component (3.3-2.4 Ma) of IODP Site 306-U1313

We present Plio-Pleistocene records of sediment color, %CaCO3, foraminifer fragmentation, benthic carbon isotopes (d13C) and radiogenic isotopes (Sr, Nd, Pb) of the terrigenous component from IODP Site U1313, a reoccupation of benchmark subtropical North Atlantic Ocean DSDP Site 607. We show that (inter)glacial cycles in sediment color and %CaCO3 pre-date major northern hemisphere glaciation and are unambiguously and consistently correlated to benthic oxygen isotopes back to 3.3 million years ago (Ma) and intermittently so probably back to the Miocene/Pliocene boundary. We show these lithological cycles to be driven by enhanced glacial fluxes of terrigenous material (eolian dust), not carbonate dissolution (the classic interpretation). Our radiogenic isotope data indicate a North American source for this dust (~3.3-2.4 Ma) in keeping with the interpreted source of terrestrial plant wax-derived biomarkers deposited at Site U1313. Yet our data indicate a mid latitude provenance regardless of (inter)glacial state, a finding that is inconsistent with the biomarker-inferred importance of glaciogenic mechanisms of dust production and transport. Moreover, we find that the relation between the biomarker and lithogenic components of dust accumulation is distinctly non-linear. Both records show a jump in glacial rates of accumulation from Marine Isotope Stage, MIS, G6 (2.72 Ma) onwards but the amplitude of this signal is about 3-8 times greater for biomarkers than for dust and particularly extreme during MIS 100 (2.52 Ma). We conclude that North America shifted abruptly to a distinctly more arid glacial regime from MIS G6, but major shifts in glacial North American vegetation biomes and regional wind fields (exacerbated by the growth of a large Laurentide Ice Sheet during MIS 100) likely explain amplification of this signal in the biomarker records. Our findings are consistent with wetter-than-modern reconstructions of North American continental climate under the warm high CO2 conditions of the Early Pliocene but contrast with most model predictions for the response of the hydrological cycle to anthropogenic warming over the coming 50 years (poleward expansion of the subtropical dry zones).