Generalized atoms in molecules approach (GAIM approach)

This thesis involves two parts. The first is a new-proposed theoretical approach called generalized atoms in molecules (GAIM). The second is a computational study on the deamination reaction of adenine with OH⁻/nH₂O (n=0, 1, 2, 3) and 3H₂O. The GAIM approach aims to solve the energy of each atom variationally in the first step and then to build the energy of a molecule from each atom. Thus the energy of a diatomic molecule (A-B) is formulated as a sum of its atomic energies, EA and EB. Each of these atomic energies is expressed as, EA = Hᴬ + Vₑₑᴬᴬ + 1/2Vₑₑᴬ<>ᴮ EB = Hᴮ + Vₑₑᴮᴮ + 1/2Vₑₑᴬ<>ᴮ where; Hᴬ and Hᴮ are the kinetic and nuclear attraction energy of electrons of atoms A and B, respectively; Vₑₑᴬᴬ and Vₑₑᴮᴮ are the interaction energy between the electrons on atoms A and B, respectively; and Vₑₑᴬ<>ᴮ is the interaction energy between the electrons of atom A with the electrons of atom B. The energy of the molecule is then minimized subject to the following constraint, |ρA(r)dr + |ρB(r)dr = N where ρA(r) and ρB(r) are the electron densities of atoms A and B, respectively, and N is the number of electrons. The initial testing of the performance of GAIM was done through calculating dissociation curves for H₂, LiH, Li₂, BH, HF, HCl, N₂, F₂, and Cl₂. The numerical results show that GAIM performs very well with H₂, LiH, Li₂, BH, HF, and HCl. GAIM shows convergence problems with N₂, F₂, and Cl₂ due to difficulties in reordering the degenerate atomic orbitals Pₓ, Py, and Pz in N, F, and Cl atoms. Further work for the development of GAIM is required. Deamination of adenine results in one of several forms of premutagenic lesions occurring in DNA. In this thesis, mechanisms for the deamination reaction of adenine with OH⁻/nH₂O, (n = 0, 1, 2, 3) and 3H₂O were investigated. HF/6-31G(d), B3LYP/6-31G(d), MP2/6-31G(d), and B3LYP/6-31+G(d) levels of theory were employed to optimize all the geometries. Energies were calculated at the G3MP2B3 and CBS-QB3 levels of theory. The effect of solvent (water) was computed using the polarizable continuum model (PCM). Intrinsic reaction coordinate (IRC) calculations were performed for all transition states. Five pathways were investigated for the deamination reaction of adenine with OH⁻/nH₂O and 3H₂O. The first four pathways (A-D) begin with by deprotonation at the amino group of adenine by OH⁻, while pathway E is initiated by tautomerization of adenine. For all pathways, the next two steps involve the formation of a tetrahedral intermediate followed by dissociation to yield products via a 1,3-hydrogen shift. Deamination with a single OH⁻ has a high activation barrier (190 kJ mol⁻¹ using G3MP2B3 level) for the rate-determining step. Addition of one water molecule reduces this barrier by 68 kJ mol⁻¹ calculated at G3MP2B3 level. Adding more water molecules decreases the overall activation energy of the reaction, but the effect becomes smaller with each additional water molecule. The most plausible mechanism is pathway E, the deamination reaction of adenine with 3H₂O, which has an overall G3MP2B3 activation energy of 139 and 137 kJ mol⁻¹ in the gas phase and PCM, respectively. This barrier is lower than that for the deamination with OH⁻/3H₂O by 6 and 2 kJ mol⁻¹ in the gas phase and PCM, respectively.

Empirical likelihood based longitudinal studies

In longitudinal data analysis, our primary interest is in the regression parameters for the marginal expectations of the longitudinal responses; the longitudinal correlation parameters are of secondary interest. The joint likelihood function for longitudinal data is challenging, particularly for correlated discrete outcome data. Marginal modeling approaches such as generalized estimating equations (GEEs) have received much attention in the context of longitudinal regression. These methods are based on the estimates of the first two moments of the data and the working correlation structure. The confidence regions and hypothesis tests are based on the asymptotic normality. The methods are sensitive to misspecification of the variance function and the working correlation structure. Because of such misspecifications, the estimates can be inefficient and inconsistent, and inference may give incorrect results. To overcome this problem, we propose an empirical likelihood (EL) procedure based on a set of estimating equations for the parameter of interest and discuss its characteristics and asymptotic properties. We also provide an algorithm based on EL principles for the estimation of the regression parameters and the construction of a confidence region for the parameter of interest. We extend our approach to variable selection for highdimensional longitudinal data with many covariates. In this situation it is necessary to identify a submodel that adequately represents the data. Including redundant variables may impact the model’s accuracy and efficiency for inference. We propose a penalized empirical likelihood (PEL) variable selection based on GEEs; the variable selection and the estimation of the coefficients are carried out simultaneously. We discuss its characteristics and asymptotic properties, and present an algorithm for optimizing PEL. Simulation studies show that when the model assumptions are correct, our method performs as well as existing methods, and when the model is misspecified, it has clear advantages. We have applied the method to two case examples.