Exploration of the Potential Energy Surfaces, Prediction of Atmospheric Concentrations, and Prediction of Vibrational Spectra for the HO2···(H2O)n (n = 1−2) Hydrogen Bonded Complexes

The hydroperoxy radical (HO2) plays a critical role in Earth's atmospheric chemistry as a component of many important reactions. The self-reaction of hydroperoxy radicals in the gas phase is strongly affected by the presence of water vapor. In this work, we explore the potential energy surfaces of hydroperoxy radicals hydrogen bonded to one or two water molecules, and predict atmospheric concentrations and vibrational spectra of these complexes. We predict that when the HO2 concentration is on the order of 108molecules·cm-3 at 298 K, that the number of HO2···H2O complexes is on the order of 107molecules·cm-3 and the number of HO2···(H2O)2 complexes is on the order of 106molecules·cm-3. Using the computed abundance of HO2···H2O, we predict that, at 298 K, the bimolecular rate constant for HO2···H2O + HO2 is about 10 times that for HO2 + HO2.

Ability of the PM3 Quantum Mechanical Method to Model Intermolecular Hydrogen Bonding between Neutral Molecules

The PM3 semiempirical quantum-mechanical method was found to systematically describe intermolecular hydrogen bonding in small polar molecules. PM3 shows charge transfer from the donor to acceptor molecules on the order of 0.02-0.06 units of charge when strong hydrogen bonds are formed. The PM3 method is predictive; calculated hydrogen bond energies with an absolute magnitude greater than 2 kcal mol-' suggest that the global minimum is a hydrogen bonded complex; absolute energies less than 2 kcal mol-' imply that other van der Waals complexes are more stable. The geometries of the PM3 hydrogen bonded complexes agree with high-resolution spectroscopic observations, gas electron diffraction data, and high-level ab initio calculations. The main limitations in the PM3 method are the underestimation of hydrogen bond lengths by 0.1-0.2 for some systems and the underestimation of reliable experimental hydrogen bond energies by approximately 1-2 kcal mol-l. The PM3 method predicts that ammonia is a good hydrogen bond acceptor and a poor hydrogen donor when interacting with neutral molecules. Electronegativity differences between F, N, and 0 predict that donor strength follows the order F > 0 > N and acceptor strength follows the order N > 0 > F. In the calculations presented in this article, the PM3 method mirrors these electronegativity differences, predicting the F-H- - -N bond to be the strongest and the N-H- - -F bond the weakest. It appears that the PM3 Hamiltonian is able to model hydrogen bonding because of the reduction of two-center repulsive forces brought about by the parameterization of the Gaussian core-core interactions. The ability of the PM3 method to model intermolecular hydrogen bonding means reasonably accurate quantum-mechanical calculations can be applied to small biologic systems.

Parallel-recusive filter structures for the computation of discrete transforms

A general approach is presented for implementing discrete transforms as a set of first-order or second-order recursive digital filters. Clenshaw's recurrence formulae are used to formulate the second-order filters. The resulting structure is suitable for efficient implementation of discrete transforms in VLSI or FPGA circuits. The general approach is applied to the discrete Legendre transform as an illustration.

Subcellular Localization of the Non-Structural Proteins 3C and 3CD of the Honeybee Virus Deformed Wing Virus

Apis mellifera L., the European honeybee, is a crucial pollinator of many important agricultural crops in the United States. Recently, honeybee colonies have been affected by Colony Collapse Disorder (CCD), a disorder in which the colony fails due to the disappearance of a key functional group of worker bees. Though no direct causalrelationship has been confirmed, hives that experience CCD have been shown to have a high incidence of Deformed Wing Virus (DWV), a common honeybee virus. While the genome sequence and gene-order of DWV has been analyzed fairly recently, few other studies have been performed to understand the molecular characterization of the virus.Since little is known about where DWV proteins localize in infected host cells, the objective of this project was to determine the subcellular localization of two of the important non-structural proteins that are encoded in the DWV genome. This project focused on the protein 3C, an autocatalytic protease which cleaves itself from a longer polyprotein and helps to cut all of the other proteins apart from one another so that they can become functional, and 3D, the RNA-dependent RNA polymerase (RdRp) which is critical for replication of the virus because it copies the viral genome. By tagging nested constructs containing these two proteins and tracking where they localized in living cells, this study aimed to better understand the replication of DWV and to elicit possible targetsfor further research on how to control the virus. Since DWV is a picorna-like virus, distantly related to human viruses such as polio, and picornavirus non-structural proteins aggregate at cellular membranes during viral replication, the major hypothesis was that the 3C and 3CD proteins would localize at cellular organelle membranes as well. Using confocal microscopy, both proteins were found to localize in the cytoplasm, but the 3CDprotein was found to be mostly diffuse cytoplasmic, and the 3C protein was found to localize more specifically on membranous structures just outside of the nucleus.

Assessing the Validity of Brain Glucose Concentration Measurement Using Microdialysis

Brain functions, such as learning, orchestrating locomotion, memory recall, and processing information, all require glucose as a source of energy. During these functions, the glucose concentration decreases as the glucose is being consumed by brain cells. By measuring this drop in concentration, it is possible to determine which parts of the brain are used during specific functions and consequently, how much energy the brain requires to complete the function. One way to measure in vivo brain glucose levels is with a microdialysis probe. The drawback of this analytical procedure, as with many steadystate fluid flow systems, is that the probe fluid will not reach equilibrium with the brain fluid. Therefore, brain concentration is inferred by taking samples at multiple inlet glucose concentrations and finding a point of convergence. The goal of this thesis is to create a three-dimensional, time-dependent, finite element representation of the brainprobe system in COMSOL 4.2 that describes the diffusion and convection of glucose. Once validated with experimental results, this model can then be used to test parameters that experiments cannot access. When simulations were run using published values for physical constants (i.e. diffusivities, density and viscosity), the resulting glucose model concentrations were within the error of the experimental data. This verifies that the model is an accurate representation of the physical system. In addition to accurately describing the experimental brain-probe system, the model I created is able to show the validity of zero-net-flux for a given experiment. A useful discovery is that the slope of the zero-net-flux line is dependent on perfusate flow rate and diffusion coefficients, but it is independent of brain glucose concentrations. The model was simplified with the realization that the perfusate is at thermal equilibrium with the brain throughout the active region of the probe. This allowed for the assumption that all model parameters are temperature independent. The time to steady-state for the probe is approximately one minute. However, the signal degrades in the exit tubing due to Taylor dispersion, on the order of two minutes for two meters of tubing. Given an analytical instrument requiring a five μL aliquot, the smallest brain process measurable for this system is 13 minutes.