Cancer clusters constitute geographical areas where the frequency of cancer diagnoses during a given period of time occur more frequently than expected by chance. Cancer clusters can impact perceptions of risk and generate significant anxiety in communities. Unfortunately, cluster investigations rarely yield the answers citizens seek around a definitive cause of cancer due to the long latency of cancer and other factors. As a result, health officials may appear to be withholding information and not doing enough to address public concerns. Effective cancer risk communication may also be hindered by other stakeholders such as the media, who sometimes sensationalize risks from environmental hazards, which can distort the public’s perceptions of risks. The result may be a community dissatisfied with a cluster investigation’s results, or worse, a community that distrusts local leaders and doesn’t understand the information reported by expert officials. The four studies comprising this dissertation aimed to summarize key issues with the communication of and investigation of cancer clusters in Canada; test the impact of different types of cancer information on risk perceptions; and explore whether individual characteristics and skills were linked to positive attitudes about coping with cancer risks. An analysis of cancer news coverage and interviews with Canadian public health officials revealed that communities may be receiving inadequate and inconsistent information about cancer risks during cluster investigations. In addition, an experiment and survey revealed information trustworthiness and individuals’ numeracy and health literacy to be important factors shaping cancer risk perceptions and attitudes. This work has significant implications for risk communicators and educators seeking improved methodologies of cancer risk communication and risk education to (1) manage differences in cancer risk perceptions between experts and non-experts (2) enhance public trust in institutions and perceptions of expert competence and (3) inform future educational interventions that promote cancer coping beliefs. / Dissertation / Doctor of Science (PhD) / Cancer clusters constitute geographical areas where the frequency of cancer diagnoses during a given period of time occur more frequently than expected by chance. Cancer clusters can generate significant anxiety in communities and influence perceptions of personal safety and health. As cluster investigations rarely yield the answers citizens seek around a definitive cause of cancer, health officials may appear to be withholding information and not doing enough to address public concerns. The objectives of this dissertation were to summarize key issues with the communication of and investigation of cancer clusters in Canada; test the impact of different types of cancer information on risk perceptions; and explore whether individual characteristics, expertise and skills were linked to positive attitudes about coping with cancer risks. This work provides insights into the diverse ways that people interpret cancer information and perceive risk and identifies improved methods of conducting cancer risk communication and risk education.

This study investigated the relationship between cluster B personality disorder characteristics and neuropsychological test performance. Specifically, the study examined whether neuropsychological test patterns could be used to support personality disorder diagnoses. The participants were 109 university students who were given a neuropsychological test battery and personality assessment. When controlling for Axis I psychopathology, no relationship was found between neuropsychological performance and borderline characteristics. However, narcissistic characteristics were associated with poorer working memory, divided attention, and verbal associative memory, and greater cognitive flexibility. Histrionic characteristics were associated with better list learning and selective auditory attention. This study suggests that some cluster B characteristics are associated with neuropsychological performance, but additional research is needed to clarify the nature of this relationship.

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Quantum chemical methods to predict experimental chiroptical properties by solving the time-dependent Schrödinger equation are useful in the assignment of absolute configurations. Chiroptical properties, being very sensitive to the electronic structure of the system, require highly-accurate methods on the one hand and on the other, need to be able to be computed with limited computational resources. The calculation of the optical rotation in the solution phase is complicated by solvent effects. In order to capture those solvent effects, we present a study that uses conformational averaging and time-dependent density functional theory calculations that incorporate solvent molecules explicitly in the quantum mechanical region. While considering several controllable parameters along which the system's optical rotation varies, we find that the sampling of the dynamical trajectory and the density functional chosen have the largest impact on the value of the rotation. In order to eliminate the arbitrariness of the choice of density functional, we would prefer to use coupled cluster theory, a robust and systematically improvable method. However, the high-order polynomial scaling of coupled cluster theory makes it intractable for numerous large calculations, including the conformational averaging required for optical rotation calculations in solution. We therefore attempt to reduce the scaling of a linear response coupled cluster singles and doubles (LR-CCSD) calculation via a perturbed pair natural orbital (PNO++) local correlation approach which uses an orbital space created using a perturbed density matrix. We find that by creating a "combined PNO++" space, incorporating a set of orbitals from the unperturbed pair natural orbital (PNO) space into the PNO++ space, we can obtain well-behaved convergence behavior for both CCSD correlation energies and linear response properties, including dynamic polarizabilities and optical rotations, for the small systems considered. The PNO++ and combined PNO++ methods require aggressive truncation to keep the computational cost low, due to an expensive two-electron integral transformation at the beginning of the calculation. We apply the methods to larger systems than previously studied and refine them for more aggressive truncation by exploring an alternative form of the perturbed density and a perturbation-including weak pair approximation. / Doctor of Philosophy / Theoretical chemistry attempts to provide connections between the structure of molecules and their observable properties. One such family of observables are chiroptical properties, or the effect of the medium on the light which passes through it. These properties include the scattering, absorption and change in polarization of light. Light being classically an electromagnetic field, chiroptical properties can be derived by treating molecules quantum mechanically and the light classically. The prediction of chiroptical properties on computers using the principles of quantum mechanics is still a growing field, being very sensitive to the method used, and requiring considerations of factors such as conformations and anharmonic corrections. Matching experimental properties is an important step in the creation of a reliable method of predicting properties of systems in order to provide more information than can be obtained through experimental observation. This work begins by addressing the problem of matching experimentally obtained quantities. Our results show that current time-intensive methods still fall short in the matching of experimental data. Thus, we then move on to approximating a more robust but computationally expensive method in order to be able to use a more accurate method on a larger scale than is currently possible. On obtaining positive results for small test systems, we test the new method on larger systems, and explore possible improvements to its accuracy and efficiency.

Single atom catalysis is a relatively new form of heterogeneous catalysis. While single atom catalysts probably are already used in a lot of catalysis, their identification and characterization has only recently become common place. As we now have the ability to synthesis relatively pure systems consisting of single atoms and then to characterize them, there are many interesting questions that we can answer about them. In this work we will use a combination of several different types of characterizations such as kinetic measurements, diffuse reflectance infrared Fourier transform spectroscopy, extended x-ray absorption fine structure, and many more to better understand how single atoms react and how we can attempt to make such systems more active. The work here is primarily based around Ir single atoms and/or small clusters on three different supports MgAl2O4, TiO2, and CeO2. In each of these cases we attempt to understand how the Ir and the support catalytically oxidize CO into CO2 through a kinetic, and if possible, mechanistic study. Through these mechanistic studies we attempt to isolate the most important parameters of the catalyst so that we can create a more active catalyst. There are, of course, many different ways that we can use this information. The most obvious is by changing the catalyst support, but as the breadth of the research presented here will show, we can also optimize catalytic activity through using mixtures of single atoms with larger species as well as by changing the nuclearity of the said species, i.e., we can increase activity by controlling the size of the catalysts. However, in order to be able to control the activity in this way, we must 1) know how the size affects the activity and 2) know how the reaction conditions affect the size, i.e., we must establish the catalyst size is stable during reaction. Each of these topics are discussed to some extent here. Additionally, we also discuss how different sites of single atoms on the same support might differ and we show that we can create such different sites. On the whole, we have studied single atom and small cluster catalysis in many different directions based on systems of Ir for CO oxidation. This work is also performed with the intent to compare these Ir systems to similar systems of Rh, Pt, Pd, etc. However here we will only discuss the Ir pieces. / Doctor of Philosophy / In this work we study various properties of Ir single atom and subnanometer cluster catalysts for CO oxidation in hopes that we might be able to design a better catalyst with this information. A catalyst is a substance that facilitates a chemical reaction but is not consumed. For this work we will be considering the reaction of carbon monoxide (CO), which is a common pollutant and highly toxic gas, with O2 to create CO2, a much less dangerous pollutant. Our catalyst thus makes this reaction happen much faster and thus allows us to remove CO from exhaust streams, such as car exhaust, better. A single atom catalyst is a catalyst that is primarily a single atom on a metal oxide support. A subnanometer cluster catalyst is thus a catalyst that is smaller than one nanometer (0.00000004 inches). These are typically 10-20 atoms grouped together. This size is interesting as it is bigger than a single atom, but it is still much smaller than a classical catalyst nanoparticle and is thus controlled or dominated by different properties. In this work we will look at how different characteristics of the singe atom and cluster catalysts affect how good of a catalyst it is. The first is how the amount of single atoms and nanoparticles affect the overall activity of the catalyst. This study will tell us what the best mixture of single atoms is. The second study is how small clusters of Ir/MgAl2O4 react differently than single atoms and large nanoparticles. This tells us what the best size for Ir/MgAl2O4 catalysts are. The third study tells us how Ir/TiO2 single atom catalysts react which is useful when compared to Ir/MgAl2O4 and Ir/CeO2 (Chapter 7). The combination of single atom studies then allows us to make predictions on which supports (apart from Ir/MgAl2O4, Ir/TiO2, and Ir/CeO2) will be the best for CO oxidation. The fourth study compares different single atoms (all of Ir/TiO2) and shows how they behave differently, this is another possibility to increase the effectiveness of the catalyst. The fifth study discusses how different conditions affect the size of the Ir/TiO2 catalysts. Specifically, whether they exist as single atoms, subnanometer clusters, or larger clusters. All of these different studies represent another way that we can potentially increase catalytic activity and hopefully will allow our group, or another group to create even more active catalysts.

Forest measurements are inherently spatial. Soil productivity varies spatially at fine scales and tree growth responds by changes in growth-age trajectories. Measuring spatial variability is a perquisite to more effective analysis and statistical testing. In this study, current techniques of partial redundancy analysis and constrained cluster analysis are used to explore how spatial variables determine structure in a managed regular spaced plantation. We will test for spatial relationships in the data and then explore how those spatial relationships are manifested into spatially recognizable structures. The objectives of this research are to measure, test, and map spatial variability in simulated forest plots. Partial redundancy analysis was found to be a good method for detecting the presence or absence of spatial relationships (~95% accuracy). We found that the Calinski-Harabasz method consistently performed better at detecting the correct number of clusters when compared to several other methods. While there is still more work that can be done we believe that constrained cluster analysis has promising applications in forestry and that the Calinski-Harabasz criterion will be most useful. / Master of Science

One of the central problems limiting the application of accurate {em ab initio} methods to large molecular systems is their high computational costs, i.e., their computing and storage requirements exhibit polynomial scaling with the size of the system. For example, the coupled cluster singles and doubles method with the perturbative inclusion of triples: the CCSD(T) model, which is considered to be the ``gold standard'' of quantum chemistry scales as 𝑂(N⁷) in its canonical formulation, where $N$ is a measure of the system size. However, the steep scaling associated with these methods is unphysical since the property of dynamic electron correlation or dispersion (for insulators) is local in nature and decays as R⁻⁶ power of distance. Different reduced-scaling techniques which attempt to exploit this inherent sparsity in the wavefunction have been used in conjunction with the coupled cluster theory to calculate ground-state properties of molecular systems with hundreds of heavy atoms in reasonable computational time. However, efforts towards extension of these methods for describing response properties like polarizabilities, optical rotations, etc., which are related to the derivative of the wavefunction with respect to external electric or/and magnetic fields, have been fairly limited and conventional reduced-scaling algorithms have been shown to yield large and often erratic deviations from the full canonical results. Accurate simulation of response properties like optical rotation is highly desirable as it can help the experimental chemists in understanding the structure-activity relationship of different chiral drug candidates. In this work, we identify the reasons behind the unsatisfactory performance of the pair natural orbital (PNO) based reduced-scaling approach for calculating linear response properties at the coupled cluster level of theory and propose novel modifications, which we refer to as PNO++, (A. Kumar and T. D. Crawford. Perturbed Pair Natural Orbitals for Coupled-Cluster Linear-Response Theory. 2018, {em manuscript in preparation}) that can provide the necessary accuracy at significantly lower computational costs. The motivation behind the PNO++ approach came from our works on the (frozen) virtual natural orbitals (FVNO), which can be seen as a precursor to the concept of PNOs (A. Kumar and T. D. Crawford. Frozen Virtual Natural Orbitals for Coupled-Cluster Linear-Response Theory. {em J. Phys. Chem. A}, 2017, 121(3), pp 708 716) and the improved FVNO++ method (A. Kumar and T. D. Crawford. Perturbed Natural Orbitals for Coupled-Cluster Linear-Response Theory. 2018, {em manuscript in preparation}). The essence of these modified schemes (FVNO++ and PNO++) lie in finding suitable field perturbed one-electron densities to construct ``perturbation aware" virtual spaces which, by construction, are much more compact for describing response properties, making them ideal for applications on large molecular systems. / Ph. D.

With the great promise that metagenomics holds in exploring virome composition and discovering novel virus species, there is a pressing demand for comprehensive and up-to-date reference databases to enhance the downstream bioinformatics analysis. In this study, a RNA virus reference database (RVRD) was developed by manual and computational curation of RNA virus genomes downloaded from the three major virus sequence databases including NCBI, ViralZone, and ViPR. To reduce viral sequence redundancy caused by multiple identical or nearly identical sequences, sequences were first clustered and all sequences except one in a cluster that have more than 98% identity to one another were removed. Other identity cutoffs were also examined, and Hepatitis C virus genomes were studied in detail as an example. Using the 98% identity cutoff, sequences obtained from ViPR were combined with the unique RNA virus references from NCBI and ViralZone to generate the final RVRD. The resulting RVRD contained 23,085 sequences, nearly 5 times the size of NCBI RNA virus reference, and had a broad coverage of RNA virus families, with significant expansion on circular ssRNA virus and pathogenic virus families. Compared to NCBI RNA virus reference in performance evaluation, using RVRD as reference database identified more RNA virus species in RNAseq data derived from wastewater samples. Moreover, using RVRD as reference database also led to the discovery of porcine rotavirus as the etiology of unexplained diarrhea observed in pigs. RVRD is publicly available for enhancing RNA virus metagenomics. / Master of Science / Next-generation sequencing technology has demonstrated capability for the detection of viruses in various samples, but one challenge in bioinformatics analysis is the lack of well-curated reference databases, especially for RNA viruses. In this study, a RNA virus reference database (RVRD) was developed by manual and computational curation from the three commonly used resources: NCBI, ViralZone, and ViPR. While RVRD was managed to be comprehensive with broad coverage of RNA virus families, clustering was performed to reduce redundant sequences. The performance of RVRD was compared with NCBI RNA virus reference database using the pipeline FastViromeExplorer developed by our lab recently, the results showed that more RNA viruses were identified in several metagenomic datasets using RVRD, indicating improved performance in practice.