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Cricetidae ranks as the second-most species-rich and abundant mammalian family, with limited studies on eastern North American records prior to the Pleistocene. While cricetids has been previously noted at the early Pliocene Gray Fossil Site (GFS), this study provides a detailed description of eight taxa: Postcopemys (two species), Symmetrodontomys, Oryzomyini, Peromyscus, Neotoma, Neotomodon, and Xenomys. Postcopemys is the most common cricetid taxon at GFS, followed by Peromyscus and Neotoma. These records expand the stratigraphic and geographic range of multiple genera. Distinctive morphological features of GFS taxa suggest presence of several new species. The GFS cricetid assemblage exhibits diverse body sizes and dietary preferences, setting GFS apart from other contemporaneous sites and emphasizing its spatial and temporal uniqueness. The Appalachian region represents a biodiversity hotspot today, and GFS was likely an important habitat for cricetid evolution during the Pliocene.

AroA catalyzes the sixth step of the shikimate biosynthetic pathway which produces aromatiG amino acids in plants and bacteria, but is absent in mammals. This makes AroA an attractive antimicrobial target. The transition state (TS) structures of AroA- and acid-catalyzed 5-eno/pyruvyl shikimate-3-phosphate (EPSP) hydrolysis were studied in atomic detail by kinetic isotope effect (KIE) measurement. Enzymes bind their transition states more tightly than any other species, so molecules that closely resemble the transition state would have a high affinity for the enzyme and be good inhibitors. Radiolabelled EPSPs were synthesized and a KIE measurement method was developed. Six KIEs were measured for both the AroA- and acid-catalyzed reactions. KIEs for the AroA reaction indicate a cationic TS structure. The acid-catalyzed reaction may employ a slightly different mechanism with an earlier TS. A computational TS model was found and its KIEs were calculated. It demonstrated good agreement with the experimental values at most positions. The model is being modified to improve the agreement with the experimental KIEs. This TS structure will be a good starting point for inhibitor design. All these efforts, hopefully, can make a positive contribution to the development of antimicrobial drugs. / Thesis / Master of Science (MSc)

The main objective of this study is to explore the complex fluid flow phenomena that result in the generation of a high frequency noise in counterbalance valves through an experimental and numerical investigation of the flow. Once the influence of the different components involved in noise generation is established, a secondary objective is the introduction of design modifications that eliminate the undesired effect without altering the operation envelope or the performance of the valve. A hydraulic test bench was used to carry out an experimental investigation of the noise generation process. A computer based data acquisition system was used to record pressure fluctuations, flowrates and hydraulic oil temperatures in a production valve under a variety of operational conditions. Extensive experimental measurements and numerical modeling lead to the hypothesis that noise generation is the result of an acoustic resonance triggered by shear layer instability at the valve inlet. The pressure gradients developed when the shear layer entrains the stagnant fluid in the valve main cavity cause the layer to become unstable and oscillate. The oscillation frequency will depend on a great number of factors such as valve geometry, pressure and velocity gradients and the density and viscosity of the fluid. It is postulated that the observed noise is generated when this frequency matches one of the resonant frequencies of the valve cavity. The proposed mechanism is theoretically poorly understood and well beyond simplified analysis, its accurate numerical simulation is computational very intensive requiring sophisticated CFD codes. The numerical investigation was carried out using STAR–CCM+, a commercially available CFD code featuring 3-D capabilities and sophisticated turbulence modeling. Streamline, pressure, velocity-vector and velocity-scalar plots were obtained for several valve configurations using steady and unsteady state flow simulations. An experimental and numerical analysis of an alternative valve geometry was carried out. Experimental results demonstrated a greatly reduced instability range. The numerical analysis of the unsteady behavior of the shear-layer streamlines for both valves yielded results that were compatible with the experimental work. The results of this investigation promise a great positive impact on the design of this type of hydraulic valves.

Quantum mechanics is basically a mathematical recipe on how to construct physical models. Historically its origin and main domain of application has been in the microscopic regime, although it strictly seen constitutes a general mathematical framework not limited to this regime. Since it is a statistical theory, the meaning and role of probabilities in it need to be defined and understood in order to gain an understanding of the predictions and validity of quantum mechanics. The interpretational problems of quantum mechanics are also connected with the interpretation of the concept of probability. In this thesis the use of probability theory as extended logic, in particular in the way it was presented by E. T. Jaynes, will be central. With this interpretation of probabilities they become a subjective notion, always dependent on one's state of knowledge or the context in which they are assigned, which has consequences on how things are to be viewed, understood and tackled in quantum mechanics. For instance, the statistical operator or density operator, is usually defined in terms of probabilities and therefore also needs to be updated when the probabilities are updated by acquisition of additional data. Furthermore, it is a context dependent notion, meaning, e.g., that two observers will in general assign different statistical operators to the same phenomenon, which is demonstrated in the papers of the thesis. It is also presented an alternative and conceptually clear approach to the problematic notion of "probabilities of probabilities", which is related to such things as probability distributions on statistical operators. In connection to this, we consider concrete numerical applications of Bayesian quantum state assignment methods to a three-level quantum system, where prior knowledge and various kinds of measurement data are encoded into a statistical operator, which can then be used for deriving probabilities of other measurements. The thesis also offers examples of an alternative quantum state assignment technique, using maximum entropy methods, which in some cases are compared with the Bayesian quantum state assignment methods. Finally, the interesting and important problem whether the statistical operator, or more generally quantum mechanics, gives a complete description of "objective physical reality" is considered. A related concern is here the possibility of finding a "local hidden-variable theory" underlying the quantum mechanical description. There have been attempts to prove that such a theory cannot be constructed, where the most well-known impossibility proof claiming to show this was given by J. S. Bell. In connection to this, the thesis presents an idea for an interpretation or alternative approach to quantum mechanics based on the concept of space-time. / QC 20100810

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20630号 / 工博第4368号 / 新制||工||1679(附属図書館) / 京都大学大学院工学研究科物質エネルギー化学専攻 / (主査)教授 安部 武志, 教授 阿部 竜, 教授 作花 哲夫 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Type 2 diabetes is a major health concern worldwide. One of its complications is postprandial hyperglycemia, i.e., high blood glucose concentrations, caused by glucose fast release from dietary polysaccharides into the bloodstream after meals. α-Glucosidase inhibitor drugs reduce postprandial hyperglycemia by inhibiting maltase/glucoamylase (MGAM) and sucrase/isomaltase (SI). MGAM and SI transform polysaccharides into absorbable monosaccharides, and inhibiting them delays monosaccharide release into the blood. The three commercially available α-glucosidase inhibitors are limited by their absorption abilities, inhibition efficacies, and side effects, which highlights the need for more specific α-glucosidase inhibitors. Because enzymes catalyze their reactions by tightly binding to their cognate transition states (TS), TS analogs can be powerful inhibitors and potential drugs. The measurement and interpretation of kinetic isotope effects (KIEs) is the only method that can directly determine TS structures on large molecules. In this work, methods to prepare radioisotopically labelled maltose were developed, as well as methods to measure KIEs on acid- and enzyme-catalyzed maltose hydrolysis. However, the methods developed did not achieve the required precision for TS analysis. Also, KIEs were calculated computationally for a model reaction of maltose hydrolysis. / Thesis / Master of Science (MSc)

One of the major factors in RF circuit design is the ability to predict the performance of these circuits in the presence of uncertainty in the key design parameters. This is referred to as uncertainty quantification in the mathematical literature. Uncertainty about the key design parameters arises mainly from the difficulty of controlling the physical or geometrical features of the underlying design, especially at the nanometer level. With the constant trend to scale down the process feature size, uncertainty quantification becomes crucial in shortening the design time. This thesis presents a new approach to statistically characterize the variability of the Harmonic Balance analysis and its application to Intermodulation distortion analysis in the presence of uncertainty in the design parameters. The new approach is based on the concept of Polynomial Chaos (PC) and Stochastic Galerkin (SG) methods. However, unlike the traditional PC, the proposed approach adopts a new mathematical formulation that decouples the Polynomial Chaos problem into several problems whose sizes are equal to the size of the original Harmonic Balance problem. The proposed algorithm produces significant CPU savings with equivalent accuracy to traditional Monte Carlo and standard PC approaches.

This thesis has two major goals; first to develop scalable scripts for steady-state analysis, then to perform a case study on the dynamic properties of a vertical pile. The scripts are based on the numerical library PETSc for parallel linear algebra. This opens up the opportunity to use the scripts to solve large-scale models on supercomputers. The performance of the scripts are verified against problems with analytical solutions and the commercial software ABAQUS. The case study compares the numerical results with those obtained from an approximate solution.   The results from this thesis are verified scripts that can find a steady-state solution for linear-elastic isotropic solids on supercomputers. The case study has shown differences between numerical and semi-analytical solutions for a vertical pile. The dynamic stiffness show differences within reasonable limits but the equivalent viscous damping show larger differences. This is believed to come from the material damping in the soil that has been excluded from the approximate solution.   These two results make it possible for further case studies on typical three-dimensional problems, that result in large-scale models, such as the dynamic properties of a slanted pile or pile-groups. The scripts can easily be expanded and used for other interesting research projects and this is the major outcome of from this thesis.

Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, mathematical modeling provides a quantitative framework for testing in silico the efficacy of new treatments and combination therapies, as well as understanding unknown biological mechanisms. In this dissertation we present two mathematical models of prostate cancer immunotherapy defined as systems of ordinary differential equations. The first work, introduced in Chapter 2, provides a mathematical model of prostate cancer immunotherapy which has been calibrated using data from pre-clinical experiments in mice. This model describes the evolution of prostate cancer, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, this model serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models. The Chapter 3 is devoted to the description of a human prostate cancer mathematical model. The potential effect of immunotherapies on castration-resistant form has been analyzed. In particular, the model includes the dendritic vaccine sipuleucel-T, the only currently available immunotherapy option for advanced prostate cancer, and the ipilimumab, a drug targeting the cytotoxic T-lymphocyte antigen 4 , exposed on the CTLs membrane, currently under Phase II clinical trial. From a mathematical analysis of a simplified model, it seems likely that, under continuous administration of ipilimumab, the system lies in a bistable situation where both the no-tumor equilibrium and the high-tumor equilibrium are attractive. The schedule of periodic treatments could then determine the outcome, and mathematical models could help in deciding an optimal schedule.