Interest rates and foreign exchange spots are widely used within financial products. It is important to understand the risk arising from products that depend on interest rates and/or foreign exchange spots. In this project, the Hull-white model, a non-parametric and a semi-parametric bootstrap will be investigated for simulations of the interest rate of USD, EUR and SEK in conjunction with its corresponding foreign exchange spot. Models were first studied for dollar interest rates and the best model was selected by using variance/autocovariance tests and quantile tests. The chosen model was then used in the simulation of the interest rate in conjunction with the foreign exchange spots. The result from the tests demonstrated that the non-parametric bootstrap model performed the best and was used to simulate the interest rate in conjunction with the foreign exchange spots. The multiple simulations were used to back test a synthetic portfolio using a quantile test. The simulated distribution was found to be acceptable which therefore simulates an acceptable risk. We used data up until 2015 for the tests, this for not including the federal reserve raising the interest rate in the later part of 2015. Avoiding changes in the Fed funds rate was necessary as they are not predictable from sampling from historical data as is done in the model but they do have a very large impact on the shorter end of the curve. The findings in this project suggests that the non-parametric bootstrap model could be used in multiple curve simulations, which could be used for calculations of potential future risk for financial products. This is very important for companies involved with financial products, since strict rules and regulations have to be followed regarding risks within these products.

Detection and analysis of protein-ligand binding sites is an important area of research in drug discovery. The FTMap web server is an established computational method for detection of binding hot spots, or regions on the protein surface that contribute disproportionately to the ligand binding free energy. This body of work primarily focuses on the utilization and advancement of FTMap for the study of protein-ligand interactions and their applications to drug discovery. First, the driving forces behind why some proteins require compounds beyond Lipinski’s rule-of-five (bRo5) guidelines are evaluated for 37 protein targets. Three types of proteins are identified on the basis of their binding hot spots, described by FTMap, and their ligand binding affinity profiles. We describe the multifaceted motivations for bRo5 drug discovery for each group of targets, including increased binding affinity, improved selectivity, decreased toxicity, and decreased off-target effects. Second, the conservation of surface binding properties in protein models is evaluated, with particular emphasis on their utility in drug discovery. Here, the probe-binding locations determined by FTMap are used to generate a binding fingerprint, and the Pearson correlation between the binding fingerprint of an experimental structure and a predicted model indicates the level of surface property conservation, without any knowledge of the protein function a priori. This analysis was performed on the protein models submitted to the Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 12 and 14, and results were correlated with well-established structure quality metrics. Third, development of the publicly-available FTMove web server (https://ftmove.bu.edu) is described for detection of binding sites and their respective strengths across multiple different conformations of a protein. FTMove was tested on 22 proteins with known allosteric binding sites, and reliably identified both the orthosteric and allosteric binding sites as highly ranked binding sites. The results yield important insight into the dynamics and druggability of such binding sites. Finally, high throughput affinity purified, mass spectrometry data is evaluated for identification of protein-metabolite interactions (PMIs) in Escherichia coli. A detailed search for known PMIs in both the Protein Data Bank and KEGG database is described, and the resulting curated sets of 21 recovered and 37 potentially novel PMIs in E. Coli are presented. Finally, high confidence novel PMIs were evaluated with the template-based small molecule docking program, LigTBM. / 2023-01-26T00:00:00Z

The Utah Department of Transportation (UDOT) Traffic and Safety Division continues to advance the safety of roadway sections throughout the state. In an effort to aid UDOT in meeting their goal, the Department of Civil and Environmental Engineering at Brigham Young University (BYU) has worked with the Statistics Department in developing analysis tools for safety. The most recent of these tools has been the development of a hierarchical Bayesian Poisson Mixture Model (PMM) of traffic crashes known as the Utah Crash Prediction Model (UCPM), a hierarchical Bayesian Binomial statistical model known as the Utah Crash Severity Model (UCSM), and a Bayesian Horseshoe selection method. The UCPM and UCSM models helped with the analysis of safety on UDOT roadways statewide and the integration of the results of these models was applied to Geographic Information System (GIS) framework. This research focuses on the addition of roadway attributes in the selection and analysis of “hot spots.” This is in conjunction with the framework for highway safety mitigation migration in Utah with its six primary steps: network screening, diagnosis, countermeasure selection, economic appraisal, project prioritization, and effectiveness evaluation. The addition of roadway attributes was included as part of the network screening, diagnosis, and countermeasure selection, which are included in the methodology titled “Hot Spot Identification and Analysis.” Included in this research was the documentation of the steps and process for data preparation and model use for the step of network screening and the creation of one of the report forms for the steps of diagnosis and countermeasure selection. The addition of roadway attributes is required at numerous points in the process. Methods were developed to locate and evaluate the usefulness of available data. Procedures and systemization were created to convert raw data into new roadway attributes, such as grade and sag/crest curve location. For the roadway attributes to be useful in selection and analysis, methods were developed to combine and associate the attributes to crashes on problem segments and problem spots. The methodology for “Hot Spot Identification and Analysis” was enhanced to include steps for the inclusion and defining of the roadway attributes. These methods and procedures were used to help in the identification of safety hot spots so that they can be analyzed and countermeasures selected. Examples of how the methods are to function are given with sites from Utah’s state roadway network.

Studies about hot spots of crimes have found that crimes are clustered; few places have many crimes. There is a consensus among criminologists that opportunities for crimes are important when explaining hot spots, at some places, there are more opportunities than at other places. The same applies for hot spots of robberies. Most studies done on the subject are quantitative, relatively little is done using a qualitative approach. Furthermore, little research is done in a Swedish or Scandinavian context. To fill these research gaps this study use participant observations to research five hot spots of robberies in Malmö. The research will try to answer which characteristics are important to explain why the places are hot spots and what the similarities and differences there between the places are. This will be analyzed using the Routine Activity Theory and the Crime Pattern Theory. The findings suggest that place-specific things are important to explain why the places are hot spots, but when using the theories several places are similar.

Protein-protein interactions (PPI) represent a challenging target class in contemporary small molecule drug discovery. The difficulty arises because PPI sites are structurally and physicochemically different from conventional drug binding sites. Moreover, we currently lack a good understanding of the druggability of PPI targets: that is, how the structure and properties of a PPI interface site relates to the properties of small molecules that can bind to that site with high affinity. Efforts to achieve potent drug-like small molecule inhibitors of PPI interfaces, involving a wide range targets, historically have largely been unsuccessful, leading to the conclusion that new inhibitor chemotypes are needed to inhibit this class of target. In this thesis, I describe the application of two approaches to identify inhibitors of the PPI interface between Kelch-like ECH associated protein 1 (KEAP1) and Nuclear factor (erythroid-derived 2)-like 2 (Nrf2): (i) screening a library of synthetic macrocycles, and (ii) fragment-based lead discovery. I validate and characterize the hit compounds obtained. In the case of the fragment hits, I investigate what features of the compounds are required for binding to the target (Chapter Two). In parallel, I investigate the structure of the hot spot ensemble at the KEAP1/Nrf2 binding interface using three complementary methods: alanine scanning mutagenesis, fragment screening, and in silico probe mapping using the FTMap algorithm (Chapter Three). This analysis brings insight into the druggability of KEAP1, and advances our understanding of the utility and limitations of those three widely used methods for characterizing the hot spot ensembles at PPI interfaces (Chapter Three). Finally, to gain additional insight into the energetics of KEAP1/Nrf2 binding, I probe the additivity of combinations of alanine mutants (Chapter Four). I use the results to propose a quantitative approach to categorizing the various degrees of additivity that can be observed at PPI interfaces, and discuss the possible structural basis for these behaviors. The model potentially provides a more general framework for understanding the binding energetics at PPI interfaces using combinations of mutations.

<p>This dissertation, while sometimes broad in scope, attempts to tie together the author’s work with the goal of better understanding the phenomenon of explosives initiation at the mesoscale. Discussion of the need for such an investigation will be covered, including how mesoscale phenomena and the dominant theory of explosives initiation–hot spot theory–are intimately related. Furthermore, some difficulties in designing mesoscale experiments will be mentioned. Sample preparation–one of the more difficult tasks in this regime–will be covered, including single crystal growth, mechanical machining with a quasi-CNC machine, laser machining, and hacks to a tungsten wire saw for novel sample production. The author will go on to show work done in a quasi-static regime, at low strain rates, and at medium- high strain rates. These novel experiments start to show how pore collapse functions in single-crystal HMX. Additional work with thermophosphors, which may be relevant to future experiments, is also presented. New experimental diagnostics designed and constructed by the author are laid out for future reference, along with improvements to a gas gun apparatus.</p>

No description available.

Master of Science

High total organic carbon (TOC) and low clay content are two criteria to identify the "sweet spots" in shale gas plays. Recently, machine learning has been proved to be effective to estimate TOC and clay from well loggings. The remaining questions are what algorithm we should choose in the first place and whether we can improve the already built models. Automatic machine learning (AutoML) appears as a promising tool to solve those realistic questions by training multiple models and compares them automatically. Two wells with conventional well loggings and elemental capture spectroscopy are selected from a shale gas play to test the AutoML's ability in TOC and clay estimation. TOC and clay content are extracted from the Schlumberger's ELAN interpretation and calibrated to cores. Generalizability is proved in the blind test well and the mean absolute test errors for TOC and clay estimation are 0.23% and 3.77%. 829 data points are used to generate the final models with the train-test ratio of 75:25. The mean absolute test errors are 0.26% and 2.68% for TOC and clay, respectively, which are very low for TOC ranging from 0-6% and clay from 35-65%. The results show the AutoML's success and efficiency in the estimation. The trained models are interpreted to understand the variables effects in predictions. 235 wells are selected through data quality checking and feed into the models to create TOC and clay distribution maps. The maps provide guidance on where to drill a new well for higher shale gas production. / Master of Science / Locating "sweet spots", where the shale gas production is much higher than the average areas, is critical for a shale reservoir's successful commercial exploitation. Among the properties of shale, total organic carbon (TOC) and clay content are often selected to evaluate the gas production potential. For TOC and clay estimation, multiple machine learning models have been tested in recent studies and are proved successful. The questions are what algorithm to choose for a specific task and whether the already built models can be improved. Automatic machine learning (AutoML) has the potential to solve the problems by automatically training multiple models and comparing them to achieve the best performance. In our study, AutoML is tested to estimate TOC and clay using data from two gas wells in a shale gas field. First, one well is treated as blind test well and the other is used as trained well to examine the generalizability. The mean absolute errors for TOC and clay content are 0.23% and 3.77%, indicating reliable generalization. Final models are built using 829 data points which are split into train-test sets with the ratio of 75:25. The mean absolute test errors are 0.26% and 2.68% for TOC and clay, respectively, which are very low for TOC ranging from 0-6% and clay from 35-65%. Moreover, AutoML requires very limited human efforts and liberate researchers or engineers from tedious parameter-tuning process that is the critical part of machine learning. Trained models are interpreted to understand the mechanism behind the models. Distribution maps of TOC and clay are created by selecting 235 gas wells that pass the data quality checking, feeding them into trained models, and interpolating. The maps provide guidance on where to drill a new well for higher shale gas production.

No / Skin pigmentation is a multistep process of melanin synthesis by melanocytes, its transfer to recipient keratinocytes and its degradation. As dyspigmentation is a prominent marker of skin ageing, novel effective agents that modulate pigmentation safely are being sought for both clinical and cosmetic use. Here, a number of plant extracts were examined for their effect on melanogenesis (by melanin assay and Western blotting) and melanin transfer (by confocal immunomicroscopy of gp100-positive melanin granules in cocultures and by SEM analysis of filopodia), in human melanocytes and in cocultures with phototype-matched normal adult epidermal keratinocytes. Mulberry, Kiwi and Sophora extracts were assessed against isobutylmethylxanthine, hydroquinone, vitamin C and niacinamide. Compared with unstimulated control, all extracts significantly reduced melanogenesis in human melanoma cells and normal adult epidermal melanocytes. These extracts also reduced melanin transfer and reduced filopodia expression on melanocytes, similar to hydroquinone and niacinamide, indicating their effectiveness as multimode pigmentation actives.