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Immunological and biochemical investigation of branched polyethylenimines to neutralize PAMP-induced inflammation, eradicate multidrug-resistant biofilms, and broaden antibiotic spectrum in acute and chronic wounds
Get PDFInnate immunity has considerable specificity and can discriminate between individual species of microbes. In this regard, pathogens are “seen” as dangerous to the host and elicit an inflammatory response capable of destroying the microbes. This immune discrimination is achieved through the recognition of microbe-specific molecules (e.g., lipopolysaccharide, lipoteichoic acid, and peptidoglycan) by toll-like receptors on host cells. Lipopolysaccharide (LPS), lipoteichoic acid (LTA), and peptidoglycan (PGN) arising from dangerous bacteria are known as Pathogen-Associated Molecular Pattern (PAMP) molecules. PAMPs impede wound healing by lengthening the inflammatory phase of healing and contributing to the development of chronic wounds. Preventing PAMPs from triggering the release of inflammatory cytokines will restore the optimal inflammatory response. However, successful drugs are elusive because PAMPs originate from many different species of Gram-negative and Gram-positive bacteria. Therefore, the need exists for a universal broad-spectrum therapeutic against LPS, LTA, and PGN bacterial PAMPs. We envision our discoveries as topical agents applied to acute and chronic wounds because, in addition to the active moiety of the agent preventing TNF-α cytokine release, it also disables antibiotic resistance mechanisms and disrupts the biofilm matrix. This versatility of this agent suggests that it may be an ideal therapeutic agent for use in the hundreds of millions of non-chronic skin or soft-tissue infections (SSTIs), and the 4.5 million chronic wound infections, that occur each year. This work is innovative because we fill the technological gap with multi-purpose agents that disable PAMPs, dissolve biofilms, and overcome antibiotic resistance mechanisms, making them superior to existing technology
Politeness AND Executing Inclusive Metadata with OK Libraries & Archives
Get PDFAs of 2018, Oklahoma ranked as the politest state in the country. This ranking speaks to a powerful intention. Often politeness and respect are defined as synonymous, but these words are not the same. During this humanity driven conversation, Suzette Chang, Founder/CEO of Thick Descriptions an organization that disrupts traditional educational methods with anthropology to help humans thrive where they are building stronger communities, attendees will learn and discuss how politeness can be a barrier to accurately and respectfully describing materials related to underrepresented communities. Participants will laugh/smile/giggle/reflect and strategically think about how to be polite AND offer inclusive metadata
Minutes of a Regular Meeting, The University of Oklahoma Board of Regents, January 19-20, 2023
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Projecting Future Locations for Commercial Wind Energy Development in the Conterminous United States using a Logistic Regression-Cellular Automata Model
Get PDFPressures to decarbonize the United States’ electricity production, reduce dependence on foreign energy imports, and the declining levelized cost of renewable electricity is making wind energy an increasingly appealing means of meeting electricity demand in the United States. However, the installation of new commercial wind farms to meet this demand requires knowledge of the most suitable locations for their installation, which depends on a combination of environmental, technical, economic, political, and social characteristics. Wind Farm Site Suitability (WiFSS) models are frequently enlisted to assist in this decision-making process in countries around the world for both onshore and offshore wind farm siting decisions. However, existing WiFSS models serve to assess present-day wind farm siting potential, rather than project specific locations for future wind energy development. Taking cues from Socio-Environmental Systems (SES) models of urban growth, this dissertation presents a Logistic Regression-Cellular Automata (LRCA) model, henceforth referred to as WiFSS-LRCA, conceived to produce maps that identify scenarios of potential future locations and timing of future commercial wind farms across the Conterminous United States (CONUS) between now and the year 2050.
Following a review of existing WiFSS modeling approaches, and of common practices by which WiFSS modeling studies select and represent their predictors, the niche that WiFSS-LRCA serves to fill was consequently identified. The majority of WiFSS studies take a Geographic Information Systems-based Multi-Criteria Decision Analysis (GIS-MCDA) approach that combines spatial data layers corresponding to selected predictors to construct a composite suitability surface. Other common approaches include Non-GIS-MCDA models that rank discrete potential wind farm sites to prioritize their order of development, Bayesian Network (BN) models that construct and convey probabilistic relationships between predictors, and Logistic Regression (LR) models that perform either spatial or non-spatial assessment of a wind farm’s suitability of presence based on the log-odds of a linear combination of predictors. The common limitation of these modeling approaches is their lack of a temporal component, meaning that they can assess WiFSS only at a single point in time. WiFSS-LRCA fills this niche by combining an LR equation with the decision rules of Cellular Automata (CA) to iteratively advance the computed probabilities of each grid cell, based on areas constrained from development and neighboring grid cells that already contain wind farms.
WiFSS-LRCA enlists a large set of predictors ranging from wind speed to legislation in effect in order for the model to represent the influence that environmental, technical, economic, political, and social predictors have on wind farm siting decisions. Data were aggregated at 20 different grid cell resolutions, collated in four different predictor configurations, and adjustments to the model’s constraint, neighborhood effect, and equation-based scenario transition rules were incorporated into the model’s construction, facilitating WiFSS-LRCA’s sensitivity and scenario analysis of model outputs by end-users. WiFSS-LRCA incorporates both calibration of its LR equation’s predictors and validation of the model’s performance to determine its ability to correctly identify the observed locations of present-day wind farms. Subsequently, the model constructs a WiFSS map whose interpretation and predictive accuracy are informed by the calibration and validation process. Construction of scenarios that modify WiFSS-LRCA’s predictors allow for the model to consider the impacts of changes in these predictors on the locations of future wind energy development (e.g., new transmission line construction, opinions of wind energy improving with time, increasing temperatures due to climate change).
The ability of WiFSS-LRCA to produce suitability surfaces with verifiable accuracy is greatest under the following conditions: when running the model over an individual U.S. state rather than the CONUS, when using a smaller grid cell size, when using a more complete (Full configuration) or more refined (Reduced configuration) set of predictors, and when the selected study area contains a larger number of present-day commercial wind farms. Across most study areas, however, WiFSS-LRCA is typically able to correctly identify 75-85% of grid cells that do and do not contain commercial wind farms, with these classifications most often associated with high wind speed, proximity to transmission lines, legislation that supports wind energy development, and large tracts of undeveloped land. CONUS-level model runs indicate five regions as being the most suitable for present wind energy development: Southern California, the Pacific Northwest, the Central Plains, the Great Lakes, and the Northeastern United States. CONUS-level model runs have a tendency to over(under)-estimate grid cell probabilities within (outside) the Central Plains and Great Lakes, which makes state-level model runs useful for revealing smaller-scale differences in the probabilities computed within these five broad regions.
Subsequent iterations of WiFSS-LRCA out to the year 2050 show projected wind energy development to remain concentrated within these same regions. Many of the grid cells initially classified as false positive in the model’s first iteration are those that gain wind farms in subsequent iterations, particularly false positive grid cells that were part of high-probability hotspots identified by Getis-Ord statistics. Running WiFSS-LRCA over states outside of these five regions projects wind energy development potential in low-probability areas (as shown in this dissertation for Florida and Kentucky) with projected wind farms in these states concentrated closer to existing infrastructure and away from protected natural areas. The Odds Ratios (ORs) computed during WiFSS-LRCA’s initial calibration provide geographical insight into its projections, with grid cells characterized by high wind speed, undeveloped land, and ambitious Renewable Portfolio Standards (RPS) being the most likely to gain wind farms in future decades. The model’s projections are, however, shown to be sensitive to end-user definitions of parameters, with neighborhood effect and constraint definitions greatly affecting the location and timing of projected wind farm locations. The scenario setup, by contrast, is shown to mostly influence the timing of these projections, with grid cell size moderately affecting both.
Multiple limitations exist in the application and interpretation of WiFSS-LRCA. Firstly, the lack of existing LRCA approaches to assessing wind farm siting potential meant few standards existed to guide this model’s development, such as the setting of default constraints and establishing cutoff statistics for refining the model’s enlisted predictors. Secondly, the use of an LR equation to construct suitability surfaces in the model’s first iteration means that both classes of the dependent variable must be filled, requiring a study area to contain at least two commercial wind farms, compromising the model’s reliability in runs over the Southeastern United States. Finally, the lack of spatial stratification during WiFSS-LRCA’s calibration and validation means that the model is trained to recognize predictors associated with wind energy development in regions where many wind farms exist, namely the Central Plains and Great Lakes, hence the greater number of Type 2 errors in CONUS-level model runs outside of these regions. Selecting stratified samples of grid cells that contain wind farms from different parts of the CONUS could be incorporated into WiFSS-LRCA to address this bias. Other directions for future work with WiFSS-LRCA include the following: optimization to assess offshore wind energy development potential by training the model with proposed offshore wind farm sites surrounding the CONUS; adapting WiFSS-LRCA to run over multiple states simultaneously to identify predictors that influence wind farm siting decisions at regional spatial scales; and performing projections of other types decentralized land-use change, such as solar energy development given similarities in the required model predictors
Three Essays on Energy Finance
No full textThe field of energy finance is currently confronted with new challenges in the ever-evolving global landscape, characterized by dynamic changes in fundamental factors and shifting economic conditions. Understanding the information that influences energy product prices continues to be a promising yet challenging aspect within financial research in the 2020s.
This dissertation addresses key challenges in energy finance studies in the current era. It examines the impact of price uncertainty on oil investments, focusing on decision-making under tail risks. Additionally, it investigates the information spread in natural gas markets, including the motivations and accuracy of forecasters. Furthermore, it explores the shift from pit trading to electronic trading in oil markets, analyzing price discovery in the US and Europe. The research aims to provide valuable insights for investors, decision-makers, and market participants in navigating the complexities of energy finance.
Chapter 1 emphasizes the significance of incorporating non-normality assumptions in analyzing real option investments. It assesses the impact of price change tail risks - skewness, kurtosis, and volatility on the likelihood of exercising real options by scrutinizing oil well operators' investment decisions. The results demonstrate a significant influence of these factors, revealing the substantial implications of tail risks on real investments: a one standard deviation increase in skewness escalates the closing probability by 13%, while a comparable rise in kurtosis augments the drilling probability by 11%. In contrast, volatility diminishes these probabilities, with reductions of 44% and 33% in closing and drilling respectively. These robust findings dismiss the theory of personal preferences as an explanatory factor for these impacts.
Chapter 2 undertakes an investigation into the relations between the decision-making attributes, herding and timing choices, and forecast accuracy in the realm of natural gas storage change forecasts. The empirical evidence reveals a prevalence of anti-herding, despite the negative association between anti-herding and accuracy. Forecasts issued later in each forecasting cycle demonstrate higher accuracy. Decision-making with respect to herding and timing contributes to approximately 50% of the cross-sectional variation in accuracy. We discern an enhancement in accuracy with a moderate anti-herding decline since 2013; however, these developments are contingent upon forecasters' average performance or timing choices. The results suggest that forecasters make trade-offs between forecast accuracy with signaling considerations and early dissemination advantages.
Chapter 3 studies the impact on price discovery when commodity futures move to electronic trading, using data from the West Texas Intermediate (WTI) and Brent oil markets. Electronic trading resulted in explosive increases in trading in both futures markets. After the shift, WTI futures became significantly more important for (indeed, the sole contributor to) price discovery but Brent futures became significantly less important. Our findings show that liquidity increases are not necessarily accompanied by price discovery improvements and suggest that the benefits of electronic trading need to be assessed from the standpoint of liquidity improvements as well as price discovery changes
Catalytic dehydration of poly (vinyl alcohol-co-ethylene) with heterogeneous acid catalysts
Get PDFThis study investigates the role of acid site density, pore size, and solvent selection in the
catalytic dehydration of Poly (vinyl alcohol-co-ethylene) (EVOH) using zeolite catalysts. The first
set of experiments reveals that catalysts with lower acid site densities and larger pores exhibit
higher maximum rates and better activity retention after deactivation, attributed to enhanced
mass transfer through additional available pores. Conversely, zeolites with higher acid site
densities demonstrate consistently higher maximum rates overall, but lower acid site density
zeolites outperform them when normalized, emphasizing the significance of larger pores and the
mass transfer limited nature of this reaction. External active sites are found to play a crucial role
in the polymer dehydration reaction, as their removal leads to reduced maximum rates and faster
catalyst deactivation.
The investigation of solvents shows that use of DMSO results in limited reactivity with
zeolite catalysts, potentially due to competition for acid sites or inhibition of certain transition
steps. In contrast, a water-propanol mixture proves effective in dissolving EVOH while reducing
the degree of competition compared to DMSO. Thus, this solvent offers better activity while also
enabling additional chemistry like alcohol-induced ether formation. However, the ratio of water
to 1-propanol significantly impacts catalyst performance, with a 12.5% water loading resulting in
optimum rates possibly due to a combination of water competing for the active sites and the
degree of solvation of the polymer.
However, challenges in analyzing results with NMR due to sampling inconsistencies make
inviable a detailed study of the underlying mechanism and rates for this reaction under solvated
environment. Cryomilling was used to improve random sampling, but heterogeneities in the final
polymer sample persisted, accentuating the difficulties of characterization. DSC and FTIR analyses
confirm these heterogeneities and highlight the need to find techniques that analyze
representative samples to achieve accurate measurement
“She takes rest as seriously as working:” How resilient professional caregivers think about and practice rest
Get PDFDo resilient employees need less rest? This study explored that question by investigating how resilient professional caregivers think about and practice rest. Analysis revealed that highly communicatively resilient professional caregivers acknowledged the material reality of the body, labeled here bounded physicality. Bounded physicality is the limited ability to engage physically in space and time. A sample of highly communicatively resilient professional caregivers was collected using atypical survey-based case selection and standard deviation analysis. Eleven positively deviant (PD) caregivers and five corroborators were subsequently interviewed about their meanings and practices of rest. Additionally, five professionals who scored extremely low on the communicative resilience measure and four who were average were also interviewed as a validation effort. Constant comparative analysis of participants’ interview responses (N = 25) revealed that PD caregivers constructed rest as the proactive pursuit of holistic restoration and held a multifaceted interpretive schema of rest. Namely, they viewed rest as a (a) strategic defense and (b) normal indispensable joy, and practiced rest as (c) multimodal care. Additionally, they resisted the ideal worker norm (IWN) by protecting, prioritizing, and pursuing rest. Finally, PD caregivers experienced positive consequences of rest on their personal, relational, and professional wellbeing. As anticipated, these findings contrasted with non-PD caregivers’ interview responses. Taken together, this scholarship extends organizational communication theory, including literatures on positive organizational scholarship, the communicative theory of resilience, the ideal worker norm, and meanings of work (MOW) and rest. Ultimately, highly communicatively resilient professional caregivers build crucial reserves through rest, which challenges the view that resilient employees need less rest
Support for Smoking in Cars when Passengers Are Present
Get PDFBackground: Smoke-free policies protect people in the US from exposure to secondhand smoke in public spaces and the workplace, but fewer protections exist for non-smokers in private cars. This dissertation examined demographic, regulatory, and occupational characteristics associated with support for and against smoking being allowed inside cars when others and children, specifically, were present. Methods: Data derived from 128,835 participants in the 2018-19 wave of the Tobacco Use Supplement to the Current Population Survey (TUS-CPS). For each manuscript, multinomial logistic regressions were conducted using SAS 9.4 to analyze characteristics associated with thinking that smoking should “never,” “under some conditions,” and “always” be allowed in a car. Models were calculated separately for participant responses to two items asking about smoking in cars “when others are present” and “when children are present”, with “never allowed” as the referent response for smoking in cars. Results: Manuscript I: Those who identified as White, male, non-Hispanic, and everyday smokers were more likely to indicate that smoking should be allowed in cars under some conditions or always when others and when children were present. Manuscript II: Characteristics of those who were more likely to indicate that smoking should be allowed in cars under some conditions or always when others including children were present included living in a state which restricted smoking in cars when children under age 12 were present, not having a smoke free rule in their home, and indicating that smoking should be allowed in multiunit housing, recreational areas like bars and clubs, and indoor work areas. Manuscript III: Among those who were currently employed, those exposed to smoke at their place of work were more likely to think smoking should under some conditions and always be allowed in cars when others and when children were present. Conclusions: This dissertation identified specific characteristics linked to indicating that smoking in cars should be allowed. This project also provides guidance on specific populations who are at higher risk of thinking smoking in a car is acceptable
