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Applications in Opioid Analysis with FAIMS Through Control of Vapor Phase Solvent Modifiers
No full textField Asymmetric Ion Mobility Spectrometry (FAIMS), often coupled with mass spectrometry (MS), offers rapid analytical ion filtration, useful in reducing matrix interference and differentiating isomeric compounds. This study focuses on augmenting FAIMS-MS analyses through the incorporation of gas phase solvents, or modifiers, inducing dynamic microsolvation, a particularly potent method of analysis enhancement.
Initially, the research utilized a bubbler-based system, incorporating a humidity sensor, improving modifier concentration control when using water. Our application in opioid detection and differentiation revealed considerable advancements. Notably, an increased peak capacity in an opioid mixture and successful separation of the isobaric opioid pair, alfentanil and ortho-isopropyl furanyl fentanyl, were observed. However, inherent reproducibility issues limited the system to water utilization.
To address these limitations, we developed an innovative system using ultrasonic nebulization and precision pumping for facilitated solvent delivery. The system offered heightened control and flexibility, enabling in-depth evaluations of the influence of modifier concentrations on the FAIMS-MS analysis of opioids. Modifiers generally employed are polar protic solvents, like water or isopropyl alcohol, which bind tightly to analytes, potentially enhancing resolving power. We explored alternatives, using solvents like pyridine, toluene, and cyclohexane, allowing more subtle interactions targeting specific classes of analytes, specifically the aromatic functionality in opioids.
Additionally, we delved into the development of a continuous ambient desorption/ionization source for FAIMS, the ultrasonic nebulization corona discharge (USN-CD). Developed via modification of the USN-based modifier system, it demonstrated superior signal intensities compared to standard nano electrospray ionization sources for heroin and fentanyl. However, thermal degradation issues arose with more thermally labile biomolecular compounds. Mitigating this requires further development, crucial for establishing USN-CD as a viable universal ionization source.
Altogether, this research not only deepens our understanding of the implementation and progression of ancillary apparatus in FAIMS-MS but also pioneers the pathway for future innovations. It forms a robust base for the advancement of FAIMS technology, particularly in refining the analysis of opioids, while also broadening its potential for diverse applications
Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems
No full textLightweight cryptography plays a vital role in securing resource-constrained deeply-embedded systems such as implantable and wearable medical devices, smart fabrics, smart homes, radio frequency identification tags, sensor networks, and privacy-constrained usage models. The National Institute of Standards and Technology (NIST) initiated a standardization process for lightweight cryptography, a relatively-long multi-year effort, which eventually concluded in February 2023. Side-channel attacks (SCAs) exploit the vulnerabilities of a system by observing and analyzing side-channel information leakages. Fault analysis attacks are a type of active SCAs, where an intelligent adversary injects bit/byte faults into the implementation of a cryptographic cipher to recover the secret key. This dissertation tackles active fault attacks by applying different error detection strategies as countermeasures to the crucial components of different state-of-the-art lightweight cryptosystems in their hardware applications. The case studies include lightweight cryptographic ciphers - QARMA, Welch-Gong ciphers WAGE and WG-29, and ASCON, the winner of the NIST standardization process for lightweight cryptography in February 2023. The proposed error detection schemes are designed to be architecture-oblivious as well as low-cost in hardware constructions of the ciphers listed above. The schemes are benchmarked on the field-programmable gate array (FPGA) hardware platform for error coverage and performance evaluation via implementation overheads. The results of the proposed works in this dissertation lead to more reliable lightweight cryptography, immune against fault analysis attacks
Synthesis, Characterization, and Separation of Loaded Liposomes for Drug Delivery
No full textLiposomes are viable candidates for drug delivery vehicles due to their ability to protect loaded compounds, dampen side effects of drugs, and be delivered to target sites in the body. The amphipathic nature of these lipid vesicles makes them very customizable through careful selection of bilayer and aqueous core components as well as synthetic and downsizing methods. However, synthetic methods may result in unencapsulated compounds remaining present alongside loaded liposomes, which require removal before the liposome suspension can be used.
This work explores the use of model DOPC as well as DOPC and CHOL liposomes and the methods that were optimized to produce, load, and purify them. Thin film hydration and extrusion were used to make and load the 50-200 nm sized vesicles. All liposomes were characterized using DLS, and loaded liposomes were successfully separated from free Cyt c via size-exclusion chromatography with specified parameters. Additional optimization is still needed to disrupt the liposomes for encapsulation efficiency determination as well as characterization of fluorescently dyed liposomes for future studies
“We Need to Have a Place to Vent and Get Our Frustrations Out”: Addressing the Needs of Mothering Students in Higher Education using a Positive Deviance Framework
No full textThis study examined the experiences of mothering students at four different colleges using a positive deviance (PD) framework. PD is an approach that seeks to identify positive behavioral patterns that help members of a community overcome structural barriers (Gross, et al. 2017). The Positive Deviance Framework was applied to investigate how some mothering students are successful in college and how their experiences could potentially help new or struggling mothering students. Eleven mothering students were interviewed to determine what interventions could assist mothering students who lack representation in the traditional college environment, a situation that often leads to feelings of isolation. This study found that mothering students find confidence in their experience as a mother and felt that this confidence keeps them organized and focused despite their immense responsibilities to care for their children, support their families financially, and find the time to complete their schoolwork. This study also found that mothering students are willing to find solutions to help other parents who attend college. They have wisdom that could help mothering students who are new to college, a single parent, or struggling in any way. This suggests that a parent support group for parenting students could address feelings of isolation, thereby creating a community of mothering students with shared experiences and knowledge or skills that could be passed on. This has the potential to increase retention and degree completion of mothering students and other parenting students
Contribution of Contextual Factors and Neuropathology to Dementia
No full textAlzheimer’s disease (AD) is a progressive neurodegenerative disorder that has extensive biological heterogeneity. It is not clear the extent to which this heterogeneity may be detected in participants without dementia, how it relates to incident AD dementia, and whether contextual factors may change how neuropathology relates to incident AD dementia. Therefore, this dissertation was completed using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 1,703) and the Czech Brain Aging Study (CBAS; n = 385) to address the following aims: to assess biological heterogeneity in participants without dementia, to relate this heterogeneity to incident AD dementia, and to determine whether contextual factors modify the association between neuropathology profiles and incident AD dementia.
First, we identified latent subgroups of neuropathology analyzed using latent profile analysis and measured via structural magnetic resonance imaging and described differences between profiles in sociodemographic characteristics, cognition, and contextual factors. Four profiles emerged in ADNI, and two profiles emerged in CBAS with significant differences in nearly all volumetric regions. Differences emerged in magnitude rather than pattern of atrophy. Participants belonging to profiles with less severe atrophy were younger, more likely to have normal objective cognition, and less likely to progress to AD dementia compared to participants belonging to profiles with more severe atrophy.
Next, we related the neuropathology profiles and contextual factors to incident AD dementia and assessed whether contextual factors modified the association between neuropathology profiles and incident AD dementia using survival analysis. Participants belonging to profiles with greater atrophy had a higher risk of incident AD dementia compared to participants belonging to profiles with less severe atrophy, contextual factors were related to a reduced risk of incident AD dementia, and contextual factors modified the association between neuropathology profiles and incident AD dementia on the multiplicative (CBAS) and additive (ADNI and CBAS) scales.
Contrary to past work, we identified biological heterogeneity based on magnitude instead of the distribution of atrophy in different brain regions. Evidence for resilience, or an attenuating effect of contextual factors on the atrophy-incident AD dementia relationship, was found on the multiplicative (CBAS) and additive (ADNI and CBAS) scales. Results suggest that although more severe atrophy relates to incident AD dementia risk, contextual factors can reduce this association. Future research should aim to uncover the mechanisms underlying the moderating role of contextual factors on the atrophy-incident AD dementia association
Meeting Diverse Student Needs: An Examination of a Physical Education Teacher Alumnus’ Culturally Responsive Teaching Beliefs and their Enactment
No full textThe purpose of this study was to examine how one physical education teacher education (PETE) alumnus teaching in a CED urban school perceives culturally responsive teaching in physical education (PE), as well as how they enact it in their classes. Guided by culturally responsive teaching and self-efficacy, this study examined John, a physical education teacher education alumnus teaching in a CED urban elementary school. John completed the culturally responsive teaching self-efficacy scale (CRTSE; Siwatu, 2007), five semi-structured interviews, five reflection journal prompts, and eight voice memos over the course of 12 weeks. Additional data collection included text messages, emails, and a reflexive journal kept by the researcher. Pre-existing data included a semi-structured interview with John completed in May 2021, a completed CRTSE scale from May 2021, and documents, such as lesson plans, reflections, and assignments from John’s teacher preparation program.
The two themes that were constructed from this data were: (1) Nurturing knowledge for self-awareness and (2) Empowering instructional excellence through knowledge utilization. The first theme discusses how John was able to gain knowledge of himself and the world around him, the school and community, and his students. The second theme describes how John was able to utilize this knowledge to empower instructional excellence through his curriculum, structure of the school, and the structure of his classroom. The findings from this study help to illuminate strategies that both PETE programs and in-service teachers can utilize to meet the needs of CED students
Anaerobic Digestion of Brewery Waste Including Spent Yeast and Hops
No full textFlorida is home to more than 300 craft breweries, that brew over a million barrels of beer annually. Brewing is an energy intensive process and produces large quantities of high strength waste including spent grains, yeast, hops, and high strength wastewater. Brewery wastewater, and spent yeast have high Chemical Oxygen Demand (COD) concentrations between 5000 mg/L – 10,000 mg/L, and 200,000 mg/L – 300,000 mg/L respectively. Brewery effluent wastewater is sent to Publicly Owned Treatment Works (POTWs) which implement high surcharges based on the strength of brewery effluent (COD concentration). While some of the spent yeast and hops can be diverted as animal feed along with spent grains, brewers are still left with huge quantities of yeast and hops to dispose. Furthermore, brewing requires large energy inputs in the form of heating fuel, and electricity.
Anaerobic digestion is a process in which microbes biodegrade organic material in the absence of oxygen. The byproduct of this process is digestate, and biogas, which predominantly consists of methane, and carbon dioxide. Methane can be harnessed as an energy source to offset energy requirements in breweries, and the digestate can be used as an organic fertilizer. Anaerobic digestion has been used by industrial breweries for onsite treatment of high strength effluent wastewater, and energy generation. However, the implementation of anaerobic digestion in smaller breweries faces obstacles including long pay back periods, and high capital costs. While anaerobic co-digestion of spent yeast, and brewery wastewater has yielded promising results, literature on mono-digestion of yeast waste, and co-digestion of yeast, and hops is sparse. Furthermore, spent hop metabolites including alpha and beta acids have antimicrobial properties that can inhibit methane production from anaerobic microbes found in rumens of ruminants. However, the effects of hops on anaerobic digestion of brewery waste are not explored. This study seeks to evaluate anaerobic mono-digestion of spent yeast, and co-digestion of spent yeast and hops.
In this study, Biomethane Potential Assays (BMPs) were set up using yeast and hops at varying substrate to inoculum ratios of 2.5 (Phase 1), and 1.7 (Phase 2). Both phases were set up with varying hop dosages of 0%, 20 % hops, and 40 % hops. Cumulative methane yields of 0.16 ml CH4/mg COD, and 0.15 ml CH4/mg COD were obtained for 20 % hops, and 40 % hops during Phase 1. Cumulative methane yields of 0.17 ml CH4/mg COD, 0.15 ml CH4/mg COD, and 0.11 ml CH4/mg COD were obtained from yeast only (0% hop dosage), 20 % hops, and 40 % hops respectively during Phase 2. In both phases, lower Gompertz methane rate constants were obtained for higher hop dosages.
Anaerobic Sequencing Batch Reactor (ASBR) studies were conducted in two phases. Duplicate ASBRs were operated at varying Organic Loading Rates (OLR) between 500 mg COD/L/day– 950 mg COD/L/day, and Solids Retention Times (SRTs) of 90 days, and 190-days during Phase 1 to evaluate mono-digestion of yeast. Average methane yields of 0.25 ml CH4/mg COD, and 90 % Total Chemical Oxygen Demand (TCOD) degradation was obtained during Phase 1. Phase 2 of the ASBR studies evaluated co-digestion of spent yeast, and hops with ASBRs operated at 0 % (yeast only), and 20 % hop dosages at OLRs between 700 COD/L/day to 900 mg COD/L/day, and SRT of 190 days. The digesters with 20 % hops provided lower methane yields, and COD degradation than the yeast only (0 % hops) reactor.
Results showed that addition of 20% hops dosage did not produce significant inhibitory effect in the BMP assays, whereas 40% hops dosage resulted in lower methane yields, and lower Gompertz rate constant. During the ASBR studies, mono-digestion of spent yeast produced methane yields comparable to yields obtained in literature for co-digestion of spent yeast with brewery effluent. Addition of 20 % hops dosage to the ASBR reactor resulted in lower methane yields, and lower TCOD degradation, however, long term studies are required to investigate this further
Exploratory Data-Driven Models for Water Quality: A Case Study for Tampa Bay Water
No full textWater, a crucial resource for sustaining life, covers approximately 70% of the earth\u27s surface. Nonetheless, the quality of water is deteriorating rapidly due to the rapid growth of urban areas and industries, which is a worrying trend causing harm to human health and the ecosystem. Water quality forecasting has a key role in water resources management by enabling effective pollution control, ecosystem monitoring, and decision-making.
Previously, traditional statistical models were used to forecast water quality, but they were unable to examine the non-linear relationships between water quality parameters, and they assumed that all datasets were distributed normally. This study uses Random Forest (RF) and Artificial Neural Networks (ANN) to predict and forecast water quality for multiple water quality parameters for different water sources using ambient temperature, rainfall, and land use as predictor variables for Tampa Bay Water.
The result from this study indicates that distance to the Alafia River was the major influencing factor for groundwater quality models with a feature importance value of 0.58, season with a feature importance of 0.9 was the highest significant parameter that impacted seawater quality models, and land use having a feature importance of 0.8 contributed highly to surface water quality models. The results of the comparison between RF and ANN in forecasting water quality indicate that RF performed better than ANN in most cases, with R2 values of 0.95 and 0.56 being the highest for groundwater and seawater, respectively. However, for some surface water quality models, ANN outperformed RF with an R2 value of 0.28.
Overall, this research highlights the efficacy of machine learning techniques in water quality prediction, with RF performing slightly better than ANN. Forecasted water quality results for July 2023 to December 2024 showed that groundwater quality remains relatively stable, and seawater and surface water quality were significantly influenced by changes in ambient temperature, land use, and rainfall. The findings emphasize the importance of considering these variables in water resource management and decision-making, particularly for seawater and surface water sources, while emphasizing the possibility of utilizing machine learning for prediction and forecasting water quality
The Rise of Text Analysis: Using Machine Learning to Explain the Variation in Going Concern Accuracy
No full textAuditors are required to issue modified audit opinions if they have sufficient doubts about the client’s ability to continue as a going concern. These going concern opinions represent an important information resource for financial statement users to evaluate client performance, and are associated with a number of negative capital market outcomes (e.g. negative returns, increased cost of capital, etc.). Despite being used by capital market participants, going concern opinions are commonly plagued with Type I errors (false positive) and Type II errors (false negative), making them a particularly noisy measure. The purpose of this study is to determine whether machine learning can be leveraged to reduce this noise by (1) identifying disclosure patterns where going concern accuracy is likely lower (higher) and (2) developing measures from these disclosures that can help predict variation in going concern accuracy. Specifically, I use a machine learning technique (Top2Vec) to identify differences in disclosure topics among financially distressed clients’ Risk disclosures (Item 1A) and Management Discussion and Analysis disclosures (Item 7) conditioned on the accuracy of the going concern opinion (accurate, Type I error or Type II error). I find significant differences in the topics that are discussed among Type I error/Type II error clients compared to clients receiving accurate going concern opinions/evaluations. Accurate going concern opinions are the situation that clients receive going concern opinions in the current year and file for bankruptcy protection in the subsequent year. Accurate going concern evaluations not only include the situations of accurate going concern opinions but also include the situations that clients do not receive going concern opinions in the current year and do not file for bankruptcy protection in the subsequent year (e.g., an accurate omission of a going concern opinion). In the Type I error settings (ignoring Type II errors in this analysis), the probability of accurate going concern opinion is higher if clients disclose human capital and supply chain risks, or if clients disclose tax related factors. The probability of accurate going concern evaluation is higher (lower) if clients disclose human capital, dispersion, legal, and macro-economic risks (funding, financial condition, debt, operational, attestation, and stock market risks), and the probability is lower if clients disclose the facts regarding growing potentials, stocks, and political contributions. In the Type II error settings (ignoring Type I errors in this analysis), the probability of accurate going concern opinion is higher (lower) if clients disclose bankruptcy and operational risks (development, supply chain, and environmental risks), or if clients disclose the facts regarding bankruptcy, performance changes, and costs (operational performance and tax). The probability of accurate going concern evaluation is higher (lower) if clients disclose macro-economic, intellectual property, and investment risks (development and oil/gas risks), or if clients disclose the facts regarding human capital (loan and operational performance). After providing evidence of which disclosure topics are associated with going concern accuracy, I then examine whether machine learning can be used to create measures (based on the textual information disclosed in Item 1A and Item 7) to improve models attempting to determine whether an observed going concern opinion is accurate. My findings support the validity and effectiveness of these machine learning developed proxies in predicting accurate going concerns, identifying Type I errors, and identifying Type II errors. I further demonstrate their superiority over other common text-based measures that do not utilize machine learning. The findings of this study have important implications for auditors, regulators, and academia
Microalgal Cultivation Characteristics Using Commercially Available Air-cushion Packaging Material as a Photobioreactor
No full textAir-cushion (AC) packaging has become widely used worldwide. ACs are air-filled, dual plastic packaging solutions commonly found surrounding and protecting items of value within shipping enclosures during transit. Herein, we report on a laboratory assessment employing ACs as a microalgal photobioreactor (PBR). Such a PBR inherently addresses many of the operational issues typically encountered with open raceway ponds and closed photobioreactors, such as evaporative water loss, external contamination, and predation. Using half-filled ACs, the performance of microalgal species Chlorella vulgaris, Nannochloropsis oculata, and Cyclotella cryptica (diatom) was examined and the ash-free dry cell weight and overall biomass productivity determined to be 2.39 g/L and 298.55 mg/L/day for N. oculata, 0.85 g/L and 141.36 mg/L/day for C. vulgaris, and 0.67 g/L and 96.08 mg/L/day for C. cryptica. Furthermore, maximum lipid productivity of 25.54 mg/L/day AFDCW and carbohydrate productivity of 53.69 mg/L/day AFDCW were achieved by C. cryptica, while maximum protein productivity of 247.42 mg/L/day AFDCW was attained by N. oculata. Data from this work will be useful in determining the applicability and life-cycle profile of repurposed and reused ACs as potential microalgal photobioreactors depending upon the end product of interest, scale utilized, and production costs