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From Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability: Practice-Motivated Approach to Measurement Planning and Data Processing
No full textWhen we process data, it is important to take into account that data comes with uncertainty. There exist techniques for quantifying uncertainty and propagating this uncertainty through the data processing algorithms. However, most of these techniques do not take into account that in real world, measuring instruments are not 100% reliable -- they sometimes malfunction and produce values which are far off from the measured values of the corresponding quantities. How can we take into account both uncertainty and reliability? In this paper, we consider several possible scenarios, and we show, for each scenario, what is the natural way to plan the measurements and to quantify and propagate the resulting uncertainty and reliability
Applying Multi-Scale Computational Approaches To Study Disease Related Biomolecules
Get PDFComputational biophysics plays a significant role in understanding biological processes in various biology systems and provides new sights to investigate disease-related biomolecules. During the doctoral research, I utilized multi-scale computational approaches, including structural modeling, Molecular Dynamics (MD) simulation, electrostatic analysis (DelPhi, DelPhiForce), and machine learning based Hybridizing Ions Treatment-2 (HIT-2) program, to investigate biomolecules. My research includes bound ions effects on kinesin Ncd binding to microtubule, ion concentration effects on kinesin BimC binding affinity, microtubule dynamics, etc. Ions are crucial for biomolecular interactions, especially for highly charged biomolecules. Bound ion effects are difficult to study in implicit solvent models. Based on the machine learning approach, a hybrid solvent method was developed to combine the explicit solvent model with implicit solvent model to study protein-protein interactions. The hybrid approach treats the bound ions explicitly and the free ions implicitly. The work applies the hybrid approach to a kinesin-tubulin complex, which demonstrates that the bound ions, especially the interfacial bound ions, play significant roles in kinesin-microtubule binding. The hybrid approach is not only capable of handling kinesin-tubulin complexes, but also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc. Microtubules are key players in several stages of the cell cycle and are also involved in transportation of cellular organelles. Therefore, understanding the interactions among tubulins is crucial for characterizing microtubule dynamics. Studying microtubule dynamics can help researchers make advances in the treatment of neurodegenerative diseases and cancer. A series of computational approaches were utilized to study the electrostatic interactions at the binding interfaces of tubulin monomers. The calculations explained that due to the electrostatic interactions, the tubulins always preferred to form α/β tubulin dimmers. The interactions between two protofilaments are the weakest, thus the protofilaments are easily separated from each other. The study elucidates some mechanistic details of microtubule dynamics and also identifies important residues at the binding interfaces as potential drug targets for the inhibition of cancer cells. BimC family proteins are bipolar motor proteins belonging to the kinesin superfamily which promote mitosis by crosslinking and sliding apart antiparallel microtubules. Understanding the binding mechanism between BimC and the microtubule is crucial for researchers to make advances in the treatment of cancer and other malignancies. By combining molecular dynamics (MD) simulations with a series of computational approaches, the electrostatic interactions at the binding interfaces of BimC and the microtubule under three different potassium chloride (KCl) concentrations were studied. We found the electrostatic features on the motor domains of BimC provide the strongest attractive interactions to the microtubule at 0 mM KCl compared to the complex at 50 and 150 mM KCl concentrations, which is validated by experimental conclusions. Furthermore, important salt bridges and residues at the binding interfaces of the complexes were identified, which illustrate the details of the BimC/microtubule interactions. The identified important residues involved in salt bridges are potential hot spots of drug targets for designing new drugs to cancer therapy
Culturally Sustaining Pedagogy and QRIS: Leveraging Systems to Improve Academic Achievement of Marginalized Students
No full textCulturally sustaining pedagogy is being utilized to support racially, ethnically, and linguistically diverse students in some classrooms and schools across the nation. Minoritized students currently have higher disciplinary rates and lower graduation rates. This leads to the question: at what age should educators begin implementing culturally sustaining pedagogy (CSP) to support positive academic, social, and emotional outcomes in young children? This thesis explores how preschool programs implement culturally sustaining practices in their classroom environments through interactions with students, family/community engagement, and social justice activities. I also examine how the implementation of CSP improves academic, social, and emotional outcomes among minoritized children. CSP is the ideal approach to creating high-quality programs for marginalized students in early childhood programs. Quality Rating Improvement Systems (QRIS), which work with early childhood programs, to improve their quality, provide a potential system that can be leveraged to support the national implementation of CSP. I propose implementing CSP through QRIS will improve the quality of early childhood programs and thus, improve outcomes in early childhood education for minoritized students. In this thesis, I provide background on QRIS and examine the ways QRIS can leverage training, practice-based coaching, and ongoing assessments to further the implementation of culturally sustaining pedagogy in preschool classrooms nationwide. Implementing CSP in QRIS would establish a need for early childhood coaches who specialize in CSP to provide on-site coaching to early childhood programs nationally. QRIS systems implementing CSP would fundamentally shift the way early childhood thinks about quality and the ways in which we assess quality for marginalized communities
Staggered Boards and Human Capital Disclosure
No full textThis article examines the effects of staggered boards on human capital management (HCM) disclosure quality. I find that firms with staggered boards exhibit significantly better HCM disclosure scores than non-staggered boards. Additionally, firms that transition from staggered boards to non-staggered boards are shown to experience significant decreases in their HCM disclosure scores. These results are robust to the exclusion of firms that switch to or from staggered boards to non-staggered boards, propensity score matching, and alternative disclosure quality measures. While various cross-sections and the usefulness of HCM disclosure information for analyst forecasts are explored, the results were not significant
Our Author Is Crazy
No full textThis thesis delves into the realm of young adult (YA) fantasy literature, exploring how its meta-narrative elements, fast-paced story beats, and relatable characters can facilitate the acquisition of healthy coping mechanisms among its readers. In the preface, I discuss the importance of providing YA audiences with narratives that not only entertain but also serve as tools for navigating the complexities of adolescence. Drawing upon psychological theories and literary analysis, I argue that meta-awareness in YA fantasy can offer readers a unique perspective on their own struggles and encourage them to develop resilience and coping skills. The accompanying YA Fantasy story follows Raz, a character aware of his fictional existence, the Author\u27s existence, and the reader\u27s existence. After being created in the opening chapter, he is thrust into OC World, where he meets his new housemates. A group of highly intense OCs with their own problems and quirks that Raz can\u27t wrap his head around. However, that\u27s the least of his concerns, as the Author has a bet with him that they didn\u27t bother to tell Raz about. Now Raz has to figure out the nature of the bet, survive this new crazy world and his housemates, all while hoping his own mental mindscape holds up. By intertwining the theoretical framework presented in the preface with the narrative exploration of the YA fantasy story, this thesis aims to demonstrate the potential of literature to serve as a catalyst for personal growth and resilience in young readers as well as tell an engaging and fun story
Red State: Excerpts From A Novel-In-Progress
No full textRed State, is a series of fictional pieces that are part of a larger project; a novel-in-progress, also entitled Red State. These pieces tell the story of two childhood friends, Jill Meyer, and Kat Eckert, and the scenes follow the trajectory of their friendship and explore the impact that trauma and privilege have had on their development. Over the course of three decades, the pieces go back and forth in time from the late 80\u27s in Abilene, TX to present day (2016-2017)
Modeling The Spatiotemporal Variations Of The Magnetic Field In Active Regions On The Sun Using Deep Neural Networks
Get PDFSolar active regions are areas on the Sun\u27s surface that have especially strong magnetic fields. Active regions are usually linked to a number of phenomena that can have serious detrimental consequences on technology and, in turn, human life. Examples of these phenomena include solar flares and coronal mass ejections, or CMEs. The precise predictionof solar flares and coronal mass ejections is still an open problem since the fundamental processes underpinning the formation and development of active regions are still not well understood. One key area of research at the intersection of solar physics and artificial intelligence is deriving insights from the available datasets of solar activity that can help us understand solar active regions better. Some machine learning models have been employed to forecast solar flares from a 6-hour to 48-hour time span, thanks to advancements in artificial intelligence. Support Vector Machine (SVM) [5,42], K-Nearest-Neighbor (KNN) [27], Extremely Randomized Trees (ERT) [36], and deep neural network [35] are some of the machine learning models that have been used in forecasting solar flares, but the results are not good. This is due to the models being trained with a specific set of active region parameters and an imbalanced dataset with few positive flare cases. As a result, there is a need to understand space weather and the basis by which these events occur. In this study, we applied a deep learning architecture originally designed for video prediction to predict the changes happening on the Sun in continuous time by using time series Helioseismic and Magnetic Imager data captured by the Solar Dynamics Observatory (SDO) and compared it against a no-change baseline and a regression baseline. In addition, we expanded our study to examine the changes in active regions by incorporating the 3D viewing geometry and the sunâ??s rotation, which helped the models focus on the changes in the active regions. We proposed using log-scale normalization to normalize the data and using the Cascading Convolutional Neural Network to predict the changes in active regions. To improve the performance of the model, we included the gradient information and the Structural Similarity Index in the training of the model by adding them as part of the loss function. In this dissertation, we demonstrated that deep neural networks can be trained to predict changes in active regions. It is our hope that further development of this work will lead to a better understanding of various physical phenomena related to space weather
Water Sufficiency For Organismal Function In Dryland Critical Zones
Get PDFWater availability is crucial for organismal survival and growth in dryland environments, affecting both ecological interactions and carbon dynamics. The goal of this thesis is to develop soil water release curves (SWRCs) that link soil water potentials (Ψ) to soil water content (θ). Using the SWRCs, temporal soil water sufficiency curves are developed, which quantify the amount of time that dryland critical zones have enough water to sustain the physiology of organisms. These curves allow for effectively indicating water availability across different species, coverage types, and soil conditions, enhancing our understanding of water dynamics in drylands and contributing important parameters for a variety of studies. I examine the interaction between water, soil, and plant dynamics at two sites: the Ivey pecan farm in Tornillo, Texas and the Jornada Experimental Range in Las Cruces, New Mexico. I assess physical soil properties, including depth, texture, and ground cover types such as bare ground, creosote, mesquite, and grass. At the Ivey Pecan Orchard, fine and coarse sites were sampled to analyze variations in soil texture. Data from moisture sensors for the period of 2011-2021 were cross-verified with direct soil gravimetric measurements and SWRCs at corresponding depths. A corresponding adjustment in data allowed for accurate quantifications of soil moisture and subsequently conversions of these measurements into water potentials using the Fredlund-Xing (1994) model, thus providing a detailed view of moisture trends across different soil coverages and textures. At the Jornada Experimental Range, it was found that shallow soils at depths of 5 and 10 cm experienced significant increases in water loss (retained water less well), whereas deeper soils exhibited more water retention stability. Our refined data showed that the upper 30 cm of soils under creosote and mesquite shrubs typically maintained water availability above the wilting point of creosote (-6 MPa) only slightly more than 50% of the time. Thus, we conclude that shallow (0-30 cm) soils in the shrubland has insufficient water availability for sustained plant health year-round, which is consistent with seasonal grass dieback at the site. Shrub species, such as creosote and mesquite, likely compensate with access to deeper water sources via their rooting structures. Preliminary correlations of soil moisture data with carbon exchange measured via eddy flux tower were inconclusive, but further modeling could reveal important connections between water sufficiency and net carbon balance. The development of temporal soil water sufficiency curves and their ability to predict water availability for organisms contribute to a broader understanding of organism water availability in drylands. This tool provides a solid foundation for future studies in drylands and works to advance the understanding of soil-plant-atmosphere relations in dryland critical zones
Produced Water: Characterization And Treatment
Get PDFIn recent years, environmental concerns have urged companies in the energy sector to modify their industrial activities to facilitate greater environmental stewardship. For example, the practice of unconventional oil and gas extraction has drawn the ire of regulators and various environmental groups due to its reliance on millions of barrels of fresh water for hydraulic fracturing well stimulation, which is generally withdrawn from natural sources and public water supplies. Additionally, this process generates two substantial waste streams, which are collectively characterized as flowback and produced water. Whereas flowback water is comprised of various chemical additives that are used during hydraulic fracturing; produced water is a complex mixture of microbiota, inorganic and organic constituents derived from the petroliferous strata. Numerous treatment modalities have been employed over the years to eradicate bacterial communities in industrial wastewater. Oxidizing agents and chemical additives such as ozone, per-manganate, glutaraldehyde, and chlorine, are effective in treating microbial contaminants that are typically found in domestic wastewater. However, the chemical complexity of produced water from fracking requires novel approaches because microbes have developed mechanisms to overcome the typical disinfectants. In this work, we provide contrast the benefits of treating vs deep-injecting produced water, highlighting the umbrella of opportunities available once the wastewater is treated accordingly. The work also includes a discussion of the various bacterial communities that have been previously found in hydraulic fracturing wastewater as well as the analytical tools typically employed in their characterization. Finally, we test the effectiveness of bacteriophages for the eradication of two model bacteria from produced water: Pseudomonas aeruginosa and Bacillus megaterium. These bacteria were grown in low salinity produced water and exposed to their corresponding phage. Overall, total inactivation of the P. aeruginosa population was achieved, as well as inactivation of B. megaterium. These promising results provide a potentially useful tool for bacterial elimination in the overall PW treatment at an industrial scale. Particularly since phage treatment is a rapid and cost-effective alternative