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Temporal and Spatial Properties of the Negative Bold Response in the DMN
No full textThe Negative Blood Oxygen Level Dependent (BOLD) Response (NBR) remains less explored than its positive counterpart (PBR) in functional Magnetic Resonance Imaging (fMRI) studies. This dissertation aims to advance the understanding of the NBR by investigating its task-specific spatial patterns, comparing its specificity to the PBR, validating its reproducibility across different subject groups, examining its relationship with functional connectivity patterns during cognitive tasks, and assessing its linearity with respect to stimulus duration.Firstly, we analyzed the spatial patterns of NBR and PBR across a diverse set of cognitive tasks using a novel paradigm that grouped twelve tasks into four cognitive domains. Our findings revealed that both NBR and PBR exhibit distinct, task-specific spatial patterns, with the NBR predominantly occurring within the Default Mode Network (DMN). Notably, the NBR demonstrated greater task specificity compared to the PBR, suggesting a unique role in differentiating cognitive processes.
To validate these observations, we replicated the analyses in a separate cohort, confirming the reproducibility and consistency of the task-specific NBR spatial patterns across different subject groups. This consistency underscores the reliability of NBR patterns and their potential generalizability.
Additionally, we investigated the functional connectivity patterns of major brain networks during cognitive tasks. Our analyses showed that functional connectivity within networks such as the DMN and the dorsal attention network (DAN) remained stable across different tasks. This implies that task-specific BOLD signal changes are likely not due to alterations in functional connectivity but result from local neural dynamics.
Furthermore, we assessed the linearity of the NBR with respect to stimulus duration. Contrary to our initial hypothesis, the NBR did not exhibit a clear linear relationship with stimulus duration, differing from the established linearity of the PBR. This suggests fundamental differences in neurovascular coupling mechanisms between positive and negative BOLD responses and indicates that linear models may not suffice for accurately analyzing NBR data.
Methodologically, we developed a robust fMRI data analysis pipeline incorporating motion correction, distortion correction using TopUp, spatial normalization via an in-house technique utilizing outputs from FreeSurfer and ANTs, and noise reduction through ICA-AROMA. This pipeline ensured high-quality preprocessing and reliable first-level statistical analyses.
In conclusion, this research enhances the understanding of the NBR by highlighting its greater task specificity and non-linearity compared to the PBR. The findings emphasize the importance of considering both activation and deactivation processes in cognitive neuroscience. They have significant implications for theoretical models of brain function, analytical approaches in fMRI research, and potential clinical applications, paving the way for future investigations into the complex dynamics of neural activation and deactivation
Case Study: A Comparison of Pedagogical Content Knowledge Between Coaches and Coaches/Mentees
No full textThis multiple case study dissertation aimed to examine one of the domains of pedagogical content knowledge, knowledge of content and students, between different types of elementary coaches and between coach and their respective collaborating teachers. It also investigated the impact a coaches’ background experiences have on the dynamic between coaches and teachers and the perceptions' teacher have on the effectiveness of coaching. The theoretical framework used in this qualitative study was Ball, Thames, and Phelps’ (2008) definition of PCK. Data was collected from six coaches–four instructional coaches and two math coaches–and eleven k-5th grade teachers. Data collection involved a survey, LMT assessment, and semi-structured interviews, and a thematic analysis method was conducted. The findings from the cross-case analysis resulted in ten themes, with the majority having multiple categories. One finding to one of the research questions was that there were no differences in knowledge of content and students between mathematics coaches and general instructional coaches, but other areas to further investigate emerged. Another finding was that coaches were either within the same capacity as their respective teachers or had extra knowledge of content and students. Although the majority of the coaches’ knowledge of content and students was at a higher level according to their LMT score, it does not necessarily mean that coaches are working with teachers in improving knowledge of content and students. In addition, more research is recommended in creating a pedagogical content knowledge instrument that is specific for coaches
Utilizing Concurrent Data Accesses for Data-Driven and AI Applications
No full textIn the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical, especially as these applications increasingly underpin modern daily life. Traditionally, architectural optimizations in computing systems have concentrated on data locality, utilizing temporal and spatial locality to enhance data access performance by maximizing data and data block reuse. However, as poor locality is a common characteristic of data-driven and AI applications, utilizing data access concurrency emerges as a promising avenue to optimize the performance of evolving data-driven and AI application workloads.This dissertation advocates utilizing concurrent data accesses to enhance performance in data-driven and AI applications, addressing a significant research gap in the integration of data concurrency for performance improvement. It introduces a suite of innovative case studies, including a prefetching framework that dynamically adjusts aggressiveness based on data concurrency, a cache partitioning framework that balances application demands with concurrency, a concurrency-aware cache management framework to reduce costly cache misses, a holistic cache management framework that considers both data locality and concurrency to fine-tune decisions, and an accelerator design for sparse matrix multiplication that optimizes adaptive execution flow and incorporates concurrency-aware cache optimizations.Our comprehensive evaluations demonstrate that the implemented concurrency-aware frameworks significantly enhance the performance of data-driven and AI applications by leveraging data access concurrency.Specifically, our prefetch framework boosts performance by 17.3%, our cache partitioning framework surpasses locality-based approaches by 15.5%, and our cache management framework achieves a 10.3% performance increase over prior works. Furthermore, our holistic cache management framework enhances performance further, achieving a 13.7% speedup. Additionally, our sparse matrix multiplication accelerator outperforms existing accelerators by a factor of 2.1.As optimizing data locality in data-driven and AI applications becomes increasingly challenging, this dissertation demonstrates that utilizing concurrency can still yield significant performance enhancements, offering new insights and actionable examples for the field. This dissertation not only bridges the identified research gap but also establishes a foundation for further exploration of the full potential of concurrency in data-driven and AI applications and architectures, aiming at fulfilling the evolving performance demands of modern and future computing systems
Ultrasound Image Guided Robot Arm for Targeted Delivery of Therapeutic Drugs and MicroRNA for Cancer Therapy
No full textMolecular imaging has revolutionized medical diagnostics by providing detailed insights into biological processes at the molecular level within the living subject. Ultrasound Molecular Imaging (USMI) has emerged as a promising diagnostic imaging modality by utilizing targeted contrast agents to unveil crucial molecular information, including vascular biomarkers associated with cancer and other diseases. Despite its potential, the transition of Ultrasound Contrast Agents (UCA) from preclinical evaluation to FDA-approved clinical use faces challenges due to the short in vivo half-life of Micro-Bubbles (MBs), necessitating repeated administrations for comprehensive assessments. Moreover, conventional ultrasound imaging methods suffer from limited scanning areas and single-target focus, leading to low throughput in preclinical evaluations.This thesis addresses these challenges by proposing a robot-assisted whole-body scanning pipeline for preclinical evaluations in Ultrasound Molecular Imaging. By integrating a robotic arm into the imaging setup, this approach enhances scanning flexibility and precision, enabling scans across the entire body of a mouse. This extension of the imaging time window allows for comprehensive assessments without the need for repeated contrast agent administrations. Additionally, the ability to simultaneously scan multiple targets within the same session significantly increases the throughput of preclinical assessments, thereby improving the efficiency and reliability of Ultrasound Molecular Imaging in clinical translation
Estimation of kinetic parameters of the Coronavirus main protease by Bayesian regression and utility for drug design
No full textThe main protease (MPro) plays a crucial role in the Coronavirus life cycle and is a target for newly developed antivirals against SARS-CoV-2. MPro is conserved across various members of the coronavirus family. The enzyme of SARS-CoV-2 and other members of the coronavirus family share similar structures and functions, commonly existing as obligate dimers. However, MPro from MERS-CoV exhibits weaker dimerization and often exists as a monomer under biochemical assay conditions, which may not accurately reflect the conditions relevant to antiviral therapy in infected cells.Interestingly, because ligand binding increases dimerization, the addition of ligands has been reported to enhance MPro activity at low concentrations before reducing it at higher concentrations. This phenomenon, known as ligand-induced dimerization, was observed not only in biochemical assays of MERS-CoV MPro but also of SARS-CoV-2 mutated MPro with weakened dimerization. Unfortunately, there are currently no published biochemical models that quantitatively fit these non-monotonic concentration-response curves. This poses a significant challenge in estimating IC50 from these curves, which is an important metric for drug potency and commonly used in drug screening. As a result, predicting compound behavior in cellular models becomes challenging.To address this challenge, we developed an enzyme kinetic model that integrates dimerization and ligand binding. We utilized Bayesian regression for the model to fit datasets published in the aforementioned study of SARS-CoV-2 MPro. Subsequently, we adjusted the model to achieve a global fit for multiple datasets of MERS-CoV MPro and estimate IC50 values. Finally, we examined the correlation between estimated IC50 and cellular EC50, demonstrating that our model is capable of predicting cellular EC50 for the biphasic curves observed in MERS-CoV MPro
Quantifying the Sorption Behavior of Polypropylene Toward Methyl Salicylate and Phenylcyclohexane Under FDA Surrogate Testing Protocol for Recycled Plastics Use in Food Contact Materials
No full textThe increasing use of recycled polypropylene in food packaging raises concerns about chemical migration. FDA’s surrogate testing protocol using hexane or heptane as a diluent has evaluated the efficiency of industrial recycling processes in removing contaminants from reclaimed polyethylene terephthalate (PET). Different conditions to optimize surrogate testing of polypropylene (PP) may be needed due to the different sorption behavior of PP relative to PET. This study examines how the interaction between the surrogate contaminant, diluting solvent, and polymer impacts the sorption of surrogate contaminants into PP. Methyl salicylate (MS) and phenylcyclohexane (PCH) were selected as surrogate contaminants based on the current FDA testing protocol and solubility parameters. Swelling and sorption experiments were performed at 40°C for up to 14 days on different types of PP (monophasic homopolymer polypropylene (h-PP), monophasic random copolymer (r-PP), and/or heterophasic block copolymer (heco-PP)) in n-hexane, 2-propanol, and ethanol as diluting solvents. Sorption of 1% (v/v) MS and PCH from each diluting solvent into h-PP was quantified by GC-MS. Results showed that the PP polymers in n-hexane swelled 10%-155% more than the alcohols. Both MS and PCH sorption in n-hexane required a much shorter time, approximately 12 h, to reach equilibrium in comparison to the alcohols, which required 2–10 days. The equilibrium sorption concentration of 1% MS into h-PP from n-hexane was 55% and 136% higher, respectively, compared to sorption of MS from 2-propanol and ethanol. On the other hand, the equilibrium sorption concentration of 1% PCH into h-PP from 2-propanol and ethanol was 11-12% higher compared to the sorption of MS from n-hexane. Our data indicates that solvent swelling cannot be overlooked when determining realistic initial contamination levels in PP. This research will assist the FDA in updating the Recycled Plastics Guidance for Industry and enhance the FDA’s ability to fulfill its mission of protecting and promoting public health
Chasing dreams or paying bills: How multiple jobs and calling influence work-life conflict
No full textThis study explores the relationship between multiple job-holding, work as a calling, and work-life conflict (WLC). The result of the comparison of the level of Work Life Conflict of multi-job holders who were separated into two groups by how they perceived their work (i.e., solely to generate income, n=61; or to support pursuing a calling, n=40) and single-job holders (n=177) yielded no significant difference. The results of the moderators of availability via communication technology (CT) and schedule flexibility revealed a significant interaction effect between work type and availability via CT on WLC, indicating that higher levels of availability via CT exacerbate WLC for those pursuing callings compared to those holding jobs. Additionally, work identified as a calling correlated higher on WLC levels than work identified as a job. Implications and future directions are discussed
Nanopore sensing for environmental and biomarker analysis
No full textNanopore stochastic sensing is a powerful analytical tool for detecting target molecules through a nanoscale pore. The analyte and electrolyte ions are subjected to a voltage bias which drives them to translocate through the nanopore, resulting in disruptions in the ionic current. These disruptions are translated to blockage events which can serve as a signature of the analyte. Owing to its unique features of single-molecule and label-free sensing, nanopore technique has been exploited in a wide array of applications such as detection of metal ions, proteins, DNA, microRNA, toxic agents etc. In this dissertation, projects showcasing nanopore’s sensing capability of different biomarkers and in the detection of a wide range of target molecules based on non-covalent interactions are presented. Particularly in the first two projects, nanopore detection of ferric ions relevant to environmental regulation as well as a biomarker for human health and a miRNA-based biomarker for oral cancer and oral related diseases are summarized. Ferric ions, which are benign if present in balanced quantities but can be toxic otherwise, are detected by using an engineered multifunctional nanopore and a chelating organophosphonic acid ligand. The chelate complex formed after ferric ions bind to ligand gives significantly different event signatures than the free ligand in the solution enabling ferric ion detection. Even in the presence of interfering ions, the ferric ions could be recognized easily because of the conformational changes brought in the nanopore lumen by the interaction of the interfering metal ions with the His-tags of the nanopore which in turn resulted in variations in the characteristics of blocking events. In the second project, miR31, an oral cancer biomarker, is selectively detected with the help of an engineered nanopore, and a DNA based probe. Several probes with variations in length, composition and position of the overhangs or probes with no overhangs were compared and studied as the probes play a crucial role in capturing the target of interest with high specificity. Our strategically designed probe emerged as the most effective in capturing the target even in presence of large background from human saliva samples and enhanced the sensitivity of the system. In the first two projects, nanopores are utilized for selective and specific detection of certain target molecules. However, in order to analyze diverse range of analytes, numerous sensing systems have to be constructed which can be a time-consuming and challenging task. To circumvent this limitation, in the third project, diverse recognition sites based on various non-covalent interactions are incorporated into the α-hemolysin protein pore to achieve detection of not just a single analyte but broad category of molecules such as cations, anions, aromatic and hydrophobic compounds
Resolvent analysis of turbulent flows: Extensions, improvements and applications
No full textThis thesis presents several advances in both physics-based and data-driven modeling of turbulent fluid flows. In particular, the present thesis focuses on resolvent analysis, a physics-based framework that identifies the coherent structures that are most amplified by the Navier-Stokes equations when they are linearized about a known turbulent mean flow via a singular value decomposition (SVD) of a discretized operator. This method has proven to effectively capture energetically-relevant features observed in various flows. However, it has some shortcomings that the present work intends to alleviate. First, the original formulation of resolvent analysis is restricted to statistically-stationary or time-periodic mean flows. To expand the applicability of this framework, this thesis presents a spatiotemporal variant of resolvent analysis that is able to account for time-varying systems. Moreover, sparsity (which manifests in localization) is also incorporated to the analysis through the addition of an l1-norm penalization term to the optimization associated with the SVD. This allows for the identification of energetically-relevant coherent structures that correspond to spatio-temporally localized amplification mechanisms, for flows with either a time-varying or stationary mean. The high computational cost associated with the discretization and analysis of a large discretized of the mean-linearized Navier-Stokes operator represents the second drawback of resolvent analysis. As a second contribution, this thesis provides an analytic form of resolvent analysis for planar flows based on wavepacket pseudomode theory, avoiding the numerical computations required in the original framework. The third contribution focuses on the characterization of the energetically-dominant coherent structures that arise in turbulent flow traveling through straight ducts with square and rectangular cross-sections. First, resolvent analysis is applied to predict the coherent structures that arise in this flow, and to study the sensitivity of this methodology to the secondary mean flow components that display a distinct pattern near the duct corners. Next, a data-driven causality analysis is performed to understand the physical mechanisms involved in the evolution of coherent structures near the duct corners. To do this, a nonlinear Granger causality analysis method is developed and applied to proper orthogonal decomposition coefficients of direct numerical simulation data, revealing that the structures associated with the secondary velocity components are behind the formation and translation of the near-wall and near-corner streamwise structures. A general discussion and future prospects are discussed at the end of this thesis
Estimation of Platinum Oxide Degradation in Proton Exchange Membrane Fuel Cells
No full textThe performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the platinum catalyst. The production of platinum oxide is a major cause of the degradation of the fuel cell system, negatively affecting its performance and durability. In order to predict and prevent this degradation, this research examines a novel method to estimate degradation due to platinum oxide formation and predict the level of platinum oxide coverage over time. Mechanisms of platinum oxide formation are outlined and two methods are compared for platinum oxide estimation. Linear regression and two Artificial Neural Network (ANN) models, including a Recurrent Neural Network (RNN) and Feed-forward Back Propagation Neural Network (FFBPNN), are compared for estimation. The estimation model takes into account the influence of cell temperature and relative humidity.Evaluation of relative errors (RE) and root mean square error (RMSE) illustrates the superior performance of RNN in contrast to GT-Suite and FFBPNN. However, both RNN and GT-Suite showcase an average error rate below 5% while the FFBPNN had a higher error rate of approximately 7%. The RMSE of RNN shows mostly less compared to FFBPNN and GT-Suite, however, at 50% training data, GT-Suite shows lowest RMSE. These findings indicate that GT-Suite can be a valuable tool for estimating platinum oxide in fuel cells with a relatively low RE, but the RNN model may be more suitable for real-time estimation of platinum oxide degradation in PEM fuel cells, due to its accurate predictions and shorter computational time. This comprehensive approach provides crucial insights for optimizing fuel cell efficiency and implementing effective maintenance strategies