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Texas ScholarWorks was established to provide open, online access to the products of the University's research and scholarship, to preserve these works for future generations, to promote new models of scholarly communication, and to help deepen community understanding of the value of higher education.
UT Tower and campus image credit: Earl McGehee, CC-BY, https://www.flickr.com/photos/ejmc/7452145850
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Exploring N- and C-terminal Fragment Ion Biases in UV-Photodissociation Mass Spectrometry of Intact Proteins
(2024) Lam, Raymond; Brodbelt, Jennifer S.
193 nm UV-photodissociation (UVPD) is a powerful ion activation method in tandem mass spectrometry (MS/MS) for analyzing complex biomolecules and proteins. Sample ions are isolated by their mass-to-charge m/z ratio and exposed to pulsed UV light, causing absorption of UV photons and cleavage along the amide backbone to generate informative fragment ions. The high energy deposition of UVPD and preservation of higher-order structure and modifications makes it particularly appealing for analysis of large, modified, heterogeneous, or multimeric protein states that prove challenging for conventional tandem mass spectrometry methods. It is expected that fragment ions containing the N- and C-terminal ends of each protein ion should be produced and detected equally upon amide backbone cleavage. Data among recent 193 nm UVPD-MS analyses and other MS/MS methods (such as collision-induced dissociation) show bias in the production of N- and C-terminal ions in different m/z mass-to-charge regions of mass spectra. Owing to the development of UVPD for top-down protein analysis, this method was examined in more detail for the N-term/C-term bias and was the focus of the present study. Among a set of six proteins prepared in denaturing solution conditions to generate standard and “supercharged” charge states, fragment ion identifications from proteins with greater numbers of basic residues were biased towards N-terminal ions in lower m/z regions, while proteins with acidic residues biased C-terminus-containing fragment ions in the same region. The backbone sites of generated fragment ions showed precursor charge-state dependence on the degree of ion current bias but remained intrinsically biased towards N/C-terminal fragment ions, while explorations of the charge of fragment ions proved insufficient to provide rationale for the migration of fragment ions into different m/z regions. This promotes further study of biases in top down MS/MS analysis of proteins, particularly as applied to MS techniques attempting to resolve information from spectrally noisy yet information-rich UVPD fragment ions, like proton-transfer charge reduction and internal fragment ion assignment.
Next generation 3D printing platform for the fabrication of personalized pills
(2023-12) Kulkarni, Vineet R.; Williams, Robert O., III, 1956-; Maniruzzaman, Mohammed; Smyth, Hugh D.C.; Huang, Siyuan; Zhang, Feng
6 in 10 adults in the US suffer from chronic conditions which can range from heart and metabolic diseases to cancer and HIV. The commonality of all these chronic disorders is the use of multi-dose multi-drug regimens, which leads to poor patient compliance and deteriorates the patient’s quality of life. Switching to a one multi-drug-containing pill (polypill) a day approach would help improve patient compliance especially in pediatric and geriatric population. Developing these polypills using conventional manufacturing methods in the pharmaceutical industry is challenging. Use of upcoming technologies can help solve this problem. Three-dimensional (3D) printing is a manufacturing technique that enables the development of dosage forms containing multiple drugs with complex structures and release profiles. The use of this technology to develop a polypill tailored specifically for the patient for reduced dosing will help increase compliance and medication adherence and also help reduce the overall costs of the medication. While setting this process up the goal was to work with material extrusion based 3D printers operating on the principles of fused deposition modeling (FDM) and pressure assisted microsyringe (PAM) based extrusion, due to their smaller footprint. An inhouse printer was set up using a conveyor belt to implement a continuous printing platform with unlimited print area. The main goal with these printing based systems was to understand how the different processing conditions affect the formulation composition and in turn affects the properties of the final printed dosage form. The effect of changing layer orientation while printing using the continuous setup was evaluated against the conventional batch process. Effect of print geometry and infill density was evaluated to understand the effect on release performance. Aspirin and Nifedipine were selected as model drugs for this study. PAM can be used with direct powder blends for printing. We set up a single step extrusion process for developing self-emulsifying granules that could replace the powder blend. The improved performance of our granules helped eliminate the drawbacks of drug segregation and distribution. These studies demonstrate the promising utility of 3D printing for personalizing drug delivery.
Histomechanical characterization and microstructure-based modeling of right ventricular myocardium
(2023-12) Kakaletsis, Sotirios; Rausch, Manuel Karl; Huang, Rui; Dortdivanlioglu, Berkin; Lejeune, Emma; Ravi-Chandar, Krishnaswamy
Right ventricular histomechanics have been historically overlooked, thus limiting our ability to describe the mechanisms underlying severe pathological conditions of the right heart. In this dissertation we set out to investigate the histomechanics of the right ventricular myocardium both in health and disease (pulmonary arterial hypertension), using a large animal (ovine) model. To this end, we combine mechanical testing, histology analysis, magnetic resonance imaging and microstructure-based modeling. Our computational approach is threefold, involving established homogenized models, novel machine learning metamodels and the use of embedded, discrete fiber networks. First, we found that the right ventricular myocardium in health exhibits nonlinear, anisotropic mechanical response. The homogenized models successfully captured this behavior at the cost of considerable computational time, subsequently accelerated by the machine learning metamodels. Moreover, we found that pulmonary arterial hypertension induced extracellular collagen deposition, spatially-dependent wall thickening, and increased stiffness at the low strain regime. Our embedded fiber network approach was able to account for these remodeling effects. Finally, throughout this work we have been making our experimental data and computational implementations publicly available, establishing for the first time a complete pipeline for the characterization of the right ventricular myocardium.
Ultrasonic and vibrational methods to determine changes of state of lithium-ion cells
(2023-12) McGee, Tyler Michael; Ezekoye, Ofodike A.; Haberman, Michael R. (Michael Richard), 1977-; Arguelles, Andrea; Khani, Hadi
Lithium-ion batteries (LIBs) are the chosen power source for battery electric vehicles and battery energy storage systems. These high-power, high-capacity applications subject LIBs to challenging operating environments where mechanical, electrical, and thermal abuse is likely. In these applications, thousands to hundreds of thousands of cells are connected in series and parallel, which creates a challenging monitoring problem. The search for improvements to the battery management system (BMS), including new sensing modalities, is a very active and growing field. This work investigates the use of mechanical inspection of lithium-ion batteries using dynamic mechanical loading for state estimation. Ultrasonic inspection is used to monitor cells as they undergo normal charge-discharge cycling and different amounts of thermal loading, sometimes to thermal runaway. By specifically monitoring the ultrasonic signal characteristics of the signal amplitude (SA) and time of flight shift (TOFS), we can monitor changes to the cell's stiffness, density, and attenuation which result from changes in the cell's state of charge (SOC), temperature, or the presence of damage from thermal abuse. We find that ultrasonic signal characteristics warn of impending cell failure up to 25 minutes in advance of traditional monitoring sensors. A transfer matrix model employing a Bloch-Floquet formalism which accounts for the repeating layered scheme of the cell is introduced to explore ultrasonic wave dispersion due to the layered structure and internal losses due to the cell's polymeric components. Experimentally obtained ultrasonic signal characteristics were corroborated with this periodic transfer matrix model (PTMM) which can simulate SA and TOFS by using the appropriate SOC or temperature-dependent material properties of cell components. The PTMM validates experimental measurements, and helps demonstrate which cell components dominate the characteristics of ultrasonic wave propagation in the thickness direction of LIB pouch cells. The results from US inspection demonstrate its applicability to provide advanced warning of cell failure and also to detect the presence of damage from previous thermal abuse. The same chemo-mechanics that drive changes in the cell's ultrasonic response should also affect the cell's modal response. One can imagine implementing modal testing on cell packs as a part of routine maintenance, or making use of ambient vibrations as the excitation for modal testing in applications like BEVs . As such, this work also explores the viability of vibrational inspection for state estimation, focusing primarily on SOC and state of health (SOH) estimation. The surface velocity of lithium-ion pouch cells confined in a fixed-fixed configuration is measured with a scanning laser Doppler vibrometer (SLDV) while the cells are subjected to base excitation using an electrodynamic a shaker. SLDV scans are performed after the cell has been charged or discharged to specific SOC and repeated across numerous cycles. Results from these experiments show that the modal frequency of a cell shifts towards higher frequency with increasing SOC. These results were corroborated with an effective material model of the cell which was created with multiscale homogenization of the cell's components including their microscale heterogeneity. This material model is created using material properties of constituents at full charge and at full discharge, and is input to a finite element simulation of the resonance frequency of the cell. We find good agreement between the resonance frequency predicted by the multiscale model and transmissibility measurements at 0% and 100% SOC. The experimental results for continued cycling showed an increase in modal frequency at the fully charged and fully discharged states with cell aging. While identifying the chemo-mechanical cause of the changing cell modal response with aging remains a challenge, the correlation between SOH and modal response illustrates how the technique can be used for both SOC and SOH estimation.
Designing key-value stores for emerging memory and disaggregation technologies
(2023-12) Lee, Sekwon; Chidambaram, Vijay; Keeton, Kimberly; Bornholt, James; Rossbach, Christopher J.; Aguilera, Marcos K.
With the increasing convergence of applications to the cloud, cloud-based key-value stores (KVSs) should offer high performance, scalability, elasticity, utilization, and crash resilience. However, conventional storage technologies and monolithic server models make it challenging to achieve these goals. The transition to the new emerging memory and disaggregation technologies, such as PM (Persistent Memory), RDMA (Remote Direct Memory Access), and CXL (Compute Express Link), can readily offer opportunities to achieve these goals. However, these new technologies have distinct characteristics from the conventional technologies. Thus, to efficiently and reliably utilize them, KVSs must be carefully designed to avoid sub-optimal design choices without compromising their inherent hardware-guaranteed benefits. In this dissertation, we seek to answer the following question: how can we achieve a high-performance, scalable, elastic, and crash-recoverable KVS for disaggregated persistent memory (DPM)? In particular, we explore solutions to achieve these goals by introducing new indexing, caching, and partitioning techniques. We design new indexing data structures for a high-performance, scalable, and crash-recoverable data storage at PM, employ caching strategies for high performance by reducing expensive accesses to DPM, and tailor partitioning techniques to achieve elastic, scalable resource deployment. This dissertation first presents Recipe, a principled approach for converting concurrent DRAM indexes to crash-consistent indexes for PM. The main insight behind Recipe is that isolation provided by a certain class of concurrent DRAM indexes can be translated to crash consistency when the same index is used in PM. We present a set of conditions that enable the identification of this class of DRAM indexes, and the actions to be taken to convert each index to be persistent. Next, we presents Dinomo, the first key-value store for DPM based on RDMA interconnects that simultaneously achieves high common-case performance, scalability, and elasticity. Dinomo uses a novel combination of techniques such as ownership partitioning, disaggregated adaptive caching, selective replication, and lock-free and log-free PM indexing to achieve these goals. Finally, we present Shift, a cache-conscious KVS designs for CXL disaggregated memory. Shift sheds new light on the existing PM indexes and partitioning schemes originally proposed for the different system domains to achieve a high-performance, scalable, elastic, crash-recoverable KVS for CXL disaggregated memory. Furthermore, Shift employs lock intention log to improve the PM indexes to be partial-failure-resilient and non-hierarchical processing to take both advantages of KN cache and direct accesses to CXL disaggregated memory.