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Spatial Mapping of Unique Cellular Populations as a Foundation for Functional Characterization
No full textExpression of spatial molecular gradients is a defining feature in essentially every phase of cellular organ development. While much of the research regarding these gradients has been conducted in developing systems, there is strong evidence suggesting that spatial gradients play an important role in adulthood as well. A robust example of post-developmental gradients is seen in the liver, which is uniquely organized into metabolic zones that are characterized by the graded expression of gene markers, proteins, nutrients, and oxygen levels. This makes the liver an ideal organ to model studies of post-developmental gradients in more complex systems. Here, we establish methodology for studying gradients of expression via fluorescent in situ hybridization technique, multiplex RNAscope. In doing so, we also create the first spatial atlas of the ten Frizzled receptors, which are involved in canonical Wnt signaling, in adult mouse liver and, in parallel, utilize publicly available single-cell RNA sequencing data to define Frizzled receptor expression in various hepatic cell types, revealing surprising zonation of Frizzled receptor 6 (Chapter 2). Once established, we then translate this methodology to study molecular spatial gradients in the brain. For this, we turn to the striatum, given its heterogenous cellular composition of dopamine receptor-expressing neurons and their roles in a wide-range of neurodegenerative and psychiatric disease. In Chapter 3, using multiplex RNAscope, we elucidate striatal dopamine receptor expression of spiny projection neurons (SPNs) along both dorsal-ventral and rostral-caudal axes. We confirm the expression of SPNs that express only one dopamine receptor subtype versus SPNs that co-express multiple subtypes. These subpopulations are not only displayed in gradients along each axis but are also transcriptomically unique in a species-conserved manner. Further exploration of a unique SPN subpopulation that co-expresses D1 and D2 dopamine receptors reveals that these cells possess distinct electrophysiological qualities compared to SPNs that express the D1 receptor alone. Lastly, we show that these SPN subpopulations may uniquely contribute to genetic risk in a plethora of neurological-related conditions. Overall, this dissertation represents a foundation for studying post-developmental gradients and cell-type heterogeneity in adult systems for further characterization of their roles in both health and disease
Book launch and Panel presentation: Health, Parenting, and Community Perspectives on Black Fatherhood: Defying Stereotypes and Amplifying Strengths
No full textThe purpose of the Book launch and Panel presentation: “Health, Parenting, and Community Perspectives on Black Fatherhood: Defying Stereotypes and Amplifying Strengths” was to conduct a launch of new book published in September 2024. Dr. Tasha Alston, Dr. Brianna Lemmons, and Dr. Latrice Rollins are the Co-Authors/Co-Editors of the new book. This book launch/talk included a panel presentation with Community Leaders (Healthy Start Inc- Pittsburgh) from Pittsburgh who lead community work on fatherhood, the Editor of Lexington books, faculty members at the University of Pittsburgh (Oakland) that have a vested interest in fatherhood research, Black fathers, strengthening Black families, and Black communities
Quantum dynamics simulation of the advection-diffusion equation
No full textThe advection-diffusion equation is simulated via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) variational quantum time evolution (VarQTE), and (3) adaptive variational quantum dynamics simulation (AVQDS). These schemes were originally developed for the Hamiltonian simulation of many-body quantum systems. The finite-difference discretized operator of the transport equation is formulated as a Hamiltonian and solved without the need for ancillary qubits. Computations are conducted on a quantum simulator (IBM Qiskit Aer) and a superconducting quantum hardware (IBM Fez). The former emulates the latter without the noise. The actual hardware implementation experiences significant noise. The results of the quantum simulator are compared with data from direct numerical simulation (DNS) with infidelities of the order . In the quantum simulator, Trotterization is observed to have the lowest infidelity and is suitable for fault-tolerant computation. The AVQDS algorithm requires the lowest gate count and circuit depth. The VarQTE algorithm is the next best in terms of gate counts, but the number of its optimization variables is directly proportional to the number of qubits. Due to current hardware limitations, Trotterization cannot be implemented, as it has an overwhelming large number of operations. Meanwhile, AVQDS and VarQTE can be executed at the hardware level. These algorithms present a new paradigm for computational transport phenomena on quantum computers
A Mixed Methods Approach to Modeling Performance Losses In Thermoelectric Generators Due to Material Sublimation
No full textThermoelectric generators (TEGs) serve as critical components in remote power systems, particularly in radioisotope thermoelectric generators (RTGs) used for deep-space missions. These devices leverage the Seebeck effect to convert heat into electrical energy. However, the long-term performance of TEGs, especially those based on silicon-germanium (SiGe) alloys, can degrade due to sublimation of the thermoelectric materials under high-temperature conditions. This sublimation leads to a non-uniform reduction in the cross-sectional area of the thermoelectric legs, affecting their electrical and thermal conductivities and ultimately impacting overall power output. This study introduces a mixed-methods model that combines finite differencing and an analytic unicouple-level model to assess the sublimation effects on SiGe-based unicouples over time. The model discretizes thermoelectric legs into axial slices, enabling the modeling of temperature-dependent sublimation rates axially along the unicouple. Experimental correlations from NASA JPL are used to model mass loss as a function of temperature, which is subsequently translated into reductions in cross-sectional leg area. The proposed model provides a robust framework for evaluating the end-of-life performance of RTGs, offering insights into the mitigation of sublimation effects through advanced materials and coatings
Optimization and Demagnetization Analysis of Permanent Magnet Machines using Hot Rolled Permanent Magnets
No full textElectric vehicle development and research have greatly expanded in recent years due to zero-emission vehicles being incentivized for emission reductions. A key part of electric vehicles is the electric motors they use. The main motor topology currently used is the permanent magnet machine. Using permanent magnets, consisting of rare earth elements allows for high efficiency and high energy density machines. Rare earth elements, however, are a controversial material to use, due to their difficulty to obtain and highly variable price. As a result, there has been increased research into alternate materials that do not rely on rare earth elements. In this research, one of these developed materials, an emerging permanent magnet material, will be used in optimization. This optimization aims to create motors with these new materials and match the performance of commercial-grade motors that use traditional permanent magnets. Specifically, both surface permanent magnet and interior permanent magnet motors will be created. They will specifically be created with a focus on mitigating demagnetization that can happen during normal operation. Finally, this thesis will examine a brief fault analysis using the optimized motors. The fault analysis will include looking at optimized motors that do not include demagnetization mitigation in their creation. Ansys Optislang and Ansys Motorcad will be utilized heavily to accomplish the two optimizations
Statistical Inference Methods to Identify Tumor Microenvironment Heterogeneity
No full textThe tumor microenvironment (TME) is a dynamic ecosystem that is continually tested and shaped by the tumor cells. It protects the tumor cells from being attacked by the host immune system and facilitates tumor growth. Therefore, it is of significant interest to understand the cell- cell interplays within the TME to help demystify the mechanisms of immune evasion. However, current computational methods fail to reveal the whole picture of the cell-cell interactions since the activated cell functions in the cell are not fully captured. Several text mining models and factorization analysis methods have been developed to solve this problem; nevertheless, they require strong hidden assumptions that are not met in the context of molecular biology. The goal of this work is to explore a way to quantify and estimate the utility of upregulated functions (specific biological activities that a cell would perform) for cell differentiation and specialization, through which to understand and interpret the communications between cells in a causal relationship. To this end, I developed three aims and address them step by step: 1) scGEM is a nonparametric Bayesian model based on a Dirichlet process to identify correlated gene co- expressing modules within the cell subtypes. The gene modules identified by scGEM are expected to follow cell differentiation path and reflect cellular functions at a higher resolution than the current methods; 2) CRCAtlas uses scGEM to investigate gene modules of colorectal cancer and then employs a pharmacological computational method to locate the ligand receptor pairs that explain such correlations; 3) IOhub is introduced as one of the largest publicly curated databases for immuno-oncology research. By incorporating the gene modules from single cells, IOhub has the potential to discover new biomarkers to predict clinical response to immune checkpoint blockade. Overall, this dissertation contributes to the understanding of cancer immunology and the advancement of precision medicine
Characterizing Replication Stress in Glioblastoma
No full textThis dissertation investigated glioblastoma multiforme (GBM), a highly aggressive brain malignancy with poor outcomes. The disease’s heterogeneity promotes treatment resistance and recurrence, with limited biomarkers available o guide therapy. MGMT promoter methylation, the sole clinical biomarker for temozolomide response, has limited predictive utility, underscoring the need for novel approaches.
This dissertation examined replication stress (RS), a key feature of GBM linked to DNA damage and genetic instability. Using advanced tissued-based multiplexed imaging technologies, RS was characterized at single-cell resolution in tumor specimens. High-dimensional biomarkers, including the Multidimensional Replication Stress Index (MRSI), were developed to quantify RS and identify therapeutic vulnerabilities.
The researched aimed to (1) identify and validate a panel of RS markers using multiplexed imaging, (2) develop the MRSI metric for quantifying RS data, (3) characterize RS profiles in primary GBM, and (4) assess RS modulation following drug treatment in a clinical trial for recurrent GBM. These efforts integrated imaging data with computational modeling to uncover novel biomarkers and stratify patients for personalized therapy.
This dissertation’s innovative use of multiplexed imaging enabled detailed visualization of protein dynamics and RS at multiple scales. Findings advanced biomarker development for GBM and provided a framework for addressing RS-driven vulnerabilities in other cancers, with potential implications for improving precision oncology
The All of Us Research Program Training and Education Platform Summary Report
No full textThe NNLM All of Us Program Center (NAPC) developed, maintained, and enhanced the All of Us Training and Education Platform (TEP) from August 2017- April 2025. This work was initiated to develop a central repository for educational content related to the All of Us Research Program, specifically to host compliance training on universal knowledge and skills to meet early program requirements for Consortium staff, and eventually graduated to role-based content and modular learning paths to increase Consortium staff knowledge and skills in priority areas that support engagement, enrollment, retention and return of value for the All of Us, and to increase researcher knowledge and skills to support their access and use of the All of Us Researcher Workbench. At the time of report, over the life of the TEP and across two Learning Management Systems (LMS), NAPC has built, launched and hosted 57 trainings, including version updates, refreshers, annual renewal courses, and reference courses, and supported over 27,000 learners in over 71,000 training completions, including more than 20,000 researchers and more than 6,000 NIH and All of Us Consortium staff
No Substitutions: Preparing for Open Science Training by Sharing Your Own Research Protocol
No full textBreaking Barriers in Research: Enabling Compliance & Innovation At Pitt with REDCap and The Advara Suite
No full textThe Health Science Information Technology (HSIT) Application Support Team, composed of Becky King and Linda Stevanus-Schmadel, provides essential support to Pitt and UPMC research study teams by assisting with REDCap projects, Qualtrics, and Advarra products (OnCore, eReg, eDCF). In 2024, HSIT answered 6,500 support tickets, with almost 600 more in January 2025, as demand continues to rise. This poster, presented by Becky King, highlights ticketing trends, the impact of eConsent, new Pitt IRB guidelines on eConsent, and the implementation of Advarra OnCore, eReg, and eDCF. A QR code linking to a REDCap test eConsent project demo will be included, allowing attendees to explore its role in improving research efficiency. This poster provides insights into optimizing workflows and leveraging HSIT support for study success