VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 50 freely available and openly licensed textbooks are among our most downloaded items.
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Recent Submissions
A Graphical Approach to Identifying Structural Bias Using Directed Acyclic Graphs: Its Application to Two-Wave Nonequivalent Control Group Designs
Shin, Jaehyun (Virginia Tech, 2026-03-18)
It is well known that the analysis of covariance (ANCOVA) and the change-score analysis (CSA) can produce quite different treatment-effect estimates when applied to data from two-wave nonequivalent control-group designs, a phenomenon known as the Lord's paradox. Pearl's (2009) structural causal model (SCM) provides a useful and intuitively appealing tool to address the Lord's paradox. Using the SCM, Kim and Steiner (2021) combined the backdoor criterion with the path-tracing rules and showed that it identified the exact bias for the CSA. Though they implied that this graphical causal model approach could be applied to the ANCOVA case in a similar way, they did not explicitly show the details. Therefore, in the present study, to examine their implication, I applied the graphical approach to the ANCOVA and compared the results with the bias derived by the population ordinary least squares (OLS) method (Lüdtke and Robitzsch, 2025). The comparison exhibited a discrepancy, though the core part of the bias obtained by the graphical approach was correct. Specifically, the discrepancy occurred in the terms that were proportional to the core part of the bias implied by each backdoor path. This means that, though the detection of the sources of bias and the identification of the conditions to eliminate the bias could be completed by the graphical approach, the exact quantification of the bias was not possible. To resolve this shortcoming, I applied the so-called regression anatomy formula, also known as the Frisch–Waugh–Lovell (FWL) theorem in econometrics, and found that the proportional term could be expressed as the residualization-induced scaling factor. I then extended this graphical approach to different data-generating scenarios within two-wave nonequivalent control-group designs and confirmed that it worked well in all cases. The residualization procedure makes a graphical approach self-contained to identify the exact structural bias.
From Policy to Pathways: A One Health Assessment of Antimicrobial Resistance (AMR) in Wastewater and Surface Waters
Okeshola, Idowu Kayode (Virginia Tech, 2026-03-17)
Antimicrobial resistance (AMR) represents a complex environmental and public health challenge driven by interactions among engineered systems, natural ecosystems, and global antimicrobial use practices. While antimicrobial misuse in clinical and agricultural settings contribute to the evolution of resistance, environmental pathways such as wastewater discharge and watershed processes play a critical role in the dissemination of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). We applied a One Health (i.e., humans-animals-environment) framework to assess AMR policies, the extent to which wastewater infrastructure addresses AMR, and AMR inputs to surface waters. Comparative analysis of antibiotic use policies in Nigeria, Germany, and the United States through a comprehensive literature review highlighted how national stewardship strategies and regulatory differences can influence resistance dissemination across socioeconomic contexts. Longitudinal field studies in an urban watershed were conducted to assess the impact of wastewater treatment plant effluent and tributary inputs on shaping microbial communities and antibiotic resistance dynamics. Amplicon sequencing, shotgun metagenomic, and physicochemical measurements were used to characterize microbial community, ARGs, and metal resistance genes (MRGs) across spatial gradients. Treated effluent showed limited impact on downstream microbial communities and resistance gene profiles, highlighting treatment efficiency, whereas tributaries contributed distinct microbial signatures and elevated resistance signals. These findings highlight the need to integrate environmental monitoring, antibiotic stewardship, and infrastructure investment to address AMR while advancing understanding of environmental pathways that influence its dissemination.
Characterization of Antibiotic Resistance Genes using Network-based Approaches
Moumi, Nazifa Ahmed (Virginia Tech, 2026-03-17)
Antibiotic resistance is a natural evolutionary response to the selective pressures created by antibiotic use, but its rapid acceleration has become a major public health crisis. Resistance emerges through both vertical inheritance of chromosomal mutations and horizontal transfer of antibiotic resistance genes (ARGs) across diverse bacterial lineages, enabling the spread of drug-resistant infections and undermining effective treatment. Because ARGs circulate across clinical, agricultural, and environmental settings, mitigating resistance requires robust surveillance and mechanistic understanding of how ARGs function, move, and pose risk in real microbial communities.
This dissertation develops network-based, multi-scale frameworks for studying ARGs from individual genomes to complex environmental metagenomes. In Chapter 2, we investigate what distinguishes ARGs from other genes within bacterial genomes using protein–protein interaction networks. By applying machine learning to interaction profiles, we identify patterns that differentiate ARGs from non-ARGs and reveal interaction signatures linked to resistance mechanisms and dissemination potential. Chapter 3 extends this analysis to metagenomic settings by extracting ARG genomic neighborhoods from metagenomic assembly graphs, enabling context-aware characterization of ARG mobility and horizontal gene transfer potential within microbial communities. Chapter 4 advances from genomic context to ecological risk by introducing a hazard quantification framework that scores ARGs based on their co-occurrence with mobile genetic elements, virulence factors, pathogens, and other resistance determinants, and applies this framework across diverse environments to study how hazards shift over time in response to external pressures. Finally, Chapter 5 synthesizes these insights into a predictive framework for identifying ARGs directly from metagenomic data. By integrating protein language model embeddings with graph-derived features from gene neighborhood graphs, this context-aware model captures both sequence-level signals and neighborhood structure, improving ARG recovery in complex metagenomic samples.
Collectively, this work provides an integrated view of ARGs across biological scales, connecting molecular interaction patterns, genomic neighborhood organization, and environmental hazard to build more accurate and interpretable approaches for resistome profiling and hazard characterization.
Measurable residual mutated NPM1 before allogeneic transplant for acute myeloid leukemia
Al-Ali, Rasha W.; Gui, Gege; Ravindra, Niveditha; Andrew, Georgia; Mukherjee, Devdeep; Wong, Zoe C.; Huang, Ying; Gerhold, Jason; Holman, Matt; Jacobsen, Austin; D'angelo, Julian; Miller, Jeffrey; Elias, Karina; Auletta, Jeffery J.; El Chaer, Firas; Devine, Steven M.; Jimenez, Antonio Martin Jimenez; De Lima, Marcos JG G.; Litzow, Mark R.; Kebriaei, Partow; Saber, Wael; Spellman, Stephen R.; Zeger, Scott L.; Page, Kristin M.; Radich, Jerald P.; Lindsley, R. Coleman; Dillon, Laura W.; Hourigan, Christopher S. (Springer Nature, 2026-02-01)
Adaptation to a Whole-Body Powered Exoskeleton: Human-Exoskeleton Coordination During Load-Handling Tasks
Park, Hanjun; Kim, Sunwook; Nussbaum, Maury A.; Srinivasan, Divya (Springer, 2026-03)
Whole-body powered exoskeletons can augment human performance and reduce physical strain in occupational settings, but little is known about how users adapt to these complex devices during practical work scenarios. We compared novice and experienced users during simulated, occupationally relevant load-handling tasks. Six novice users completed exoskeleton familiarization and stationary load-handling tasks in three sessions while five experienced users performed the tasks once. Task performance, biomechanical demands, and perceived workload were compared in each novice session vs. the experienced group. Novice performance improved substantially across sessions, with task completion time reduced by nearly 50% and movement jerk by 30%. However, performance gaps still persisted in session three, compared to the experienced users. Novices also used consistently lower angular velocities (up to 52% lower) and adopted greater hip flexion throughout the sessions. In contrast, differences in shoulder flexion, muscle activity, perceived exertion, and workload diminished more rapidly, with novices approaching experienced levels by session three. Novice users adapted to using a powered exoskeleton over multiple sessions, especially in movement patterns and muscle activation, but differences in task completion time, jerk index, and angular velocities indicated that novices did not attain the skilled coordination and efficiency of experienced users after three sessions. Our results highlight the likely need for extended familiarization and training for the current powered exoskeleton design and provide baseline data for the novice learning curve in occupational settings.