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Recent Submissions
The Peripheral-Central Neuroimmune Interface: The Influence of Monocyte Reprogramming on Chronic Neuroinflammation in the Dentate Gyrus Following Traumatic Brain Injury
Harris, Elizabeth Ann (Virginia Tech, 2026-09-15)
Traumatic brain injury (TBI) is a leading cause of chronic morbidity with over 3 million people in the US alone living with a TBI-induced long-term disability. While the immediate effects of the impact itself can be devastating, long-term neurocognitive sequelae are most commonly due to ongoing secondary injury driven by chronic neuroinflammation. The dentate gyrus (DG) is a specialized region of the hippocampus responsible for memory contextualization and mood regulation. The DG also acts as a gateway organizing signals between the cortex and outer hippocampus, and hilar interneurons located in this region are critical for maintaining tight regulation of excitatory and inhibitory impulses. These interneurons are selectively vulnerable to secondary brain injury, leading to impaired cognitive and behavioral function. Peripheral-derived mononuclear cells (PDMs), namely monocytes and macrophages, have been recently implicated as key drivers in chronic neuroinflammation. Previous work has demonstrated that EphA4 is upregulated in infiltrating monocytes following brain injury and that blockade of EphA4 causes PDMs to adopt a less inflammatory, pro-resolving phenotype. Despite these findings, the precise, long-term impacts of infiltrating PDM activity on the DG microenvironment and interneuron populations has never been investigated. I hypothesize that a persistent PDM-driven proinflammatory cytokine profile drives hilar interneuron loss, and that deletion of EphA4 in monocytes would limit neuronal loss and improve long-term memory and mood outcomes through modulation of the local cytokine profile and reducing chronic glial activation. The work included in this dissertation characterized the temporospatial profile of the dentate gyrus microenvironment over four months following TBI and determined that chronic hilar interneuron loss and cognitive decline are associated with TNF-TNFR signaling. These studies also determined that EphA4 deletion in monocytes reduced hilar interneuron loss, chronic glial activation, and improved long-term neurobehavioral outcomes through downregulation of classical proinflammatory TNF and NF-κB signaling pathways. In addition, dual deletion of monocyte-specific EphA4 and Tie2 reversed these neuroprotective effects, indicating that PDM-associated EphA4/Tie2 signaling may be a key target for future therapeutic strategies.
Single-Cell and Spatial Transcriptomic Dissection of Brain Cellular Responses to Nutritional and Injury Perturbations
Lin, Yu (Virginia Tech, 2026-09-15)
Single-cell and spatial transcriptomic technologies have transformed our ability to resolve the cellular composition of complex tissues, such as the brain, and provide single-cell-level expression profiles. Yet interpreting these data requires computational approaches that move beyond cataloging discrete cell types to capture the continuous, context-dependent transcriptional states that cells adopt in response to perturbation. In this work, we develop and apply single-cell, single-nucleus, and spatial transcriptomic approaches to characterize how the developing and injured brain responds to two classes of perturbation-nutritional and injury-reading out each challenge as a shift in cell-type composition and cell-state programs.
In Chapter 2, we apply spatial transcriptomics together with single-nucleus multi-omics (paired snRNA-seq and snATAC-seq) to investigate how excess maternal folic acid supplementation affects offspring brain development. We find that maternal folic acid excess alters gene programs governing neurogenesis and axon myelination in a region-specific manner, and identify maturing excitatory neurons of the hippocampal dentate gyrus as particularly vulnerable, exhibiting coupled changes in gene expression and chromatin accessibility within ribosomal biogenesis pathways critical for synaptic formation.
In Chapter 3, we extend the nutritional perturbation to postnatal timing by modeling an abrupt prenatal-to-postnatal drop in folate availability—a "folate cliff"—in the developing cerebellum. Combining behavioral assays with bulk transcriptomic analysis, we observe a nonlinear behavioral dose-response and a convergent disruption of glial and myelination programs, marked by upregulation of Gfap and downregulation of Pdgfra and Mbp. We interpret these changes as a coordinated glial/myelin disruption signature, while noting the limited sample size and the cell-type resolution deferred to future work.
In Chapter 4, we develop StateCommute, a computational framework to present cells in pathway space and integrate non-negative matrix factorization, compositional (differential-abundance) analysis, trajectory inference, and cell-cell communication modeling to resolve transcriptional states across distinct brain injuries. Applying it to a harmonized atlas of viral encephalitis (VEEV), organophosphorus nerve-agent (OPNA) exposure, and traumatic brain injury (TBI), we identify cellular states and pathway-level programs that are shared across injuries as well as those specific to each-recovered without reference to condition labels yet anchored in genuine, injury-driven transcriptional change.
Together, these chapters demonstrate how single-cell and spatial transcriptomic approaches can be used to characterize cellular responses to diverse biological perturbations during brain development and injury. In addition to providing biological insights into the mechanisms, this work introduces and applies computational methods for integrating multimodal and spatial transcriptomic data to better define cellular states and their interactions. Collectively, these findings provide a framework for studying dynamic cellular responses in the brain and establish a foundation for future investigations of brain development, injury, and neurological disease.
Modulation and Control for High-Frequency GaN-Based Bidirectional AC--DC Converters
Chen, Xingyu (Virginia Tech, 2026-09-15)
Alternating-current--direct-current (AC--DC) converters serve as a critical interface between the electric grid and direct-current (DC) loads. With the increasing demand for data center power consumption, renewable energy systems, energy storage systems, electric vehicles (EVs), AC--DC converters are required to reach high efficiency, high power density, high reliability and also support both rectifier-mode and inverter-mode operations. Gallium nitride (GaN) high-electron-mobility transistor (HEMT) devices provide superior switching performance compared with conventional silicon (Si) metal--oxide--semiconductor field-effect transistors (MOSFETs), enabling high-frequency and high-power-density converter designs. Critical-conduction-mode (CRM) operation can further utilize the advantages of GaN devices by achieving zero-voltage switching (ZVS). However, as the switching frequency increases, new challenges arise in zero-current-detection (ZCD) sensing, digital modulation, closed-loop control and increased circulating current. Furthermore, the current distortion issue under non-unity power factor (PF) operation is another challenge for inverter mode operation. This dissertation investigates the sensing, modulation, and control of high-frequency GaN-based bidirectional totem-pole (TP) AC--DC converters.
First, a reliable ZCD-sensing-based CRM modulation method is developed for GaN-based TP AC--DC converters. The noise propagation path in the ZCD sensing circuit is analyzed, and a noise-oriented circuit design method is proposed, including component selection guidelines and printed-circuit-board (PCB) layout rules. To avoid false triggering and clamp the maximum switching frequency, a window-based ZCD signal processing method is introduced. In addition, the closed-loop control challenge of high-frequency CRM TP-power factor correction (PFC) converters is addressed. Conventional constant-on-time (COT) control can suffer from input-current total harmonic distortion (THD) due to the resonance between the inductor and switch output capacitances, while variable-on-time (VOT) operation with voltage-mode control (VMC) is sensitive to parameter tolerances. Therefore, an average-current-mode control (ACMC) method with adaptive compensator gain is proposed to directly regulate the AC current and improve the dynamic response near the AC voltage zero-crossing region. The proposed ZCD sensing circuit and closed-loop control methods are verified on a 1~kW GaN-based TP-PFC prototype. Experimental results show noise-free ZCD operation with negligible sensing delay, and the measured input-current THD is 1.97% at full load and remains below 5% over the tested operating range.
Second, a sensor-less CRM modulation method is proposed to eliminate the dedicated ZCD sensing circuit and reduce the impact of system response delay. Although improved ZCD circuits can achieve reliable operation, practical converter systems may still suffer from severe electromagnetic-interference (EMI) noise, increased bill-of-materials (BOM) cost, and delay-induced performance degradation, especially when the switching frequency is pushed even higher. Quantitative analysis shows that even a response delay at the 50~ns level can significantly increase the inductor-current ripple under ultra-high-frequency operation. To address this issue, a model-based CRM modulation method is developed, where the synchronous-rectifier (SR) conduction time is calculated based on the sensed average inductor current. The proposed model avoids the complex timing calculation of the second-order resonance between the inductor and switch output capacitances while maintaining good accuracy. The tolerance analysis shows that the model has low sensitivity to the output-capacitance tolerance. With printed-circuit-board (PCB) winding inductors, the proposed method has strong potential for mass-production applications. Experimental comparison with ZCD-based modulation shows reduced inductor current and smaller circulating current. The model-based method is further applied to a 2.2~kW two-channel interleaved TP-PFC prototype with PCB inductors, achieving a measured peak efficiency of 99.02%.
Finally, high-frequency CRM operation is extended to inverter-mode applications. Under non-unity-PF operation, the zero-crossing instants of the AC current and AC voltage no longer coincide, which causes an extremely high switching frequency near the AC current zero crossing. To solve this issue, discontinuous-conduction-mode (DCM) modulation with stepwise resonant-dead-time adjustment is proposed to clamp the switching frequency. A ZCD window is also applied to avoid undesired triggering caused by DCM current ringing. In addition, the AC-voltage zero-crossing blanking time used in conventional unipolar modulation can cause current distortion in inverter-mode operation. Although bipolar modulation can avoid this issue, it increases the switching frequency and reduces the benefit of CRM operation. Therefore, a unipolar--bipolar hybrid modulation method is proposed. In this method, blanking time is applied around the AC current zero crossing instead of the AC voltage zero crossing, and bipolar modulation is only used near the voltage zero-crossing region. A compare-value adjustment method is further proposed to achieve seamless transitions between modulation modes without using conventional action-qualifier software forcing. Hardware experiments verify the effectiveness of the proposed DCM frequency-clamping method and hybrid modulation strategy.
Overall, this dissertation provides a systematic study of sensing, modulation, and control techniques for high-frequency GaN-based bidirectional TP converters. The proposed methods improve the reliability of CRM operation, reduce current distortion, mitigate delay and tolerance issues, and extend CRM operation from rectifier-mode PFC applications to bidirectional applications.
Impact of the Kcnt1 R428Q Mutation on Neuronal Subtype Excitability and Therapeutic Approaches in a Mouse Model
Safari, Mona (Virginia Tech, 2026-09-15)
Gain-of-function (GOF) mutations in KCNT1, encoding the sodium-activated potassium channel KNa1.1, are associated with severe developmental and epileptic encephalopathies, including epilepsy of infancy with migrating focal seizures (EIMFS). However, the cell-type–specific mechanisms underlying seizure generation and effective targeted therapies remain unclear. Here, we generated a mouse model carrying the Kcnt1-R409Q (human R428Q) mutation and found that this GOF variant selectively reduces the excitability of somatostatin (SST) interneurons, while excitatory neurons and parvalbumin (PV) interneurons remain largely unaffected. Selective expression of the mutation in medial ganglionic eminence (MGE)-derived interneurons was sufficient to recapitulate the seizure phenotype. We next tested a selective, high-potency KCNT1 inhibitor and found that it robustly suppressed seizures in vivo. Electrophysiological recordings revealed that KCNT1 inhibition increased interneuron excitability, reversing the hypoexcitability observed in mutant mice, while excitatory neuron function remained unchanged. To assess whether this approach extends to other channelopathies, we evaluated the compound in a Dravet syndrome model caused by loss-of-function of the NaV1.1 sodium channel (SCN1A). Treatment significantly reduced seizure frequency, decreased SUDEP incidence, and improved survival. At the cellular level, KCNT1 inhibition enhanced PV interneuron excitability, indicating a shared mechanism across distinct genetic epilepsies. Together, these findings identify KCNT1 as a key regulator of neuronal excitability and demonstrate that its inhibition can modulate circuit function and suppress seizures. This work highlights the potential of KCNT1 inhibition as a therapeutic approach for multiple forms of drug-resistant epilepsy.
Development of an Energy Management Strategy for an All-Wheel Drive Electric Vehicle
Weng, Tony (Virginia Tech, 2026-09-15)
As part of the EcoCAR EV challenge, a energy management strategy was developed for a 2023 Cadillac Lyriq. To assist in testing the proposed strategy, a forward propagating model was developed in MATLAB and Simulink using virtual vehicle components. To validate the model, output data from simulation was compared with output data from the vehicle obtained on an all-wheel drive dynamometer under the same drive cycle. For this study, a power minimization equation with a torque split algorithm was used to find the optimal torque split across all torque-speed domains. This searches for the front and rear drive unit split ratios that minimizes power loss. Using driver demand and vehicle speed as inputs, a fuzzy logic controller was used to improve vehicle drivability by reducing jerk in high oscillating torque split regions. In depth simulations of UDDS, HWFET, and competition specified trapezoidal drive cycles were conducted to validate this method. The results show that the proposed strategy reduces energy consumption by 3.28% for UDDS, 1.46% for HWFET, and 2.41% for the trapezoidal drive cycles when compared to a baseline capability split.