Soluble peptides from the HIV-1 (Human Immunodeficiency Virus) envelope heptad repeat-2 domain, known as HIV fusion inhibitors, can inhibit viral entry by blocking formation of the gp41 6-helix bundle required for membrane fusion and infection. However, this treatment is unfeasible because it requires twice-daily subcutaneous injections with high risk and cost. The Lieber Lab is working to engineer hematopoietic stem and progenitor cells (HSPCs) to express HIV fusion inhibitors in vivo, potentially offering sustained protection against HIV. In my work I used SIVmac239 (Simian Immunodeficiency Virus) challenged Rhesus Macaques sera and developed viremia (from another study by Lieber lab). My goal was to test whether anti-gp41 antibodies from these animals cross-reacted with synthetic gp41-derived fusion inhibitor peptides, specifically C46-v2o, C34-SFT, and Enfuvirtide(T20). If antibodies interfered with fusion inhibitors, their therapeutic effect would be severely compromised. In my project, I developed an Enzyme-Linked Immunosorbent Assay (ELISA) to measure antibody titers. These peptides were coated, then blocked with 3% bovine serum albumin, and incubated with diluted Macaque serum to allow antibody binding. I used anti-monkey immunoglobulin-G conjugated with Horseradish Peroxidase for detection of antibody binding. I optimized the serum dilution to 1:200 to reduce background signal and concluded SIV-challenged Macaques had detectable antibody levels against C46-v2o and C34-SFT, but not T20. Ongoing work will determine more detailed IC50 antibody titers in serum samples. Notably, animals with high viral loads exhibited higher levels of antibodies against HIV fusion inhibitors. T20 is a promising candidate for sustained HIV inhibition, as no detectable antibodies means it’s less susceptible to pre-existing immune responses. These findings provide valuable insights into how fusion inhibitors interact with the immune system and help refine strategies for HSPC-based HIV therapies, bringing us closer to a long-term, self-sustaining approach for HIV prevention. 
 

Before a child says their first word, they begin to produce and practice sounds they hear. Early vocalizations play a crucial role in speech development and language acquisition. However, most research on infant vocalizations focuses on children in Western, industrialized societies. This study contributes to the growing body of literature on diverse linguistic environments, specifically examining emergent sounds in the Panãra community, an Indigenous group in the Brazilian Amazon with approximately 700 speakers. Ten infants aged 2-21 months wore recording devices that collected a recording of their language environment over a day. Alongside shared ethnographic observations, I manually annotated selected 30-second audio segments for a fine-grained analysis of child vocalizations. I am currently analyzing the frequency and types of child vocalizations (i.e. vocal play, canonical babbling, variegated babbling) in infants' speech, and I plan to explore how these vocalizations may differ across the age range studied. I predict that child vocalizations will become more complex with increasing age, following pre-speech vocal development stages broadly found across cultures. My findings will contribute to a broader understanding of how language learning varies across cultural settings, vocalization stages, and the role of the environment to language development. 

Brains can somehow maintain functionality despite significant neuron loss. However, we do not yet fully understand what factors contribute to this robustness or under what conditions brains become fragile to neuron loss. Research in our lab has identified two types of neurons: those that, when removed, lead to large changes in the network’s expected activity patterns, and those that do not appear to be so critical. My research aims to address this gap. I study the network properties that confer robustness in an ideal system: the Drosophila fly, the most complex organism with a fully mapped brain at ~140,000 neurons. I am focusing on one particular brain region, the Antennal Motor and Mechanosensory Center (AMMC), because it is a primary sensory region that receives direct connections from the fly’s ear (the antennae) and contributes to auditory-driven behaviors such as courtship, which we can easily measure.  I have found that many anatomically distinct neurons share high level network properties. I hypothesize that the morphological and network properties of these neurons make them special. Here, I investigate the morphological features of several neurons, such as arborization patterns, neurotransmitter profile, and synaptic partners, and also search for genetic driver lines through a large database that will help us test the impact of these neurons in a living fly. Investigating the relationship between neural properties and robustness to removal of a neuron is crucial for developing a deeper understanding of how brain circuitry copes with injuries and the progression of neurodegenerative diseases.

Direct Analysis in Real Time Mass Spectroscopy (DART+ MS) is a chemical identification tool that uses a superheated gas stream to ionize chemical samples, producing a distinct chemical signal that can be used to identify the composition of an unknown sample. DART+ MS is used reliably in fields like forensics, food safety, pharmaceuticals, and more recently, environmental protection. At the Wasser Research Lab, at the Center for Environmental Forensic Science, we work to protect endangered species such as African Elephants. Using Direct Analysis in Real Time Mass Spectroscopy, we seek to find if elephant ivory from different regions in Africa has distinct chemical signatures, allowing us to geolocalize ivory samples based on their DART+ MS signatures. Current methods of elephant geolocation include genetic testing, but results can often be ambiguous; By using this completely different, complementary approach, we could improve our estimates of these inconclusive tests. If there is a chemical difference in the ivory of Elephants from the Savannah and Forest regions of Africa, then we can trace the origins of ivory obtained from illegal seizures, aiding in the conservation efforts of African elephants. Chemical distinctions aside, we also hope to answer questions about the effects of certain chemical preservatives on ivory samples and whether the DART+ MS signal varies along the length of the cut of the tusk, establishing best practices for sampling. Ultimately, our goal is to determine if DART+ MS proves to be a reliable and quick method of identifying elephant ivory for conservation efforts. By bridging cutting-edge technology with conservation science, we hope this research will make a significant impact on efforts to combat the illegal ivory trade and wildlife crime. 

Somatosensory neurons innervate the skin, where their peripheral axons detect signals like touch and pain. The neurons relay stimuli to the brain via peripheral axons in the skin and spinal cord axons in the spinal cord. Given their superficial location, somatosensory axons are susceptible to damage. Axon damage can cause tingling, increased pain, or sensory inhibition, and reinnervation in mammals is often slow or incomplete. I use injury models in zebrafish to study the mechanisms of successful axon regeneration in an adult vertebrate with optically accessible skin. I aim to reveal conserved regeneration patterns of somatosensory neurons. Furthermore, I seek to understand the extent of reinnervation success and observe the prevalence of hyperinnervation post-injury. Using in vivo confocal microscopy and adult zebrafish skin models, I created a methodology to capture somatosensory reinnervation over a three-week span following a scale pluck injury. Zebrafish scales separate epidermal and dermal layers of skin, and scale removal induces regeneration of epidermal skin and surrounding dermal tissue. I use transgenic zebrafish with fluorescent labels for dorsal root ganglion DRG neurons and osteoblast cells Tg(p2rx3a:mCherry);Tg(sp7:EGFP). DRG neurons are the primary somatosensory neuron in adult zebrafish, and osteoblasts allow me to view the scale alongside axon reinnervation. For image acquisition, I designed a 3d-printed chamber for zebrafish mounting and intubation within our confocal microscope. For analysis, I developed Image J macros which use threshold analysis to quantify changes in axon density of specific regions of regenerating axons. Dermal axons tend to regenerate first while superficial axons in the epidermis regenerate secondarily in conjunction with the novel scale. To examine skin layer differences, I separate epidermal and dermal layers to compare the reinnervation trends between superficial and dermal axons. With this data, I can gain insight in the regeneration potential of somatosensory neurons.

Fentanyl is a synthetic opioid that has become the leading driver of the U.S. opioid epidemic, contributing to over 70,000 overdose deaths annually. Opioid use disorder (OUD) is characterized by cycles of dependence, withdrawal, and relapse, with most fatal overdoses occurring during relapse, yet existing treatments for OUD do not effectively prevent relapse. Understanding how fentanyl affects brain activity and behavior is critical for developing more effective therapies. I investigated how fentanyl exposure modulates locomotion and the neural activity in the nucleus accumbens (NAc) across abstinence, dependence, withdrawal, and relapse. I hypothesized that each stage would show distinct neural activation patterns and that fentanyl exposure would reduce exploration and locomotion, reflecting compulsive drug-seeking behavior. To test this, I implanted silicon probes in the NAc of mice to monitor neural activity while tracking movement and behavior with high-resolution video. Mice received increasing fentanyl doses over five days, followed by a withdrawal period and, finally, a relapse challenge dose. I analyzed their behavior using deep learning-based pose estimation for correlations with neural activity across different stages of fentanyl exposure. I expect neural recordings to show that fentanyl significantly alters NAc activity, with each phase displaying unique neural patterns. I also expect fentanyl-exposed mice to show reduced exploratory movement, consistent with behavioral inflexibility and compulsive drug-seeking tendencies characteristic of OUD. These findings could provide critical insights into how fentanyl disrupts brain function and behavior, helping to identify new targets for addiction treatment. This research lays the groundwork for future studies on relapse prevention, with the goal of improving OUD therapies and reducing overdose deaths.

The mechanisms guiding the sensory detection of pain and the subsequent sensitization of damaged tissue to mechanical and thermal stimuli are relatively well understood. However, mechanisms guiding the transformation of nociception into the negative feelings associated with pain remain largely unknown. This affective component, notably in chronic pain, translates into an intense emotional impact on patients and can contribute to the development of comorbid psychiatric disorders. The elderly population have a propensity to be socially isolated and face exacerbated effects of chronic pain. In 2021, an estimated 20.9% of U.S adults suffer from chronic pain with persons over 65 years of age having the greatest propensity of acquiring the disease. Due to this, clinical intervention models call for a more holistic approach to pain intervention that incorporates lifestyle and nutritional factors, extending beyond pharmacological treatments. One of these promising non-pharmacological interventions is positive social interaction, which has been shown to alleviate pain and suffering.  Several studies show that humans who maintain strong social bonds recover from injuries faster than people without them. However, it has not yet been evaluated the extent to which this phenomenon occurs in geriatric animals and its relative efficacy as a social intervention to alleviate chronic pain in injured mice. My project seeks to gauge whether social intervention can alleviate chronic pain symptoms in aged mice and to unveil the underlying mechanisms guiding these successful non-pharmacological treatments. I will achieve this through two aims: evaluation of social self-administration as an intervention for chronic pain, and transcriptomic analysis to identify gene expression changes as a result of social interaction. Future research will include miniscope endomicroscopy recordings to visualize cell activity within major brain regions, and comparison of cell ensemble activity between groups of mice will lead to the identification of structures encoding behavioral shifts caused by pain.

Pulmonary Arterial Hypertension (PAH) is a deadly vascular disease, affecting the blood vessels of the lungs, with no existing cure. PAH is characterized by pulmonary arterial smooth muscle cell (PASMC) hypertrophy and hyperplasia, which increases resistance to blood flow within the pulmonary arteries, leading to rapid symptom progression and eventual death from right heart failure. My mentor and I hypothesize that defects in PASMC differentiation and alignment may contribute to PAH. To test whether alignment and phenotypic responses differ in patients with PAH, we designed a micropatterned collagen scaffold atop a glass coverslip. Explanted PASMCs from patients with PAH or failed donors (controls) were cultured on alternating 10-µm wide x 10-µm deep microchannels or unpatterned constructs and alignment, protein expression, and cellular morphology were compared across conditions. I evaluated 3 PAH and 3 control subjects and have collected preliminary data for each condition (control versus PAH), with three technical replicates each. Through these preliminary studies, I have demonstrated success of my model with consistent alignment observed on patterned substrates. Excitingly, PASMCs from patients with PAH expressed significantly decreased levels of the contractile protein, Calponin, when compared with control cells, including after responding to cues that promote alignment and contractility. This suggests that PAH PASMCs remain in an inappropriately synthetic or proliferative state. Moving forward, I plan to evaluate additional micropatterns by varying dimensions of rectangular and sine waves designs using an ablation protocol with a 2-photon microscope laser. Subsequent evaluation will include immunofluorescent staining of contractile and other SMC markers as well as transcriptomic evaluation of cellular responses to micropatterning. This work will enhance understanding of whether SMC abnormalities contribute to disease initiation and progression in PAH and will contribute to the broader effort of developing more complex models of pulmonary vascular disease.

Speech technology is often evaluated under idealized conditions that privilege certain speaker profiles: native English speakers in optimal acoustic environments. This approach overlooks the reality that English, as a global lingua franca, is spoken by billions of non-native speakers. Similarly, speakers with speech disorders face potential exclusion. Accurate phonemic transcription is crucial both for analyzing speech patterns in post-stroke aphasia and Computer-Assisted Pronunciation Training (CAPT). We evaluate automatic phonemic transcription under realistic conditions, including varied noise levels, L2 accents, and speech variations. We find that standard models perform suboptimal under realistic conditions, and that applying vocabulary refinement and data augmentation improves error rates by 12-28 percentage points. To demonstrate the viability of our phonemic transcription models, we develop Machine Aided Pronunciation Learning via Entertainment (MAPLE). MAPLE maintains real-time performance on consumer devices, demonstrating the practical applicability of robust socioculturally-aware phonemic transcription in educational environments.

Integrating complex animal behavior with peripheral physiological recording is critical for revealing the neural basis of behavior. Traditional peripheral physiological recording methods constrain natural behavior due to cable tethers, and manually annotating behavior often introduces subjectivity. We have recently published two pipelines that independently overcome these confounds: (1) mechano-acoustic (MA) devices that provide wireless, minimally invasive peripheral recording based on finely-tuned accelerometers, and (2) a computer vision based machine learning package (Simple Behavioral Analysis, SimBA) for supervised behavioral classification from recorded videos. Here, we developed a comprehensive machine learning model to classify behavioral states using MA device accelerometer data, using SimBA to validate and extend model outcomes. We test this model by analyzing the effect of anesthesia and other consciousness-altering drugs on mice. Lastly, we extend this approach for closed-loop applications. This work contributes to the growing field of bio-signal processing, offers a data-driven approach to automated behavior classification, and provides the groundwork for answering many diverse questions in neuroscience and related fields.

While some species cannot persist in urban areas, coyotes (Canis latrans) thrive in cities in part thanks to their varied diet and creative scavenging. Urban coyotes consume more anthropogenic foods and have more diverse diets than wild populations, and the quality of the anthropogenic foods they consume varies with the landscape of the city. In addition, consumption of particular anthropogenic foods can bring coyotes into conflict with resident human populations. What are coyotes eating in Seattle, and what does their diet composition say about the specific urban environments they inhabit? DNA metabarcoding, a technique used to genetically identify the species present in a sample, provided an initial idea of coyote diet composition. However, the metabarcoding data lacks resolution for plants, invertebrates, and some anthropogenic foods. This study investigates the diet composition of Seattle’s coyotes through traditional scat analysis, building on previous metabarcoding work to identify key diet items. Traditional analysis allows for better identification of plant and invertebrate species via the identification of hard-items such as bones, exoskeletons and seeds, and can provide additional resolution where metabarcoding primers lack specificity. In particular, traditional analysis contextualizes the dietary role of chicken— the presence or absence of physical items such as feathers clarifies if coyotes are eating domestic chickens or anthropogenic foods. I estimate the percent composition of each item in a given sample and compare these results to the metabarcoding data in order to compare the strengths of traditional and genetic techniques for diet analysis. My anticipated results provide valuable information regarding the dietary role of invertebrates, the plants coyotes consume and disperse, and if coyotes are consuming domestic chickens— highlighting the advantages of traditional analysis used in conjunction with metabarcoding. These results will help refine the methods of omnivore diet research and inform action to prevent human-wildlife conflict.

Following spinal cord injury (SCI), respiratory function is often impaired due to limited respiratory muscle function. Decreased respiratory function can lead to breathlessness, impaired coughing, reduced exercise tolerance, and increased respiratory infection risks. Previous studies have shown that transcutaneous spinal cord stimulation (tSCS) at cervical and lower thoracic levels can increase vital capacity by targeting respiratory and abdominal muscles in individuals with cervical SCI. This case series study aims to evaluate the effects of tSCS combined with arm crank exercise on respiratory function after SCI. We recruited three individuals with cervical motor-complete SCI, who were randomly assigned to the active tSCS or sham stimulation group. Two participants underwent 24 training sessions with active tSCS. One participant completed 24 training sessions training with sham stimulation. Spirometry was conducted with real-time tSCS at baseline at different spinal locations. Spirometry was also conducted without real-time tSCS before and after 24 training sessions to assess the long-term effect. Forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and peak expiratory flow (PEF) were measured. Out of all locations tested, T6-T7 showed the largest improvement across all spirometry parameters. Participants in the active tSCS group showed improvements in all parameters after 24 sessions. The participant in the sham group showed decreased PEF. The data collected thus far suggests that tSCS may modulate the spinal neural network responsible for respiratory function. Furthermore, tSCS combined with exercise has potential to improve respiratory function in people living with SCI. A larger sample size is necessary to evaluate the long-term efficacy of this novel non-invasive therapy on respiratory function to improve health after SCI.

Multiple System Atrophy (MSA) is a fatal neurodegenerative disease caused by alpha-syn deposition in the brain and spinal cord. This results in severely declined autonomic and motor functions.  In rare cases of MSA, there is pure autonomic system failure, only including dysregulation of blood pressure (BP) control and pelvic organ functions including bowel movement. Blood pressure changes could be extremely dramatic, with uncontrolled drops below 60 mmHg and elevation sometimes over 250 mmHg, resulting in the inability to even stand for more than one minute without feeling faint. Overall, this greatly impacts an individual’s quality of life and mortality. On average, life expectancy after MSA diagnosis is about 6 to 10 years, though this can vary based on factors such as age at onset and symptom severity. Currently, treatment options primarily focus on mitigating symptoms. This case study reports the effect of non-invasive transcutaneous spinal cord stimulation, using on-skin electrodes, on cardiovascular and bowel function. We recruited a male in his 60’s with MSA diagnosed 15 years ago, showing pure autonomic system failure. We measured both acute and long-term effects of stimulation on blood pressure by monitoring continuous BP during stimulation and also had the patient maintain a 24-hour blood pressure log pre- and post-stimulation. Upon examining the data that I analyzed, cervical spinal cord stimulation elevated blood pressure more than thoracic or lumbar stimulation. The participant also recorded his bowel management and stool quality for 5-7 days before and after the sessions. Spinal cord stimulation initiated bowel movements immediately after the intervention. Further research is warranted to better understand the effects of cervical spinal stimulation on blood pressure regulation and bowel function.

Individuals with spinal cord injury (SCI) often experience reduced exercise capacity due to impaired cardiovascular control, which limits their participation in rehabilitation and daily activities. Although epidural spinal cord stimulation (eSCS) has demonstrated efficacy in restoring activity tolerance, its invasive nature and high cost hinder its widespread clinical adoption. To overcome these limitations, this research aims to develop a non-invasive, closed-loop transcutaneous spinal cord stimulation (tSCS) system that automatically adjusts stimulation levels based on real-time physiological signals. As a validation study for the hypothesis that exercise tolerance can be modulated using tSCS with activity dependent stimulation intensities, electrocardiogram and photoplethysmography data were collected from four SCI participants during exercise. I processed these cardiovascular signals using Fast Fourier Transform for heart rate variability (HRV) analysis in Python. I am also involved in developing a predictive machine learning model responsible for controlling tSCS intensity to improve exercise tolerance. It estimates exercise tolerance metrics, such as oxygen consumption volume, based on the HRV parameters. In the system, data are transmitted via Bluetooth Low Energy (BLE) protocols from physiological monitoring units to a processing unit, after on-board computation it then performs automatic adjustment of stimulation intensity. I have established a stable BLE connection within the system, and the final integrated system is anticipated to enhance rehabilitation outcomes by improving cardiovascular control during exercise and providing a clinically viable method to restore exercise capacity in individuals with SCI. Future studies will focus on optimizing algorithm efficiency for real-time performance and validating the system through clinical trials to further assess its impact on rehabilitation outcomes.

This project aims to enhance EEG source localization by addressing electrode misplacement, which can possibly lead to errors in brain activity reconstruction. We developed a optimization algorithm on the quasi-static electromagnetic model to optimize electrode positions. Using the multipole expansion method, our model minimizes discrepancies between recorded and predicted EEG signals. Our work has applicability to many clinical scenarios, like stroke activity localization, and can enhance existing brain activity reconstruction protocols.

Multitasking, such as walking and talking, is common for humans and other animals, yet we are limited in how many behaviors we can perform simultaneously. The neural circuit mechanisms that limit multitasking are not well understood. Uncovering these mechanisms will help us understand how brains combine some, but not all, behaviors during normal function, but also in the context of aging and neurological disorders such as Parkinson’s disease, where multitasking gets compromised. The fruit fly Drosophila melanogaster walks and “sings” by vibrating a wing during courtship, in a natural example of multitasking. These stereotyped behaviors are controlled by a relatively simple brain, which can be experimentally driven via artificial stimulation of key neurons, making the fly an amenable model to study multitasking. I therefore developed a platform to record and manipulate the interaction between locomotion and “singing”. I will activate sing-inducing neurons during two contexts, when flies are stationary (single-tasking) vs. moving (multitasking). I hypothesize that singing characteristics will change depending on context. For example, multitasking may decrease the likelihood of singing because the fly’s nervous system is “busy” controlling locomotion. Alternatively, locomotor context may make it easier to drive wing vibrations because of the higher activity levels in the circuits involved. My results will therefore help uncover how neural circuit interactions shape an animal’s ability to multitask.

In this work, I propose an improved remeshing approach for particle method approximations. Particle approximation methods are a flexible tool for approximating solutions to nonlinear continuity equations, and are especially useful for aggregation-diffusion equations, which have important applications in fields ranging from modeling physical processes to neural networks. They work by decomposing functions into constituent parts, called particles. By tracking the motion and mass associated with each of these particles over time, we then use these to construct a high-resolution approximation to a desired solution. However, particle methods suffer from accuracy decay over time, necessitating remeshing (resetting particle positions) to maintain a useful approximation. It's important that the techniques used for this remeshing preserve existing structures, so that our approximation exhibits the same qualities as the true solution of the underlying equation. For instance, existing remeshing techniques often preserve conservation of mass, but not entropy decay. By combining remeshing techniques to periodically merge clustered particles and introduce new particles, I'm developing a method that maintains approximation accuracy and preserves structural properties. I present the results of the numerical analysis done using Python, as well as an implementation of the method using a finite-difference approach, which examines the approximation at various steps through time. This approach is expected to preserve the structure of the true solution within the particle method approximation, contributing to the development of robust particle methods for a broad class of partial differential equations.

Glioblastomas (GBMs) are highly aggressive brain tumors with poor patient prognosis, necessitating improved preclinical models to evaluate therapeutic strategies. My lab develops cerebral organoids from human pluripotent stem cells, seeded with primary patient tumors to model GBM progression and therapeutic screening. Developing biologically relevant neural organoids provides a platform for integrating patient-derived GBM samples, enabling disease modeling and treatment testing. This study aims to optimize the embedding, cryosectioning and immunofluorescence (IF) staining protocols used to screen key molecular markers and cell populations within the organoids to validate their suitability for GBM tumor engraftment. Fixed organoids, along with embryonic and adult mouse brain tissues, are embedded in OCT to preserve structure and cryosectioned (12–20 μm). IF staining is optimized by adjusting fixation time, permeabilization, blocking reagents, and antibody concentrations to improve specificity and reduce background fluorescence. Markers analyzed so far include SOX2 (neural precursors), PAX6 (radial glia), FOXG1 (forebrain), and TUJ1 (neuronal differentiation). Mouse brain cryosections from newborn (P0) and adult (P56) stages serve as positive controls to validate antibody specificity and distinguish true signals from autofluorescence or non-specific staining. Images are acquired via Olympus scanner and analyzed using OlyViA and NIH Fiji (Enhanced ImageJ). Current efforts focus on optimizing section thickness for clearer images and refining blocking conditions to minimize non-specific binding. We expect the detected fluorescent markers will mirror known cellular and tissue expression patterns, confirming that the organoids exhibit normal human fetal neurodevelopmental characteristics and are biologically relevant for GBM modeling. Future work will expand marker validation to include GFAP (astrocytes), DCX (neurogenesis marker), TBR2 (intermediate progenitors), OLIG2 (oligodendrocyte progenitors), PTPRZ1 (radial glia), IBA1 (microglia) and other cell lineage-specific markers. Establishing reliable staining and imaging conditions is a crucial step toward developing our organoid model to be suitable for exploring GBM tumor biology and potential therapeutic responses.

The sensation of acute pain is fundamental to survival, indicating tissue damage that motivates an animal to engage in adaptive protective behaviors. Chronic pain, however, is persistent pain beyond typical recovery window and serves little adaptive function. The negative emotional component inherent in chronic pain contributes to the development of comorbid psychiatric disorders such as depression, social aggression, and social withdrawal. Our research aims to understand the bidirectional relationship between pain and social behavior, by evaluating mechanical sensitivity and changes in social motivation, reward, and interaction following a neuropathic injury. Using social self-administration (SSA), pair-housed mice were placed in operant chambers and underwent voluntary lever press trials for the reward of social interaction with their cage mate. Mice also underwent mechanical hypersensitivity response assays called von Frey where increasing weights of plastic filament were applied to the hind paw. Following baseline von Frey testing and the acquisition of the SSA task, mice then received a spared nerve injury (SNI) to induce neuropathic pain. After surgery recovery, mice were returned to the lever press and von Frey trials at different post-operative windows. Pain sensitivity was determined by the filament weight in which the animal withdrew their paw during von Frey. Changes in social behavior were measured via changes in lever press frequency and interactions during trials. Behavior changes were quantified using Simple Behavior Analysis (SimBA) machine learning to classify interactions during social trials. Once the trials were completed, brain tissue from regions associated with reward and social neural circuitry was collected and investigated using transcriptomic methods. Our data found sexually divergent social adaptations and gene expression following chronic pain. Future experiments will further delineate these sex-specific adaptations following a traumatic injury. This research can inform social intervention as an adjunct or alternative treatment to pharmacological pain intervention and its comorbidities.

Magnetoencephalography (MEG) is a powerful, noninvasive type of brain imaging that uses magnetic field readings from outside the skull to reconstruct the neuronal current sources that produce them in accordance with Maxwell’s equations. However, as these magnetic fields do not have unique current sources, algorithms are structured with constraints to guarantee the correct solution. In this project, we design a novel algorithm to reconstruct neural current sources. Using a cone-shaped beam with its vertex at the origin and a spherical-head model, we show we can reproduce any signal produced from within the cone using a current distribution on the cone’s surface, effectively allowing us to spatially localize the current source responsible for a given dataset of MEG measurements. I have employed this algorithm on an artificially produced dataset using MATLAB and assessed its effectiveness through reconstruction error analyses and visual techniques like heat maps. Future work will include testing the method on phantom-head data. We anticipate this algorithm is adaptable to non-spherical head geometries and cases involving multiple significant current sources, and we are working towards these advancements. Unlike other inverse methods, we expect our approach to assume minimal a priori knowledge about the brain’s conductivity profile, making it easier to implement in cases where detailed information about the subject's neural anatomy is limited.

Over 200,000 doctors and nurses in the U.S. who use live X-ray imaging (fluoroscopy) to guide medical procedures are exposed to harmful radiation. Over time, this exposure increases their risk of cancer, cataracts, and other health problems. The current solution, which involves wearing heavy lead aprons, provides some protection but does not entirely block radiation. Furthermore, these heavy lead aprons often cause long-term problems, such as chronic back, neck, and joint pain in over 50% of users. Over the past two years, I have helped develop a new portable radiation shield designed to provide full-body protection while reducing physical strain. This shield features telescoping poles that adjust for ergonomic positioning and support large lead sheets while remaining compact, easy to maneuver, and compatible with sterile environments. To evaluate its effectiveness, a phantom model is used to measure scattered radiation during live X-ray imaging. Two shielding methods are tested: a standard lead apron and the portable shield we have created. Radiation sensors are placed at the head, neck, chest, and legs to compare exposure levels. A paired t-test determines whether the portable shield significantly reduces radiation compared to the lead apron. At least 30 test trials per shielding condition are conducted to ensure accurate results, with a target of ≥95% radiation reduction. Based on our initial calculations, I expect a 15-fold decrease in radiation exposure with our portable shield compared to traditional lead aprons. This research evaluates a new way to protect healthcare workers from harmful radiation exposure while reducing physical strain and helping improve safety in medical settings.