With respiratory diseases, namely COVID, becoming exceedingly present, new diagnostic techniques are important in delivering quick and accurate results to patients. With the broad aim of creating a COVID breath test diagnostic, the project focuses on building a classifier able to detect various volatile organic compounds (VOCs). I applied a panel of VOCs to the antennae of the moth Manduca sexta and recorded the change in voltage across the antenna over time, also known as an electroantennogram (EAG). After recording the voltage response it is essential to extract important features associated with the response to avoid overfitting in the classifier. Each odor will have a unique dose-response curve. By identifying the pattern of intensity-related response for each odor, I have extracted important information that can be useful in classifying EAGs. Going forward, this project will be scaled to include COVID-related VOCs and multichannel experiments to measure electrical response in multiple areas of the antenna simultaneously. Multi-channel recordings will provide an increased number of important features for the classifier to use in learning the unique voltage signature related to each odor in our panel.
Preliminary data has shown that the administration of floral odors to the antenna elicits unique voltage signatures when recorded in single-channel EAGs. I predict that if we record from two sites in the antenna, we will observe a set of unique dose-response curves relative to the differential expression of olfactory sensors in the base and the tip of the antenna. These sets of dose-response curves may provide our classifier with better parameters to categorize and detect the presence of odors associated with COVID, as two dose-response curves must be consistent with an assigned odor’s electrical signature instead of only one. This may improve our chances of creating a classifier able to reliably differentiate between COVID and non-COVID odors.

The glymphatic system, which is primarily active during sleep, is a network of astroglial perivascular channels within the brain that allows for cerebrospinal fluid (CSF) influx and exchange. Glymphatic exchange plays a crucial role in the clearance of amyloid, a hallmark in the development of Alzheimer’s. Recently, a bidirectional relationship between Alzheimer's disease and sleep has also been suggested with amyloid deposition associated with mid-life sleep disruption. However, the mechanistic link between sleep disruption, particularly over chronic time scales, and the development of Alzheimer’s pathology remains unclear. This study investigated whether chronic sleep disruption, similar to that experienced in aging population, impacts downstream Alzheimer’s-related neuropathology. We hypothesized chronic sleep disruption will result in decreased glymphatic function and increased amyloid plaque burden. This experiment utilized a chronic sleep disruption model using Lafayette Sleep Fragmentation chambers, where mice underwent either chronic sleep disruption every two minutes during normal sleeping periods (daylight hours) or normal sleeping conditions (sham) from 10 weeks to 18 weeks of age (n=120). After eight weeks of sleep disruption or sham exposure, glymphatic function was assessed by dynamic in vivo near infrared imaging following stereotactic CSF tracer injection. Animals were perfusion fixed, cryosectioned, and glymphatic function was further assessed by measurement of fluorescent cerebrospinal fluid tracers in brain tissue. Aquaporin-4 localization, amyloid plaque deposition, and markers of astroglial and microglial activation were assessed by immunofluorescence. The collected data demonstrated that sleep disruption significantly increased neuropathological outcomes. The measured impact of glymphatic function was also correlated with these downstream pathological effects. These findings could be an indicator of interactions between neurological disease progression and an inflammatory expression after sleep disruption. They can also shed more light on the complex relationship between Alzheimer’s disease progression, the glymphatic system, and chronic sleep disruption.

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide and has been established as a risk factor for neurodegenerative diseases such as Alzheimer’s disease (AD). Neurofibrillary tangles (NFTs), aggregates of intracellular tau, are hallmarks of AD and are observed in the post-TBI brain; however, the mechanisms that contribute to tau aggregation and accumulation are not well understood. One key mechanism that may contribute to this tau aggregation is decreased clearance by the glymphatic system, a perivascular pathway that clears solutes, including tau, from the brain. PS19 mice with tau pathology were crossed with Aqp4-/- mice lacking the astroglial water channel aquaporin-4 (AQP4) or Snta1-/- mice lacking perivascular localization of AQP4. Behavioral tests were performed on the PS19:Aqp4 transgenic crosses at 4 or 6 months of age. Brain tissue was collected and stained for markers of phosphorylated-tau (p-tau) pathology. Sham or mild TBIs were performed on PS19:Snta1 transgenic crosses at 3 months of age. I performed behavioral testing at 4 months (1 month post-TBI) or 6 months (3 month post-TBI). Brain tissue was collected and stained for markers of p-tau pathology. I imaged this immunostained tissue and quantified the pathological tau burden. I observed that Aqp4 deletion was sufficient to exacerbate tau pathology in PS19 mice at 6 months old, in the absence of TBI, and more advanced tau pathology was observed in PS19+Snta1-/- mice at 6 months old (3 months post-TBI) compared to PS19+Snta1+/+ that also received a TBI. Loss of AQP4 or loss of perivascular AQP4 promotes tau pathology in a mouse model of tau pathology. These studies may provide a mechanistic basis for the vulnerability of the post-traumatic brain to tau aggregation and neurodegeneration and suggest that targeting glymphatic dysfunction may be useful in the prevention and treatment of neurodegeneration.

In this study, we present a dexterous implementation of the Rubber Hand Illusion (RHI) in virtual reality (VR). The RHI is a classic perceptual illusion in which a sense of embodiment of a non-self object is elicited by synchronously and congruously stroking both a visible non-self object (i.e., a rubber hand) and the subject’s actual hand, hidden from view. While powerful, the classic RHI experiment is constrained by physical reality. Here, we present a new VR-RHI implementation that integrates Unity’s collider-based physics system and SteamVR’s hand pose estimation algorithm to achieve real-time rendering of real-world collisions. This enables precise visuotactile concordance and thus induction of the RHI over a virtual hand. Data from healthy, right-handed human VR-RHI participants (n=17) demonstrated a strong, bounded, linear correlation between VR render offset and proprioceptive drift till a certain threshold. We have designed and validated a new gaze drift metric that uses integrated eye-tracking hardware and SDK support for gaze-object collision to allow gaze-based self-localization. Based on preliminary results, we believe using gaze may refine the proprioceptive drift metric by minimizing the required movement of the subject’s body and contralateral hand while self-localizing after RHI induction. In addition, we have implemented a new feature of the experiment to separate the visual and tactile sensations by showing the subject the actual hand location rendered in the virtual environment during the induction. During these trials, the subject is aware of the offset, but preliminary results suggest that we are still able to induce the illusion. Furthermore, we have also implemented a new induction method where we use movement to induce the illusion rather than tactile sensations. Finally, we have improved the experiment protocol by automating data collection and experimental loops so that the experiment can run without a third party.