Office of Undergraduate Research
Permanent URI for this communityhttp://hdl.handle.net/1903/20157
Emphasizing equitable and inclusive access to research opportunities, the University of Maryland's Office of Undergraduate Research (OUR) empowers undergraduates and faculty to engage and succeed in inquiry, creative activity, and scholarship. This collection includes materials shared by undergraduate researchers during OUR events. It also encompasses materials from Undergraduate Research Day 2020, Undergraduate Research Day 2021, and Undergraduate Research Day 2022, which were organized by the Maryland Center for Undergraduate Research.
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Item Spread Prevention and Eradication of Resistant Bacterial Growth(2024) Sripathi, Neha; Kim, Joshua; Dinh, Phuclam; Rayford, Amber; Breymaier, Nicholas; Zhang, Cristina; Hillman, Mackenzie; Stein, Daniel; Stein, DanielDiseases caused by drug resistant bacteria are becoming a pressing public health threat due to a lack of new antibiotics and the evolution of multidrug resistance. Drug resistance is caused by mutant or novel genes known as resistance genes. CRISPR-Cas9 gene editing has been shown in recent studies to successfully edit resistance genes to increase susceptibility to antibiotics. We aim to use bacteriophage M13 to transduce a CRISPR-Cas9 system into nalidixic acid resistant Escherichia coli and resensitize it to nalidixic acid. We aim to improve upon the efficiency of previous studies.Item Biomarker Research Applications in Alzheimer's Disease(2021-05) Cieslak, Zofia; Acha, Beatrice; Hemani, Danny; Kubli, Anjali; Lee, So Min; Mgboji, Rejoyce; Nallani, Madhulika C.; Park, Michael J.; Samson, Mahalet; Wu, Benjamin; Smith, J. Carson; Smith, J. CarsonAlzheimer’s Disease (AD) affects millions of older individuals and is a growing problem without an accessible diagnosis method, drug target for treatment, or model of the longitudinal progression of the disease. The project, led by University of Maryland Gemstone Team BRAIN, aims to determine how changes in memory, visuospatial ability, the plasma amyloid β 42/40 ratio, and the total hippocampal volume can be used to accurately predict the onset and progression of AD. Using the Alzheimer’s Disease Neuroimaging Initiative, a database that compiles data from nationwide studies, we analyze cognitive function (memory and visuospatial ability), plasma biomarkers (amyloid β 42/40 ratio), and brain imaging (hippocampal volume). Data analysis consists of using programs such as Python and JASP to analyze data from the ADNI database, and finding significant relationships between variables through statistical analysis. Our results suggest that the impact of the e4 allele on memory and visuospatial ability over time may be strong in people who show early cognitive decline, independent of age, sex and education, and that hippocampal volume loss is greater in people who carry the e4 allele independent of covariates. Furthermore, it is unclear if plasma biomarkers reflect brain pathology. Team BRAIN’s future research goals include addressing disparities in AD development among different demographic and socioeconomic groups, using our findings to work towards a novel and cost-effective approach to diagnosing and treating AD to eradicate boundaries in the access to care, applying machine learning to propose a model of prediction and longitudinal progression, and expanding the variable set to include more biomarkers.Item Autocycle: Design, Construction, and Validation of an Autonomous Bicycle(2021) Allen, Michael; Bartolomei, Jacob; Carter, Jeremy; Grill, Cooper; Khrenov, Mikhail; Mirenzi, John; O'Leary, Joseph; Rose, Isaac; Ruderman, Evan; Sanguesa, Andoni; Swaisgood, Logan; Gomez, RomelEfficient urban transportation has time and time again proved to be a difficult problem to rectify. One modern solution is the bike-sharing system, where many bicycles are available either at hubs or spread across a city for short-term use. However, usage is limited to those located close enough to a bicycle hub that traveling to and from it is time-effective. As for hubless bike-shares, bicycles require redistribution over time to remain conveniently available to many. We propose the creation of an electric bicycle that can either be used by a cyclist manually or operated autonomously using locomotion, sensing, balance, and control systems. We have concluded that such a concept is possible and achievable, as we are making significant progress toward developing working prototypes for the self-stability and autonomous navigation of the Autocycle. Once those milestones are completed, we will integrate the two systems together in the final prototype. Our Undergraduate Research Day presentation will showcase the research and data we have collected up until this point and outline our future goals for the project. With the completion of this prototype, we want to show that such a bicycle could be implemented into a larger bike-sharing system that autonomously manages distribution and allows users to summon a bicycle to their location, expanding the range of use and encouraging environmentally-friendly transportation solutions in an urban setting.Item Interpretable Deep Learning for Toxicity Prediction(2020) Banerjee, Aranya; Boby, Kevin; Lam, Samuel; Li, Jeffrey; Polefrone, David; San, Robert; Schlunk, Erika; Wynn, Sean; Yancey, Colin; Feizi, SoheilTeam TOXIC (“Understanding Computational Toxicology”) seeks to apply interpretability techniques to machine learning models which predict drug safety. Currently, such models have been developed with relative accuracy and are used in industry for drug development. However, because they are not sufficiently rooted in chemical knowledge, they are not widely used in regulatory processes. To contribute towards a solution, we evaluate existing explanation methods for toxicity predction models trained on open-source data sets. Additionally, we are working towards models involving the usage of more interpretable data representations. Ultimately, we hope to demonstrate a proof-of-concept for an interpretable model for predicting drug safety which can illustrate its reasoning.Item Improving Non-Contact Tonometry through Advanced Applanation Techniques and Measurement of Corneal Deformation(2020) Muessig, James; Ackman, Moshe; Cho, Lauren; Do, Kun; Green, Aaron; Klueter, Sam; Krakovsky, Eliana; Locraft, Ross; Wu, Hongyi; Lin, Jonathan; Scarcelli, GiulianoGlaucoma, a disease characterized by increased intraocular pressure (IOP) in the eyes, is the leading cause of preventable blindness worldwide. Accurate measurement of IOP is essential to early diagnosis of glaucoma in order to begin treatment and prevent long-term vision loss. Currently, non-contact tonometry, known as an “air-puff test”, is the most common diagnostic method despite its inaccessibility, discomfort, high cost, and reliance on an expert to operate. In order to improve upon this method, we designed an accurate and less invasive measurement system utilizing a novel depth-mapping neural network and a microcontroller-driven valve system. We applanated eyes with a variable-intensity air puff while imaging the deformation with a single camera. Our neural network then processed the image data and generated a three-dimensional deformation map. We compared our results to accepted tonometry measurements in order to validate the accuracy of our system as an alternative diagnostic device. With a lower pressure puff and simplified imaging setup, we were able to accurately measure IOP, improving existing diagnostic techniques in optometry.Item Robotic Habitat Technologies for Minimizing Crew Maintenance Requirements(2020) Broemmelsiek, Rachel; Calderwood, Micah; Callejon Hierro, Jaime; Cueva, Rachel; Harvey, Rachel; Holmes, Scott; Khawaja, Imran; Kleyman, William; Mnev, Peter; Orlando, Wilson; Queen, Jessica; Shenk-Evans, Micah; Skinner, Thomas; Akin, Dave; Bowden, MaryNASA’s Lunar Gateway aims to be deployed later in the decade and will serve as an outpost orbiting the moon. This habitat will be utilized as a base for lunar operations as well as future missions to Mars. Unlike the International Space Station (ISS), which maintains three to six astronauts at any given time, the Lunar Gateway will be uncrewed for eleven months out of the year. Over 80% of crew time onboard the ISS is dedicated to logistics, repair, and maintenance, leaving minimal time for scientific research and experimentation. In order to maintain Gateway, robotic systems must be implemented to accomplish maintenance and operational tasks. This paper discusses our team’s proposed dexterous robotic system, which will address routine and contingency operational and maintenance tasks on Gateway. The project is experimentally-based, and split into three approaches: cataloging robotic capabilities via robot/taskboard interactions, logistics management of Cargo Transfer Bags (CTBs), and software development of an AprilTag situational development system. This research project utilizes the unique capabilities of the University of Maryland (UMD) Space Systems Laboratory (SSL), which houses various dexterous robotic manipulators, mock-ups of space habitats, and the Neutral Buoyancy Research Facility (NBRF), a 50-foot diameter, 25-foot deep water tank used to simulate microgravity conditions. By incorporating robotic systems into the architecture of the Lunar Gateway, it will allow for the lunar outpost to be continually operated and maintained while uncrewed, and will allow for astronauts, when present, to focus on maximizing scientific discoveries.Item Artificially Intelligent Medical Assistant Robot (AIMAR)(2020) Ronin, Dana; Horne, Nina; Daniel, Paulos; Jacobson, Ben; Kuo, Kevin; Marsandi, Michelle; Offenberg, Natalie; Utz, Ryan; Vandergriff, Johan; Deane, AnilHealthcare providers face financial, regulatory, and logistical obstacles in supplying quality care. Applying robotics and artificial intelligence (AI) to healthcare reduces demands on providers, increases accuracy by supplementing medical diagnoses, and improves patient outcomes. Team AIMAR (Artificially Intelligent Medical Assistant Robot) has constructed a modular robotic healthcare AI system, consisting of advanced diagnostic features as supplements to a generic base. The team focused on analyzing images with machine learning to identify skin conditions. The base robot can move around the home or hospital, pick up objects, and interact with patients and doctors. The patient can log in using face authentication so that patient data is secure, and interact verbally and visually through the user interface. New features can easily be added to the base robot's existing integrated features, making AIMAR adaptable for many desired contexts.Item Investigation of 1P-LSD as a Novel Drug Therapy for Autism Spectrum Disorders(2020) Foster, Kayla; Hansen, Abigail; Lee, Matthew; Mohammed, Alan; Morrell, Matthew; Nguyen, Thach-Vu; Olson, Caroline; Pascale, Lucas; Sunny, NishanthAutism spectrum disorders (ASD), defined by repetitive behaviors or impaired social communication, is a prevalent yet relatively misunderstood set of conditions. ASD encompasses a series of neurodevelopmental disorders that have various physiological manifestations (Goines & Ashwood, 2013). Due to the heterogeneity of ASD, the true mechanisms leading to the development of ASD and its symptoms remain unclear and require more research (Rossignol & Frye, 2012; Watts, 2008). The purpose of this project is to test whether or not 1P-LSD, an analogue of LSD (lysergic acid diethylamide), has the potential to treat symptoms of ASD, specifically the hyperexcitation of N-methyl-D-aspartate (NMDA) receptors in the brain which causes the neuronal excitotoxicity highly implicated in the pathology of ASD. We will determine the two highest doses of 1P-LSD which do not result in any hallucinogenic side effects in Phase 1 of this protocol and utilize these doses towards treatment of symptoms associated with ASD in Phase 2 of this protocol. We will monitor NMDA receptor activity, which is usually impaired in ASD, following microdosing of 1P-LSD. For these experiments, we will be using an autistic mouse model (Slc6a4) compared to normal mice (C57BL/6J). The efficacy of the treatment model will be assessed by measuring the levels of a subunit of the NMDA receptor, the NR2B subunit, using western blotting and immunohistochemistry, and by measuring the levels of glutamate using gas chromatography-mass spectrometry (GC-MS).Item Inhibiting Degranulation in Immune Cell Signaling Pathways(2020) Fadul, Naja; Kasica, Zachary; Laurence, Kyeisha; Moy, Stephanie; Murugan, Sindhu; Pamala, Chinmayi; Robinson, Morgan; Shah, Rohan; Shrestha, Mansu; Smith, Marcus; Vashi, Bhavya; Frauwirth, KennethAllergies are a pervasive issue and require novel ways of alleviating symptoms. Existing treatments are focused on symptom management and immunotherapy, but there is also potential to target the molecules involved in the downstream pathway, particularly the PLCγ enzymatic pathway. Our research aimed to identify important target molecules involved in this pathway that result in the degranulation of mast cells. We intended to inhibit these molecules in order to hinder mast cell degranulation and therefore prevent allergic symptoms. Our results were tested and measured in MC/9 (mouse mast cell) and RBL-2H3 (rat basophilic) cell lines with multiple cell degranulation assays such as the beta-hexosaminidase and tryptase assay. The results were evaluated based on the comparative effect as well as specificity of inhibitors on mast cell degranulation. We aim to find the most ideal inhibitor for the PLCγ, SK, S1P2 enzymatic pathways in the signalling cascade in order to most effectively reduce degranulation and thus reduce the allergic response.Item Localizing Chemotherapeutic Drug Release to Treat Stage III Colorectal Cancer(2020) Sebastian, Ria; Atalla, Anthony; Coley, Morgan; Hamers, Matthew; Tiberino, Matthew; Nagler, Matthew; Nassar, Yomna; Nichols, Alison; Minahan, Eva; Karodeh, Nima; McGrath, Jennifer; Wendeu-Foyet, Kevin; Kofinas, Peter; Ayyub, OmarThese studies focused on the incorporation of chemotherapeutic drugs into biodegradable polymers, specifically poly(lactide-co-caprolactone) (PLCL), as a localized form of cancer treatment. In conjunction with the surgical resection of a tumor, this polymer can be used to deposit drugs directly at the site and minimize the risks posed by systemic chemotherapy. The methodology focused on Stage IIIA colorectal cancer due to its high recurrence rate and the common use of surgery as a form of treatment. In our experiments, data was collected to compare the various physical, chemical, and mechanical properties between PLCL fiber mats loaded with Capecitabine in order to evaluate the most ideal drug release pattern. Results found that the combinations we had tested thus far had shown a delayed release, meaning at least a week passed before initial drug dissociation from the polymer. Current results suggest a possible relationship between molecular weight and the delay period length, which has implications in future research. Different polymers will also be studied to assess the chemical impact on the release patterns we found in our data.