Undergraduate Research Day Projects
Permanent URI for this communityhttps://hdl.handle.net/10657/2212
Organized by the University of Houston Office of Undergraduate Research and Major Awards, Undergraduate Research Day is an annual event showcasing exceptional scholarship undertaken by the UH undergraduate community.
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Browsing Undergraduate Research Day Projects by Subject "Biomedical Engineering"
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Item Biased Agonism at the Angiotensin II Type 1 Receptor(2023-04-13) Siddiqui, AniqueThis research investigates the differing preferences among students and professors towards two distinct methods of teaching writing in English Literature classes: process (type A) and product (type B) classes. Process classes are defined as classes that contain assignments building up to a finished draft, and product classes are defined as classes that only grade finished drafts. Through the use of questionnaires and interviews, students and professors expressed their satisfaction towards the ease, quantity of grades, and improvement of critical reading and writing skills for both class types. Through this data, we can claim that students tend to prefer the quantity of work for process classes because they better provide the opportunity to improve writing skills. Furthermore, professors have differing opinions on the ease of process versus product classes. While product classes have less to grade, professors also have to interact with completely new topics in each assignment (unlike the build-up of one topic in process classes). Finally, professors face difficulty in deciphering what students prefer due to the sometimes isolating environment of teaching. This research bridges the gap between student and professor and creates a conversation about the potential benefits and drawbacks of certain class types for both parties.Item Investigation of LRG-I as A Biomarker For Stress Urinary Incontinence In A Rabbit Model?(2023-04-13) Tran, HoangStress Urinary Incontinence (SUI) is a condition that primarily affects females, with a prevalence ranging from 15% to 80% (Koch). To develop new treatments for SUI, appropriate animal models must be used to investigate proposed treatments. Previous research have reported elevated levels of LRG-I (logFC 3.91) (Mitulovi?, Koch, etc) in women with SUI compared to healthy women1,2. This study investigates LRG-I concentration in the female New Zealand White rabbit animal model, which has previously been reported as a model for SUI (Corona-Quintanilla). This study aims to determine whether LRG-I is a viable biomarker for SUI in female New Zealand White rabbits. Additionally, LRG-I serum concentrations are compared between old multiparous animals that have undergone neuromodulation treatment and those that have not investigated the effect of treatment.Item Representation Learning for Feature Analysis in Synthetic Neuronal Signals(2023-04-13) Roelofs, LillianThe current standard for neural signal spike detection uses an amplitude-thresholding approach, which is biased towards high-amplitude signals and evoked signals with artifacts. Because detection is an initial step in signal analysis, it is critical to have an accurate representation of spike occurrence and shift the detection paradigm to focus on the signal shape. One limitation of the current approach is that it tends to disregard low-amplitude signals, which carry important information such as sensory and pain response. Thus, improvements in understanding the shapes and characteristics of neural signal features is vital to enhance signal analysis quality and neuromodulation techniques. We propose the use of a generative autoencoder model to learn a representation of neuronal signal characteristics using an unsupervised training approach. In this work, a synthetic neural signal dataset is constructed using Multi-Electrode-Array recordings (MEArec) software. The synthetic data is used to train an autoencoder, a model which learns a latent representation of the input data, and outputs a reconstructed version of it. The accuracy of the autoencoder in recreating the input data reflects the quality of its latent representation. The use of synthetic data allows for the direct correlation of latent representation learning and validation loss with ground truth neuronal features. We hypothesize that by learning a robust representation of data with known ground truths, the latent space of the autoencoder will contain salient information on neuronal feature characteristics. In future work, this learned representation will be leveraged to detect and identify salient features within experimental data.