BSU

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Assessing Pim-1 Kinase Inhibition as an Effective Treatment for Acute Myeloid Leukemia

Proviral integration site for malignancy-1 (PIM-1) is a serine/threonine kinase proto-oncogene that has many important functions, namely cell proliferation and signal transduction, in the context of Acute Myeloid Leukemia (AML). Generally, PIM-1 kinase, which arises from the expression of the PIM-1 oncogene, can cause poor prognosis of AML. Additionally, PIM-1 kinase, also a substrate of Triad1, stabilizes as expression of Triad1 decreases during the prognosis of AML. Thus, this project looks at PIM-1 kinase inhibition and aims to determine its effectiveness in treatment for AML and how it can affect potential therapeutic interventions. AZD1208, a potent pan-PIM inhibitor, is theorized to be an alternative treatment for AML instead of chemotherapy. This project involves a luciferase reporter assay, which is used to track PIM-1’s gene activation. The assay allows for analyzing PIM-1s impact on signaling pathways and helping determine its oncogenic potential through measuring the fluorescent activity in the Pim-1 kinase promoter

MEIS1 and HOXB13 Support Tumor Suppression By Regulating Decorin and Lumican

Prostate cancer is the second leading cause of cancer-related death in American men. While prostate cancer is very treatable, there remains a need for alternative treatments for those who are resistant to androgen based therapies. MEIS1 and HOXB13 regulation is associated with proteoglycans like decorin and lumican to promote a tumor suppressive phenotype. Therefore, this project aims to elucidate how the transcription factors HOXB13 and MEIS1 work together to act as tumor suppressors by regulating DCN and LUM to inhibit prostate cancer growth. To do this, cancer cell lines 22Rv1 were injected into immunocompromised mice to create xenografts. Then, protein and RNA were harvested from the xenograft tumors. The protein was quantified using BCA analyses before running on Western blots and the RNA was quantified through qPCR. When the cell lines were modulated to overexpress MEIS1, there was an abundance of DCN and LUM. However, when HOXB13 was knocked out the DCN and LUM levels dropped. Analysis of protein-protein interaction prediction through HADDOCK suggests MEIS1-HOXB13 interaction near the C-terminus of the MEIS1 protein. Overall, these results shed light on how MEIS1 and HOXB13 work together to modulate decorin and lumican, providing insight on more effective treatments

(VIRTUAL) Radians: Units are Important and Why the unit circle is not

Have you ever wondered why we have radians as a unit of measure for angles? Aren\u27t degrees good enough? I like them. After all, a right angle is a nice, round 90degrees – so why complicate things? While degrees are useful, radians bring a unique significance. Units matter, even in the world of mathematics

MLK 2025

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Edge Computing and Al Intern opportunity

Robust and efficient face recognition and verification is becoming increasingly important in the modern age, due to the escalating presence of online interactions. For example, a manager may want to check remote employee authentication, but only has a video feed of them, or outside intrusions in secure environments. Thus, Face recognition and verification programs must be optimized. To achieve this, this internship utilized the deepface framework for python. The contemporary facial recognition pipeline consists of four core stages: detection, alignment, representation, and verification. Experiments involving distinct combinations of facial recognition models, face detectors, distance metrics and alignment models performed on the Labelled Faces in the Wild database were conducted in order to optimize the facial recognition pipeline. This internship provided valuable practical experience in this field and equipped me with necessary skills to contribute meaningfully

AR Imaging for Applications

My project aims to develop an augmented reality (AR) application using Android Studio to animate murals across the city of Aurora. We plan to remodel the way people interact with static public art pieces by turning them into interactive digital experiences. This project, commissioned by APS Data Technologies through a city contract with the City of Aurora, used augmented reality imaging to overlay animations onto existing murals, improving engagement and overall boosting accessibility to art. The development process involved a significant amount of research into AR technology, user interface design and GUI, and backend integration to ensure users have a seamless experience. The contributions through this internship project were mainly among the technical aspects, as we worked on the code required to optimize the application\u27s performance during real-time user interactions

Five-Year Spatiotemporal Water Quality Assessment of Horseshoe Lake Using Sentinel-2 Imagery and Google Earth Engine (GEE)

Satellite-derived data analysis offers a cost-effective alternative to traditional in-situ measurements for monitoring water quality dynamics in critical water bodies. This study leverages remote sensing techniques to assess multiple zones within Horseshoe Lake, Illinois, identified based on physical barriers, hydrological flow, and proximity to water treatment facilities. To ensure scientific rigor, we applied unsupervised clustering techniques such as K-means, ISODATA, and DBSCAN to Sentinel-5P parameters. These techniques validated whether the selected zones are hydrologically distinct, allowing for a more precise characterization of spatial variations in water quality. Using Google Earth Engine (GEE), we retrieved cloud-free composite images spanning 2020-2024 and chose key water quality indicators for further analysis: chlorophyll-α concentration, turbidity, and phosphorus levels. We then applied statistical trend analysis and cross-indicator validation to explore parameter interdependence and assess how major meteorological events influence water quality trends. This study provides a comprehensive assessment of pollution dynamics within Horseshoe Lake, offering a robust methodology for long-term water quality monitoring. Future work will focus on integrating these analytical capabilities into a public dashboard, enabling users to monitor lake conditions in real time from any personal device

Quantifying Ozempic’s Impact: Sentiment-Based Drug Evaluation with BERT and Mistral Models

Semaglutide, sold under the name Ozempic, is a medication that aids in blood sugar management as well as weight loss in individuals diagnosed with type 2 diabetes. Ozempic is frequently advertised as an effective weight loss drug due to the uncontrolled popularity stemming around it from social media. The purpose of this study is to understand the public opinion and real-life testimonials regarding the use of Ozempic via Youtube comments. I trained BERT and Mistral models for sentiment classification and thematic pattern recognition using a labelled dataset of generated content. Three major perspectives were identified to focus on: users of Ozempic, the families of users of Ozempic, and the general public. This study utilized natural processing language (NLP) to analyze sentiment patterns, issues repeated over time, and the general perception of the effectiveness of the drug of different people. The results provide information on the qualitative effects experienced by users of Ozempic outside the clinical setting and the views that shape the perception of Ozempic in public