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COMPARING THE EFFECTS OF PROTON AND PHOTON THERAPY ON TUMOR AGGRESSIVENESS IN ADVANCED CANCERS
No full textRadiotherapy, a cornerstone of cancer treatment, can paradoxically induce increased tumor aggressiveness in some cases, complicating patient outcomes. This study compares the effects of proton and photon therapies on the aggressiveness of cancer cells that survive radiation treatments. We examined two types of advanced cancer known for poor prognosis and high recurrence rates: Glioblastoma Multiforme (GBM) and High-Grade Serous Ovarian Cancer (HGSOC). Given the aggressive nature of these cancers and the limited success of available treatments, there is a critical need to develop new strategies that utilize radiation therapy but minimize the adverse effects.
Photon radiation, the conventional treatment, utilizes high-energy photons to ionize biomolecules and disrupt cellular functions, leading to cell death. Although photon radiation is effective in killing cancer cells, it is also linked to the development of secondary cancers and more aggressive cancer phenotypes that become resistant to treatment. On the other hand, proton radiation, a more targeted therapy, minimizes collateral damage to surrounding healthy tissues by using hydrogen ions to deliver precise radiation doses to tumors.
To compare radiation-induced tumor aggressiveness between proton and photon radiation, we examined pluripotency, epithelial-mesenchymal transition (EMT), and tumor immune evasion traits in irradiated HGSOC and GBM cells. We integrated a SORE6-GFP reporter to identify cancer stem cell (CSC) populations expressing SOX2/OCT4 and a 3’ UTR-ZEB1-GFP reporter to detect mesenchymal cell populations. Transduced cells were treated with 0, 1, 2, 4, and 8 Gy of 250 MeV proton and 6 MeV photon beams, and GFP levels were measured via flow cytometry 72 hours post-radiation. By calculating the ratio of live GFP reporter-expressing cells to the total number of live cells, we observed an increase in the ratio of CSCs and mesenchymal cells following both proton and photon treatments. RT-qPCR analysis assessed gene expression changes in irradiated cells, examining pluripotency genes POU5F1, SOX2, LIN28A, and EMT transcription factors ZEB1, ZEB2, SNAI1, and SLUG.
To assess the potential for immune evasion in cells following radiation, we conducted RT-qPCR analysis to examine changes in the expression levels of immune checkpoint genes PD-L1 and HLA-G, as well as MHC class I genes HLA-A, HLA-B, and HLA-C. To determine which type of radiation induced a more immune-favorable response, we developed an Immune Response Score to measure the ratio of immune checkpoint gene expression to MHC class I gene expression. Overall, proton radiation resulted in a more immune-favorable response in cancer cells, with the exception of LN18 cells.
This project further examined the responses of a patient-derived HGSOC cell line and its cisplatin-resistant subline to proton and photon radiation treatments. We incorporated the SORE6-GFP reporter, the 3’ UTR-ZEB1-GFP reporter, and an apoptosis assay. Our data indicate that the cisplatin-resistant subline showed greater resistance to both types of radiation compared to the cisplatin-sensitive subline; however, proton radiation induced a higher rate of cell death. When calculating the ratio of live GFP reporter-expressing cells to the total number of experimental cells, we observed that photon radiation therapy increased CSC and mesenchymal cell populations, while proton radiation therapy did not
SUSPICIOUS ACTIVITY RECOGNITION USING COMPUTER VISION
Get PDFThis culminating experience project explored innovative methods for enhancing anomaly detection in video surveillance systems, a vital concern for public safety and security management. The research questions addressed were: Q1) What emergent approaches in Activity-based Human Action Recognition (AbHAR) lead to significant advancements in surveillance technology driven by human cognition? Q2) How can the processing and detection phases of video monitoring systems be optimized for improved efficiency? Q3) What are the advantages of ensemble approaches over individual algorithms in enhancing the robustness and accuracy of anomaly detection systems in video surveillance?
The findings were: (Q1), That deep learning methodologies, particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, significantly elevate the ability to recognize and interpret human actions, thereby improving anomaly detection systems. Compared to traditional machine learning methods, CNNs demonstrate improved accuracy in spatial feature extraction, by 50% and LSTM networks perform better in detecting sequential patterns with 51% accuracy, underscoring the advantages of deep learning in enhancing AbHAR-based systems (Q2) Integrating edge computing with advanced algorithms significantly enhances the efficiency of video processing and anomaly detection, reducing latency by 30% and increasing real-time processing capability by 20%, which is crucial for effective surveillance operations. This combination allows the system to process data closer to the source, thus enabling faster decision-making and reducing bandwidth usage. Additionally, by optimizing processing at the edge, the system can better handle high volumes of video data with improved accuracy and speed, essential for timely threat detection and response. (Q3) The study underscored the benefits of ensemble methods, which combine multiple algorithms to improve detection accuracy by 10% and reduce false positives, thereby optimizing the response to security threats. By leveraging diverse algorithmic strengths, ensemble methods provide a more robust system that adapts to varied surveillance environments and evolving anomaly patterns. This approach not only enhances overall reliability but also supports proactive threat management, ensuring more accurate and timely security interventions.
The conclusions were: (Q1) deep learning models significantly enhance action recognition capabilities, (Q2) edge computing optimizes processing for timely anomaly detection and (Q3) ensemble techniques improve system robustness and accuracy. Areas for further study include: (1). investigating multi-modal data integration to enrich contextual analysis, (2). leveraging innovative learning paradigms to reduce dependency on labeled datasets, and (3). refining algorithms for effective real-time applications across diverse surveillance settings
IDENTIFICATION OF DISTRESSED REAL ESTATE PROPERTIES USING NATURAL LANGUAGE PROCESSING/MACHINE LEARNING
Get PDFThe Real Estate industry is a great asset class that involves constructing, buying, and selling property. Although technology has made significant progress in buying and selling real estate properties online, via Zillow and Redfin, the ways to find distressed real estate properties as an investment opportunity seem to be lacking. This culminating experience project explored how to use machine learning to classify a property as distressed or non-distressed. The research questions are: Q1. How can Natural Language Processing methods like Latent Dirichlet Allocation (LDA) be leveraged to identify distressed and non-distressed real estate properties? (Tijare & Rani, 2020) and Q2. How can machine learning models help in categorizing real estate listings into distressed vs non-distressed properties based on textual features? (Narozhnyi & Kharchenko, 2024). The findings in Q1 are that the LDA model with 4 Topic modellings generated the highest coherence score of 94% and identified the keywords associated with distressed properties. The findings for Q2 are choosing the Multi-Layer Perceptron (MLP) machine learning model, which had the highest F1 scores of 94% and accuracy of 93% and predicted the distressed properties probability. In Q1, we conclude that 4 Topic modelling generated the highest coherence values and hence the keywords predicted distressed properties. In Q2, we conclude that the MLP machine learning model generated the highest F1 score of 94% and testing accuracy of 93% and performed the best to predict distressed properties. Future study can be expanded by studying different asset classes like multifamily, commercial and mixed-use properties, and additional geographical regions could offer a broader perspective
Stressors, Caffeine Consumption, and Mental Health Concerns among College Students
Get PDFThe purpose of this study was to understand the relationship between mental health challenges, life balance concerns and caffeine consumption among college students. As caffeine is considered a psychoactive and cognitive enhancer that enhances physical performance, consuming an excess of caffeine can result in caffeine intoxication, which may include experiencing negative side effects.
Utilizing quantitative methods and availability sampling, 61 college students completed a self-administered online survey via Instagram and, with the assistance of professors, in classrooms. The self-administered Depression, Anxiety, Stress Scale 21 (DASS-21) reveals that college students are experiencing high levels of depression, anxiety, and stress. A majority of participants reported that they spend their free time working on coursework and do not see their family, friends, and loved ones as much as they would like to. The self-administered Caffeine Consumption Questionnaire-R (CCQ-R) reveals that college students are consuming caffeinated beverages, and coffee is shown to be the most consumed. Although there was no correlation between caffeine consumption and mental health, college students experienced negative side effects when consuming caffeine
MACHISMO: THE IMPACT IT HAS ON HISPANIC MALE COLLEGE STUDENTS RECEIVING MENTAL HEALTH SERVICES
Get PDFMachismo is an ideology held within the Hispanic community that endorses the expression of men’s dominance and power as well as aspects of bravery, honor, dominance, and reserved emotions. Higher rates of belief in machismo are connected to higher post-traumatic distress, stress, and depression. The goal of the study is to determine to what extent Machismo has an impact on male Hispanic college students’ beliefs about mental illness and their willingness to seek help. The following is a quantitative study. This study utilized male college students enrolled in the Bachelor of Social Work and Master of Social Work programs. Additional participants attending a two-year community college or a four-year university were recruited through social media platforms. A Qualtrics questionnaire was administered using scales to assess machismo, willingness to seek help, and beliefs towards mental illness. This population was chosen due to the stigma of mental health and mental health services among Hispanic males (n=23). The findings from this study provide social work practice with a better understanding of how to apply interventions and treatment for male Hispanic college students with mental illness. Results of the study indicate that Hispanic male college students who have higher identification with machismo have higher beliefs in negative stereotypes of mental illness but do not have a statistically significant relationship with a willingness to seek help
THE BARRIERS TO NATURAL OUTDOOR SPACES: PERSPECTIVES FROM PEOPLE WITH MOBILITY DISABILITIES
Get PDFPast research has predominantly examined the accessibility challenges encountered in built environments, particularly in urban parks and playgrounds. However, there is a gap in understanding the experiences of individuals with mobility disabilities in natural outdoor spaces, particularly state and national parks (SNPs). This study aims to address this gap by investigating the experiences of individuals with mobility disabilities within SNPs. Through qualitative data collection methods, including in-depth interviews, this research explores the perspectives of seven individuals who have visited SNPs in the past decade. The major findings covered a range of topics, including demographics, personal experiences, transportation barriers, infrastructure accessibility, societal attitudes, and the availability of information related to accessibility within SNPs. By capturing firsthand accounts and insights from individuals with mobility disabilities, this study contributes to a better understanding of the accessibility challenges and opportunities within natural outdoor environments, with implications for policy, design, and management practices aimed at enhancing accessibility and inclusion in SNPs