As the use of car dashboard cameras (dashcams) has increased, the availability of dashcam imagery has also increased. In recent years, dashcam imagery has been predominantly used in conjunction with computer vision techniques for autonomous vehicle systems. However, this research explores an alternative application of these technologies in the domain of public safety and security. Specifically, we apply object detection to dashcam imagery to address the challenge of identifying vehicles associated with active Amber Alerts. With the goal of aiding law enforcement in locating abducted children more efficiently, we employ the YOLO (You Only Look Once) object detection model, a state-of-the-art deep learning framework known for its real-time performance and accuracy. Our methodology involves training and fine-tuning the YOLO model on a custom dataset of dashcam footage, incorporating diverse environmental conditions such as varying lighting, weather, and traffic scenarios. Experimental results demonstrate that the model achieves high precision and recall rates in detecting target vehicles, validating its effectiveness for real-world deployment. This research highlights the potential of leveraging deep learning and computer vision techniques to address critical public safety challenges, offering a novel application of these technologies beyond their traditional use in autonomous driving. Our findings contribute to the growing body of work in computer science that seeks to harness AI for societal benefit.
Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for over 90% of cases, and is characterized by aggressive growth, early metastasis, and resistance to therapy. A comprehensive understanding of the molecular mechanisms driving PDAC is essential for improving diagnosis, prognosis, and treatment. In this study, a multiomics approach was applied by analyzing both DNA methylation and RNA-sequencing datasets obtained from The Cancer Genome Atlas Pancreatic Adenocarcinoma project.The methylation dataset included significantly more tumor samples than normal samples, and a similar imbalance was observed in the RNA-seq dataset. This disparity posed a challenge for direct feature selection, as it could lead to a model biased toward tumor-associated features. To address this issue, six data imbalance correction techniques were evaluated and compared: Random Oversampling, Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic (ADASYN) for oversampling, along with Random Undersampling, Cluster Centroids, and AllKNN for undersampling. Identifying the most effective imbalance correction method is essential for improving feature selection accuracy and facilitating the discovery of novel genes associated with pancreatic ductal adenocarcinoma (PDAC). A deeper understanding of these oncogenes could contribute to the development of non-invasive diagnostic tests and personalized treatment strategies for PDAC.
Aging is a complex biological process influenced by a range of genetic, environmental, and physiological factors. Studying normal aging can help us better comprehend age related diseases and potentially lead to the identification of therapeutic targets. In this study, we use large transcriptomes collected from mouse and human brains (Tabula Muris and GTEx) to investigate genes, gene networks, and biological pathways that are selectively engaged at different biological ages through brain aging. We use a novel network biology platform called NetDecoder to determine which genes are highly utilized within brain specific biological networks; high utility genes are those that encode for important proteins that are crucial to a specific function, even if they are not differentially expressed. Our approach is unique because we can recover genes relating to the aging brain that are not differentially expressed, meaning they likely would not be pinpointed by other labs.
Menstrual cups have become increasingly popular in recent years for their environmental benefits, cost-effectiveness, and user comfort. Most menstrual cups are made using silicone, taking advantage of its flexible and leak-proof material properties. However, there has been limited research on the hydrolytic degradation of silicone biomaterials, particularly in the acidic vaginal environment, raising potential safety concerns. The objective of this research project is to study the hydrolytic degradation of silicone under acidic conditions to better understand the safety profile of biomedical devices like menstrual cups. Our initial study tested 40 silicone samples over a 29-day period at 37 °C and 67 °C in a 1 M hydrochloric acid (HCl) solution. Results of this accelerated study indicated a maximum mass loss of 11.4 %. Future studies will be performed using a vaginal fluid simulant (VFS) primarily composed of a lactic acid buffer system to assess physiologically relevant degradation behavior and to characterize potentially toxic degradation products. Ultimately, this research aims to develop a standardized workflow for studying the degradation of polymeric biomaterials in a VFS that could also be applied to other biomedical devices such as intravaginal ring (IVR) drug delivery systems.
The Internet of Things (IoT) encompasses a variety of systems and devices that enable data exchange across networks. With this interleaved connectivity comes an inherent vulnerability to attacks. Traditional intrusion detection in IoT environments has been primarily human-reliant, but modern malicious methods surpass manual approaches. Machine Learning (ML)-based Intrusion Detection Systems (IDS) show promise but require refinement to match human-monitored IDS effectiveness.This study involved a literature review of research involving the NetFlow dataset NF-ToN-IoT-v2, created in 2022 to enable ML-based IDS development. With balancing, the dataset includes approximately 16 million net-flows, with 63.99% attack and 36.01% benign. The data’s imbalanced nature was addressed through methods like down sampling to reduce training bias. A hyper-parameter tuning pipeline was used to optimize algorithm testing and cross-validation, especially for different data balancing methods.The algorithms tested based on previous research found during literature review include Naïve Bayes, Random Forest, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and XGBoost. Comparative analysis using confusion matrices and bar plots enabled the evaluation of algorithm effectiveness. Overall, this research highlights the potential of ML approaches in IoT IDS development, through leveraging NF-ToN-IoT-v2 to enhance detection accuracy and bridge the gap between human-monitored and ML-driven solutions.
Plastic pollutants are a significant environmental concern. Biodegradable plastics are a large area of research because if plastics are accidentally released into the environment, biodegradable plastics will break down into harmless byproducts. A blister pack is a type of packaging that consists of plastic pockets that hold individual pills. Current blister packs on the market are not biodegradable and contribute to environmental harm. The goal for this research project is to find an eco-friendly material to replace current blister packs that can also handle chemical reagents (such as medical reagents). Initial testing focused on developing a film from cassava starch that was adapted from the literature. The standard ASTM D543 was used to evaluate the resistance of the material to chemical reagents. The samples were placed under strain using a 3D printed strain jig, the chemical reagent was applied, and the samples were held at fixed temperature for varied amounts of time. After chemical exposure, the samples were tested to determine changes in mechanical properties. These results will be used to determine if cassava starch can replace traditional plastic blister packs to open the door to many environmentally friendly swaps in the medical field.
Tumor ablation is an effective, minimally invasive technique for cancer removal. The procedure uses medical imaging and a needle-like probe, which is guided to the target cancerous tissue where it is subsequently heated or cooled to a cytotoxic level. Thus, surrounding tissue must be separated from the cancerous tissue to prevent damage to healthy tissue. Saline and carbon dioxide are current methods of separation, but both migrate from the site due to gravity and cause risk of postoperative pain. To create a stable, stationary, and thermally protective barrier, a biocompatible foam has been developed with FDA-approved materials to optimize tissue separation for a typical 60 minute procedure. As progress continues, further characterization of the foam is being tested using rheology, which mimics deformation during foam injection and quantifies stability as a function of time and deformation rate. Current project goals involve developing a freeze-dried procedure that maximizes the shelf life of the foam and minimizes preparation steps for future commercialization and clinical use. Continued testing is essential for confirming previous qualitative tests of the foam’s material properties and providing data required for publication and implementation of these foams in a clinical setting.
This project seeks to develop a mechanically flexible cooling pad that can be used by medical patients to provide targeted pain or inflammation relief to injured or surgical areas. We are seeking to develop a device that is fully temperature controlled and can be used for long intervals of time up to several hours. We have identified several possible configurations to maximize cooling power while retaining as much geometrical flexibility as possible. We are currently pursuing two distinct cooling methods, and working to engineer a complete system for both methods that is able to sense and adjust temperatures produced by the cooling pad. In this poster we will describe some of the key geometrical and experimental variables under study, and work needed for continued improvement.
Amanita muscaria, commonly known as fly agaric or fly amanita, is a mushroom renowned for its distinctive appearance and psychoactive properties attributed to its compounds, ibotenic acid, and muscimol. Contemporary interest in Amanita muscaria has surged, driven by anecdotal reports of perceived psychological and medicinal benefits. However, no clinical studies exist thus far. This study employs thematic analysis of discussions from the “r/AmanitaMuscaria” subreddit on Reddit to explore users’ reasons for its consumption and the positive and negative experiences associated with this mushroom. A total of 998 principal posts and their associated 9,542 comments were analyzed, revealing thematic trends in adverse effects, perceived positive outcomes, reasons for use, modes of consumption, and thought perceptions. Findings highlight that users experienced more positive than adverse effects, and adverse effects experienced were minimal and primarily self-limiting. These findings may be particularly salient in clinical settings, as medical providers might find it challenging to uncover Amanita muscaria use among their patients unless presented with severe adverse effects. Future research is recommended to investigate Amanita muscaria’s pharmacology further to inform patients and medical providers of safe practices. Finally, an innovative methodological strategy is warranted to examine Reddit posts in-depth to understand users’ perceptions and attitudes.
Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for over 90% of cases, and is characterized by aggressive growth, early metastasis, and resistance to therapy. A comprehensive understanding of the molecular mechanisms driving PDAC is essential for improving diagnosis, prognosis, and treatment. In this study, a multiomics approach was applied by analyzing both DNA methylation and RNA-sequencing datasets obtained from The Cancer Genome Atlas Pancreatic Adenocarcinoma project.The methylation dataset included significantly more tumor samples than normal samples, and a similar imbalance was observed in the RNA-seq dataset. This disparity posed a challenge for direct feature selection, as it could lead to a model biased toward tumor-associated features. To address this issue, six data imbalance correction techniques were evaluated and compared: Random Oversampling, Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic (ADASYN) for oversampling, along with Random Undersampling, Cluster Centroids, and AllKNN for undersampling. Identifying the most effective imbalance correction method is essential for improving feature selection accuracy and facilitating the discovery of novel genes associated with pancreatic ductal adenocarcinoma (PDAC). A deeper understanding of these oncogenes could contribute to the development of non-invasive diagnostic tests and personalized treatment strategies for PDAC.
Alcohol use disorders (AUD) are a critical public health issue in the United States linked to elevated morbidity and mortality. Mutual help groups (MHGs), which provide peer advice and support, are among the most widespread forms of treatment for individuals with AUD. Twelve-step MHGs like Alcoholics Anonymous (AA), are the most utilized and evidence-based interventions for AUD. In recent years, several secular 12-step/AA alternative MHGs have emerged, including Self-Management and Recovery Training (SMART) Recovery, LifeRing, and Women For Sobriety (WFS). The outcomes and mechanisms of these 12-step alternative MHGs are poorly understood. In the present study, we conducted a systematic review with the goal of updating the scientific literature on outcomes, moderators, and mechanisms of change of SMART Recovery, LifeRing, and WFS for alcohol use problems in adults with AUD. Our review was pre-registered with PROSPERO and followed PRISMA guidelines. Alcohol-related outcomes, such as alcohol abstinence/reduction in alcohol use, heavy drinking, and other negative consequences were examined. Additionally, we included analysis of engagement-related outcomes, like membership characteristics, moderators of engagement/involvement, and mechanisms of change for MHOs. Preliminary Results from our qualitative review suggest differential alcohol-related and engagement-related outcomes by MHG. These findings highlight the importance of defining similarities and differences between MHGs, as individual differences in patient history and/or ideology disprove notions of universal MHG suitability. Study findings provide valuable insights into the different mechanisms and moderators of 12-step alternative MHGs that may inform future precision medicine strategies.
Non-suicidal self-injury (NSSI) and suicide are major public health concerns and represent behaviors most practitioners will encounter during their career. Unfortunately, many mental health providers lack confidence in their skills for treating suicidal and/or self-injuring patients. Factors such as concerns about liability, emotional contagion and suicide severity appeared to moderate willingness to treat. There is a lack of research whether these patterns emerge regarding providers’ willingness to treat NSSI, a known risk factor for suicide. This study aimed to explore therapists’ willingness to treat clients with NSSI, Depression, or suicide risk. Participants were emailed through midwestern state licensing lists and asked to answer questions about a hypothetical patient from one of the three conditions. Analyses included chi-square, ANOVA, and regression. Participants showed a lower willingness to treat or accept a patient who has a history of Suicide compared to NSSI or Depression. In addition, both regression models identified significant variables, those being confidence, negative attitudes towards self-harm, and liability concerns that associated with willingness to accept or treat. We also found that the perceived risk of the patient negatvely correlated with willingness to treat. Mental health providers could benefit from increased training about suicide to build their skills and confidence.
