Illinois Mathematics and Science Academy
Illinois Mathematics and Science Academy: DigitalCommons@IMSANot a member yet
9795 research outputs found
Sort by
Experimental Paradigm for Studying Abnormal Hip Torque Coupling During Gait Initiation After Stroke
No full textPost-stroke lower limb impairments cause significant challenges in balance during gait initiation. While studies have mainly focused on behavioral impacts of these impairments, the understanding of neutral constraints persist a significant gap. This study is aimed to investigate the abnormal hip extension- adduction coupling previously found in individuals with stroke. We hypothesize that when stepping with the non paretic limb, the reduced ability in the paretic standing limb to abduct while extending will impact frontal plane balance. To test our hypothesis, we propose to induce the abnormal hip extension-adduction coupling by increasing hip extension torque demand in the stance limb during gait initiation with a longer step and/or by applying resistance through a passive exosuit. Towards this end, here we present the initial development and validation of the proposed paradigm in one healthy participant, under two (normal and long) steps x (with and without) resistance conditions. Our experimental setup successfully increased hip extension torque demand, which is expected to induce abnormal hip extension- adduction coupling in individuals with stroke. The findings from this experiment will show the effects of neural constraints on the lower limb after stroke during tasks such as gait initiation which may be effective for future therapeutic interventions
Topic Modelling Approaches for Identification of Topics within Clinical Notes of Emergency Department Patients with Opioid Misuse
No full textOpioid misuse is a significant public health challenge in the US, with escalating impacts on emergency medical services and emergency departments. Patients with opioid misuse are often treated as a homogenous population when there are likely subgroups that may influence optimal clinical care. This study aims to investigate and identify these latent groups among patients with opioid misuse. A sample of 1200 UI Health emergency department encounters were retrospectively reviewed and annotated for the presence of opioid misuse using previously published methodology. Of these, 570 cases were positive for opioid misuse. A latent Dirichlet allocation model was then trained on the clinical notes from these patient encounters. Coherence scores for models encompassing 2-19 topics were calculated. The final model was chosen by balancing coherence scores with model complexity. The optimal model was determined to be a 9-topic model with the following topics represented: overdose/altered mental status, skin and soft tissue infection, cardiac disease, limb pain, mental health, critical illness, physical rehabilitation, respiratory conditions, and gastrointestinal/liver disease. These findings highlight the heterogeneity that exists within the population of patients with opioid misuse utilizing the emergency department and suggest that personalized treatment approaches should be investigated to improve patient outcomes
Investigating the Effects of Light Exposure During Sleep and Circadian Rhythm in Adolescents and Young Adults
No full textThis research investigates the impact of light on sleep and circadian rhythm in adolescents and young adults. Light serves as a primary regulator of circadian rhythms, influencing sleep-wake cycles and overall health. By synthesizing findings from various studies, we explore the effects of light intensity, duration, and wavelength on sleep patterns and circadian timing. Studies demonstrate a gradual shift towards later bedtimes during adolescence, potentially linked to changes in circadian timing. Furthermore, research highlights the differential sensitivity to light between pre- to mid-pubertal and late to post-pubertal adolescents, with shorter wavelengths of light exerting a more significant impact on circadian rhythm and sleep architecture. Understanding these dynamics is crucial for designing interventions to promote healthy sleep habits and overall well-being in adolescents and young adults. To investigate our meta-analysis of prior studies, we distributed 50 actigraphy watches to Northwestern students, which tracked their sleep schedule and daily light exposure, in order to identify any variable conclusions or patterns. This investigation aims to contribute valuable insights into the role of light in shaping sleep patterns and circadian rhythms during critical developmental stages
MMWave Reflections for Object Detection
No full textThis project explores the potential applications of millimeter-wave (mmWave) radar technology for object and activity recognition. Using a Texas Instruments radar wave card and several cases, the data retrieved demonstrates the capability of mmWave radar to distinguish between different objects. Mmwave is unique in the sense that it uses the reflection points of wifi-waves which are more adverse in their nature to collect data. The methodology feeding this into neural network models to interpret the unique signatures of various subjects. By analyzing the point cloud data generated by mmWave radar, which includes spatial coordinates, velocity, range, intensity, and bearing angle algorithms capable of recognizing eleven distinct object classes and several human activities, future research can be done for advanced applications in cyber-security, through device and keyboard recognition, and in automotive and pedestrian safety, by accurately identifying and tracking moving objects. MmWave radar technology is unique in its ability to penetrate adverse weather conditions and other obstacles yet still offer detailed spatial information, which presents a promising alternative to conventional imaging and sensing methods like other waves such as X- ray, ultrasonic, or magnetic
Design and Synthesis of Potential Treatments for Leishmaniasis
No full textLeishmaniasis is a parasitic disease that mainly infects mammals through bites from sandflies. Not only is it primarily seen affecting people in low-income areas, but also people with other infections that debilitate the immune system, like HIV. Immunosuppressed people get infected more easily, which in turn gives the disease more hosts to spread. Medicinal treatments for Leishmaniasis are extremely lacking in efficiency and accessibility. Some treatments that have been used before to fight Leishmaniasis have been proven to be toxic to humans, overly expensive, ineffective, or take too long to begin its effect. The corresponding carboxylic acids were reacted with amine, DIPEA, and HATU solution to create three new compounds. Column chromatography was used to work up and purify the compounds. The products were then subjected to IR and NMR testing. The objective is to develop a leishmaniasis treatment that is affordable, efficient in less than ten days, and safe for people with immunosuppressive diseases like HIV
Single-Cell Analysis of ChP-BAM Co-cultured Organoids
No full textThe choroid plexus (ChP) is a part of the blood-brain barrier that is responsible for cerebrospinal fluid (CSF) secretion, which washes out toxins and enables nutrient transport. Correspondingly, the ChP serves as a niche for immune cells (e.g. Border Associated Macrophages (BAMs). Studies suggest that ChP and immune dysfunction increases the incidence of Alzeimer’s Disease (AD), however, in vitro models are limited to induced microglia (iMGs). The human cell modeling group at the Rush Alzheimer’s Disease Center therefore created an in-vitro organoid ChP-iMG co-culture model to more accurately model BAMs. Single nuclei RNA sequencing (10X genomics kit) was used to characterize this new model. I performed data analysis within R using the Seurat package to clean, normalize, cluster, and visualize the data. I used proteinatlas.org and other scientific articles to identify unique cell types based on gene expression. We observed marked changes in gene expression (e.g. SPP1, CTSD, POSTN LPL) in immune cells that were co-cultured with the ChP (henceforth: iBAMs) compared to iMGs. These findings suggest that iBAMs are a better model to study the interaction between the ChP and immune system in Alzheimer’s disease and future treatments
Classifying Admission Characteristics of TBI Patients Using K-means Clustering
No full textTraumatic Brain Injury (TBI) remains a significant health concern that often results in long-term cognitive impairments, coma, or even mortality. Current classification methods are primarily reliant on the Glasgow Coma Scale (GCS) and face limitations in representing the complexity and variability of TBIs. This study utilizes unsupervised learning through a k-means algorithm to cluster TBI patients at Beth Israel Deaconess Medical Center between 2008 and 2019 based on admission characteristics, in order to enhance prognosis and treatment approaches. Analysis of clusters uncovers diverse patient profiles which reveal correlations between age, GCS scores, and post-hospital outcomes. Clusters characterized by extreme age or GCS scores demonstrate varied mortality rates suggesting the ineffectiveness of GCS as a sole classifier. Younger age emerged as a highly expected yet crucial predictor of favorable outcomes. The study establishes the potential of clustering algorithms in patient stratification, offering insights for prognosis and post-hospital outcome prediction. However, there are limitations stemming from a lack of generalizability due to a single-hospital dataset. More validation across diverse datasets is required for broader clinical applicability in critical care settings for TBI patients
Mathematical Modeling of the Optimal Light Wavelength for Increasing Biomass and Cell Size of Chlorella Vulgaris as a Basis for Enhancing Biofuel Production
No full textThe pressing need for eco-friendly fuel sources due to limited fossil fuels and rising population elucidates microalgae including Chlorella vulgaris (C. vulgaris) as a sustainable biofuel. Yet, high production costs hinder their commercial viability, which can be addressed by optimized lighting. However, a gap exists concerning the optimal wavelength of light to enhance biomass growth and cell size in C. vulgaris. This experiment investigated the impact of varying light wavelengths (400-650 nm) on biomass growth and cell size to develop a predictive mathematical model aimed at increasing productivity of commercial units. Separate containers were established for four groups that were each exposed to different light wavelengths: blue (400-490 nm), green (510-530 nm), red (630-650 nm), and control (no light), and a12hr:12 hr light- dark cycle was used. Biomass concentration was measured using a spectrophotometer over 10 days and the data for each condition was regression fitted to a logistic growth curve. Cell size was measured on the last day using a light microscope. C. vulgaris exposed to blue light (400-490 nm) had the largest positive change in biomass, followed by red (630-650 nm) and green (510-530 nm). C. vulgaris exposed to red light had significantly smaller cell sizes, while other groups had comparably larger cell sizes. The derived mathematical model can be extrapolated to large-scale plants. Overall, the null hypothesis can be rejected, as One Way ANOVA p \u3c 0.001. This has implications for reducing the cultivating and harvesting costs of C. vulgaris
Neural Network Compression and Storage Using Linear Feedback Shift Registers (LFSRs)
No full textThis research paper explores the application of Linear Feedback Shift Registers (LFSRs) to enhance the compression of neural networks. LFSRs, which employ a linear function to determine input bits based on previous states, are commonly used for generating bit sequences and pseudo-random numbers that can be used to generate pseudo-random weight approximations. Compressed neural networks offer a transformative solution by significantly reducing memory demands. The study looks at weight visualizations of neural networks and explores possible LFSR approximation with it which could potentially improve storage efficiency without compromising model performance. We hope to find some patterns that we can use to optimize the compression process, leading to more efficient neural network implementations. By leveraging the properties of LFSRs in conjunction with neural network weight visualization techniques, we aim to uncover novel strategies for enhancing neural network compression while maintaining or even improving model accuracy. This approach has the potential to contribute to the field of neural network compression and pave the way for more streamlined and resource-efficient deep learning applications
Quantification of Cells with Modifications Relating to the RB1 Pathway
No full textThe retinoblastoma tumor suppressor (RB1) is a vital tumor suppressor gene. It prevents the cell from transitioning to the S phase from G phase by inhibiting E2F activity which limits cell proliferation and facilitates a stable exit from the cell cycle. Inactivation of RB1 thus allows for the expression of genes necessary for the cell cycle to progress and results in the production of proteins and DNA. RB1 also regulates KDM5A which is a direct repressor of metabolic regulatory genes. Therefore a lack of RB1 causes dysregulation of KDM5A which can lead to downregulation of H3K4me3 levels, effectively silencing the metabolic genes. The EGFR pathway leads to the activation of CDK4/6, which inactivates the RB1 tumor suppression by initiating the phosphorylation of RB1. We raised multiple cell lines which were modified for the presence of RB1 and EGFR TKIs, then added ki67 (a marker for proteins) and EdU (a marker for DNA). By using Zeiss Microscopy Software to quantify the cell images, this project gave insight into the correlation between the expression of KDM5A and E2F within the RB1 pathway