Illinois Mathematics and Science Academy
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Segmentation-Based Morphological Profiling of Resistance Cells via Gromov-Wasserstein Distance Matrices
No full textUnderstanding and identifying distinct morphological features of cells is crucial for studying cancer progression and treatment response. This project specifically focuses on Pancreatic cancer cells as the cancer remains one of the deadliest malignancies, with a five-year survival rate below 10%. Recently, the FDA approved the drug Sotorasib to treat cancers with the mutation KRAS G12C, which Pancreatic cancer cells contain. However, resistance to Sotorasib and other treatments, including cisplatin, gemcitabine, and trametinib, remains a challenge for scientists in developing treatments for pancreatic cancer.
The project aims to develop a computational pipeline that segments images of pancreatic cancer cells that have been separately treated with the four drugs. Therefore, a deep-learning-based segmentation model, with an accuracy of ~94% across all conditions was built and used. The segmentation model accurately isolates individual cells, allowing for quantitative analysis of morphological features. By applying GW distance to a pair of cells, we compare the morphological distributions of treated colonies of cells on the four treatments to identify phenotypic shifts indicative of resistance.
Initial results suggest distinct morphological signatures for each treatment. This method provides an interpretable framework for predicting whether or not a colony will be resistant to a certain treatment
A novel Microcantilever Biosensing platform for PFOA Detection
No full textPerfluorooctanoic acid (PFOA) is a type of per- and polyfluoroalkyl substance (PFAS), which pose health risks to humans. Commonly referred to as “forever chemicals” due to their extremely long lifespans, drinking water PFAS contamination from industrial processes and other sources has been recognized by the EPA as a cancer risk, and regulates the maximum level allowed. Unfortunately, current PFAS detection systems are expensive, require sophisticated machinery, and are not as selective, sensitive, and specific as could be desired. We designed and tested a microcantilever platform for a PFOA biosensor. Anti-albumin antibodies were immobilized on the cantilever surface. When this was exposed to a solution containing PFOA, antibody antigen interactions took place, exerting a stress on the surface of the cantilever tip. This stress led to deflection of the tip, which was measured optically. In a more concentrated PFOA solution, more binding interactions will take place, leading to a greater stress and greater tip deflection. Thus if you have deflection data from an unknown sample, the PFOA concentration can be determined. Preliminary data shows that this approach can predict with high sensitivity the concentration of PFOA in solution
Data Science to Identify Inequalities in CPS
No full textThe positive narrative of academic recovery told by Chicago Public Schools (CPS) emphasizes gains in student involvement and achievement. However, a more thorough examination of budgetary allocations, teacher migration, attendance patterns, and standardized test distributions (IAR and STAR360) indicates notable inequalities that affect student results. Attendance rates reveal information about accessibility and student involvement, whereas faculty migration draws attention to the difficulties in keeping skilled teachers, especially in schools with little funding. The distribution of standardized tests highlights the need for fair academic support by revealing achievement discrepancies across various demographic groups. Furthermore, institutional priorities are reflected in budget allocations, which frequently expose disparities in funding across communities and schools. By looking at these elements, we may better comprehend the challenges CPS faces in creating fair learning environments and guaranteeing that every kid makes steady academic development
Effects of Compressive Velocity on Lipid Monolayer Shear Banding Collapse
No full textFound in alveoli in the form of lung surfactants, the structure of a lipid monolayer is composed of hydrophobic tails surrounded by air and hydrophilic heads that assimilate with water. As we breathe in and out, lung surfactants expand and contract to optimize air intake volume and pressure, causing collapse under high compressive stresses and strains during exhalation. We can experimentally observe the compression and collapse of lipid monolayers using fluorescence microscopy images on a Langmuir trough. The lipid dyes visualize coexisting phases during compression and collapse, including condensed domains and the matrix surrounding them. Depending on the starting conditions (ie. composition, temperature, etc), collapse can take different forms, such as out-of-plane collapse (folding, crumpling, vesiculation), or in-plane collapse (shear banding).
Shear banding is a type of collapse where the lipid domains shift from a hexagonal organization into horizontal condensed rows under high compression. In this study, elastic continuum mechanics is used to model the collapse of lipid monolayer systems computationally, through MATLAB and ABAQUS. Fluorescence microscopy images of lipid monolayers on Langmuir troughs provide data for shear banding collapse and allow us to compare our computational results to experimental results.
Previous computational experimentation has studied shear banding while compressing at one speed. In this study, we test whether different compressive velocities affect strain localization. We look at homogenous cases as well as heterogeneous cases that involve large models and small models of domain geometry pulled from experimental data. By further studying the lipid monolayer collapse, we can open gateways of research into the field of respiratory diseases
With or Without Highways?
Get PDFCan you imagine your life without your GPS (Global Positioning System)? Although GPS seems to be an automatic and standard service for all users. The users alone contribute to at least 30% of the program’s ability to work accurately. Join us in exploring a model representing the calculations performed by navigational devices that predict travel times and the optimal roads for this travel time. If you get lost on the way to this presentation, use the GPS
Severity Assessment of Diabetic Retinopathy Through Automated Segmentation
No full textDiabetic Retinopathy (DR) is a microvascular complication of diabetes mellitus that impacts the retinal vasculature, leading to progressive vision impairment and potential blindness if left untreated. This study investigates the efficacy of automated segmentation techniques in the evaluation of DR severity. The objective is to establish a precise and efficient methodology for the quantification of critical retinal biomarkers, including blood vessels, microaneurysms, and optic discs. This study used advanced image processing algorithms such as Hough Circle Transformation, Canny Edge Detection and Contrast Limited Adaptive Histogram Equalization to extract these features from fundus images. Refinement to the images was achieved through contour detection and elliptical element filtering. The performance of each method on the features were assessed by pixel-wise evaluation and the methods yielded high accuracy. The automated segmentation of fundus images allows for the assessment of DR severity, which enables ophthalmologists to perform timely therapeutic interventions. This method enhances diagnostic accuracy and minimizes human error in DR diagnosis, optimizing patient management strategies. This approach found in the study not only aids ophthalmologists but also provides quality care to patients through early detection and continuous monitoring of the retinal features, particularly for underserved diabetic populations at risk for vision threatening retinopathy
Global form of flavor symmetry groups in Twisted A_{2N} Class S Theories
No full textFour-dimensional quantum field theories with N=2 supersymmetry provide a rich theoretical laboratory to test general ideas about quantum field theory. A large class of such theories, known as Class S, arise from compactification of a six-dimensional N = (2,0) theory on a punctured Riemann surface. The N = (2,0) theories have an ADE classification, and those of type A,D, and E_6 give rise to a twisted sector of the corresponding class S theories. These theories, in turn, can have interesting 2-group symmetries, which provide additional tools for understanding their physics. In this work, we complete the preliminary step of determining the 0-form symmetry group of twisted A_{2N} theories, whose 2-group symmetries have not yet been studied
