Illinois Mathematics and Science Academy
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Build Your Own World!
No full textYou will be introduced to an activity that allows you to create a unique planet and learn about earth and space science in the process! In making your planet, you will use knowledge of formation, plate boundaries, landforms, weather, climate, wind currents, and more. You will sketch out planetary details and create a raised relief map that shows aspects of your planet. If you love Earth Science and project-based learning, this is the session for you. Bring a pencil and a positive attitude
Using Artificial Intelligence To Discover Novel Biological Targets For Endocarditis Infections
No full textEnterococcus faecium is a gram-positive bacterium known to cause a variety of infections in humans, including endocarditis, a bacterial infection of the heart’s inner lining. PandaOmics is an AI driven platform for finding therapeutic biological targets of different diseases by aggregating biological and biomedical datasets to produce an organized list of potential biological targets for a specific disease based on a variety of criteria. The platform combines data of gene expression, methylation, and proteomics to create a ranked list of genes that are potential therapeutic targets, looking at factors such as small molecules, safety considerations, protein class, biological process involvement, novelty, and pharmaceutical development to identify the best candidates. PandaOmics was used to identify potential small molecule novelty biological targets of the disease endocarditis. Biological targets were mainly sorted based on being small molecule and novelty targets. The dataset created by PandaOmics identified specific genes as the best potential targets for endocarditis, as they matched a majority of criteria the AI search was looking for. These identified biological targets can then be further explored in a laboratory setting to better understand how endocarditis occurs in the body and create potential treatments for endocarditis infections
Determining Systematic Uncertainties in the Search for the Doubly Charged Higgs Boson
No full textTo resolve parity violation through the weak force, the doubly charged Higgs boson arises through the Left- Right Symmetric model. In search of the boson at the Large Hadron Collider, I determine systematic uncertainties to determine the accuracy of our mass limit. Specifically, analyzing the impact of the parton distribution function (PDF) , which determines how quarks and gluons interact in a collision, I determined the impact of the PDF on the signal of the boson. Afterwards, generating the data via Monte Carlo at a higher mass of 5000 GeV, I measured the significance of the intrinsic width on the signal. In particular, I compare the events generate at each mass from plus or minus one standard deviation in comparison to the baseline
The Effects of Chemotherapeutic Stress on NAT10 Expression in U937 Cells
No full textThe efficacy of chemotherapeutic drug regimens has been a longstanding concern for cancer treatments, highlighting the need for research on how they could become more effective. A possible answer could lie in the field of epitranscriptomics, in which chemical modifications are made to RNA to affect gene expression. In this study, we aimed to find the effects of drug treatment duration on the expression levels and subcellular location of Nacetyltransferase 10 (NAT10), an enzyme that has been associated with poor prognosis in multiple cancer types. We observed an increase in NAT10 levels as the duration of Daunorubicin treatment increased, yet our results with Cytarabine were inconclusive, leaving room for further experimentation. Additionally, we found that there was increased NAT10 expression in the cytoplasm when treated with Daunorubicin, but decreased expression when treated with Cytarabine. Building on this, we generated multiple NAT10 clones with different mutations to express these localization phenotypes. While our results put NAT10 as a promising drug target for AML patients, further research is needed to validate these findings. Using the generated DNA clones will help us uncover the implications of NAT10 expression and localization in drug resistance and cancer progressio
Analyzing and Characterizing Heme Binding Peptides
No full textSelf-assembling peptides have a variety of uses in biological materials science, from conductive nanowires to applications in biomedicine. This research hopes to facilitate future heme binding ⍺-helical peptide designs by employing machine learning methods to identify patterns in peptide sequences that promote heme binding while maintaining an ⍺-helical structure to mitigate the bias in previous research towards β-sheet forming peptides.
Physical and structural properties of the peptides determined from their sequences, including charge distribution, ⍺-helical propensity, heme binding propensity, and hydrophobic interactions motivated the rational design of initial experimental peptide sequences. \u3e200 ixteen-amino-acid-long peptide sequences were synthesised using Solid Phase Peptide Synthesis, and characterized using Fourier Transformation Infrared spectroscopy, Circular Dichroism (CD) spectroscopy and Ultraviolet-visible (UV-Vis) spectroscopy to assess secondary structure and heme binding efficiency.
A linear combination analysis routine of the CD and UV/vis data was developed using python to automate and quantify the fidelity of ⍺-helix formation and heme binding. These data will then be used as part of the experimental validation method in our machine learning protocol, consisting of a Long Short Term Memory model that was trained on the Protein Data Bank and experimental data, aimed to generate potential patterns of successful structures for experimentation
Heuristic-Guided Genetic Preparation Of Ansatz for Variational Quantum Eigensolvers
No full textVariational Quantum Eigensolvers (VQEs) are an ernerging application of near-term quantum computers due to their low computational requirements and high resistance to errors. The success of VQEs depends on the chosen ansatz, or pararneterized circuit, to approximate the ground state of a problem Hamiltonian. Ansatz selection presents a challenge, as it involves a trade-off between representation accuracy (which typically requires a high circuit depth) and hardware efficiency (which limits expressibility). This project presents a new method that utilizes a reinforcement-leaming-based heuristic to estimate the fitness of genetically generated ansatz, considering measures of expressibility, entanglement, and hardware costs. The protocol trains a genetic algorithm to generate candidate ansatz that can model a class of problems similar to a training Hamiltonian, optimizing both efficiency and accuracy. This project tests the method on various rnolecules (LiH and H2) and finds a significant reduction in the number of two-qubit gates and parameters compared to traditional hardware-efficient methods while maintaining accuracy. Future work will focus on applying particle swarm optimization to optimize the hyperparameters and further using learning to refine the fitness function, enhancing the efficiency and accuracy of ansatz generation
How AI Really Works
No full textThis session will discuss, in as simple a way as possible, how generative artificial intelligence works, using simple demonstrations to show the basic principles of large language models. The hope is that this will help dispel the tendency to view AI as some sort of black magic and allow people to consider carefully how it can be useful as a tool—and when it can’t