Engineering – Page 5 – UROP Spring Symposium 2021

Engineering

Testing and Formulation of Icephobic Coatings

The buildup of ice on surfaces can be detrimental to the performance of essential infrastructures, technologies, and transportation systems such as aircrafts, ships, turbines, powerlines, and more. Icephobic coatings can act as a shield against the accumulation of ice on surfaces, providing a cost-effective way of protecting the integrity and safety of systems and reducing or eliminating potential maintenance costs created by ice damage. There are numerous combinations of reagents, solvents, and catalysts that, when reacted together, form a product exhibiting icephobic properties. An ideal icephobic coating will have an ice adhesion strength, defined as the force required to debond a specified area of ice from a substrate, of less than 100 kPa. Additionally, it is desired for these coatings to be optically clear and to have fast curing, or drying, rates. The purpose of this research is to find a formula that provides low ice adhesion strength, optimal optical properties, and a fast curing rate. Low adhesion strength and clear coatings are relatively simple to produce, but these properties in conjunction with a fast curing rate (ideally of just a few minutes) poses a challenge. Making use of polyurethane as the base polymer for icephobicity, the amounts and choices of reagents, solvents, and catalysts were changed to produce different formulas. Ice adhesion numbers were tested for all samples, and the samples were compared to one another to determine which gave the most desired results. By changing the amounts of one variable (such as solvent, catalyst, oil, or diisocyanate amount) and keeping all others constant, the affect that each variable has on the icephobic properties of the formula could be studied.

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Chimeric Antigen Receptor T Cell Design through Systems Biology

Chimeric antigen receptor (CAR) T cells are a promising solution for treating cancers that are drug resistant, but are currently unsuccessful in solid tumors due to their stronger defense mechanisms that prevent the CAR T cells from recognizing the tumor. This project aims to identify the defense mechanisms in triple negative breast cancer related to infiltrating B cells and plasma cells in order to improve CAR T cell targeting in these tumors. We analyzed publicly available single cell RNA sequencing data from 12 patients with treatment-naive triple negative breast cancer and identified B cell and plasma cell subsets within these tumors. A total of 13 subsets were identified, with different patients displaying distinct subtypes of B and plasma cells. We compared the survival probability associated with the genes identified for each subset of cells. Survival analysis indicated that no single set of genes was associated with increased survival. Interestingly, clusters 1 and 2, denoted by genes IGKV-3, HLA-DRA, and MS4A1, were associated with patients that had increased immune infiltrate, which may indicate that these cells could be used to improve targeting of CAR T cell therapy.

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Analysis of the Human Ovarian Cell Landscape using Histological and Immunofluorescent Staining

Improvements in cancer treatments have led to the increase of cancer survival rates in recent decades. While these treatments are life-saving, they have cytotoxic effects on the ovaries, thus depleting the ovarian follicle supply. The number of follicles in the ovaries is nonrenewable, leading to premature ovarian insufficiency (POI). In vitro follicle culture could serve as a broad fertility preservation option for these patients, but much is still unknown about the mechanisms driving early follicle development. Single cell sequencing of ovarian tissue from deceased donors can be used to characterize the role of stromal cells in follicle development as well as transcriptional differences between follicles at the different stages of development. To verify the cell populations identified using single cell sequencing we will use hematoxylin and eosin (H&E) and immunofluorescent staining to characterize the spatial landscape of follicles and stroma in donor ovarian tissue. I will quantify follicles at different stages of development across donors and use immunofluorescent staining to validate and support the sequencing data. This work will deepen our understanding of follicle development and supporting cell types, leading to development of human follicle culture systems and a broad fertility preservation option for cancer survivors.

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Chimeric Antigen Receptor T Cell Design through Systems Biology

CAR T cell cancer therapy is a new, promising treatment that offers an alternative to the existing methods of fighting cancer, such as chemotherapy and radiation therapy. However, at present it is primarily useful in counteracting blood cancers like leukemia, and is far less effective on solid tumors. To circumvent this issue, sc-RNAseq data from triple-negative breast cancer (TNBC) patients was analyzed using bioinformatic techniques in R to identify adjuvant targets. Cells were clustered into groups using genetic markers, allowing for survival analysis on certain genes to predict which pathways were associated with improved patient recovery. Once identified, these genes will be tested in vitro, using CAR T cells to record cessation of cancer cells with and without these pathways enabled. Such findings have the potential to have a significant impact on the field of cancer therapy, as they offer a treatment to tumors such as TNBC that have previously been difficult, if not impossible, to successfully treat.

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Role of Actin in Health and Disease

Our research culminates to answer the question: what is extracellular actin’s role in health and disease? By examining three different forms of extracellular actin – cell surface bound, free circulating, and those associated with the extracellular matrix – we can understand their application and how they can be used to innovate the tools we currently have to work with diseases.

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Chimeric Antigen Receptor T Cell Design through Systems Biology

Chimeric antigen receptor (CAR) T cells are an emerging therapy for drug-resistant cancers. So far, they have only been effective on hematological malignancies such as leukemia, but have had little success on solid tumors such as triple negative breast cancer (TNBC), due to the immunosuppressive nature of solid tumors that prevents the CAR T cells from operating effectively. In this study, we used publicly available single cell RNA sequencing data to study how fibroblasts in diseased and healthy patients affect and communicate with the CAR T cells. We used Seurat to cluster the different cells in the tumor microenvironment in healthy and diseased tissue and ran a survival analysis for each cluster. Our results show us that the cluster containing macrophages, which is defined by the FOS, DNAJB1, ZFP36, JUNB, and KLF4 genes, has a significant effect on the survival of patients with TNBC. We hope to confirm these computational results through in vivo experimentation of mice infected with TNBC. These data will shed light on how to better engineer CAR T cells to make them more effective in targeting solid tumors.

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Bioinformatics and Biochemical studies on cancer proteins

Protein sequence data is abundant, but ancestry information of those proteins and experimental analysis of the structure of those protein sequences are less available, and costly to produce. Thus, machine learning algorithms are being developed to predict protein structure. For structure prediction, available data is collected and parsed for the specific properties one’s algorithm requires, such as torsion angles for tertiary structure prediction. After an algorithm is trained against this data, the algorithm can be tested in its correctness of predicting protein structure. As a new algorithm is developed, an increase in accuracy and precision is expected.

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Machine Learning Methods for Robust ECG Beat Detection

This research was conducted to determine what types of machine learning algorithms were best to determine whether an individual is experiencing heart arrhythmias—such as atrial fibrillation, a type of arrhythmia that can lead to a number of fatal conditions such as blood clots, stroke, and heart failure. Using Python and the sklearn library, a number of machine learning models were tested for accuracy of ECG peak detection, which included Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Classification and Regression Trees, Gaussian Naive Bayes, and Support Vector Machines. The accuracies varied based on the proportion of noise within the ECG file, so an introspective algorithm was developed to choose the optimal peak detection algorithm based on the estimated noise level measured in an ECG. This can be used in the future as a convenient way to accurately determine the presence of heart arrhythmias using wearable devices such as a smart watch.

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The Effect of Disaster-induced Displacement on Social Behaviour: The Case of Hurricane Harvey

In recent decades, whether climate change is real has become more and more of a partisan issue among everyday people. Over 97% of the scientific community, however, agrees that the climate has been changing due to human activity. In order to see if and how people’s views change when they actually experience a natural disaster that is likely due to climate change, we conduct an analysis of over 100,000 tweets before and after Hurricane Harvey.The analysis consists of labelling each tweet as part of one of six categories (climate change, Hurricane Harvey, sports, politics, policing, and other). Then, if the tweet relates to climate, the disposition of the tweet’s author towards climate change was also noted. Currently, in order to make the analysis of data more efficient, several machine learning classifiers are being built in the programming languages python and R. Once these classifier models are created, we will benchmark them and then use the best performing model to label new tweets at a much faster pace than a team of human coders. With the fully labeled corpus of climate change tweets, the study will be able to shed light on behavioral and political changes before and after climate change-induced events. Keywords: climate change, hurricane, Harvey, twitter, politics

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Using C++ to create a predictive model of twitter data to analyze social and political behavior.

As much as the media covers natural disasters extensively, little is known about the underlying effects aside from the “who was hurt or killed and what was destroyed” coverage of the disaster. Natural disasters often cause geographic displacement of individuals from their homes and communities to new ones. These events result in more than just the loss of physical property, as community and neighborhood attributes encompass many different aspects of an individual’s social relationships and the cultural institutions. Moving to a new community changes these attributes and will affect an individual’s political attitudes and behaviors. The ongoing post-Harvey migration provides a unique opportunity to examine the effect of social contact and political context in a case where these encounters could not have otherwise been anticipated by the individuals affected. This research study seeks to examine the political consequences of Hurricane Harvey by studying post-Harvey migration patterns as an unexpected or exogenous shock via data analysis techniques such as Natural Language Processing using C++ programming. This analysis will allow us to study variations in the decision of displaced individuals to move to different areas and how this affects their associated experiences once they move, and how people’s political attitudes and behaviors change in response to rapid demographic shifts in their communities in the future. This research is therefore able to address “How does local context affect political opinions and behaviors?” Using hand-coded twitter data, we were able to create various predictive models coded in R, Python, and C++. My specific research focuses on building a C++ predictive model. The C++ uses a score based system on NLP and even caught mistakes made during manual coding. We were able to determine that natural disasters such as Hurricane Harvey do influence social and political behaviors of the people affected by using an analysis of social media such as twitter. We also determined that the C++ predictive model had a very accurate prediction on sample twitter data.

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