Broad Street Scientific 2020-2021

BROAD STREET SCIENTIFIC

VOLUME 10 | 2020-21

The North Carolina School of Science and Mathematics Journal of Student STEM Research

Front Cover Aurelia aurita Aurelia aurita, also known commonly as the moon jellyfish, is a translucent jellyfish that is found along the Atlantic coasts of Europe and North America. It is commonly recognized for its four horseshoe-shaped gonads, visible in the image to the left.

Photo credit: ©Hans Hillewaert

Biology Section Panellus stipticus Panellus stipticus, also known commonly as the bitter oyster, is a bioluminescent species of fungus that is found in Asia, Europe, Australia, and North America. The fungus is known for its luminescent gills and kidney shaped caps.

Photo credit: Ylem, via Wikimedia Commons

Chemistry Section Bathocyroe fosteri Bathocyroe fosteri is a species of bioluminescent comb jellyfish found at intermediate to deep depths in oceans around the globe. Bathocyroe fosteri is commonly found near the Mid-Atlantic Ridge.

Photo credit: Marsh Youngbluth, via Wikimedia Commons

Engineering Section Janicella spinicauda Janicella spinicauda is a species of bioluminescent shrimp commonly found in the Gulf of Mexico, Atlantic Ocean, and Pacific Ocean. The shrimp use their light emitting abilities to camouflage themselves from predators.

Photo credit: Danté Fenolio

Mathematics and Computer Science Section Mycena lucentipes Mycena lucentipes is a species of bioluminescent gilled mushroom found in the Atlantic Forest in Brazil. The species is relatively new, and is known for its glow-in-the-dark caps.

Photo credit: Henrique R. Domingos, via Wikimedia Commons

Physics Section Milky Seas Effect The Milky Seas Effect is a natural phenomenon in the world’s oceans in which the water glows varying shades of blue light. This light is caused by the presence of bioluminescent dinoflagellates in the water.

Photo credit: Johanna Brehmer, via Wikimedia Commons

TABLE of CONTENTS 4

Letter from the Chancellor

5

Words from the Editors

6

Broad Street Scientific Staff

7

Essay: Computing the Brain: When Neuroscience and Technology Collide

SHERRY LIU, 2022

9

Photography: Distant Glimmers

GABE HART, 2021

Biology 10 Assessing the Effectiveness of the Hericium erinaceus Extract as Acetylcholinesterase Inhibitors

CHAYSE CHANDLER, 2021

16

Investigation of the Possible Synergistic Effects Between MSDC-0160 and Rapamycin in Treating Parkinson’s Disease

NNAMDI OBI, 2021

22

The Effect of Small Molecules Jh-RE-06 and T2AA on Mutagenic Translesion

Synthe sis in Arabidopsis thaliana

LIXIN YANG, 2021

Chemistry 28 Computational Design of Pathogen-Specific Hsp90 Inhibitor to Target Malarial Parasites

ANIKA BHADRIRAJU, 2021

35

Computational Design of a Multi-Target NMDAR/AChE Ligand for the Symptomatic Treatment of Alzheimer’s Disease

TIANNING "T.T." CHEN, 2021

44

Compatibility of Melamine Foams Hydrophobically Modified for Crude Oil Absorption with a Janus-Style Autonomous Propulsion Mechanism

NRITHYA RENGANATHAN, 2021

Engineering 51

Evaluating the Efficiency of Using Martian Materials as Fuel to Reach Martian Orbit

JOSHUA KUPRAS, 2021 ONLINE

Mathematics and Computer Science 56

The Fundamental Theorem of Calculus: Solving MAA-CMJ Problem #1177

ARNAV SINGH, JANE CROWLEY AND JUSTIN INDLA, 2021

61

Self-Driving Car Mask R-CNN Object Detection Model Efficiency with Different

Levels of Training

AKSHAR YECCHERLA, 2022 ONLINE

67

Optimal Training Methods for Neural Networks When Predicting Stock Market Prices

EVAN ZHANG, 2021

Physics 76

Examining X-Ray Emissions from Repeating FRB 180916.J0158+65

KYLIE BURGESS, 2021

84

Investigating the Properties of GW170729 using a Newtonian Approximation

SHARDOOL DESHPANDE, 2021

Featured Article 88

An Interview with Dr. Pamela Douglas

LETTER from the CHANCELLOR “Above all, don't fear difficult moments. The best comes from them..” ~Rita Levi-Montalcini I am proud to introduce the tenth edition of the North Carolina School of Science and Mathematics’ (NCSSM) scientific journal, Broad Street Scientific. Each year students at NCSSM conduct significant scientific research, and Broad Street Scientific is a student-led and student-produced showcase of some of the impressive research being done by our students.

Though NCSSM (like nearly all schools) has had to make significant adjustments during the past year in response to the COVID-19 global pandemic, I am heartened by the response of our students, faculty, and staff to the many challenges they have faced. Their resilience, optimism, and determination to pursue research goals — no matter the obstacles — reflect their passion for scientific inquiry and reinforce the power of research to address the challenges we face as a human family. While we are always proud of our students, I am especially proud of those who found a way to pursue their research goals this year and to share their results with the broader scientific community.

They inspire confidence in us that they will continue their good work beyond their time at NCSSM, and will continue to develop solutions and technologies that will keep us healthy and improve our lives.

Opened in 1980, NCSSM was the nation’s first public residential high school where students study a specialized curriculum emphasizing science and mathematics. Teaching students to do research and providing them with opportunities to conduct high-level research in biology,

4 | 2020-2021 | Broad Street Scientific

chemistry, physics, computational science, engineering and computer science, math, humanities, and the social sciences are critical components of NCSSM’s mission to educate academically talented students to become state, national, and global leaders in science, technology, engineering, and mathematics. I am thrilled that each year we continue to increase the outstanding opportunities NCSSM students have to participate in research.

Each edition of Broad Street Scientific features some of the best research students conduct at NCSSM under the guidance of our outstanding faculty and in collaboration with researchers at major universities. For thirty-six years, NCSSM has showcased student research through our annual Research Symposium each spring and at major research competitions such as the Regeneron Science Talent Search and the International Science and Engineering Fair.

The publication of this journal provides another opportunity to share with the broader community the outstanding research being conducted by NCSSM students.

I would like to thank all of the students and faculty involved in producing Broad Street Scientific, particularly faculty sponsors Dr. Jonathan Bennett and Mr. Matthew Meers, and senior editors Akshra Paimagam, Daniel Jin, and Sriya Mantena. Explore and enjoy!

Dr. Todd Roberts Chancellor

WORDS from the EDITORS Welcome to the Broad Street Scientific, NCSSM’s journal of student research in science, technology, engineering, and mathematics. In this tenth edition of Broad Street Scientific, we aim to showcase the breadth and depth of research conducted by our students and encourage readers to be scientifically literate individuals. This year we are especially proud of our student researchers as they continued exploring with unwavering dedication and enthusiasm despite COVID-19 and our country’s societal reckoning. Now, it is more important than ever to keep up with new advancements so that we can hold informed perspectives and make informed decisions. We hope you enjoy this year’s edition!

This year’s theme is bioluminescence: a visual dive into the diverse organisms that have the incredible ability to produce and emit their own light. Through chemiluminescence, the production of light through chemical reactions, bioluminescent organisms are able to utilize different patterns of light production for a variety of functions.

From hunting to communication, bioluminescence plays many different roles in the lives of these organisms. This is reminiscent of how STEM plays so many different roles in our lives; from powering our homes to protecting us from diseases, STEM research works to better people’s lives.

Bioluminescence itself is also a focus of science, inspiring scientists to develop new ways for the production of light. The bioluminescent organisms in this year’s visuals

remind us of not only how innovative science can be, but also of how beautiful it is.

We would like to thank the faculty, staff, and administration of NCSSM for continuing to support and build the scientific community that NCSSM represents. Now in its 41st year, NCSSM continues to nurture a stimulating academic environment that encourages motivated students to apply their interests towards solving real-world problems.

To many, NCSSM serves as a symbol of passion and determination in the next generation of young people that will no doubt change the world. We would like to give special thanks to Dr. Jonathan Bennett and Mr. Matthew Meers for their invaluable support and guidance throughout the publication process. We would also like to thank Chancellor Dr. Todd Roberts, Dean of Science Dr. Amy Sheck, and Director of Mentorship and Research Dr. Sarah Shoemaker. Lastly, we would like to acknowledge Dr. Pamela Douglas, Chief of Cardiology at Duke, for speaking with us about her unique career path and perspectives in STEM, and for imparting important advice to young scientists so that they, too, may tread fearlessly and find their own path forward.

Akshra Paimagam, Sriya Mantena, and Daniel Jin Editors-in-Chief

Broad Street Scientific | 2020-2021 | 5

BROAD STREET SCIENTIFIC STAFF Editors-in-Chief

Publication Editors

Biology Editors

Chemistry Editors

Engineering Editors

Mathematics and Computer Science Editors

Physics Editors

Faculty Advisors

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Daniel Jin, 2021 Sriya Mantena, 2021 Akshra Paimagam, 2021

Mac Barnes, 2022 Kaylene Eun, 2021 Harjas Kaur, 2022 Sara Talekar, 2022 Ashley Wang, 2021 Lucia Wang, 2022

Prabuddha Dastidar, 2022 Melissa Du, 2021 Suhani Ramchandra, 2022

Sreenidhi Elayaperumal, 2022 Cliff McGinnis, 2021 Hrishika Roychoudhury, 2022

Vish Ravichandran, 2022 Mariah Snuggs, 2022

Alvin Chen, 2021 Dheepthi Mohanraj, 2022 Pracheeti Shikarkhane, 2022

Andrei Mistreanu, 2021 Dhruv Yalamanchi, 2021

Dr. Jonathan Bennett Mr. Matthew Meers

COMPUTING THE BRAIN: WHEN NEUROSCIENCE AND TECHNOLOGY COLLIDE Sherry Liu Sherry Liu was selected as the winner of the 2021 Broad Street Scientific Essay Contest. Her award included the opportunity to interview Dr. Pamela Douglas, Ursula Geller Professor of Research in Cardiovascular Diseases in the Department of Medicine at Duke University and Director of the Multimodality Imaging Program at Duke Clinical Research Institute.

“The computer is incredibly fast, accurate, and stupid.

Man is unbelievably slow, inaccurate, and brilliant. The marriage of the two is a challenge and opportunity beyond imagination.” - Leo Cherne At first glance, the human brain appears to be the most powerful computer in the world. It maintains an impressive 3.5 quadrillion bytes of memory and processes information at 2.2 billion megaflops per second, all while consuming less power than a typical light bulb (Fischetti).

The two share the same basic function as well: to process data to make informed decisions. Yet take a closer look and you’ll see that the human brain and the computer take vastly different approaches to achieve their goals.

Integrating the advantages of each is key to advancing both neuroscience and computer science - and the future of humanity.

To begin exploring these differences, we turn to the distinct structures of the computer and the brain. At the heart of computers are transistors - minute devices that switch between two states to represent 0 and 1. Millions of transistors are packed into a microchip, and together they carry out sequential commands (Woodford).

Computer data are concrete - they are physically stored and any changes must be explicitly, and often laboriously, programmed. In contrast, memories in the mind exist in many regions at once and are constantly edited, often unconsciously. To explain this, we examine the neuron - the basic unit of the brain. Neurons are branch-shaped cells that communicate with other nerve cells through connections called synapses. While transistors can only be turned on or off, the state of a neuron fluctuates as its threshold, or activation limit, changes. When stimuli from surrounding cells reach this threshold, the neuron propagates an electric signal that in turn may activate the thresholds of other neurons (Woodruff). Many neurons firing over time allows your brain to write and execute its own unique “program.”

The beauty of the brain lies in its adaptability, or neuroplasticity. Through adjusting the thresholds and synapses of each neuron, we can identify patterns, learn from them, and generalize our findings to novel problems.

This phenomenon is what allows you to recognize a familiar face from years ago and a child to understand social

norms after observing relatively few examples. These are tasks that computers must be specifically programmed to perform with far less accuracy, if any. This flexibility also gives rise to uniquely human traits like creativity and open-mindedness at the crux of innovation.

Computers have advantages of their own. While the number of neurons is fixed at roughly 100 billion, transistors have yet to reach anything resembling a limit: Moore’s law predicts the number of transistors in a microchip to double every eighteen months due to research efforts (Herculano-Houzel, Bell). Machines also execute programs with incredible speed and reliability, and their sheer constancy befits them for tasks like searching through catalogs and calculating trajectories that take orders of magnitude longer to perform manually.

Knowing the distinguishing strengths of man and machine, it’s impossible not to envision a world where the two join forces. Pioneers of the neural network did just that. In 1943, neuroscientist Walter McCulloch and logician Walter Pitts united in an effort to model the complex decisions of the brain. While their goal was not novel, their key breakthrough lay in framing the brain as a probabilistic model that alters its state to fit stimuli.

Suddenly, the intangible goal of “thinking like the brain” became a task perfectly fit for computers: “calculating changing probabilities.” Armed with this powerful observation, the two researchers created the first neural network, an algorithm that adjusts the weights of “nodes” like the brain adjusts the thresholds of neurons in order to minimize the difference between observed and calculated outputs. Several layers of weights extract patterns in input data and correlate them with outcomes. This approach revolutionized computer science, merging the raw power of machines and the human ability to learn through past examples and launching an age of rapid growth for artificial intelligence that continues today. In 1996, IBM’s Deep Blue supercomputer defeated the world champion in chess for the first time. Modern neural networks can be found interpreting radiology scans, guiding self driving cars, translating texts, and recommending movies, all while assisting the very species that inspired their creation.

Motivated by the success of neural network algorithms, which still use traditional sequential microchips, researchers are working to mimic the brain at the most Broad Street Scientific | 2020-2021 | 7

fundamental level by redesigning computer hardware.

Neuromorphic chips are microchips packed with artificial neurons that exchange electric signals in bursts at varying intensities, simulating the brain’s structure as well as function (Zaleski). This developing technology has the capability to enhance neural networks in efficiency and scope, making artificial intelligence ubiquitous in tools ranging from smartphones to medical devices.

As some work to mimic the brain, others are inserting the brain itself into computers. Key to this is mapping the connectome, or every neural connection, and integrating it with technology. Currently, the human connectome is too complex to process, although advancements in image analysis, coincidentally through neural networks, may soon change this. Meanwhile, the open sourced project OpenWorm has scanned the connectome of the worm species C. elegans and inserted it into an extraordinary robot whose movement is solely controlled by the worm’s brain, no code required (Sarma et al.).

Still others, including Elon Musk through his startup NeuraLink, are exploring ways to allow humans to control technology using Brain Machine Interface (BMI), a device that translates brain signals into commands understood by computers. A common example of this is the cochlear implant, which stimulates the auditory nerve using electric signals to restore hearing. Further, breakthroughs in mind controlled arm prosthetics are giving amputees sensations of touch (Moore). Fully integrated BMI would be revolutionary, allowing for seamless communication between brain and computer, monitoring of attention levels, and hands-free applications (Shih et al.).

As the line between computer and brain blurs, a common fear is that machines are intruding on our humanity. For a reality check, we turn to the Turing test, which states that a computer must converse with a human without being detected as a machine to demonstrate human intelligence (Rouse). This much is clear: artificial intelligence passing the Turing test does not exist presently and will not exist in the foreseeable future. The greatest concerns in technology lie not in computers themselves, but in how we use, or abuse, them to shape the minds of users. These complex challenges will require experts in both neuroscience and computer science to collaborate, as they so often do, to ensure technology molds the human brain for good.

From these insights it is clear that computers and brains each possess distinct strengths which, when combined, have astonishing applications and capabilities. Whether it’s neurons in the brain, transistors on a microchip, or two separate disciplines, individual components are strongest when they form connections. As the future unfolds for both man and machine, let us take a valuable lesson from those at the intersection between neuroscience and computer science. Let us not be afraid to seek exposure to ideas beyond any single approach, for the greatest 8 | 2020-2021 | Broad Street Scientific

ingenuities arise when seemingly unrelated ideas come together.

Works Cited Bell, Lee. “What is Moore's Law? WIRED explains the theory that defined the tech industry.” WIRED, 28 August 2016, wired.co.uk.

Fischetti, Mark. “Computers vs. Brains.” Scientific American, vol. 305, 1 November 2011, 10.1038/scientificamerican1111-104.

Friston, Karl. “The History of the Future of the Bayesian Brain.” US National Library of Medicine National Institutes of Health, 15 August 2012, ncbi.nlm.nih.gov.

Herculano-Houzel, Suzana. “The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost.” Proceedings of the National Academy of Sciences of the United States of America, 26 June 2012, 10.1073/pnas.1201895109.

Korte, Martin. “The impact of the digital revolution on human brain and behavior: where do we stand?” Dialogues in Clinical Neuroscience, 22 June 2020, ncbi.nlm.nih.gov.

Moore, Nicole. “‘It’s like you have a hand again’: An ultra-precise mind-controlled prosthetic” University of Michigan, 4 March 2020, https://news.umich.edu/.

“Neuromorphic Computing.” Intel, intel.com.

Rouse, Margaret. “Turing Test.” TechTarget, June 2019, searchenterpriseai.techtarget.com.

Sarma et al. “OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans.” The Royal Society, 10 September 2018, 10.1098/ rstb.2017.0382.

Shih et al. “Brain-Computer Interfaces in Medicine.”

Mayo Clinic Proceedings, March 2012, 10.1016/j. mayocp.2011.12.008.

Small et al. “Brain health consequences of digital technology use.” Dialogues in clinical neuroscience, 22 June 2020, 10.31887/DCNS.2020.22.2/gsmall.

Woodford, Chris. “Transistors.” Explain That Stuff!, 21 September 2020, explainthatstuff.com.

Woodruff, Alan. “What is a Neuron?” Queensland Brain Institute, 13 August 2019, qbi.uq.edu.au.

DISTANT GLIMMERS Gabe Hart Gabe Hart was selected as the winner of the 2021 Broad Street Scientific Photo Contest. His award included the opportunity to have his photographs featured in the 2021 volume of the Broad Street Scientific. Gabe's photographs are shown below.

Galactic Neighbor The Triangulum Galaxy (Messier 33) is the third largest galaxy in the Local Group of galaxies, after the Andromeda Galaxy and the Milky Way, and is approximately 2.7 million light-years distant.

Space Cloud Approximately 1350 light-years away, the Orion Nebula's gas has collapsed down to form the Trapezium star cluster in its core. The two nebulae visible are part of the same complex, and only appear separated because of a dark dust lane in front of them.

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ASSESSING THE EFFECTIVENESS OF THE HERICIUM ERINACEUS EXTRACT AS ACETYLCHOLINESTERASE INHIBITORS Chayse Chandler Abstract Alzheimer’s Disease (AD), a type of dementia, is attributed to the overproduction of the amyloid-beta (Aß) protein caused by the cleavage of the amyloid precursor protein. This project focuses on deriving therapeutics to control the Aß protein by finding an effective acetylcholinesterase (AChE) inhibitor (AChE-I) to treat AD. Research indicates that low levels of acetylcholine are correlated with high levels of the Aß protein; thus, an AChE-I will hypothetically decrease the level of the Aß protein. The most effective known AChE-I is galantamine, an alkaloid isolated from Lycoris radiata. This project focuses on assessing the ability of the extract isolated from Hericium erinaceus as an AChE-I. The first paralysis assay with model organism Drosophila melanogaster, which express the genes for AChE, demonstrated that high concentrations of the H. erinaceus isopropyl extract significantly increased paralysis in comparison with isopropyl alcohol. This suggests that the extracts are toxic, a characteristic of AChE-Is. An Ellman’s Assay concluded that all the galantamine concentrations and the 0.05g/mL concentration of the extract significantly decreased the AChE concentration (p<0.01). The second experiment of the paralysis assay with wildtype D. melanogaster and lower concentrations demonstrated no significance with the two lowest concentrations of galantamine and extracts compared to alcohol, which suggests the concentrations are safe.

1. Introduction Normal aging is characterized by the deterioration of the mental and physical functions. One of the outcomes of this physiological process is dementia, which is defined by the gradual deterioration of intellectual abilities. 30% of all adults above the age of 80 are affected by this disorder. Primary dementia, or Alzheimer’s Disease (AD), is a point of interest in research because it affects 50-70% of all dementia patients [4]. There are approximately 20 million AD cases worldwide making it the most common progressive neurodegenerative disease [6], the sixth leading cause of death in the United States (US), and the cause of death for 33% of seniors in the US. In 2019 dementia related illnesses cost the government 290 billion dollars and in 2050 this is expected to expand to 1.1 trillion dollars [3]. The justifications for researching this neurodegenerative disease vary from the loss of life to the economic toll and demonstrates the importance of finding therapeutics and therapies to lessen symptoms such as loss of mental function.

The cause of the symptoms that arise from AD is largely unknown, but researchers have identified that a significant factor is the overproduction of the amyloid-beta (Aβ) protein. According to the amyloid hypothesis, the expression of the amyloid precursor protein (APP) creates an imbalance of the production and clearance of Aβ [6]. This results in the trademark symptoms of cerebral atrophy, neuritic plaques, and neurofibrillary tangles. Cerebral atrophy is the loss of brain cells, neuritic plaques (also known as amyloid plaques) are extracellular deposits of Aβ, and neurofibrillary tangles are aggregates of the tau protein [4].

Based on this hypothesis, there are two categories of strat10 | 2020-2021 | Broad Street Scientific

egies that current researchers are prioritizing to treat AD. One strategy is attempting to subdue specific symptoms, and the second strategy is targeting the overproduction of Aβ. This experiment will focus on the second of the two strategies. Previous research demonstrates that the overproduction of Aβ is linked to abnormal cholinergic (compounds that mimic ACh or butyrylcholine) levels in the basal forebrain, which is hypothesized to be a major determinant of the resulting neurodegeneration characterized in AD [4]. Low levels of acetylcholine (ACh) are related to the production of Aβ and the onset of symptoms [6]. Due to this correlation, a current strategy to treat AD is to control and elevate the level of ACh in the synaptic cleft. This can be done by inhibiting an enzyme called acetylcholinesterase (AChE), which hydrolyzes this neurotransmitter.

Therefore, this method would increase ACh by inhibiting the enzyme AChE to reduce the overall expression of Aβ.

Research suggests that AChE inhibitors (AChE-I) cause an increase in the amount of ACh and decrease the effects of AD; however, these drugs have a number of negative side effects including inflammation and gastrointestinal issues, and the derivatives of AChE-Is used as therapeutics are not completely effective [6]. Therefore, scientists are looking towards naturally occurring compounds to reduce the side effects and increase the ability to inhibit AChE. The current hypothesis is that using pure compounds from plants and fungi will act as a replacement for the already existing drugs; in fact, current therapeutics utilize alkaloids, which are nitrogen-based chemicals found in plants and fungi. Galantamine, the positive control for this study, is an alkaloid isolated from Lycoris radiata and is the current prescribed drug for AChE-I. Research has demonBIOLOGY RESEARCH

strated that these compounds will lessen the side effects by increasing levels of ACh and boosting cholinergic activity [2]. The goal is to then find new compounds in plants or fungi that would be more effective than the already existing drugs [6].

Current research supports that pure compounds isolated from medicinal plants and fungi meant to treat arthritis, memory, or snake bites are a natural alternative to already existing drugs. Plant and fungal extracts contain a wide variety of phytochemicals and naturally occurring compounds; thus, it can be inferred that extracts allow for a broader approach than focusing on just alkaloids. One study found that alkaloids isolated from the Lycoris radiata were chemically similar to galantamine (current drug for AChE inhibitors), delayed paralysis, inhibited genes coding for AChE, and prevented inflammation [6]. For this study we will be isolating extracts from the fungi Hericium erinaceus. H. erinaceus, commonly known as Lion’s Mane, is clinically supported to enhance short term memory, and is being sold as a memory inducer, and also improves gastrointestinal function. Furthermore, this fungus is extremely bioactive and many of its compounds have been sold as alternative medicines. Compounds have been isolated from the mycelia including polysaccharides, proteins, lectins, phenols, terpenoids. The two classes of terpenoid compounds, hericenones and erinacines, have been isolated from the fruiting bodies of this fungi and have been found to stimulate nerve function [5]. Overall, this organism is not only an alternative medicine, but it also produces bioactive compounds supported to improve nerve function.

The model organisms used in this study were the wildtype Drosophila melanogaster and were chosen as the model organisms because they are easy to handle, reproduce quickly, have genes responsible for the expression of the enzyme AChE, and have strain that contains the human Aß protein.

The suppression of symptoms caused by AD is crucial in researching therapeutics because it affects such large portions of the population. AD has been linked to a deficient amount of the neurotransmitter ACh. Therefore, a popular approach to combat AD is to control levels of this neurotransmitter within the system by inhibiting AChE, an enzyme that breaks down ACh. However, these drugs are not 100% effective in inhibiting AChE and cause negative side effects. By utilizing medicinal plants, many of these side effects are suppressed and many known alkaloids, such as galantamine, are more effective and currently used as approved therapeutics for AD. 2. Materials and Methods During the experimental process, galantamine was maintained as the positive control and isopropyl alcohol as the negative control. There were two paralysis assay experiments conducted where one was high concentrations BIOLOGY RESEARCH

of galantamine and H. erinaceus extract, while the other was low concentrations. An Ellman’s Assay was then performed to quantify the AChE activity in the presence of the treatments.

2.1 - Isolating H. erinaceus Extracts and Creating Galantamine Solutions The extracts isolated from the H. erinaceus were created by reconstituting a specified amount of the H. erinaceus powdered extract with 20mL of 99.99% isopropyl alcohol.

The concentrations created were 0.5g/mL (10.0g of the powdered extract), 0.25g/mL (5.0g of the powdered extract), 0.125g/mL (2.5g of the powdered extract), 0.05g/ mL (1g of the powdered extract), and 0.025g/mL (0.5g of the powdered extract). Over the course of 72 hours, the mixtures were vigorously shaken every hour during the daytime. After 72 hours, the H. erinaceus solutions were filtered through a Whatman paper into new vials. Then, the liquid portion of the solution was saved and stored in the freezer at approximately 4°C to prevent the solution from evaporating while the solid portions were disposed of.

This project used similar concentrations of galantamine to those utilized by Xin et al., which were intended for the model organism Caenorhabditis elegans [6]. These concentrations are 10µM, 30µM, and 50µM and were made by mixing 57.47µg, 172.41µg, and 287.35µg of galantamine, respectively, with 20mL of isopropyl alcohol. The 100µM concentration was made by mixing 574.4µg of galantamine with 2mL of isopropyl alcohol.

To create the lower concentrations of the H. erinaceus extract, a 1:3 serial dilution was performed using the 0.025g/ mL solution resulting in the concentrations of 16.67g/L, 5.56g/L, 1.85g/L, and 0.617g/L. Also, a similar method was used for the lower concentrations of galantamine. A 1:3 serial dilution was performed using the 10µM solution to create 3.33µM, 1.11µM, 0.37µM, and 0.123µM. 2. 2 - Culturing D. melanogaster D. melanogaster were cultured by mixing 1oz of Formula 4-24 Instant Drosophila Medium with 1 oz of water into a fly vial and adding a pinch of dry yeast (approximately 5-12 colonies). This project was conducted without using anesthesia to transfer the D. melanogaster between vials, and the cultures of D. melanogaster were kept in a relatively cold and dark environment.

2.3 - Paralysis Assay There were two basic experiments that utilized a paralysis assay during this project including wildtype D. melanogaster with high concentrations of galantamine and H. erinaceous extracts and wildtype with low concentrations.

The high concentration experiment used the 100µ, 50µM, and 30µM concentrations of galantamine and 0.5g/mL, 0.25g/mL, 0.125g/mL, and 0.05g/mL of the H. erinaceus extract. The low concentration experiment used the 10µM, Broad Street Scientific | 2020-2021 | 11

3.33µM, 1.11µM, 0.37µM, and 0.123µM concentrations of galantamine and 0.025g/mL, 16.67g/L, 5.56g/L, 1.85g/L, and 0.617g/L of the H. erinaceus extract. To perform the assay, 0.5mL of the treatment was added to the D. melanogaster medium and the paralysis assay was conducted over a ten hour period or until all of the D. melanogaster in that vial were paralyzed. Statistical analysis was performed using the Mantel-Cox test to compare the paralysis curves of certain treatments.

2.4 - Ellman’s Assay The Colorimetric Acetylcholinesterase Ellman’s Assay was performed using the protocol provided by Amplite [1]. 50µL of the reagents were added to each well of a 96-well plate, and then 50µL of the working solution was added. The reagents included the AChE standards (concentrations found in Table 1), galantamine (100µM, 50µM, 30µM, and 10µM), H. erinaceus extracts (0.5g/mL, 0.25g/mL, 0.125g/mL, 0.05g/mL, and 0.025g/mL), and isopropyl alcohol. In addition to the treatments, 5µL of the AS4 standard (concentration is 37.04mM/mL of AChE) was added to the isopropyl alcohol, H. erinaceus extracts, and galantamine. The 96-well plate was then put through a plate reader with an absorbance of 410nm. The absorbance values of the standards were graphed against the concentration of each standard found in Table 1. A linear fit was applied to have a basic formula based on concentration and absorbance. This formula was used to find the AChE concentration of each reagent. The H. erinaceus extracts had a slightly orange tint that likely impacts the results for the Ellman’s Assay. Therefore, all concentrations were normalized by observing the absorbance when the extracts of varying concentrations were introduced to the acetylcholinesterase buffer. An average was calculated for each concentration of the H. erinaceus extract and was subtracted from the absorbance values. Statistical significance was quantified by comparing alcohol and each treatment using a T Test.

Table 1: Shows the different standards and their concentrations for the Ellman’s Assay.

Standard AS7 AS6 AS5 AS4 AS3 AS2 AS1

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3. Results The first experiment was a paralysis assay with wildtype D. melanogaster and higher concentrations of the treatments. During this experiment, the H. erinaceus extracts significantly (p<0.0001) decreased time until paralysis compared to galantamine and the negative control. The results are depicted in Figure 1. Additionally, subjective visual analysis revealed supporting confirmation that the H. erinaceus extracts were causing paralysis in the D. melanogaster. This can be observed due to the similar physiological responses between the positive control and H. erinaceus extract treatments before they died.

Figure 1: Figure shows the survival curve of all treatments in this experiment with higher concentrations and wildtype D. melanogaster. The curves were significantly different and demonstrate that higher concentrations in the H. erinaceus extracts increased paralysis.

The results of the Ellman’s Assay are depicted in Figure 2. The Ellman’s Assay revealed that the AChE concentration was significantly lower for all galantamine concentrations (p<0.01) compared to the alcohol (Fig. 2a). Conversely, the H. erinaceus extract with the concentration of 0.05 g/mL was significantly lower than alcohol while the 0.25 g/mL, 0.125 g/mL, and 0.025 g/mL concentrations were all insignificantly lower than the alcohol. The other concentrations were not significant compared to the alcohol (Fig. 2b).

Concentration (mM/mL) 1000.0 333.3 111.1 37.0 12.3 4.11 1.37

BIOLOGY RESEARCH

Figure 2a:

Figure 2b:

Figure 2: Figure shows the two different results from the Ellman’s Assay when AS4 is added. G1-G4 are the galantamine treatments with G1 corresponding with 100µM, G2 with 50µM, G3 with 30µM, and G4 with 10µM. E1-E4 are the H. erinaceus extract treatments with E1 corresponding with 0.5g/mL, E2 with 0.25g/mL, E3 with 0.125g/mL, E4 with 0.05g/ mL, and E5 with 0.025g/mL. The symbol AL refers to isopropyl alcohol. a) the results of galantamine treatments and b) the results of the H. erinaceus extract treatments. ** indicates p<0.01 and * indicates p<0.5.

The second experiment was with wildtype D. melanogaster and the lower concentrations of the H. erinaceus extracts and galantamine. These concentrations included 10µM, 3.33µM, 1.11µM, 0.37µM, and 0.123µM of galantamine and 0.025g/mL, 16.67g/L, 5.56g/L, 1.85g/L, and 0.617g/L of the H. erinaceus extracts. When the wildtype D. melanogaster were exposed to the treatments, the paralysis curves were significantly different (p<0.001) across all treatments.

Additionally, the concentrations of 1.85g/L and 0.617g/L of the H. erinaceus extract and 0.37µM and 0.123µM of the galantamine demonstrated no significance in comparison to the alcohol treatment (p > 0.5). The results of this experiment are demonstrated in Figure 3.

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Figure 3a:

Figure 3b:

Figure 3: Depicts the results of the second experiment involving a paralysis assay of wildtype D. melanogaster and lower concentrations of galantamine and H. erinaceus extracts. a) shows all of the treatments (p < 0.001) and b) shows the lowest of the concentrations and alcohol (p > 0.5).

4. Discussion Overall, this experiment was able to sufficiently support that the H. erinaceus extract is an AChE-I at certain concentrations. Each of the two experiments involving a paralysis assay had a specific purpose in demonstrating the effectiveness of the H. erinaceus extracts and galantamine as AChE-Is. The purpose of the first experiment with high concentrations of galantamine and extract and wildtype D. melanogaster was to demonstrate that the H. erinaceus extracts and galantamine increase toxicity because AChE-Is are toxic at high concentrations. The data demonstrate that the isopropyl alcohol had a significantly higher time until paralysis; thus, the positive control and H. erinaceus extracts were toxic at high concentrations. These data provide supporting evidence that there are shared characteristics between AChE-Is and the H. erinaceus extracts. Additionally, during paralysis assay experiments, subjective visual analysis revealed that the D. melanogaster seemed to have a similar physiological response to the drugs premortem. The selected D. melanogaster exposed to either galantamine and H. erinaceus extract treatments seemed to convulse before paralyzing. Since this is a shared response between the two treatments, it can be deduced that the H. erinaceus extracts are inhibiting something and triggering similar responses in comparison to galantamine.

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The goals of the second experiment involving a paralysis assay with low concentrations and wildtype D. melanogaster were to provide support that lower concentrations of these solutions were less toxic and had no significance to alcohol. This is an important step because it demonstrates the efficiency of these compounds as future therapeutics when patients must be able to survive the treatment.

During this experiment, the survival curves of each treatment were significant (p<0.001). Additionally, the survival curves of the two lowest concentrations of both galantamine and H. erinaceus extracts compared to alcohol were not significant. This demonstrates that at low concentrations the H. erinaceus extract solutions are no longer toxic compared to alcohol. Additionally, the D. melanogaster cultures, exposed to all concentrations except 10μM of galantamine and 0.025g/mL of the H. erinaceus extracts, were not paralyzed approximately 15 hours after the experiment was conducted. This is an important observation because it indicates that the inhibition is temporary and the D. melanogaster must be given a periodic dose of the AChE-Is to be efficient.

The first two experiments, as described above, are intended to show key similarities between galantamine (a known AChE-I) and the H. erinaceus extracts of varying concentrations as well as to demonstrate the potential of this extract as a therapeutic for AD. The purpose of the final experiment, the Colorimetric Ellman’s Assay, was to quantify AChE activity. The data demonstrated that galantamine significantly reduced the AChE concentration in comparison with the alcohol treatment. This suggests that the AChE was inhibited and that galantamine is an effective AChE-I. The results from the H. erinaceus extracts suggested that the concentration of 0.05g/mL was most effective at inhibiting AChE because the absorbance was significantly lower than that of alcohol. Additionally, the average AChE concentration of the 0.025g/mL H. erinaceus extract was lower than alcohol but was not significant. Due to the large amount of variance, the lack of significance may be attributed to experimental error. The other concentrations of the extract were not significantly different from the alcohol treatment. From this it can be inferred that the lower concentrations inhibited AChE, while the higher concentrations did not. The reason why higher concentrations were not an effective AChE-I can be attributed to multiple factors including that higher concentrations may inhibit other enzymes more or may somehow inhibit AChE too much and cause the opposite effect of increasing AChE concentration. Nonetheless, further research is needed to observe the effects of lower concentrations and observe how it would affect inhibition to deduce whether H. erinaceus extracts would be an effective AChE-I.

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5. Conclusion From the data presented above it can be concluded that the H. erinaceus extract is a potential AChE-I and a potential drug for AD patients. First, it demonstrates an increased toxicity with high concentrations in comparison to isopropyl alcohol which is a known characteristic of AChE-Is. Second, there is a decrease in toxicity with lower concentrations which provides support that the patients can safely take this extract. Finally, the Ellman’s Assay showed that low concentrations inhibited AChE more than high concentrations. All these experiments were intended to show the similarities to galantamine, the positive control and known AChE-I. The validity of this conclusion can be strengthened with two different steps. The first method is to conduct more trials with lower concentrations of the H. erinaceus extract with the Ellman’s Assay. The second would be to expose a strain of D. melanogaster that expresses the human Aß1-40 proteins (denoted as AD D. melanogaster in this paper) to lower concentrations of galantamine and H. erinaceus extracts. The former could demonstrate that a decrease in the extract concentration could be more effective as AChE-Is than the concentrations already tested because some inhibitors are most effective at low concentrations.

The result would support the ability of the extract to better inhibit AChE with decreasing concentrations. This would not only support that the H. erinaceus extract is an AChE-I, but also that low concentrations remain effective which is important when manufacturing drugs in the future.

Future experimentation with AD D. melanogaster and lower concentration of the H. erinaceus extract is likely the most important future outlook. The purpose of finding AChE-Is is to treat AD, so this experiment demonstrates the direct effect of H. erinaceus on AD symptoms and its use as a future therapeutic. It would also be interesting to see the correlation with the results of the Ellman’s Assay results. If there is a decrease in paralysis for the concentrations above 0.05g/mL, then it could be inferred that the H. erinaceus extract is a potential treatment for AD, but is likely not an AChE-I. The question then arises of how this extract interacts with one’s body and why it is efficient for treating AD. If there is a decrease in paralysis for the concentration of 0.05g/mL, this would suggest that this concentration acts as an AChE-I and is efficient when treating AD, supporting the theory that AChE-Is are an effective treatment. If paralysis increases with concentrations above 0.05g/mL, then it supports that high concentrations are likely not AChE-Is and are not an effective treatment. Finally, if there is an increase in paralysis with the 0.05g/mL concentration, then multiple questions arise as to whether AChE-Is are effective treatments, given that the 0.05g/mL is an AChE-I. All these conclusions would not only support the hypothesis of this project, but also provide further insight into using AChE-Is to treat AD. BIOLOGY RESEARCH

This project was affected by the COVID-19 pandemic, thus, there are limitations to the research. First, due to the fact this project was completed in two separate environments, one in a well-controlled environment and the other not, there are likely other factors that could be affecting the data. Furthermore, the two paralysis assays were conducted in separate environments. Second, the best model organism for this project would have been the C. elegans due to its ability to model AD efficiently; however, D. melanogaster were chosen to accommodate for the lack of lab equipment. Third, this project was hoping to use a model organism that expressed the human Aß1-40 which was not possible due to a change in the timeline and lack of appropriate equipment.

6. Acknowledgements I would like to thank my mentor, Dr. Kim Monahan, the Research in Biology program, and my peers in this research program for guiding and supporting me through the research process. Further thanks to Dr. Amy Sheck, the Glaxo Endowment, and the North Carolina School of Science and Mathematics for giving me the opportunity to conduct my own research.

7. References 1. Amplite Colorimetric Acetylcholinesterase assay kit. (n.d.). AAT Bioquest. https://www.aatbio.com/products/ amplite-colorimetric-acetylcholinesterase-assay-kit 2. Cardoso-Lopes, E. M., Maier, J. A., Silva, M. R., Regasini, L. O., Simote, S. Y., Lopes, N. P., Pirani, J. R., Bolzani, V. D., & Young, M. C. (2010). Alkaloids from stems of Esenbeckia leiocarpa Engl. (Rutaceae) as potential treatment for Alzheimer disease. Molecules, 15(12), 9205-9213.

3. Alzheimer’s Association. Facts and figures. (n.d.). Alzheimer’s Disease and Dementia. Retrieved January 12, 2021, from https://www.alz.org/alzheimers-dementia/ facts-figures 4. Muir, J. L. (1997). Acetylcholine, aging, and Alzheimer’s disease. Pharmacology Biochemistry and Behavior, 56(4), 687-696. Science Direct.

5. Thongbai, B., Rapior, S., Hyde, K. D., Wittstein, K., & Stadler, M. (2015). Hericium erinaceus, an amazing medicinal mushroom. Mycological Progress, 14(10).

6. Xin, L., Yamujala, R., Wang, Y., Wang, H., Wu, W., Lawton, M. A., Long, C., & Di, R. (2013). Acetylcholineestarase-inhibiting alkaloids from Lycoris radiata delay paralysis of amyloid beta-expressing transgenic C. elegans CL4176. PLoS ONE, 8(5), e63874.

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INVESTIGATION OF THE POSSIBLE SYNERGISTIC EFFECTS BETWEEN MSDC-0160 AND RAPAMYCIN IN TREATING PARKINSON’S DISEASE Nnamdi Obi Abstract Parkinson’s Disease (PD) is a chronic neurological disorder that destroys dopamine-generating neurons in the brain. The standard treatment for the disease, Levodopa (L-Dopa), becomes unsustainable as PD progresses, meaning alternative treatments need to be discovered. In this experiment, two such possible alternative treatments, Rapamycin and MSDC0160 (Mitoglitazone), were tested on Caenorhabditis elegans. It is theorized that MSDC-0160 and Rapamycin work synergistically, meaning C. elegans treated with a combination of these two treatments would exhibit less dopaminergic neuron degeneration than C. elegans administered either treatment separately. To test this hypothesis, a combination treatment was prepared with Rapamycin and MSDC-0160 at concentrations of 200 μM and 100 μM, respectively. Both treatments were also tested separately. Analysis of collected data through an ANOVA test and Tukey-Kramer post-hoc test (p = .05) showed that the combination treatment of Rapamycin and MSDC-0160 (p-value = 0.0245) was significantly more effective at treating C. elegans expressing Parkinson’s Disease than Rapamycin at 200µM (p-value = .627), MSDC at 100µM (p-value = .612), and MSDC at 10µM (p-value = .261). These results suggest a more viable treatment for PD and link the functions of both MSDC-0160 and Rapamycin.

1. Introduction In Parkinson’s Disease (PD), dopaminergic neurons are destroyed, inhibiting movement. Patients with PD may experience tremors, slowed movement, a loss of automatic movements, speech changes, or impaired posture or balance [1]. There is no cure for PD; however, there are multiple medications, such as Levodopa (L-Dopa), a dopamine substitute, designed to combat its symptoms [7].

L-Dopa and other dopamine substitutes work by replacing the dopamine lost due to the destruction of dopaminergic neurons. However, this treatment becomes unsustainable as PD progresses and symptoms worsen resulting in higher dosages becoming necessary for the same effect. Furthermore, PD patients who have taken L-Dopa frequently exhibit Levodopa-induced dyskinesia (LID), which results in uncontrolled movements in the form of smooth tics [7].

These problems make L-Dopa and other dopamine substitutes unreliable for long-term treatment [2].

The main cellular symptoms of PD are Lewy bodies, an abnormal aggregation of Alpha-Synuclein proteins in dopaminergic neurons [1]. This occurs due to the disruption of cellular homeostatic responses such as autophagy or metabolism [5]. Many new treatments focus on breaking down Alpha-Synuclein proteins in Lewy bodies by restoring these disrupted cellular processes to restore proper neuron function. These new treatment methods utilize the disruption of certain pathways to restore these cellular processes.

Multiple treatments for Parkinson’s Disease, such as the modulation of the mitochondrial pyruvate carrier (MPC) using Thiazolidinedione Insulin Sensitizers (TZD) like 16 | 2020-2021 | Broad Street Scientific

MSDC-0160 (Mitoglitazone), are currently under investigation. MSDC-0160 prevents pyruvate produced during glycolysis from entering the mitochondrial membrane by inhibiting the Mitochondrial Pyruvate Carrier (MPC) Complex, which contains the proteins MPC1 and MPC2 [4]. When the movement of pyruvate across the mitochondrial membrane is halted, the cell is forced to metabolize amino and fatty acids instead of pyruvate to continue cellular respiration [5]. This change in cell metabolism leads to unknown downstream effects that induce autophagy in the cell, leading to the breakdown of Alpha-Synuclein proteins [5]. However, most TZDs also activate the peroxisome proliferator–activated receptor-γ (PPARγ), which causes side effects such as fluid retention, weight gain, and an increased risk for bladder cancer [4]. On the other hand, MSDC-0160 can modulate MPC without affecting PPARγ [4], making it a viable candidate for the treatment of PD. Another treatment method is the modulation of the mammalian target of Rapamycin (mTOR) intracellular pathway with Rapamycin. Rapamycin has neuroprotective effects because it restores the autophagic cellular pathway through inhibition of the mTOR pathway [5]. mTOR is a kinase that is part of two different complexes: mTORC1 and mTORC2 [8]. Activation of the mTORC1 complex inhibits autophagy, which is important for breaking down the alpha-synuclein proteins associated with PD [8]. Thus, overactivation of the mTOR pathway, which disrupts autophagy and the subsequent breakdown of Alpha-Synuclein proteins, is associated with PD [5]. Through inhibition of the mTOR pathway, Rapamycin induces autophagy and the creation of lysosomes (lysosomal biogenesis), which causes the cellular autophagic process to be restored to BIOLOGY RESEARCH

normal [5]. This restoration allows for Alpha-Synuclein proteins to be broken down, destroying the Lewy bodies and restoring proper neuron function. However, though Rapamycin inhibits mTORC1, it is not able to do so completely [9]. This means that there needs to be a stronger or complementary inhibitor of mTOR along with Rapamycin for maximum neuroprotective effects [9].

The downstream effects caused by MSDC-0160 that induce autophagy and the mTOR pathway inhibited by Rapamycin are theorized to be related [5]. Preventing pyruvate uptake by mitochondria changes the metabolic balance signals in a way that stops the activation of mTOR while activating the autophagic pathway [5]. There are competing theories on why this is true. One theory revolves around the cellular energy sensor AMPK, which functions as an adaptor molecule ensuring cellular energy homeostasis, stimulation of glycolysis, and autophagy activation [5]. Its activation due to the change in metabolism caused by inhibition of the MPC complex inhibits mTORC1, directly linking the cellular energy state with autophagy. The inhibition of mTORC1 by AMPK in tandem with Rapaymcin could allow for a more complete inhibition of mTORC1, which would induce stronger neuroprotective effects (Figure 1).

ments and trials to be conducted at a relatively quick pace [3]. C. elegans are also relatively easy to work with because when placed in liquid, their thrashing behavior (Figure 2) is a direct way of measuring dopaminergic degeneration [3]. Normal C. elegans (abv. worms) thrash quickly in the water, while C. elegans that lack dopamine due to dopaminergic neuron degeneration thrash at a slower pace [3].

Furthermore, their translucent bodies allow for the use of microscopes and fluorescence to visualize the aggregation of alpha-synuclein protein in their dopaminergic neurons [3].

Figure 2. A C. elegans worm performing one thrash [10].

If the way that MSDC-0160 and Rapamycin work is related, then combining these two treatments is expected to increase thrashing rates in C. elegans with dopaminergic neuron degeneration in comparison to using either drug separately. If this hypothesis holds true, the synergistic effects between the two treatments could suggest a more viable treatment for PD and a possible relationship between the MPC and mTOR cellular pathways.

2. Methodology

Figure 1. Simple diagram demonstrating the theorized relationship between MSDC-0160 and Rapamycin.

C. elegans, which are nematode worms, are an ideal model organism for modeling PD [3], as much of the genetic material concerning the brain is conserved between humans and C. elegans [6]. For example, the MPC-1 protein is conserved in C. elegans. Moreover, they grow quickly and have a short lifespan, allowing for many different experiBIOLOGY RESEARCH

For this experiment, two C. elegans strains, N2 (wild type) and OW13, were used. The OW13 strain over-expressed Alpha-Synuclein protein, meaning that its dopaminergic neurons and its Alpha-Synuclein protein expressed GFP fluorescence [3]. Both the OW13 and N2 strands were originally transferred from two prepared plates from the Caenorhabditis Genetics Center (CGC). C. elegans from these plates were then chunked to plates seeded with OP-50 and allowed to grow. To ensure that both strains of worms continued to grow normally over the course of the experiment, they were constantly transferred to new OP-50 seeded plates via both picking and chunking.

All worms were maintained at 20°C. To measure dopaminergic neuron degeneration, the motility of each worm was assessed via a thrashing assay. In the thrashing assay (L4), or early adult stage, worms were moved from their growth plates seeded with OP50 into an agar pad with the M9 buffer. After the worms adjusted for Broad Street Scientific | 2020-2021 | 17

one minute, they were observed under a microscope and the number of thrashes performed in the M9 buffer over a one-minute interval was counted. A thrash was defined as a complete body bend, meaning that the worm bends its body one direction, then in the opposite direction, and then comes back to its original position.

Before testing treatments, a baseline experiment was conducted where both worm strains were tested to ensure that their thrashing rates significantly differed from each other. Thrashing assays were conducted on both worm strains, and analysis of this baseline data showed that both worm strains had thrashing assays that differed significantly, with the OW13 strain having a lower thrashing rate than the N2 Strain.

In the main experiment, treatments were dissolved in DMSO to form stock solutions. These stock solutions were then diluted down to obtain two concentrations of MSDC0160 (10µM, 100µM) and one concentration of Rapamycin (200µM). To administer each drug to the worms, they were grown on plates containing the specific treatment.

These treatment plates were prepared by spreading 100 µM of the drug and 70µM of OP-50 on unseeded plates.

Both the N2 and OW13 worm strains were administered the same treatments. The N2 strain was the control group and the OW13 strain was the experimental group. Both the N2 and OW13 strains were cultivated in similar environments and incubated on their respective treatment plates for 48 hours at 20°C. The data collection for this experiment was stretched out over several weeks. Due to time constraints because of COVID-19, the combination treatment could not be tested in each round and was only able to be tested during the final round of treatment. Every treatment round contained DMSO-only (no treatment) plates for each group, which acted as a control group within each strain.

The first round of data collection started on September 16, 2020. Four treatment plates were prepared for both the N2 and OW13 strains: two Rapamycin 200 µM treatment plates and two no-treatment plates.

The second round of data collection began on September 18. Two treatment plates were prepared for both the N2 and OW13 strains: one MSDC-0160 10 µM treatment plate and one no-treatment plate.

The third round of data collection was on September 23. Two treatment plates were prepared for both the N2 and OW13 strains: one MSDC-0160 100 µM treatment plate and one no-treatment plate.

The final round of data collection began on September 26 and was used to test all the treatments in relation to each other. Five treatment plates were prepared for both the N2 and OW13 strains: one MSDC-0160 10 µM treatment plate, one MSDC-0160 100 µM treatment plate, one combination plate (MSDC-100 µM and Rapamycin 200 µM), one Rapamycin (200 µM) treatment plate, and one no-treatment plate (control).

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3. Results The data collected from the baseline experiment were analyzed through an unpaired one-tailed t-test (p = 0.05) (Figure 3). Analysis of the data showed that thrashing rates from OW13 and N2 worms differed significantly, with N2 strain worms having an average thrashing rate of 83.7 thrashes/minute and OW13 strain worms having an average thrashing rate of 66.3 thrashes/minute (p = 0.0487).

Figure 3. Graph shows mean number of thrashes/ minute in both strains. A thrash was defined as a full body bend, meaning that the worm bends its body one direction, then in the opposite direction, and then comes back to its original position. Statistics performed using an equal-variance, unpaired, and one-tailed t-test. Error bars represent Standard Error (SE) of the mean. An asterisk (*) denotes a P value less than 0.05.

In the main experiment, data from the first three treatment rounds were analyzed through an unpaired onetailed t-test (p = 0.05), like the baseline experiment.

Analysis of the data showed that in the first round of treatment for the OW13 Strain, the 200 µM Rapamycin treatment significantly (p = 0.0014) increased the amount of thrashing from 63.2 to 87.2 thrashes per minute (Figure 4). In the second treatment round, the control group had a mean thrashing rate of 70.5, while the MSDC-0160 (10 µM) treatment had a mean thrashing rate of 67.0 thrashes/minute, with no significant difference between the two treatments. In the third treatment round, the Control group had a mean thrashing rate of 71 thrashes/minute, while the MSDC-0160 (100 µM) treatment had a mean thrashing rate of 71.5 thrashes/minute, with no significant difference between the two treatments. For the N2 strain, no significant differences between the treatment groups in its first three treatment rounds were found (Figure 5).

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Figure 4. Each graph shows the mean number of thrashes/minute for two treatments over three rounds of testing in the C. elegans strain OW13. First round of data: Control had a mean of 63.2, Rap (200 µM) had a mean of 87.2. Second round of data: Control: 70.5, MSDC (10 µM): 67.0. Third round of data: Control: 71, MSDC (100 µM): 71.5. Statistics between each control group and its corresponding treatment were performed using an unpaired one-tailed t-test.

Error bars represent the Standard Error (SE) of the mean. ** = P < 0.01. MSDC = MSDC-0160. Rap = Rapamycin.

thrashing rate of 49.2 thrashes/minute. The MSDC (10 µM) treatment had a significantly higher (p = .0242) average thrashing rate compared to the control group, with 77.9 thrashes per minute. Additionally, the MSDC (100 µM) treatment also had a non-significantly higher average thrashing rate than the control group, with 60.4 thrashes per minute. Compared to the control group, the 200 µM Rapamycin treatment also had a significantly (p = .0206) higher average thrashing rate, with 75.3 thrashes per minute. The combination treatment composed of MSDC at 100 µM and Rapamycin at 200 µM had a non-significantly higher average thrashing rate than the control group, with an average thrashing rate of 50.8 thrashes/min (Figure 6).

For the OW13 strain C. elegans, the analysis showed that in the final round of treatment the Control group had an average thrashing rate of 56.9 thrashes per minute. The MSDC (10 µM) treatment had a non-significantly higher average thrashing rate, with 73.8 thrashes per minute. Additionally, the MSDC (100 µM) treatment had a non-significantly lower average thrashing rate with 39.0 thrashes per minute. The 200 µM Rapamycin treatment also had a higher average thrashing rate with 69.2 thrashes per minute; however, this increase was not significant. The combination treatment composed of MSDC at 100 µM and Rapamycin at 200 µM had a significantly higher (p = .0258) average thrashing rate than the control group with an average thrashing rate of 79.4 thrashes/min (Figure 6).

Figure 5. Each graph shows the mean number of thrashes/minute for two treatments over three rounds of testing in the C. elegans strain N2. First round of data: Control had a mean of 87.2, Rap (200 µM) had a mean of 79.8. Second round of data: Control: 66.8, MSDC (10 µM): 79.8. Third round of data: Control: 85.2, MSDC (100 µM): 82.1. Statistics between each control group and its corresponding treatment were performed using an unpaired one-tailed t-test. Error bars represent the Standard Error (SE) of the mean.

There were no significant differences that could be determined from this analysis. MSDC = MSDC-0160.

Rap = Rapamycin.

The data in the final round of treatment from both N2 and OW13 strain C. elegans were analyzed through a single factor ANOVA Test (p = .05), and a Tukey-Kramer post hoc test was also performed to observe pairwise comparisons between treatment groups and the control (Figure 6).

For the N2 strain C. elegans, the analysis showed that in the final round of treatment, the control group had an average BIOLOGY RESEARCH

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Figure 6. Mean number of thrashes per group. Error bars represent Standard Error (SE) of the mean. An asterisk (*) denotes a P value less than 0.05. MSDC = MSDC-0160. Rap = Rapamycin. †This treatment was a combination of MSDC-0160 (100 µM) and Rapamycin (200 µM).

4. Discussion Analysis of the data showed that combination treatment of Rapamycin (200 µM) and MSDC-0160 (100 µM) had a more significant (p-value = 0.0245) effect on treating C. elegans expressing Parkinson’s Disease than Rapamycin (p-value = .627), MSDC-100 µM (p-value = .612), and MSDC-10 µM (p-value = .261), which suggests that MSDC-0160 and Rapamycin display synergistic effects when used together.

The results of this experiment also give more credence to the theory that MSDC-0160 and Rapamycin are connected through the cellular energy sensor AMPK. Additionally, it supports the theory that AMPK works in tandem with Rapamycin in order to inhibit mTORC1. Knowing 20 | 2020-2021 | Broad Street Scientific

the relation between these two pathways is important in designing further treatment compounds; furthermore, this information could be used for designing future treatments for Parkinson’s Disease, which currently lacks a long-term and viable treatment.

In the first three rounds of treatment for the OW13 strain, the only treatment that significantly increased the amount of thrashing in C. elegans was the Rapamycin (200 µM). This could suggest that MSDC-0160 isn’t effective at raising the number of thrashes of C. elegans by itself, but this conclusion is contradicted by previous research [5].

Additionally, in the final round of testing, the MSDC (100 µM) treatment was the least effective treatment at improving thrashing rates, even compared to the MSDC (10 µM) treatment. These results indicate that a 100µM concentration of MSDC-0160 is harmful to C. elegans, causing them to exhibit lower thrashing rates. To have a better idea of how effective MSDC-0160 is, further experimentation is needed.

In the N2 strain, the control group displayed an unusually low thrashing rate, which may have arisen from an error in preparing the control group growth plate that could have negatively affected the worms. This low thrashing rate for the Control group caused both the MSDC (10 µM) and Rapamycin (200 µM) treatments to significantly vary from the control group. Furthermore, both the MSDC (100 µM) and Combination treatment exhibited low thrashing rates, which could be due to both human error and possible toxicity of both treatments. However, there were no significant differences in the thrashing rates for the first three treatment rounds. For this reason it’s likely that the N2 strain isn’t affected significantly by MSDC0160 and Rapamycin treatments. This is consistent with the expected results; however, further testing is needed to confirm this.

5. Conclusion In this experiment, two treatments, Rapamycin and MSDC-0160, were combined to assess possible synergistic effects that could suggest a more viable treatment for Parkinson’s Disease. Analysis of the data showed that the combination treatment of Rapamycin (200 µM) and MSDC-0160 (100 µM) had a more significant (p-value = 0.0245) effect on treating C. elegans expressing Parkinson’s Disease than Rapamycin (p-value = .627), MSDC-100 µM (p-value = .612), and MSDC-10 µM (p-value = .261), suggesting that MSDC-0160 and Rapamycin exhibit synergistic effects.

This experiment could be improved further by using more combinations of Rapamycin and MSDC-0160. For example, a combination treatment of Rapamycin (200 µM) and MSDC-0160 (10 µM) could also be tested. More concentrations of Rapamycin could also be tested. Furthermore, these treatments could be compared to L-Dopa, the BIOLOGY RESEARCH

standard treatment for Parkinson’s Disease.

One possible future area of study could be looking at the cellular energy sensor AMPK and seeing how it is affected

by MSDC-0160, which would allow the potential connection between the MSDC-0160 and Rapamycin pathways to be studied further.

6. Acknowledgements I would like to thank my mentor, Dr. Kim Monahan, for helping me with this research. I would also like to thank Dr. Amy Sheck, Dr. Heather Mallory, NCSSM, and the Glaxo Foundation for supporting this research.

7. References [1] Mayo Foundation for Medical Education and Research.

(2020). Parkinson’s disease - Symptoms and causes. Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/ parkinsons-disease/symptoms-causes/syc-20376055 [2] Ferrazzoli, D., Carter, A., Ustun, F. S., Palamara, G., Ortelli, P., Maestri, R., Yücel, M., & Frazzitta, G. (2016).

Dopamine Replacement Therapy, Learning and Reward Prediction in Parkinson’s Disease: Implications for Rehabilitation. Frontiers in behavioral neuroscience, 10, 121. https://doi.org/10.3389/fnbeh.2016.00121

[7] Nonnekes, J., Timmer, M. H., de Vries, N. M., Rascol, O., Helmich, R. C., & Bloem, B. R. (2016). Unmasking levodopa resistance in Parkinson’s disease. Movement disorders : official journal of the Movement Disorder Society, 31(11), 1602–1609. https://doi.org/10.1002/mds.26712 [8] Robida-Stubbs, S., Glover-Cutter, K., Lamming, D. W., Mizunuma, M., Narasimhan, S. D., Neumann-Haefelin, E., Sabatini, D. M., & Blackwell, T. K. (2012). TOR signaling and rapamycin influence longevity by regulating SKN-1/Nrf and DAF-16/FoxO. Cell metabolism, 15(5), 713–724. https://doi.org/10.1016/j.cmet.2012.04.007 [9] Thoreen CC, Sabatini DM. (2009). Rapamycin inhibits mTORC1, but not completely. Autophagy. Jul;5(5):7256. doi: 10.4161/auto.5.5.8504. Epub 2009 Jul 22. PMID: 19395872.

[10] Hart, Anne C., ed. Behavior (July 3, 2006), WormBook, ed. The C. elegans Research Community, WormBook, doi/10.1895/wormbook.1.87.1, http://www. wormbook.org.

[3] Cooper, J. F., & Van Raamsdonk, J. M. (2018). Modeling Parkinson’s Disease in C. elegans. Journal of Parkinson’s disease, 8(1), 17–32. https://doi.org/10.3233/JPD-171258 [4] Ghosh, A., Tyson, T., George, S., Hildebrandt, E. N., Steiner, J. A., Madaj, Z., Schulz, E., Machiela, E., McDonald, W. G., Escobar Galvis, M. L., Kordower, J. H., Van Raamsdonk, J. M., Colca, J. R., & Brundin, P. (2016). Mitochondrial pyruvate carrier regulates autophagy, inflammation, and neurodegeneration in experimental models of Parkinson’s disease. Science translational medicine, 8(368), 368ra174. https://doi.org/10.1126/scitranslmed.aag2210 [5] Quansah, E., Peelaerts, W., Langston, J. W., Simon, D. K., Colca, J., & Brundin, P. (2018). Targeting energy metabolism via the mitochondrial pyruvate carrier as a novel approach to attenuate neurodegeneration. Molecular neurodegeneration, 13(1), 28. https://doi.org/10.1186/ s13024-018-0260-x [6] Brenner S. (1974). The genetics of Caenorhabditis elegans. Genetics, 77(1), 71–94.

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THE EFFECT OF SMALL MOLECULES Jh-RE-06 AND T2AA ON MUTAGENIC TRANSLESION SYNTHESIS IN ARABIDOPSIS THALIANA Lixin Yang Abstract Cancerous cells in the body lose most healthy DNA repair systems, forcing them to undergo translesion synthesis (TLS), a cellular alternate repair strategy. Because TLS often promotes further drug resistance and mutations, limiting successful chemotherapy treatment, the inhibition of TLS in cancerous cells poses a possible improvement to chemotherapeutics. In recent research, two novel small molecules have been shown to inhibit specific pathways of TLS: Jh-RE-06 (Wojtaszek et al., 2019) and T2AA (Inoue A, Kikuchi S, Hishiki A, et al., 2014). In this project, the possibility of a synergistic effect between both drugs in inhibiting both pathways of translesion synthesis is investigated. Model organism Arabidopsis thaliana seeds were exposed to UV rays to induce TLS. Once successful, 40 seeds were plated on Murashige and Skoog (MS) plates and allowed to germinate. Seedlings were then exposed to control, Jh-RE-06, T2AA, and Jh-RE-06+T2AA treatments. A root bending assay revealed significant differences in root angle and length in the individual Jh-RE-06 and T2AA treatments compared to the control, while the Jh-RE-06+T2AA treatment revealed a greater significant difference compared to individual drug treatments. The data suggest TLS reduction from individual Jh-RE-06 and T2AA treatments but a greater synergistic effect when combining both Jh-RE-06 and T2AA. 1. Introduction Cancer is a genetic disease characterized by abnormal cell growth in body cells, caused by mutations to genes that control the way our cells function. Nearly one out of every three people in the United States will be diagnosed with some form of cancer during their lifetime, making the disease one of the leading causes of death in the country [1].

The genetic mutations that contribute to the development of cancer affect three main types of genes: proto-oncogenes, tumor suppressor genes, and DNA repair genes [5].

DNA repair genes, which are involved in fixing damaged DNA, are especially important to consider when studying the effect of cancer on organisms. Cells with mutations in DNA repair genes tend to develop mutations in other genes, which when combined together cause the cells to become cancerous. Many cancerous cells eventually develop into tumors, which have the ability to spread into and invade other tissues in the body [5]. Once cancer becomes metastatic and spreads throughout the entire body, the survival rate of the cancer patient dramatically decreases.

Over the past several years, research has been conducted worldwide in an attempt to find new ways to stop the growth of cancer cells. Although other treatment options are available, chemotherapy remains one of the most popular ways to treat cancer. In some cases, chemotherapy is combined with another treatment plan in order to fully stop tumor or cancer growth, and it remains the standard treatment for most types of cancers, including AML (acute myeloid leukemia), colon/rectal cancer, Hodgkin Lymphoma (HL), and lung cancer [2]. Despite the popularity 22 | 2020-2021 | Broad Street Scientific

of chemotherapy, its success rates are often low when used as the only treatment plan. In 2014, scientists reported that the overall contribution of curative cytotoxic chemotherapy to 5-year survival in adults was estimated to be 2.3% in Australia and 2.1% in the USA [3]. In addition, many chemotherapeutic treatments, even in regular amounts, can produce toxicity and severe side effects, such as nausea, vomiting, leukopenia, and cardiomyopathy, along with other potentially life-threatening symptoms [16]. As a result, much of the research done today is moving towards finding new ways of improving the survival rates of patients undergoing chemotherapy for cancer.

In order to look into ways of improving chemotherapy, understanding how it treats cancers is necessary. Most chemotherapy drugs kill cancer cells by severely damaging their DNA through creating mutations in their genetic sequence [5]. Because healthy cells inside the body are subjected to DNA damaging exposure every day, they have numerous DNA repair systems that are both accurate and efficient. However, as the cells become cancerous, they sometimes lose one of the DNA repair systems, so they begin to become more reliant upon TLS (translesion synthesis) as an alternate repair strategy [4]. TLS relies on specialized TLS DNA polymerases used to copy over damaged DNA, but the process is not very accurate. Cancer cells and tumors that survive the TLS process often acquire many new mutations that make them more resistant to future treatment [4]. Cisplatin, one of the most popular chemotherapy drugs, is mostly successful in destroying tumors, but they frequently grow back following treatment [4].

Cisplatin-induced DNA damage is persistent in cultured BIOLOGY RESEARCH

BIOLOGY RESEARCH

merization of the Rev1 CTD at its Rev7 binding site and blocks the Rev1-Rev7 interaction. In vivo, Jh-RE-06 was found to inhibit mutagenic TLS and enhance cisplatin-induced toxicity in cultured mouse and human cell lines [6].

Furthermore, administering Jh-RE-06 with cisplatin suppressed the growth of human melanomas in mice [6].

AtRev1

Replication Fork

PCNA

UBZ

AtRev3 AtRev7 Replication Fork

TLS Pol ζ Complex

AtRev3 AtRev7 PIP-BOX

CTD

AtPolk

Rev1 Independent Pathway

RIR

TLS Y Family Polymerase

Rev1 Dependent Pathway

AtPolk

Pol 𝛅𝛅

A.

UBZ

ζ

𝛅𝛅

cancer cells, which retain unaffected replication fork progression due to the upregulation of TLS [18]. Research on this topic has been driven towards finding drugs that specifically aim to inhibit the mutagenic DNA repair pathway (TLS) in order to improve the long-term effectiveness of chemotherapy on cancer.

In order to study TLS in cancer, different model organisms that are dependent on TLS for DNA replication and division can be used. Plants are constantly exposed to UV damage and have evolved to use TLS in order to continue to replicate and divide even though it means retaining the newly acquired mutations in their DNA [17].

The plant model organism, Arabidopsis thaliana has two key TLS pathways: the Rev1 dependent pathway and the Rev1 independent pathway [10]. On the Rev1 dependent pathway, the DNA polymerase Rev1, a key component of TLS, recruits a second TLS DNA polymerase consisting of a complex of Rev3 and Rev7 proteins. There are two components of the Rev1 interaction in order for TLS to begin (Fig. 1a). One interface of TLS recruits POL ζ polymerases (Rev3 and Rev7) through an interaction with the c-terminus of Rev1, while the other interface recruits insertion TLS polymerases of the Y polymerase family (AtPolk) [12].

On the Rev1 independent pathway, the TLS Pol ζ complex interacts with the PIP box of PCNA to enable TLS, while the UBZ-containing residues of the AtPolk protein bind to the UBZ domain on the PCNA. In this pathway, the final PCNA complex binds to Pol δ to begin TLS (Fig. 1b). Both pathways can independently start TLS, so efforts were made to look into small molecules that can each inhibit one pathway in order to inhibit TLS rates.

On the Rev1 independent pathway, the interaction between Rev1 and the Pol ζ complex was investigated for inhibition possibilities. Small molecule Jh-RE-06 was found to effectively disrupt TLS on the Rev1 dependent pathway by interrupting the Rev1-Rev7 interaction [6]. Previously, it was thought that small molecule interruption of the Rev1-Rev7 interaction was not possible because the binding surface of the Rev1 CTD was very shallow. However, Jh-RE-06 was found to be able to bind to the Rev1 C-terminal domain (CTD), which plays a role in coordinating TLS by inducing dimerization of the protein (Fig. 1a) [6].

The main job of the Rev1 CTD is to recruit TLS DNA polymerases through an interaction with the Rev7 protein. Jh-RE-06 is able to bind to the Rev1 CTD by inducing dimerization of the protein such that the compound is almost entirely encapsulated within the Rev1 CTD dimer.

The formation of the Rev1 CTD dimer not only creates a large binding pocket for Jh-RE-06 but also conceals the Rev7 binding surface of the Rev1 CTD, thus blocking the Rev1 CTD interaction with Rev7 [6]. By interfering with the interaction between Rev1 and the Rev7 component of the second TLS DNA polymerase, TLS would be interrupted and tumors would be less likely to develop mutations that are hard to treat. In vitro, Jh-RE-06 induces di-

𝛅𝛅

Figure 1: Two pathways are used in TLS: the Rev1 dependent pathway and the Rev1 independent pathway. (A) In the Rev1 dependent pathway, TLS Y family polymerases (AtPolk in Arabidopsis) and the TLS Pol ζ complex (made up of AtRev3 and AtRev7) all bind to the C-terminus of the Rev1 polymerase, which interacts with the PCNA. (B) In the Rev1 independent pathway, the TLS Pol ζ complex interacts with the PIP box of PCNA to enable TLS, while the UBZ containing residues of the AtPolk binds to the UBZ domain on the PCNA. In order to target the Rev1 dependent pathway, the PIP-box interaction was investigated. Small molecule T2AA was found to inhibit the interaction of PCNA (proliferating cell nuclear antigen) with DNA polymerase δ, effectively stopping the TLS process on the Rev1 independent pathway [7]. PCNA has diverse functions in cell cycle regulation and DNA synthesis within the DNA replication process, but it has the ability to support cancer growth. In TLS, the AtRev7/AtRev3 polymerase complex binds to PCNA through a short sequencing motif called the PIPbox, while the AtPolk polymerase binds to the UBZ domain of the PCNA (Fig. 2). After these two interactions happen, the PCNA continues down the replication fork to DNA polymerase Pol δ, beginning the TLS process on the independent pathway [7]. Since the PCNA/PIP-box and Pol δ interaction are essential to TLS, inhibitors of the interaction such as small molecule T2AA should inhibit DNA replication and translesion synthesis [7].

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When looking at the entire process of TLS in Arabidopsis, the Rev1 dependent pathway and Rev1 independent pathway have the ability to start TLS on their own without help from the other pathway. Thus, both small molecules Jh-RE-06 and T2AA will be used on Arabidopsis thaliana in hope that each molecule would inhibit one pathway, effectively reducing overall TLS rates and offering a level of improvement for chemotherapeutics and decrease in rates of tumor regrowth and mutation.

2.2 – Inducing TLS in Arabidopsis sprouts: For Arabidopsis plants to resort to translesion synthesis as their main method of DNA replication and to mimic the natural sunlight, the following UV treatment protocol was used: Seeds were exposed every other day to 15 minutes of UV radiation under a UV flashlight (wavelength of 385 nm). This was repeated 5 times for a total of 10 days of UV treatment and 105 minutes of UV radiation.

2.3 – Treatments: The wild type (columbia) Arabidopsis strain was used in all four treatments. All seeds of all treatments were under identical light conditions and plate mixtures.

Control: negative control, had no treatment.

Jh-RE-06: positive control, was known to induce translesion synthesis in previous research done by Duke and MIT [6].

T2AA: new drug hypothesized to further induce TLS in Arabidopsis sprouts.

Jh-RE-06 + T2AA: used to investigate the possible synergistic effects between both drugs.

Figure 2: (A) The specific locations where Jh-RE-06 and T2AA small molecules are hypothesized to inhibit. (B) When inhibition drugs are used, small molecule drug Jh-RE-06 attaches to the binding pocket of Rev7 and conceals the binding surface that Rev7 uses to bind to the C-terminus of Rev1, effectively inhibiting the Rev1 dependent pathway. Similarly, the small molecule drug T2AA disrupts the interaction of PCNA with DNA polymerase δ and inhibits the Rev1 independent pathway of translesion synthesis.

2. Methods 2.1 – Sterilization: All Arabidopsis seeds were treated with the following protocol for sterilization: soak in 6-8% sodium hypochlorite (household bleach) for 5 minutes, then rinse with water three times. Seeds were then plated on Murashige and Skoog (MS) plant media plates for optimal growth and kept under 24h light conditions. Early trials proved to be unsuccessful with the presence of mold on all plates despite efforts to keep the workplace sterile. It was determined that the mold was originating from the seeds themselves, so a decontamination protocol using 6.8% sodium hypochlorite solution (household bleach) was used in order to disinfect seeds.

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Table 1: Display of each treatment (plate) of Arabidopsis seeds, including the seed strain, number of seeds that are plated on each MS nutrient plate, the treatment of drugs (Jh-RE-06 and T2AA small molecules), and the concentration of each drug.

Strain of Arabidopsis WT (columbia) WT (columbia) WT (columbia) WT (columbia)

Number of seeds 10 10 10 10

Treatment None Jh-RE-06 T2AA Jh-RE-06 + T2AA

Concentration N/A 10 µM 15 µM 15 µM

2.4 – Root Bending Assay: In order to determine the effectiveness of both small molecules on the inhibition of TLS after treatment, the following protocol was used in order to measure root bending and relative root length in Arabidopsis sprouts.

Seeds were first grown on horizontal surfaces while being subject to UV treatment in order to induce TLS. Germination and sprout/root growth appeared after ~ 10 days, after which plates were turned vertically up (perpendicular to table) so that sprouts could grow up while roots grew downwards. This position was maintained for around 10 days. The individual treatments were administered to the Arabidopsis seedlings. Plates were then rotated 90 degrees BIOLOGY RESEARCH

so that seedlings were horizontal and parallel to the surface of the table. After 5 days, root bending was analyzed as the roots reoriented themselves to grow downwards. The angle of root bending and root length were measured using software to analyze data.

(A)

2.5 – Statistical Analysis: After drugs were administered and root bending assay was performed, all root bend angles and root lengths of each Arabidopsis seedling were measured using ImageJ software. Mean, standard deviation, SEM were calculated along with a one-way ANOVA test in order to analyze data and determine significance between treatment results.

2.6– Imaging: Seedlings and plates were photographed with a cell phone camera with a phone microscope attached. Photographs were then uploaded to software ImageJ, where root length and root angle were measured.

(B)

3. Results Drug treatments of Jh-RE-06 and T2AA provided evidence of significant reductions in TLS rates through the one-way ANOVA test. Root bending measures show that the treatments of Jh-RE-06, T2AA, and Jh-RE-06+T2AA all had a significant difference as compared to the control with a p<0.01 (Fig. 3b). The average root bending angle for the control group was 66 degrees, while the average angles for Jh-RE-06 and T2AA were 120 and 110 degrees, respectively, depicting a significant increase in angle measures (Fig. 3b). However, the root bending angle data were nonsignificant between Jh-RE-06 and T2AA treatments. In the Jh-RE-06+T2AA combination group, the angle measurement was markedly higher with an average of 160 degrees. Similarly, the combined Jh-RE-06+T2AA treatment had a significant difference between the separate Jh-RE-06 and T2AA treatments with a p-value of less than 0.01.

Root length measures for each treatment suggested similar results as root bending angles under the ANOVA statistical analysis. All drug treatments of Jh-RE-06, T2AA, and both drugs combined had a significant TLS inhibition rate as compared to the control with a p-value of less than 0.01 (Fig. 3a). The average root length of the control treatment was 0.44 cm. The average root lengths of the individual Jh-RE-06 and T2AA treatments were 0.31 and 0.31 cm respectively, showing a similar decrease when compared to the control. In the combination treatment of both Jh-RE-06 + T2AA, the root length decreased further to an average of 0.22 cm, showing a significant difference when compared to both separate treatments of Jh-RE-06 and T2AA (p<0.05). Similar to the results of the root bending data, there was not a significant difference when comparing treatments of Jh-RE-06 and T2AA. BIOLOGY RESEARCH

Figure 3. Jh-RE-06 and T2AA both reduce TLS rates and have a synergistic effect when combined. Seeds treated with 6-8% sodium hypochlorite (household bleach) for 5 min, left on treatment plates for 5 days while root bending assay was performed. (A) Root lengths were measured after treatmentss of control, Jh-RE-06, T2AA, and Jh-RE-06 +T2AA (B) Root angles were measured after treatments of control, JhRE-06, T2AA, and Jh-RE-06 +T2AA Error bars represent SEM. Statistical analysis: one-way ANOVA. 4. Discussion Translesion synthesis remains an unfavorable DNA repair pathway when considering cell survival and preventing mutagenesis in chemotherapy, due to its tendency for inaccuracies when copying over damaged DNA. The inhibition of TLS leads to a lower chance of tumor mutation Broad Street Scientific | 2020-2021 | 25

and cancer recurrence, improving the efficacy of cancer treatments.

For the first objective in this project, Arabidopsis seedlings needed to have translesion synthesis (TLS) induced in order for the experiment to proceed. Large amounts of ultraviolet light were known to cause DNA damage to Arabidopsis plant cells and induce TLS using a variety of DNA polymerases as an alternate repair pathway in an attempt to maintain cellular division [12]. With the UV protocol that was used in this experiment, Arabidopsis seedlings had to resort to using TLS for cellular repair in order to bypass the DNA damage caused by the UV light. Since TLS enabled Arabidopsis seedlings to continue with cellular division, the plants remained healthy and growing under extreme exposure to UV light, which signified that the seedlings were in fact using TLS as their primary source of DNA repair.

The UV protocol used induced damage to seedling cells, assuring that they will use TLS as their form of cellular repair. Results from the root bending assay suggest that both Jh-RE-06 and T2AA individually have significant effects on reducing TLS rates as compared to the control when comparing root angle and length. In Arabidopsis seedlings, TLS offers cellular repair and replication abilities to each damaged cell. Therefore, a greater root angle from the treatments signifies a greater inhibition of TLS due to the delayed reorientation of roots after being inverted. Similarly, a shorter root indicates reduced TLS rates due to not being able to perform cellular division and repair efficiently (see figure 4). When comparing both drugs, the root angle and length data are nonsignificant, providing evidence that Jh-RE-06 and T2AA have roughly the same effect on TLS in Arabidopsis. This discovery raises more thought regarding how much each of the two pathways contributes to overall TLS rates. Since each drug has roughly the same effect, each pathway (Rev1 dependent and Rev1 independent) contributes about the same effort in regulating TLS. Neither pathway is contributing more to the TLS process than the other when looking at the root bending and length data. The statistically significant data when looking at the individual treatments of Jh-RE-06 and T2AA further supports the credibility and accuracy of the two scientific papers in which the molecules were investigated (Wojtaszek et al., 2019 and Actis et al., 2019), and that the two drugs actually inhibit the specific pathways in TLS that they were postulated to interrupt.

Further data suggest that combining Jh-RE-06 and T2AA creates a synergistic effect between the two drugs; using a combination of both drugs resulted in a greater root angle and shorter root length, signifying higher TLS inhibition rates compared to using each drug individually.

These data suggest that each small molecule (Jh-RE-06 and T2AA) inhibits one pathway of the TLS process (the Rev1 dependent pathway and the Rev1 independent pathway), effectively significantly decreasing TLS rates. These results 26 | 2020-2021 | Broad Street Scientific

highlight the possibility that Jh-RE-06 and T2AA may play a future role in improving chemotherapeutics in cancer patients. The complete inhibition of TLS would ensure a decrease in tumor mutation and cancer recurrence rates, improving the cancer survival rate in organisms. However, there are limitations to consider for the conclusions of this experiment. The exact process of TLS (proteins involved, polymerase interactions) varies between different organisms. It is unclear whether the combination of Jh-RE-06 and T2AA will provide a decrease in TLS rates in other model organisms. It is beyond the scope of this study to accurately determine the effectiveness of this treatment in other organisms with cancer, but future research will be done in this area.

Figure 4. Photographs of each treatment depicting the bent roots and various root lengths after assay was performed.

5. Conclusion This research aimed to identify two small molecules that could inhibit the Rev1 independent and Rev1 dependent pathways of translesion synthesis in order to inhibit the process in cancerous cells. Based on data analysis and qualitative data of the Arabidopsis sprouts, it can be concluded that small molecules Jh-RE-06 and T2AA are able to inhibit each pathway of TLS, yielding statistically significant data when using both molecules on their own and also when combining both drugs into one treatment. The methodology used in this experiment allowed for a phenotypic reveal of the protein to protein interactions of translesion synthesis in Arabidopsis, adding on to the already existing research related to the topic. The promise that both small molecules show in inhibiting TLS could mean opening up future paths in TLS research to determine if these drugs could possibly be used to improve chemotherapeutics for cancer patients.

To better understand the implications of these results, future research in this project may be done in performing more trials to see how consistent the results are with BIOLOGY RESEARCH

bigger samples. Similarly, different concentrations of each drug, different model organisms, and adjusting protocols can be researched in order to improve the reduction of TLS rates. In addition, more work can go into looking at both drugs computationally to determine the specific binding and inhibition locations on both TLS pathways, investigating if the drugs are affecting any other part of the cell besides TLS. 6. Acknowledgements I would like to thank my mentor, Dr. Kim Monahan, and my Research in Biology class for guiding and supporting me through the research process. Further thanks to Dr. Amy Sheck, the Glaxo Endowment, and the North Carolina School of Science and Mathematics for giving me the opportunity to conduct my own research.

7. References [1] Cancer Prevalence: How Many People Have Cancer? (n.d.). Retrieved from https://www.cancer.org/content/ cancer/en/cancer/cancer-basics/cancer-prevalence.html [2] Chemotherapy. (2017, April 27). Retrieved from https://www.mayoclinic.org/tests-procedures/chemotherapy/about/pac-20385033 [3] Morgan, G., Ward, R., & Barton, M. (2004). The contribution of cytotoxic chemotherapy to 5-year survival in adult malignancies. Clinical oncology (Royal College of Radiologists (Great Britain)), 16(8), 549–560. https://doi. org/10.1016/j.clon.2004.06.007

[8] Sandoiu, A. (n.d.). ‘Supramolecule’ helps immune cells ‘eat up’ cancer. Retrieved from https://www.medicalnewstoday.com/articles/322334 [9] How does cancer do that? Sizing up cells and their shapes. (2020, January 21). Retrieved from https://www.cancercenter.com/community/ blog/2018/02/how-does-cancer-do-that-sizing-up-cellsand-their-shapes [10] Yamanaka, K., Chatterjee, N., Hemann, M. T., & Walker, G. C. (2017). Inhibition of mutagenic translesion synthesis: A possible strategy for improving chemotherapy?. PloS genetics, 13(8), e1006842. https://doi. org/10.1371/journal.pgen.1006842 [11] Plenderleith I. H. (1990). Treating the treatment: toxicity of cancer chemotherapy. Canadian family physician Medecin de famille canadien, 36, 1827–1830.

[12] Sakamoto A. N. (2019). Translesion Synthesis in Plants: Ultraviolet Resistance and Beyond. Frontiers in plant science, 10, 1208. Retrieved from https://doi. org/10.3389/fpls.2019.01208 [13] Gomes, L. R., Rocha, C., Martins, D. J., Fiore, A., Kinker, G. S., Bruni-Cardoso, A., & Menck, C. (2019).

ATR mediates cisplatin resistance in 3D-cultured breast cancer cells via translesion DNA synthesis modulation.

Cell death & disease, 10(6), 459. Retrieved from https:// doi.org/10.1038/s41419-019-1689-8

[4] Trafton, A., & MIT News Office. (2019, June 6). Drug makes tumors more susceptible to chemo. Retrieved from http://news.mit.edu/2019/drug-chemo-effective-tumors-0606 [5] What Is Cancer? (n.d.). Retrieved from https://www. cancer.gov/about-cancer/understanding/what-is-cancer [6] Wojtaszek et al., A Small Molecule Targeting Mutagenic Translesion Synthesis Improves Chemotherapy, Cell (2019), https://doi.org/10.1016/j.cell.2019.05.028 [7] Actis et al., Small molecule inhibitors of PCNA/PIPbox interaction suppress translesion DNA synthesis, Bioorganic & Medicinal Chemistry, Volume 21, Issue 7, 2013, Pages 1972-1977, ISSN 0968-0896. Retrieved from https://doi.org/10.1016/j.bmc.2013.01.022.

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COMPUTATIONAL DESIGN OF PATHOGEN-SPECIFIC HSP90 INHIBITOR TO TARGET MALARIAL PARASITES Anika Bhadriraju Abstract Malaria is a re-emerging disease that has become one of the leading causes of morbidity and mortality in the tropics. The demand for viable treatments for the disease has increased due to the parasite’s resistance to conventional antimalarials such as chloroquine, atovaquone, and artemisinin. The Heat Shock 90 (Hsp90) protein is critical to normal growth and development in eukaryotes and specifically assists malarial parasites, such as Plasmodium falciparum (Pf), in surviving the sudden change between two physiologically divergent habitats: cold-blooded Anopheles mosquitoes and warm-blooded humans. This project deals with the computational design of a pathogen-specific inhibitor that targets the Plasmodium falciparum Hsp90 (PfHsp90) Protein. After examining the differences in conformation between the Pf and human Hsp90 (HuHsp90) orthologues, a Pf-specific hydrophobic pocket was located and focused on to improve selective binding to PfHsp90 over HuHsp90. Various target ligands were docked into both proteins using the Molegro Virtual Docker. Existing 7-azaindole compounds, such as IND311, and other established inhibitors, including PU-H71, were improved through computational screening to bind with high affinity to the ATP binding site of the protein while targeting the hydrophobic cavity found only in PfHsp90. One novel compound was found to have superior binding affinity to the Pf protein while showing lower binding affinity in the human orthologues, indicating the potential for parasitic inhibition while reducing the likelihood of side effects to humans.

1. Introduction Malaria is a re-emerging disease that has risen to become one of the leading causes of morbidity and mortality in the tropics [1]. According to the World Health Organization (WHO), an estimated 3.4 billion people in 92 different countries live at risk of developing malaria [2]. The 2018 World Malaria report stated that there were 219 million cases of malaria globally in 2017 and 435,000 deaths from the disease [2]. Plasmodium falciparum, the most prevalent species of malaria parasitic protozoa, is credited for 99% of cases in Africa and is responsible for a majority of malaria deaths globally [1].

A leading issue in malaria prevention is the parasite’s resistance to conventional antimalarials. This resistance can arise from a number of factors, including incomplete treatment of active infection, high levels of parasite genetic and metabolic adaptability, and overuse of drugs for treatment [3]. Chloroquine, one of the first synthetic antimalarial drugs, exhibited a strong antimalarial potency and high safety, allowing it to become the most extensively used drug in malarial treatment [15]. However, with the emergence of chloroquine-resistant P. falciparum strains, the drug proved ineffective and forced the development of additional antimalarial drugs such as atovaquone, antifolates, and aminoquinolines [16]. Through time, each of these molecules has fallen from prominence due to parasitic resistance. To circumvent these issues, researchers are currently looking for alternative malaria treatment options [7].

1.1 – Heat Shock 90 (Hsp90) Protein The Heat Shock Protein 90 (Hsp90) is a highly conserved 28 | 2020-2021 | Broad Street Scientific

homodimer protein that is critical for the growth and development of eukaryotic cells [4]. This class of proteins is classified according to their molecular mass, where Hsp90 refers to a subset of Heat Shock proteins having a molecular weight of 90-kDa [4]. The protein works to mediate the functioning of various cellular signaling pathways as well as to manage folding and protein-protein interactions [4]. Specifically, the ATPase activity of Hsp90 is essential for the active conformation and release of client proteins such as mutated signaling proteins and epidermal growth factor receptors [5]. When inhibited, the Hsp90 client proteins cannot attain an active form and are degraded, leading to growth arrest and apoptosis of the cell [6].

The Hsp90 proteins are specifically crucial to P. falciparum, which develops through a complex life cycle involving two host organisms: cold-blooded Anopheles mosquitoes and warm-blooded humans [4]. In order to survive the sudden change between the two physiologically divergent habitats, the parasite requires the Hsp90 protein for its ability to buffer rapid heat shocks [5]. Inhibiting Hsp90 function has been shown to not only interfere with essential cellular functions, but also to halt many parasitic resistance mechanisms [10].

1.2 – Hsp90 Protein Structure Hsp90 is composed of three conserved domains: the N-terminal domain, the C-terminal domain, and the middle domain [4]. Binding of ATP occurs in the ATP binding cleft of the N-terminal domain, making this domain a prime target for current Hsp90 inhibitors [4]. A flexible “ATP Lid” resides at the entrance of the ATP binding domain and is linked by hinges to serve as a rotatable cover that can close CHEMISTRY RESEARCH

over the binding pocket [7]. While analysis of the structural differences between the Plasmodium falciparum Hsp90 (PfHsp90) protein and human orthologues indicates nearly identical ATP binding pockets, the Glycine-Rich Hinge Loop (GHL), or the short hinge at the C-terminal base of the lid, shows variation (Fig. 1) [7]. The GHL in human Hsp90 (HuHsp90) is presented in a curved conformation by which residue Glycine 135 is clipped to Glycine 114 [7]. However, the GHL is in a straight conformation in the PfHsp90, allowing for the presence of a Pf-specific hydrophobic cavity between the side chain of Arginine 98 and the GHL [7].

hydrogen bond to residue Asn 37, which is a residue of high importance in inhibition [7]. The 1,3-benzodioxole ring is responsible for extending deeper into the ATP binding site, making these rings a point of focus for future inhibitor derivatives.

Previous studies employed multiple layers of virtual drug screening to find IND311 derivatives, specifically IND31119, as other prime candidates for an inhibitor [7].

The IND311 derivatives were designed to specifically target the hydrophobic cavity in the PfHsp90 protein [7]. Since the IND31119 inhibitor contains a chiral structure, previous studies docked both the S and the R-enantiomer and optimized these structures for the pose that is most stably bound in the protein [7]. The R-enantiomer was chosen to best inhibit protein function as it indicated increased stability compared to the IND31119 S-enantiomer [7].

Figure 1. Analysis of GHL in proteins. Left: PfHsp90 protein. Right: HuHsp90 protein It is possible to design an inhibitor that works to target the Pf-specific hydrophobic pocket while binding to the ATP binding domain with high affinity. This allows the inhibitor to hinder protein function and parasitic development.

1.3 – Analysis of Current Inhibitors In order to design potential inhibitors, current inhibitors for PfHsp90 were docked in the PfHsp90 protein and human orthologs using Molegro Virtual Docker. Geldanamycin (GA), an ansamycin antibiotic, and radicicon (Rad), a macrocyclic lactone, have been used in the past to inhibit Hsp90 protein function [6]. However, due to the toxicity caused by GA, Rad, and their derivatives through in vitro studies, PU-H71, with its attractive pharmacokinetic profile, has emerged as a viable alternative to target the protein and was chosen to be docked for analysis (Fig. 2) [6].

Figure 2. Left: IND31119 R-enantiomer. Right: PU-H71 After being docked in the PfHsp90 protein, it was found that all cyclic components of PU-H71 remain above the GHL of the protein (Fig. 3) [6]. The docking results show a CHEMISTRY RESEARCH

Figure 3. Analysis of PU-H71 inhibitor. (a) PU-H71 with 1,3-benzodioxole ring highlighted. (b) PU-H71 docked in PfHsp90 The predicted binding models indicate that the aliphatic chain at the 1-position and carboxyl group at the 2-position of the 7-azaindole core in IND31119 (R) are responsible for jointly filling the Pf-specific hydrophobic cavity (Fig. 4 & 5) [7]. The 7-azaindole core of the compound remains above the GHL while the 6-methylhept-5-en-2-yl chain at the 5-position is found to extend deeper into the ATP binding pocket of the protein [7]. This chain targets residues Asn 37, Asn 92, and Ile 173 while forming hydrogen bonds with Asn 37 [7].

Figure 4. Analysis of IND3119 (R) inhibitor. (a) IND31119 with 6-methylhept-5-en-2-yl chain, aliphatic chain, carboxyl group, and 7-azaindole core highlighted. (b) IND31119 (R) docked in PfHsp90 Broad Street Scientific | 2020-2021 | 29

Figure 5. Naming of carbon positions on 7-azaindole core of IND3119 (R) By competing with ATP at the binding site, the inhibitors are able to stop ATPase activity of the protein, further hindering the development of the parasite. Based on PU-H71’s observed strong affinity to the ATP binding site and the ability for IND31119 (R) to target the Pf-specific hydrophobic pocket, these inhibitors can be used to create future derivatives that combine the strengths of each.

2. Methods 2.1 – Protein Analysis and Binding Cavity Identification Protein structures determined by X-ray crystallography of PfHsp90 and HuHsp90 were acquired from the Protein Data Bank (PfHsp90 PDB code: 3k60; HuHsp90 PDB Code: 1byq) [9,17]. These structures were imported and viewed in the Molegro Virtual Docker software, which uses a heuristic search algorithm and differential evolution to predict binding cavity and docking poses [8]. With the use of the MolDock cavity prediction algorithm and previous analysis of current Hsp90 inhibitor binding sites, the active sites of the proteins were located (Fig. 6 & 7) [8].

Figure 6. Active site of PfHsp90 protein. (a) Binding cavity predicted with MolDock cavity prediction algorithm. (b) Binding cavity close up image with yellow circle around Pf-specific hydrophobic cavity.

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Figure 7. Active site of HuHsp90 protein. (a) Binding cavity predicted with MolDock cavity prediction algorithm. (b) Binding cavity close up image with GHL and ATP lid.

2.2 – Computational Compound Design Nineteen novel compounds were designed and analyzed based on their ability to target the hydrophobic pocket and extend deeper into the ATP binding domain of the protein.

The key structural elements of IND31119 (R) and PU-H71 were identified through an initial screening and essential moieties were incorporated into the novel variants. Compounds were designed in three different phases: 1) effect of chirality and 6-methylhept-5-en-2-yl chain in IND31119 (R) on binding in the ATP binding domain, 2) effect of aliphatic chain and carboxyl group in IND31119 (R) on targeting hydrophobic cavity, 3) incorporation of the 1,3-benzodioxole ring into inhibitor. These became different classes of compounds (classes H, J, and K, respectively). The novel compounds, as well as observed inhibitors PU-H71 and IND31119 (R), were docked in both PfHsp90 and HuHsp90.

Ten docking poses were generated from every compound in each protein. The optimal poses were chosen based on MolDock-calculated Rerank scores and interaction with residues. The binding affinity for the optimal pose in the PfHsp90 and HuHsp90 protein was found using Molegro’s Virtual Docker while the protein-ligand interactions were observed with Molegro’s Ligand Energy Inspector.

2.3 – Analysis of Pharmacokinetic Properties Top compounds from each class were screened using Stardrop’s ADME QSAR probabilistic scoring models to observe Oral Non CNS score and Lipinski Rule of 5 score [13]. The Lipinski Rule of 5 score is a set of drug-likeness guidelines based on the compound’s molecular weight, lipophilicity, and number of H-bond donors and acceptors [11, 12]. The Oral Non CNS score is based on the Oral Non CNS Scoring profile on the Stardrop software and is a measure of toxicity. Optimal Oral Non CNS values should be close to 1.

CHEMISTRY RESEARCH

3. Results and Discussion 3.1 – Computational Docking Analysis The binding affinity, a measure of binding strength, was calculated for the twenty-one docked inhibitors (Table 1).

Inhibitor structures are also shown (Fig. 8). The H class of compounds focused on adjusting the length of the 6-methylhept-5-en-2-yl chain at the 5-position of the 7-azaindole core and eliminating the chirality of the IND31119 (R) inhibitor with the addition of a methyl group. As shown by the binding affinity of H1, the elimination of the chiral structure of the IND31119 (R) inhibitor improved binding to the PfHsp90 binding cavity. Compounds H1, H2, and H3 were considered in developing the next class of compounds. The J class of compounds focused on targeting the hydrophobic cavity of the PfHsp90 protein with changes to the aliphatic chain at the 1-position and carboxyl group at the 2-position of the 7-azaindole core of the IND31119 (R) inhibitor. As shown by the binding affinity of J5, complete elimination of the aliphatic chain at the 1-position allowed for improved polar-polar interactions and increased hydrogen bonding. J5 was employed in the final phase of compound design involving the incorporation of the 1,3-benzodioxole ring from the PU-H71 inhibitor at the 5-position of J5. Compounds were tested with the addition of the 1,3-benzodioxole ring (K1 and K4) and of only the benzene ring (K2 and K3). Compounds K1 and K4 differ in the presence of oxygen atoms at the 1- and 3-positions of the benzodioxole ring. Compounds K2 and K3 differ in the presence of a nitrogen atom at the 5-position of the 7-azaindole core.

Figure 8. Structure of current inhibitors (IND31119 (R) & PU-H71) and novel compounds (H1-K4)

CHEMISTRY RESEARCH

Table 1. Molecular docking results of current inhibitors (IND31119 (R) & PU-H71) and novel compounds (H1-K4) in PfHsp90 and HuHsp90. A lower binding affinity value indicates better binding. Units are arbitrary. Compounds that advanced from H class are highlighted in blue. Compounds that advanced from J class are highlighted in orange. Compound that was selected as the most effective inhibitor is highlighted in green.

Compound IND3119 R PU-H71 H1 H2 H3 H4 H5 H6 H7 H8 J1 J2 J3 J4 J5 J6 J7 K1 K2 K3 K4

PfHsp90 Binding Affinity -23.98 -37.17 -25.50 -25.55 -25.70 -23.76 -23.92 -23.02 -24.76 -24.19 -25.84 -23.99 -27.10 -25.03 -27.23 -27.15 -25.99 -39.94 -32.19 -29.23 -32.25

HuHsp90 Binding Affinity -23.14 -34.68 -23.88 -25.51 -24.95 -24.44 -24.99 -23.97 -23.92 -25.67 -24.88 -25.61 -24.50 -24.81 -26.84 -27.70 -25.81 -35.97 -33.45 -29.63 -33.00

3.2 – Pharmacokinetic Property Analysis of Novel Compounds Optimal compounds from each round of screening were imported into the Stardrop software and screened for their Lipinski Rule of 5 score and Oral Non CNS score to assess the druggability and toxicity of the molecule (Table 2). The novel compounds show an improvement in Lipinski Rule of 5 score with final compound K1 reaching an optimal score of 1. Although compound K1 shows significant improvement in Oral Non CNS score, compounds J5 and H3 show the highest Oral Non CNS scores of all screened novel compounds. Since other tested compounds show a higher Oral Non CNS score than compound K1, there is potential for improvement in Oral Non CNS score in putative compound K1.

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Table 2. Pharmacokinetic screening of potential inhibitors. Units are arbitrary. Compounds that advanced from H class are highlighted in blue. Compounds that advanced from J class are highlighted in orange. Compound that was selected as the most effective inhibitor is highlighted in green. Compound K1 shows an improvement in Oral Non CNS score (optimal Oral Non CNS score = 1) and an optimal Lipinski Rule of 5 score (optimal Lipinski Rule of 5 score = 1).

Compound

Lipinski Rule of 5

Oral Non CNS Score

IND31119 R

0.9999

0.03189

PU-H71 H1 H2 H3 J5 K1

1 0.9992 0.4832 0.4832 1 1

0.04065 0.01472 0.02393 0.1425 0.1425 0.1199

3.3 – Computational Protein Interaction Analysis of Optimal Inhibitor Novel compound K1 was chosen as the putative inhibitor and was analyzed for electrostatic interactions and hydrogen bonding (Fig. 9). Strong hydrogen bonding was shown between K1 and PfHsp90 within the hydrophobic pocket of PfHsp90 (Fig. 9a). A hydrogen bond was formed between the nitrogen at the 1-position of the 7-azaindole core and residue Val 122, indicating that the removal of the aliphatic chain improved binding. Bonds were also formed between the nitrogen at the 7-position of the 7-azaindole core and residue Phe 124 and between the single bonded oxygen on the carboxyl chain at the 2-position and residue Thr 101.

Based on pairwise steric and hydrogen bonding energies, Molegro Ligand Energy Inspector calculates detailed information about the interactions between a particular pose and certain residues within the protein active site [14].

These interactions are characterized by an EPair score, with a higher score indicating greater interactions [14]. Molegro Ligand Energy Inspector was used to observe the interactions between desired residues and K1, IND31119 (R) and PU-H71 (Table 3). The EPair values that were calculated between each observed residue and K1 proved to be higher than those of IND31119 (R) and PU-H71 with the exception of residue Arg 98. Residue Asn 37 and Phe 124 are located at the floor of the ATP Binding pocket of the PfHsp90 protein [7]. A higher EPair value for K1 confirms that the inhibitor is able to extend and target the ATP binding pocket with greater affinity, compared to IND31119 (R) and PU-H71.

Residue Gly 123 resides on PfHsp90’s GHL, which allows for the presence of the hydrophobic cavity [7]. A higher EPair value between Gly 123 and K1 indicates that K1 is targeting the Pf-specific hydrophobic cavity of the protein.

Lastly, due to the positively charged guanidinium end and flexible side chain of Arg 98, strong interactions to Arg 98 prove to improve affinity of the inhibitor to the binding site [7]. Unlike other observed residues, Arg 98 interactions are not improved with the K1 compound, showing potential for improvement in future inhibitor designs.

Table 3. Ligand-protein interactions of K1, IND31119 (R), and PU-H71 in PfHsp90 protein calculated from Molegro Energy Inspector. Values are the EPair score between each residue and inhibitor. Units are arbitrary. K1 shows improved interactions with Asn 37, Phe 124, and Gly 123 but weaker interactions with Arg 98.

Residue

K1

Asn 37 Phe 124 Arg 98 Gly 123

-21.219 -16.510 -10.635 -11.302

IND31119 (R) -17.876 -1.410 -18.413 -8.848

PU-H71 -14.799 -12.801 -6.709 -11.535

4. Conclusion and Future Work Figure 9. K1 computational hydrogen bonding analysis. (a) K1 Structure. b) K1 hydrogen bonding (shown with blue dotted lines) in PfHsp90. Hydrogen bonding exists between K1 and residues Phe 124, Thr 101, and Val 122.

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In this project, a pathogen-specific inhibitor was computationally designed to bind to the P. falciparum Hsp90 (PfHsp90) protein. Malaria is a disease that has become difficult to treat due to the malarial parasites developing resistance to conventional antimalarial drugs [3]. The Heat Shock Protein 90 (Hsp90) is essential to the growth and development of eukaryotes, but is specifically crucial to the malarial parasite and its ability to buffer rapid heat shocks when CHEMISTRY RESEARCH

traveling between two physiologically divergent hosts [4].

After analyzing previous literature, it was found that a hydrophobic cavity exists in PfHsp90 proteins as compared to human orthologs (HuHsp) due to the open conformation of the Glycine-Rich Hinge Loop (GHL) [7]. By targeting this hydrophobic pocket, a pathogen-specific inhibitor can potentially be developed.

Current inhibitors, IND31119 (R) and PU-H71, were docked in both PfHsp90 and HuHsp90 and analyzed in conjunction with previous studies. The aliphatic chain at the 1-position and 6-methylhept-5-en-2-yl chain at the 5-position of the 7-azaindole core of the IND31119 (R) inhibitor became prime targets for adjustment in novel compounds due to their ability to target the Pf-specific hydrophobic pocket and ATP binding pocket, respectively [7]. The 1,3-benzodioxole ring of PU-H71 became of interest in future derivatives due to its high binding affinity within the PfHsp90 protein.

Novel compounds were designed from IND31119 (R) in three separate phases: 1) improve binding to the ATP binding pocket, 2) improve binding to the hydrophobic pocket of PfHsp90, 3) incorporate ring structures of the PU-H71 inhibitor. Compound K1 proved to have the highest binding affinity to PfHsp90. To design compound K1, there was an addition of a methyl group to eliminate chirality from IND31119 (R), an elimination of the aliphatic chain at the 1-position of IND31119 (R) to increase polar interactions, and an incorporation of the 1,3-benzodioxole rings from PU-H71. When screened in Stardrop software, compound K1 proved to increase Lipinski Rule of 5 and Oral Non CNS scores, showing reduced toxicity. K1 also improved interactions with critical residues, such as Phe 124, Gly 123, and Asn 37. With the results shown in this study, K1 shows strong potential to be used as a pathogen-specific inhibitor to circumvent the issue of parasitic resistance and improve malarial prophylaxis.

Future goals include further improving compound K1 to bind to PfHsp90 with greater affinity while increasing Oral Non CNS score. Since previous studies prove Arg 98 as a strong target for PfHsp90 inhibitors, additional derivatives should focus on targeting this residue [7]. Future work will also involve the development and execution of a synthesis scheme to test the efficacy of these inhibitors in vitro.

6. References

5. Acknowledgements

[9] Bank, R. (n.d.). 1BYQ: HSP90 N-terminal domain bound to ADP-MG. Retrieved November 12, 2020, from https:// www.rcsb.org/structure/1BYQ

I would like to thank Dr. Tim Anglin and Dr. Michael Bruno for their mentorship and guidance and Mr. Robert Gotwals for his assistance in computational work and the usage of computer software. I would also like to thank the North Carolina School of Science and Mathematics Foundation for their support, as well as the Summer Research and Innovation Program. Lastly, I would like to thank my Research in Chemistry peers.

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[1] Nöteberg, D., Hamelink, E., Hultén, J., Wahlgren, M., Vrang, L., Samuelsson, B., & Hallberg, A. (2003). Design and Synthesis of Plasmepsin I and Plasmepsin II Inhibitors with Activity in Plasmodium falciparum-Infected Cultured Human Erythrocytes. Journal of Medicinal Chemistry, 46(5), 734746. doi:10.1021/jm020951i [2] Fact sheet about Malaria. (n.d.). Retrieved November 12, 2020, from https://www.who.int/news-room/fact-sheets/ detail/malaria [3] Hyde, J. E. (2007). Drug-resistant malaria − an insight. FEBS Journal, 274(18), 4688-4698. doi:10.1111/ j.1742-4658.2007.05999.x [4] Corbett, K. D., & Berger, J. M. (2010). Structure of the ATP-binding domain of Plasmodium falciparum Hsp90.

Proteins: Structure, Function, and Bioinformatics, 78(13), 2738-2744. doi:10.1002/prot.22799 [5] Hsp90-Targeted Library. (n.d.). Retrieved November 12, 2020, from https://www.chemdiv.com/wp-content/ uploads/2014/02/HSP90.pdf [6] Shahinas, D., Folefoc, A., Taldone, T., Chiosis, G., Crandall, I., & Pillai, D. R. (2013). A Purine Analog Synergizes with Chloroquine (CQ) by Targeting Plasmodium falciparum Hsp90 (PfHsp90). PLoS ONE, 8(9). doi:10.1371/journal. pone.0075446 [7] Wang, T., Bisson, W. H., Mäser, P., Scapozza, L., & Picard, D. (2014). Differences in Conformational Dynamics between Plasmodium falciparum and Human Hsp90 Orthologues Enable the Structure-Based Discovery of Pathogen-Selective Inhibitors. Journal of Medicinal Chemistry, 57(6), 2524-2535. doi:10.1021/jm401801t [8] Thomsen, R., & Christensen, M. H. (2006). MolDock: A New Technique for High-Accuracy Molecular Docking. Journal of Medicinal Chemistry, 49(11), 3315-3321. doi:10.1021/jm051197e

[10] Shahinas, D., Folefoc, A., & Pillai, D. (2013). Targeting Plasmodium falciparum Hsp90: Towards Reversing Antimalarial Resistance. Pathogens, 2(1), 33-54. doi:10.3390/ pathogens2010033

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[11] (n.d.). Lipinski Rule of Five. Retrieved November 12, 2020, from http://www.scfbio-iitd.res.in/software/drugdesign/lipinski.jsp [12] Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the Rule of 5 and drugability. Advanced Drug Delivery Reviews, 101, 89-98. doi:10.1016/j. addr.2016.05.007 [13] Optibrium. (n.d.). ADME QSAR models. Retrieved November 12, 2020, from https://www.optibrium.com/ stardrop/stardrop-adme-qsar-models.php [14] Molegro Virtual Docker User Manual. (n.d.). (2019).

Molexus IVS. http://molexus.io/molegro/MVD_Manual. pdf.

[15] Parhizgar, A. R., & Tahghighi, A. (2017). Introducing New Antimalarial Analogues of Chloroquine and Amodiaquine: A Narrative Review. Iranian journal of medical sciences, 42(2), 115–128.

[16] Mott, B. T., Tripathi, A., Siegler, M. A., Moore, C. D., Sullivan, D. J., & Posner, G. H. (2013). Synthesis and Antimalarial Efficacy of Two-Carbon-Linked, Artemisinin-Derived Trioxane Dimers in Combination with Known Antimalarial Drugs. Journal of Medicinal Chemistry, 56(6), 2630-2641. doi:10.1021/jm400058j [17] Bank, R. (n.d.). 3K60: Crystal structure of N-terminal domain of Plasmodium falciparum Hsp90 (PF07_0029) bound to ADP. Retrieved November 12, 2020, from https://www. rcsb.org/structure/3k60

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CHEMISTRY RESEARCH

COMPUTATIONAL DESIGN OF A MULTI-TARGET NMDAR/AChE LIGAND FOR THE SYMPTOMATIC TREATMENT OF ALZHEIMER’S DISEASE Tianning “T.T.” Chen Abstract Alzheimer’s Disease (AD) remains as a leading cause of death in the United States. Extensive research over the past decades have not yielded any therapies that prevent, halt, or reverse its progression. This study aims to enhance existing symptomatic therapy by designing a dual inhibitor molecule that simultaneously antagonizes both currently approved inhibitory targets: the N-methyl-D-aspartate receptor (NMDAR) and the acetylcholinesterase enzyme (AChE). Current NMDAR and AChE inhibitors were selected to be merged into hybrid compounds. One high affinity hybrid was then selected for further pharmacokinetic optimization using various human Ether-à-go-go-Related Gene (hERG) blockage risk studies and pharmacophore guidelines. Of the ten resulting derivative compounds, a novel dual inhibitor was found to have a much lower risk of QT prolongation than its parent compounds, as well as high affinity for both NMDAR and AChE. Future work on designing multi-target drugs using the merged framework could lead to promising progress in mitigating the symptoms of multifactorial diseases like Alzheimer’s.

1. Introduction 1.1 – Symptomatic Treatment of Alzheimer’s Disease via NMDAR and AChE Inhibition Alzheimer’s Disease (AD) is a highly prevalent, irreversible form of dementia. One of the top six causes of death in the United States, AD affects a range of processes related to memory, cognition, personality, and emotions, causing people to lose their ability to communicate or even carry out basic tasks. However, decades of intensive research have yet to produce successful preventive or curative Alzheimer’s medications, although current treatments can still significantly ease behavioral symptoms [1]. All five FDA-approved prescription drugs inhibit two different proteins: the N-methyl-D-aspartate receptor (NMDAR) and the acetylcholinesterase enzyme (AChE) [2]. Researchers have not firmly concluded why targeting these two proteins has succeeded more than other treatment plans, but both influence many cognitive pathways in the brain.

NMDAR is an excitatory ion channel that plays a role in gene expression and synaptic development but can also lead to excitotoxicity and even neuronal death, if overstimulated [3,4]. The GluN2B subtype of NMDAR (NR2B) has been of special interest, due to its involvement in neurological conditions like schizophrenia or ischemic stroke and its concentration in many cognitive centers in the brain, including the cerebral cortex and hippocampus [3,5,6]. NR1/NR2B residues identified as important for binding include Ala75, Thr110, Tyr109, Phe113, Leu135, Ser132, Ile82, Phe114, Ile111, Gln110, Ala107, Glu236, Pro177, and Phe176 (Fig. 1) [3,7].

CHEMISTRY RESEARCH

Figure 1. The two FDA-approved target proteins for Alzheimer’s treatment, NMDAR and AChE, with their respective secondary structures displayed on the left. On the right, in higher magnification, their Molegro-predicted cavities are highlighted in green and the locations of some of the NMDAR and AChE residues significant for binding are labeled in white [2,3,7,8].

AChE helps to break down the neurotransmitter acetylcholine, which plays a role in many learning and memory processes. Some researchers have proposed a cholinergic hypothesis that low levels of acetylcholine in the body contribute to the development of Alzheimer’s [2]. Since AChE reduces levels of acetylcholine by catalyzing its decomposition into acetate and choline, targeting of this enzyme may aid the body’s efforts to maintain healthy levels of acetylcholine, and thus reduce symptoms. AChE’s binding gorge, where acetylcholine typically binds, begins with a narrower rim - the peripheral anionic binding site (PAS) and widens at the base - the catalytic active site (CAS). The Broad Street Scientific | 2020-2021 | 35

residues at the PAS include Phe290, Trp279, Tyr121, Tyr 70, and Asp72 (Fig. 1) [2,8]. Recent research suggests that targeting the PAS may not only block the entrance of the gorge, therefore restricting acetylcholine’s ability to bind, but also reduce AChE’s proaggregatory interactions with Aβ proteins, which have been shown to play a dominant role in advancing the AD pathology [1,2].

1.2 – Multi-Target Merged Hybrid Design One FDA-approved medication—memantine—targets NMDAR, while three others—donepezil, galantamine, and rivastigmine—target AChE. The fifth FDA-approved medication targets both NMDAR and AChE by combining doses of memantine and donepezil to treat mild-to-moderate Alzheimer’s [2]. Unfortunately, polypharmacy increases the risk for drug-drug interactions and undesired side effects. To remedy this, drug pathways can be combined in a single molecule rather than divided among separate drugs [9]. However, any current hybrid compounds are the result of parent compounds connected by a carbon linker of varying length, and thus are very bulky [10,11,12]. High molecular weight (Mw) can lead to poor drug-likeness and, specifically, low blood-brain barrier penetration, thus impairing the passage of the drug from the bloodstream to the brain tissue.

An alternative is creating bifunctional drugs by using a merging strategy that allows for the similarity of two compounds to inform the hybrid design process. The goal is to have both parent compounds’ pharmacophores as equally present and preserved as possible in the final molecule.

Instead of adding a linker between two compounds or creating a single point of connection, any highly similar structural elements are combined into a common scaffold, onto which the essential structural motifs of the parent compounds are incorporated [9]. This allows the merged drug to interact with both intended targets while possessing a comparable molecular weight and drug-likeness to its parent compounds (Fig. 2) [9].

It’s important to note that while the targeted pathways and binding cavity residues for NMDAR and AChE are different, the high similarity of numerous other properties (including cavity size/shape, inhibitor structure similarities, and therapeutic intervention timing) of NMDAR and AChE inhibition make the multi-target merged hybrid design a feasible inhibitory option. The commonality of these properties indicates that the structures passed down from both the NMDAR and AChE parent compounds to the hybrid molecule will result in similar pharmacodynamic effects.

In addition, the variability in donepezil’s binding mechanisms (multiple “inward” and “outward” orientations [14]) suggests that there is also a margin of variability allowed in the ring structures on either end of the ligand molecule. Due to the high occurrence of tyrosine, tryptophan, and phenylalanine residues, multiple regions of the 36 | 2020-2021 | Broad Street Scientific

AChE binding gorge have very similar ligand interactions (such as π-π or cation-π interactions) [15], especially those that are separated by a distance equal to the distance between certain ring structures in AChE inhibitors. Moreover, multiple ring structures have been demonstrated to achieve π-stacking interactions with the same AChE gorge residue. For example, donepezil’s indanone moiety and its benzyl ring can interact with either W117 in the CAS or W317 in the PAS (PDB code 4EY7, chain A) [14]. Thus, the ring structures typically responsible for certain interactions with PAS residues may be easily replaced not only by ring structures from the other half of the molecule, but also by similar ring structures in NMDAR inhibitors.

Figure 2. Of the three above strategies for pharmacophore addition, the merged framework creates molecules with the least molecular weight. The merged framework incorporates highly similar structural elements from each parent compound into one common scaffold (green triangle). Structural variants from each parent compound are then incorporated as appropriate (red and blue).

However, certain risks arise from the incorporation of NMDAR inhibition into a multi-target drug system. Liu et al. warned that insufficient NMDAR selectivity could lead to hazardous inhibitory activity in their 2020 study of NR2B inhibitors. Specifically, the authors discussed targets for which NMDAR antagonists have had high inhibitory effects in the past; one of these proteins was the human Ether-à-go-go-Related Gene (hERG) channel [3].

The hERG channel is a voltage-gated, rapidly acting potassium ion channel (Kv), whose blockage can delay repolarization and cause Long QT Syndrome, or LQTS (where CHEMISTRY RESEARCH

the QT interval in the PQRST complex of an ECG wave is prolonged) [20]. This phenomenon can lead to serious cardiovascular conditions, such as polymorphic ventricular arrhythmia (torsades de pointes, TdP). All currently identified drugs causing TdP have been correlated with hERG channel blockage [20,26]. Yet, Recanatini et al. noted that 2-3% of all prescriptions may contain LQTS-inducing medications. Therefore, screening for potential hERG inhibition in silico is critical [26].

Based on these findings, this study aims to design a hybrid molecule that retains high affinity to NMDAR’s NR2B subtype and AChE’s PAS residues in addition to pharmacokinetic safety and effectiveness by merging current NMDAR and AChE inhibitors.

2. Materials and Methods 2.1 – Computational Drug Evaluation Software The computational software used in this study included ChemDraw, Molegro Virtual Docker (MVD), Molegro Data Modeller (MDM), and StarDrop.

ChemDraw was used to generate SMILES codes for the current NMDAR and AChE inhibitors as well as the hybrid molecules designed within this project. The SMILES codes were then converted into Protein Data Bank (PDB) files via an online SMILES translator.

These PDB files, and those for NMDAR (PDB code 5EWJ [3]) and AChE (PDB code 2CKM [10]), were imported into Molegro Virtual Docker (MVD), which simulates protein-ligand interactions. After published experimental data was used to select one of the NMDAR or AChE binding cavities detected by MVD, the Molegro Docking Wizard program generated a list of poses by running the docking algorithm 25 times, with a maximum of 1500 iterations per run. For each ligand, the five poses with the lowest PLANTS Scores (and therefore the highest binding affinities) were chosen for further examination in the three-dimensional workspace. MVD highlights the predicted interactions between a molecule and the protein residues around it. This feature was used not only to ensure that the conformations of the poses were similar to known binding modes [3,7,10,14,16,17] and that the ligands were specifically interacting with AChE’s PAS residues, but also to determine which parts of the molecule were most important in lowering binding energy. Given that all poses had reasonable arrangements within the cavity, the pose with the highest affinity for each ligand (according to the Molegro Rerank scores) was selected for analysis in Molegro Data Modeller.

Molegro Data Modeller (MDM) was used to select the inhibitors with the highest affinities for NMDAR and AChE. Although MVD is limited to pose affinity comparisons for a single ligand, MDM can compare pose affinities across multiple ligands using the Molegro binding affinity algorithm, which estimates binding energy. The MDM CHEMISTRY RESEARCH

binding affinity scores were used to roughly estimate which inhibitors would have the best binding affinities to both proteins.

All ligands were also run through StarDrop, which provides numerical predictions for a myriad of pharmacokinetic interactions. Molecular weight (daltons) and bloodbrain barrier penetration (log([brain]:[blood])) were studied to assess the success of the merging strategy while lipophilicity (logP) and hERG inhibition (pIC50) were analyzed to ensure minimal pharmacokinetic risk. StarDrop also provides scoring profiles to assess how well molecules might be absorbed (Lipinski’s Rule of 5), especially when used as oral drugs targeting the central nervous system.

The Oral CNS Score is based on values ranging from predicted lipophilicity to cytochrome affinity. These profiles were used to determine which of the hybrid molecules designed in this study had the greatest potential for pharmacokinetic stability and safety, as well as which of the derivative compounds of that hybrid had the lowest predicted hERG inhibition (during Phase Two and Phase Three of the study; see section 2.2 below).

2.2 – Phases of Molecule Design Process Phase One consisted of selection of known or proposed NMDAR and AChE inhibitors from existing drugs and published drug candidates. Phase One NMDAR candidates were dubbed N[#] by this study in arbitrary order: N1, N2, N3, etc. (with the exception of ifenprodil and traxoprodil, which retained their original names), and Phase One AChE inhibitors were identified by their published names [2,3,5,10,13,17,18,19,20,21,22,23,24,25]. These collected molecules were then computationally analyzed for binding affinity and overall pharmacokinetic safety and effectiveness through MVD, MDM, and StarDrop software. Six NMDAR inhibitors and six AChE inhibitors were selected to advance to Phase Two. For Phase Two, the highest affinity molecules for NMDAR and AChE were integrated into hybrids using the merged framework (Fig. 2). Hybrids named H1-H6 were derived from the top NMDAR inhibitors (N8, N10, N2, N3, N5, and N7, respectively) merged with the highest affinity AChE inhibitor AP2238. Hybrids H7-H11 were derived from the top AChE inhibitors (AP2238, Memoquin, Bis(7)-tacrine, Propidium, HuprineX, and Donepezil, respectively) merged with the highest affinity NMDAR inhibitor N8. One high affinity hybrid was selected to advance to Phase Three.

Due to hybrid H1’s risk for hERG inhibition (according to previous literature [3] and Stardrop predictions of hERG pIC50 = 5.434), Phase Three was focused on researching the structural risk factors for hERG blockage to direct the H1 modification process and design derivative compounds D1-D10.

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3. Results & Discussion 3.1 – Phase One: Evaluation of Current Inhibitors Twelve high affinity NMDAR inhibitors and thirteen high affinity AChE inhibitors were studied from previous literature. Six NMDAR inhibitors and six AChE inhibitors were selected to advance to Phase Two due to their high predicted affinities, as calculated by MDM software (Table 1).

Table 1. Structures and MDM binding affinity scores of the top six highest affinity inhibitors for both NMDAR and AChE, in order of their predicted affinity. Higher magnitude binding affinity scores indicate higher predicted binding affinity. Units are arbitrary.

Figure 3. H1 scheme of phamacophore merging strategy used. NMDAR inhibitor N8 and AChE inhibitor AP2238 were merged to create the hybrid H1.

3.2 – Phase Two: Hybrid Design Process The six highest affinity molecules for NMDAR were integrated into the single highest affinity molecule for AChE, using the merged framework (Fig. 3), and vice versa.

During this hybrid design process, the primary objective was the preservation of as many of the substituents and functional groups of each pair of NMDAR and AChE inhibitors as possible; most of these substituents or functional groups had been declared important for increasing or decreasing certain pharmacodynamic or pharmacokinetic effects by their respective studies. For example, Claiborne et al. studied the effects of substituents on the phenyl rings of a cyclic benzamidine series of NR2B antagonists.

Their results indicated that the trifluoromethoxy group was significant for increasing affinity [6]. Thus, during the merging of N8 and AP2238 to create H1, the trifluoromethoxy group from N8 was added to the common scaffold despite the lack of any fluorine group in the other parent compound, AP2238 (Fig. 3).

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In most cases, however, some structural elements of a parent molecule were found to be more essential than others based on the published binding affinity or binding mechanism data available; these elements were prioritized when adding structural variants from the parent compounds to the hybrid scaffold. For example, the phenyl ring in AP2238 supposedly increases AChE affinity through OH-π interactions with Tyr124 as well as π-π interactions with Tyr341 (these chain IDs are from a different AChE model; these residues are the equivalents of PAS residue Tyr121 and residue Tyr334 in this study’s protein model; see Fig. 1 for more detailed residue locations) [2,17]. However, integrating this AP2238 phenyl ring linker with the phenyl group connected to the N8 benzamidine core would have resulted in a 5 ring structure, which would certainly exceed the optimal length of 17.5Å for NMDAR inhibitors as well as the desired molecular weight (<< 500Da) for preserving blood-brain barrier penetration [9,13]. Consequently, the AP2238 phenyl ring linker was eliminated during the integration of AP2238 and N8 to create H1, in the hope that the strong π-interactions of the benzamidine or coumarin ring structures would compensate for the lost OH-π and π-π interactions of the AP2238 phenyl ring linker, or even restore them. This prioritization of affinity-increasing structures was implemented in the design of all eleven hybrid molecules (H1-H11).

After compounds H1-H11 were designed, their computationally predicted pharmacodynamics and pharmacokinetics were assessed to determine which hybrid would be advanced to Phase Three for pharmacokinetic risk reduction. High binding affinity was the main priority during the Phase Two selection process, but overall pharmacokinetic CHEMISTRY RESEARCH

profiles were examined in tandem to increase the chance that the selected compound would yield high affinity and pharmacokinetically safe derivative compounds during the subsequent optimization processes. Thus, in Figure 4 where StarDrop Oral CNS scores were plotted against Molegro NMDAR and AChE binding affinity scores, the most upper right data points indicate the most ideal compounds, with high affinity scores prioritized over high Oral CNS scores.

Novel compound H1 displayed high predicted binding affinity: the magnitude of H1’s AChE affinity score was the second highest out of all 36 compounds screened (in Phase One and Phase Two combined) and almost double that of donepezil, the most widely used AChE drug (-26.5518). H1 also displayed great potential for safe pharmacokinetics according to StarDrop Oral CNS scoring, which rated H1’s capability as an oral drug at least 1.8x higher than any of the other top five highest affinity compounds (Table 2). H1 was selected to advance to Phase Three for further pharmacokinetic optimization.

Figure 4. Graphed above are the StarDrop Oral CNS Scores compared to the computational NMDAR (top) and AChE (bottom) binding affinities for the model and candidate molecules. The upper right data points are most ideal. H1 was selected for having the desired compound properties.

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Table 2. MDM binding affinity scores and Oral CNS scores for the five highest affinity compounds for NMDAR and AChE out of all 36 compounds screened in Phases One and Two. Higher MDM affinity scores indicate higher predicted affinity. StarDrop Oral CNS Scores range from 0-1, with higher scores indicating safer pharmacokinetic properties.

3.3 – Phase Three: Pharmacokinetic Risk Reduction As predicted by the Liu et al. study on NR2B inhibitors, H1 displayed moderately high risk for hERG inhibition (pIC50 = 5.434, higher than its parent NMDAR compound N8, whose pIC50 = 5.155) [3]. Thus, Phase Three of this study centered on reducing the predicted hERG inhibition of compound H1.

The hERG channel has six transmembrane helices and some unique structural elements that may explain the high occurrence of adverse drug interactions. In particular, the hERG channel does not contain two proline residues that limit the cavity volume of other Kv channels. The absence of these two residues may account for the channel’s more frequent blockage by larger, “trapped” drug molecules [26].

There are also many other conjectures as to what increases risk of hERG inhibition.

Kawai et al. suggested that lipophilicity and hERG inhibition rates have a positive correlation in their 2007 study of traxoprodil derivatives [20]. Traxoprodil is a “cornerstone” NMDAR inhibitor whose base structure has been utilized in numerous research studies to derive new generations of NMDAR inhibitors, many of which are in this study. Due to this structural connection, traxoprodil’s pharmacokinetic profile is critical for understanding the potential pharmacokinetic behaviors of many NMDAR inhibitors in this study, including hybrid compound H1.

In the Kawai et al. study, compounds with sufficient in vivo activity as well as reduced hERG activity had clogP (calculated lipophilicity partition coefficient) values ranging from 1.5-2.5. If they had clogP values lower than 1.5, compounds ran the risk of low blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB) penetration. For compounds with higher clogP values, experimental data showed hERG binding IC50 < 1.5 µM. Through their traxoprodil modification efforts, Kawai et al. demonstrated that lower hERG inhibitory activity may be found in compounds with fewer aromatic rings, more oxygen and fluorine substitutions, and fewer methyl groups.

Recanatini et al. identified a residue of significance in hERG (open-state) binding that explains the Kawai et al.

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observation. The presence of the residue Phe656 in the hERG channel allows for strong hydrophobic interactions, which is why clogP can be a useful indicator of hERG inhibition [26]. Another hERG protein residue, Tyr652, can also prove useful to the molecule revision process because of its π-cation interactions with trapped drug molecules; this interaction helps explain the hypothesized higher hERG inhibition rates of N-protonated compounds [26].

Recanatini et al. also presented some specific pharmacophores (all containing ionizable nitrogens) that seemed to increase risk for hERG blockage, based on various data studies using three-dimensional quantitative structure-activity relationship (3D-QSAR) models. One pharmacophoric scheme depicted a single ionizable nitrogen connected to two aromatic fragments on one side, creating a hydrophobic “handle,” and one aromatic fragment on the opposite side, creating a hydrophobic “tail” (Fig. 5, top: “N” and “Arom”). This “drain-plug” hypothesis proposes a binding mechanism wherein the “tail” of the molecule enters the channel cavity - with the protonatable nitrogen helping to orient the molecule through π-cation interactions with Tyr652 - and the “handle” effectively plugs the entrance [26]. Recanatini et al. also specified another two studies that had studied pharmacophores with an ionizable feature linked to three other aromatic groups. In one, however, the higher hERG blockage risk was correlated with the presence of a para-substituted phenyl ring that was one of the three aromatic fragments linked to the basic nitrogen; in the other, the aromatic moieties were linked to the protonatable nitrogen by distances ranging from 4.6-9.1Å and separated from each other by distances ranging from 10.314.7Å (Fig. 5, bottom right: “N” and “Arom”) [26]. A fourth study on pharmacophores with high risk for QT prolongation presented an ionizable nitrogen linked to four hydrophobic groups from a distance of 5.2-7.5Å. These four hydrophobic centers were themselves separated by a distance ranging from 3.1-8.2Å (Fig. 5, bottom left: “N” and “h”).

minishing lipophilic character, and reducing structural similarities to certain high hERG risk pharmacophores (as presented by the Recanatini et al. study) [20,26]. However, many other factors were taken into consideration during this molecule optimization process. Past research studies focusing on the derivative generation of compounds N8 and AP2238 (H1’s parent compounds), such as the AP2469 catechol replacement of AP2238’s original 6,7-methoxy-2H-2-chromenone nucleus, were used to guide H1 transformations [5,6,27,28]. It was also important to maintain the previous pharmacodynamic and pharmacokinetic property goals from Phase One and Phase Two of this study: binding affinity and blood-brain barrier penetration. Thus, transformations were applied to H1 based on these considerations, and the ten H1 derivatives designed were evaluated using four properties of interest: NMDAR binding affinity, AChE binding affinity, hERG pIC50, and blood-brain barrier penetration. The property values for each derivative were graphed on a radar plot for an approximate visual estimation of how well each derivative performed in each category. The ideal compound would perform well in all categories; on the radar plot, the area of a derivative’s plotted data values would be large and square-like. D5 was selected as the most ideal compound due to its fulfillment of these values (Fig. 6: D5, bolded blue line).

Figure 5. Risk factor assessments for hERG inhibition. Recanatini et al. studied a large set of LQTS-inducing molecules and specified that a nitrogen (or an ionizable nitrogen) linked to four hydrophobes (h) or three aromatic rings (Arom) in certain arrangements led to increased risk of hERG inhibition [26].

Figure 6. The derivatives with the greatest area and the most equal score balance for all four attributes are most ideal. D5 (bolded blue line) was selected as the most ideal compound. (A) MDM NMDAR Binding Affinity Scores. Units are arbitrary. (B) MDM AChE Binding Affinity Scores. Units are arbitrary. (C) StarDrop hERG pIC50 scores. (D) StarDrop blood-brain barrier penetration scores predict log([brain]:[blood]).

The design of H1 derivatives D1-D10 was mainly directed by the goals of decreasing molecular weight, di40 | 2020-2021 | Broad Street Scientific

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The NMDAR and AChE binding affinity, hERG pIC50, and BBB penetration scores for D5 are listed in Table 3, alongside those for donepezil, the most widely used AChEI drug for Alzheimer’s; N8, the highest affinity NMDAR inhibitor studied in this project; AP2238, the highest affinity AChE inhibitor studied in this project; and H1, the hybrid designed in Phase Two that became the parent compound of D5. Despite moderate BBB penetration scores, D5 proved to be the optimal inhibitor in terms of low predicted hERG inhibition and high estimated affinity for both NMDAR and AChE. Table 3. Binding affinities and pharmacokinetic properties of the most widely used FDA-approved AD drug, donepezil, as well as the highest affinity inhibitors for NMDAR and AChE, N8 and AP2238 (Phase One), and the inhibitors designed in this study, hybrid H1 (Phase Two) and its derivative D5 (Phase Three).

pounds like D5 have high potential to set a safer and more effective standard of symptomatic care not only for Alzheimer’s, but also for other diseases with similarly numerous subpathologies.

5. Acknowledgements I would like to thank Dr. Tim Anglin and Dr. Michael Bruno for their critical guidance throughout this project, as well as Mr. Bob Gotwals for his specialized assistance with computational work and my Research in Chemistry Program peers for their support. I’d also like to acknowledge the NCSSM Science Department, the NCSSM Summer Research and Innovation Program, and the NCSSM Foundation for making this experience possible.

6. References [1] Alzheimer’s Disease Fact Sheet. (n.d.). National Institute on Aging. Retrieved November 12, 2020, from http:// www.nia.nih.gov/health/alzheimers-disease-fact-sheet [2] Agatonovic-Kustrin, S., Kettle, C., & Morton, D. W. (2018). A molecular approach in drug development for Alzheimer’s disease. Biomedicine & Pharmacotherapy, 106, 553–565. https://doi.org/10.1016/j.biopha.2018.06.147

4. Conclusions & Future Directions This project has aimed to improve a current FDA-approved AD treatment—a combination therapy comprising one NMDAR-targeting and one AChE-targeting drug— by utilizing a merged framework to design a multi-target directed ligand (MTDL). Analysis of the computational binding data for the ligands designed in this study affirms the capacity of the merging strategy to generate hybrid compounds with comparable molecular weight to their parent compounds as well as high affinity to the parents’ inhibitory targets. Novel compound D5 displays high predicted affinity for both NMDAR and AChE, as well as low hERG blockage risks and adequate BBB penetration. D5 is a derivative of the H1 hybrid drug, whose design resulted from the merging of parent compounds N8 and AP2238.

Future work could be focused on optimizing the bloodbrain barrier penetration score and other pharmacokinetic property scores of compound D5. A synthesis scheme for the dual inhibitor with optimal predicted affinity and pharmacokinetics should be developed. The NMDAR and AChE IC50 values for the synthesized compound can then be experimentally determined in vitro. Multi-target comCHEMISTRY RESEARCH

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[13] Tamiz, A. P., Whittemore, E. R., Zhou, Z.-L., Huang, J.-C., Drewe, J. A., Chen, J.-C., Cai, S.-X., Weber, E., Woodward, R. M., & Keana, J. F. W. (1998). Structure− Activity Relationships for a Series of Bis(phenylalkyl) amines: Potent Subtype-Selective Inhibitors of N-Methyl-d-aspartate Receptors. Journal of Medicinal Chemistry, 41(18), 3499–3506. https://doi.org/10.1021/jm980235+ [14] Silva, M. A., Kiametis, A. S., & Treptow, W. (n.d.).

Donepezil Inhibits Acetylcholinesterase via Multiple Binding Modes at Room Temperature. Journal of Chemical Information and Modeling. Retrieved November 12, 2020, from https://pubs.acs.org/doi/10.1021/acs. jcim.9b01073?ref=pdf [15] Dougherty, D. A. (1996). Cation-pi interactions in chemistry and biology: A new view of benzene, Phe, Tyr, and Trp. Science (New York, N.Y.), 271(5246), 163–168. https://doi.org/10.1126/science.271.5246.163 [16] Atanasova, M., Stavrakov, G., Philipova, I., Zheleva, D., Yordanov, N., & Doytchinova, I. (2015). Galantamine derivatives with indole moiety: Docking, design, synthesis and acetylcholinesterase inhibitory activity. Bioorganic & Medicinal Chemistry, 23(17), 5382–5389. https://doi. org/10.1016/j.bmc.2015.07.058 [17] Piazzi, L., Rampa, A., Bisi, A., Gobbi, S., Belluti, F., Cavalli, A., Bartolini, M., Andrisano, V., Valenti, P., and Recanatini, M. (2003, May 13). 3-(4-{[Benzyl(methyl)amino]methyl}phenyl)-6,7-dimethoxy-2H-2chromenone (AP2238) Inhibits Both Acetylcholinesterase and Acetylcholinesterase-Induced β-Amyloid Aggregation: A Dual Function Lead for Alzheimer’s Disease Therapy (world) [Rapid-communication]. American Chemical Society. https://doi.org/10.1021/jm0340602 [18] Rosini, M., Simoni, E., Bartolini, M., Tarozzi, A., Matera, R., Milelli, A., Hrelia, P., Andrisano, V., Bolognesi, M. L., & Melchiorre, C. (2011). Exploiting the lipoic acid structure in the search for novel multitarget ligands against Alzheimer’s disease. European Journal of Medicinal Chemistry, 46(11), 5435–5442. https://doi.org/10.1016/j. ejmech.2011.09.001 [19] Saeed, A., Mahesar, P. A., Zaib, S., Khan, M. S., Matin, A., Shahid, M., & Iqbal, J. (2014). Synthesis, cytotoxicity and molecular modelling studies of new phenylcinnamide derivatives as potent inhibitors of cholinesterases. European Journal of Medicinal Chemistry, 78, 43–53. https://doi. org/10.1016/j.ejmech.2014.03.015

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[20] Kawai, M., Ando, K., Matsumoto, Y., Sakurada, I., Hirota, M., Nakamura, H., Ohta, A., Sudo, M., Hattori, K., Takashima, T., Hizue, M., Watanabe, S., Fujita, I., Mizutani, M., & Kawamura, M. (2007). Discovery of (−)-6-[2-[4-(3-fluorophenyl)-4-hydroxy-1-piperidinyl]1-hydroxyethyl]-3,4-dihydro-2(1H)-quinolinone—A potent NR2B-selective N-methyl d-aspartate (NMDA) antagonist for the treatment of pain. Bioorganic & Medicinal Chemistry Letters, 17(20), 5558–5562. https://doi. org/10.1016/j.bmcl.2007.08.014 [21] Kawai, M., Nakamura, H., Sakurada, I., Shimokawa, H., Tanaka, H., Matsumizu, M., Ando, K., Hattori, K., Ohta, A., Nukui, S., Omura, A., & Kawamura, M. (2007). Discovery of novel and orally active NR2B-selective N-methyl-d-aspartate (NMDA) antagonists, pyridinol derivatives with reduced HERG binding affinity. Bioorganic & Medicinal Chemistry Letters, 17(20), 5533–5536. https://doi.org/10.1016/j.bmcl.2007.08.039 [22] Kawai, M., Sakurada, I., Morita, A., Iwamuro, Y., Ando, K., Omura, H., Sakakibara, S., Masuda, T., Koike, H., Honma, T., Hattori, K., Takashima, T., Mizuno, K., Mizutani, M., & Kawamura, M. (2007). Structure–activity relationship study of novel NR2B-selective antagonists with arylamides to avoid reactive metabolites formation.

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[26] Recanatini, M., Poluzzi, E., Masetti, M., Cavalli, A., & De Ponti, F. (2005). QT prolongation through hERG K(+) channel blockade: Current knowledge and strategies for the early prediction during drug development.

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[23] Wright, J. L., Gregory, T. F., Kesten, S. R., Boxer, P. A., Serpa, K. A., Meltzer, L. T., Wise, L. D., Espitia, S. A., Konkoy, C. S., Whittemore, E. R., & Woodward, R. M. (2000). Subtype-selective N-methyl-D-aspartate receptor antagonists: Synthesis and biological evaluation of 1-(heteroarylalkynyl)-4-benzylpiperidines. Journal of Medicinal Chemistry, 43(18), 3408–3419. https://doi.org/10.1021/ jm000023o [24] Borza, I., Kolok, S., Gere, A., Agai-Csongor, E., Agai, B., Tárkányi, G., Horváth, C., Barta-Szalai, G., Bozó, E., Kiss, C., Bielik, A., Nagy, J., Farkas, S., & Domány, G. (2003). Indole-2-carboxamides as novel NR2B selective NMDA receptor antagonists. Bioorganic & Medicinal Chemistry Letters, 13(21), 3859–3861. https://doi. org/10.1016/s0960-894x(03)00708-x [25] Borza, I., Kolok, S., Galgóczy, K., Gere, A., Horváth, C., Farkas, S., Greiner, I., & Domány, G. (2007).

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COMPATIBILITY OF MELAMINE FOAMS HYDROPHOBICALLY MODIFIED FOR CRUDE OIL ABSORPTION WITH A JANUS-STYLE AUTONOMOUS PROPULSION MECHANISM Nrithya Renganathan Abstract The need for crude oil remediation alternatives that are low cost, environmentally friendly, efficient, and easily recoverable is increasing by the day, as oil spills have catastrophic effects on both water quality and ocean wildlife. This paper tests the compatibility of surface-modified hydrophobic foams with a Janus-style propulsion mechanism as a means of improving their efficiency and recoverability as a crude oil absorption method. Melamine, the base foam chosen for this preparation, has a high absorptive capacity and structure that is able to undergo hydrophobic surface modification through a facile dip-coating process inducing the self-assembly of tannic acid and n-octadecylamine. The Janus-style, or asymmetric, loading of the foams with a camphor/stearic acid fuel propels the foam through its gradual discharge due to its surface tension gradient with water, as described by the Marangoni effect. Using video analysis and measurements of the degree and speed of oil uptake, it is seen that the attractive interactions of the loaded melamine foam induce movement towards other hydrophobic materials, effectively creating a smart device for on-the-fly oil absorption. This method of preparation has the potential to aid viability of the foams in practical remediation applications, including cargo transportation, quality sensing, and water treatment.

1. Introduction As the demand for energy has risen, the production and consumption of oil have been increasing drastically and are projected to grow by 11.9 percent between the years of 2018 and 2024 [1]. Over the past few decades, stations including drilling rigs, oil tankers, refineries, pipelines, and transport equipment have spilled enormous amounts of oil into our water systems [2], bringing long-term ecological disaster for marine wildlife [3] and detrimentally affecting the humans who depend on these ecosystems to survive [4]. From 2009 to 2019, there were over 180,000 tons of oil spilled into our water systems [5]. Cleanup methods currently being used to mitigate these oil spills include skimmers, dispersants and in situ burning, which are either inefficient, not selective, time consuming, introduce toxic fumes or secondary pollutants, or have high application expenses [6].

As the need for effective pollution remediation strategies grows every day, researchers in this field have been looking for viable alternatives to these methods. Technologies used as environmental remediation materials to fix this problem must be easily synthesized, efficient, and cost effective so that they are able to be implemented in treatment facilities on a large scale [6]. Surface modification of synthetic foams made of materials such as polyurethane [7, 8, 9, 10] or melamine [11, 12, 13] have gathered a lot of interest, particularly in the past 5 years, in the oil remediation field for their absorption potential, oil selectivity, and simple tunability processes [6]. Melamine sponges in 44 | 2020-2021 | Broad Street Scientific

particular are excellent candidates for modification. Their high levels of porosity allow them to absorb many times their mass, and their three-dimensional structures are able to undergo surface modification through a simple dip-coating process, as shown in Figure 1 [11, 12, 13].

Figure 1. Schematic describing dip-coating foam modification, which is widely used for its simplicity as a material tunability process.

These foams have a low fabrication cost, are flexible, and can be modified for very high levels of oil selectivity.

They are characterized by water contact angles over 150 degrees, which qualify them to be labeled as superhydrophobic [14]. However, absorptive materials as a whole have been difficult to implement in practical oil remediation applications, as they may include components that are toxic to the environment and are difficult to recovere from the water [6, 14, 11]. To this end, Zhang et al. have recently reported an environmentally friendly facile one-pot dip-coating process to fabricate a superhydrophoCHEMISTRY RESEARCH

bic melamine sponge with a water contact angle of 152°, inspired by hydrophobic materials found in nature [12].

This process used a mixture of tannic acid and n-octadecylamine that is self-assembled onto the melamine foams to give a rough surface texture, similar to that of the lotus leaf, that is able to repel water and selectively absorb oil.

Additionally, there have been reports of fabricated foams that exhibit magnetic character [15,16]. In the presence of a magnetic field, these foams can be guided towards areas with oil and later be recovered with greater ease. However, the infrastructure required to manipulate these foams can be inefficient on a wide range and increases operation costs [17].

The recoverability of these absorptive foams still remains an issue that must be dealt with before they can be implemented on a large scale. Taking inspiration from recent nanoparticle research in the field of Janus micromotors, Li et al. have reported a novel method of foam modification that attempts to solve this problem by fabricating a stearic acid-modified PVA foam and loading one end of it with a camphor-based fuel source [17]. Camphor, while toxic to humans in very large doses, is a substance that can be readily consumed by bacteria and does not accumulate in the environment [18, 19]. As these foam motors rest on the water’s surface, they gradually release the fuel by employing the Marangoni effect, a phenomenon caused by the difference in surface tensions between camphor and water. This modification leads to the foams being able to propel themselves to collect paraffin oil from water without external stimuli, as shown in Figure 2, and also aggregate together in water to form dimers and trimers for easy removal (Fig. 2) [17].

Figure 2. Autonomous propulsion of the foam described by Li et al. [17] that takes advantage of the Marangoni effect to absorb oil without external guidance.

However, the stearic acid-modified PVA base for this motor does not qualify as superhydrophobic, as it has a water contact angle less than 150̊ (138̊) and had a lower absorption capacity when compared to reported melamine foams with the same paraffin liquid model oil [12].

Our work tests the compatibility of this camphor-based fuel source as a Janus-style propulsion mechanism with the highly selective and porous melamine base foam described by Zhang et al. [12]. The resulting foams will be characterized to verify if they have higher levels of effiCHEMISTRY RESEARCH

ciency for on-the-fly absorption when compared to their non-autonomous counterparts while exhibiting the same aggregation abilities for simple foam removal described by Li et al. [17]. If this camphor-based fuel can be applied to melamine foams, effectively creating smart devices for efficient oil absorption without external guidance, it will have great promise to make implementing these foams more viable in practical remediation efforts.

2. Experimental Design 2.1 – Materials Melamine foams and paraffin oil were obtained from the local grocery store. N-octadecylamine, tannic acid and ethanol, to be used in the surface modification dip coating solution, were bought from Sigma-Aldrich. Camphor and stearic acid, to be used as the fuel source, were also bought from Sigma-Aldrich.

2.2 – Preparation of Tannic-Acid/Octadecylamine Modified Foams The unmodified melamine sponges were first cut into blocks measuring 5 x 5 x 10 mm or 3 x 5 x 12 mm and massed. They were then cleaned with ethanol and distilled water under ultrasonication and left to dry in an oven at 80 degrees Celsius (176̊ F). To prepare the solution in which these foams were dip coated, 0.4 grams of tannic acid and 0.4 grams of octadecylamine were mixed with 50 milliliters of ethanol. This mixture was mixed thoroughly with 10 pipette droplets of hydrochloric acid of pH 2. The dried melamine foams were put in this solution and stirred for 12 hours. After the 12 hours passed, the sponges were cleaned and washed with distilled water. Then, they were dried in an oven at 80̊ C. Our synthesis of hydrophobically modified foams follows the method described by Zhang et al. and is summarized in Figure 3 [12].

Figure 3. Surface modification of porous foams via dip-coating method 2.3 – Characterization of Modified Foams The foams were first placed on a water bath to be tested for flotation and dipped in the water to observe their non-wetting behavior. To record selective uptake of the foams, the mass increases of the modified foam after being immersed in oil and water were compared to those of an unmodified foam. If a sponge showed hydrophobic character and oil selectivity, the water contact angle (WCA) Broad Street Scientific | 2020-2021 | 45

of the sponge was tested to give a measure of its hydrophobicity. A digital camera was used to record the interactions of water droplets in three places across the foam at ambient temperature, allowing the water contact angle to be calculated using image analysis software. Water contact angles between 145̊ and 155̊ were sought to verify the self-assembly of tannic acid and n-octadecylamine onto the melamine foams (Fig. 4).

2.6 – Characterization of Autonomous Hydrophobic Foam After the asymmetric preparation process, the foams were then tested for their flotation and movement abilities. They were first placed in a rectangular container of water with dimensions 20 cm by 30 cm and a depth of 3 cm without any external stimuli. Then, they were placed in the same water bath with an additional autonomous foam, and finally with a thin coating of liquid paraffin sprayed on the water’s surface as a model oil spill. Data for these tests were recorded by taking videos of the foams’ movements and applying the Vernier Logger Pro video analysis tool to them. In the case of the paraffin oil coating on the water’s surface, the mass uptake was compared to that of an immobile hydrophobic foam placed under the same conditions for 180 seconds.

3. Results and Discussion

Figure 4. Measures of water contact angles and their relationship to hydrophobicity qualifiers.

Pink arrow shows expected WCA (152 ̊ ) of modified melamine foams, as described by Zhang et al. [12].

2.4 – Incorporation of Camphor Using Ethanol Solvent To prepare the solution in which the modified foams were dipped, camphor was dissolved into pure ethanol at a concentration of 500 mg/mL. This mixture was loaded into one end of the hydrophobic foam at 80̊ C and was left to dry (Fig. 5). After drying, the mass of the foam was recorded to verify camphor uptake.

3.1 – Characterization of Melamine Surface-Modified Foam After the first step of the melamine modification process, the foams were placed on the surface of a water bath and compared to an unmodified melamine foam. As seen in Figure 6a, the modified foam was able to rest on the water’s surface, while the unmodified foam sank to the bottom of the water bath. This hydrophobicity was further characterized by immersing the foam in water and observing the bubble formation around its surface, which takes place in these hydrophobic foams due to the Cassie-Baxter nonwetting effect (Fig. 6b) [12]. The hydrophobic character of these foams was then quantitatively measured by finding their water contact angle (WCA), in order to verify that the tannic acid and n-octadecylamine had properly self-assembled onto them. Using image analysis on photos such as the one in Figure 6c, the water contact angle of these foams had an average value of 145̊, or near superhydrophobic.

Figure 5. Partial dip-coating process to prepare the autonomous hydrophobic foam (S.A. = stearic acid).

2.5 – Alternative Incorporation of Camphor Using Stearic Acid Mixture To prepare the second version of the solution for dipping modified foams, the camphor was dissolved in stearic acid using a mass ratio of 0.75 g/g (camphor/S.A.). This mixture was loaded onto one end of the hydrophobic foam at 80̊ C and left to cool (Fig. 5). After cooling, the mass of the foam motor was recorded and compared to the modified foam’s mass to verify the camphor and stearic acid mixture uptake.

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Figure 6a. Comparison of modified and unmodified foams in water bath. 6b. Non-wetting behavior of hydrophobically modified foam immersed in water bath. 6c. Water contact angle image of modified foam.

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3.2 – Camphor/Ethanol Modified Foams The hydrophobic foams were partially submerged into a solution of ethanol and camphor (Fig. 7a). After the foams were left to dry and the ethanol was able to evaporate, it was verified that the foams were able to incorporate the precipitated camphor by recording their average masses before and after this preparation (Fig. 7b). It was found that the foams, which originally had an average mass of 0.016 grams, increased in mass to an average of 0.0978 grams, seeing a mass increase of over 700 percent. This meant that the camphor was indeed able to be absorbed into the melamine framework.

3.3 – Camphor/Stearic Acid Modified Foams The foams used in the second camphor incorporation process were partially submerged into a mixture of camphor and stearic acid at 80̊ C and left to cool into a solid form. As mentioned in Section 3.2, these foams had thinner and elongated dimensions of 3 x 5 x 12 mm in order to increase the contact area between the foams and water’s surface and preserve surface tension. To verify that this mixture was able to be taken up by the foams, the average masses before and after this second method of preparation were recorded (Fig. 9). It was found that the foams increased in mass from an average of 0.004 grams to 0.056 grams, meaning that the camphor/stearic acid mixture was able to fill the pores of the hydrophobic melamine foam framework. This modification process led to the creation of foams that were able to display movement abilities, as described in Section 3.5.

Figure 7a. Visual of partial dip coating in the camphor/ethanol solution. 7b. Average increase in foam mass after the partial dip coating and ethanol solvent evaporation process recorded.

The incorporation of pure camphor led to a shift in the center of mass toward the camphor-modified end, as well as a decrease in hydrophobic character as seen by the lowering of water contact angle in (Fig. 8b). This decrease was possibly due to the ethanol solvent washing away the tannic acid/n-octadecylamine self-assembled structure.

These two changes led to an uneven resting on the water’s surface, breaking the surface tension and preventing the foam’s Marangoni effect-induced motion. To counteract this breaking of surface tension, foams of thinner and elongated dimensions were used when preparing the camphor/stearic acid mixture modified foams to increase the ratio of surface contact area to foam volume.

Figure 8a. Uneven resting of camphor/ethanol modified hydrophobic foams on water’s surface. 8b. Decrease in water contact angle on the camphor-prepared end of the hydrophobic foam.

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Figure 9. Uptake of the camphor/stearic acid mixture after the partial dip coating and cooling process recorded by average increase in foam mass.

3.4 – Foam Selectivity To measure selectivity of the unmodified, immobile hydrophobic, and autonomous hydrophobic foams, their dry masses and masses after being immersed in distilled water and paraffin oil were recorded (Fig. 10). Qualitatively, it is seen that the unmodified foam absorbed both paraffin oil and water and was therefore not selective in its absorption abilities. Meanwhile, both immobile and autonomous hydrophobic foams selectively absorbed oil and not water, demonstrating the retention of high oil selectivity after the camphor/stearic acid mixture foam preparation. The autonomous foam’s incorporation of camphor into its framework lowers its absorptive capacity, which is a property that should be optimized in further study.

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stance, with Figure 12b-d showing the distance between the foams at 0, 11, and 14 seconds, respectively.

Figure 10. Selectivity of unmodified, hydrophobic, and autonomous hydrophobic foams as shown by their uptake of water and paraffin oil 3.5 – Camphor/Stearic Acid Foam Movement Abilities As mentioned in Section 3.3, the thinner and elongated foams prepared with a mixture of camphor and stearic acid resulted in hydrophobic foams that displayed movement capabilities. To characterize this motion, foams were first placed into a water bath with no additional external stimuli and video recorded. It was found that these foams would move in a circular pattern. Figure 11a shows the path of an autonomous foam over a 10 second time interval. These foams exhibited movement lifetimes of up to 25 minutes.

Figure 11b graphically depicts the velocity of these undisturbed foams over a 600 second period. The velocity seems to decrease gradually, with an exponential-like decrease, demonstrating the gradual release of camphor over this time interval as a means of propulsion.

Figure 11a. Autonomous foam motion over a 10 second time interval without external stimuli. 11b. Velocity of autonomous foam over a 10-minute period (Exponential fit of v = 7.88e-.002 x t) Following these tests, the autonomous foam’s aggregation abilities were then measured. Two autonomous foams were placed at a distance of 7 centimeters from each other and their movement was analyzed. While a pair of immobile hydrophobic foams would not combine together at this distance, the autonomous foams displayed the ability to move towards each other and form a dimer. Figure 12a shows the movement paths from one such aggregation in48 | 2020-2021 | Broad Street Scientific

Figure 12a. Aggregation path of autonomous foams placed 7 centimeters apart. 12b. Positions at t = 0 s. 12c.

Positions at t = 11 s. 12d. Positions at t = 14 s (Scale bars = 1 cm). Red arrows provided to show the direction of released camphor from the foam.

Finally, preliminary tests were done to observe the autonomous foam’s ability to actively collect oil. As shown in Figure 13a, paraffin oil was sprayed across the surface of the water bath, and an autonomous and immobile foam was placed in these conditions for 180 seconds. The mass of each foam was recorded before and after the testing period. Since the immobile foams had different dimensions when compared to the autonomous foams, these measurements were standardized in order to compare them (Fig. 13b). These tests show that the autonomous foams were able to absorb oil to a greater degree, but this property needs to be further analyzed in more standardized conditions to be fully understood.

Figure 13a. Immobile and autonomous foam in paraffin-coated water bath (white arrow). Red arrows provided to show the direction of released camphor from the foam. 13b. Scaled recorded oil uptake of immobile and autonomous foams after 180 seconds in paraffin-coated water bath.

4. Conclusion Based on this work, the attractive interactions of the loaded hydrophobic melamine foams induce movement towards other hydrophobic materials, effectively creating a smart device for on-the-fly oil absorption and coagulation. One of the current drawbacks to using absorptive technologies in oil remediation is that they are very diffiCHEMISTRY RESEARCH

cult to recover from water. The autonomous foam’s ability to collect oil by sampling a large area and later aggregating increases its viability and cost effectiveness in practical application. These foams have great promise in implementation for remediation efforts, cargo transportation, and water quality sensing. In the future, verification of these results and study of this propulsion mechanism with other surfactant modification methods and in response to external stimuli such as non-stagnant water baths, soaps, and varying acidity levels can be done to further characterize and understand this system.

5. Acknowledgments Research for this project was conducted under the guidance and supervision of Dr. Tim Anglin and Dr. Michael Bruno at the North Carolina School of Science and Mathematics. Funding for this project was provided by the NCSSM Foundation and NCSSM Chemistry department. I would like to thank my research mentors and my Research in Chemistry peers for their support throughout the process of my project.

6. References [1] OPEC. (2020). World Oil Outlook 2020. https://woo. opec.org/pdf-download-es/ [2] Fernandes, L. J., Barbosa-Póvoa, A. P., & Relvas, S. (2010). Risk Management Framework for the Petroleum Supply Chain. 20th European Symposium on Computer Aided Process Engineering, 28, 157-162. https://doi. org/10.1016/S1570-7946(10)28027-6 [3] Washington (DC): National Academies Press (US) (2003). National Research Council (US) Committee on Oil in the Sea: Inputs, Fates, and Effects. 5, Biological Effects of Oil Releases. https://www.ncbi.nlm.nih.gov/books/ NBK220710/ [4] D’Andrea, M. A., & Reddy, G. K. (2018) The Development of Long-Term Adverse Health Effects in Oil Spill Cleanup Workers of the Deepwater Horizon Offshore Drilling Rig Disaster. Frontiers in public health, 6, 117. https://doi.org/10.3389/fpubh.2018.00117 [5] ITOPF. (2019). Oil Tanker Spill Statistics. https:// www.itopf.org/knowledge-resources/data-statistics/statistics

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[6] Gatou, M., Lagopati, N., Tsoukleris, D., & Pavlatou, E. A. (2020). Commercial Sponges as A Novel Technology for Crude Oil Removal from Seawater and Industrial Wastewater: A Review. Biomedical Journal of Scientific & Technical Research, 25, 4, 19426-19436. https://biomedres.us/fulltexts/BJSTR.MS.ID.004251.php [7] Su, C. (2009). Highly hydrophobic and oleophilic foam for selective absorption. Applied Surface Science, 256, 5, 1413-1418. https://doi.org/10.1016/j.apsusc.2009.08.098 [8] Meng, H., Yan, T., Yu, J., & Jiao, F. (2018). Super-hydrophobic and super-lipophilic functionalized graphene oxide/polyurethane sponge applied for oil/water separation. Chinese Journal of Chemical Engineering, 26, 5, 957963. https://doi.org/10.1016/j.apsusc.2009.08.098 [9] Peng, L., Yuan, S., Yan, G., Yu, P., & Luo,Y. (2014).

Hydrophobic Sponge for Spilled Oil Absorption. Journal of Applied Polymer Science, 131. 10.1002/app.40886.

[10] Zhang, X., Liu, D., Ma, Y., Nie, J., & Suia, G. (2017) Super-hydrophobic graphene coated polyurethane (GN@ PU) sponge with great oil-water separation performance.

Applied Surface Science, 422, 116-124. https://doi. org/10.1016/j.apsusc.2017.06.009 [11] Zhao, Jun., Guo, Q., Wang, X., Xia, H., & Chen, Y. (2016). Recycle and reusable melamine sponge coated by graphene for highly efficient oil-absorption. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 488, 93-99. https://doi.org/10.1016/j.colsurfa.2015.09.048 [12] Zhang, L., Dong, D., Shao, L., Xia, Y., Zeng, T., & Wang, Y. (2019). Cost-effective one-pot surface modified method to engineer a green superhydrophobic sponge for efficient oil/water mixtures as well as emulsions separation. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 576, 43-54. https://doi.org/10.1016/j. colsurfa.2019.05.022 [13] Nguyen, D. D., Tai, N., Lee, S., & Kuo, W. (2012).

Superhydrophobic and superoleophilic properties of graphene-based sponges fabricated using a facile dip coating method. Energy & Environmental Science 5(7):79087912. 10.1039/C2EE21848H. [14] Deng, Y., Peng, C., Dai, M., Lin, D., Ali, I., Alhewairini, S. S., Zheng, X., Chen, G., Li, J., & Naz, I. (2020). Recent development of super-wettable materials and their applications in oil-water separation. Journal of Cleaner Production, 266, 121624. https://doi.org/10.1016/j.jclepro.2020.121624

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[15] Yin, Z., Li, Y., Song, T., Bao, M., Li, Y., Lu, J., Li, Y. (2020). An environmentally benign approach to prepare superhydrophobic magnetic melamine sponge for effective oil/water separation. Separation and Purification Technology, 236, 116308. https://doi.org/10.1016/j.seppur.2019.116308 [16] Li, Z., Wu, H., Chen, W., He, F., & Li, D. (2019).

Preparation of magnetic superhydrophobic melamine sponges for effective oil-water separation. Separation and Purification Technology, 212, 40-50. https://doi. org/10.1016/j.seppur.2018.11.002 [17] Li, X., Mou, F., Guo, J., Deng, Z., Chen, C., Xu, L., Luo, M., & Guan, J. (2018). Hydrophobic Janus Foam Motors: Self-Propulsion and On-The-Fly Oil Absorption. Micromachines, 9, 23. 10.3390/mi9010023.

[18] International Programme on Chemical Safety (IPCS).

Camphor, 1,7,7-trimethyl bicyclo (2,2,1)-heptan-2-one. http://www.inchem.org/documents/pims/pharm/camphor.htm.

[19] Jones K. H., Smith R. T., Trudgill P. W. (1993). Diketocamphane enantiomer-specific ‘Baeyer-Villiger’ monooxygenases from camphor-grown Pseudomonas putida. J Gen Microbiol, 139(4):797-805. 10.1099/00221287-139-4-797.

PMID: 8515237.

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EVALUATING THE EFFICIENCY OF USING MARTIAN MATERIALS AS FUEL TO REACH MARTIAN ORBIT Joshua Kupras Abstract With NASA’s Artemis and Gateway programs, and private companies such as SpaceX planning manned missions to Mars by 2030, Mars is looking more and more to be the next step in human exploration. While the technology to get to Mars exists today, the technology to get back to Earth has not been explored well, making any trip a one-way trip. The purpose of the research was to find out if a viable rocket fuel could be made from materials easily found on Mars. A fuel would be considered viable if all of its compounds could be easily acquired on Mars and its specific impulse (ISP) was above 75 seconds in standard gravity. Specific impulse is the amount of time an engine can generate thrust equal to its fuel weight.

The compounds would be considered easily acquirable if refined amounts could be obtained without an industrial setting.

Using data from three Martian landers, these materials were selected and then acquired. Testing was then done on various mixtures by suspending a rocket on a pendulum. The highest ISP achieved was about 55 seconds, but with only a couple of newtons of thrust, and a slow burn time, the materials used were found to be unsuitable for rocket fuel. However, one mixture, that of potassium chlorate, sulfur, and carbon, was found to be highly explosive, well suited for possible mining operations on Mars.

1. Introduction While many great advances have been made in propulsion technology to send explorers to Mars, very few have been made to get them back. A one-way trip is economically and technologically viable, but few human astronauts would accept that mission. Conventional methods such as transporting fuel to the surface or simply landing a rocket capable of sending humans back involve a hefty risk. This was significantly easier with the Apollo missions due to both the closeness of the moon to the Earth and the significantly lower gravity of the moon compared to Mars.

Attempting to land fuel on the surface would require an aerobraking process and a gentle landing, both of which are risky with today’s technology. Attempting to land a rocket large enough to take back off from the surface is not technologically possible or economically feasible at the current time.

Space exploration organizations are already researching processes to develop viable fuel on Mars, however their considered techniques rely on finding water or olivine deep in the regolith, requiring significant remote-operated mining equipment prior to humans landing on Mars (Oze et al. 2005). In addition, these processes will require an extreme amount of time to generate any usable amount of fuel.

Approximately 1 km/s of change in velocity (ΔV) is required to obtain a Martian orbit from the surface at the minimum safe orbit of 200km, and 1.7 km/s for a TransEarth Injection, for a total of 2.7 km/s from the surface of Mars to Earth SOI (George et al. 1998). For a comparison, 2.7 km/s ΔV is about the same amount of ΔV needed to land on the Moon from geostationary orbit. Since orbital rendezvous have been very successful in the past, at least ENGINEERING RESEARCH

in Earth’s gravity, requiring no new or special technology, the goal of the fuel will be to get a rocket to Martian orbit. The required ISP to reach 1 km/s ΔV, with a generous buffer for maneuvering, was set at 75 seconds in standard gravity, 9.81m/s2.

The Martian regolith contains many volatile materials such as magnesium, potassium, sulfur, and small amounts of chlorate and perchlorate-based molecules (Hecht et al., 2009; Hanley et al., 2012). While these chlorate and perchlorate compounds are toxic, their reactivity makes them perfect for solid fuel. This reactivity has been exploited in boosters such as the SRB on the Shuttle Program. Chlorate and erchlorate compounds could be easily filtered out through electrostatic cleaning or simple water washings and distillation, and refined with bacteria from the Dechloromonas genus (Davila et al., 2013; Anupama et al. 2015). Sulfur, another key ingredient of rocket fuel, is abundant in the Martian crust in the form of sulfates, which can first be turned into hydrogen sulfide by sulfur-reducing bacteria and then to elemental sulfur through the Claus process (King et al., 2010; Camacho, 2009; Nirupama et al., 1989). Additionally, copious amounts of elemental sulfur may exist deep in the crust as well as the surface regolith (Schubert et al., 1990). Carbon, abundant in the atmosphere, could be reduced to solid carbon with the help of a gallium, indium, and tin alloy coated in cerium oxide. When combined, these materials create an incredibly reactive substance that could be used as rocket fuel.

The purpose of this study was to create a way for future explorers to be able to return to Earth, removing the one-way-trip element. The hypothesis was that a rocket powered by fuel made from Martian materials will have sufficient ISP and thrust to be a viable option to fuel a return vehicle.

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15mm (Figs. 1 and 2).

2. Materials and Methods 2.1 – Mixture Methodology With all the elements available in the Martian regolith, choosing suitable elements was critical (Table 1). The elements had to be easily accessible on the surface, easily refinable, and easily mixed requiring no compounds not attainable on Mars. (Per)Chlorate compounds were chosen as the oxidizer, as obtaining (per)chlorate from the regolith should be relatively easy with simple water washings or electrostatic cleaning. Being easily obtainable, sulfur, carbon, and iron were chosen as the fuels. Extensive testing was conducted on mixes of these elements, with the best mixes and ratios chosen for their burn times, heat produced, and vigor during combustion.

Figure 1. Image of final nozzle.

Table 1. Chart of compounds available in Martian regolith as detected by several landers.

Oxide SiO2 Al2O3 TiO2 Fe2O3 MnO CaO MgO K2O Na2O P2O5 SO3 Cl Total

VL-1 Wt% 43 7.3 0.66 18.5 5.9 6 <.15 6.6 0.7 89

VL-2 Wt% 43 7 0.55 17.8 5.7 6 <.15 8.1 0.5 89

Pathfinder Wt% 44 7.5 1.1 16.5 5.6 7 0.3 2.1 4.9 0.5 89.5

2.2 – Engineering a Nozzle To better represent the conditions in which the fuel would be used, a nozzle was engineered to provide greater thrust and to pressurize the fuel inside. Several iterations were designed and milled, with the goal of creating a narrow enough opening to produce maximum thrust while staying wide enough to avoid too much internal pressure that could lead to a catastrophic failure in either splitting the tube itself or popping the nozzle off the tube. A hose clamp was used to secure the nozzle inside the tube. The final restriction was 46%, with a width of 9mm, a wall thickness of 3mm, a height of 24mm, an angle of constriction of 125 degrees, an angle to output from throat of 15 degrees, a constriction width of 4.17mm, and an output width of 52 | 2020-2021 | Broad Street Scientific

Figure 2. Cross-sectional schematic of final nozzle design.

2.3 – Compacting the Fuels Using Hydration and Dehydration The amount of fuel that could be stored in each tube was increased from ~8g to ~10g due to creation of a compaction process, allowing for a longer and more robust burn. (Per)Chlorate compounds are very soluble. Obtained in a powdered form, the mixture could be solidified in the combustion chamber by repeatedly saturating and desaturating the final mixture in layers. A small amount of mixture was placed into the combustion chamber. Water was then dripped onto the powder until saturated, forming a glue-like substance. This glue-like substance could easily and safely be compacted (Fig. 3). This process was repeated until the tube was full, and then it was left to dry. Ethanol was later substituted for water as it would dry more quickly. When dry, the fuel would remain a solid, compacted mixture.

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Figure 3. Image of Hydrated-Dehydrated Potassium Chlorate-Carbon-Sulfur mix in its hydrated form.

2.4 – Engineering a Way to Manually Measure Force A pendulum device was designed with the tube and the fuel inside hanging at the bottom of a pendulum perpendicular to the shaft (Figs. 4 and 5). At an angle of 90 degrees from the resting position, the rocket would be thrusting equal to its weight. By measuring the angle produced during thrust, thrust could be measured relative to its thrust at 90 degrees. While less precise than digitally measuring the force produced, it would be accurate. This pendulum was then constructed, although due to time constraints only two tests were performed.

Figure 4. Image of test rocket firing in pendular testing device.

Figure 5. Image of pendular testing device at an angle 3. Results 3.1 – Data One test was performed with a test mixture of potassium chlorate, sulfur, and carbon , and the other was performed with a control Estes C6-0 rocket engine (Fig. 4).

The angle was measured continuously while the rocket was firing. The test rocket performed significantly worse than the control rocket (Fig. 6).

Figure 6. Graph of pendular testing device data of the control (Estes C6-0) and chlorate carbon sulfur test fuel.

3.2 – Data Analysis 3.2.1 – Analysis of Fuels Potassium perchlorate, potassium chlorate, and sodium chlorate were explored as possible oxidizers. Sodium chlorate was quickly rejected from mixes due to both potassium

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perchlorate and chlorate being more energetic. Potassium perchlorate was soon rejected from mixes due to its slow burn time. Iron was also rejected, as the element would drastically increase the activation energy of the mix and slow down the reaction, making ignition during testing difficult and producing little thrust.

Ultimately, a mixture of 67.83% potassium chlorate, 19.94% sulfur, 7.46% carbon, and 4.77% bentonite clay that had been soaked in ethanol then left to partially dehydrate was selected. While potassium chlorate and sulfur has been known to be unstable, the addition of iron completely stabilized the mixture from ignition on contact friction and heat but increased the activation energy of the mix substantially, sometimes preventing ignition. A similar result was accomplished with the addition of carbon; the mixture would not ignite on friction but would still ignite with the fuse. Carbon was chosen over iron in all later mixes for both this stabilizing property and for being more energetic in the mixtures. A small amount of bentonite clay was later added to the mix to further increase the stability.

3.2.2 – Analysis of Pendulum Data Using the force pendulum, the thrust of the rocket could be measured by the angle. The graph is in an angle versus time format, with a common model rocket to act as a control (Fig. 6). The data from the control roughly matched up with the manufacturer’s specifications. The Martian fuel test, however, only had a maximum angle of 2 degrees, leading to an estimated ISP of 55 seconds.

3.2.3 – Calculations The ultimate goal was to calculate ISP, with a target number of 75 seconds in standard gravity, 9.81m/s2. It is calculated using thrust force, mass flow rate, and standard gravity. Force was calculated through the sine of the angle relative to the resting position of the test rocket multiplied by its weight. Force can then be used in conjunction with the mass flow rate of the fuel and the standard gravity of either Earth (9.81 m/s2) or Mars (3.71 m/s2) to get the ISP on each respective planet. 75 seconds in Earth gravity was chosen as the target because that metric allowed for ample buffer for the associated engine to obtain the ΔV needed to escape Martian gravity.

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4. Discussion While the fuels tested did not yield promising results, the energetic nature of the Hydrated-Dehydrated potassium Chlorate-Carbon-Sulfur mix (HDCCS) should be explored for possible mining and research uses. A colony could easily create these explosives to be used in mining operations to gather resources or research deeper parts of the surface. However, mass producing these perchlorate-based fuels will be difficult, as perchlorates are toxic and the purification process requires copious amounts of water. Creating any hydrated-dehydrated versions will require even more water, and due to the mixture’s stickiness it leaves behind a residue.

5. Conclusions Based on the results from testing, the test fuels are not viable for a return vehicle from Mars. However, due to time constraints, these results are inconclusive and these fuels need to be tested more extensively in the future. Specifically, the mixture of potassium chlorate, carbon, and sulfur (PCCS), which had an energy output that neared explosive failure, should be further tested. With the tests performed, the hypothesis that the test fuels can reach Martian orbit should be rejected, but further tests should be performed to ensure that the difficulties with time and testing were not factors in this conclusion.

Due to time constraints, it was not possible to test all mixtures of elements easily obtainable in the regolith.

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Any future work on regolith to fuel conversion should be broadened to include more substances such as methane. A more rigid and thicker walled combustion chamber should be used in conjunction with more secure methods of attaching the nozzle to attain the maximum thrust possible.

HDCCS should be studied for the possible applications on Mars in situations such as mining operations and excavating into the regolith to study the composition at lower levels. PCCS or variations thereof should also be studied as a possible fuel in a more secure testing area with improved materials to avoid possible explosion.

6. Acknowledgments Matthew Tuckey was invaluable in the logistics of my research, from letting me use his lab and fume hood to conduct my tests, to helping me to procure and safely ignite my compounds.

7. References Davila, A. F., Willson, D., Coates, J. D., & McKay, C. P. (2013). Perchlorate on Mars: A chemical hazard and a resource for humans. International Journal of Astrobiology, 12(4), 321–325. https://doi.org/10.1017/ S1473550413000189

Hecht, M. H., Kounaves, S. P., et al. (2009). Detection of Perchlorate and the Soluble Chemistry of Martian Soil at the Phoenix Lander Site. Science, 325(5936), 64–67. doi: 10.1126/science.1172466.

Ivanov, M. S., Markelov, G. N., Ketsdever, A. D., & Wadsworth, D. C. (1999). Numerical Study of Cold Gas Micronozzle Flows. AIAA Journal.

Oleson, S., McGuire, M., Burke, L., Fincannon, J., Warner, J., Colozza, T., Fittje, J., Martini, M., Packard, T., Hemminger, J., & Gyekenyesi, J. (2010, July 25). Mars earth return vehicle (Merv) propulsion options. 46th AIAA/ASME/SAE/ASEE Joint Propulsion Conference &amp; Exhibit. 46th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Nashville, TN. https://doi. org/10.2514/6.2010-6795 Oze, C., and Sharma, M. ( 2005), Have olivine, will gas: Serpentinization and the abiogenic production of methane on Mars, Geophys. Res. Lett., 32, L10203, doi:10.1029/2005GL022691.

Penelope L. King, Scott M. McLennan; Sulfur on Mars. Elements ; 6 (2): 107–112. doi: https://doi.org/10.2113/gselements.6.2.107

Dichmann, Donal et al., 2013, Ways to the Moon: Trajectory Options From Earth GEO Transfer Orbit to the Moon, Presentation.

Schubert, G., and Spohn, T. ( 1990), Thermal history of Mars and the sulfur content of its core, J. Geophys. Res., 95( B9), 14095– 14104, doi:10.1029/JB095iB09p14095.

Esrafilzadeh, D., Zavabeti, A., et al. (2019). Room temperature CO2 reduction to solid carbon species on liquid metals featuring atomically thin ceria interfaces. Nature Communications, 10. https://www.nature.com/articles/ s41467-019-08824-8

Shafirovich, E. Y., & Goldshleger, U. I. (1992). Combustion of Magnesium Particles in CO2/CO Mixtures. Combustion Science Technology, 84(1-6), 33–43. https://www. tandfonline.com/doi/abs/10.1080/00102209208951843

George, LE, KOS, LD, Interplanetary Mission Design Handbook: Earth-to-Mars Mission Opportunities and Mars-to-Earth Return Opportunities 2009-2024, NASA / TM-1998-208533, NASA, July 01, 1998, https://ntrs.nasa. gov/archive/nasa/casi.ntrs.nasa.gov/19980210557.pdf

Smith, P. H. (2009). Detection of Perchlorate and the Soluble Chemistry of Martian Soil at the Phoenix Lander Site.

Science, 325(5936), 64–67. doi: 10.1126/science.1172466.

Guidotti, R. A., Odinek, J., & Reinhardt, F. W. (2006).

Characterization of Fe/KClO4 Heat Powders and Pellets. Journal of Energetic Materials, 24(4), 271–305. doi: 10.1080/07370650600896566.

Hanley, J., Chevrier, V. F., et al. (2012). Chlorate salts and solutions on Mars. Geophysical Research Letters, 39(8). doi: 10.1029/2012gl051239.

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THE FUNDAMENTAL THEOREM OF CALCULUS: SOLVING MAA-CMJ PROBLEM #1177 Arnav Singh, Jane Crowley, Justin Indla Abstract The Fundamental Theorem of Calculus states that . This powerful theorem allows us to establish a relationship between differentiation and integration without using Riemann sums. We utilize this fundamental characteristic in coordination with Leibniz Differentiation to examine a continuous function f(x) that satisfies By utilizing Leibniz Differentiation to infinitely differentiate the given function, we can construct a Maclaurin series for is the only possible such continf(x). Then, by analyzing its higher-order derivatives, we can conclude that uously differentiable function that satisfies this equation.

1. Introduction

The Mathematical Association of America (MAA) - College Mathematics Journal (CMJ) problem 1177 [1] is defined as follows: Find all continuous functions that satisfy the following equation for all x ∈ R: (1) An elementary function is a single variable function composed of a combination of constants, power, radical, exponential, logarithmic, trigonometric, inverse trigonometric, hyperbolic, and inverse hyperbolic functions.

Functions that are not elementary functions include those such as the sine integral, the error function, and the Cantor function. All elementary functions are also analytic functions [2]. Analytic functions are defined as functions that can be expressed as convergent power series. Absolute value and piece-wise functions are not analytic at junction points. One characteristic of analytic functions is that they are infinitely differentiable, and therefore the Taylor series for an analytic function, f(x), can be written as, (2) 1.1 Answer is an Infinitely Differentiable Function Only To prove that f(-x) is infinitely differentiable, we first look into why f(-x) must be differentiable at least one time.

Because f(x) and sin(t) are both continuous, the Theory of Definite Integrals guarantees an antiderivative [3] for both functions. Because f(x) and sin(t) are both continuous, an antiderivative for the similarly continuous product of the two must also exist. If we were to define such a function, g(x,t), it would yield:

Then, we can also conclude that g(x,t) is continuous because it is the product of two continuous functions. Since all continuous functions have an antiderivative, it follows that there exists a function, G(x), such that: (4) From here, we add a constant to G(x). Since all constants are continuous functions, the sum of two constant functions is a continuous function. We integrate g(x,t) from 0 to x, obtaining the original integral equation: (5) This antiderivative, H(x), by the Fundamental Theorem of Calculus, is differentiable [3][4]. As H(x) is differentiable, so is f(-x).

Taking the derivative of f(-x) with respect to x using Leibniz Differentiation [5] then yields: (6)

Where

is the integrand.

From here, we assume the integrand, in this case h(x,t), to also be continuous. This allows us to integrate −f′(−x), which in turn allows us to take a second derivative of f(−x), and so on and so forth. This reasoning can be applied to an infinite number of derivatives for f(x), thereby assuming f(x) to be infinitely differentiable.

(3)

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2. Methods 2.1 Derivative of the Integral Equation Chain Rule and Leibniz Differentiation [5] both play an important role when taking the derivative with respect to x for both sides of Equation 1.

2.1.3 Higher Order Derivatives Leibniz’s rule can be reapplied to find further derivatives of this nature for f(x) [5].

Continuing this differentiation and keeping a(x)=0, b(x)=x, and g(x,t)=f(x-t)sin(t), we find the derivative for of any order, n.

2.1.1 Derivative of the Left-hand Side We will take the derivative of the left-hand side of the integral equation by using Chain Rule.

Chain Rule: for differentiable functions f(x) and g(x), if . y=f(u) and u=g(x), then On the left side of the equation, d/dx f(-x), was calculated by employing chain rule.

This results in, (7) (10)

2.1.2 Derivative of the Right-hand Side We will take the derivative of the right-hand side of the integral equation by using Leibniz Differentiation [5].

Leibniz’s Differentiation Rule states that: on the open interval -∞ < a(x), b(x) < ∞ for the integral the following is true,

(8)

Where,

By utilizing Leibniz’s Differentiation Rule, we set a(x)=0, b(x) = x, and Therefore, the derivative of the integral equation , is equal to

(11)

(9) This can be continued for any order derivative, n.

The right-hand side of the integral equation, had to be treated differently than the left-hand side because the presence of multiple variables required us to take a partial derivative, as opposed to the total derivative we took on the left-hand side.

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2.2 Computational Analysis: In order to accurately and quickly determine whether a potential function held true for all real values of x, we developed an algorithm to test both f(-x) and for all real values of x. This algorithm then compares the and resulting values for f(-x) and highlights in green or red if they are equal or not equal, respectively. The algorithm then computes whether the chosen f(x) satisfies the equation, for Broad Street Scientific | 2020-2021 | 57

We can split the multiplication in the integrand into two integrals here.

the given domain.

3. Results 3.1 Representing f(x) as a Series Expansion: When attempting to find a solution to f(x) that satiswe fies the integral equation, knew that the integral equation is infinitely differentiable using Leibniz Differentiation. With this knowledge in hand, we chose to represent f(x) as a Taylor polynomial.

Since we already know that f(0) = 1, we set out to create a Taylor polynomial centered at x = 0, also known as a Maclaurin series. Taylor polynomials are often used to express approximations of more complicated functions and are of the form,

(14)

At this step, we go through and use integration by parts to continue simplifying the equation.

(12)

where a is the point around which the polynomial is centered.

To construct our Taylor polynomial, we utilize the values of the derivative of f(x), at its center, x = 0.

As we took higher order derivatives, we noticed a pattern after the first two derivatives: f(0) = 1, f ’(0) = 0, and f ’’(0) = 1. We initially thought that the Taylor polynomial would be an infinite series based off of this pattern and tried to match it to a previously existing series. However, as we took higher order derivatives past the second derivative, we noticed that the derivatives evaluated at x = 0 yielded only 0, meaning that when substituting for the Taylor polynomial, the only terms that had non-zero coefficients were the x2 term and the constant term. After the second derivative, every derivative evaluated at x = 0 still yielded 0. Thus, we concluded that the function was a polynomial, represented by a finite series.

When we test a variety of trigonometric functions for f(x) they all fail to satisfy the integral equation, indicating that an infinite series would not be suitable for f(x).

3.2 Proof that : By plugging in for f(x) in the integral equation, we can algebraically confirm our solution,

(13)

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(15)

Here, we need to repeat integration by parts one more time to simplify our integrand to sin(x).

(16)

Thus,

satisfies this equation.

For further testing, we verify that is a solution through computational analysis.

The results obtained upon entering as f(x) in our function testing algorithm are shown in the following table (Table 1).

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Table 1: The iterations of the algorithm for

The third derivative here, -f ’’’(-x), leaves us with a lone integral without any outside terms, similar to the original f(-x). However, the original integral equation has the important addition of a constant term. The constant, 1, in the integral equation is what causes f(0) and f ’’(0) to be non-zero. For any f n(0), the integral in the expression will be evaluated from 0 to 0 (equals 0), meaning that f n(0)’s value will be determined by the outside terms; hence, f(0) = 1. For the second derivative, f(0) is present and multiplied with a non-zero cos(x) term (which equals 1 at x = 0) , meaning f ’’(0) is non-zero. f ’(0) is 0 because f(0) is multiplied by the sine function, which equals 0, at x = 0.

In the third derivative, f ’’(0) and f(0) will not be present in any further derivatives because they cancel each other out.

As a result, after the third derivative, all terms outside the integral will be 0, and since the integral itself is 0, we can then conclude that for all n≥3, f n(0)=0. From here, we can then write a two-term Taylor polynomial centered at 0 that will be the only continuously differentiable solution for f(x) that satisfies the integral equation.

In this case, the Taylor polynomial itself is actually equal to f(x) because all its derivatives beyond the second derivative are 0.

It can then be shown that: (18) 4. Discussion

After proving that is the only continuously differentiable solution to f(x), there still remains the possibility that there exists another, non-differentiable Seeing as holds true for all real values of function, g(x), that satisfies the equation. This potentially x, we can confirm that is a solution to this could include logarithmic, rational, cube root, and pieceequation. wise functions, among others, that are not differentiable for under the real values of x. We looked into the possibili3.3 is the Only Continuously Differentiable ty of a piecewise function consisting of our solution parabFunction that Satisfies the Integral Equation ola, and subsequent translations of it in intervals of π/2 , We know for the first three derivatives, the following π, and 2π. We chose these specific translations so that the holds true: parabolas are connected at a single point at the end of the interval, forming a repeating “w” shape. We believe that

this can produce an additional solution because of the periodic nature of the sine function in the integrand of the

integral equation. We were unable to find a version of this function that was successful, however, it is possible that there exists another variation of this or a different piecewise function that we have yet to consider. This possibility (17) warrants future investigation.

Another possible avenue for future investigation is whether we can replace sin(t) in the integrand with a cos(t). This yields a new integral equation,

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(19) When attempting to construct a series to satisfy this new integral equation, things become far more complicated as the series expansion at x = 0 changes from a simple polynomial to a complex hypergeometric function.

One final avenue to explore is to investigate what difference replacing the f(-x) in the integral equation with a f(x) would make and how it would change the function that satisfies the integral equation. Implementing this change would yield the integral equation, (20) 5. Conclusion In conclusion, we were able to find a continuous function f(x) such that the following equation holds true: (21) The solution, , was found by treating f(x) as an elementary, and subsequently analytic, function, which allowed us to take infinitely many derivatives of the original integral equation. This was done by means of Leibniz Differentiation, which outlines how to take the partial derivative of a multivariable function under an integral. A Maclaurin series of f(x) could be constructed by finding the derivatives of f(x) and evaluating them at x = 0.

Several derivatives in, it became apparent that the Maclaurin series that represented f(x) was finite, and we were able to determine that f(x) was thus a polynomial solution. We confirmed it was indeed a solution by two means: a. Plugging back into the original function and using integration by parts to set both sides of the equation equal for all real values of x b. Plugging into our algorithm and testing both sides of the integral equation for all real values of x.

Research and Innovation Program and to conduct this research.

7. References [1] Oman, G., & Curtis, C. N. (2020). Problems and Solutions. The College Mathematics Journal, 51(3), 225-232. doi:10.1080/07468342.2020.1735882 [2] Michigan State University Department of Mathematics. (N.D.). Basic Properties of Analytic Functions. Retrieved from users.math.msu.edu/users/yanb/425-ch2. pdf.

[3] Benestad, C., Pendharkar, H., Sternberg, N., Taylor G., & Winders S. (2015). Antiderivatives Math 120 Calculus I. Clark University. Retrieved from mathcs.clarku.edu/~ma120/antiderivatives.pdf.

[4] Krantz, S. G. (1999). Handbook of Complex Variables.

Birkhäuser Boston, 22. doi:10.1007/978-1-4612-1588-2.

[5] Haile, P. (2020, February). Differentiating an Integral. Yale University. Retrieved from www.econ.yale. edu/~pah29/409web/leibniz.pdf.

Thus, we have shown that both satisfies and is the only elementary, analytic, and continuously differentiable function to do so.

6. Acknowledgments The authors would like to thank Professors Nicholas Koberstein, Todd Lee, and Cheryl Gann for their mentorship, time, and for this opportunity. Their suggestions and assistance greatly improved the quality and depth of our research. The authors would also like to thank the North Carolina School of Science and Mathematics (NCSSM) for giving us the opportunity to be a part of its Summer 60 | 2020-2021 | Broad Street Scientific

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SELF-DRIVING CAR MASK R-CNN OBJECT DETECTION MODEL EFFICIENCY WITH DIFFERENT LEVELS OF TRAINING Akshar Yeccherla Abstract Object detection is a crucial part of self-driving car technology because it must be very accurate. Even one slip-up in object detection could put a driver in a dangerous situation. The study aimed to see how well object detection models would perform under various levels of training. An image dataset from real-life footage of a car driving throughout a neighborhood, TensorFlow, Matterport Mask R-CNN and Keras packages were used to train nine different Mask R-CNN object detection models. All of the Mask R-CNN models were trained using the same training procedures. The efficiency of these models was evaluated using the loss and mean average precision (mAP) metrics. It was concluded that higher steps per epoch values would lead to significant gains in the efficiency of an object detection model. A potential future study could be made with access to hardware that can train these models faster, so more data could be collected to analyze the effects of higher training levels.

1. Introduction 1.1 Self-Driving Cars Self-driving cars are an emerging technology that continues to be tested and implemented by many companies, notably Google and Tesla. Tesla has already released car models that can go into autopilot mode. The most crucial aspect of self-driving cars is safety. Self-driving cars must be able to detect many different objects, such as other cars (driving or stopped), pedestrians, traffic signals, and road signs, to make accurate decisions such as stopping and turning. The software in self-driving cars must be able to detect and correctly classify these objects, as just one simple mistake could prove fatal for the driver and other drivers or pedestrians nearby. Cars use machine learning to detect and recognize such objects from their live feed almost instantly. The models are trained on large amounts of images and videos taken in various conditions (rainy, snowy, etc.) so they can respond to many different situations that could arise on the road.

1.2 Convolutional Neural Networks Self-driving cars use convolutional neural networks, or CNNs, to detect and recognize objects in their live feed; CNNs are the most widely used networks for object recognition. A CNN is made up of layers of nodes containing neurons that learn to optimize their output based on the data on which they are trained (O’Shea and Nash, 2015).

These neurons take input, perform a single calculation and return output. The basic structure of a CNN consists of three types of layers: convolutional layers, pooling layers, and fully-connected layers. The convolutional layer takes in input (in this case, images of cars). The neurons in the convolutional layer receive input in the form of pixels. The neurons classify pixels as being part of MATHEMATICS AND COMPUTER SCIENCE RESEARCH

an object according to the weights of the object detection model. This output is then processed by the pooling layer. The pooling layer assists with the calculations of the convolutional layer by optimizing calculation time.

The pooling layer does this by continuously reducing the size of the representation of the image. The fully-connected layer is responsible for the output, which is the bounding boxes and classification of the objects. We created and tested Mask Region Based CNN (R-CNN) models for this project.

1.3 Mask R-CNN Mask R-CNN, an improved extension of Region-Based CNN (R-CNN), is a type of CNN that is very efficient at creating object detection models and is used by some self-driving car prototypes. Region-Based CNN is a type of CNN that looks at an image and proposes regions of interest (RoIs) that could potentially contain an object (He et al., 2018). During training, a Mask R-CNN model takes in a binary mask of an image, where zeros are filled in when an object is not present, and ones are filled in when an object is present. Despite training data usually containing rectangular bounding boxes for where an object like a car is located, Mask R-CNN will still attempt to output a pixel-perfect mask of the object. This is beneficial since it provides more than just the bounding box from the output of other models such as R-CNN and Fast R-CNN. The process of creating a pixel-perfect mask is conducted by assigning a binary mask to each RoI during testing, which enables the Mask R-CNN model to give a pixel-perfect prediction since the model can remove the pixels that do not contain an object. Furthermore, Mask R-CNN takes less training time in comparison to R-CNN and Fast R-CNN, which is also a reason why it was selected for this study.

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1.4 The Problem Companies want to spend a lot of time making sure the object recognition model is accurate and can be relied upon, as lives are on the line. Companies want to be sure that these models are running at peak efficiency. The models should not classify random objects as vehicles or vice versa; they must make predictions accurately. It is important for companies to know how well object detection models perform given various levels of training.

This study aimed to determine how the level of training of a Mask R-CNN model would impact its efficiency. The relation between the level of training and the performance of the model was evaluated by several metrics, such as loss and mAP. Multiple Mask R-CNN models were trained and evaluated, and differences in efficiency between models were measured.

2. Data Preparation and Computational Approach 2.1 Dataset and Data Preparation The dataset used to train and test the model was a dataset meant for object detection of common obstacles a self-driving car could encounter, such as cars, trucks, pedestrians, and traffic lights. The data for this work were obtained from Kaggle, and were created by Alin Cijov (Cijov, 2020). The dataset had six columns. The ’frame’ column contained the name of the image file, which could be used to access the image through the path. The ”xmin”, ”xmax”, ”ymin”, and ”ymax” variables determine the coordinates of the rectangular bounding box that contains the target object. The ”class id” variable refers to the target object contained within the bounding box. The number 1 corresponds to the car being contained within the bounding box. For our purposes, this is the only object that should be detected, as it would be too computationally intensive to identify multiple objects in an image.

The dataset was taken and filtered such that the only target class would be cars for the aforementioned reasons.

The dataset was also ordered by the ”frame” variable, which contains the file names of the images so that the rows corresponding to each image would be consecutive. This was done to make sure there was no overlap between the rows selected for training and the rows selected for testing. The data cleaning was performed with the Python ”pandas” package.

2.2 Object Detection Model Creation and Training A Mask R-CNN model was developed with Python 3.7 (Python Software Foundation, 2019) using the TensorFlow v1.15.3 and Keras v2.2.4 packages and the Matterport Mask-RCNN framework (Matterport, 2019).

The programs for training and testing the model were built and tested using PyCharm Community Edition v2020.2 (JetBrains, 2020); training was performed on Google Colab (Google, 2017), a free platform for running 62 | 2020-2021 | Broad Street Scientific

Python scripts for data science, utilizing a Google Cloud GPU. The base structure of the code for the Mask R-CNN testing and training model was taken from the Machine Learning Mastery article by Brownlee (Brownlee, 2019a).

Each row in the dataset corresponds to one bounding box for one image. This means that if an image has multiple bounding boxes present, then the bounding box data for that image are contained within multiple rows of the dataset. Due to the long time and expensive computation it would take to process one image at a time, one row was processed at a time. Out of the over 120,000 total rows present in the dataset, the first 10,000 rows (corresponding to 2097 images) were selected for training and a continuous set of 2000 rows (corresponding to 291 images) were selected for testing. A relatively small number of images were selected due to the intensive computational nature of training the model.

2.3 Model Evaluation Before continuing, it is important to define some terminology. An epoch is one pass over the dataset. The steps per epoch is the number of batches trained per epoch. A total of 9 separate models were trained, with combinations of 10, 25, or 50 steps per epoch and 1,2, or 3 epochs. These models were initialized using pre-determined weights from the Mask R-CNN COCO configuration file. During training, the loss per epoch was recorded.

The models were evaluated by the loss and mAP metrics calculated from the testing set. The loss metric is an estimate of how closely the model would fit the actual bounding boxes, and the mean average precision (mAP) metric is a standard R-CNN evaluation metric that describes the precision and efficiency of the model. Data on loss progression, mAP metrics, and example images were recorded.

3. Results and Discussion 3.1 Overview This section will look at some primary aspects of the object detection models that were trained. The metrics present in this section are the loss per model, the drop in loss per model, the mean average precision (mAP) per model, and the object detection models’ output after being given one image.

3.2 Loss For Mask R-CNN, the loss function is defined as the sum of the loss for classification (the object that the program identifies), the loss for bounding boxes, and the loss for masks (He et al., 2018) (Girshick, 2015). The loss function is a good estimate for how the predictions for the bounding box made by the Mask R-CNN model fit the actual given bounding boxes. The rule of thumb is that the MATHEMATICS AND COMPUTER SCIENCE RESEARCH

higher the loss value, the worse the model. For this reason, a loss that is close to 0 is optimal and approaches 0 for long training times relative to the size of the training set. The loss function metric also serves as a good debugging check to make sure that the model is being trained correctly, as a very high loss (around 30) would indicate there is something wrong with the training conditions. Table 1 depicts the loss function values for each model and the drop in the loss for each model.

Table 1. Object Detection Models - Final Loss vs. Initial Loss.

10 Steps 25 Steps 50 Steps

Initial Loss 3.0262

1 Epoch

2 Epochs

3 Epochs

2.3684

1.4192

1.6362

2.4864

1.6681

1.3319

1.3513

2.9038

1.7201

1.3498

1.0083

It can be seen here that the general trend is that the loss drops over time. However, the loss value increases from epoch 2 to epoch 3 for steps per epoch sizes of 10 and 25, which was a surprising result. Repeated trials could improve the accuracy of the result.

The loss function best matches that of an inverse log function (as shown in Figure 1). The rate at which the loss decreases goes down significantly as training time progresses, so drop in the loss cannot be used as a steadfast metric for evaluating across models with different steps per epoch, especially given that the initial loss was different for each model. Note that the initial loss is the same for each model with the same value of steps per epoch since the models were trained consecutively. The takeaway here is that it seems that a higher number of epochs lead to considerable gains in efficiency.

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Figure 1. Theoretical loss value graph over time (from (Brownlee, 2019b)). This graphic does not reflect data collected from this study.

3.3 Object Detection Model Example Images Figure 2 contains each of the nine models’ output for one given image. This is useful for looking through some of the common patterns in the model. The code snippet for displaying these predictions was taken from the Towards Data Science article ”Object Detection Using Mask R-CNN on a Custom Dataset” by Renu Khandelwal (Khandelwal, 2019).

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Figure 2. Object Detection of One Image by 9 Models (Row values correspond to the steps per epoch) Figure 3 shows the actual bounding boxes of the cars in the image.

Figure 3. Actual Car Bounding Boxes for Testing Image It is clear that the model improves as it gets higher levels of training. The model on the lowest levels settings (10 steps per epoch, 1 epoch) has bounding boxes all over the place, while the models with higher trained settings (50 steps per epoch) are not detecting any objects that aren’t cars. It can also be noted that the colored regions aren’t always box-shaped, they try to fit the exact object; this is a unique feature of Mask R-CNN. There are major differences as you increase the number of epochs. Importantly, the model stops picking 64 | 2020-2021 | Broad Street Scientific

up objects that are not cars, and the masks start to capture the whole car instead of just a part of the car. When the number of steps per epoch were increased, a similar effect occurs. The cars aren’t being detected as two separate objects and the masks are starting to fit the shape of the car better than lower levels.

3.4 mAP Metrics Mean average precision (mAP) is commonly used to evaluate how precise an R- CNN model is. mAP stands for ”mean Average Precision,” and it is a metric that shows how precise a model is (Hui, 2018). The mAP metric ranges from 0 to 1, with a higher mAP signifying to a more precise model. The mAP metric is determined by calculating an average precision (AP) rating for each image in the testing set, which is also a value between 0 and 1, and averaging the AP values over the set of testing images (Hui, 2018).

The AP value is calculated by first calculating Intersection over Union (IoU). IoU compares the predicted bounding box to the actual bounding box (or ”ground truth” box) (Hui, 2018). The IoU metric is the area of the intersection (overlap) between the predicted and ground truth bounding boxes, divided by the union (total area) between the predicted and ground truth bounding boxes. This metric is also between 0 and 1, as it is standardized by the division by the union of the areas. An IoU of 0 means that the actual and predicted bounding boxes do MATHEMATICS AND COMPUTER SCIENCE RESEARCH

not intersect, while an IoU of 1 means that the bounding boxes perfectly match. Based on this metric, there are four types of predictions that can be made (Hui, 2018): • • • •

True Positive: A car is detected when it should’ve been False Positive: A car is detected when it shouldn’t have been (no car present) True Negative: A car is not detected when there was no car present in the region False Negative: A car is not detected, but is present in the region

To distinguish a true positive from a false positive in the scenario where a predicted bounding box partially captures a car, an IoU greater than a certain threshold results in a true positive, and an IoU less than a certain threshold results in a false positive (Hui, 2018). The standard threshold for the IoU is 0.5 and will be for this study as well (Hui, 2018). Based on those four types of predictions, there are two measurements, precision and recall: (Note, ”True Positives” refers to the number of true positives in one image, and the same holds for the other types of predictions)

Letting the function of precision in terms of recall being P (r), the AP measurement is

Otherwise known as the area of the curve below P (r) over the interval 0 ≤ r ≤ 1. Since Precision and Recall are standardized, they are between 0 and 1, giving us an AP value between 0 and 1.

The AP value is calculated for each image, and the average AP out of all images in the test dataset gives us the mAP (which stands for mean AP). Below is a table of the mAPs for the nine models.

Table 2. Object Detection Models - mAP Values 10 Steps 25 Steps 50 Steps

1 Epoch 0.126 0.454 0.466

2 Epochs 0.265 0.498 0.509

3 Epochs 0.249 0.415 0.505

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There is a conclusion that can be drawn based on these mAP values. That takeaway is that using more steps per epoch leads to higher model efficiency. The difference between the mAP values for 10 steps per epoch and 25 steps per epoch is significant, around a 0.250 to 0.300 increase in model efficiency overall. This difference isn’t as large between the 25 steps per epoch and 50 steps per epoch models, but still significant.

The general trend is that the model efficiency increases if trained for more epochs, as more pass-throughs of the dataset means that the model will perform better (for a low number of epochs). However, there is one exception in the data table to this finding: the drop in mAP from the second epoch to the third epoch. The change is almost negligible for the 50 steps per epoch models but is drastic for the 25 steps per epoch models and decently significant in the 10 steps per epoch model. This most likely isn’t a result of over-fitting, as it is evident from the example images shown earlier that the model can use some improvement. This is because the masks contain area that doesn’t contain a car. Furthermore, many models are generally trained at more than 3 epochs. This just seems like an anomaly from a theoretical standpoint, but it is present in the data. It’s most likely that the efficiency will increase if trained for more epochs than just 3, but due to the limitations on the access to technology that can train fast (Google Colab has extremely slow training times), it cannot be said for sure based on the experimental data.

One potential reason for why this may be the case is as the car detection model gets better, it will begin to detect trucks (which are similar to cars) with more confidence.

This was observed in a few testing images. In the dataset, trucks are given a different class id than cars, meaning that they are not included in the actual bounding boxes that were used in the training data. This will come up as a false positive calculation, which will bring down the AP value.

4. Conclusion In this study, nine Mask R-CNN models were trained at different levels and their efficiency was measured. The major takeaway from the results is that increasing the number of steps per epoch leads to significant gains in the efficiency of a Mask R-CNN model. The efficiency of our model did not increase when the number of epochs increased between the second and third epochs, but increased between the first and second epochs. After the first epoch, the difference between the mAP values of the 50 steps per epoch model and the 10 steps per epoch model was 0.340, which is significant.

One major limitation of this study was that there weren’t a lot of data points. This is because training and testing the models was a time-consuming process. Mask R-CNN claims to have a fast training time, which is Broad Street Scientific | 2020-2021 | 65

true, but the Mask R-CNN paper (He et al., 2018) refers to the use of an 8-GPU machine. Only one GPU was used to train our models. It took around three days to train all the models and about a day and a half to extract the mAP values. If this study were to be repeated, getting more data points might be the best thing to do. In future research, I may pay for training the models on Amazon Web Services (AWS) Cloud Computing, which will lead to faster training times.

Overall, the applications of this study are very useful not only to self-driving cars but also to R-CNN models in general. Higher steps per epoch values yield massive gains in model efficiency. It may be useful for self-driving car companies to maximize this value as much as they possibly can afford. These companies have access to highly advanced machines that can yield fast training times and have a long time overall to train them, so they can train their models for a large number of epochs and a large number of steps per epoch for very large amounts of training data. The models that were trained in this study performed well overall given the low amount of training levels they had relative to usual models. I have no concern over the performance of self-driving car object detection models given how long they are trained and tested for.

5. Acknowledgements Thank you to Kenan Hasanaliyev for helping me learn how to use Google Colab, proofreading this paper, and giving me some good suggestions on what to change. Thank you to Alin Cijov for putting together the self-driving car dataset. It is not an easy feat to put together a database of that size. Thank you to Jason Brownlee for putting together a great tutorial on getting started with Mask R-CNN. Thank you to Robert Gotwals for teaching me many Data Science skills in his course at the NCSSM Online Program and for providing resources that helped me a lot in the process of writing this paper.

Cijov, A. (2020). Self-driving cars - object detection dataset. Retrieved 2020-11-28, from https://www.kaggle. com/alincijov/self-driving-cars Girshick, R. (2015). Fast r-cnn. Retrieved from https:// arxiv.org/pdf/1504.08083.pdf Google. (2017). Google colab. Retrieved from https://colab.research.google.com/ He, K., Gkioraxi, G., Dollár, P., & Girshick, R. (2018). Mask r-cnn. Retrieved from https://arxiv.org/pdf/1703.06870. pdf Hui, J. (2018). map (mean average precision) for object detection. Retrieved 2020-12-04, from https:// jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173 JetBrains. (2020). Pycharm community edition version 2020.3. Retrieved from https://www.jetbrains.com/pycharm/download/ Khandelwal, R. (2019). Object detection using mask r-cnn on a custom dataset. Retrieved 2020-12-04, from https:// towardsdatascience.com/object-detection-using-mask-rcnn-on-a-custom-dataset-4f79ab692f6d Matterport. (2019). Matterport mask rcnn. Retrieved from https://github.com/matterport/Mask_RCNN O’Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. Retrieved from https://arxiv.org/ pdf/1511.08458.pdf Python Software Foundation. (2019). Python version 3.7.

Retrieved from http://www.python.org

6. References Brownlee, J. (2019a). How to train an object detection model with keras. Retrieved 2020-11-28, from https:// machinelearningmastery.com/how-to-train-an-objectdetection-model-with-keras/ Brownlee, J. (2019b). How to choose loss functions when training deep learning neural networks. Retrieved 202012-04, from https://machinelearningmastery.com/howto-choose-loss-functions-when-training-deep-learningneural-networks/

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OPTIMAL TRAINING METHODS FOR NEURAL NETWORKS WHEN PREDICTING STOCK MARKET PRICES Evan Zhang Abstract Since the technological revolution in the early 2000s, predicting stock market prices with computer-generated models has gained traction. In this paper, optimal training methods for Long-Short Term Memory (LSTM) neural networks were analyzed. The dataset was split into three clusters, a control (Dow Jones Industrial), a “similar” group, and a “differing” group. Data from two large tech companies, Facebook and Microsoft, were used as validation data. The “similar” cluster of data consists of large tech companies (Apple, Amazon, Netflix, Google), and the “differing” group consists of four large automobile companies (Toyota, Honda, Ford, Lincoln). After the LSTM model was trained on these three clusters of data, the average error and direction prediction accuracy of the predicted price compared to the actual price were checked. It was found that, on average, the iterative method produces better predictions than the sequential method, and that training a model using a “similar” or “differing” cluster of data does not produce significantly different results.

1. Introduction In the late 2000s and early 2010s, predicting stock markets with algorithms and mathematical models was a popular and important objective. In particular, algorithmic trading, where a computer program follows a set of pre-defined instructions to buy, sell, or short a stock, exploded in popularity due to its theoretical ability to generate profits at rates unparalleled by any human trader (Seth, 2020). In reality, however, professional traders were still far more accurate at predicting stock market trends, mostly due to the variability and non-linearity that are inherent in a pre-defined mathematical model of the stock market. The weight of each variable fluctuates situationally; for example, a moving average may be more indicative of an upwards trend in one situation, whereas the Relative Strength Index (RSI) may be more powerful in another situation (Chen, 2020).

More recently, neural networks have been implemented as a means of predicting stock market prices. The stock market is a textbook example of neural network implementation; there is tons of data available, many variables and metrics can be devised from the closing price of each stock over time, and the non-linearity of the stock market is well suited to a neural network, which can generate powerful non-linear models. This development has completely changed the world of algorithmic trading, as according to a recent article, over 80% of the stock market is now automated (Li, 2020). Recent models can accurately predict the direction (up/down trend) of the stock market with around 60%−85% accuracy (Chong et al., 2017) (H. Hu et al., 2018).

Previous research on using neural networks for stock market predictions have used various model architectures, from FLANN (Functional Link Artificial Neural NetMATHEMATICS AND COMPUTER SCIENCE RESEARCH

works) to GAN-FD (Generative Adversarial Network) models (Mohapatra et al., 2007) (Zhou et al., 2018). In this paper, the optimal training methods for a simpler LSTM (Long-Short Term Memory) neural network were analyzed, by splitting the dataset into three clusters and deploying two training methods.

2. Preliminary Statistical Analysis The stock data were retrieved from Yahoo Finance’s historical data section and split into three groups (clusters): a control group (Dow Jones Industrial), a “similar” group (i.e., similar tech companies — Amazon, Apple, Netflix and Google), and a “differing” group (i.e., large automobile companies — Ford, Honda, Toyota, Lincoln). All datasets except for Facebook range from December 31, 2009, to December 31, 2018, a 9-year span. Facebook’s range is shorter because it officially went public on May 18, 2012.

One purpose of this paper is to determine whether training a neural network with a control, a “similar” cluster of companies, or a “differing” cluster of companies, would produce more accurate predictions of stock market prices. In other words, clusters of stock data for companies in similar fields are grouped and used to train the neural networks for comparison. The “similar” cluster of data consists of large tech companies, and the “differing” cluster of data consists of large automobile companies. The words “similar” and “differing” are used to define the data groups because the test data consists of stock data from two large tech companies (i.e., Facebook and Microsoft).

Before we begin training our model, however, we need to determine the correlation between our test data and our control, “similar” and “differing” groups.

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2.1 – Cluster of “Similar” Companies The raw values (Fig. 1) and scaled values (Fig. 2) of stock price histories for four large tech companies (Amazon, Apple, Netflix, and Google) are shown. The non-scaled values (Fig. 1) do not provide a good representation for the data feeding into the neural network because the input data are scaled (Fig. 2). The stocks in Fig. 2 are scaled from 0 to 1 with a MinMaxScaler, and provide a better representation for the input data since they properly scale companies like Apple, who have more distributed shares and therefore a lower stock price.

(1) where r denotes the correlation coefficient, xi and yi denote the values of the x and y variables in the sample, and and denote the mean of the x and y variables in the sample (Zaiontz, 2020). r > 0.5 indicates the two data samples have a strong correlation in the same direction, whereas r < −0.5 indicates the two data samples have a strong correlation in the opposite direction. −0.5 ≤ r ≤ 0.5 indicates weak or no correlation within the dataset.

Table 1. Table of Pearson Coefficient values within the similar group.

Amazon Apple Netflix Google

Figure 1. Raw values of stocks for four large tech companies

Amazon 1 0.917 0.981 0.948

Apple 0.917 1 0.904 0.931

Netflix 0.981 0.904 1 0.927

Google 0.948 0.931 0.927 1

Ignoring the correlation coefficients of 1 between the same companies, the average value of the Pearson Coefficients in Table 1 is 0.935, which indicates a strong positive correlation in this cluster of data. These results confirm what is expected from the empirical observations 2.2 – Cluster of Control and “Differing” Companies The raw stock values for the “Differing” group (four large automobile companies) are shown in Figure 3. The scaled values for both the control group (DJI) and the differing group are depicted in Figure 4. Observing the trends in Figure 4, there seems to be no noticeable correlation within this dataset.

Figure 2. Scaled values of stocks for four large tech companies.

Looking at the scaled stock values of the four companies (Fig. 2), all companies have upwards trending stocks, and seem to be roughly correlated with each other. This is because there are more and more consumers of technology as time passes. There was a 139% increase in internet users from the beginning of 2010 to the end of 2018 (the range of the stock data used in the present analysis) (Internet World Statistics, 2020).

Pearson’s Correlation Coefficient is used to further analyze the correlation between such clusters of data. The correlation coefficient is defined as: 68 | 2020-2021 | Broad Street Scientific

Figure 3. Raw values of stocks for four large automobile companies.

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(which consists of two large tech companies) and the tech company cluster due to their similarity. Simultaneously, weak or no correlation is expected between the test data and the automobile company cluster. The average value of Pearson Coefficients between test data and control and “similar” groups (Table 4) is 0.931, while the average value between the test data and the “differing” group (automobile companies) is 0.109. As expected, the test data are clearly correlated with the other large tech companies and the DJI, but not very correlated with the car companies.

Table 4. Table of Pearson Coefficient values comparing input data and test data.

Figure 4. Scaled values of stocks for four large automobile companies and the DJI. Table 2. Table of Pearson Coefficient values within the non-similar groups.

DJI Toyota Honda Ford Lincoln

DJI

Toyota Honda

Ford

Lincoln

1 0.774 -0.393 -0.217 0.922

0.774 1 -0.057 0.299 0.837

-0.217 0.299 0.559 1 0.02

0.922 0.837 -0.277 0.02 1

-0.393 -0.057 1 0.559 -0.277

The average value of non-one Pearson Coefficients (Table 2) is 0.498, indicating that there is weak or no correlation within this dataset. Although this was not expected, the data still serve as a fair alternative to the “similar” group.

2.3 – Cross-Comparison Between Two Clusters The correlation between the cluster of tech companies and the cluster of DJI and automobile companies is also calculated in terms of Pearson Coefficients (Table 3). It seems that the two clusters do not have a significant correlation, as the average Pearson Coefficient value is 0.463.

DJI Toyota Honda Ford Lincoln Amazon Apple Netflix Google

Facebook

Microsoft

0.933 0.433 -0.536 -0.451 0.836 0.886 0.878 0.879 0.966

0.956 0.623 -0.463 -0.387 0.828 0.983 0.941 0.97 0.966

3. Neural Network Model 3.1 – Input Variables 20 input variables were taken from Qiu et al. and were used in this study. Five of those variables were obtained directly from the dataset, including the opening price (Ot), closing price (Ct), highest price (Ht), and lowest price (Lt), as well as the volume of the stock (Vt). The rest of our inputs were created from these five main variables (Table 5).

Table 3. Table of Pearson Coefficient values when cross-comparing the two clusters.

DJI Toyota Honda Ford Lincoln

Amazon

Apple

Netflix

Google

0.927 0.567 -0.436 -0.437 0.767

0.959 0.694 -0.466 -0.339 0.848

0.912 0.587 -0.382 -0.372 0.777

0.975 0.71 -0.465 -0.28 0.888

2.4 – Input Data vs Test Data Comparison A strong correlation is expected between the test data MATHEMATICS AND COMPUTER SCIENCE RESEARCH

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Table 5. Table of input variables. Terminology is from Qiu and Song 2016 (Qiu et al., 2016).

Figure 5. LSTM Input Gate: Decides what values from the input to update the memory state (Colah, 2015).

Using these definitions, a list of the following 20 input variables was compiled, as shown here: Ct, Ot, Ht, Ct, OBVt, MA5, BIAS6, PSY12, ASY5, ASY4, ASY3, ASY2, ASY1, stochKt, MOM4, OSCP5, RSI14, MACDt, Signalt, MACD-Differencet.

This data were then preprocessed and scaled using a MinMaxScaler with a feature range from 0 to 1. In this study, the period was set at 14, which means that when predicting the price for any given day, the model will look at the past 14 data points to make that prediction. Therefore, the dimensions of our array are (x, 14, 20), where x is the number of test cases, 14 is the period, and 20 is the number of input variables.

3.2 – Model Architecture This model uses a LSTM (Long Short-Term Memory) Network for the time series forecasting. The LSTM is a variant of a recurrent neural network that is trained through backpropagation (Brownlee, 2020). Instead of neurons that are traditionally found in a feedforward neural network, LSTM networks have memory blocks that are connected through layers. Each memory block has components that allow it to control the flow of information through it. It does this through three gates that serve to protect and control the cell state (Colah, 2015). The three gates are shown in Figures 5, 6 and 7. The LSTM memory block as a whole is shown in Figure 8.

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Figure 6. LSTM Forget Gate: Decides what information to throw away from the memory block (Colah, 2015).

Figure 7. LSTM Output Gate: Decides what information to output based on the input and memory state of the block (Colah, 2015).

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The loss function used for the present model is MeanSquared Error (MSE), defined as: (3) where denotes the actual value on the t−th day and yt denotes the predicted value on the t−th day. Using this loss function should slowly minimize the distance between the values predicted by the model and the actual closing price.

4. Training Methods Figure 8. Image of LSTM memory block as a whole (Colah, 2015).

Figure 9. Image of LSTM Model Architecture The model has three layers: a dense input layer with 14 memory blocks, a hidden LSTM layer with 45 memory blocks and an output layer. The number of neurons in the dense layer was chosen because period is 14. The number of neurons in the LSTM hidden layer is determined by the equation: (2) where Ns is the number of samples in the training data set (2265), Ni is the number of input neurons (14), No is the number of output neurons (1), and the value α is some arbitrary scalar typically ranging from 2 to 10 (Hobs, 2015) (Hagan et al.). The range of hidden neurons is therefore 15.1 ≤ Nh ≤ 75.5, and this model sets the units of the LSTM layer to be the integer closest to the middle of that range.

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This paper also analyzes whether there is a difference between different training methods on each cluster. Training, especially when there is a large amount of data in the dataset, is oftentimes computationally expensive. Optimizing the training process is necessary to speed up the time needed to train an accurate model. In this paper, three methods of training were used. For each of the three training methods, the number of times trained on a dataset is fixed and denoted as k, while n is the number of datasets in the cluster.

Method 1 (Linear): Train the model on a single dataset k times. In this experiment, a model was trained on just the DJI as a control.

Method 2 (Sequential): Train the model sequentially, by training the model on one dataset k/n times, then training the model on the next dataset, and so forth.

Method 3 (Iterative): Train the model n times iteratively, by training the model on one dataset, moving on to the next dataset, and doing this process k/n times.

In our experiment, we set k = 12, and n = 4. To prevent overfitting and practice basic early stoppage (Wong et al., 2020), a cross-validation dataset (Facebook and Microsoft data from October 9, 2014, to December 18, 2015, and October 4, 2016, to December 13, 2017) was used in training.

Table 6 shows all the parameters of our model: Table 6. Table of Model Parameters Parameter Architecture

Value Dense (14) → LSTM(45) → Dense(1) Number of Input Variables 20 Period 14 Epochs 12 1 Batch Size Loss Function Optimizer

Mean Squared Error adam

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Table 7. DJI Model. Avg MSE: 3.221, Avg DPA: 0.516

5. Results and Discussion For our analysis, four test cases were used, two from Facebook, two from Microsoft. The test cases span two periods of time, one from October 9, 2014, to December 18, 2015 (FB 1 & MSFT 1), and the other from October 4, 2016, to December 13, 2017 (FB 2 & MSFT 2). Each of these timeframes consisted of 300 data points.

Two metrics were deployed to measure the accuracy of the model. The first metric is Mean Squared Error (MSE), as defined in Equation 3. The other metric is Direction Prediction Accuracy (DPA), defined as: (4) (5) where denotes the actual value on the i−th day, yi denotes the predicted value on the i−th day, and Pi tells whether the predicted direction of the model was correct (H. Hu et al., 2018). Some argue that DPA may be a more important metric than MSE since investors are ultimately trying to maximize profits rather than to minimize forecast errors (Lee, 2007).

5.1 – Results The MSE and DPA of our models, including plus-minus 2 standard deviations (i.e., approximately 95% confidence intervals), are summarized in Tables 7 through 11.

We trained the models as follows: DJI model: Each model was trained 12 times on just DJI data.

Sequential - Tech: The model was trained 3 times on Amazon data, 3 times on Apple data, 3 times on Netflix data, and then 3 times on Google data.

Iterative - Tech: The model was trained on Amazon data, Apple data, Netflix data, and then Google data. This training process repeats 4 more times for each model.

Sequential - Auto: The model was trained 3 times on Toyota data, 3 times on Honda data, 3 times on Ford data, and then 3 times on Lincoln data.

Iterative - Auto: The model was trained on Toyota data, Honda data, Ford data, and then Lincoln data. The training process repeats 4 more times for each model.

For each of these training methods, 30 differently initialized models were generated and used to obtain the results shown in Tables 7 through 11.

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Test Case FB 1 FB 2

MSE 3.364±1.433 7.568±6.423

DPA 0.530±0.031 0.509±0.035

MSFT 1 MSFT 2

1.026±0.321 0.926±0.821

0.497±0.026 0.527±0.055

Table 8. Sequential Training Method with “Similar” Cluster. Avg MSE: 2.606, Avg DPA: 0.521

Test Case FB 1 FB 2 MSFT 1 MSFT 2

MSE 3.066±1.838 5.733±5.956 0.762±0.194 0.862±1.007

DPA 0.532±0.044 0.513±0.054 0.498±0.050 0.539±0.076

Table 9. Iterative Training Method with “Similar” Cluster. Avg MSE: 2.504, Avg DPA: 0.519

Test Case FB 1 FB 2 MSFT 1 MSFT 2

MSE 2.966±1.410 5.508±5.373 0.774±0.161 0.768±0.83

DPA 0.532±0.054 0.509±0.054 0.505±0.045 0.531±0.058

Table 10. Sequential Training Method with “Differing” Cluster, Avg MSE: 2.793, Avg DPA: 0.522

Test Case FB 1 FB 2 MSFT 1 MSFT 2

MSE 3.190±1.277 6.076±4.292 0.898±0.206 1.007±0.814

DPA 0.535±0.026 0.519±0.030 0.495±0.036 0.539±0.034

Table 11. Iterative Training Method with “Differing” Cluster. Avg MSE: 2.228, Avg DPA: 0.520

Test Case FB 1 FB 2 MSFT 1 MSFT 2

MSE 2.740±0.658 4.736±2.423 0.752±0.091 0.685±0.434

DPA 0.537±0.039 0.514±0.054 0.501±0.039 0.530±0.052

5.2 – Discussion The iterative method with the “differing” cluster of data produced the most accurate results and had the lowest confidence intervals out of all of the models. In addition, on average, the iterative training method produced more accurate results compared to the sequential training method. Training the model on just the DJI produced the worst predictions, even though the average Pearson’s CorrelaMATHEMATICS AND COMPUTER SCIENCE RESEARCH

tion Coefficient between the DJI and Facebook/Microsoft indicates a strong positive correlation between the data (0.945). The efficacy of the models could be better understood by analyzing their accuracy as they train through the epochs.

In the “similar” cluster of data, the training loss is quickly minimized and has occasional fluctuations, as depicted in Figure 10. In contrast, the iterative model for the “differing” cluster of data has great fluctuations in its error as depicted in Figure 11, which may be due to the fact the model can’t approach a proper local minimum since it sees completely different data for each iteration in training.

Since the elements within the tech group are correlative but the elements within the car group are not, the loss fluctuates more when using the car dataset to train the model.

Figure 11. Different methods applied to “differing” cluster.

More importantly, the validation loss seems to consistently favor the iterative and sequential training methods over the control. This may be due to the fact that the two clusters of data have more unique data compared to the control, therefore reducing overfitting of the data. The iterative model seems to do slightly better than the sequential methods in the graph with the validation data, though the difference is too marginal in these two test samples The differences in Direction Prediction Accuracy between the models are too marginal to be conclusive.

Figure 10. Different methods applied to “similar” cluster.

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6. Concluding Remarks In this paper, unique training methods for LSTM neural networks were analyzed and compared when predicting stock market prices. It was concluded that, on average, the iterative method produces better predictions than the Broad Street Scientific | 2020-2021 | 73

sequential method, and that training a model using a “similar” or “differing” cluster of data does not produce significantly different results.

7. Further Research This paper provides a great foundation for further research regarding stock market price prediction with Long-Short Term Memory models, and from it, there are many possibilities for further research. Our loss function in this paper was Mean-Squared Error, which effectively minimized the actual and predicted price discrepancy; however, as noted by Lee, investors are ultimately trying to maximize profits rather than minimize forecast errors (Lee, 2007). As such, Lee suggests that the loss function be MFTR (mean forecast trading returns) or MCFD (mean correct forecast directions), both of which take into account whether the actual price increased or decreased compared to the predicted price.

Variables aside from the ones from Qiu and Song’s pa per are also possible. Some of these variables include GDP acceleration, inflation, unemployment, and news sentiment (McMillan, 2016) (Chen, 2009) (Atkins et al., 2018).

If this were done, the user would need to be careful not to overdo the number of features in the model (Amatriain, 2015). Different groupings for the clusters of data or larger clusters of data could also be used, which supposedly should improve accuracy (Halevy et al., 2009). Our stock scraping web application, which creates a zipped folder of multiple stocks, can help streamline the process of acquiring large amounts of data (Zhang, 2020). In the future, we plan to increase the amount of data inputted in to the network and generate more models to narrow the confidence intervals.

For anyone who would like to replicate the experiment, the code and datasets are available on Kaggle (Zhang, 2020).

8. Acknowledgements Thanks to Dr. John Muth for inspiring me to work on Data Science and Software Engineering projects. I couldn’t thank you enough for the impact you’ve made on my interests and future career path.

9. References Amatriain, X (2015). In Machine Learning, What is Better: More Data or better Algorithms. https://www.kdnuggets.com/2015/06/machine-learning-more-data-better-algorithms.html.

Atkins, A., Niranjan, M. & Gerding, E. (2018). Financial news predicts stock market volatility better than close price, The Journal of Finance and Data Science Volume 4, Issue 2, June 2018, Pages 120-137. https://doi. org/10.1016/j.jfds.2018.02.002.

Brownlee, J. (2016). Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras. https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/.

Chen, J. (2020). Relative Strength Index (RSI). https:// www.investopedia.com/terms/r/rsi.asp.

Chen, Shiu-Sheng (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators, Journal of Banking Finance Volume 33, Issue 2, February 2009, Pages 211-223. https://doi.org/10.1016/j.jbankfin.2008.07.013.

Chong, E., Han, C. & Park, F. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies, Expert Systems With Applications 83 187-205. https://doi.org/10.1016/j. eswa.2017.04.030.

Colah (2015). Understanding LSTM Networks. https:// colah.github.io/posts/2015-08-UnderstandingLSTMs/.

H. Hu, Tang, L., Zhang, S. & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends, Neurocomputing, https://doi. org/10.1016/j.neucom.2018.01.038.

Hagan, M., Demuth, H., Beale, M. & Jesús, O. (2014). Neural Network Design 2nd Edition. http://hagan.okstate. edu/NNDesign.pdf.

Halevy A., Norvig, P. & Pereira, F. (2009). The Unreasonable Effectiveness of Data. 2009 IEEE Computer Society. https://static.googleusercontent.com/media/research. google.com/en//pubs/archive/35179.pdf.

Hobs (2015). How to choose the number of hidden layers and nodes in a feedforward neural network? https://stats. stackexchange.com/a/136542.

Internet World Statistics (2020). Internet Growth Statistics. https://www.internetworldstats.com/emarketing. htm.

Lee, Tae-Hwy (2007). Loss Functions in Time Series Forecasting. http://www.faculty.ucr.edu/~taelee/paper/ lossfunctions.pdf.

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Li, Y. (2019). 80% of the stock market is now on autopilot. https://www.cnbc.com/2019/06/28/80percent-of-thestock-market-is-now-on-autopilot.html.

Mohapatra, C. & S. Ron (2007). FLANN Based Model to Predict Stock Price Movements of Stock Indices. http:// ethesis.nitrkl.ac.in/62/1/10307001.pdf.

McMillan, David G. (2016). Which Variables Predict and Forecast Stock Market Returns? Available at SSRN: https://ssrn.com/abstract=2801670 or http://dx.doi. org/10.2139/ssrn.2801670.

Qiu M. & Song Y. (2016). Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. PLoS ONE 11(5): e0155133. doi:10.1371/journal.pone.0155133.

Seth, S. (2020). Basics of Algorithmic Trading: Concepts and Examples. https://www.investopedia.com/articles/ active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp.

Wong S., Chan, J., Azizi, L. & Xu, R. (2020). Time-varying neural network for stock return prediction. https://arxiv. org/pdf/2003.02515.pdf.

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EXAMINING X-RAY EMISSIONS FROM REPEATING FRB 180916.J0158+65 Kylie Burgess Abstract FRB 180916.J0158+65 is a fast radio burst (FRB) with a period of 16.35 +/- 0.18 days. Its cycle consists of approximately 12 consecutive days during which no radiation is observable, followed by 4 days during which bursts are able to be released.

FRBs are distant, bright signals that have only been observed in the radio spectrum and whose origins are unknown. Due to its repetitive nature, FRB 180916 presents the unique opportunity to observe an FRB in a section of the spectrum other than radio. Detection, or lack thereof, of the FRB in other sections of the electromagnetic spectrum will provide further context about its progenitor. In this study, previous X-ray observations of the location of FRB 180916 were searched for X-ray radiation. We analyzed data from the archives of two telescopes: Chandra, which took pointed observations of FRB 180916 during December of 2019, and ROSAT, which scanned FRB 180916 during an all-sky survey from 1990 to 1991.

We analyzed the Chandra data with Ciao software and the ROSAT data with HEASARC software. The data did not reveal any significant X-ray radiation between the energies of 0.07 keV and 7.29 keV from FRB 180916.

1. Introduction 1.1 – Fast Radio Bursts Fast Radio Bursts (FRBs) are extremely bright radio signals originating from distant sources. They were discovered in 2007 when undergraduate David Narkevic was reviewing data from a 2001 Parkes Observatory study with radio astronomer Dr. Duncan Lorimer (Mack, 2014). The pulse profile from the ”Lorimer Burst” is shown in Figure 1. FRBs are unique because they have a dispersion measure and redshift that imply they are 106 times farther away from Earth than a pulsar with a similar peak flux density.

Therefore, the source of the FRB must be extremely energetic compared to known radio emitters (Petroff et al., 2019). Although most observed FRBs are isolated events, a recent CHIME study has identified nine that are known to repeat, one of which (FRB 180916.J0158+65) has a known periodicity (Dolch et al., 2020)(Amiri et al., 2020). The origins of FRBs, although unknown, are a subject of current discussion.

1.1.1 – Calculated and Observed Properties 1.1.1.1 – Propagation Effects Multiple phenomena are responsible for the distortion of radio signals as they travel from the source to Earth, the most important and well-studied of which being dispersion. Dispersion measure is defined as the constant of proportionality relating the frequency of a wave with the difference in time between when the wave would have arrived if it were traveling through a vacuum and when it actually arrived (Berkeley.edu). Lower-frequency waves are slowed down more by the intergalactic medium, so the arrival time of a wave decreases with frequency. The Lorimer Burst is an example of a signal with obvious dispersion (Fig. 1). Radio waves from the burst are first ob76 | 2020-2021 | Broad Street Scientific

served at higher frequencies. As time goes on, waves from lower frequencies arrive, creating a curve in the frequency-time plot.

Figure 1. The light curve and frequency-time plot of the Lorimer burst, the first detected FRB. The burst shows obvious dispersion in the frequency-time plot (Lorimer et al., 2007).

For a signal that spans across a bandwidth of frequencies, the time delay caused by dispersion can be written as: (1) Here v1 is the lowest frequency observed in gigahertz, v2 is the highest frequency observed in gigahertz, and the integral of nedl from 0 to d is the dispersion measure (Berkeley.edu).

Dispersion measure (DM) is considered one of the most important parameters of radio sources because it can be used to provide an estimate for the distance of the source.

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Dispersion measures for FRBs range between 103.5 and 1909.8 pc/cm3. The dispersion measure is equal to the average electron density between the observation point and the source times the distance from the source. Therefore, in order to determine the distance of an object, the dispersion measure must be divided by ne, which can be approximated as 0.05cm-3.

Dispersion measure can be calculated from the frequency-pulse phase plots for FRBs. The DM of FRBs is one of their distinguishing factors, as although they have similar peak flux densities to pulsars, their DMs are much larger, suggesting a greater distance from Earth.

1.1.1.2 – Calculated Properties Of the most basic calculated properties of FRBs is distance. A distance limit can be derived using equation 1.

Once distance constraints are found, the luminosity of the source, L, can be calculated using the distance constraint, dL, the differential flux per unit logarithmic frequency interval, Svδv, and a parameter correcting for redshift, (1 + z): (2) Brightness temperature, or the thermodynamic temperature of a black body of equivalent luminosity, is a useful derived property for all radio sources. For FRBs, this brightness value is extremely large, with a value of greater than 3.5 × 1035 K (Petroff et al., 2019).

1.1.2 – Previous Research Few attempts to study FRBs in other sections of the spectra have been made. One of the most notable attempts to detect FRBs in the X-ray spectrum was led by Dr. Paul Scholz of the National Research Council of Canada (Scholz et al., 2017). Scholz et al. took simultaneous radio and X-ray observations of known repeater FRB 121102. During the project, XXM-Newton and the Green Bank Telescope observed the repeater between August and September of 2016, then Chandra and the Green Bank Telescope observed between January and December of 2017. Twelve bursts were detected in the radio spectra, but the bursts were not detected in the X-ray spectra.

If the source had been from a magnetar giant flare, we would expect to see short duration X-ray bursts with a similar timescale to the FRB. X-ray bursts have been observed from galactic magnetars. Although no X-ray emissions were found during the study, this may be because the range of photon energies able to be observed by XMM and Chandra is only a fraction of the possible energies at which X-ray bursts can emit (Fig. 2). In addition, since it is possible that not all FRBs have the same progenitor, other FRBs may be visible at this range of energies.

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Figure 2. Each line represents the theoretical possible photon energies of a giant flare spectrum. The shaded region is the range of photon energies the Chandra and XMM telescopes are able to observe (Scholz, 2017). The majority of energy from bursts was therefore out of the range of the Chandra and XMM-Newton telescopes.

1.2 – X-Ray Astronomy The primary data set after an observation is an events list, which is a table of received photons and their properties. The events list can be binned using different axes to create different plots. For example, an image is created by binning on spatial axes. A spectrum is binned on spectral axes. Light curves are binned on time and energy axes.

Before data can be binned, though, it must be manipulated into a usable form. While collecting data, an X-ray telescope may dither, or move in the sky. Therefore, the data must be non-dithered to display the true positions of photons in relation to one another. A bad pixel file will be created for the data, which detects “bad pixels” and deletes them from the data set. Finally, calibration data for the X-ray telescope will be held in a database called CALDB. CALDB is updated often, as calibration changes over time for a telescope. All of the software and databases necessary for X-ray analysis, including ds9, the image viewer, are contained in the Ciao software package (Harvard.edu).

1.3 – FRB 180916.J0158+65 1.3.1 – Past Observations The CHIME survey that determined the period of FRB 180916.J0158+65 found the FRB to have a periodicity of 16 days, with a 4-day window of bursts (Amiri et al., 2020).

The number of pulses detected in each window is not constant—during some windows multiple bursts were detected, while in others, no bursts were detected. According to current data, the probability that the 16.35-day periodicity was caused by random bursts is 10-7 (Amiri et al., 2020).

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19 bursts have been observed, and the dynamic spectra of the 17 bursts observed during the CHIME study are shown in Fig. 3. The DM of the bursts is estimated to be about 348.82 pc cm-3. The right ascension of the FRB is 01h 58m 00.7502s, and the declination is +65̊43’ 00.3152”.

Figure 4. The relativistic wind of the pulsar interacts with the stellar wind of the O/B star to create a spiral. Radio emissions can only escape the system through the back wall. Diagram from Lyukitov et al., 2020.

Figure 3. The dynamic spectra of 17 bursts observed during the CHIME survey (Amiri et al., 2020). The arrival phase within the cycle is displayed in the top left corner of each burst. Each pulse has been corrected for dispersion, assuming a dispersion measure of 348.82 pc cm-3.

1.3.2 – Current Models As with other FRBs, most current models focus on neutron stars as the progenitor of FRB 180916.J0158+65. In particular, most models focus on neutron-star binaries, as the period of 16 days is in the range of a period of such a binary. Lyukitov et al. (2020) proposed that the periodic FRB could originate from a mild pulsar in a tight binary with an O/B star. In this model, the relativistic wind of the pulsar interacts with the stellar wind of the O/B star to create a spiral around the binary (Fig. 4). Radio emissions are unable to escape the system except through the back wall of the spiral. As a result, the radio emissions are only observable as pulses that occur in the 4-day window of the binary’s phase. In such a model, mass would also be transferring from the O/B star to an accretion disk around the neutron star. This type of binary system, known as a low-mass X-ray binary system, results in the synthesis of X-rays. Whether these X-rays are observable is unknown, as they might be masked by the winds of the binary system.

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Yang Zou (2020) proposed that the periodic FRB could originate from a magnetar in a binary system with another dense object. In this model, the periodicity of the FRB is caused by precession of the neutron star caused by its orbit.

Other theories include: • FRBs are caused by magnetars with a super-long period (the longest known period of a magnetar is currently 6.7 hours)(Beniamini et al., 2020).

• FRBs are caused by a white dwarf in a neutron starwhite dwarf binary system filling its Roche lobe and transferring mass to the neutron star in short bursts (requires high eccentricity)(Gu et al.,2020).

• FRBs are created during axion emission by cosmic superstrings (Chernoff et al., 2020).

1.4 – Significance of Research The period of the system provides insight into the emission mechanisms of FRB 180916.J0158+65. Since the period is roughly 16 days, the source of the FRB is likely engaged in a neutron-star binary system. If the binary system is emitting X-rays while creating radio bursts, it may be engaged in mass transfer, or one of the components may be a flaring magnetar. Searching for X-ray emissions from FRB 180916.J0158+65 will validate or eliminate possible models. The FRB may serve as a Rosetta stone for the origins of all FRBs. In addition, setting upper and lower limits on source luminosity, depending on the radiation observed, will provide more information about the FRB and how it should be observed in the future. Setting bounds on source luminosity will also provide insight into what internal processes are occurring within the FRB. Some mechanisms for radiation emission may be ruled out if they would result in a source luminosity that is outside of the bounds determined in our research.

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1.5 – Research Question Our research question is as follows: The periodic nature of FRB 180916.J0158+65 has inspired multiple models describing its origins. The periodic FRB may act as a Rosetta Stone for the progenitor of other FRBs. By examining the behavior of FRB 180916.

J0158+65 in all regions of its spectra, models may be narrowed down or validated. How do the X-ray spectra of the periodic FRB compare with its radio spectra? What implications does this have for the emission mechanisms and origins of the FRB? 2. Methods and Results To answer our research question, we examined previous X-ray observations of FRB 180916.J0158+65. We searched for X-ray data near the source in databases for the Chandra and ROSAT missions. We used the Ciao suite of software to bin the Chandra X-ray data on multiple axes.

We manipulated each event file to create images, which we then filtered by the region of the FRB. We generated light curves and spectra from the filtered data.

To analyze the ROSAT data, we utilized software from the FTools and XANADU packages. The data were collected from the National Aeronautics and Space Administration’s High Energy Astrophysics Science Archive Research Center (HEASARC). In particular, we used XSelect, the command line interface for FTools, to filter the data by the region of the FRB, then generate images and light curves from the data.

Through close analysis of the data products, we determined if any X-ray radiation is detectable from the source in either data set. More information on X-ray data analysis can be found in section 1.2. Since no radiation from the source is present, we calculated the upper bound of the source luminosity based on the flux for the region of the FRB. To do so, we used the images generated to define a region of about 5 arcseconds around the projected location of the FRB (Figs 5, 6, 7 and 8). We then used Ciao and HEASoft to calculate the flux in that region during the observation. Finally, we used luminosity equation 2 to find the upper limit of radiation from FRB 180916 (Table 1).

In order to compare our data to the data of a known X-ray emitter, we analyzed data from magnetar SGR 052566. We created images of the magnetar using Ciao and HEASoft (Figs 9 and 10) and found its X-ray luminosity using the process described above (Table 1). Finally, we compared the luminosity of the magnetar to the upper limit placed on the luminosity of FRB 180916.

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Figure 5. Image of Chandra observation 23081 of FRB 180916, compiled with the ds9 image viewer (Joye Mandel, 2003). The yellow circle shown has a radius of 0.1 arcminutes and is centered at declination 01:58:00.7502 and right ascension +65:43:00.3152, which is the location of the FRB in Epoch J2000 celestial coordinates. Each pixel represents a photon, with its position corrected based off its point of collision with the X-ray telescope. Blue pixels denote hard X-rays, green pixels denote intermediate X-rays, and red pixels denote soft X-rays. The density of photons is constant throughout the observed area, so there is no evidence of a point source.

Figure 6. Light curve created from Chandra observation 23081 of FRB 180916, compiled with the dmextract script of the Ciao software (Fruscione et al., 2006). The light curve does not show significant peaks or trends, and the number of counts per minute is low. This implies the radiation from the region is consistent with background radiation.

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Figure 7. Image of ROSAT observation 930503 of the region of the sky containing FRB 180916, compiled with the ds9 image viewer through the XSelect tool.

The yellow circle shown has a radius of 5 arcminutes and is centered at the location of the FRB. Each pixel represents a photon, with its position corrected based off its point of collision with the X-ray telescope. Blue pixels denote hard X-rays (energy of over 0.4 keV), while red pixels denote soft X-rays (energy of lower than or equal to 0.4 keV). The density of photons is constant throughout the observed area, so there is no evidence of a point source.

Figure 8. Light curve created from ROSAT observation 930503 of the region of the sky containing FRB 180916, compiled with the XSelect tool of the FTools package. The light curve does not show significant peaks or trends, and the number of counts per minute is low. This implies the radiation from the region is consistent with background radiation.

Figure 9. Image of Chandra observation 01957, compiled with the ds9 image viewer (Joye Mandel, 2003).

The yellow circle shown has a radius of 0.1 arcminutes and is centered at right ascension 05 26 00.7 and declination -66 04 35, which is the location of SGR 0525- 66 in Epoch J2000 celestial coordinates. Each pixel represents a photon, with its position corrected based off its point of collision with the X-ray telescope. There exists an obvious concentration of photons in the specified region, as well as an obvious cloud of photons around the region. This cloud denotes the Large Magellanic Cloud, in which the magnetar resides.

Figure 10. Image of ROSAT observation 932907 of the region of the sky containing SGR 0525-66, compiled with the ds9 image viewer through the XSelect tool.

The yellow circle is centered at the location of SGR 0525-66. Each pixel represents a photon, with its position corrected based off its point of collision with the X-ray telescope. Blue pixels denote hard X-rays (energy of over 0.4 keV), while red pixels denote soft X-rays (energy of lower than or equal to 0.4 keV).

The image shows an obvious X-ray source at the location of SGR 0525-66.

Multiple key characteristics differ between the Chandra and the ROSAT missions and their resultant data. The

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Chandra telescope data were collected from the ACIS-S instrument. Therefore, the energy range observed was 0.20 to 8.86 keV (Chandra.Harvard.edu, 2008). The ROSAT data, collected by the PSPCC instrument, has an energy range of 0.5 to 0.9 keV (heasarc.gsfc.nasa.gov, 2001).

The Chandra telescope is able to collect a wider range of energies and therefore is more likely to capture emissions from the FRB. Another characteristic of X-ray telescopes is angular resolution. This value describes the minimum angular distance between two objects required for them to be distinguishable from each other. The angular resolution of the ROSAT telescope was 5 arcseconds, while the angular resolution of the Chandra telescope is 0.5 arcseconds.

Therefore, the Chandra telescope is able to distinguish between objects with a smaller angular distance between them as compared to the ROSAT telescope.

The raw data from both the ROSAT mission and the Chandra mission are saved in the form of a .FITS file. A .FITS file is a table that records the time, position, and pulse height of each photon event sensed by the telescope.

The .FITS files for the ROSAT mission, along with the calibration data for the telescope, can be downloaded from the online HEASARC archives. The .FITS files for the Chandra telescope are available in the Chandra archives.

This does not mean that FRB 180916 does not release X-ray radiation. The source may be too dim for any X-rays to be significant against noise when observed from Earth.

In addition, the FRB might not have released any bursts during either observation. If X-rays are released only during bursts or are released at a greater luminosity during bursts, a lack of observed bursts would make the FRB appear invisible in the X-ray spectrum. An important next step is determining if radio observations were taken of the source simultaneously to the X-ray observations and, if so, if radio bursts were observed.

The upper limit placed on the luminosity of the source is 1.515 x 1042 erg/s. Had the FRB had a greater luminosity than the upper limit, the radiation it produced would have been significant against background radiation. An important characteristic of the upper limit is it is much greater than either luminosity calculated for SGR 0525-66. Therefore, had SGR 0525-66 been located at the same distance as FRB 180916, it would not have been able to be observed.

Therefore, an X-ray source such as a magnetar could be the source of FRB 180916, but be too dim in the X-ray spectrum for X-rays to be observed.

Table 1. The raw flux and source luminosity values for the background noise, FRB 180916 region, and a known X-ray source for both the Chandra and ROSAT telescopes. The luminosity of the source was calculated using equation 2.

We determined that no significant radiation between the energies of 0.07 keV to 7.29 keV was present in either observation. We also defined the upper limit of the luminosity of FRB 180916 to be 1.515 x 1042 erg/s. We also determined that a strong source of X-rays, such as a magnetar, would not have the luminosity necessary to be visible from the location of FRB 180916. However, no conclusions were drawn about radiation outside of the energy range from 0.07 keV to 7.29 keV. To expand upon current research, we can analyze more data from other sources. Currently, there exist public data of FRB 180916.J0158+65 from NuStar and XMM Newton.

The NuStar observation is particularly significant because it observes the FRB in the 3 to 79 keV range, from which the FRB has never been observed (Harrison, F.A. et al., 2013). In addition, a recent X-ray survey was conducted with the eROSITA X-ray telescope. The data are not yet public, but proposals to engage in eROSITA research before their publication are being accepted.

We can also continue observing the FRB in the radio spectrum to search for changes or modulations in its period. Signs of slow spin-down may suggest the source of the FRB is engaged in a binary system. However, a faster decrease in period may imply the source is a magnetar.

Source Name ROSAT FRB 180916 ROSAT SGR 052566 Chandra FRB 180916 Chandra SGR 052566

Raw Flux (erg/ Source Luminosity (erg/s) cm2/s) -13 5.701 x 10 1.515 x 1042

2.397 x 10-11

8.059 x 1036

3.934 x 10-11

1.011 x 1044

1.090 x 10-12

3.017 x 1035

4. Discussion Events recorded in the region of FRB 180916 were uniform with the background for both the Chandra and ROSAT observations. As a result, we can conclude that FRB 180916 was not observed in either data set.

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5. Conclusion

6. Acknowledgements We would like to thank Dr. Jonathan Bennett of the North Carolina School of Science and Mathematics for his guidance and mentorship during our research. We would Broad Street Scientific | 2020-2021 | 81

like to extend our gratitude to the North Carolina School of Science and Mathematics and the North Carolina School of Science and Mathematics Foundation for providing the resources that made our research possible. We would also like to thank Dr. Dongzi Li of the CHIME/FRB Collaboration for allowing access to data from the CHIME radio observation of FRB 180916.

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INVESTIGATING THE PROPERTIES OF GW170729 USING A NEWTONIAN APPROXIMATION Shardool Deshpande Abstract Gravitational waves are recently discovered extrasolar messengers that carry information from astrophysical events. Unlike electromagnetic radiation, gravitational waves weakly interact with matter and are therefore virtually unimpeded as they propagate through space. We study the gravitational wave signal GW170729 from a black hole binary merger to obtain source parameters for the event. We apply a Newtonian approximation to model the inspiral stage of the coalescence. We compare the efficacy of this model with the computationally intensive general relativistic model used in more complex analyses. Using data from the Laser Interferometer Gravitational-Wave Observatory (LIGO), we analyze the time-frequency evolution of the signal using a linear regression. From this regression, we obtain the key parameter of chirp mass. Using the chirp mass, we then calculate other important parameters such as the total mass of the system and the masses of the two merging black holes. We analyze the data further to obtain the luminosity distance and the total energy lost in gravitational waves. Results for these parameters fall within one standard deviation of the results obtained

by LIGO’s general relativistic model. Finally, we discuss the limitations of the Newtonian model in comparison to the relativistic model.

1. Introduction One of the most sensational discoveries ever made in the field of astrophysics occurred in September 2015, when the Laser Interferometer Gravitational-Wave Observatory detected a gravitational-wave signal from a binary black hole merger. This detection confirmed Albert Einstein’s prediction of the existence of gravitational waves in his theory of general relativity and has paved the way for more discoveries in this budding field [1]. With gravitational waves, we can gather data that can be difficult or impossible to gather from electromagnetic (EM) radiation. For example, gravitational waves can help us discover binary black holes. These systems are weak sources of EM radiation, but emit copious amounts of gravitational waves.

The waves are emitted as the black hole orbits shrink.

Another useful characteristic of gravitational waves is the fundamentally weak nature that allows them to travel long distances without interacting with any matter [2]. This information is free of any distortion otherwise experienced

by EM radiation as it reaches observers on Earth. These properties make gravitational waves a valuable tool in astrophysics. In this project, we will analyze a set of LIGO data to locate a signal. We will then estimate parameters from this signal and compare them to LIGO’s official results.

spiral inward, they lose energy in the form of gravitational waves. The second stage, the merger, is marked by the coalescence of the black holes, or when the distance between them is less than the sum of their Schwarzschild radii. The Schwarzschild radius is simply the boundary beyond which even light cannot escape the black hole’s gravitational pull.

The final stage, or ring-down, involves the newly formed black hole settling into its equilibrium state [5].

An important term to note is the chirp, which refers to the increase in frequency (Fig. 1, Middle).

2. Background 2.1 – A Newtonian-based Model for Emission of Gravitational Waves There are three stages in a black hole merger (Fig. 1, Top). In the first stage, the black holes spiral around their common center of mass in nearly circular orbits. As they 84 | 2020-2021 | Broad Street Scientific

Figure 1. Top: Stages of merger matched with straintime plot. Middle: A numerical relativity template of the coalescence. Bottom: Keplerian black hole separation in Schwarzschild radii and the black holes’ relative velocities in terms of the speed of light [1].

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2.2 – Assumptions of Model In this project, we used a Newtonian-based model that operates under a series of assumptions [5]. First, we assumed that the orbit of each black hole is circular. Further analysis revealed that elliptical orbits become nearly circular during the inspiral [6]. Treating the black holes as point masses that orbit a common center of mass, we may write a modified version of Kepler’s third law to describe the system:

background noise (Fig. 2).

(1) Here, is the system’s angular frequency and G is the universal gravitational constant. The mass and radius of the smaller black hole are denoted by m and r, while those characteristics of the larger black hole are denoted by M and R. The chirp mass, , is an important parameter that influences the time-frequency evolution of the system.

The chirp mass is a special ratio of the black hole masses as shown in Equation 2: (2) Our model combines Kepler’s Third Law with expressions based on dimensional analysis for the system’s power emission and mass quadrupole moment. The combination allows us to express the chirp mass of a system as a function of frequency [5]: (3) In this equation, c is the speed of light and f is the chirp frequency. We include the coefficient to account for general relativity. This constant is necessary because Newtonian mechanics alone does not account for gravitational-wave emission. Since we seek to calculate the masses of the system before coalescence, it is useful to define the total mass. Under the assumption that black hole coalescence starts to occur once their separation is equal to the sum of their Schwarzschild radii, we are able to derive an expression for the total mass of the system [5]:

Figure 2. A raw strain-time graph of GW170729. The horizontal axis represents time in seconds, and the vertical axis displays the dimensionless strain on the order of 10-20.

3.2 – Data Processing We utilized the Python libraries of Numerical Python (NumPy) and MatPlotlib as well as the LSC Algorithm Library Suite (LALSuite). To identify the chirp signal in the data, we implemented a series of procedures to eliminate noise and maximize the signal-to-noise ratio (SNR).

First, we applied a Fourier transform to the time-domain data using a built-in NumPy function. We then whitened the data by dividing the frequency domain by an estimate of the amplitude spectral density of the noise. This down-weighted frequencies that were dominated by background noise.

With the data now clean, we move on to the next procedure: matched filtering. Matched filtering identifies the chirp in the signal with the use of a template. We obtained this template by generating a waveform. The PyCBC library contains a built-in function to generate this waveform. We used the SEOBNRv4opt waveform in this project as it models spinning, non-precessing black holes.

We converted the template to the frequency domain. Finally, we imposed the template on the whitened data and integrated over all frequencies. This effectively amplified the signal. We then converted this product back into the time-frequency domain to produce a graphic (Fig. 3).

(4) Here, fc represents the maximum frequency reached by the chirp. Equation 4 will be useful in our procedures.

3. Procedures 3.1 – Data Collection The data from GW170729 were collected from the Laser Interferometer Gravitational-Wave Observatory’s Observing Run 1 [4]. We then downloaded an HDF5 file containing strain-time measurements for a span of 28 seconds surrounding the signal. The data are dominated by PHYSICS RESEARCH

Figure 3. A signal-to-noise time series of GW170729.

The visible peak at 7.3 seconds represents the peak of the chirp.

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3.3 – GW170729 Parameter Estimation To convert our data to the form conducive to parameter estimation, we generated a pseudocolor plot on which we graphed the time-frequency evolution. Aside from time and frequency, there was a third array with “power” values, which are comparable to strain measurements.

Figure 4. The horizontal axis displays the time from the start of the data file in seconds. The vertical axis records the frequencies of GWs detected at each time.

The square root of the power of the signal is displayed by varying the color from dark purple to yellow.

Next, we used a power threshold to filter out the remaining noise in the figure. We set the threshold to 6 and scanned the time and frequency arrays for coordinate pairs that map to a power greater than 6. Locating the chirp at about 7.3 seconds, we can confirm that the rapid increase in frequency with respect to time in Figure 4 was indeed the chirp. We write the filtered values into a comma-separated values (CSV) file. We then opened this file in Microsoft Excel to analyze it.

The first parameter we estimated was the chirp mass, which can then allow us to calculate the total mass and individual masses of the two coalescing black holes. We can examine the relationship between time-frequency evolution and chirp mass by integrating Equation 3. The result is the following equation: (5) Here, f1 is the initial frequency, f2 is the frequency at time , and is our unknown chirp mass. Since is the only unknown variable in this equation, we take the approach of fitting the data with a line. The slope of this line allows us to solve for chirp mass. By subtracting from both sides, we are able to fit the equation with a linear regression. In order to obtain a meaningful linear fit, however, we plotted the time on the horizontal axis with on the vertical axis (Fig. 5).

The graph is not a one-to-one function due to the existence of some noise we could not remove. This is because a few of the chirp’s time values had more than one frequency of high power. However, we do not expect this inconsistency to significantly impact our results. We obtained a slope of -0.00067 from the linear fit. We can solve for the chirp mass by setting the coefficient of in equation 5 equal to the slope of the graph. This process yields a chirp mass of 31.56 solar masses. We calculate a total mass of 73.12 solar masses using Equation 4 and the highest chirp 86 | 2020-2021 | Broad Street Scientific

Figure 5. The vertical axis is a function of frequency that linearizes Equation 5. frequency from Figure 3. To estimate the individual masses, we first compute the mass ratio , which is related to the total mass and chirp mass as follows: (6) Solving this equation for , we obtain a mass ratio of approximately 0.56. Now that we have determined the total mass and mass ratio, we can solve for the masses of the black holes. We obtain a primary mass of 40.87 solar masses and a secondary mass of 32.26 solar masses.

We next solve for the luminosity distance dL with the assumption that the intensity of radiation is inversely proportional to the square of the distance from the source.

The GW intensity can be modeled using dimensional analysis [5]. Thus, the luminosity distance from the source is given by: (7) In Equation 7, m represents the secondary mass, M represents the primary mass, and h represents the strain at the highest chirp frequency. We generate a waveform to identify the peak strain (Fig. 6).

Figure 6. A strain-time waveform for GW170729.

We substitute a value of into Equation 7.

Evaluating the expression, we calculate a luminosity distance of about 1925 pc.

To find the total energy radiated in gravitational waves, we assume the final separation before merger to be the sum of the black holes’ Schwarzschild radii, which allows us to use the relation below [5]: (8) PHYSICS RESEARCH

We simply substitute the primary and secondary masses into the expression to estimate a loss of 4.5 in gravitational waves.

4. Discussion of Results Our results are compiled in Table 1.

Table 1. Comparison of results to LIGO’s Numerical Relativity-based analysis

6. References [1] Abbott, B., Abbott, R., Abbott, T., Abernathy, M., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R., & others (2016). Observation of gravitational waves from a binary black hole merger. Physical Review Letters, 116(6), 061102.

[2] Black, E., & Gutenkunst, R. (2003). An introduction to signal extraction in interferometric gravitational wave detectors. American Journal of Physics, 71(4), 365–378.

[3] Eisenstein, R. (2019). Numerical Relativity and the Discovery of Gravitational Waves. Annalen der Physik, 531(8), 1800348.

Our Newtonian model results fall within the range provided by the numerical relativity analysis in column 2, which indicates that a simple Newtonian-based model can reach the accuracy of general relativity. The relatively high percent error in the luminosity distance calculation can be attributed to uncertainty in the peak strain measurement.

We simply used a template to estimate the peak strain, which was only an approximation of the actual data. While our results are reasonably accurate, there are limitations to this approach. Since spacetime around the system is relatively flat during the inspiral, Newtonian gravity holds.

But as the black holes approach each other at the end of the inspiral, spacetime begins to curve and becomes non-linear. Solving Einstein’s equations is necessary to model this stage, which is why fits to numerical relativity simulations are more accurate [3]. Our analysis also does not take other signal parameters into account. These include the component spins, peak luminosity, and polarization angle. We can account for component spins in future analyses by additionally considering angular momentum in the equations described above.

[4] B.P. Abbott et al (2019). GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs. Phys. Rev. X, 9, 031040.

[5] Mathur, H., Brown, K., & Lowenstein, A. (2017). An analysis of the LIGO discovery based on introductory physics. American Journal of Physics, 85(9), 676–682.

[6] Peters, P. (1964). Gravitational Radiation and the Motion of Two Point Masses. Phys. Rev. 136, B1224–B1232.

5. Acknowledgements I would like to acknowledge Dr. Jonathan Bennett, my mentor at the North Carolina School of Science and Mathematics, for his guidance through the research process. I would also like to acknowledge the North Carolina School of Science and Mathematics and the North Carolina School of Science and Mathematics Foundation for funding the Summer Research and Innovation Program.

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AN INTERVIEW WITH DR. PAMELA DOUGLAS

From left to right, top to bottom: Daniel Jin, BSS Publication Editor-In-Chief; Sriya Mantena, BSS Editor-In-Chief; Akshra Paimagam, BSS Editor-In-Chief; Dr. Jonathan Bennett, BSS Faculty Advisor; Shery Liu, 2021 BSS Essay Contest Winner; and Dr. Pamela Douglas, Ursula Geller Professor of Research in Cardiovascular Diseases in the Department of Medicine at Duke University and Director of the Multimodality Imaging Program at Duke Clinical Research Institute.

How and when did you become interested in cardiology?

tures and how that information could drive improvements in outcomes or improvements in care and outcomes.

I really became interested when I was working in the intensive care unit in my second year of residency. We really enjoy taking care of very sick patients because every brain cell is focused on getting this person out of danger and back to stability and that was very intriguing to me. As I started thinking about a fellowship or specialty after medical school, what was most interesting about cardiology was that clinical science was very respected in cardiology at that time. In most other specialties in internal medicine, if you wanted to be academic, you tended to need to do bench research. I was very interested in the intersection between clinical care and clinical science and not so much interested in basically going back to school to figure out how to become a bench researcher. I figured I'd spent my four years of medical school to become a clinician and a clinical scientist. So that was really how I made that decision was that I could do that combination of things, being a clinician, which I really enjoyed and a clinical scientist, which was intellectually stimulating to me.

Probably the most memorable project was working on heart disease in athletes. [This project] was done in the setting of the Hawaii Ironman Triathlon over more than 15 years. We traveled to Hawaii every fall and set up a noninvasive laboratory on the beach to study the athletes before and after doing this incredible day of exercise, 2.4 mile swim, 112 mile bike ride followed by a marathon run, to see what it did to their heart.

Can you summarize your research in layperson terms? What has been one of your most memorable projects?

The bulk of my research is related to cardiac imaging, taking pictures of the heart and focused rather not on the pictures, but the information that we could get from the pic88 | 2019-2020 | Broad Street Scientific

And lo and behold their hearts. Some of the hearts looked fine, but some of the hearts looked awful and they also leaked muscle proteins, just like people having a heart attack. This was totally memorable to be where we were doing these studies, but also I had an incredible group of people to work with and found important discoveries with implications for clinical care.

How does your work in the clinic motivate your research? How do you navigate the dynamic between your work in the clinic and the lab?

You know, clinical care is all about the patient and the patient is totally at the center of that. When you're doing clinical research, you're using patient data, but the center of it is your research hypothesis. Hopefully you've selected a hypothesis that has important clinical implications and you've used it by having a question with patients like, so FEATURED ARTICLE

you have chest pain, which tests should I order? I have half a dozen options and which is the best test? It sounds like a very simple question, but it turns out to have animated decades of research.

So I think they're very interrelated and the dynamic is of course that you need to leave one mind and start the other, but that dynamic in terms of keeping your enthusiasm fresh is you change, change venues and change thinking and change teams and, it keeps it interesting.

What are some of the greatest challenges you face in your work, and what have you learned from them?

I think the greatest challenge is to be creative and innovative, because if you can do that then of course the next challenge is convincing somebody to pay for the research that you want to do, and then doing the research and then can we think of somebody to publish it. I would say overall for my career, probably the biggest challenge is being a woman in a totally male dominated field.

When I became a cardiologist, less than 3% of the cardiologists in the country were women. And now it’s maybe about 12%. And so you can see that over nearly 35, 40 years, we've not made a lot of improvement in that, and this is in spite of the proportion of people graduating from medical schools, half women and from internal medicine residency, half women. And then when you get into cardiology, it's just a cliff. And that has been both a challenge, but also, I mean, there's a silver lining in that as well. It's certainly a challenge to have to work twice as hard and be twice as good, but it's created some incredible bonds with women who were doing the same thing, and also the opportunity to change the culture of the field, which I think we are doing successfully now, so that it's much more friendly, not only to women, but other underrepresented groups.

As the President of the American Society of Echocardiography, the American College of Cardiology, and the Chief of Cardiology at both the University of Wisconsin and Duke University, how do you promote diversity and inclusion in medical fields? How would you describe the impacts of these efforts?

I think my biggest impact has actually been with the American College of Cardiology. I was president from 2005 to 2006 of that organization and then subsequently asked to lead their diversity and inclusion initiative, which we started in 2017. I have led that initiative and task force to the present, and I'll be rotating off in the spring. That has been a real learning curve for me. I joke that, you know, at the beginning of this, that I knew diversity and inclusion because I had experienced it, but there is a science to diversity and inclusion and there are best practices and there FEATURED ARTICLE

are things that work and don't work and ways of going about it.

And I've had to learn all those things. We started out with doing a strategic plan, in which we really tried to define, “Why diversity?” We did it not in terms of individuals saying “I've been discriminated against,” or “It's harder for me than it is for him,” but in terms of what would make the field better and what the benefits to the field are in diversity.

And so our whole approach has been that we will do better science. We will be more innovative. We will have more functional teams. We will make better strategic decisions.

We will take better care of our patients. We will also be accessing much more talent and promoting and developing that talent if we take a diversity and inclusion lens to everything that we do. And we have approached this through changing the culture through education and communication, through collection and publication of data, through really changing how the American College of Cardiology does business. It's got diversity and inclusion principles.

Now it has diversity competencies for leadership. We have diversity professional development programs for underrepresented groups. And in addressing the pipeline, a deep pipeline issue as well as that residency cliff issue. And we have successfully changed the culture in the ACC and it's starting to trickle out to the rest of cardiology. We haven't changed the field entirely. There's a lot of work left to be done.

And, you know, the Black Lives Matter movement has shown us how much systemic, structural prejudice there is in our institutions and in our professions, not just about race, but also about sex. And so there's a lot more work to be done to do that, but the impact of this effort, I think, has been really large in the organization. I'll be modest here, but you would see it on my CV that ACC a few years ago created a diversity and inclusion leadership award, and last year they named it after me. So I think it's a recognition that not only have I done a lot, but that it's really truly valued by the profession.

As a researcher working in the medical field, what can you say about the ethical issues that you’ve faced in your work? How do you think students interested in going into STEM fields with possible human impacts, such as medicine, should prepare themselves?

I think ethics is very important, and if you try to create a clinical trial, the first thing you need to do is create a hypothesis for which there's equipoise, which is a really important word in clinical research. It means we don't know the answer. And so that we would be equally comfortable if a patient had X therapy or Y therapy.

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Having equipoise is at the core of ethics, because if you don't have equipoise, you can't ethically do this study. You can have a guess as to which way it's going to turn out, right? That's your hypothesis and you structure the organization and the details of the trial design around that hypothesis.

But equipoise is really critical. And then how that plays out in terms of informed consent—which populations do you test? I think it is very important. A lot of this, I don’t know that at a high school or college level is critically important to work on, unless you have a particular interest in the science of patient engagement, and that is increasingly becoming recognized as we transition to clinical care.

When we talk a lot about patient-centeredness, I'm not sure we always do it very well, but clinical research also needs to be patient centered and how you include patients in the research, not as subjects you do something to, but as people who help design the study and help conduct the study and the interpretation of the results. I think those things are all very important. They're very rich areas of research alone. How do you engage patients? How do you recruit them and retain them?

Why did you choose to study heart disease in women specifically?

I was doing all this athlete research and somebody asked me to write a chapter in a gynecology textbook, obviously about women, on exercise. So I said sure, and proceeded diligently to go to the library to read up on everything I could about exercise and women. There was a whole literature about how cardiac stress testing was less accurate in women than in men. And I thought, well, how come we don't know this? If I take a woman as a patient and do this test, how come I don't know that I need to interpret those results differently than I do for a man?

If you take a mixed population of people whose results need to be interpreted one way and then another way and mix them all together, you're blending apples and oranges, and you're not going to get very clear indications for either group, quite frankly, because they will each dilute the other.

And so that's how I got engaged in it. I took it upon myself and I wrote an editorial in Circulation, wrote a perspective, and said, “Hey, you know, we don't know the information about half our patients. How come we don't know this?

We need to study heart disease in women.” There were some early things that we were studying a lot like bypass surgery because it was fairly new in the 80s and it turned out women did worse than men. I thought, well, that's really interesting. Let's find out why women did worse than 90 | 2019-2020 | Broad Street Scientific

men. And a lot of it turned out to be about body size and the size of the bypass grafts. They were much smaller in women because women were smaller and so they tended to close off sooner than in men, which was an important finding in women.

It also was an important finding because the same was true for small men. When you're looking at risk for bypass surgery, you need to look at vessel size and body size. If we hadn't done the research to try to understand the difference in outcome with women, we would never know that for men.

At NCSSM, many students are interested in pursuing STEM fields in college and beyond. Do you have any advice for these students?

I would urge you to think and learn broadly. If you're interested in STEM, increasingly, medical schools are very interested in something called holistic review, which is not just how well you've done in your Organic Chemistry class or on the MCATs but, what kind of person you are. Are you culturally competent? Are you empathetic? Can you relate to people? Are you ethical?

Be a human being. An empathetic, warm human being who can take care of others, and know how to access the literature and think critically. These are all things that are really important. I would also have to say that when somebody comes to a medical school application or a residency application, and they've published in the sciences and have a very strong letter of recommendation from a research mentor, that's very powerful. So do both. Be successful in science, but also be successful as a well-rounded human being.

How has COVID affected your day-to-day work as well as your research goals?

Our research building is in downtown Durham and it closed in March of last year. I have not been to my office for 10 months. I don't even know what's in there anymore. We do almost everything by Zoom and email now.

We miss the contact and we miss the social interstices of a meeting where you’re, you know, walking somebody back to the office or you meet them in the break room. I stopped doing clinical work at that time, being in a highrisk group because of COVID, and have not gone back to doing clinical work. So that was a big change, and also very personal. In terms of day-to-day work, that was about a third of my time.

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What do you enjoy doing outside of work?

What motivates you?

Having a little bit more flexibility in my time, one thing that I've done is intensified a meditation and yoga practice that I’ve had since the pandemic. It’s really nice to be able to be flexible with my time and not have to show up in the office in the morning and leave late at night. Just things that you do at home, basically, scratch the cats. I have a garden. We do have a beach house that we are very lucky enough to have an escape to periodically to get a change of scenery and the North Carolina coast.

I guess there's a certain selfish motivation. I do the stuff that I enjoy. But then the question would be, why do I enjoy it? On the selfish side, it's intellectually interesting.

But I think if it didn't make a difference, I wouldn't do it.

When we do a clinical research project, one of the things that we start very, very early on asking ourselves is how is this going to change care? How is this going to have an impact? If it's just going to be an intellectual finding that checks a box, I'm not very interested. I want to do work that has an impact. Whether it's an impact from the research or an impact from a diversity and inclusion initiative to really improve people's lives and improve our field, I think we all want to improve the world.

What do you see for the future of cardiovascular medicine? What plans do you have to continue your research?

I'm actually writing two grants right now. One is a sub study grant for a large cohort of patients in rural South Appalachia in the Mississippi Delta who have an incredibly high burden of disease, but have basically never been studied at all. This is a fascinating group to study the logistics of getting medical equipment and personnel to the deep, rural South, which has been challenging. We've met that by using an artificial intelligence guided ultrasound machine, which guides an unskilled observer to take pictures. It’s all very exciting and forward looking, but also very human in where the sample lives and wanting to understand the social determinants of health in an area where we don't even know the medical determinants of health, because these people just don't have access to medical care.

The other grant that I'm writing is a large 8,000 patient cohort of patients living with HIV in whom we're doing a cardiac prevention trial. HIV imposes an additional cardiovascular risk above the usual risk factors, and we’re doing a randomized trial using statin medications. We'll be following these patients to look at all different kinds of accelerated aging in the kidneys, liver, muscles and so on as a consequence of HIV. These are all problems that have become relevant because of the incredible success of HIV treatments in the last two decades. Now, these patients are living long enough and healthy enough to start encountering chronic diseases, and it turns out at a much higher rate than usual.

I think the future is wide open. There are so many populations to study, so many questions to ask. I think we're going to broaden the scope of less conventional research, such as implementation research, to bring what we learned in conventional studies to the clinic. It's a time of very rapid change. 10 or 20 years ago, we would have said, it's very narrow. Just how do we get drugs and devices to the market and then make sure we don't have side effects afterwards. Now it's much bigger than that - it makes it very exciting.

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What gave you the confidence to enter such a male-dominated field? What pushed you to take on that challenge?

I didn't see the barriers. If you ski and you're skiing in trees, if you focus on the trees, you will hit the tree, so you've got to focus on the gaps. And so I just said, well, this is what I want to do. I don't see any reason why I shouldn't do it, so I just plowed on. I was the third woman to go through our fellowship program ever at Penn. And I was the first woman hired on faculty in cardiology at Penn, and then later, in 2000, when I went to Wisconsin, as chief, I was the only woman on the faculty in cardiology. Somebody asked me the question, “Well, you're going to be the only woman in the whole cardiology division, how are you going to deal with that?” My answer was I'm the boss. It doesn't bother me at all. And how are the men going to deal with it?

Do you have any tips for us to implement practices of diversity and inclusion in the teams we’re a part of such as classes and clubs?

I'd urge you to do some thinking to really internalize why you want to implement these practices and what you hope to gain. Yes, it's moral justice, social justice. It's morally the right thing to do to have diverse teams. But beyond that, why does it serve the enterprise and how does it serve the enterprise? That elevates it from being “How do I treat you” to “How do we do this better together.” If you start there and then look at the barriers to being better through that diversity lens, then I think you'll find specific strategies and tactics that you might engage in to make things more diverse. I would also make sure that you listen to underrepresented colleagues. If they're not joining a club, why aren't they joining a club? Why did they feel excluded?

Why does this club's purpose not resonate with them and what might resonate better? Listening is really important to identifying those barriers and you might be surprised as to what you learn.

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