Broad Street Scientific Journal 2020

BROAD

STREET

SCIENTIFIC

VOLUME 9 | 2019-20

The North Carolina School of Science and Mathematics Journal of

Student STEM Research

Front Cover

Human Eye

The human eye is one of the most complex parts of

our physiology. Our eyes can differentiate between

approximately 10 million different colors and can even

detect a single photon of light. In the eye, light travels

through the cornea and pupil to the crystalline lens,

which focuses the light onto the retina. The light falling

onto the retina is converted to electrical signals that

travel through the optic nerve to the brain, creating

what we experience as vision. Image Credit: Dhyamis

Kleber.

Approximate Scale: 20 millimeters

Biology Section

Tissue with Bronchogenic Carcinoma

Tissue is an extracellular matrix of similar cells from the

same origin that together carry out a specific function.

Most cancers arise in epithelial tissue. One such example

is Bronchogenic Carcinoma, a lung cancer that begins in

the tissue lining the airway of the lungs and spreads outwards,

creating more and more mutated cells, eventually

forming a tumor. Lung and bronchus cancers account

for about 13 percent of new cancer cases in the United

States. Image Credit: Microscopy U.

Approximate Scale: 250 micrometers

Chemistry Section

Fluorescence Image of Neuronal Culture

The cell is the basis for all life on Earth. From the 30

meter long Blue Whale to the 370 nanometer long Pelagibacter

ubique, every living organism on the planet

consists of at least one cell. Every individual cell consists

of genetic material suspended in a cytoplasm contained

within a membrane. The human body is made of about

37.2 trillion cells. Image Credit: Jan Schmoranzer, 2011

Olympus BioScapes Digital Imaging Competition.


Engineering Section

TIGER Domain

Organelles are to cells like organs are to the body. They are

smaller than cells and harder to see, so new discoveries are

revolutionary and unique. The TIGER domain, discovered

in 2018, is a space where messenger RNAs (mRNAs)

encoding certain kinds of proteins find the appropriate

environment to grow. TIGER stands for TIS granules

(purple) and Endoplasmic Reticulum (green). The central

black area is the cell nucleus. Image Credit: Dr. Christin

Mayr, Memorial Sloan Kettering Cancer Center.


Mathematics and Computer Science Section

Side View Image of Live SKOV3 (Ovarian Cancer) cell

Proteins are complex molecules that play critical roles in

the human body and are required for the structure, function,

and regulation of the body’s tissues and organs. Sideview

images of a live SKOV3 (Ovarian Cancer) cell show

it undergoing compression by an an atomic force microscope.

The green shows the fluorescently tagged protein

Histone 2B, while the magenta shows KRas-tail, a protein

that localizes to the cell membrane. Image Credit: Chad M.

Hobson, UNC-Chapel Hill Department of Physics and Astronomy.


Physics Section

Nine Essential Amino Acids

Amino acids are the organic building blocks of proteins. In

the body, proteins are used to facilitate chemical reactions,

provide structure, act as chemical messengers, store energy,

and transport other molecules. Out of the 20 amino acids

used by humans, 9 are essential amino acids, which cannot

be made by the body and must be supplied by our diet.

These 9 essential amino acids are histidine, isoleucine, leucine,

lysine, methionine, phenylalanine, threonine, tryptophan,

and valine. Image Credit: MolView, Eleanor Xiao.

Approximate Scale: 0.4-1 nanometer per amino acid

Back Cover

Color-coded Distribution of Neurons in Transgenic

Mouse Dentate Gyrus

To understand the brain, scientists need to map how neurons

are wired to form circuitries in healthy and disease

states. The traditional approach of tissue slicing is difficult

and labor intensive as the neuronal circuitries are vast,

three dimensional, and tightly entangled. A new tissue

clearing solution, OPTIClear, selectively adjusts the optical

properties of tissue without damaging their structural

components. Image Credit: Dr. Wutian Wu, The University

of Hong Kong (HKU) and Imperial College London.


TABLE of CONTENTS

4 Letter from the Chancellor

5 Words from the Editors

6 Broad Street Scientific Staff

7 Essay: The Forsaken Victims of Climate Change

MEGAN MOU, 2021

Biology

10 Small Molecule Activation of Wnt/β-Catenin Signaling Pathway on Neurodegeneration

Rates of Dopaminergic Neurons in C. elegans

ARIBA HUDA, 2020

18 Quorum Quenching: Synergistic Effects of Plant-Derived Compounds on Biofilm

Formation in Vibrio harveyi

PREETI NAGALAMADAKA, 2020

25 Emotional Processing and Working Memory in Schizophrenia with Neutral and

Negative Stimuli: An fMRI Study

CINDY ZHU, 2020

Chemistry

33 Sesamol as a Novel Redox Mediator for the Electrochemical Separation of Carbon

Dioxide from Flue Gas

MADISON HOUCK, 2020

40 Modeling the Effect of Chemically Modified Non-Antibiotic Tetracyclines with

β-Amyloid Fibrils to Treat Alzheimer's Disease

RACHEL QU, 2020

46 Synthesis of a Tau Aggregation Inhibitor Related to Alzheimer's Disease

EMMA STEUDE, 2020

Engineering

51 A Telemetric Gait Analysis Insole and Mobile App to Track Postoperative Fracture

Rehabilitation

SAATHVIK A. BOOMPELLI, 2021 ONLINE

57 Differences in Reliability and Predictability of Harvested Energy from Battery-less

Intertermittently Powered Systems

NITHYA SAMPATH, 2020

Mathematics and Computer Science

62 Applying Machine Learning to Heart Disease Diagnosis: Classification and Correlation

Analyses

SAHIL PONTULA, 2020 ONLINE

69 Modeling the Effect of Stem Cell-Targeted Immunotherapy on Tumor Size

AMBER POSPISTLE, 2021 ONLINE

Physics

76 The Cosmic Radiation Shielding Properties of Lunar Regolith

ELEANOR MURRAY, 2020

81 Modeling the Gravitational Lens of the Einstein Ring MG1131+0456

NATHANIEL S. WOODWARD, 2020

Featured Article

89 An Interview with Mr. Erik Troan

LETTER from the CHANCELLOR

"For 300 days I’ve been fortunate to be a part of something that strives to represent and benefit all humanity. As I

look out and reflect on our shared world, that has been the greatest honor. . . I can’t wait to pay it forward to the next

explorers and watch them fly even higher."

~ Christina H. Koch ‘97

I am very happy to introduce the ninth edition of

the North Carolina School of Science and Mathematics’

(NCSSM) scientific journal, Broad Street Scientific, with the

words of NASA astronaut and NCSSM graduate Christina

H. Koch ‘97, who recently set a record for the longest single

spaceflight by a woman. We are proud of Christina, and

of each of our NCSSM students who conduct significant

scientific research during the academic year. Broad Street

Scientific is a student-led and student-produced showcase

of some of the impressive research currently being

conducted by students at NCSSM.

As I write this foreword, those of us in North

Carolina and in much of the United States are responding

to stay-at-home orders as a result of the COVID-19 global

pandemic. The importance of scientific inquiry is amplified

in times like these, when cutting-edge technologies and

treatments are literally saving lives each day, and our

researchers accelerate their search for both a cure for

this virus and a vaccine to prevent its return. At NCSSM,

students are given regular opportunities to develop their

research skills and share their results as part of the broader

scientific conversation around the world. Many go on to

join that larger community of researchers, asking difficult

questions and providing guidance grounded in data to

those who may address the next global challenge. We are

proud to be a part of their educational journey.

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,

chemistry, physics, computational science, engineering

and computer science, math, humanities, and the social

sciences is a critical component 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 that our students conduct at NCSSM

under the guidance of our outstanding faculty and in

collaboration with researchers at major universities. For

thirty-five 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 sponsor Dr. Jonathan Bennett, and senior editors

Olivia Fugikawa, Jason Li and Eleanor Xiao. Explore and

enjoy!

Dr. Todd Roberts, Chancellor

4 | 2019-2020 | Broad Street Scientific

WORDS from the EDITORS

Welcome to the Broad Street Scientific, NCSSM’s journal

of student research in science, technology, engineering,

and mathematics. In this ninth edition of the 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. In rapidly changing

times, it is important to keep up with new advancements

so that we can hold informed perspectives and make informed

decisions as a society. We hope you enjoy this

year’s edition!

This year’s theme is the composition of the human

body: a visual dive into the components that connect each

of us as one species. Human anatomy and physiology is the

subject of research for many scientific disciplines, ranging

from biology and chemistry to physics and engineering. A

deep understanding of human anatomy is key to promoting

health and wellness in our society, and the years of research

that have gone into studying the human body have

produced significant improvements in healthcare and the

quality of life. We chose the composition of the human

body as this year’s theme not just because of its importance

to research, but also because of the incredible scientific

images produced during the research process. Technology

has enabled us to acquire high resolution images of

many biological components, from tissues and individual

cells down to proteins and nucleic acids. The anatomical

images used in this year’s issue display the many levels of

biological organization. The biological levels of organization

order the different complexities of life from simplest

to most complex. In living organisms, these levels are:

atoms, molecules, organelles, cells, tissues, organs, organ

systems, organisms. The importance of every working

component cannot be understated; all individual parts of

a system must function properly to produce a working

whole. This aspect of anatomy and physiology reminds us

that we are all part of something bigger, just as how every

bit of research done, even by high schoolers, contributes to

a greater society of scientific thinkers.

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 40th 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 for his

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, the Broad Street Scientific would like to acknowledge

Mr. Erik Troan, Founder and Chief Technology Officer at

Pendo, for speaking with us about his unique career path

and perspectives on STEM and entrepreneurship, and for

imparting important advice to young scientists and entrepreneurs

so that they, too, may tread fearlessly and find

their own path forward.

Olivia Fugikawa, Jason Li, and Eleanor Xiao

Editors-in-Chief

Broad Street Scientific | 2019-2020 | 5

BROAD STREET SCIENTIFIC STAFF

Editors-in-Chief

Olivia Fugikawa, 2020

Jason Li, 2020

Eleanor Xiao, 2020

Publication Editors

Henry Chen, 2021

Kaylene Eun, 2021

Daniel Jin, 2021

Esha Shakthy, 2021

Marina Takara, 2021

Ashley Wang, 2021

Biology Editors

Alisha Kamath, 2021

Ishaan Maitra, 2020

Akshra Paimagam, 2021

Chemistry Editors

Suraj Rao, 2020

Varun Varanasi, 2020

Andrew Zhen, 2021

Engineering Editors

Sahil Azad, 2021

Sriya Mantena, 2021

Mathematics and

Computer Science Editors

Alvin Chen, 2021

Aakash Kothapally, 2020

Physics Editors

Andrei Mistreanu, 2021

Will Staples, 2020

Faculty Advisor

Dr. Jonathan Bennett


THE FORSAKEN VICTIMS OF CLIMATE CHANGE

Megan Mou

Megan Mou was selected as the winner of the 2019-2020 Broad Street Scientific Essay Contest. Her award included the

opportunity to interview Mr. Erik Troan '91, founder and CTO of Pendio.io. This interview can be found in the Featured

Article section of the journal.

Mother Earth is dying. Nowadays, it seems like there

is always a headline that bears news of yet another

environmental tragedy, and 2019 was a record year:

20% of the Amazon ravaged by forest fires, almost 900

deaths in the hottest England heat wave in history, and

1 billion animals killed by Australia’s wildfires added up

to the one of the most environmentally devastating years

in history. The worst part of it all? Humans are directly

linked to causing these catastrophes—yet we are still not

doing enough to prevent them. Research has shown that

humans are by far the greatest contributor of CO 2

, with

two thirds of human emissions due to the burning of fossil

fuels (Johnson, 2019). And when carbon dioxide and other

gases enter our atmosphere, they remain for centuries,

inflicting terrible damage on our planet.

Why do our activities have such a great effect on

Earth’s climate? To answer this, we must fully understand

the science behind global warming. The cause can be

summed up in one phrase: the enhanced greenhouse

effect. When solar radiation reaches Earth’s atmosphere,

some is reflected back into space, and the rest is absorbed

by the land and ocean, causing Earth to radiate heat

towards space. The greenhouse gases that naturally exist

in our atmosphere are responsible for absorbing some of

this heat, keeping Earth warm enough to sustain life ("The

Causes of Climate Change," 2019). The problem now is

that too much of these greenhouse gases (CO 2

, methane,

nitrous oxide, water vapor, and chlorofluorocarbons,

to name a few) is being released due to human activity,

driving up average annual temperatures. The recent U.S.

Fourth National Climate Assessment found that between

93% to 123% of observed 1951-2010 warming was due to

human activities (it is possible to be greater than 100%

because natural factors in isolation, such as volcanoes and

solar activity, would most likely have resulted in a slight

cooling over the last few decades, offsetting part of the

human-associated warming) (Hausfather, 2017). Most

people who are aware of climate change are also aware of

its general environmental impacts, such as melting glaciers

and rising sea levels, as well as disruption of habitats and

loss of biodiversity. However, what many are not aware of

is the disproportionately large impact that climate change

has on those living in extreme poverty.

There are several ways that climate change perpetuates

a cycle of poverty among the most vulnerable members

of our population. Poorer communities are not only more

susceptible to the detrimental effects of global warming,

they are also less able to cope with the aftermath of these

effects. If we continue to be bystanders, climate change

could force an additional 100 million people into extreme

poverty by 2030 (Giovetti, 2019). The most significant

threat that climate change poses to the poor is the

destruction of resources. 3 out of 4 people living in poverty

rely on agriculture and natural resources to survive—for

them, climate change is literally a matter of life and death.

So when increasingly unpredictable weather and natural

disasters such as sustained droughts or severe storms hit

farmers living in Haiti, Timor-Leste, or Zimbabwe, their

families face starvation and dehydration as their crops and

water sources are wiped out. Climate change threatens

the world’s food and water supply—research shows that

climate and natural disasters alone have triggered food

crises for 29 million people. The undermining of food

production and loss of resources cause extreme instability

in different communities, leading to a rise in conflicts over

arable land, freshwater, and livestock. In fact, thousands

have been killed in Nigeria, where diminishing land

and water resources have recently been exacerbated by

the effects of climate change, prompting long-standing

tension to spark into full-fledged violence (Schwartz,

2019). Clearly, existing social and economic problems are

only intensified by the consequences of climate change,

and millions of people are losing their lives as a result.

Those who have not been killed are being forced to flee.

Rising sea levels, prolonged droughts, and natural disasters

drive millions to move away from their homes in search of

better livelihoods. Almost all of these displacements occur

in developing countries, where residents have insufficient

resources to cope with change and destruction. This cycle

of poverty continues for refugees, many of whom do not

have access to education and must depend on humanitarian

aid. Furthermore, many first-world countries outsource

CO 2

in the form of factories to developing countries,

whose labor force is eager for new jobs (Plumer, 2017).

In short, climate change makes rich countries richer and

poorer countries poorer.


Lastly, climate change multiplies the number of health

issues that exist in poorer regions. A warmer climate means

warmer freshwater sources, which in turn provide a more

habitable place for harmful bacteria and microbes to grow.

The World Health Organization (WHO) estimates that

3.575 million people die from water-related diseases per

year, and with increased temperatures drying out available

water sources, people driven desperate by thirst are forced

to choose between the risks of drinking contaminated

water or dying of thirst. Additionally, the increased smog

caused by warmer atmospheres, coupled with severe air

pollution, has made it impossible to breathe in places such

as Delhi, where the quality of air reached such high toxicity

that experts deemed it equivalent to smoking 50 cigarettes

a day (Paddison, 2020). In fact, the WHO claims that over

90% of the world population breathe in some form of

toxic air, leading to an abundance of diseases like stroke

and lung cancer (Fleming, 2018). Even within the U.S.,

poorer communities in both rural and urban areas bear the

greatest burden of climate change, as seen by lack of health

insurance, dependence on agriculture-based economies,

and no funds to recover from natural disasters. In urban

areas, which produce 80% of greenhouse gas emissions in

North America, the poor live in neighborhoods with the

greatest exposure to climate and extreme weather events

(Chappell, 2018). Poorer Americans, while to a much

lesser extent, face some of the same disadvantages as those

living in developing countries in terms of environmental

inequality. So what exactly is being done to save our planet

and its poorest inhabitants?

One thing is for sure: not enough. The overall global

response to climate change can be characterized as

extremely uneven. Persistent skepticism from certain

global leaders, many of whom are motivated by economic

interests, is slowing cooperative efforts to address the

issue of climate change. In particular, President Trump’s

decision to withdraw from the Paris climate agreement will

trigger both short-term and long-term damage—for one, it

will be less likely for the U.S., the second-highest ranking

country in production of greenhouse gases, to reduce

carbon emissions without international obligations, and

countries that were already hesitant about membership

are more likely to back off as well (“The Uneven Global

Response to Climate Change,” 2019). President Trump's

decision in times like these is brutal, as the urgency of

action is greater now than ever before, as indicated by the

World Meteorological Organization’s 2019 report. The

report showed a continued increase in greenhouse gases

to new records during the period of 2015-2019, with CO 2

growth rates nearly 20% higher than the previous five

years (“Global Climate in 2015-2019,” 2019). Surrounded

by a constant whirlwind of bad news, it can feel like all

hope is lost for planet Earth.

But hope is not lost. The global effort to address

climate change is moving forward as a whole, even

without the current support of the U.S. government.

Plenty of countries are setting goals to reduce emissions

and implement renewable energy. Morocco has invested

heavily in solar power, and India has implemented a

prohibition of new coal plants (“The Uneven Global

Response to Climate Change,” 2019). China, the world’s

number one CO 2

contributor at a whopping 29% of

global emissions, has made great strides in reducing air

pollution (“Each Country's Share of CO 2

Emissions,”

2019). The youth movement across multiple nations, led

by activist Greta Thunberg, is on the rise, and hundreds

of new green technologies are making their way towards

the market. The most promising of these innovations

include solar cells that incorporate the mineral perovskite,

which convert UV and visible light with a stunning 28%

efficiency (as compared to the average 15-20%), graphenebased

batteries that power electric vehicles, and carbon

capture and storage that traps CO 2

at its source and isolates

it underground (Purdue University, 2019).

While these global pledges and new technologies

hold great promise for future sustainability, it is up to us

to actively implement more environmentally conscious

decisions into our daily lives. Reduce, reuse, and recycle in

that order. Eat a more plant-based diet. Conserve energy

at every moment possible. Always be civically engaged.

We owe it to not only the animals, our children, and our

home; we owe it to those who contribute least to the

climate change devastation but feel its effects most deeply.

We must not let our privilege go wasted.

References

Chappell, C. (2018, November 27). Climate change in

the US will hurt poor people the most, according to a

bombshell federal report. Retrieved January 7, 2020, from

https://www.cnbc.com/2018/11/26/climate-changewill-hurt-poor-people-the-most-federal-report.html.

Each Country's Share of CO 2

Emissions. (2019, October

10). Retrieved January 8, 2020, from https://www.ucsusa.

org/resources/each-countrys-share-co2-emissions.

Fleming, S. (2018, October 29). More than 90% of

the world's children are breathing toxic air. Retrieved

January 9, 2020, from https://www.weforum.org/agenda/2018/10/more-than-90-of-the-world-s-children-arebreathing-toxic-air/.

Giovetti, O. (2019, September 25). How the effects of

climate change keep people in poverty. Retrieved January

7, 2020, from https://www.concernusa.org/story/effectsof-climate-change-cycle-of-poverty/.

Global Climate in 2015-2019: Climate change accelerates.


(2019, September 24). Retrieved January 8, 2020, from

https://public.wmo.int/en/media/press-release/global-climate-2015-2019-climate-change-accelerates.

Hausfather, Z. (2017, December 13). Analysis: Why

scientists think 100% of global warming is due to humans.

Retrieved January 8, 2020, from https://www.carbonbrief.org/analysis-why-scientists-think-100-of-globalwarming-is-due-to-humans.

Johnson, S. (2019, November 22). What Are the Primary

Heat-Absorbing Gases in the Atmosphere? Retrieved January

7, 2020, from https://sciencing.com/primary-heatabsorbing-gases-atmosphere-8279976.html.

Paddison, L. (2020, January 3). 5 Environmental News

Stories To Watch In 2020. Retrieved January 9, 2020,

from https://www.huffpost.com/entry/environ-

ment-heat-wave-climate-change-elections_n_5def-

87d7e4b05d1e8a57cb90.

Plumer, B. (2017, April 18). How rich countries "outsource"

their CO 2

emissions to poorer ones. Retrieved

January 9, 2020, from https://www.vox.com/energy-and-environment/2017/4/18/15331040/emissions-outsourcing-carbon-leakage.

Purdue University. (2019, November 12). New material

points toward highly efficient solar cells. Retrieved

January 9, 2020, from https://www.sciencedaily.com/

releases/2019/11/191112164944.htm.

Schwartz, E. (2019, November 19). Quick facts: How

climate change affects people living in poverty. Retrieved

January 8, 2020, from https://www.mercycorps.org/articles/climate-change-affects-poverty.

The Causes of Climate Change. (2019, September 30).

Retrieved January 7, 2020, from https://climate.nasa.gov/

causes/.

The Uneven Global Response to Climate Change. (2019,

November 18). Retrieved January 7, 2020, from https://

www.worldpoliticsreview.com/insights/27929/the-uneven-global-response-to-climate-change.


SMALL MOLECULE ACTIVATION OF

WNT/ β-CATENIN SIGNALING PATHWAY ON

NEURODEGENERATION RATES OF DOPAMINERGIC

NEURONS IN C. ELEGANS

Ariba Huda

Abstract

Parkinson’s Disease (PD) is a neurodegenerative disease characterized by loss of midbrain dopaminergic (mDA) neurons.

While there are several medical treatments available for PD, they often come with significant side effects and do not act

as definite cures. Past studies have indicated that Wnt/β-Catenin signaling is critical for the generation of dopamine

(DA) neurons during development and for further neurorepair. This study investigates the roles of small molecules,

Wnt Agonist 1 and Pyrvinium, in Wnt signaling and their effects on neurodegeneration. Wnt signaling was modeled

by Caenorhabditis Elegans (C. elegans), nematodes that display dopamine-dependent behavior in response to neurodegeneration.

24 hour exposure to Wnt Agonist 1 has been shown to significantly reduce neurodegeneration as observed

through locomotor behavior and chemotaxation. Currently, work is being done to measure BAR-1 and other Wnt related

ortholog gene expression within Wnt Agonist 1 exposed worms. Analysis of the functions behind the Wnt/β-Catenin

signaling pathway in the generation and neurorepair of mDA neurons will allow further understanding of the potential

for PD stem cell therapies.

1. Introduction

1.1 – Parkinson’s Disease

Parkinson’s Disease (PD) is a neurodegenerative disease

that results from the progressive cell death of dopaminergic

(DA) neurons. Currently, various medications

are prescribed to patients to control symptoms such as

cognitive decline and loss of motor function, but there is

no definitive cure. The most common therapy is the drug

levodopa (L-DOPA), which is used to stimulate dopamine

production in neurons associated with motor skill. However,

while L-DOPA is efficient at managing the extent

of symptoms, it also has various side effects on patients,

ranging from physiological to psychological. At present,

research is being conducted to identify the clinical applications

of stem cell therapy in Parkinson’s Disease as well as

the genetic factors behind PD [1]. These discoveries could

contribute to the development of a new therapy option

geared towards reducing the adverse effects of medications

on diagnosed patients.

Dopamine neurons are located in the human nigrostriatal

pathway, a brain circuit that connects neurons in the

substantia nigra pars compacta with the dorsal striatum.

Despite lack of a specific cause for neuronal loss, DA neuron

loss has been linked to genetic mutations and environmental

toxins [2]. Studies have shown that DA neurons

have a distinctive phenotype that could contribute to their

vulnerability. An example of this is the opening of L-type

calcium channels, which results in elevated mitochondrial

oxidant stress and susceptibility to toxins [2]. Moreover,

DA neurons are susceptible to degeneration because of

extensive branching and amounts of energy required to

transmit nerve signals along these branches [3].

1.2 – Wnt/β-Catenin Signaling Pathway

Due to the significance of the Wnt signaling pathway

for the healthy functioning of the adult brain, dysregulation

of these pathways in neurodegenerative disease has

become notable. Wnt/β-Catenin signaling is also critical

for the generation of DA neurons in embryonic stem cells

[4]. Since several of the biological functions disrupted in

PD are partially controlled by Wnt signaling pathways,

there is potential for therapy centered around targeting

these pathways [4].

In an activated state, Wnt proteins act as extracellular

signaling molecules that activate the Frizzled receptor.

Following the activation of Frizzled, the LRP receptor undergoes

phosphorylation, inducing the translocation of the

destruction complex, a complex of proteins that degrades

β-catenin, to the region of membrane near the two receptors

(Fig. 1). The activated dishevelled (Dsh) proteins cause

the inhibition of the destruction complex which prevents

β-catenin phosphorylation. Overall, an activated state of

the Wnt/β-Catenin signaling pathway causes an increase

in β-Catenin levels.

The transcription factor TCF mediates the genetic action

of Wnt signaling patterns, leading to the induction

of Wnt targeted genes. When β-Catenin levels increase,

they are translocated to the mitochondria, dislodging the

Groucho protein from TCF, and binding to TCF leading

to the transcription of Wnt targeted genes. The expressed

genes regulate cellular growth and proliferation. Without

10 | 2019-2020 | Broad Street Scientific BIOLOGY

Wnt stimulation, cytoplasmic β-Catenin levels are kept

low through continuous proteasome-mediated degradation.

Figure 1. Activated Wnt Signaling Pathway. Dsh is

activated when Wnt binds to the Frizzled receptor.

Consequently, the inhibition of the destruction complex

leads to increased expression of β-Catenin.

(GSK-3β), a protein found in the destruction complex

of the Wnt/β-Catenin signaling pathway [7]. The phosphorylation

of a protein by GSK-3β inhibits activity of its

downstream target. GSK-3β is active in a number of pathways,

including cellular proliferation, migration, glucose

regulation, and apoptosis.

1.3 – Wnt Agonist 1 and Pyrvinium

The most notable Wnt activators work by inhibiting

the GSK-3β enzyme found in the β-Catenin destruction

complex [7]. By inhibiting GSK-3β, Wnts disrupt the ability

of the complex to degrade β-Catenin, allowing β-Catenin

to accumulate in the nucleus and relay the Wnt signal

for transcription. Further, it has been shown that GSK-3β

dysregulation contributes to PD-like pathophysiology and

accumulation of alpha-synuclein [7]. Wnt Agonist 1 (Fig.

3) is a cell-permeable, small molecule agonist of the Wnt

signaling pathway. Wnt Agonist 1 induces accumulation of

β-Catenin by increasing TCF transcriptional activity and

altering embryonic development [8]. Studies have found

that the stimulation of Wnt/β-Catenin signaling pathway

with the Wnt agonist has been able to reduce organ injury

after hemorrhagic shock [9].

Recent studies have found that the canonical

Wnt/β-Catenin pathway is a key mechanism in controlling

DA neuron cell fate decision from neural stem cells

or progenitors in the ventral midbrain [5]. Wnt acts as a

morphogen which activates several signaling pathways,

specifically regulating the development and maintenance

of midbrain dopaminergic (mDA) neurons [6]. Wnt proteins

have also been shown to regulate the steps of the processes

DA neuron specification and differentiation (Figure

2). Previous research in mice found that the Wnt/β-Catenin

signaling pathway is required to rescue mDA neuron

progenitors and promote neurorepair [4].

Figure 3. Wnt Agonist 1 Structure, (C

19 H 19 ClN 4 O 3 ). A

crystalline solid. Formula weight: 350.4

Figure 2. The development of midbrain dopamine

neurons. mDA neurons arise from neural progenitors

in the ventral midline, and are divided into 3

separate phases: regional and neuronal specification

(phase I), early (phase II), and late differentiation

(phase III) [20].

New approaches in stem cell research have utilized developmental

molecules to program embryonic stem cells.

The key ingredient for this is glycogen synthase kinase

BIOLOGY

Pyrvinium (Fig. 4) has been found to be a small molecule

inhibitor of the Wnt signaling pathway through activation

of casein kinase 1a [10]. In most eukaryotic cell

types, the casein kinase 1 family of protein kinases are

enzymes that function as regulators of signal transduction

pathways. Studies have demonstrated that pyrvinium

binds to and activates CK1α, a part of the β-Catenin destruction

complex. CK1α members play a critical role in

Wnt/β-Catenin signaling, acting as both a Wnt activator

and Wnt inhibitor [10].

Clinical research in neurodegenerative diseases has not

been done with Wnt Agonist 1 and Pyrvinium. However,

Wnt Agonist 1 has been shown to be effective in vivo, de-


creasing tissue damage in rats with ischemia-reperfusion

injury [21]. Pyrvinium salts have been shown to inhibit the

growth of cancer cells [10]. Additionally, Pyrvinium has

been shown to be an effective anthelmintic, a drug used to

treat infections of animals with parasitic worms [22].

the other hand, if worms are exposed to increasing concentrations

of Pyrvinium, they will display higher rates of

neurodegeneration due to CK1α activation and inhibited

Wnt/β-Catenin signaling. Furthermore, if worms are exposed

to respective treatments during development, their

neurodegeneration rates will increase or decrease more

drastically compared to adult worms who are not exposed

to any treatment.

Figure 4. Pyrvinium Structure, (C26H28N3 + ). Formula

Weight: 382.53

1.4 – C. Elegans Model

C. elegans is a popular model for neurodegenerative

research. The transparency of C. elegans makes it easy to

facilitate the study of specific neurons and genetic manipulation

[11]. C. elegans also present locomotor behavioral responses

to neurodegeneration. The identification of genes

that cause monogenic forms of PD allows for easy modeling

in C. elegans. Studying a C. elegans model of PD can provide

insight into the cellular and molecular pathways involved

in human disease. C. elegans are also able to be used

to identify disease markers and test potential treatments.

Outcome measures are used to detect disease modifiers

such as survival of dopamine neurons, dopamine dependent

behaviors, mitochondrial morphology and function,

and resistance to stress. Behavioral markers studied in C.

elegans to detect PD include basal slowing, ethanol preference,

area restricted searching, swimming induced paralysis,

and accumulation of α-synuclein [12]. Dopamine

neuron location sites are present within hermaphroditic

and male specific worms (Fig. 5). This project will further

investigate the role of C. elegans in neurodegenerative research

in place of previously used vertebrate models.

1.5 – Hypothesis

Due to the significant role Wnt/β-Catenin signaling

plays for DA neuron differentiation in development, activated

signaling within worms will decrease neurodegeneration

rates. Thus, if worms are exposed to increasing

concentrations of Wnt Agonist 1, they will display lower

rates of neurodegeneration due to activated Wnt/β-Catenin

signaling and increased β-Catenin expression. On

Figure 5. Top represents a hermaphrodite C. elegans

and bottom represents a male C. elegans with their respective

DA neuron sites. R&L stands for right and

left side. CEPD neurons are mechanosensory neurons.

ADE neurons are anterior deirid neurons. PDE

neurons are post embryonically born posterior deirid

neurons. All neurons have DOP-2 dopamine receptor.

R5a, R7a, and R9a neurons are male specific

sensory ray neurons [25].

2. Methods

This study consists of a preliminary experiment, two

main experiments, and a secondary experiment. The two

C. elegans strains used in this project were the OW13 and

N2 type worms. OW13 overexpressed alpha-synuclein

and displayed PD-like symptoms. N2 worms acted as the

control group, representing wildtypes with no genetic alterations.

The same treatments were administered to both

worms for each of the following experiments. Treatments

administered included three separate concentrations of

Wnt Agonist 1 and Pyrvinium, as well as dimethyl sulfoxide

(DMSO).

2.1 – C. elegans Maintenance

N2 and OW13 strains were purchased from the

Caenorhabditis Genetics Center (CGC) at the University

of Minnesota. The CGC is funded by the National Institute

of Health (P40 OD010440). Worms were placed on

nematode growth media (NGM) plates that were spotted

with approximately 30μl solution of LB Broth and

OP50, a strain of E. coli in the C. elegans diet. Worms were

placed onto new plates approximately every 48-72 hours.

C. elegans maintenance protocols were followed through

WormBook [13].


2.2 – Preparation

Wnt Agonist 1 Preparation: Stock Wnt Agonist 1 was

purchased from Selleckchem. 1mg Wnt Agonist 1 was dissolved

into 2.5851 mL, 0.5170 mL, and 0.2585 mL DMSO

for 1 mM, 5mM, and 10 mM concentrations, respectively.

Dilutions were stored in 4 o C.

Pyrvinium Preparation: Stock Pyrvinium was purchased

from Selleckchem. 1 mg Pyrvinium was dissolved

into 0.8685 mL, 0.1737 mL, and 0.0896 mL DMSO for 1

mM, 5 mM, and 10 mM concentrations, respectively. Dilutions

were stored in 4 o C.

2.3 – Preparation: Experimental Design

Activating Wnt Signaling: During each trial, 6 NGM

plates spread with different concentrations of treatment

were prepared for each strain of worm. L4 stages of each

strain (OW13 and N2) were exposed to three different

concentrations (1 mM, 5 mM, and 10 mM) of Wnt Agonist

1. Worms were kept on the plate for approximately

24 hours until they reached the adult stage. Then, approximately

half of the worms were transferred to perform the

thrashing assays and the remaining worms remained on

the plate until the next generation of worms were apparent.

Inhibiting Wnt Signaling: During each trial, 6 NGM

plates, spread with different concentrations of treatment,

were prepared for each strain of worm. L4 stages of each

strain (OW13 and N2) was exposed to three different

concentrations (1 mM, 5 mM, and 10 mM) of Pyrvinium.

Worms were kept on the plate for approximately 24 hours

until they reached the adult stage. Then, some worms were

transferred to perform the thrashing assays, and the rest of

the worms remained on the plate until the next generation

of worms were apparent.

Thrashing Assay: Agar Pads were prepared and approximately

30 worms were picked onto each pad. A drop of

M9 buffer was then added. Five minutes after transfer, the

number of body bends in 20s intervals was sequentially

filmed then counted for each of the worms on the assay

plate. This was done after 24 hour exposure to treatment

and when the next generation of worms (born to exposure)

had reached adult stage. Trials were run 3 times for

each treatment group and values were averaged.

2.4 – Statistical Measurements

Averages of locomotor behavior and chemotaxation

were calculated and recorded after experimentation. Data

were analyzed using unpaired Student t-tests with unequal

variance. This is due to differences in sample size per treatment

group. Error bars were calculated using standard error

of the mean (SEM).

3. Results

3.1 – Preliminary Data

To determine that OW13 and N2 strains displayed significant

differences in neurodegeneration, a preliminary

thrashing assay was conducted on OW13 and N2 worms

with exposure only to DMSO. These worms represented

a non-treatment group. A single thrash is a complete body

bend (Fig. 6).

Figure 6. Single thrash (body bend) within 20 second

interval. Movement follows from top to bottom.

We can conclude that the number of body bends per 20

second interval is an appropriate measure of neurodegeneration

and displays statistically significant differences between

N2 and OW13 strains of worms. OW13 worms

thrashed at a rate of 34.58 bends per 20 second interval and

N2 worms thrashed at a rate of 23.42 bends per 20 second

interval (Fig. 7). The OW13 worms with overexpressed

α-synuclein showed a significant increase in thrashing in

comparison to the wildtype N2 worms. Overexpression

of α-synuclein is the main marker of dopamine deficiency

and Parkinson’s Disease. This demonstrates that higher

rates of thrashing correlate to higher levels of dopamine

deficiency within the OW13 strain.

BIOLOGY


Figure 7. No treatment thrashing score analysis: the

number of body bends per 20 second interval were

counted for 30 worms per each strain. Then the values

were averaged and graphed above. N2 worms

moved at an average of 23.42 body bends per interval,

with a standard deviation of 3.86, median of 23.5,

and standard error of the mean (SEM) of 0.79. OW13

worms moved at an average of 34.58 body bends per

interval, with a standard deviation of 4.44, median

of 35, and SEM of 0.811. Significance (p<0.05 = *) is represented

through bold connecting lines. This shows

that worms with Parkinson’s Disease (OW13) move

at a faster rate than wildtype (N2) worms. Error bars

± SEM.

4. Main Results

After determining the effectiveness of the analysis of

thrashing scores in measuring neurodegeneration, worms

were exposed to their own respective treatments (Tab.

1). These treatments were either Wnt Agonist 1 (1 mM,

5mM, or 10 mM concentration), Pyrvinium (1 mM, 5mM,

or 10mM concentration), or DMSO. The number of body

bends per 20 second interval were counted for 30 worms

per each strain and concentration. The values were averaged

and graphed below.

Table 1. Organization of worm groups and treatments

administered. (Key: 1 = 1mM treatment, 5 =

5mM treatment, 10 = 10mM treatment)

Wnt Agonist 1

Effect on

Cancer

OW13 A1, A5, A10 C1, C5,

C10

N2 B1, B5, B10 D1, D5,

D10

DMSO

(Control)

3 plates of

worms exposed

to equal concentration

3 plates of

worms exposed

to equal concentration

Exposure to Wnt Agonist 1 significantly decreased

thrashing rates in comparison to the control DMSO worms

(Fig. 8). However, this does not show the effect was concentration

dependent. Exposure to Pyrvinium at 5mM

and 10mM concentrations killed the worms. 1mM Pyrvinium

exposure also resulted in a significant decrease in

thrashing rates. In the N2 groups, there is no significant

difference between the treatment groups and the control

N2 group. Pyrvinium decreased thrashing in relation to

the 10mM Wnt Agonist 1 exposure, contradicting the

original hypothesis.

Next generation adult worms were collected on the

same treatment plates after approximately 5 days. This

study further aimed to observe any developmental significance

in activated Wnt/β-Catenin signaling within genetically

predisposed offspring.

Figure 8. Thrashing score analysis across various

treatments after 24 hour exposure: 10 different treatment

groups of 30 worms were measured: OW13

Wnt Agonist 1 (1mM) (μ = 24.615 , ̃x = 25, SEM= 0.475);

OW13 Wnt Agonist 1 (5mM) (μ = 20.625 , ̃x = 21, SEM=

0.739); OW13 Wnt Agonist 1 (10mM) (μ = 20.923 , ̃x = 21,

SEM= 0.630); OW13 Pyrvinium (1mM) (μ = 29.167, ̃x =

29, SEM= 0.632); N2 Wnt Agonist 1 (1mM) (μ = 25.375 , ̃x

= 25, SEM= 0.789); N2 Wnt Agonist 1 (5mM) (μ = 27.846

, ̃x = 28, SEM= 0.414); N2 Wnt Agonist 1 (10mM) (μ =

27.75 , ̃x = 28.5, SEM= 0.567); N2 Pyrvinium (1mM) (μ

= 23 , ̃x = 23.5, SEM= 0.833); OW13 DMSO (μ = 34.58 , ̃x

= 35, SEM= 0.811); N2 DMSO (μ = 23.42 , ̃x = 23.5, SEM=

0.705). Significance (p<0.05 = *, p<0.01 = **, p<0.001 =

***) is represented through dotted connecting lines.

Error bars ± SEM.

Similar to the adult worms (Fig. 8), exposure to Wnt

Agonist 1 significantly decreased thrashing rates in comparison

to the control DMSO next generation worms (Fig.

9). However, there is no significance supporting a concentration

dependent effect. Exposure to Pyrvinium at 5mM

and 10mM concentrations killed the worms and 1mM

Pyrvinium exposure also resulted in significant decrease in

thrashing rates. In the N2 groups, there is no strong significance

between the treatment groups and the control N2

group. Pyrvinium decreased thrashing in relation to the


10mM Wnt Agonist 1 exposure. The Pyrvinium results

might be related to the dual function Pyrvinium has in

both activating and inhibiting Wnt signaling.

Wnt Agonist 1, and 1mM Pyrvinium were significant (Fig.

10). Next generation OW13 worms thrashed less when exposed

to 1 mM Wnt Agonist 1 and more when exposed

to 5 mM Wnt Agonist 1, but less for both in N2 strains.

These inconsistent results lead us to conclude that there

is no overall significant difference or correlation between

adult worms exposed to treatments and their offspring.

5. Discussion

Figure 9. Thrashing score analysis across various

treatments of next generation worms, born into

treatment exposure: 10 different treatment groups

of 30 worms were measured: OW13 Wnt Agonist 1

(1mM) (μ = 20.929 , ̃x = 21, SEM= 0.793); OW13 Wnt Agonist

1 (5mM) (μ = 20.438 , ̃x = 20, SEM= 0.653); OW13

Wnt Agonist 1 (10mM) (μ = 24.333 , ̃x = 21, SEM= 0.328);

OW13 Pyrvinium (1mM) (μ = 22.769 , ̃x = 22, SEM= 0.553);

N2 Wnt Agonist 1 (1mM) (μ = 21.75 , ̃x = 25, SEM= 0.405);



N2 Pyrvinium (1mM) (μ = 26.5 , ̃x = 236.5, SEM= 0.496);

OW13 DMSO (μ = 34.58 , ̃x = 35, SEM= 0.811); N2 DMSO

(μ = 23.42 , ̃x = 23.5, SEM= 0.705). Significance (p<0.05

= *, p<0.01 = **, p<0.001 = ***) is represented through

dotted connecting lines. Error bars ± SEM.

Using the above data, adult thrashing scores and next

generation thrashing scores were compared per strain.

Figure 10: Adult versus next generation worm

thrashing score analysis: Data collected in both Figure

10 and 11 were replotted adjacent to each other in

order for comparison between adult and next generation

worms of each strain. Significance (p<0.05 = *,

p>0.05 = ns) is represented through dotted connecting

lines. Error bars ± SEM.

The thrashing differences between adult and next generation

worms exposed to 1mM Wnt Agonist 1, 5mM

BIOLOGY

5.1 – Wnt/β-Catenin Signaling Activation on Neurodegenerative

Rates

Previous research has shown that Wnt/β-Catenin signaling

is critical for the generation of dopamine neurons in

embryonic stem cells [4]. The generation of DA neurons

increases dopamine levels in the brain, thus decreasing

neurodegeneration.

The number of body bends per 20 second interval is an

appropriate measure of neurodegeneration, and displays

statistically significant differences between N2 and OW13

strains of worms (Fig. 7). This further shows that higher

values of body bends per 20s interval correlate to increased

neurodegeneration.

Wnt Agonist 1 effectively decreases neurodegeneration

rates in adult and next generation worms in comparison

to worms exposed to DMSO (Fig. 8 & 9). This decrease

in neurodegeneration, however, is not significant in a

concentration dependent manner. Pyrvinium in a 1mM

concentration has also been shown to decrease neurodegeneration,

which differs from the original hypothesis.

This could be due to the dual function of CK1α members

as Wnt activators or Wnt inhibitors. Pyrvinium activates

CK1α in the β-catenin destruction complex. Further, there

is no consistently significant difference in neurodegeneration

rates between adult and next generation worms (Fig.

10). This also differs from the original hypothesis which

suggests that Wnt activation might be a more effective

treatment during development.

5.2 – Limitations

Wnt/β-catenin signaling has been suggested to be

different in C. elegans than in vertebrates. In metazoans

(cnidarians, nematodes, insects and vertebrates), Wnts

are secreted glycoproteins that function as extracellular

signals [23]. Evolutionary conservation suggests that cell

signaling functioning in response to Wnts were part of a

“developmental toolkit” from at least 500 million years ago

from the common ancestor to modern metazoans [23]. Although

C. elegans uses Wnt/β-catenin signaling similar to

other metazoans, it also has a second Wnt/β-catenin signaling

pathway that uses extra β-catenins for up-regulation

of target genes, distinct from other species [23]. This

could lead to varied results in activated Wnt signaling in

comparison to other organisms.

In order to conduct thrashing assay, adult worms were


picked from the treatment plates to agar pads spotted with

M9 buffer. Often worms were picked incorrectly and died

very quickly. Thrashing scores of these worms were discarded

from analysis.

6. Current Work

Expression patterns of Wnt related genes during larval

development have been extensively studied using transgenic

reporter gene based assays. Wnts have been established

to act as morphogens, providing cells in developing tissue

with positional information in long-range concentration

gradients [14]. Sawa and Korswagen [14] looked at Wnt

related genes, BAR-1, POP-1, GSK-3, PRY-1, MOM-2,

MOM-5, KIN-19, which are orthologs of significant genes

that partake in Wnt/β-Catenin signaling. BAR-1 (β-catenin/armadillo-protein

1) functions as a transcriptional activator,

and along with POP-1 (ortholog of Tcf), regulates

cell fate decision during larval development [15]. GSK-3

(Glycogen synthase kinase-3) is the ortholog of human

GSK3β, a key enzyme in Wnt signaling and phosphorylation

of β-catenin [16]. PRY-1 is the ortholog of Axin-1

and is a part of the destruction complex in negatively regulating

BAR-1/β-catenin signaling [17]. MOM-2 codes a

Wnt ligand for members of the Frizzled family as well as

regulates cell fate determination [18]. MOM-5 (ortholog

of Frizzled receptor) couples to the β-catenin signaling

pathway, leading to the activation of disheveled proteins

[14]. KIN-19, ortholog of CK1 (Casein Kinase 1), has been

shown to transduce Wnt signals [19].

Future work in this study will be done to extensively

look at Wnt related gene expression within worms that

show decreased neurodegeneration. This will be done

through cDNA synthesis and real time polymerase chain

reaction. We expect to see BAR-1 expression, the ortholog

of β-catenin, and other Wnt related genes to have increased

expression within worms exposed to Wnt Agonist

1 in all concentrations. However, we expect to see less expression

of GSK-3 as decreased β-catenin is expected to be

degraded through phosphorylation.

7. Conclusions

Currently, there are no disease modifying treatments

for Parkinson’s Disease. Current PD treatments involve

the use of dopaminergic drugs to restore dopamine concentration

and motor function. These treatments do not

alter the course of PD, but they do provide improvement

in motor symptoms of patients [1]. Numbers of cell-based

treatments have responded to the need for targeted delivery

of physiologically released dopamine. One option that

recent studies have considered is the introduction of stem

cells into the striatum [1]. Lineage tracing based on Wnt

target genes has provided evidence for Wnts as significant

stem cell signals that have been detected in various organs

[16]. Wnt proteins or Wnt agonists have been used to

maintain stem cells in culture, allowing stem cells to expand

in a self-renewing state [24].

Though C. elegans do not have the same Wnt/β-catenin

signaling system as vertebrates, they are a valuable model

to test whether or not Wnt targeted therapies are effective

treatments to increase dopamine production in neurons

and decrease PD symptoms. The use of Wnt activators on

model organisms have not been well studied, especially in

the context of neurodegenerative disease.

As more studies and trials are completed on the effects

of activated Wnt/β-catenin signaling, especially through

the exposure to various agonists, we can see how organisms

respond physiologically, genetically, and behaviorally

to such changes. Further experimentation should also

consider potential side effects of such treatments as well

as the toxicity of molecules used for activation. Analysis

should further study if Wnt signaling is able to rescue neurodegeneration

by inducing DA neuron development or

through neurorepair. The most effective clinical treatment

of Parkinson’s disease can be achieved by expanding the

field and examining potential therapies.

8. 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. I would also like to

thank Angelina Katsanis and Emile Charles for being my

lab assistants over the summer. Further thanks to Dr. Amy

Sheck, the Glaxo Endowment, and the North Carolina

School of Science and Mathematics for allowing me the

opportunity to experience research.

9. References

[1] Stoker, T. B., & Greenland, J. C. (2018). Parkinson’s

Disease: Pathogenesis and Clinical Aspects. Codon Publications.

[2] Surmeier, D. J., Guzmán, J. N., Sánchez-Padilla, J., &

Goldberg, J. A. (2010). What causes the death of dopaminergic

neurons in Parkinson’s disease?. In Progress in

brain research (Vol. 183, pp. 59-77). Elsevier.

[3] Mamelak, M. (2018). Parkinson’s disease, the dopaminergic

neuron and gammahydroxybutyrate. Neurology

and therapy, 7(1), 5-11.

[4] L'Episcopo, F., Tirolo, C., et al. (2014). Wnt/β‐catenin

signaling is required to rescue midbrain dopaminergic progenitors

and promote neurorepair in ageing mouse model

of Parkinson's disease. Stem Cells, 32(8), 2147-2163.

[5] Chen, L. W. (2013). Roles of Wnt/β-catenin signaling


in controlling the dopaminergic neuronal cell commitment

of midbrain and therapeutic application for Parkinson’s

disease. Trends In Cell Signaling Pathways In Neuronal

Fate Decision, 141.

[6] Arenas, E. (2014). Wnt signaling in midbrain dopaminergic

neuron development and regenerative medicine for

Parkinson's disease. Journal of molecular cell biology, 6(1),

42-53.

[7] Credle, J. J., George, J. L., et al. (2015). GSK-3 β dysregulation

contributes to Parkinson’s-like pathophysiology

with associated region-specific phosphorylation and

accumulation of tau and α-synuclein. Cell Death & Differentiation,

22(5), 838-851.

[8] Wnt Agonist I (CAS 853220-52-7). (n.d.). Retrieved

from https://www.caymanchem.com/product/19903/

wnt-agonist-i.

[9] Kuncewitch, M., Yang, W. L., et al. (2015). Stimulation

of Wnt/beta-catenin signaling pathway with Wnt agonist

reduces organ injury after hemorrhagic shock. The journal

of trauma and acute care surgery, 78(4), 793.

[10] Thorne, C. A., Hanson, A. J., et al. (2010). Small-molecule

inhibition of Wnt signaling through activation of casein

kinase 1α. Nature chemical biology, 6(11), 829.

[11] Wolozin, B., Gabel, C., Ferree, A., Guillily, M., &

Ebata, A. (2011). Watching worms whither: modeling

neurodegeneration in C. elegans. In Progress in molecular

biology and translational science (Vol. 100, pp. 499-514).

Academic Press.

[12] Vartiainen, S. U. V. I., & Wong, G. A. R. R. Y. (2005).

C. Elegans models of parkinson disease.

[13] Wang, D., Liou, W., Zhao, Y., & Ward, J. (2019, September

3). Retrieved from http://www.wormbook.org/

chapters/www_strainmaintain/strainmaintain.html.

The Axin-like protein PRY-1 is a negative regulator of a

canonical Wnt pathway in C. elegans. Genes & Development,

16(10), 1291-1302.

[18] mom-2 (gene) - WormBase : Nematode Information

Resource. (n.d.). Retrieved from https://wormbase.org/

species/c_elegans/gene/WBGene00003395#0-9g-3.

[19] Peters, J. M., McKay, R. M., McKay, J. P., & Graff,

J. M. (1999). Casein kinase I transduces Wnt signals.

Nature, 401(6751), 345-350.

[20] Ferri, A. L., Lin, W., Mavromatakis, Y. E., Wang, J.

C., Sasaki, H., Whitsett, J. A., & Ang, S. L. (2007). Foxa1

and Foxa2 regulate multiple phases of midbrain dopaminergic

neuron development in a dosage-dependent

manner. Development, 134(15), 2761-2769.

[21] Kuncewitch, M., Yang, W., Nicastro, J., Coppa, G.

F., & Wang, P. (2013). Wnt Agonist Decreases Tissue

Damage and Improves Renal Function After Ischemia-Reperfusion.

Journal of Surgical Research, 179(2),

323.

[22] Holden-Dye, L., & Walker, R. (2014). Anthelmintic

drugs and nematocides: studies in Caenorhabditis elegans.

WormBook: the online review of C. elegans biology,

1-29.

[23] Jackson, B. M., & Eisenmann, D. M. (2012). β-catenin-dependent

Wnt signaling in C. elegans: teaching an

old dog a new trick. Cold Spring Harbor perspectives in

biology, 4(8), a007948.

[24] Zeng, Y. A., & Nusse, R. (2010). Wnt proteins are

self-renewal factors for mammary stem cells and promote

their long-term expansion in culture. Cell stem cell, 6(6),

568-577.

[25] (n.d.). Retrieved from http://home.sandiego.edu/~cloer/loerlab/dacells.html.

[14] Sawa, H. and Korswagen, H. C. (2013). Wnt signaling

in C. elegans. Retrieved from http://www.wormbook.

org/chapters/www_wntsignaling.2/wntsignal.html

[15] BAR-1 (protein) - WormBase : Nematode Information

Resource. (n.d.). Retrieved from https://wormbase.

org/species/c_elegans/protein/CE08973#062--10.

[16] Main. (n.d.). Retrieved from https://web.stanford.

edu/group/nusselab/cgi-bin/wnt/.

[17] Korswagen, H. C., Coudreuse, D. Y., Betist, M. C.,

van de Water, S., Zivkovic, D., & Clevers, H. C. (2002).

BIOLOGY


QUORUM QUENCHING: SYNERGISTIC EFFECTS

OF PLANT-DERIVED COMPOUNDS ON BIOFILM

FORMATION IN VIBRIO HARVEYI

Preeti Nagalamadaka

Abstract

Sixteen million people die annually due to diseases caused by antibiotic resistant bacteria, sixty-five percent of which form

biofilms. Biofilms offer one thousand times more resistance to antibiotics with their exopolysaccharide matrix. Many bacteria,

including Vibrio harveyi, a model for Vibrio cholera, opt for this structure to evade antibiotics. Because biofilm matrix

production is regulated by quorum sensing, efforts are underway to find quorum quenchers. This project focused on testing

combinations of plant-derived quorum quenchers that function by different mechanisms to find if they were more effective

than individual compounds at inhibiting biofilm formation in Vibrio harveyi. In previous literature, neohesperedin

and naringenin were found to inhibit HAI-1 and AI-2 signaling. Cinnamaldehyde also disrupted the DNA-binding ability

of the regulator LuxR. V. harveyi biofilms were grown in the presence of quorum quenching compounds with dimethyl

sulfoxide as a control, stained with Crystal Violet and quantified by OD. Naringenin alone was found to decrease biofilm

formation, whereas cinnamaldehyde and neohesperedin alone showed no detectable effect. Combinations of naringenin

and cinnamaldehyde showed a synergistic effect on inhibiting biofilm formation. Through studying V. harveyi, optimized

quorum quenching could be utilized to counter V. cholera and other biofilm-spread diseases.

1. Introduction

To conserve energy, bacteria coordinate metabolically

expensive activities through quorum sensing. Small

amounts of bacteria bioluminescing are metabolically

wasteful because it will not produce significant light, but

a large group of coordinated bacteria bioluminescing simultaneously

has an ecologically stronger effect, conserving

energy and benefitting all bacteria. Bacteria use chemical

signals to communicate with each other and induce

changes in the bacterial population. When there is a high

cell density of bacteria, molecules called autoinducers are

produced. Upon reaching a threshold concentration, the

whole bacterial population is signaled to alter its gene expression

in unison – a process called quorum sensing [1].

Autoinducers collectively control the activity of metabolically

expensive bacterial functions such as biofilm formation,

pathogenesis, bioluminescence, conjugation and

secretion of virulence factors [1]. In many species such

as Pseudomonas aeruginosa, Helicobacter pylori, Vibrio fischeri,

Vibrio cholerae and Vibrio harveyi, quorum sensing is a

means for bacterial survival and host pathogenesis [1][2].

Some bacteria use a single type of autoinducer in quorum

sensing, usually acyl homoserine lactones (AHLs) or

autoinducing peptides (AIPs) [3], while others use many

types of autoinducers. The single autoinducer LuxIR system

is common in many pathogenic gram-negative bacteria,

but the systems present in V. harveyi, P. aeruginosa and

V. cholerae differ because they have multiple components

and autoinducers. V. harveyi respond to three different

autoinducers (Fig. 1): V. harveyi autoinducer-1 (HAI-1),

Cholera autoinducer-1(CAI-1), and Autoinducer-2 (AI-2)

[4]. HAI-1 is a homoserine lactone (HSL) N-(3-hydroxybutanoyl)-HSL

which is a type of AHL. CAI-1 is a 3-hydroxytridecan-4-one,

and AI-2 is a furanosylborate diester

[4]. AI-2 is found in quorum sensing pathways among different

species and is thought to contribute to interspecies

communication. HAI-1, CAI-1, and AI-2 are recognized

by the sensor kinases LuxN, CqsS, and LuxQ/P respectively

[4]. The low concentration signal is received at these

receptors and is transduced by the phosphorylation phosphotransferase

LuxU which then phosphorylates LuxO

[5]. This activates the transcription of small regulatory

RNAs (sRNAs) that prevent the translation of LuxR [5].

The LuxR protein then goes on to regulate the expression

of over hundreds of genes involved in biofilm formation,

virulence factor secretion or bioluminescence. At

higher concentrations of autoinducers LuxN, LuxQ, and

LuxP switch to phosphatases, dephosphorylating LuxO.

Since dephosphorylated LuxO is inactive, no sRNAs will

be formed and thus the LuxR mRNA will be stable and

translated [6].

The quorum sensing pathway was first observed in

the bioluminescent species V. harveyi and is responsible

for regulating its bioluminescence, colony morphology,

biofilm formation and virulence factor production [7].

Biofilms are communities of bacteria stuck to a surface

encapsulated in an exopolysaccharide matrix. The gene

responsible for the production of this matrix is regulated

by the LuxR protein. Due to their exopolysaccharide

coats, bacteria from biofilms can evade the host immune

system and survive longer in harsh environments, leading

to critical economic problems and nosocomial infections.

Because quorum sensing is integral to the survival of bio-


films, research is underway to inhibit it, termed quorum

quenching [1][3][8]. To detect the efficacy of quorum

quenching, biofilm mass and certain virulence factors can

be quantified.

Many quorum sensing pathogens like Vibrio cholerae,

Providencia stuartii, Heliobacter pylori and Candida albicans

cause harmful infections in humans [2][11][12]. Quorum

sensing pathways in these species are analogous to pathways

in V. harveyi. Quenching these pathways can be impactful

because it offers an alternative approach to target

bacterial infections via quorum sensing [2][11]. Quorum

quenching works by competitively inhibiting the receptor

sites of the autoinducers, degrading autoinducers, or stopping

production of autoinducers completely [3][12]. The

most effective quorum quenchers are those that inhibit

biofilm formation and virulence factor secretion without

slowing the growth of bacteria, because inhibiting bacterial

growth can lead to more selective pressure and resistance

[8].

Many quorum quenching agents have been found in

marine organisms and herbal plants [2][11][12]. Some

specific plant compounds found to inhibit biofilms are

cinnamaldehyde (derived from cinnamon bark extract),

neohesperidin and naringenin (both derived from citrus

extracts) [9][10]. Cinnamaldehyde works by decreasing

the DNA binding ability of the LuxR transcript. Neohesperidin

inhibits the efficacy of the HAI-1 autoinducer in V.

harveyi. Naringenin inhibits quorum sensing via the AI-2

and HAI-1 autoinducer pathways. Because all three quorum

quenchers have different mechanisms of actions in

quorum quenching, combinations of these molecules were

tested on V. harveyi for possible synergistic effects in the

reduction of biofilm formation.

2. Materials and Methods

Figure 1. Shows the HAI-1, CAI-1 and AI-2 autoinducers

received by kinases/phosphatases LuxN, CqsS

and LuxP/Q respectively. The signal transduction

pathway regulating the expression of LuxR is also

shown via the LuxR transcript. LuxR functions in

the regulation of quorum sensing controlled behaviors

[4].

Quorum quenching can have a wide range of effects

in medicine. It can be responsible for inhibiting biofilm

formation and decreasing the pathogenicity of bacteria.

Because quorum sensing controls biofilm formation and

virulence factor secretion, it has the potential to decrease

pathogenicity. For example, the quorum sensing regulated

type three secretion system found in many gram-negative

bacteria infects eukaryotic cells via the secretion of specific

proteins. The quorum quencher naringenin has been

shown to decrease the virulence of the type three secretion

system [9]. Furthermore, the same quorum sensing pathways

control the secretion of exopolysaccharides needed

for maintaining biofilm structure [10].

BIOLOGY

2.1 – Compounds

Cinnamaldehyde, naringenin and neohesperidin were

purchased from Sigma-Aldrich. All compounds were dissolved

in dimethyl sulfoxide (DMSO) at a concentration of

10 mg/mL and stored at -20°C.

2.2 – Media and Bacterial Growth

Vibrio harveyi strain BB120 (wild-type) was purchased

from ATCC. The Luria Marine (LM) medium was used

to grow V. harveyi. Overnignt cultures were incubated at

30°C without shaking.

2.3 – Individual Compound Biofilm Formation Assay

This experiment determined the effect of each of the

quorum quenchers individually. Overnight culture of V.

harveyi BB120 was diluted in a 1:100 ratio in LM media.

One mL of the diluted culture was placed into each well

in a 24-well plate. Wells received concentrations of compounds

previously found to successfully inhibit quorum

sensing in V. harveyi [9][10]. Each well received 6.25 µg/

mL of naringenin, 12.5 µg/mL of neohesperidin, 13.22 µg/

mL of cinnamaldehyde, or 1.32 µL of DMSO as a control.

The plates were incubated at 26°C with no shaking for 24


hours to stress bacteria into forming biofilms [9]. The biofilm

mass was quantified by Crystal Violet staining. The

plates were first washed with deionized water three times.

They were then stained with 2 mL of 0.1% Crystal Violet

solution for 20 minutes. The dye not associated with the

biofilm was washed out with deionized water. All the dye

associated with the biofilm was dissolved in 1 mL of 33%

acetic acid. The absorbances of these acetic acid and dye

samples were taken at 570 nm by a spectrophotometer.

The optical density (OD) was used as a means to quantify

the biofilm. This experiment was carried out 6 times with

6 replicated wells per plate.

2.4 – Multiple Compounds Biofilm Formation Assay

This experiment determined the effect of combinations

of quorum quenching compounds. This was the same assay

as Individual Compound Biofilm Formation Assay except

each well received 1.32 µL of DMSO as a control, 6.25

µg/mL of naringenin,12.5 µg/mL of neohesperidin, 13.22

µg/mL of cinnamaldehyde, or a combination of the compounds.

Combinations tested include cinnamaldehyde/

naringenin and neohesperidin/naringenin. Half concentrations

of naringenin (3.125 µg/mL) and cinnamaldehyde

(6.61 µg/mL) were tested in later combinations to determine

whether a synergistic effect was present. Plates were

stained with Crystal Violet and the OD associated with

biofilm was measured. Each combination experiment was

replicated 3 times with 4 replicated wells per plate.

2.5 – Cellular Growth Assay

This experiment determined whether combinations of

compounds altered the growth rates of the bacteria. Overnight

culture of V. harveyi BB120 was diluted in a 1:100 ratio

in Luria Marine (LM) media. Each tube received either

3.97 µL of DMSO as a control, 6.25 µg/mL of naringenin

and 12.5 µg/mL of neohesperidin, 6.25 µg/mL of naringenin

and 13.22 µg/mL of cinnamaldehyde, or 6.25 µg/mL

of naringenin, 12.5 µg/mL of neohesperidin and 13.22 µg/

mL of cinnamaldehyde. The cultures were grown for 24

hours at 30°C with shaking. Optical densities of the broth

were taken roughly every 2 hours for the first 8 hours.

Additionally, samples were taken of the broth at 6 hours

and 24 hours. The samples were serially diluted and plated

on LM agar plates and colony forming units were counted

after 24 hours of growth. This experiment was replicated

twice.

3. Data Analysis

To analyze the results of my experiments, the statistical

software JMP 10 was used to run ANOVA tests and

first determine the presence of a treatment effect. If the

ANOVA test showed an effect, a Tukey’s Honest Significance

Difference (HSD) test was conducted to discern the

significant differences among means. Some data were ad-

ditionally graphed as percent biofilm inhibition, further

emphasizing the presence of a synergistic effect. Biofilm

inhibition percentages were calculated as (control OD -

treatment OD)/(control OD)*100.

4. Results

4.1 – Individual Compound Biofilm Formation Assay

To measure the effectiveness of individual plant-derived

compounds on quorum sensing inhibition, the individual

compound biofilm formation assay was conducted

on V. harveyi biofilms. As determined by the Tukey’s honest

significance test, naringenin at a concentration of 6.25

µg/mL significantly decreased biofilm mass. Cinnamaldehyde

and neohesperidin showed no detectable biofilm inhibition

(Fig 2).

Figure 2. Individual Compound Biofilm Formation

Assay. Shows the results of the individual plant-derived

compounds on biofilm formation in V. harveyi

to see which compounds were most effective. Error

bars show mean +/- 1 SEM. An ANOVA test was conducted

and showed p < 0.0037* (Table 1). A Tukey’s

Significance test was then conducted. Different

letters correspond to significant differences as discerned

by Tukey’s HSD test.

Table 1. ANOVA table from the Individual Compound

Biofilm Formation Assay (Fig. 2).

Source DF Sum of

Squares

Compound

Mean

Square

F Ratio

Prob > F

3 0.34467 0.11489 6.2299 0.0037*

Error 20 0.36883 0.018442

Corr.

Total

23 0.7135

4.2 – Multiple Compounds Biofilm Formation Assay

Because naringenin was consistently effective, combinations

with naringenin were tested. The cinnamaldehyde

and naringenin combination (Fig. 3a) showed a significant


difference between the control and the individual compounds,

but no significant difference between the combination

and the individual compounds, which is indicative

of naringenin overpowering the combination.

Table 2. ANOVA test conducted on the combination

of naringenin and cinnamaldehyde (Fig. 3a).

Source DF Sum of

Squares

Mean

Square

F

Ratio

Prob > F

a)

3 0.34688 0.115628 12.0778 0.0002*

Error 17 0.16275 0.009574

Corr.

Total

20 0.50963

Table 3. ANOVA test conducted on the combination

of neohesperidin and naringenin (Fig. 3b).

Source DF Sum of

Squares

Mean

Square

F

Ratio

Prob > F

b)

Compound

Compound

3 0.41184 0.115628 17.9586 0.0001*

Error 20 0.15289 0.009574

Corr.

Total

23 0.56473

Figure 3. Combinations with naringenin. a) Shows

V. harveyi biofilm formation when treated with the

combination of cinnamaldehyde and naringenin.

Naringenin concentrations were 6.25 µg/mL and

cinnamaldehyde concentrations were 13.22 µg/mL.

The ANOVA test showed p < 0.0002* (Table 2), indicating

that there is indeed some difference between

the compounds. The Tukey’s HSD test was then conducted

to find specific differences. b) Shows V. harveyi

biofilm formation when treated with the combination

of naringenin and neohesperidin. The ANOVA

test showed p < 0.0001* (Table 3), indicating that there

is indeed some difference between the compounds.

Tukey’s HSD test was then conducted to find specific

differences. Different letters on the graphs above

indicate differences in significance according to

Tukey’s HSD test.

The neohesperidin and naringenin combination (Fig.

3b) showed naringenin significantly different from the

control and neohesperidin indistinguishable from the

control. Furthermore, the combination of naringenin and

neohesperidin was indistinguishable from naringenin,

supporting the theory that neohesperidin showed no detectable

effect. In these combinations the raw OD values of

the combinations were low, so in subsequent experiments

biofilm formation was increased through less volume of

LM media in all the wells to better detect an inhibitory

effect of the combinations. Since naringenin seemed

to overpower combinations, half the concentration of

naringenin (3.125 µg/mL) was tested with cinnamaldehyde

(Fig. 4). The combination with full concentrations

of naringenin and cinnamaldehyde showed a significant

difference in biofilm formation compared to the other

individual compounds, suggesting an interaction between

the compounds. Because this combination had a greater

percent biofilm inhibition than the individual compounds’

percents of inhibition combined (Fig. 5), it was concluded

that a synergistic effect was present. However, the

combination with half the concentration of naringenin

and full concentration of cinnamaldehyde was indistinguishable

from the full concentration of naringenin alone,

showing the necessity of naringenin for the synergy (Fig.

5). A similar experiment was conducted to determine the

effects of cinnamaldehyde concentration on this synergy

by testing combinations of naringenin with half the concentration

of cinnamaldehyde (6.61 µg/mL). A synergistic

effect was present in the combinations of naringenin

with both full and half concentrations of cinnamaldehyde,

indicating that the concentration of cinnamaldehyde is

not as integral as that of naringenin for the synergy (Fig.

6 and 7).

BIOLOGY


Figure 4. Combinations with Varied Concentrations

of Naringenin. Effects on biofilm formation of the

combination of naringenin at full (6.25 µg/mL) and

half (3.125 µg/mL) concentrations with cinnamaldehyde

(13.22 µg/mL). Error bars show 1 SEM. ANOVA

test p < 0.0001* (Table 4). Letters correspond to differences

discerned by Tukey’s HSD Test.

Table 4. ANOVA test conducted on full concentrations

of cinnamaldehyde with varied concentrations

of naringenin (Fig. 4).

Source DF Sum of

Squares

Compound

Mean

Square

F

Ratio

Prob > F

5 0.94506 0.189011 29.7551 <0.0001*

Error 18 0.11434 0.006352

Corr.

Total

23 1.0594

Figure 6. Combinations with Varied Concentrations

of Cinnamaldehyde. Effects on biofilm formation of

the combination of cinnamaldehyde at full (13.22 µg/

mL) and half (6.61 µg/mL) concentrations with naringenin

(6.25 µg/mL). Error bars show 1 SEM. ANOVA

test p < 0.0001* (Table 5). Letters correspond to differences

discerned by Tukey’s HSD Test.

Table 5. ANOVA test conducted on full concentrations

of naringenin tested with various concentrations

of cinnamaldehyde (Fig. 6).

Source DF Sum of

Squares

Compound

Mean

Square

F

Ratio

Prob > F

5 2.21176 0.4423 31.761 <0.0001*

Error 18 0.25065 0.0139

Corr.

Total

23 2.4624

Figure 5. Percent Biofilm Inhibition of Varied Naringenin

Concentrations. Biofilm inhibition percentages

calculated as (control OD - treatment OD)/(control

OD)*100. Error bars show 1 Standard Error of the

Mean (SEM).

Figure 7. Percent Biofilm Inhibition of Varied Cinnamaldehyde

Concentrations. Shows the biofilm

inhibition percentages calculated as (control OD -

treatment OD)/(control OD)*100. Error bars show 1

SEM.

4.3 – Multiple Compounds Cellular Growth Assay

The experiment was conducted to show the growth rate

of the V. harveyi bacteria when treated with combinations

used in biofilm formation assays. Because the growth rates


of bacteria remained unchanged when treated with combinations

of compounds, it is understood that the treatments

are inhibiting only quorum sensing, not altering the mortality

of V. harveyi.

Figure 8. Cellular Growth Assay. Shows the growth

curve of V. harveyi treated with DMSO (control), cinnamaldehyde

(13.22 µg/mL)/naringenin (6.25 µg/mL),

neohesperidin (12.5 µg/mL)/naringenin (6.25 µg/mL)/

cinnamaldehyde (13.22 µg/mL), neohesperidin (12.5

µg/mL)/naringenin (6.25 µg/mL). Error bars show

mean +/- 1 SEM.

5. Discussion

The results of our study showed naringenin alone was

able to inhibit biofilm formation, whereas neohesperidin

and cinnamaldehyde alone at the concentrations tested

showed no consistent detectable effect on biofilm inhibition

(Fig. 2). This study confirmed previous studies showing

that naringenin strongly suppressed AI-2 and HAI-1

mediated quorum sensing in V. harveyi. Because cinnamaldehyde

was thought to inhibit the master regulatory protein

LuxR, it was hypothesized to be the most effective.

However, the variable effects of cinnamaldehyde as well

as the consistently undetectable effects of neohesperidin,

responsible for the inhibition of HAI-1 mediated quorum

sensing, were unexpected. Surprisingly, there was a

synergistic effect present when both cinnamaldehyde and

naringenin were used to inhibit biofilm formation. It is

important to note the combinations of cinnamaldehyde/

naringenin and neohesperidin/naringenin showed low

raw OD values. Only 1 mL of LM media was administered

to each well in later experiments to increase baseline

biofilm formation, so inhibition could more easily be detected.

In the earlier experiments, naringenin seemed to

overpower combinations as the combination was not significantly

different from naringenin alone. Therefore, half

concentrations of naringenin were tested with decreased

BIOLOGY

media per well. Synergy was detected with the full concentration

of cinnamaldehyde and naringenin.

One explanation for this synergy is that once naringenin

blocks AI-2 and HAI-1 mediated quorum sensing,

cinnamaldehyde is left to inhibit only the effects of CAI-1

mediated quorum sensing (Fig. 1). Cinnamaldehyde may

be more effective at decreasing the DNA-binding affinity

of relatively small amounts of LuxR, which would explain

the apparent synergy and why the synergy depended heavily

on the concentration of naringenin present. It is also

possible that there are more components to this pathway.

If naringenin inhibits other unknown components of this

pathway that cinnamaldehyde cannot, cinnamaldehyde

alone might be ineffective because of the activation of multiple

components of the pathway. The inconsistent results

of cinnamaldehyde could be explained by the presence

of these additional, confounding components. In combinations,

however, naringenin could block the unknown

components and cinnamaldehyde would be able to work

effectively. In addition, it is confirmed that these compounds

inhibit only quorum sensing and not cell growth

rates because results of the cellular growth assay show a

similar curve with and without the treatments (Fig. 8).

Future directions include altering the concentrations of

quenchers to see if greater inhibition and optimization are

possible, elucidating the exact mechanisms of these quorum

quenchers, and observing the effects of combinations

of different quorum quenchers on biofilm formation. It

would be interesting to see which autoinducers contribute

the greatest effect on biofilm formation by testing combinations

of naringenin or cinnamaldehyde with a quorum

quencher that acts on CAI-1 mediated quorum sensing.

Another approach would be to test knockout strains of

V. harveyi that only respond to AI-2 and HAI-1 mediated

quorum sensing to see if decreased biofilm formation

occurs. As further research ensues, the mechanisms of the

quorum quenchers will be better known and can be optimized

in combinations to produce more synergistic effects

on decreasing biofilm formation.

More broadly than decreasing biofilm formation, quorum

sensing inhibition has the potential to act as an alternative

to antibiotics. Because quorum quenching effectively

reduces biofilm formation and does not kill bacteria,

it can be a less selective alternative therapy to antibiotics

[12]. Since quorum sensing controls not only biofilm formation

but also the secretion of other virulence factors [2],

the inhibition of quorum sensing can decrease virulence

factor production. The production of these factors can also

be measured to detect synergistic effects among quorum

quenchers. The applications of this project are in inhibiting

biofilm formation, which can be used as the foundation for

biofilm-resistant hospital materials, alternative antibiotic

treatments or as a model for V. cholera. These applications

are important as a model for V. cholera because the V. cholera

biofilm plays a critical role in pathogenesis and disease


transmission [13]. A cholera outbreak occurred in Haiti in

2010 when UN peacekeepers introduced the disease to the

earthquake devastated country, taking upwards of 10,000

lives. In 2018, there were cholera outbreaks in Yemen and

Somalia. Additionally, costs of outbreaks were estimated

to be $38.9-$64.2 million in 2005 [14]. Through further

studying this synergistic effect of quorum quenchers on

decreasing biofilm formation in V. harveyi, the mortality

rates and economic costs due to infectious diseases including

cholera can decrease in the future.

6. Acknowledgments

This work would not have been possible without the

endless guidance and support of my mentor, Amy Sheck.

I would like to extend gratitude to Kimberly Monahan, as

well. Also, I would like to thank Nick Koberstein for help

with statistical analysis. Furthermore, I would like to thank

the Research in Biology Classes of 2019 and 2020 for their

friendship and advice. Lastly, I am highly indebted to the

Glaxo Endowment to the North Carolina School of Science

and Mathematics for providing the funding for this

project.

7. References

[1] Bassler, B. L. & Losick, R. (2006) Bacterially speaking.

Cell, 125, 237-246.

[2] Defoirdt, T. (2017) Quorum-sensing systems as targets

for antivirulence therapy. Trends in Microbiology, 26, 313-

328.

[3] Vadakkan, K., Choudhury, A. A., Gunasekaran, R.,

Hemapriya, J. & Vijayanand, S. (2018) Quorum sensing intervened

bacterial signaling: pursuit of its cognizance and

repression. Journal of Genetic Engineering and Biotechnology,

16, 239-252.

[7] Lilley, B. N. & Bassler, B. L. (2000) Regulation of quorum

sensing in Vibrio harveyi by LuxO and Sigma-54. Molecular

Microbiology, 36, 940-954.

[8] Kalia, V. C., Patel, S. K. S., Kang, Y. C., & Lee, J. (2019)

Quorum sensing inhibitors as antipathogens: biotechnological

applications. Biotechnology Advances, 37, 68-90.

[9] Vikram, A., Jayaprakasha, G. K., Jesudhasan, P. R.,

Pillai, S. D. & Patil, B. S. (2010) Suppression of bacterial

cell-cell signaling, biofilm formation and type III secretion

system by citrus flavonoids. Journal of Applied Microbiology,

109, 515-527.

[10] Packiavathy, I. A. S. V., Sasikumar, P., Pandian, S. K.,

& Ravi, A. V. (2013) Prevention of quorum-sensing-mediated

biofilm development and virulence factors production

in Vibrio spp. by curcumin. Applied Microbiology and Biotechnology,

97, 10177-10187.

[11] Ta, C. A. K. & Arnason, J. T. 2015. Mini review of

phytochemicals and plant taxa with activity as microbial

biofilm and quorum sensing inhibitors. Molecules, 21, E29.

[12] Kalia, V.C. (2013) Quorum sensing inhibitors: an

overview. Biotechnology Advances, 31, 224-245.

[13] Silva, A. J. & Benitez, J. A. (2016) Vibrio cholera Biofilms

and Cholera Pathogenesis. PLOS Neglected Tropical

Diseases, 10(2), e0004330.

[14] Tembo, T., Simuyandi, M., Chiyenu, K., Sharma, A.,

Chilyabanyama, O. N., Mbwili-Muleya, C., Mazaba, M. L.,

& Chilengi, R. (2019) Evaluating the costs of cholera illness

and cost-effectiveness of a single dose oral vaccination

campaign in Lusaka, Zambia. PLOS One, 14(5), e0215972.

[4] Anetzberger, C., Pirch, T. & Jung, K. (2009) Heterogeneity

in quorum sensing-regulated bioluminescence of

Vibrio harveyi. Molecular Microbiology, 73(2), 267-277.

[5] Tu, K. C. & Bassler, B. L. (2007) Multiple small RNAs

act additively to integrate sensory information and control

quorum sensing in Vibrio harveyi. Genes Dev, 21, 221-223.

[6] Brackman, G., Deifoirdt, T., Miyamoto, C., Bossier,

P., Calenbergh, S. V., Nelis, H., & Coenye, T. (2008) Cinnamaldehyde

and cinnamaldehyde derivatives reduce virulence

in Vibrio spp. by decreasing the DNA-binding activity

of the quorum sensing response regulator LuxR. BMC

Microbiology, 8, 149-163.


EMOTIONAL PROCESSING AND WORKING MEMORY

IN SCHIZOPHRENIA WITH NEUTRAL AND NEGATIVE

STIMULI: AN fMRI STUDY

Cindy Zhu

Abstract

Schizophrenia (SZ) is a psychiatric disorder that results in abnormalities with emotional processing and working memory.

Working memory (WM) and Emotional Processing (EP) are supported by specific neural regions in the brain; however,

we do not know how these regions interact to influence task performance. To explore the effects of emotional valence

on working memory, an emotional n-back task was used with a focus on Neutral and Negative emotional valence and

on working memory and emotional processing regions in the brain. In the neutral condition, we observed recent-onset

schizophrenia (RO) participants having a lower activation than genetic high-risk (HR) participants in working memory

regions (ACC, Left ACC, Right ACC), and in ventral regions (Left NAcc). RO showed lower activation than control

(CON) participants in the Left DLPFC. These results align with behavioral results - RO did not show a significant difference

in D′ values between Neutral and Negative conditions while controls did, which may reflect the belief that RO

patients have a greater ability to focus on the working memory task due to tunneling and focus less on the emotional valence

as compared to the control patients. When exploring the correlation between brain activity and task performance,

we found CON and RO exhibiting a positive correlation, while HR showed a negative correlation. In conclusion, during

a cognitively demanding task, RO participants exhibit fewer differences in working memory and emotional regulation

between conditions, which supports prevailing theories of emotional blunting and tunneling. In addition, surprising

differences between HR and RO groups showed HR with overactivation, as opposed to prevailing theories that HR will

show levels of activation intermediate between CON and RO.

1. Introduction

Schizophrenia is characterized by significant impairments

in working memory and emotional regulation.

Impairments in working memory (WM), the temporary

storage and manipulation of information held for a limited

period of time, are considered a core neurocognitive deficit

in patients with schizophrenia (SZ) and can significantly

impact quality of life. Deficits in emotional regulation, or

the conscious effort in modulating emotional response to

goal-unrelated or irrelevant emotional stimuli, interfere

with social life and daily functioning, and may also be associated

with the development of psychotic symptoms

[1]. These deficits in WM are present prior to the onset

of illness, such as in genetically high-risk individuals, or

in patients who are medication-free in their first episode

of illness [2]. Thus, gaining a better understanding of the

interplay of emotional regulation and working memory in

patients with schizophrenia may help improve patient diagnosis

and quality of life.

Emotional blunting, or blunted affect, is one of the core

negative symptoms of schizophrenia and results in difficulty

in expressing emotions in reaction to emotional stimuli

due to problems with emotional processing. There are

mixed findings of activation during emotionally evocative

stimuli between people with and without schizophrenia in

areas associated with emotion, with some studies reporting

no differences in activation, while others report diminished

activation [3]. Emotional processing abnormalities

in schizophrenia have been shown to reduce activation

in brain regions associated with emotional processing in

response to emotionally evocative stimuli [4]. It remains

unclear how these alterations in emotional valence processing

may impact WM functions for individuals with

schizophrenia.

Working memory is an important component of higher

cognition, such as goal-directed behavior. Deficits in

schizophrenia may relate to a number of other core symptoms

in schizophrenia [5]. Working memory impairment

is often associated with differing DLPFC activation, which

is implicated in executive functions, goal-directed planning,

and inhibition, and is a part of working memory circuitry

[6]. The effects of these deficits can be studied using

n-back tasks, which test working memory by utilizing continuous

updating and order memory [2].

Emotional regulation processes and working memory

can be studied in conjunction using emotional working

memory paradigms through functional magnetic resonance

imaging (fMRI). Task-based fMRI allows for both

behavioral and aggregate neural activation measurements

and can help identify how emotion and WM interact. An

emotional one-back task was used with neutral and negative

conditions. Participants were instructed to indicate

when a new stimulus (image with emotional valence)

was the same as the stimulus presented one before, which

required short-term memory engagement. By using an

BIOLOGY


emotional one-back task, the influence of the emotional

valence (i.e. whether a stimulus is neutral or negative) on

WM can be calculated.

The influence of emotion on cognition is an essential

topic to research, but research on this topic has received

less attention than others in schizophrenia literature. In

controls, it has been shown that emotional stimuli can

garner more attention than neutral stimuli. This extra attention

may facilitate the processing of emotional stimuli.

In patients with schizophrenia, there may be reduced activation

and worsened performance due to the addition

of emotional valence to the WM task. DLPFC has an important

role in the integration of emotional and cognitive

information [7]. Studies of negative valence and cognition

tend to produce more robust results due to the arousing

nature of the images used. Other studies [4] also explore

the interaction between EP and WM, but only with control

and schizophrenia patient groups. By including HR

participants, which have been found to show similar

working memory deficits as those with schizophrenia [2],

in our study of the interaction of WM and EP, we address a

new set of questions in how high-risk participants perform

compared to controls and RO and if they exhibit deficits

similar to RO participants.

The overarching goal of this study is to identify how

WM and emotional processes interact, particularly in the

context of schizophrenia. In order to achieve this aim, we

administered an emotional n-back task to 76 participants

(35 control, 20 HR, 21 RO) to determine group differences

in regional activation and WM performance associated

with psychotic illness. We hypothesized that RO participants

would perform consistently across neutral and negative

conditions (because emotional blunting may result in

less expressed performance differences between valences),

while control participants would have more impaired performance

in the negative condition compared to the neutral

condition. Moreover, we hypothesized that changes in

performance between conditions will differ between subject

groups. When looking at brain activation, we hypothesized

that the control participants would exhibit a greater

change in ventral regions when comparing between the

neutral and negative conditions and that control participants

would have a greater change in activation in WM

regions between neutral to negative. We also hypothesized

that genetic high-risk participants would exhibit brain activation

and task performance intermediate between CON

and RO. Finally, we investigated links between activation

and behavior to see if changes in activation correlated with

changes in task performance.

2. Methods

2.1 – Participants

Twenty-one patients with recent-onset schizophrenia

and twenty genetic high-risk patients were recruited from

the UNC Healthcare System. Thirty-five healthy control

subjects were also included. All participants provided

written consent to the study approved by the University

of North Carolina- Chapel Hill IRB. All participants were

between the ages of 16-45, of any ethnicities or gender,

had no presence of metallic implants or devices interfering

with MRI, and were not pregnant. Inclusion criteria for

recent-onset schizophrenia (RO) patients were: (1) Meet

DSM-IV criteria for SZ or schizophreniform disorder, (2)

No history of major central nervous system disorder or intellectual

disability (IQ<65), (3) Must have illness for <5

years, (4) No current diagnosis of substance dependence,

and no substance abuse for 6 weeks. RO patients were also

instructed to refrain from taking benzodiazepine medications

on the morning of testing but instead to bring their

medication with them to take after scanning. Inclusion

criteria for genetic high risk (HR) patients were: (1) Must

have first degree relative with psychotic disorder, (2) Must

not meet DSM-IV criteria for past or current Axis I psychotic

disorder on bipolar affective disorder, (3) No history

of major central nervous system disorder or intellectual

disability (IQ<65), (4) No current treatment with antipsychotic

medication. Healthy controls (CON) were excluded

if they had history of a DSM-IV axis I psychiatric disorder,

family history of psychosis, history of current substance

abuse/dependence, history or current medical illness that

could affect brain morphology, or clinically significant

neurological or medical problems that could influence the

diagnosis or the assessment of the biological variables in

the study. All participants gave written informed consent

consistent with the IRB of UNC if over 18 or assent and

parent/guardian provided consent for minors prior to

their participation in the study.

2.2 – Emotional One-back Task and Procedure

Each participant completed an emotional one-back task

with an auditory component during a functional magnetic

imaging (fMRI) session with 8 runs. The emotional oneback

task consists of a visual tracking task, using images

with either Positive, Neutral, or Negative valence, as defined

by the International Affective Picture System (IAPS)

[8]. Further analysis was performed with only Neutral and

Negative Valences. Patients with schizophrenia report

feeling negative emotion strongly but are less outwardly

expressive of this negative emotion [9]. By focusing on the

Negative valence, we want to observe if RO exhibit similar

activation to CON during negative emotional situations,

as the contexts in which RO patients experience negative

emotion is different than those without SZ. All images in

the run were of the same valence, and subjects were asked

to press a button when they saw the same image two times

in a row (Fig. 1). A control condition with no n-back

task was also included. The auditory component, which

occurred simultaneously with the visual component, involved

subjects hearing irrelevant standard and pitch devi-


ant tones at random intervals, but for the study’s purposes,

it was excluded from further analysis.

The task was divided into 8 runs, with 2 runs of each

valence, and 2 control runs with no n-back task. Each

run lasted 200.58s, with 14 target images, 56 non-target

images. Each image was presented for 500ms and the inter-stimulus

interval was either 1500ms or 3000ms.

Figure 1. Emotional One-Back Task. Participants

were asked to press a button when the picture shown

matches the picture shown one before.

2.3 – Behavioral Analysis

To analyze the performance of participants during the

task, D′, or sensitivity index, was used as a metric, because

it considers accuracy and sensitivity. Paired t-tests were

performed to investigate within-group differences in D′

between Neutral and Negative task conditions. To investigate

differences between groups (CON, HR, and RO)

and condition (Neu and Neg) and the interaction effects

of both factors on D′, a 2-way ANOVA was used. Finally,

a one-way ANOVA was used to investigate group differences

for neutral and negative conditions, only considering

one condition at a time. A Tukey comparison of means

was performed for One-way and Two-way ANOVA’s that

had significant results to further interpret the results.

2.4 - Neuroimaging Analysis

2.4.1 - Imaging Data Acquisition

A General Electric 3.0 T MRI scanner with a functional

gradient-echo echo-planar imaging sequence allowing

for full-brain coverage (TR: 2000 ms; TE: 27 ms; FOV:

24 cm; image matrix: 64×64; flip angle: 60; voxel size:

3.75×3.75×3.8mm 3 ; 34 axial slices) was used. Each functional

run had 120 time-points. Structural MRIs were acquired

before fMRIs to obtain 3D coplanar anatomical T1

images using a spoiled gradient-recalled acquisition pulse

sequence (TR: 5.16 ms; TE: 2.04 ms; FOV: 24 cm; image

matrix: 256×256; flip angle: 20; voxel size: 0.94×0.94×1.9

mm 3 ; 68 axial slices).

2.4.2 - Preprocessing

Functional data analyses were carried out using the

University of Oxford’s Center for Functional Magnetic

Resonance Imaging of the Brain (FMRIB) Software Library

(FSL) release 6.0 [11]. Image preprocessing consisted

BIOLOGY

of using the Brain Extraction Tool (BET) to remove nonbrain

structures, motion correction, time-slice correction

and spatial filtering using a Gaussian kernel of full width

half maximum 5 mm, high-pass temporal filtering. The

functional images were co-registered to the structural images

in their native space. These images were then normalized

to the Montreal Neurological Institute standard

brain. The default adult template in FMRIB’s Linear Image

Registration Tool (FLIRT) was used for registration [10].

FMRIB’s Improved Linear Model (FILM) was used for

pre-whitening in order to estimate and account for each

voxel’s time series auto-correlation.

2.4.3 - Whole Brain Analysis

Whole Brain Analysis was performed to ensure that

the tasks activated regions known to be involved in WM

and EP. These whole-brain maps were thresholded for significance

to obtain a significant rate of p=0.05 (corrected

for false discovery rate due to multiple comparisons) with

Randomise, which tests for correlation inferences using

permutation methods, implemented in FSL [11]. The Dorsal

Attention and Ventral Attention networks [12] were

used to check that existing patterns of activation in control

participants were consistent with working memory and

emotional processing regions. The control mean group

activation was then overlaid on top of the two attention

networks to see where activation overlapped.

2.4.4 - Region of Interest Selection

Regions of Interest (ROI) involved in working memory

circuitry and/or emotional processing were selected

(Fig. 2). Dorsal, or working memory regions, include the

whole anterior cingulate cortex (ACC), left and right anterior

cingulate cortex (Left ACC, Right ACC), left and right

dorsal lateral prefrontal cortex (Left DLPFC, Right DLP-

FC), and the interparietal sulcus (IPS). Ventral regions,

or regions involved in emotional processing, include left

and right amygdala (Left AMY, Right AMY), left and right

nucleus accumbens (Left NAcc, Right NAcc), and left and

right Orbitofrontal cortex (Left OFC, Right OFC).

2.4.5 - fMRI Statistics

Using FSL version 6 [11], the FEAT first-level analysis

was performed on each run per subject, where a general

linear model is used to model the expected hemodynamic

response function, which models the evoked hemodynamic

response to a neural event. In the second-level analysis,

three contrast of interest (Neutral, Negative, Neutral

> Negative) were generated for each subject across multiple

runs to investigate the interaction between emotional

valence and type of model design (NeuVisTarg > NegVis-

Targ or NeuVis > NegVis), and each emotional valence

and type of model design (NeuVisTarg and NegVis). In the

final analysis, only event designs Neutral, Negative, and

Neutral>Negative contrasts were considered.


Table 1: Demographic information for the participants. CON denotes Healthy controls, HR denotes High Risk

Participants and RO denotes Recent Onset Participants.

CON (n=35) HR (n = 20) RO (n = 21)

Mean SD/% Mean SD/% Mean SD/% p(CON-HR) p(CON-RO) p(HR-RO)

Age 27.01 6.07 30.62 7.89 25.29 4.92 0.068 0.284 0.015

Male 19 54.29% 5 25% 17 80.95% 0.037 0.0457 0.0004

Female 16 45.71% 15 75% 4 19.05% 0.037 0.0457 0.0004

These contrasts were then submitted to the third level

analysis to examine differences between groups in task-related

brain activity. Region of Interest analysis was then

conducted using FEATQUERY, and beta values, which

represent the direction and magnitude of regional brain

activation, were extracted from working memory (WM)

and emotional processing (EP) of the brain.

We conducted paired t-tests to investigate the differences

between neutral and negative conditions within the

same subject group. To investigate differences between

groups (CON, HR, and RO) and condition (Neu and

Neg) and the interaction effects of both factors, a 2-way

ANOVA was used. Finally, a one-way ANOVA was used

to investigate group differences for neutral and negative

conditions for different regions. Post Hoc analysis was performed

with a Tukey comparison of means for one way

and Two-way ANOVA’s that had significant results, in order

to isolate specific effects (or differences).

3. Results

3.1 - Basic Information

The demographic information of these three groups

(Healthy Controls, High-Risk participants, and Recent

Onset Participants) are listed in Table 1. There was no significant

difference between CON and RO regarding age.

However, there is a significant age difference between

CON-HR and HR-RO, which may be due to the greater

ages of the HR participants. High-Risk participants may

be of greater age because the tendency for schizophrenia

to manifest in patients in their late teens or early twenties

does not restrict the pool of HR participants. As a result,

there is a wider age range for HR participants to be chosen

from (unrestricted by diagnosis), while recent-onset

schizophrenia typically occurs before the age of 35, which

may explain the difference in mean age. There is a significant

difference between all groups (CON-HR, CON-RO,

RO-HR) regarding sex (male and female). This is due to

the high percentage of Male RO and a high percentage of

Female HR.

3.2 - Behavioral Performance on the Emotional One-back

Task

3.2.1- Within-Group Analysis

Control participants performed significantly worse

during the neutral valence than the negative valence (t

= -2.216, p = 0.035) but the recent-onset and high-risk

participants exhibited no significant difference in performance

between the neutral and negative valences (Fig. 3).

Figure 2. Regions of Interest used in Analysis.

2.5 - Brain Activity and WM Performance

In order to explore links between brain activity and

WM performance, linear regression was performed

with extracted beta values from selected ROI and neutral

and negative conditions as the independent variable and

D′ values as the dependent variable. Subjects without

behavioral data were excluded. These regressions were

performed on patient groups (CON, RO and HR participants)

and the correlation was identified.

Figure 3. Within Group D′ Values for Neutral and

Negative Condition.


3.2.2 - Between-Group Analysis

A Two-way ANOVA with D′ as the dependent variable

revealed no significant main effects, including Subject

Group, Valence, and Subject Group by valence interaction.

While not significant, the HR group had the best task performance

(highest group mean D′), followed by the control

group and lastly the RO group.

3.3 - Whole Brain Imaging Analysis

For Neutral and Negative conditions, the activation of

the control means overlapped with dorsal and ventral regions.

Some specific regions include the anterior cingulate

cortex and the intraparietal sulcus. Neutral>Negative exhibited

less overlap, but since this condition is calculated

from the difference between Neutral and Negative, it is

expected that there would be lower activation in those regions.

Activation in visual regions is expected given that it

is a visual task (i.e. the occipital activation).

3.4 - Region of Interest Analysis

3.4.1 - Within-Group Analysis

CON, HR and RO subjects did not show significant

within-group changes in activation between Neutral and

Negative valence in Dorsal or Ventral Regions (Fig. 5).

While not significantly different, in control participants,

all regions but the left AMY had greater activation in the

Neutral condition. In high-risk participants, all dorsal regions

but the left and right DLPFC had greater activation

in the Neutral condition, and in ventral regions, all but

the left AMY and left NAcc had greater activation in the

Negative condition. Finally, in recent-onset participants,

all dorsal regions but the right ACC and right DLPFC had

greater activation in the Negative condition and all ventral

regions but the right AMY had greater activation in the

Negative condition.

Figure 4. Whole Brain Results showing Mean Control

Activation across conditions.

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a) Neutral Condition b) Negative Condition c) Neutral > Negative Condition

Figure 5. Within-Group Average Beta Values for

Neutral and Negative Condition.

3.4.2 - Between-Group Analysis

We observed significant main effects of group in working

memory regions, specifically ACC (F=4.158, p=0.018),

Left ACC (F=3.434, p=0.0348), and Right ACC (F=4.3281,

p=0.015). Upon further investigation, the group difference

lies between RO and HR groups in the Neutral condition,

with greater activation in the HR group (Table 2). We also

observed significant main effects in emotional processing

regions, specifically, Left NAcc (F = 4.4413, p= 0.013).

Similarly in the Left NAcc, the group difference lies between

RO and HR groups in the Neutral condition, with

greater activation in the HR group (Table 2). As a follow-up

analysis of the Two-way ANOVA, the One-way

ANOVA was used to identify which valence the significant

differences found with the Two-way ANOVA were associated

with. There was a significant difference between

groups in the Left DLPFC during the Neutral condition

(F=4.0098, p=0.02227). After performing post-hoc analysis,

the difference was shown to be between the RO and

CON groups, with RO having significantly less activation

than CON (Fig. 6). The examination of beta values suggests

differences in the Neutral condition may be driven


Table 2. Significant 2-way ANOVA and Post-Hoc Test Results

2-way ANOVA

Region F value p-value Group Sig. Diff Tukey Comparison of Means p-value Greater Activation

ACC 4.158 0.018 HR-RO 0.013 HR

Left ACC 3.438 0.035 HR-RO 0.027 HR

Right ACC 4.328 0.015 HR-RO 0.011 HR

Left NAcc 4.4413 0.013 HR-RO 0.009 HR

by greater deactivation or less activation for RO subjects in

dorsal and ventral regions. While not significant, HR and

Control activation is greater than RO for all regions in the

Neutral condition. HR shows the greatest activation across

all regions except the Left DLPFC (control greater), while

controls show greater activation than RO in all regions.

In the Negative condition, there are no significant differences

between groups. HR shows greater activation in

all regions, while controls show greater activation than

RO in all regions but the Left DLPFC. Finally, to assess

the change in activation from the Neutral condition to

the Control condition, Neutral > Negative activation was

evaluated. While there were no significant differences between

groups, in dorsal regions except for the right DLP-

FC and the IPS, RO exhibited greater deactivation. This

deactivation can be attributed to RO having greater activation

in Negative conditions than Neutral conditions for

those regions.

3.5 - Brain Activity and Task Performance

We explored the impact of brain activity in working

memory and emotional processing regions on participants’

performance by the subject group during Neutral

and Negative conditions. For control participants, there

was a significant link between activation and task performance

in the Right NAcc in the Neutral condition.

This linear relationship (t=2.125, p=0.0429) is positively

correlated, with greater activation linked with a higher

D′ (Fig. 7). High-Risk participants showed a significant

negative correlation between activation and task performance

in the Left ACC (t=-2.312, p=0.034) and Left NAcc

(t=-3.086, p=0.007) for the Neutral condition and in the

Left NAcc (t=-2.876, p=0.011) , Right NAcc (t=-3.294,

p=0.005) , and Right Orbitofrontal Cortex (t=-2.486,

p=0.024) for the Negative condition. As presented in Figure

7, when brain activation increased for High-Risk participants

in the regions above, their D′ decreased, showing

worse task performance with increased activation. Recent

Onset participants exhibited a significant positive correlation

between brain activity and task performance in

the Left ACC (t=2.303, p=0.040) and Left Orbitofrontal

Cortex (t=2.225, p=0.046) during the Negative condition.

Both Control and Recent Onset participants showed a

strong positive correlation between brain activity and

task performance, while High-Risk participants showed a

strong negative correlation.

a) Neutral Condition

b) Negative Condition

c) Neutral > Negative Condition

Figure 6. Between Group Average Beta Values for

different conditions.


4. Discussion

The behavioral and fMRI findings provide insights

into working memory and emotional regulation deficits

in patients with genetic high risk and recent-onset

schizophrenia. Our behavioral results show that patients

with RO Schizophrenia and participants in the HR group

did not exhibit a significant change in task performance

between the Neutral and Negative conditions, while the

control participants did. Imaging results of controls are

consistent with other studies and show activation in Dorsal

and Ventral regions. These results support the belief

that the dorsal and ventral regions chosen for this study

are activated during WM and emotional regulation tasks.

Our results may show the effects of emotional blunting

in RO participants, since in the Neutral > Negative condition,

for all ventral regions, RO did not exhibit significant

differences. Emotional blunting may cause this, as RO

participants are less impacted by the valence of the task

and redirect capacity to other functions, such as working

memory. This is reflected in their performance results as

well, with the least change in D′ from Neutral to Negative,

even if overall performance is worse. Increased activation

in WM regions was linked with better performance in

participants. The D′ values reflect this, as HR consistently

had higher activation than Controls and RO and also

performed better in the task.

When looking at the activation in the Neutral condition

between groups, we observed significant abnormal

deactivation/lower activation in ACC, Left ACC, Right

ACC, Left DLPFC and Left NAcc. In previous studies,

schizophrenic patients often have diminished activation

when compared to controls [3]. In looking at ventral

regions, the underactivation of the Amygdala in patients

with schizophrenia in response to negative stimuli may

not be due to underactivation in the Negative condition,

but due to overactivation in response to neutral stimuli

[3]. These findings reflect the emotional experience

reported by people with schizophrenia, where they often

experience more negative emotions in response to neutral

stimuli [3]. Previous studies show that other brain

regions also showed less activation, such as the ACC and

DLPFC [3], which our results reflect.

We also observed that HR participants showed consistent

overactivation in dorsal and ventral regions compared

to control participants for both the Neutral and Negative

conditions, which was surprising. Based on previous studies,

we expected HR to show levels of cognitive performance

intermediate between healthy controls and schizophrenic

patients [13]. Even though HR activation tended

to be more similar to control activation in scale, it was always

greater than Control and RO activation. One possible

interpretation is that HR participants are suffering from

WM and emotional processing deficits, so the activation is

greater in order to compensate for these deficits; RO may

just have less activation overall due to task tunneling and

having a lower capacity to spend on both WM and emo-

Figure 7. Significant links between D′ and Activation.

tional regulation.

Interestingly, our results suggest that during an effortful

cognitive task, less brain activity was associated with a significant

reduction in CON and RO patients’ performance

and more brain activity was associated with a significant

reduction in HR participants’ performance. HR exhibited

the opposite trend compared to RO and Control participants

when examining brain activation and task performance.

Further connectivity analysis could help us gain a

better understanding of why this occurs.

This study has several limitations. First, the sample size

of the participant groups (Con, HR, RO) was relatively

small and there were significant differences regarding participant

age and gender between groups, which may limit

the interpretability of the results. Secondly, only Neutral,

Negative, and Neutral > Negative conditions of the task

were considered. By utilizing the positive conditions and

other brain circuits, we might be able to provide more information

about the differences between groups during an

emotional one-back task. There was no co-variate included

for medication in the RO group, which may affect our

results. Finally, the behavioral results were only for a subset

of participants, which may limit the interpretability of

the D′ values and the linkage with performance.

In the future, we hope to utilize these results in patient

diagnosis and patient outcome. We also hope to explore

the interplay of symptoms of schizophrenia with WM and

emotional regulation and include different neural circuits

based on the regions involved.

BIOLOGY


5. Conclusion

In summary, by using an emotional one-back task

allowing for the analysis of the interplay of WM and ER,

we demonstrated between-group differences attributed

to emotional blunting and surprising results for highrisk

participants. Our findings indicate that RO patients

tend to show deactivation/lesser activation in WM and

ER regions, which can be attributed to limited capacity

(and less activation overall), with fewer changes due

to emotional valence. Interestingly, our results suggest

that during an effortful cognitive memory task with no

emotional valence, HR does not fall between CON and SZ

as an intermediate, but instead exhibits greater activation.

Another surprising finding of HR was that less brain

activity was associated with a significant reduction in

CON and RO patients’ performance, while more brain

activity was associated with a significant reduction in HR

participants’ performance. Understanding the WM and

emotional processing deficits in schizophrenia is a critical

target for improving diagnosis and recovery outcomes in

schizophrenia.

6. Acknowledgments

I would like to acknowledge Dr. Andrea Pelletier-Baldelli,

Dr. Aysenil Belger and Mr. Josh Bizzell from the UNC

Department of Psychiatry for their help with understanding

the cognitive side of this project, as well as providing the

data used here, Mr. Robert Gotwals for his guidance during

the research process, and the Research in Computational

Sciences Program at NCSSM.

7. References

[1] Guimond, S., Padani, S., Lutz, O., Eack, S., Thermenos,

H., & Keshavan, M. (2018). Impaired regulation of emotional

distractors during working memory load in schizophrenia.

Journal of psychiatric research, 101, 14–20.

[2] Seidman, L. J., Thermenos, H. W., Poldrack, R. A.,

Peace, N. K., Koch, J. K., Faraone, S. V., & Tsuang, M. T.

(2006). Altered brain activation in dorsolateral prefrontal

cortex in adolescents and young adults at genetic risk for

schizophrenia: An fmri study of working memory. Schizophrenia

research, 85(1-3), 58–72.

[5] Forbes, N., Carrick, L., McIntosh, A., & Lawrie, S.

(2009). Working memory in schizophrenia: A meta-analysis.

Psychological medicine, 39(6), 889–905.

[6] Eryilmaz, H., Tanner, A. S., et al. (2016). Disrupted

Working Memory Circuitry in Schizophrenia: Disentangling

fMRI Markers of Core Pathology vs Other Aspects of

Impaired Performance. Neuropsychopharmacology, 41(9),

2411–2420. doi:10.1038/npp.2016.55

[7] Gray, J. R., Braver, T. S., & Raichle, M. E. (2002). Integration

of emotion and cognition in the lateral prefrontal

cortex. Proceedings of the National Academy of Sciences,

99(6), 4115–4120.

[8] Lang, P. J., Bradley, M. M., Cuthbert, B. N., et al. (1999).

International affective picture system (iaps): Instruction

manual and affective ratings. The Center for Research in

Psychophysiology, University of Florida.

[9] Kring, A. M. [A. M.], & Moran, E. K. (2008). Emotional

Response Deficits in Schizophrenia: Insights From

Affective Science. Schizophrenia Bulletin, 34(5), 819–834.

doi:10.1093/schbul/sbn071

[10] Jenkinson, M., Bannister, P., Brady, M., & Smith, S.

(2002). Improved optimization for the robust and accurate

linear registration and motion correction of brain images.

Neuroimage, 17(2), 825–841.

[11] Smith, S. M., Jenkinson, M., et al. (2004). Advances in

functional and structural mr image analysis and implementation

as fsl. Neuroimage, 23, S208–S219.

[12] Thomas Yeo, B., Krienen, F. M., et al. (2011). The organization

of the human cerebral cortex estimated by intrinsic

functional connectivity. Journal of neurophysiology, 106(3),

1125–1165.

[13] Bang, M., Kim, K. R., Song, Y. Y., Baek, S., Lee, E., &

An, S. K. (2015). Neurocognitive impairments in individuals

at ultra-high risk for psychosis: Who will really convert?

Australian & New Zealand Journal of Psychiatry, 49(5),

462–470.

[3] Kring, A. M. [Ann M], & Elis, O. (2013). Emotion deficits

in people with schizophrenia. Annual review of clinical

psychology, 9, 409–433.

[4] Becerril, K., & Barch, D. (2011). Influence of Emotional

Processing on Working Memory in Schizophrenia. Schizophrenia

Bulletin, 37(5), 1027–1038. doi:10.1093/schbul/

sbq009


SESAMOL AS A NOVEL REDOX MEDIATOR FOR

THE ELECTROCHEMICAL SEPARATION OF CARBON

DIOXIDE FROM FLUE GAS

Madison Houck

Abstract

The increasing presence of carbon dioxide in the atmosphere has contributed to overall rising temperatures over the past

several years. In particular, flue gas, which contains a mixture of carbon dioxide, nitrogen, and oxygen, is a common way

that large quantities of carbon dioxide are introduced to the atmosphere. Previously, the redox couple hydroquinone and

benzoquinone were applied to a fuel cell capable of separating carbon dioxide from other gases, but the negative environmental

impacts of these chemicals have prompted a search for an environmentally friendly option. This project seeks to

apply the proton-coupled electron transfer (PCET) reaction of sesamol to achieve the same effect. Cyclic voltammetry was

used to evaluate the electrochemical response of sesamol. Cyclic voltammograms show that sesamol undergoes a quasi-reversible

reaction in sodium bicarbonate, with peaks that correspond to a redox couple appearing after one cycle or after a

deposition. Additionally, half-cell liquid phase testing was performed, confirming that sesamol’s redox reaction creates a

pH gradient that drives carbon dioxide to be released at the anode. Furthermore, the construction of a fuel cell reveals that

with applied voltage, carbon dioxide concentration increases on the permeate side of the cell when sesamol is utilized as a

redox mediator. Future work can be done to further evaluate the efficiency of a sesamol fuel cell as compared to a quinone

fuel cell, and to confirm that the system selectively transports carbon dioxide, and not other components of flue gas.

1. Introduction

As global temperatures continue to rise as a result of

carbon dioxide’s increasing presence in the atmosphere,

the wide-reaching impacts of climate change have driven

researchers to search for new carbon dioxide remediation

techniques [1]. Many have been developed, including the

use of metal-organic frameworks, the direct reduction of

carbon dioxide, and the use of nucleophiles to bind to carbon

dioxide [2]. However, some of these techniques fall

short when it comes to practical applications because of the

large amounts of carbon dioxide that enter the atmosphere

through flue gas. Flue gas is created when fuel or coal is

burned, and is composed of oxygen, nitrogen, water vapor,

carbon dioxide, and trace amounts of other gases. Many of

the carbon dioxide capture technologies mentioned above

are rendered useless when exposed to flue gas; oftentimes,

water or nitrogen gas interfere with the mechanisms used.

Some technologies are simply not selective for carbon dioxide,

and some are structurally damaged by the presence

of other gases [3].

This problem prompted research into an applicable

technology that uses a fuel cell to electrochemically separate

carbon dioxide from other gases. These technologies

rely on carbon dioxide’s unique sensitivity to pH changes.

A pH gradient is established in the fuel cell that captures

carbon dioxide, and has no impact on the gases that might

be present in the mixture. Such a gradient is established

through the use of a redox mediator that undergoes a reversible

proton-coupled electron transfer reaction. The

Figure 1. Schematic for a fuel cell capable of separating

carbon dioxide from flue gas electrochemically

by establishing a pH gradient.

most widely studied redox mediator is a quinone redox

couple (shown in Figure 1). Quinones undergo a reversible

redox reaction. The reversibility of the reaction is important

because it means that the fuel cell can be used repeatedly,

an important consideration when considering any

wide-scale application. Additionally, the redox reaction

that the quinone couple undergoes is a proton-coupled

electron transfer reaction (PCET). While electrons are being

transferred to oxidize or reduce a species, a proton is

also transferred. It is this transfer of protons that drives

the pH gradient. As voltage is applied to the fuel cell, the

quinone is reduced at the cathode and oxidized at the an-

CHEMISTRY


ode. As it is reduced, protons are consumed, and thus the

local pH becomes basic. The basic pH leads to the capture

of carbon dioxide as a bicarbonate ion because this capture

proves sensitive to subtle pH changes. At the other end of

the cell, quinone is reduced, releasing protons, decreasing

the pH, and regenerating carbon dioxide gas from bicarbonate

ions, that then exits the cell. Carbon dioxide is the

only gas that exits; nitrogen, water, or oxygen are not impacted

by pH changes and do not cross the cell [3].

Unfortunately, although a quinone couple has been

demonstrated to work in a fuel cell to help transfer carbon

dioxide across a membrane, it is not a perfect solution.

Quinones have negative impacts on the environment and

enter largely as air pollutants; since the goal of the fuel cell

is environmental remediation, there has been a push to

move away from using quinones in practical applications

in this technology [4]. Additionally, a species in a common

quinone couple, hydroquinone, is a suspected carcinogen

[5], prompting a search for a more environmentally friendly

option that has similar redox behavior. One compound

that has been singled out for its quinone-like properties is

sesamol [4]. Isolated from sesame seeds, sesamol is unlikely

to have a negative impact on the environment. It is composed

of a fused ring structure, and has a hydroxyl group

attached to the benzene ring. Sesamol’s redox mechanism

is shown in Figure 2 and is slightly more complex than

that of a quinone; the reaction is quasi-reversible. The first

step of the reaction is irreversible, but it creates a quinone

structure (2-hydroxymethoxybenzoquinone or 2-HMBQ)

that then undergoes a second reduction reaction (to form

2-hydroxymethoxyhydroquinone or 2-HMHQ). This second

reaction is reversible, and largely mimics a quinone’s

redox behavior in that it transfers a proton as well [6].

Figure 2. Scheme proposed for the oxidation of sesamol

in aqueous solutions, forming 2-hydroxymethoxybenzoquinone

that undergoes further reduction.

This project aims to identify sesamol as a more environmentally

friendly alternative to quinones in a fuel cell

used for the separation of carbon dioxide from other gases.

First, sesamol’s quasi-reversible reaction was studied

through cyclic voltammetry in sodium bicarbonate and

saturated with both argon and carbon dioxide to examine

if conditions present in a fuel cell would fundamentally alter

the mechanism of the reaction. Two peaks corresponding

to the reversible redox reaction appeared upon repeated

sweeps, demonstrating that a reversible reaction begins

to occur after an irreversible step. Next, it was confirmed

that sesamol undergoes a PCET reaction; this was accomplished

through the use of half-cell liquid-phase testing,

which saw an increased current and gas evolution when

in sodium bicarbonate and saturated with carbon dioxide.

Finally, sesamol was used as a redox mediator in a fuel cell

and carbon dioxide transport across the cell was achieved.


2.1 – Materials

The polypropylene membrane, Celgard 3501 was a generous

gift from Celgard (Charlotte, NC). The Toray Carbon

Paper 060 electrode was purchased from The Fuel Cell

Store. Gases were industrial grade purchased from Airgas.

All other chemicals were purchased from Sigma-Aldrich,

and used without further purification.

2.2 – Cyclic Voltammetry

Cyclic voltammetry was performed on 10 mM 2,6-dimethylhydroquinone,

10mM 2,6-dimethylbenzoquinone,

and 1mM sesamol with 0.5 M sodium bicarbonate as the

analyte solution using an eDAQ potentiostat (ER466) and

a three electrode arrangement. The concentration of sesamol

was decreased in order to better examine each peak

that appeared. Each trial was performed in a 4 mL conical

vial. The reference electrode was silver/silver chloride, the

counter electrode was platinum/titanium, and the working

electrode was glassy carbon. The working electrode

was polished with a 0.3μM alumina suspension, and then

rinsed with acetone and water. The reference and counter

electrodes were rinsed with acetone and water. The

electrodes were all cleaned between each trial. Solutions

that were saturated with gas underwent 10 minutes of gas

sparging with argon gas, and then 10 more minutes of

sparging with carbon dioxide gas, as necessary. The pH of

each sample was measured with a Vernier pH Probe. Each

trial consisted of three sweeps in total, measuring the continued

electrochemical response of each sample. The scan

rate was 100mV/s for each trial, and data were collected

from -1.0V to 1.5V.

2.3 – Half Cell Liquid Phase Testing

Half-cell testing was performed in a 4 mL conical vial.

The working electrode was a 1cm by 3cm Toray Carbon

Paper 060 electrode (Fuel Cell Store), the reference electrode

was silver/silver chloride, and the counter electrode

was platinum. The carbon paper electrode was rinsed with

acetone and water between each trial, as were the counter

and reference electrodes. The catalyst was added from a

10mg/mL solution of 20% wt. platinum on carbon black

in methanol, which was drop-casted onto the carbon pa-

34 | 2019-2020 | Broad Street Scientific CHEMISTRY

per electrode at a concentration of 10μL/cm 2 and allowed

to evaporate. A voltage of 0.5V was applied using eDAQ

Chart software. Each solution was tested at a concentration

of 100mM in 0.5M sodium bicarbonate or 0.5M sodium

sulfate.

2.4 –Fuel Cell Construction

To construct the fuel cell, first a polypropylene membrane

(Celgard 3501) was soaked in a solution mixture of

5mM 2,6-DMBQ and 5mM 2,6-DMHQ or 5mM 2-HMBQ

and 5mM 2-HMHQ for 24 hours. Sesamol was oxidized

electrochemically by applying a voltage of 900mV for 60

seconds to the entire sample, then taking half of that sample

by volume and applying a voltage of 400mV for an additional

60 seconds [6]. Two carbon paper electrodes were

cut to 25cm 2 and placed on either side of the membrane,

with the catalyst layer facing the membrane. The catalyst

was added from 10uL/cm 2 of a 50mg/mL solution of 20%

wt. platinum on carbon black in methanol. The membrane

electrode assembly was applied to a fuel cell, and efficiency

was evaluated using fourier-transform infrared spectroscopy.

The permeate side of the fuel cell was attached to the

FTIR. Carbon dioxide was flowed into the fuel cell at a rate

of 5 standard cubic centimeters (SCCM) per minute, with

the sweep gas (argon) at a high flow rate, preventing the

CO 2

from crossing the membrane. Then, after 10 minutes,

once the fuel cell was saturated, the CO 2

was turned off and

the argon flow rate was decreased significantly. An FTIR

scan was taken with no additional voltage applied to measure

how much carbon dioxide was diffusing across the

membrane initially. Then, a voltage of 2.5 V was applied

with a voltmeter and FTIR scans were taken after 1, 5, and

10 minutes. The voltage was then turned off for 5 minutes

and another FTIR scan was taken to examine if CO 2

was

simply leaking across the fuel cell over time.

The redox reaction of 2,6-dimethylhydroquinone has

been well studied because of its reversibility [4]. It has a

clear oxidation peak (at 0V) and reduction peak (at -0.2V).

The reaction proceeds this way under argon and carbon

dioxide saturated conditions. Upon repeated scans, the

shape of the voltammogram does not change appreciably.

a)

b)

c)

3. Results

3.1 – Cyclic Voltammetry

Figure 4. Cyclic voltammograms for sesamol in 0.5M

acetic acid with (a) no gas saturation (oxidation steps

labelled), (b) argon gas saturation, and (c) carbon dioxide

gas saturation.

Figure 3. Cyclic voltammogram of 2,6-DMHQ in .5M

sodium bicarbonate.

Cyclic voltammetry was also used to characterize sesamol’s

redox reaction. The quasi-reversible scheme proposed

by Brito et al. is supported by the appearance of

secondary peaks referred to as “shoulder peaks” after one

voltage cycle (Fig. 4a). The oxidation peak that initially appears

around 0.7V represents the irreversible step that creates

2-HMBQ. After this species is present, the reduction

peak at 0V corresponds to the creation of 2-HMHQ which

CHEMISTRY


is then oxidized. This oxidation peak, one of the shoulder

peaks, is noticeable in the next scan. This occurs around

0.4V. Additionally, after the shoulder peak appears, the

shape remains the same upon repeated scans, indicating a

continued and predictable electrochemical response. Under

argon saturated conditions, the voltammograms appear

similar. Additionally, when the sample was purged

with argon and then saturated with carbon dioxide, the

voltammogram shape remains consistent. Past a potential

of 1V, the aqueous solution begins to oxidize, thus the pertinent

data remain between -1V and 1V on the voltammogram.

a)

The redox mechanism of sesamol was next examined

in a solution of sodium bicarbonate instead of acetic acid

(Fig. 5). The sodium bicarbonate solution yields a much

higher pH (9.52 instead of 2.49), which could be important

to consider in the mechanism of sesamol oxidation, as it is

assumed that both protons and electrons are transferred.

There is less peak definition in the sodium bicarbonate;

however, despite this, shoulder peaks can still be observed,

albeit at slightly different potentials. Here, the irreversible

oxidation step likely occurs between 0.3V and 0.4V, and

the shoulder peak first appears around 0.1V. There is the

slight appearance of a reduction peak around -0.4V which

corresponds to the reduction of the 2-HMBQ. Under argon

and carbon dioxide conditions, the general shape of

the voltammogram is retained.

3.2 – Half Cell Liquid Phase Testing

Half-cell liquid phase testing was performed on samples

of hydroquinone and sesamol (Fig. 6). When potentials

were applied, the current was measured and the sample

b)

a) b)

100mM hydroquinone

solvent

saturated

with carbon

dioxide?

0.5 M no no

NaHCO 3

0.5 M yes yes

NaHCO 3

gas

evolved?

0.5 M no no

Na 2

SO 4

c)

Figure 5. Cyclic voltammograms of 1mM sesamol in

.5M sodium bicarbonate with (a) no gas saturation,

(b) argon gas saturation, and (c) carbon dioxide gas

saturation.

0.5 M yes no

Na 2

SO 4

c) d)

100mM sesamol

solvent

saturated

with carbon

dioxide?

0.5 M no no

NaHCO 3

0.5 M yes yes

NaHCO 3

0.5 M no no

Na 2

SO 4

0.5 M yes no

Na 2

SO 4

gas

evolved?

Figure 6. Data compiled from half-cell testing on (a)

100mM 2,6-DMHQ and (c) 100mM sesamol. Photograph

of carbon paper electrode after half-cell testing

was performed on (b) 2,6-DMHQ and (d) sesamol,

showing gas evolution.


was observed for the evolution of a gas. When a gas is

evolved, it indicates that carbon dioxide is being released

at the anode, after the oxidation of the redox mediator and

the subsequent release of protons, driving down the local

pH. At a lower local pH, carbon dioxide is released from

a bicarbonate ion back into its gaseous form and exits the

solution. The presence of a gas therefore indicates that

the reaction transfers protons as well as electrons; if only

electrons were transferred, the pH would not change and

the carbon dioxide would remain in solution. A gas was

only evolved when specific conditions were met; firstly,

the sample had to have been saturated with carbon dioxide.

Secondly, the solvent had to be sodium bicarbonate.

A different aqueous solvent, sodium sulfate, was used as

a negative control for gas evolution. As expected, gas was

evolved in a carbon dioxide saturated solution of 100mM

hydroquinone in 0.5M NaHCO 3

. Additionally, it was

found that a 100mM sesamol solution in 0.5 M NaHCO 3

also saw gas evolution at the same potential. When each of

these redox mediators were in a solution of 0.5M Na 2

SO 4

,

no gas evolution was seen.

3.3 – Fuel Cell Testing

A fuel cell was tested with no redox mediator, a quinone

redox mediator, and a sesamol redox mediator, and relative

carbon dioxide concentrations were measured with FTIR.

When the membrane placed in the fuel cell was soaked

in a solution of 0.5 M NaHCO 3

, little to no difference was

seen in the relative concentrations of carbon dioxide on

the permeate side of the cell even when voltage was applied

and subsequently turned off. The carbon dioxide

peak was identified to be between 2300 and 2330 cm -1 .

The data were baseline shifted to begin at the same transmittance

value because the FTIR reported different initial

transmittance values with each measurement.

When the membrane was soaked in 5mM 2,6-DMHQ+

5mM 2,6-DMBQ, the carbon dioxide peak increased in

size as the voltage was applied to the cell for progressively

longer amounts of time. The most carbon dioxide was

present on the permeate side when a voltage of 2.5V had

been applied for 10 minutes, as represented by the yellow

peak in Figure 8.

Figure 8. FTIR spectra of permeate side of a fuel cell

with membrane soaked in 5mM 2,6-DMHQ+ 5mM 2,6-

DMBQ in .5 M NaHCO 3

. Spectra were taken with no

voltage applied (blue), with 2.5V applied after 1 min

(orange), after 5 min (gray), and after 10 min (yellow).

When the membrane was soaked in a sesamol solution

that had been oxidized electrochemically to form 5mM

2-HMBQ + 5mM 2-HMHQ, a similar carbon dioxide increase

was seen over time (Fig. 9). The largest peak was

seen when the fuel cell had been running for ten minutes.

Additionally, when the voltage was turned off for 5 minutes,

the concentration of carbon dioxide decreased again,

above any peak that occured when voltage had been applied.

Figure 7. Normalized FTIR spectra of permeate side

of a fuel cell with membrane soaked in 0.5 M NaH-

CO 3

(no redox mediator). Spectra were taken with no

voltage applied (blue), with 2.5 V applied after 1 min

(orange), after 5 min (gray), after 10 min (yellow) and

after the voltage had been turned off for 5 min (light

blue).

Figure 9. FTIR spectra of permeate side of a fuel cell

with membrane soaked in 5mM 2-HMBQ + 5mM

2-HMHQ (generated from the oxidation of sesamol)

in .5 M NaHCO 3

. Spectra were taken with no voltage

applied (orange), with 2.5 V applied after 1 min (gray),

after 5 min (yellow), after 10 min (blue) and after the

voltage had been turned off for 5 min (green).

CHEMISTRY


The sesamol fuel cell saw the greatest increase in peak

height over the ten minutes voltage was applied (Fig. 10).

Although this does not directly correlate to an increase in

fuel cell function, since FTIR testing cannot quantify efficiency,

it does indicate that sesamol is a functional redox

mediator that is qualitatively on par with quinone.

Figure 10. Graph representing peak height vs. time

the voltage has been applied measured from the initial

peak height. Compiled data from bicarbonate

fuel cell (green), quinone fuel cell (blue), and sesamol

fuel cell (yellow).

4. Discussion

The cyclic voltammograms of sesamol above (Fig. 4

and 5) support the proposed scheme for the oxidation of

sesamol, which involves the creation of a quinone species.

The presence of shoulder peaks points to the creation of

a new compound (2-hydroxymethoxybenzoquinone) in

the first step of the reaction, and the subsequent reversible

reaction these compound(s) undergo. When the sample

is purged with argon, (Fig. 4b and 5b), none of the voltammograms

change shape dramatically, suggesting that

none of the peaks were due to the presence of oxygen in

the sample. Additionally, carbon dioxide does not interfere

with the progression of the reaction, as when the samples

are purged with carbon dioxide, the voltammograms

retain their general shape (Fig. 4c and 5c). Although the

cyclic voltammograms in bicarbonate have less defined

peaks (see Fig. 3), this may be attributed in part to the increased

pH and in part to the drift that Ag/AgCl reference

electrodes undergo as they age. Further research could be

done into the impact of pH on sesamol’s redox reaction to

determine if the movement of the peaks represents the impact

of an increased concentration of OH- ions available,

as these are required for the first irreversible oxidation

step of the reaction, and decreased concentration of H+

ions available, as these are required for the reduction of

2-HMBQ to 2-HMHQ. By examining how the pH impacts

the progression of the redox reaction, the optimal pH of a

fuel cell could be determined for maximum carbon dioxide

transport [3].

Half-cell testing results fully support the hypothesis that

sesamol undergoes a proton-coupled electron transfer reaction

because in sodium bicarbonate, a carbon dioxide saturated

solution saw gas evolution at a 0.5V potential. This

gas evolution would not be possible without the transfer

of protons creating an acidic pH at the anode, subsequently

driving the release of carbon dioxide dissolved in solution

as a gas, observed on the electrode. It is important to note

that since only a 0.5V potential was applied, none of the

gas evolution would be expected to be due to water-splitting

[7]. Additionally, the platinum catalyst was chosen

because it does not contribute heavily to water-splitting,

even at higher potentials [3].

Fuel cell testing suggests that quinone and sesamol redox

mediators, rather than the simple application of voltage,

are responsible for moving carbon dioxide across the

fuel cell. Additionally, the decrease in concentration observed

when the voltage is removed dispels the theory that

carbon dioxide is leaking over to the permeate side of the

cell over time. Further work is necessary to evaluate the

efficiency of each fuel cell, as this project’s set-up was not

equipped to evaluate percent carbon dioxide transported,

only observe relative decreases or increases in concentration.

Plotting peak height against time reveals that sesamol

and quinone have similar efficiencies in this setting. All evidence

of carbon dioxide transport was qualitative. Percent

carbon dioxide efficiency can be evaluated with different

applied potentials as well, not just 2.5V. Further research

can also be done to verify that the mechanism is selective

for carbon dioxide by pumping a mixture of nitrogen, oxygen

and carbon dioxide, rather than pure carbon dioxide,

across a fuel cell to further emulate flue gas conditions.

One area of concern is that the platinum catalyst will catalyze

water-splitting and that oxygen could be released on

the permeate side with carbon dioxide, although previous

works have found that platinum on carbon black does not

generate oxygen in large amounts as other similar metal

catalysts might [3].

5. Conclusion and Future Work

It has been shown that sesamol undergoes a quasi-reversible

redox reaction in sodium bicarbonate; it is assumed

that two non-toxic quinones are created, 2-hydroxymethoxybenzoquinone

and 2-hyroxymethoxyhydroquinone,

that further reduce and oxidize reversibly while transfering

protons. The appearance of shoulder peaks in the cyclic

voltammograms supports this reaction scheme, and their

relative size and placement indicate that such a reaction is

reversible and repeatable. Through half-cell testing, it was

confirmed that protons are transferred, thus creating an in

situ pH gradient that releases carbon dioxide that has been

dissolved in solution at the anode. Based on the results of

preliminary fuel cell testing, it can be concluded that sesamol’s

quasi-reversible proton-coupled electron transfer

reaction functions to transport carbon dioxide across a fuel

cell like that of a quinone, and can be used as a more environmentally-friendly

choice of mediator to separate CO 2


from flue gas.

6. Acknowledgments

The author would like to thank the North Carolina

School of Science and Mathematics (NCSSM) Science Department,

the NCSSM Foundation, the NCSSM Summer

Research and Innovation Program and research mentor

Dr. Michael Bruno. Additionally, this research would not

have been possible without the generous gift of polypropylene

membrane from Celgard (Charlotte, NC).

7. References

[1] Wuebbles, D. (2017). USGCRP: Climate Science Special

Report: Fourth National Climate Assessment, Volume

I. U.S. Global Change Research Program. DOI: 10.7930/

J0J964J6.

[2] Rheinhardt, J. (2017). Electrochemical Capture and

Release of Carbon Dioxide. ACS Energy Letters 2 (2), 454-

461 DOI: 10.1021/acsenergylett.6b00608

[3] Watkins, D. (2015). Redox-Mediated Separation of

Carbon Dioxide from Flue Gas. Energy & Fuels 29 (11),

7508-7515 DOI: 10.1021/acs.energyfuels.5b01807

[4] Gandhi, M. (2018). In Situ Immobilized Sesamol-Quinone/Carbon

Nanoblack-Based Electrochemical Redox

Platform for Efficient Bioelectrocatalytic and Immunosensor

Applications. ACS Omega 3 (9), 10823-10835 DOI:

10.1021/acsomega.8b01296

[5] Bolton, J (2017). Formation and Biological Targets of

Quinones: Cytotoxic versus Cytoprotective Effects. Chemical

Research in Toxicology 30 (1), 13-37, DOI: 10.1021/

acs.chemrestox.6b00256

[6] Brito, R. (2014). Elucidation of the Electrochemical

Oxidation Mechanism of the Antioxidant Sesamol on a

Glassy Carbon Electrode. Journal of the Electrochemical

Society 161 (5), 27-32, DOI: 10.1149/2.028405jes

[7] Stretton, T. (n.d.). Standard Reduction Table. Retrieved

November 13, 2019, from http://www2.ucdsb.on.ca/tiss/

stretton/Database/Standard_Reduction_Potentials.htm.

CHEMISTRY


MODELING THE EFFECT OF CHEMICALLY MODIFIED

NON-ANTIBIOTIC TETRACYCLINES WITH β-AMYLOID

FIBRILS TO TREAT ALZHEIMER’S DISEASE

Rachel Qu

Abstract

Alzheimer’s Disease is a neurodegenerative disorder in which memory and comprehensive abilities are lost over time.

There is currently no known cure, but the disease has been linked to the aggregation of extracellular β-amyloid plaques.

The tetracycline family of antibiotics has been shown to reduce plaque formation, but use in more complex treatments

involves the risk of bacterial resistance. This project explores the use of tetracycline’s non-antibiotic analogs to reduce

β-amyloid aggregation. Certain chemically modified non-antibiotic tetracyclines (CMTs) were selected to be modeled

alongside the known β-amyloid aggregation inhibitors tetracycline, doxycycline, and minocycline. These were then analyzed

computationally using Molegro to predict the binding affinities of certain CMTs to the β-amyloid protein fibril.

CMT-3 (6-deoxy-6-demethyl-4-dedimethylamino tetracycline), CMT-4 (7-chloro-4-dedimethylamino tetracycline),

CMT-5 (tetracycline pyrazole), and CMT-7 (12-deoxy-4-dedimethylamino tetracycline) were seen to bind more effectively

than known inhibitors. The same compounds were then analyzed using StarDrop, helping to determine how effective

the compounds could perform as oral drugs, and CMT-3 and CMT-7 were suggested to be more suitable in acting as

oral drugs. This information was then used in studies with transgenic Caenorhabditis elegans to confirm results. Treatment

in more complex models, like vertebrates, could be applied in the future to develop a novel treatment method for Alzheimer’s

Disease.

1. Introduction

1.1 – Alzheimer's Disease

Alzheimer’s Disease (AD), the most common type of

dementia, is a neurodegenerative disorder in which memory

and comprehensive abilities are slowly lost over time

[1]. Most symptoms appear in more elderly individuals,

with symptoms generally appearing after age 60 [2]. Features

of the disease include the loss of connections between

neurons. Damage appears to initially take place in the hippocampus,

spreading outward as progression occurs [3].

By 2060, the number of Americans with the disease is projected

to hit around 14 million. Currently, effective prevention

methods do not exist, as there is no known cure

for Alzheimer’s [2]. This is detrimental because AD is one

of the highest ranking causes of death in the United States.

AD occurs when brain cells that typically process, store,

and retrieve information degenerate and die [4]. There are

two supposed causes for this disease, traced back to β-amyloid

(Aβ) peptides and tau proteins. The accumulation of

intracellular neurofibrillary tangles (NFTs), composed of

tau proteins, used to stabilize microtubules when phosphorylated,

is associated with AD. In addition, extracellular

plaques are also exhibited in patients with AD. Aβ

peptides, created from the breakdown of amyloid precursor

protein (APP) primarily form these plaques [5]. The

amyloid hypothesis assumes that mistakes in the process

governing the production, accumulation, and/or disposal

of Aβ proteins are the primary cause of AD. These proteins

accumulate in the brain, disrupting communication

between brain cells and killing them.

1.2 – β-Amyloid Fibrils

Amyloid precursor protein (APP) is cut by other proteins

into smaller separate sections. One of these sections

becomes Aβ proteins, which tend to accumulate. It is currently

believed that small, soluble aggregates of Aβ are

more toxic [4]. First, they form small clusters, or oligomers,

and then chains of clusters called fibrils. The fibrils

then form mats called β-sheets. Finally, the mats come

together to form the plaques seen in AD [4]. Because the

cleavage process by γ-secretase that forms Aβ is not always

entirely precise, Aβ peptides of different lengths can be

formed [6]. Aβ-42 is one of these, thought to be especially

toxic [3].

Because Aβ production and its subsequent fibril formation

is assumed to be a cause of AD, inhibiting Aβ aggregation

could lead to a potential cure for AD. Since Aβ’s structure

relies on hydrogen bonds for β-sheet mat formation,

disruption by certain compounds can potentially be used

to prevent aggregation. The alternating hydrogen bond

donor-acceptor pattern has been thought to be complementary

to the β-sheet conformation of Aβ-42 [7]. Previous

compounds known to act as alleviants or deterrents

of AD have been seen to contain certain common features

that suggest the hydrogen bonds donor-acceptor-donor

patterns are responsible for interrupting the β-sheet formation

of Aβs [7].


1.3 – Tetracycline and CMTs

Even though there is no definitive cure, there are still

treatment options that exist to alleviate AD. Many are

natural products with medicinal properties, but other options

also exist. Some compounds like aged garlic extract,

curcumin, melatonin, resveratrol, Ginkgo biloba extract,

green tea, and vitamins C and E have been tested in Alzheimer’s

patients and show promise [8-9]. Tetracycline and

its analogs like doxycycline and minocycline have also been

shown to do the same [10].

Observing tetracycline and some of its analogs showed

similarities in structure between the three compounds.

These specifically included a pattern of alternating hydrogen

bond donors and acceptors attached to adjacent rings

on all three compounds. It was hypothesized that this alternating

donor-acceptor-donor pattern had some basis in

affecting Aβ fibrils, since tetracyclines have been shown

to dissolve these structures after formation [10]. This was

based on the fact that hydrogen bond interactions hold

the β-sheet’s pleated sheet structure together. Since the

donor-acceptor-donor pattern mimics the patterns in the

sheet, it is possible that structures like tetracyclines can disrupt

this hydrogen bonding and thus the structure of the

sheet itself. By disrupting hydrogen bonds in β-sheets, the

protein no longer retains its shape and theoretically loses

its original structure. By this basis, the fibril formation

would be disrupted or reversed and Aβ would not be able

to continue to form its characteristic plaques.

Though tetracycline, doxycycline, and minocycline

have been shown to be effective, they all exhibit antibacterial

characteristics [10]. Antibiotic activity is linked to the

presence of the dimethylamine group present on the structure

(Fig. 1). This could lead to poor side effects, specifically

bacterial antibiotic resistance, if clinically used to treat

AD. Removal of this group while maintaining the primary

structure of the donor-acceptor-donor pattern would ideally

lead to the formation of an improved AD drug.

Chemically modified non-antibiotic tetracyclines

(CMTs) have been synthesized and can be applied to Aβ

aggregation inhibition. They have similar structures to

tetracycline but remove the dimethylamine group responsible

for antibiotic properties. Since studies have shown

that tetracycline and its analogs can be effective through

preventing and inhibiting Aβ aggregation, the possible use

of CMTs that still contain the alternating H-bond donor/

acceptor pattern but do not have antibiotic properties is of

interest.

CHEMISTRY

1.4 – Hypothesis/Goals

It is hypothesized that the treatment of AD can be approached

through the use of CMTs to reduce Aβ aggregation,

which can be modeled both computationally and in

vivo through the use of Caenorhabditis elegans. The goals of

this project were to first computationally model Aβ interactions

with different compounds using Molegro to evaluate

aggregation inhibition efficiency, and then evaluate the

same compounds for effectiveness as oral drugs in Star-

Drop. After computational modeling, selected compounds

were to be used with a transgenic version of the model organism

C. elegans to test the in vivo effects of selected compounds

through a paralysis assay.

Figure 1. Structure of tetracycline indicating alternating

hydrogen bond donor-acceptor-donor

pattern (circled in red) and dimethylamine group

(squared in blue).


Molegro Virtual Docker and Molegro Data Modeller

were used to analyze the binding affinity of multiple chemically

modified tetracyclines (CMT-1, CMT-3, CMT-4,

CMT-5, CMT-6, CMT-7, CMT-8) to Aβ proteins. Aβ

structure 2MXU was taken from the Protein Data Bank.

CMT structures were created in ChemDraw and translated

to PDB files by putting the SMILES code through an

online translator. After docking CMT structures, their

Ligand Rerank scores were converted to binding affinity

scores using Molegro’s Binding Affinity algorithm. Star-

Drop was used to analyze the ability of the drug to reach

the target cells using Lipinski’s Rule of Five and an Oral

CNS scoring profile. Data from Molegro and StarDrop

were taken into account to rank the drug performance and

narrow the list of potential drugs.

CL2006 strain of Caenorhabditis elegans was obtained

from the Caenorhabditis Genetics Center at the University

of Minnesota and grown on Nematode Growth Medium

plates. CMT-3 was purchased from Echelon BioSciences.

All other chemicals were obtained from Sigma Aldrich and

dissolved in water before use.

C. elegans were propagated at 15 degrees Celsius on solid

Nematode Growth Medium (NGM). Age synchronization

was performed by picking worms of the same life stage,

L1s, and plated on plates seeded with tet-resistant zim-2

Escheria coli. After 48 hours, plates were treated with 100

μL of vehicle or drugs (100 μM each). After 24 hours, the

temperature was raised to 24 degrees Celsius and paralysis

was scored until 120 hours of age. Paralysis was evaluated


by touching the worms’ heads; paralysis was scored if the

worm did not move or only moved the head.

3. Results/Discussion

3.1 – Molecular Docking Studies

Molegro is a computational platform for predicting

protein-ligand interactions. In Molegro, the protein

2MXU was taken from the Protein Data Bank as a model

of Aβ fibril aggregation. 2MXU shows a fibril structure

composed of multiple peptide monomers. Two different

approaches were taken with this protein. One was of a

smaller fibril structure and the other of a dimer with a cavity

located between. Because the mechanism behind how

drugs like tetracycline work to dissolve the preformed fibril

is not currently known, both approaches were used.

With the fibril structure approach, the ligand bonds to the

outside of the fibril protein in a smaller cavity. With the

dimer structure approach, the ligand binds between the

two monomers (Fig. 2).

binding when more negative. Through using Molegro to

model the interactions between protein and ligand, it was

possible to find that in both the fibril and dimer formation,

CMTs repeatedly and consistently outperform tetracycline

and its natural analogs (Tab. 1).

Table 1. Molegro data table of binding affinities,

where more negative values are more effective, for

tetracycline and analogs with both the fibril (top)

and dimer (bottom) approaches.

Molecule Fibril binding affinity (avg.)

Tetracycline -9.69

Doxycycline -10.10

Minocycline -8.28

CMT-3 -14.43

CMT-1 -13.45

Molecule Dimer binding affinity (avg.)

Tetracycline -11.00

Doxycycline -12.33

Minocycline -9.37

CMT-3 -17.17

Among the CMTs themselves, however, there were

varied results. By testing different CMTs, it was possible

to find the more effective Aβ aggregation inhibitor to use

in further treatment. CMTs-1, 3, 4, 5, 6, 7, and 8 were all

used (Fig. 3). Because the same trend was seen in both the

fibril and dimer approach, with CMTs outperforming tetracycline

and its natural analogs, the calculated binding

affinities were only compared for the fibril approach. The

compounds with the most effective binding affinities were

shown to be CMT-3, CMT-4, CMT-5, and CMT-7 (Tab.

2).

Figure 2. Molegro screencap of Aβ fibril protein

2MXU with tetracycline (green) integrated into both

fibril structures (top) and dimer structure (bottom).

When modeling the effectiveness of different ligands on

Aβ aggregation inhibition efficiency, tetracycline and its

analogs can be interpreted as effective through the analysis

of binding affinities. Binding affinities show more effective

Figure 3. Structures of CMTs (in order: 1, 3, 4, 5, 6, 7, 8).


Table 2: Molegro data table of binding affinities for

CMT's using the fibril approach

Molecule

CMT-1 (4-dedimethylaminotetracycline)

CMT-3 (6-deoxy-6-demethyl-4-dedimethylaminotetracycline)

CMT-4 (7-chloro-4-de-dimethylamino

tetracycline)

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Binding affinity

(average)

-13.45

-14.43

-17.03

CMT-5 (tetracycline pyrazole) -16.01

CMT-6 (4-dedimethylamino.

4-hydroxytetracycline)

CMT-7 (12-deoxy-4-de-dimethylamino

tetracycline)

CMT-8 (4-dedimethylaminodoxycycline)

-11.32

-15.26

-14.09

3.2 – Drug Efficiency Analysis

StarDrop was used in determining how the different

CMTs would perform as oral drugs in treating AD. Lipinski’s

Rule of Five and an Oral Central Nervous System

(CNS) Profile were both performed. Lipinski’s Rule of

Five evaluates if a compound will be a likely active drug

in humans. The criteria include a maximum value of 5 for

octanol-water partition coefficient (logP) which measures

hydrophobicity, a maximum of 500 Da molecular weight

(MW), a maximum of 5 hydrogen bond donors (HBD)

and 10 hydrogen bond acceptors (HBA). It was shown

that CMT-3 and CMT-7 were the only two CMTs that

fulfilled all four criteria, with even tetracycline and doxycycline

only meeting three of the four requirements (Tab.

3). Since tetracycline, doxycycline, and minocycline are all

used in practice as drugs, Lipinski’s Rule of Five is only one

guideline for drug design and not absolutely required for

effective drugs. However, it can still be used as a guideline

to corroborate the potential effectiveness of CMT-3 and

CMT-7.

An Oral CNS profile was also performed using Star-

Drop. Because AD is related to the brain, the drugs need

to be able to be analyzed in the context of the central nervous

system. While none of the compounds exhibited ideal

Oral CNS profiles, many outperformed tetracycline. Since

tetracycline is known to be able to be used in the context

of treating the brain [10], the criteria used for a drug to

be deemed effective was an Oral CNS Profile score higher

than that of tetracycline. This allowed CMT-3, 4, 5, 7, and

8 to all be predicted as effective Oral CNS drugs (Tab. 4).

From the results of StarDrop and Molegro combined,

it was determined that CMT-3 and CMT-7 were the best

candidates for binding to the fibril and for being an effective

drug capable of treating AD through the context of Aβ

aggregation. This information was then applied to experimental

treatments.

Table 3: StarDrop data table of CMTs, tetracycline,

doxycycline, and minocycline through the analysis

of Lipinski’s Rule of Five. The color scale ranges

from green to red, with darker greens being indicators

of more effective drugs, and darker reds being

indicators of less effective drugs.

Lipinski

Rule of

Five

Compound logP MW HBD HBA

0.5 tetracycline -0.8803 444.4 6 10

0.5 doxycycline -0.2456 444.4 6 10

1 minocycline 0.4239 457.5 5 10

0.5 CMT-1 -0.3233 401.4 6 9

1 CMT-3 0.7645 371.3 5 8

0.5 CMT-4 0.9379 435.8 6 9

0.5 CMT-5 -0.3333 397.4 6 9

0.5 CMT-6 -0.9992 417.4 7 10

1 CMT-7 0.4774 385.4 5 8

0.5 CMT-8 0.1577 401.4 6 9

3.3 – Caenorhabditis elegans

Caenorhabditis elegans (C. elegans) do not naturally express

Aβ. However, transgenic strains of the nematode

exist in which it is possible to model the effectiveness of

Alzheimer’s drugs, using temperature-sensitive expression

strains. The CL2006 strain of C. elegans, when raised

at normal conditions, expresses typical wild-type growth.

When shifted to higher temperatures, they conditionally

express the Aβ peptide and upon adulthood, paralysis

is induced. By treating C. elegans with AD drugs, the percentage

of nematodes in which paralysis is expressed will

decrease, as Aβ plaques are lessened. The more effective

the drug is, the more paralysis should be reduced. Previous

studies with tetracycline show that, 48 hours after the

temperature shift, around 35 percent of C. elegans treated

with doxycycline, minocycline, and tetracycline remained

healthy while 100 percent of untreated worms were paralyzed

[10].

A similar paralysis assay for the CL2006 strain of C. elegans

was performed with CMT-3 and it was found that

CMT-3 was more effective at preventing paralysis than

tetracycline and doxycycline, with doxycycline being the

least effective (Fig. 4).

3.4 – Discussion

Molegro and StarDrop data indicated that the most effective

CMTs to treat AD would be CMT-3 and CMT-7,

properly known as 6-deoxy-6-demethyl-4-dedimethyl-


Table 4. StarDrop data table of CMTs, tetracycline, doxycycline, and minocycline through the analysis of an

Oral CNS Scoring Profile. The color scale ranges from green to red, with darker greens being indicators of

more effective drugs, and darker reds being indicators of less effective drugs.

Oral

CNS

Scoring

Profile

Compound logP logS 2C9

pKi

hERG

pIC50

BBB

log([brain])

:([blood])

BBB

category

HIA

category

P-gp

category

2D6

affinity

category

PPB90

category

0.03053 tetracycline -0.8803 3.002 4.742 2.599 -1.577 - - yes low low

0.08814 doxycycline -0.2456 2.833 4.739 2.659 -1.3 - + yes low low

0.1369 minocycline 0.4239 2.591 4.8 3.168 -1.221 - + yes low low

0.02148 CMT-1 -0.3233 1.843 4.91 2.232 -1.217 - - yes low low

0.03198 CMT-3 0.765 1.369 4.833 2.665 -1.094 - - yes lo low

0.06815 CMT-4 0.9379 1.837 5.02 2.371 -1.198 - + yes low low

0.03579 CMT-5 -0.3333 2.412 5.232 2.096 -1.552 - - yes low low

0.01162 CMT-6 -0.9992 2.177 4.971 2.035 -1.3 - - yes low low

0.03813 CMT-7 0.4774 1.556 4.886 2.429 -0.9871 - - yes low low

0.07435 CMT-8 0.1577 1.73 4.88 2.422 -1.206 - + yes low low

amino tetracycline and 12-deoxy-dedimethylamino tetracycline

respectively. CMT-3 is commercially available as

incyclinide while CMT-7 is not, so CMT-3 was the only

CMT taken into consideration in experimental data. In future

work, if other CMTs are available, they should also

be used to measure effectiveness as AD drugs, especially

CMT-7.

Figure 4. Paralysis assay of C. elegans paralysis 24

hours after temperature shift to 24 o C.

It has been shown that tetracycline and doxycycline are

effective at reducing paralysis in C. elegans [10]. Results

shown here support this while suggesting that CMT-3 is

also effective at reducing paralysis, potentially even better

than the two tetracycline analogs.

However, though data suggest that CMT-3 is overall

more effective than tetracycline and doxycycline, the data

show large drops and fluctuations during the time period

measured, probably due to a smaller sample size in each

trial performed. Additionally, there is a 10-hour long time

period lost in data collection that distorts the pattern of

paralysis as time goes on. In future work, the experimental

section should be replicated with a larger sample size and

ideally consistent 2-hour intervals throughout the entire

approximately 24-hour period, working to confirm the results

shown here.

4. Conclusion

It has been concluded through computational analysis

that CMTs can be predicted to interact with Aβ in a similar

manner to tetracycline and its natural analogs. Especially

successful CMTs include CMT-3 and 7, and in vivo

trials with model organism C. elegans, specifically temperature-dependent

strain CL2006, can model paralysis correlated

to Aβ expression. Experimental data shows that

CMT-3 does partially recover paralysis in this strain and

suggests that other CMTs may do the same.

Tetracycline and its analogs can be used for multiple

medicinal applications and can interact with Aβ aggregation

in transgenic models of C. elegans to protect them

from the paralysis phenotype. Certain CMTs have been

computationally modeled to behave in the same manner

to a similar and possibly better degree. This is especially

applicable to CMT-3 (6-deoxy-6-demethyl-4-dedimethylamino

tetracycline) and CMT-7 (12-deoxy-4-dedimethylamino

tetracycline) which have been suggested to be

better at preventing aggregation with the preformed Aβ

fibril and exhibit better interactions as oral drugs. Paralysis

assays performed with a transgenic model of C. elegans in

an in vivo study seem to exhibit a pattern indicating CMT-

3’s ability to prevent paralysis caused by Aβ aggregation

and may outperform both tetracycline and doxycycline in


its role as an aggregation inhibitor, though future replications

must be done to corroborate this phenomenon.

CMT-3 is therefore a promising compound to be used

in future studies as a proposed treatment method for preventing

or curing AD. It would be done through the same

method tetracycline and its natural analogs exhibit, while

still preventing associated bacterial antibiotic resistance,

and reducing side effects that would occur in patients

through treatment of AD with tetracycline.

5. Acknowledgements

This research was supported by the North Carolina

School of Science and Mathematics Science Department,

the NCSSM Foundation, and the NCSSM Summer Research

and Innovation Program. The author would also

like to thank research mentor Dr. Michael Bruno and Dr.

Kim Monahan for her assistance in C. elegans research.

6. References

[1] Xu, J., Kochanek, K. D., Sherry, M. A. ;, Murphy, L.,

& Tejada-Vera, B. (2007). National Vital Statistics Reports,

Volume 58, Number 19 (05/20/2010). Retrieved from http://

www.cdc.gov/nchs/deaths.htm.

Biological Chemistry, 287(41), 34786–34800. https://doi.

org/10.1074/jbc.M112.357665

[8] Bui, T. T., & Nguyen, T. H. (2017, September 26).

Natural product for the treatment of Alzheimer’s disease.

Journal of Basic and Clinical Physiology and Pharmacology,

Vol. 28, pp. 413–423. https://doi.org/10.1515/

jbcpp-2016-0147

[9] Wu, Y., Wu, Z., Butko, P., et al. (2006). Amyloid-β-induced

pathological behaviors are suppressed

by Ginkgo biloba extract EGB 761 and ginkgolides in

transgenic Caenorhabditis elegans. Journal of Neuroscience,

26(50), 13102–13113. https://doi.org/10.1523/JNEURO-

SCI.3448-06.2006

[10] Diomede, L., Cassata, G., et al. (2010). Tetracycline

and its analogues protect Caenorhabditis elegans from β

amyloid-induced toxicity by targeting oligomers. Neurobiology

of Disease, 40(2), 424–431. https://doi.org/10.1016/j.

nbd.2010.07.002

[2] Matthews, K. A., Xu, W., Gaglioti, A. H., Holt, J. B.,

Croft, J. B., Mack, D., & McGuire, L. C. (2019). Racial

and ethnic estimates of Alzheimer’s disease and related

dementias in the United States (2015–2060) in adults aged

≥65 years. Alzheimer’s and Dementia, 15(1), 17–24. https://

doi.org/10.1016/j.jalz.2018.06.3063

[3] National Institute on Aging (2017). What Happens

to the Brain in Alzheimer's Disease? National Institute

on Aging. https://www.nia.nih.gov/health/what-happens-brain-alzheimers-disease

[4] Alzheimer's Association (2017). Beta-amyloid and the

amyloid hypothesis. Alzheimer’s Association. https://www.

alz.org/national/documents/topicsheet_betaamyloid.pdf

[5] Markaki, M., & Tavernarakis, N. (2010). Modeling

human diseases in Caenorhabditis elegans. Biotechnology

Journal, 5, 1261–1276. https://doi.org/10.1002/

biot.201000183

[6] Murphy, M. P., & Levine, H. (2010). Alzheimer’s

Disease and the Amyloid-β Peptide. Journal of Alzheimer’s

Disease, 19(1), 311–323. https://doi.org/10.3233/jad-

2010-1221

[7] Kroth, H., Ansaloni, A., et al. (2012). Discovery and

structure activity relationship of small molecule inhibitors

of toxic β-amyloid-42 fibril formation. Journal of

CHEMISTRY


SYNTHESIS OF A TAU AGGREGATION INHIBITOR

RELATED TO ALZHEIMER’S DISEASE

Emma G. Steude

Abstract

A multitargeted approach is suggested to be most effective in inhibiting the formation of tau aggregates in Alzheimer’s

disease. Two promising targets for treatment of Alzheimer’s are the initial hyperphosphorylation of tau, caused by an

overexpression of the GSK-3β protein, and early tau aggregation itself. To improve drug effectiveness, the structure of a

known inhibitor molecule targeting both of these stages of tau aggregation was adjusted to increase binding affinity with

the GSK-3β enzyme. These adjusted molecules were screened using Molegro. The candidate molecules with the highest

calculated binding affinities were further evaluated. One of these novel compounds was then synthesized and assayed for

its ability to inhibit the GSK-3β protein, resulting in a comparable efficacy to the original known molecule’s multitargeted

structure. The novel molecule has promising GSK-3β inhibition results and maintained structural features to attack

early tau aggregation. This indicates possible effectiveness in inhibiting the future stages of tau aggregation indicative of

Alzheimer’s disease.

1. Introduction

According to the Alzheimer’s Association [1], one in

ten Americans aged 65 and older have Alzheimer’s disease.

The neural damage from Alzheimer’s disease can result in

severe memory loss, degradation of motor functions, and

eventual death. Despite the severity of the disease, there is

currently no cure. This is partly because a specific target

has yet to be definitively identified and experimentally determined

to cause Alzheimer’s disease. Currently, the two

most researched explanations are the amyloid hypothesis

and the tau hypothesis. The amyloid hypothesis suggests

that amyloid-β aggregates to create plaques that disrupt

normal neuron communication at the synapses. However,

none of the developed amyloid beta-targeted drugs have

proven to stop or even slow disease progression [2]. More

recently, the tau hypothesis is being researched. Tau is

thought to disrupt transport in the neuron itself and may

be a more promising target for prevention of Alzheimer’s

disease.

The tau protein is located along the axon of neural cells

and is complex, having six isoforms. In healthy brains, tau

functions to aid transport of signals across the axon of

neurons in the brain. In the brains of Alzheimer’s disease

patients, however, tau hyperphosphorylates, causing the

tau to break from the microtubule that it was stabilizing.

Drifting from its usual position near the axon, the tau protein

can then form aggregates with other hyperphosphorylated

tau proteins [3]. The formation of tau aggregates is

thought to be a significant factor in causing Alzheimer’s

disease.

There are, in fact, multiple levels of tau aggregation

and, consequently, multiple targets for drug design. Hyperphosphorylated

tau proteins may aggregate to form

β-sheets, which later aggregate to form oligomers. Soluble

oligomers then form insoluble paired helical filaments

(PHF), which further aggregate to form neurofibrillary

tangles [3]. Researchers are still unsure which specific aggregation

level would cause Alzheimer’s disease, but experimental

correlation suggests that overall aggregation

plays a significant role in the progression of the disease.

As there is no direct target for disease treatment, focusing

on early stages of the aggregation process may be the

best alternative. The inhibition of early aggregation may

prevent subsequent, more complex aggregates from forming.

Furthermore, targeting multiple steps of aggregation

with a multitargeted drug may be more effective in preventing

the disease than targeting a single stage. The hyperphosphorylation

stage and the early levels of aggregation

itself are two plausible targets. Drug interaction with

these targets can be tested with the GSK-3β protein and

the AcPHF6 peptide. GSK-3β is a kinase that, when overexpressed,

hyperphosphorylates the tau protein, enabling

tau to aggregate. The AcPHF6 peptide, on the other hand,

is a segment of the tau protein that models aggregation.

This peptide is involved in both the microtubule-binding

property of normal tau as well as PHF formation in hyperphosphorylated

tau. Therefore, one can inhibit many

stages of tau protein aggregation by inhibiting the GSK-

3β protein’s hyperphosphorylation of tau and by inhibiting

the early aggregation shown through the AcPHF6 peptide.

In essence, by targeting tau aggregation at two early levels,

further aggregation may be effectively inhibited [4].

Previous research used Thiadiazolidinedione (TZD) as

a lead compound to identify candidate compounds that act

against both the GSK-3β protein and the AcPHF6 peptide

[4]. One derived molecule, Model 30 [4] was promising

for multitargeted tau inhibition (Fig. 1). It was suggested

that structural adaptations at the R1 and R2 positions

could further increase the efficacy of this molecule. With

these adaptations, the compound is hypothesized to more

effectively inhibit tau aggregation at the GSK-3β target


site, while preserving the characteristics necessary to inhibit

the AcPHF6 peptide. This work aims to confirm this

hypothesis by developing a more potent variant of this

derivative with a greater efficacy against GSK-3β while

maintaining the structural features thought to be required

for inhibition of the AcPHF6 peptide.

Figure 1. Structure of Model 30, with suggested structural

adaptations at R1 and R2.

2. Methods

2.1. Inhibitor Design through Computational Modeling

TZD derivative Model 30 was selected as a starting

point for computational modeling [4]. Candidate molecules

were ultimately designed by selecting modifications

that followed the proposed structural adaptations (Fig. 2).

Figure 3. The active site pocket of the GSK-3β protein,

with identifiers Lys85 and Val135.

To assess the candidates’ suitability in medical application,

the molecules with the best predicted effectiveness

were then imported into StarDrop. The Oral CNS Scoring

Profile was run in StarDrop and the results were reviewed,

taking note of the Lipinski Rule of Five data as well as

the blood brain barrier (BBB) log([brain]:[blood]) value.

The StarDrop data for the newly designed structures were

compared to those of the original compound, Model 30.

The candidates that compared most favorably to Model 30

were selected for possible synthesis.

2.2 – Synthesis, Purification, and Analysis

The reaction proceeds by reacting the appropriate aldehyde

precursor with TZD (1:1) under microwave irradiation

at 80 °C for 30 minutes with half the molar amount of

Ethylenediamine diacetate (EDDA) to catalyze the reaction

(Fig. 4) [4]. The chosen compound, T006129, was synthesized

using this method with its respective R-group (Fig.

5).

Figure 2. Candidate molecules designed with base

structure of Model 30.

The candidate structures were then imported into Molegro

Virtual Docker along with the GSK-3β protein structure

[5]. Cavities on the protein were detected, and the active

site was identified to be around Val135 and Lys85 on

the protein (Fig. 3). The candidates were then docked in

the active site of GSK-3β. Poses were compared using the

binding affinity scores, where more negative scores suggested

better binding effectiveness.

Figure 4. Reaction scheme proposed to synthesize the

proposed inhibitors [4].

The thick, caramel-colored product was then diluted

with water and collected by filtration, being sure to scrape

out as much of the mixture as possible. After vacuum filtering

with a heavy wash of water, the compound was then

purified by crystallization. To achieve this, a considerable

amount of hot ethanol was added by pipette to the solution

of product, which was heated and stirred until fully

dissolved. Then, the solution was taken off the hot plate

CHEMISTRY


and chilled water was added by drop until crystals began

to form. Once this occurred, the beaker was carefully set

in ice and not disturbed to allow for crystallization to complete.

The crystals were collected by vacuum filtration

and dried. Thin layer chromatography (TLC) was used to

monitor the progress of the reaction.

in solution (1mM). The assay protocols were adjusted to

fill cuvettes for analysis by fluorescence emission in a spectrophotometer.

Four solutions were prepared and tested using the above

assays. A solution with the GSK-3β enzyme, but no ATP

or inhibitor, was used to create the lower boundary for

expected fluorescence. A solution with ATP, but no GSK-

3β or inhibitor, determined the upper boundary with the

greatest amount of ATP to be expected. Next, a solution

with the enzyme and ATP, but no inhibitor, showed the

regular activity of GSK-3β. Lastly, all three components

were put in the solution to find the impact of the inhibitor

on GSK-3β activity. The % inhibition was calculated for

the specific inhibitor concentration.

3. Results and Discussion

3.1 – Computational Analysis

Molegro Computational Analysis showed a clear increase

in predicted inhibition when the substituents with

the suggested properties were added. The adapted molecules

have better predicted binding affinities compared to

the Model 30 structure (Fig. 6). The more negative scores

suggest a better inhibition for the GSK-3β enzyme.

Figure 5. Respective R-groups for the proposed synthesis

procedure.

Fourier-transform infrared spectroscopy (FTIR) was

then used to determine whether the inhibitor was in fact

created. Heptane, ethyl acetate, and chloroform were unable

to sufficiently dissolve the product for liquid FTIR,

so KBr pellets were created to run FTIR. Since KBr pellets

are susceptible to water vapor, the crushed inhibitor

was mixed with uncrushed KBr before being compressed.

A blank KBr pellet was used as the background to further

reduce the influence of the O-H bond of water on the resulting

graph. FTIR graphs from the reactants and the

product were compared to determine whether the reaction

occurred correctly.

2.3 – Assay to Test GSK-3β Inhibition

The BioVision ATP Colorimetric/ Fluorometric Assay

Kit was paired with the BPS Bioscience GSK-3β Assay Kit

to determine the molecule’s inhibitory effect on the GSK-

3β protein by inducing measurable fluorescence. Properly

functioning GSK-3β consumes ATP in its reaction with a

substrate, so inhibited GSK-3β would leave high ATP levels.

Since the measured fluorescence directly corresponds

to the amount of ATP left in solution, inhibition can be

tested by measuring fluorescence of the solution.

To prepare the product for the assays, just enough dimethyl

sulfoxide (DMSO) was added to dilute the product

Molecule

Inhibitor Binding Affinities

Binding

Affinity

Model 30 -27.886

Molecule

Binding

Affinity

T002052 -30.7393 PyrroleC 3

O 2

-31.7228

T006129 -31.1837 PyrroleNH 2

-29.5741

T010305 -33.3667 T01113w/

OCH 3

-29.3512

Figure 6. Molegro computational binding affinities

for Model 30 and six candidate inhibitors.

With these promising computational inhibition results,

these six candidate compounds were imported into

StarDrop to assess suitability in medical application. The

Lipinski Rule of Five and the Oral CNS Scoring Profile

data were compared between molecules. The compounds

seem to be viable in most of the metrics computed by

StarDrop. See the StarDrop Data in Supplemental Materials,

below. Between molecules, the most variation is

with respect to the blood brain barrier permeability (BBB

log([brain]:[blood])). Compounds with more positive BBB

values can penetrate the BBB more easily, increasing their

efficacy in reaching the target site in the brain and, therefore,

requiring a lower dosage. Disappointingly, when

comparing the BBB permeability to the binding affinity

scores (Fig. 7), there were few compounds that noticeably

stood out from the rest in both areas.


the nitrogen on the indole and the carboxylic acid group,

were preserved in the product (Fig. 8). The aldehyde peaks,

on the other hand, were absent in the product, confirming

that the appropriate reaction did occur.

Figure 7. Blood brain barrier permeability compared

to the computational binding affinities for Model 30

and candidate molecules. The upper left data points

are most ideal in each respect.

Generally, candidates with better predicted inhibition

had worse predicted permeability and vice versa. This

made choosing compounds to synthesize more difficult.

Model 30 had the most effective predicted BBB permeability

(Fig. 7), but other molecules had preferable computational

binding affinities. Considering that no compound

stands out for excellent scores in both variables and that a

goal of this particular research project is to more effectively

inhibit GSK-3β particularly, the candidates with more

promising binding affinity scores were selected for possible

synthesis. These were molecules T0103050, PyrroleC 3

0 2

,

T006129, and T002052. Attempting to use the reaction

mechanism previously proposed and taking the availability

of laboratory materials into consideration, candidate molecule

T006129 was then chosen to be synthesized.

3.3 – Assay Analysis

When the GSK-3β inhibition assay was performed,

product T006129 was experimentally determined to affect

the performance of the GSK-3β protein. The molecule had

inhibitory results when added to the reaction solution (Fig.

9). As the experiment was not able to be repeated in the

limited time frame, a true IC50 score for T006129 could

not be determined. Even so, initial results show that inhibitor

T006129 had a 53% inhibition at a 0.98μL concentration,

which is comparable to Model 30’s IC50 score of

0.89 ±0.21μL. The promising GSK-3β inhibition results of

T006129 point to inhibition of hyperphosphorylation of

tau. At the same time, the molecule maintains its structural

features to attack early tau aggregation, suggesting that

T006129 may be effective in inhibiting future stages of tau

aggregation. These results support that this research approach

may yield new and even more effective compounds.

By refining the T006129 inhibitor’s IC50 score with more

GSK-3β assay trials and testing the other promising inhibitor

candidates defined in this study, the GSK-3β inhibition

results may be more accurately compared. This additional

research would pave the way to testing the impact of the

early multitargeted approach on later stages of tau aggregation.

3.2 – Synthesis Analysis

The FTIR analysis of the product shows promising results

for the synthesis of molecule T006129 (Fig. 8). With

a yield of approximately 10%, product T006129 was concluded

to have been produced.

Figure 9. GSK-3β assay fluorescence results for compound

T006129 are shown above. The % inhibition

was found by dividing the amount of ATP used by the

enzyme with the inhibitor present by the amount of

ATP used without the inhibitor present.

4. Conclusion

Figure 8. FTIR data comparing the components of the

T006129 product to the aldehyde reactant.

Prominent absorption lines of the reactant, including

CHEMISTRY

The multitargeted approach to treating Alzheimer’s disease

with the tau hypothesis has yet to be further tested.

While multitargeted molecules have been proposed, focusing

on specialized binding properties may increase drug

effectiveness and decrease undesirable side effects. The

Model 30-derived candidate molecules outlined in this paper

were computationally predicted to have more interaction

with the GSK-3β protein than the baseline Model 30

molecule based on their computed binding affinities. One


such novel molecule was successfully synthesized. The assay

suggests that this inhibitor was comparable to Model

30, with a 53% inhibition at a 0.98μL concentration compared

to a 50% inhibition at a 0.89 ±0.21μL concentration

respectively. Further investigation is required to refine the

measurement of this IC50 score and determine the early

aggregation inhibition as modeled by the AcPHF6 peptide.

A proper IC50 score of inhibitor T006129 could not be

determined in the limited time frame. Even so, the comparable

results suggest that the performance of T006129

and the other inhibitors should continue to be explored.

Among the other candidate compounds explored, compounds

T0103050 and T00252 computationally outperformed

T006129 when taking blood brain barrier permeability

into consideration with binding affinity.

Future studies should refine the IC50 estimate of

T006129 with multiple replicates of assay data. The other

candidate inhibitors should also be synthesized and tested

for their performance against GSK-3β. Additionally, it

would be wise to take factors like the AcPHF6 inhibition

and blood brain barrier permeability into consideration.

These inhibitors are ultimately meant to be multitargeted

drugs. While the focus in this study was to improve the

GSK-3β inhibition specifically, the inhibition of early tau

aggregation and the dosage efficiency of the compound

must be considered in future studies. The results of this

study suggest that a multitargeted inhibitor with improved

GSK-3β inhibition may be created, but a considerable

amount of research is imperative before a treatment for

Alzheimer’s disease can ultimately be proposed.

7. References

[1] Alzheimer’s Association. (2018). 2018 Alzheimer’s

Disease Facts and Figures, Alzheimers & Dementia, 14(3),

367–429. doi: 10.1016/j.jalz.2018.02.001

[2] Myths. (n.d.). Retrieved from http://www.alz.org/

alzheimers-dementia/what-is-alzheimers/myths PDB ID:

1Q4L

[3] Kuret, J., et al. (2005). Pathways of tau fibrillization.

Biochimica Et Biophysica Acta (BBA) - Molecular Basis

of Disease, 1739(2-3), 167–178. doi: 10.1016/j.bbadis.2004.06.016

[4] Gandini, A., et al. (2018). Tau-Centric Multitarget Approach

for Alzheimer’s Disease: Development of First-in-

Class Dual Glycogen Synthase Kinase 3β and Tau-Aggregation

Inhibitors. Journal of Medicinal Chemistry, 61(17),

7640–7656. doi: 10.1021/acs.jmedchem.8b00610

[5] Bertrand, J., et al. (2003). Structural Characterization

of the GSK-3β Active Site Using Selective and Non-selective

ATP-mimetic Inhibitors. Journal of Molecular Biology,

333(2), 393–407. doi: 10.1016/j.jmb.2003.08.031

5. Acknowledgements

I would like to acknowledge Dr. Timothy Anglin, Dr.

Michael Bruno, Dr. Kat Cooper, Dr. Darrell Spells, Mr.

Bob Gotwals, the NCSSM Foundation, the NCSSM Science

Department, the Summer Research and Innovation

Program, Dr. Sarah Shoemaker, and my Research in

Chemistry Peers for making this research possible.

6. Supplementary Materials

6.1 StarDrop Data

Compound T002052 T006129 T010305 Pyrrole

C 3

0 2

Pyrrole

NH 2

T01113w/ Model 30

OCH 3

Oral CNS Scoring

Profile Score

Lipinski Rule of

Five Score

BBB

log([brain]:[blood])

0.1482 0.1183 0.1899 0.1219 0.1478 0.2131 0.2373

1 1 1 1 1 1 1

-0.4836 -1.284 -1.032 -1.284 -0.6675 -0.7091 -0.1947


A TELEMETRIC GAIT ANALYSIS INSOLE AND

MOBILE APP TO TRACK POSTOPERATIVE FRACTURE

REHABILITATION

Saathvik A. Boompelli

Abstract

Accurate and objective monitoring of a fracture’s healing process is essential to both patient quality of care and determination

of the chances of nonunion and postoperative intervention. In recent years, due to industrialization, injury rates in

developing countries, notably road traffic injuries (RTIs), have drastically increased. Ill-equipped countries such as Kenya

may have only 60 orthopedic surgeons for a population of 36.9 million, with no rigorous rehabilitation protocol or quality

post-operative care. This work focuses on the development of a telemetric gait analysis insole that works in conjunction

with a mobile application. This technique automates the tedious and resource-intensive process of tracking postoperative

fracture rehabilitation. This is done through analyzing patient ground reaction forces (GRFs), which have been found to

correlate well with weight-bearing ability, fracture healing, and delayed union. Four force-sensitive resistors (FSRs) that

function through polymer thick film technology are placed in the insole under the primary areas for force measurement.

An Arduino microcontroller compiles the data and sends it to a Python program via a Bluetooth module. The Python

program performs peak analysis to determine the average peak Vertical Ground Reaction Force (VGFR) of the strides.

This value is sent to a cloud database that subsequently sends the data to a mobile application made accessible to a healthcare

professional, who can send back weight-bearing recommendations based on the data. Features include access to the

cloud database, a graph of weekly values, emailing functionality, and an ability to begin a trial through a smartphone. This

approach addresses a commonly overlooked lapse in the healthcare system of many countries and provides an objective

method to track fracture healing.

1. Introduction

1.1 – Motivation

1.1.1 – Lower Extremity Injury Prevalence

The economic boom occurring in developing countries

comes at the cost of new and challenging problems, including

the rapid increase of Road Traffic Injuries (RTIs) with

the urbanization of rural areas. There is often a lack of legislation

regulating the education of the local population on

how to navigate the new infrastructure being constructed

in their areas. This results in a higher prevalence of fractures

and lower-extremity injuries in developing countries

(Fig. 1), accounting for more than 1.27 million deaths per

year, and more deaths than HIV/AIDS, tuberculosis, and

malaria combined. However, the number of RTI injuries

is more jarring, with 20-50 injured due to RTIs for every

RTI related death[1]. These 60 million injured patients expected

in the next ten years place a significant burden on

local, unequipped medical facilities. Regional economies

are also significantly impacted, with much of the local productivity

reliant on human labor.

1.1.2 – Rural-Medical Practitioners and Under-Qualification

This problem of exorbitant amounts of injuries is compounded

by the lack of qualified specialists and an excessive

number of under-qualified physicians referred to as

Rural Medical Practitioners (RMPs) who have no formal

education. These RMPs tend to over prescribe painkillers

in place of legitimate procedures, taking advantage of their

uneducated clientele. Due to the inaccessibility of expensive

equipment such as X-ray imaging devices, RMPs use

subjective and often inaccurate methods to track the rehabilitation

of patients with lower-extremity fractures. This

research aims to connect underserved communities with

more educated specialists, who are often located in larger

metropolitan areas.

Figure 1. Graph depicting concentrations of Road

Traffic Deaths across the globe [15]

1.1.3 – Delayed-Union and Non-Union

Approximately 5% to 10% of fractures worldwide proceed

to nonunion [4], which is the improper bonding of a

ENGINEERING


fracture (Fig. 2). This leads to permanent disabilities and a

significantly higher need for healthcare resources, which

often can not be provided. Classically, the reasons for delayed-union

and non-union are complications including

inadequate reduction, loss of blood supply, and infection,

all of which are extremely prevalent in developing areas

[6]. A well designed post-operative rehabilitation protocol

can be implemented to identify any stagnations in the

healing process. However, physicians in rural areas often

cannot provide quality postoperative care, leaving their

patients without outpatient facilities after surgery.

1.2 – Background

1.2.1 – Telemetric Medicine

Telemetric medicine is a budding subfield of biomedical

engineering that allows for patient-doctor communication

from thousands of miles away. This has massive potential

in addressing the problems faced by underserved communities,

including a lack of qualified specialists, by giving

them access to these specialists. Currently telemetric medicine

has been limited to first world countries, where elderly

and disabled patients who are not capable of traveling to

a hospital can be provided with quality care from the comfort

of their home. However, current trends have shown

rapid increases in the number of smartphones available to

rural populations in developing countries due to a decline

in phone price. This has allowed for fast data transmission

to these areas. For example, vaccine reminders and natural

disaster alerts have become commonplace. This research

takes advantage of this framework by allowing for direct

patient to doctor communication over long distances.

1.2.2 – Weight-Bearing Ability

Weight-bearing ability of the afflicted limb has been

shown to greatly correlate with radiological evidence of

fracture union and overall healing (Fig. 3) [4]. According

to study by S. Morshed, 84% of patients indicated that

weight bearing ability was the most important clinical criteria

for diagnosis of delayed union and non-union. Despite

weight-bearing ability being the most critical factor

in the diagnosis of non-union, it is not often used due to

the subjectivity of clinical observation and patient feedback

being very unreliable and incomparable over the long

term. This research presents an objective method to track

gait parameters, such as weight bearing ability, and allows

for repeatability- crucial in the tracking of a patient’s rehabilitation

protocol.

Figure 2. X-Ray of a Humeral Nonunion [16]

1.1.4 – Benefits of Early Partial Weight Bearing

Early weight bearing is often absent from many rural

rehabilitation protocols, with “just rest” being prescribed,

as that is mistakenly seen as the best way to heal a fracture.

However, early partial weight-bearing has been found to

improve fracture healing, maintain bone stock and density,

and keep the fracture and implants aligned early in the

recovery process [5].

Figure 3. Graph depicting relationship between

weight bearing and fracture healing [17]

1.2.3 – Review of Insole-Based Gait Analytics Systems

Currently a majority of gait analysis is performed in

two specific methods: in a laboratory with force plates and

3-D motion tracking or in a doctor’s office with a clinician

making visual observations. The first method is extremely

expensive and provides highly specialized and unnecessary

data points while the second method is very subjective and

is often not repeatable over several trials and patient visits.

The benefits of insole-based gait analytics have been

identified as an accurate and cheap alternative to laborato-

52 | 2019-2020 | Broad Street Scientific ENGINEERING

ry and clinician-based gait analysis. Gait analysis systems

measure a range of data types including foot angle, stride

distance, step distance, step count, cadence, and speed . To

measure the extremely different statistics that fall under

the gait analysis the insoles use different sensor types for

each statistic. Accelerometers are used for stride length and

velocity, gyroscopes are used for orientation, flex-sensors

are used for plantar and dorsi-flexion, electric field sensors

for height above the floor, and finally force-sensitive resistors

are used for force parameters which is the focus of

this work [2].

1.2.4 – Ground Reaction Force and Biomechanics

The method utilized to measure weight bearing ability

is the temporospatial gait parameter known as vertical

ground reaction force (VGRF). In biomechanics, it is defined

as the force exerted by the ground on the body, when

in contact with the ground. Research has also suggested

that ground reaction forces correlate well with callus mineralization

and weight-bearing ability [3]. An increase in

ground reaction force indicates increased healing for the

patient. While weight-bearing is subjective, ground reaction

force measurements give healthcare providers a

singular statistic that can be stored and automatically processed

by computers in a setting where human involvement

is scarce and valuable. The VGRF graph (Fig. 4) displays

the active loading peak, the point of greatest force in

the gait cycle, which is the target statistic our insole aims

to measure.

plates. Force plates are traditionally used in gait analysis

but are not viable as even a slight increase in cost can exponentially

decrease the number of viable users. The insole

was also designed to be less than 300 grams, as any heavier

weight has been found to greatly alter gait parameters,

resulting in inaccurate data [2]. Small, individual sensors

were used in place of larger sensors, minimizing hysteresis

and inaccuracy in measurements across multiple trials.

2.2 – Force-Sensitive Resistors and Placement

The insole uses 4 force-sensitive resistors (FSRs) that

utilize polymer-thick film technology and provide a resistance

differential based on the force exerted. Polymer-thick

film sensors are constructed by the deposition of several

dielectric layers via a screen-printing process, making

them extremely cheap and lightweight compared to capacitive

sensors. To maximize cost efficiency, 4 locations on

the sole of the foot, which were identified via a pedobarograph

(Fig. 5) to have the greatest concentration of force,

are where the FSRs would be placed. The locations on the

sole of the foot determined to have the greatest concentrations

of force were under the big toe, metatarsal head I,

metatarsal head V, and the heel [11].

Figure 4. Vertical Ground Reaction Force graph over

a single stride [18]

2. – Materials and Insole Design

2.1 – Insole Design Criteria

The insole was chosen as the primary data acquisition

device due to the low relative cost in comparison to force

Figure 5. Pedobarograph representing areas of highest

force concentrations [19]

ENGINEERING


2.3 – Hardware Structure and Data Pathway

The data from the FSRs are then sent to an ankle-mounted

Arduino Nano microcontroller attached via

a Velcro strap. The Arduino microcontroller compiles the

data received and uses a Bluetooth module to send the data

via Bluetooth low energy signals (Fig. 6). In gait analysis,

wireless technology is heavily utilized and beneficial as

wired technology greatly affects the target gait parameters,

resulting in non-representative data. The data are then received

by the data analytics software, which is written in

the Python programming language.

Table 1. Consecutive VGRF Values (Newtons) Acquired

from Insole (Left→Right and Top→Bottom)

0 0 0 0

203.2 364.8 389.6 406.4

429.2 403.2 324.8 194.4

0 0 0 0

0 0 0 0

183.2 348.8 367.2 373.6

388.4 364.8 0 0

0 0 0 0

208.8 336.0 365.6 372.8

393.6 404 212.8 0

0 0 0 0

Figure 6. Insole hardware wiring including an Arduino

Nano and HC-O6 Bluetooth Module

3. Data Analysis and Software

3.1 – Python Code and Peak Analysis

The Python program performs time-series peak analysis

to acquire the active loading peak of the ground reaction

force. Table 1 is an example of a raw dataset (in

Newtons) that requires peak analysis. Each data point in

Table 1 is taken every 30 milliseconds from the insole and

is stored in the table. Figure 7 is a plot of the data in Table

1 and depicts how peak analysis functions. The red point

in each step represents the active loading peak or the max

VGRF for that step. An algorithm included within the

code performs this process by defining a step as an array

of non-zero digits separated by zeroes, where the largest

value in that array is extracted and tagged as the VGRF of

that individual step. The non-zero digits are the instances

when the foot is in contact with the ground, while the zero

digits are when the foot is in the air, providing no data to

the force sensors. To provide robust and accurate data, the

program is designed to average the VGRF value over multiple

strides (N>6) during the trial. Six strides were chosen

as the minimum number of steps needed to provide representative

gait analysis data including ground reaction force

[7].

3.2 – Google Sheets API

The average VGRF value is then printed to a Google

Sheets API, allowing it to be accessed from any device. The

Google Sheets API provides the healthcare provider with

the week number relative to the beginning of the rehabil-

Figure 7. Peak Analysis Visualization

itation protocol, VGRF values, a visual representation via

a graph that is updated after every entry from the patient,

and an automatic risk assessment. As this work aims to

address the lack of qualified specialists, the API automates

the tedious aspect of screening at-risk patients during

rehabilitation. To achieve this, the API tags any patients

with a stagnation or reversal of VGRF as high risk, and

tags patients with increases in VGRF as low risk. Using

this novel approach, the healthcare provider can address

patients with a high risk of delayed or nonunion quickly

and accurately.

3.3 – Smartphone Application

To provide patient-centered care, a mobile app that

provides patients with data and communication tools for

proper management of their injury was developed (Fig. 8)

through the MIT App Inventor program. The application

includes access to the cloud database, a graph of weekly

values, and emailing functionality. The app also includes

Bluetooth pairing capability which allows the patient to

begin a trial through their smartphone. The design is focused

on simplicity of use. Upon initialization of the app,

the user is shown a welcome screen in which they can visit

their history or begin a test. Patient history includes a table


and graph of weekly data to aid patients in visualization

of the healing progress of their fracture. Testing is done

over individual IP servers in order to protect privacy and

connect with the API database.

hysteresis for the optimized and designed insole. A 2-Sample

T-Test for the final hysteresis values was performed

with 30 trials, resulting in a significance value of a=.05.

The test results in a p-value of 0.021 indicating statistical

significance.

5. Conclusion and Future Work

Figure 8. Mobile Application Initialization Pathway


4.1 – Hysteresis and Quality Analysis

FSRs are rarely used in biomedical devices, as consistency

and accuracy are one of the most important criteria

for success, but FSRs have classically been more suscep

tible to inaccuracies. An experiment was designed to test

the quality of the insole, the extent of hysteresis and the

inaccuracy of the sensor over time.

5.1 – Conclusion and Real-World Application

This work addresses a commonly overlooked lapse in

the healthcare system of a significant portion of the world’s

population. The addressed issue of increased road traffic

injuries will continue to exponentially grow in coming

years. Novel solutions to the proper healthcare and rehabilitation

of these injuries will be required to maintain a

high quality of life for those afflicted. The hardware utilized

is optimized for the specific use-case of a rural and

low-income setting in conjunction with a software application

designed to utilize the overdeveloped sector of

communications technology in developing countries with

many rural villagers owning smartphones.

5.2 – Future Work

5.2.1 – Machine Learning

Machine learning is another budding field of technology

that is uprooting the medical sector by automating

tasks that could previously only be performed by a trained

physician. However, a drawback of machine learning and

artificial neural networks is the vast amount of data needed

to train and create an accurate model [14]. The data that

are collected in the field from this work could contribute

to gait analysis databases and allow for even more automation

in this field, thereby improving the quality of life of

underserved communities in developing countries.

5.2.2 – Other gait disorders

Research has shown that other common disorders such

as Parkinson’s Disease and Cerebral Palsy have clear lower-extremity

gait related symptoms [12][13]. As the modern

hospital moves more towards the patient’s home, telemetric

medicine will become heavily utilized with medical

devices such as the one presented in this work.

Figure 9. Hysteresis or inaccuracy overtime: optimized

vs unoptimized sensor

Hysteresis or deviation is the inaccuracy of a sensor’s

indicated values from actual values which occurs in all sensors

and is accounted for. Figure 9 plots sensor hysteresis

against hours of use under the stress from weight that simulates

human locomotion. The control was a single unoptimized

FSR, and it was compared to the insole designed

with FSRs optimized by placement, size, and resistance.

The graph below shows clear reductions in the amount of

ENGINEERING

6. Acknowledgments

I would like to express my gratitude towards Dr. Sambit

Bhattacharya of Fayetteville State University for guiding

me in this project. His input allowed me to find solutions

in ways I never would have thought to.

7. References

[1] Mathew, G., & Hanson, B. P. (2009). Global burden of

trauma: Need for effective fracture therapies. Indian journal

of orthopaedics, 43(2), 111.


[2] Bamberg, S. J. M., Benbasat, A. Y., Scarborough, D. M.,

Krebs, D. E., & Paradiso, J. A. (2008). Gait analysis using a

shoe-integrated wireless sensor system. IEEE transactions

on information technology in biomedicine, 12(4), 413-

423.

[3] Seebeck, P., Thompson, M. S., Parwani, A., Taylor, W.

R., Schell, H., & Duda, G. N. (2005). Gait evaluation: a tool

to monitor bone healing?. Clinical Biomechanics, 20(9),

883-891.

[4] Morshed, S. (2014). Current options for determining

fracture union. Advances in medicine, 2014

[5] Dorson, Jill R. "Biofeedback aids in resolving the paradox

of weight-bearing." (2018).

[6] Nunamaker, D. M., Rhinelander, F. W., & Heppenstall,

R. B. (1985). Delayed union, nonunion, and malunion.

Textbook of Small Animal Orthopaedics, 38.

[15] Naboureh, A., Feizizadeh, B., Naboureh, A., Bian, J.,

Blaschke, T., Ghorbanzadeh, O., & Moharrami, M. (2019).

Traffic accident spatial simulation modeling for planning

of road emergency services. ISPRS International Journal

of Geo-Information, 8(9), 371.

[16] Edginton, J., & Taylor, B. (2019). Humeral Shaft Nonunion.

OrthoBullets

[17] Joslin, C. C., Eastaugh-Waring, S. J., Hardy, J. R. W.,

& Cunningham, J. L. (2008). Weight bearing after tibial

fracture as a guide to healing. Clinical biomechanics, 23(3),

329-333.

[18] Larson, P.(2011). Vertical Impact Loading Rate in

Running: Linkages to Running Injury Risk.

[19] Tekscan, F-Scan System. https://www.tekscan.com/

products-solutions/systems/f-scan-system.

[7] Falconer, J., & Hayes, P. W. (1991). A simple method

to measure gait for use in arthritis clinical research. Arthritis

& Rheumatism: Official Journal of the American

College of Rheumatology, 4(1), 52-57.

[8] Rao, U. P., & Rao, N. S. S. (2017). The rural medical

practitioner of India. Evol Med Dent Sci, 6.

[9] Naeem, Z. (2010). Road traffic injuries–changing

trend?. International journal of health sciences, 4(2)

[10] Bachani, A., Peden, M. M., Gururaj, G., Norton, R., &

Hyder, A. A. (2017). Road traffic injuries.

[11] Razak, A., Hadi, A., Zayegh, A., Begg, R. K., & Wahab,

Y. (2012). Foot plantar pressure measurement system:

A review. Sensors, 12(7), 9884-9912

[12] Zhou, J., Butler, E. E., & Rose, J. (2017). Neurologic

correlates of gait abnormalities in cerebral palsy: implications

for treatment. Frontiers in human neuroscience, 11,

103

[13] Hausdorff, J. M. (2009). Gait dynamics in Parkinson’s

disease: common and distinct behavior among stride

length, gait variability, and fractal-like scaling. Chaos:

An Interdisciplinary Journal of Nonlinear Science, 19(2),

026113

[14] Gal, Y., Islam, R., & Ghahramani, Z. (2017). Deep

bayesian active learning with image data. In Proceedings

of the 34th International Conference on Machine Learning-Volume

70 (pp. 1183-1192). JMLR. org.


DIFFERENCES IN RELIABILITY AND

PREDICTABILITY OF HARVESTED ENERGY FROM

BATTERY-LESS INTERMITTENTLY POWERED

SYSTEMS

Nithya Sampath

Abstract

Solar and radio frequency (RF) harvesters serve as viable alternative energy sources for battery-powered devices in which

the battery is not easily accessible. However, energy harvesters do not consistently produce enough energy to sustain an

energy consumer; thus, both the energy availability and execution of the consumer process are intermittent. By simulating

intermittent systems with large-scale energy demands using specifically designed circuit models, parameters including

harvested voltage, voltage across the capacitor, and voltage across the consumer were determined. The probability

of energy availability was then computed based on the number of energy events that had previously occurred for both

harvested solar and radio frequency. A metric designated as the η-factor was computed from the probability plots for the

solar and radio frequency data to quantify the reliability of each power source. The η-factor for harvested solar energy

was significantly higher than that of harvested radio frequency energy, indicating that harvested solar energy is more

consistently available than harvested radio frequency energy. Finally, the effects of various obstacles between the radio

frequency transmitter and receiver on the output voltage were determined. Increasing the distance between the transmitter

and receiver, as well as placing people and metal between the two, resulted in a significant drop in energy availability as

compared to foam and wood obstacles. Quantifying the reliability of different harvested sources would help in identifying

the most practical and efficient forms of renewable energy. Determining which obstacles cause the most obstruction to a

signal can aid in optimizing the strategic placement of harvesters for maximum energy efficiency.

1. Introduction

Battery-powered devices such as pacemakers and neurostimulators

are not suitable in many systems due to their

inaccessible placement and frequent need for battery replacement.

Harvested energy from solar, radio frequency,

and heat sources is an attractive alternative to batteries

in such systems; however, energy availability from these

sources is not consistent and is therefore characterized as

intermittent. There are two components to these intermittently

powered systems: the energy harvester and the energy

consumer. The energy consumer utilizes the energy

captured by the energy harvester. For the energy consumer

to remain functional, a certain power threshold must be

met. There is often a discrepancy between the amount of

energy required to power the consumer and the amount

of energy supplied to the system by the energy harvester,

causing the device to cyclically turn on and off as shown

in Figure 1. This sporadic energy harvesting pattern leads

to an interrupted, or intermittent, execution of the energy

consumer [1]. Most renewable energy sources, including

solar and wind power plants, have an intermittent power

output [2].

An energy event is defined as the generation of a significant

voltage in a given time interval. In this study, a

minimum voltage of 2.8V during a period of 5 minutes was

considered significant. Indicating that N energy events

have occurred signifies that 5N minutes have passed since

the energy consumer last shut off. The time designated for

an energy event in this investigation was selected to test

the burstiness of energy and accurately determine when

the probability of a future energy event reaches 0. A capacitor

was included in the circuit to store and release energy

slowly into the consumer. Without the capacitor, energy

flowing directly from the harvester to the consumer would

instantly terminate the execution of the consumer software

in the absence of an energy event [3].

Figure 1. Graph of voltage vs. time for an energy consuming

device. The device turns on when it reaches

a threshold voltage, approximately 2 Volts, and begins

to consume energy at a rate greater than the rate

at which harvested energy is supplied to the circuit.

When the voltage supplied to the energy consuming

device drops, the device shuts off and the voltage is

allowed to increase again from the harvested power

supply. This cycle repeats.

ENGINEERING


The objective of this study is to model an energy harvesting

system on a small scale using solar harvester and

radio frequency harvester units. This model will be used

to analyze the burstiness of energy by computing the likelihood

of power availability, given that N consecutive energy

events have occurred. It is hypothesized that all harvested

energy will be available in short bursts, consistent

over small periods of time. The reliability of various harvested

sources in terms of their power availability will also

be determined by computing the η-factor. The η-factor is

a metric that compares the experimental energy harvesters

to a random energy harvester, in which energy events are

independent. This is not the case for real energy harvesters,

in which energy events are dependent upon each other.

It is predicted that the η-factor of solar energy will be

higher than that of radio frequency energy, both of which

will be less reliable than wall power, which has a η-factor

of 1.0. Lastly, the effect of various obstacles and distances

between an RF transmitter and receiver will be determined.

It is expected that obstacles with a higher density,

such as people, will allow for less energy to be harvested

than obstacles such as foam or wood. Obstacles were considered

only for harvested RF energy, as solar energy is

traditionally harvested without obstruction. This research

currently has great relevance as the world begins to look

towards renewable energy sources to replace fossil fuels.

Investigating the patterns of energy availability and consumption

allows for precise prediction of energy patterns

and optimization of execution process scheduling.


The setup for both the solar and the radio frequency experiments

involved two main circuits as shown in Figure

2. The first, designated as the harvester circuit, captured

energy either from the sun or from the transmitted RF

signal and converted it into electrical energy. There were

4 main components to this circuit: the solar or radio frequency

harvester unit, the capacitor, the LTC (Load Tap

Changer), and the consumer. In this experiment, the load

was an MSP-430 device that was constantly running an

energy-consuming software and required 2.8 Volts to turn

on. The purpose of the LTC in the circuit was to ensure

that given a certain amount of energy input, the amount

of voltage output would be 3.3 Volts. This amplified input

voltage pronounces the absence of sufficient energy.

The voltage was recorded across the energy consumer and

energy harvester, which captured energy from the sun

or radio frequency transmitter. The weather conditions

during the experiment are important to consider when

dealing with solar data. A light sensor was used to record

the full-spectrum, infrared, and visible light levels. The device

also retrieved real-time weather data from Weather.

com, including the temperature and UV index. The experiment

was conducted under consistent weather conditions,

sunny and minimal cloud coverage, and the harvester was

placed directly across from a window facing west. The system

tended to reach peak energy in the afternoon, when

the most sunlight was available.

Figure 2. Diagram of circuit setups for solar and radio

frequency harvesting experiments. Output voltages

from each component in the harvester circuit — the

harvester unit, the capacitor, the LTC, and the load

— were connected to the Arduino and recorded.

For the radio frequency experiment, an infrared sensor

was also used to record the presence and absence of people;

a person walking in front of the sensor and blocking the

signal was considered as an absence of an energy event. A

secondary logger circuit was constructed to record voltages

at each point in the harvester circuit. It consisted of a

Raspberry Pi 3 and an Arduino Uno, powered from the

wall. Arduino and Python programs were used to record

and analyze the voltage data, calculate η-factors, and construct

plots of energy availability.

The obstacle experiment with a radio frequency harvester

involved a similar setup, except for an infrared sensor.

Python and Arduino programs were used to record the

voltages at various points in the circuit. Data were collected

over a span of 2 minutes each for four different obstacles–metal,

wood, person, and foam–over three different

distances – 1 meter, 2 meters, and 3 meters. The voltages

were recorded from the D out

pin on the MSP-430, which

is directly related to the amount of radio frequency input

received. The absence of an obstacle between the transmitter

and the receiver served as a control to which the other

voltage values were compared. The approximate densities

and thicknesses of the obstacles used are listed in Table

1. The density of a human was approximated to be 1.01

grams per cubic centimeter [4].

3. Results

This study aimed to test the burstiness properties of

harvested energy by analyzing the probability of energy

availability. The idea that energy was available in short


Table 1. Approximate thicknesses and densities of

obstacles used in experiment. The thicknesses were

measured and densities were calculated using measurements

of mass and volume. The thickness measurement

for a human is very approximate as it is

harder to measure with precision.

Approximate Thickness and Density of Obstacles

ENGINEERING

Thickness (cm) Density (g/cm 3 )

Human 25 1.01

Metal Whiteboard 2.0 0.85

Wood 2.0 0.70

Foam 0.3 0.13

bursts was suggested by earlier data collected in the lab;

however, the data collection did not occur for long enough

to prove conclusions supporting that idea. To determine

whether the energy-consuming device was turned on and

how much energy was being supplied to the circuit, voltage

data was recorded at various locations in the circuit. In

Figure 3 (solar) and Figure 4 (radio frequency), the horizontal

axes contain both positive and negative values for

the number of previous energy events. Negative numbers

of energy events correspond to a continuous absence of

energy events. These graphs support the hypothesis of the

burstiness of harvested energy since the probability does

not oscillate as the number of energy events increase.

Figure 3. Probability plot for harvested solar energy.

This plot displays energy occurring in short bursts.

The mean η-factor is 0.8595 (n = 6). The data collection

for this experiment spanned three days.

Figure 3 also depicts that the correlation between the

probability and the number of energy events decreases

near N = 70. This is consistent with the designated definition

of an energy event, as 70 energy events lasting 5

minutes each would cumulate to 5.83 hours. This is the

approximate time for which sufficient sunlight was facing

the energy consumer in the experiment location. This feature

is not represented in Figure 4 as the amount of harvested

radio frequency energy was not dependent upon the

time of day.

Figure 4. Probability plot for harvested radio frequency

energy. This plot displays energy occurring

in short bursts. The mean η-factor is 0.3657 (n = 6).

The data collection for this experiment spanned two

weeks.

The reliability of various harvested sources based on

the η-factor was compared. The findings reinforce the

hypothesis of a given harvested energy source being more

reliable than another due to the variance in reliability patterns.

The mean η-factor for harvested solar power was

0.8595 and the mean -factor for harvested radio frequency

power was 0.3657. The standard deviation of the solar data

was 0.0018 and the standard deviation of the radio frequency

data was 0.0762. A student’s t-test was performed

over the 6 trials for each of the solar and radio frequency

experiments. The t-test yielded a two-tailed p-value of less

than 0.0001, indicating a statistically significant difference

between the η-factors for harvested solar and harvested

radio frequency energy. The η-factors for both harvested

solar and harvested radio frequency energy fall below the

ideal standard of 1.0, the η-factor for wall and battery power.

This can be observed in Figure 5, where the probability

of energy availability, given that any number of energy

events have occurred, is 1.0 [5]. The higher η-factor for

solar power suggests that harvested solar energy is more

reliable than harvested radio frequency.

Finally, various obstacles and distances separated a radio

frequency transmitter and receiver to determine how

they would affect the amount of harvested energy. Varying

obstacles are likely to have varied effects on the amount of

energy able to be harvested, due to the density and thickness

of the object. The results shown in Figure 6 indicate

that metal and people had a more pronounced effect on the

ability of the receiver to harvest energy from the transmitter

as compared to wood and foam. Foam had a slightly

higher voltage input value than the absence of an obstacle

at 1 meter, but this difference is too small to be significant

and was likely caused by random variation. Additionally,


as distance increases, the received signal input decreases

for all obstacles. Over larger distances, fewer signals can be

received and converted into electrical energy.

Figure 5. Theoretical probability plot for wall power.

Wall power has a η-factor of 1.0 since it is not intermittent.

Figure 6. Graph depicting D out

for various obstacles

and distances in the RF obstacle experiment. The D out

value is correlated with the radio frequency input,

so higher D out

values correspond to higher radio frequency

input.

4. Discussion

The major objectives for this investigation included

investigating the burstiness of energy, comparing the reliability

of harvested solar and harvested radio frequency

energy, and exploring the effects of various obstacles and

distances on the amount of harvested radio frequency energy.

Graphs of the probability of energy availability given

that a certain number of energy events had occurred were

constructed and analyzed from which η-factors were calculated

(Fig. 3 and 4). The average D out

values for different

obstacles and distances were also plotted against a control

group, comparing which obstacles had the greatest effect

on D out

values (Fig. 6).

Figures 3 and 4 indicate the burstiness of both harvested

solar and radio frequency energy. The probability of an energy

event occurring becomes relatively high, after many

have already occurred. This correlation does not continue

in the case of harvested solar energy. Increasing the time

interval t, which defines an energy event, could have had

an influence on this data. A larger value of t may represent

sections of the graph after the correlation ends for solar

energy, causing probabilities in the middle to appear closer

to 1 than they truly are. Selecting a smaller value of t

may not show where the correlation ends, suggesting that

the correlation does not in fact end. In Figure 4, there is

a spike in probability at approximately N=-1. This indicates

that if there was no energy event in the previous time

interval, then the probability of a future energy event is

extremely high. This suggests that when a person walks in

front of the sensor, they cause the absence of a single energy

event, but they are not likely to cause the absence of a

second energy event. In other words, most people walk by

the sensor rather than standing in front of it. Inaccuracies

in the infrared sensor, indicating if a person was blocking

the signal, may have also influenced the results. Finally, the

Arduino could only record voltages to 2 decimal places,

limiting the precision of the data analysis. This analysis of

the burstiness of energy will allow for the optimization of

scheduling execution processes.

The calculations of the η-factor and the results of the

student’s t-test suggest that harvested solar power is more

reliable than harvested radio frequency power, supporting

the original hypothesis. The increased standard deviation

for the radio frequency-factor could be attributed to the

increased variance among trials in the patterns of people

passing in front of a sensor over a given time, compared

to the more stable pattern of light reaching a solar panel.

Data collection for the harvested radio frequency energy,

which lasted 2 weeks, spanned a longer period than that of

solar data, which lasted 3 days. The rationale behind this

was that the absence of radio frequency energy events is

less frequent than the absence of solar energy events, so

more data are needed to gain a holistic understanding of

the radio frequency energy patterns. As in the first experiment,

the reliability of the sensor and Arduino device

could have also influenced the data collected. These results

indicate that harvested solar energy may be a more suitable

alternative to harvested radio frequency energy in terms of

reliability and predictability.

The results presented in Figure 6 suggest that people

and metal, more than other obstacles, obstruct the radio

frequency signal from being received and converted into

electrical energy. Thus, people were used to obstruct the

signal in the first radio frequency experiment in order to

induce the absence of an energy event. Factors that may

have influenced the results include the presence of multiple

objects between the transmitter and receiver, such

as a foam board being held by a hand. These results are

relevant to the real-world application of harvested radio

frequency energy, including Wi-Fi routers and cell towers.

In addition, the results indicate which obstacles are more

likely to cause the absence of an energy event and can in-


form the placement of radio frequency energy harvesters.

There were limitations in some aspects of the model

regarding accurately modeling large-scale solar and radio

frequency energy harvesting systems. For the radio frequency

experiment, the model fails to account for the real-life

conditions of multiple obstacles or static obstacles,

which could decrease the likelihood of energy events by

allowing for less energy to be successfully harvested.

Future research may involve testing different forms of

harvested energy under various weather conditions. The

differences in amounts and reliability of harvested energy

between different types of piezoelectric energy could be

investigated, including energy harvested from mechanical

stress inside a person’s shoe or energy harvested from mechanical

stress on a tile. As solar cell technology advances,

the reliability of different types of solar panels could also

be tested.

In the search for fossil fuel replacements in this era of

climate crisis, the reliability of different harvested energy

sources should be considered. Harvested solar energy was

found to be more reliable than harvested radio frequency

energy, and this should be a factor in deciding which

types of renewable energy to invest in and implement on a

large scale. These harvesters, particularly radio frequency

harvesters, should be placed in a way that minimizes obstruction,

particularly from people and metal. In this way,

energy efficiency can be maximized. Through harvesting

energy from common and everyday sources like the sun

and radio frequency signals, the first steps are taken towards

ensuring a more sustainable and energy-efficient

future.

[3] Islam, B., Luo, Y., Lee, S., & Nirjon, S. (2019, April).

On-device training from sensor data on batteryless platforms.

In Proceedings of the 18th International Conference

on Information Processing in Sensor Networks.

[4] Density of Human body in 285 units and reference

information (2019). Retrieved November 9, 2019.

[5] Islam, B., Luo, Y., & Nirjon, S. (2019). Zygarde:

Time-Sensitive On-Device Deep Intelligence on Intermittently-Powered

Systems. arXiv preprint arXiv:1905.03854.

5. Acknowledgements

Thanks to Dr. Shahriar Nirjon and Bashima Islam of the

Department of Computer Science at UNC-Chapel Hill

and to Dr. Sarah Shoemaker, Ms. Shoshana Segal, and

Mr. Chris Thomas of the NCSSM Mentorship and Research

Program for advice and assistance throughout the

research process.

6. References

[1] Lucia, B., Balaji, V., Colin, A., Maeng, K., & Ruppel,

E. (2017). Intermittent computing: Challenges and opportunities.

In 2nd Summit on Advances in Programming

Languages (SNAPL 2017). Schloss Dagstuhl-Leibniz-Zentrum

fuer Informatik.

[2] Fuchs, E., & Masoum, M.A. (2015). Power Quality in

Power Systems and Electrical Machines (2nd ed.). Academic

Press Inc.

ENGINEERING


APPLYING MACHINE LEARNING TO HEART DISEASE

DIAGNOSIS: CLASSIFICATION AND CORRELATION

ANALYSES

Sahil Pontula

Abstract

Classification machine learning has emerged to quickly and efficiently analyze large sets of data and make predictions

about potentially unknown variables. It has seen application in numerous fields, from sorting through ionosphere signals

to predicting weather. Here, we report on the application of classification algorithms to heart disease diagnosis, run on

a sample dataset of 303 patients. We test several models and compare their results to determine accuracy in predicting

presence or absence of heart disease. Furthermore, we conduct statistical and graphical analyses to determine correlations

and causations between different attributes linked to cardiovascular disease. We believe that the methods demonstrated

here show promise for large-scale applications, for both more complex and comprehensive datasets and real-time data

collected in a clinical setting.

1. Introduction

1.1 Heart Disease

1.1.1 Angina

Approximately 9.8 million Americans are thought to

suffer from angina pectoris, or simply angina, annually,

and it is suspected that 500,000 new cases emerge each year

[1]. Angina is a common symptom of heart attacks (myocardial

infarctions) and is the result of insufficient blood

supply to the heart (cardiac) muscle, known as ischemia.

In ischemia, the heart does not receive enough oxygen to

pump adequately. Despite angina’s relative commonness,

it can be difficult to distinguish between other causes of

chest pain. The chest pain of the patients whose data we

investigate here is classified as anginal or non-anginal (and

the cause may be known or unknown).

Symptoms of angina include chest pain, pain in the

limbs or jaw that accompanies chest pain, nausea, sweating,

fatigue, and shortness of breath, though symptoms for

females can be different from the conventional symptoms,

and as such treatments will vary. Angina can be classified

as stable or unstable. Stable angina is more common and

can occur during exercise (and disappear upon resting). It

occurs when the heart is required to pump much harder, is

short-lasting, and is often predictable. Unstable angina is

more dangerous and may indicate risk of a heart attack (it

is unpredictable and longer-lasting). A third type of angina,

variant or Prinzmetal’s angina, is rarer and is caused by

a spasm in the coronary arteries [2].

Most often, the ischemia that causes angina is due to

coronary artery disease. When coronary arteries are

blocked by fatty deposits, or plaques, the condition is

known as atherosclerosis. Exercise-induced angina occurs

when the effects of ischemia are felt during exercise (reduced

blood flow through coronary arteries cannot match

the oxygen demand).

Risk factors of angina include tobacco use, diabetes,

high blood pressure, high blood cholesterol levels, elderly

age, sedentary lifestyle, obesity, and stress. We investigate

just some of these factors in analyzing the given dataset.

1.1.2 Age

It is known that individuals of age 65 or older are more

susceptible to developing heart disease, including myocardial

infarctions (heart attacks) and heart failure. With older

age, the heart cannot beat as quickly during exercise or

stress, even though the heart rate is fairly constant. Aging

commonly causes the arteries to grow stiffer, resulting in

a condition known as arteriosclerosis. This, in turn, results

in complications such as hypertension. Other changes

in the cardiovascular system that result from aging include

arrhythmias (irregular heartbeats or abnormal heart

rhythms) and edema.

1.1.3 Gender

At younger ages, males are more vulnerable to heart disease

than women; on average, men suffer their first attack

at age 65, while women suffer their first attack at age 72

[3]. Nevertheless, heart disease is the most prevalent cause

of death in both genders. Many factors may contribute to

the greater vulnerability of males, including higher rates of

risk factors such as smoking and the fact that hormones in

younger women protect against heart disease. Additionally,

the symptoms may differ between the genders; some

researchers have noted that during heart attacks, women

are more likely to suffer from abnormal symptoms.

Ironically, the survival rate after developing heart disease

shows a trend opposite to that of acquiring it. Survival

rates have been found to be lower in women, perhaps because

women are less likely to obtain advice or beneficial

62 | 2019-2020 | Broad Street Scientific MATHEMATICS AND COMPUTER SCIENCE

medications than men or because women are generally

older and suffer from other health complications [3].

1.1.4 Cholesterol

Cholesterol, in normal levels, is essential for many body

functions, including building new cell walls and making

steroid hormones. In excess amounts, however, cholesterol

tends to accumulate in arterial walls, resulting in a

condition known as atherosclerosis. This disease results in

narrowed arteries and blocked or slowed blood flow, causing

angina or heart attacks. Note that blood-borne cholesterol

is generally characterized as low-density lipoproteins

(LDLs) or high-density lipoproteins (HDLs). LDLs are

responsible for the atherosclerosis-causing fatty plaques.

HDLs are thought to function in clearing the blood of excess

cholesterol. Additionally, high levels of triglycerides,

another type of fat, are suspected to correlate with occurrences

of heart disease [4].

1.1.5 Electrocardiograms (ECGs)

ECGs provide a convenient way to measure the electrical

activity of the heart, and hence find common application

in diagnosing cardiac disorders. A representative signal

of normal cardiac electrical activity is shown in Figure

1. The P wave is associated with atrial depolarization, the

QRS complex is associated with ventricular depolarization

and atrial repolarization, and the T wave is associated with

ventricular repolarization. The measurements are recorded

from a set of electrodes placed on the skin and the results

are typically presented as a “12-lead ECG,” which has

12 voltage-time graphs.

The ST segment connects the points marked S and T

in Figure 1. ST depression, wherein the ST segment is unusually

low relative to the baseline, is sometimes associated

with heart disease. It is generally indicative of myocardial

ischemia, which is primarily caused by coronary artery

disease (CAD), in which blood flow is greatly reduced for

much the same reason as in atherosclerosis. ST depression

is also seen in patients with unstable angina. The ST segment

may be depressed but also upsloping, in which case

ischemia is generally not the cause. Instead, it is likely a

variant of the normal sinus rhythm. Additionally, studies

have shown that the ST segment slope may provide an accurate

diagnosis of CAD [5].

1.2 Classification Algorithms

Here we describe the algorithms (and other related concepts)

used in the classification analysis of the heart disease

dataset.

1.2.1 KNN Algorithm

The KNN, or k-nearest neighbors, algorithm (Fig. 2)

is part of supervised machine learning techniques (“supervised”

indicates that labeled input data is being used for

training/testing). The algorithm assumes that data similarly

classified are also close together geometrically (usually

measured by the Euclidean distance).

Figure 1. A sample ECG signal for a normal (sinus)

rhythm, showing the P wave, QRS complex, and T

wave [6].

MATHEMATICS AND COMPUTER SCIENCE

Figure 2. The steps of the KNN classification algorithm.

Note how distances are computed for the new

data point between it and members of the preexisting

classes before a decision is made for its classification

[7].

For classification, it works by computing the distance between

a data point and all other data points, selecting the

k nearest neighbors thus found (where k is defined by the


user), and classifying according to the most frequently occurring

label. However, this algorithm can slow significantly

as the size of the dataset grows. In addition, the user

must decide on the value of k. If k is too small, the dataset

could be overfitted, leading to significant variance, while

if k is too large, the data may be oversampled, resulting in

bias.

1.2.2 Decision Tree Analysis

Decision trees function in classification and regression

in machine learning. They are conventionally drawn such

that their root is at the top. Conditions, or internal nodes,

are places where the tree splits into branches (edges).

When the branch does not split any more, we have found

the decision (leaf). Decision trees allow relations and important

features to be easily seen.

Decision trees are often based off of the technique of

recursive binary splitting, where the split points are selected

based on a cost function. This allows selection of

the splits that maximize the tree’s accuracy. Sub-splits are

also chosen recursively in this manner, so recursive binary

splitting is also known as the greedy algorithm, as the only

goal is to minimize cost. This means the root node, at the

top of the tree, is the best classifier. Gini scores, G = ∑p k

(1

−p k

), provide a good way to quantify how good a split is by

measuring how independent or mixed the two resulting

groups are. An ideal split would have G = 0.

Note, however, that trees that are too long may overfit

the data, and as such the number of training inputs used

for splitting is often limited.

1.2.3 Support Vector Machine Analysis

This supervised learning algorithm’s objective is to

find a hyperplane in n-dimensional space, where n is the

number of features in the data, that classifies the data by

separating the classes as much as possible. Many planes

are possible to separate two given classes, but the one that

maximizes the distance between data of different classes,

or margin distance, is chosen.

Support vectors are data points that lie closer to the hyperplane

and affect its position, directly affecting the margin

distance. Note that the margin distance is optimized by

a loss function known as the hinge loss function.

1.2.4 Neural Network Analysis

Typically, neural networks (NN) are the preferred

method of choice for classification tasks such as this one.

These structures consist of an input layer, an output layer,

and a series of hidden layers in between. All layers may

consist of multiple neurons (or nodes), which take in some

input value, perform a computation using some algorithm,

and propagate an output value to the next neuron. In the

end, the output from the output layer determines the result.

For classification tasks, the NN uses training data to

adjust the “weight” of the algorithm/computation that the

neurons use, where it can correct the values of the “weight”

to match the outputs of the training data. Once trained, the

NN can be used on a test set, predicting the appropriate

classifications. The results can be compared with the true

classifications to determine the accuracy of the model, and

eventually the model can be used with real-world data.

1.2.5 Random Forest Analysis

Random forests consist of many individual decision

trees working as an ensemble. Each tree outputs a class

prediction, and whichever class is predicted the most by

the trees is chosen as the model’s prediction. The random

forest algorithm is one of the best among supervised classification

techniques, because it works by integrating the

predictions of many independent, uncorrelated models

(trees), compensating for the individual errors of any one

tree [8].

1.2.6 Naive Bayesian Analysis

This classification technique is based on Bayes’s theorem

for computing conditional probabilities. It assumes

independence among predictors/features in the data, and

uses Bayes’s theorem to predict the probability of a given

data point falling into a certain class based on the predictors.

Naive Bayes is a quick, efficient way to classify data with

multiple classes. Unfortunately, it is accurate only if the

assumption of independence between predictors holds.

Additionally, Naive Bayes is often better with categorical

variables, as it assumes numerical variables are normally

distributed. Nevertheless, the fast nature of the algorithm

makes it a preferred method for real-time and multi-class

prediction [9].

1.2.7 Gini Index

This index measures the probability of a specific variable

being wrongly classified when randomly chosen. G =

0 or G = 1 indicate that all elements are in only one class

or the elements are randomly distributed across the classes,

respectively. G = 0.5 indicates equal distribution of the

elements in the classes.

The Gini index is defined as G = 1 − ∑(p i

) 2 , with p i

being

the probability an element is classified in a specific class.

In decision trees, the feature with the lowest Gini index is

usually chosen as the root node [10].

1.3 Objective

In this investigation, we have two primary purposes.

First, we attempt to use statistical analysis in R/RStudio,

including correlograms and the Bayesian information

criterion, to identify potential correlations and causations

among the variables in the heart disease dataset, described

in more detail below. Second, we use classification machine

learning algorithms, also in R/RStudio, to predict

the presence of heart disease in arbitrary patients.


2. Datasets

The data for this work were obtained from https://

archive.ics.uci.edu/ml/datasets/heart+Disease. Data were

collected from 303 patients. The original dataset consists

of 75 variables, but we only consider a subset of the 14

most important variables (which were used in published

experiments). The last variable is the class variable, referring

to the presence or absence of heart disease. Values of

1, 2, 3, and 4 denote presence (according to a scheme used

by the creators of the dataset), while a value of 0 denotes

absence. The 14 variables in the dataset are as follows:

1. Age, numerical

2. Gender, categorical

3. Chest pain type, categorical

4. Resting BP (mmHg), numerical

5. Serum cholesterol concentration (mg/dl), numerical

6. Fasting blood sugar concentration (mg/dl), categorical

7. Resting EKG results, categorical

8. Maximum heart rate (bpm), numerical

9. Presence of exercise-induced angina, categorical

10. ST segment depression due to exercise, numerical

11. Slope of peak exercise ST segment, categorical

12. Number of major vessels colored by fluoroscopy,

categorical

13. Status of defect, categorical

14. Diagnosis of heart disease, categorical

3. Data Preparation and Modeling

In order to clean the data, data munging was performed

in R/RStudio. The raw data were read as a CSV file directly

from the URL above. The columns were then renamed

to readable forms and missing data were found and

corrected for. Six rows had missing data, and we chose to

simply delete these from the dataset. We then reclassified

the categorical variables, changing the numbers to more

readable text.

4. Correlation Analysis


For preliminary analysis, pairwise histograms and correlations

(scatter plots and correlation coefficients) were

produced for the five numerical variables. From the histograms,

it was noted that most numerical variables showed

only slight skew and the only variable with considerable

skew (ST segment depression) could not be easily corrected

with normalization transformations. As such, the data

were not transformed in any way. Furthermore, a correlogram

was generated, taking the numerical variables in

pairs, to visualize any correlations present.

Linear regressions were then created with ST segment

depression as the dependent variable. A multiple regression

model was also developed to predict ST segment depression.

To examine causality, Bayesian information criterion

(BIC) values were generated between ST segment depression

and each of the other variables. If the value for the

association between ST segment depression and a given

variable was more than 10 less than the value with no coupling,

a causal relationship was possible.

Lastly, two scatterplots were generated with ST segment

depression as the dependent variable and maximum

heart rate as the independent variable. Points were

grouped by two categorical variables, namely slope of peak

exercise ST segment and type of chest pain.

5. Classification Analysis

After designating the diagnosis variable as the class variable,

Gini indices were generated for all other attributes.

Lower values indicated more important variables, namely

those that would give greater equality in the distribution of

data into classes. These variables, in turn, could be used as

root nodes in decision trees. The attributes with the lowest

Gini indices were plotted using jitterplots, where the

dependent variable was heart disease diagnosis, to examine

data clustering.

With these important variables identified, various classification

algorithms were then tested. Among these were

the random forest, partition, neural network, support vector

machine (SVM), and Naive Bayes models. These algorithms

were run on two of the “best” categorical variables

(according to the Gini coefficients) and the best numerical

variable, ST segment depression. k = 3 clusters were used

for the neural network model, based on the jitterplots. Tables

were generated to document the accuracy of the algorithms

in predicting presence or absence of heart disease

in the 303 patients.

Additionally, a k-nearest neighbors (KNN) algorithm

was run. 70% of the dataset was designated for training,

and the remaining 30% was used to test the model. A cross

table of the model’s predictions and the actual diagnoses

was then produced and compared with the results of the

other classification algorithms.

6. Results

6.1 Correlation Analysis

The pairwise correlations and histograms for the numerical

variables are shown in Figure 3. The correlogram

for the numerical data are shown in Figure 4. Note from

both figures that most numerical data are distributed over

a range of values (i.e. more continuous than discrete). We

do not see particularly strong correlations, but this does

not rule out associations between the variables, as considerable

scatter may be present in the data.

Linear regressions between ST segment depression and

each of the other numerical variables are shown in Figure


5. Again, we see no strong correlations. At discrete values

of age and resting blood pressure, we note that the data

points take on a range of ST segment depressions. From

the multiple regression plot shown in Figure 6, we again

see considerable scatter. Indeed, when the actual ST segment

depression is 0, the predicted depression adopts a

wide range of values.

Figure 5. Four linear regressions with ST segment

depression as the dependent variable. Age (years),

maximum heart rate (bpm), resting blood pressure

(mmHg), and blood cholesterol concentration (mg/

dl) are the independent variables.

Figure 3. Pairwise histograms and correlations for

the numerical variables. Note how the age histogram

is fairly normally distributed. The histograms

for resting BP and cholesterol level show right skew,

the histogram for maximum HR shows left skew,

and the histogram for ST depression shows significant

right skew. No strong correlations appear to be

present, the strongest being between age and maximum

HR.

Figure 6. A plot of the actual ST segment depression

and the predicted depression using a multiple regression

model with all four other numerical variables.

Figure 4. A correlogram for the numerical data in the

heart disease dataset. The results shown confirm the

findings from the pairwise plots. No strong correlation

are apparent. Note that the color indicates the

the direction of the correlation (positive, negative,

or no correlation) and the size of the circle indicates

the relative strength of the correlation.

Conducting BIC analyses with ST segment depression

as the dependent variable, it was found that half of the

variables had a possible causal relationship. Among these

were chest pain type, maximum heart rate, presence or absence

of exercise-induced angina, slope of the peak exercise

ST segment, and presence or absence of heart disease.

The latter two had the lowest BIC values.

Figure 7 shows plots with ST segment depression and

maximum heart rate, grouped by two categorical variables.

All variables showed evidence of correlation via the regressions

or BIC analyses. Grouping by slope, we see that flat

and downsloping ST segments show considerable scatter,

with the downsloping datapoints suggesting potential outliers

at high depressions. The upsloping ST segments seem

to be concentrated at low depressions. A similar trend is

seen in the second graph, where asymptomatic chest pains

suggest outliers. Nevertheless, none of the four categories

shows distinctive patterns in the plot.


algorithms. Similar accuracies among many of the algorithms

can be explained given that the dataset is not too

complex, so for most data points, the algorithms operate

similarly and give the same predictions. The accuracy of

the KNN algorithm, using all non-class variables as predictors,

was 64.9% for absence and 46.3% for presence of heart

disease (using the test dataset). We see that the partition

model using ST segment depression as a predictor has the

best accuracy for both absence and presence.

Figure 7. Two scatter plots with ST segment depression

as the dependent variable. The data points are

grouped by the categorical variables chest pain type

and slope of ST segment during peak exercise.

6.2 Classification Analysis

By finding Gini values for each of the non-class variables,

it was found that gender, fasting blood sugar concentration,

and presence/absence of exercise-induced angina

were most important. None of the numerical variables

were major predictors of diagnosis. Nevertheless, we show

jitterplots for the three aforementioned categorical variables

as well as the numerical variable with the lowest Gini

index in Figure 8. The three plots for categorical variables

show four distinct clusters, as expected. The plot with fasting

blood sugar (FBS) shows a concentration of data at low

FBS values, while the plot with exercise-induced angina

shows that most patients lacked both it and heart disease.

In the plot with ST segment depression (the numerical

variable), it is apparent that most patients presented with

low ST segment depression (especially those without heart

disease), with a few outliers at high depression values.

Figure 8. Four jitterplots for the variables with the

lowest Gini indices. Note the distinct clustering tendency

of the datapoints, indicating that most of these

variables are good clustering attributes and likely

important parts of any classification algorithm.

Table 1 shows the results of five of the classification

Table 1. The results of the classification algorithms,

excluding KNN. The first and second numbers in

each cell indicate the accuracy in predicting absence

or presence of heart disease, respectively. The partition

model appears to have the optimal accuracy in

predicting both absence and presence, using ST segment

depression as a predictor. Factors were chosen

based on Gini indices and the correlation analysis.

Naive

Bayes

ECG Result

(%)

Exercise-

Induced

Angina (%)

ST Segment

Depression

(%)

58.1 57.7 85.6 54.0 86.9 46.7

SVM 57.5 59.9 85.6 54.0 81.9 59.1

Neural

Net

57.5 59.9 85.6 54.0 71.3 67.9

Partition 57.5 59.9 85.6 54.0 91.3 68.6

Random

Forest

57.5 60.0 85.6 54.0 81.9 54.0

7. Discussion and Conclusion

In this paper, we have conducted correlation and classification

analyses for a dataset containing heart disease data

for 303 patients. We find that, among the numerical variables

in the dataset, all pairwise correlations are weak, the

strongest existing between age and maximum heart rate.

These variables exhibit a weak negative correlation, which

could exist for multiple reasons. For example, it has been

found that aging slows the natural electrical activity of the

heart’s pacemaker, the sinoatrial (SA) node [11]. The cause

has not been exactly identified, but it is suspected to be due

to changes in the ion channels of sinoatrial myocytes, cells

in the SA node. And yet, the correlation is not at all strong.

This is an indication that multiple factors contribute to

maximum heart rate and similar measures of heart health,

including exercise and diet. Without knowing the exact

details of the patients whose data were used, we suspect

that the considerable scatter in the maximum heart rateage

regression is due to a variety of lifestyles. This claim

is supported by the relatively wide range taken on by the

histograms in Figure 3.

By creating linear regressions with ST segment depres-



sion as a dependent variable, we have found that weak

positive correlations exist between it and blood cholesterol

levels, age, and resting blood pressure, while a negative

correlation exists between it and maximum heart rate. We

can theorize why these correlations exist, but more data

would be required to confirm their statistical significance.

High blood cholesterol levels are a known risk factor for

atherosclerosis and CAD due to the formation of fatty

plaques blocking blood vessels. Given that ST depression

is an indication of these diseases, the correlation is expected.

Age and high blood pressure (hypertension) are also

related to the occurrence of heart disease, though they are

not dominant risk factors. The correlation with maximum

heart rate is likely not statistically significant, as it has actually

been found that accelerated heart rate (e.g. tachycardia)

is a risk factor for heart disease, particularly in men [12].

We have also demonstrated the ability to apply classification

machine learning algorithms to predict the presence

or absence of heart disease. Using Gini coefficients and

jitterplots, we are able to ascertain the variables most important

in the classification. Here, the most important numerical

variable was, as expected, ST segment depression,

the numerical variable that gave the most comprehensive

indication of cardiovascular disease. Using this variable,

it was found that the partition model performed the best,

predicting absence of heart disease with an accuracy of

over 90% and presence with an accuracy of about 70%.

The primary limitations of this work include the lack

of sufficient data to draw statistically significant conclusions

and the inability to attribute heart disease to a limited

number of causes or risk factors. We have seen the high

level of variance in this dataset, and a principal component

analysis (PCA) may provide a way to dimensionally reduce

the data to only a few key variables. In addition, gathering

more data on variables including, but not limited to,

genetics and race may provide a more holistic and accurate

model to predict cardiovascular disease. Nevertheless,

this research takes the first step in expanding classification

machine learning techniques to a clinical setting, and we

envision it helping automate the process of heart disease

diagnosis.

8. Acknowledgements

The author would like to acknowledge the help and support

of Mr. Robert Gotwals of the North Carolina School

of Science and Mathematics (NCSSM) and the NCSSM

Online Program for making this research project possible.

Research (2018). https://www.mayoclinic.org/diseases-conditions/angina/symptoms-causes/syc-20369373

[3] Publishing, H.H.: The heart attack gender gap. https://

www.health.harvard.edu/heart-health/the-heart-attackgender-gap

[4] Cholesterol and Heart Disease. WebMD (2018).

https://www.webmd.com/heart-disease/guide/heart-disease-lower-cholesterol-risk#1

[5] Finkelhor, R.S., Newhouse, K.E., Vrobel, T.R., Miron,

S.D., Bahler, R.C.: The st segment/heart rate slope as a predictor

of coronary artery disease: comparison with quantitative

thallium imaging and conventional st segment criteria.

American heart journal 112(2), 296–304 (1986)

[6] McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith,

L.A.: A dynamical model for generating synthetic electrocardiogram

signals. IEEE transactions on biomedical engineering

50(3), 289–294 (2003)

[7] KNN Classification using Scikit-learn. https://www.

datacamp.com/community/tutorials/k-nearest-neighbor-classification-scikit-learn

[8] Yiu, T.: Understanding Random Forest. Towards Data

Science (2019). https://towardsdatascience.com/understanding-random-forest-58381e0602d2

[9] Ray, S., Analytics, B.: 6 Easy Steps to Learn Naive Bayes

Algorithm (with code in Python) (2019). https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/

[10] Gini Index For Decision Trees. QuantInsti (2019).

https://blog.quantinsti.com/gini-index/

[11] Larson, E.D., Clair, J.R.S., Sumner, W.A., Bannister,

R.A., Proenza, C.: Depressed pacemaker activity of sinoatrial

node myocytes contributes to the age-dependent

decline in maximum heart rate. Proceedings of the National

Academy of Sciences 110(44), 18011–18016 (2013)

[12]Perret-Guillaume, C., Joly, L., Benetos, A.: Heart rate

as a risk factor for cardiovascular disease. Progress in cardiovascular

diseases 52(1), 6–10 (2009)

9. References

[1] Angina Pectoris (2019). https://emedicine.medscape.

com/article/150215-overview

[2] Angina. Mayo Foundation for Medical Education and


MODELING THE EFFECT OF STEM CELL-TARGETING

IMMUNOTHERAPY ON TUMOR SIZE

Amber Pospistle

Abstract

Cancer stem cells (CSCs) are associated with aggressive tumors and are believed to be a driving factor in tumor growth

due to their ability to differentiate and their high reproductivity [17]. Immunotherapy treatments targeting CSCs have

been shown to be promising in reducing tumor size in experimental studies involving mice [15]. In this paper, a computational

model of stem cell-targeted immunotherapy is proposed. The interaction of cytotoxic T-cells (CTCs) and dendritic

cells (DCs) with cancer cells is modeled. The ordinary differential equation model will be used to assess the effectiveness of

dendritic cell vaccines and T-cell adoptive therapy with or without chemotherapy in reducing tumor size and growth. The

results of the model show that immunotherapy treatments combined with chemotherapy are the most effective treatment

for reducing tumor size and growth. The model confirms that CSCs are likely a driving factor in tumor growth.

1. Introduction

Immunotherapy is one of the newest treatments for

cancers. According to the World Health Organization,

the number of new cancer cases is expected to increase by

70 percent over the next 20 years [10]. Immunotherapy

is effective in treating several types of cancer including

non-Hodgkin’s lymphoma, multiple myeloma, prostate

cancer, renal cell carcinoma, malignant melanoma, colorectal

cancer, and small-cell lung cancer [13]. This treatment

stimulates the adaptive or acquired immune system

to kill tumor cells and has fewer side effects than chemotherapy

[19]. For patients with melanoma, a type of cancer

unresponsive to chemotherapy, immunotherapy may be

more effective [19]. Immunotherapy is most effective for

patients in the early stages of cancer [19]. There are multiple

types of immunotherapy including dendritic cell vaccines,

adoptive T-cell therapies, and stimulating the immune

system through vaccines and/or cytokines [17] [19].

Tumor cells have specific antigens that trigger immune

responses [1]. Tumor antigens are often derived from viral

proteins, point mutations, or encoded by cancer-germline

genes. Antigens may be the product of oncogenes or tumor

suppressor genes, over-expressed genes, products of oncogenic

viruses, or oncofectal antigens. Tumor antigens may

be present on the cell surface or in the bloodstream. Most

types of cancer have identified antigens. Antigens are classified

as tumor-specific (only associated with tumor cells)

or tumor-associated (associated with both tumor cells and

normal cells) [20]. Different types of cancer and different

patients have different tumor antigens. When antigens are

recognized by lymphocytes (white blood cells), T lymphocytes

(T-cells) multiply and kill the cancer cells [10].

According to the Tumor Immune Surveillance hypothesis,

immune cells including monocytes, macrophages,

dendritic cells (DCs), and natural killer cells provide a

short-lived response that kills tumor cells and captures debris

from dead tumor cells. T-cells and B cells (B lymphocytes)

provide long-lived antigen-specific responses and

have an effective memory [3] [10]. Dendritic cells allow

T-cells to recognize an antigen. The effectiveness of dendritic

cells in presenting an antigen to T-cells influences

the effectiveness of immunotherapy. As a result, dendritic

cells are a major target when developing immunotherapy

treatments [19].

In addition to T-cell receptors recognizing an antigen,

the T-cell needs to receive a costimulatory signal to become

activated. When a T-cell is activated, T-cell receptors

bind to antigen peptides on major histocompatibility

complex class (MHC) molecules presented by dendritic

cells or macrophages [10] [17]. In order for the T-cell receptor

to bind to the class I MHC molecules, a glycoprotein

called CD8 is needed. Once the T-cell receptors bind to

the cell with the antigen, the cytotoxic T-cell will release

cytokines (chemical messengers) that have antitumor effects.

The cytotoxic T-cell will also release cytotoxic granules

including perforin and granzymes. Perforin forms a

pore in the cell membrane and allows granzymes to enter

the cell which leads to cell apoptosis. Cytotoxic T-cells can

also kill the target cell when FasL (Fas ligand) on the T-cell

surface binds with Fas on the target cell surface. Activated

CD4+ T- cells (also known as T-helper cells) can secrete

cytokines such as interleukins to promote the growth of

the cytotoxic T-cell population [10][17].

Initially, these immune cells can destroy tumor cells

or entire tumors. However, pathways such as PD-L1 and

PD-L2 can inhibit the activation of T-cells [17]. PD-L1 is

expressed on the surface of up to 30 percent of solid tumors

[14]. Tumor cells develop mechanisms to evade

immunosurveillance including producing immuno suppresive

cytokines or altering their expression of interleukins

which may cause inactivation or prevent maturity of

DCs. In addition, CD4+ and CD8+ T-cell responses may

be suppressed by the immune system to prevent damage to



healthy cells. Regulatory cells including regulatory T-cells,

proteins, and natural suppressor cells can suppress immune

system response [21]. Regulatory T-cells play a negative

regulation role on immune cells including cytotoxic T-cell

lymphocytes [19]. T helper type 2 cells, neutrophils (a type

of white blood cell), and activated M2 macrophages can

inhibit cytotoxic T-cell immune response [16]. Therefore,

regulatory T-cells play a major role in the effectiveness of

immunotherapy treatments.

Tumor cells may change their expression of antigens

to evade immune response [3]. Immunotherapy blocks

inhibitory pathways used by tumors [17], in contrast to

targeted kinase inhibitors which are prone to becoming

unresponsive after tumor cells evade it. Immunotherapy is

more effective over time as it activates the body’s immune

system and does not target one specific characteristic of a

constantly evolving tumor [14].

Multiple types of immunotherapy have been studied

in experimental and clinical trials. Immunotherapy treatments

are often given in combination for increased effectiveness.

Dendritic cell vaccines are one promising treatment.

To develop a dendritic cell vaccine, dendritic cells

are extracted from the patient. The cells from the patient

are then injected with tumor antigens and inserted back

into the patient in the form of a vaccine. The immune response

against the vaccine causes the immune system to

recognize the tumor antigen and kill the tumor cells [2]

[17]. Without the vaccine, the patient’s immune system

would not develop a strong response against the tumor

antigen, as the antigen belongs to the patient. Dendritic

cells are the best type of cell for presenting tumor antigens

to other cells such as effector cells or T-cells [2].

A similar treatment with T-cells, called T-cell adoptive

therapy, has also become a promising immunotherapy

treatment. In adoptive T-cell therapy, naive T-cells from

the patient are isolated. The naive T-cells are activated in

vitro by DCs with the tumor antigen. The activated T-cells

with the antigen are readministered to the patient [17].

Most adoptive therapies use CD8+ T-cells that destroy a

tumor cell with a specific antigen by binding to its complex

of class I major histocompatibility (MHC) proteins

[10]. The goal of T-cell adoptive therapy is to increase the

activated T-cell population with receptors that recognize

antigens specific to the tumor. Previous research has found

that T-cell adoptive therapy can destroy large tumors [17]

[21] [23]. In this paper, T-cell adoptive therapy will be referred

to as T-cell treatment.

Many solid tumors are heterogeneous, containing both

cancer stem cells (CSCs) and non-CSCs (nCSCs). CSCs can

self-renew and differentiate into or produce other types of

cancer cells [17]. CSCs have been implicated as a driving

factor in tumor growth and recurrence of tumors [4] [17].

CSCs have been identified for many types of cancer including

head and neck squamous cell carcinoma, pancreatic,

breast, and lung cancer [17]. However, cancer treatments

including chemotherapy and radiation have been ineffective

in killing CSCs. As a result, cancer stem-cell based

immunotherapies are of increasing interest in cancer research.

Due to the complexity of the tumor microenvironment

and the high cost of cancer research, computational models

of the cancer immune system have been created to model

the effectiveness of immunotherapy compared to traditional

treatments [17]. A past computational model of

dendritic cell vaccinations found that the treatment is most

effective when given multiple times based on a set interval.

Two computational models were proposed to find the optimal

time for dendritic cell vaccinations [2][19]. Multiple

models of the interaction of T-cells and cancer [8][9] and

other mathematical models of tumor-immune interaction

[1][5][6][7] have been proposed. However, there are few

computational models of T-cell adoptive therapy or stem

cell-targeted immunotherapy.

This paper will model the interaction of cancer stem

cells, non-cancer stem cells, cytotoxic T-cells (CTCs), and

dendritic cells (DCs) through ordinary differential equations.

This work will model the effect of CSCs and nCSCs

on tumor size, determine the cell population in which

dendritic vaccinations are the most effective, model T-cell

treatment with inoculation of different cell populations,

and determine how chemotherapy and immunotherapy

together impact tumor size.

2. Computational Approach

For this paper, the computational model was created

using STELLA Architect [18], a differential equation solver

that can model the different cell populations, inflows

(replication), and outflows (cell death). The model consists

of two cancer cell populations: CSCs and nCSCs. Populations

of DCs and CTCs specific to CSCs and nCSCs were

included in the model. A DC can be immature (doesn’t

have tumor antigen), mature with CSC-specific antigens,

or mature with nCSC-specific antigens. A CTC can be

considered naive, mature, CSC-specific, or nCSC-specific.

Several assumptions were made in the model:

1. CSCs self-renew and nCSCs can renew at a varying

rate based on the type of cancer.

2. CSCs can become two nCSCs and a nCSC can

become a CSC. A CSC can also become two CSCs

through replication or become one CSC and one

nCSC.

3. Immature DCs can become mature through the

consumption of antigens. If the immature DC

consumes a CSC or a nCSC, the mature DC will be

CSC-specific or nCSC-specific respectively.

4. Mature DCs in the model will present antigens

to naive CTCs causing the CTCs to become

activated. The CTCs will be able to kill cells with

the same antigens as the DC. The CTC will be


Table 1. List of parameters used in the ordinary differential equations [17]

Parameter Parameter Meaning Parameter Value

S Total Population of CSCs See Equation 1

P Total Population of nCSCs See Equation 2

T s

Total Population of Activated CSC-specific CTCs See Equation 3

T p Total Population of Activated nCSC-specific CTCs See Equation 4

D s

Total Population of Mature CSC-specific DCs See Equation 5

D p Total Population of Mature nCSC-specific DCs See Equation 6

C Concentration of Chemotherapy See Equation 7

α S

Growth rate of CSC population due to symmetric replication See Figure Captions

ρ PS

Transition rate from nCSCs to CSCs See Figure Captions

ρ SP

Transition rate from CSCs to nCSCs See Figure Captions

β S

Death rate of CSCs due to CSC-specific CTCs 6.2 × 10 -8 cells -1 day -1

δ S

Death rate of CSCs due to natural causes See Figure Captions

Γ S

Death rate of CSCs due to chemotherapy 1.4 × 10 -3 day -1 (µg/mL) -1

α P

Growth rate of nCSCs due to replication See Figure Captions

α SP

Growth rate of nCSC population due to asymmetric division of CSCs See Figure Captions

β P

Death rate of nCSCs due to nCSC-specific CTCs 6.2 × 10 -8 cells -1 day -1

δ P

Death rate of nCSCs due to natural causes See Figure Captions

Γ P

Death rate of nCSCs due to chemotherapy 5.0 × 10 -3 day -1 (µg/mL) -1

χ TS T n S

Saturated Activation Rate of CTCs due to mature CSC-specific DCs 4.5 × 10 4 aCTCs day -1

T s

Mature CSC-specific DC EC50 for CTC Activation Rate 2.5 × 10 4 mDCs

δ Ts

Death Rate of Activated CSC-specific CTCs due to natural causes 0.02 day -1

χ TS T n P

Saturated Activation Rate of CTCs due to mature nCSC-specific DCs 4.5 × 10 4 aCTCs day -1

T P Mature nCSC-specific DC EC50 for CTC Activation Rate 2.5 × 10 4 mDCs

δ TP Death Rate of Activated nCSC-specific CTCs due to natural causes 0.02 day -1

γ DS D Maturation rate of CSC-specific DCs due to consumption of cancer cells 0.0063 day -1 cancer cell -1

β DS Death Rate of CSC-specific DCs due to CSC-specific CTCs 6.2 × 10 -8 cells -1 day -1

δ DS Death Rate of CSC-specific DCs due to natural causes 0.2 day -1

γ DP D Maturation rate of nCSC-specific DCs due to the consumption of cancer cells 0.0063 day -1 cancer cell -1

β DP Death Rate of nCSC-specific DCs due to nCSC-specific CTCs 6.2 × 10 -8 cells -1 day -1

δD P

Death Rate of nCSC-specific DCs due to natural causes 0.2 day -1

e c

Elimination Rate of Chemotherapy 50 day -1

CSC-specific or nCSC-specific based on the antigen

of the DC cell.

5. All cells die.

In STELLA, seven stocks were used to represent the following

populations: CSCs, nCSCs, activated nCSC-specific

CTCs, activated CSC-specific CTCs, mature CSC-specific

DCs, mature nCSC-specific DCs, as well as the concentration

of chemotherapeutic agent.

In this paper, five models involving tumor inoculation,

dendritic cell vaccines, adoptive T-cell therapy, and chemotherapy

will be simulated. Each model will be discussed

individually and has a different STELLA model. The following

ordinary differential equations using variables defined

in Table 1 [17] were used:

(1)

(2)

(3)

(4)

(5)



Eq. 1 represents the growth of CSCs through symmetric

replication, the transition of nCSC to CSC, the transition

of CSCs to nCSCs, killing of CSCs by CTCs, natural

death of CSCs, and the death of CSCs by chemotherapy.

Eq. 2 represents the growth of nCSCs through asymmetric

replication, growth of the nCSC population through replication,

the division of a CSC into two nCSCs, killing of

nCSCs by nCSC-specific CTCs, natural death of nCSCs,

and chemotherapy killing nCSCs. Eq. 3 represents the activation

of naive CTCs (which become saturated) by mature

CSC-specific DCs and the natural death of activated

CSC-specific CTCs. Eq. 4 represents the activation of naive

CTCs (which become saturated) by mature nCSC-specific

DCs and the natural death of activated nCSC-specific

CTCs. Eq. 5 represents the maturation of immature DCs

when they encounter tumor cells with CSC-specific antigens

and the killing of mature CSC-specific DCs. Eq. 6

represents the maturation of immature DCs when they encounter

tumor cells with nCSC-specific antigens and the

killing of mature nCSC-specific DCs. Eq. 7 represents the

elimination of chemotherapy from the body. Host- specific

parameters will be in the figure captions in the results section.

These host-specific parameters vary based on patient

and type of cancer.

3. Model 1: Tumor Inoculation with CSCs and nCSCs

In Model 1, the effect of tumor inoculation with CSCs

and nCSCs on tumor size was studied. The tumor was inoculated

with 50,000 CSCs or 50,000 nCSCs on Day 0. All

cell populations were set to 0 in STELLA except for the cell

population that was inoculated. The simulation in STEL-

LA started on Day 12 (the time at which cancer cells would

take root in a host) and ended on Day 25.

4. Model 2: Dendritic Cell Vaccine Before Tumor Inoculation

In Model 2, the effects of a dendritic cell vaccine prior to

tumor inoculation on tumor size were modeled. Dendritic

cell vaccines were given on Day 0, Day 7, and Day 14. The

tumor was inoculated on Day 22. There were four types

of treatments simulated: no DC vaccination, DC vaccination

with CSC-specific DCs and nCSC-specific DCs, DC

vaccination with CSC-specific DCs, and DC vaccination

with nCSC-specific DCs. In all simulations, a population

of 10,000 CSCs and 90,000 nCSCs were used to inoculate

the tumor. On each day the vaccine was given, 1 million

mature DCs were added to the appropriate DC population

for the DC vaccination with CSCs and the vaccination

with nCSCs. For the mixed DC vaccine treatment, each

(6)

(7)

vaccine contained 100,000 CSC- specific DCs and 900,000

nCSC-specific DCs. For the no treatment simulation, the

tumor was only inoculated. The DC vaccines were simulated

in STELLA using the PULSE option. Prior to inoculation,

all cell populations except for the appropriate

mature DC population, if applicable, were set to zero. The

inoculation of the tumor was modeled in STELLA with

PULSE. The simulation was run from Day 0 to Day 49.

5. Model 3: T-Cell Treatment After Tumor Inoculation

In Model 3, the effects of T-Cell treatment after tumor

inoculation on tumor size were modeled. T-Cell treatment

was given on Day 1, Day 8, and Day 15. There were four

types of treatments: no treatment, mixed treatment with

both CSC-specific and nCSC-specific CTCs, treatment

with CSC-specific CTCs, and treatment with nCSC-specific

CTCs. T-cell treatments consisted of 1,000,000 CSCs

or nCSCs. For mixed treatment, treatments consisted of

50,000 CSC-specific CTCs and 950,000 nCSC-specific

CTCs. All cell populations were set to 0 for Day 0. T-Cell

treatments were modeled in STELLA using PULSE. The

inoculation of the tumor on Day 8 was modeled in STEL-

LA using PULSE which allowed for an inflow of 5,000

CSCs and 95,000 nCSCs.

6. Model 4: Modeling the Effectiveness of T- Cell Treatment

and Chemotherapy

In Model 4, there were six treatments modeled: no treatment,

chemotherapy only, CSC-specific T-Cell treatment

only, nCSC-specific T-cell treatment only, combined chemotherapy

and CSC-specific T-cell treatment, and combined

chemotherapy and nCSC-specific T-cell treatment.

The tumor was inoculated on Day 0 with 5,000 CSCs and

95,000 nCSCs. All other populations were set to zero. For

nCSC-specific T-cell treatment and CSC-specific T-cell

treatment, one million activated CTCs were added to the

appropriate population on Day 20 and 27 using PULSE.

For chemotherapy treatment, one injection of 6,000 µg/

mL chemotherapeutic agent was given per day from days

20-24 and 27-31. Chemotherapy was modeled in STEL-

LA using PULSE. For combined chemotherapy and T-cell

treatment, the methods for modeling chemotherapy and

T-cell treatment were used together in the STELLA model.

7. Model 5: Modeling the Effectiveness of DC Vaccines

and Chemotherapy

In Model 5, there were six treatments modeled: no

treatment, chemotherapy only, mature CSC-specific DC

vaccines only, nCSC-specific DC cell vaccines only, combined

chemotherapy and CSC-specific DC vaccines, and

combined chemotherapy and nCSC-specific DC vaccines.


The tumor was inoculated on Day 0 with 5,000 CSCs and

95,000 nCSCs. All other populations were set to zero. For

nCSC-specific DC vaccines and CSC-specific DC vaccines,

one million mature DCs were added to the appropriate

population on Day 20 and 27 using PULSE. For chemotherapy

treatment, one injection of 6,000 µg/mL chemotherapeutic

agent was given per day from days 20-24 and

27-31. Chemotherapy was modeled in STELLA using

PULSE. For combined chemotherapy and DC vaccines,

the methods for modeling chemotherapy and DC vaccines

were used together in the STELLA model.


For all models, the effectiveness of the treatment is

shown in terms of tumor size. Tumor size was calculated

by adding together the CSC population and nCSC population

based on the results of the STELLA model and dividing

by 100,000 using Excel [11]. All plots were created using

Mathematica [22]. The host-specific parameters used

for each model are reported in the figure captions. Refer

to Table 1 for the meaning of each parameter and its value.

both CSCs or nCSCs or only nCSCs. All T-cell treatments

were effective in reducing tumor size when compared to

no treatment.

Figure 2. This figure shows the evolution of tumor

size after dendritic cell vaccines with CSC-specific

DCs and/or nCSC-specific DCs. The host-specific parameters

are α S = 0.6 day-1 , δ S

= 0.2 day -1 , ρ SP

= 0.1 day -1 ,

α P

= 0.2 day -1 , δ P

= 0.14 day -1 , α SP

= 2.0 day -1 , and ρ PS

= 4.4

× 10 −4 day -1 .


size after inoculation with 50,000 CSCs or nCSCs. The

host-specific parameters are α S

= 0.5 day -1 , δ S

= 0.2 day -1 ,

ρ SP

= 0.15 day -1 , α P

= 0.2 day -1 , δ P

= 0.15 day -1 , α SP

= 1.8 day -1 ,

and ρ PS

= 5.3 × 10 −4 day -1 .

Inoculating a tumor with cancer stem cells leads to

significant tumor growth, and inoculating a tumor with

nCSCs leads to a significantly lower and comparatively

negligible tumor growth in the same host-specific conditions

(Fig. 1). These data support the finding that CSCs

drive tumor growth [17].

DC vaccines prior to tumor inoculation are effective in

slowing tumor growth when compared to no treatment

(Fig. 2). The CSC-specific DC vaccine was most effective

in reducing tumor size compared to the mixed DC vaccine

and nCSC DC vaccine. The mixed DC vaccine was the second

most effective in reducing tumor size.

CSC T-Cell treatment is significantly more effective in

reducing tumor size compared to the mixed T-cell treatment,

nCSC T-cell treatment, and no treatment (Fig. 3).

In other words, activated CTCs cultured with mature dendritic

cells that were pulsed with CSCs were more effective

in reducing tumor growth compared to those pulsed with



size after different T-cell treatments and no treatment.

The host-specific parameters are α S

= 0.7, δ S

=

0.19 day -1 , ρ SP

= 0.24 day -1 , α P

= 0.2 day -1 , δ P

= 0.1 day -1 , α SP

=

2.8 day -1 , and ρ PS

= 1.3 × 10 −4 day -1 .


size after T-cell treatments, chemotherapy, and

combined T-cell and chemotherapy treatments. The


= 0.5 day -1 , δ S

= 0.2 day -

1

, ρ SP

= 0.15 day -1 , α P

= 0.2 day -1 , δ P

= 0.15 day -1 , α SP

= 1.8 day-

1

, and ρ PS

= 5.3 × 10 −4 day -1 .

Chemotherapy is a more effective treatment for treating

tumors compared to only CSC T-cell treatment or

nCSC T-cell treatment (Fig. 4). The combined chemotherapy

treatments and T-cell treatments were most effective

in reducing tumor size. CSC T-cell treatment with

chemotherapy was the most effective followed by com-


bined nCSC T-cell treatment with chemotherapy and chemotherapy

only. All treatments had the same tumor size

until Day 20, when the first chemotherapy and/or immunotherapy

treatment was given. All treatments involving

chemotherapy saw an oscillating decrease in tumor size

before an increase in tumor size after the last day of chemotherapy

treatment. All treatments with chemotherapy

had similar tumor size at the end of the simulation which

was significantly less than treatments without chemotherapy.

Treatments that did not involve chemotherapy saw a

significant increase in tumor size, which continued until

the end of the simulation.


size after DC vaccines, chemotherapy, and combined

chemotherapy treatment and DC vaccines. The


= 0.5 day -1 , δ S

= 0.2 day -1 ,

ρ SP

= 0.15 day -1 , α P

= 0.2 day -1 , δ P

= 0.15 day -1 , α SP

= 1.8 day -1 ,

and ρ PS = 5.3 × 10−4 day -1 .

Treatments with chemotherapy, especially those combined

with immunotherapy treatments, are more effective

in reducing tumor size compared to no treatment or only

immunotherapy treatment (Fig. 4 and 5). The combined

treatment with CSC-specific DC Vaccine treatment and

chemotherapy was most effective in reducing tumor size

(Fig. 5). The combined treatment with nCSC-specific DC

vaccine treatment and chemotherapy was the second most

effective treatment for reducing tumor size. However, all

chemotherapy treatments had a very similar tumor size at

the end of the simulation. In addition, the immunotherapy

treatments and no treatment had a very similar tumor size

by the end of the simulation.

All results suggest that immunotherapy treatments

targeting CSCs are the most effective in reducing tumor

growth. After CSC-specific immunotherapy treatment,

T-cell receptors will recognize CSC-specific antigens, bind

to antigen peptides on MHC Class I molecules on CSCs,

and kill the CSCs through the release of cytokines, cytotoxic

granules, or the FasL ligand. CSC-specific dendritic

cell vaccines and T-cell adoptive therapy allow the immune

system to directly target and kill CSCs. In contrast,

nCSC-specific dendritic cell vaccines and T-cell adoptive

therapy are less effective in reducing tumor size, since

cytotoxic T-cells will kill nCSCs and lead to comparably

negligible tumor growth due to lower renewal rate and inability

to differentiate [17] (Fig. 1).

9. Conclusions

In this paper, a computational model was used to

study how dendritic cells and cytotoxic T-cells impact

tumor-immune interaction with CSCs and nCSCs. The

model found that tumor inoculation with CSCs produced

larger tumors compared to tumors inoculated with nCSCs.

CSC-specific DC vaccines are more effective in reducing

tumor growth than mixed or nCSC-specific DC vaccines

(Fig. 2). CSC-specific T-cell treatment was more effective

in reducing tumor growth than mixed or nCSC-specific

T-cell treatment (Fig. 3). Cancer treatments involving

chemotherapy were most effective in reducing tumor

growth compared to immunotherapy treatments alone

(Fig. 4 & 5). However, combined chemotherapy and immunotherapy

treatments (CSC-specific DC vaccines or

CSC-specific T-cell treatment) were most effective in reducing

tumor size. T-cell treatment was more effective in

reducing tumor size after inoculation compared to dendritic

cell vaccines. The CSC-specific T-cell treatment and

chemotherapy treatment was the most effective treatment

overall for reducing tumor growth after inoculation. This

model could be improved to include the role of T-helper

cells in activating cytotoxic T-cells [19] [17]. Mature

dendritic cells present antigens to both cytoxic T-cells

and T-helper cells through MHC Class I and MHC Class

II [17]. T-helper cells give cytotoxic T-cells a costimulatory

signal that is needed for T-cells to become activated

as well as proliferate [17]. The model could add the role of

T-regulatory cells that inhibit T-cell activation and proliferation

[16][17][19]. In the model, exponential growth

was assumed that may not occur in vivo. Some cancer cells

that were inoculated may have been destroyed in vivo prior

to the cancer cells taking root which may be a potential

source of error. In addition, DC cells can lead to death of

CSC and nCSCs but was not considered in the model, since

the effect on the cell populations is considered negligible

compared to cell death caused by CTCs. Tumors are also

believed to affect the maturation of dendritic cells and cytotoxic

T-cells, which was not considered in the model as

the effects are currently unclear. The proposed computational

model could have large implications in studying immunotherapy

effectiveness and combined immunotherapy

and chemotherapy treatments for many types of cancer

including pancreatic cancer, breast cancer, squamous head

and neck cancer, and brain tumors. The host-specific parameters

and chemotherapy treatment parameters can be

modified for a patient or a type of cancer. In addition, the

renewal rate of CSCs and nCSCs can be modified for each

type of cancer.

10. Acknowledgements

The author thanks Mr. Robert Gotwals for his assistance

with this work and the Broad Street Scientific editors.


11. References

[1] Al-Tameemi, M., Chaplain, M., d’Onofrio, A. (2012).

Evasion of tumours from the control of the immune system:

consequences of brief encounters. Biology direct,

7(1), 31.

[2] Castiglione, F., Piccoli, B. (2006). Optimal control in a

model of dendritic cell transfection cancer immunotherapy.

Bulletin of Mathematical Biology, 68(2), 255-274.

[3] Cheng, M., Chen, Y., Xiao, W., Sun, R., Tian, Z.

(2013). NK cell-based immunotherapy for malignant diseases.

Cellular molecular immunology, 10(3), 230.

[4] Dashti, A., Ebrahimi, M., Hadjati, J., Memarnejadian,

A., Moazzeni, S. M. (2016). Dendritic cell based immunotherapy

using tumor stem cells mediates potent antitumor

immune responses. Cancer letters, 374(1), 175-185.

[5] DePillis, L. G., Eladdadi, A., Radunskaya, A. E. (2014).

Modeling cancer-immune responses to therapy. Journal of

pharmacokinetics and pharmacodynamics, 41(5), 461-478.

[6] de Pillis, L. G., Radunskaya, A. (2003). A mathematical

model of immune response to tumor invasion. In Computational

Fluid and Solid Mechanics 2003 (pp. 1661-1668).

Elsevier Science Ltd.

[7] de Pillis, L. G., Radunskaya, A. E., Wiseman, C. L.

(2005). A validated mathematical model of cell-mediated

immune response to tumor growth. Cancer research,

65(17), 7950-7958.

[8] Kirschner, D., Panetta, J. C. (1998). Modeling immunotherapy

of the tumor–immune interaction. Journal of

mathematical biology, 37(3), 235-252.

[9] Kuznetsov, V. A., Makalkin, I. A., Taylor, M. A., Perelson,

A. S. (1994). Nonlinear dynamics of immunogenic

tumors: parameter estimation and global bifurcation analysis.

Bulletin of mathematical biology, 56(2), 295-321.

[10] Mahasa, K. J., Ouifki, R., Eladdadi, A., de Pillis, L.

(2016). Mathematical model of tumor–immune surveillance.

Journal of theoretical biology, 404, 312-330.

[11] Microsoft Excel [Software] Available from https://

products.office.com/en-us/excel.

[12] de Mingo Pulido, A., Ruffell, B. (2016). Immune regulation

of the metastatic process: implications for therapy.

In Advances in cancer research (Vol. 132, pp. 139-163).

Academic Press.

[13] Morisaki, T., Matsumoto, K., Onishi, H., Kuroki,

H., Baba, E., Tasaki, A., Tanaka, M. (2003). Dendritic

cell-based combined immunotherapy with autologous tumor-pulsed

dendritic cell vaccine and activated T cells for

cancer patients: rationale, current progress, and perspectives.

Human cell, 16(4), 175-182.

[14] Ohaegbulam, K. C., Assal, A., Lazar- Molnar, E., Yao,

Y., Zang, X. (2015). Human cancer immunotherapy with

antibodies to the PD-1 and PD-L1 pathway. Trends in

molecular medicine, 21(1), 24-33.

[15] Palucka, K., Banchereau, J. (2012). Cancer immunotherapy

via dendritic cells. Nature Reviews Cancer, 12(4),

265.

[16] Rhodes, A., Hillen, T. (2019). A Mathematical Model

for the Immune-Mediated Theory of Metastasis. bioRxiv,

565531.

[17] Sigal, D., Przedborski, M., Sivaloganathan, D., Kohandel,

M. (2019). Mathematical modelling of cancer stem

cell-targeted immunotherapy. Mathematical Biosciences,

318, 108269.

[18] STELLA Architect (Version 1.6.2) [Software] Available

from https://www.iseesystems.com/store/products/

stellaarchitect.aspx.

[19] Qomlaqi, M., Bahrami, F., Ajami, M., Hajati, J. (2017).

An extended mathematical model of tumor growth and its

interaction with the immune system, to be used for developing

an optimized immunotherapy treatment protocol.

Mathematical biosciences, 292, 1-9.

[20] Vigneron, N. (2015). Human tumor antigens and cancer

immunotherapy. BioMed research international, 2015.

[21] Wilson, S., Levy, D. (2012). A mathematical model

of the enhancement of tumor vaccine efficacy by immunotherapy.

Bulletin of mathematical biology, 74(7), 1485-

1500.

[22] Wolfram Mathematica (12.0 Student Version) [Software]

Available from https://www.wolfram.com/mathematica/.

[23] Yu, J. L., Jang, S. R. J. (2019). A mathematical model

of tumor-immune interactions with an immune checkpoint

inhibitor. Applied Mathematics and Computation,

362, 124523.



THE COSMIC RADIATION SHIELDING PROPERTIES OF

LUNAR REGOLITH

Eleanor Murray

Abstract

Lunar regolith is the most accessible material for use as radiation shielding for human habitation on the Moon. This study

aims to determine the thickness of lunar regolith shielding necessary to protect humans from cosmic radiation and its secondaries,

including neutrons. We measured the percentage of thermal neutrons that passed through prepared LHS-1 Lunar

Highlands Simulant samples of different thicknesses using the Neutron Powder Diffraction Facility at the PULSTAR

Reactor of NC State University, prepared for a neutron transmission experiment. In addition, we modeled an analogue

of the experiment using GEANT4 software. We present the results of the neutron experiments as well as preliminary

results of the necessary thickness at which the radiation dose will drop to safe levels based on GEANT4 simulations of the

interactions of galactic cosmic rays with the lunar regolith-like material.

1. Introduction

As NASA prepares for a Moon landing in 2024 with the

Artemis program, leading towards “sustainable missions

by 2028” [1], the In-Situ Resource Utilization (ISRU) of

lunar regolith will be integral. Particularly on a large scale,

the costs of ISRU will need to be balanced with the potential

costs of transporting materials from Earth. One potential

cost is that of launching radiation shielding materials

to the Moon. However, this cost could be mitigated by using

a material readily available on the Moon as radiation

shielding: the lunar regolith. Eckart found that a 1 meter

layer of lunar regolith would prevent crew members from

receiving more than about 3 cSV of radiation from each

solar particle event [2]. The main types of radiation on the

Moon are galactic cosmic rays (GCR) and solar particle

events (SPE). Galactic cosmic rays are a constant source of

radiation, while solar particle events occur only occasionally

but expose astronauts to a greater momentary amount

of radiation. During an SPE, astronauts can stay in an area

with more shielding, but GCR is harder to protect against

since it is constant [3]. The protons in the SPE are also

“fairly easy to stop, compared to GCR” [4]. More research

is necessary in order to protect astronauts on long-term

missions from the GCR.

2. Lunar Regolith as Radiation Shielding

On Earth, humans are protected from excessive amounts

of solar radiation by Earth’s magnetic field. The Moon’s

magnetic field is much weaker [5], so radiation shielding

will be necessary for human habitation. Eckart [2] writes

that calculations have found layers of lunar regolith 50-100

cm deep to be sufficient in decreasing radiation doses from

the GCR and SPE to acceptable levels [6]. Miller [6] found

that less than 50 cm of lunar regolith should be enough to

stop the most damaging and common galactic cosmic rays

(GCR) as well as protons from solar particle events (SPE).

This study found that lunar regolith, and several simulants

are comparable to aluminum in their radiation-shielding

properties by measuring the dose reduction per unit areal

density [6]. However, this study did not investigate the

impact of neutrons in this measurement. As galactic cosmic

rays impact the lunar surface, they produce secondary

radiation, which includes neutrons. Both the protons from

above and the neutrons from below will pose major risks

to astronauts [7]. A greater understanding of the radiation

shielding properties of lunar regolith is essential to planning

potential lunar habitats that will attenuate radiation

to safe levels.

3. Safe Limits of Radiation

Determing a safe level of radiation is difficult as studies

use varying units. A gray is a measure of how much energy

was absorbed; one gray is equal to one joule per kilogram.

Sieverts are similar but relate to the expected amount of biological

damage; the conversion between grays and sieverts

depends on the type and energy of the radiation. Globus

and Strout surveyed the literature and found that in the

context of orbital space settlements, the general population

should not receive more than 20 millisieverts per year [4].

The World Nuclear Association states that less than 100

millisieverts per year is harmless [8]. In addition, NASA

lists 500 milligray-equivalents as the limit of radiation to

the blood-forming organs of astronauts per year[9]. The

radiation on the moon comes from Solar Particle Events

(SPE), which are occasional but deliver large amounts of

radiation, and Galactic Cosmic Rays (GCR), which are

constant, deliver smaller amounts of radiation, come from

far away galaxies as opposed to the Sun, and are harder

to shield against than SPE. When these charged particles

impact the regolith of the Moon, nuclear reactions occur

and produce free neutrons, adding to the radiation dose

humans would receive [7].

76 | 2019-2020 | Broad Street Scientific PHYSICS

4. Direct Measurements of Lunar Radiation

Previous research, such as the Lunar Exploration Neutron

Detector (LEND), took data from approximately 50

kilometers above the moon instead of at its surface [10].

The LEND examined albedo neutrons, or the neutrons

that are created in the lunar surface and then bounce back

towards space [11], instead of the neutrons that are created

in the regolith and move downwards where they would

contribute to the radiation dose experienced by astronauts.

During the Apollo 17 mission, astronauts successfully

deployed the Lunar Neutron Probe Experiment (LNPE).

This probe consisted of a 2-meter-long rod that was placed

in the lunar regolith to measure the rates of capture of low

energy neutrons at various depths. This experiment is one

of the few measurements of neutrons on the lunar surface.

However, the experiment was designed with low energy

neutrons in mind, while the radiation dose from neutrons

also includes high energy neutrons. The LNPE report also

included only preliminary data; and calibration of the data

had not been completed [12].

These limitations on the available data require GEANT4

simulations as well as an empirical experiment to model

the radiation dose on the Moon as it varies with depth of

lunar regolith.

6. Method 1: Nuclear Reactor

6.1 - Methodology

We investigated the feasibility of using lunar regolith

as shielding against neutrons from the radiation environment

of the Moon. LHS-1 Lunar Highlands Simulant was

used as a proxy for lunar regolith. The simulant was contained

by aluminum boxes (show schematically in Fig. 1)

that were manufactured on the NC School of Science and

Mathematics campus.

Figure 1. Diagram of Aluminum Boxes for Samples

Ten samples that are two inches by two inches across

with varying thicknesses x were prepared. The smallest

thickness was ¼ inch, and the largest thickness was 2 ½

inches, increasing in ¼ inch increments.

5. Lunar Regolith Simulants

Lunar regolith is difficult to obtain, so most research

on lunar regolith involves JSC-1 lunar regolith simulant,

which is produced by NASA. We utilized LHS-1 Lunar

Highlands Simulant since its availability is higher than that

of JSC-1 Lunar Regolith Simulant. However, their percent

compositions are roughly similar, as shown in Table 1.

Table 1. Percent Composition by weight of oxides.

Data compiled from references [17-19].

Oxide Lunar Regolith LHS-1 JSC-1

(Highlands)

Na .6 2.30 2.7

Mg 7.5 11.22 9.01

Al 24.0 26.24 15.02

Si 45.5 44.18 47.71

Ca 15.9 11.62 10.42

Ti .6 .79 1.59

Fe 5.9 3.04 10.79

Since an estimate of the linear stopping power of a compound

can be made by assuming linear stopping power is

additive [13], the linear stopping powers of lunar regolith,

JSC-1 Lunar Regolith Simulant, and LHS-1 Lunar Highlands

Simulant should be roughly equal.

PHYSICS

Figure 2a. Image of Neutron Powder Diffraction Facility.

Image credit: Mr. Scott Lassell [14]

The samples were taken to the NC State PULSTAR reactor,

and were experimented on at the Neutron Powder

Diffraction Facility (shown in Figure 2a) prepared for

a transmission experiment. A diagram of the neutron

transmission experiment carried out on these samples

is shown in Figure 2b. The de Broglie wavelength of the

neutrons was 1.478 angstroms [15], corresponding to a kinetic

energy of approximately 0.03745 electron-volts. The

number of neutrons that hit the detector was measured

both without a target and after passing through the samples.


The attenuation length is defined as the depth of the material

where the intensity of the radiation decreases to 1/e of

the initial intensity [16]. The attenuation length of the LHS-

1 lunar regolith simulant is approximately 6.51 g/cm 2 . The

mass attenuation coefficient is equivalent to the chance that

any one neutron will interact per centimeter divided by the

density of the material [13]. In this case, the mass attenuation

coefficient is 0.15 cm 2 /g.

Figure 2b. Diagram of Neutron Transmission Experiment

6.2 - Results

The equation (N/N 0

)=e -b*t was used to analyze the data,

where N is the number of neutrons that passed through the

sample in one minute, N 0

is the intensity of neutrons in the

initial beam in one minute, b is a constant that represents

the probability any one neutron will interact per centimeter,

and t is the thickness of the lunar regolith simulant sample

in centimeters. N=3168e (-.18*t) was found to fit the data, so for

any one neutron, there is an 18% chance that it will interact

per centimeter of lunar regolith simulant. Figure 3 shows

the thickness of lunar regolith simulant in centimeters on the

x-axis and the neutron fluence over one minute per cm 2 on

the y-axis.

7. Method 2: GEANT4 Simulations

7.1 - Methodology

GEANT4 simulation software was run in batch mode for

each thickness, and a short Python program was used to analyze

the output to count the number of neutrons that passed

through the material. Figure 5 below shows a side view of

this scenario, where the blue rectangle represents the material

defined to be similar by percent composition to LHS-1 Lunar

Highlands Simulant, the gray bar is “Shape 2,” the green tracks

show the neutrons, and the neutrons start from the middle

of the left side of the material. Only 100 neutrons are shown

for clarity. Neutrons were counted as having passed through

the material if they hit Shape 2, which was a thin cylinder

modeled as bone with a diameter of 1.27 cm, centered at the

position of the point source and thickness x centimeters from

the point source.

Figure 3. Neutron Fluence vs. Thickness of Sample

The data were also linearized to give the equation

y=-.18*t+ln(N 0

) where y=ln(N). The slope of this graph (Fig.

4) again shows that there is an 18% chance that any one neutron

will interact per centimeter of lunar regolith simulant.

Figure 4. ln(Neutron Fluence) per cm 2 vs. Thickness of

Sample

Figure 5. GEANT4 Simulation of Neutrons Passing

Through the Material.

7.2 - Results

Figure 6 shows the number of neutrons per centimeter

squared that passed through the material vs. the thickness

of the material in centimeters.

According to the equation, (N/N 0

)=e -bt , there is approximately

a 12% chance per centimeter that any particular

neutron will interact with the material. The attenuation

length is 9.8 g/cm 2 , and the mass attenuation coefficient is

0.10 cm 2 /g. Figure 7 shows the same data, but linearized,

so that y=-bt+ln(N 0

), and y=ln(N). Here, b equals approximately

0.12, so there is a 12% chance per centimeter that

a neutron will interact with the lunar regolith simulant.


Figure 6. Number of Neutrons/cm 2 vs. Thickness of

Material.

discrepancy is the differences in geometry of the two experiments;

in the nuclear reactor experiment, the neutron

detector was 135 centimeters [14] away from the neutron

source, while in the GEANT4 experiment, the detector was

set right behind the sample with the neutron source set at

the front of the sample. Therefore in the nuclear reactor

experiment, the distance between the neutron source and

detector was always 135 centimeters, while in GEANT4,

the distance between the neutron source and detector

varied between 0.635 centimeters and 6.35 centimeters in

0.635 centimeter increments. Additionally, the aperture of

the collimated neutron beam at the reactor had an internal

diameter of 1.3 centimeters [14], while in GEANT4, the

neutrons came from a collimated point source. Other possible

sources of the discrepancy are the slightly different

percent compositions between the LHS-1 Lunar Highlands

Simulant used in the nuclear reactor experiment and the

material used in the GEANT4 experiment. In the future,

more simulations can be done with protons simulating the

GCR, including neutron secondaries, to find the thickness

of lunar regolith at which the radiation dose drops to safe

levels.

9. Conclusion

Figure 7. ln(Number of Neutrons) vs. Thickness of

Material.

The percent difference between the value of b, or the

percent chance that any one neutron will interact per

centimeter, found with the nuclear reactor (18%) and the

value found with GEANT4 simulations (12%) is 40%. The

percent difference in the attenuation lengths is 36%, and

the percent difference in the mass attenuation coefficients

is 40.%. A possible source of this error is the differences

in the geometry of the detectors; the neutron detector at

the nuclear reactor is set approximately 1 meter behind the

sample while the detector in GEANT4 is set directly behind

the sample.

8. Discussion

Based on the empirical method described earlier, about

6.5 g/cm 2 is the attenuation length of 0.03745 eV neutrons

in LHS-1 Lunar Highlands Simulant. For any one neutron,

there is about an 18% chance that it will interact with the

simulant per centimeter. However, a similar experiment

modeled in GEANT4 found a 12% chance per centimeter

that any one neutron will interact with the simulant, and

an attenuation length of 9.8 g/cm 2 . The percent difference

between the chances each neutron will interact per centimeter

is 40%, and the percent difference between the attenuation

lengths is 36%. A possible explanation for the

PHYSICS

Thermal neutrons have an 18% chance per centimeter

of interacting with LHS-1 Lunar Highlands Simulant, with

an attenuation length of 6.51 g/cm 2 and a mass attenuation

coefficient of .15 cm 2 /g. The radiation shielding properties

of lunar regolith should be similar to LHS-1 Lunar Highlands

Simulant since their percent compositions are similar.

This information is useful to the designs of prospective

lunar habitats. Simulations in GEANT4 show reasonable

agreement with the thermal neutron transmission experiment.

However, thermal neutrons are not a great model

for cosmic radiation, so GEANT4 will be used to model the

interactions of lunar regolith with the full radiation environment

of the Moon in order to determine the thickness

of lunar regolith at which the radiation dose falls to safe

levels for long-term human habitation.

10. Acknowledgements

The author would like to thank their mentor, Dr. Jonathan

Bennett, the physics chair at the North Carolina

School of Science and Mathematics (NCSSM), Mr. Benjamin

Wu for his assistance with coding a program for data

analysis, as well as Ms. Bec Conrad, the manager of the

Fabrication and Innovation Laboratory at NCSSM, and

the NCSSM Foundation. Additionally, the author would

like to thank Dr. Quinsheng Cai and Mr. Scott Lassell

for their assistance in utilizing the PULSTAR Reactor at

North Carolina State University, the developers and forum

of GEANT4, as well as the Center for Lunar and Asteroid

Surface Science (CLASS) Exolith Lab for providing the


LHS-1 Lunar Highlands Simulant used in this study.

11. References

[1] “What is Artemis?” NASA.gov, https://www.nasa.

gov/what-is-artemis. Accessed 28 Oct. 2019.

[2] Eckart, Peter. The Lunar Base Handbook. The Mc-

Graw-Hill Companies, Inc., 1999

[3] Blanchett, Amy and Laurie Abadie. “Space Radiation

Won’t Stop NASA’s Human Exploration.” NASA.gov, 12

Oct. 2017, https://www.nasa.gov/feature/space-radiation-won-t-stop-nasa-s-human-exploration

[4] Globus, Al and Joe Strout. “Orbital Space Settlement

Radiation Shielding”. National Space Society, April 2017.

[5] Halekas, Jasper S. and Robert P. Lin. “Determining Lunar

Crustal Magnetic Fields and Their Origin”. Lunar and

Planetary Institute, https://www.lpi.usra.edu/meetings/

LEA/presentations/tues_pm/2_Halekas_Lunar_Crustal_

Magneti_Breakout1.pdf. Accessed 27 Oct. 2019

[13] Knoll, Glenn F. Radiation Detection and Measurement,

page 41. 3rd. ed., John Wiley and Sons, Inc., 2000

[14] Lassell, Scott. Personal interview. 23 Oct. 2019

[15] “Neutron Powder Diffraction Facility.” NCSU.

edu, https://www.ne.ncsu.edu/nrp/user-facilities/neutron-diffraction-facility/.

[16] X-Ray Attenuation Length. http://henke.lbl.gov/optical_constants/atten2.html.

Accessed 4 Aug. 2019

[17] “LHS-1 Lunar Highlands Simulant Fact Sheet.” UCF.

edu, Mar. 2019, http://sciences.ucf.edu/class/wp-content/

uploads/sites/58/2019/02/Spec_LHS-1.pdf

[18] McKay, David S. et al. “JSC-1: A New Lunar Soil Simulant.”

USRA.edu, 1994, https://www.lpi.usra.edu/lunar/

strategies/jsc_lunar_simulant.pdf

[19] Turkevich, A. L. “The average chemical composition

of the lunar surface.” NASA Astrophysics Data System,

http://adsabs.harvard.edu/full/1973LPSC....4.1159T. Accessed

17 May 2019.

[6] Miller J., et al. 2009. “Lunar soil as shielding against

space radiation”, page 167. Radiation Measurements

[7] Barry, Patrick L. “Radioactive Moon.” NASA.gov, 8

Sept. 2005, https://science.nasa.gov/science-news/science-at-nasa/2005/08sep_radioactivemoon

[8] “What is Radiation?” World Nuclear Association,

https://www.world-nuclear.org/information-library/safety-and-security/radiation-and-health/radiation-and-life.aspx.

Accessed 13 Aug. 2019.

[9] NASA Space Flight Human-System Standard: Volume

1, Revision A: Crew Health. NASA.gov, pp. 22, 30 July

2014. https://standards.nasa.gov/standard/nasa/nasa-std-

3001-vol-1. Accessed 22 Oct. 2019

[10] “Building the LRO Spacecraft.” NASA, https://lunar.

gsfc.nasa.gov/spacecraft.html. Accessed 3 Oct. 2019

[11] Litvak, M. L. et al. “Global maps of lunar neutron

fluxes from the LEND instrument.” Journal of Geophysical

Research, vol. 117, June 2012, pp. 1-2, https://www.

researchgate.net/publication/235979801_Global_maps_

of_lunar_neutron_fluxes_from_the_LEND_instrument.

Accessed 28 Oct. 2019

[12] Woolum, Dorothy S. et al. “The Apollo 17 Lunar

Neutron Probe Experiment.” Astrophysics Data System,

http://adsabs.harvard.edu/full/1973LPI.....4..793W. Accessed

28 Oct. 2019


MODELING THE GRAVITATIONAL LENS OF THE

EINSTEIN RING MG1131+0456

Nathaniel S. Woodward

Abstract

We present the results of modeling the distribution of mass in the elliptical lensing galaxy of the Einstein ring MG1131+0456

using numerical and computational modeling methods. We applied two mass models of varying complexity to this system:

a point mass and a singular isothermal sphere. In constructing these models, we relied on image construction through

ray-tracing. Results from the ray tracing method qualitatively agree with both the literature and the observed radio imaging

of MG1131. Total mass derived from the ray tracing model was within the range predicted by previous models. Current

work aims to extend the ray-tracing model to include a singular isothermal ellipsoid as well as applying this model to

gravitational lenses that have not been analyzed.

1. Introduction

Gravitational lensing is a phenomenon predicted by

Einstein’s general theory of relativity where the path of

light is determined based on the curvature of the spacetime.

Gravitational lensing is a powerful tool when applied

to problems in modern physics and has allowed physicists

to measure universal constants such as the Hubble constant

and the deceleration parameter at a high degree of

accuracy, which quantify the expansion and deceleration

of the expansion of the universe, respectively [1][2]. Gravitational

lensing has also allowed physicists to determine

distributions of dark matter in galaxies and throughout the

universe and construct more accurate models of galaxies

[3]. In recent years, gravitational lensing analysis has been

conducted primarily through the comparison of observation

studies and computational studies that rely on mass

models to represent the lensing object. In this paper we

will examine the gravitational lens MG1131+0456 and

aim to calculate the mass distribution and total mass. We

will compare our results directly to those of Chen et al. to

verify our model [4]. After testing our model against the

literature, we hope to determining the total mass and mass

distribution of systems that have not been studied.

1.1 – MG1131+0456 as a gravitational lens

MG1131+0456 is an extensively studied gravitationally

lensed system and because of this we can characterize it

based on the previous literature. The literature suggests

that the source of MG1131+0456 is a distant quasar within

the range of 3500 to 4200 Mpc [6]. The radiation emitted

by this quasar is believed to be lensed by a massive elliptical

galaxy and the resulting images have been analyzed in

the infrared and radio wave wavelength [5][6]. The exact

orientation of this galaxy has been debated in the literature

and because of the uncertainty, we will work to examine

a variety of orientations in our model. Figure 1 displays

MG1131+0456 in the radio wavelength as recorded in the

CfA-Arizona Space Telescope LEns Survey of gravitational

lenses (CASTLeS) [7]. The image of MG1131+0456 in

the radio wavelength expresses two distinct features: a near

complete Einstein ring and two distinct compact images.

Previous studies have utilized these features to suggest the

source image may be composed of two compact components

[5]. One of these components is collinear with the

lens and the observer and is lensed into an Einstein ring.

The other compact component is slightly offset from the

line connecting the observer and lens and as a result, is

lensed into the two compact multiple images. We will conduct

our gravitational lensing analysis under the assumed

geometry of the source where the quasar is composed of

two distinct compact components.

Figure 1. The gravitational lens MG1131+0456 in radio

imaging. Credit: Hewitt et al. 1988. [5]

1.2 – Distance Values

The distance to the source has been debated throughout

PHYSICS


the literature, but based on redshift data, we can construct

a range of possible distances. Work by Tonry et al. estimated

a source redshift ranging from z = 1.9 to z = 5 [6].

We assumed the predictions by Tonry et al. as bounds for

all possible redshifts for the source quasar. We first solve

the equation for recession velocity v from the relativistic

Doppler effect, shown in Eq. 1 where γ is the Lorentz factor

and z is the redshift.

From the range of redshifts, we use the source redshift

to calculate a recession velocity using Eq. (2).

(1)

(2)

After calculating a recession velocity, we can apply

Hubble’s law in Eq. 3 with a Hubble constant value of

69.8 km/s/Mpc from the results of Freedman et al. [8]

to determine the distance to the source.

(3)

Using Eq. 2 and Eq. 3, we computed a source distance

in the range of 3500 to 4200 Mpc. While the redshift of

the source is debated, the redshift of the lensing object

is agreed upon as 0.84 [6]. Using the same procedures,

we calculated a distance to the lens of 2400 Mpc [6]. We

will refer to the distance to the source and the distance

to the lens as D s and D l respectively.

Cartesian everywhere except the lensing plane, we assumed

that D s − D l = D ls . As shown in Figure 2, under

the thin lens approximation, this assumption holds.


2.1 – Preliminary Point Mass Model

Determining a mass model for a lensing object is an integral

part of the lensing analysis. First we conducted an

analysis modeling the lensing galaxy as a point mass. The

aim of this analysis was simply to validate that our distance

values from the literature for the lensing system were approximately

correct. In creating our point mass model for

the lensing galaxy, we set all distance values equal to the

values determined from the literature.

Examining the data from Hewitt et al. [5], shown in

Figure 1, we designated the two compact components as

multiple images and computed the Einstein ring radius.

The Einstein ring angular radius is a characteristic of a

lensing object and can be calculated for a point mass model

by Eq. 4, where θ E

is the Einstein ring angular radius and

θ+ and θ− are angular deviation of the positive and negative

multiple images respectively [9]:

The simplicity of the point mass model allowed us to

iterate through possible distances within the range of 3482

Mpc to 4183 Mpc and calculate a system mass using Equation

5:

(4)

(5)

Under the assumption that D s − D l = D ls , we can

reduce Eq. (5) to;

(6)

Figure 2. We can see that under the thin lens approximation

where the lensing object is represented

as a plane, our distance approximation holds. In

addition to displaying distance values, this diagram

also shows the angular values that describe a lensing

system, where θ is the angle of observance, β is the

angular deviance of the source from the optical axis,

andˆα and α are the deflection vector and reduced

deflection angle, respectively. Credit: [9]

We also define a variable D ls that represents the distance

between the lens and the source. Under the thin

lens approximation, which assumes spacetime to be

By nature of the point mass model, there exists a possible

mass such that at every point within the source

distance range an Einstein ring and multiple images

would form to match the data. This yields a range of

possible masses calculated from the bounds of the distance

range. With a known Einstein ring angular radius

and lens distance, we are left one free parameter of the

source distance. We calculate the total mass of our point

mass model using Eq. 6 by iterating through source distances

derived from Tonray et al.’s redshift predictions.

Our analysis predicted a mass in the range of 2.9 * 10 41

kg to 4.0 * 10 41 kg. A ray tracing diagram of our point

mass model is shown in Figure 3 which displays the distance

range and shows light rays emitted from two distinct

compact components. Our predicted values from

the point mass model are within ≈ 20% compared to the

result of 5.1 * 10 41 kg predicted by Chen et. al. [4].


Figure 3. The ray tracing diagram above displays

how the literature suggests the lensing system

MG1131+0456 is composed. There are two luminous

points of the source quasar representing the compact

components of the source. Rays of light travel from

these points to the lensing plane and some of these

rays are deflected to reach the observer. The diagram

also displays the minimum and maximum distance

values for the source quasar which are labeled ”Min”

and ”Max.” Dashed lines correspond to locations of

virtual images for the multiple images.

2.2 – SIS Mass Model

After completing our rudimentary analysis using the

point mass model, we extended our model to describe an

SIS mass distribution. A Singular Isothermal Sphere model,

or SIS, is a simple, but effective model for gravitational

lensing systems. An SIS model represents a galaxy as a

sphere of gas in hydrostatic equilibrium[9]. In this model,

the motions of particles in the spherical gas cloud in

hydrostatic equilibrium are analogous to the orbital motion

of objects in the galaxy. Equations that describe an

SIS model are derived directly from the ideal gas law and

the equation of hydrostatic equilibrium [9]. The SIS model

and its deflection of light rays are shown in Figure 4.

The SIS mass model allows us to include a non-uniform

mass distribution which is fundamental to an accurate approximation

of a galaxy. Every parameter of the SIS mass

model depends on the velocity dispersion or σ. The velocity

dispersion is analogous to the temperature of a cloud of

particles in hydrostatic equilibrium, but for the motion of

planets and stars in a galaxy, it describes the characteristic

speed of masses in motion [9]. The velocity dispersion can

be treated as a free parameter, but often is estimated to

225 km/s. With the velocity dispersion known or parameterized,

we can calculate properties of an SIS mass model

using the equations [9]:

(7)

(8)

The mass within radius R or M(R) is a function of the

impact radius R, which is the radial distance to the center

of lens from the point a ray of light impacts the lensing

plane, and grows without bound as R approaches infinity

(shown in Eq. 7). The mass density within a radius R or

Σ(R) is inversely proportional to R and therefore predicts a

singularity of infinite density as R approaches zero (shown

in Eq. 8). By limiting the upper bound of R to some radial

extent, we ensure that the total mass is finite, but a finite

core density can only be achieve through extending to a

more complex mass model. A key characteristic of the SIS

model is shown in Eq. 10, a deflection angle independent

of R.

(10)

Figure 4. A source object of varying brightness is

shown to be emitting light which impacts the lensing

plane at arbitrary points. We can see that the

lens plane contains a spherical lensing object which

deflects the incident rays of light. Some of the deflected

rays of light impact the observer and become

images of the source.

2.3 – Ray Tracing SIS Model

We chose to devise a new method to characterize our

lensing system by tracking the path of individual light

rays as they impacted the lensing plane. We predict an

image based off the light rays calculated to impact the

observer. In creating this model, we chose to work primarily

with the SIS model as it reduces the complexity

of the ray tracing model and is shown to be a reasonable

model for an elliptical galaxy. The SIS lowers complexity

because it is spherically symmetric, so to compute

light deflection we only require the impact radius.

First we defined a source matrix where every cell

has an assigned unitless brightness value. We then convert

these brightness values into pixels by converting

all brightness values into percent maximum brightnes.

The source matrix that we use to construct the source

plane and the corresponding source plane are shown in

Figure 5.

(9)

PHYSICS


lens (x l , y l , z l ). We defined z s = −D ls and z l = 0. We use

these two points and the center of lens (0, 0, 0) to define

the plane that will contain the entire path of our deflected

light ray. We will first rotate about the z-axis such that the

impact point (x l , y l , z l ) has an x-component of zero (rotating

the y-axis to the impact point).

(11)

Figure 5. We initially define the source numerically

with a matrix having cell values corresponding to

point sources of light. We determine the brightness

of each point source of light based on the ratio to the

maximum brightness in the source matrix.

Similar to our construction of the source plane, we define

a lensing plane with coordinates (x l

, y l

) to represent

our SIS and use the origin of our lensing plane as one of

the two points to define the optical axis (with the other

point being the observer). To model light rays traveling in

all directions from the source, for every luminous point on

the source plane, we extended a light ray to every possible

point on the lensing plane. We can determine the incident

light ray directly from its lens plane and source plan coordinates

using simple vector algebra.

To allow light rays to travel between the source plane

and the lensing plane, we extended our coordinate system

to a three dimensional space. We assign each plane in

three-dimensional space a constant z-coordinate. This assumes

that our lens and image planes are parallel. We hope

to incorporate tilt in the source plane in further work.

In designating the z-coordinates of our planes, we again

based our distance values in the literature. The distance to

the lens was found to be 2400 Mpc and, as stated in our discussion

of the point mass model, the distance to the source

is a variable range. We chose the lower bound of the range,

3500 Mpc, initially to work with for our distance to the

source. With these values from the literature and the use

of our conversion function, we successfully combined our

source and image planes into one three dimensional space.

2.3.1 – Coordinate Manipulation Vector Verification

After creating the shell of our lensing model, we implemented

a method to verify if a light ray impacted the

observer by calculating a deflected vector to represent the

light ray and determining if this deflected ray impacted the

observer. While this method is computationally intensive,

it maintains the needed level of complexity to describe the

system.

First, we aimed to define a plane that contains the entire

path of the light ray, thus reducing the dimensions by one.

We achieved this through a series of coordinate rotations.

As previously stated, we can define the path of light between

the source point (x s , y s , z s ) and impact point on the

Next, we perform another coordinate rotation to

rotate the source position on to the y-axis, therefore allowing

the entire path of our light ray to be described

the only the y and z components. We will rotate relative

to the y-axis at an angle θ z . This rotation is shown in

Figure 6b.

(a) z-axis rotation

(12)

(b) y-axis rotation

Figure 6. (a) We extend a ray of light from an arbitrary

point on the source plane to an arbitrary point

on the lens plane. We then rotate the coordinate

system about the z-axis by an angle θ y to align the

impact location with the y-axis. (b) Viewing the system

from top-down, we rotate the coordinate system

about the y-axis such that the source location also

has an x-component of zero. Having both the source

and the impact vectors with a zero x-component vector

allows us to define the incident path of light in

two dimensions.

After these two coordinate transformations, we can reduce

our three-dimensional vectors for the source location

and lens location on the lensing plane to two dimensional

vectors with components (z, y). Our lens and source vectors

can be represented respectively as:


We can determine the components of a ray starting at the

source and pointing to the impact point on the lens by subtracting

the source vector from the impact vector. Therefore

our incident vector I is,

Because under the SIS model, the deflection angle α is

constant, we can define a deflection matrix G which will

represent our lensing galaxy. Matrix G acts as a rotation

matrix and deflects the incident vector byˆα. This matrix

is shown below and the vector deflection is represented in

Figure 7.

deflected vector is parallel to the ID vector then it will impact

the observer and an image will be formed. To check if

the deflected vector is parallel to the ID vector, we verify

that the ratio between the y and z components are equal

for both the deflected and ID vector. We recognized the

limitations of a matrix representation for both the source

image and lens plane and incorporated an allowed variance

in the y-component. The best value for allowed deviance is

still being determined.

After determining that an image would be formed, we

increase the brightness value at (x l

, y l

) by the source image

brightness at the point S(x , y s

). We repeat this process for

s

all luminous points on the source plane, thus checking for

image formation for every possible ray of light from the

source.

So far, we have worked under the assumption that the

center of our source plane lies on the optical axis. The deviance

of a source image from the optical axis is referred

to as β and is shown as an angular value in Figure 2. The

literature suggests that the source of MG1131+0456 is

composed of two distinct compact components: one on the

optical axis which is lensed into an Einstein ring and one

that deviates from the optical axis and is multiply imaged

[5]. Assuming that the images shown in Figure 1 are in

fact multiply imaged, we can calculate an angular deviance

using SIS lensing equations [9].

(13)

(14)

Figure 7. Having reduced the path of light to a two

dimensional plane, we apply the deflection matrix

G(α) to deflect the light ray.

To reduce needless computation, before calculating the

deflection vector, we check if the observer exists within an

allowed variance from the plane that describes the path of

the ray of the light. We know that the path of the incident

ray and the deflected ray are entirely contained in the plane

defined by the source, the center of the lensing object, and

the impact point on the lensing object. The observer is not

in this plane or within a small range, then it is not possible

for the light ray to be seen. The best value for allowed deviance

is still being determined. If the observer is in the plane

of the ray’s path, then in order to calculate the deflection

vector, we we must multiply the deflection matrix and the

incident vector:

To verify if the deflected vector impacts the observer, we

define an ID vector that starts at the impact location on

the SIS and points to the observer. We know that if the

Through summing these equations, we can solve for Beta

to be:

(15)

Using the data from Hewitt et al. we applied Eq. (15) to

calculate a β value of 2.8x10 −6 radians [5]. From an angular

deviance, we use simple trigonometry to calculate linear

deviance in the source plane using the relation, where D β is

deviance vector in the source plane:

(16)

Having calculated a deviance in the source plane, we recognize

that the SIS model is axially symmetric and any

direction of deviance will be equivalent in its effect. We

designate an arbitrary direction of deviance relative to the

x-axis of π/4 radians and with this arbitrary direction, we

can determine the x and y components of deviance. These

components are shown in Eq. 17 using the small angle approximation

for tan(θ) where θ D

is the angle of deviance

and D x

and D y

are the x and y components of deviance respectively:

and (17)

PHYSICS


With x and y components of our deviance, we can

simply add our respective components to all x and y coordinates

of images in the source plane and thus shift all

luminous points by the deviance. This process is shown in

Figure 8.

we designate the velocity dispersion of our SIS model as a

free parameter. We require the velocity dispersion to be a

free parameter because it is one of the largest contributors

of both the total mass and mass density, both of which are

values we aim to calculate. We have shown through Eq.

(7) that the SIS total mass equation requires a radial limit

and because of this we allow the radial limit of the lensing

SIS to vary within the range of 10 5 pc to 2x10 6 pc. We have

calculated the angular deviance between the two source

components to be θ D

= 2.8x10 −6 radians (Fig. 8).

2.6 — Calculating Variance

Having defined free parameters, before predicting results,

we must construct a method to quantify the goodness

of fit for our predicted image. We recognize that the

source data are pixelated just as our predicted data, but

their respective resolutions are not necessarily the same

(Fig. 9). We aim to reduce each resolution to a 100x100

image such that we can directly compare individual pixel

values to calculate a source variance.

Figure 8. In the figure, we can see that the source is

originally aligned with the optical axis. We allow

for a source offset from the optical axis by shifting

the source plane with a deviance vector D which we

can decompose into its components D x and D y . With

these components, we can transfer every coordinate

in the aligned source plane to the deviated source

plane by adding D x and D y to the x and y coordinates,

respectively.

2.4 — Modeling the Source Quasar

To begin testing our lensing model, we must construct

an accurate representation for our source. The literature

suggests that the source is likely a distant quasar with two

extended compact components embedded in a galaxy [10].

Our initial method of modeling assumes a symmetric distribution

of matter in the quasar. However this representation

resulted in a needless increase in computational load

compared to the results of the point source model. We determined

that a point source model for the source quasar

served as the most effective model.

2.5 — Free Parameters of the Ray Tracing Model

In order to apply our model to MG1131+0456, we must

designate free parameters. From previous work by Tonry

et al. [6], there is a firm lower bound at redshift z s

= 1.9

and an estimated upper bound at redshift z s

= 5.0. Previous

findings suggest the source distance as a viable free

parameter as it is uncertain and constrained. Additionally,

Figure 9. We can reduce a pixelated portion of an image

to one individual pixel with a brightness equal

to the average of all of the preexisting pixels. In this

diagram, we reduce an image with a pixel ratio of 4:1,

as apparent because we average the brightness values

of a 4x4 square of pixels.

First, we determine the ratio of pixels between the current

image and the ideal 100x100 image. For example, if

our predicted image was 400x400, we would have a pixel

ratio of 4:1 from the predicted to the ideal. Using this, we

average every 4x4 square of pixels, which do not overlap,

in the predicted image to form one pixel in the reduced image.

This process is shown in Figure 9. Additionally, background

noise is apparent throughout the image which adds

inherent variation when compared to our computational

model. To resolve background noise, we apply a lower

bound to pixel brightness which filters out noise while retaining

the structure of MG1131, shown in Figure 10a.

3. Results

The best fit model for MG1131 using the ray tracing

model resulted in a velocity dispersion of 255 km/s,


(a) Reduced Astronomical Data (b) Best Fit Model (c) Overlap Image

Figure 10: (a) Using the methods previously described, we have reduced the radio image of MG1131 to a

100x100 pixelated image where pixel brightness again correlates to percent maximum brightness. This

image allows for direct pixel to pixel comparisons between the ray tracing model and the data. (b) Using the

ray tracing model we determined the best fit model compared to the reduced and noise-filtered radio imaging

of MG1131 using parameters of source distance, velocity dispersion, radial extent of the lensing SIS, and

the angular deviance of the source components. (c) By overlapping the best fit image and the observed data

of MG1131 we better understand the limitations of the ray tracing model. The observed data for MG1131 are

shown in red, the computed image is shown in green, and all overlap is yellow. We are unable to represent

the ellipticity that the observed image contains which significantly limits the accuracy of our model. Additionally,

the best fit model does include multiple images, but they are not aligned with the multiple images

present in MG1131.

a source distance of 3482 Mpc, a β deviance of 2.8x10 −6

arcseconds, and a lensing radius of 12500 pc. This simulation

was run using a SIS with a radial extent of 25000

pc. Using these parameters and Eq. (7) we calculate a total

mass of the lensing galaxy to be 6.98x10 11 M sun

. Comparing

our results to the work of Chen et al. [4] which predicted

values in the range of 1.17x10 12 M sun

to 2.57x10 11 M sun

, our

best fit model is within this range. Our predicted mass using

the ray tracing model varied by 46% and 92% compared

to the least and most massive models presented by Chen et

al. However, compared to the average of the values predicted

by Chen et al., 7.14x10 11 M sun

, there is a percent differnce

of 2.2%. The best fit model is shown in Figure 10.

4. Discussion and Future Work

The best fit model using the ray tracing method qualitatively

fits the radio image of MG1131 having an Einstein

ring and two distinct multiple images of the offset source.

However, due to the limitations of the SIS model when

applied to an elliptical lens and the assumption of point

sources, we cannot represent all features of MG1131 in

this model. While the ray tracing model cannot reflect the

complex optical strucutre of MG131, our results are within

the range of values predicted by Chen et al. [4] which suggest

that our model may be able to predict a realistic mass

for complex gravitational lenses.

In future work, we aim to further apply the ray tracing

model to previously analyzed systems to test its mass predictions

against those of the literature. We are first examining

the system B2045 +265 shown in Figure 11.

Previous work by Fassnacht et al. concluded that B2045

is "a radio galaxy lensing a radio-loud quasar" [11]. We

have shown through the comparing mass predictions for

MG1131 with those of Chen et al. [4] that the ray tracing

method is viable for modeling strong lensing events

by galaxies. The similar structure of B2045 leaves it as an

optimal candidate to retest the ray tracing model’s accuracy

in mass predictions. Following the literature, we plan to

model B2045 with an SIS model and a source represented

as multiple point sources [11].

5. Conclusion

The ray tracing model has shown to be a viable method

for computationally modeling gravitational lenses but has

several limitations at high levels of complexity. We have

shown that regardless of these complexity limitations, the

ray tracing model predicts a realistic mass model for the

gravitational lens MG1131 through comparing our results

to the literature [4]. Future work aims to extend the ray

tracing model to include a singular isothermal ellipsoid as

the mass model to allow for ellipticity in lensed images as

well as applying the ray tracing model to B2045 as well as

unanalyzed systems.

PHYSICS


org/10.1086/175857.

[5] Hewitt, J. N., et al. “Unusual radio source MG1131

0456: a possible Einstein ring”. In: Nature 333.6173 (1988),

pp. 537–540. doi: 10.1038/333537a0.

[6] Tonry, John L. and Kochanek, Christopher S. “Redshifts

of the Gravitational Lenses MG 1131+0456

and B1938+666”. In: The Astronomical Journal 119.3

(Mar. 2000), pp. 1078–1082. issn: 0004-6256.doi:

10.1086/301273. url: http://dx.doi.org/10.1086/301273.

[7] C.S. Kochanek, E.E. Falco, C. Impey, J. Lehar, B. Mc-

Leod, H.-W. Rix. “CASTLES”.

Figure 11: Images of B2045 from the CASTLeS lensing

database and aim to construct a rudimentary model

of the system using the ray tracing model. Credit: [7]

[11]

6. Acknowledgements

I would like to thank Dr. Jonathan Bennett from The

North Carolina School of Science and Mathematics for

guidance throughout the research process as well as suggesting

the field of gravitational lensing. Additionally, Dr.

Bennett’s advice was crucial in designing the ray tracing

method. I would also like to thank The North Carolina

School of Science and Mathematics and The North Carolina

School of Science and Mathematics Foundation for providing

me with this opportunity and the funding necessary

to produce this work.

[8] Freedman, Wendy L., et al. “The Carnegie-Chicago

Hubble Program. VIII. An Independent Determination of

the Hubble Constant Based on the Tip of the Red Giant

Branch”. In: The Astrophysical Journal 882.1 (Aug. 2019),

p. 34. issn: 1538-4357. doi: 10.3847/1538- 4357/ab2f73.

url: http://dx.doi.org/10.3847/1538-4357/ab2f73.

[9] Congdon, Arthur B. and Keeton, Charles. Principles of

Gravitational Lensing. Springer International Publishing,

2018. isbn: 978-3-030-02122-1. doi: 10.1007/978-3-030-

02122-1.

[10] Kochanek, C. S., et al. “The Infrared Einstein Ring in

the Gravitational Lens MG J1131 0456 and the Death of

the Dusty Lens Hypothesis”. In: The Astrophysical Journal

535.2 (2000), pp. 692–705. doi: 10.1086/308852.

[11] Fassnacht, C. D., et al. “B2045 265: A New Four-Image

Gravitational Lens from CLASS”. In: The Astronomical

Journal 117.2 (1999), pp. 658–670. doi: 10.1086/300724.

7. References

[1] Deceleration Parameter: COSMOS. url: http://astronomy.swin.edu.au/cosmos/D/Deceleration%20Parameter.

[2] Huchra, John P. url: https://www.cfa.harvard.edu/~dfabricant/huchra/hubble/.

[3] Blandford, R. “Cosmological Applications of Gravitational

Lensing”. In: Annual Review of Astronomy and

Astrophysics 30.1 (Jan. 1992), pp. 311–358. doi: 10.1146/

annurev.astro.30.1.311.

[4] Chen, G. H.; Kochanek, C. S.; and Hewitt, J. N. “The

Mass Distribution of the Lens Galaxy in MG 1131+0456”.

In: The Astrophysical Journal 447 (July 1995), p. 62.

issn: 1538-4357. doi: 10.1086/175857. url: http://dx.doi.


AN INTERVIEW WITH MR. ERIK TROAN

From left, Jason Li, BSS Editor-In-Chief; Eleanor Xiao, BSS Publication Editor-In-Chief; Dr. Jonathan Bennett, BSS

Faculty Advisor; Olivia Fugikawa, BSS Editor-In-Chief; Megan Mou, 2020 BSS Essay Contest Winner; and Mr. Erik

Troan '91, founder and CTO of Pendo.io.

To get us started, you are a self-proclaimed technology

geek. When was it and what was it that first got you

interested in computer programming and engineering?

I’ve been doing [CS and Eng] since I was probably five or

six years old. Going back a few decades ago, people didn’t

have computers at home, but my father was at IBM so he

was always around them. He put me in a programming

class at the library when I was probably six or seven. We

also had a little computer at school so those two things

made me kind of interested in exploring the world. I know

what I enjoyed about it then is probably what I still enjoy

about it now; it’s a combination of problem solving while

doing it in an abstract environment where you really can

control a lot of things. Many of you probably enjoy math.

Math is fun because there is a right and a wrong, correct?

You get to solve a problem. You’re solving a puzzle, but

you’re not constrained by inconveniences like physics,

gravity, forces, or having to go build things and buy equipment.

For me, growing up, it was always a way I could

build and explore things. I could do things that didn’t have

the constraints of the physical world or have to get materials

and put them together. Physical things break and then

you have to fix them. So I think that level of control over

computer software is really what made me interested in it.

You’ve highlighted NCSSM as one of the most valuable

parts of your education. What was your favorite part of

your experience there?

The other people. It’s all about the kids. It’s about the social

scene—and I don’t necessarily mean social like parties, but

social in terms of, you’re in a peer group that’s smart. I’ve

probably said something like that at Convocation: you’re

not going to sit in a group of people as smart and as energetic

and as intellectual and as curious as you are ever

again and that leaves a mark. You really do realize there

are like-minded people out there; you have interesting

conversations and you’re solving interesting problems. I

remember I took a fractal class senior year and just the excitement

of the kids getting together to solve the problem,

and to really learn about it, is what really made an impact

on me.

I learned the importance of communication, so I learned

to write. I had some very good instructors. I learned from

FEATURED ARTICLE


them that writing was something that helped you communicate,

and if you want to become effective in this world,

you’re not going to do it mostly one-on-one; you’re going

to do it one-to-many. Even in the world of the Internet,

Youtube and everything else, writing is probably the most

basic way that you’re going to communicate to a lot of people.

I also really fell in love with history, of all things. I had a

great U.S. History teacher. That’s followed me my whole

life. I’m still a history buff; I read history books all the time.

I was so fortunate, just like you, to have instructors who

pushed me and made me better all the time. I have a son

who is [in NCSSM Online], and it’s great watching him

online and how he kicks me out of his room so he can do

his classes because he’s getting that extra push and really

interesting problem-solving material. He just started his

new Bioinformatics class this week and you can just see the

lights back on in his eyes.

What do you do at Pendo now? What does a day look

like for you?

There are two different parts to that question. My official

job at Pendo is that I’m Chief Technology Officer, and that

I run all of Product Engineering. All the products that we

build come out of my team; it’s a team of about 110 people

now. [Our product] runs in the cloud (out on the internet)

because it’s a software product. The team deploys that

product, keeps it running, manages automated testing, and

does 24/7 monitoring. All of that is all under me. So [my

job is to] get the product, work with the team that defines

what we should build, figure out how to architect it, build

it, get it shipped to customers, and keep it up and running.

What do I do every day? I go to meetings pretty much all

day—the most important part of my job is helping with

communication within my team or across teams. I don’t

make the big decisions. If I’m making a decision, there’s

probably something wrong. It’s much more about getting

the right people in the room, enabling them to make the

decision and make sure they feel supported and empowered

to make the decision. Asking questions is also a big

part of it. I actually sat in your chair a few hours ago going

through whether or not we should use a third-party partner

for something new we’re rolling out. There was a team

of three people that told me two or three weeks ago that

they were really excited about it. Now they all changed

their mind and all I did was ask them questions about their

decision. “Why?” “Have you thought about this?” “What

about that?” I didn’t make the decision though. We were

going with their recommendation, but I helped to validate

and make sure they had the confidence that they thought

through everything. So I do that with both Engineering

and Product. The other thing I’ll do is work with Sales,

Product and Marketing to try to understand the market.

I’ll talk to customers about what Pendo does for them and

where they are having a problem. I think I have three or

four customers that I’m an executive sponsor for, so another

part of my job is making sure that they are successful.

It’s a lot of talking. I joke that the CTO is actually Chief

Talking Officer; some technical in it, but not much.

One thing in a fast-growing organization, you’ll find, is the

problems don’t change; the problems change scale. When

you solve a problem for a team of 20 with the right solution

for 20, when you get to a [team of] 100, that solution

probably doesn’t work any more. For example, you might

realize that you didn’t have a process for something at 20.

That didn’t matter at the time because two people went to

lunch once a week, saw each other informally at lunch and

communicated, “That’s not working,” or “Fine, I’ll fix it for

you.” Then they go back and they fix it. When you get to

100 people, those two people could now sit on different

floors and don’t go to lunch any more. All of a sudden,

you have a hole where things that were solved informally

can’t be solved informally any more because the roles that

you need to have around the table drift apart. That doesn’t

mean you screwed up, or that your process is wrong, it just

means that you’ve grown a lot, and you have to make sure

you’re constantly looking for things where you missed or

where you can do better. So as you hire specialists, they can

thrive without there being gaps between all the specialists.

Do you miss the coding part of your job or the engineering

part of your job?

No, because I still do it. This is one of those things where

it’s either the best thing about me or the worst thing about

me as the CTO. I’m still technical and still want to code.

It means I have to hire and surround myself with people

who complement that aspect of me. I have two VP’s of Engineering

who don’t code. So, I brought people in to take

things that gave me a little more room to still be technical.

[But] I have to be very careful with what I code. I tend to

code things that are longer term, things that aren’t going

to be blocking a customer, or things that are not a promise

that we are going to get it done by a certain day. I’ve been

told that most of the team loves that I [code], because it

makes me relatable and I can understand what they’re going

through a little more. But it also does mean that there

are other parts of my job that I delegate and I do differently

because I insist on continuing to code.

What is your advice to students who want to learn

more about coding?

Go to school. Go to college. I’m a huge believer in formal

computer science education. I hire people who don’t have

it, so I do hire coding academies and people who have just

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picked it up, but the easy path to success if you want to be

in a computer software job is to go to college in computer

and software. You will learn things in school that you

won’t learn anywhere else. I took a lot of graduate classes

as an undergraduate and I still use those lessons. Some of

the best engineers have Master’s degrees. I haven’t hired a

Master’s degree who’s not a good engineer. These things

go together. People get smart in graduate school and undergraduate

for a reason, so don’t let that go.

While you’re [in school], do as many internships, real

world projects, volunteer projects, and capstone projects

as you can. You’ve got to go solve problems. You’ve got to

exercise the muscles in your brain to let you try different

ways of approaching problems, because there are all sorts

of different ways to do it. Unless you’ve done it 20 times,

you’re not going to know which [solution] is the best. College

and graduate school absolutely accelerate that process.

What is your advice to students who hope to start their

own business?

To start a company, you’ve got to find a problem that you

are passionate about, not a solution you are passionate

about. I tried a [starting a company with a] solution that I

really liked and that company fizzled and failed because it

wasn’t a problem.Find something you think is worth solving.

Find customerswho think it’s worth solving—ideally,

before you build it. You can’t always do that, but if you

can find some way to get people who will validate this as a

problem worth paying for, there’s nothing to replace that.

One of the reasons I got really attracted to Pendo was, our

CEO gave me a problem that I understood, was excited

about and resonated with my career. He started talking to

potential customers who said, “Oh yeah! Talk to this guy,

they said this.” So he could clearly come in with a head start

towards, “Was this a valuable problem to solve? What was

important about the problem? How is this really going to

help customers?”

Another thing I would say is: it is very easy, and I’ve been

guilty of this in my career, to take customers for granted.

They are going to make you successful. The only things

that make you successful are their willingness to pay and

your willingness to make themsuccessful. So don’t take

them for granted, especially if you’re talking about low

prices and products. It’s very easy to say, “Yeah, we have to

make it up in volume.” or “If any one customer is unhappy,

we can kind of blow it off.” If one customer is unhappy, it’s

probably a thousand customers who are unhappy. Make

your customers happy. Your customers will carry you everywhere.

We have been very lucky here. We’ve done five

venture-funding rounds. Every one of them was preempted,

so we never went to look for money. Investors came

to us, wanting to put money in. The reason that happened

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was our customers. The first one that found us was because

we had already sold to six other portfolio companies,

and they started hearing about Pendo, Pendo, Pendo.

“What is this thing? Why are you guys all using it?” They

were so happy with the value we were delivering that they

told their investors about it, who told their partners about

it. It happened again and again. It wasn’t any magic. We

have seven core values, and one of them is “maniacal focus

on the customer.” We are absolutely dedicated to them. I

think out of everything I’ve learned, that is the one thing

that I have discounted in my career, and I wish I hadn’t. It’s

so important.

Many of the students here at NCSSM view failure as

something shameful. How did you overcome the failures

that you encountered in your life, and how did you

learn from them?

Practice. I used to be a private pilot. There’s a saying

around that, “The only way to avoid mistakes is through

experience, and the only way to get experience is by making

mistakes.” Failure is a side effect of taking a chance,

taking a risk, pushing your zone. If you don’t fail, you’re

not trying very hard. And I get it—I watched my kids go

through middle school and high school. I get that we built

a society for students where failure can be very, very expensive.

You guys get that one “C” in a class, and there’s

colleges you might not get into or scholarships you might

not get. You’re in a weird place in high school. Don’t mistake

how you succeed in that microcosm for how you’re

going to be successful in life. You’re going to be successful

in life by growing, learning, and trying new things. One

of the reasons that [the US] is still one of the premiere

economies in the world is that we don’t punish failure. I

failed in my last company. It was a big crater in the ground,

burned through tens of millions of dollars of other people’s

money. When I helped raise money for Pendo, people saw

that as a plus.They didn’t say, “Well, your last one failed,

why would I invest in you now?” They said, “Hey, what’d

you learn?” and I said “This and this and this, oh my god

I learned so much,” and they’d say, “Great, let’s do this.”

You’re lucky to be in a country that, as you move out of

that narrow, high-achieving academic track that you’re on,

failure won’t always be punished. So embrace it and try

things. You have to try new things or you won’t get better.

I have a really smart PhD [employee] here, who worked

on a project for 6 months. He spent 6 months of engineering

on something. It didn’t work. At the end of the day,

we had to shelve it. But if we didn’t try, we would always

be wondering “What if?” We had to figure out other ways

to solve it, and that approach was a bad one. So, I get why

kids at Science and Math and in high school are scared of

failure; I think you’ve been taught your whole lives to be

scared of failure. Just try and get over it when you’re in college.

It’s going to be very hard to do anything special unless


you take some gambles. You can’t win every gamble. But

when you fail, look at why. I learned more from the company

that didn’t work than from the companies that did.

I’ve hired people who have been fired, by the way. The

question is, “What did you learn from your failure? Why

did it not work? What was it about you that had to change?”

and if they say “it was the company, there were bad people

there, they didn’t listen to my ideas, they did everything I

told them not to” — run away. That’s not learning. If they

say “Yeah, the company had this culture, and I brought this

culture, and I had trouble adapting,” or, “I really thought

this was gonna work out, and I pushed hard for it. I think I

alienated some people” — those are the kind of people you

want, because they’ll probably be better next time.

What do you enjoy doing outside of work?

I scuba dive. I’m actually a scuba instructor, so I teach one

or two classes a year. I’d like to get back into flying; I’m

looking into doing that in the next year or two. I’m a pretty

avid snowskier; I try and go out west a couple times a year.

I run hundreds of miles a year—that’s not something I enjoy,

I do it because I’m old. I like to cook. It’s the highlight

whenever we can get the whole family around the table. I

have 3 teenage boys, so it doesn’t happen that often, but

when we can all sit down to a meal, our meals tend to last

one and a half, two hours. They’re very long, drawn-out

meals after the food’s all gone. We don’t see each other that

much so we enjoy that. I like to travel. Whenever I can,

I’ll take a trip overseas—not for work, but if I can travel

personally, it’s great to just be in a new place and wake up

somewhere else. I also like to read. I read a ton.

As a scientist who works in the entrepreneurial side of

things, what do you have to say about the role of entrepreneurship

in advancing science?

First of all, I’m not a scientist, I’m an engineer. I consider

those really, really different. When I didn’t go to grad

school, I turned away from being a scientist. Scientists discover

new things. Engineers try and apply science to problems.

I think engineering and entrepreneurship are really

tightly coupled. My personal belief is that most of the time

if you engineer something new, you probably need a new

company to really make it successful. The iPhone is a huge

counterexample to that, but most truly novel engineering

projects have a company to go off of because [they’re]

bringing something new to the market.

research grants and universities where you don’t need to

have that immediate payback period.

Some of the most interesting things that come out of science

start as an intellectual curiosity. They didn’t start off

as solving a real-world problem, but they all feed into real-world

problems, into the hands of people like me, who

are engineers. When you start crossing those wires too

much, you get into bad positions like you have in medicine,

where a lot of research in medicine is being privatized.

If it isn’t for a disease that has enough payback, then

it gets shelved, so you start getting the wrong incentives

around basic research.

How important is entrepreneurship for applying the

knowledge that comes out of science, or for doing engineering?

I think for building real solutions to real problems, it’s critical.

If all we had in the world was fifty Fortune 50 companies,

then we wouldn’t get innovation, and we wouldn’t

get new products to solve new problems out there. The

easiest example is alternate energy. It’s not Exxon and BP

that are bringing alternate energy and clean energy to

the world. They have a business—they dig up oil, refine

it, burn it, and make a lot of money doing it. It’s hard to

take a business like that and go do something that’s new

and different. It’s a risk. It’s a lot easier to take a risk as an

entrepreneur because you have a lot to gain, but if you’re

running a big company, how do you value that risk? You

have a lot to lose.

[Entrepreneurship] is critical. I think universities are

normally pretty bad at it. Stanford is uniquely good at it,

and I don’t know if it’s [because they’re] Stanford or if

it’s because they’re sitting in the middle of Silicon Valley

and have hundreds of billions of investment capital sniffing

around looking for the next opportunity all the time.

Mostly it’s disappointing when you look at universities

that have so much pressure to raise money and own part

of their inventions that they aren’t good at getting these

companies up or bringing the technology up. Which is too

bad because I think there’s a huge amount of symbiosis between

those two.

Science is harder. A lot of science has around 30 or 40

year payback periods, and it’s not that they’re not interesting

topics and that they’re not important. If you’re lucky

enough to be a scientist, and you’re able to do it in an entrepreneurial

sense, I think that’s great. But for most science,

I think, you need to figure out how to do it through

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