Scientia Winter 2020 Edition

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aboutthe the tr iple hel ix about triple helix

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The Interdisciplinary World of Neuroscience:AN INQuiry with Professor David Freedman

The Interdisciplinary World of y on lymphatic e ef of Notch4 deficienc Neuroscience: AN INQuiry with Professor vessel formati and subcuta neou s fat / Corinne Stonebraker David ph Freedman enotype in mice

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The effect of Notch4 deficiency on lymphatic vessel formation and subcutaneous fat phenotype in mice / DONIA BALLAN

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A symphony of strings:AN INQuiry with Professor Savdeep Sethi / JESSICA METZGER

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Searching for Infrared Galaxies in the MUSE Optical Data / Kathryn Downey

On Becoming a Population Health Scientist: An Inquiry with Dr. Stacy Lindau / Hanna Czeladko

P R O D U C E D BY T H E T R I P L E H E L I X AT T H E U N I V E R S I T Y O F C H I C AG O L AYO U T A N D D E S I G N BY B O N N I E H U , P R O D U C T I O N D I R EC TO R C O - E D I TO R S - I N - C H I E F : R I TA K H O U R I , M A R I T H A WA N G S C I E N T I A B OA R D : J O S H E V E R T S , S W E TA N A R AYA N , M O L LY S U N

Abstracts

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Quantum Information and Computing: An Inquiry With PROFESSOR Fred Chong / ROHAN KUMAR

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Towards Measurement of Antimatter Absorption Cross-Section: Deuteron Identification in ALICE / Julia BOOK

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Characterization of the fecal microbiota of Alzheimer's Tg and WT male mice with respect to Alzheimer's pathogenesis / SHAYNA COHEN

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Nocte is Required for Motor Neuron Terminal Development / Katherine DELONG

Stretchable and StiffnessChangeable Substrate for Electrode Devices / Bernadette Miao

ACKNOWLEDGEMENTS

TA B LE O F CO NTE NTS

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Resistin Increases Breast Cancer Cell Invasiveness by Inducing their Mesenchymal Transition / Julianna Bianco

a bout the triple helix

Dear Reader, The Triple Helix, Inc. (TTH) is the world’s largest student-run organization dedicated to evaluating the true impact of historical and modern advances in science. Of TTH’s more than 20 chapters worldwide, The University of Chicago chapter is one of the largest and most active. Our TTH chapter continues to proudly share some of the most distinct publications and events on campus, engaging the minds and bodies of our institution and the public as “a global forum for science in society.” Our mission, to explore the interdisciplinary nature of the sciences and how they shape our world, remains the backbone of our organization and the work we do. In addition to Scientia, we publish The Science in Society Review (SISR) and an online blog (E-Pub), while also creating events to discuss the most current, pressing topics at the intersection of science and our society.

Our organization is driven by talented undergraduate individuals—writers, editors, and the executive board that come from all backgrounds and interests. The intellectual diversity of TTH members allows us to bring you the vast array of knowledge, research, and perspectives that we present in our works. We consciously strive to help each member grow, not only as a writer or editor, but also as a leader, who will continue to ask the very questions that lead us to innovation and advancement as a society.

Over the years, TTH UChicago has expanded from just one journal and an online blog to a holistic outlet for all undergraduates on our campus.

Everyone—whether in “the sciences” or not—is affected by it, contributes to it, and has to interact with it on a daily basis. We wanted to continue to grow the platform of “the sciences,” to make it accessible to everyone. We now have insightful opinion pieces through SISR, brief reportings through E-Pub, workshops and discussions through events, and original research and interviews with leading professors through Scientia—a whole ecosystem of knowledge that we hope challenges you to think and to actively join the evergrowing dialogue on “the sciences.”

Today, I invite you to join us, The Triple Helix team, in celebrating the newest release of Scientia, one of our two biannual print publications. Scientia— unique to the UChicago chapter—was inspired by and continues to embody the motto of our university: Crescat scientia; vita excolatur (Let knowledge grow from more to more; and so be human life enriched).

As you read our latest issue, I hope you are reminded of its essence and let your knowledge grow.

Best wishes, EDWARD ZHOU President of The Triple Helix at the University of Chicago

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Dear Reader,

a bout S cientia

For this edition, our front cover features an artistic representation of string theory against a backdrop of constellations, providing a snapshot of the exciting articles in this journal, which range from particle physics and theoretical frameworks of the universe to neuroscience and medicine. We are humbled to share the works of students on campus in addition to articles featuring the worldclass research being done by our own faculty here at UChicago!

Our student researchers had the opportunity to interview professors who hail from different walks of life and are involved in rigorous scientific research at the University. This issue features Professors David Freedman, Savdeep Sethi, and Fred Chong, in addition to Dr. Stacy Lindau. The research topics investigated by these individuals include how the interplay between machine learning, artificial intelligence, and neuroscience can mimic human processing, the nuances of the string landscape theory, the benefits and applicability to quantum computer modeling in areas such as cryptology, and female sexual function in the context of breast cancer. This edition also features four impressive fulllength articles on original undergraduate research ranging from insights into how resistin increases the invasiveness of breast cancer cells to preliminary methods for identifying deuteurons and anti-deuterons using the Large Hadron Collider (LHC) at CERN. We are also excited to share several abstracts of students’ works, highlighting the diverse research being conducted by our peers. It is truly a privilege for us to be able to highlight all of these works, and we certainly have a passionate team of writers and editors to thank.

Scientia is always looking to broaden our scope and expand the reach of our publication. If you are completing a research project and want to see it in print, or if there is a professor performing eyeopening research that you would like to share, consider writing for us! We encourage all interested writers to contact a member of our team, listed in the back. In the meantime, please enjoy this edition of Scientia, presented by The Triple Helix.

Sincerely, RITA KHOURI and MARITHA WANG Co-Editors-in-Chief of Scientia

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THE INTERDISCIPLINARY WORLD OF NEUROSCIENCE AN INQUIRY WITH P R O F E S S O R D AV I D FREEDMAN / CORINNE STONEBRAKER Professor David Freedman is a Professor of Neurobiology and the Chair of the Graduate Program in Computational Neuroscience at the University of Chicago.

Credit: David Freedman

The visual system is a powerful tool for experiencing and processing the world around us. Whether you are taking a test, saying hello to a friend, or driving a car, your visual system is constantly making sense of incoming stimuli to flexibly guide higher cognitive processes. David Freedman, a Professor of Neurobiology and the Chair of the Graduate Program in Computational Neuroscience at the University of Chicago, studies how sensory inputs like vision are cognitively linked to motor function through decision making, learning, and memory. Deciphering the links between underlying neural circuits is fundamental to fully understanding higher cognitive functioning in the brain. Professor Freedman uses a combination of behavioral observations, neurophysiological data, and machine learning to create specially structured algorithms, termed neural networks, that mimic human processing.

Professor Freedman’s research

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is augmented by Artificial Intelligence, especially when it comes to learning and memory. Freedman and his laboratory have wholeheartedly embraced the idea that neuroscience can inform A.I. technology, and conversely, that A.I. technology can help neuroscientists better understand the brain.

“I think there’s a tremendous potential for interplay between A.I. and neuroscience.

Even the architecture of neural network models is inspired by the idea that you have multiple layers of neurons in a hierarchy where signals come in the bottom and propagate to different layers, and that the connections between different layers are key for storing knowledge in that network and for transforming inputs into outputs,” he says, emphasizing this critical exchange.

More recently, advances in A.I. technology have also been contributing back to neuroscience, especially in terms of analyzing data. These artificial networks are

Neural networks are specially structured algorithms that are modeled after mechanisms that exist in the human brain. Credit: pixabay.com

used to answer questions about everything from categorization to the limits of memory storage. Using machine learning tools to train a class of artificial neural networks called recurrent neural networks, Professor Freedman and his team have been able to implement features that mimic those that exist in real neurons, such as excitation and inhibition. When recurrent neural networks were assigned the same categorization and decision-making tasks that are administered to monkeys, they behaved in a very similar fashion. This mimicry was further evidenced when the activity patterns of the individual artificial neurons were found to look very similar to those seen in the brain.

He elaborates: “It suggests that these artificial networks might be solving these sorts of tasks that we’ve been studying for decades now in animals using the same kinds of solutions that the brain is using.” In the era of big data, A.I. and artificial neural networks are helpful in analyzing massive amounts of experimental data and solving cognitive-style tasks the way a real brain would.

Self-driving cars are one such application of A.I. that would require

human-like cognitive capabilities. Driving often confers situations that necessitate short-term memory. For example, the A.I. algorithms governing a self-driving car must keep track of the behavior of the car in front of it, extracting knowledge about what that car has done in the recent past to make a prediction about what will happen in the future. Tasks which require interpreting sensory information and comparing it to recently formed memories in order to make a decision are the crux of Professor Freedman and his laboratory’s research goals. These involve a different type of “thinking” than some of the tasks for which the use of A.I. is particularly well-known, such as the direct stimulus-input, solution-output mechanism of facial recognition. “This is a more dynamic, ongoing process where there are different events that unfold over time. Those are the kinds of problems we’re interested in now, both in the real brain and behavior and also in the artificial network domain,” Professor Freedman explains.

The beauty of neuroscience, according to Professor Freedman, lies in its scope. It ranges “from molecules, synapses, cells, and proteins

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Professor Freedman in his lab. Credit: David Freedman

all the way to studying how activity in circuits drives behavior, and what can go wrong in neural circuits, which leads to disease,” Freedman says. However, it is also very unified—“throughout all levels, computation and theory is mixed in,” he elaborates.

Pervasive in every level is the influence of other academic fields: neuroscience liaises closely with mathematics, linguistics, engineering, computer science, chemistry, philosophy, psychology, and medicine. From his own background, Professor Freedman is familiar with the interdisciplinary nature of the field, having spent his first years of college studying electrical engineering. “In high school, I had basically no exposure to neuroscience,” he explains. In fact, at the time, most were unaware of the field in general. However, at the recommendation of a friend, Freedman sat in on a class about the sensory systems of the brain. “It was so fascinating that I kind of kept taking every course I could find that was like that,” he says. However, having a background in electrical engineering was

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practical for Professor Freedman and often proved useful in designing experimental setups. When advising students interested in neuroscience research, Professor Freedman likes to tell them that “with the complexities of running an experimental lab, it’s helpful to have a bit of breadth.”

Professor Freedman’s passion for neuroscience research began with the same class that motivated him to switch to studying cognitive science. Intrigued by the way that sensory signals are integrated into one’s ongoing perception of the world, Freedman describes the class as “the first introductory course I took in college where the answer to a lot of the questions I had was, ‘Well, we don’t know yet.’” At first, he considered studying medicine. But after becoming involved in research at the laboratories of Walter Makous and David R. Williams at the University of Rochester, Professor Freedman decided to pursue graduate school in neuroscience.

For undergraduates thinking of a career in neuroscience, Freedman emphasizes the

importance of understanding the day-to-day experience of working in a lab environment.

Some people enjoy reading, writing, and learning about neuroscience, but not necessarily working on research projects– and vice versa. Fundamentally, it comes down to finding where you fit within the huge disciplinary range of the field. “There are some neuroscience research labs that are really like a biology lab, and there are others that are really computer science or theoretical math groups,” he concludes.

At UChicago, the neuroscience major has become very popular with students on the pre-medicine track, in addition to students interested purely in neuroscience research. Remarking on this observation, Professor Freedman asks, “What does the academic world look like to an undergraduate?

Are there a number of people that feel a pull back towards the academic world as a place to stay?” While a significant number of premed students are involved with academic research, some do see it as a means to an end in terms of strengthening their medical school application. However, research and academia have never been wholly separate from medicine–if anything, the two are only becoming more and more intertwined. The “Physician-Scientist” is someone who holds a degree in both medicine and science, plays a critical role in translational medicine and clinical research, and helps connect research findings to their corresponding health care applications. “I interview students here for the MSTP [Medical Scientist Training Program], and I always ask people what they want to do that really requires both fields and expertise,” Freedman comments. “People sometimes end up focusing more on the clinical side or more on the research side. But they are in the best position right now to make the best judgment about how useful new research, or a new product or device is and applying it.”

Freedman essentially delineates the notion that if one cannot inform medicine with research, then he or she is missing out on a significant way to improve patient quality of care, the ultimate goal of medicine.

A large flock of snow geese arrange themselves in a highly specific way when traveling at high altitudes. Freedman believes that the way complex, natural systems like these organize themselves can give clues to how the brain is structured. Credit: flickr.com

Fundamentally, the organization of the brain draws in those fascinated by complexity and the rules that govern it.

Elaborate, organized systems are inherent to the natural world. “The emergent properties of an intricate network like the brain are not so different from the way large flocks of birds arrange themselves, or how insects can build huge, methodical structures,” Freedman explains. At its core, the study of neuroscience lends itself to a rather philosophical understanding of ourselves, our own cognitive functions, and the world around us. As Professor Freedman puts it, “Neuroscience offers the opportunity to understand something about yourself. Our experience is an emergent property of the activity in our brains. In a way, the more you can understand about that process, the more you’re understanding something about yourself.”

Corinne Stonebraker is a third-year at the College studying Neuroscience and Biology. She is fascinated by the intersection of science and medicine, particularly how psychiatry and neurology are being redefined by basic and translational clinical research.

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THE EFFECT OF NOTCH4 deficiency

on lymphatic vessel formation and subcutaneous fat phenotype in mice Donia Ballan 1 , Carrie Shawber 1 1 Columbia University Medical Center

Lipedema is a disorder characterized by the buildup of subcutaneous fat, which is unaffected by diet or exercise. Despite affecting about 11% of adult women worldwide and leading to many other complications, its pathobiology is poorly understood. The lymphatic system is believed to impact lipedema. Notch4, a protein, has been suggested to be involved in lymphatic vessel formation and may therefore affect lipedema. To study the role further, immunohistochemistry staining for LYVE1 and Podoplanin was performed on Notch4 (gene) null and wild-type mice to evaluate the capillary and collecting lymphatics, respectively. Notch4 female mice failed to develop proper lymphatic vessel infrastructure in the skin that correlated with changes in Podoplanin+ and LYVE1+ vessels. Analysis of lipid accumulation in the subcutaneous fat content by Oil-Red-O staining shows that female Notch4 null mice developed a similar condition to lipedema and had significantly greater subcutaneous fat content. This study concludes that Notch4 is required for proper lymphatic vessel formation and that without it, mice develop a lipedema-like condition.

Since the Notch signaling pathway is highly conserved, Notch4 likely plays a similar role in humans.

This is a crucial step in understanding the causes and development of lipedema.

Lipedema Lipedema is a type of fat disorder that affects about 11% of adult women worldwide [1]. It usually manifests at puberty, suggesting it is estrogen-regulated [2;3]. Men with lipedema often have extremely high estrogen levels and low testosterone levels, further indicating that sex hormone levels may affect its development [4]. The condition is characterized by widespread, uniform, and abnormal distribution of subcutaneous fat mostly restricted to the lower extremities [5]. This fat lies directly under the dermis layer of the skin and cannot be lost through diet and exercise [6]. Lipedema is often

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misunderstood and misdiagnosed as obesity because of similarities in their phenotypes.

However, obesity is characterized by the buildup of visceral fat, which can be lost through diet and exercise.

The mechanism of this subcutaneous fat buildup is unknown. The lymphatic system has been implicated in lipedema, but its exact role is not understood. When lymphatics are malfunctioning, lymph fluid and fat buildup occur [8]. In support of this, lymph fluid can promote fat differentiation of adipocyte progenitors, which often develop into fat [9]. Furthermore, as lipedema progresses, a buildup of lymph fluid is observed as the fat

begins to hinder the lymphatic system [10]. The buildup of fluid has been proposed to result in subsequent progression to lipedema, leading to greater accumulation of fat [11].

Lymphatics and Fat The lymphatic system is uniquely designed to transport and remove extracellular fluid or lymph, a transparent fluid containing lymphocytes, throughout the body [5]. Lymphatic capillaries are made of a single layer of lymphatic endothelial cells (LECs) that form intercellular junctions and allow for the passage of fluid [12]. Lymphatic vessels express different levels of Podoplanin and LYVE1 depending on the vessel type. In humans without lymphatic issues, LECs high in Podoplanin are localized to collecting ducts, whereas lymphatic capillaries express LYVE1 [13]. Podoplanin is essential for lymphatic valve formation and LYVE1 is required for effective transportation of fluid from the extracellular matrix to lymph nodes [14].

Alterations in the expression of these two proteins suggest improper lymphatic vascular development and may contribute to lymphatic dysfunction.

Notch Notch is a highly conserved signaling pathway found in all mammals that controls cell differentiation. This pathway consists of four different receptors: Notch1, Notch2, Notch3, and Notch4. The role of Notch in lymphatic development has only recently begun to be investigated. Deletion of Notch1 in mouse LECs has been shown to cause defects in valve formation and failed separation of lymphatic vessels from cardinal veins responsible for carrying blood [16, 17]. These studies illustrate that Notch1 functions as both a positive and negative regulator of lymphatic development depending on its cellular context.

The role of Notch4 in lymphatic development is not yet understood. Unpublished data from the Shawber Lab suggest that Notch4 might play a role in lymphatic development. Activation of Notch1 or Notch4 in humans has been shown to induce integrin Îą9, fibronectin EIIIA and Cx37 expression, all of which are required for lymphatic valve formation [20].

This study aims to further understand the role of Notch 4 in lymphatic development and its role in lipedema. It is hypothesized that if Notch4 expression is altered, then irregularities in the lymphatic structure in mice will be seen as well as a difference in subcutaneous fat. This study aims

epidermis

subcutaneous adipose

dermis

hypodermis striated smooth muscle cell layer

Figure 1. Skin tissue This image illustrates the different layers of skin.

Subcutaneous fat is found directly under the dermis.

to determine the role of Notch4 in the dermal lymphatics and subcutaneous fat phenotype.

To address this hypothesis, the subcutaneous lymphatic and fat phenotype were determined by staining for the lymphatic proteins Podoplanin and LYVE1, as well as determining the adipose phenotype by Oil-O-Red staining.

MATERIALS AND METHODS Preparation To analyze the role of Notch4 in lymphatic vessel formation, the following mice strains were used: Notch4+/-, Notch4-/-, and wild-type mice [21]. Prior to receiving tissues for the study, skin samples were obtained from 6-week-old and 15-week-old mice and tissue fresh frozen in optimal cutting temperature compound (OCT). Frozen sections were prepared for immunohistochemistry and Oil-Red-O staining.

Immunochemistry Staining The mice tissues were stained as described in [22]. Skin sections were double stained for the presence of LYVE1 and Podoplanin, a marker of LECs. Angiobio Podoplanin was used to stain for Podoplanin. Abcam LYVE1 was used to stain for LYVE1. Secondary antibody donkey anti-rabbit 488 (Invitrogen) was used to detect the LYVE1 antibody and goat anti-hamster 594 (Invitrogen) was used to detect the Podoplanin antibody.

Oil Red O Staining (ORO) of Lipids The mice tissues were stained with Oil-Red-O solution and hematoxylin. This procedure stains fat

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200 um

Figure 2. Notch4 deficiency leads to decrease LYVE1+ lymphatic vessels in 6-week-old female mice.

Six-week-old female wild-type and Notch4 null mice were stained for LYVE1. In figure 2a, the mean SD of LYVE1+ vessel density is shown. Wild-type mice had significantly greater density of Notch4 null mice. The sample size is 12 (n=12). Figure 2b is a pair of representative images in 10x magnification that show the presence of LYVE1+ vessels in tissues. The arrows point towards LYVE+ vessels.

red and nuclei blue.

two groups was determined by a Student’s t-test.

Imaging Microscopy and imaging were performed using a Zeiss LSM 510 Meta Confocal Microscope.

Images were processed using Zeiss LSM image browser software.

RESULTS

Image Quantification ImageJ software (NIH) was used for quantitative analyses of images, which were normalized by litter and area. The images were cropped to measure the area above the hypodermis to the epidermis to determine accurate distinctions in LYVE1+ lymphatic vessel density, Podoplanin+ lymphatic vessel density, and subcutaneous fat density between wild-type and Notch4 -/- mice. Significance between the

Notch4-deficiency correlates with reduced LYVE1+ vessels at maturation To analyze the role of Notch4 in the dermal capillary lymphatics, wild-type (Notch4 +/+) and Notch4 -/- were stained for LYVE1. Notch4 -/mice had a significant reduction in the density of LYVE1+ vessels when compared to wildtype mice (Figure 2). A p-value of 0.0360 was obtained through a t-test comparing the LYVE1+ vessel density in Notch4 -/- and wild-type mice.

Since LYVE1 is required for lymphatic vessels to effectively transport fluid, the results illustrate that Notch4-deficient mice do not have the lymphatic infrastructure needed to properly

Figure 3. Notch4 deficiency increases Podoplanin+ lymphatic vessels in 6-week-old female mice.

The mean of Podoplanin+ lymphatic vessels are shown (Âą SD). The sample size is 12 (n=12).

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Figure 4. Notch4 deficiency increases subcutaneous fat density in 6-weekold and 15-week-old female mice.

Wild-type and Notch4-/- female mice were stained at 6 weeks (maturation) and 15 weeks with Oil-Red-O to measure subcutaneous fat density. The sample size for each group was 12 mice (n=12). The density of subcutaneous fat in tissue were normalized by litter and area. Shown are mean ±SD.

uptake fluid.

Notch4-deficiency correlates with increased Podoplanin+ vessels at maturation Wild-type, Notch4−/−, and Notch4+/− mice were stained with Podoplanin to evaluate the dermal collecting lymphatics. Notch4-deficient mice had a significantly increased density of Podoplanin+ vessels when compared to wildtype mice (Figure 3). A p-value of 0.0439 was obtained through a Student’s t-test comparing the Podoplanin+ vessel density in Notch4 -/and wild-type mice. Since LECs that are high in Podoplanin are usually localized in collecting ducts, this suggests Notch4-deficient mice do not have properly developed lymphatic vessels.

Female Notch4 null mice demonstrate a progressive increase in subcutaneous lipid content Tissues from both 6-week-old and 15-week-old female mice were stained with Oil-Red-O, a stain that marks lipids.

Notch4-deficient female mice have greater subcutaneous lipid density compared to the wild-type females (Figure 4, p=0.0553). At 15 weeks, the dermal lipid content in Notch4 null female mice is significantly greater than the lipid content in female wild-type mice (Figure 4, p = 0.0008). These p-values were calculated using a two-tailed t-test comparing wild-type and Notch4-deficient mice within each age group.

This shows that the Notch4-deficient mice

Figure 5. Notch4 deficiency does not significantly increase subcutaneous fat density in 6-week-old male mice.

Wild-type and Notch4-/- male mice were stained at 6 weeks with OilRed-O to measure subcutaneous fat density. The sample was 12 mice (n=12). The density of subcutaneous fat in tissue were normalized by litter and area. Shown are mean ±SD.

develop significantly more subcutaneous fat after estrous, the reoccurring phases in females affected by reproductive hormones that are similar to how lipedema patients progressively develop more fat after puberty.

Loss of Notch4 has no effect on subcutaneous fat density in males Tissues from 6-week-old male mice were stained with Oil-Red-O similarly to the female tissues. There are large variations in the data and no significant difference in subcutaneous fat between wild-type and Notch4 -/- mice (Figure 5, p= 0.4274).

This p-value was calculated using a two-tailed t-test comparing wild-type and Notch4-deficient mice. The presence of Notch4 having no effect in male mice between the two groups is consistent with lipedema being present only in females.

DISCUSSION In order to investigate the development of lipedema, this study assessed the effect of Notch4 deficiency on lymphatic vessel formation and subcutaneous fat density in mice. Compared to wild-type mice, female Notch4−/− mice had a significantly reduced density of LYVE1+ capillary lymphatic vessels and an increase in Podoplanin+ collecting lymphatic vessels. Notch4−/− female mice also had a greater density of subcutaneous fat compared to wild-type mice. The difference in subcutaneous fat density became more

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pronounced as the mice grew older and went through several cycles of estrous.

Notch4 is expressed in dermal lymphatics, but its role is unknown [20]. Staining of 6-weekold mouse tissue revealed that Notch4 deficiency results in a significant loss in dermal capillary lymphatics (Figure 2). Since LYVE1 is required for effective transport of fluid from the extracellular matrix to lymph nodes, the results illustrate that Notch4-deficient mice do not have the lymphatic infrastructure needed to properly uptake fluid [14]. As this fluid can promote fat differentiation of adipocyte progenitors, it may be the reason for subcutaneous fat buildup [9]. While lymphatic capillaries and vessels usually express high levels of LYVE1, those in Notch4-deficient mice expressed higher levels of Podoplanin, which is usually found in the cells of lymphatic collecting ducts instead of vessels [13]. Notch4-deficient mice had a significantly increased density of Podoplanin+ vessels when compared to wild-type mice, a finding that suggests that they had improper lymphatic development and function (Figure 3). Together, these results indicate that Notch4 is critical for proper lymphatic valve formation.

When lymphatics malfunction, lymph fluid and fat buildup has been observed [8]. Notch4deficient 6-week-old female mice had a greater density of subcutaneous fat than the wild-type mice (Figure 4; 6-week data, p= 0.0553). At 15 weeks, the difference became more pronounced (Figure 4; 15-week data, p= 0.0008). This demonstrates the progressive nature of the lipedema-like condition.

The results also show that Notch4-deficient female mice developed a similar phenotype to that of lipedema. The decrease in LYVE1+ lymphatic density correlates with a progressive increase in subcutaneous fat in female Notch4-/- mice.

These results also correlated with an increase in Podoplanin+ vessel density, which is not found in wild-type mice. These data suggest that increased subcutaneous fat observed in lipedema is because of insufficient lymphatic vessels.

In 6-week-old male mice, great variations and insignificant differences between wild-type and Notch4-/- mice were observed within each sample (Figure 5; p= 0.4274). This large p-value indicates that the differences observed are due to natural variability, unlike the extreme differences found in the female mice. This is consistent with the observation that lipedema primarily affects females and is believed to be linked to sex hormone levels [2; 4].

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Despite affecting at least 11% of women worldwide, the causes of lipedema and the mechanism by which subcutaneous fat is built up is poorly understood. Current treatments for lipedema are only palliative, and effective treatments might be developed if scientists are able to understand why and how it develops. The lipedema-like condition observed in mice occurred when there was a lack of LYVE1+ vessels that are needed for the uptake of lymph fluid, which can promote adipocyte differentiation. This result suggests that the buildup of subcutaneous fat might occur as a result of the excess of lymph fluid. Future studies can analyze whether removing lymph fluid through other means can prevent lipedema-like conditions in mice. This may lead to an alternative solution to lipedema, instead of the invasive liposuction procedures that are only tentative solutions. In addition, since the lipedemalike condition only occurred in female Notch4-/mice, there is evidence to support that lipedema is a sex hormone-regulated disorder. Subsequent studies on the effect of regulating hormones in male and female Notch4-/- mice will allow us to understand whether estrogen is required for lipedema or whether testosterone prevents the development of lipedema.

CONCLUSION This data reveals that Notch4 has a role in proper lymphatic vessel development and is a positive regulator of lymphatic growth. Without Notch4, female mice developed a lipedemalike condition. Since Notch4 is part of a highly conserved pathway, it likely serves a similar role in humans and its absence might be a factor that causes lipedema. Future studies in humans are needed to examine if lipedema patients fail to express Notch4 or if they have Notch4 mutations.

The effects of the change in expression of LYVE1+ and Podoplanin+ vessels as well as hormone levels on lipedema should also further be pursued.

Donia Ballan is a first-year student at the University of Chicago interested in majoring in Biology and/or Statistics. She is on the premedical track and is pursuing a career in the medical field.

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particular visceral fat, is associated with cardiometabolic risk

doi:10.1242/dev.083865 https://www.ncbi.nlm.nih.gov/

factors in obese women. Plos One, 12(9). doi:10.1371/journal.

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pone.0185403 18. Niessen, K., Zhang, G., Ridgway, J. B., Chen, H., Kolumam, 7. Wold LE, Hines EA Jr, Allen EV. Lipedema of the legs: A

G., Siebel, C. W., & Yan, M. (2011). The Notch1-Dll4 signaling

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in lymphatic valve formation. Development, 141(12), 24462451. doi:10.1242/dev.101188 https://www.ncbi.nlm.nih.gov/

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21. Krebs, L.T., Y. Xue, C.R. Norton, J.R. Shutter, M. Maguire, J.P. Sundberg, D. Gallahan, V. Closson, J. Kitajewski, R. Callahan,

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Sethi gives a workshop on “String Theory and Generalized Geometry” held at the Banff International Research Station for Mathematical Innovation and Discovery of the University of British Columbia. Credit: youtube.com

A SYMPHONY OF STRINGS AN INQUIRY WITH PROFESSOR S AV D E E P S E T H I / JESSICA METZGER

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Professor Savdeep Sethi is a Professor of Physics and faculty member at the Enrico Fermi Institute as well as the director of the Kadanoff Center for Theoretical Physics at the University of Chicago. Credit: Savdeep Sethi.

Professor Savdeep Sethi didn’t always know he wanted to be a theoretical physicist. Thanks to a great high school physics teacher, Sethi says that, “physics was already top of the list” when he entered his undergraduate studies at Cornell University as an engineering physics major. But Sethi wasn’t sure which subtopic he would end up in. He enjoyed his REU research in plasma physics at the Cornell supercomputer facility; however, throughout undergrad, he drifted closer and closer to fundamental theory, taking many math and graduate-level physics courses. By the end of undergrad, he had completed an additional degree in mathematics and “knew [he] wanted to go

to grad school right away” to study theory.

When Sethi entered graduate school in 1991 at Harvard University, the majority of his peers were very excited about experimental condensed matter and other applied disciplines of physics. However, he was interested in the more abstract goal of “trying to understand the interplay between quantum mechanics and gravity,” two theories that physicists had struggled to unify for decades. String theory, which explains fundamental physics through tiny vibrating strings, is currently one of the leading candidate theories to account for both quantum mechanics and gravity in a unified framework; however, at the time, it was much less well-known and

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well-developed. During his first year of classes at Harvard, Sethi oscillated between string theory, which only one senior faculty member at Harvard was researching, and the more widely researched topic of condensed matter theory. Eventually, he decided, “if [he] didn’t give string theory a shot, [he]’d regret it,” and went on to study string theory under his advisor, Dr. Cumrun Vafa.

Luckily, the middle of Sethi’s graduate studies coincided with big developments in string theory known as the “second superstring revolution.” Physicists had just found evidence of a phenomenon called “strong-weak coupling duality,” where some strongly-coupled theories (i.e. involving particles interacting with very strong forces) are equivalent to other weakly-coupled theories that are often easier to solve. As long as there are certain symmetries available, one can handle the strongly-coupled theory in the language of its weakly-coupled counterpart. This led to the unification of different string theories, as everyone discovered their different versions of the theory were all equivalent. This brought on a flurry of research within the area, thus leading to the birth of the “revolution.” Influenced by these developments, Sethi wrote his graduate thesis on this duality, providing tests of said duality in different theories, while introducing a number of new aspects within this up-and-coming field.

After graduate school, Sethi went on to a post-doctoral fellowship at the Institute for Advanced Study in Princeton, NJ. During this time, there was another revolution— cosmologists found that the expansion of the universe was accelerating due to a mysterious source of energy known as “dark energy.” It turns out that dark energy occupies 70% of the energy density of the universe, yet is at a scale that is about 10120 times smaller than might be expected from quantum mechanics. This presented a problem to string theorists, because all versions of string theory up until then

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produced either zero or an enormous amount of dark energy. In the wake of these revolutionizing observations, the work that Sethi and others did earlier helped lay the groundwork for later developments.

As a student, he had written a paper which had shed light on some strange features in certain string theory vacua that were later used to construct the “string landscape,” which has become one of the leading string theory explanations for dark energy.

The “string landscape” is the collection of possible string vacua, and our universe is thought to sit in one of the “valleys” of this landscape that give positive dark energy.

By some estimates, there are 10272,000 such vacua. For decades now, this explanation has been widely accepted by string

The “string landscape’ is the collection of possible string vacua , and our universe is thought to sit in one of the “valleys’ of this landscape that give p o s i tive d a r k e n e rg y.

theorists, with few questioning it.

After his post-doc, Sethi went on to a faculty position at UChicago, where he has been for the last 20 years, teaching many courses especially at the graduate level.

He still spends considerable time working on the string landscape story to this day.

However, after his initial work, he became skeptical of the evidence for this popular string landscape view. For one thing, even with the wide variety of possible configurations, there are very few that give our universe such a low vacuum energy scale, making the universe’s current configuration incredibly unlikely.

Many scientists have resorted to anthropic reasoning to explain this fine-tuning (i.e. they reason that if the energy scale weren’t so low, the universe wouldn’t have been habitable enough to produce humans to observe it), which many, including Sethi, see as flawed and too philosophical, since they believe that there should be a more fundamental scientific reason for it. Sethi believes that much of the string landscape theory is based on faulty reasoning, tending to “mix-and-match” different theories in a way that isn’t logically consistent. For many years, he was one of the most vocal critics of the string landscape theory, which he describes as a “lonely” and “unpleasant” position. “You see a lot of the sociology of the field,” he says. “The proponents would basically try to insist that the issues you have with their constructions are just technical issues, and the main ideas are right.”

However, over time, Sethi has noticed a change in the currents, as more people have come to adopt his skeptical view of the string landscape constructions.

In 2017, he wrote a paper explaining what was wrong with the most popular constructions of the string landscape.

This paper became one of the progenitors of the “swampland” story, a view separate from the string landscape explanation which requires dark energy to come from different sources. More and more people have begun to question the string landscape story for a variety of different reasons, and Sethi points out that last summer, attendees of a string theory conference were divided 50-50 on the issue.

Sethi still believes in the string landscape, just not how it’s constructed.

He mostly insists that we use the correct constructions from the start, even if it means going back and re-working things that we left off years ago. He believes that there is bound to be a reckoning eventually, although he doesn’t know how long it will take; in particular, he notes that progress

is hampered by people’s tendencies to take shortcuts.

Besides his work on the string landscape story, he has another main project called “T T-bar deformation,” which observes what happens when you change the energy scale of some systems. It turns out that you can add a deformation to a system that, at low energies, looks like local quantum field theory, but at high energies, looks like string theory. Sethi is excited about the potential for this theory to unify disparate theories like quantum field theory and gravity. Although the project is in the messy, early stages—Sethi points out that there’s “no clean picture” yet—like a true scientist, he finds it “lots of fun to explore.”

Jessica Metzger is a third-year Physics and Math major. She wants to go into physics research.

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SEARCHING FOR INFRARED GALAXIES IN THE MUSE OPTICAL DATA

Kathryn Downey 1 , Hanae Inami 2 1 University of Chicago 2 Hiroshima University

Infrared galaxies radiate 90% of their light in the infrared, and thus, many are not optically bright enough to be selected in spectroscopic surveys. This problem was lessened by using spectroscopic surveys with integral field unit (IFU) instruments which can simultaneously take imaging and spectroscopic data of an entire field-of-view. An optical IFU instrument called the Multi Unit Spectroscopic Explorer (MUSE) took optical spectra without pre-selections of target galaxies.

An automated approach was used to match galaxies in an infrared catalog to optical sources in the MUSE redshift catalogs within a 3-arcsecond radius. For sources with multiple matches, images of the galaxies were displayed, the locations of matching sources were marked, and the correct match was manually identified. Out of the 129 infrared galaxies in the catalog, 30 had no matches, 21 had too many matches, and 78 had one match. The optical catalogs provide two redshift calculations: spectroscopic and photometric. For fainter sources like infrared galaxies, these calculations often have a notable discrepancy. However, no obvious redshift discrepancy was found in this work. The galaxies were also classified by their powering mechanism (i.e. supermassive black hole or extreme star formation) using 3 classification diagrams that compared the flux ratios of different emission lines.

INTRODUCTION Infrared galaxies are galaxies that radiate >90% of their light in the infrared range, and they are 100 to 1,000 times brighter in the infrared range than typical galaxies. Infrared galaxies are scientifically interesting because they are the source of the majority of star formation of the universe. They can provide information on the star formation process and the history of the universe.

Many sky surveys are done in the optical range because it is possible to use ground-based telescopes. Since light intensity of infrared galaxies peaks in the infrared range, they are much fainter in the optical range. Thus, in surveys that require the selection of which objects to observe, the fainter infrared galaxies are often left out.

Unlike many previous surveys, spectroscopic

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surveys with integral field unit (IFU) instruments, which concurrently take imaging and spectroscopic data, do not require scientists to pre-select targets.

Instead, they take spectra for an entire field-ofview. This means that MUSE was able to collect optical data for infrared galaxies that were not bright enough in the optical range to be selected as targets in previous surveys. The MUSE optical survey observed two regions of the sky: MUSE Wide and MUSE Deep, which were observed for 1 hour/field and 10 hours/field respectively [1;2]. This survey overlapped with the infrared observation done by the GOODS-Herschel survey [3].

The goal of this research was to use the sources from two catalogues, the GOODSHerschel infrared catalogue and the MUSE optical

Figure 1a. Shows the locations of objects in the sky.

GOODS-Herschel infrared sources are shown in red. MUSE Wide and Deep optical sources are shown in green and blue respectively.

catalogue, and then find the corresponding optical data for each infrared observation. The new optical information was then used to analyze the infrared galaxies’ redshifts and classify them.

Matching MUSE Optical Data with known Infrared Galaxies The first goal of this project was to match the galaxies in an infrared catalog to the optical sources in the MUSE catalog. An automated program was run that matched every infrared galaxy to all optical sources within a 3 arcsecond range, as shown in Fig. 1. The result was the following: of 129 galaxies, 30 galaxies did not have a single optical source within range, 53 had 1 match, and 36 had 2 or more matches.

However, there should only be one corresponding optical source for each galaxy, so it needed to be determined which source was the correct one. To do this, a manual matching process was implemented. The locations of optical data were plotted on top of an infrared galaxy image, as shown in Fig. 2. Then, each image was manually inspected and, if possible, the correct source was identified. Fig. 2 shows an example of a galaxy with a clear match because the source at the center of the galaxy was most likely a correct match, while the source in the outer edges was probably incorrect. However, this identification was not possible for some galaxies because there were too many optical sources within a single infrared galaxy. The result of the matching process was that 30 galaxies had no matches, 78 had 1 match, and 21 had 2 or more matches.

Figure 1b. Shows the same infrared sources, but color coded differently.

Infrared galaxies with no optical matches are red, and those with one or more matches are blue.

Redshift Discrepancy The MUSE catalogue calculates the redshift values of its optical sources in two ways: photometric redshift (photo-Z) and spectroscopic redshift (spec-Z). Photometric redshift is calculated by finding the redshift in the entire spectral energy distribution. Spectroscopic redshift is found by finding the shift in individual spectral

Figure 2.

The background of this diagram is an infrared (70 Îźm) image of the infrared galaxy. A red x was placed in the center of the galaxy according to the location provided by the infrared catalogue. A red circle was overlaid to denote the 3 arc second range that all possible matching optical sources were within.

A black + was placed over the location of each optical source.

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Figure 3a. Shows the overall spectral energy distribution of an object which is used to calculate its photometric redshift.

lines. Examples of spectral energy distribution and spectral lines are shown in Fig. 3. Since the spectral energy distribution has less data points, its calculation is much more efficient but has a lower resolution result.

In the MUSE catalogue, there is a known discrepancy between the two redshift calculations that occurs for faint objects due to systematic errors in the survey. Since infrared galaxies are a faint subset of the catalogue, it would be useful to see if this discrepancy was present in this set of infrared galaxies.

To do this, the spectroscopic redshift was plotted against the discrepancy in the two redshift calculations, as shown in Fig. 4.

Figure 3b. Shows a zoomed in portion of the spectral energy distribution and the spectral lines which are used to calculate spectroscopic redshift.

For the data within one standard deviation, there appeared to be no discrepancy between photo-Z and spec-Z. This implies that the overall catalogue’s discrepancy is not due to the existence of infrared galaxies. This graph also reveals two more important pieces of information. Two of the infrared galaxies were at high redshift values which means they are very interesting as they can provide information about star formation in the early universe. Additionally, there were 6 outliers that fell outside of one standard deviation. It was concluded that they were included due to mistakes made in the manual matching process and were excluded from later results.

Figure 4. Bottom plot shows the spectroscopic redshift vs. the discrepancy in redshift. With the exception of outliers, this discrepancy was shown to be essentially negligible. The top graph shows the distribution of galaxies according to their redshifts.

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Figure 5. Bottom plot shows the spectroscopic redshift vs. the discrepancy in redshift.

With the exception of outliers, this discrepancy was shown to be essentially negligible. The top graph shows the distribution of galaxies according to their redshifts.

Spectral Line Classification Infrared galaxies can be classified into two major categories: active galactic nuclei (AGN) or starburst galaxies. AGN are powered by the accretion of matter into a supermassive black hole. AGN can be further classified by their ionization lines. Seyfert galaxies are AGN with strong high-ionization lines. Low-ionization nuclear emission-line regions (LINERs) are, as their name implies, AGN that have strong in lowionization lines. Unlike AGN, Starburst galaxies are powered by extraordinarily high rates of star formation. Starburst galaxies typically have star formation rates over 100 times greater than that of the Milky Way. It is also possible for a galaxy

to be classified as composite which means that it has characteristics of both categories.

In order to classify the infrared galaxies, the spectral line fluxes of each object needed to be calculated. To do this, the spectrum of each object and a 40-angstrom cutout around each spectral line was plotted. If there was only one spectral line inside the cutout, a single Gaussian curve fit was applied as shown in Fig. 5. If there were two spectral lines, a double Gaussian fit was applied. The flux of each spectral line was found by calculating the integral of each curve.

Each fit produced a corresponding uncertainty which was used to find the signal-to-noise ratio of each line.

If the signal-to-noise ratio was less than three, it was assumed that no spectral line was found and that it was instead obscured by high levels of background noise. In these cases, the maximum flux that could be obscured by this noise was found by using an amplitude 3 sigma levels above the mean of the continuum emission (3-sigma upper limit) as shown in Fig. 6.

The calculated fluxes of each spectral line were plotted on three BPT diagrams. A BPT diagram, named after the initials of its three creators, plots the ratios of different Balmer emission lines to low ionization emission lines [4]. Since the two types of galaxies are powered through different mechanisms, they are strong in different emission lines. The ratios of different lines are used so that AGN are concentrated in the top right corner of the diagram and starbursts are concentrated in the bottom left corner.

200 um

Figure 6a. Shows the best possible Gaussian fit (yellow) to the flux data (blue). An emission line would be expected where the red highlight is, but the background noise is too high to observe it. This means that the Gaussian fit has too low of a signal to noise ratio.

Figure 6b. The maximum flux that could be obscured by this level of noise was found and plotted in Fig. 6b.

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A Using this type of diagram, many different papers have proposed experimentally determined dividing lines between the two galaxy types. The first classification diagram, Fig. 7a, plotted the Nitrogen line (NII) divided by Hydrogen (Hα) against Oxygen (OIII) divided by Hydrogen (Hβ) [5-6]. The use of two lines in this diagram allows galaxies which fall between the lines to be classified as composite, meaning they have properties of both galaxy types. However, many objects do not have an observable Nitrogen (NII) line, so a new model was created that used Sulfur (SII) instead [5,7]. This diagram shown in Fig. 7b was able to classify more objects than before and subdivide the AGN into LINERs and Seyfert galaxies. The final diagram, shown in Fig. 7c, replaced one axis with the mass of the object, which was calculated in the optical catalogue, and added a composite classification [8]. This recovered many objects which did not have observable NII or SII lines.

B

Cross-Checking Classification Since all three graphs classify the same set of galaxies, it was necessary to see if each diagram’s classification was consistent with that of the other diagrams. To do this, Fig. 7c was recreated, but color-coded according to how the galaxies were classified in the other two diagrams (Fig. 8). The diagrams are mostly consistent with each other except for a few galaxies that were close to dividing lines.

C

DISCUSSION

Figure 7. All three figures show different variations of BPT diagrams.

Fig. 7a plots NII/Hα vs. OIII/Hα. Fig. 7b replaces the NII line with SII in order to include more galaxies. Fig. 7c replaces the x-axis with the galaxy’s mass. The coral dots represent galaxy’s whose fluxes are directly calculated. Blue arrows represent the maximum flux calculation and point in the direction that the flux could also exist as.

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A matching process was designed to find the corresponding source in the MUSE catalogue for each infrared galaxy in the GOODS-Herschel infrared catalogue. An automated process was created to find optical sources within 3 arcseconds of each infrared galaxy. Then, each galaxy was manually matched to the correct source. Out of 129 galaxies, a single optical match was found for 78 (59%). The new optical data was then used to create a graph that compares the discrepancy in spec-Z and photo-Z. No significant discrepancy was found besides outliers due to mismatched sources. Lastly, three diagrams were used to classify galaxies as AGN, starburst, or composite. The majority of galaxies were able to be classified in a way that was consistent between diagrams.

Figure 9. Reproduced from Chanial, 2003. [9] Displays a set of example SED templates that an infrared galaxy’s SED could be matched to. See Fig. 3.1 for an example infrared galaxy SED.

REFERENCES 1. Inami, H. et al. VizieR Online Data Catalog: MUSE Hubble Ultra Deep Field Survey. II. A&A, 608, 26 (2017) 2. Urrutia T. et al. VizieR Online Data Catalog: MUSE-Wide Survey DR1 catalog. A&A, 624, 24 (2019) 3. Elbaz D. et al. VizieR Online Data Catalog: GOODS-Herschel North and South catalogs. A&A, 533, 26 (2011) Figure 8. The data in both graphs is identical to that in Fig. 7c, but the color code is different.

Each galaxy in Fig. 8a is color-coded according to how it is classified in Fig. 7a and the galaxies in Fig. 8b are color-coded according to Fig. 7b. Coral represents AGN, blue represents composite, purple represents starburst, and yellow represents a galaxy that was not classified in the figure.

FUTURE PLANS The separation between AGN and starbursts is not as sharp as the BPT diagrams would imply. Infrared galaxies will often have contributions from both types of sources. For instance, a galaxy may emit 60% of its energy from an AGN but 40% from high rates of star formation. To find this information, template matching is extraordinarily useful. Each type of infrared galaxy has an expected shape to its spectral energy distribution called an SED template. Example SED templates are shown in Fig. 9. By matching each galaxy’s SED to a variety of templates, it is possible to produce a more nuanced description of an object’s classification and the contribution of AGN and star formation to its emissions.

4. Baldwin, J. A. Phillips, M. M., & Terlevich, R. Classification parameters for the emission-line spectra of extragalactic objects. PASP, 93, 5 (1981) 5. Kewley, L. J. et al. Theoretical Modeling of Starburst Galaxies.

ApJ, 556, 121 (2001) 6. Kauffmann, et al. The host galaxies of active galactic nuclei.

MNRAS, 346, 1055 (2003) 7. Kewley, L. J. et al. The host galaxies and classification of active galactic nuclei. MNRAS, 372, 961 (2006) 8. Juneau, S. et al. A New Diagnostic of Active Galactic Nuclei: Revealing Highly Absorbed Systems at Redshift >0.3. ApJ, 736, 26 (2011) 9. Chanial, P. Étude multi-longueurs d’onde des galaxies normales et à ambée de formation d’étoiles de l’univers local, thèse de doctorat, Université Paris VII. (2003)

Kathryn Downey is a second-year student at the University of Chicago majoring in Astrophysics and potentially minoring in Data Science. She hopes to attend graduate school to study computational astrophysics.

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ON BECOMING A POPULATION HEALTH SCIENTIST: AN INQUIRY WITH D R . S TA C Y L I N D A U / HANNA CZELADKO

As an architect of solutions to social injustice and a practicing physician, Dr. Stacy Lindau has focused her career on how best to advocate for her patients and connect them with essential resources outside of the doctor’s office. Dr. Lindau attended Brown University Medical School and is currently a practicing gynecologist at the University of Chicago. She teaches geriatrics and health services fellows at UChicago Medicine about health disparities, sexuality and aging, and research methods.

Before embarking on the path of a physician-scientist, Dr. Lindau was interested in political science and public policy. Dr. Lindau completed her undergraduate education at the University of Michigan-Ann Arbor, where she majored in political science as a student in the Honors College for Literature, Science, and the Arts (LAS). As an undergraduate, Dr. Lindau also studied secondary education and had plans for a career as a teacher. She later went on to receive a master’s degree in public policy from the University of Chicago.

She had no idea at this point in time that she would one day become intimately involved in population health research and social medicine.

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In fact, while attempting to find a teaching position in New York after college, Dr. Lindau inadvertently ended up in the television division of the Wall Street Journal.

During her time in The Wall Street Journal newsroom, Dr. Lindau recalled that she was good with her hands and became a wellrespected editor among her colleagues due to her trusted editing skills.

As a part of her work, Dr. Lindau spent much of her time reading endless strings of numbers on ticker tape conveying information about the sale of stocks and trades. She soon found herself fascinated by the factors governing a person’s decision-making and their relationship to the trade information contained on the ticker tape. At the same time, Dr. Lindau began volunteering at a local hospital in New York. Piecing together the many skills and interests she had acquired in New York, Dr. Lindau decided to embark on the physician-scientist career path and applied to medical school.

During her time as a medical student, Dr. Lindau took a history and physical examination course intended to give first and second year medical students the necessary skills for patient interactions. Dr.

Dr. Stacy Lindau is a professor of Obstetrics and Gynecology and Geriatrics and the director of the South Side Health and Vitality Studies (SSHVS) at the University of Chicago Urban Health Initiative.

Lindau was taught to take sexual histories and other relevant information. As HIV and AIDS were both widespread diseases at this time, she understood that anybody having unprotected sex could be at risk of these diseases. As a result, Dr. Lindau believed that “age was not a reason not to ask” and took sexual histories from patients of all ages.

After asking an older patient about their sexual history, the conversation

always took a turn. Dr. Lindau recalled that “there would be a sparkle in their eye and a story to tell.” Dr. Lindau found it strange that her medical student peers, and even the leading geriatricians, omitted questions about sexual history when speaking to older patients. This “felt like hypocrisy” to Dr. Lindau and ultimately sparked her curiosity about sexual function and aging.

Currently, Dr. Lindau focuses not only on sexual function in the context of aging, but also in the context of cancer and other illnesses. In particular, the Bionic Breast Project, a collaboration with Dr. Sliman Bensmaia, has become one of her largest endeavors. Funded by the National Cancer Institute, this project focuses on discovering how women think and feel about their breast function, particularly the sensory functions.

Up to this point, breast function has been poorly studied beyond lactation.

Ongoing research relies on face-toface interviews, online surveys, and psychophysical sensory testing involving women from the Chicago area. As part of the Bionic Breast Project, Dr. Lindau and Dr. Bensmaia hope to restore sensory function to women who have undergone mastectomies. Part of this process will involve performing nerve block studies in collaboration with the Department of Anesthesia at UChicago Medicine as a means of better understanding the sensory pathways of the breast.

When asked about her motivation behind this project, Dr. Lindau quickly replied that patient questions and frustrations have always informed her research. As a practicing gynecologist, helping women with cancer preserve and recover their sexual function comprises the focus of her clinical practice.

Over half of Dr. Lindau’s patients have or have had breast cancer and many have asked for help in recovering sexual function while fighting cancer.

According to Dr. Lindau, many of these patients feel a tremendous loss from their mastectomy and do not feel prepared

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for the implications of a mastectomy on their sexual function. Currently, patient advisors make important contributions to the Bionic Breast project, helping direct the trajectory of research.

Other motivating factors include massive gaps in knowledge concerning female sexual function in the context of cancer. Dr. Lindau noted that research on preserving and restoring sexual function for men with prostate cancer has come very far. On the contrary, analogous research concerning women and breast cancer lags far behind, despite a similar prevalence of prostate and breast cancer.

Massive investments have been made in penile reconstruction and transplants, even though penile cancer and injuries that warrant reconstruction have an astonishingly low prevalence. In contrast, approximately 100,000 women a year in the U.S. undergo the removal of one or both breasts, and relatively little research exists to help preserve and restore female sexual function.

Due to what Dr. Lindau describes as “a deep frustration with waste and injustice in healthcare,” she has also founded several community-based asset mapping organizations, including MAPSCorps and Community RX. MAPSCorps and Community RX both study how best to connect people with community-based support and assets to promote health and help community members manage their needs. In partnership with youth employment agencies across the city, MAPSCorps hires high school students and pairs them with science-oriented college students that provide the training necessary for gathering systematic data about all operating businesses and organizations within a given community.

MAPSCorps currently operates in five U.S. regions, and its operation in Chicago relies on a network of organizations across 61 communities city-wide.

Through a current study led by the Community RX program, Dr. Lindau will

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Dr. Stacy Lindau and her lab. WomanLab is a virtual platform where every woman—and everyone who loves and cares for women—can learn facts about sex.

Approximately 100,000 women a year in the U. S . undergo the removal of one or both breasts , and relatively lit tle research exists to help preser ve and restore female sexual function.

Dr. Stacy Lindau strongly believes in creating and spreading knowledge that people and communities can use to sustain excellent health and vitality.

target caregivers of community-residing people with dementia. This study focuses primarily on the black population of the South Side of Chicago, as minorities are more likely to be diagnosed with Alzheimer’s and to age at home with Alzheimer’s as opposed to aging in a hospital or other facility. Through this research, Dr. Lindau hopes to draw attention to the disproportionate burden placed on family members of minority groups in terms of providing unpaid caregiving to family members with dementia. She intends to uncover how best to connect caregivers with community-based support to help them and the people they care for navigate their diagnosis.

Dr. Lindau also plans to reach as many people worldwide as possible through her blog-based website, WomanLab. Having already reached over 115,000 people in more than 200 countries, this platform aims to provide unique information about female sexuality and sexual function in the context of aging, cancer, and other illnesses. WomanLab has grown substantially since it first began in January of 2017. Dr. Lindau believes that clearly “there is a need for the kind of information we are providing.”

Hanna Czeladko is a third-year student at the University of Chicago majoring in Biochemistry and Chemistry.

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RE SISTI N I NCRE ASE S B RE AST CANCER CELL INVASIVENE S S BY INDUCING THEIR ME SENCHYMAL TR ANSITION

Julianna Bianco 1 1 Endocrine Lab, Lenox Hill Hospital of Northwell Health

Breast cancer is the most common cancer in women and has been found to have increased metastasis (the spread of cancer cells beyond the primary cancer site) and poorer prognoses in obese women. Resistin is an adipokine that plays a key role in the development of insulin resistance. One of the key steps in cancer metastasis is the epithelial to mesenchymal transition (EMT). The purpose of this study was to investigate the potential effect of resistin on breast cancer cell invasiveness and EMT. MCF-7 and MDA-MB-231 breast cancer cells were used. In vitro assays were performed, and mRNA expressions were measured in qRT-PCR analyses. Changes in the cell morphology and cell motility were also explored in separate assays. Resistin was found to upregulate the expression of cytokine SOCS3 (p < 0.05), and resistin-treated cells also experienced cell separation and cellular protrusion formation as well as increased cell motility. These morphological changes were most likely caused by the upregulation of EMT markers and cyclin D1, a key cell cycle protein (p < 0.05).

This study gives insight into resistin-induced EMT and highlights the importance of resistin levels in breast cancer.

INTRODUCTION Breast cancer is the most common cancer among women, accounting for nearly 1 in 3 diagnosed cancers and 16% of all female cancers. Although genetic predisposition is the main contributing factor for cancer initiation and progression, obesity also plays a significant role.

It is estimated that about 20% of cancer deaths in American women 50 years or older were caused by obesity [1] and approximately 35% of adults in the United States are considered obese.

The pathways of chronic diseases like diabetes have also been linked to the pathways of obesity, which may affect the prognoses of cancer patients [2]. It is well-established that obesity levels in women correlate with cancer incidence and is negatively associated with cancer survival, regardless of their menopausal status [3].

Today, cancer metastasis constitutes the main

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cause of mortality in cancer patients [4]. Many studies report that obese women in the highest quintile of the body mass index have double the death rate from breast cancer metastasis when compared to women in the lowest quintile [1], but the mechanism between obesity and the decreased breast cancer metastasis is still poorly understood [5]. Accumulating evidence suggests that adipose tissue plays a key role in the pathophysiology of cancer metastasis [6].

Until recently, adipose tissue was considered only as an energy storage and as a thermal insulator, but it has recently been classified as an important endocrine organ. Adipose tissue produces a variety of cytokines and other compounds that play different roles in the regulation of metabolic homeostasis [7].

There are two types of adipose tissue: white and

brown. Obesity is characterized by an increase in adipocyte size and number in white adipose tissue (WAT) and increased pro-inflammatory cytokine production. The excessive lipid production that takes place in obese humans leads to the increased secretion of adipokines (cytokines in adipose tissue) from WAT. These adipokines have pro-inflammatory qualities, and inflammation has been found to be positively correlated with increased cancer risk. One commonly secreted adipokine is leptin [2], which has been found to induce breast cancer cell migration and invasion by inducing cellular transformation, specifically the epithelial to mesenchymal transition (EMT) of cells. But leptin is only one of the many adipokines secreted by WAT [8].

EMT is an early event in cancer metastasis in which cells lose their epithelial characteristics and gain mesenchymal-like features. During this process, cells lose their well-developed junctions and polarization [9], becoming unorganized and spindle-shaped [10]. EMT is a mechanism for cancer to metastasize, which is defined as the movement of cells from one part of the body to the other [4] resulting in tumor growth and cancer progression. In one study, cells that were treated with transforming growth factor beta (TGF-β) underwent EMT and became resistant to apoptosis [9], suggesting a novel role of this pathway. EMT has been found to take place in cancer cells after the activation of specific transcription factors and genes as well, but the causes of this change in signaling pathways is still unknown [4].

Another adipokine secreted by WAT is resistin, named after ‘resistance to insulin’ [6].

Resistin plays a key role in the development of insulin resistance, becoming known as a molecular link between diabetes and obesity.

Resistin has been classified as pro-inflammatory, but its specific functions are still highly disputed [11]. Multiple clinical and in vivo studies involving genetic or diet-induced obese animal models found that serum resistin levels correlate with WAT mass, reaching its highest levels in states of obesity [12]. Accordingly, weight loss is accompanied by a decrease in serum resistin levels [13]. Recent evidence suggests that resistin also plays a role in breast cancer tumor progression [14] and is associated with poor cancer prognoses [5].

Obesity leads to an increase of lipid production in adipocytes, causing an increased

release of hormones and proteins [2] like resistin. And so, because of the greater amount of WAT in obese humans, resistin levels may also be increased, suggesting that resistin is a possible mechanism between obesity and cancer metastasis. The purpose of this study is to investigate the potential effects of resistin on breast cancer cell invasiveness and EMT.

MATERIALS AND METHODS Cell Cultures MCF-7 and MDA-MB-231 human breast cancer cell lines were used. MCF-7 are breast cancer cells that have an epithelial phenotype and grow in clusters. MDA-MB-231 are highly invasive, fibroblastic breast cancer cells with a star-shaped morphology [8]. Both cell lines were grown in respective cell cultures.

In Vitro Assay An in vitro assay was performed with both MCF-7 and MDA-MB-231. Duplicate plates were stimulated with 4 ul of 12.5 ng/ml of Human Resistin Recombinant Protein. Two plates per cell line were left unstimulated as a control. The experiment was performed three times.

RNA Extraction After six hours, the plates were removed from the incubator and RNA was extracted from the cells. Once a pellet of RNA was created, it was diluted in DNase/RNase-free distilled water. The RNA concentrations of each sample were then measured.

Reverse Transcription RNA was reversely transcribed to cDNA by using qScript cDNA SuperMix and a Thermal Cycler. The cDNA was then diluted 10X with DNase/RNase-free distilled water.

Quantitative reverse-transcription PCR Quantitative reverse-transcription PCR was performed using various primers. Relative mRNA expressions of the target genes were calculated using the ΔΔCt method and were normalized to that of 18S house-keeping gene.

Cell morphology assay A time-course treatment of MCF-7 cells was performed. After 24 hours, the first set of

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duplicate plates, labeled Day 5, were stimulated with 4 ul of 12.5 ng/ml resistin (dilution of 1:1000).

The following day, the Day 4 cells were stimulated.

The next day, Day 3 cells were stimulated. This was repeated until Day 1 was reached. Throughout the course of the experiment, two plates were left unstimulated as controls. After the final day of stimulation, nine representative images were taken of each condition. The images were analyzed by evaluating the number of particles (cell clusters or single cells) and the size of each particle (measured by the number of pixels).

Scratch migration wound healing assay MCF-7 and MDA-MB-231 cells were grown in separate 6-well cell culture plates until confluent monolayers were formed. A scratch was created by scraping through the monolayer with a micropipette tip. Two wells of each plate were then treated with 12.5 ng/ml of resistin and the plates were put back into the incubator. Five images from each plate (total of 10 images per condition) were taken at different time intervals (3, 6, 15, and 18 hours). From each image, scratch width was measured in six different areas and averaged. For the data analyses, the scratch width at each time interval was normalized to 0 hours and presented as percentage of resistin treatment vs. control.

Statistical analysis Statistical analyses were performed by unpaired t-test. Data were represented as mean values Âą SEM. Data was accepted as statistically significant when p < 0.05.

Figure 1. Effect of resistin on SOCS3 mRNA expression MCF-7 (A) and MDA-MB-231 (B) cells were treated with 12.5 ng/ ml resistin at a concentration of 1:1000 for 6 hours. RNA was then extracted using TRIzol (Thermo Fisher Scientific, Hanover Park, IL). Expression levels of SOCS3 mRNA were measured by using qRT-PCR analysis. This treatment mediated a positive effect on resistin levels, indicating that resistin at a concentration of 12.5 ng/ml is sufficient enough to affect the cells (* p < 0.05).

Figure 2. Effect of resistin on MCF-7 cell morphology MCF-7 were treated with 12.5 ng/ml of resistin at a concentration of 1:1000 for 1 to 5 days in duplicate plates where c depicts the control plate. After the last day of stimulation, nine representative images at a magnification of 20X were taken of each condition. After two days of treatment with resistin, individual cells started to separate from their cellular clusters. After three days of treatment, the formation of cellular protrusions was observed, indicated by the yellow arrows. These effects developed during the entire five day time-course experiment. The observed morphological changes are indicative for initiation of EMT.

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200 um

Figure 3. Resistin-induced morphological changes in breast cancer cells- image analysis MCF-7 were treated with 12.5 ng/ml of resistin at a concentration of 1:1000 for 1 to 5 days in duplicate plates where c depicts the control plate. After the last day of stimulation, nine representative images at a magnification of 20X were taken of each condition.

These images were then analyzed by Image) software. (A) Number of particles (* p < 0.033 compared to control) (B) Mean particle size (** p < 0.0001 compared to control).

RESULTS Resistin increases SOCS3 mRNA expression In a series of time-course and concentrationdependent experiments that were previously performed, the treatment conditions for resistin in breast cancer cell lines were optimized. Suppressor of cytokine signaling 3 (SOCS3) is a proinflammatory cytokine and one of the members of the SOCS family which functions by inhibiting the tyrosine kinase activity of JAK2. Since it was previously known that resistin upregulates SOCS3 transcriptional expression [11], SOCS3 can be used as a downstream target gene as evidence of resistin level optimization. SOCS3 mRNA was measured using qRT-PCR analysis. Resistin at concentration of 12.5 ng/ml for 6 hours was sufficient to mediate a positive effect on SOCS3 (p < 0.05) in both MCF-7 and MDA-MB-231 breast cancer cells, as illustrated in Figure 1.

Resistin induces breast cancer cell morphological changes After optimizing the treatment conditions, the effect of resistin on breast cancer cellular morphology was investigated. MCF-7 cells, not MDA-MB-231, were used because MCF-7 cells grow in clusters, making it easier to study their morphological changes. After two days of treatment with resistin, individual cells started to separate from their cellular clusters. On the third day of treatment, formation of cellular protrusions was observed. These effects were

observed during the entire five day time-course experiment, as demonstrated in Figure 2. The observed morphological changes are indicative of an initiation of EMT and the loss of tight junction cell adhesion proteins. Results from the cell morphology image analysis demonstrated that resistin treatment resulted in a time-dependent decrease of the particle sizes (p < 0.033; Day 5 as compared to control) as well as a decrease in the number of particles (p < 0.0001; Day 5 as compared to control) after five days of treatment, as shown in Figure 3.

Resistin potentiates the mesenchymal transition of breast cancer cells To solidify the evidence for the initiating effect of resistin on EMT in breast cancer cells, the expression of fibronectin and vimentin, which are well-utilized mesenchymal markers [10], were measured by using qRT-PCR analysis. Resistin upregulated the mRNA expression levels of these two markers in both MCF-7 and MDA-MB-231 cells (p < 0.05), as demonstrated in Figure 4.

Furthermore, the expression of key transcription factors Snail (Snail 1), Slug (Snail 2), Twist, and ZEB1 [15] involved in EMT were measured in the presence and absence of resistin. The results illustrated in Figure 5 demonstrate that resistin significantly upregulated the expression of Snail, Slug, Twist, and ZEB1 in both MCF-7 and MDAMB-231 cells (p < 0.05).

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C = control

R = resistin treatment

Figure 4. Effect of resistin on mesenchymal gene expression levels in breast cancer cells MCF-7 (A & B) and MDA-MB-231 (C & D) cells were treated with 12.5 ng/ml resistin at a concentration of 1:1000 for 6 hours. RNA was then extracted using TR1zol (Thermo Fisher Scientific, Hanover Park, IL). Expression levels of fibronectin and vimentin mRNAs were measured by using qRT-PCR analysis. Both mesenchymal markers (Chen et al., 2017) were significantly upregulated (* p < 0.05).

C = control

R = resistin treatment

Figure 5. Effect of resistin on mRNA expression of key EMT transcription factors in breast cancer cells MCF-7 (A, B, C, & D) and MDA-MB-231 (E, F, G, & H) cells were treated with 12.5 ng/ml resistin at a concentration of 1:1000 for 6 hours.

RNA was then extracted using TRIzol (Thermo Fisher Scientific, Hanover Park, IL). Expression levels of Snail (Snail 1), Slug (Snail 2), Twist, and ZEB 1 mRNAs were measured by using qRT-PCR analysis. All six key EMT transcription factors (Zeisberg & Melson, 2009) were significantly upregulated (* p < 0.05).

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200 um

C = control

R = resistin treatment

Figure 6. Effect of resistin on breast cancer cell motility Scratch migration wound-healing assay of MCF-7 (A) and MDA-MB-231 (B) cells subjected to resistin treatment (12.5 ng/ml) for up to 18 hours. The scratch was completely filled after 24 hours. Cells were grown in duplicate plates. Five images from each plate (total of ten images per condition) were taken at different time-intervals (0, 3, 6, 15, and 18 hours). From each image scratch width was measured in six different areas and averaged. For the data analyses, the scratch width at each time-interval was normalized to 0 hour and presented as percentage of resistin treatment vs. control. MCF-7 cells, * p < 0.0004, ** p < 0.0001, *** p < 0.0014; MDA-MB-231 cells, * p < 0.0153, ** p < 0.0036, *** p < 0.0034, compared to each time-interval control (C & D).

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C = control

R = resistin treatment

Figure 7. Effect of resistin on mRNA expression of cyclin D1MCF-7 (A) and MDA-MB-231 (B) cells were treated with 12.5 ng/ml resistin at a concentration of 1:1000 for 6 hours. RNA was then extracted using TRIzol (Thermo Fisher Scientific, Hanover Park, IL). Expression levels of cyclin D1 mRNA was measured by using qRT-PCR analysis. This key protein of the cell cycle that can be used to mark cell cycle progression (Masamha & Benbrook, 2009) was significantly upregulated (* p < 0.05).

Resistin increases breast cancer cell motility After 6 hours, it was observed that in the resistin stimulated wells, the scratch was closing faster than in the control (p < 0.05), as illustrated in Figure 6. This suggests that the resistin treatment induced cell proliferation and migration in both MCF-7 and MDA-MB-231 cells.

Resistin upregulates key cell cycle protein Finally, the effect of resistin on cyclin D1 mRNA expression was examined with the hypothesis that resistin may have an effect on cell cycle progression. The results from the qRT-PCR analyses demonstrated that resistin significantly upregulated cyclin D1 mRNA expression levels (p < 0.05) in both MCF-7 and MDA-MB-231 cells, as illustrated in Figure 7.

DISCUSSION Cancerous cells have been found to have lower levels of intercellular adhesiveness compared to their epithelial counterparts [4].

As illustrated in Figure 2, resistin treatment resulted in the separation of single cells from the cellular clusters and the formation of cellular protrusions, signs indicative of an initiation of EMT and subsequently the loss of tight junction cell adhesion proteins such as E-cadherin and

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occludin. The loss of these proteins promotes cellular transformation. Cases of breast cancer with a low gene expression of occludin and E-cadherin have extremely poor prognoses [9], most likely because of the EMT caused by the loss of these proteins. The loss of epithelial cell adhesion molecule E-cadherin is correlated with the upregulation of fibronectin and vimentin [10].

Fibronectin is a protein found in the extracellular matrix and vimentin is an intermediate filament that holds organelles within the cytosol of the cell [16]. The upregulation of both of these proteins correlated with increased metastatic potential and cancer cell invasiveness [10]; the data illustrated in Figure 5 supports this information, demonstrating that the morphological changes that took place throughout the five-day experiment was in fact EMT. Similarly, the upregulation of transcription factors Snail, Slug, Twist, and ZEB1 have been found to be positively correlated to EMT. Snail is a widely used downstream target for the multiple signaling pathways of EMT processes [16], two of these signaling pathways being TGF-β and Wnt [17]. Recent studies [8] demonstrated that leptin (the most commonly secreted adipokine [2]) induces EMT by activating the Wnt/β-catenin signaling pathway as well. All currently known EMT processes that take place during the metastasis of cancer appear to be associated with Snail upregulation. In fact, all zinc finger proteins in this family have been found to mediate EMT and to be positively correlated with protection against cell death. The two previously mentioned transcription factors, fibronectin and vimentin, have also been found to upregulate mesenchymal cell marker expressions and to downregulate multiple epithelial markers like occuldin. Snail and Slug suppress E-cadherin expression, similar to Twist which suppresses E-cadherin and upregulates fibronectin, thus working alongside Snail and Slug during cancer metastasis [16]. Twist has been found to do this independently, however, utilizing the β-catenin pathway [17]. Twist has also been found to be transcriptionally active during cell differentiation, further demonstrating its role in cancer metastasis. Zinc finger E-box binding homebox 1 (ZEB1) is another gene that represses E-cadherin expression [16]. ZEB1 activation follows the activation of the Snail family, correlating it to the TGF-β and Wnt signaling pathways [17].

All six genes have been found to suppress the

expression of E-cadherin, indicating a major role in EMT. This offers an explanation as to why the morphological changes of resistin treated breast cells took place by suggesting a possible molecular mechanism.

From the scratch migration assay and analysis of cyclin D1 expression, it can be inferred that resistin increases breast cancer cell line motility by promoting cell cycle progression.

Cyclin D1 is a key protein required through the G1 phase of the cell cycle that is rapidly synthesized and accumulated in the nucleus. Cyclin D1 is a regulatory subunit of cyclin-dependent kinases CDK4 and CDK6. Namely the complex of cyclin D1 with CDK4 promotes the passage through the G1 phase during the cell cycle. Once the cell enters the S phase of the cycle, it is committed to DNA replication and division [18]. An increase in cyclin D1 may suggest an increased rate of this process, thus leading to an increased proliferation of breast cancer cells. By analyzing both Figures 2 and 4, it can be inferred that the migration of cells in Figure 4 is that of mesenchymal cells.

These transformed cells are more aggressive and invasive than their epithelial counterparts, suggesting an explanation behind why resistin causes breast cancer cells to experience induced migration.

indicating a positive correlation.

The results demonstrated that resistin induced both genetic and morphological changes in breast cancer cells. Based on these findings, a model for resistin-induced EMT in breast cancer is presented: resistin transmits signals via TGF-β, Wnt, and β-catenin pathways, leading to the upregulation of mesenchymal genes and transcription factors. This upregulation could also be hypothesized to allow nuclear accumulation of cyclin D1, ultimately inducing the epithelialmesenchymal transition of breast cancer cells in response to resistin treatment. Collectively, this study gives insight into resistin-induced EMT and highlights the importance of resistin levels in breast cancer. Further studies should aim to utilize a complete mechanism of these effects.

Julianna Bianco is a second-year student at the University of Chicago. She is majoring in Biology with a specialization in Global Health and minoring in Health and Society. Julianna hopes to attend medical school and continue to research cancer and/or gastrointestinal disease in a more global context.

CONCLUSION Despite advances in treatment, metastasis remains the main cause of mortality in cancer patients, contributing to 90% of all deaths from solid tumors [4]. Cancer metastasis is a multistep process involving interplay between the cancer cells and the host distant site. One of the key steps in metastasis is EMT. But similar to breast cancer, many of the causes and mechanisms of EMT remain unknown. In order to further investigate the mechanism behind the morphological changes, the expression of tight junction cell proteins was analyzed. This experiment has reported a novel function of resistin as an inducer of EMT in breast cancer cells, providing in vitro evidence of some novel and important players of the resistin network.

This is most likely the first reported finding that resistin induces the mesenchymal transition of breast cancer cells. The results of this study draw parallel conclusions to other studies on the role of adipokines in the EMT of breast cancer,

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2. Ray, A., & Cleary, M. P. (2012). Obesity and breast cancer: A

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4. Gupta., G. P., & Massaguè, J. Cancer metastesis: building a

transition of breast cancer via the upregulation of pyruvate kinase M2. Journal of Experimental & Clinial Cancer Research, 35(166), 1-10. (2016).

16. Zeisberg, M., & Neilson, E. G. Biomarkers for epithelialmesenchymal transistions. J.Clin. Invest., 119, 1429-1437. (2009).

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17. Lamouille, S. et al. Molecular mechanisms of epithelial-

5. Lee, J. O. et al. Resistin, a fat-derived secretory factor,

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promotes metastasis of MDA-MB-231 human breast cancer cells through ERM activation. Scientific Reports, 6, 1-11. (2016).

6. Assiri, A. M. A. et al. Resistin, visfatin, adiponectin, and leptin: risk of breast cancer in pre- and postmenopausal Saudi females and their possible diagnostic and predictive implications as novel biomarkers. Disease Markers, 2015, 1-9. (2015).

7. Coelho, M. et al. Biochemistry of adipose tissue: an endocrine organ. Archives of Medical Science, 9(2), 191–200 (2013).

8. Yan, D. et al. Leptin-induced epithelial- mesenchymal transition in breast cancer cells requires B-Catenin activation via Akt/GSK3- and MTA1/Wnt1 protein-dependent pathways. The Journal of Biological Chemistry, 287(11), 8598-8612. (2012).

9. Runkle, A. E., & Mu, D. Tight junction proteins: From barrier to tumorigenesis. Cancer Letters, 337(1), 41-48. (2013).

10. Chen, Q. et al. TGF-β1 induces EMT in bovine mammary epithelial cells through the TGFβ1/Smad signaling pathway. Cell Physiol Biochem, 43, 82-93. (2017).

11. Pirvulescu, M. et al. A novel pro- inflammatory mechanism of action of resistin in human endothelial cells: Up-regulation of SOCS3 expression through STAT3 activation. Biochemical and BioPhysical Research Communications, 422(2), 321-326. (2012).

12. Lee, J. H. et al. Cirulating resistin in lean, obese, and insulinresistant mouse models: lack of association with insulinemia and glycemia. Am J Physiol Endocrinol Metab, 288, E625-E632.

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13. Valsamakis, G. et al. Modest weight loss and reduction in waist circumference after medical treatment are associated with favorable changes in serum adipocytes. Metabolism, 53(4), 430-434. (2004).

14. Deshmukh, S. K. et al. Resistin and interleukin-6 exhibit racially-disparate expression in breast cancer patients, display

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clinical biochemistry perspective. Clinical Biochemistry, 45,

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mesenchymal transition. Nat Rev Mol Cell Biol., 15(3), 178-196.

Professor Fred Chong is the Seymour Goodman Professor in the Department of Computer Science at the University of Chicago, and the Lead Principal Investigator EPiQC, an NSF Expedition in Computing.

QUANTUM INFORMATION AND COMPUTING AN INQUIRY WITH PROFESSOR FRED CHONG / ROHAN KUMAR

What do you think of when you see the word ‘computer’? Maybe you think about the thin laptop in your backpack or the phone in your pocket. Perhaps instead you imagine the colossal processors in Facebook’s data centers, or a futuristic robot controlled by artificial intelligence.

Regardless of what comes to mind, there is a high chance that it shares one key characteristic with all the examples listed above: It operates on bits. A bit, which is an abbreviation for the term “binary digit,” is a type of data where information can be represented with either a ‘1’ or a ‘0’. All the computers you know, from microwaves to desktop computers, operate using long chains of bits to carry out computations.

They are, quite literally, the foundation of the history of computers.

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There has been a significant lack of research ‘connecting the theor y and algorithms of quantum computation to the machines that are being built .’

However, what if instead of using bits, we were able to use different forms of information to do computing? How would this be possible, and would there be advantages in using particular kinds of information when building computing systems? These are the questions that Professor Frederic Chong and his research group are focused on. Professor Chong is the Seymour Goodman Professor of Computer Science at The University of Chicago. His research is focused on quantum computation, a rapidly developing field of computing that, instead of bits, uses qubits to encode and process information.

Unlike bits, qubits can store values that can be expressed as a combination of both a ‘0’ and ‘1,’ such as a state halfway in between the two.

While many institutions and corporations alike are investing heavily in this area, Professor Chong’s research is different than other research in the field as it focuses on the software side of quantum computation. In his words, while there are large strides being made to build more reliable physical quantum machines, there has been a significant lack of research “connecting the theory and algorithms of quantum computation to the machines that are being built.” This has resulted in large

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inefficiencies in the execution of quantum algorithms on existing hardware. Professor Chong’s work, however, focuses on minimizing these inefficiencies by reducing the number of qubits and the time required to execute these quantum algorithms.

In order to achieve this goal, Professor Chong directs an “Expedition in Computing” under the National Science Foundation called “Enabling Practical-Scale Quantum Computing” (EPiQC). This group, which spans 5 universities and researchers with backgrounds from theoretical computing to experimental physics, aims to find ways to reduce the time and resources taken to execute algorithms on the order of thousands of times. According to Professor Chong, using software and systems design to tackle these problems means saving as much as twenty years of research in building quantum hardware to run the same algorithms.

Despite his expertise in the field, Professor Chong was not always interested in quantum computation. In fact, the field didn’t exist when he was a teenager. Instead, he was primarily interested in computer science throughout his high school years.

More specifically, he has always fostered curiosity about the physics of computing, particularly at the level of chip design and fabrication. He explains that understanding the fundamentals of how chips constitute computers allowed him to build “a strong intuition for the efficiency of algorithms,” such as how fast they will run, how much power they will use, and how many resources they will take, from an early age.

To understand computing systems and architecture further, Professor Chong studied Computer Science and Electrical Engineering at the Massachusetts Institute of Technology, obtaining his Bachelor’s Degree, Master’s Degree, and PhD. In his undergraduate years, he met a student named Isaac Chuang, who would invite him to join one of the first teams in the world to work on the computer architecture of quantum computers many years later. This

In a talk that brought together Yale quantum physicists, computer scientists, and electrical engineers, Professor Fred Chong discusses how to close the gap between quantum algorithms and hardware through software-enabled vertical integration and co-design. Credit: youtube.com.

began Professor Chong’s journey in the field of quantum technology, which he believes will be impactful to the world in a number of ways.

Professor Chong believes the most immediate observable result of quantum computing will be the effects it has on scientific research. Quantum computing could vastly improve our ability to model complicated systems, which can impact human life in a number of ways. For example, some estimates say that about 2% of the world’s energy consumption is used to fix nitrogen in the process of making fertilizer for crops and farms. However, compared to the efficiency with which plants fixate nitrogen, the method we currently use to do so is highly inefficient. However, according to Professor Chong, quantum computers could help us model and understand the process of nitrogen fixation more thoroughly, allowing us to potentially save

vast amounts of energy in the global food supply chain. Other impacts of quantum modelling include better material design, more effective photovoltaic cells, more efficient delivery routes, and machine learning.

Another impact of quantum computing, which will likely take more time before coming into effect, is on cryptography.

Cryptography is the study of ‘scrambling’ information such that only the intended recipient of the information can ‘unscramble’ and interpret it. In other words, when information exchange occurs between two parties, cryptography explores how to make the information in question unreadable to all external parties. Cryptography is central to our everyday lives. Every time you use email, social media, or your credit card, you are implicitly trusting that no third party can track your information and activity as you do so. Email providers, social media

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companies, and banks use cryptography to encrypt your information in order to make sure that this is the case. Currently, the most widely used cryptographic method is called RSA encryption, invented by Ronald Rivest, Adi Shamir, and Leonard Adelman in 1977. Simply put, its security is based on the fact that it takes computers operating on regular bits on the order of thousands of years to factorize products of very large prime numbers. As such, by the time a third party were to ‘read’ RSA-encrypted information, the information in question would be completely useless, protecting the interests of the person whose information was encrypted.

Despite RSA encryption’s large popularity, the advent of quantum computation would flip its effectiveness on its head. This is because large quantum computers could theoretically run Shor’s Algorithm, an algorithm that takes a much shorter amount of time to factorize the product of two very large prime numbers.

Thus, while it might take a classical computer thousands of years to decrypt RSA encrypted information, it would take a sufficiently advanced quantum computer running Shor’s Algorithm only on the order of minutes to do the same. If experimentally possible, this would render our current cryptographic systems highly vulnerable. As such, we will need to change our encryption schemes to be more resistant to quantum

Quantum computation will likely change the face of large scale cr yptography in the digital world.

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algorithms in the future. According to Professor Chong, many of these new, quantum-proof algorithms will require some degree of quantum computation and communication themselves to enhance the security of new encryption schemes.

As such, quantum computation will likely change the face of large-scale cryptography in the digital world.

Quantum computing is a powerful tool that will potentially revolutionize the scope of science and technology. Thus, while its uncertain and unintuitive nature may be unsettling to some, Professor Chong, drawing on his interdisciplinary career spanning theory and application, simply relishes in it as another exciting problem awaiting solutions.

Rohan is a first-year student at the University of Chicago looking to major in Physics and Mathematics. He is fascinated by research in quantum technology and looks forward to getting involved in the field.

TOWARDS ME ASU REMENT OF ANTI MAT TER AB SORPTION CROS S -SECTION : DEUTERON I DENTI FICATION I N ALICE

Julia Book 1 , Alberto Caliva 2 University of Chicago 2 GSI Helmholtz Centre for Heavy Ion Research

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The ALICE detector on the Large Hadron Collider (LHC) at CERN is optimized to study the products of heavy ion collisions in the LHC. The LHC and all associated experiments are currently undergoing upgrades in anticipation of its third run. One of the improvements under consideration for ALICE in the LHC Run III upgrade would enable ALICE to measure the inelastic cross-section of anti-deuterons produced in those collisions, providing insight on matter-antimatter asymmetry by comparing the cross-sections of deuterons and anti-deuterons. In preparation of the measurement, a method of effectively identifying deuterons and anti-deuterons in the detector must be established, and a thorough simulation of the experiment must be conducted to enable effective data-taking. We consider how data from the inner detectors can be used to improve anti-deuteron identification and establish the parameters necessary for initial simulations of the experiment. Specifically, we consider particle identification data from the Time Projection Chamber (TPC), Transition Radiation Detector (TRD), and Time of Flight Detector (TOF) in particle identification. Data from the TPC and TOF were used to identify the deuteron and anti-deuteron peaks, but TRD data was insufficient to lower the signal-to-background ratio on those peaks.

Therefore, simulation of the experiment to improve identification techniques is required.

INTRODUCTION ALICE (A Large Ion Collider Experiment) is one of the four major experiments along the LHC (Large Hadron Collider) at CERN, an international particle physics laboratory in Geneva, Switzerland.

ALICE is a general purpose heavy ion detector which focuses on the lead-lead collisions produced by the LHC. At the collision point, energies of the colliding particles are so high that the protons and neutrons making up the nuclei “melt,” dissolving into their composite quarks and gluons. This “quark-gluon plasma” mimics the conditions of the early universe in a way that is directly observable by researchers. ALICE contains 18 separate detector systems to analyze quark-gluon plasma and resulting particles, which are optimized together to clearly identify particle type and energy [1].

Figure 1. A schematic diagram of the experimental setup over one HMPID chamber [5].

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This range of detector systems provides us with the opportunity to take a new measurement of the absorption cross-section for anti-deuterons (deuterons being the deuterium nucleus, consisting of one proton and one neutron, and antideuterons being their anti-matter counterpart).

Measurements of the absorption cross-section in ALICE are optimized in the 1 to 4 GeV range, the relevant range for cosmic ray measurements.

This measurement will contribute to results in cosmology and astrophysics, including models of the propagation of cosmic rays. Cosmic ray propagation could be informative with regards to the production of ions and other conditions of the early universe.

Additionally, the two most recent measurements of the cross section were performed at 25 GeV and 13.3 GeV [2;3]. These measurements inform the parameterizations of simulation software such as GEANT4. GEANT4 is a tailored simulation environment used by particle physicists looking to develop data analysis tools, compare simulated experimental results with known theoretical models, and identify when data has diverged from these models in a significant way.

However, the parameterizations are only predicted to agree with data within eight percent, and need additional tweaking at energies below 100 MeV to raise accuracy to the necessary point [4]. These parameterizations also don’t take into account contributions to the cross-section due to Coulomb scattering, an effect where particles are deflected by the nucleus of an atom, which is a significant effect in most detectors. By measuring the crosssection at lower energies, this experiment will provide the data necessary to update and refine those parameterizations.

In order to define the accuracy with which this measurement can be performed in ALICE, a method of effectively identifying the deuteron and anti-deuteron signals and an analysis of the signal-to-noise ratio in those signals was developed using experimental data from previous LHC runs. This paper discusses the development of the preliminary particle identification methods, and next steps for improving those methods.

MATERIALS AND METHODS Detector systems used in the measurement The anti-deuteron absorption cross-section will be measured in ALICE by comparing the

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Figure 2. A schematic drawing of ALICE’s cross section Note the TPC, TRD, and TOF systems which encircle the ring, and the outer HMPID (of which only 4 chambers are visible) [1].

number of collision-derived anti-deuterons which either enter an absorber or pass completely through it. There are a number of reasons why an anti-deuteron may not make it through the absorber, such as Coulomb scattering, inelastic scattering, and absorption. The same scattering types apply to both deuterons and anti-deuterons, so the difference between the deuteron and antideuteron total cross-sections is the anti-deuteron absorption cross section.

During the LHC Run III upgrade, it is proposed that ALICE be modified to include absorbers in front of two of the High Momentum Particle Identification detector (HMPID) panels in the detector. The HMPID system is optimized for identification of the heavier particles more commonly produced in lead-lead collisions than in proton-proton collisions, and will be used to ‘count’ deuterons that make it through the absorbers. ALICE’s inner detector systems will be used to identify deuterons and antideuterons first produced in the collisions, while a combination of track matching techniques and geometric cuts will be used to match those hits in the HMPID with identification as deuterons.

Materials under consideration for the absorbers include aluminum, graphite, and silicon.

Preliminary identification uses three of ALICE’s 18 detector systems, those thought to be most likely to assist in the identification of deuterons and anti-deuterons. We consider using

the Time Projection Chamber (TPC), Time of Flight detector (TOF), and the Transition Radiation Detector (TRD) for particle identification. A fourth detector, the Multi-wire Proportional Chamber (MPC) component of the HMPID, is used to count particles which exit the absorber.

Taking advantage of the positioning of the detectors (as shown in Figure 2), we consider the TPC, TOF, and TRD with full azimuthal coverage to determine particles which are deuterons or antideuterons, and could have entered the HMPID based on their momenta and trajectory.

The TPC is optimized for a combination of track separation (that is, differentiating one particle from another) and particle identification (determining a particle’s species), and therefore is used to make the initial particle identification in this measurement. It is the innermost of the detector systems used in this analysis and consists of a cylindrical gas-filled time-projection chamber. The time-projection chamber records the charge and location of a particle as it moves through the chamber as a function of time, providing data regarding a particle’s charge and momentum and distinguishes between particles as they move through the detector.

The TOF is optimized for particle identification at the intermediate momentum range (relative to particles detected in ALICE), up to about 4 GeV for protons, and similarly for deuterons [1].

Like the TPC, it provides information about a particle’s momentum, but also provides better energy resolution in our region of interest around 4 GeV. However, it does not record information about a particle’s charge.

Data from the TPC and the TOF are recorded, and compared to theoretical estimates of a deuteron or anti-deuteron’s behavior in that detector. With no interference in the measurement, we would expect that a strong deuteron signal will appear as a Gaussian distribution about the mean deuteron behavior, and similarly for anti-deuterons. Therefore, we can assign a value to each particle detected in the TPC and TOF, called “nσ TPC” and “nσ TOF” respectively, which refer to the number of standard deviations a particular particle’s behavior falls from the expected deuteron behavior in a given detector. These nσ values are used in the analysis.

The TRD’s main purpose in ALICE is to identify high momentum electrons in the central barrel, but it is generally effective at identifying any charged particles at high momenta [6]. In this

experiment, we attempt to use the TRD for particle tracking in order to reduce uncertainty in particle matching of hits in the TPC and TOF detectors, since this matching uncertainty is the main source of uncertainty in the TOF response. The TRD consists of six layers, each of which records hits separately. A detected particle can have hits in any number of layers, as determined by the particle’s behavior. These hits are collectively treated as tracklets, track segments joining a set of cluster measurements inside the same chamber.

Once deuterons and anti-deuterons are identified using the inner detectors, we will use the HMPID to detect the absorption signal, by using the Multi-Wire Proportional Chamber (MWPC) in the HMPID to check for tracks matching those (anti-)deuterons which entered the absorbers.

Because the MWPC is a simple hit counter, we will rely on the particle identification done in the inner detectors, combined with a geometric extrapolation of the particle’s momentum, to determine whether and where a particle track is expected in the HMPID. When no matching track is found, we record an absorption signal.

For this experiment, a method of identifying deuterons and anti-deuterons must be developed and well-understood in advance of data-taking for LHC Run III. Therefore, rather than relying on simulated data, we use data from previous LHC runs to develop our anti-deuteron identification procedures. Because the detectors under consideration generally stay the same over the course of the upgrade, the extrapolating procedures from pre-upgrade to post-upgrade data are highly reliable. We expect that higher luminosity will lead to proportionally higher statistics, but that it will not materially affect the measurement.

RESULTS Particle identification begins with an analysis of the TPC data. Each particle hit in the TPC was recorded and plotted in a histogram containing nσ TPC and particle momentum, as shown in Figure 3. The calculation of nσ TPC produces a statistical distribution in which the majority of true deuterons will fall within ±σ of the mean, and particles three or more sigma away from the mean are not considered deuteron candidates.

In this figure, all particles produced in a collision have been included, and there is no clear peak in

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Figure 4. The projection of nσ TPC across the x axis (as shown in Fig. 3) for particles with energies from 0.8 to 0.9 GeV. Figure 3. Number of sigmas in the TPC vs momentum, for the deuteron hypothesis Note that at this stage, any visible peak in the nσ = 0 region merges with those of other particles at p > 1 GeV.

the nσ = 0 region. This is to be expected, as both deuterons and anti-deuterons are relatively rare events in the Pb-Pb collisions recorded in ALICE. In order to further understand the behavior of particles in this detector, the data was projected across the y-axis at a variety of energies. A sample of those projections can be seen in Figures 4-6. Though a deuteron peak appears in projections of the lower momentum bins (for example, the 0.8 to 0.9 GeV range shown in Figure 4), when referencing the projections with the source histogram, this peak is obscured by occurrences of other particles at momenta above 1 GeV, and therefore the TPC data cannot alone be used to produce clean particle identification. The obscuring of the deuteron peak at higher energies is illustrated in the projections in the 1.7-1.8 GeV and 2.3-2.4 GeV ranges (Figures 5 and 6 respectively), which show the peak being overtaken by the high numbers of similarly behaving particles in the TPC. The absence of clear peaks at higher momenta means that TPC alone is insufficient to cleanly identify the deuterons. However, this data is still crucial as a preliminary filter for identification. To that end, for subsequent analyses we filter the data to only consider particles which fall within ±σ of the deuteron hypothesis in the TPC and combine the data from the TPC with data from the TOF for overall identification.

After filtering those particles which are still considered deuteron candidates after TPC analysis, we once again compare each candidate particle with the deuteron hypothesis, this time

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Figure 5. The projection of nσ TPC across the x axis (as shown in Fig. 3) for particles with energies from 1.7 to 1.8 GeV.

Figure 6. The projection of nσ TPC across the x axis (as shown in Fig. 3) for particles with energies from 2.3 to 2.4 GeV.

considering a particle’s TOF response. We analyze TOF response by calculating nσ TOF, in the same manner described for calculating nσ TPC above. This data is shown in Figure 7.

This figure represents each of the particles which pass the basic track quality cuts and have a value of nσ TPC less than 3.

Once the TPC filtering is applied, a clear peak in the TOF response begins to develop

Figure 7. Number of sigmas in the TOF vs momentum, for the deuteron hypothesis Note the clear peak developing in the 1 to 4 GeV momentum range (the area of interest for this experiment).

in the area of interest—the 1 to 4 GeV region.

The data from this detector in that region was also projected across the y-axis. A sample of those projections can be seen in Figures 8 and 9. Similar peaks appear at all momenta in the region of interest and are clear enough to be fit to a Gaussian function with an exponential tail and two exponential background functions. This corresponds to the functional form expected of the anti-deuteron TOF response, with the main source of background corresponding to matching inefficiencies between TPC and TOF tracks—that is, the TPC and TOF tracks from different particles being identified as the same particle, and vice versa.

We were able to successfully fit each projection with a reduced χ² (defined as χ² divided by number of degrees of freedom) between 0.8 and 1.2 for each fit, and a mean reduced χ² of 0.95, indicating very high quality fits overall.

Despite the high quality of the fits, the signal-to-background ratios of each fit were regularly lower than the desired 10:1. (Signal-tobackground ratio is defined here as the integral of the signal portion of the fit over the background portion, in the region from -4 to 4 nσ TOF.) A major source of this background is in matching tracks from the TPC to tracks in the TOF, which occurs when tracks reconstructed in the inner tracking system and TPC are propagated outward to the outer detectors. This propagation uses track parameters which are used to search for hits in the TOF detector with matching parameters.

Matching hits can potentially belong to the same

Figure 8. A sample of projections on nσ in the TOF from 1.8 to 2.0 GeV Peaks are fit by a Gaussian with exponential tail and exponential background. Fig. 8 shows the projection of nσ TOF across the x axis (as shown in Fig. 7) for particles with energies from 1.8 to 2.0 GeV.

Figure 9. A sample of projections on nσ in the TOF from 3.8 to 4.0 GeV Projection of nσ TOF across the x axis (as shown in Fig. 7) for particles with energies from 3.8 to 4.0 GeV.

particle, and ensuring that particles are correctly co-identified is one of the chief sources of background.

Though the TRD does not directly collect data that can be used for particle identification, the number of hits a particle obtains in the TRD was a potential area for background reduction.

In particular, TRD data could be used if filtering particles based on the number of hits they obtained in the TRD lowered the signal-tobackground ratio in the fits discussed above. We consider the possibility that a higher number of hits in the TRD would be associated with particles that had not Coulomb scattered, and therefore the ratio of true to fake track matches would be higher. If that were the case, requiring a minimum

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Figure 10. Projection of nσ TOF across the x axis for particles with energies from 2.2 to 2.4 GeV with a minimum of one TRD tracklet

Figure 12. Two-dimensional histogram with a sample of signalto-background ratio in the TOF response (cf. Figures 10, 11), sorted by momentum and minimum required number of TRD tracklets

ratio above 3.5 GeV, below 2 GeV signal-tobackground ratio is best at lower minimum numbers of TRD tracklets, due to the higher statistics obtained when no additional data cut is made. Overall, no statistically significant correlation was found. Therefore, the TRD does not offer any possibility to significantly improve the signal-to-background ratio, and other methods will be sought for use in the final analysis.

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Figure 11. Projection of nσ TOF across the x axis for particles with energies from 2.2 to 2.4 GeV with hits in all 6 TRD chambers

DISCUSSION

number of TRD hits would make the identification clearer by limiting the number of non-deuterons in the sample.

In order to determine whether TRD hits could be used to reduce signal-to-background ratio, each particle which passed the TPC 3σ threshold was sorted by number of TRD tracklets, and the projections and fits based on TOF data were repeated for each number of TRD hits. Figures 10 and 11 show a characteristic pair of projections in the same momentum range, with a minimum (1) and maximum (6) number of TRD hits. Though the sample requiring six hits (Figure 10) has lower statistics than the sample requiring only one hit (Figure 11), the fit parameters and corresponding signal-to-background ratio are mostly unchanged.

A summary of signal-to-background ratios as a function of momentum and number of hits in the TRD is shown in Figure 12. Though the TRD cut slightly improves the signal-to-background

The combination of particle identification in the TPC and TOF establishes an identifiable deuteron signal in the detector. However, the 10:1 signal-to-background ratio achieved when only the TPC and TOF data are used is insufficient to enable clean deuteron identification, due to the high background inefficiency in matching particles form the TPC to the TOF. The distance and material in the detector between the TPC and the TOF introduces ample opportunity for multiple Coulomb scattering, resulting in an increase of “fake matching”— particles with apparently matching parameters, wherein the inner particle is matched with a different particle which scattered in the material between the TPC and the TOF such that it appears to have matching parameters. This fake matching is the primary source of background in the TOF signal.

In an effort to lower this background, a data cut based on the number of TRD tracklets

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associated with the particle was considered.

However, the reduction in data volume with this cut was significant (event rates dropped by about half when hits in all six TRD chambers were required) and the change in signal-to-noise ratio was not statistically significant. Therefore, other methods in improving track matching efficiency and background filtering must be sought. In particular, a computer simulation of the proposed experiment will be used to determine the most effective ways to filter for this measurement, by enabling scientists to directly examine the particular behaviors expected of deuterons as they interact with the absorber and write code designed to identify those behaviors.

Because the software used to simulate the experiment uses the parameterizations of antideuteron behavior based on measurements at higher energies, the particle identification methods from this study will be integrated into any simulation work to improve its accuracy.

REFERENCES 1. ALICE Collaboration. The ALICE experiment at the CERN LHC; Institute of Physics Publishing and SISSA (2008).

2. F. B. et al (IHEP-CERN Collaboration). Absorption crosssections of 25 GeV/c antideuterons in Li, C, Al, Cu, and Pb; Physics Letters 31B (1970).

3. S. P. D. et all. Measurements of antideuteron absorption and stripping cross sections at the momentum 13.3 GeV/c; Nuclear Physics B 31 (1970).

4. V. U. et all. Antinucleaus-nucleus cross sections implemented in GEANT4; Physics Letters B (2011).

5. G. DeCataldo (ALICE Collaboration). HMPID contribution to the ALICE physics program in run-3, in HMPID; Weekly Meeting (CERN, 2018).

6. The ALICE transition radiation detector: Construction, operation, and performance, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 881, 88 (2018).

Julia Book is a fourth-year student at the University of Chicago majoring in Physics and minoring in Medieval Studies. She hopes to attend graduate school to further her study of high-energy particle physics, continuing her year-round interest in neutrino physics.

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A B STR ACTS CHAR ACTERIZ ATION OF TH E FECAL MICROB IOTA OF AL ZH EI MER ’ S TG AND W T MALE MICE WITH RE SPECT TO AL ZHEIMER ’ S PATHOG ENE SIS

Shayna Cohen 1 , Hemraj B. Dodiya 1 , Monica Olszewski 1 , Ian Weigle 1 , Carlos Roman-Santiago 1 , Karen Shi 1 , Thomas Kuntz 1 , Jack A. Gilbert 2 , Sangram S. Sisodia 1 1 The University of Chicago, Department of Neurobiology 2 Department of Pediatrics, University of California at San Diego

An altered gut microbiome has been associated with Alzheimer’s Disease (AD), which is typically characterized by the excess accumulation of amyloid protein outside neurons of the brain. In previous studies, we have shown that AD transgenic male mice treated with microbiome-altering antibiotic cocktail (ABX) have reduced amyloid beta (Aβ) plaque formation in the brain. In these ABX-treated mice, the original gut microbiome was restored to original transgenic levels after fecal microbiota transplantation (FMT) from age-matched mice, establishing a causal relationship between AD-affiliated Aβ plaque burden and changes in the gut microbiome. Nevertheless, it is unknown whether using APPPS1-21 (a widely used strain for AD mice), transgenic (Tg), or Wild-Type (WT) mice as fecal microbiota donors would have different effects on Aβ pathology. In this study, the microbiota profile between male WT and APPPS1-21 Tg mice at pre-weaning age P22 and 7 weeks were compared. It was hypothesized that there would be no significant difference in the plaque burden caused by WT or Tg donors. To test this hypothesis, fecal microbiota from WT and Tg male donors were transplanted into ABX-treated male mice. The experimental mice were orally gavaged with Tg or WT fecal slurry daily, and both cecal and fecal microbiome profiles from these mice were evaluated along with plaque burden analysis through brain sections. A significantly higher abundance of the bacteria B. uniformis, a significant portion of the fecal bacteria population, was found in Tg pups compared with WT pups. At 7 weeks of age, prevotella, lactobacillus, and Akkermansia Muciniphila showed higher abundance in Tg male mice compared with WT male mice. Overall, there were no significant differences between WT and Tg donor-caused plaque burden, suggesting that plaque pathology is even more strongly connected with the gut microbiome.

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NOCTE IS REQUIRED FOR MOTOR NEURON TERMINAL DEVELOPMENT Katherine DeLong 1 , Meike Lobb-Rabe 1 , and Robert Carrillo 1 Department of Molecular Genetics and Cell Biology, University of Chicago

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Complex neural circuits are required for all aspects of behavioral and cognitive function. Although the mechanisms underlying the formation of these circuits have been intensely studied for several decades, we still lack a complete understanding of how neurons wire with precision and how synaptic growth is coordinated during development. The Drosophila melanogaster neuromuscular system provides an ideal model to uncover the molecules and mechanisms that regulate these developmental programs due to the nearly invariant and modular circuitry along the larval body axis between motor neurons and their muscle targets. One molecule, Dpr10, has been implicated in synaptic connectivity and growth. In a previous biochemical screen searching for proteins that bind to Dpr10, we identified Nocte, which is expressed in muscles, glia, and several other cell types. Preliminary studies showed that Nocte is required for circadian rhythm entrainment. However, Nocte has not been previously characterized during larval development. We utilize genetic manipulations, dissections, immunohistochemistry, and fluorescent imaging to delve into Nocte expression and function in the Drosophila neuromuscular system. Loss of Nocte results in aberrant terminal morphology and axon pathfinding errors, suggesting that it is required for normal neuromuscular circuit development. The mechanisms underlying these Nocte functions are currently under investigation. Many neurological diseases have an underlying synaptic dysfunction etiology; thus, unraveling the mechanisms that control synaptic function may allow for identification of novel therapeutic targets.

STRETCHABLE AND STIFFNESS-CHANGEABLE SUBSTRATE FOR ELECTRODE DEVICES Bernadette Miao 1 , Hongzhang Wang 1 , Sihong Wang 1 1 Pritzker School of Molecular Engineering, University of Chicago

Brain-machine devices connect the internal brain with the external world through electrode arrays. These devices produce electrical impulses to measure, stimulate, and restore abnormal brain signaling activity from neurological diseases. Such electrical impulses also provide deep-brain stimulation which can be used to control and treat neurological conditions such as Parkinsonâ&#x20AC;&#x2122;s disease and epilepsy. To provide effective treatment for neurological disorders, these electrode array devices must be biocompatible to prevent tissue damage during insertion and allow for long-term implantation. Here, we present a stretchable and stiffness-changeable substrate that can be incorporated into electrode arrays. This magnetic field-induced stretchable and stiffnesschangeable substrate improves on the biocompatibility of current designs and allows for wireless control with a magnetic field. To make the substrate, we combined polydimethylsiloxane (PDMS), iron particles, and a liquid metal (gallium/indium). We chose PDMS due to its biocompatibility and ease of fabrication, and we incorporated iron particles and the liquid metal to create and enhance the mechanism of stiffness change. To create a uniform composite, we used sonication to evenly disperse the composite particles, producing a mixture that resulted in a film that was 5-6 microns thick when spin-coated and cured. This thin film is necessary for the substrate design, as the individual brain neuron is about 10 microns, and a thinner structure increases biocompatibility by decreasing tissue damage and inflammation. Future plans include the measurement of stiffness changes under a magnetic field and integration of the substrate with a conductive electrode layer. This biocompatible substrate design heightens the ability of brain-machine devices to treat neurological disorders.

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