Volume 110, Number 4, July–August 2022 American Scientist

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WICKED PROBLEMS • MIND MAPS • DISASTER RESILIENCE DRUG DELIVERY • SUSTAINABLE EDUCATION • REGENERATION

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Converging on Public Health

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Scientist 194 From the Editors 195 Online Preview

Volume 110 • Number 4 • July–August 2022

CONTENTS

212

196 From Polymaths to

235 First Person: Joseph DeSimone 3D printing medical devices

238 Fixing Joints That

Cyborgs—Convergence Is Relentless Multidisciplinary collaborations lead to humanity-helping breakthroughs.

Appear Beyond Repair A lunchtime sketch of an engineered ligament helped launch a revolution in sports medicine.

IOANNIS PAVLIDIS, ERGUN AKLEMAN, AND ALEXANDER M. PETERSEN

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SPECIAL ISSUE: Convergence Science

CATO LAURENCIN AND DEBOLINA GHOSH

242 Cancer Nanomedicine 222 Unlocking Joint Pain with Microscale Devices Organ-on-chip devices model osteoarthritis at a microscale so scientists can study the disease and test drugs to relieve pain.

Serendipity and unexpected observation lead to new concepts. PATRICK COUVREUR

242

MEAGAN MAKARCZYK, MICHAEL GOLD, AND HANG LIN

202 Replacing Injections with Oral Medications Hydrogels move medical treatments home. NICHOLAS A. PEPPAS AND OLIVIA L. LANIER

204 Suffocating from Medical Bias A systems engineering approach to equitable health care solutions

226 A New System for Disaster Research Connecting researchers, building networks, and training newcomers to the field can disrupt cycles of disaster loss. LORI PEEK

222

GILDA A. BARABINO AND HARRIET B. NEMBHARD

208 First Person: Hongkui Zeng The brain cartographer

244 It Takes a Whole School Reforming education to promote sustainable communities ARJEN E. J. WALS AND ROSALIE G. MATHIE

249 Scientists’ Nightstand Heroism’s roots • A thriving innovation ecosystem 253 Sigma Xi Today From the President: A whole new world of opportunities • Key thought sessions at IFoRE ’22 • Faces of GIAR: Anabel Ford and Jeff Toney • Pariser Global Lectureship for Innovation in Physical Sciences

212 The World Needs Wicked Scientists How can we train the next generation of researchers to tackle society’s most vexing problems? MARK MORITZ AND NICHOLAS C. KAWA

218 Engineering Infrastructure in service to well-being HENRY PETROSKI www.americanscientist.org

ON THE COVER Public health research is benefitting from the Converging concept of convergence, on Public Health a solutions-oriented approach that brings together scientists with many specialties across the traditional boundaries between fields of study to tackle vexing problems. (Cover illustration by Carlos Zamora.) SPECIAL ISSUE

WICKED PROBLEMS • MIND MAPS • DISASTER RESILIENCE DRUG DELIVERY • SUSTAINABLE EDUCATION • REGENERATION

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Scientist July–August 2022

232 The Power of Nexus Planning Achieving sustainability entails weighing trade-offs and collaborating among interconnected sectors. LUXON NHAMO, SYLVESTER MPANDELI, AND TAFADZWANASHE MABHAUDI Special Issue: Convergence Science

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From the Editors

What is Convergence Science?

AMERICAN

Scientist www.americanscientist.org VOLUME 110, NUMBER 4

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n 2016, the National Science Foundation (NSF) named the growth of convergence science as one of its 10 Big Ideas. But what does convergence science actually mean? How does it differ from the standard idea of interdisciplinary research? The 2014 National Academies report Convergence: Facilitating Transdisciplinary Integration of Life Sciences, Physical Sciences, Engineering, and Beyond acknowledges that “the goal of merging expertise to address complex problems is not new.” But it goes on to clarify: “At the heart of the current momentum for convergence, however, is the realization that physical and biological sciences can each benefit from being more fully integrated into the intellectual milieu of the other.” Writing in this report as the chair of the Committee on Key Challenge Areas for Convergence and Health of the National Research Council, Joseph DeSimone (whose First Person interview appears on pages 235–237) notes that “convergence provides us with an opportunity not only to discuss strategies to advance science but also to elevate discussions on how to tackle fundamental structural challenges in our research universities, funding systems, policies, and partnerships.” In a way, what defines convergence science is its overarching goal of solving what the NSF calls “vexing research problems,” meaning ones that are complex and focus on societal needs. Another primary characteristic is the deep integration of researchers across disciplines to reach these goals by developing novel ways of framing research questions. This special issue explores advances that have emerged from the application of convergence science to public health concerns. What makes public health particularly compelling? The 2016 Massachusetts Institute of Technology report Convergence: The Future of Health—for which this issue’s authors Cato Laurencin (pages 238–241) and Nicholas Peppas (pages 202–203) were scientific advisors— emphasizes that “an accelerated convergence research strategy can lead to truly major advances in fighting cancer, dementia, and diseases of aging, infectious diseases, and a host of other pressing health challenges.” The report notes that convergence science also has potential to alleviate the increasing humanitarian and fiscal costs of health care. This point is echoed by Victor J. Dzau, president of the National Academy of Medicine, who wrote in The Lancet in 2018 that “improving the health of populations requires an understanding of the myriad factors that influence health” and “requires scientific understanding of education, social services, economic development, environment, nutrition and food marketing, and urban design.” Writing in PNAS Nexus in 2022, Dzau and his colleagues point out that “responsibility for solving these challenges lies far beyond the health and medical arena. They are fundamentally connected to changes in our environment, our communities, our cultures, how we live and work, and society writ large.” The articles in this issue therefore focus on factors related to public health in the broadest sense, such as disaster resilience, the availability of resources such as food and water, infrastructure’s role in controlling disease, sustainable approaches to education, and the need to center equity in discussions of health care. Our authors also look at the concept of convergence itself, examining its effects in solving so-called wicked problems, and highlighting breakthroughs in which a convergence approach has made a difference. The issue showcases problem-solving work in which researchers in different fields have collaborated to come up with solutions, such as cutting-edge treatments for cancer or chronic diseases, maps of the brain, organ-on-a-chip devices, medical implants, and regenerative medicine. We hope you find these articles about new approaches to science and research to be inspiring and thought-provoking. —Fenella Saunders (@FenellaSaunders) 194

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Editor-in-Chief Fenella Saunders Special Issue Editor Corey S. Powell Managing Editor Stacey Lutkoski Digital Features Editor Katie L. Burke Senior Contributing Editors Efraín E. RiveraSerrano and Sarah Webb Contributing Editors Sandra J. Ackerman, Marla Broadfoot, Emily Buehler, Christa Evans, Jeremy Hawkins, Jillian Mock, Laura Poole, Diana Robinson, Heather Saunders, Mark Teich Editorial Intern Jasmine Johnson Editorial Associate Mia Evans Art Director Barbara J. Aulicino SCIENTISTS’ NIGHTSTAND Book Review Editor Flora Taylor AMERICAN SCIENTIST ONLINE Digital Managing Editor Robert Frederick Social Media Specialist Kindra Thomas Publisher Jamie L. Vernon EDITORIAL CORRESPONDENCE American Scientist P.O. Box 13975 Research Triangle Park, NC 27709 919-549-4691 • [email protected] CIRCULATION AND MARKETING NPS Media Group • Beth Ulman, account director ADVERTISING SALES [email protected] • 800-243-6534 SUBSCRIPTION CUSTOMER SERVICE American Scientist P.O. Box 193 Congers, NY 10920 800-282-0444 • [email protected] PUBLISHED BY SIGMA XI, THE SCIENTIFIC RESEARCH HONOR SOCIETY President Nicholas A. Peppas Treasurer David Baker President-Elect Marija Strojnik Immediate Past President Robert T. Pennock Executive Director Jamie L. Vernon American Scientist gratefully acknowledges support for “Engineering” through the Leroy Record Fund. Sigma Xi, The Scientific Research Honor Society is a society of scientists and engineers, founded in 1886 to recognize scientific achievement. A diverse organization of members and chapters, the Society fosters interaction among science, technology, and society; encourages appreciation and support of original work in science and technology; and promotes ethics and excellence in scientific and engineering research. Printed in USA

Online Preview | Find multimedia at americanscientist.org

ADDITIONAL DIGITAL CONTENT Collection on Convergence Science

Building a Brain Atlas

Public health researchers have for years recognized the need for collaboration and cross-disciplinary expertise to solve pressing concerns. For more on the topic, check out this special issue’s companion blog featuring content from the American Scientist archives.

Our Q&A with Hongkui Zeng (pages 208–210) just scratched the surface of her work with the BRAIN Institute. The online version of the article includes a 3D rendering of more than 1,700 individual neurons in a mouse brain, as well as an extended audio version of the interview.

Engineering Cheaper, Better, and Faster Care

Health Consequences of Racism

Joseph DeSimone’s work on 3D-printed devices is changing the game for fast and affordable drug delivery systems and medical implants (pages 235–237). Listen to the podcast version of the interview to hear more about his work, including how the technology could be used to develop better treatments for children with cleft palates.

The conversation started by Gilda A. Barabino and Harriet B. Nembhard’s article on medical biases (pages 204–207) is continued on our Science Culture blog with a review of Anne Pollock’s book Sickening: Anti-Black Racism and Health Disparities in the United States. Pollock uses case studies to illustrate structural inequality in the U.S. health care system.

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Ioannis Pavlidis, Ergun Akleman, and Alexander M. Petersen | Multidisciplinary collaborations lead to humanity-helping breakthroughs.

From Polymaths to Cyborgs– Convergence Is Relentless

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he first draft of the human genome—a historic map of our species’ genetic instruction manual—was completed not by biologists but by a computer science group at the University of California, Santa Cruz. Parsing the complexity of 2.85 billion nucleotides, written across more than 20,000 genes, required technical assistance from and close collaboration with researchers from many disciplines. Ultimately, the Human Genome Project included researchers from engineering, informatics, ethics, physics, biology, and chemistry. The Human Genome Project is a powerful example of the convergence approach in science. In a 2014 report, the National Research Council defined convergence science as the integration of multidisciplinary approaches aiming to address complex questions. For more than half a century, convergent approaches have become increasingly common and impactful in science, prompting some historians of ideas, such as Peter Watson, to identify ongoing convergence as the ultimate scientific trend. Others, including a study panel led by Mihail Roco and William Bainbridge at the National Science Foundation, have proposed that scientific research actually cycles between periods of convergence and divergence, the latter being the fragmentation of science into distinct disciplines. From the 18th century to the mid20th century, divergence flourished and highly specialized areas of science were spawned. Then convergence was needed to bring the pieces together to

solve problems spanning multiple specialties. This return to convergence, however, has proven challenging. Each specialty has formed its own culture and does not readily welcome change. For example, John Bowlby, the British psychologist who developed attachment theory, experienced ferocious attacks from his fellow psychoanalysts when he attempted to bring a biological perspective to behavioral studies in the post–World War II period.

Convergent approaches have led to breakthroughs with enormous social implications, such as the creation of mRNA vaccines for SARS-CoV-2 and the development of genomic drugs. Informed by historical and quantitative analysis, our research team takes a more nuanced, comprehensive, and unifying approach to convergence. We deconstruct the definition of convergence into two parts: its essence (or aims) and its methods (or means to attain those aims). In its essence, convergence strives to provide all-encompassing answers to grand challenges, such as describing the en-

tire human genome or determining how the human brain works. (See First Person: Hongkui Zeng, pages 208–210.) In recent years, convergent approaches have led to breakthroughs with enormous social implications, such as the creation of mRNA vaccines for SARS-CoV-2 and the development of genomic drugs. The goals of convergence are consistent with the goals of scientific inquiry itself. But the methods scientists use to tackle big questions have changed and evolved over the course of history. By differentiating between convergence’s essence and methods, we replace a static definition of convergence bound to the present historical period with a dynamic one having timeless relevance. In this new framework, science evolution is not a relay with the baton being passed back and forth between convergence and divergence, but a marathon of ever-morphing convergence. In our conceptualization, divergence is a tactic employed during a specific period—the 1700s, 1800s, and early 1900s—to manage in-depth investigations. Divergent methodologies have brought significant benefits to scientific research, but they have also been seriously undermining the transition back to convergence in recent decades. We believe that, for utilitarian reasons alone, it is likely that divergent methods will eventually be phased out. New methods that can negotiate both the breadth and depth of knowledge will take their place, bringing cultural congruency across the currently splintered scientific community.

QUICK TAKE The goal of convergence science is to find answers to big questions. The methods of this approach, however, have changed and evolved over the history of Western science.

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Using convergent approaches, researchers have made breakthroughs with enormous social implications, such as the development of genomic drugs and mRNA vaccines.

In the 21st century, artificial intelligence may enhance the abilities of researchers whose expertise spans multiple fields as they tackle problems of great depth and breadth.

GSK

The Laboratory for Genomics Research (LGR) explores how gene mutations cause diseases and develops technologies to rapidly accelerate the discovery of new medicines. Genomics as a field is an example of convergence science in action, as it necessarily marries biology and computational science. In the future, labs like LGR could use artificial intelligence and machine learning to guide drug development, solve complex problems, and analyze enormous volumes of data.

Ancient Natural Philosophy Long before the rise of the scientific method, convergence was the traditional mode of inquiry. For ancient natural philosophers, the goals of intellectual inquiry were not so different from the overarching goals of modern science. They viewed their studies as a holistic effort to explain nature—an approach that intimately tied protoscience to convergence. If we consider the famously polymathic Aristotle to have been a representative example of these early scholars, they practiced what they preached. Aristotle studied subjects in physics, biology, and many other areas of scientific inquiry, synthesizing a sophisticated worldview based on his diverse knowledge. He was also the father of logic, providing early scientists with a deductive method for drawing inferences. Aristotelian philosophy was a remarkable showcase of convergence, the impact of which lasted well into the Renaiswww.americanscientist.org

sance. Although many of Aristotle’s specific findings were later proved incorrect, his philosophical system demonstrated the lasting value of a convergent approach to science in which holistic explanations reign supreme. For nearly 2,000 years in the Western world, convergence was pursued solely within the minds of scholars. Exceptional individuals such as Aristotle, Leonardo da Vinci, and Galileo were largely responsible for holding science’s evolving convergent state, in much the same way that the mythical Atlas held up the heavens and sky. Science was still nascent, its body of knowledge growing but still manageable for singular scholars. Instruments were limited, so the amount of data collected and analyzed was limited as well. In many cases, data were nonexistent; for instance, the natural philosopher Democritus correctly deduced the atomic nature of matter purely through thought processes. Special Issue: Convergence Science

Explosion of Data and Disciplines The state of scientific inquiry changed radically during the Industrial Revolution in the 18th and 19th centuries. New instruments such as aneroid barometers, sextants, and theodolites allowed for precise measurements in meteorology, naval navigation, and surveying, respectively. Such abundant and reliable measurements produced everincreasing amounts of data begging for analysis. New and more rapid forms of communication, such as scientific journals, gave rise to thriving knowledge networks. During this period, Western science and the economy were linked in a positive reinforcement loop: Scientific advances brought economic growth, which in turn brought further scientific advances. For all these reasons, the body of scientific knowledge and its underpinnings mushroomed in short order, rendering expansive, individual polymathic inquiries like those of Aristotle practically impossible. An era of specialization dawned in science, in which scholars focused on in-depth investigations, trying to make the most of their new informational powers. As a result, many disparate scientific disciplines, including chemistry, biology, 2022

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Color loci 43–64 64–68 68–71 71–74 74–77 77–80 80–85 85–91 91–97 97–151

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Color loci 43–64 64–68 68–71 71–74 74–77 77–80 80–85 85–91 91–97 97–151

Ƃ Reprinted by permission from Springer Nature

Thomas Marent/Minden Pictures

By using artificial intelligence to collate and analyze photographs of more than 4,500 passerine birds, researcher Christopher R. Cooney from the University of Sheffield and his colleagues were able to validate a long-standing theory that the plumage of birds in the tropics (such as the multicolored tanager from Colombia on the right) is more

colorful than the plumage of birds from temperate regions. The maps (left) show global mean color loci scores—a measure of color variation in the plumage of a particular bird—for males (top) and females (bottom). This analysis is an early example of what the authors call cyborg team convergence in action.

and civil and mechanical engineering, were established and institutionalized. Upon closer examination, however, science’s attraction to convergence never really withered; what changed during this period is that convergence emerged from within, rather than across, disciplines. For example, Darwin’s theory of evolution in biology— an all-encompassing causal account of living organisms—inspired generalized theories of complex system evolution in other disciplines, such as social science and economics. The Atom, the Moon, and the Genome Around the middle of the 20th century, the arc of convergence bent in a new direction. Science had grown so big that individual scholars could not traverse its breadth the way their ancient predecessors had done, so researchers from different disciplines began to work together in teams to solve complex challenges. In this era, convergence would not take place within the minds of polymaths like Aristotle or emerge semi-disguised from within disciplines such as systems evolution; rather, it would be forged in multidisciplinary teams. The Manhattan Project in the 1940s was a stunning demonstration of the power of this approach, ushering humanity into the nuclear age. Similarly, in the 198

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1960s NASA’s Apollo space program leveraged multidisciplinary team convergence to send humans to the Moon. And as the 20th century was ending, multidisciplinary team convergence in the Human Genome Project marked the beginning of the genomic era. In 60 short years, multidisciplinary team

Multidisciplinary team convergence worked well in 20thcentury “Big Science” projects because it was directly and effectively managed by government institutions. convergence advanced humanity far beyond what thousands of years of prior scientific progress had achieved. In all these “Big Science” projects, multidisciplinary team convergence worked well because it was directly and effectively managed by government institutions, such as the military, NASA, and the National Institutes of Health (NIH). The NIH, for example, not only successfully completed the

Human Genome Project, but did so under budget. The project also marked the beginning of a more inclusive era in science, as more female scientists joined the team. Thus the Human Genome Project demonstrated that multidisciplinary team convergence had some capacity for equity, which may have been aided by the more progressive times and by the appeal that convergent research holds for female scientists—who are often drawn to more nascent fields that are less established and, therefore, less exclusionary, according to researchers Diana Rhoten of the Social Science Research Council and Stephanie Pfirman of Barnard College. Open Software and the Brain At the start of the 21st century, the wheels of change started spinning yet again. At the end of the 1990s, large government agencies such as the NIH and NASA started putting more emphasis on funding multidisciplinary teams and using public-private partnerships to drive scientific inquiry. Self-organized, multidisciplinary teams of scientists now competed to win small and midsize grants from funding agencies, such as the National Science Foundation (NSF) and the NIH. The agencies also started issuing solicitations on convergent themes such as the nexus of computing and

health and the nexus of computing and behavioral sciences. At the same time, the internet was globalizing and democratizing knowledge access, as ubiquitous sensing and computing flooded the world with data, and artificial intelligence (AI) allowed researchers to use that data in new ways. Crucially, a wave of opensource scientific software bestowed tremendous powers on the average researcher. For example, psychologists could now use open programming scripts featuring sophisticated machine learning algorithms to analyze behavioral data, and even contribute to the development of such scripts through cross-disciplinary training. Now, for the first time in a long time, it has become feasible for any intelligent, curious person to play Aristotle and comb through ideas from many disparate fields, thanks to online educational materials for everything and everyone, the proliferation of open data sets, and the abundance of ready-touse expertise, packaged in freely available open software modules. With these “black boxes,” scientists can know the inputs, outputs, and best-use cases, but do not need to know the intricate internal algorithmic details. This is a prime example of modern convergent approaches facilitating both investigational breadth and depth, where the depth is provided by the open software. As these tactics proliferate, the practice of convergence science is becoming more personal and decentralized. The combination of open data and packaged expertise in the form of these software modules encourages scientists to expand beyond their disciplinary boundaries, gradually becoming polymaths to the point that they start producing their own open software and data, perpetuating the process. We call this emerging form of decentralized, discipline-fluid research polymathic team convergence. Polymathic team convergence is close to, but distinct from, what we call multidisciplinary team convergence, in which researchers from different disciplines work together on collaborative projects but stay in their respective, narrow disciplinary lanes. In polymathic teams, individual researchers carry multiple disciplines in their own heads. On a polymathic team convergent project on human behavior, for example, there would be no pure computer scientists or psychologists; instead, each of the www.americanscientist.org

Courtesy of Alexander Petersen & Ioannis Pavlidis

This graphic representation of multidisciplinary convergence depicts the research collaboration network of about 1,000 scholars sampled from U.S. computer science departments (magenta) and biology departments (green) in 2015. Links represent collaborations, and node size is proportional to a scholar’s centrality within this network. The cross-disciplinary bridge formed by computing scholars extending into the biology domain represents the genomics nexus, where computer scientists and their surrounding biology collaborators are forming a new convergent culture.

researchers on the team would have mixed expertise and be capable of working across the computational and psychological aspects of the project. In our research, we’ve found strong evidence that, in parallel with multidisciplinary team convergence, polymathic team convergence gained traction in brain research during the 2010s. During that period, governments around the world began setting up initiatives to map and understand the functioning of the human brain using crossdiscipline research approaches. In this context, multidisciplinary teams made great strides; for example, a neurally controlled robotic arm for people with tetraplegia was developed by Leigh Hochberg and colleagues in 2012. This robotic arm opened the door to mindcontrolled prosthetics that stand to transform the lives of millions of people who are disabled. The technology is currently undergoing clinical trials, one Special Issue: Convergence Science

of which—the BrainGate2 trial at Massachusetts General Hospital—is scheduled to conclude in 2026. Polymathic teams also expanded significantly and published important work during this time. In brain science during the 2010s, the annual growth and citation impact of polymathic team publications outpaced that of single discipline team publications by 3 percent and 6 percent, respectively. This polymathic team trend is exemplified in the field of transcriptomics. Transcriptomes reflect differences in gene expression between different cells and can reveal molecular organization in the brain and other organs. The key to understanding neurodegenerative diseases lies in this molecular organization. Although transcriptomes hold exceptional promise for brain science, they represent complex biological entities and are notoriously difficult to reconstruct computationally. Accord2022

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ingly, polymathic teams that traverse biology and computing (for example, Manfred G. Grabherr and colleagues from the Broad Institute) have excelled in the development of transcriptomic methods that have already impacted research into brain disorders such as Alzheimer’s disease. We expect the growth and impact of polymathic team convergence to accelerate during the 2020s, with this approach eventually becoming the dominant form of convergence. Our prediction is based on favorable underlying conditions. Presently, a new generation of researchers is being trained through a wave of convergence grants, such as the Bridge2AI grants from NIH, which aim to connect biomedical and computer sciences. These young researchers are also afforded the tools of open science, which facilitate mastery of multiple disciplines. As these researchers mature, so too will polymathic team convergence. Cyborg Science If we were to make a bold prediction about where convergence will go in the mid-21st century, we would bet that polymathic teams will be enhanced with AI, a development we call cyborg team convergence. We believe AI will accelerate convergence by enhancing ideation and solve the breadth–depth conundrum in research by combining and comparing collections of data far too big for an unaided human mind to handle. In terms of ideation, major advances are often the product of scientific novelty, arising from atypical combinations conceived by talented scholars. AI excels in combinatorial analysis, provided the combined elements have been properly encoded. For instance, in a recent publication, Vahe Tshitoyan and colleagues at the University of California, Berkeley, presented an AI system that predicts novel material combinations worth investigating for their thermoelectric properties. Novel thermoelectric materials can be applied in highly efficient cooling and energy scavenging, thus making significant contributions to sustainable energy solutions. Importantly, this work is an early example of AI intervention in the discovery process, which thus far has depended exclusively on human talent, experience, and luck. Luck is what AI interventions promise to replace, while enhancing talent and partly making up for experience. 200

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AI will also be indispensable in allowing teams to investigate problems of great depth and breadth. A glimpse of the vast possibilities is offered by a recent publication by Christopher Cooney and colleagues from the University of Sheffield in England. Cooney’s team used AI to analyze thousands of digitized bird images from a museum collection to prove that birds are more colorful closer to the equator, thereby validating a long-standing evolutionary theory that plumage colorfulness is greater in tropical regions. Charles Darwin and Alfred Russel Wallace spent decades in the 19th century documenting the same finding, but only anecdotally. In essence, Cooney’s team was able to negotiate a broad problem’s considerable depth thanks to a machine partnership. We envision researcher–machine partnerships in which AI will help

Ultimately, convergence matters because it is extremely effective at supporting science in its inexorable progress toward making the world a better place. researchers find novel and insightful ways to combine topics, and to dig deeply into data archives that would otherwise be overwhelming. For instance, brain–machine interfaces, such as those being developed by the company Neuralink, would allow researchers to connect with AI engines, enhancing the researchers’ ability to analyze voluminous human behavior data, scan the ever-expanding scientific literature, and in general perform currently challenging or impossible tasks. Assuming the open data and open software trends continue, such human–machine partnerships stand to benefit scientific productivity because they can provide a head start to convergent teams at large. Through history, convergence has evolved to accommodate the everchanging state of scientific inquiry. It started as a solitary, expansive endeavor by polymathic scholars. When this became untenable, convergence attempted to provide total answers

from within scientific disciplines even while divergence appeared to be the dominant form of inquiry. Eventually, convergence assumed its modern form by attempting to integrate within multidisciplinary teams. Currently, polymathic team convergence is emerging, increasingly supported by technology, and is a process that may ultimately lead to cyborg team convergence. Convergence keeps enhancing science’s efficiency and impact by joining together many different strands of knowledge and insight. The names of the research team members who gave us the mRNA vaccines or genomic drugs may not be household names like Aristotle and Leonardo da Vinci, but their impact on society is massive. Ultimately, convergence matters because it is extremely effective at supporting science in its inexorable progress toward making the world a better place. Convergence is science’s destiny. Bibliography National Research Council. 2014. Convergence: Facilitating transdisciplinary integration of life sciences, physical sciences, engineering, and beyond. Washington, DC: The National Academies Press. Pavlidis, I., A. M. Petersen, and I. Semendeferi. 2014. Together we stand. Nature Physics 10:700–702. Petersen, A. M., D. Majeti, K. Kwon, M. E. Ahmed, and I. Pavlidis. 2018. Crossdisciplinary evolution of the genomics revolution. Science Advances 4:eaat4211. Petersen, A. M., M. E. Ahmed, and I. Pavlidis. 2021. Grand challenges and emergent modes of convergence science. Humanities and Social Sciences Communications 8:1–15. Tshitoyan, V., et al. 2019. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571:95–98. Woelfle, M., P. Olliaro, and M. H. Todd. 2011. Open science is a research accelerator. Nature Chemistry 3:745–748. Wuchty, S., B. F. Jones, and B. Uzzi. 2007. The increasing dominance of teams in production of knowledge. Science 316:1036–1039.

Ioannis Pavlidis is the Eckhard-Pfeiffer Distinguished Professor of Computer Science at the University of Houston. He is a polymathic researcher with background in computing, psychology, and engineering. Ergun Akleman is a professor in the departments of visualization and of computer science and engineering at Texas A&M University. He is a pioneer of visual storytelling in science and has published extensively in various areas of computer graphics research. Alexander M. Petersen is an associate professor in the Department of Management of Complex Systems at the University of California, Merced—an example of the nascent institutionalization of convergence in the academe. Twitter for Pavlidis: @ipavlidis

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Nicholas A. Peppas and Olivia L. Lanier | Hydrogels move medical treatments home.

Replacing Injections with Oral Medications

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ew things in life are more debilitating than a chronic disease, and these conditions are even more mentally draining for patients when their cause is mysterious. Take, for instance, the collection of conditions known as inflammatory bowel diseases (IBDs). In the United States, some 60,000 individuals are diagnosed with IBDs each year, and a total of about 3 million individuals are living with the disease. Why the body suddenly sets off this painful and incapacitating inflammation of the gastrointestinal tract is not clearly understood, but it seems to involve aberrant signaling by cytokines, molecules that can act as danger signals to immune cells.

A cutting-edge therapy for these diseases are antibody infusions that help silence these false alarm signals, but the treatments are onerous. The infusions must be administered intravenously or by injection, sometimes as often as once every two weeks, a schedule that is particularly costly for patients of lower socioeconomic status. A 2021 study from the Centers for Disease Control found that the prevalence of IBDs among nonHispanic Black participants had risen over the past two decades, and their rates of hospitalization and death from the diseases were disproportionately higher than for other groups. Also, African Americans are often diagnosed later and are less likely to receive spe-

cialist treatment. A more broadly accessible mechanism of treatment delivery is urgently needed. Protecting Proteins Antibody treatments of this type fall under the umbrella of a new class of medications called protein therapeutics. Since the development of recombinant human insulin in the late 1970s, protein therapeutics have experienced exponential growth. They can be used to treat numerous diseases, ranging from diabetes to immune disorders to cancer. These highly specific and targetable therapies can treat diseases that have resisted other methods. However, administration of protein therapies has been so far limited

Anionic material complexed Small mesh size pH ~ 2 Targeting moiety binds to macrophage receptor

Cytosolic delivery of siRNA Phagocytosis

Increasing pH Release payload Cationic swelling Anionic material decomplexed Increased mesh size pH ~ 5–7

The size and shape of a negatively charged, or anionic, hydrogel (shown as black crossed lines at center) changes with pH. The hydrogel takes up positively charged nanoparticles (purple spheres) containing a therapeutic agent, in this case a small-interfering RNA (siRNA) that can silence troublesome genes associated with irritable bowel diseases. In the acidic environment of the gastrointestinal tract, the hydrogel has a small mesh size and protects its therapeutic payload from the harsh environment. Once the hydrogel transits

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siRNA-loaded cationic nanogels are uptaken by macrophages in the intestines

to the intestine, the pH level increases, which causes the hydrogel to swell (or decomplex); its mesh size increases and it releases its nanoparticle payload only in this target environment. The nanoparticles have a molecular group, called a moiety, that binds to the receptor of macrophage cells in the small intestine. The nanoparticles are taken into the cell by phagocytosis, where they swell because of their positive (or cationic) charge and then release their siRNA payload into the cytosol, the aqueous component of the cytoplasm of a cell.

Courtesy of Olivia L. Lanier, made with BioRender

Protect payload

to injection-based approaches, which have been shown time and again to result in decreased patient compliance. For conditions such as these, the development of oral treatments that could be taken at home would go far in allowing patients of all backgrounds to regain a normal quality of life. Although the oral route has been successful in many applications, it presents a number of challenges that are difficult to overcome. The stomach maintains a highly acidic environment that can damage the structure of complex and fragile protein molecules. Additionally, both the stomach and the small intestine contain numerous enzymes that efficiently break down proteins as part of the natural digestive process. Even if proteins can be successfully delivered to the intestine, they must penetrate a thick mucosal layer that coats the inner surface of the intestine to reach the intestinal epithelium while avoiding degradation enzymes. Once they reach the epithelium, they face the most difficult challenge of all: traversing through the tissues of the epithelium and entering circulation. Intestinal absorptive cells have tight junctions between them that greatly slow, or fully prevent, the passive diffusion of large macromolecules such as proteins. And proteins cannot diffuse directly into a cell membrane, so some delivery system must be developed. Tailored Gels One class of materials that have shown promise are hydrogels, crosslinked networks of biocompatible polymers that respond to changes in pH. These hydrogels contain acidic molecular groups that have a strength comparable to what is found in the body, so they can swell and shrink in a controlled, reversible manner that can be tuned to specific physiological conditions. The hydrogels can be formulated as microparticles or nanoparticles and loaded with a protein therapy. These nanogels protect the payload throughout its transit through the gastrointestinal tract, where the change in the acidity in the intestine causes the hydrogel to change shape and release the protein. Our lab has a long history in the development of hydrogels, which we have researched for use as synthetic replacement materials for cartilage, vocal cords, and intraocular lenses. For www.americanscientist.org

medications, we have taken a convergent approach across materials science, chemistry, and molecular biology to investigate hydrogel oral delivery for insulin to treat diabetes, calcitonin for possible  treatment of osteoporosis in postmenopausal women, and interferon beta for treatment of multiple sclerosis (which would replace painful multiple sclerosis procedures based on intramuscular injections, for which compliance is poor). A pioneering new hydrogel orally delivers hematological factor IX, a clotting agent whose absence from the blood is the main cause of a type of hemophilia B. We have also studied new hydrogel platforms for the oral delivery of drugs

Hydrogels can swell and shrink in a controlled, reversible manner that can be tuned to specific physiological conditions and protect a payload throughout its transit through the gastrointestinal tract. that have a high isoelectric point, a property that determines the pH at which the molecule has no net electric charge related to the surrounding tissue, thereby affecting its uptake and retention. Drugs in this class include therapeutic agents for Crohn’s disease and hormones used to treat diseases associated with growth. Similar work is now being conducted on targeted treatments for cancer—in particular, those for which health disparities have been well documented to exist for historically disadvantaged groups. Crossing Cell Membranes As proteins don’t easily cross cell membranes, we are adapting our research on biomaterials to try delivering other treatments that can overcome this issue. A new approach to mitigate false cytokine signaling— such as that underlying IBDs and maybe also Crohn’s disease, ulcerative colitis, and even celiac disease— is RNA interference (RNAi). This approach uses what are called smallinterfering RNAs (siRNAs), a short double strand of RNA that can degrade messenger RNA signals, thereby silencing Special Issue: Convergence Science

undesirable genes. In the case of IBDs, the siRNA treatment needs to target macrophages in order to tone down the immune system’s inflammatory effects. To carry the siRNAs, we created negatively charged nanogels with a pH response that was optimized to attract positively charged nanoparticles containing siRNAs to their interiors, but then to repel them once they reach their targets so that they don’t remain bound to the nanogels. Once the nanogels arrive intact at the mucosa of the upper small intestine, they swell because of the relatively high pH there. Cell membranes have been shown to allow positively charged nanoparticles to translocate across them, so the released nanoparticles can penetrate the cells and deliver the siRNA inside (see figure on the facing page). With the development of these approaches, we hope that oral delivery systems will be more accessible for patients in areas of the world without immediate access to health care. In addition, oral systems have been shown to improve patient compliance and to reduce side effects, thereby lowering medical costs. We are hopeful that, before long, our work developing oral delivery systems for therapeutics that traditionally have been delivered by injections could streamline and make more equitable the treatment of many diseases. Bibliography Horava, S. D., and N. A. Peppas. 2016. Design of pH-responsive biomaterials to enable the oral route of hematological factor IX. Annals of Biomedical Engineering 44:1970–1982. Liu, W., Y. Wang, J. Wang, O. L. Lanier, M. E. Wechsler, N. A. Peppas, and Z. Gu. 2021. Macroencapsulation devices for cell therapy. Engineering doi:10.1016/j.eng.2021.10.021 Peters, J. T., M. E. Wechsler, and N. A. Peppas. 2021. Advanced biomedical hydrogels: Molecular architecture and its impact on medical applications. Regenerative Biomaterials 8:1–21. Schoellhammer, C. M., R. S. Langer, and C. G. Traverso. 2017. Blood, guts, and hope. American Scientist 105:32–35. Spencer, D. S., et al. 2021. Cytocompatibility, membrane disruption, and siRNA delivery using environmentally responsive cationic nanogels. Journal of Controlled Disease 332:608–619. Nicholas A. Peppas is the Cockrell Family Regents Chair in Engineering and director of the Institute for Biomaterials, Drug Delivery, and Regenerative Medicine at the University of Texas at Austin. Olivia L. Lanier is a Provost Early Career Fellow in the department of biomedical engineering at the University of Texas at Austin. Email for Peppas: [email protected] 2022

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Gilda A. Barabino and Harriet B. Nembhard | A systems engineering approach to equitable health care solutions

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he United States is in the midst of a public health crisis, reeling from two serious pandemics: COVID-19 and systemic racism. Everyone is familiar with the impact of the virus. The categorization of racism as a pandemic may seem less obvious, but when viewed through the lens of systems engineering, racism in the American health care system can be seen to contain tightly linked problems of medicine, technology, design, leadership, and ethics. The intersections are myriad, bound in racial disparities that pervade all aspects of life, including such basic functions as the ability to breathe.

For Black people and other racially minoritized groups, the health care system—which should provide equitable treatment and care—is tainted by disparate access, poor quality of care, unequal outcomes, and distrust between individuals and health care providers. The extent to which racial biases lead to health care disparities is influenced by demographics; environmental, social, and economic conditions; and policies and practices that pervade all aspects of life. Our research takes a systems engineering approach to examining racism and health care, with particular atten-

A patient at a COVID-19 isolation center in the Banaadir region of Somalia has her blood oxygen level tested by a health care provider using a finger pulse oximeter. These medical devices are not reliable on patients with darker skin tones, and the resulting underdiagnosis of

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tion on the disproportionate effect of health crises on Black people. Systems engineering is an interdisciplinary field of engineering that focuses on the design, operations, and management of complex systems; it considers the technical and business needs of all stakeholders in a system in order to provide quality products and outcomes over a long-range life cycle. The broad scope of this approach allows researchers to tackle large problems without losing sight of the details. The goal of our work is to identify the racial biases inherent in the health care system, and suggest solutions toward a better and

hypoxemia in Black patients may in part explain why there have been high levels of mortality among African Americans during the pandemic. The authors argue that a systems engineering approach could help root out racial biases in medical devices and the health care system.

© World Health Organization/Ismail Taxta

Suffocating from Medical Bias

more equitable future. Engineering and technology have long played an important role in health care, from the design and manufacture of early medical devices to the current deployment of electronic medical records and artificial intelligence for health screening. However, biased algorithms and technologies, along with a lack of diverse innovators, mean that not all people benefit equally from these advances. To address the racial pandemic in health care, we need to recognize that engineering and technology are influenced by society and operate within the context of social and political forces. Even neutral-seeming medical devices or drug protocols can contain harmful, embedded racial biases. Those biases must be acknowledged and addressed if we are to create a more humane and effective system of health care. Breathing Room The system of racial disadvantage across housing, schooling, employment, wealth, and health is well documented. Social scientist David R. Williams of Harvard University, who studies social influences on health, has described this societal system as the ”house that racism built,” noting how the system of racism (with institutional, cultural, and individual discrimination components) is impacted by social forces with multiple pathways to undermine health. To better understand interrelated racial disparities across multiple domains, University of Washington sociologist Barbara Reskin describes race discrimination as a system of dynamically related subsystems in which disparities systematically favor certain groups and are mutually reinforcing. Where someone lives can dictate where they are schooled, which can in turn constrain employment opportunities; it can also determine whether they have access to quality health care or are exposed to environmental elements that are detrimental to their health. For Black people, racism and its relationship with breathing have a long history that was brought into sharp focus in 2020 by the murder of George Floyd by suffocation, and by the COVID-19 pandemic that has seen disproportionate numbers of Black people succumbing to respiratory disease. Gabriel O. Apata (an independent scholar of philosophy, sociology, and www.americanscientist.org

the African diaspora) explains in his 2020 article “I Can’t Breathe” the ways in which racism targets breathing itself, with “social air” representing freedom and suffocation representing its negation. Apata’s suffocation theory describes racial suffocation as the slow, methodical, and invisible process by which racially unjust policies, practices, and behaviors combined with the lack of access to resources and opportunities systematically squeeze the social air breathed by Black people, leading to their deaths.

Protecting Black lives means protecting Black breath, which means addressing the discriminatory design in medical devices and more broadly in the health care system. The normal respiration rate for a healthy adult who has no difficulty breathing is 12 to 20 breaths per minute while at rest. A device called a spirometer measures the amount of air a person can breathe in a single deep breath as well as the rate of that breath. The spirometer has a long and sordid history in the subjugation of Black people. Lundy Braun, a professor of medical science and Africana studies at Brown University, has shown how this device has adversely shaped medical research and practice for many decades. The legacy of slavery as embodied by disparate treatment and health outcomes for Black people takes many forms, one root of which is to devalue Black lives—often through misrepresented biological differences between Black and white people. Braun found that in the mid-19th century, plantation physician and slaveholder Samuel Cartwright used a spirometer to compare the lung capacities of enslaved Black people with those of white people. His findings perpetuated the misconception that Black people have lower lung capacity than white people, which provided Special Issue: Convergence Science

support for racially defined inferiority. This fallacy endures as race continues to be used out of context in medical measurements, and most modern spirometers still include a race-based lung function algorithm that assumes lower lung capacity for people of color, thereby resulting in undertreatment of breathing conditions in people of color. When oxygen saturation is below 90 percent, there is danger to vital organs such as the heart, brain, lungs, and kidneys. The ubiquitous fingertip pulse oximeter, which measures how much oxygen is making it into the blood, does not reliably work on people with darker skin. Since the population drawn upon for the development of the pulse oximeter was not racially diverse (a design flaw) and measurement of light transmitted through skin can vary with skin tone, discrepancies in readings for Black people are not surprising. Multiple studies, including a 2020 paper in the New England Journal of Medicine by Michael Sjoding (assistant professor of pulmonary and critical care medicine at the University of Michigan Medical School) and his colleagues, show that fingertip pulse oximeters produce biased results that make it more likely that medical providers will underdiagnose occult hypoxemia in Black patients. Given that oxygen is a frequently administered medical therapy, there are widespread consequences of this racial bias. Philosophers Shen-yi Liao of the University of Puget Sound and Vanessa Carbonell of the University of Cincinnati have used spirometers and pulse oximeters as case studies to demonstrate how racism is encoded in medical devices. They argue that these devices materialize oppression, meaning that the tools’ inherent racial biases produce tangible harm based on racial difference, thereby perpetuating an unjust hegemonic system. The measurement of breath and oxygen is fundamental to the care of all patients, and biased readings can have life-threatening implications for Black patients with a range of medical issues. For example, most patients with sickle cell anemia develop  abnormal pulmonary function, which is characterized by airway obstruction, restrictive lung disease, and hypoxemia. Pulmonary complications of the disease, which turn fatal in a large proportion of patients, may be underappreciated by health care providers. Poor under2022

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+ racial bias

ability of health providers to deliver fair health care

+ knowledge of current reality of racial bias

– availability of fair medical devices +

+

R1

+ patients’ health status

need for health care interventions

R2

–

equity-driven health care markets

+ government policies to reduce racism in public health

R3

+ +

+

engineering education to reduce racism in medical device design

Gilda A. Barabino and Harriet B. Nembhard

This diagram of a systems thinking causal loop shows reinforcing cycles in the health care system. The innermost reinforcing loop (R1, black) illustrates the links between the availability of unbiased medical devices and the ability to receive care. The middle reinforcing loop (R2, green) shows interventions that can help create a more equitable health care system. The outermost reinforcing loop (R3, orange) incorporates an educational system that teaches engineering students to consider potential biases when conceiving of new medical devices so that equity is built into the product from the beginning.

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his colleagues have demonstrated racial bias in algorithms used by health care systems; those algorithms assign lower risk scores to Black people, on average, leading to an underestimation of their health care needs. The question becomes what practices, interventions, and resolutions will help to achieve equitable health

Health Care System Complexity Our work on complex systems and systems thinking provides useful approaches to studying the dynamic cause and effect of racism in health care. The word system in an engineering context means interrelated parts or components that cooperate in some way. In such systems, there is a need to focus on the interactions between components. The complexity of the American health care system has led to layers of crises in safety, quality, cost, and access. Moreover, as health care providers gain experience and seek to

40%

percent with SaO2 ≤ 88% when SpO2 92% – 96%

standing of health complications for Black people has many causes, including false assumptions about biological differences, racial biases in the designs of medical devices, and researchers and clinicians ignoring societal factors. COVID-19 is also having disproportionate impacts on Black and brown populations. A 2021 study led by National Cancer Institute epidemiologist Meredith S. Shiels found that in 2020 more than twice as many Black, American Indian, Alaska Native, and Latino men and women than white and Asian men and women had died of COVID-19. Biased breathing measurements may have contributed to this disproportionate outcome because COVID-19 can cause lung complications such as  pneumonia or acute respiratory distress syndrome (ARDS). Sepsis, another possible complication of COVID-19, can also cause lasting harm to the lungs. With these adverse outcomes in the balance, health care providers, engineers, and device designers ought to take greater action to fight discriminatory design. There is nothing more fundamental to life than taking a breath. Protecting Black lives means protecting Black breath, which means addressing the discriminatory design in medical devices, and more broadly in the health care system. For example, Ziad Obermeyer at the University of California, Berkeley, School of Public Health and

care for all people. Achuta Kadambi, leader of the Visual Machines Group at the University of California, Los Angeles, describes a framework that identifies three ways that racial and gender bias can permeate medical devices— physical bias, interpretation bias, and computational bias—and argues that fairness should be considered equally important as effectiveness when evaluating new technology. All three types of biases are demonstrated in the technologies used to measure breath: the pulse oximeter is an example of physical bias; the spirometer is an example of interpretation bias; and health care algorithms are an example of computational bias.

30%

95% Cl 20%

10%

0 Asian

Black

Hispanic

White

Adapted from V. S. M. Valbuena, et al. 2022.

Pulse oximeters are more likely to report falsely high oxygen levels in Black patients than in patients of other races. The gold standard for measuring oxygen saturation (SaO2) is with an arterial blood gas test; pulse oximeters measure peripheral oxygen saturation (SpO2) through a less invasive—but less accurate—method. The normal level of oxygen saturation is 92 percent to 96 percent; readings below 88 percent indicate that a patient has dangerously low blood oxygen saturation. Black patients are far more likely than patients of other races to receive a pulse oximeter measurement in the normal range when their actual level of oxygen saturation is dangerously low. (CI indicates the confidence interval.)

make individual improvements, localized patterns of behavior in patient care emerge. There is no single point of control, and no one is “in charge.” Consequently, the health care providers within this complex system can typically be influenced more than they can be controlled. Applying systems thinking to the multifaceted American health care system ensures that the larger goal of fair and equitable care does not get lost as individual stakeholders address their specific areas of expertise. It is a holistic approach to analysis that focuses on how parts of a system interrelate over time and how they work within the context of larger systems. As systems thinkers, engineers are well positioned to apply a systems approach to derive solutions to health disparities stemming from racism. We have been incorporating some of these connections into engineering education. But we can do more. A systems thinking causal loop diagram provides an overview of how racial bias can be influenced within the complex system of health care (see figure at top of page 206). The innermost reinforcing loop shows how the availability of fair medical devices affects the ability of health care providers to deliver fair health care, and thus patients’ resulting health status. The middle reinforcing loop shows how knowledge of racial bias, equity-driven health care markets, and government policies to fight racism add to the availability of fair medical devices. For example, last year the U.S. Food and Drug Administration released its “Artificial Intelligence/ Machine Learning-Based Software as a Medical Device Action Plan,” in which the agency commits to supporting efforts to evaluate and improve the use of AI in medical devices with the goal of identifying and eliminating bias. The outermost reinforcing loop conceptualizes the role and importance of engineering education. Engineering programs train their students in the systems thinking necessary to approach large and complex problems without losing sight of the big picture. Nearly every disciplinary area in engineering touches on health care, and biomedical engineers in particular are trained to combine a systems perspective with an understanding of the intricacies of health care. It is typical that biomedical engineering students have foundational www.americanscientist.org

courses in biomaterials, biomechanics, and bioinstrumentation that include training on modeling, acquisition, and analysis of data collected from living systems, as well as courses on medical device design fundamentals and prototyping, often culminating in a

As systems thinkers, engineers are well positioned to apply a systems approach to derive solutions to health disparities stemming from racism.

The critical work ahead of us in engineering education is to enhance understanding of the causal relationships in American heath care, as well as the increasingly vast research that is expanding our knowledge of racism as a health determinant. Then we must collaborate to use that knowledge to create a better future, where medical devices and the provision of health care are more equitable for all. References Apata, G. O. 2020. “I can’t breathe”: The suffocating nature of racism. Theory, Culture and Society 37(7–8):241–254. Braun, L. 2014. Breathing Race into the Machine: The Surprising Career of the Spirometer from Plantation to Genetics. Minneapolis: University of Minnesota Press. Griffin, P., et al. 2016. Healthcare Systems Engineering. Hoboken, New Jersey: John Wiley & Sons, Inc. Kadambi, A. 2021. Achieving fairness in medical devices. Science doi:10.1126/science.abe9195

yearlong senior design capstone project. There are opportunities in these courses and others throughout the engineering curriculum to improve pedagogy and promote inclusive practices to reduce racism in design. For example, students should be trained in the Societal Readiness Level, an expansive framework that provides a nine-stage scale for assessing the readiness of a product or innovation to be integrated into society. These frameworks need to be incorporated into the way we design and assess medical devices. Such work is also in alignment with the Accreditation Board for Engineering and Technology’s principles of diversity and inclusion, which state that these efforts to improve equity and representation are critical to innovation. Moreover, we have the opportunity to ensure that engineering students are working in broadly interdisciplinary teams. These teams can and should include numerous parties: public health students and faculty members to understand how racism can affect health; anthropology and history colleagues to bring context to how medical oppression has impacted people; and medicine and nursing colleagues to bring awareness of patients’ lived experiences and how they are affected by the end designs of devices and systems. This convergent approach will support a network of embracing and transmitting systems of thinking about equity in health care. Special Issue: Convergence Science

Liao, S. Y., and V. Carbonell. 2022. Materialized oppression in medical tools and technologies. American Journal of Bioethics. Published online. doi:10.1080/15265161.2022.2044543 Obermeyer, Z., B. Powers, C. Vogeli, and S. Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science doi:10.1126/science.aax2342. Reskin, B. 2012. The race discrimination system. Annual Review of Sociology 38:17–35. Shiels, M. S., et al. 2021. Racial and ethnic disparities in excess deaths during the COVID-19 pandemic, March to December 2020. Annals of Internal Medicine doi:10.7326/M21-2134. Sjoding, M. W., R. P. Dickson, T. J. Iwashyna, S. E. Gay, and T. S. Valley. 2020. Racial bias in pulse oximetry measurement. New England Journal of Medicine 383:2477–2478. Valbuena, V. S. M., et al. 2022. Racial bias in pulse oximetry measurement among patients about to undergo extracorporeal membrane oxygenation in 2019–2020: A retrospective cohort study. Chest 161:971–978. Williams, D. R., J. A. Lawrence, B. A. Davis, and C. Vu. 2019. Understanding how discrimination can affect health. Health Services Research doi:10.1111/1475-6773.13222.

Gilda A. Barabino is the president of Olin College of Engineering and a professor of biomedical and chemical engineering. She has broad interests in global health and interdisciplinary research and education, and she is an advocate for health equity. Harriet B. Nembhard is the dean of the College of Engineering and a professor of industrial and systems engineering at the University of Iowa. Her research takes a multidisciplinary approach to improving complex systems across manufacturing and health care, and she advocates for equity and inclusion in STEM education. Email for Barabino: [email protected] 2022

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First Person | Hongkui Zeng

The Brain Cartographer

What is the BRAIN Initiative aiming to accomplish, and how does your work fit within that mission?

The primary goal of the BRAIN Initiative is to develop neurotechnologies that will facilitate the understanding of the circuit function of the brain and how it changes in diseases. In conjunction with that goal, the BRAIN Initiative is also addressing series of questions using those technologies. They’ve set up seven different priority areas, from cells to circuits, from manipulation to the development of computational series, to explain how the brain works. The number one priority out of the seven is to understand the cellular composition and diversity of the brain—basically coming up with a catalog of cell types and understanding the relationships, structures, and functions of those cell types—as a foundation to understand the brain itself. To understand how a car works, you need a list of parts, and you need to know what each part is doing and how the parts work together to allow the car to run. Cell types are the parts list of the brain. The BRAIN Initiative established a BRAIN Initiative Cell Census Network [BICCN] that is tasked with generating the cell type catalog for the mammalian brain, including major species from mice and rodents, up to humans and nonhuman primates. We at the Allen Institute have been interested in the cell type question from the very beginning. We generated a brain atlas by profiling all 20,000 genes in the mouse genome and mapping how they are distributed in the brain. We’ve been doing that in the human brain as well. We have already established multi208

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ple transcriptomic technology platforms that allow us to profile individual cells in the brain very efficiently. You used the term “brain circuit,” which brings to mind a digital computer. To what degree is that a useful paradigm?

It is a very good analogy, comparing the brain to a computer. In a computer, individual chips, or units, are connected to each other in a wiring diagram in order to perform computations between those different individual units. The brain is a much more advanced computer. The individual cells and cell types are the units. But the cell types are much more flexible than a computer chip, and they are not all the same unit. They’re diverse. They have different properties. They can change and tune their properties. They don’t just perform linear functions. The wiring diagram of the brain is the interconnections between the cells—the neurons of the brain. That wiring diagram is extremely complex and specific, which allows the brain to compute in sophisticated ways that we don’t understand yet. How do you break down a task—such as mapping the brain, which is so big and complicated—into tangible units?

Fortunately, the brain is already broken down into units: the individual cells. What we need to do is characterize and measure the different properties of the cells. We call the genes that a cell expresses transcriptomics. Transcription is the expression of the gene. If we obtain a transcriptome, it’s a collection of the expression level of all the genes in the genome. Then there’s the shape of the

Erik Dinnel /Allen Institute

An average human brain is anything but average: It contains roughly 100 billion neurons, linked together by at least 100 trillion synaptic connections, bathed in ever-shifting chemicals and reacting to ever-changing stimuli. Making sense of all that complexity might seem like an unattainable goal, but Hongkui Zeng is working on it. She has sought to break down the problem into comprehensible chunks, investigating activity patterns, genetics, and cell types in the brain. Zeng also started on a reduced scale, first studying fruit flies, then moving up to mouse brains. Now, as the director of the Allen Institute for Brain Science in Seattle, Zeng is going all in. She is a key player in the BRAIN (Brain Research Through Advancing Innovative Neurotechnologies) Initiative, a vast, interdisciplinary alliance created to map out exactly how the human brain functions—and what happens when it malfunctions. Special issue editor Corey S. Powell spoke with Zeng about how she is approaching the colossal challenge of understanding the human mind. This interview has been edited for length and clarity. cell, the firing action, and the potential firing properties of the cells. There are other properties you can measure. You do all these different quantitative measurements from the same cell, and then you can use clustering analysis to look for similarities in the properties between cells. When you use clustering analysis, you can identify types of cells. A cell type is a group of cells that have similar properties with each other, and different properties from other types. The complex issue here is that the cellular properties are very diverse. That’s why we’ve developed technologies to crack the cells in different ways. Preferably you want to measure the different properties in the same cell so that you can correlate and understand the relationships between properties better. If you measure property A in cell 1 and then measure property B in cell 2, it’s very difficult to understand how properties A and B are related to each other, and how they’re related to cells 1 and 2. But if you measure A and B in 1, and A and B in 2, and then A and B in many other different cells, the cluster analysis will make sense and help you understand whether A and B characterize different types of cells, and whether A and B always go together. We call that multimodal analysis. Another important feature is scalability. There are millions to billions of cells. A mouse brain contains 75 million cells. A human brain contains 82 billion cells. If you want to gain a systematic understanding—if you want to have a complete parts list—you have to profile millions of cells from the mouse brain. Choosing the right technology to scale up is very impor-

tant. Currently, the most scalable approach is single-cell transcriptomics, which describes the entire repertoire of gene expression in a cell. Singlecell transcriptomics is a revolutionary technology developed within the past 10 years. It’s now being widely used to profile millions of cells. How much of the cell categorization is qualitative and how much of it involves discrete, quantitative differences?

Everything is quantitative. You can assess qualitatively, but that is not good enough for an unbiased comparison between different kinds of cells. You always have to be quantitative. Cell types are organized in a hierarchical manner. You can group cells into big categories—let’s say excitatory neurons and inhibitory neurons. They’re qualitatively different because they express two different neurotransmitters, but within each of those two different categories there will be many different types. Within each type, there could be many subtypes. So the difference between the types becomes smaller and more quantitative. The difference between subtypes within each type can be even more subtle, and sometimes it becomes a continuum. At what point do you stop? How many categories do you find useful?

That’s something neuroscientists and biologists in general have been debating for a long time. It’s still an open question. You may have heard the debate between lumpers and splitters. Some people really want to know the details and just keep splitting groups into subtypes, whereas others—the lumpers—only care about major differences. Where should we stop? The major distinctions and major type levels are obvious, but as to where we stop, that comes from the multimodal analysis. We know that single-cell transcriptomics is the most scalable approach. We can use that method to classify cells into types. However, when you get to the lower branches, it’s very hard to find out how many types there are. At that point, it is important to bring in additional features: the shape of the cell, the connections of the cell, and the physiological properties of the cell. Then we see how the different properties are covariant within a cell. When you introduce multimodal criteria, it helps to discretize, or it helps to prioritize the ambiguity between cells. www.americanscientist.org

Of course, sometimes the process brings more ambiguity. Sometimes it brings less. But when it gets really confusing, then you say, “Let’s stop. We can’t divide these cells into types anymore.” But if there’s a point where you can clearly separate cells into groups based on multiple pieces of evidence, then you continue. When you’re looking at the cell types by genomics, how do you mark the cells so that you know what you’re looking at?

When we do genomics or transcriptomics studies, we don’t need to mark the cells. The transcriptomics profile is already so rich. It has expression information on every gene. Once we cluster them, we can use marker genes— basically genes that tell apart the types. We use the characteristic genes that tell apart different clusters or types as the label markers. The marker genes are the most differentially expressed genes. With clustering analysis, you group the cells,

Think of learning, disease, and cognition as players in an orchestra. Then you can begin to pick out how the players work together to make music. say, into two clusters. Then you compute the average gene expression level for every gene between the two clusters. You identify the discrete differences and find the genes with the biggest difference between the two types. Or, if you have many types, you identify the genes that are most unique, most differentially expressed in your cluster versus all the other clusters. Those are the cell-type marker genes. When you go into other kinds of analysis, where you don’t have this rich information, you then need to label the cells. You use those marker genes to label. For example, you use the promoter of that marker gene to express a fluorescent protein, and you can decide that any cells labeled by this fluorescence belong to this transcriptomic type. Now you can measure its firing properties, its connections, its functions, things like that. Special Issue: Convergence Science

You alluded to three orders of magnitude in the jump from the mouse brain to the human brain, just in terms of cell numbers, never mind complexity. Where are you in laying the groundwork for doing the equivalent project on the human brain?

The groundwork would be on both the conceptual and technical levels. At the technical level, we have to scale up from what we do in the mouse. We probably don’t need to scale up proportionally, profiling a thousand times more cells in order to understand the human brain. But at least tenfold, maybe one-hundredfold. The reason I say that we don’t need to do a thousandfold more is because of the conceptual advances we’re generating now in the mouse brain. We understand the organization, the basic parts list, how they’re distributed spatially, how they compose different systems of the brain. There’s the motor system, sensory, cognitive, memory, the innate behaviors. There are all these different parts of the brain. We now can provide the cellular composition for each part of the brain—the general network. That information can be carried over into the human brain, which helps us to develop a strategy to sample the different parts of the human brain for comparison with the mouse brain to see whether the cellular diversity is really a diversity difference, or just a numbers difference. You go to some regions, you sample, and you find that actually the number of cell types in a homologous human region and mouse region doesn’t change much, even though the number of cells in those two regions changes a hundredfold. That just means there are more cells of a given type in a human brain compared to a mouse brain. That’s a very important thing to find out, though—whether there are a thousand times more cell types in a human brain, or if it’s just more cells of each type. How does cell type scale up, and in what way? Having a comprehensive mouse map will help us to understand that question. We can analyze the complexity one region at a time and know what to look for. Did you run into any unexpected hurdles working on the mouse model that will help your process when you move on to the human brain?

Initially we didn’t know whether we’d be able to scale up efficiently. Maybe cost would be a hurdle. Whether there’s 2022

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solved computationally. For example, the analysis of the genomics data as well as the analysis of the images—the light microscopy, and especially the electron microscopy images—takes a lot of computation. Especially for the electron microscopy images, there’s a lot of AI and machine learning involved in learning how to reconstruct faster. We’re dealing with really large-scale data sets now, and computation is critical.

Allen Institute

How has your work deepened our understanding of the brain and brain disorders?

This digital reconstruction of human neurons (overlaid on a slice of brain tissue donated by a brain surgery patient) shows the electrical information that Allen Institute researchers are able to capture from live human neurons, as well as their three-dimensional shape and gene expression. The colors represent different types of human neurons in the medial temporal gyrus of the neocortex, the outermost shell of the mammalian brain.

a technique that can give us sufficient resolution or not, and whether the technique would be cheap enough to allow us to profile many cells or not. There were hurdles at the beginning, but it’s amazing to see the technological progress over the past few years. Sequencing has become cheaper and cheaper. There are techniques that have emerged that allow us to scale up. A technique that overcame one of the major hurdles is called spatial transcriptomics, or spatially resolved transcriptomics. My expectation is that our current single-cell transcriptomics will be replaced by spatial transcriptomics in the near future. With the previous single-cell transcriptomics, you have to dissociate the brain. You need to disrupt the brain tissue, add some proteinase or something, and really digest it and pull it apart. Then you isolate the single cells, sort them, put them in microfluid, and you get the transcriptomes of each cell. In that kind of process, you lose the spatial context of the cells completely. You don’t know which cell comes from where. But the spatial transcriptomics allows you to profile gene expression of brain sections in situ. For spatial transcriptomics, you lay down the brain section on a barcoded array on a chip, and you allow the expressed genes—the transcripts—to be bound onto that barcoded array. That gives you not only what genes are expressed, but in which spots those genes are detected. It gives you both 210

American Scientist, Volume 110

spatial information and gene expression information at the same time. To create a brain atlas, you need to know not only what cells there are in the brain, but also where they are. A brain cell atlas without this spatial information is not a brain cell atlas. Coupling spatial and transcriptomic information together was a major hurdle for us, but there are techniques now that allow us to do that. How do you put a map of cell connections and a map of cell types together to understand what’s actually going on in that brain?

The idea is to understand how the cell types are wired together. For that, you need to label the different cell types. Then, using microscopy, you label different cell types, collect the imaging data, and trace. You literally trace individual neurons to find how they’re connected, where the synapses are on the neurons. And you derive, manually or computationally, the wiring diagram. How do the principles of convergence science help you bring different types of expertise together to work on these problems?

We have to bring people with different expertise together—genomics people, neuroscientists, imaging and microscopy people, computational people. The major challenge is letting each of them understand what the others are doing. Very often, the problem has to be

The work we’ve done in the mouse brain lays a much more detailed and comprehensive foundation for us to begin to understand the human brain and its normal functions, as well as changes in a disease situation. I emphasize the words detailed and comprehensive because you can’t look at a human brain and figure out exactly what’s happening with fuzzy observation techniques. By laying things out clearly, at a specific cell type level, it allows you to really dissect what is happening. A lot of this knowledge can be translated into humans. Humans and mice, after all, are 90 percent similar to each other. Of course there are also areas that cannot be translated, but we can identify the similarities and the differences between a mouse brain and a human brain. And then, in terms of human diseases, you’re able to dissect which particular parts of the brain or parts of the pathways are changed, and what kind of changes happen, in a disease context. It gives you a much clearer, crisper picture than what we had before. We provide a detailed map to allow you to understand the variations of the brain. Think of learning, disease, and cognition as players in an orchestra. Then you can begin to pick out how the players work together to make music. In cognition, which parts are activated? This group and this one are working together and generating this outcome, whereas that group is forming memories, and this change is related to some kind of disease. And you can begin to derive an active map. Instead of the static map that we initially have, you build activities, build dynamics, and build function into that map.

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