A girl stops by an image of the cosmic microwave background (CMB) at Shanghai Astrology Museum in Shanghai, China Sunday, Jul. 18, 2021. The planetarium, with a total floor space of 38,000 square meters and claimed to be the world’s largest, opens to visitors from July 18. (FeatureChina via AP Images)
There was a time before time when the universe was tiny, dense, and hot. In this world, time didn’t even exist. Space didn’t exist. That’s what current theories about the Big Bang posit, says Vijay Balasubramanian, the Cathy and Marc Lasry Professor of Physics. But what does this mean? What did the beginning of the universe look like? “I don’t know, maybe there was a timeless, spaceless soup,” Balasubramanian says. When we try to describe the beginning of everything, “our words fail us,” he says.
Yet, for thousands of years, humans have been trying to do just that. One attempt came 75 years ago from physicists George Gamow and Ralph Alpher. In a paper published on April 1, 1948, Alpher and Gamow imagined the universe starts in a hot, dense state that cools as it expands. After some time, they argued, there should have been a gas of neutrons, protons, electrons, and neutrinos reacting with each other and congealing into atomic nuclei as the universe aged and cooled. As the universe changed, so did the rates of decay and the ratios of protons to neutrons. Alpher and Gamow were able to mathematically calculate how this process might have occurred.
Now known as the alpha-beta-gamma theory, the paper predicted the surprisingly large fraction of helium and hydrogen in the universe. (By weight, hydrogen comprises 74% of nuclear matter, helium 24%, and heavier elements less than 1%.)
The findings of Gamow and Alpher hold up today, Balasubramanian says, part of an increasingly complex picture of matter, time and space. Penn Today spoke with Balasubramanian about the paper, the Big Bang, and the origin of the universe.
Balasubramanian is Cathy and Marc Lasry Professor in the Department of Physics and Astronomy in the Penn School of Arts and Sciences and a member of the Penn Bioengineering Graduate Group.
A team of researchers led by the School of Arts & Science’s Wei Guo offers new insights into a mechanism that promotes tumor growth. “This information could be used to help clinicians diagnose cancers earlier in the future,” says Guo.
In many instances, the physical manifestation of cancers and the ways they are subsequently diagnosed is via a tumor, tissue masses of mutated cells and structures that grow excessively. One of the major mysteries in understanding what goes awry in cancers relates to the environments within which these structures grow, commonly known as the tumor microenvironment.
These microenvironments play a role in facilitating tumor survival, growth, and spread. Tumors can help generate their own infrastructure in the form of vasculature, immune cells, signaling molecules, and extracellular matrices (ECMs), three-dimensional networks of collagen-rich support scaffolding for a cell. ECMs also help regulate cellular communications, and in the tumor microenvironment ECMs can be a key promoter of tumor growth by providing structural support for cancerous cells and in modulating signaling pathways that promote growth.
Now, new research led by the School of Arts & Science’sWei Guo and published in the journal Nature Cell Biology has bridged the complex structural interactions within the tumor microenvironment to the signals that trigger tumor growth. The researchers studied cancerous liver cells grown on ECMs of varying stiffness and discovered that the stiffening associated with tumor growth can initiate a cascade that increases the production of small lipid-encapsulated vesicles known as exosomes.
“Think of these exosomes as packages that each cell couriers out, and, depending on the address, they get directed to other cells,” says Ravi Radhakrishnan, professor of bioengineering in the School of Engineering and Applied Science and a co-author of the paper.
“By recording the number of packages sent, the addresses on these packages, their contents, and most importantly, how they’re regulated and generated, we can better understand the relationship between a patient’s tumor microenvironment and their unique molecular signaling signatures, hinting at more robust personalized cancer therapies,” Radhakrishnan says.
While studying exosomes in relation to tumor growth and metastasis has been well-documented in recent years, researchers have mostly focused on cataloging their characteristics rather than investigating the many processes that govern the creation and shuttling of exosomes between cells. As members of Penn’s Physical Sciences Oncology Center (PSOC), Guo and Radhakrishnan have long collaborated on projects concerning tissue stiffness. For this paper, they sought to elucidate how stiffening promotes exosome trafficking in cancerous intracellular signaling.
“Our lab previously found that high stiffness promotes the secretion of exosomes,” says Di-Ao Liu, co-first author of the paper and a graduate student in the Guo Lab. “Now, we were able to model the stiffening processes through experiments and identify molecular pathways and protein networks that cause this, which better links ECM stiffening to cancerous signaling.”
Artist-in-residence and visiting scholar Rebecca Kamen has blended AI and art to produce animated illustrations representing how a dyslexic brain interprets information.
A work that Penn artist-in-residence Rebecca Kamen produced for the show, “Dyslexic Dictionary” at Arion Press in San Francisco. Here, she reinterprets Ph.D. candidate Dale Zhou’s network visualization. (Image: Cat Fennell)
Communicating thoughts with words is considered a uniquely human evolutionary adaptation known as language processing. Fundamentally, it is an information exchange, a lot like data transfer between devices, but one riddled with discrete layers of complexity, as the ways in which our brains interpret and express ideas differ from person to person.
Learning challenges such as dyslexia are underpinned by these differences in language processing and can be characterized by difficulty learning and decoding information from written text.
Artist-in-residence in Penn’s Department of Physics and Astronomy Rebecca Kamen has explored her personal relationship with dyslexia and information exchange to produce works that reflect elements of both her creative process and understanding of language. Kamen unveiled her latest exhibit at Arion Press Gallery in San Francisco, where nine artists with dyslexia were invited to produce imaginative interpretations of learning and experiencing language.
The artists were presented with several prompts in varying formats, including books, words, poems, quotes, articles, and even a single letter, and tasked with creating a dyslexic dictionary: an exploration of the ways in which their dyslexia empowered them to engage in information exchange in unique ways.
Undiagnosed dyslexia
“[For the exhibit], each artist selected a word representing the way they learn, and mine was ‘lens,’” explains Kamen. “It’s a word that captures how being dyslexic provides me with a unique perspective for viewing and interacting with the world.”
From an early age, Kamen enjoyed learning about the natural sciences and was excited about the process of discovery. She struggled, however, with reading at school, which initially presented an obstacle to achieving her dreams of becoming a teacher. “I had a difficult time getting into college,” says Kamen. “When I graduated high school, the word ‘dyslexia’ didn’t really exist, so I assumed everyone struggled with reading.”
Kamen was diagnosed with dyslexia well into her tenure as a professor. “Most dyslexic people face challenges that may go unnoticed by others,” she says, “but they usually find creative ways to overcome them.”
This perspective on seeing and experiencing the world through the lens of dyslexia not only informed Kamen’s latest work for the exhibition “Dyslexic Dictionary,” but also showcased her background in merging art and science. For decades, Kamen’s work has investigated the intersection of the two, creating distinct ways of exploring new relationships and similarities.
“Artists and scientists are curious creatures always looking for patterns,” explains Kamen. “And that’s because patterns communicate larger insights about the world around us.”
The researchers studied different information-seeking approaches by monitoring how participants explore Wikipedia pages and categorically related these to two ideas rooted in philosophical understandings of learning: a “busybody,” who typically jumps between diverse ideas and collects loosely connected information; and a more purpose-driven “hunter,” who systematically ties in closely related concepts to fill their knowledge gaps.
They used these classifications to inform their computational model, the knowledge network. This uses text and context to determine the degree of relatedness between the Wikipedia pages and their content—represented by dots connected with lines of varying thickness to illustrate the strength of association.
In an adaption of the knowledge network, Kamen was classified as a dancer, an archetype elaborated on in an accompanying review paper by Dale Zhou, a Ph.D. candidate in Bassett’s Complex Systems Lab, who had also collaborated with Kamen on “Reveal.”
“The dancer can be described as an individual that breaks away from the traditional pathways of investigation,” says Zhou. “Someone who takes leaps of creative imagination and in the process, produces new concepts and radically remodels knowledge networks.”
Dani Smith Bassett is J. Peter Skirkanich Professor in Bioengineering with secondary appointments in the Departments of Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry.
David Lydon-Staley is an Assistant Professor in the Annenberg School for Communications and Bioengineering and is an alumnus of the Bassett Lab.
The model of tubule packing developed by the Hughes Lab shows the tubules repelling each other and shifting around.
A recent study by Penn Bioengineering researchers sheds new light on the role of physics in kidney development. The kidney uses structures called nephrons and tubules to filter blood and pass urine to the bladder. Nephron number is set at birth and can vary over an order of magnitude (anywhere from 100,000 to over a million nephrons in an individual kidney). While the reasons for this variability remain unclear, low numbers of nephrons predispose patients to hypertension and chronic kidney disease.
Now, research published in Developmental Cell led by Alex J. Hughes, Assistant Professor in the Department of Bioengineering, demonstrates a new physics-driven approach to better visualize and understand how a healthy kidney develops to avoid organizational defects that would impair its function. While previous efforts have typically approached this problem using molecular genetics and mouse models, the Hughes Lab’s physics-based approach could link particular types of defects to this genetic information and possibly highlight new treatments to prevent or fix congenital defects.
Alex J. Hughes, Assistant Professor in Bioengineering
Louis Prahl, NIH F32 Postodctoral Fellow
During embryonic development, kidney tubules grow and the tips divide to make a branched tree with clusters of nephron stem cells surrounding each branch tip. In order to build more nephrons, the tree needs to grow more branches. To keep the branches from overlapping, the kidney’s surface grows more crowded as the number of branches increase. “At this point, it’s like adding more people to a crowded elevator,” says Louis Prahl, first author of the paper and Postdoctoral Fellow in the Hughes Lab. “The branches need to keep rearranging to accommodate more until organ growth stops.”
To understand this process, Hughes, Prahl and their team investigated branch organization in mouse kidneys as well as using computer models and a 3D printed model of tubules. Their results show that tubules have to actively restructure – essentially divide at narrower angles – to accommodate more tubules. Computer simulations also identified ‘defective’ packing, in which the simulation parameters caused tubules to either overlap or be forced beneath the kidney surface. The team’s experimentation and analysis of published studies of genetic mouse models of kidney disease confirmed that these defects do occur.
This study represents a unique synthesis of different fields to understand congenital kidney disease. Mathematicians have studied geometric packing problems for decades in other contexts, but the structural features of the kidney present new applications for these models. Previous models of kidney branching have approached these problems from the perspective of individual branches or using purely geometric models that don’t account for tissue mechanics. By contrast, The Hughes Lab’s computer model demonstrates the physics of how tubule families interact with each other, allowing them to identify ‘phases’ of kidney organization that either relate to normal kidney development or organizational defects. Their 3D printed model of tubules shows that these effects can occur even when one sets the biology aside.
Hughes has been widely recognized for his research in the understanding of kidney development. This new publication is the first fruit of his 2021 CAREER Award from the National Science Foundation (NSF) and he was recently named a 2023 Rising Star by the Cellular and Molecular Bioengineering (CMBE) Special Interest Group. In 2020 he became the first Penn Engineering faculty member to receive the Maximizing Investigators’ Research Award (MIRA) from the National Institutes of Health (NIH) for his forward-thinking work in the creation of new tools for tissue engineering.
Pediatric nephrologists have long worked to understand the cause of these childhood kidney defects. These efforts are often confounded by a lack of evidence for a single causative mutation. The Hughes Lab’s approach presents a new and different application of the packing problem and could help answer some of these unsolved questions and open doors to prevention of these diseases. Following this study, Hughes and his lab members will continue to explore the physics of kidney tubule packing, looking for interesting connections between packing organization, mechanical stresses between neighboring tubule tips, and nephron formation while attempting to copy these principles to build stem cell derived tissues to replace damaged or diseased kidney tissue. Mechanical forces play an important role in developmental biology and there is much scope for Hughes, Prahl and their colleagues to learn about these properties in relation to the kidney.
Other authors include Bioengineering Ph.D. students and Hughes Lab members John Viola and Jiageng Liu.
This work was supported by NSF CAREER 2047271, NIH MIRA R35GM133380, Predoctoral Training Program in Developmental Biology T32HD083185, and NIH F32 fellowship DK126385.
University of Pennsylvania scientist Nader Engheta has been selected as a 2023 recipient of the Benjamin Franklin Medal, one of the world’s oldest science and technology awards. The laureates will be honored on April 27 at a ceremony at the Franklin Institute in Philadelphia.
Engheta, H. Nedwill Ramsey Professor in Electrical and Systems Engineering, is among nine outstanding individuals recognized with Benjamin Franklin Medals this year for their achievements in extraordinary scientific, engineering and business leadership.
“As a scientist and a Philadelphian, I am deeply honored and humbled to receive the Franklin Medal. It is the highest compliment to receive an award whose past recipients include some of my scientific heroes such as Albert Einstein, Nikola Tesla, Alexander Graham Bell, and Max Planck. I am very thankful to the Franklin Institute for bestowing this honor upon me.”
Larry Dubinski, President and CEO of The Franklin Institute, says, “We are proud to continue The Franklin Institute’s longtime legacy of recognizing individuals for their contributions to humanity. These extraordinary advancements in areas of such importance as social equity, sustainability, and safety are significantly moving the needle in the direction of positive change and therefore laying the groundwork for a remarkable future.”
The 2023 Benjamin Franklin Medal in Electrical Engineering goes to Engheta for his transformative innovations in engineering novel materials that interact with electromagnetic waves in unprecedented ways, with broad applications in ultrafast computing and communication technologies.
“Professor Engheta’s pioneering work in metamaterials and nano-optics points the way to new and truly revolutionary computing capabilities in the future,” says University of Pennsylvania President Liz Magill. “Penn inaugurated the age of computers by creating the world’s first programmable digital computer in 1945. Professor Engheta’s work continues this tradition of groundbreaking research and discovery that will transform tomorrow. We are thrilled to see him receive the recognition of the Benjamin Franklin Medal.”
Engheta founded the field of optical nanocircuits (“optical metatronics”), which merges nanoelectronics and nanophotonics. He is also known for establishing and& developing the field of near-zero-index optics and epsilon-near-zero (ENZ) materials with near-zero electric permittivity. Through his work he has opened many new frontiers, including optical computation at the nanoscale and scattering control for cloaking and transparency. His work has far-reaching implications in various branches of electrical engineering, materials science, optics, microwaves, and quantum electrodynamics.
“This award recognizes Dr. Engheta’s trailblazing advances in engineering and physics,” says Vijay Kumar, Nemirovsky Family Dean of Penn Engineering.“ The swift and sustainable technologies his research in metamaterials and metatronics offers the world are the result of a lifelong commitment to scientific curiosity. For over 35 years, Nader Engheta has personified Penn Engineering’s mission of inventing the future.”
Nader Engheta is the H. Nedwill Ramsey Professor in the Departments of Electrical and Systems Engineering and Bioengineering in the School of Engineering and Applied Science and professor of physics and astronomy in the School of Arts & Sciences at the University of Pennsylvania.
Penn Engineering’s newly established ASSET Center aims to make AI-enabled systems more “safe, explainable and trustworthy” by studying the fundamentals of the artificial neural networks that organize and interpret data to solve problems.
ASSET’s first funding collaboration is with Penn’s Perelman School of Medicine (PSOM) and the Penn Institute for Biomedical Informatics (IBI). Together, they have launched a series of seed grants that will fund research at the intersection of AI and healthcare.
Teams featuring faculty members from Penn Engineering, Penn Medicine and the Wharton School applied for these grants, to be funded annually at $100,000. A committee consisting of faculty from both Penn Engineering and PSOM evaluated 18 applications and judged the proposals based on clinical relevance, AI foundations and potential for impact.
Artificial intelligence and machine learning promise to revolutionize nearly every field, sifting through massive amounts of data to find insights that humans would miss, making faster and more accurate decisions and predictions as a result.
Applying those insights to healthcare could yield life-saving benefits. For example, AI-enabled systems could analyze medical imaging for hard-to-spot tumors, collate multiple streams of disparate patient information for faster diagnoses or more accurately predict the course of disease.
Given the stakes, however, understanding exactly how these technologies arrive at their conclusions is critical. Doctors, nurses and other healthcare providers won’t use such technologies if they don’t trust that their internal logic is sound.
“We are developing techniques that will allow AI-based decision systems to provide both quantifiable guarantees and explanations of their predictions,” says Rajeev Alur, Zisman Family Professor in Computer and Information Science and Director of the ASSET Center. “Transparency and accuracy are key.”
“Development of explainable and trustworthy AI is critical for adoption in the practice of medicine,” adds Marylyn Ritchie, Professor of Genetics and Director of the Penn Institute for Biomedical Informatics. “We are thrilled about this partnership between ASSET and IBI to fund these innovative and exciting projects.”
Seven projects were selected in the inaugural class, including projects from Dani S. Bassett, J. Peter Skirkanich Professor in the Departments of Bioengineering, Electrical and Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry, and several members of the Penn Bioengineering Graduate Group: Despina Kontos, Matthew J. Wilson Professor of Research Radiology II, Department of Radiology, Penn Medicine and Lyle Ungar, Professor, Department of Computer and Information Science, Penn Engineering; Spyridon Bakas, Assistant Professor, Departments of Pathology and Laboratory Medicine and Radiology, Penn Medicine; and Walter R. Witschey, Associate Professor, Department of Radiology, Penn Medicine.
Optimizing clinical monitoring for delivery room resuscitation using novel interpretable AI
Elizabeth Foglia, Associate Professor, Department of Pediatrics, Penn Medicine and the Children’s Hospital of Philadelphia
Dani S. Bassett, J. Peter Skirkanich Professor, Departments of Bioengineering and Electrical and Systems Engineering, Penn Engineering
This project will apply a novel interpretable machine learning approach, known as the Distributed Information Bottleneck, to solve pressing problems in identifying and displaying critical information during time-sensitive clinical encounters. This project will develop a framework for the optimal integration of information from multiple physiologic measures that are continuously monitored during delivery room resuscitation. The team’s immediate goal is to detect and display key target respiratory parameters during delivery room resuscitation to prevent acute and chronic lung injury for preterm infants. Because this approach is generalizable to any setting in which complex relations between information-rich variables are predictive of health outcomes, the project will lay the groundwork for future applications to other clinical scenarios.
The Heilmeier Award honors a Penn Engineering faculty member whose work is scientifically meritorious and has high technological impact and visibility. It is named for the late George H. Heilmeier, a Penn Engineering alumnus and member of the School’s Board of Advisors, whose technological contributions include the development of liquid crystal displays and whose honors include the National Medal of Science and Kyoto Prize.
Bassett, who also holds appointments in Physics & Astronomy in Penn Arts & Sciences and in Neurology and Psychiatry in the Perelman School of Medicine, is a pioneer in the field of network neuroscience, an emerging subfield which incorporates elements of mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits. They lead the Complex Systems lab, which tackles problems at the intersection of science, engineering and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease.
Bassett will deliver the 2022-23 Heilmeier Award Lecture in Spring 2023.
Twin siblings and scholars Dani S. Bassett of Penn and Perry Zurn of American University collaborated over half a dozen years to write “Curious Minds: The Power of Connection.” (Image: Tony and Tracy Wood Photography)
Twin academics Dani S. Basset, J. Peter Skirkanich Professor and director of the Complex Systems Lab, and Perry Zurn, a professor of philosophy at American University, were recently featured as guests on NPR radio show “Detroit Today” to discuss their new book, “Curious Mind: The Power of Connection.”
In their book, Basset and Zurn draw on their previous research, as well as an expansive network of ideas from philosophy, history, education and art to explore how and why people experience curiosity, as well as the different types it can take.
Basset, who holds appointments in the Departments of Bioengineering and Electrical and Systems Engineering, as well as the Department of Physics and Astronomy in Penn Arts & Science, and the Departments of Neuroscience and Psychiatry in Penn Perelman’s School of Medicine, and Zurn spoke with “Detroit Today” producer Sam Corey about what types of things make people curious, and how to stimulate more curiosity in our everyday lives.
According to the twin experts, curiosity is not a standalone facet of one’s personality. Basset and Zurn’s work has shown that a person’s capacity for inquiry is very much tied to the overall state of their health.
“There’s a lot of scientific research focusing on intellectual humility and also openness to ideas,” says Bassett. “And there are really interesting relationships between someone’s openness to ideas, someone’s intellectual humility and their curiosity and also their wellbeing or flourishing,”
Twin siblings and scholars Dani S. Bassett of Penn and Perry Zurn of American University collaborated over half a dozen years to write “Curious Minds: The Power of Connection.” (Image: Tony and Tracy Wood Photography)
With appointments in the Departments of Bioengineering and Electrical and Systems Engineering, as well as the Department of Physics and Astronomy in Penn Arts & Science, and the Departments of Neuroscience and Psychiatry in Penn Perelman’s School of Medicine, Dani S. Bassett is no stranger to following the thread of an idea, no matter where it might lead.
Those wide-ranging fields and disciplines orbit around an appropriate central question: how does the tangle of neurons in our brains wire itself up to learn new things? Bassett, J. Peter Skirkanich Professor and director of the Complex Systems Lab, studies the relationship between the shape of those networks of neurons and the brain’s abilities, especially the way the shape of the network grows and changes with the addition of new knowledge.
To get at the fundamentals of the question of curiosity, Bassett needed to draw on even more disciplines. Fortunately, they didn’t have to look far; Bassett’s identical twin is Perry Zurn, a professor of philosophy at American University, and the two have investigated the many different ways a person can exhibit curiosity.
Bassett and Zurn have now published a new book on the subject. In Curious Minds: The Power of Connection, the twins draw on their previous research, as well as an expansive network of ideas from philosophy, history, education and art.
“It wasn’t clear at the beginning of our careers that we would even ever have a chance to write a book together because our areas were so wildly different,” Bassett says – but then, as postgraduates, Zurn was studying the philosophy of curiosity while Bassett was working on the neuroscience of learning. “And so that’s when we started talking. That talking led to seven years of doing research together,” Bassett says. “This book is a culmination of that.”
How exactly do philosophy and neuroscience complement each other? It all starts with the book’s first, and most deceptively simple question: what is curiosity? “Several investigators in science have underscored that perhaps the field isn’t even ready to define curiosity and how it’s different from other cognitive processes,” says Bassett. The ambiguity in the neuroscience literature motivated Bassett to turn to philosophy, “where there are really rich historical definitions and styles and subtypes that we can then put back into neuroscience and ask: ‘Can we see these in the brain?’”
Developing new soft materials requires new data-driven research techniques, such as autonomous experimentation. Data regarding nanometer-scale material structure, taken by X-ray measurements at a synchrotron, can be fed into an algorithm that identifies the most relevant features, represented here as red dots. The algorithm then determines the optimum conditions for the next set of measurements and directs their execution without human intervention. Brookhaven National Laboratory’s Kevin Yager, who helped develop this technique, will co-teach a course on it as part of a new Penn project on Data Driven Soft Materials Research.
The National Science Foundation’s Research Traineeship Program aims to support graduate students, educate the STEM leaders of tomorrow and strengthen the national research infrastructure. The program’s latest series of grants are going toward university programs focused on artificial intelligence and quantum information science and engineering – two areas of high priority in academia, industry and government.
Chinedum Osuji, Eduardo D. Glandt Presidential Professor and Chair of the Department of Chemical and Biomolecular Engineering (CBE), has received one of these grants to apply data science and machine learning to the field of soft materials. The grant will provide five years of support and a total of $3 million for a new Penn project on Data Driven Soft Materials Research.
Osuji will work with co-PIs Russell Composto, Professor and Howell Family Faculty Fellow in Materials Science and Engineering, Bioengineering, and in CBE, Zahra Fakhraai, Associate Professor of Chemistry in Penn’s School of Arts & Sciences (SAS) with a secondary appointment in CBE, Paris Perdikaris, Assistant Professor in Mechanical Engineering and Applied Mechanics, and Andrea Liu, Hepburn Professor of Physics and Astronomy in SAS, all of whom will help run the program and provide the connections between the multiple fields of study where its students will train.
These and other affiliated faculty members will work closely with co-PI Kristin Field, who will serve as Program Coordinator and Director of Education.
