Computing at the speed of light may reduce the energy cost of training AI. (Narongrit Doungmanee via Getty Images)
Penn Engineers have developed a new chip that uses light waves, rather than electricity, to perform the complex math essential to training AI. The chip has the potential to radically accelerate the processing speed of computers while also reducing their energy consumption.
The silicon-photonic (SiPh) chip’s design is the first to bring together Benjamin Franklin Medal Laureate and H. Nedwill Ramsey Professor Nader Engheta’s pioneering research in manipulating materials at the nanoscale to perform mathematical computations using light — the fastest possible means of communication — with the SiPh platform, which uses silicon, the cheap, abundant element used to mass-produce computer chips.
The interaction of light waves with matter represents one possible avenue for developing computers that supersede the limitations of today’s chips, which are essentially based on the same principles as chips from the earliest days of the computing revolution in the 1960s.
In a paper in Nature Photonics, Engheta’s group, together with that of Firooz Aflatouni, Associate Professor in Electrical and Systems Engineering, describes the development of the new chip. “We decided to join forces,” says Engheta, leveraging the fact that Aflatouni’s research group has pioneered nanoscale silicon devices.
Their goal was to develop a platform for performing what is known as vector-matrix multiplication, a core mathematical operation in the development and function of neural networks, the computer architecture that powers today’s AI tools.
Nader Engheta is the H. Nedwill Ramsey Professor in Electrical and Systems Engineering, Bioengineering, Materials Science and Engineering, and in Physics and Astronomy.
NASA’s James Webb Space Telescope has produced the deepest and sharpest infrared image of the distant universe to date. Known as Webb’s First Deep Field, this image of galaxy cluster SMACS 0723 is rich with detail. Thousands of galaxies—including the faintest objects ever observed in the infrared—have appeared in Webb’s view for the first time. The image shows the galaxy cluster SMACS 0723 as it appeared 4.6 billion years ago. The combined mass of this galaxy cluster acts as a gravitational lens, magnifying much more distant galaxies behind it. Webb’s Near-Infra Red Cam has brought those distant galaxies into sharp focus—they have tiny, faint structures that have never been seen before, including star clusters and diffuse features. (Image: NASA, ESA, CSA, and STScI)
Could the universe be twice as old as current estimates put forward? Rajendra Gupta of the University of Ottawa recently published a paper suggesting just that. Gupta claims the universe may be around 26.7 billion years rather than the commonly accepted 13.8 billion. The news has generated many headlines as well as criticism from astronomers and the larger scientific community.
Penn Today met with professors Vijay Balasubramanian and Mark Devlin to discuss Gupta’s findings and better understand the rationale of these claims and how they fit in the broader context of problems astronomers are attempting to solve.
How do we know how old the universe actually is?
Balasubramanian: The universe is often reported to be 13.8 billion years old, but, truth be told, this is an amalgamation of various measurements that factor in different kinds of data involving the apparent ages of ‘stuff’ in the universe.
This stuff includes observable or ordinary matter like you, me, galaxies far and near, stars, radiation, and the planets, then dark matter—the sort of matter that doesn’t interact with light and which makes up about 27% of the universe—and finally, dark energy, which makes up a massive chunk of the universe, around 68%, and is what we believe is causing the universe to expand.
And so, we take as much information as we can about the stuff and build what we call a consensus model of the universe, essentially a line of best fit. We call the model the Lambda Cold Dark Matter (ΛCDM).
Lambda represents the cosmological constant, which is linked to dark energy, namely how it drives the expansion of the universe according to Einstein’s theory of general relativity. In this framework, how matter and energy behave in the universe determines the geometry of spacetime, which in turn influences how matter and energy move throughout the cosmos. Including this cosmological constant, Lambda, allows for an explanation of a universe that expands at an accelerating rate, which is consistent with our observations.
Now, the Cold Dark Matter part represents a hypothetical form of dark matter. ‘Dark’ here means that it neither interacts with nor emits light, so it’s very hard to detect. ‘Cold’ refers to the fact that its particles move slowly because when things cool down their components move less, whereas when they heat up the components get excited and move around more relative to the speed of light.
So, when you consider the early formation of the universe, this ‘slowness’ influences the formation of structures in the universe like galaxies and clusters of galaxies, in that smaller structures like the galaxies form before the larger ones, the clusters.
Devlin: And then taking a step back, the way cosmology works and pieces how old things are is that we look at the way the universe looks today, how all the structures are arranged within it, and we compare it to how it used to be with a set of cosmological parameters like Cosmic Microwave Background (CMB) radiation, the afterglow of the Big Bang, and the oldest known source of electromagnetic radiation, or light. We also refer to it as the baby picture of the universe because it offers us a glimpse of what it looked like at 380,000 years old, long before stars and galaxies were formed.
And what we know about the physical nature of the universe from the CMB is that it was something really smooth, dense, and hot. And as it continued to expand and cool, the density started to vary, and these variations became the seeds for the formation of cosmic structures.
The denser regions of the universe began to collapse under their own gravity, forming the first stars, galaxies, and clusters of galaxies. So, this is why, when we look at the universe today, we see this massive cosmic web of galaxies and clusters separated by vast voids. This process of structure formation is still ongoing.
And, so, the ΛCDM model suggests that the primary driver of this structure formation was dark matter, which exerts gravity and which began to clump together soon after the Big Bang. These clumps of dark matter attracted the ordinary matter, forming the seeds of galaxies and larger cosmic structures.
So, with models like the ΛCDM and the knowledge of how fast light travels, we can add bits of information, or parameters, and we have from things like the CMB and other sources of light in our universe, like the ones we get from other distant galaxies, and we see this roadmap for the universe that gives us it’s likely age. Which we think is somewhere in the ballpark of 13.8 billion years.
Vijay Balasubramanian is the Cathy and Marc Lasry Professor in the Department of Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania. He is a member of the Penn Bioengineering Graduate Group.
Mark Devlin is the Reese W. Flower Professor of Astronomy and Astrophysics in the Department of Physics and Astronomy in the School of Arts & Sciences at Penn.
Nader Engheta was puzzled when he got a call from the psychology department about a fish. In the early 1990s, Engheta, a newly minted associate professor of electrical engineering in Penn’s School of Engineering and Applied Science, was a respected expert in radio wave technologies. But in recent years, his work had been expanding into subjects at once more eccentric and fundamental.
Nader Engheta was puzzled when he got a call from the psychology department about a fish.
In the early 1990s, Engheta, a newly minted associate professor of electrical engineering in Penn’s School of Engineering and Applied Science, was a respected expert in radio wave technologies. But in recent years, his work had been expanding into subjects at once more eccentric and fundamental.
Engheta’s interest in electromagnetic waves was not limited to radio frequencies, as a spate of fresh publications could attest. Some studies investigated a range of wave interactions with a class of matter known as a “chiral media,” materials with molecular configurations that exhibit qualities of left or right “handedness.” Others established practical electromagnetic applications for a bewildering branch of mathematics called “fractional calculus,” an area with the same Newtonian roots as calculus proper but a premise as eyebrow-raising as the suggestion a family might literally include two-and-a-half children.
Electromagnetic waves are organized on a spectrum of wavelengths. On the shorter end of the spectrum are high-energy waves, such as X-rays. In the middle, there is the limited range we see as visible light. And on the longer end are the lower-energy regimes of radio and heat.
Researchers tend to focus on one kind of wave or one section of the spectrum, exploring quirks and functions unique to each. But all waves, electromagnetic or not, share the same characteristics: They consist of a repeating pattern with a certain height (amplitude), rate of vibration (frequency), and distance between peaks (wavelength). These qualities can define a laser beam, a broadcasting voice, a wind-swept lake, or a violin string.
Engheta has never been the kind of scholar to limit the scope of his curiosity to a single field of research. He is interested in waves, and his fascination lies equally in the physics that determine wave behavior and the experimental technologies that push the boundaries of those laws.
So, when Edward Pugh, a mathematical psychologist studying the physiology of visual perception, explained that green sunfish might possess an evolutionary advantage for seeing underwater, Engheta listened.
Soon, the two Penn professors were pouring over microscope images of green sunfish retinas.
Read Devorah Fischler’s full story about Nader Engheta and watch an accompanying video at Penn Today.
Nader Engheta is H. Nedwill Ramsey Professor of Electrical and Systems Engineering at Penn Engineering, with secondary appointments in the departments of Bioengineering, Materials Science and Engineering, and Physics and Astronomy in the School of Arts & Sciences.
(Left to Right) Vijay Kumar, Nemirovsky Family Dean of Penn Engineering, Nader Engheta, H. Nedwill Ramsey Professor in Electrical and Systems Engineering, and Michele Marcolongo, Drosdick Endowed Dean of Villanova University’s College of Engineering
On April 26, scholars from all over the world gathered at Villanova University to celebrate extraordinary innovation in the physics and technology of light.
The symposium, titled “Sculpting Waves with Complex Materials,” explored the richness and breadth of Engheta’s impact.
In a glass-paneled lecture hall nestled between flowering dogwoods and limber pines, speakers attested to Engheta’s technical acumen and intellectual creativity, describing his pathbreaking work in light-matter interaction.
Andrea Alù, Distinguished Professor at the City University of New York, Einstein Professor of Physics at the Graduate Center, CUNY and former Penn Engineering postdoctoral fellow, cited Engheta as “one of the original pioneers of the field of complex electromagnetic structures and modern metamaterials,” and the “father” of four influential fields: analog computing with metamaterials, plasmonic cloaking, non-zero-index metamaterials and optical nanocircuits.
Two faculty affiliated with the Department of Bioengineering at the University of Pennsylvania have been elected to the American Academy of Arts & Sciences. They join nearly 270 new members honored in 2023, recognized for their excellence, innovation, leadership, and broad array of accomplishments.
Nader Engheta, the H. Nedwill Ramsey Professor.
Nader Engheta is the H. Nedwill Ramsey Professor, with affiliations in the departments of Electrical and Systems Engineering (primary appointment), Bioengineering (secondary appointment) and Materials Science and Engineering (secondary appointment) in the School of Engineering and Applied Science; and Physics and Astronomy (secondary appointment) in the School of Arts & Sciences. His current research activities span a broad range of areas including optics, photonics, metamaterials, electrodynamics, microwaves, nano-optics, graphene photonics, imaging and sensing inspired by eyes of animal species, microwave and optical antennas, and physics and engineering of fields and waves. He has received numerous awards for his research, including the 2023 Benjamin Franklin Medal in Electrical Engineering, the 2020 Isaac Newton Medal and Prize from the Institute of Physics (U.K.), the 2020 Max Born Award from OPTICA (formerly OSA), induction to the Canadian Academy of Engineering as an International Fellow (2019), U.S. National Academy of Inventors (2015), and the Ellis Island Medal of Honor from the Ellis Island Honors Society (2019). He joins four other Penn faculty elected to the Academy this year.
Read the announcement and the full list of Penn electees in Penn Today.
Susan Margulies, Ph.D. (Photo: Jack Kearse)
Susan Margulies, Professor in the Wallace H. Coulter Department of Biomedical Engineering in the College of Engineering at Georgia Tech, was also elected. Margulies is both Professor Emeritus in Penn Bioengineering and an alumna of the program, having earned her Ph.D. with the department in 1987. Margulies is an expert in pediatric traumatic brain injury and lung injury. She previously served as Chair of Biomedical Engineering at Georgia Tech/Emory University and in 2021 became the first biomedical engineer selected to lead the National Science Foundation’s (NSF) Directorate of Engineering.
Read the announcement of Margulies’ elected to the Academy at Georgia Tech.
A tailored silicon nanopattern coupled with a semi-transparent gold mirror can solve a complex mathematical equation using light. (Image credit: Ella Maru studio)
Researchers at the University of Pennsylvania, AMOLF, and the City University of New York (CUNY) have created a surface with a nanostructure capable of solving mathematical equations.
Powered by light and free of electronics, this discovery introduces exciting new prospects for the future of computing.
Engheta is the founder of the influential field of “optical metatronics.” He creates materials that interact with photons to manipulate data at the speed of light. Engheta’s contribution to this study marks an important advance in his quest to use light-matter interactions to surpass the speed and energy limitations of digital electronics, bringing analog computing out of the past and into the future.
“I began the work on optical metatronics in 2005,” says Engheta, “wondering if it were possible to recreate the elements of a standard electronic circuit at nanoscale. At this tiny size, it would be possible to manipulate the circuit with light, rather than electricity. After achieving this, we became more ambitious, envisioning collections of these nanocircuits as processors. In 2014, we were designing materials that used these optical nanostructures to perform mathematical operations, and in 2019, we anted up to entire mathematical equations using microwaves. Now, my collaborators and I have created a surface that can solve equations using light waves, a significant step closer to our larger goals for computing materials.”
The study, recently published in Nature Nanotechnology, demonstrates the possibility of solving complex mathematical problems and a generic matrix inversion at speeds far beyond those of typical digital computing methods.
The solution converges in about 349 femtoseconds (less than one trillionth of a second), orders of magnitude faster than the clock speed of a conventional processor.
Nader Engheta is the H. Nedwill Ramsey Professor in the Departments of Electrical and Systems Engineering and in Bioengineering in the School of Engineering and Applied Science and Professor in Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania.
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.
