Aurelia Butler

Imagine cells navigating through a complex maze, guided by chemical signals and the physical landscape of their environment. A team of researchers at the University of Maryland, Baltimore County (UMBC) has contributed an important discovery about how cells move, or migrate, through this maze of bodily tissues, using the fruit fly egg chamber as a model system. Potential implications include better understanding of diseases like cancer and advancing medical treatments.
Published iniScience, the team’s study combines biological experiments and mathematics to reveal new insights into cell migration. By integrating mathematical modeling with advanced imaging, the team discovered that the physical shape of the egg chamber, combined with chemical signals called chemoattractants, significantly influences how cells move.
“This paper takes an interdisciplinary focus with tight collaboration between a mathematical framework and experimental design,” says UMBC mathematician and co-author Brad Peercy. “The results promote the idea that complex distribution of chemical attractants can explain specific variations in migratory movement.”
Peercy’s enthusiasm highlights the study’s innovative approach, which merges precise mathematical models with real-world biological experiments to uncover patterns that were previously invisible.
Following the breadcrumbs
The team’s work focuses on border cells, a type of cell in fruit fly egg chambers, which are a model system for studying cell migration because of their similarities to processes in human development and disease. The team found that the border cells’ movement wasn’t only driven by continuously increasing chemical concentrations from one end of the egg chamber to the other, as earlier models suggested. Instead, the physical structure of the tissue — narrow tubes alternating with wider gaps — played a critical role.
“This was the first time that we characterized that there were these patterns of migration behavior that ended up correlating to aspects of the tissue geometry,” explains biologist Alex George, a co-author who completed his Ph.D. at UMBC in 2024 and will begin a postdoctoral fellowship at the Geisel School of Medicine at Dartmouth in a few weeks. He likens the migration process to Hansel and Gretel following breadcrumbs through a forest: On a flat plain, the trail is clear, but in a landscape with ravines and valleys, the breadcrumbs pool in unexpected ways, complicating the path.

To understand this, co-author Naghmeh Akhavan, who completed her Ph.D. in mathematics at UMBC this spring, developed mathematical models that simulate how cells respond to both chemical signals and tissue geometry together. “Alex’s experiments showed that the speed is not exactly the way previous models showed it,” she says. Her models revealed that cells speed up in narrow tubes and slow down in larger gaps, a pattern confirmed by George’s imaging.
Both approaches — wet-lab experiments and modeling — bring unique strengths to the work. Putting them together “is like unveiling the invisible from two different perspectives,” George says. “My experiments would refine her model, and her model would refine my experiments.”
And then, “When our model shows exactly what Alex found in his experiments, we love that,” Akhavan adds.
New strategies, new discoveries
The study’s broader impact lies in its potential to inform fields beyond developmental biology. Cell migration is critical in processes like wound healing, immune responses, and cancer metastasis.
“Most research on how cells navigate the world has focused only on chemical signals or only on structural ones, so this is one of the first studies to consider how those two things impact each other, which is likely to be relevant in many cases,” explains UMBC biologist and co-author Michelle Starz-Gaiano. By showing how tissue geometry and chemical signals interact, the research could guide new strategies for controlling cell movement via medical treatments.
The team’s work continues to evolve, including recent experiments at the Advanced Imaging Center at the Janelia Research Campus in Virginia, where George used specialized microscopes to capture previously unseen dynamics of the relevant chemoattractants. These findings will further refine the team’s models, opening new avenues for research.
“We are developing new experimental strategies both on the biology and the math side of things,” Starz-Gaiano says, “so it will be exciting to see where this will take us next.” More

Specialized robots that can both fly and drive typically touch down on land before attempting to transform and drive away. But when the landing terrain is rough, these robots sometimes get stuck and are unable to continue operating. Now a team of Caltech engineers has developed a real-life Transformer that has the “brains” to morph in midair, allowing the dronelike robot to smoothly roll away and begin its ground operations without pause. The increased agility and robustness of such robots could be particularly useful for commercial delivery systems and robotic explorers.
The new robot, dubbed ATMO (aerially transforming morphobot), uses four thrusters to fly, but the shrouds that protect them become the system’s wheels in an alternative driving configuration. The whole transformation relies on a single motor to move a central joint that lifts ATMO’s thrusters up into drone mode or down into drive mode.
The researchers describe the robot and the sophisticated control system that drives it in a paper recently published in the journal Communications Engineering.
“We designed and built a new robotic system that is inspired by nature — by the way that animals can use their bodies in different ways to achieve different types of locomotion,” says Ioannis Mandralis (MS ’22), a graduate student in aerospace at Caltech and lead author of the new paper. For example, he says, birds fly and then change their body morphology to slow themselves down and avoid obstacles. “Having the ability to transform in the air unlocks a lot of possibilities for improved autonomy and robustness,” Mandralis says.
But midair transformation also poses challenges. Complex aerodynamic forces come into play both because the robot is close to the ground and because it is changing its shape as it morphs.
“Even though it seems simple when you watch a bird land and then run, in reality this is a problem that the aerospace industry has been struggling to deal with for probably more than 50 years,” says Mory Gharib (PhD ’83), the Hans W. Liepmann Professor of Aeronautics and Medical Engineering, director and Booth-Kresa Leadership Chair of Caltech’s Center for Autonomous Systems and Technologies (CAST), and director of the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT). All flying vehicles experience complicated forces close to the ground. Think of a helicopter, as an example. As it comes in for a landing, its thrusters push lots of air downward. When that air hits the ground, some portion of it bounces back up; if the helicopter comes in too quickly, it can get sucked into a vortex formed by that reflected air, causing the vehicle to lose its lift.
In ATMO’s case, the level of difficulty is even greater. Not only does the robot have to contend with complex near-ground forces, but it also has four jets that are constantly altering the extent to which they are shooting toward each other, creating additional turbulence and instability.
To better understand these complex aerodynamic forces, the researchers ran tests in CAST’s drone lab. They used what are called load cell experiments to see how changing the robot’s configuration as it came in for landing affected its thrust force. They also conducted smoke visualization experiments to reveal the underlying phenomena that lead to such changes in the dynamics.
The researchers then fed those insights into the algorithm behind a new control system they created for ATMO. The system uses an advanced control method called model predictive control, which works by continuously predicting how the system will behave in the near future and adjusting its actions to stay on course.
“The control algorithm is the biggest innovation in this paper,” Mandralis says. “Quadrotors use particular controllers because of how their thrusters are placed and how they fly. Here we introduce a dynamic system that hasn’t been studied before. As soon as the robot starts morphing, you get different dynamic couplings — different forces interacting with one another. And the control system has to be able to respond quickly to all of that.” More

Open heart surgery is a hard thing to practice in the real world, and airplane pilots cannot learn from their mistakes midair. These are some scenarios where virtual reality solves really hard problems, but the technology has limits. That’s the upshot of a review of experimental research on VR, published in the journal Nature Human Behavior.
“Virtual reality is not for everything,” said Jeremy Bailenson, lead author and director of Stanford’s Virtual Human Interaction Lab. “What we’ve long showed in the lab is that VR is great when used sparingly and thoughtfully. Otherwise, the pros typically don’t outweigh the cons.”
As a medium, VR is very intense, added Bailenson, who is the Thomas More Storke Professor and professor of communication in the School of Humanities and Sciences. It blocks out the real world. VR can make users uncomfortable or even experience “simulator sickness,” a type of motion sickness.
While companies like Meta and Apple have invested heavily in the tech, betting on wide consumer adoption, the review’s findings show it’s better used in short doses — minutes not hours — and only for certain things. The researchers recommend saving VR for “DICE” experiences, those which if done in the real world would be dangerous, impossible, counterproductive, or expensive.
Some examples of what to do (and not do) in VR, from the paper’s five core findings:
1. Travel to awesome or personally challenging places. Don’t go to VR for run-of-the-mill meetings.
Stroll through the ruins of Pompeii or visit the Grand Canyon. The visceral nature of VR lends itself well to experiences where “being there” matters. In fact, some psychologists are using VR in exposure therapy, allowing people to face something they fear while they are physically safe. One study found that people treated for fear of flying with VR had no return of their symptoms three years later.

VR’s value fades if the environment is not dramatic. While there were hopes during the pandemic that people would turn to VR for more engaging meetings, that idea failed to catch on.
“If you are just sitting there staring and not moving your body, you probably can do that on a computer and save yourself some headset time,” Bailenson said.
2. Learn surgery or public speaking. Don’t solve basic math problems.
Educators had great hopes for virtual reality ever since simulators were first used to train pilots in 1929. As the technology developed, though, it became apparent that VR did not add much to abstract learning that can be taught well on a chalkboard.
Instead, virtual reality is best used with learning skills that are procedural, requiring one step and then another, as might be done in surgery or dissection situations. Spatial tasks where movement and immersion are helpful also work well in VR, such as practicing nonverbal behavior or performing in front of a crowd.
“The key with VR is to focus on learning scenarios that are jaw-droppingly special in that medium, as opposed to assuming that any media experience works better in a headset,” Bailenson said.

3. Try on a new identity in VR, but make sure it’s the right fit.
Self-perception changes how people behave, studies have shown, whether in a virtual world or the real one. For instance, if people choose more athletic avatars, they tend to move around more. Those with taller avatars tend to negotiate more aggressively. The opposite is also true, which means users should be careful when choosing an avatar, Bailenson advised.
“Understand that whatever avatar you’re going to use is going to change the way that you behave inside VR and for some time after you leave,” he said. “So be thoughtful and use platforms that allow you to choose an avatar that either matches your actual or ideal self.”
4. Take a VR fitness class. Don’t try to learn how to throw a baseball.
Athletic training is a great use for virtual reality, except when it comes to precision movements. Users have a hard time judging distance in the virtual world, the review found. It’s a persistent problem the technology has yet to overcome.
“High-level spatial activities are great in VR, but when you’re looking for down-to-the-centimeter accuracy, you should be wary of using commercial VR applications,” Bailenson said.
5. You can run in VR, but you can’t hide.
People are easily identified by how they move their bodies, research has shown, so users should be aware that even if their avatar itself masks their identity, the millions of movement data points automatically collected by the system can identify them. There is no true anonymity when wearing VR headsets.
“In VR you move your body, and the scene responds. That’s what makes the medium so special,” Bailenson said. “Natural body movements are so important that the medium literally can’t run if you turn off movement tracking.” More

Large language models (LLMs) — the advanced AI behind tools like ChatGPT — are increasingly integrated into daily life, assisting with tasks such as writing emails, answering questions, and even supporting healthcare decisions. But can these models collaborate with others in the same way humans do? Can they understand social situations, make compromises, or establish trust? A new study from researchers at Helmholtz Munich, the Max Planck Institute for Biological Cybernetics, and the University of Tübingen, reveals that while today’s AI is smart, it still has much to learn about social intelligence.
Playing Games to Understand AI Behavior
To find out how LLMs behave in social situations, researchers applied behavioral game theory — a method typically used to study how people cooperate, compete, and make decisions. The team had various AI models, including GPT-4, engage in a series of games designed to simulate social interactions and assess key factors such as fairness, trust, and cooperation.
The researchers discovered that GPT-4 excelled in games demanding logical reasoning — particularly when prioritizing its own interests. However, it struggled with tasks that required teamwork and coordination, often falling short in those areas.
“In some cases, the AI seemed almost too rational for its own good,” said Dr. Eric Schulz, lead author of the study. “It could spot a threat or a selfish move instantly and respond with retaliation, but it struggled to see the bigger picture of trust, cooperation, and compromise.”
Teaching AI to Think Socially
To encourage more socially aware behavior, the researchers implemented a straightforward approach: they prompted the AI to consider the other player’s perspective before making its own decision. This technique, called Social Chain-of-Thought (SCoT), resulted in significant improvements. With SCoT, the AI became more cooperative, more adaptable, and more effective at achieving mutually beneficial outcomes — even when interacting with real human players.

“Once we nudged the model to reason socially, it started acting in ways that felt much more human,” said Elif Akata, first author of the study. “And interestingly, human participants often couldn’t tell they were playing with an AI.”
Applications in Health and Patient Care
The implications of this study reach well beyond game theory. The findings lay the groundwork for developing more human-centered AI systems, particularly in healthcare settings where social cognition is essential. In areas like mental health, chronic disease management, and elderly care, effective support depends not only on accuracy and information delivery but also on the AI’s ability to build trust, interpret social cues, and foster cooperation. By modeling and refining these social dynamics, the study paves the way for more socially intelligent AI, with significant implications for health research and human-AI interaction.
“An AI that can encourage a patient to stay on their medication, support someone through anxiety, or guide a conversation about difficult choices,” said Elif Akata. “That’s where this kind of research is headed.” More

Numbers have a funny way about them. Young math students are taught various strategies to make problem-solving easier. Comparing fractions? Find a common denominator or convert to decimals. The strategies get more complex when doing the kind of math used to describe the activities of DNA, RNA, or protein sequences.
In science, when you make a model, its parameters determine its predictions. But what do you do when different sets of parameters result in the same predictions? Call one half 2/4 or 3/6 — either way, the result’s the same. In physics, such parameter sets are called gauge freedoms. They play a key role in how we understand electromagnetism and quantum mechanics. Surprisingly, gauge freedoms also arise in computational biology when trying to model how different mutations interact.
Now, Cold Spring Harbor Laboratory (CSHL) quantitative biologists have developed a unified theory for gauge freedoms in models of biological sequences. Their solution could have countless applications, from plant breeding to drug development.
Granted, most folks have never heard of gauge freedoms. So, how common are they? When it comes to computer models used to describe massive genetic datasets, they’re basically everywhere, says CSHL Associate Professor Justin Kinney, who co-led this study with Associate Professor David McCandlish.
“Gauge freedoms are ubiquitous in computational models of how biological sequences work,” Kinney says. “Historically, they’ve been dealt with as annoying technicalities. We’re the first to study them directly in order to get a deeper understanding of where they come from and how to handle them.”
Until now, computational biologists have accounted for gauge freedoms using a variety of ad hoc approaches. Kinney, McCandlish, and their colleagues were looking for a better way. Together, they developed a unified approach. Their new mathematical theory provides efficient formulas scientists can use for all sorts of biological applications. These formulas will allow scientists to interpret research results much faster and with greater confidence.
The investigators also published a companion paper that reveals where these gauge freedoms ultimately come from. It turns out they’re needed for models to reflect symmetries in real biological sequences. Perhaps counterintuitively, making biological models behave in a simple and intuitive way requires them to be larger and more complex. “We prove that gauge freedoms are necessary to interpret the contributions of particular genetic sequences,” McCandlish adds.
Together, the studies strongly suggest that Kinney and McCandlish’s unified approach isn’t just a new strategy for solving theoretical problems. It may prove fundamental for future efforts in agriculture, drug discovery, and beyond. More

A team of physicists at the University of Cambridge has unveiled a breakthrough in quantum sensing by demonstrating the use of spin defects in Hexagonal Boron Nitride (hBN) as powerful, room-temperature sensors capable of detecting vectorial magnetic field at the nanoscale. The findings, published in Nature Communications, mark a significant step toward more practical and versatile quantum technologies.
“Quantum sensors allow us to detect nanoscale variations of various quantities. In the case of magnetometry, quantum sensors enable nanoscale visualisation of properties like current flow and magnetisation in materials leading to the discovery of new physics and functionality,” said Dr Carmem Gilardoni, co-first author of this study at Cambrdge’s Cavendish Laboratory. “This work takes that capability to the next level using hBN, a material that’s not only compatible with nanoscale applications but also offers new degrees of freedom compared to state-of-the-art nanoscale quantum sensors.”
To date, nanoscale quantum magnetometry at ambient conditions is only possible with the nitrogen vacancy (NV) centre defect in diamond. While a powerful technology, these sensors have limitations that result from their fundamental photophysics. In particular, the NV centre is a single-axis sensor, with limited dynamic range for magnetic field detection. In contrast, the hBN sensor development by the team in Cambridge does not share these limitations and instead presents a multi-axis sensor of magnetic field with large dynamic range.
The team’s work demonstrates the capabilities of this new sensor, as well as providing a mechanistic understanding of the origin of its advantageous properties for sensing. Importantly, the team uncovered that the low symmetry, and fortuitous excited state optical rates are responsible for the dynamic range and vectorial capabilities.
hBN is a two-dimensional material, similar to graphene, that can be exfoliated to just a few atomic layers thick. Atomic-scale defects in the hBN lattice absorb and emit visible light in a way that is sensitive to local magnetic conditions, making it an ideal candidate for quantum sensing applications.
In this study, the team investigated the response of the hBN defect fluorescence to variations in magnetic field, using a technique known as optically detected magnetic resonance (ODMR). By carefully tracking the spin response and combining this with detailed analysis of the dynamics of photon emission, the team could uncover the underlying optical rates of the system and their connection to the defect symmetry, and how this combination results in a robust and versatile magnetic field sensor.
“ODMR isn’t a new technique — but what we have shown is that probes built using the hBN platform would allow this technique to be applied in a variety of new situations. It’s exciting because it opens the door to imaging magnetic phenomena and nanomaterials in a way we couldn’t before,” said Dr Simone Eizagirre Barker, co-first author of the paper.
“This sensor could open the door to studying magnetic phenomena in new material systems, or with higher spatial resolution that done before,” said Prof Hannah Stern, who co-led the research with Prof Mete Atatüre at the Cavendish Laboratory. “The 2D nature of the host material also opens exciting new possibilities for using this sensor. For example, the spatial resolution for this technique is determined by the distance between the sample and sensor. With an atomically-thin material, we can potentially realise atomic scale spatial mapping of magnetic field.” More

Recent research points to the potential utility of a familiar sounding foe-herpes virus-in the fight against cancer.
The idea: the virus has evolved to commandeer cellular machinery in order activate signaling pathways inside cells and these strategies can be repurposed to bolster immunotherapy against diseases like cancer.
T cells are front line defenders against pathogens, like viruses, and cancer because they can kill infected or malignant cells.
Scientists have for years been trying different techniques to direct these immune cells to protect against disease.
CAR-T therapy is one such example of prompting the body’s own immune system to attack certain forms of cancer using T cells.
However, the therapeutic potential of T cells can be limited by the suppressive environment present within tumors that impairs T cell survival and function.
The University of Michigan team identified herpes virus saimiri, which infects the T cells of squirrel monkeys, as a source of proteins that activate pathways in T cells that are needed to promote T cell survival.

The work, led by the lab of Adam Courtney, Ph.D., in the Department of Pharmacology and the U-M Rogel Cancer Center, exploits this ability in order to investigate whether a modified viral protein could be used to activate transcription factors known as STAT proteins.
The approach is borne of observations that stimulation of the JAK-STAT5 pathway by cytokines like interleukin-2 (IL-2) helps boost the therapeutic ability of T cells to kill cancer cells.
The team engineered a variant of the tyrosine kinase interacting protein from the herpes virus to bind LCK (a kinase active in resting T cells) and recruit it to activate STAT5.
In this way, the team determined that direct activation of STAT5 could sustain T cell function in tumors of mouse models of melanoma and lymphoma.
Their findings hint at a new approach — using genes from organisms with proven ability to modulate human cells — to enhance the power of immunotherapy.
Ph.D. candidate Yating Zheng, of the Department of Pharmacology at U-M Medical School is first author of the paper. More

In a major leap forward for genetic and biomedical research, two scientists at the University of Missouri have developed a powerful new artificial intelligence tool that can predict the 3D shape of chromosomes inside individual cells — helping researchers gain a new view of how our genes work.
Chromosomes are the tiny storage boxes that hold our DNA. Since each cell has about six feet of DNA packed inside it, it must be folded up tightly to fit. This folding not only saves space — it also controls which genes are active or inactive. But when the DNA doesn’t fold the right way, it can disrupt normal cell functions and lead to serious diseases, including cancer.
Historically, scientists have relied on data that averaged results from millions of cells at once. That makes it almost impossible to see the unique differences between individual cells. But the new AI model developed by Yanli Wang and Jianlin “Jack” Cheng at Mizzou’s College of Engineering changes that.
“This is important because even cells from the same part of the body can have chromosomes folded in very different ways,” Wang, a graduate student and lead author of the study, said. “That folding controls which genes are turned on or off.”
Studying single cells is tricky because the data is often messy or incomplete. But the new AI tool is specially designed to work with those challenges. It’s smart enough to spot weak patterns in noisy data, and it knows how to estimate a chromosome’s 3D shape even when some information is missing.
It also understands how to “see” biological structures correctly, even when they’re rotated. Compared to a previous deep learning AI method, Mizzou’s tool is more than twice as accurate when analyzing human single-cell data.
The team has made the software free and available to scientists around the world. That means researchers can now use it to better understand how genes function, how diseases start and how to design better treatments.
“Every single cell can have a different chromosome structure,” Cheng, a Curators’ Distinguished Professor of Electrical Engineering and Computer Science, said. “Our tool helps scientists study those differences in detail — which can lead to new insights into health and disease.”
The researchers now plan to improve the AI tool even further by expanding it to build the high-resolution structures of entire genomes. Their goal: to give scientists the clearest picture yet of the genetic blueprint inside our cells. More