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AI approach elevates plasma performance and stability across fusion devices

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AI approach elevates plasma performance and stability across fusion devices


Achieving a sustained fusion reaction is a delicate balancing act, requiring a sea of moving parts to come together to maintain a high-performing plasma: one that is dense enough, hot enough, and confined for long enough for fusion to take place.

Yet as researchers push the limits of plasma performance, they have encountered new challenges for keeping plasmas under control, including one that involves bursts of energy escaping from the edge of a super-hot plasma. These edge bursts negatively impact overall performance and even damage the plasma-facing components of a reactor over time.

Now, a team of fusion researchers led by engineers at Princeton and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have successfully deployed machine learning methods to suppress these harmful edge instabilities — without sacrificing plasma performance.

With their approach, which optimizes the system’s suppression response in real-time, the research team demonstrated the highest fusion performance without the presence of edge bursts at two different fusion facilities — each with its own set of operating parameters. The researchers reported their findings on May 11 in Nature Communications, underscoring the vast potential of machine learning and other artificial intelligence systems to quickly quash plasma instabilities.

“Not only did we show our approach was capable of maintaining a high-performing plasma without instabilities, but we also showed that it can work at two different facilities,” said research leader Egemen Kolemen, associate professor of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment. “We demonstrated that our approach is not just effective — it’s versatile as well.”

The costs of high-confinement

Researchers have long experimented with various ways to operate fusion reactors to achieve the necessary conditions for fusion. Among the most promising approaches involves operating a reactor in high-confinement mode, a regime characterized by the formation of a steep pressure gradient at the plasma’s edge that offers enhanced plasma confinement.

However, the high-confinement mode has historically come hand-in-hand with instabilities at the plasma’s edge, a challenge that has required fusion researchers to find creative workarounds.

One fix involves using the magnetic coils that surround a fusion reactor to apply magnetic fields to the edge of the plasma, breaking up the structures that might otherwise develop into a full-fledged edge instability. Yet this solution is imperfect: while successful at stabilizing the plasma, applying these magnetic perturbations typically leads to lower overall performance.

“We have a way to control these instabilities, but in turn, we’ve had to sacrifice performance, which is one of the main motivations for operating in the high-confinement mode in the first place,” said Kolemen, who is also a staff research physicist at PPPL.

The performance loss is partly due to the difficulty of optimizing the shape and amplitude of the applied magnetic perturbations, which in turn stems from the computational intensity of existing physics-based optimization approaches. These conventional methods involve a set of complex equations and can take tens of seconds to optimize a single point in time — far from ideal when plasma behavior can change in mere milliseconds. Consequently, fusion researchers have had to preset the shape and amplitude of the magnetic perturbations ahead of each fusion run, losing the ability to make real-time adjustments.

“In the past, everything has had to be pre-programmed,” said co-first author SangKyeun Kim, a staff research scientist at PPPL and former postdoctoral researcher in Kolemen’s group. “That limitation has made it difficult to truly optimize the system, because it means that the parameters can’t be changed in real time depending on how the conditions of the plasma unfold.”

Raising performance by lowering computation time

The Princeton-led team’s machine learning approach slashes the computation time from tens of seconds to the millisecond scale, opening the door for real-time optimization. The machine learning model, which is a more efficient surrogate for existing physics-based models, can monitor the plasma’s status from one millisecond to the next and alter the amplitude and shape of the magnetic perturbations as needed. This allows the controller to strike a balance between edge burst suppression and high fusion performance, without sacrificing one for the other.

“With our machine learning surrogate model, we reduced the calculation time of a code that we wanted to use by orders of magnitude,” said co-first author Ricardo Shousha, a postdoctoral researcher at PPPL and former graduate student in Kolemen’s group.

Because their approach is ultimately grounded in physics, the researchers said it would be straightforward to apply to different fusion devices around the world. In their paper, for instance, they demonstrated the success of their approach at both the KSTAR tokamak in South Korea and the DIII-D tokamak in San Diego. At both facilities, which each have a unique set of magnetic coils, the method achieved strong confinement and high fusion performance without harmful plasma edge bursts.

“Some machine learning approaches have been critiqued for being solely data-driven, meaning that they’re only as good as the amount of quality data they’re trained on,” Shousha said. “But since our model is a surrogate of a physics code, and the principles of physics apply equally everywhere, it’s easier to extrapolate our work to other contexts.”

The team is already working to refine their model to be compatible with other fusion devices, including planned future reactors such as ITER, which is currently under construction.

One active area of work in Kolemen’s group involves enhancing their model’s predictive capabilities. For instance, the current model still relies on encountering several edge bursts over the course of the optimization process before working effectively, posing unwanted risks to future reactors. If instead the researchers can improve the model’s ability to recognize the precursors to these harmful instabilities, it could be possible to optimize the system without encountering a single edge burst.

Kolemen said the current work is yet another example of the potential for AI to overcome longstanding bottlenecks in developing fusion power as a clean energy resource. Previously, researchers led by Kolemen successfully deployed a separate AI controller to predict and avoid another type of plasma instability in real time at the DIII-D tokamak.

“For many of the challenges we have faced with fusion, we’ve gotten to the point where we know how to approach a solution but have been limited in our ability to implement those solutions by the computational complexity of our traditional tools,” said Kolemen. “These machine learning approaches have unlocked new ways of approaching these well-known fusion challenges.”



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‘Dancing molecules’ heal cartilage damage

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In November 2021, Northwestern University researchers introduced an injectable new therapy, which harnessed fast-moving “dancing molecules,” to repair tissues and reverse paralysis after severe spinal cord injuries.

Now, the same research group has applied the therapeutic strategy to damaged human cartilage cells. In the new study, the treatment activated the gene expression necessary to regenerate cartilage within just four hours. And, after only three days, the human cells produced protein components needed for cartilage regeneration.

The researchers also found that, as the molecular motion increased, the treatment’s effectiveness also increased. In other words, the molecules’ “dancing” motions were crucial for triggering the cartilage growth process.

The study was published today (July 26) in the Journal of the American Chemical Society.

“When we first observed therapeutic effects of dancing molecules, we did not see any reason why it should only apply to the spinal cord,” said Northwestern’s Samuel I. Stupp, who led the study. “Now, we observe the effects in two cell types that are completely disconnected from one another — cartilage cells in our joints and neurons in our brain and spinal cord. This makes me more confident that we might have discovered a universal phenomenon. It could apply to many other tissues.”

An expert in regenerative nanomedicine, Stupp is Board of Trustees Professor of Materials Science and Engineering, Chemistry, Medicine and Biomedical Engineering at Northwestern, where he is founding director of the Simpson Querrey Institute for BioNanotechnology and its affiliated center, the Center for Regenerative Nanomedicine. Stupp has appointments in the McCormick School of Engineering, Weinberg College of Arts and Sciences and Feinberg School of Medicine. Shelby Yuan, a graduate student in the Stupp laboratory, was primary author of the study.

Big problem, few solutions

As of 2019, nearly 530 million people around the globe were living with osteoarthritis, according to the World Health Organization. A degenerative disease in which tissues in joints break down over time, osteoarthritis is a common health problem and leading cause of disability.

In patients with severe osteoarthritis, cartilage can wear so thin that joints essentially transform into bone on bone — without a cushion between. Not only is this incredibly painful, patients’ joints also can no longer properly function. At that point, the only effective treatment is a joint replacement surgery, which is expensive and invasive.

“Current treatments aim to slow disease progression or postpone inevitable joint replacement,” Stupp said. “There are no regenerative options because humans do not have an inherent capacity to regenerate cartilage in adulthood.”

What are ‘dancing molecules’?

Stupp and his team posited that “dancing molecules” might encourage the stubborn tissue to regenerate. Previously invented in Stupp’s laboratory, dancing molecules are assemblies that form synthetic nanofibers comprising tens to hundreds of thousands of molecules with potent signals for cells. By tuning their collective motions through their chemical structure, Stupp discovered the moving molecules could rapidly find and properly engage with cellular receptors, which also are in constant motion and extremely crowded on cell membranes.

Once inside the body, the nanofibers mimic the extracellular matrix of the surrounding tissue. By matching the matrix’s structure, mimicking the motion of biological molecules and incorporating bioactive signals for the receptors, the synthetic materials are able to communicate with cells.

“Cellular receptors constantly move around,” Stupp said. “By making our molecules move, ‘dance’ or even leap temporarily out of these structures, known as supramolecular polymers, they are able to connect more effectively with receptors.”

Motion matters

In the new study, Stupp and his team looked to the receptors for a specific protein critical for cartilage formation and maintenance. To target this receptor, the team developed a new circular peptide that mimics the bioactive signal of the protein, which is called transforming growth factor beta-1 (TGFb-1).

Then, the researchers incorporated this peptide into two different molecules that interact to form supramolecular polymers in water, each with the same ability to mimic TGFb-1. The researchers designed one supramolecular polymer with a special structure that enabled its molecules to move more freely within the large assemblies. The other supramolecular polymer, however, restricted molecular movement.

“We wanted to modify the structure in order to compare two systems that differ in the extent of their motion,” Stupp said. “The intensity of supramolecular motion in one is much greater than the motion in the other one.”

Although both polymers mimicked the signal to activate the TGFb-1 receptor, the polymer with rapidly moving molecules was much more effective. In some ways, they were even more effective than the protein that activates the TGFb-1 receptor in nature.

“After three days, the human cells exposed to the long assemblies of more mobile molecules produced greater amounts of the protein components necessary for cartilage regeneration,” Stupp said. “For the production of one of the components in cartilage matrix, known as collagen II, the dancing molecules containing the cyclic peptide that activates the TGF-beta1 receptor were even more effective than the natural protein that has this function in biological systems.”

What’s next?

Stupp’s team is currently testing these systems in animal studies and adding additional signals to create highly bioactive therapies.

“With the success of the study in human cartilage cells, we predict that cartilage regeneration will be greatly enhanced when used in highly translational pre-clinical models,” Stupp said. “It should develop into a novel bioactive material for regeneration of cartilage tissue in joints.”

Stupp’s lab is also testing the ability of dancing molecules to regenerate bone — and already has promising early results, which likely will be published later this year. Simultaneously, he is testing the molecules in human organoids to accelerate the process of discovering and optimizing therapeutic materials.

Stupp’s team also continues to build its case to the Food and Drug Administration, aiming to gain approval for clinical trials to test the therapy for spinal cord repair.

“We are beginning to see the tremendous breadth of conditions that this fundamental discovery on ‘dancing molecules’ could apply to,” Stupp said. “Controlling supramolecular motion through chemical design appears to be a powerful tool to increase efficacy for a range of regenerative therapies.”



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New understanding of fly behavior has potential application in robotics, public safety

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Why do flies buzz around in circles when the air is still? And why does it matter?

In a paper published online July 26, 2024 by the scientific journal Current Biology, University of Nevada, Reno Assistant Professor Floris van Breugel and Postdoctoral Researcher S. David Stupski respond to this up-until-now unanswered question. And that answer could hold a key to public safety — specifically, how to better train robotic systems to track chemical leaks.

“We don’t currently have robotic systems to track odor or chemical plumes,” van Breugel said. “We don’t know how to efficiently find the source of a wind-borne chemical. But insects are remarkably good at tracking chemical plumes, and if we really understood how they do it, maybe we could train inexpensive drones to use a similar process to find the source of chemicals and chemical leaks.”

A fundamental challenge in understanding how insects track chemical plumes — basically, how does the fly find the banana in your kitchen? — is that wind and odors can’t be independently manipulated.

To address this challenge, van Breugel and Stupski used a new approach that makes it possible to remotely control neurons — specifically the “smell” neurons — on the antennae of flying fruit flies by genetically introducing light-sensitive proteins, an approach called optogenetics. These experiments, part of a $450,000 project funded through the Air Force Office of Scientific Research, made it possible to give flies identical virtual smell experiences in different wind conditions.

What van Breugel and Stupski wanted to know: how do flies find an odor when there’s no wind to carry it? This is, after all, likely the wind experience of a fly looking for a banana in your kitchen. The answer is in the Current Biology article, “Wind Gates Olfaction Driven Search States in Free Flight.” The print version will appear in the Sept. 9 issue.

Flies use environmental cues to detect and respond to air currents and wind direction to find their food sources, according to van Breugel. In the presence of wind, those cues trigger an automatic “cast and surge” behavior, in which the fly surges into the wind after encountering a chemical plume (indicating food) and then casts — moves side to side — when it loses the scent. Cast-and-surge behavior long has been understood by scientists but, according to van Breugel, it was fundamentally unknown how insects searched for a scent in still air.

Through their work, van Breugel and Stupski uncovered another automatic behavior, sink and circle, which involves lowering altitude and repetitive, rapid turns in a consistent direction. Flies perform this innate movement consistently and repetitively, even more so than cast-and-surge behavior.

According to van Breugel, the most exciting aspect of this discovery is that it shows flying flies are clearly able to assess the conditions of the wind — its presence, and direction — before deploying a strategy that works well under these conditions. The fact that they can do this is actually quite surprising — can you tell if there is a gentle breeze if you stick your head out of the window of a moving car? Flies aren’t just reacting to an odor with the same preprogrammed response every time like a simple robot, they are responding in context-appropriate manner. This knowledge potentially could be applied to train more sophisticated algorithms for scent-detecting drones to find the source of chemical leaks.

So, the next time you try to swat a fly in your home, consider the fact that flies might actually be a little more aware of some of their natural surroundings than you are. And maybe just open a window to let it out.



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Lampreys possess a ‘jaw-dropping’ evolutionary origin

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One of just two vertebrates without a jaw, sea lampreys that are wreaking havoc in Midwestern fisheries are simultaneously helping scientists understand the origins of two important stem cells that drove the evolution of vertebrates.

Northwestern University biologists have pinpointed when the gene network that regulates these stem cells may have evolved and gained insights into what might be responsible for lampreys’ missing mandibles.

The two cell types — pluripotent blastula cells (or embryonic stem cells) and neural crest cells — are both “pluripotent,” which means they can become all other cell types in the body.

In a new paper, researchers compared lamprey genes to those of the Xenopus, a jawed aquatic frog. Using comparative transcriptomics, the study revealed a strikingly similar pluripotency gene network across jawless and jawed vertebrates, even at the level of transcript abundance for key regulatory factors.

But the researchers also discovered a key difference. While both species’ blastula cells express the pou5 gene, a key stem cell regulator, the gene is not expressed in neural crest stem cells in lampreys. Losing this factor may have limited the ability of neural crest cells to form cell types found in jawed vertebrates (animals with spines) that make up the head and jaw skeleton.

The study will be published July 26 in the journal Nature Ecology & Evolution.

By comparing the biology of jawless and jawed vertebrates, researchers can gain insight into the evolutionary origins of features that define vertebrate animals including humans, how differences in gene expression contribute to key differences in the body plan, and what the common ancestor of all vertebrates looked like.

“Lampreys may hold the key to understanding where we came from,” said Northwestern’s Carole LaBonne, who led the study. “In evolutionary biology, if you want to understand where a feature came from, you can’t look forward to more complex vertebrates that have been evolving independently for 500 million years. You need to look backwards to whatever the most primitive version of the type of animal you’re studying is, which leads us back to hagfish and lampreys — the last living examples of jawless vertebrates.”

An expert in developmental biology, LaBonne is a professor of molecular biosciences in the Weinberg College of Arts and Sciences. She holds the Erastus Otis Haven Chair and is part of the leadership of the National Science Foundation’s (NSF) new Simons National Institute for Theory and Mathematics in Biology.

LaBonne and her colleagues previously demonstrated that the developmental origin of neural crest cells was linked to retaining the gene regulatory network that controls pluripotency in blastula stem cells. In the new study, they explored the evolutionary origin of the links between these two stem cell populations.

“Neural crest stem cells are like an evolutionary Lego set,” said LaBonne. “They become wildly different types of cells, including neurons and muscle, and what all those cell types have in common is a shared developmental origin within the neural crest.”

While blastula stage embryonic stem cells lose their pluripotency and become confined to distinct cell types fairly rapidly as an embryo develops, neural crest cells hold onto the molecular toolkit that controls pluripotency later into development.

LaBonne’s team found a completely intact pluripotency network within lamprey blastula cells, stem cells whose role within jawless vertebrates had been an open question. This implies that blastula and neural crest stem cell populations of jawed and jawless vertebrates co-evolved at the base of vertebrates.

Northwestern postdoctoral fellow and first author Joshua York observed “more similarities than differences” between the lamprey and Xenopus.

“While most of the genes controlling pluripotency are expressed in the lamprey neural crest, the expression of one of these key genes — pou5 — was lost from these cells,” York said. “Amazingly, even though pou5 isn’t expressed in a lamprey’s neural crest, it could promote neural crest formation when we expressed it in frogs, suggesting this gene is part of an ancient pluripotency network that was present in our earliest vertebrate ancestors.”

The experiment also helped them hypothesize that the gene was specifically lost in certain creatures, not something jawed vertebrates developed later on.

“Another remarkable finding of the study is that even though these animals are separated by 500 million years of evolution, there are stringent constraints on expression levels of genes needed to promote pluripotency.” LaBonne said. “The big unanswered question is, why?”

The paper was funded by the National Institutes of Health (grants R01GM116538 and F32DE029113), the NSF (grant 1764421), the Simons Foundation (grant SFARI 597491-RWC) and the Walder Foundation through the Life Sciences Research Foundation. The study is dedicated to the memory of Dr. Joseph Walder.



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