Two Georgia Tech Ph.D. students created a student-run, faculty-graded, fully-accredited course that links math, engineering and machine learning.

Andrew Rosemberg, with assistance from Michael Klamkin, both student researchers with the U.S. National Science Foundation AI Research Institute for Advances in Optimization (AI4OPT), designed the course to bridge gaps they saw in existing classrooms.

“While Georgia Tech offers excellent courses on optimization, control, and learning, we found no single class that connected all these fields in a cohesive way,” Rosemberg said. “In our research, it was clear these topics are deeply interconnected.”

Problem-driven learning

The course starts with fundamental problems and works backward to the methods required to solve them. Rosemberg said this approach was intentional. He said that courses often center around methods in isolation rather than showing how the methods contribute to the larger context. This keeps the course focused on problem-driven discovery.

The class also serves as a way for Rosemberg and Klamkin to strengthen their own teaching and mentoring skills.

Goals and structure

The primary goal of the course is to help students build a clear understanding of how mathematical programming, classical optimal control, and machine learning techniques such as reinforcement learning connect to one another. Students are also working to produce a structured book by the end of the semester.

“The hope is that this resource will not only solidify our own learning but also serve as a guide for other students who want to approach these problems in the future,” Rosemberg said.

Responsibilities are distributed across participants, with each student delivering lectures, reviewing peers’ work, and contributing to collective discussions. Rosemberg and Klamkin provide additional support where needed, while faculty mentor and director of AI4OPT, Pascal Van Hentenryck, ensures the class stays aligned with broader academic objectives.

Student ownership and collaboration

Rosemberg noted that the student-led model gives students a deeper sense of ownership, making them responsible for their own learning, and having a stronger impact. This model allows students to determine what to learn and why, which promotes critical thinking.

The course uses GitHub as its primary workflow platform. Rosemberg said adds transparency and prepares students for real-world research practices.

“GitHub functions much like university systems such as Canvas or Piazza. It also has the added benefit of making all contributions visible to the world,” Rosemberg explained. “This helps students take pride and ownership of their work, while also introducing them to Git, an essential tool for software development and modern STEM research.”

Emerging insights and challenges

Students have begun aligning their research with course themes, including shaping qualifying exam topics around the intersections of operations research, optimal control and reinforcement learning. Rosemberg said exploring the comparative strengths of these fields side by side has been one of the most rewarding outcomes.

Balancing independence with guidance has proven to be the greatest challenge. He said they have been evolving alongside the students in real time and have learned to emphasize mutual responsibility to promote the collective progress of the class.

Looking ahead

Rosemberg said future iterations of the course may place more emphasis on setting expectations early, given the effort required to deliver a lecture in this format.

His advice for others who may want to replicate the model is to focus on building a committed core team.

“Start with a small, motivated group,” Rosemberg said. “Like a startup, success depends less on the structure and more on the dedication of the people involved.”

Researchers at Georgia Tech have analyzed the seasonal differences of sulfate aerosols — a major pollutant in the United States — to examine the long-term impact from sulfur dioxide (SO₂) emission reductions since the enactment of the Clean Air Act amendments in 1990. 

School of Earth and Atmospheric Sciences Professor Yuhang Wang and his team studied the factors affecting SO₂ and sulfate concentrations during winter and summer in the “Rust Belt” — from New York through the Midwest — and the Southeast regions of the U.S. over two decades (2004 to 2023). Supported by the National Science Foundation and Georgia Tech’s Brook Byers Institute for Sustainable Systems, the team also developed an ensemble machine learning approach to project seasonal patterns until 2050. 

“Power plants, particularly those burning coal and oil, are a major source of SO₂ emissions in these regions,” says Wang, who co-authored, with Ph.D. students Fanghe Zhao and Shengjun Xi, the study recently published in Environmental Science & Technology Letters. 

Seasonal differences in atmospheric chemistry 

In the U.S., the chemistry in the atmosphere varies among the seasons. During summer, solar radiation from ample sunlight activates oxidant reactions that produce hydrogen peroxide (H₂O₂) in the atmosphere. The supply of H₂O₂ is determined by the amount of emitted air pollution, and once in the atmosphere, H₂O₂ can oxidize SO₂ quickly into sulfate aerosols in the aqueous phase. 

Sulfate aerosols from the oxidation of SO₂ contribute to the formation of particulate matter less than 2.5 micrometers in diameter (PM2.5). Particulate sulfate poses significant environmental and public health risks, including air pollution, acid rain, and circulatory and respiratory issues. 

“The supply of H₂O₂ in summer is eight times greater than in winter — a huge difference — which means sulfate concentrations are generally higher in summer and a reduction in SO₂ emissions leads to a proportional decrease in sulfate concentrations,” explains Wang. “When SO₂ emissions exceed the available supply of H₂O₂ in winter, the reduction in sulfate concentrations can be much smaller because of a ‘chemical damping’ effect that causes sulfate levels to decline more slowly than SO₂ emissions.” 

Narrowing the disparities between seasonal sulfate levels 

The study’s two-decade observations revealed distinct patterns in the reduction of SO₂ emissions and sulfate concentrations during winter and summer. 

While SO₂ emissions significantly decreased in both seasons­ over time — primarily from the Clean Air Act and more power plants transitioning from coal to natural gas — the reduction of sulfate concentrations initially showed large seasonal differences. However, over the past decade, the disparity between winter and summer sulfate levels narrowed as SO₂ emissions decreased.

According to Wang, the seasonal disparity of sulfate was caused by changing chemical regimes in winter over time. Although the lower supply of H₂O₂ remained stable in winter, SO₂ wintertime emissions were higher from 2004 to 2013, then dropped below the level of H₂O₂ after 2013 — reaching parity with the levels of reduced SO₂ emissions in the summer. 

“When you have this complexity of atmospheric chemistry, there is a non-linear effect in winter — as SO₂ emissions decreased, sulfate aerosol production efficiency increased until 2013, then flattened as of today. The reduction in sulfate aerosols initially lagged behind the decrease in SO₂ emissions but eventually caught up as a result of sustained air quality control efforts,” says Wang. “Conversely, there is a simple, linear effect in summer — the more SO₂ emissions, the more sulfate aerosols in the atmosphere — and if you reduce one, the other is reduced by the same proportion.”

Decades-long full impact 

From now until 2050, the researchers’ machine learning projections indicate a continuing decrease of winter and summer sulfate levels, which are currently around 20 percent, as SO₂ emission controls achieve comparable efficacy across the seasons. 

“We’re now seeing the full impact from the Clean Air Act,” concludes Wang, “and the nation’s sustained effort in pollution reduction is key to improving air quality and health outcomes.”

On a clear polymer chip, soft and pliable like a gummy bear, a microscopic lung comes alive — expanding, circulating, and, for the first time, protecting itself like a living organ. 

For Ankur Singh, director of Georgia Tech’s Center for Immunoengineering, watching immune cells rush through the chip took his breath away. Singh co-directed the study with longtime collaborator Krishnendu “Krish” Roy, former Regents Professor and director of the NSF Center for Cell Manufacturing Technologies at Tech and now the Bruce and Bridgitt Evans dean of engineering and University Distinguished Professor at Vanderbilt University. Rachel Ringquist, Roy’s graduate student, and now a postdoctoral fellow with Singh, led the work as part of her doctoral dissertation. 

“That was the ‘wow’ moment,” Singh said. “It was the first time we felt we had something close to a real human lung.”

Lung-on-a-chip platforms provide researchers a window into organ behavior. They are about the size of a postage stamp, etched with tiny channels and lined with living human cells. Roy and Singh’s innovation was adding a working immune system — the missing piece that turns a chip into a true model of how the lung fights disease.

Now, researchers can watch how lungs respond to threats, how inflammation spreads, and how healing begins.
 

The Human Stakes

For millions of people struggling with lung disease, everyday life can feel nearly impossible, whether it’s climbing stairs, carrying groceries, or even laughing too hard. Doctors and scientists have attempted for decades to unlock what really happens inside fragile lungs.

"This unique lung-on-a-chip model opens new, preclinical pathways of discovery that will allow researchers to better understand the interplay of immune responses to severe viral infections and evaluate critical antiviral treatments,” said Roy.

For Singh, the Carl Ring Family Professor in the George W. Woodruff School of Mechanical Engineering with a joint appointment in the Wallace H. Coulter Department of Biomedical Engineering, this research is deeply personal. He lost an uncle when an infection overwhelmed his cancer-weakened immune system.

“That experience stays with you,” Singh reflected. “It made me want to build systems that could predict and prevent outcomes like that, so fewer families go through what mine did. I think about my uncle all the time. If work like this means fewer families lose someone they love, then it’s worth everything.”

That motivation pushed his team to reimagine what a lung-on-a-chip could do, setting the stage for the breakthroughs that followed.
 

When the Lung Fought Back

The turning point came when Roy’s and Singh’s team peered through a microscope and saw something no one had ever witnessed on a chip: blood and immune cells coursing through tiny vessel-like structures, behaving just as they do in a living lung.

For years, researchers had struggled to add immunity to organ-on-a-chip systems. Immune cells often died quickly or failed to circulate and interact with tissue the way they do in people. the team solved that problem, creating a chip where immune cells could survive and coordinate a defense.

“It was an amazing breakthrough moment,” Singh said.

The true test came when the team introduced a severe influenza virus infection. The lung mounted an immune response that closely mirrored what doctors see in patients. Immune cells rushed to the site of infection, inflammation spread through tissue, and defenses activated in response.

“That was when we realized this wasn’t just a model,” Singh said. “It was capturing the real biology of disease.”

Singh and Roy’s research is published in the journal Nature Biomedical Engineering.
 

A More Human Approach

For decades, lung research has relied on animal models. But mice don’t get asthma like children. Their bodies don’t mount the same defenses.

“Five mice in a cage may respond the same way, but five humans won’t,” Singh explained. “Our chip can reflect that difference. That’s what makes it more accurate, and why it could dramatically reduce the need for animal models.”

Krish Roy emphasized its potential.

“The Food and Drug Administration’s strategic vision on reducing animal testing and developing predictive non-animal models aligns perfectly with our work. This device goes further than ever before in modeling human severe influenza and providing unprecedented insights into the complex lung immune response,” he said.

Fighting More Than the Flu

What began with influenza now expands to a wider range of diseases. Roy and Singh believes the platform can be used to study asthma, cystic fibrosis, lung cancer, and tuberculosis. The researchers are also working to integrate immune organs, showing how the lung coordinates with the body’s defenses.

The long-term vision is personalized medicine: chips built from a patient’s own cells to predict which therapy will work best. Scaling, clinical validation, and regulatory approval will take years, but Singh is undeterred.

“Imagine knowing which treatment will help you before you ever take it,” Singh said. “That’s where we’re headed.”

Where we’re headed, the future doesn’t wait for illness. Instead, it anticipates it, intercepts it, and rewrites the outcome.

 

Georgia Tech postdoctoral researcher Rachel Ringquist was the first author leading the study.

This research was supported by Wellcome Leap, with additional funding from the National Institutes of Health, Carl Ring Family Endowment, and the Marcus Foundation.

Ringquist, R., Bhatia, E., Chatterjee, P. et al. An immune-competent lung-on-a-chip for modelling the human severe influenza infection response. Nature Biomedical Engineering, September 2025 Vol.9 No.9

DOI: https://doi.org/10.1038/s41551-025-01491-9

Smart manufacturing, data-driven design, and artificial intelligence aren’t just buzzwords — they are fields that are creating high-paying, high-tech careers across the country. In rural communities across Georgia, these advanced manufacturing roles are growing, but the talent pipeline isn’t keeping pace.

“It’s not just about creating jobs, it’s about filling them,” says Tom Kurfess, Regents’ Professor in mechanical engineering and executive director of the Georgia Tech Manufacturing Institute (GTMI). “To do that, we need to show students how exciting and innovative manufacturing can be. Manufacturing has really changed over the past few years. Today, going from an idea to a physical part is much easier to do. It is fun and exciting to bring ideas to life and to actually hold the results in your hands.”

GTMI is working to reignite student interest in the art and science of making through its new K–12 initiative: the Advanced Manufacturing Pathways (AMP) Program. Modeled after Georgia Tech’s Rural CS Initiative, AMP empowers schools with faculty expertise, cutting-edge equipment, and a hands-on curriculum to give students early exposure to the tools, technologies, and creativity behind modern manufacturing while building a pipeline of future talent ready to thrive in high-tech careers.

Funded by the Southwest Georgia Regional Commission (SWGRC), AMP is kicking off in three school districts this fall — Decatur County, Thomas County, and the city of Thomasville  — with plans to expand to additional schools in the spring of 2026. The program will start by engaging more than 200 students through hands-on learning, virtual instruction, and in-person lab experiences led by Georgia Tech researchers and faculty.

“Here in Southwest Georgia, we believe that opportunities like this are vital for integrated learning in schools and for growing our future workforce,” says Beka Shiver, economic development and transportation planner for SWGRC. “Workforce development and K-12 integration are at the heart of our Southwest Georgia Ecosystem Building Project, and we are so pleased to be able to provide funding for this program.”

The launch of the AMP Program is centered around Design, Build, Race, a course putting a modern spin on the classic pinewood derby. Students will use digital design, 3D printing, and machining to build and race custom cars, while also learning how to collect and analyze performance data to improve their designs and predict outcomes. The course blends engineering with data science, sparking curiosity and showing students how modern manufacturing is powered by both technical skills and smart data. 

“This program delivers real-world industry experience to students while strengthening the talent pipeline that drives innovation, competitiveness, and resilience in advanced manufacturing”, says Steven Ferguson, interim director of operations at GTMI and one of the project’s leaders. “After more than 20 years of driving education and workforce development innovation, I’m more energized than ever to help launch the AMP program to open doors for students and advance U.S. manufacturing leadership.”

Building the Blueprint

Before it evolved into the AMP Program, Design, Build, Race was a course developed by GTMI research engineer Kyle Saleeby in 2023. Originating in GTMI’s Advanced Manufacturing Pilot Facility (AMPF), the course was designed to introduce Morehouse and Georgia Tech students to the possibilities of modern manufacturing through digital design, 3D printing, machining, and competitive creativity.

“Even after the first week, it was powerful to watch students discover how exciting it is to design and manufacture a competition-ready car in a matter of hours,” said Saleeby. “That’s when I knew we were onto something special.”

Saleeby teamed up with Ferguson to transform the course into a broader initiative. The duo engaged colleagues from STEM@GTRI and secured funding from SWGRC to modify the curriculum and scale the course for a high school audience. 

“We are thrilled that we have been able to take the lessons learned during the development of the Rural Computer Science Initiative and expand opportunities for students in Southwest Georgia,” says Sean Mulvanity, a senior research associate in the Georgia Tech Research Institute. Mulvanity is one of the founders of the initiative and has been a key contributor to the AMP Program. “We hope this program can grow and expose students across the state to the field of advanced manufacturing.” 

Though granted by the SWGRC, funds for the program were provided by Georgia Artificial Intelligence in Manufacturing, a statewide initiative founded by GTMI and Georgia Tech’s Enterprise Innovation Institute to advance AI-driven manufacturing.

To bring AMP into classrooms, Southern Regional Technical College helped set up labs and provide technical support, ensuring schools were ready to launch. 

“At all levels, the community has rallied around this program,” says Saleeby. “Providing students with a unique experience learning advanced manufacturing technologies will open countless career opportunities. I cannot wait to see where they go.” 

Georgia Tech School of Electrical and Computer Engineering (ECE) Professor Muhannad Bakir has been named the inaugural GlobalFoundries Termed Chair in Packaging and 3D Heterogeneous Integration.

The position was established in the spring of 2025 to be awarded to a distinguished ECE faculty member who has demonstrated excellence in research and teaching in the areas of chiplet-based integrated circuit (IC) systems, 2.5D, and 3D IC technologies.

“I am grateful to GlobalFoundries for establishing this chair position in ECE, and honored to be the inaugural recipient,” Bakir said. “Advanced packaging and heterogeneous integration are a key differentiator and driver of innovation in virtually all leading-edge electronic systems from handheld devices to data centers powering AI. ECE’s partnership with GlobalFoundries will position the School for many unique research and educational programs development to support 2.5D and 3D technologies.”

3D heterogeneous integration (3DHI) is a cutting-edge technology that merges various ICs and components, including processors, memory, sensors, and RF modules, into one 3D package.

Bakir is currently the Dan Fielder Professor in ECE and the director of the 3D Systems Packaging Research Center supported by the Institute for Matter and Systems, where he oversees an interdisciplinary approach to electronic packaging research.

Read the full article

 

Maintaining balance while walking may seem automatic — until suddenly it isn’t. Gait impairment, or difficulty with walking, is a major liability for stroke and Parkinson’s patients.  Not only do gait issues slow a person down, but they are also one of the top causes of falls. And solutions are often limited to time-intensive and costly physical therapy.

A new wearable electronic device that can be inserted inside any shoe may be able to address this challenge. The device, developed by Georgia Tech researchers, is made of more than 170 thin, flexible sensors that measure foot pressure — a key metric for determining whether someone is off-balance. The sensor collects pressure data, which the researchers could eventually use to predict which changes lead to falls.

The researchers presented their work in the paper, “Flexible Smart Insole and Plantar Pressure Monitoring Using Screen-Printed Nanomaterials and Piezoresistive Sensors.” It was the cover paper in the August edition of ACSApplied Materials & Interfaces. 

Pressure Points

Smart footwear isn’t new — but making it both functional and affordable has been nearly impossible. W. Hong Yeo’s lab has made its reputation on creating malleable medical devices. The researchers rely on the common commercial practice of screen-printing electronics to screen-print sensors. They realized they could apply this printing technique to address walking difficulties.

“Screen-printing is advantageous for developing medical devices because it's low-cost and scalable,” said Yeo, the Peterson Professor and Harris Saunders Jr. Professor in the George W. Woodruff School of Mechanical Engineering. “So, when it comes to thinking about commercialization and mass production, screen-printing is a really good platform because it's already been used in the electronics industry.”

Making the device accessible to the everyday user was paramount for Yeo’s team. A key innovation was making sure the wearable is thin enough to be comfortable for the wearer and easy to integrate with other assistive technologies. The device uses Bluetooth, enabling a smartphone to collect data and offer the future possibility of integrating with existing health monitoring applications.

Possibilities for real-world adaptation are promising, thanks to these innovations. Lightweight and small, the wearable could be paired with robotics devices to help stroke and Parkinson’s patients and the elderly walk. The high number of sensors could make it easier for researchers to apply a machine learning algorithm that could predict falls. The device could even enable professional athletes to analyze their performance.

Regardless of how the device is used, Yeo intends to keep its cost under $100. So far, with funding from the National Science Foundation, the researchers have tested the device on healthy subjects. They hope to expand the study to people with gait impairments and, eventually, make the device commercially available. 

“I'm trying to bridge the gap between the lack of available devices in hospitals or medical practices and the lab-scale devices,” Yeo said. “We want these devices to be ready now — not in 10 years.”

With its low-cost, wireless design and potential for real-time feedback, this smart insole could transform how we monitor and manage walking difficulties — not just in clinical settings, but in everyday life. 

Crossing a room shouldn’t feel like a marathon. But for many stroke survivors, even the smallest number of steps carries enormous weight. Each movement becomes a reminder of lost coordination, muscle weakness, and physical vulnerability.

A team of Georgia Tech researchers wanted to ease that struggle, and robotic exoskeletons offered a promising path. Their findings point to a simple but powerful shift: exoskeletons that adapt to people, rather than forcing people to adapt to the machine. Using artificial intelligence (AI) to learn the rhythm of patients’ strides in real time, the team showed how these devices can reduce strain and increase efficiency. They also demonstrated how the technology can help restore confidence for stroke survivors. 

The Robot Finds the Rhythm

A robotic exoskeleton is a wearable device that helps people move with mechanical support. Traditional exoskeletons require endless manual adjustments — turning knobs, calibrating settings, and tweaking controls. 

“It can be frustrating, even nearly impossible, to get it right for each person,” said Aaron Young, associate professor in the George W. Woodruff School of Mechanical Engineering. “With AI, the exoskeleton figures out the mapping itself. It learns the timing of someone’s gait through a neural network, without an engineer needing to hand-tune everything.”

The software monitors each step, instantly updates, and fine-tunes the support it provides. Over time, the exoskeleton aligns its movements with the unique gait of the person wearing it. In this study, the research team used a hip exoskeleton, which provides torque at the hip joint — in other words, adding power to help stroke survivors walk or move their legs more easily.
 

Taking Smarter Steps

Walking after a stroke can be tough and unpredictable. A patient’s stride can change from one day to the next, and even from one step to the next. Most exoskeletons aren’t built for that kind of variation. They are designed around the steady, even gait of healthy young adults, which can leave stroke survivors feeling more unsteady than supported.

Young’s breakthrough, detailed in IEEE Transactions on Robotics, is a neural network — a type of AI that learns patterns much like the human brain does. Sensors at the hip pick up how someone is moving, and the network translates those signals into just the right boost of power to support each step. It quickly figures out a person’s unique walking pattern. But lead clinician Kinsey Herrin said the AI’s learning doesn’t stop there. It keeps adjusting as the patient walks, so the exoskeleton can stay in sync even during stride shifts.

“The speed really surprised us,” Young said. “In just one to two minutes of walking, the system had already learned a person’s gait pattern with high accuracy. That’s a big deal, to adapt that quickly and then keep adapting as they move.”

Tests showed the system was far more accurate than the standard exoskeleton. It reduced errors in tracking stroke patients’ walking patterns by 70%.

Young emphasized that this research is about more than metrics. “When you see someone able to walk farther without becoming exhausted, that’s when you realize this isn’t just about robotics — it’s about giving people back a measure of independence,” he said.
 

Adapting Anywhere

Every exoskeleton comes with its own set of sensors, so the data they collect can look completely different from one device to the next. A neural network trained on one machine often stumbles when it’s moved to another. To get around that, Young’s team designed software that works like a universal adapter plug — no matter what device it’s connected to, it converts the signals into a form the AI can use. After just 10 strides of calibration, the system cut error rates by more than 75%.

“The goal is that someone could strap on a device, and, within a minute, it feels like it was built just for them,” Young said.


A Step Toward the Future

While the study centered on stroke survivors, the implications are far broader. The same adaptive approach could support older adults coping with age-related muscle weakness, people with conditions like Parkinson’s or osteoarthritis, or even children with neurological disabilities. 
Young and his team are now running clinical trials to measure how well the AI-powered exoskeleton supports people in a wide range of everyday activities.

“There’s no such thing as an ‘average’ user,” Young said. “The real challenge is designing technology that can adapt to the full spectrum of human mobility.”

If Georgia Tech’s exoskeleton can rise to that challenge, the promise goes well beyond the lab. It could mean a world where technology doesn’t just help people walk — it learns to walk with them.

Inseung Kang, who holds a B.S., M.S., and Ph.D. from Georgia Tech, is the paper’s lead author and now an assistant professor of mechanical engineering at Carnegie Mellon University. He explained that the real promise is in what comes next. 

“We’ve developed a system that can adjust to a person’s walking style in just minutes. But the potential is even greater. Imagine an exoskeleton that keeps learning with you over your lifetime, adjusting as your body and mobility change. Think of it as a robot companion that understands how you walk and gives you the right assistance every step of the way.”

 

Aaron Young is affiliated with Georgia Tech’s Institute for Robotics and Intelligent Machines.

This research was primarily funded by a grant (DP2HD111709-01) from the National Institutes of Health New Innovator Award Program. Georgia Tech researchers have created the first lung-on-a-chip with a functioning immune system, allowing it to respond to infections much like a real human lung. The breakthrough, published in Nature Biomedical Engineering, provides a more accurate way to study diseases, test therapies, and reduce reliance on animal models. With potential applications in conditions from influenza to cancer, the technology opens the door to personalized medicine that predicts how individual patients will respond to treatment.

Fat grafting is a potentially life-saving surgical technique, often used to fill and repair severe injuries. Also used in cosmetic treatments, the procedure works to move fat from one part of the body to another. Yet not all parts of fat tissue are helpful and can be damaged or even harmful when transplanted.

To improve fat grafting, a team of students from in the Master of Biomedical Innovation and Development (MBID) program in the Wallace H. Coulter Department of Biomedical Engineering used tools in IMS’s Materials Properties Characterization Facility (MPCF) to isolate and remove harmful fat tissue.

The team designed and machined a hydrostatic press in the MPCF for their research. This kind of press applies equal pressure from all directions, which is different from a hydraulic press that only pushes in one direction.

To build their hydrostatic press, they:

• Drilled a hole into a block of aluminum
• Filled that hole with liquid
• Then pushed down on the liquid with a piston

This setup allowed them to apply over 36,000 pounds of force for 10 minutes using a special machine called a servohydraulic test frame.

Their results showed that applying this pressure to their samples would destroy the parts of the fat cells that weren’t needed, while keeping the important structural parts and healing factors. This made the fat tissue more consistent and could make it safer to use in surgeries.