Resilience hubs are trusted, community-serving facilities designed to support residents and coordinate communication and resources in everyday life; and before, during, and after disruptions. Environmental disruptions such as hurricane damage, coastal erosion, flood damage, extreme heat, and wildfire destruction are occurring more frequently and with greater economic costs. 

On November 21, 2024, a team from Georgia Tech met with nine other organizations at the Penn Center on St. Helena Island in South Carolina to work towards developing targeted resilience strategies to cope with environmental disaster events. More specifically, the Penn Center workshop’s overall goal was the co-creation of paths toward building community-led and -engaged, scientifically supported resilience hubs, addressing the unique challenges faced by coastal and inland vulnerable communities in the Southeastern United States.

A common definition of community resilience is “the sustained ability of a community to use available resources to respond to, withstand, and recover from adverse situations.”

Part of the process to build these action research partnerships and resilience plans is to bring together community leaders, government representatives, and an interdisciplinary team of researchers—many of whom are from Georgia Tech. Georgia Tech researchers bring expertise from science, engineering, design, humanities, and social sciences.

As part of the workshop, 15 Georgia Tech architecture students presented their design models for a multipurpose 20,000 square-foot building designed for the Penn Center campus which is steeped in African American history.

Some of the researchers at Georgia Tech attending the workshop and supporting the development of Southeastern community-focused resilience strategies included: 

• Sofía Pérez-Guzmán, assistant professor in the School of Civil & Environmental Engineering; 
• Allen Hyde, associate professor in the School of History and Sociology, and faculty member of the Institute for People and Technology; 
• Danielle Willkens, associate professor in the School of Architecture and faculty member of the Institute for People and Technology; 
• Alexander Robel, associate professor in the School of Earth and Atmospheric Sciences; 
• Jennifer Hirsch, director of the Center for Sustainable Communities Research and Education at Georgia Tech;  
• Valerie M. Thomas, Anderson-Interface Chair of Natural Systems and professor in the H. Milton School of Industrial and Systems Engineering with a joint appointment in the School of Public Policy; 
• Joe F. Bozeman III, assistant professor in the School of Civil and Environmental Engineering with a joint appointment in the School of Public Policy; 
• Russell Clark, lead principal investigator of the Coastal Equity and Resilience Hub and senior research scientist at the Institute for People and Technology; 
• Nicole Kennard, assistant director for community-engaged research in the Brook Byers Institute for Sustainable Systems; and 
• Jung-Ho Lewe, senior research engineer in the Daniel Guggenheim School of Aerospace Engineering.

Participating partner organizations in addition to the Penn Center include:

• 7 Dimensions Outreach
• Atlanta Preservation Center
• Center for Sustainable Communities 
• Coastal Conservation League 
• Community Church Atlanta
• Furman University
• Gullah Geechee Futures Project
• University of South Carolina: Arnold School of Public Health, the EJ Strong Program, and the Department of Environmental Health Science
• Willson Center for Humanities and Arts at the University of Georgia

 

This work is supported by a Georgia Tech Sustainability Next research seed grant. The seed grant program is administered by the Brook Byers Institute for Sustainable Systems (BBIS) in collaboration with the Renewable Bioproducts Institute (RBI), the Strategic Energy Institute (SEI), and the Institute for People and Technology (IPaT). The program nurtures promising areas for future large-scale collaborative sustainability research, research translation, and high-impact outreach; provides mid-career faculty with leadership and community-building opportunities; and broadens and strengthens the Georgia Tech sustainability community as a whole.

When Kostas Konstantinidis proved that many microbes — like plants and animals — are organized into species, he upended a long-held scientific belief. Scientists widely believed that bacteria, due to their unique genetic exchange mechanisms and the vast size of their global populations, did not — and could not — form distinct species.

New research from Konstantinidis and collaborators further challenges this notion, suggesting that not only do bacteria form species, but they also maintain cohesive species through a process that is somewhat “sexual." 

“The next question for us was how individual microbes in the same species maintain their cohesiveness. In other words, how do bacteria stay similar?” said Konstantinidis, the Richard C. Tucker Professor in Georgia Tech’s School of Civil and Environmental Engineering. 

Bacterial and other microbes are thought to evolve primarily through binary fission, meaning asexual reproduction, while also engaging in infrequent genetic exchange. Using a novel bioinformatic method for detecting gene transfer, along with a new trove of whole genome data, Konstantinidis and an international team of researchers tested their hypothesis for how species emerge and are maintained. They found that bacteria evolve and form species more “sexually” than previously thought. 

Their research was published in the journal Nature Communications.

To investigate how microbial species maintain their distinct identities, the team analyzed the complete genomes of microbes from two natural populations. They collected and sequenced over 100 strains of Salinibacter ruber (a salt-loving microbe) from solar salterns in Spain. Then they analyzed a set of previously published Escherichia coli genomes isolated from livestock farms in the U.K. They compared the genomes of closely related microbes to see how genes were being exchanged.

They found that a process called “homologous recombination” plays a major role in keeping microbial species together. Homologous recombination occurs when microbes exchange DNA with each other and integrate the new DNA into their genome by replacing their own similar DNA. They observed that recombination occurs frequently and randomly across the entire genome of microbes, and not just in a few specific regions. 

“This may be fundamentally different from sexual reproduction in animals, plants, fungi, and non-bacterial organisms, where DNA is exchanged during meiosis, but the outcome in terms of species cohesion may be similar,” Konstantinidis said. “This constant exchange of genetic material acts as a cohesive force, keeping members of the same species similar.” 

The researchers also observed that members of the same species are more likely to exchange DNA with one another than with members of different species, further contributing to distinct species boundaries.

“This work addresses a major, long-lasting problem for microbiology that is relevant for many research areas,” Konstantinidis said. “That is, how to define species and the underlying mechanisms for species cohesion.”

This research has implications for several fields, from environmental science and evolution to medicine and public health, and offers valuable insights for identifying, modeling, and regulating clinically or environmentally important organisms. The methodology developed during the research also provides a molecular toolkit for future epidemiological and micro-diversity studies.

 

Note: The research was made possible by contributions from the groups of Ramon Rossello-Mora at IMEDEA in Majorca, Spain, and Rudolf Amann at the Max Planck Institute for Marine Microbiology in Bremen, Germany, who obtained data from the natural microbial populations and equally contributed to the data analysis and interpretations. 

Citation: Conrad, R.E., Brink, C.E., Viver, T. et al. Microbial species and intraspecies units exist and are maintained by ecological cohesiveness coupled to high homologous recombination. Nat Commun 15, 9906 (2024). 

DOI: https://doi.org/10.1038/s41467-024-53787-0

Funding: U.S. Department of Energy, U.S. National Science Foundation, European Regional Development Fund

Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate.

 “What is happening within the field is revolutionary,” says School of Earth and Atmospheric Sciences Associate Chair and Professor Annalisa Bracco, adding that because many climate-related processes — from ocean currents to melting glaciers and weather patterns — can be described with physical equations, these advancements have the potential to help us understand and predict climate in critically important ways. 

Bracco is the lead author of a new review paper providing a comprehensive look at the intersection of AI and climate physics.

The result of an international collaboration between Georgia Tech’s Bracco, Julien Brajard (Nansen Environmental and Remote Sensing Center), Henk A. Dijkstra (Utrecht University), Pedram Hassanzadeh (University of Chicago), Christian Lessig (European Centre for Medium-Range Weather Forecasts), and Claire Monteleoni (University of Colorado Boulder), the paper, ‘Machine learning for the physics of climate,’ was recently published in Nature Reviews Physics. 

“One of our team’s goals was to help people think deeply on how climate science and AI intersect,” Bracco shares. “Machine learning is allowing us to study the physics of climate in a way that was previously impossible. Coupled with increasing amounts of data and observations, we can now investigate climate at scales and resolutions we’ve never been able to before.”

Connecting hidden dots

The team showed that ML is driving change in three key areas: accounting for missing observational data, creating more robust climate models, and enhancing predictions, especially in weather forecasting. However, the research also underscores the limits of AI — and how researchers can work to fill those gaps.

“Machine learning has been fantastic in allowing us to expand the time and the spatial scales for which we have measurements,” says Bracco, explaining that ML could help fill in missing data points — creating a more robust record for researchers to reference. However, like patching a hole in a shirt, this works best when the rest of the material is intact.

“Machine learning can extrapolate from past conditions when observations are abundant, but it can’t yet predict future trends or collect the data we need,” Bracco adds. “To keep advancing, we need scientists who can determine what data we need, collect that data, and solve problems.”

Modeling climate, predicting weather

Machine learning is often used when improving climate models that can simulate changing systems like our atmosphere, oceans, land, biochemistry, and ice. “These models are limited because of our computing power, and are run on a three-dimensional grid,” Bracco explains: below the grid resolution, researchers need to approximate complex physics with simpler equations that computers can solve quickly, a process called ‘parameterization’.

Machine learning is changing that, offering new ways to improve parameterizations, she says. “We can run a model at extremely high resolutions for a short time, so that we don’t need to parameterize as many physical processes — using machine learning to derive the equations that best approximate what is happening at small scales,” she explains. “Then we can use those equations in a coarser model that we can run for hundreds of years.”

While a full climate model based solely on machine learning may remain out of reach, the team found that ML is advancing our ability to accurately predict weather systems and some climate phenomena like El Niño. 

Previously, weather prediction was based on knowing the starting conditions — like temperature, humidity, and barometric pressure — and running a model based on physics equations to predict what might happen next. Now, machine learning is giving researchers the opportunity to learn from the past. “We can use information on what has happened when there were similar starting conditions in previous situations to predict the future without solving the underlying governing equations,” Bracco says. “And all while using orders-of-magnitude less computing resources.”

The human connection

Bracco emphasizes that while AI and ML play a critical role in accelerating research, humans are at the core of progress. “I think the in-person collaboration that led to this paper is, in itself, a testament to the importance of human interaction,” she says, recalling that the research was the result of a workshop organized at the Kavli Institute for Theoretical Physics — one of the team’s first in-person discussions after the Covid-19 pandemic.

“Machine learning is a fantastic tool — but it's not the solution to everything,” she adds. “There is also a real need for human researchers collecting high-quality data, and for interdisciplinary collaboration across fields. I see this as a big challenge, but a great opportunity for computer scientists and physicists, mathematicians, biologists, and chemists to work together.”

 

Funding: National Science Foundation, European Research Council, Office of Naval Research, US Department of Energy, European Space Agency, Choose France Chair in AI.

DOI: https://doi.org/10.1038/s42254-024-00776-3

 

A researcher in Georgia Tech’s School of Interactive Computing has received the nation’s highest honor given to early career scientists and engineers.

Associate Professor Josiah Hester was one of 400 people awarded the Presidential Early Career Award for Scientists and Engineers (PECASE), the Biden Administration announced in a press release on Tuesday.

The PECASE winners’ research projects are funded by government organizations, including the National Science Foundation (NSF), the National Institutes of Health (NIH), the Centers for Disease Control and Prevention (CDC), and NASA. They will be invited to visit the White House later this year.

Hester joins Associate Professor Juan-Pablo Correa-Baena from the School of Materials Science and Engineering as the two Tech faculty who received the honor.

Hester said his nomination was based on the NSF Faculty Early Career Development Program (CAREER) award he received in 2022 as an assistant professor at Northwestern University. He said the NSF submits its nominations to the White House for the PECASE awards, but researchers are not informed until the list of winners is announced.

“For me, I always thought this was an unachievable, unassailable type of thing because of the reputation of the folks in computing who’ve won previously,” Hester said. “It was always a far-reaching goal. I was shocked. It’s something you would never in a million years think you would win.”

Hester is known for pioneering research in a new subfield of sustainable computing dedicated to creating battery-free devices powered by solar energy, kinetic energy, and radio waves. He co-led a team that developed the first battery-free handheld gaming device.

Last year, Hester co-authored an article published in the Association of Computing Machinery’s in-house journal, the Communications of the ACM, in which he coined the term “Internet of Battery-less Things.”

The Internet of Things is the network of physical computing devices capable of connecting to the internet and exchanging data. However, these devices eventually die. Landfills are overflowing with billions of them and their toxic power cells, harming our ecosystem.

In his CAREER award, Hester outlined projects that would work toward replacing the most used computing devices with sustainable, battery-free alternatives.

“I want everything to be an Internet of Batteryless Things — computational devices that could last forever,” Hester said. “I outlined a bunch of different ways that you could do that from the computer engineering side and a little bit from the human-computer interaction side. They all had a unifying theme of making computing more sustainable and climate-friendly.”

Hester is also a Sloan Research Fellow, an honor he received in 2022. In 2021, Popular Sciene named him to its Brilliant 10 list. He also received the Most Promising Engineer or Scientist Award from the American Indian Science Engineering Society, which recognizes significant contributions from the indigenous peoples of North America and the Pacific Islands in STEM disciplines.

President Bill Clinton established PECASE in 1996. The White House press release recognizes exceptional scientists and engineers who demonstrate leadership early in their careers and present innovative and far-reaching developments in science and technology.

Georgia Tech researchers have developed biosensors with advanced sleuthing skills and the technology may revolutionize cancer detection and monitoring. 

The tiny detectives can identify key biological markers using logical reasoning inspired by the “AND” function in computers — like, when you need your username and password to log in. And unlike traditional biosensors comprised of genetic materials — cells, bits of DNA — these are made of manufactured molecules.

These new biosensors are more precise and simpler to manufacture, reducing the number of false positives and making them more practical for clinical use. And because the sensors are cell-free, there’s a reduced risk for immunogenic side effects.

“We think the accuracy and simplicity of our biosensors will lead to accessible, personalized, and effective treatments, ultimately saving lives,” said Gabe Kwong, associate professor and Robert A. Milton Endowed Chair in the Wallace H. Coulter Department of Biomedical Engineering, who led the study, published this month in Nature Nanotechnology. 

Breaking With Tradition

The researchers set out to address the limitations in current biosensors for cancer, like the ones designed for CAR-T cells to allow them to recognize tumor cells. These advanced biosensors are made of genetic material, and there is growing interest to reduce the potential for off-target toxicity by using Boolean “AND-gate” computer logic. That means they’re designed to release a signal only when two specific conditions are met.

“Traditionally, these biosensors involve genetic engineering using cell-based systems, which is a complex, time-consuming, and expensive process,” said Kwong.

So, his team developed biosensors made of iron oxide nanoparticles and special molecules called cyclic peptides. Synthesizing nanomaterials and peptides is a simpler, less costly process than genetic engineering, according to Kwong, “which means we can likely achieve large-scale, economical production of high-precision biosensors.”

Unlocking the AND-gate

Biosensors detect cancer signals and track treatment progress by turning biological signals into readable outputs for doctors. With AND-gate logic, two distinct inputs are required for an output. 

Accordingly, the researchers engineered cyclic peptides — small amino acid chains — to respond only when they encounter two specific types of enzymes, proteases called granzyme B (secreted by the immune system) and matrix metalloproteinase (from cancer cells). The peptides generate a signal when both proteases are present and active.

Think of a high-security lock that needs two unique keys to open. In this scenario, the peptides are the lock, activating the sensor signal only when cancer is present and being confronted by the immune system. 

“Our peptides allow for greater accuracy in detecting cancer activity,” said the study’s lead author, Anirudh Sivakumar, a postdoctoral researcher in Kwong’s Laboratory for Synthetic Immunity. “It’s very specific, which is important for knowing when immune cells are targeting and killing tumor cells.”

Super Specific

In animal studies, the biosensors successfully distinguished between tumors that responded to a common cancer treatment called immune checkpoint blockade therapy — ICBT, which enhances the immune system — from tumors that resisted treatment. 

During these tests, the sensors also demonstrated their ability to avoid false signals from other, unrelated health issues, such as when the immune system confronted a flu infection in the lungs, away from the tumor.

“This level of specificity can be game changing,” Kwong said. “Imagine being able to identify which patients are responding to the therapy early in their treatment. That would save time and improve patient outcomes.”

The first step toward this simpler, precise form of cancer diagnostics began with an ambitious but humble ($50,000) seed grant from the Petit Institute for Bioengineering and Bioscience five years ago for a collaboration between Kwong’s lab and the lab of M.G. Finn, professor and chair in the School of Chemistry and Biochemistry.

It evolved into a multi-institutional project supported by grants from the National Science Foundation and National Institutes of Health that included researchers from the University of California-Riverside, as well as Georgia Tech faculty researchers Finn and Peng Qiu, associate professor in the Coulter Department.

“The progression of the research, from an initial seed grant all the way to animal studies, was very smooth,” Kwong said. “Ultimately, a collaborative, multidisciplinary effort turned our early vision into something that could have a great impact in healthcare.”

 

Citation: Anirudh Sivakumar, Hathaichanok Phuengkham, Hitha Rajesh, Quoc D. Mac, Leonard C. Rogers, Aaron D. Silva Trenkle, Swapnil Subhash Bawage, Robert Hincapie, Zhonghan Li, Sofia Vainikos, Inho Lee, Min Xue, Peng Qiu, M. G. Finn, Gabriel A. Kwong. “AND-gated protease-activated nanosensors for programmable detection of anti-tumour immunity.” Nature Nanotechnology (January 2025).  https://doi.org/10.1038/s41565-024-01834-8

Funding: This research was supported in part by National Institutes of Health (NIH) grants 5U01CA265711, 5R01CA237210, 1DP2HD091793, and 5DP1CA280832.