The field of exposomics is revolutionizing health science by unlocking the power of advanced technologies to study the complex interactions between environmental, social, and psychological factors that shape our biology. By analyzing the molecular fingerprints left in our bodies from every breath we take, every meal we eat, and every environment we encounter, researchers are uncovering new insights into disease prevention and personalized medicine.

Led by the Banbury Exposomics Consortium, an interdisciplinary group of scientists gathered at Cold Spring Harbor’s Banbury Center to define the core principles of this rapidly evolving field. Gary Miller, PhD, a foremost expert in exposomics and faculty member at Columbia University Mailman School of Public Health, was the lead organizer of the Consortium.

Exposomics explores how environmental factors such as pollutants in our water and food, social stressors, and psychological factors shape our biology. By studying these combined exposures, researchers can uncover how they collectively influence health, from metabolism and heart function to brain health and disease risk.

The young field is already proving its transformative potential. Researchers analyzing molecular evidence identified a specific industrial solvent as the culprit behind kidney disease clusters among factory workers. In another study, scientists merged satellite pollution mapping with residential location information to reveal how airborne particulates prematurely age the brain.

These discoveries are made possible by cutting-edge technologies and tools such as wearable sensors that track chemical exposures in real-time, satellite imagery that maps pollution down to city blocks, and ultra-sensitive mass spectrometers that detect compounds present at just one part per trillion.

While genetics provides our biological blueprint, it explains only a fraction of chronic disease risk. The exposome captures everything that happens to us, from industrial chemicals to social stressors. Unlike traditional studies examining single exposures in isolation, exposomics integrates advanced tools to understand how environmental, social, and psychological factors collectively interact with our biology.

Systematically analyzing these complex interactions can improve drug development, uncover hidden drivers of disease, and address health disparities. The approach bridges precision medicine and population health.

Miller and colleagues outline critical priorities for advancing exposomics, including the development of more sensitive technologies, creating a human exposome reference to enable analysis and contextualization at the population scale, and implementing standardized protocols to enable AI-driven analysis of complex datasets.

Newly launched U.S. and European exposomics hubs now provide the infrastructure for worldwide collaboration, standardizing methods, harmonizing data, and training researchers in the cross-disciplinary skills needed to advance this field.

“We’re now building the first systematic framework to measure how all exposures — from chemical to social — interact with biology across the lifespan,” says Miller. “Our goal is to create actionable strategies for healthier lives.”

The article discusses new research from the University of Massachusetts Amherst that shows upgrading electrical and broadband infrastructure using a “dig once” approach is nearly 40% more cost-effective than replacing them separately. The study found that co-undergrounding – burying both electric and broadband internet lines together – saves costs, making it feasible for smaller towns in Massachusetts to make undergrounding upgrades.

Using computational modeling across various infrastructure upgrade scenarios, the researchers found that co-undergrounding is 39% more cost-effective than separately burying electrical and broadband wires. They also explored how aggressively towns should pivot to putting lines underground, considering factors such as the cost of converting lines from above ground to underground, the cost of outages, and the hours of outages that can be avoided if lines are underground.

A case study in Shrewsbury, Massachusetts, found that an aggressive co-undergrounding strategy over 40 years would cost $45.4 million but save $55.1 million from avoiding outages, considering factors like spoiled food, damaged home appliances, missed remote work hours, and increased use of backup power sources.

The researchers also took into account additional benefits such as increased property values from the aesthetic improvement of eliminating overhead lines, resulting in a net benefit of $11.3 million. They concluded that aggressively converting just electrical wires to underground was less expensive but had a significantly lower net benefit than co-undergrounding.

Future research directions include quantifying the impacts of co-undergrounding across various geographic locations and scenarios, investigating alternative underground routing options, and other potential outage mitigation strategies.

The study’s findings aim to help decision makers prioritize strategic planning for infrastructure upgrades, considering factors like soil composition, network type, and land use variables. The ultimate goal is to encourage utilities and towns to think strategically about upgrading electrical and broadband infrastructure using a “dig once” approach.

Riding the crest of technological advancements, researchers at Osaka Metropolitan University have developed a groundbreaking machine learning-powered fluid simulation model that significantly reduces computation time without compromising accuracy. This innovative technique opens doors to potential applications in offshore power generation, ship design, and real-time ocean monitoring.

Predicting fluid behavior with precision is crucial for industries relying on wave and tidal energy, as well as for designing maritime structures and vessels. Traditional particle methods are commonly used but require extensive computational resources. The new AI-powered surrogate model uses graph neural networks to simplify and accelerate fluid simulations, making waves in fluid dynamics research.

However, researchers acknowledge that AI is not without its limitations. “AI can deliver exceptional results for specific problems but often struggles when applied to different conditions,” said Takefumi Higaki, an assistant professor at Osaka Metropolitan University’s Graduate School of Engineering and lead author of the study.

The team aimed to create a tool that is consistently fast and accurate. They first compared different training conditions to determine what factors were essential for high-precision fluid calculations. Then, they systematically evaluated how well their model adapted to different simulation speeds and various types of fluid movements.

The results demonstrated strong generalization capabilities across different fluid behaviors. “Our model maintains the same level of accuracy as traditional particle-based simulations, throughout various fluid scenarios, while reducing computation time from approximately 45 minutes to just three minutes,” Higaki said.

This research marks a significant step forward in high-performance fluid simulation, offering a scalable and generalizable solution that balances accuracy with efficiency. Such improvements extend beyond the lab, enabling faster and more precise fluid simulations that can accelerate the design process for ships and offshore energy systems.

The study was published in Applied Ocean Research, highlighting the potential of AI to revolutionize ocean simulations and unlock new possibilities for industries reliant on wave and tidal energy.