• Adding satellite monitoring to bridge inspections reduces the number of structures labeled high risk by about one third.
• Among the bridges that still rank as high risk, roughly half could benefit from ongoing observations from space.
• The biggest gains could occur in regions such as Africa and Oceania, where bridge monitoring is currently limited or almost nonexistent.

A researcher from the University of Houston is helping identify vulnerable bridges across the planet and offering a new way to address potential failures before they become catastrophic.

In a global analysis of 744 bridges published in Nature Communications, Pietro Milillo and collaborators from several international institutions evaluated the condition of bridges around the world. Their results showed that bridges in North America are generally in the poorest condition, followed by those in Africa. The team also proposed a strategy that could transform how infrastructure is monitored worldwide by using satellites to track bridge stability and detect warning signs early.

Aging Infrastructure and a Growing Risk

Many of the bridges identified in the study are approaching the limits of their intended lifespan. Construction of bridges in North America surged during the 1960s, meaning many of these structures are now decades old and nearing or surpassing their original design life.

To address this challenge, researchers are turning to space based monitoring systems that rely on Synthetic Aperture Radar. This technology captures high resolution images frequently and covers large areas of the planet, while also providing access to extensive historical data.

“Our research shows that spaceborne radar monitoring could provide regular oversight for more than 60 percent of the world’s long-span bridges,” said Milillo, co author of the study and an associate professor of civil and environmental engineering at UH. “By integrating satellite data into risk frameworks, we can significantly lower the number of bridges classified as high-risk, especially in regions where installing traditional sensors is too costly.”

Detecting Tiny Movements From Space

The international research team included Dominika Malinowska from Delft University of Technology (TU Delft) and the University of Bath, Cormac Reale and Chris Blenkinsopp from the University of Bath, and Giorgia Giardina from TU Delft. They relied on a remote sensing method known as Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR).

This technique can complement traditional inspections by identifying extremely small shifts in structures. The system can measure movements as small as a few millimeters caused by slow geological processes such as landslides or ground subsidence. It can also reveal unusual patterns across wide areas that might signal emerging structural issues.

Bridges represent some of the most fragile components of transportation systems, yet the current approaches used to monitor them have clear limitations. Visual inspections carried out in person can be costly and sometimes subjective. They are also typically performed only twice a year, which means early warning signs of deterioration may go unnoticed between inspections.

Structural Health Monitoring (SHM) sensors provide a more continuous way to track structural performance. However, these systems are usually installed only on newer bridges or structures already known to have issues. According to the study, fewer than 20% of the world’s long span bridges are equipped with these sensors, leaving many structures without consistent monitoring.

A Satellite Based Monitoring Solution

“Remote sensing offers a complement to SHM sensors, can reduce maintenance costs, and can support visual inspections, particularly when direct access to a structure is challenging,” said Millilo. “For bridges specifically, MT-InSAR allows for more frequent deformation measurements across the entire infrastructure network, unlike traditional inspections, which typically occur only a few times per year and require personnel on the ground.”

Said Malinowska. “While using MT-InSAR to monitor bridges is well-established in academic circles, it has yet to be routinely adopted by the authorities and engineers responsible for them. Our work provides the global-scale evidence showing this is a viable and effective tool that can be deployed now.”

The researchers found that adding MT-InSAR data to bridge risk evaluations can improve accuracy. The technique analyzes satellite pixels known as persistent scatterers (PS), which have stable radar reflections. Using these signals reduces uncertainty and allows engineers to better prioritize which bridges require maintenance or closer inspection.

The approach proposed by the research team combines monitoring information from SHM sensors with satellite observations from systems such as the European Space Agency’s Sentinel-1 and the recently launched NASA NISAR mission. Integrating these data sources into a bridge’s structural vulnerability score allows engineers to receive more frequent updates than traditional inspection schedules provide.

With more consistent monitoring, authorities can gain a clearer picture of a bridge’s condition and make better decisions about maintenance and risk management.

Another major difficulty is detecting and predicting when ions move through crystals in a liquid-like manner. Standard computational techniques that attempt to calculate the properties of such dynamically disordered systems demand extremely high computing power, making large-scale studies impractical.

Machine Learning Predicts Raman Signals of Liquid-Like Ion Motion

To address these challenges, researchers developed a machine learning (ML) accelerated workflow that combines ML force fields with tensorial ML models to simulate Raman spectra. Their findings show that strong low-frequency Raman intensity can act as a clear spectroscopic indicator of liquid-like ionic conduction.

When ions move through a crystal lattice in a fluid-like way, their motion temporarily disturbs the lattice symmetry. This disturbance relaxes the usual Raman selection rules and produces distinctive low-frequency Raman scattering. These spectral signals can be directly connected to high ionic mobility.

The new approach allows scientists to simulate the vibrational spectra of complex and disordered materials at realistic temperatures with near-ab initio accuracy while significantly reducing computational cost. When applied to sodium-ion conducting materials such as Na₃SbS₄, the method revealed pronounced low-frequency Raman features. These signals arise from symmetry breaking caused by rapid ion transport and provide a reliable indicator of fast ionic conduction. The results also help explain earlier experimental observations and open the door to high-throughput screening for new superionic materials.

Raman Features Reveal Superionic Conductors

The researchers further tested the method using sodium-ion conducting systems. The workflow successfully identified Raman signatures linked to liquid-like ion motion. Materials that displayed strong low-frequency Raman features also showed high ionic diffusivity and dynamic relaxation of the host lattice.

By contrast, materials where ion transport occurs mainly through hopping between fixed positions did not produce these Raman signatures. This distinction highlights how Raman signals can reveal the underlying transport mechanism inside a material.

Accelerating Discovery of Advanced Battery Materials

By extending the breakdown of Raman selection rules beyond traditional superionic systems, the study provides a broader framework for interpreting diffusive Raman scattering across many classes of materials. The ML-accelerated Raman pipeline connects atomistic simulations with experimental measurements, allowing scientists to evaluate candidate materials more efficiently.

This strategy introduces a powerful new route for data-driven discovery in energy storage research. By helping researchers quickly identify fast-ion conductors, the method could accelerate the development of high-performance solid-state battery technologies.

The findings were recently published in the online edition of AI for Science, an international journal focused on interdisciplinary artificial intelligence research.

Researchers previously believed this condition developed mainly from a mix of genetic susceptibility and lifestyle or environmental influences. However, findings published in Hepatology show that in certain cases, a single inherited mutation can play a central role in triggering the disease.

The team traced the variant to the MET gene, which plays an important role in liver repair and how the body processes fat. When this gene does not function properly, fat begins to build up inside liver cells. This buildup can lead to inflammation. Over time, inflammation can progress to fibrosis and scarring that stiffens the liver. In more advanced stages, the condition can develop into cirrhosis, which may cause permanent liver damage or even liver cancer.

Metabolic dysfunction-associated steatotic liver disease affects roughly one-third of adults worldwide. Its more severe form, metabolic dysfunction-associated steatohepatitis, is expected to become the leading cause of cirrhosis and the primary reason for liver transplants in the near future.

“This discovery opens a window into how rare inherited genetic variants can drive common diseases,” says lead author Filippo Pinto e Vairo, M.D, Ph.D., medical director of the Program for Rare and Undiagnosed Diseases at Mayo Clinic’s Center for Individualized Medicine. “It provides new insights into this disease pathogenesis and potential therapeutic targets for future research.”

Family Case Reveals the Genetic Clue

The discovery began with genomic analysis of a woman and her father who both had metabolic dysfunction-associated steatohepatitis. Interestingly, neither of them had diabetes or high cholesterol, two of the most common risk factors associated with fat accumulation in the liver.

Because the usual explanations did not apply, researchers performed an extensive genetic analysis, examining DNA across more than 20,000 genes. During this search, they identified a small but potentially meaningful alteration in the MET gene.

Working together with scientists from the Medical College of Wisconsin’s John & Linda Mellowes Center for Genomic Sciences and Precision Medicine, led by Raul Urrutia, M.D., the research team confirmed that this mutation interfered with a critical biological process.

Genes are made up of chemical letters that carry instructions for how the body functions. In this case, a single swapped letter within the DNA sequence disrupted the message, preventing the liver from properly processing fat. This rare genetic variant found in the family has not previously been documented in scientific literature or public genetic databases.

“This study demonstrates that rare diseases are not rare but often hidden in the large pool of complex disorders, underscoring the immense power of individualized medicine in identifying them, and enabling the design of advanced diagnostics and targeted therapies,” Dr. Urrutia says.

Large Genomic Study Finds Similar Variants

To understand whether this mutation might appear in other patients, researchers analyzed data from Mayo Clinic’s Tapestry study. This large exome sequencing initiative aims to identify genetic factors that influence disease.

The Tapestry project has examined germline DNA from more than 100,000 participants across the United States, creating an extensive genomic database that supports research into both established and emerging health conditions.

Among nearly 4,000 adults in the Tapestry study who had metabolic dysfunction-associated steatotic liver disease, about 1% carried rare variants in the same MET gene that may contribute to the condition. Nearly 18% of these variants occurred in the same key region identified in the original family, strengthening the evidence that this gene plays a role in liver disease.

“This finding could potentially affect hundreds of thousands, if not millions, of people worldwide with or at risk for metabolic dysfunction-associated steatotic liver disease,” says Konstantinos Lazaridis, M.D., a lead author and the Carlson and Nelson Endowed Executive Director for the Center for Individualized Medicine.

Dr. Lazaridis also emphasized the importance of the Tapestry study in revealing hidden genetic factors behind disease.

“Once a pathogenic variant is discovered, interrogating our Tapestry data repository is giving us a clearer lens into the hidden layers of disease, and this discovery is one of the first to demonstrate its scientific significance,” Dr. Lazaridis says. “This finding highlights the profound value of studying familial diseases and the merit of large-scale genomic datasets, which can reveal rare genetic variations with broader implications for population health.”

Precision Genomics Helps Solve Medical Mysteries

The findings also highlight the growing role of genomic medicine in clinical care at Mayo Clinic. Researchers and clinicians are increasingly using advanced genetic technologies to help uncover the causes of complex diseases.

Since it launched in 2019, the Program for Rare and Undiagnosed Diseases has provided more than 3,200 patients with access to comprehensive genomic testing. The program works with nearly 300 clinicians across 14 divisions at Mayo Clinic to deliver precision diagnostics for patients with difficult-to-diagnose conditions, including rare liver diseases.

Researchers say future studies will examine how this discovery involving metabolic dysfunction-associated steatotic liver disease could guide the development of targeted treatments and improve how the disease is diagnosed and managed.

But the history of plague goes back even further. An earlier form of Y. pestis appeared about 5,000 years ago during the Bronze Age. This ancient strain infected people across Eurasia for nearly two millennia before disappearing. Unlike the medieval plague, however, this earlier version could not be transmitted by fleas. For years, scientists have struggled to understand how the disease managed to spread across such a vast region without that transmission pathway.

Ancient Sheep Provides a Critical Clue

Researchers have now uncovered an important piece of the puzzle. An international team that includes University of Arkansas archaeologist Taylor Hermes identified the first evidence of Bronze Age plague in a nonhuman host. The scientists detected Y. pestis DNA in the remains of a domesticated sheep that lived about 4,000 years ago.

The animal came from Arkaim, a fortified settlement in the Southern Ural Mountains of present day Russia near the border with Kazakhstan. The finding suggests that livestock may have played a role in the spread of plague during the Bronze Age, helping explain how the disease traveled so widely across Eurasia.

The research was published in Cell under the title “Bronze Age Yersinia pestis genome from sheep sheds light on hosts and evolution of a prehistoric plague lineage.” The international collaboration includes researchers from Harvard University and leading institutions in Germany, Russia and South Korea.

Searching Ancient DNA for Clues

Hermes co leads a major research project that studies ancient livestock DNA. By examining genetic material preserved in bones and teeth, his team is tracing how domesticated animals such as cattle, goats and sheep spread from the Fertile Crescent across Eurasia. These movements helped shape the rise of nomadic cultures and early empires.

“When we test livestock DNA in ancient samples, we get a complex genetic soup of contamination,” Hermes said. “This is a large barrier to getting a strong signal for the animal, but it also gives us an opportunity to look for pathogens that infected herds and their handlers.”

Working with ancient DNA is challenging and time consuming. Scientists must separate the DNA of the animal from many other sources found in the sample. Microorganisms living in the soil where bones were buried leave behind their own genetic traces. Researchers can also accidentally introduce DNA from their own skin cells or saliva.

The fragments recovered from ancient remains are extremely small. Many pieces measure only about 50 base pairs. For comparison, the full human genome contains more than 3 billion base pairs.

Animal remains also tend to be less well preserved than human remains, which are usually carefully buried. Animals were often cooked and eaten, and their bones discarded in waste piles where exposure to heat and weather gradually breaks down genetic material.

The Moment of Discovery

While studying livestock remains excavated from Arkaim in the 1980s and 1990s, Hermes and his colleagues noticed something unexpected. One sheep bone contained DNA belonging to Yersinia pestis.

“It was alarm bells for my team. This was the first time we had recovered the genome from Yersinia pestis in a non-human sample,” Hermes said. “We were extra excited because Arkaim is linked to the Sintashta culture, which is known for early horse riding, impressive bronze weaponry and substantial geneflow into Central Asia.”

How Did Bronze Age Plague Spread?

Researchers have previously found identical Bronze Age plague strains in human remains located thousands of kilometers apart. The question has been how the disease managed to travel such long distances.

“It had to be more than people moving. Our plague sheep gave us a breakthrough. We now see it as a dynamic between people, livestock and some still unidentified ‘natural reservoir’ for it, which could be rodents on the grasslands of the Eurasian steppe or migratory birds,” Hermes said.

A natural reservoir is an animal species that carries a pathogen without becoming sick. In the Middle Ages, rats served as the reservoir for Y. pestis, while fleas acted as the vector that spread the bacterium. Today, bats often fill this role for viruses such as Ebola and the Marburg virus.

Lessons From an Ancient Epidemic

Hermes recently received a five year grant from Germany’s Max Planck Society worth 100,000 Euros to continue excavations in the Southern Urals near Arkaim. His team will search for additional human and animal remains that may contain traces of Y. pestis.

The Bronze Age was a period when the Sintashta culture began managing larger livestock herds while also becoming skilled horse riders. Increased interaction with animals and expanding travel across the steppe may have exposed people to disease reservoirs in the environment.

Although these events happened thousands of years ago, Hermes believes the findings carry an important message today. Expanding economic activities into natural environments can disrupt ecosystems and increase the risk of disease spillover.

“We should appreciate the delicate inner workings of the ecosystems we might disturb and aim to preserve the balance,” Hermes said.

“It’s important to have a greater respect for the forces of nature,” he said.