The Sciences
Researchers develop AI algorithm to accurately detect heart murmurs in dogs
Researchers have developed AI Algorithm to detect heart murmurs in dogs, improving early diagnosis of cardiac disease
Researchers at the University of Cambridge have developed a machine learning algorithm capable of accurately detecting heart murmurs in dogs—a critical indicator of cardiac disease, particularly prevalent in smaller breeds like the King Charles Spaniel. This innovative approach has the potential to transform veterinary care, offering an accessible tool for early diagnosis and treatment of heart conditions.
Heart murmurs are a key sign of mitral valve disease, the most common heart issue affecting adult dogs. Statistically, approximately one in every 30 dogs seen by a veterinarian presents with a heart murmur, with higher rates observed in small breeds and older dogs. Given the frequency of such conditions, timely detection is essential. Early intervention can significantly enhance a dog’s quality of life and longevity, making effective screening methods vital for veterinarians.
Dr. Andrew McDonald, the study’s first author from the Department of Engineering at Cambridge, emphasized the importance of early detection, according to a statement issued by the University: “Heart disease in humans is a huge health issue, but in dogs it’s an even bigger problem. Most smaller dog breeds will have heart disease when they get older, but obviously dogs can’t communicate in the same way that humans can, so it’s up to primary care vets to detect heart disease early enough so it can be treated.”
The Algorithm’s Development
The research team began with an algorithm initially designed for human heart sound analysis. Recognizing the similarities between mammalian heart function, they adapted this technology to analyze audio recordings from digital stethoscopes used on dogs. The algorithm demonstrated an impressive sensitivity of 90% in detecting heart murmurs, a level of accuracy comparable to that of expert cardiologists.
Professor Anurag Agarwal, the lead researcher and an expert in acoustics and bioengineering, noted the absence of a dedicated database for canine heart sounds. “As far as we’re aware, there are no existing databases of heart sounds in dogs, which is why we started out with a database of heart sounds in humans,” he explained in a statement issued by the University of Cambridge. “Mammalian hearts are fairly similar, and when things go wrong, they tend to go wrong in similar ways.”
The team refined the algorithm to not only detect but also grade heart murmurs
To build a robust dataset, the researchers collected heart sound data from nearly 800 dogs undergoing routine examinations at four veterinary specialist centers across the UK. Each dog received a thorough physical examination and an echocardiogram performed by a cardiologist, who graded any detected murmurs and identified underlying cardiac issues. This effort resulted in the largest dataset of dog heart sounds ever compiled.
Expanding the Dataset for Better Outcomes
Co-author Professor Jose Novo Matos, a small animal cardiology specialist, highlighted the need for diverse data to improve the algorithm’s effectiveness: “Mitral valve disease mainly affects smaller dogs, but to test and improve our algorithm, we wanted to get data from dogs of all shapes, sizes, and ages. The more data we have to train it, the more useful our algorithm will be, both for vets and for dog owners.”
The team refined the algorithm to not only detect but also grade heart murmurs, distinguishing between mild and advanced disease requiring further intervention. This innovation aims to empower general veterinarians, reducing the need for expensive specialized scans and consultations with cardiologists.
Promising Results and Future Implications
The algorithm’s performance was encouraging: it aligned with cardiologists’ assessments in over half of the cases, and in 90% of instances, it was within one grading unit of the cardiologist’s evaluation. Dr. McDonald pointed out the practical implications of these findings: “The grade of heart murmur is a useful differentiator for determining next steps and treatments, and we’ve automated that process.”
Novo Matos remarked on the transformative potential of this technology, seeing it as a supportive tool rather than a job threat. “So many people talk about AI as a threat to jobs, but for me, I see it as a tool that will make me a better cardiologist,” he said. With the veterinary profession facing time constraints and a shortage of specialists, this algorithm could streamline the process of identifying dogs that need urgent care.
A Path Forward for Veterinary Medicine
The researchers’ ultimate goal is to equip veterinarians with the means to make informed decisions regarding treatment, enhancing the quality of life for their canine patients. “Knowing when to medicate is so important, in order to give dogs the best quality of life possible for as long as possible,” said Agarwal.
Supported by organisations such as the Kennel Club Charitable Trust and the Medical Research Council, this research marks a significant step forward in the use of machine learning for veterinary applications. As technology continues to evolve, it holds the promise of not only advancing animal health but also improving the human-animal bond through better care and understanding.
Sustainable Energy
Can ammonia power a low-carbon future? New MIT study maps global costs and emissions
Under what conditions can ammonia truly become a low-carbon energy solution? MIT researchers attempt to resolve this
Ammonia, long known as the backbone of global fertiliser production, is increasingly being examined as a potential pillar of the clean energy transition. Energy-dense, carbon-free at the point of use, and already traded globally at scale, ammonia is emerging as a candidate fuel and a carrier of hydrogen. But its climate promise comes with a contradiction: today’s dominant method of producing ammonia carries a heavy carbon footprint.
A new study by researchers from the MIT Energy Initiative (MITEI) attempts to resolve this tension by answering a foundational question for policymakers and industry alike: under what conditions can ammonia truly become a low-carbon energy solution?
A global view of ammonia’s future
In a paper published in Energy and Environmental Science, the researchers present the largest harmonised dataset to date on the economic and environmental impacts of global ammonia supply chains. The analysis spans 63 countries and evaluates multiple production pathways, trade routes, and energy inputs, offering a comprehensive view of how ammonia could be produced, shipped, and used in a decarbonising world.
“This is the most comprehensive work on the global ammonia landscape,” says senior author Guiyan Zang, a research scientist at MITEI. “We developed many of these frameworks at MIT to be able to make better cost-benefit analyses. Hydrogen and ammonia are the only two types of fuel with no carbon at scale. If we want to use fuel to generate power and heat, but not release carbon, hydrogen and ammonia are the only options, and ammonia is easier to transport and lower-cost.”
Why data matters
Until now, assessments of ammonia’s climate potential have been fragmented. Individual studies often focused on single regions, isolated technologies, or only cost or emissions, making global comparisons difficult.
“Before this, there were no harmonized datasets quantifying the impacts of this transition,” says lead author Woojae Shin, a postdoctoral researcher at MITEI. “Everyone is talking about ammonia as a super important hydrogen carrier in the future, and also ammonia can be directly used in power generation or fertilizer and other industrial uses. But we needed this dataset. It’s filling a major knowledge gap.”
To build the database, the team synthesised results from dozens of prior studies and applied common frameworks to calculate full lifecycle emissions and costs. These calculations included feedstock extraction, production, storage, shipping, and import processing, alongside country-specific factors such as electricity prices, natural gas costs, financing conditions, and energy mix.
Comparing production pathways
Today, most ammonia is produced using the Haber–Bosch process powered by fossil fuels, commonly referred to as “grey ammonia.” In 2020, this process accounted for about 1.8 percent of global greenhouse gas emissions. While economically attractive, it is also the most carbon-intensive option.
The study finds that conventional grey ammonia produced via steam methane reforming (SMR) remains the cheapest option in the U.S. context, at around 48 cents per kilogram. However, it also carries the highest emissions, at 2.46 kilograms of CO₂ equivalent per kilogram of ammonia.
Cleaner alternatives offer substantial emissions reductions at higher cost. Pairing SMR with carbon capture and storage cuts emissions by about 61 percent, with a 29 percent cost increase. A full global shift to ammonia produced with conventional methods plus carbon capture could reduce global greenhouse gas emissions by nearly 71 percent, while raising costs by 23.2 percent.
More advanced “blue ammonia” pathways, such as auto-thermal reforming (ATR) with carbon capture, deliver deeper emissions cuts at relatively modest cost increases. One ATR configuration achieved emissions of 0.75 kilograms of CO₂ equivalent per kilogram of ammonia, at roughly 10 percent higher cost than conventional SMR.
At the far end of the spectrum, “green ammonia” produced using renewable electricity can reduce emissions by as much as 99.7 percent, but at a significantly higher cost—around 46 percent more than today’s baseline. Ammonia produced using nuclear electricity showed near-zero emissions in the analysis.
Geography matters
The study also reveals that the viability of low-carbon ammonia depends heavily on geography. Countries with abundant, low-cost natural gas are better positioned to produce blue ammonia competitively, while regions with cheap renewable electricity are more favourable for green ammonia.
China emerged as a potential future supplier of green ammonia to multiple regions, while parts of the Middle East showed strong competitiveness in low-carbon ammonia production. In contrast, ammonia produced using carbon-intensive grid electricity was often both more expensive and more polluting than conventional methods.
From research to policy
Interest in low-carbon ammonia is no longer theoretical. Countries such as Japan and South Korea have incorporated ammonia into national energy strategies, including pilot projects using ammonia for power generation and financial incentives tied to verified emissions reductions.
“Ammonia researchers, producers, as well as government officials require this data to understand the impact of different technologies and global supply corridors,” Shin says.
Zang adds that the dataset is designed not just as an academic exercise, but as a decision-making tool. “We collaborate with companies, and they need to know the full costs and lifecycle emissions associated with different options. Governments can also use this to compare options and set future policies. Any country producing ammonia needs to know which countries they can deliver to economically.”
As global demand for low-carbon fuels accelerates toward mid-century, the study suggests that ammonia’s role will depend less on ambition alone, and more on informed choices—grounded in data—about how and where it is produced.
The Sciences
Why Octopuses Have Three Hearts, And Why Their Bodies Are Stranger Than Fiction
And the reason is surprisingly practical
Octopuses are already odd enough — eight arms, no bones, a brain that wraps around their throat — but one detail still stops people in their tracks: they have three hearts. Not two. Not one. Three.
And the reason is surprisingly practical.
Three Hearts for a Tough Life Underwater
Two of the hearts — called branchial hearts — do a very specific job: each one pushes blood through a gill, where it can pick up oxygen. The third, the systemic heart, takes that oxygen-rich blood and pumps it to the rest of the body.
In other words: two hearts to breathe, one heart to live.
Why Their Blood Is Blue
Another strange thing: their blood isn’t red at all.
It’s blue — literally blue — because it’s based on copper, not iron.
The copper-based protein, hemocyanin, works better in the cold, low-oxygen parts of the ocean where many octopuses live. It keeps them alive in places where most animals wouldn’t last a minute. But it’s not very efficient, so their bodies need extra pumping power to keep the oxygen flowing.
Evolution’s answer? Give them more hearts.
A Heart That Stops When They Swim
Here’s the part that sounds almost fictional: when an octopus swims, its main heart actually stops.
Imagine going for a swim and your heart taking a break halfway through. That’s why octopuses prefer to crawl on the seafloor. Swimming is simply too tiring — it literally costs them heartbeats.
The Ocean’s Quiet Genius
When you combine all of this — the blue blood, the three hearts, the bizarre nervous system, the ability to vanish into their surroundings — you get one of the most unusual and surprisingly intelligent creatures on the planet.
Octopuses don’t just survive in harsh oceans; they’ve evolved in ways that feel almost alien. And maybe that’s why we’re endlessly fascinated by them — they remind us how strange and creative life can be.
The Sciences
Most Earthquake Energy Is Spent Heating Up Rocks, Not Shaking the Ground: New MIT Study Finds
How do earthquakes spend their energy? MIT’s latest research shows heat—not ground motion—is the main outcome of a quake, reshaping how scientists understand seismic risks
When an earthquake strikes, we experience its violent shaking on the surface. But new research from MIT shows that most of a quake’s energy actually goes into something entirely different — heat.
Using miniature “lab quakes” designed to mimic real seismic slips deep underground, geologists at MIT have, for the first time, mapped the full energy budget of an earthquake. Their study reveals that only about 10 percent of a quake’s energy translates into ground shaking, while less than 1 percent goes into fracturing rock. The vast majority — nearly 80 percent — is released as heat at the fault, sometimes creating sudden spikes hot enough to melt surrounding rock.
“These results show that what happens deep underground is far more dynamic than what we feel on the surface,” said Daniel Ortega-Arroyo, a graduate researcher in MIT’s Department of Earth, Atmospheric and Planetary Sciences, in a media statement. “A rock’s deformation history — essentially its memory of past seismic shifts — dictates how much energy ends up in shaking, breaking, or heating. That history plays a big role in determining how destructive a quake can be.”
The team’s findings, published in AGU Advances, suggest that understanding a fault zone’s “thermal footprint” might be just as important as recording surface tremors. Laboratory-created earthquakes, though simplified models of natural ones, provide a rare window into processes that are otherwise impossible to observe deep within Earth’s crust.
MIT researchers created the “microshakes” by applying immense pressures to samples of granite mixed with magnetic particles that acted as ultra-sensitive heat gauges. By stacking the results of countless tiny quakes, they tracked exactly how the energy distributed among shaking, fracturing, and heating. Some events saw fault zones heat up to over 1,200 degrees Celsius in mere microseconds, momentarily liquefying parts of the rock before cooling again.
“We could never reproduce the full complexity of Earth, so we simplify,” explained co-author Matěj Peč, MIT associate professor of geophysics. “By isolating the physics in the lab, we can begin to understand the mechanisms that govern real earthquakes — and apply this knowledge to better models and risk assessments.”
The work also provides a fresh perspective on why some regions remain vulnerable long after previous seismic activity. Past quakes, by altering the structure and material properties of rocks, may influence how future ones unfold. If researchers can estimate how much heat was generated in past quakes, they might be able to assess how much stress still lingers underground — a factor that could refine earthquake forecasting.
The study was conducted by Ortega-Arroyo and Peč, along with colleagues from MIT, Harvard University, and Utrecht University.
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