The Microsoft co-founder said “there’s a lot of commitment to this cause in the United States.”

Two research groups said they will produce peer-reviewed documents assessing the current and future national impacts of climate change.

A global race to recruit U.S. scientists is heating up as President Donald Trump’s sweeping cuts to research funding and agencies trigger an exodus.

Experts say this is the nation's first state lodging tax that raises money for the environment and coping with climate change.

Nature Climate Change, Published online: 06 May 2025; doi:10.1038/s41558-025-02353-7

Author Correction: Heterogeneous pressure on croplands from land-based strategies to meet the 1.5 °C target

As the saying goes, time is money. That’s certainly evident in the transportation sector, where people will pay more for direct flights, express trains, and other ways to get somewhere quickly.

Still, it is difficult to measure precisely how much people value their time. Now, a paper co-authored by an MIT economist uses ride-sharing data to reveal multiple implications of personalized pricing.

By focusing on a European ride-sharing platform that auctions its rides, the researchers found that people are more responsive to prices than to wait times. They also found that people pay more to save time during the workday, and that when people pay more to avoid waiting, it notably increases business revenues. And some segments of consumers are distinctly more willing than others to pay higher prices.

Specifically, when people can bid for rides that arrive sooner, the amount above the minimum price the platform can charge increases by 5.2 percent. Meanwhile, the gap between offered prices and the maximum that consumers are willing to pay decreases by 2.5 percent. In economics terms, this creates additional “surplus” value for firms, while lowering the “consumer surplus” in these transactions.

“One of the important quantities in transportation is the value of time,” says MIT economist Tobias Salz, co-author of a new paper detailing the study’s findings. “We came across a setting that offered a very clean way of examining this quantity, where the value of time is revealed by people’s transportation choices.”

The paper, “Personalized Pricing and the Value of Time: Evidence from Auctioned Cab Rides,” is being published in Econometrica. The authors are Nicholas Buchholz, an assistant professor of economics at Princeton University; Laura Doval, a professor at Columbia Business School; Jakub Kastl, a professor of economics at Princeton University; Filip Matejka, a professor at Charles University in Prague; and Salz, the Castle Krob Career Development Associate Professor of Economics in MIT’s Department of Economics.

It is not easy to study how much money people will spend to save time — and time alone. Transportation is one sector where it is possible to do so, though not the only one. People will also pay more for, say, an express pass to avoid long lines at an amusement park. But data for those scenarios, even when available, may contain complicating factors. (Also, the value of time shouldn’t be confused with how much people pay for services charged by the hour, from accountants to tuba lessons.)

In this case, however, the researchers were provided data from Liftago, a ride-sharing platform in Prague with a distinctive feature: It lets drivers bid on a passenger’s business, with the wait time until the auto arrives as one of the factors involved. Drivers can also indicate when they will be available. In studying how passengers compare offers with different wait times and prices, the researchers see exactly how much people are paying not to wait, other things being equal. All told, they examined 1.9 million ride requests and 5.2 million bids.

“It’s like an eBay for taxis,” Salz says. “Instead of assigning the driver to you, drivers bid for the passengers’ business. With this, we can very directly observe how people make their choices. How they value time is revealed by the wait and the prices attached to that. In many settings we don’t observe that directly, so it’s a very clean comparison that rids the data of a lot of confounds.”

The data set allows the researchers to examine many aspects of personalized pricing and the way it affects the transportation market in this setting. That produces a set of insights on its own, along with the findings on time valuation. 

Ultimately, the researchers found that the elasticity of prices — how much they change — ranged from four to 10 times as much as the elasticity of wait times, meaning people are more keen on avoiding high prices.

The team found the overall value of time in this context is $13.21 per hour for users of the ride-share platform, though the researchers note that is not a universal measure of the value of time and is dependent on this setting. The study also shows that bids increase during work hours.

Additionally, the research reveals a split among consumers: The top quartile of bids placed a value on time that is 3.5 times higher than the value of the bids in the bottom quartile.

Then there is still the question of how much personalized pricing benefits consumers, providers, or both. The numbers, again, show that the overall surplus increases — meaning business benefits — while the consumer surplus is reduced. However, the data show an even more nuanced picture. Because the top quartile of bidders are paying substantially more to avoid longer waits, they are the ones who absorb the brunt of the costs in this kind of system.

“The majority of consumers still benefit,” Salz says. “The consumers hurt by this have a very high willingness to pay. The source of welfare gains is that most consumers can be brought into the market. But the flip side is that the firm, by knowing every consumer’s choke point, can extract the surplus. Welfare goes up, the ride-sharing platform captures most of that, and drivers — interestingly — also benefit from the system, although they do not have access to the data.”

Economic theory and other transportation studies alone would not necessarily have predicted the study’s results and various nuances.

“It was not clear a priori whether consumers benefit,” Salz observes. “That is not something you would know without going to the data.”

While this study might hold particular interest for firms and others interested in transportation, mobility, and ride-sharing, it also fits into a larger body of economics research about information in markets and how its presence, or absence, influences consumer behavior, consumer welfare, and the functioning of markets.

“The [research] umbrella here is really information about where to find trading partners and what their willingness to pay is,” Salz says. “What I’m broadly interested in is these types of information frictions and how they determine market outcomes, how they might impact consumers, and be used by firms.”

The research was supported, in part, by the National Bureau of Economic Research, the U.S. Department of Transportation, and the National Science Foundation. 

Farmers today face a number of challenges, from supply chain stability to nutrient and waste management. But hanging over everything is the need to maintain profitability amid changing markets and increased uncertainty.

Fyto, founded by former MIT staff member Jason Prapas, is offering a highly automated cultivation system to address several of farmers’ biggest problems at once.

At the heart of Fyto’s system is Lemna, a genus of small aquatic plants otherwise known as duckweed. Most people have probably seen thick green mats of Lemna lying on top of ponds and swamps. But Lemna is also rich in protein and capable of doubling in biomass every two days. Fyto has built an automated cropping system that uses nitrogen-rich wastewater from dairy farms to grow Lemna in shallow pools on otherwise less productive farmland. On top of the pools, the company has built what it believes are the largest agricultural robots in the world, which monitor plant health and harvest the Lemna sustainably. The Lemna can then be used on farms as a high-protein cattle feed or fertilizer supplement.

Fyto’s systems are designed to rely on minimal land, water, and labor while creating a more sustainable, profitable food system.

“We developed from scratch a robotic system that takes the guesswork out of farming this crop,” says Prapas, who previously led the translational research program of MIT’s Tata Center. “It looks at the crop on a daily basis, takes inventory to know how many plants there are, how much should be harvested to have healthy growth the next day, can detect if the color is slightly off or there are nutrient deficiencies, and can suggest different interventions based on all that data.”

From kiddie pools to cow farms

Prapas’ first job out of college was with an MIT spinout called Green Fuel that harvested algae to make biofuel. He went back to school for a master’s and then a PhD in mechanical engineering, but he continued working with startups. Following his PhD at Colorado State University, he co-founded Factor[e] Ventures to fund and incubate startups focused on improving energy access in emerging markets.

Through that work, Prapas was introduced to MIT’s Tata Center for Technology and Design.

“We were really interested in the new technologies being developed at the MIT Tata Center, and in funding new startups taking on some of these global climate challenges in emerging markets,” Prapas recalls. “The Tata Center was interested in making sure these technologies get put into practice rather than patented and put on a shelf somewhere. It was a good synergy.”

One of the people Prapas got to know was Rob Stoner, the founding director of the Tata Center, who encouraged Prapas to get more directly involved with commercializing new technologies. In 2017, Prapas joined the Tata Center as the translational research director. During that time, Prapas worked with MIT students, faculty, and staff to test their inventions in the real world. Much of that work involved innovations in agriculture.

“Farming is a fact of life for a lot of folks around the world — both subsistence farming but also producing food for the community and beyond,” Prapas says. “That has huge implications for water usage, electricity consumption, labor. For years, I’d been thinking about how we make farming a more attractive endeavor for people: How do we make it less back-breaking, more efficient, and more economical?”

Between his work at MIT and Factor[e], Prapas visited hundreds of farms around the world, where he started to think about the lack of good choices for farming inputs like animal feed and fertilizers. The problem represented a business opportunity.

Fyto began with kiddie pools. Prapas started growing aquatic plants in his backyard, using them as a fertilizer source for vegetables. The experience taught him how difficult it would be to train people to grow and harvest Lemna at large scales on farms.

“I realized we’d have to invent both the farming method — the agronomy — and the equipment and processes to grow it at scale cost effectively,” Prapas explains.

Prapas started discussing his ideas with others around 2019.

“The MIT and Boston ecosystems are great for pitching somewhat crazy ideas to willing audiences and seeing what sticks,” Prapas says. “There’s an intangible benefit of being at MIT, where you just can’t help but think of bold ideas and try putting them into practice.”

Prapas, who left MIT to lead Fyto in 2019, partnered with Valerie Peng ’17, SM ’19, then a graduate student at MIT who became his first hire.

“Farmers work so hard, and I have so much respect for what they do,” says Peng, who serves as Fyto’s head of engineering. “People talk about the political divide, but there’s a lot of alignment around using less, doing more with what you have, and making our food systems more resilient to drought, supply chain disruptions, and everything else. There’s more in common with everyone than you’d expect.”

A new farming method

Lemna can produce much more protein per acre than soy, another common source of protein on farms, but it requires a lot of nitrogen to grow. Fortunately, many types of farmers, especially large dairy farmers, have abundant nitrogen sources in the waste streams that come from washing out cow manure.

“These waste streams are a big problem: In California it’s believed to be one of the largest source of greenhouse gas emissions in the agriculture sector despite the fact that hundreds of crops are grown in California,” Prapas says.

For the last few years, Fyto has run its systems in pilots on farms, trialing the crop as feed and fertilizer before delivering to its customers. The systems Fyto has deployed so far are about 50 feet wide, but it is actively commissioning its newest version that’s 160 feet wide. Eventually, Fyto plans to sell the systems directly to farmers.

Fyto is currently awaiting California’s approval for use in feed, but Lemna has already been approved in Europe. Fyto has also been granted a fertilizer license on its plant-based fertilizer, with promising early results in trials, and plans to sell new fertilizer products this year.

Although Fyto is focused on dairy farms for its early deployments, it has also grown Lemna using manure from chicken, and Prapas notes that even people like cheese producers have a nitrogen waste problem that Fyto could solve.

“Think of us like a polishing step you could put on the end of any system that has an organic waste stream,” Prapas says. “In that situation, we’re interested in growing our crops on it. We’ve had very few things that the plant can’t grow on. Globally, we see this as a new farming method, and that means it’s got a lot of potential applications.”

What if data could help predict a patient’s prognosis, streamline hospital operations, or optimize human resources in medicine? A book fresh off the shelves, “The Analytics Edge in Healthcare,” shows that this is already happening, and demonstrates how to scale it. 

Authored by Dimitris Bertsimas, MIT’s vice provost for open learning, along with two of Bertsimas’ former students — Agni Orfanoudaki PhD ’21, associate professor of operations management at University of Oxford’s Saïd Business School, and Holly Wiberg PhD ’22, assistant professor of public policy and operations research at Carnegie Mellon University — the book provides a practical introduction to the field of health care analytics. With an emphasis on real-world applications, the first part of the book establishes technical foundations — spanning machine learning and optimization — while the second part of the book presents integrated case studies that cover various clinical specialties and problem types using descriptive, predictive, and prescriptive analytics. 

Part of a broader series, “The Analytics Edge in Healthcare” demonstrates how to leverage data and models to make better decisions within the health care sector, while its predecessor, “The Analytics Edge,” dives into the science of using data to build models, improve decisions, and add value to institutions and individuals. 

Bertsimas, who is also the associate dean of business analytics and the Boeing Leaders for Global Operations Professor of Management at the MIT Sloan School of Management, is the innovator behind 15.071 (The Analytics Edge), a course on MIT Open Learning’s MITx that has attracted hundreds of thousands of online learners and served as the inspiration behind the book series. Bertsimas took a break from research and his work at MIT Open Learning to discuss how the field of analytics is transforming the health care system and share some surprising ways analytics are already being used in hospitals. 

Q: How is the field of analytics changing the way hospitals provide care and manage their operations?

A: As an academic, I’ve always aspired to educate, write publications, and utilize what we do in practice. Therefore, I founded Holistic Hospital Optimization (H20) with the goal of optimizing hospital operations with machine learning to improve patient care. We have developed a variety of tools at MIT and implemented them at hospitals around the world. For example, we manage patients’ length of stay and their deterioration indexes (a computerized tool that predicts a patient’s risk of clinical deterioration); we manage nurse optimization and how hospitals can allocate human resources appropriately; and we optimize blocks for surgeries. This is the beginning of a change where analytics and AI methods are now being utilized quite widely. My hope would be that this work and this book will accelerate the effect of using these tools. 

Additionally, I have taught a nine-lecture course twice with Agni and Holly at the Hartford Hospital System, where I realized that these analytics methods — which are typically not taught in medical schools — can be demonstrated for health care practitioners, including physicians, nurses, and administrators. To have an impact, you need to have appropriate methods, implement them, and apply them, but you also need to educate people on how to use them. This links well with my role at Open Learning, where our objective is to educate learners globally. In fact, Open Learning is launching this fall Universal AI, a dynamic online learning experience that provides comprehensive knowledge on artificial intelligence, preparing a global audience of learners for employment in our rapidly evolving job market. 

Q: What are some surprising ways analytics are being used in health care that most people wouldn’t expect?

A: Using analytics, we have reduced patients’ length of stay at Hartford Hospital from 5.67 days to five days. We have an algorithm that predicts patients’ probability of being released; therefore, doctors prioritize the patients with the highest probability, preparing them for discharge. This means that the hospital can treat far more patients, and the patients stay in the hospital less time.

Furthermore, when hospitals saw an increase in nurse turnover during the Covid-19 pandemic, we developed an analytics system that takes into account equity and fairness and decreases overtime costs, giving preferred slots to nurses and decreasing overall turnover substantially. These are just two examples; there are many others where an analytical perspective to health care and medicine has made a material difference. 

Q: Looking ahead, how do you see artificial intelligence shaping the future of health care?

A: In a very significant way — we use machine learning to make better predictions, but generative AI can explain them. I already see a movement in that direction. It’s really the evolution of AI that made this possible, and it is exciting. It’s also important for the world, because of its capabilities to improve care and save lives. 

For example, through our program at the Hartford Hospital System, we discovered that a patient was getting worse and predicted through analytics that they would get even worse. After our prediction, the doctors examined the patient more closely and discovered the patient had an early case of sepsis, a life-threatening condition in which the body responds improperly to an infection. If we hadn’t detected sepsis earlier, the patient might have died. This made an actual difference in saving a person’s life. 

Q: If you had to describe “The Analytics Edge in Healthcare” in one or two words, what would they be, and why? 

A: The book is a phased transition in health care because it is capable of affecting the health care sector in a way that has not been done before. The book really outlines my work in health care and its applications in the last decade.

If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving. 

One approach to counter this is known as eco-driving, which can be installed as a control system in autonomous vehicles to improve their efficiency.

How much of a difference could that make? Would the impact of such systems in reducing emissions be worth the investment in the technology? Addressing such questions is one of a broad category of optimization problems that have been difficult for researchers to address, and it has been difficult to test the solutions they come up with. These are problems that involve many different agents, such as the many different kinds of vehicles in a city, and different factors that influence their emissions, including speed, weather, road conditions, and traffic light timing.

“We got interested a few years ago in the question: Is there something that automated vehicles could do here in terms of mitigating emissions?” says Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in the Department of Civil and Environmental Engineering and the Institute for Data, Systems, and Society (IDSS) at MIT, and a principal investigator in the Laboratory for Information and Decision Systems. “Is it a drop in the bucket, or is it something to think about?,” she wondered.

To address such a question involving so many components, the first requirement is to gather all available data about the system, from many sources. One is the layout of the network’s topology, Wu says, in this case a map of all the intersections in each city. Then there are U.S. Geological Survey data showing the elevations, to determine the grade of the roads. There are also data on temperature and humidity, data on the mix of vehicle types and ages, and on the mix of fuel types.

Eco-driving involves making small adjustments to minimize unnecessary fuel consumption. For example, as cars approach a traffic light that has turned red, “there’s no point in me driving as fast as possible to the red light,” she says. By just coasting, “I am not burning gas or electricity in the meantime.” If one car, such as an automated vehicle, slows down at the approach to an intersection, then the conventional, non-automated cars behind it will also be forced to slow down, so the impact of such efficient driving can extend far beyond just the car that is doing it.

That’s the basic idea behind eco-driving, Wu says. But to figure out the impact of such measures, “these are challenging optimization problems” involving many different factors and parameters, “so there is a wave of interest right now in how to solve hard control problems using AI.” 

The new benchmark system that Wu and her collaborators developed based on urban eco-driving, which they call “IntersectionZoo,” is intended to help address part of that need. The benchmark was described in detail in a paper presented at the 2025 International Conference on Learning Representation in Singapore.

Looking at approaches that have been used to address such complex problems, Wu says an important category of methods is multi-agent deep reinforcement learning (DRL), but a lack of adequate standard benchmarks to evaluate the results of such methods has hampered progress in the field.

The new benchmark is intended to address an important issue that Wu and her team identified two years ago, which is that with most existing deep reinforcement learning algorithms, when trained for one specific situation (e.g., one particular intersection), the result does not remain relevant when even small modifications are made, such as adding a bike lane or changing the timing of a traffic light, even when they are allowed to train for the modified scenario.

In fact, Wu points out, this problem of non-generalizability “is not unique to traffic,” she says. “It goes back down all the way to canonical tasks that the community uses to evaluate progress in algorithm design.” But because most such canonical tasks do not involve making modifications, “it’s hard to know if your algorithm is making progress on this kind of robustness issue, if we don’t evaluate for that.”

While there are many benchmarks that are currently used to evaluate algorithmic progress in DRL, she says, “this eco-driving problem features a rich set of characteristics that are important in solving real-world problems, especially from the generalizability point of view, and that no other benchmark satisfies.” This is why the 1 million data-driven traffic scenarios in IntersectionZoo uniquely position it to advance the progress in DRL generalizability.  As a result, “this benchmark adds to the richness of ways to evaluate deep RL algorithms and progress.”

And as for the initial question about city traffic, one focus of ongoing work will be applying this newly developed benchmarking tool to address the particular case of how much impact on emissions would come from implementing eco-driving in automated vehicles in a city, depending on what percentage of such vehicles are actually deployed.

But Wu adds that “rather than making something that can deploy eco-driving at a city scale, the main goal of this study is to support the development of general-purpose deep reinforcement learning algorithms, that can be applied to this application, but also to all these other applications — autonomous driving, video games, security problems, robotics problems, warehousing, classical control problems.”

Wu adds that “the project’s goal is to provide this as a tool for researchers, that’s openly available.” IntersectionZoo, and the documentation on how to use it, are freely available at GitHub.

Wu is joined on the paper by lead authors Vindula Jayawardana, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS); Baptiste Freydt, a graduate student from ETH Zurich; and co-authors Ao Qu, a graduate student in transportation; Cameron Hickert, an IDSS graduate student; and Zhongxia Yan PhD ’24. 

Tuberculosis, the world’s deadliest infectious disease, is estimated to infect around 10 million people each year, and kills more than 1 million annually. Once established in the lungs, the bacteria’s thick cell wall helps it to fight off the host immune system.

Much of that cell wall is made from complex sugar molecules known as glycans, but it’s not well-understood how those glycans help to defend the bacteria. One reason for that is that there hasn’t been an easy way to label them inside cells.

MIT chemists have now overcome that obstacle, demonstrating that they can label a glycan called ManLAM using an organic molecule that reacts with specific sulfur-containing sugars. These sugars are found in only three bacterial species, the most notorious and prevalent of which is Mycobacterium tuberculosis, the microbe that causes TB.

After labeling the glycan, the researchers were able to visualize where it is located within the bacterial cell wall, and to study what happens to it throughout the first few days of tuberculosis infection of host immune cells.

The researchers now hope to use this approach to develop a diagnostic that could detect TB-associated glycans, either in culture or in a urine sample, which could offer a cheaper and faster alternative to existing diagnostics. Chest X-rays and molecular diagnostics are very accurate but are not always available in developing nations where TB rates are high. In those countries, TB is often diagnosed by culturing microbes from a sputum sample, but that test has a high false negative rate, and it can be difficult for some patients, especially children, to provide a sputum sample. This test also requires many weeks for the bacteria to grow, delaying diagnosis.

“There aren’t a lot of good diagnostic options, and there are some patient populations, including children, who have a hard time giving samples that can be analyzed. There’s a lot of impetus to develop very simple, fast tests,” says Laura Kiessling, the Novartis Professor of Chemistry at MIT and the senior author of the study.

MIT graduate student Stephanie Smelyansky is the lead author of the paper, which appears this week in the Proceedings of the National Academy of Sciences. Other authors include Chi-Wang Ma, an MIT postdoc; Victoria Marando PhD ’23; Gregory Babunovic, a postdoc at the Harvard T.H. Chan School of Public Health; So Young Lee, an MIT graduate student; and Bryan Bryson, an associate professor of biological engineering at MIT.

Labeling glycans

Glycans are found on the surfaces of most cells, where they perform critical functions such as mediating communication between cells.In bacteria, glycans help the microbes to enter host cells, and they also appear to communicate with the host immune system, in some cases blocking the immune response.

“Mycobacterium tuberculosis has a really elaborate cell envelope compared to other bacteria, and it’s a rich structure that’s composed of a lot of different glycans,” Smelyansky says. “Something that’s often underappreciated is the fact that these glycans can also interact with our host cells. When our immune cells recognize these glycans, instead of sending out a danger signal, it can send the opposite message, that there’s no danger.”

Glycans are notoriously difficult to tag with any kind of probe, because unlike proteins or DNA, they don’t have distinctive sequences or chemical reactivities that can be targeted. And unlike proteins, they are not genetically encoded, so cells can’t be genetically engineered to produce sugars labeled with fluorescent tags such as green fluorescent protein.

One of the key glycans in M. tuberculosis, known as ManLAM, contains a rare sugar known as MTX, which is unusual in that it has a thioether — a sulfur atom sandwiched between two carbon atoms. This chemical group presented an opportunity to use a small-molecule tag that had been previously developed for labeling methionine, an  amino acid that contains a similar group.

The researchers showed that they could use this tag, known as an oxaziridine, to label ManLAM in M. tuberculosis. The researchers linked the oxaziridine to a fluorescent probe and showed that in M. tuberculosis, this tag showed up in the outer layer of the cell wall. When the researchers exposed the label to Mycobacterium smegmatis, a related bacterium that does not cause disease and does not have the sugar MTX, they saw no fluorescent signal.

“This is the first approach that really selectively allows us to visualize one glycan in particular,” Smelyansky says.

Better diagnostics

The researchers also showed that after labeling ManLAM in M. tuberculosis cells, they could track the cells as they infected immune cells called macrophages. Some tuberculosis researchers had hypothesized that the bacterial cells shed ManLAM once inside a host cell, and that those free glycans then interact with the host immune system. However, the MIT team found that the glycan appears to remain in the bacterial cell walls for at least the first few days of infection.

“The bacteria still have their cell walls attached to them. So it may be that some glycan is being released, but the majority of it is retained on the bacterial cell surface, which has never been shown before,” Smelyansky says.

The researchers now plan to use this approach to study what happens to the bacteria following treatment with different antibiotics, or immune stimulation of the macrophages. It could also be used to study in more detail how the bacterial cell wall is assembled, and how ManLAM helps bacteria get into macrophages and other cells.

“Having a handle to follow the bacteria is really valuable, and it will allow you to visualize processes, both in cells and in animal models, that were previously invisible,” Kiessling says.

She also hopes to use this approach to create new diagnostics for tuberculosis. There is currently a diagnostic in development that uses antibodies to detect ManLAM in a urine sample. However, this test only works well in patients with very active cases of TB, especially people who are immunosuppressed because of HIV or other conditions.

Using their small-molecule sensor instead of antibodies, the MIT team hopes to develop a more sensitive test that could detect ManLAM in the urine even when only small quantities are present.

“This is a beautifully elegant approach to selectively label the surface of mycobacteria, enabling real-time monitoring of cell wall dynamics in this important bacterial family. Such investigations will inform the development of novel strategies to diagnose, prevent, and treat mycobacterial disease, most notably tuberculosis, which remains a global health challenge,” says Todd Lowary, a distinguished research fellow at the Institute of Biological Chemistry, Academia Sinica, Taipei Taiwan, who was not involved in the research.

The research was funded by the National Institute of Allergy and Infectious Disease, the National Institutes of Health, the National Science Foundation, and the Croucher Fellowship.

Deepfakes are now mimicking heartbeats

In a nutshell

• Recent research reveals that high-quality deepfakes unintentionally retain the heartbeat patterns from their source videos, undermining traditional detection methods that relied on detecting subtle skin color changes linked to heartbeats.
• The assumption that deepfakes lack physiological signals, such as heart rate, is no longer valid. This challenges many existing detection tools, which may need significant redesigns to keep up with the evolving technology.
• To effectively identify high-quality deepfakes, researchers suggest shifting focus from just detecting heart rate signals to analyzing how blood flow is distributed across different facial regions, providing a more accurate detection strategy...

The agency plans to eliminate offices that track planet-warming emissions and regulate air pollution.

The requirements earned initial approval last week from the International Code Council.

The move came days after the Justice Department sued two states over their legal challenges against the oil and gas industry.

The legendary investor said the U.S. electric grid needs "incredible improvement" during his last shareholder meeting as Berkshire Hathaway chief executive.

Documents stolen by a worldwide hacking ring were cited by Exxon Mobil in court as it battled a series of climate lawsuits.

The administration said in its proposed budget that the Low-Income Home Energy Assistance Program is "unnecessary."

Some 17,000 homes, businesses and other structures burned to the ground in the January fires. It’s uncertain how much will be rebuilt.

HSBC Holdings has walked back some of its emissions goals, a move it said was necessary because of the slow pace of decarbonization in the wider economy.

Companies offering adaptation are “emerging as a complementary and increasingly investable part of the broader climate response,” the report says.