It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Friday, May 23, 2025
Study shows how El Niño and La Niña climate swings threaten mangroves worldwide
New international research from Tulane University is the first study to demonstrate global-scale patterns in how El Niño-Southern Oscillation (ENSO) influences mangrove growth and degradation. (Photo by Daniel Friess)
Credit: Photos courtesy Daniel Friess, Tulane University
A new international study led by researchers at Tulane University shows that the El Niño and La Niña climate patterns affect nearly half of the world's mangrove forests, underscoring the vulnerability of these vital coastal ecosystems to climatic shifts. Mangroves are shrubs or trees that grow in dense thickets mainly in coastal saline or brackish water.
The research, published in Nature Geoscience, isbased on nearly two decades of satellite data from 2001 to 2020 and is the first study to demonstrate global-scale patterns in how El Niño-Southern Oscillation (ENSO) influences mangrove growth and degradation.
Previously, impacts had only been documented at individual sites, such as a dramatic die-off in northern Australia in 2015 when more than 40 million mangrove trees perished along a 1,200-mile stretch of coastline.
“We wanted to know whether these events were isolated or part of a broader pattern,” said lead author Zhen Zhang, a postdoctoral scholar at Tulane School of Science and Engineering. “Our findings confirm that ENSO has large-scale, recurring effects on mangrove ecosystems around the world.”
El Niño is a climate pattern of Pacific Ocean temperature and wind shifts that affect global weather. El Niño brings warm waters to the eastern Pacific; La Niña brings cool waters there. These changes disrupt rainfall, storms, and temperatures worldwide—causing floods, droughts, and shifts in hurricane activity.
El Niño is known for triggering coral bleaching, droughts, wildfires, and now, researchers have confirmed it also plays a major role in mangrove health.
The study identified a striking “seesaw” effect: During El Niño events, mangroves in the Western Pacific experience widespread degradation, while those in the Eastern Pacific see increased growth. The opposite occurs during La Niña events, with growth in the west and decline in the east.
Researchers pinpointed sea level changes as the key driver behind these patterns. For example, El Niño often causes sea levels to drop temporarily in the Western Pacific, increasing soil salinity and leading to mangrove dieback.
The research team, including collaborators from Xiamen University and the National University of Singapore, used satellite-derived Leaf Area Index data, which measures plant productivity based on leaf density, alongside oceanic and climate datasets to assess mangrove health over time.
Tulane Earth and Environmental Sciences professor Daniel Friess, a co-author of the study, said mangrove forests provide essential services to hundreds of millions of people worldwide, including storm protection, carbon storage and fisheries support. But their existence depends on a narrow set of environmental conditions, making them particularly sensitive to climate variations like El Niño.
“Mangroves are one of the most valuable ecosystems on the planet, yet they exist in a delicate balance with their environment,” Friess said. “A better understanding of how this unique habitat is influenced by changing environmental conditions will help us conserve and restore them, while supporting the coastal communities that rely on them.”
EDITOR’S NOTE:Research team photos by Daniel Friess are available here.
New study shows the "seesaw" effect of El Niño and La Niña causes floods, droughts and shifts in hurricane activity, stressing mangrove forests worldwide. (Photo by Daniel Friess)
ws the "seesaw" effect of El Niño and La Niña causes floods, droughts and shifts in hurricane activity, stressing mangrove forests worldwide. (Photo by Daniel Friess)
Credit
Mangrove photos courtesy Daniel Friess, Tulane University
Analysis of newly launched children’s ready-to-eat cereals from 2010 to 2023 revealed concerning nutritional shifts: notable increases in fat, sodium, and sugar alongside decreases in protein and fiber. Children’s cereals contain high levels of added sugar, with a single serving exceeding 45% of the American Heart Association’s daily recommended limit for children. These trends suggest a potential prioritization of taste over nutritional quality in product development, contributing to childhood obesity and long-term cardiovascular health risks.
Corresponding Authors: To contact the corresponding authors, email Shuoli Zhao, PhD, (szhao@uky.edu) and Qingxiao Li, PhD, (qli@agcenter.lsu.edu).
Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.
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Researchers have identified a type of chemical compound that, when applied to insecticide-treated bed nets, appears to kill the malaria-causing parasite in mosquitoes.
Published today in the journal Nature, the multi-site collaborative study represents a breakthrough for a disease that continues to claim more than half a million lives worldwide every year. A lab at Oregon Health & Science University played a key role, and National Institute of Allergy and Infectious Diseases, of the National Institutes of Health, supported the research.
Michael Riscoe, Ph.D., professor of molecular microbiology and immunology in the OHSU School of Medicine, designed and synthesized the anti-malarial drugs, termed ELQs, that were then screened in the lab of Flaminia Catteruccia, Ph.D., the study’s senior author and Irene Heinz Given Professor of Immunology and Infectious Diseases at the Harvard T.H. Chan School of Public Health.
ELQ drugs refer to a class of experimental antimalarial drugs known as endochin-like quinolones.
“It was a very clever and novel idea by Dr. Catteruccia and her colleagues to incorporate anti-malarial drugs into bed nets and then to see if the mosquitoes would land on the nets and take up the drug,” Riscoe said. “The idea is the drug kills the parasites that cause malaria instead of the mosquitoes, and our data shows this works.”
Risco said further research is necessary to determine whether the best strategy in the field is to incorporate the antimalarial ELQs together with insecticides in the fibers that are woven into bed nets or simply to use them alone to blunt disease transmission.
Malaria is a potentially lethal infection that is spread from person to person by mosquitoes. After a significant decline in cases and deaths caused by malaria since the turn of the century, progress has stalled in recent years due to increasing insecticide resistance. In 2023 alone, there were 263 million new cases of malaria worldwide and more than half a million deaths.
“Insecticide resistance is now extremely common in the mosquitoes that transmit malaria, which jeopardizes many of our most effective control tools,” said Alexandra Probst, M.Pharm, lead author of the study and a Ph.D. candidate in Catteruccia’s lab at Harvard. “By targeting malaria-causing parasites directly in the mosquito, rather than the mosquito itself, we can circumvent this challenge and continue to reduce the spread of malaria.”
Catteruccia’s lab screened 81 compounds for blocking malaria parasite growth within the mosquito. This work identified two ELQ drugs from Riscoe’s chemical collection as top hits from the initial screen. The ELQs were even effective when added to materials like those used in mosquito nets.
Tests showed infected mosquitoes that landed on the surface of these materials were cured of their infections. The ELQs remained stable, continued to work for a long time and were effective even in mosquitoes that were resistant to traditional insecticides.
“If an infected mosquito hits or lands on netting containing either of the ELQs, it’s essentially disinfected. It absorbs the treatment via its legs, and that kills the parasites that it’s carrying,” said Mike Rubal, Ph.D., a scientist at the Southwest Research Institute in San Antonio, Texas, who contributed to the Nature article. “The best defense against malaria has been insecticide-treated bed nets, but mosquitoes are developing a resistance to those prevention methods. This novel approach targets the source of the disease.”
The next step is to test this strategy in the field with ELQ-impregnated bed nets, which is set to begin later this year.
“This work has potential to significantly blunt the transmission of malaria,” Riscoe said. “I think that it will evolve and develop to be a key element to our success in eradicating malaria around the world.”
In vivo screen of Plasmodium targets for mosquito-based malaria control
Article Publication Date
21-May-2025
Cloudy with a chance of lifesaving and more cost-effective weather predictions
Penn professor Paris Perdikaris and collaborators developed Aurora, a machine-learning model that has predictive capabilities for air quality, ocean waves, tropical cyclone tracks, and weather.
As extreme weather events become more common, researchers are turning to higher- quality information. However, interpreting these massive datasets presents another set of challenges, such as maintaining accuracy and keeping costs down. Paris Perdikaris of the School of Engineering and Applied Science and collaborators at Microsoft Research have created Aurora, a low-cost model that can predict a wide range of environmental events.
When Hurricane Katrina reached the Gulf Coast in 2005, emergency responders were blindsided by a storm surge that defied predictions. In Japan six years later, the destructive scale of a tsunami triggered by a massive earthquake outpaced early warnings. The 2020 wildfires that engulfed California overwhelmed air quality models.
In each of these disasters, comprehensive modeling—encompassing tropical cyclones, ocean waves, air quality, and broader climate variables—could have enhanced emergency responses, saved lives, and cut damage repair costs. However, processing such vast amounts of numerical data has traditionally been computationally intensive and expensive, often hindering timely decision-making.
Now, Paris Perdikaris of the University of Pennsylvania and his collaborators at Microsoft Research have developed a machine learning model capable of accurately forecasting a variety of Earth systems, including air quality, ocean waves, and tropical cyclone tracks. Their new model, Aurora, outperforms existing traditional systems at a fraction of the cost, and their findings could help emergency service providers better prepare for extreme weather events. Their findings are published in Nature.
“Earth’s climate is perhaps the most complex system we study—with interactions spanning from quantum scales to planetary dynamics,” says Perdikaris, an associate professor at the School of Engineering and Applied Science. “With Aurora, we addressed a fundamental challenge in Earth system prediction: how to create forecasting tools that are both more accurate and dramatically more computationally efficient.”
For example, the team’s model correctly predicted landfall of 2023’s Typhoon Doksuri—the costliest Pacific typhoon to date—in the northern Philippines four days ahead of the event, while official forecasts erroneously predicted landfall off the coast of northern Taiwan.
Perdikaris explains that the numerical models that have been the backbone of weather prediction for decades involve complex systems of differential equations derived from physics principles. He notes that instead of solving equations, Aurora identifies complex relationships in historical Earth system data and uses these to generate predictions.
“This makes Aurora dramatically faster—generating predictions in seconds rather than hours— while maintaining or even exceeding the accuracy of traditional models,” he says.
Better, faster, stronger
To achieve better results for less time and money, the team turned to a “foundation model,” an artificial intelligence (AI) model trained on a wide variety of data—much like OpenAI’s GPT. Aurora is trained on more than one million hours of diverse geophysical data, including temperature, wind speeds, humidity, ocean wave heights, and atmospheric chemical compositions, Perdikaris says. These come from weather analyses, reconstructions of historical weather, forecasts, and climate simulations.
The learning process involves two key phases, he explains. First, Aurora is fed this diverse data, learning to predict the evolution of Earth system variables with a six-hour lead time and providing the model with fundamental insights into planetary dynamics. Then, during fine-tuning, this pretrained model can be adapted to perform specific tasks, such as using chemical composition data to predict air quality or pressure patterns associated with storm systems to track tropical storms.
The model achieves faster predictions, the researchers explain, because it learns patterns directly from extensive observational and simulation datasets—bypassing the need for explicit mathematical equations typically required in traditional model—and employs AI techniques specifically designed to leverage parallel processing capabilities of graphics processing units.
The enhanced accuracy of their approach arises from several key factors. First, the model identifies and utilizes subtle patterns and correlations within data that conventional physics-based approaches might miss or not explicitly represent. Secondly, its neural network architecture is particularly well-suited for capturing complex physical processes occurring simultaneously at multiple scales.
Perdikaris says Aurora also employs transfer learning, which means that knowledge gained from one area, such as atmospheric dynamics used in weather forecasting, enhances its predictive performance in other domains, including air quality modeling or predicting tropical cyclone formation.
“This cross-domain learning is central to the foundation model philosophy that guides my broader research program,” says Perdikaris.
Beating the supercomputers
In testing Aurora’s predictive abilities, the team looked at a series of recent weather events as case studies and pitted their new AI against extant systems.
Perdikaris says that Aurora’s hurricane forecasting achievements are particularly remarkable. “When we compared Aurora to official forecasts from agencies like the National Hurricane Center, China Meteorological Administration, and others, Aurora outperformed all of them across different basins worldwide.”
To examine air quality, the team looked at a sandstorm that took place in Iraq in June 2022, one of a series that resulted in more than 5,000 hospitalizations. Their AI accurately predicted it one day in advance at a fraction of the cost it takes to run a forecast on the Copernicus Atmosphere Monitoring Services, the gold standard in Earth observation and atmospheric monitoring.
Perdikaris adds that what’s particularly impressive is the model’s ability to handle the challenges of air quality data—sparse observations, large dynamic ranges in pollutant concentrations, and complex chemical reactions through hundreds of equations—while accounting for human-generated emission pattern changes, like those seen during COVID-19.
Aurora “did not have any prior knowledge about atmospheric chemistry or how nitrogen dioxide, for instance, interacts with sunlight— that wasn't part of the original training,” says co-first author Megan Stanley of Microsoft Research. “And yet, in fine-tuning, Aurora was able to adapt to that because it had already learned enough about all of the other processes.”
In testing Aurora’s predictive capabilities for the heights and directions of ocean waves, the team conducted a case study of Typhoon Nanmadol, which struck the southern coast of Japan in 2022 and was the most intense typhoon that year. Their model exceeded expectations by perceiving intricate wave patterns in greater detail, drawing from prevailing wind patterns, and accurately capturing the typhoon’s waves.
The forecast for Aurora
“What makes these results particularly exciting is that they demonstrate how a single foundational approach can be applied across diverse domains,” says Perdikaris. “It’s something we’re now expanding to other scientific applications in my group.”
The researchers are interested in extending their model to generate predictions on Earth systems such as local and regional weather, seasonal weather, and extreme weather events like floods and wildfires. Perdikaris believes that this may represent a potential paradigm shift in how information on Earth systems is disseminated to key decision makers.
“The most transformative aspect is democratizing access to high-quality forecasts,” he says. “Traditional systems require supercomputers and specialized teams, putting them out of reach for many communities worldwide. Aurora can run on modest hardware while matching or exceeding traditional model performance.”
For cities and local governments, Perdikaris notes that this means having localized, high-resolution predictions for air quality, extreme rainfall, or heat waves without relying on downscaled global models. He says that the computational efficiency allows for more frequent updates and forecasts that better quantify uncertainty, which is critical for risk management.
“What excites me most about this technology is its broader applicability,” says Perdikaris. “At Penn, we’re exploring how similar foundation model approaches can address other prediction challenges beyond weather—from urban flooding to renewable energy forecasting to air quality management—making powerful predictive tools accessible to communities that need them most.”
Paris Perdikaris is an associate professor in the Department of Mechanical Engineering and Applied Mechanics in the School of Engineering and Applied Science at the University of Pennsylvania.
Megan Stanley is a senior researcher in the Machine Intelligence group at Microsoft Research.
Other authors include Johannes Brandstetter of Johannes Kepler University Linz and Microsoft Research, Chun-Chieh Wu of National Taiwan University; Ana Lucic and Max Welling of the University of Amsterdam and Microsoft Research; Anna Allen and Alexander T. Archibald of the University of Cambridge; Richard E. Turner of the University of Cambridge and Microsoft Research; Haiyu Dong, Kit Thambiratnam, and Jonathan A. Weyn of Microsoft Corporation; Wessel P. Bruinsma, Patrick Garvan, Elizabeth Heider, and Maik Riechert of Microsoft Research; and Cristian Bodnar and Jayesh K. Gupta of Microsoft Research and Silurian AI.
This research was supported by the Department of Energy’s Advanced Scientific Computing Research program (DE-SC0024563) and the Engineering & Physical Sciences Research Council Prosperity Partnership) between Microsoft Research and the University of Cambridge (EP/T005386/1).
C.B., W.P.B., M.S., J.B., P.G., M.R., J.A.W., H.D., J.K.G., K.T., E.H., M.W. and P.P. own Microsoft stock. W.P.B., M.S., P.G., M.R., J.A.W., H.D. and K.T. are Microsoft employees. C.B. and J.K.G. are employees of Silurian AI Inc. and own Silurian AI Inc. stock. The remaining authors declare no competing interests.
Paid maternity leave policies could be costing women tech jobs
Groundbreaking research reveals 22% drop in interview likelihood for women at struggling companies after national mandate for extended paid leave
Institute for Operations Research and the Management Sciences
BALTIMORE, MD, May 22, 2025 – Well-intentioned employer-paid maternity leave policies may be the catalyst for the unintended consequence of costing women jobs in the technology workforce instead of boosting their participation, according to a groundbreaking study in the INFORMS journal Management Science.
These counterintuitive findings – resulting from a study of more than 4 million IT job applications across 7,000+ companies – raises urgent questions about how workplace policies are designed and implemented, especially in high-skill sectors like tech where gender equity already lags. The study, “Does Employer-Paid, Job-Protected Maternity Leave Help or Hurt Female IT Workers? Evidence from Millions of Job Applications,” analyzes the impact of a 2017 Indian law that expanded paid maternity leave from 12 to 26 weeks for companies with 10 or more employees.
“Less profitable companies are 22% less likely to interview female applicants after the implementation of extended paid leave mandates, raising a bevy of significant concerns for employers and policymakers,” says Sofia Bapna of the University of Minnesota.
The release of this study comes amid heightened debate over paid family leave policies worldwide. In the U.S., conversations continue about expanding paid leave. New York recently implemented the nation’s first paid prenatal leave law on Jan. 1, 2025, providing 20 hours of paid leave annually for prenatal medical appointments.
The U.S. maintains strong legal protections against gender-based hiring discrimination, but this study underscores a critical reality: policies don’t operate in a vacuum. Economic incentives – and disincentives – still shape employer behavior. As more states and companies expand paid family leave, this research signals a need for thoughtful policy design that includes cost-sharing mechanisms, gender-neutral benefits and anti-bias safeguards. Without these, even well-intentioned reforms risk reinforcing the very inequities they aim to correct.
“While the intent of the law was to support women’s participation in the workforce, our findings reveal a critical backfire,” continued Bapna, a professor in the Information and Decision Sciences Department in the Carlson School of Management at the University of Minnesota. “Companies that can’t absorb the cost of extended maternity leave are effectively closing the door on women before they even have a chance to prove themselves.”
“As policymakers push for expanded leave programs, this research serves as a crucial warning: even well-meaning policies can cause harm if not carefully designed,” says Russell Funk of the University of Minnesota. “Without complementary measure – such as antidiscrimination laws, shared parental leave and employer incentives – efforts to empower women could end up shutting them out instead.”
The authors recommend that employer-paid leave policies be accompanied by safeguards that protect women from discriminatory hiring practices.
“Creating balanced policies that benefit both employers and employees is essential for achieving true equity in the workplace,” concludes Funk, a professor in the Strategic Management and Entrepreneurship Department in the Carlson School of Management at the University of Minnesota.
About INFORMS and Management Science INFORMS is the world’s largest association for professionals and students in operations research, AI, analytics, data science and related disciplines, serving as a global authority in advancing cutting-edge practices and fostering an interdisciplinary community of innovation. Management Science, a leading journal published by INFORMS, publishes quantitative research on management practices across organizations. INFORMS empowers its community to improve organizational performance and drive data-driven decision-making through its journals, conferences and resources. Learn more at www.informs.org or @informs.
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