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The fires that devastated many in Los Angeles in January 2025 not only scarred the landscape but also changed the air.

A day after the Eaton fire burned through Altadena, California, chlorine levels in the atmosphere reached approximately 40 times the normal amount, while lead peaked at over 100 times the usual level. Atmospheric chlorine can cause respiratory irritation and distress; lead can cause damage to the brain and central nervous system.

“The Los Angeles fires burned homes and cars, which contain electronics, plastics and other synthetic materials that can give off toxic chemicals when they burn,” said Nga Lee “Sally” Ng, a professor at Georgia Tech.

Ng leads the U.S. National Science Foundation-supported Atmospheric Science and Chemistry mEasurement NeTwork (ASCENT), which includes 12 air quality measurement sites nationwide. Each site has state-of-the-art instruments that help us understand aerosols, or tiny particles in the atmosphere. The network is constantly analyzing the chemical constituents of aerosols with a diameter smaller than 2.5 micrometers, referred to as PM2.5, which contribute to more than 90% of the adverse health impacts associated with air pollution.

Researchers in the ASCENT team analyzed data from three locations across Southern California during and after the fire to reveal that certain aerosols carried a unique chemical signature associated with burning synthetic materials in urban fires.

“We now have a very powerful magnifying glass to see

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The computing world is celebrating a major milestone as Andrew Barto, professor emeritus at the University of Massachusetts Amherst, and Richard Sutton, professor of computer science at the University of Alberta, Canada, have been awarded the 2024 Association for Computing Machinery A.M. Turing Award — often called the “Nobel Prize of computing” — for “developing the conceptual and algorithmic foundations of reinforcement learning.”

The legacy in reinforcement learning

Barto and Sutton are widely recognized as pioneers of the modern computational reinforcement learning (RL), a field that addresses the challenge of learning how to act based on evaluative feedback. Their work has laid the conceptual and algorithmic foundations of RL, shaping the future of artificial intelligence and decision-making systems.

The influence of RL extends across multiple disciplines, including computer science (machine learning), engineering (optimal control), mathematics (operations research), neuroscience (optimal decision-making), psychology (classical and operant conditioning) and economics (rational choice theory). Researchers in these fields continue to be profoundly shaped by the contributions of Sutton and Barto.

From NSF Grants to AI Breakthroughs

Barto’s contributions were made possible through a series of U.S. National Science Foundation-funded projects that sustained AI research long before its recent boom. His research was supported through grants from NSF programs including the National Robotics Initiative, Robust Intelligence, Collaborative Research in Computation Neuroscience, Human-Centered Computing, Biological Information Technology and Systems, Artificial Intelligence and Cognitive

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The manufacturing technique known as 3D printing, now being used everywhere, from aircraft manufacturers to public libraries, has never been more affordable or accessible. Biomedical engineering has particularly benefited from 3D printing as prosthetic devices can be produced and tested more rapidly than ever before. However, 3D printing still faces challenges when printing living tissues, partly due to their complexity and fragility.

Now, with support from the U.S. National Science Foundation, a research team at Boston University (BU) and the Wyss Institute at Harvard University has pioneered the use of gallium, a metal that can be molded at room temperature, to create tissue structures in various shapes and sizes.

This innovative approach to fabrication, engineered sacrificial capillary pumps for evacuation (ESCAPE), was highlighted in a recent study published in Nature, where the team used gallium casts to mold biomaterials. The scaffolds left behind by these casts are then filled with cells cultured to form tissue structures. Vascular structures were some of the first produced using ESCAPE, particularly because of the challenges faced due to blood vessel complexity. Few techniques exist to build large (millimeter-scale) and small (micrometer-scale) structures in scaffolds made of natural materials, making this multiscale fabrication capability a novel approach.

“ESCAPE can be used on several tissue architectures, but we started with vascular forms because blood vessel networks feature many different length scales,”

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