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AI model ‘reads’ protein pairs, unlocking new insights into disease and drug discovery

Researchers have developed a new artificial intelligence (AI) model that can more accurately predict how proteins interact with one another—an advancement that could accelerate drug discovery and deepen insights into diseases such as cancer.

Led by Professor Zhang Yang, Senior Principal Investigator from the Cancer Science Institute of Singapore (CSI Singapore) at the National University of Singapore, and published in Nature Communications, the study introduces a paired protein language model (PPLM) that learns from two interacting proteins simultaneously, rather than analyzing them in isolation. This marks a significant shift in how AI is applied to biology, enabling more accurate prediction of protein–protein interactions that underpin nearly all cellular processes.

A truly invisible device that does not disturb its surroundings and its metamaterial shell

Metamaterials are carefully engineered materials that possess desirable properties and can be used to manipulate electromagnetic, acoustic, or other types of waves in interesting ways. Some materials scientists and engineers have been trying to use these materials to develop so-called invisible devices, or, in other words, devices that do not disturb the environment around them or reveal their presence to other technologies nearby.

Most proposed approaches for realizing invisible devices entail surrounding devices with a metamaterial shell that prevents scattering. While devices created using these strategies do not disturb their surrounding environment, they still distort what is happening within the metamaterial shell, thus they remain partly visible.

Researchers at Fudan University have introduced a new approach to realize devices that are truly and entirely invisible using metamaterials. Their proposed solution, outlined in a paper published in Physical Review Letters, was found to eliminate scattering effects both outside and inside a metamaterial cloaking shell.

World’s largest collection of Olympiad-level math problems now available to everyone

Every year, the countries competing in the International Mathematical Olympiad arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. No one had ever collected them systematically, cleaned them, and made them available—not for AI researchers testing the limits of mathematical reasoning, and not for the students around the world training for these competitions largely on their own.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), King Abdullah University of Science and Technology (KAUST), and HUMAIN have now done exactly that.

MathNet is the largest high-quality dataset of proof-based math problems ever created, and it is not closed. Comprising more than 30,000 expert-authored problems and solutions spanning 47 countries, 17 languages, and 143 competitions, it is five times larger than the next biggest dataset of its kind. The work will be presented at the International Conference on Learning Representations (ICLR 2026) in Brazil later this month.

What makes Mars’ magnetotail flap? Two spacecraft point to magnetic reconnection

The sun continuously blasts charged, magnetic field-carrying particles, or plasma, in all directions. This solar wind interacts with the magnetic fields and atmospheres of several of our solar system’s planets and other bodies, sculpting long magnetic tails of charged particles—magnetotails—that stretch into space behind them.

Magnetotails contain thin layers of electric current-carrying plasma sheets, which sometimes “flap” in an up-and-down waving motion. Spacecraft observations have revealed that flapping in Earth’s magnetotail can be driven by a process called magnetic reconnection, in which magnetic field lines rapidly break and then snap together in a new configuration, releasing stored energy. However, whether reconnection plays this same role beyond Earth has thus far been a mystery.

Yuanzheng Wen and colleagues report the first evidence that magnetic reconnection may also trigger magnetotail flapping at Mars. Their findings are published in the journal AGU Advances.

AI model accurately predicts the spread of wildfires in real time

USC researchers are developing a computational model that combines satellite data and physics-based simulations to forecast a wildfire’s path, intensity, and growth rate. If you’ve ever been evacuated from your home during a wildfire, you’ll be aware of the terrifying unpredictability of the situation. From your location on the ground—rapidly gathering a few vital belongings and attempting to identify the best route to safety—there’s no way of knowing how fast a fire is growing or which direction it’s likely to take.

That was the experience of Assad Oberai, Hughes Professor of aerospace and mechanical engineering at the USC Viterbi School of Engineering. He was evacuated from his home during the Eaton Fire in January 2025—one of the most destructive wildfires in Southern California history, burning for 24 days before full containment and leaving more than 9,400 structures destroyed and over 1,000 damaged.

“Due to changing climate, we’re seeing more of these extremely intense fires—those that burn very fast and very bright,” he reflected. “We have the data at our fingertips. It all comes down to how we put it to use.”

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