Manganese dioxide can convert amino acids into hydrogen cyanide (HCN) without requiring methane, solving a long-standing puzzle about the origin of this key prebiotic molecule on early Earth, as reported by researchers from Science Tokyo. Although HCN is central to origin-of-life theories, recent evidence suggests early Earth's atmosphere didn’t contain sufficient methane needed for classic HCN-producing reactions. The newly found chemical pathway shows that HCN could instead have been continuously supplied from abundant amino acids.

A new machine-learning-based approach to mapping forests in high resolution and simulating their future growth, developed at Michigan State University and Virginia Tech, could help landowners figure out which trees to cut and which to leave standing to enhance pine profits.

As Australia and the world face rapidly ageing populations, technology is increasingly promoted as the answer to helping older people live safely, independently and well - but experts warn that innovation alone is not enough.

“AI should be able to say ‘I’m Not Sure’ on its own.”

A new approach has been proposed to address the problem of “overconfidence”—one of the most critical risks of artificial intelligence (AI) in areas such as autonomous driving and medical diagnosis, where AI shows high confidence in incorrect predictions. A KAIST research team has developed a training method that enables AI to recognize situations involving unfamiliar or unseen knowledge, laying the foundation for reducing overconfidence and improving reliability.

In a world-first discovery, researchers have documented thousands of Israeli isopods abandoning their solitary lives to join massive, synchronized "death spirals" triggered by artificial streetlights. By experimenting with different light geometries, the team revealed how vertical beams of white light accidentally hijack the natural instincts of these crustaceans, trapping them in a mesmerizing but potentially dangerous circular march. This striking phenomenon highlights the hidden, unintended consequences of human light pollution on the secret lives of ground-dwelling wildlife.

Keratoconus is a progressive eye disease that weakens the cornea, but its earliest (subclinical) stage is difficult to detect with standard imaging that looks only at corneal shape. In this study, researchers analyzed imaging data from 359 eyes to test whether polarization‑sensitive optical coherence tomography (PS‑OCT), combined with artificial intelligence, could improve early diagnosis by revealing changes in corneal collagen organization and sublayer thickness. AI models built from PS‑OCT data were compared with models based on two widely used tomography systems. While all approaches performed similarly for healthy eyes and clear keratoconus, PS‑OCT differed in classifying subclinical cases, identifying subtle structural patterns not captured by conventional imaging. The results suggest that PS‑OCT provides complementary information about corneal microstructure and may help refine early keratoconus detection, supporting safer clinical decision‑making in eye care.