Koala population crash came before humans, genomic study reveals
Peer-Reviewed Publication
Updates every hour. Last Updated: 29-Oct-2026 11:16 ET (29-Oct-2026 15:16 GMT/UTC)
A groundbreaking genomic study has reshaped our understanding of the evolutionary history of the koala (Phascolarctos cinereus), revealing the iconic Australian marsupial experienced a severe population decline around 100,000 years ago, before the arrival of humans on the continent. All modern koalas descended from a single ancestral population that survived major climate fluctuations, including severe glacial periods.
What gives certain flowers their striking, glossy appearance? Researchers investigating the hardy ice plant uncovered a fascinating optical mystery hidden within the flower’s microscopic surface. By combining advanced imaging techniques with biomimetic material design, the study explores how nature manipulates light in unexpected ways. The findings not only deepen our understanding of plant optics but also open new possibilities for designing innovative reflective materials inspired by nature.
For over a century, doctors have used electrocardiograms (EKGs) to render the invisible electrical activity of the human heart visible, using the pulse to diagnose disease before it becomes fatal. Now, scientists have invented a way to do the exact same thing for the places where most of humanity lives: cities.
In a recent study published in the Proceedings of the National Academy of Sciences (PNAS), researchers have introduced the concept of the “Urban Pulse.” By using dense, high-frequency satellite imagery, the team has successfully tracked the dynamic, real-time metabolic activity of urban environments, effectively measuring the heartbeat of a city.
Machine learning (ML) is reshaping pipeline integrity management (PIM) from physics-based to data-driven paradigms. This systematic review comprehensively surveys ML applications across pipeline reliability design, integrity assessment, inspection, and maintenance; identifies key bottlenecks including data scarcity, poor interpretability, and limited field validation; and proposes a research roadmap toward tractable, trustworthy ML for full-lifecycle pipeline safety.