Machine learning redesigns microscopic web sensors to be five times more flexible than nature-inspired versions, enabling detection of masses as small as trillionths of a gram.

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, ...

Microgrids play a growing role in modern power systems, supporting renewable integration, local resilience, and decentralized ...

Across neuroscience, biomedical engineering and artificial intelligence, researchers from Carnegie Mellon University are exploring how pain is measured, understood and treated to support safer, more ...

What if vaccine development didn’t have to take a decade? This piece looks at how AI is helping scientists ask better ...

The signals that drive many of the brain and body's most essential functions—consciousness, sleep, breathing, heart rate and motion—course through bundles of "white matter" fibers in the brainstem, ...

Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.

This proteomics-driven approach to drug discovery improves target validation and uncovers insights into undruggable proteins, ...

Artificial intelligence allows tracing the evolution of genetic control elements in the developing mammalian cerebellum. An international research team led by biologists from Heidelberg University as ...

The global spatial biology market is projected to grow at a compound annual growth rate (CAGR) of approximately 15% over the ...

AI is ultimately a story about selfhood—and the answer will not be found in the machine, but in what mindful awareness allows us to recognize when we see ourselves reflected there.

Brain imaging technologies (e.g., MRI, PET, advanced microscopy) are foundational tools for deciphering the brain's ...