An eye implant and a pair of high-tech glasses that enable people with a severe form of vision loss to read have been approved for use by a European regulator
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Human tissue model tracks glioblastoma invasion cell by cell
Glioblastoma is a malignant brain tumor and is among the most aggressive cancers in humans. Despite multimodal therapy with surgery, radiation and chemotherapy, there is still no cure. A major reason is the tumor’s invasive behavior: Glioblastoma cells migrate far beyond the visible tumor into healthy brain tissue. These infiltrating cells cannot be completely removed and seed tumor recurrence—often within just a few months.
“To understand why glioblastoma keeps coming back, we need to look closely at the tumor cells that remain hidden in the brain after surgery,” says Dr. Matthias Schneider, deputy director of the Department of Neurosurgery at the UKB and head of the Brain Tumor Translational Research Group at the UKB and the University of Bonn. “Core2Edge allows us to study these infiltrative tumor cells in a model based entirely on human tissue, closely mirroring what we see in patients.”
The study is published in the journal Nature Protocols.
New theory on how six‑tonne Stonehenge rock was transported from Scotland thousands of years ago: On a glacier
Built from stones weighing between 2 and 25 tonnes (2 to 28 tons), the structure of Stonehenge demonstrates a scale of construction hard to imagine before the invention of the wheel. The mystery deepens when you consider that the stones are not from the local bedrock. So why these stones, and how did they get there?
Recent work I carried out with geochemist Anthony Clarke from Curtin University in Australia might have an answer to these two questions for Stonehenge’s most far-traveled stone—the Altar Stone.
The Altar Stone was sourced from 700 km (435 miles) away in northeast Scotland, from a region of bedrock geology known as the Orcadian Basin that was once a lake called Lake Ocradie. The sandstone of the Altar Stone was formed from these lake sediments. Now, new work by our international team has tested the possibility that it was transported by glaciers.
Dynamic ‘breathing’ in nanopore structures can maximize efficiency of molecule separation and diffusion
Nanoporous material-based separation technology is vital in many applications because it can precisely distinguish between and separate nearly identical chemical or biochemical molecules.
Previous research by Professor Susumu Kitagawa of Kyoto University’s Institute for Integrated Cell-Material Sciences (WPI-iCeMS) and colleagues, published in Nature, applied this technology to separate two very similar types of water molecules—regular water (H₂O) and heavy water (D₂O), which have similar overall properties but slightly different masses. But the underlying mechanisms of that separation were not well understood.
Now, a study led by Professor Shinji Saito of the Institute for Molecular Science (IMS) in Japan and published in Nature Communications in July has provided a theoretical explanation for that phenomenon, using H₂O and D₂O molecules to study nanopore behavior in a metal-organic framework.
Moon’s thick crust could amplify elusive gravitational-wave signals
Gravitational waves are tiny ripples in the fabric of spacetime that are produced when massive objects in the cosmos accelerate or collide. By detecting these waves, astrophysicists can study various cosmic events, including black hole mergers, neutron star collisions and the early evolution of the universe.
There are several gravitational-wave observatories in different geographic regions worldwide. While these detectors are highly sensitive to the tiny changes associated with ripples in spacetime, they cannot yet detect waves across all frequency ranges.
Researchers at the Chinese Academy of Sciences and Peking University recently revisited the possibility of using the moon to amplify gravitational waves with frequencies between 0.01 and 1 hertz (Hz), a range that remains largely inaccessible to current gravitational-wave detectors.
Brain-inspired AI is capable of flexible planning and problem-solving while using far less energy
The capabilities of large AI systems are constantly improving, but they consume a great deal of energy during training and operation. The human brain, by contrast, is extremely energy-efficient: It requires only around 20 watts.
Researchers at Graz University of Technology, in collaboration with international partners, have developed a novel brain-inspired AI model that can plan flexibly and solve complex problems. In doing so, it consumes significantly less energy than multilayer neural networks or large language models. The study is published in the journal Nature Machine Intelligence.
“The brain works in a completely different way from today’s AI systems,” says Wolfgang Maass from the Institute of Machine Learning and Neural Computation at Graz University of Technology. “We are trying to translate the way it works into algorithms and apply them to AI systems.”
A way to read quantum bits faster and with less hardware
Quantum computers process information in a fundamentally different way from conventional computers, using quantum bits, or qubits, that can exist in multiple states at once. This could allow them to tackle problems beyond the reach of today’s machines, from simulating new materials to optimizing complex systems.
But to extract useful results from a quantum processor, researchers must reliably measure the state of each qubit, a task that remains one of the main bottlenecks in the field.
One of the leading approaches to building quantum computers uses superconducting circuits that carry current without resistance at extremely low temperatures.