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Deafness reversed: Single injection brings hearing back within weeks

A cutting-edge gene therapy has significantly restored hearing in children and adults with congenital deafness, showing dramatic results just one month after a single injection. Researchers used a virus to deliver a healthy copy of the OTOF gene into the inner ear, improving auditory function across all ten participants in the study. The therapy worked best in young children but still benefited adults, with one 7-year-old girl regaining almost full hearing. Even more exciting: this is just the start, as scientists now aim to target other genes that cause more common forms of deafness.

Enhancement of Li+ transport through intermediate phase in high-content inorganic composite quasi-solid-state electrolytes

Quasi-solid-state electrolytes promise the safety of ceramics, the flexibility of polymers, and the conductivity of liquids—yet the “how” behind their superior ion transport has remained murky. Now, a joint team from Fudan University and the National Institute for Cryogenic & Isotopic Technologies (Romania), led by Professors Aishui Yu and Tao Huang, delivers a decisive answer in Nano-Micro Letters. Their review, “Enhancement of Li⁺ Transport Through Intermediate Phase in High-Content Inorganic Composite Electrolytes,” decodes the hidden chemistry that lets lithium sprint across solid/liquid boundaries.

The Secret Sauce: Acidic Interfaces

US HEAT-ML breakthrough accelerates fusion plasma heat protection

A public-private team of fusion pioneers – Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory – has unveiled an AI breakthrough that could reshape the future of fusion plasma research.

The new system, called HEAT-ML, can identify safe zones inside a reactor in milliseconds, replacing a process that once took more than 30 minutes.

By protecting sensitive components from the blistering heat of superheated plasma, this advance could accelerate the design and operation of next-generation fusion power plants.

An AI tool, developed by CFS, PPPL, and Oak Ridge, maps fusion plasma heat in milliseconds, protecting reactors and advancing clean energy.

Theoretical particle physicist tackles machine learning’s black box

From self-driving cars to facial recognition, modern life is growing more dependent on machine learning, a type of artificial intelligence (AI) that learns from datasets without explicit programming.

Despite its omnipresence in society, we’re just beginning to understand the mechanisms driving the technology. In a recent study, Zhengkang (Kevin) Zhang, assistant professor in the University of Utah’s Department of Physics & Astronomy, demonstrated how physicists can play an important role in unraveling its mysteries.

“People used to say is a black box—you input a lot of data and at some point, it reasons and speaks and makes decisions like humans do. It feels like magic because we don’t really know how it works,” said Zhang. “Now that we’re using AI across many critical sectors of society, we have to understand what our machine learning models are really doing—why something works or why something doesn’t work.”

Ultrafast untethered levitation device offers frictionless design for omni-directional transport

Advances in technology have led to the miniaturization of many mechanical, electronic, chemical and biomedical products, and with that, an evolution in the way these tiny components and parts are transported is necessary to follow. Transport systems, such as those based on conveyor belts, suffer from the challenge of friction, which drastically slows the speed and precision of small transport.

Researchers from Yokohama National University addressed this issue by developing an untethered levitation device capable of moving in all directions. The frictionless design allows for ultrafast, agile movement that can prove to be very valuable in machine assembly, biomedical and chemical applications via contactless transport.

The results are published in the journal Advanced Intelligent Systems.

Discover How AI is Transforming Quantum Computing

Quantum technologies have had a meteoric rise and become a key area of prioritization for governments, academics, and businesses. Government funding commitments total almost $40 billion, while private investments since 2021 total nearly $8 billion. The US agency, National Institute of Standards and Technology, released this year three new post-quantum security standards, which governments classify as ‘critical resources’ for the economy and national defense. Meanwhile, users of quantum technologies experiment with them, from industry applications in drug development and materials science to energy grid optimization and logistics efficiency.

Yet, besides a few areas, such as quantum sensing, practical and impactful quantum technologies haven’t matured for widespread use. However, when combined with classical machine learning, practical use cases emerge.

This article delves into the impact and potential of artificial intelligence and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in quantum technologies and AI, as a potential collaborator for startups to deliver investment, resources, global networks, and tailored accelerator and incubator programs.

This article covers AI and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in emerging technologies.

Science Fiction? Think Again. Scientists Are Learning How to Decode Inner Thoughts

To protect users’ privacy, they chose a passphrase to activate the device that was unlikely to come up in everyday speech: “Chitty Chitty Bang Bang,” the title of the 1964 Ian Fleming novel and 1968 movie. The technology would start translating thoughts when it detected the phrase, which, for one participant, it did with 98.75 percent accuracy.

In the tests, the researchers asked the participants—all four of whom have some trouble speaking—to either attempt saying a set of seven words or to merely think them. They found the patterns of neural activity and regions of the brain used in both scenarios were similar, but the inner thoughts produced weaker signals.

Then, the team trained the computer system on the signals produced when participants thought words from a 125,000-word vocabulary. When the users then thought sentences with these words, the device translated the resulting brain activity. The technology produced words with an error rate of 26 to 54 percent, making it the most accurate attempt to decode inner speech to date, Science reports.

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