face_with_colon_three year 2025.
Researchers at Meta have developed a wristband that translates your hand gestures into commands to interact with a computer, including moving a cursor, and even transcribing your handwriting in the air into text. It could make today’s personal devices a lot more accessible to people with reduced mobility or muscle weakness, and even unlock new ways for people to control their gadgets effortlessly.
Emerging evidence highlights the brain renin–angiotensin system (RAS) as a key regulator of reward, memory, and stress. While these discoveries established the brain RAS as a promising therapeutic target for interventions in neurological and neuropsychiatric disorders, translational progress is hampered by the lack of an integrative mechanistic framework. Here, we consolidate accumulating evidence on the molecular and system-level roles of the brain RAS in reward, memory, and stress pathways, and its dual regulatory architecture. Pharmacological RAS modulation regulates domain-specific signaling in frontostriatal reward circuits, hippocampal–prefrontal memory networks, and frontolimbic fear networks. We evaluate the transdiagnostic therapeutic potential in neurological and neuropsychiatric disorders (e.g.
A spin qubit, in which quantum information is encoded in the spin state of an electron, is one of the most promising platforms for quantum computing. Spin qubits exhibit long coherence times and are compatible with advanced semiconductor manufacturing technologies. The leading implementation of spin qubits involves confined electrons inside quantum dots, a nanoscale semiconductor architecture that behaves like a controllable artificial atom. Recent advances have enabled high-fidelity operation of single- and two-qubit gates, exceeding the threshold required for certain surface code quantum error correction techniques.
Researchers from the University of Cambridge and GlitterinTech, a startup founded by the same research group, have unveiled a fundamentally new type of optical spectrometer that delivers laboratory-grade precision in a device small enough to be embedded in portable and wearable technologies. By rethinking how spectra are measured and processed, the team has demonstrated a spectrometer costing only around $10, operating at a centimeter scale, and capable of applications ranging from industrial quality control to real-time health care monitoring.
Optical spectrometers underpin countless technologies, from chemical analysis and manufacturing to environmental sensing and medicine. Yet shrinking these instruments has historically involved painful trade-offs: Miniaturized devices typically sacrifice bandwidth, resolution or accuracy, limiting them to rough identification rather than true metrological measurements. The newly reported convolutional spectrometer overcomes these barriers by introducing a conceptually elegant operating principle grounded in the convolution theorem, offering unprecedented performance metrics compared with existing dispersive, Fourier-transform and reconstructive spectrometers.
As artificial intelligence systems grow larger and more powerful, their energy demands are rising dramatically. But recent research from the University of Massachusetts Amherst published in Nature Communications suggests that advanced AI capabilities may be achievable with dramatically lower energy consumption.
A team led by Hava Siegelmann, Provost Professor in the Manning College of Information and Computer Sciences at UMass Amherst, has developed a novel AI that more closely mirrors key aspects of how the human brain operates. Siegelmann and her lab have focused on two complementary goals: enabling AI systems to learn continuously in real time rather than only during a fixed training phase, and dramatically reducing the energy required for intelligent computation.
“Current AI systems are extraordinarily powerful, but they are also extraordinarily energy-hungry,” said Siegelmann. “Our work shows that it is possible to design AI that remains highly capable while operating much more efficiently.”
Researchers from the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, and Johns Hopkins University in Baltimore have developed a practical, comprehensive noise-modeling framework for a popular class of superconducting quantum processors. Their work, published in the journal PRX Quantum, offers a sevenfold improvement in predictive accuracy over existing approaches.
Quantum bits, or qubits, are intrinsically prone to noise—interference arising from environmental factors such as electrical and magnetic fields or temperature fluctuations—as a result of the extreme sensitivity that makes them so valuable for computing. Developing accurate noise models is key to creating the robust quantum algorithms and resilient error-correction protocols required to build truly fault-tolerant quantum computers.
“To really advance the field, we need models that can predict a wide range of behavior while utilizing a small number of parameters, rather than theoretical models that try to account for all of the fundamental physics at play in quantum interactions,” said project lead Gregory Quiroz, a senior physicist at APL and an associate research professor in the Department of Physics and Astronomy at the Johns Hopkins University Krieger School of Arts and Sciences. “The novelty of our approach lies in a unified and experimentally validated framework that connects multiple noise mechanisms and yields a coherent predictive methodology.”
A new study from the Hebrew University of Jerusalem challenges a long-held assumption in climate science by showing that aerosols—tiny particles suspended in the atmosphere—can either warm or cool the climate, depending on the time scale considered.
Led by Prof. Guy Dagan of the Fredy and Nadine Herrmann Institute of Earth Sciences, the research reveals that aerosol-cloud interactions can produce opposite climate effects in the short and long term. The findings, published in Nature Communications, offer a new explanation for why aerosols remain one of the largest sources of uncertainty in climate projections.
Aerosols come from a variety of natural and human-made sources, including air pollution, wildfires, sea spray and dust. Scientists have long known that these particles influence how clouds form and how much heat Earth retains, but accurately estimating their overall impact on climate has proved difficult.
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, researchers introduced the concept of the “Urban Pulse.” By using dense, high-frequency satellite imagery, the team successfully tracked the dynamic, real-time metabolic activity of urban environments, effectively measuring the heartbeat of a city.
Zhe Zhu, director of the Global Environmental Remote Sensing (GERS) Laboratory and associate professor of natural resources and the environment in the College of Agriculture, Health and Natural Resources (CAHNR), was the first author. He worked in close collaboration with senior author Karen C. Seto, the Frederick C. Hixon Professor of Geography and Urbanization Science at the Yale School of the Environment, alongside Michail Fragkias of Boise State University and a multi-institutional team of researchers.