Meichsner et al. review recent insights into mitochondria as dynamic signaling hubs. The authors describe how structural plasticity and interorganellar communication enable mitochondria to serve as both sources and targets of signaling, coordinating stress responses, metabolic adaptation, and innate immune pathways to safeguard cellular homeostasis.
A new technique uses an ‘anti-noise’ signal to cancel out the unavoidable quantum noise associated with precision measurements like those needed for gravitational-wave detection.
When light is used to detect motion with high-precision—for example, in accelerometers or gravitational-wave detectors—its ultimate sensitivity is limited by quantum noise, which is unavoidable. A research team has now demonstrated a tabletop device that can reduce the disruption of quantum noise by modifying a light beam before using it to make a measurement [1]. This beam preparation cancels out the noise in a manner reminiscent of noise-canceling headphones [1]. Working across a wide frequency range and potentially offering up to 77% noise reduction, the system might ultimately find additional uses in quantum information processing.
Observing gravitational waves involves detecting changes in the interference pattern created by a pair of interacting laser beams, each of which has bounced off a remote mirror whose distance changes slightly when a wave passes. Such detections require very high sensitivity, which is compromised by inherent quantum fluctuations in the light field. To reduce quantum noise, researchers currently use a technique called squeezing, in which the quantum fluctuations can be shifted from one parameter, such as the phase, to another, such as the intensity [2, 3].
Vortices in superconductors have so far been considered a disruption, as they can impair the superconducting properties. Researchers at the Karlsruhe Institute of Technology (KIT) have proved in experiments that magnetic vortices can be used as controllable quantum systems in certain materials. This means that a previously unwanted phenomenon is becoming a potential resource in quantum technologies, opening up new avenues for the development of quantum computers, highly sensitive sensor systems, and innovative approaches in materials research. These results are published in Nature.
Superconductors are materials that, under certain conditions, conduct electricity with zero resistance, entirely expelling magnetic fields. However, once the magnetic flux exceeds a critical threshold, magnetic fields start to penetrate into the material as tiny, quantized vortices. Such vortices have so far been considered unwanted disruptive factors, as they have an energy-draining effect, limiting the efficiency of superconducting systems.
Dark matter is an elusive form of matter that almost never emits, absorbs or reflects light, while only weakly interacting with regular matter. These properties make it very difficult to detect using conventional experimental techniques and instruments.
Over the past decades, physicists have inferred the existence of dark matter indirectly, by probing its influence on the gravity of stars, galaxies and other cosmological objects. As it has never been directly observed before, the exact composition and nature of dark matter remain unknown.
A hypothetical dark matter particle is the axion, an ultralight particle that is predicted to be highly abundant in the universe. Most existing work describes axions as a classical field, a wave-like entity that resembles an electromagnetic field.
For nearly 80 years, mathematicians have struggled to solve a classic geometry puzzle first posed by Paul Erdős in 1946: the planar unit distance problem. The question posed by the legendary Hungarian mathematician was, on the surface, deceptively simple.
It asks: if you take a piece of paper and add some dots, how many pairs can be exactly the same distance apart? Erdős himself proposed that the maximum number grows only slightly faster than the number of dots. Although many mathematicians agreed with him, no one could find a way to mathematically prove it.
Astronomers from Italy and Brazil have investigated a nearby red dwarf star known as Ross 318 and have discovered an exoplanet orbiting this star, which is at least six times more massive than Earth. The discovery is reported in a research paper published May 11 on the arXiv preprint server.
Located just 28 light years away from Earth, Ross 318 (also known as Gliese 48, or TIC 379084450) is a red dwarf star of spectral type M3.5V. The star has an orbital period of approximately 51.5 days and an effective temperature of 3,450 K, and showcases strong magnetic activity, which poses a major challenge for exoplanet searches.
A team of astronomers led by Giuseppe Conzo from the amateur astronomy association Gruppo Astrofili Palidoro (GAP) decided to investigate Ross 318, hoping that amidst its magnetic activity, they could verify whether an alien world orbits this star. For this purpose, they conducted a systematic re-analysis of radial velocity (RV) data from the CARMENES spectrograph and decade-long High Resolution Echelle Spectrometer (HIRES) observations. Their study was complemented by data from the Transiting Exoplanet Survey Satellite (TESS).
When they reach the bottom of a soap dispenser, frugal handwashers might try adding water to the bottle to push out the last bit of soap. But usually, the water drills right through the soap and jets out an only slightly sudsy splash.
This happens because when you push a less viscous fluid like water into a more viscous fluid like soap in a confined space, the place where the two fluids meet can be unstable, and the runnier liquid might find a path of least resistance.
If you look very closely, you might see tiny protuberances form at the place where the fluids touch, in a phenomenon physicists call “viscous fingering.” In certain types of confined spaces, the fingers form a branching pattern.
The mechanism of high-temperature (TC) superconductivity is a key challenge in condensed matter physics. Recently, Chinese scientists made significant progress in the study of high-TC nickelate superconductors.
For the first time, scientists observed a nodeless superconducting gap and discovered electron-boson coupling by measuring the electronic structures of Ruddlesden-Popper bilayer nickelate superconducting thin films. These results provide crucial evidence for two fundamental issues in the mechanism of high-TC nickelates: “superconducting gap symmetry” and “superconducting pairing mechanism.”
This study, conducted by a team led by Prof. He Junfeng from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, in collaboration with a team led by Prof. Xue Qikun and Prof. Chen Zhuoyu from the Southern University of Science and Technology (SUSTech), was published in Science.
Modern smartphones are packed with incredible technology, from high-resolution cameras and advanced graphics chips to AI processors. In premium models, this hardware includes LiDAR (light detection and ranging), which helps power augmented reality features and improve depth sensing.
And that capability could soon be in for a seriously impressive upgrade. Researchers at the Massachusetts Institute of Technology (MIT) have developed an algorithm that lets a phone’s LiDAR sensor detect objects hidden around corners. Details are in a paper published in the journal Nature.
Typically, this type of non-line-of-sight (NLOS) capability is found in labs and relies on bulky, expensive research-grade hardware. But the team’s breakthrough makes it possible for consumer LiDAR sensors to peek behind obstacles.
Over the past few decades, roboticists worldwide have introduced increasingly advanced robots that can understand human instructions, move in their surroundings and reliably complete basic manual tasks. While they perform well in some scenarios, many of these robots still struggle to translate the instructions of users into precise and executable actions that would allow them to successfully complete desired tasks.
Recently, computer scientists have been trying to improve how robots respond to user commands or queries using vision-language models (VLMs), artificial intelligence (AI) systems trained to process both images and texts. These models can typically interpret basic requests such as “place the bottle onto the plate,” yet they often do not exhibit the spatial reasoning capabilities required to interpret more elaborate instructions and translate them into executable actions in real-world settings.
Researchers at the Chinese University of Hong Kong, the Zhejiang Humanoid Robot Innovation Center Co. Ltd and other institutes recently introduced Retrieval-Augmented Manipulation (RAM), a framework that could improve the ability of robots to connect abstract instructions with three-dimensional (3D) representations of the space around them. The new framework, presented in a Science Robotics paper, was found to improve the spatial reasoning capabilities of robots, allowing them to reliably follow more elaborate instructions, without requiring task-specific training.