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Depression shown to be both cause and consequence of poor health

A large international study led by researchers at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, shows that major depressive disorder (MDD) not only increases risk for a wide range of diseases and social problems, but is also partly driven by factors such as loneliness, obesity, smoking, and chronic pain.

The study, published in Nature Mental Health, applied to systematically test which traits are causes, and which are consequences, of depression. The findings highlight the double burden of MDD: it both arises from and contributes to poor health, making prevention and treatment particularly urgent.

“We show that depression sits at the center of a web of health problems,” says Joëlle Pasman, research associate at Amsterdam UMC and Karolinska Institutet, who led the study. “It is not only a debilitating condition in itself but also increases the risk of many diseases, while at the same time being triggered by social, behavioral, and medical factors.”

Social experiments assess ‘artificial’ altruism displayed by large language models

Altruism, the tendency to behave in ways that benefit others even if it comes at a cost to oneself, is a valuable human quality that can facilitate cooperation with others and promote meaningful social relationships. Behavioral scientists have been studying human altruism for decades, typically using tasks or games rooted in economics.

Two researchers based at Willamette University and the Laureate Institute for Brain Research recently set out to explore the possibility that (LLMs), such as the model underpinning the functioning of the conversational platform ChatGPT, can simulate the observed in humans. Their findings, published in Nature Human Behavior, suggest that LLMs do in fact simulate in specific social experiments, offering a possible explanation for this.

“My paper with Nick Obradovich emerged from my longstanding interest in altruism and cooperation,” Tim Johnson, co-author of the paper, told Tech Xplore. “Over the course of my career, I have used computer simulation to study models in which agents in a population interact with each other and can incur a cost to benefit another party. In parallel, I have studied how people make decisions about altruism and cooperation in laboratory settings.

Scorpion-inspired pressure sensors let robots feel their surroundings

Nature, the master engineer, is coming to our rescue again. Inspired by scorpions, scientists have created new pressure sensors that are both highly sensitive and able to work across a wide variety of pressures.

Pressure sensors are key components in an array of applications, from and industrial control systems to robotics and human-machine interfaces. Silicon-based piezoresistive sensors are among the most common types used today, but they have a significant limitation. They can’t be super sensitive to changes and work well across a range of pressures at the same time. Often, you have to choose one over the other.

Smart microrobots learn to communicate and collaborate in water

In a major step toward intelligent and collaborative microrobotic systems, researchers at the Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN) at Chemnitz University of Technology have developed a new generation of autonomous microrobots—termed smartlets—that can communicate, respond, and work together in aqueous environments.

These tiny devices, each just a millimeter in size, are fully integrated with onboard electronics, sensors, actuators, and . They are able to receive and transmit optical signals, respond to stimuli with motion, and exchange information with other microrobots in their vicinity.

The findings are published in Science Robotics, in a paper titled “Si chiplet–controlled 3D modular microrobots with smart communication in natural aqueous environments.”

Hydroxyl adsorption identified as key factor in electrocatalytic ammonia production

Compared with the energy-intensive Haber-Bosch process, renewable energy-driven electrocatalytic nitrate reduction reaction (NO3−RR) provides a low-carbon route for ammonia synthesis under mild conditions. Using nitrate from wastewater as the nitrogen source and water as the hydrogen source, this route has the potential to produce ammonia sustainably while mitigating water pollution.

Copper (Cu)-based catalysts show a good performance for NO3−RR to ammonia. However, they suffer from issues including high overpotential, competing nitrite (NO2–) formation, and low overall energy efficiency.

In a study published in ACS Catalysis, a team led by Prof. Bao Xinhe and Prof. Gao Dunfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences, along with Prof. Wang Guoxiong from Fudan University, proposed hydroxyl (*OH) as a selectivity descriptor for via NO3−RR over Cu catalysts.

Self-consistent model incorporates gas self-gravity effects to address accretion across cosmic scales

A research team led by Prof. Jiao Chengliang at the Yunnan Observatories of the Chinese Academy of Sciences, along with collaborators, has introduced a self-consistent model that addresses long-unresolved theoretical gaps in the study of self-gravitating spherical accretion. The study was recently published in The Astrophysical Journal.

Accretion, the fundamental astrophysical process by which matter is drawn onto a central celestial object (such as a black hole or star), underpins our understanding of phenomena ranging from to black hole growth. For decades, the classical Bondi model—developed in the 1950s and still widely used today—has served as the backbone of research.

However, this foundational framework overlooks a critical factor: the self-gravity of the gas being accreted. This omission, the researchers note, can drastically alter flow structures and accretion rates in high-density astrophysical environments, limiting the model’s accuracy in key scenarios.

Zigzag graphene nanoribbons create ‘string light’ configuration for tomorrow’s electronics

Organic chemistry, the chemistry of carbon compounds, is the basis of all life on Earth. However, metals also play a key role in many biochemical processes. When it comes to “marrying” large, heavy metal atoms with light organic compounds, nature often relies on a specific group of chemical structures: porphyrins. These molecules form an organic ring; in its center, individual metal ions such as iron, cobalt, or magnesium can be “anchored.”

The porphyrin framework forms the basis for hemoglobin in human blood, photosynthetic chlorophyll in plants, and numerous enzymes. Depending on which metal is captured by the porphyrin, the resulting compounds can display a wide range of chemical and physical properties. Chemists and materials scientists have long sought to exploit this flexibility and functionality of porphyrins, including for applications in .

However, for —even molecular ones—to function, they must be connected to each other. Wiring up individual molecules is no easy task. But this is precisely what researchers at Empa’s nanotech@surfaces laboratory have achieved, in collaboration with synthetic chemists from the Max Planck Institute for Polymer Research.

Two quantum computers with 20 qubits manage to simulate information scrambling

Four RIKEN researchers have used two small quantum computers to simulate quantum information scrambling, an important quantum-information process. This achievement illustrates a potential application of future quantum computers. The results are published in Physical Review Research.

Still in their infancy, quantum computers are only just beginning to be used for applications. But they promise to revolutionize computing when they become a mature technology.

One possible application for quantum computers is simulating the scrambling of quantum information—a key phenomenon that involves the spread of information in ranging from strange metals to .

Simulations reveal pion’s interaction with Higgs field with unprecedented precision

With the help of innovative large-scale simulations on various supercomputers, physicists at Johannes Gutenberg University Mainz (JGU) have succeeded in gaining new insights into previously elusive aspects of the physics of strong interaction.

Associate Professor Dr. Georg von Hippel and Dr. Konstantin Ottnad from the Institute of Nuclear Physics and the PRISMA+ Cluster of Excellence have calculated the interaction of the pion with the Higgs field with unprecedented precision based on . Their findings were recently published in Physical Review Letters.

Physics-inspired computer architecture solves complex optimization problems

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as arranging telecommunications, scheduling, and travel routing to maximize efficiency.

Unfortunately, today’s technologies run into limits for how much processing power can be packed into a computer chip, while training artificial-intelligence models demands tremendous amounts of energy.

Researchers at UCLA and UC Riverside have demonstrated a new approach that overcomes these hurdles to solve some of the most difficult optimization problems. The team designed a system that processes information using a network of oscillators, components that move back and forth at certain frequencies, rather than representing all data digitally. This type of computer architecture, called an Ising machine, has special power for parallel computing, which makes numerous, complex calculations simultaneously. When the oscillators are in sync, the optimization problem is solved.

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