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Experiment explores contribution of neural, epigenetic and behavioral factors to autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is estimated to be experienced by roughly 1 in 127 people worldwide. It is characterized by atypical patterns in brain development, which manifest in differences in communication, social interactions, behavior and responses to sensory information.

Past neuroscientific and suggest that a variety of factors contribute to the development of ASD. These can include , chemical alterations that influence the expressions of genes (i.e., epigenetic factors), differences in the structure of specific or neural circuits, and environmental factors, such as early life events or infections or immune responses during pregnancy.

Researchers at the Korea Brain Research Institute and University of Fukui in Japan recently carried out a study aimed at further exploring these different dimensions of ASD, focusing on , the communication between brain regions, epigenetic changes and behavioral patterns. Their findings, published in Translational Psychiatry, paint a clearer picture of the intricate underpinnings of the disorder and could inform the development of more precise tools for diagnosing it.

Outdoor air exposure to industrial solvent trichloroethylene may raise risk of Parkinson’s disease

Long-term exposure to the industrial solvent trichloroethylene (TCE) outdoors may be linked to an increased risk of Parkinson’s disease, according to a large nationwide study published in Neurology.

TCE is a chemical used in metal degreasing, and other industrial applications. Although TCE has been banned for certain uses, it remains in use today as an industrial solvent and is a persistent environmental pollutant in air, water and soil across the United States. The study does not prove that TCE exposure causes Parkinson’s disease, it only shows an association.

“In this nationwide study of older adults, long-term exposure to trichloroethylene in outdoor air was associated with a small but measurable increase in Parkinson’s risk,” said study author Brittany Krzyzanowski, Ph.D., of Barrow Neurological Institute in Phoenix. “These findings add to a growing body of evidence that environmental exposures may contribute to Parkinson’s disease.”

Data from dark-energy observatories indicate universe may ‘end in a big crunch’ at 33 billion years old

The universe is approaching the midpoint of its 33-billion-year lifespan, a Cornell physicist calculates with new data from dark-energy observatories. After expanding to its peak size about 11 billion years from now, it will begin to contract—snapping back like a rubber band to a single point at the end.

Palladium filters could enable cheaper, more efficient generation of hydrogen fuel

Palladium is one of the keys to jump-starting a hydrogen-based energy economy. The silvery metal is a natural gatekeeper against every gas except hydrogen, which it readily lets through. For its exceptional selectivity, palladium is considered one of the most effective materials at filtering gas mixtures to produce pure hydrogen.

Today, palladium-based membranes are used at commercial scale to provide pure for semiconductor manufacturing, food processing, and fertilizer production, among other applications in which the membranes operate at modest temperatures. If palladium membranes get much hotter than around 800 Kelvin, they can break down.

Now, MIT engineers have developed a new palladium that remains resilient at much higher temperatures. Rather than being made as a continuous film, as most membranes are, the new design is made from palladium that is deposited as “plugs” into the pores of an underlying supporting material. At high temperatures, the snug-fitting plugs remain stable and continue separating out hydrogen, rather than degrading as a surface film would.

One-atom-thick filter helps lithium–sulfur batteries keep their charge

Longer-lasting phones, lighter drones, electric cars that drive farther. These are just some of the possibilities thanks to a new battery separator design from University of Florida researchers and their partners.

Think of a tiny coffee filter, but this one works inside a battery. The team recently showed that a one-atom-thick filter can block sulfur chains from shuttling within the battery, potentially unlocking the long-awaited promise of lithium–sulfur batteries.

While lithium–sulfur batteries are lighter and pack more power in a lighter package compared to the more conventional lithium-ion batteries, their fatal flaw is the sulfur doesn’t cooperate well inside the system. It clumps into long chains that clog up the works, draining the battery’s power and cutting its lifespan.

AI model can manipulate time to make better predictions in a wide range of fields

In the world around us, many things exist in the context of time: a bird’s path through the sky is understood as different positions over a period of time, and conversations as a series of words occurring one after another.

Computer scientists and statisticians call these sequences time series. Although statisticians have found ways to understand these patterns and make predictions about the future, modern AI models struggle to perform just as well, if not worse, than statistical models.

Engineers at the University of California, Santa Cruz, developed a new method for time series forecasting, powered by deep learning, that can improve its predictions based on data from the near future. When they applied this approach to the critical task of seizure prediction using brain wave data, they found that their strategy offers up to 44.8% improved performance for predicting seizures compared to baseline methods. While they focused on this critical health care application, the researchers’ method is designed to be relevant for a wide range of fields. A new study in Nature Communications reports their results.

Combination of quantum and classical computing supports early diagnosis of breast cancer

Quantum computing is still in its early stages of development, but researchers have extensively explored its potential uses. A recent study conducted at São Paulo State University (UNESP) in Brazil proposed a hybrid quantum-classical model to support breast cancer diagnosis from medical images.

The work was published as part of the 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), organized by the Institute of Electrical and Electronics Engineers (IEEE). In the publication, the authors describe a hybrid that combines quantum and classical layers using an approach known as a quanvolutional neural network (QNN). They applied the model to mammography and ultrasound images to classify lesions as benign or malignant.

“What we wanted to bring to this work was a very basic architecture that used quantum computing but contained a minimum of quantum and classical devices,” says Yasmin Rodrigues, the first author of the study. The work is part of her scientific initiation project, supervised by João Paulo Papa, full professor in the Department of Computing at the Bauru campus of UNESP. Papa also co-authored the article.

Entangled states enhance energy transfer in models of molecular systems

A study from Rice University, published in PRX Quantum, has found that energy transfers more quickly between molecular sites when it starts in an entangled, delocalized quantum state instead of from a single site. The discovery could lead to the development of more efficient light-harvesting materials that enhance the conversion of energy from light into other forms of energy.

Many , including photosynthesis, depend on rapid and efficient energy transfer following absorption. Understanding how quantum mechanical effects like entanglement influence these processes at room temperature could significantly change our approach to creating artificial systems that mimic nature’s efficiency.

“Delocalizing the initial excitation across multiple sites accelerates the transfer in ways that starting from a single site cannot achieve,” said Guido Pagano, the study’s corresponding author and assistant professor of physics and astronomy.

Heat-rechargeable design powers nanoscale molecular machines

Though it might seem like science fiction, scientists are working to build nanoscale molecular machines that can be designed for myriad applications, such as “smart” medicines and materials. But like all machines, these tiny devices need a source of power, the way electronic appliances use electricity or living cells use ATP (adenosine triphosphate, the universal biological energy source).

Researchers in the laboratory of Lulu Qian, Caltech professor of bioengineering, are developing nanoscale machines made out of synthetic DNA, taking advantage of DNA’s unique chemical bonding properties to build circuits that can process signals much like miniature computers. Operating at billionth-of-a-meter scales, these molecular machines can be designed to form DNA robots that sort cargos or to function like a neural network that can learn to recognize handwritten numerical digits.

One major challenge, however, has remained: how to design and power them for multiple uses.

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