Lifeboat Foundation

A recent study from the McGovern Institute for Brain Research shows how interests can modulate language processing in children’s brains and paves the way for personalized brain research.

The paper, which appears in Imaging Neuroscience, was conducted in the lab of MIT professor and McGovern Institute investigator John Gabrieli, and led by senior author Anila D’Mello, a recent McGovern postdoc who is now an assistant professor at the University of Texas Southwestern Medical Center and the University of Texas at Dallas.

“Traditional studies give subjects identical stimuli to avoid confounding the results,” says Gabrieli, who is the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT. “However, our research tailored stimuli to each child’s interest, eliciting stronger—and more consistent—activity patterns in the brain’s language regions across individuals.”

Depth degradation is a problem biologists know all too well: The deeper you look into a sample, the fuzzier the image becomes. A worm embryo or a piece of tissue may only be tens of microns thick, but the bending of light causes microscopy images to lose their sharpness as the instruments peer beyond the top layer.

To deal with this problem, microscopists add technology to existing microscopes to cancel out these distortions. But this technique, called adaptive optics, requires time, money, and expertise, making it available to relatively few biology labs.

Now, researchers at HHMI’s Janelia Research Campus and collaborators have developed a way to make a similar correction, but without using adaptive optics, adding additional hardware, or taking more images. A team from the Shroff Lab has developed a new AI method that produces sharp microscopy images throughout a thick biological sample.

Researchers at Duke University have uncovered the molecular inner workings of a material that could underpin next-generation rechargeable batteries.

Unlike today’s popular lithium-ion batteries that feature a liquid interior, the lithium-based compound is a solid at operational temperatures. But despite its rigid interior structure, charged ions are still able to quickly travel through, making it a “super ionic” material. While researchers have been interested in this compound for some time, they have not known how lithium ions are able to pass through its solid crystalline structure so easily.

The new results answer many standing questions, showing surprising liquid-like behavior at the atomic level. With these insights in hand, as well as the machine learning models used to obtain them, researchers are set to explore similar recipes to solve many of the field’s long-standing challenges.

Numerous memory types for computing devices have emerged in recent years, aiming to overcome the limitations imposed by traditional random access memory (RAM). Magnetoresistive RAM (MRAM) is one such memory type which offers several advantages over conventional RAM, including its non-volatility, high speed, increased storage capacity and enhanced endurance.

Although remarkable improvements have been made to MRAM devices, reducing energy consumption during data writing remains a critical challenge.

A study published in Advanced Science by researchers from Osaka University proposes a new technology for MRAM devices with lower-energy data writing. The proposed technology enables an electric-field-based writing scheme with reduced energy consumption compared to the present current-based approach, potentially providing an alternative to traditional RAM.

In quantum computers, information is often carried by single photons and picked up by structures named superconducting nanostrip single-photon detectors (SNSPDs). In principle, traditional type-I superconductors would be easier to integrate into existing quantum computing architectures than the type-II materials more widely used today. So far, however, this possibility hasn’t been widely explored.

New research published in Superconductivity shows how Lixing You and colleagues at the Chinese Academy of Sciences, Shanghai, China have for the first time successfully fabricated an SNSPD using thin films of the type-I superconductor, aluminum, and used the structure to detect single photons of visible light with extremely high efficiency.

Compared with the type-II superconductors more commonly used in SNSPDs so far, aluminum is more compatible with the latest quantum computing architectures.

AI algorithms analyzes the OAM spectrum and intensity patterns of twisted light as it propagates through a turbulent medium, enabling the detection of the key features of the medium.

A breakthrough in artificial intelligence.

Artificial Intelligence (AI) is a branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. These tasks include understanding natural language, recognizing patterns, solving problems, and learning from experience. AI technologies use algorithms and massive amounts of data to train models that can make decisions, automate processes, and improve over time through machine learning. The applications of AI are diverse, impacting fields such as healthcare, finance, automotive, and entertainment, fundamentally changing the way we interact with technology.

Physicists turn to supercomputers to help build a 3D picture of the structures of protons and neutrons.

A team of scientists has made exciting advances in mapping the internal components of hadrons. They employed complex quantum chromodynamics and supercomputer simulations to explore how quarks and gluons interact within protons, aiming to unravel mysteries like the proton’s spin and internal energy distribution.

Unveiling the Parton Landscape.

CISA lists critical flaws in Mitel MiCollab (CVE-2024–41713, CVE-2024–55550) and Oracle WebLogic (CVE-2020–2883).