How does brain activity change from childhood to old age? Researchers introduce Xi–αNET, an AI model linking EEG rhythms to nerve-signal speed and myelination. Discover why alpha waves slow down as we age.
Category: robotics/AI
How “mindreading” AI detects hidden suicidal thoughts in the brains of young adults
A recent study published in Human Brain Mapping provides evidence that young adults experiencing suicidal thoughts process concepts related to death differently in their brains compared to healthy individuals. The findings indicate that these individuals reflexively associate death-related ideas with their own sense of self. This research suggests that brain imaging combined with artificial intelligence could eventually help identify people at risk for suicide based on how their brains represent specific words.
If you or someone you know is experiencing suicidal thoughts or a mental health crisis, help is available. Call or text 988 to reach the free and confidential Suicide & Crisis Lifeline, or chat live at 988lifeline.org.
While mental health professionals typically rely on patients to report their feelings, people at risk for suicide do not always disclose their struggles. Finding an objective physical measurement in the brain could help identify those in need of support.
Long-term inflammatory memory driver identified!
The researchers first gave a bout of psoriasis to mice when they were young. They discovered that about 10–15% of the memories that persisted a month later stuck around even to the end of the mouse’s life (~2 years). To see why these long-term memories lingered while their short-term counterparts faded within six months, they analyzed the DNA sequence characteristics within each of the memories by using a deep learning model customized by the third co-first author.
“When we compared the DNA sequences of short and long-term memory domains, they looked very similar in terms of the numbers and kinds of transcription factor binding sites,” says the author. “We realized we needed to develop a new metric that specifically captures memory persistence across time, not just total accessibility at any one point.”
Soto-Ugaldi’s adaptation, called PersistNet, quickly identified a telling trait: The longest lasting memory domains had an unusually high frequency of CpG dinucleotides—short DNA sequences of cytosine followed by guanine, which are known to play a key role in gene regulation. In fact, the model predicted that CpG density hardwires a timer into every memory domain: The more CpG’s, the longer the memory.
When they tested the prediction, that’s exactly what they found. “Looking across all 1,000 memory domains, we discovered that these nucleotide densities alone, and no other DNA sequence pattern, could distinguish how long each memory would linger,” says the author.
Back in the lab, the team discovered that these genetically wired densities enabled a host of epigenetic changes in memory domains, including DNA demethylation (the removal of a methyl group specifically found on CpG dinucleotides); the binding of transcription factors that prefer demethylated states; and the recruitment of a histone variant called H2A.Z, which preferentially seeks out demethylated sites and boosts chromatin accessibility while staving off future re-methylation. Together, these changes stabilized the open chromatin formation and its gene-priming activity. As the authors discovered, this structure could crucially be passed down across cellular generations, essentially keeping the doors open for life. Science Mission sciencenewshighlights.
One of the most puzzling aspects of common chronic inflammatory skin diseases such as psoriasis is how they become chronic. What allows an ongoing condition to stay dormant for months or even years, then seemingly spring back out of nowhere?
Legged robot could accelerate resource prospecting on the moon and the search for life on Mars
Planetary surface missions currently operate cautiously. On Mars, communication delays between Earth and rovers (typically between four and 22 minutes), as well as data transfer constraints due to uplink and downlink limitations, force scientists to plan operations in advance. Rovers are designed for energy efficiency and safety, and to move slowly across hazardous terrain.
As a result, exploration is typically limited to only a small portion of the landing site, with rovers typically traveling up to a few hundreds of meters per day, which makes it difficult to collect geologically diverse data.
In a study published in Frontiers in Space Technologies, a team led by Dr. Gabriela Ligeza, former Ph.D. student from the University of Basel and now a postdoctoral researcher at the European Space Agency (ESA), tested a different approach: a semi-autonomous robotic explorer which can investigate multiple targets one-by-one and collect data without constant human intervention.
What’s inside a masterpiece? Laser scans and AI map paint layers molecule by molecule
Paintings are far more than dabs of oil on canvas. They are complex works of art composed of multiple layers, from primer and glues to the pigments and protective varnishes applied by the artists. Being able to see into these layers and map their chemical makeup is essential for art historians and conservators. A new technique developed by an international team of scientists can now probe paint layers in far greater molecular detail than before.
As they describe in a paper published in the journal Science Advances, the researchers combined a technique called MALDI-MSI (matrix-assisted laser desorption/ionization mass spectrometry imaging) with an AI named MSIpredictART to help identify the specific pigments and binders present in each layer of a painting.
Current approaches looking at the internal structure of a painting have to run several different tests on tiny samples. MALDI-MSI reduces the need for multiple separate techniques by using a high-resolution laser scan to map both the pigments and the binder or glue that holds them together.
Three-in-one diode integrates sensing, memory and processing for smart cameras
Think about how easily you recognize a friend in a dimly lit room. Your eyes capture light, while your brain filters out background noise, retrieves stored visual information, and processes the image to make a match. It all happens in a fraction of a second and uses remarkably little energy. Unfortunately, artificial vision systems in smartphones, cameras, and autonomous machines operate more like an assembly line. In our recent paper published in Nature Electronics, we describe how we addressed this challenge by enabling sensing, memory, and processing within the same device, pointing to a possible route toward more efficient machine vision.
The iGaN Laboratory led by Professor Haiding Sun at the School of Microelectronics, University of Science and Technology of China (USTC), in collaboration with multiple institutions, developed the multifunctional semiconductor diode with integrated photosensing, memory, and processing capabilities.
To understand the challenge, it helps to look at the basic building block of modern digital cameras: the semiconductor p-n diode. These tiny junctions act as the light-sensing pixels in imaging systems. However, a conventional diode is usually limited to a single function. It converts light into an electrical signal, and the captured data must then be transferred to separate memory and processing units. Moving this data back and forth consumes time, power, and chip area.
Meta-Harness: End-to-End Optimization of Model Harnesses
Think of a Large Language Model (LLM) like a brilliant scholar. To do their job well, they don’t just need their own brain; they need a good workspace—a desk with the right books, a filing cabinet that’s easy to navigate, and a clear set of instructions on how to process information. In the tech world, this “workspace” is called a harness.
Up until now, these harnesses have been built by human engineers through trial and error. While we have tools to automatically improve the AI’s “brain” (the model weights), the code that actually manages the AI’s information has remained stubbornly manual.
Meta-Harness automatically optimizes model harnesses — the code determining what to store, retrieve, and present to an LLM — surpassing hand-designed systems on text classification, math reasoning, and agentic coding.
Cooperation by non-kin during birth underpins sperm whale social complexity
In an unprecedented observation, researchers in Science captured the birth of a sperm whale calf, documenting how 11 whales from two normally separate family groups coordinated closely to support the newborn for hours after its arrival.
These findings offer quantitative evidence of direct communal caregiving in cetaceans and suggest that short-term, highly coordinated cooperation during critical moments like birth may play a foundational role in maintaining the complex social structures seen in sperm whale societies.
Birth and neonatal care represent particularly revealing contexts for understanding the emergence of cooperation. Cetacean species produce a small number of offspring with long lifespans. Calves are born infrequently and represent a major maternal investment; calf survival depends heavily on immediate support after birth and early caregiving (9). Thus, births offer critical opportunities to study how individuals coordinate in high-stakes contexts. Direct quantitative observations of sperm whale births remain virtually absent (14), with only four sperm whale births being reported over the past 60 years, and all of them either anecdotal or whaling related (15–18).
Within the matrilineal social units of sperm whales, individuals take turns socializing, foraging, and caring for calves across years (19–24). Through decades of observational work (19, 21, 22, 25–28), communal allocare for calves has been identified as the central mechanism driving selection for sociality in this species. Although it has been hypothesized that communal defense and shared parental care underpin the evolution of sperm whale sociality (19, 22, 23, 26), these hypotheses have lacked direct empirical grounding during the birth of a newborn. Newborns are assumed to be negatively buoyant (20, 29) and likely require immediate physical support to breathe, and this potentially shapes the evolutionary importance of cooperative allocare within units (26, 30). Under this framework, the survival of mothers and newborns around birth creates a potentially dangerous environment in which selection is strongly imposed.
Here, we present a high-resolution, multiscale analysis of a sperm whale birth event through the integration of drone-based videography, machine learning, and longitudinal association and kinship data. We quantified how individuals across two distinct matrilines coordinated around the mother and newborn by analyzing and tracking physical support, proximity, orientation, and role distribution over time. Our results suggest that kin and non-kin engaged in sustained, cooperative, postnatal care, taking turns to support the newborn and maintain group cohesion, in contrast to historical kin-segregated foraging patterns (21). These findings provide rare quantitative evidence of direct allocare in cetaceans and can lend support to the hypothesis that transient, structured cooperation during birth is a key mechanism sustaining complex sociality in sperm whales.