After many years, this venerable technique continues to evolve, yielding many different solutions for exploring atomic-scale landscapes in biology, chemistry, materials and other disciplines.
Category: biological – Page 10
Advancements could enhance perceptual capabilities in robotics. Artificially engineered biological processes, such as perception systems, remain a challenging target for organic electronics experts due to the dependence of human senses on an adaptive network of sensory neurons that communicate by firing in response to environmental stimuli.
At the heart of language neuroscience lies a fundamental question: How does the human brain process the rich variety of languages? Recent developments in Natural Language Processing, particularly in multilingual neural network language models, offer a promising avenue to answer this question by providing a theory-agnostic way of representing linguistic content across languages. Our study leverages these advances to ask how the brains of native speakers of 21 languages respond to linguistic stimuli, and to what extent linguistic representations are similar across languages. We combined existing (12 languages across 4 language families; n=24 participants) and newly collected fMRI data (9 languages across 4 language families; n=27 participants) to evaluate a series of encoding models predicting brain activity in the language network based on representations from diverse multilingual language models (20 models across 8 model classes). We found evidence of cross-lingual robustness in the alignment between language representations in artificial and biological neural networks. Critically, we showed that the encoding models can be transferred zero-shot across languages, so that a model trained to predict brain activity in a set of languages can account for brain responses in a held-out language, even across language families. These results imply a shared component in the processing of different languages, plausibly related to a shared meaning space.
The authors have declared no competing interest.
Recent research demonstrates that brain organoids can indeed “learn” and perform tasks, thanks to AI-driven training techniques inspired by neuroscience and machine learning. AI technologies are essential here, as they decode complex neural data from the organoids, allowing scientists to observe how they adjust their cellular networks in response to stimuli. These AI algorithms also control the feedback signals, creating a biofeedback loop that allows the organoids to adapt and even demonstrate short-term memory (Bai et al. 2024).
One technique central to AI-integrated organoid computing is reservoir computing, a model traditionally used in silicon-based computing. In an open-loop setup, AI algorithms interact with organoids as they serve as the “reservoir,” for processing input signals and dynamically adjusting their responses. By interpreting these responses, researchers can classify, predict, and understand how organoids adapt to specific inputs, suggesting the potential for simple computational processing within a biological substrate (Kagan et al. 2023; Aaser et al. n.d.).
Our body isn’t just human—it’s home to trillions of microorganisms found in or on us. In fact, there are more microbes in our gut than there are stars in the Milky Way. These microbes are essential for human health, but scientists are still figuring out exactly what they do and how they help.
In a new study, published in Nature Microbiology, my colleagues and I explored how certain gut bacteria—a group known as Enterobacteriaceae—can protect us from harmful ones. These bacteria include species such as Escherichia coli (E coli). This is normally harmless in small amounts but can cause infections and other health problems if it grows too much.
We found that our gut environment—shaped by things like diet—plays a big role in keeping potentially harmful bacteria in check.
A new model shows that the denizens of a vast, ancient biome beneath the seafloor use barely enough energy to stay alive — and broadens understanding of what life can look like.
Cis-trans photoisomerization is a key process for many processes in biology and materials science, but only careful and time-consuming quantum chemistry methods can describe such reaction in detail. Here, a predictive tool is presented requiring few and affordable calculations, evaluating the efficiency of paradigmatic and modified photoswitches.
A groundbreaking discovery by researchers at the University of California, Los Angeles (UCLA) has challenged a long-standing rule in organic chemistry known as Bredt’s Rule. Established nearly a century ago, this rule stated that certain types of specific organic molecules could not be synthesized due to their instability. UCLA’s team’s findings open the door to new molecular structures that were previously deemed unattainable, potentially revolutionizing fields such as pharmaceutical research.
To grasp the significance of this breakthrough, it’s helpful to first understand some basics of organic chemistry. Organic chemistry primarily deals with molecules made of carbon, such as those found in living organisms. Among these, certain molecules known as olefins or alkenes feature double bonds between two carbon atoms. These double bonds create a specific geometry: the atoms and atom groups attached to them are generally in the same plane, making these structures fairly rigid.
In 1924, German chemist Julius Bredt formulated a rule regarding certain molecular structures called bridged bicyclic molecules. These molecules have a complex structure with multiple rings sharing common atoms, akin to two intertwined bracelet loops. Bredt’s Rule dictates that these molecules cannot have a double bond at a position known as the bridgehead, where the two rings meet. The rule is based on geometric reasons: a double bond at the bridgehead would create such significant structural strain that the molecule would become unstable or even impossible to synthesize.
Associate Professor Qing Zhong shared insights on AI breakthroughs in proteomics and multiomics, challenges in data sharing and his vision for a high-resolution lens on biology that could redefine science and healthcare.
Treating hair loss may be as simple as developing therapies to flip a molecular “switch,” according to a new study by researchers from Penn State; the University of California, Irvine; and National Taiwan University.
The researchers reviewed the biological and social evolution of human scalp hair. Based on their analysis, they proposed a novel theory that points to a molecular basis underlying the ability to grow long scalp hair.
In short, human ancestors may have always had the ability to grow long scalp hair, but the trait remained dormant until certain environmental and biological conditions — like walking upright on two legs — turned on the molecular program. The team published their findings, which they said could serve as the basis for future experimental work, in the British Journal of Dermatology.