Before determining the correct quantum theory of gravity, researchers need to know if gravity is actually quantized. Experiments testing that assumption are now being developed.
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AI Discovers Geophysical Turbulence Model
One of the biggest challenges in climate science and weather forecasting is predicting the effects of turbulence at spatial scales smaller than the resolution of atmospheric and oceanic models. Simplified sets of equations known as closure models can predict the statistics of this “subgrid” turbulence, but existing closure models are prone to dynamic instabilities or fail to account for rare, high-energy events. Now Karan Jakhar at the University of Chicago and his colleagues have applied an artificial-intelligence (AI) tool to data generated by numerical simulations to uncover an improved closure model [1]. The finding, which the researchers subsequently verified with a mathematical derivation, offers insights into the multiscale dynamics of atmospheric and oceanic turbulence. It also illustrates that AI-generated prediction models need not be “black boxes,” but can be transparent and understandable.
The team trained their AI—a so-called equation-discovery tool—on “ground-truth” data that they generated by performing computationally costly, high-resolution numerical simulations of several 2D turbulent flows. The AI selected the smallest number of mathematical functions (from a library of 930 possibilities) that, in combination, could reproduce the statistical properties of the dataset. Previously, researchers have used this approach to reproduce only the spatial structure of small-scale turbulent flows. The tool used by Jakhar and collaborators filtered for functions that correctly represented not only the structure but also energy transfer between spatial scales.
They tested the performance of the resulting closure model by applying it to a computationally practical, low-resolution version of the dataset. The model accurately captured the detailed flow structures and energy transfers that appeared in the high-resolution ground-truth data. It also predicted statistically rare conditions corresponding to extreme-weather events, which have challenged previous models.
Current flows without heat loss in newly engineered fractional quantum material
A team of US researchers has unveiled a device that can conduct electricity along its fractionally charged edges without losing energy to heat. Described in Nature Physics, the work, led by Xiaodong Xu at the University of Washington, marks the first demonstration of a “dissipationless fractional Chern insulator,” a long-sought state of matter with promising implications for future quantum technologies.
The quantum Hall effect emerges when electrons are confined to a two-dimensional material, cooled to extremely low temperatures, and exposed to strong magnetic fields. Much like the classical Hall effect, it describes how a voltage develops across a material perpendicular to the direction of current flow. In this case, however, that voltage increases in discrete, or quantized steps.
Under even more extreme conditions, an exotic variant appears named the “fractional quantum Hall” (FQH) effect. Here, electrons no longer behave as independent particles but move collectively, giving rise to voltage steps that correspond to fractions of an electron’s charge. This unusual collective behavior unlocks a whole host of exotic properties, and has made such states particularly appealing for emerging quantum technologies.
Old galaxies in a young universe?
The standard cosmological model (present-day version of “Big Bang,” called Lambda-CDM) gives an age of the universe close to 13.8 billion years and much younger when we explore the universe at high-redshift. The redshift of galaxies is produced by the expansion of the universe, which causes emitted wavelengths to lengthen and move toward the red end of the electromagnetic spectrum.
The further away a galaxy is, the more rapidly it is moving with respect to us, and so the greater is its redshift; and, given that the speed of light is finite, the more we travel to the past. Hence, measuring the age of very high redshift galaxies would be a way to test the cosmological model. Galaxies cannot be older than the age of the universe in which they are; it would be absurd, like a son older than his mother.
In work carried out with my colleague, Carlos M. Gutiérrez, at the Canary Islands Astrophysics Institute (IAC; Spain), we analyzed 31 galaxies with average redshift 7.3 (when the universe was 700 Myr old, according to the standard model) observed with the most powerful available telescope available: the James Webb Space Telescope (JWST).
Software tool can detect hidden errors in complex tissue analyses
A new software tool, ovrlpy, improves quality control in spatial transcriptomics, a key technology in biomedical research. Developed by the Berlin Institute of Health at Charité (BIH) in international collaboration, ovrlpy is the first tool to identify cell overlaps and folds in tissue sections, thereby reducing previously unrecognized sources of misinterpretations. The researchers have published their results in the journal Nature Biotechnology.
Spatial transcriptomics is a pioneering field of research in biomedicine that visualizes cellular activity within a tissue by mapping RNA transcripts and assigning this molecular activity to individual cells. So far, such analyses of tissue samples have mostly been interpreted in two dimensions. However, even very thin tissue sections of 5 to 10 micrometers thick, about one-tenth the width of a human hair, have a complex three-dimensional structure.
If this 3D arrangement is interpreted only as a flat surface, analytical errors can occur, for example, due to cell overlaps or tissue folds. This impedes the precise assignment of transcripts to individual cells and can distort downstream analysis and interpretation.
Neural crest cells: Miniature electric muscles that colonize embryonic organs
Neural crest cells are a population of stem cells that invade the embryo in early development. They play a big role in what you look like: the pigments of your eyes, of your skin, and the bone structure of your face are all neural crests. Inside your body, the neural crest will form the myelin sheath of your peripheral nervous system and the entire nervous system of your intestine, the so-called “second brain.”
Neurocristopathies are a range of pathologies resulting from defective neural crest migration. One of the most frequent ones is Hirschsprung disease; it affects 1 in 5,000 newborns. These babies lack a nervous system inside their colon because the neural crest cells didn’t make it all the way to the end of the digestive tract during embryogenesis. The condition is lethal if not surgically treated at birth and its causes remain unknown in more than half of cases.
Among the identified genes involved in Hirschsprung disease, one has stood out for more than half a century: the peptide endothelin 3. Mice and humans with genetic defects in either endothelin 3 or its receptor EDNRB develop the disease, in some cases accompanied with pigmentation or craniofacial defects.
Machine learning reveals hidden landscape of robust information storage
In a new study published in Physical Review Letters, researchers used machine learning to discover multiple new classes of two-dimensional memories, systems that can reliably store information despite constant environmental noise. The findings indicate that robust information storage is considerably richer than previously understood.
For decades, scientists believed there was essentially one way to achieve robust memory in such systems—a mechanism discovered in the 1980s known as Toom’s rule. All previously known two-dimensional memories with local order parameters were variations on this single scheme.
The challenge lies in the sheer scale of possibilities. The number of potential local update rules for a simple two-dimensional cellular automaton is astronomically large, far greater than the estimated number of atoms in the observable universe. Traditional methods of discovery through exhaustive search or hand-design are therefore impractical at this scale.
A forgotten battery design from Thomas Edison—how scientists helped reimagine it
A little-known fact: In the year 1900, electric cars outnumbered gas-powered ones on the American road. The lead-acid auto battery of the time, courtesy of Thomas Edison, was expensive and had a range of only about 30 miles. Seeking to improve on this, Edison believed the nickel-iron battery was the future, with the promise of a 100-mile range, a long life and a recharge time of seven hours, fast for that era.
Alas, that promise never reached fruition. Early electric car batteries still suffered from serious limitations, and advances in the internal combustion engine won the day.
Now, an international research collaboration co-led by UCLA has taken a page from Edison’s book, developing nickel-iron battery technology that may be well-suited for storing energy generated at solar farms. The prototype was able to recharge in only seconds, instead of hours, and achieved over 12,000 cycles of draining and recharging—the equivalent of more than 30 years of daily recharges.
Discovery of unique brain tumor subtypes offers hope for targeted glioma therapies
Researchers have uncovered the mechanisms behind three unique subtypes of mismatch repair deficient high-grade gliomas. The findings provide a clearer understanding of how these tumors develop, explain why patients respond differently to immunotherapy, and are already helping guide more precise therapies.
High-grade gliomas are a group of aggressive brain tumors and one of the deadliest tumors in children and young adults. In some children, the tumors are driven by mismatch repair deficiency (MMRD), which is characterized by hypermutation (a large and quickly accumulating number of mutations in tumor cells) and resistance to standard treatments such as chemotherapy and radiation.
Tumors driven by mismatch repair deficiency are known as primary mismatch repair deficient high‑grade gliomas (priMMRD‑HGG). Because priMMRD-HGG have high numbers of mutations, treatment has shifted to immunotherapy, which uses the body’s own immune system to fight cancer by targeting cancer cells.
DNA-binding proteins from volcanic lakes could improve disease diagnosis
Scientists have uncovered new DNA-binding proteins from some of the most extreme environments on Earth and shown that they can improve rapid medical tests for infectious diseases. The work has been published in Nucleic Acids Research. The international research team, led by Durham University and working with partners in Iceland, Norway and Poland, analyzed genetic material from Icelandic volcanic lakes and deep-sea vents more than two kilometers below the surface of the North Atlantic Ocean.
Nature is the world’s largest source of useful enzymes, but many remain undiscovered. By using next-generation DNA sequencing, the researchers were able to search huge databases containing millions of potential proteins.
This approach allowed them to identify previously unknown proteins that bind to single-stranded DNA and remain stable under harsh conditions such as high temperatures, extreme pH or high salt levels.