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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.

Deep learning detects foodborne bacteria within three hours by eliminating debris misclassifications

Researchers have significantly enhanced an artificial intelligence tool used to rapidly detect bacterial contamination in food by eliminating misclassifications of food debris that looks like bacteria. Current methods to detect contamination of foods such as leafy greens, meat and cheese, which typically involve cultivating bacteria, often require specialized expertise and are time-consuming—taking several days to a week.

Luyao Ma, an assistant professor at Oregon State University, and her collaborators from the University of California, Davis, Korea University and Florida State University, have developed a deep learning-based model for rapid detection and classification of live bacteria using digital images of bacteria microcolonies. The method enables reliable detection within three hours. The findings are published in the journal npj Science of Food.

Their latest breakthrough involves training the model to distinguish bacteria from microscopic food debris to improve its accuracy. A model trained only on bacteria misclassified debris as bacteria more than 24% of the time. The enhanced model, trained on both bacteria and debris, eliminated misclassifications.

New nanoparticles remove melanoma tumors in mice with low-power near-infrared laser

Researchers at Oregon State University have developed and tested in a mouse model a new type of nanoparticle that enables the removal of melanoma tumors with a low-power laser. After the systemically administered nanoparticles accumulate in cancerous tissue, exposure to near-infrared light causes them to heat up and destroy the melanoma cells, leaving healthy tissue unharmed.

The study led by Olena Taratula and Prem Singh of the Oregon State University College of Pharmacy represents a huge step toward solving a persistent problem with using photothermal therapy to treat melanoma, the deadliest form of skin cancer: Conventional nanoparticles require lasers with power densities that are unsafe for the skin. Findings were published in Advanced Functional Materials.

Taratula, associate professor of pharmaceutical sciences, and Singh, a postdoctoral researcher in Taratula’s lab, based their new theranostic platform —it can be used for both treatment and diagnosis—on gold nanorods. The nanorods are coated with an iron-cobalt shell and tightly loaded with a dye that heats up upon exposure to near-infrared light—invisible, low-frequency radiation able to penetrate deeply into human tissue.

Anthropic’s ‘anonymous’ interviews cracked with an LLM

In December, the artificial intelligence company Anthropic unveiled its newest tool, Interviewer, used in its initial implementation “to help understand people’s perspectives on AI,” according to a press release. As part of Interviewer’s launch, Anthropic publicly released 1,250 anonymized interviews conducted on the platform.

A proof-of-concept demonstration, however, conducted by Tianshi Li of the Khoury College of Computer Sciences at Northeastern University, presents a method for de-anonymizing anonymized interviews using widely available large language models (LLMs) to associate responses with the real people who participated. The paper is published on the arXiv preprint server.

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