Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Get the latest international news and world events from around the world.

Log in for authorized contributors

Machine learning method uncovers hidden patterns in DNA methylation

In a study recently published in Nature Communications, researchers from Berlin, Potsdam, and Jena present a new method for analyzing the epigenome. The machine-learning method identifies differentially methylated DNA regions without sample labels—a prerequisite for many existing algorithms. This makes it possible to identify previously hidden biological patterns as well as new subgroups of cells or diseases.

The activity of our genes is not determined by DNA sequence alone. The attachment of small chemical compounds—known as methyl groups—influences which genes are active and which remain silenced. DNA methylation is thus a central component of the epigenome.

Changes to the epigenome play a crucial role in the development of our bodies, influence the aging process and are relevant to numerous diseases, such as cancer. To understand such changes, researchers specifically search for differentially methylated DNA regions (DMRs). However, existing methods usually require samples under investigation to be assigned to known groups—such as healthy or diseased tissue. With complex clinical datasets, however, this information is often unknown.

Stress gene ‘stuck on’ in the brains of people with schizophrenia, study finds

Experts at the University of Sydney have found that a gene involved in regulating the body’s response to stress switches on more easily in the brains of people who live with schizophrenia.

The study, published in the American Journal of Psychiatry and carried out in collaboration with researchers at the Max Planck Institute of Psychiatry, looked at the FKBP5 gene and the corresponding FKBP51 protein, which help regulate how strongly the body responds to stress hormones such as cortisol.

Using donated brain tissue, the researchers found that for people with schizophrenia, the chemical tags that normally keep the FKBP5 gene in check had been stripped away. This change was linked to higher activity of the FKBP5 gene, opening up the possibility of developing new treatments that target it.

Simulations reveal asymmetric diffusion of magnetic skyrmions through an off-center gate

Diffusion is a fundamental natural phenomenon that can be observed across a wide range of length and time scales. It plays a key role in many fields, including physics, biology and economics. In particular, asymmetric or directional diffusion of particle systems has attracted growing interest for practical applications, including the development of unconventional artificial intelligence (AI) hardware, where it could enable nonlinear, geometry-controlled information processing.

Magnetic skyrmions are topological spin textures that can behave as particle-like objects with chiral dynamics. Recent reports have shown that even tiny thermal fluctuations can drive effective diffusion of skyrmions in ultrathin magnetic films and layered heterostructures. Some experiments have also revealed a topology-dependent sideways, wall-guided motion known as the Brownian gyromotion of skyrmions when they interact in a confined space.

Magnetic skyrmions can also exhibit exotic dynamic behaviors that cannot be reproduced by common particles. Their diffusive properties have immense potential in novel information-processing applications. However, these properties, especially in structured environments, remain largely unexplored.

Sound waves do double duty, carrying and protecting quantum information

Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have demonstrated a promising new way to protect fragile quantum information using nothing but mechanical vibrations—essentially extremely small sound waves. The breakthrough, which comes from the lab of Marko Lončar, Tiantsai Lin Professor of Electrical Engineering, paves a path toward compact, sound-based quantum networks on chips, as well as hybrid quantum systems that combine many different types of quantum bits, or qubits.

The research is published in Nature Physics. Experiments were led by Eliza Cornell, a recent Ph.D. graduate from the Lončar lab and current postdoctoral researcher at Boston University, and Zhujing Xu, a former postdoctoral scholar in Lončar’s group.

Erasable semiconductor is programmed with light

Researchers at Princeton Engineering have created a semiconductor with unique properties: It is just a few molecules thick and can repeatedly change its properties in response to light. This is a step toward building more energy-efficient sensors, optoelectronic devices and computing technologies.

Advances in semiconductors over the past 50 years have allowed engineers to create increasingly smaller semiconductor devices and more efficient computers. But this approach is reaching its physical limits—the semiconductor devices are so tiny that it is challenging to add more devices. Researchers are now creating new materials rather than shrinking existing ones.

“One of the future goals for advancing electronics is not just making materials smaller, but making them adaptable and smarter,” said Jaehoon Ji, a postdoctoral researcher and first author on the July 1 paper describing the research in Science Advances.

Scientists resurrect ancient proteins, offering new antibiotic leads

University of Oregon biologists have resurrected prehistoric proteins up to 160 million years old that carry natural antimicrobial properties. The revived molecules could inspire the design of new treatments for antibiotic-resistant infections, a pressing global health issue.

Described in a paper published in PLOS Biology on Aug. 25, the scientists worked their way up the tree of life, reconstructing peptides—short protein fragments—dating back to the earliest placental mammals, the diverse lineage that includes humans and nearly all mammals alive today. In laboratory tests, the researchers found that some of the extinct peptides were more potent against drug-resistant bacteria than some of their present-day counterparts.

Evolution’s ancient remedies could offer new starting points for scientists designing treatments that supplement or replace antibiotics that no longer work, said Matt Barber, senior author of the paper and evolutionary biologist at the UO College of Arts and Sciences.

Exact calculations sharpen view of atomic nuclei

Every high-energy nuclear collision leaves behind a trail of clues about the structure of atomic nuclei. Deciphering those clues, however, depends on the accuracy of the underlying theory. Physicists at Osaka Metropolitan University have now performed a full calculation within Glauber theory, a cornerstone framework for describing high-energy nuclear collisions.

By overcoming a computational challenge that has long forced researchers to rely on approximations to reduce computational demands, the team has shown that its full calculation can accurately reproduce experimental data and provide a reliable framework for predicting the outcomes of future experiments involving ordinary and exotic nuclei.

The study was published in Physical Review Letters on May 18 and Physical Review C on June 1. Physical Review Letters provides a brief overview of the main findings, while Physical Review C contains the complete paper with additional details, results and analysis.

Chemical physicists quantitatively model electron interactions in real quantum materials

A team of scientists from Caltech and Yale University has shown for the first time how to accurately quantify an important quantum phenomenon in metals, called the Kondo effect, for specific real materials. Unlike previous approaches, which for decades have relied on simplified models to qualitatively describe the effect, the new work uses the actual atomic and electronic structures of materials to solve the problem directly.

The work represents a step toward simulations of important quantum materials such as high-temperature superconductors, in which the motions of individual electrons depend so sensitively on what other electrons are doing at any moment that they cannot be averaged together.

The team describes the new technique and results in a paper published in Science. The lead authors are Linqing Peng (Ph. D.) and Tianyu Zhu of Yale University. Both Peng and Zhu started working on the project in the lab of Garnet Chan, Bren Professor of Chemistry and director of the Rudolph A. Marcus Center for Theoretical Chemistry at Caltech.

Bright ideas accelerate the hunt for quantum emitters

The search for materials that can power future quantum technologies is accelerating, but identifying the most promising candidates remains painfully slow. Evaluating whether a material can efficiently emit quantum light requires computationally intensive simulations, making it difficult to screen the vast number of available materials.

Now, researchers from the University of Osaka have overcome this bottleneck with a prediction method that rapidly evaluates promising quantum materials without sacrificing accuracy. They established a high-speed first-principles framework for evaluating atomic-scale color centers that emit single photons and store quantum information.

The findings are published in the journal npj Computational Materials.

AI Decodes a Hidden DNA Signal Linked to Disease-Causing Mutations

Machine learning identifies the likely “initiator” and enables new predictions about DNA mutations that can cause disease.

Every human cell depends on tens of thousands of genes being switched on at the right time and in the right amount. Specialized stretches of DNA coordinate this activity, ultimately directing the production of enzymes, hormones, proteins, and other components essential to cell structure and function. When that regulation goes wrong, cells can malfunction and contribute to disorders including cancer.

To better understand the DNA sequences that control this process, researchers in the University of California San Diego Professor James T. Kadonaga’s laboratory focused on a crucial region known as the “initiator.” This DNA segment marks the point where information encoded in a gene first begins to be converted, or expressed, into functional products.

/* */