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New tool uncovers overlooked disease-linked genes by accounting for ancestry and family ties

Every person’s DNA tells a unique story. To unlock the full potential of genetic research, scientists need tools that reflect the complexity of the people they study.

Researchers at Baylor College of Medicine and Texas Children’s Duncan Neurological Research Institute (Duncan NRI) have developed a new computational method that enables scientists to more accurately identify genetic changes linked to disease by accounting for the ancestry and family relationships found in real-world populations.

Published in Nature Genetics, the new approach, called Tractor-Mix, addresses a longstanding challenge in genetic research. Many existing methods struggle to accurately analyze people whose DNA reflects ancestry from more than one ancestral population, as well as relatives participating in the same study. As a result, researchers often must simplify their data or exclude participants altogether.

Philosophy Of Physics (@PhilosophyOfPhy) on X

The continuity equation was not the work of a single physicist. Its development grew from early hydraulic studies and the work of Daniel and Johann Bernoulli. In the eighteenth century, Jean le Rond d’Alembert produced the first partial-differential expression of mass conservation in fluid motion, and Leonhard Euler soon placed it in the general mathematical framework that became the foundation of modern fluid mechanics. It should therefore not be attributed solely to Giovanni Battista Venturi, whose later work concerned flow through constricted tubes. Its general form is ∂ρ/∂t + ∇·(ρv) = 0 where ρ is fluid density and v is the velocity field. The equation says that mass cannot simply appear or disappear: any change in the amount of fluid inside a region must be explained by fluid entering or leaving it. For steady flow through a pipe, this becomes ρ₁A₁v₁ = ρ₂A₂v₂ If the fluid is effectively incompressible, its density remains constant, giving the familiar form: A₁v₁ = A₂v₂ The meaning is simple. The same volume of fluid must pass through every section of the pipe each second. When the pipe becomes narrower, the fluid must move faster; when it becomes wider, the fluid slows down. This equation is fundamental to the study of pipes, nozzles, rivers, aircraft flow, circulation systems and computational fluid dynamics. More broadly, continuity equations appear throughout physics wherever something locally conserved, such as mass or electric charge, moves through space. The equation is not merely about fluids; it is the mathematical language of the principle that what flows into a region must either flow out or remain inside.

Brain-penetrating nanoparticles, ultrasound and microbubbles show promise in treating glioblastoma

University of Virginia Comprehensive Cancer Center scientists have developed a promising new experimental approach to targeting glioblastoma, the most common and deadliest brain cancer. The approach could overcome many of the limitations of treatments using existing drugs.

UVA’s Roger Abounader, MD, Ph.D., and colleagues have identified “microRNAs” that can simultaneously suppress multiple malfunctioning genes responsible for glioblastoma’s formation and growth. The scientists use a combination of brain-penetrating nanoparticles, focused ultrasound waves and microbubbles to deliver the miRNAs through the brain’s natural protective barrier—a barrier that typically blocks treatments for tumors and neurodegenerative diseases. The study is published in the Journal of Clinical Investigation.

“This new approach could help target numerous molecules that promote cancer growth, including those for which no drugs exist, at the same time to achieve better therapies,” said Abounader, a professor at UVA’s School of Medicine, Department of Microbiology, Immunology and Cancer Biology, Comprehensive Cancer Center and Center for RNA Science and Medicine. “We are hoping to translate our findings into future clinical trials for patients with glioblastoma and other brain tumors.”

Same carcinogen, different tumors: Mouse study reveals the role of genetic background

Why do cancers develop differently in different people—even when they are exposed to the same risk factors? An international research group, including the German Cancer Research Center (DKFZ), has demonstrated in mice that an organism’s genetic makeup significantly influences the course of cancer development.

The findings, published in Nature, provide new insights into the earliest stages of tumor development and could, in the long term, represent an important step toward more precise, personalized cancer medicine.

These ancient quasars shouldn’t exist so soon after the Big Bang

Scientists have found the oldest quasars ever seen, revealing giant black holes blazing across the universe when it was only 670 million years old. Astronomers have uncovered 31 of the oldest known quasars, including the two earliest ever detected, shining from a time when the universe was only about 670 million years old. Powered by supermassive black holes billions of times the Sun’s mass, these incredibly bright objects challenge scientists’ understanding of how such enormous black holes formed so quickly after the Big Bang.

Quasars rank among the brightest and most powerful objects in the universe. They are fueled by supermassive black holes that consume surrounding material at the centers of galaxies, producing so much energy that they can be seen across billions of light years.

Now, an international team of researchers has identified 31 of the oldest quasars ever discovered, including the two earliest known examples. These extraordinary objects were already shining with the light of roughly a trillion suns when the universe was only about 670 million years old. The discovery, published in Astronomy & Astrophysics, offers an unprecedented glimpse into one of the earliest chapters of cosmic history.

Meet Biomni—an AI-powered biomedical co-scientist

In creating a comprehensive, AI-enabled research agent for the biomedical sciences, Stanford University researchers hope to speed innovation by eliminating the tedium of scientific legwork. Biomni, an AI-powered, multiskilled biomedical research agent, is no mere chatbot. It is a full-fledged “co-scientist” capable of designing and developing complex research workflows, said Jure Leskovec, the Alfred and Rebecca Lin Professor and professor of computer science in the School of Engineering and senior author of the paper introducing Biomni in the journal Science.

“If you think of an agent as a carpenter, a carpenter without tools is just a carpenter who can talk,” Leskovec said, explaining what sets Biomni apart from popular generative AI chatbots. “With Biomni, we give the carpenter a set of tools, so it can build.”

Born for impact Biomni was born from the notion that, when working with an AI agent, a scientist should be able to describe a research problem in simple, natural language. With that in mind, the researchers designed Biomni to read the literature, form hypotheses, choose datasets and tools, write code, interpret results and suggest next-stage experiments in a complete research workflow.

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