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Category: genetics – Page 151

New model for human evolution suggests Homo sapiens arose from multiple closely related populations.

A new study in Nature challenges prevailing theories, suggesting that Homo sapiens evolved from multiple diverse populations across Africa, with the earliest detectable split occurring 120,000–135,000 years ago, after prolonged periods of genetic intermixing.

In testing the genetic material of current populations in Africa and comparing it against existing fossil evidence of early Homo sapiens populations there, researchers have uncovered a new model of human evolution — overturning previous beliefs that a single African population gave rise to all humans. The new research was published on May 17, in the journal Nature.

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Summary: Neurons in the hippocampus vary in function depending on their exact genetic identity. The study revealed these neurons, once believed to be homogeneous, are quite diverse and encode task-related information differently based on their location. This newfound understanding of neuronal diversity could lead to better comprehension of brain functions, memory capacity, and potentially advance disease treatment strategies.

Key Facts:

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Quantum biology explores how quantum effects influence biological processes, potentially leading to breakthroughs in medicine and biotechnology. Despite the assumption that quantum effects rapidly disappear in biological systems, research suggests these effects play a key role in physiological processes. This opens up the possibility of manipulating these processes to create non-invasive, remote-controlled therapeutic devices. However, achieving this requires a new, interdisciplinary approach to scientific research.

Imagine using your cell phone to control the activity of your own cells to treat injuries and diseases. It sounds like something from the imagination of an overly optimistic science fiction writer. But this may one day be a possibility through the emerging field of quantum biology.

Over the past few decades, scientists have made incredible progress in understanding and manipulating biological systems at increasingly small scales, from protein folding to genetic engineering. And yet, the extent to which quantum effects influence living systems remains barely understood.

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“We now understand that having one map of a single human genome cannot adequately represent all of humanity,” says Karen Miga, a professor at the University of California, Santa Cruz, and a participant in the new project.

People’s genomes are largely alike, but it’s the hundreds of thousands of differences, often just single DNA letters, that explain why each of us is unique. The new pangenome, researchers say, should make it possible to observe this diversity in more detail than ever before, highlighting so-called evolutionary hot spots as well as thousands of surprisingly large differences, like deleted, inverted, or duplicated genes, that aren’t observable in conventional studies.

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Tactile stimulation improved motor performance, reduced phosphorylated tau, preserved neurons and synapses, and reduced DNA

DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).

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Alzheimer’s disease (AD) is a complex neurodegenerative illness with genetic and environmental origins. Females experience faster cognitive decline and cerebral atrophy than males, while males have greater mortality rates. Using a new machine-learning method they developed called “Evolutionary Action Machine Learning (EAML),” researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital have discovered sex-specific genes and molecular pathways that contribute to the development and progression of this condition. The study was published in Nature Communications.

“We have developed a unique machine-learning software that uses an advanced computational predictive metric called the evolutionary action (EA) score as a feature to identify that influence AD risk separately in males and females,” Dr. Olivier Lichtarge, MD, Ph.D., professor of biochemistry and at Baylor College of Medicine, said. “This approach lets us exploit a massive amount of evolutionary data efficiently, so we can now probe with greater accuracy smaller cohorts and identify involved in in AD.”

EAML is an ensemble computational approach that includes nine machine learning algorithms to analyze the functional impact of non-synonymous coding variants, defined as DNA mutations that affect the structure and function of the resulting protein, and estimates their deleterious effect on using the evolutionary action (EA) score.

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