Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 55

In a network, pairs of individual elements, or nodes, connect to each other; those connections can represent a sprawling system with myriad individual links. A hypergraph goes deeper: It gives researchers a way to model complex, dynamical systems where interactions among three or more individuals—or even among groups of individuals—may play an important part.

Instead of edges that connect pairs of nodes, it is based on hyperedges that connect groups of nodes. Hypergraphs can represent higher-order interactions that represent collective behaviors like swarming in fish, birds, or bees, or processes in the brain.

Scientists usually use a hypergraph to predict dynamic behaviors. But the opposite problem is interesting, too. What if researchers can observe the dynamics but don’t have access to a reliable model? Yuanzhao Zhang, an SFI Complexity Postdoctoral Fellow, has an answer.

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Scientists have mapped an unprecedentedly large portion of the brain of a mouse. The cubic millimeter worth of brain tissue represents the largest piece of a brain we’ve ever understood to this degree, and the researchers behind this project say that the mouse brain is similar enough to the human brain that they can even extrapolate things about us. A cubic millimeter sounds tiny—to us, it is tiny—but a map of 200,000 brain cells represents just over a quarter of a percent of the mouse brain. In brain science terms, that’s extraordinarily high. A proportionate sample of the human brain would be 240 million cells.

Within the sciences, coding and computer science can sometimes overshadow the physical and life sciences. Rhetoric about artificial intelligence has raced ahead with terms like “human intelligence,” but the human brain is not well enough understood to truly give credence to that idea. Scientists have worked for decades to analyze the brain, and they’re making great progress despite the outsized rhetoric working against them.

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Memory formation, storage, and retrieval are fundamental processes that define who we are and how we interact with the world. At the cellular level, these processes rely on specialized neurons called engram cells—neuronal populations that physically encode our experiences and allow us to recall them later. Over the past few decades, scientists have made significant progress in identifying these neuronal ensembles and understanding some aspects of memory allocation.

Although sleep is widely known to be essential for memory processing and consolidation, many of its underlying mechanisms and functions are unclear. Traditional views have largely focused on sleep as a backward-looking process that serves to strengthen past experiences, but could it simultaneously help prepare the brain for new learning?

In a recent effort to tackle this question, a research team from Japan, led by Distinguished Professor Kaoru Inokuchi from the University of Toyama, uncovered a dual role for sleep in memory processing. Their paper, which will be published in Nature Communications on April 28, 2025, explores how the brain simultaneously preserves past memories while preparing for future ones during sleep periods.

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A research team has identified a previously unknown enzyme, SIRT2, that plays a key role in memory loss associated with Alzheimer’s disease (AD). The study provides critical insights into how astrocytes contribute to cognitive decline by producing excessive amounts of the inhibitory neurotransmitter GABA.

Astrocytes, once thought to only support neurons, are now known to actively influence brain function. In Alzheimer’s disease, astrocytes become reactive, meaning they change their behavior in response to the presence of amyloid-beta (Aβ) plaques, a hallmark of the disease. While astrocytes attempt to clear these plaques, this process triggers a harmful chain reaction. First, they uptake them via autophagy and degrade them by the urea cycle, as discovered in previous research. However, this breakdown results in the overproduction of GABA, which dampens brain activity and leads to memory impairment. Additionally, this pathway generates hydrogen peroxide (H2O2), a toxic byproduct that causes further neuronal death and neurodegeneration.

The research team set out to uncover which enzymes were responsible for excessive GABA production, hoping to find a way to selectively block its harmful effects without interfering with other brain functions. Using molecular analysis, microscopic imaging, and electrophysiology, the researchers identified SIRT2 and ALDH1A1 as critical enzymes involved in GABA overproduction in Alzheimer’s-affected astrocytes.

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By analyzing the data from the Atacama Large Millimeter/submillimeter Array (ALMA), astronomers have investigated a star-forming region known as G34.26+0.15. As a result, they discovered an explosive outflow in this complex. The study was reported in a paper published on April 22 on the arXiv preprint server.

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The presence of cellular defects of multifactorial nature can be hard to characterize accurately and early due to the complex interplay of genetic, environmental, and lifestyle factors. With this study, by bridging optically-induced dielectrophoresis (ODEP), microfluidics, live-cell imaging, and machine learning, we provide the ground for devising a robotic micromanipulation and analysis system for single-cell phenotyping. Cells under the influence of nonuniform electric fields generated via ODEP can be recorded and measured. The induced responses obtained under time-variant ODEP stimulation reflect the cells’ chemical, morphological, and structural characteristics in an automated, flexible, and label-free manner.

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