Ex vivo models capture the cellular and functional richness of respiratory tract tissues and enable study of viral pathogenesis.
Self-propulsion enzymatic nanomotors have shown tremendous potential in the field of diagnostics. In a study led by Wang and coworkers, nanoenzyme-driven cup-shaped nanomotors were designed for enhanced cell penetration and synergistic photodynamic/thermal treatments under single near-infrared laser irradiation. By combining the concepts of self-propulsion enzymatic nanomotors and synergistic dual-modal therapy, this work provides a new idea and tool for the application of nanomotors in the biomedical field.
đ THE FUTURE OF SCI-FI: UPLIFTING OR JUST UPLOADING? đ
Welcome back, gang! Egotastic FunTime is blasting into another galactic rantâthis time asking the big question:
Has sci-fi lost its soul? đ
From Star Trekâs hopeful utopias to todayâs server-farmed dystopias, weâre cracking open the hard drive of the future and asking if weâre still dreaming⊠or just buffering forever. đ€âš
Why is modern sci-fi obsessed with uploading instead of uplifting?
Is humanity evolving or just ghosting itself with tech?
Where did the wonder goâand can we get it back?
Grab your neural nodes and sarcastic side-eyes, because weâre deep-diving into the state of sci-fi, tech anxiety, and how imagination might just save us yet.
Mapping dynamical systems: New algorithm infers hypergraph structure from time-series data without prior knowledge
Posted in information science, mapping, neuroscience | Leave a Comment on Mapping dynamical systems: New algorithm infers hypergraph structure from time-series data without prior knowledge
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 individualsâor even among groups of individualsâmay 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 model to predict dynamic behaviors. But the opposite problem is interesting, too. What if researchers can observe the dynamics but donât have access to a reliable model? Yuanzhao Zhang, an SFI Complexity Postdoctoral Fellow, has an answer.
The biological computer system can stay alive for up to six months and is compatible with USB devices.
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 weâve 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 tinyâto us, it is tinyâbut a map of 200,000 brain cells represents just over a quarter of a percent of the mouse brain. In brain science terms, thatâs 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 âhuman intelligence,â 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 theyâre making great progress despite the outsized rhetoric working against them.
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 cellsâneuronal 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.
A research team has identified a previously unknown enzyme, SIRT2, that plays a key role in memory loss associated with Alzheimerâs 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 Alzheimerâs disease, astrocytes become reactive, meaning they change their behavior in response to the presence of amyloid-beta (AÎČ) 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 Alzheimerâs-affected astrocytes.