Observing anything and everything within the human brain, no matter how large or small, while it is fully intact has been an out-of-reach dream of neuroscience for decades.
Three new innovations from an MIT-based team enables high-resolution, high-throughput imaging of human brain tissue at a full range of scales, and mapping connectivity of neurons at single-cell resolution.
Recently Danielson, Satishchandran, and Wald (DSW) have shown that quantum superpositions held outside of Killing horizons will decohere at a steady rate. This occurs because of the inevitable radiation of soft photons (gravitons), which imprint a electromagnetic (gravitational) “which-path’’ memory onto the horizon. Rather than appealing to this global description, an experimenter ought to also have a local description for the cause of decoherence. One might intuitively guess that this is just the bombardment of Hawking/Unruh radiation on the system, however simple calculations challenge this idea—the same superposition held in a finite temperature inertial laboratory does not decohere at the DSW rate. In this work we provide a local description of the decoherence by mapping the DSW setup onto a worldline-localized model resembling an Unruh-DeWitt particle detector.
For the first time, scientists have taken near-daily measurements of the sun’s global coronal magnetic field, a region of the sun that has only been observed irregularly in the past. The resulting observations are providing valuable insights into the processes that drive the intense solar storms that impact fundamental technologies, and thus lives and livelihoods, here on Earth.
How do the characteristics of Neptune-like exoplanets, also known as exo-Neptunes, differ from each other? This is what a recent study published in Astronomy and Astrophysics hopes to address as an international team of researchers investigated a new classification known as the “Neptunian Ridge”. This complements previous classifications of “Neptunian Desert” and “Neptunian Savannah”, with the former identifying exo-Neptunes that are rare in number but orbit very close to their parent stars while the “Neptune Savannah” describes exo-Neptunes that orbit much farther out. This study holds the potential to help astronomers better understand the formation and evolution of exo-Neptunes throughout the cosmos.
For the study, the researchers used confirmed and candidate exoplanets that comprise the Kepler DR25 catalog to ascertain the characteristic variations in exo-Neptunes while providing additional insights into the formation and evolution of exo-Neptunes, as well. In the end, they determined that this “Neptunian Ridge” exists as a middle-ground between the “Neptunian Desert” and “Neptunian Savannah”, with the former hypothesized to have formed from moving inward in their system from high-eccentricity tidal migration and the latter forming from disk-driven migration, which occurs right after planetary formation.
“Our work to observe this new structure in space is highly significant in helping us map the exoplanet landscape,” said Dr. David Armstrong, who is an Associate Professor of Physics at the University of Warwick and a co-author on the study. “As scientists, we’re always striving to understand why planets are in the condition they are in, and how they ended up where they are. The discovery of the Neptunian ridge helps answer these questions, unveiling part of the geography of exoplanets out there, and is a hugely exciting discovery.”
Randal Koene discusses Whole Brain Emulation on the H+ Magazine podcast. He touches on the subjects of connectomics, neural mapping, optogenetics, and neural prosthesis.
The Allen Institute for Immunology has released its first Human Immune Health Atlas, a comprehensive single-cell reference dataset that offers unprecedented insight into the landscape of healthy human immune cells from childhood through adulthood.
Comprehensive dataset maps the landscape of healthy immune cells across the human lifespan.
Storytelling has evolved beyond mere narration—it’s now about immersion, interaction, and creating a deep emotional connection. Few organizations understand this better than National Geographic, a brand renowned for bringing the natural world to life through visually stunning, fact-driven content. With their latest digital experience, “Into the Amazon,” National Geographic has raised the bar even higher, blending cutting-edge technology with unparalleled storytelling to offer audiences a front-row seat to one of the planet’s most vital ecosystems: the Amazon rainforest.
At a time when the world’s environmental challenges are more pressing than ever, National Geographic’s “Into the Amazon” doesn’t just inform viewers; it transforms how we understand the Amazon’s role in the global ecosystem. This article explores how innovative technologies—such as interactive 3D mapping, augmented reality, and immersive media formats —are being harnessed to deliver a uniquely engaging experience. It also reflects on the broader implications for the future of digital storytelling, where technology and narrative merge to create impactful, lasting impressions.
As digital media continues to dominate, audience expectations have shifted. Passive consumption is no longer enough—today’s audiences crave interaction, engagement, and experiences that go beyond the screen. Immersive storytelling, particularly in the environmental and scientific fields, has emerged as a powerful tool to captivate and educate.
A new analysis of Mars’ gravitational field has revealed hidden structures buried beneath the remains of an ancient ocean.
The work, which was presented this week at the Europlanetary Science Congress in Berlin, could add to a growing body of evidence that suggests the Red Planet may not be as geologically “dead,” or inactive, as once believed.
Overlaid with a thick and smooth layer of sediment which may have once been a seabed, the structures are significantly denser than their surroundings — though a more precise explanation of what they might be has so far eluded researchers.
With maps of the connections between neurons and artificial intelligence methods, researchers can now do what they never thought possible: predict the activity of individual neurons without making a single measurement in a living brain.
For decades, neuroscientists have spent countless hours in the lab painstakingly measuring the activity of neurons in living animals to tease out how the brain enables behavior. These experiments have yielded groundbreaking insights into how the brain works, but they have only scratched the surface, leaving much of the brain unexplored.
Now, researchers are using artificial intelligence and the connectome—a map of neurons and their connections created from brain tissue —to predict the role of neurons in the living brain. Their paper has been published in the journal Nature.
During the worst days of the COVID-19 pandemic, many of us became accustomed to news reports on the reproduction number R, which is the average number of cases arising from a single infected case. If we were told that R was much greater than 1, that meant the number of infections was growing rapidly, and interventions (such as social distancing and lockdowns) were necessary. But if R was near to 1, then the disease was deemed to be under control and some relaxation of restrictions could be warranted. New mathematical modeling by Kris Parag from Imperial College London shows limitations to using R or a related growth rate parameter for assessing the “controllability” of an epidemic [1]. As an alternative strategy, Parag suggests a framework based on treating an epidemic as a positive feedback loop. The model produces two new controllability parameters that describe how far a disease outbreak is from a stable condition, which is one with feedback that doesn’t lead to growth.
Parag’s starting point is the classical mathematical description of how an epidemic evolves in time in terms of the reproduction number R. This approach is called the renewal model and has been widely used for infectious diseases such as COVID-19, SARS, influenza, Ebola, and measles. In this model, new infections are determined by past infections through a mathematical function called the generation-time distribution, which describes how long it takes for someone to infect someone else. Parag departs from this traditional approach by using a kind of Fourier transform, called a Laplace transform, to convert the generation-time distribution into periodic functions that define the number of the infections. The Laplace transform is commonly adopted in control theory, a field of engineering that deals with the control of machines and other dynamical systems by treating them as feedback loops.
The first outcome of applying the Laplace transform to epidemic systems is that it defines a so-called transfer function that maps input cases (such as infected travelers) onto output infections by means of a closed feedback loop. Control measures (such as quarantines and mask requirements) aim to disrupt this loop by acting as a kind of “friction” force. The framework yields two new parameters that naturally describe the controllability of the system: the gain margin and the delay margin. The gain margin quantifies how much infections must be scaled by interventions to stabilize the epidemic (where stability is defined by R = 1). The delay margin is related to how long one can wait to implement an intervention. If, for example, the gain margin is 2 and the delay margin is 7 days, then the epidemic is stable provided that the number of infections doesn’t double and that control measures are applied within a week.
