Lifeboat Foundation

Westlake University in China and the California Institute of Technology have designed a protein-based system inside living cells that can process multiple signals and make decisions based on them.

The researchers have also introduced a unique term, “perceptein,” as a combination of protein and perceptron. Perceptron is a foundational artificial neural network concept, effectively solving binary classification problems by mapping input features to an output decision.

By merging concepts from neural network theory with protein engineering, “perceptein” represents a biological system capable of performing classification computations at the protein level, similar to a basic artificial neural network. This “perceptein” circuit can classify different signals and respond accordingly, such as deciding to stay alive or undergo programmed cell death.

The concept of vectors can be traced back to the 17th century with the development of analytic geometry by René Descartes and Pierre de Fermat. They used coordinates to represent points in a plane, which can be seen as a precursor to vectors. In the early 19th century, mathematicians like Bernard Bolzano and August Ferdinand Möbius began to formalize operations on points, lines, and planes, which further developed the idea of vectors.

Hermann Grassmann is considered one of the key figures in the development of vector spaces. In his 1844 work “Die lineale Ausdehnungslehre” (The Theory of Linear Extension), he introduced concepts that are central to vector spaces, such as linear independence, dimension, and scalar products. However, his work was not widely recognized at the time.

In 1888, Giuseppe Peano gave the first modern axiomatic definition of vector spaces. He called them “linear systems” and provided a set of axioms that precisely defined the properties of vector spaces and linear maps. Hilbert helped to further formalize and abstract the concept of vector spaces, placing it within a broader axiomatic framework for mathematics. He played a key role in the development of functional analysis, which studies infinite-dimensional vector spaces.

A method for imaging spin waves in magnetic materials uses flash-like intensity variations in a laser beam to capture the wave motion at specific moments in time.

The magnetic moments, or spins, in certain materials can twirl in a coordinated wave pattern that might one day be used to transmit information in so-called spintronic devices. Researchers have developed a new way to image these spin waves using an infrared laser that essentially flashes on and off at a frequency that matches that of the spin waves [1]. Unlike other spin-wave probes, this strobe method can directly capture phase information that is relevant to certain applications, such as hybrid devices that combine spin waves with other types of waves.

A spin wave can be triggered in a magnetic material when some perturbation causes a spin to oscillate, which can then generate a wave of oscillations that ripple through neighboring spins. Spin waves have several properties that make them good candidates for information carriers. For one, they have relatively small wavelengths—a few hundred nanometers at a frequency of 1 GHz, whereas a 1-GHz photon has a wavelength of about 30 cm. This compactness could conceivably allow researchers to build spintronic components, such as waveguides and logic gates, at the nanoscale. Another advantage of these waves is that the spins remain in place, and only their orientation changes. So the heat losses that affect the moving charges in traditional electronics don’t exist.

UC Santa Barbara researchers developed a compact, low-cost laser that matches the performance of lab-scale systems. Using rubidium atoms and advanced chip integration, it enables applications like quantum computing, timekeeping, and environmental sensing, including satellite-based gravitational mapping.

For experiments requiring ultra-precise atomic measurements and control—such as two-photon atomic clocks, cold-atom interferometer sensors, and quantum gates—lasers are indispensable. The key to their effectiveness lies in their spectral purity, meaning they emit light at a single color or frequency. Today, achieving the ultra-low-noise, stable light necessary for these applications relies on bulky and expensive tabletop laser systems designed to generate and manage photons within a narrow spectral range.

But what if these atomic applications could break free from the confines of labs and benchtops? This is the vision driving research in UC Santa Barbara engineering professor Daniel Blumenthal’s lab, where his team is working to replicate the performance of these high-precision lasers in lightweight, handheld devices.

Mapping the geometry of quantum worlds: measuring the quantum geometric tensor in solids.

Quantum states are like complex shapes in a hidden world, and understanding their geometry is key to unlocking the mysteries of modern physics. One of the most important tools for studying this geometry is the quantum geometric tensor (QGT). This mathematical object reveals how quantum states “curve” and interact, shaping phenomena ranging from exotic materials to groundbreaking technologies.

The QGT has two parts, each with distinct significance:

Continue reading “Physicists measure quantum geometry for first time” »

After forty years, the creator of scar theory has observed the phenomenon in real time.

Quantum scarring is a phenomenon in which traveling electrons end up following the same repeating path.

Scars of Chaos: Visualizing Mysteries in Graphene Dots probabilities cluster along the paths of unstable orbits from their classical counterparts. These scars, while predicted, have remained elusive to direct observation—until now.
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Using an innovative combination of graphene dot fabrication and advanced wavefunction mapping via scanning tunneling microscopy, researchers captured stunning images of scars. Within stadium-shaped GQDs, they observed striking lemniscate (∞-shaped) and streak-like probability patterns. These features recur at equal energy intervals, aligning with theoretical predictions for relativistic scars—a fascinating blend of mechanics and relativity.

Continue reading “‪#‎quantum‬ — Explore” »

There’s No Turning Back

Not long ago, solving the crystal structure of a protein required an entire PhD.

Growing crystals, collecting X-ray diffraction data, and interpreting electron density maps often took years of optimization and expensive instruments. Even then, solving all protein structures was a challenge, further compounding the “protein folding problem” in biology.

Patreon: https://www.patreon.com/seanmcarroll.
Blog post with audio player, show notes, and transcript: https://www.preposterousuniverse.com/podcast/2024/12/09/298-…the-brain/

The number of neurons in the human brain is comparable to the number of stars in the Milky Way galaxy. Unlike the stars, however, in the case of neurons the real action is in how they are directly connected to each other: receiving signals over synapses via their dendrites, and when appropriately triggered, sending signals down the axon to other neurons (glossing over some complications). So a major step in understanding the brain is to map its wiring diagram, or connectome: the complete map of those connections. For a human brain that’s an intimidatingly complex challenge, but important advances have been made on tinier brains. We talk with Jeff Lichtman, a leader in brain mapping, to gauge the current state of progress and what it implies.

Continue reading “Mindscape 298 | Jeff Lichtman on the Wiring Diagram of the Brain” »

This tech offers smarter decision-making and regional analysis.

Spaid’s AI-based mapping solutions transform GIS data into detailed 3D maps with land, building, traffic, and demographic insights.