The dynamical axion quasiparticle, which is directly analogous to the hypothetical fundamental axion particle, is observed in two-dimensional MnBi2Te4, and has implications for quantum chromodynamics, cosmology and string theory.
Walgreens is expanding the number of its retail stores served by its micro-fulfillment centers as it works to turn itself around and prepares to go private.
At the level of molecules and cells, ketamine and dexmedetomidine work very differently, but in the operating room they do the same exact thing: anesthetize the patient. By demonstrating how these distinct drugs achieve the same result, a new study in animals by neuroscientists at The Picower Institute for Learning and Memory at MIT identifies a potential signature of unconsciousness that is readily measurable to improve anesthesiology care.
What the two drugs have in common, the researchers discovered, is the way they push around brain waves, which are produced by the collective electrical activity of neurons.
When brain waves are in phase, meaning the peaks and valleys of the waves are aligned, local groups of neurons in the brain’s cortex can share information to produce conscious cognitive functions such as attention, perception and reasoning, said Picower Professor Earl K. Miller, senior author of the new study in Cell Reports. When brain waves fall out of phase, local communications, and therefore functions, fall apart, producing unconsciousness.
MIT physicists have captured the first images of individual atoms freely interacting in space. The pictures reveal correlations among the “free-range” particles that until now were predicted but never directly observed. Their findings, appearing today in the journal Physical Review Letters, will help scientists visualize never-before-seen quantum phenomena in real space.
The images were taken using a technique developed by the team that first allows a cloud of atoms to move and interact freely. The researchers then turn on a lattice of light that briefly freezes the atoms in their tracks, and apply finely tuned lasers to quickly illuminate the suspended atoms, creating a picture of their positions before the atoms naturally dissipate.
The physicists applied the technique to visualize clouds of different types of atoms, and snapped a number of imaging firsts. The researchers directly observed atoms known as “bosons,” which bunched up in a quantum phenomenon to form a wave. They also captured atoms known as “fermions” in the act of pairing up in free space — a key mechanism that enables superconductivity.
A phenomenon largely ignored since its discovery 100 years ago appears to be a crucial component of diabetic pain, according to new research from The University of Texas at Dallas’s Center for Advanced Pain Studies (CAPS).
Findings from a new study published in Nature Communications suggest that cell clusters called Nageotte nodules are a strong indicator of nerve cell death in human sensory ganglia. These could prove to be a target for drugs that would protect these nerves or help manage diabetic neuropathy.
“The key finding of our study is really a new view of diabetic neuropathic pain,” said Dr. Ted Price, Ashbel Smith Professor of neuroscience in the School of Behavioral and Brain Sciences, CAPS director and co-corresponding author of the study. “We believe our data demonstrate that neurodegeneration in the dorsal root ganglion is a critical facet of the disease—which should really force us to think about the disease in a new and urgent way.”
A group of researchers affiliated with the Center for Innovation in New Energies (CINE) has developed a method for purifying materials that is simple, economical and has a low environmental impact. The scientists have managed to improve the efficiency of a film that can be used in some green hydrogen production processes.
Known as mullite-type bismuth ferrite (Bi₂Fe₄O₉), the material has been used as a photoelectrocatalyst in the production of hydrogen by photoelectron oxidation, a process in which molecules of water or biomass derivatives are oxidized using sunlight as an energy source. The role of bismuth ferrite films in this process is to absorb light and drive the electrochemical reactions that “separate” the hydrogen from the original molecules (water, glycerol, ethanol, etc.).
However, the performance of these photoelectrocatalysts has been limited in the production of hydrogen due, among other factors, to the presence of unwanted compounds in the material itself, known as secondary phases. Now, research carried out by CINE members in the laboratories of the State University of Campinas (UNICAMP) in Brazil has brought a solution to the problem: a purification method that has managed to eliminate these unwanted compounds.
In the rapidly evolving field of quantum computing, silicon spin qubits are emerging as a leading candidate for building scalable, fault-tolerant quantum computers.
A new review titled “Single-Electron Spin Qubits in Silicon for Quantum,” published in Intelligent Computing, highlights the latest advances, challenges and future prospects of silicon spin qubits for quantum computing.
Silicon spin qubits are compatible with existing semiconductor industry manufacturing processes, making them promising for universal quantum computers. They have several remarkable properties.
Despite acting through different molecular mechanisms, ketamine and dexmedetomidine both disrupt brain wave phase alignment in ways that reliably induce unconsciousness.
In the domain of artificial intelligence, human ingenuity has birthed entities capable of feats once relegated to science fiction. Yet within this triumph of creation resides a profound paradox: we have designed systems whose inner workings often elude our understanding. Like medieval alchemists who could transform substances without grasping the underlying chemistry, we stand before our algorithmic progeny with a similar mixture of wonder and bewilderment. This is the essence of the “black box” problem in AI — a philosophical and technical conundrum that cuts to the heart of our relationship with the machines we’ve created.
The term “black box” originates from systems theory, where it describes a device or system analyzed solely in terms of its inputs and outputs, with no knowledge of its internal workings. When applied to artificial intelligence, particularly to modern deep learning systems, the metaphor becomes startlingly apt. We feed these systems data, they produce results, but the transformative processes occurring between remain largely opaque. As Pedro Domingos (2015) eloquently states in his seminal work The Master Algorithm: “Machine learning is like farming. The machine learning expert is like a farmer who plants the seeds (the algorithm and the data), harvests the crop (the classifier), and sells it to consumers, without necessarily understanding the biological mechanisms of growth” (p. 78).
This agricultural metaphor points to a radical reconceptualization in how we create computational systems. Traditionally, software engineering has followed a constructivist approach — architects design systems by explicitly coding rules and behaviors. Yet modern AI systems, particularly neural networks, operate differently. Rather than being built piece by piece with predetermined functions, they develop their capabilities through exposure to data and feedback mechanisms. This observation led AI researcher Andrej Karpathy (2017) to assert that “neural networks are not ‘programmed’ in the traditional sense, but grown, trained, and evolved.”