Lifeboat Foundation

Anesthesia alters thalamocortical circuits, causing a shift from unimodal-transmodal functional geometry to a transmodal-deficient pattern. This change is associated with disrupted matrix cell connectivity, suggesting a mechanism for unconsciousness.

Volatile anesthetics reversibly abolish consciousness or motility in animals, plants, and single-celled organisms (Kelz and Mashour, 2019; Yokawa et al., 2019). For humans, they are a medical miracle that we have been benefiting from for over 150 years, but the precise molecular mechanisms by which these molecules reversibly abolish consciousness remain elusive (Eger et al., 2008; Hemmings et al., 2019; Kelz and Mashour, 2019; Mashour, 2024). The functionally relevant molecular targets for causing unconsciousness are believed to be one or a combination of neural ion channels, receptors, mitochondria, synaptic proteins, and cytoskeletal proteins.

The Meyer–Overton correlation refers to the venerable finding that the anesthetic potency of chemically diverse anesthetic molecules is directly correlated with their solubility in lipids akin to olive oil (S. R. Hameroff, 2018; Kelz and Mashour, 2019). The possibility that general anesthesia might be explained by unitary action of all (or most) anesthetics on one target protein is supported by the Meyer–Overton correlation and the additivity of potencies of different anesthetics (Eger et al., 2008). Together these results suggest that anesthetics may act on a unitary site, via relatively nonspecific physical interactions (such as London/van der Waals forces between induced dipoles).

Cytoskeletal microtubules (MTs) have been considered as a candidate target of anesthetic action for over 50 years (Allison and Nunn, 1968; S. Hameroff, 1998). Other membrane receptor and ion channel proteins were ruled out as possible unitary targets by exhaustive studies culminating in Eger et al. (2008). However, MTs (composed of tubulin subunits) were not ruled out and remain a candidate for a unitary site of anesthetic action. MTs are the major components of the cytoskeleton in all cells, and they also play an essential role in cell reproduction—and aberrant cell reproduction in cancer—but in neurons, they have additional specialized roles in intracellular transport and neural plasticity (Kapitein and Hoogenraad, 2015). MTs have also been proposed to process information, encode memory, and mediate consciousness (S. R. Hameroff et al., 1982; S. Hameroff and Penrose, 1996; S. Hameroff, 2022). While classical models predict no direct role of MTs in neuronal membrane and synaptic signaling, Singh et al. (2021a) showed that MT activities do regulate axonal firing, for example, overriding membrane potentials. The orchestrated objective reduction (Orch OR) theory proposes that anesthesia directly blocks quantum effects in MTs necessary for consciousness (S. Hameroff and Penrose, 2014). Consistent with this hypothesis, volatile anesthetics do bind to cytoskeletal MTs (Pan et al., 2008) and dampen their quantum optical effects (Kalra et al., 2023), potentially contributing to causing unconsciousness.

Honesty is the best policy… most of the time. Social norms help humans understand when we need to tell the truth and when we shouldn’t, to spare someone’s feelings or avoid harm. But how do these norms apply to robots, which are increasingly working with humans? To understand whether humans can accept robots telling lies, scientists asked almost 500 participants to rate and justify different types of robot deception.

“I wanted to explore an understudied facet of robot ethics, to contribute to our understanding of mistrust towards emerging technologies and their developers,” said Andres Rosero, Ph.D. candidate at George Mason University and lead author of the study in Frontiers in Robotics and AI. “With the advent of generative AI, I felt it was important to begin examining possible cases in which anthropomorphic design and behavior sets could be utilized to manipulate users.”

The widespread use of artificial intelligence (AI) tools designed to process large amounts of data has increased the need for better performing memory devices. The data storage solutions that could help to meet the computational demands of AI include so-called high-bandwidth memories, technologies that can increase the memory bandwidth of computer processors, speeding up the transfer of data and reducing power consumption.

Currently, flash memories are the most prominent memory solutions capable of storing information when a device is turned off (i.e., non-volatile memories). Despite their widespread use, the speed of most existing flash memories is limited and does not best support the operation of AI.

In recent years, some engineers have thus been trying to develop ultrafast flash memories that could transfer data faster and more efficiently. Two-dimensional (2D) materials have shown promise for fabricating these better performing memory devices.

Scientists have measured the magnetic moment of the muon to unprecedented precision, more than doubling the previous record.

Physicists from the Muon g-2 Collaboration cycled muons, known as “heavy electrons,” in a particle storage ring at Fermilab in the United States to nearly the speed of light. Applying a magnetic field about 30,000 times stronger than Earth’s, the muons precessed like tops around their spin axis due to their own magnetic moment.

As they circled a 7.1-meter diameter storage ring, the muon’s magnetic moment, influenced by virtual particles in the vacuum, interacted with the external magnetic field. By comparing this precession frequency with the cycling frequency around the ring, the collaboration was able to determine the muon’s “anomalous magnetic moment” to a precision of 0.2 parts per million.

A new study led by researchers at the University of Minnesota Twin Cities is providing new insights into how next-generation electronics, including memory components in computers, break down or degrade over time. Understanding the reasons for degradation could help improve efficiency of data storage solutions.

Studies of gravity variations at Mars have revealed dense, large-scale structures hidden beneath the sediment layers of a lost ocean. The analysis, which combines models and data from multiple missions, also shows that active processes in the Martian mantle may be giving a boost to the largest volcano in the solar system, Olympus Mons. The findings have been presented this week at the Europlanet Science Congress (EPSC) in Berlin by Bart Root of Delft University of Technology (TU Delft).

Irrespective of their personal, professional and social circumstances, different individuals can experience varying levels of life satisfaction, fulfillment and happiness. This general measure of life satisfaction, broadly referred to as “well-being,” has been the key focus of numerous psychological studies.

Better understanding the many factors contributing to well-being could help to devise personalized and targeted interventions aimed at improving people’s levels of fulfillment. While many past studies have tried to delineate these factors, few have done so leveraging the advanced machine learning models available today.

Machine learning models are designed to analyze large amounts of data, unveiling hidden patterns and making accurate predictions. Using these tools to analyze data collected in previous studies in neuroscience and psychology could help to shed light on the environmental and genetic factors influencing well-being.

Scientists use atomic clocks to measure the “second,” the smallest standard unit of time, with great precision. These clocks use natural oscillations of electrons in atoms, similar to how pendulums work in old grandfather clocks. The quest for an even more precise timekeeper led to the discovery of nuclear clocks, which use the transitions of atomic nuclei instead of electrons to keep time.