Despite having identical genetic instructions, female honey bee larvae can develop into either long-lived reproductive queens or short-lived sterile workers who help rear their sisters rather than laying their own eggs. Now, an interdisciplinary team led by researchers at Penn State has uncovered the molecular mechanisms that control how the conflict between genes inherited from the father and the mother determine the larva’s fate.

Two species of worms have retained remarkably similar patterns in the way they switch their genes on and off despite having split from a common ancestor 20 million years ago, according to a new study reported in Science. When gene expression did diverge between the two worms, the changes were more likely to occur in specialized cell types. For example, expression patterns in cells involved in basic functions like muscle or gut tended to be conserved, while expression patterns in more specialized cells involved in sensing and responding to the worm’s environment were more likely to diverge. Researchers were able to compare development in every single cell of these two different organisms.

For many, fitness trackers have become indispensable tools for monitoring how many calories they’ve burned in a day. But for those living with obesity, who are known to exhibit differences in walking gait, speed, energy burned and more, these devices often inaccurately measure activity — until now. 

Scientists at Northwestern University have developed a new algorithm that enables smartwatches to more accurately monitor the calories burned by people with obesity during various physical activities.

Researchers have been able to demonstrate a quantum exponential scaling advantage, using two 127-qubit IBM Quantum Eagle processor-powered quantum computers, over the cloud. The paper, “Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem,” was published in APS flagship journal Physical Review X.

Researchers at the University of Massachusetts Amherst have pushed forward the development of computer vision with new, silicon-based hardware that can both capture and process visual data in the analog domain. Their work, described in the journal Nature Communications, could ultimately add to large-scale, data-intensive and latency-sensitive computer vision tasks.

Researchers Mostafa Bedewy, at the University of Pittsburgh Swanson School of Engineering, and Ahmed Aziz Ezzat, at Rutgers University, have received a $549,947 collaborative National Science Foundation (NSF) grant to study new ways of controlling the formation of alumina-supported iron nanoparticles by using machine learning (ML) to efficiently model, characterize, simulate, and predict their growth. Their research seeks to advance understanding of these particles to improve nanomanufacturing.