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Blockchain-inspired DNA storage codec recovers data from heavily damaged sequences

A research team at the Faculty of Engineering at The University of Hong Kong (HKU), led by professor Ruibang Luo of the School of Computing and Data Science and professor Can Li of the Department of Electrical and Computer Engineering, has developed Gungnir, a blockchain-inspired DNA data storage codec. The framework improves digital information recovery from severely damaged DNA sequences, potentially extending the practical lifespan of DNA data archives from less than a decade to centuries. The paper is published in the journal Nature Communications.

DNA is a promising medium for long-term archival storage. It can hold vast amounts of information in a small physical volume, remain stable for millennia under suitable conditions and require no energy during storage. However, DNA molecules can gradually degrade and accumulate damage over time, making the original files increasingly difficult to recover. Existing codecs work best with newly synthesized DNA. They are less effective at correcting errors that build up during long-term storage.

Gungnir takes a new approach to error correction by applying blockchain-based computing techniques to DNA data storage. It uses substantial computing power to generate possible reconstructions of the original data and verify them until it identifies the correct information. In other words, greater computing power gives Gungnir stronger error correction capabilities. The design enables a DNA drive to tolerate error rates of up to 20%, a fourfold improvement over existing methods.

Tiny robot can precisely control its jump height

As our skies increasingly crowd with buzzing, hovering, flitting drones, spare a thought for the humble hopping robot. Hopping, a popular form of locomotion in the insect and amphibian worlds, is nearly two orders of magnitude more energy-efficient than flying. Unlike a mosquito that must constantly expend energy to stay aloft, a flea only works out when it jumps.

Those cost savings could also pay off in the world of robotics: A group of small, simple hopping robots could explore a location much more cheaply than flying drones. These robots could find gas leaks in an oil refinery, monitor water and fertilizer usage on a farm, or even explore other planets.

With this in mind, engineers at the University of Washington have created DirectHop, a roughly 1-gram (0.04-ounce) robot that can perform multiple hops in sequence and jump high enough to clear a standard stair step. The robot uses a tiny electric motor and three folding legs to launch itself to a specified jump height with 1-centimeter (0.4-inch) accuracy. After it lands, DirectHop can twitch to right itself and then prepare for another jump.

Spin waves inside a nano-oscillator imaged for the first time

For the first time, researchers have directly imaged the magnetization dynamics inside a spin Hall nano-oscillator—a nanoscale device that converts direct current into tunable microwave signals and is a promising building block for energy-efficient wireless communication and brain-inspired computing.

A Swedish–German team led by the University of Gothenburg and Helmholtz-Zentrum Berlin (HZB) achieved this using time-resolved scanning transmission X-ray microscopy at the MAXYMUS instrument at BESSY II. The results, published in Advanced Materials, reveal spin-wave features that had escaped previous indirect measurement techniques.

Spin Hall nano-oscillators (SHNOs) are among the most versatile devices in spintronics: A direct current driven through a nanometer-sized constriction sets the local magnetization into steady precession, turning a DC input into a tunable radio-frequency output.

Brain organoids uncover how primates develop different brain shapes

Most primate species have relatively large brains with surfaces marked by pronounced bulges and furrows. This is not the case with common marmosets (Callithrix jacchus). The brains of these small monkeys are smooth and virtually unfolded.

Researchers at the German Primate Center (DPZ)—the Leibniz Institute for Primate Research in Göttingen—investigated which cellular processes during embryonic development cause the marmoset brain to develop differently from a certain stage onward.

In experiments with brain organoids, they were able to show that certain neural progenitor cells—from which neurons arise—develop differently in marmosets, both morphologically and temporally. Several processes occurring at different levels and involving various types of progenitor cells govern this phenomenon, which ultimately leads to a reduction in the size and folding of the marmoset brain.

Central surface density of dark matter agrees with the prediction of a new theory of gravity

A researcher from Sejong University has used a new theory of gravity proposed by Erik Verlinde to predict the observed central surface density of dark matter. The study was published in Physics of the Dark Universe on Sept. 20.

Since the 1970s, the rotation speeds of galaxies have been regarded as important observational evidence for the existence of dark matter. Stars in the outer regions of galaxies move much faster than would be expected if Newtonian gravity were applied to visible matter alone. In mainstream astronomy, this phenomenon is explained by assuming that galaxies are surrounded by invisible dark matter that provides additional gravitational attraction.

However, researchers have also continued trying to explain galactic dynamics by modifying the theory of gravity itself rather than invoking dark matter. In 1983, Mordehai Milgrom proposed that Newtonian gravity might need to be modified in regimes of very weak gravitational acceleration. In 2016, Verlinde proposed a new theory of gravity, known as “emergent gravity,” aimed at explaining gravitational phenomena on galactic scales without assuming the existence of dark matter.

Physicists Tackle a Classic Quantum Problem With a Powerful New Computational Method

A new computational approach uses the real electronic structure of materials to predict a classic quantum effect far more accurately than simplified models.

Seven magnetic atoms embedded one at a time in copper have given physicists a new way to test whether computers can predict the behavior of real quantum materials without first reducing them to simplified models.

For most of the seven transition-metal impurities, calculations developed by researchers at Caltech and Yale University improved on the accuracy of conventional model-based predictions by as much as two orders of magnitude. The test involved the Kondo effect, a classic quantum problem whose general physics has been understood for decades even though its precise behavior in specific materials has remained difficult to calculate.

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