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Landscape dynamics determine the evolution of biodiversity on Earth

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Landscape dynamics determine the evolution of biodiversity on Earth


Movement of rivers, mountains, oceans and sediment nutrients at the geological timescale are the central drivers of Earth’s biodiversity, new research published today in Nature has revealed.

The research also shows that biodiversity evolves at similar rates to the pace of plate tectonics, the slow geological processes that drive the shape of continents, mountains and oceans.

“That is a rate incomparably slower than the current rates of extinction caused by human activity,” said lead author Dr Tristan Salles from the School of Geosciences.

The research looks back over 500 million years of Earth’s history to the period just after the Cambrian explosion of life, which established the main species types of modern life.

Dr Salles said: “Earth’s surface is the living skin of our planet. Over geological time, this surface evolves with rivers fragmenting the landscape into an environmentally diverse range of habitats.

“However, these rivers not only carve canyons and form valleys, but play the role of Earth’s circulatory system as the main conduits for nutrient and sediment transfer from sources (mountains) to sinks (oceans).

“While modern science has a growing understanding of global biodiversity, we tend to view this through the prism of narrow expertise,” Dr Salles said. “This is like looking inside a house from just one window and thinking we understand its architecture.

“Our model connects physical, chemical and biological systems over half a billion years in five-million-year chunks at a resolution of five kilometres. This gives an unprecedented understanding of what has driven the shape and timing of species diversity,” he said.

The discovery in 1994 of the ancient Wollemi pine species in a secluded valley in the Blue Mountains west of Sydney gives us a glimpse into the holistic role that time, geology, hydrology, climate and genetics play in biodiversity and species survival.

The idea that landscapes play a role in the trajectory of life on Earth can be traced back to German naturalist and polymath Alexander von Humboldt. His work inspired Charles Darwin and Alfred Wallace, who were the first to note that animal species boundaries correspond to landscape discontinuities and gradients.

“Fast forwarding nearly 200 years, our understanding of how the diversity of marine and terrestrial life was assembled over the past 540 million years is still emerging,” University of Sydney PhD student Beatriz Hadler Boggiani said.

“Biodiversity patterns are well identified from the fossil record and genetic studies. Yet, many aspects of this evolution remain enigmatic, such as the 100 million years delay between the expansion of plants on continents and the rapid diversification of marine life.”

In groundbreaking research a team of scientists — from the University of Sydney, ISTerre at the French state research organisation CNRS and the University of Grenoble Alpes in France — has proposed a unified theory that connects the evolution of life in the marine and terrestrial realms to sediment pulses controlled by past landscapes.

“Because the evolution of the Earth’s surface is set by the interplay between the geosphere and the atmosphere, it records their cumulative interactions and should, therefore, provide the context for biodiversity to evolve,” said Dr Laurent Husson from University of Grenoble Alpes.

Instead of considering isolated pieces of the environmental puzzle independently, the team developed a model that combines them and simulates at high resolution the compounding effect of these forces.

“It is through calibration of this physical memory etched in the Earth’s skin with genetics, fossils, climate, hydrology and tectonics by which we have investigated our hypothesis,” Dr Salles said.

Using open-source scientific code published by the team in Science in March, the detailed simulation was calibrated using modern information about landscape elevations, erosion rates, major river waters and the geological transport of sediment (known as sediment flux).

This allowed the team to evaluate their predictions over 500 million years using a combination of geochemical proxies and testing different tectonic and climatic reconstructions. The geoscientists then compared the predicted sediment pulses to the evolution of life in both the marine and terrestrial realms obtained from a compilation of paleontological data.

“In a nutshell, we reconstructed Earth landforms over the Phanerozoic era, which started 540 million years ago, and looked at the correlations between the evolving river networks, sediment transfers and known distribution of marine and plant families,” University of Grenoble PhD student Manon Lorcery said.

When comparing predicted sediment flux into the oceans with marine biodiversity, the analysis shows a strong, positive correlation.

On land, the authors designed a model integrating sediment cover and landscape variability to describe the capacity of the landscape to host diverse species. Here again, they found a striking correlation between their proxy and plant diversification for the past 450 million years.

In his 1864 novel A Journey to the Centre of the Earth, Jules Verne attributes this to his fictitious hero, Professor Otto Lidenbrock:

“Animal life existed upon the Earth only in the secondary period, when a sediment of soil had been deposited by the rivers and taken the place of the incandescent rocks of the primitive period.”

Dr Salles said: “This observation by Professor Lidenbrock to his nephew Axel fits strikingly well with our hypothesis. So, it should be no surprise that Jules Verne was greatly inspired by Humboldt’s work.”



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Charge your laptop in a minute or your EV in 10? Supercapacitors can help

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Charge your laptop in a minute or your EV in 10? Supercapacitors can help


Imagine if your dead laptop or phone could charge in a minute or if an electric car could be fully powered in 10 minutes.

While not possible yet, new research by a team of CU Boulder scientists could potentially lead to such advances.

Published today in the Proceedings of the National Academy of Sciences, researchers in Ankur Gupta’s lab discovered how tiny charged particles, called ions, move within a complex network of minuscule pores. The breakthrough could lead to the development of more efficient energy storage devices, such as supercapacitors, said Gupta, an assistant professor of chemical and biological engineering.

“Given the critical role of energy in the future of the planet, I felt inspired to apply my chemical engineering knowledge to advancing energy storage devices,” Gupta said. “It felt like the topic was somewhat underexplored and as such, the perfect opportunity.”

Gupta explained that several chemical engineering techniques are used to study flow in porous materials such as oil reservoirs and water filtration, but they have not been fully utilized in some energy storage systems.

The discovery is significant not only for storing energy in vehicles and electronic devices but also for power grids, where fluctuating energy demand requires efficient storage to avoid waste during periods of low demand and to ensure rapid supply during high demand.

Supercapacitors, energy storage devices that rely on ion accumulation in their pores, have rapid charging times and longer life spans compared to batteries.

“The primary appeal of supercapacitors lies in their speed,” Gupta said. “So how can we make their charging and release of energy faster? By the more efficient movement of ions.”

Their findings modify Kirchhoff’s law, which has governed current flow in electrical circuits since 1845 and is a staple in high school students’ science classes. Unlike electrons, ions move due to both electric fields and diffusion, and the researchers determined that their movements at pore intersections are different from what was described in Kirchhoff’s law.

Prior to the study, ion movements were only described in the literature in one straight pore. Through this research, ion movement in a complex network of thousands of interconnected pores can be simulated and predicted in a few minutes.

“That’s the leap of the work,” Gupta said. “We found the missing link.”



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AI headphones let wearer listen to a single person in a crowd, by looking at them just once

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AI headphones let wearer listen to a single person in a crowd, by looking at them just once


Noise-canceling headphones have gotten very good at creating an auditory blank slate. But allowing certain sounds from a wearer’s environment through the erasure still challenges researchers. The latest edition of Apple’s AirPods Pro, for instance, automatically adjusts sound levels for wearers — sensing when they’re in conversation, for instance — but the user has little control over whom to listen to or when this happens.

A University of Washington team has developed an artificial intelligence system that lets a user wearing headphones look at a person speaking for three to five seconds to “enroll” them. The system, called “Target Speech Hearing,” then cancels all other sounds in the environment and plays just the enrolled speaker’s voice in real time even as the listener moves around in noisy places and no longer faces the speaker.

The team presented its findings May 14 in Honolulu at the ACM CHI Conference on Human Factors in Computing Systems. The code for the proof-of-concept device is available for others to build on. The system is not commercially available.

“We tend to think of AI now as web-based chatbots that answer questions,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “But in this project, we develop AI to modify the auditory perception of anyone wearing headphones, given their preferences. With our devices you can now hear a single speaker clearly even if you are in a noisy environment with lots of other people talking.”

To use the system, a person wearing off-the-shelf headphones fitted with microphones taps a button while directing their head at someone talking. The sound waves from that speaker’s voice then should reach the microphones on both sides of the headset simultaneously; there’s a 16-degree margin of error. The headphones send that signal to an on-board embedded computer, where the team’s machine learning software learns the desired speaker’s vocal patterns. The system latches onto that speaker’s voice and continues to play it back to the listener, even as the pair moves around. The system’s ability to focus on the enrolled voice improves as the speaker keeps talking, giving the system more training data.

The team tested its system on 21 subjects, who rated the clarity of the enrolled speaker’s voice nearly twice as high as the unfiltered audio on average.

This work builds on the team’s previous “semantic hearing” research, which allowed users to select specific sound classes — such as birds or voices — that they wanted to hear and canceled other sounds in the environment.

Currently the TSH system can enroll only one speaker at a time, and it’s only able to enroll a speaker when there is not another loud voice coming from the same direction as the target speaker’s voice. If a user isn’t happy with the sound quality, they can run another enrollment on the speaker to improve the clarity.

The team is working to expand the system to earbuds and hearing aids in the future.

Additional co-authors on the paper were Bandhav Veluri, Malek Itani and Tuochao Chen, UW doctoral students in the Allen School, and Takuya Yoshioka, director of research at AssemblyAI. This research was funded by a Moore Inventor Fellow award, a Thomas J. Cabel Endowed Professorship and a UW CoMotion Innovation Gap Fund.



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Theory and experiment combine to shine a new light on proton spin

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Theory and experiment combine to shine a new light on proton spin


Nuclear physicists have long been working to reveal how the proton gets its spin. Now, a new method that combines experimental data with state-of-the-art calculations has revealed a more detailed picture of spin contributions from the very glue that holds protons together. It also paves the way toward imaging the proton’s 3D structure.

The work was led by Joseph Karpie, a postdoctoral associate in the Center for Theoretical and Computational Physics (Theory Center) at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility.

He said that this decades-old mystery began with measurements of the sources of the proton’s spin in 1987. Physicists originally thought that the proton’s building blocks, its quarks, would be the main source of the proton’s spin. But that’s not what they found. It turned out that the proton’s quarks only provide about 30% of the proton’s total measured spin. The rest comes from two other sources that have so far proven more difficult to measure.

One is the mysterious but powerful strong force. The strong force is one of the four fundamental forces in the universe. It’s what “glues” quarks together to make up other subatomic particles, such as protons or neutrons. Manifestations of this strong force are called gluons, which are thought to contribute to the proton’s spin. The last bit of spin is thought to come from the movements of the proton’s quarks and gluons.

“This paper is sort of a bringing together of two groups in the Theory Center who have been working toward trying to understand the same bit of physics, which is how do the gluons that are inside of it contribute to how much the proton is spinning around,” he said.

He said this study was inspired by a puzzling result that came from initial experimental measurements of the gluons’ spin. The measurements were made at the Relativistic Heavy Ion Collider, a DOE Office of Science user facility based at Brookhaven National Laboratory in New York. The data at first seemed to indicate that the gluons may be contributing to the proton’s spin. They showed a positive result.

But as the data analysis was improved, a further possibility appeared.

“When they improved their analysis, they started to get two sets of results that seemed quite different, one was positive and the other was negative,” Karpie explained.

While the earlier positive result indicated that the gluons’ spins are aligned with that of the proton, the improved analysis allowed for the possibility that the gluons’ spins have an overall negative contribution. In that case, more of the proton spin would come from the movement of the quarks and gluons, or from the spin of the quarks themselves.

This puzzling result was published by the Jefferson Lab Angular Momentum (JAM) collaboration.

Meanwhile, the HadStruc collaboration had been addressing the same measurements in a different way. They were using supercomputers to calculate the underlying theory that describes the interactions among quarks and gluons in the proton, Quantum Chromodynamics (QCD).

To equip supercomputers to make this intense calculation, theorists somewhat simplify some aspects of the theory. This somewhat simplified version for computers is called lattice QCD.

Karpie led the work to bring together the data from both groups. He started with the combined data from experiments taken in facilities around the world. He then added the results from the lattice QCD calculation into his analysis.

“This is putting everything together that we know about quark and gluon spin and how gluons contribute to the spin of the proton in one dimension,” said David Richards, a Jefferson Lab senior staff scientist who worked on the study.

“When we did, we saw that the negative things didn’t go away, but they changed dramatically. That meant that there’s something funny going on with those,” Karpie said.

Karpie is lead author on the study that was recently published in Physical Review D. He said the main takeaway is that combining the data from both approaches provided a more informed result.

“We’re combining both of our datasets together and getting a better result out than either of us could get independently. It’s really showing that we learn a lot more by combining lattice QCD and experiment together in one problem analysis,” said Karpie. “This is the first step, and we hope to keep doing this with more and more observables as well as we make more lattice data.”

The next step is to further improve the datasets. As more powerful experiments provide more detailed information on the proton, these data begin painting a picture that goes beyond one dimension. And as theorists learn how to improve their calculations on ever-more powerful supercomputers, their solutions also become more precise and inclusive.

The goal is to eventually produce a three-dimensional understanding of the proton’s structure.

“So, we learn our tools do work on the simpler one-dimension scenario. By testing our methods now, we hopefully will know what we need to do when we want to move up to do 3D structure,” Richards said. “This work will contribute to this 3D image of what a proton should look like. So it’s all about building our way up to the heart of the problem by doing this easier stuff now.”



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