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How humans develop larger brains than other apes

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How humans develop larger brains than other apes

A new study is the first to identify how human brains grow much larger, with three times as many neurons, compared with chimpanzee and gorilla brains. The study, led by researchers at the Medical Research Council (MRC) Laboratory of Molecular Biology in Cambridge, UK, identified a key molecular switch that can make ape brain organoids grow more like human organoids, and vice versa.

The study, published in the journal Cell, compared ‘brain organoids’ — 3D tissues grown from stem cells which model early brain development — that were grown from human, gorilla and chimpanzee stem cells.Similar to actual brains, the human brain organoids grew a lot larger than the organoids from other apes.Dr Madeline Lancaster, from the MRC Laboratory of Molecular Biology, who led the study, said: “This provides some of the first insight into what is different about the developing human brain that sets us apart from our closest living relatives, the other great apes. The most striking difference between us and other apes is just how incredibly big our brains are.”

During the early stages of brain development, neurons are made by stem cells called neural progenitors. These progenitor cells initially have a cylindrical shape that makes it easy for them to split into identical daughter cells with the same shape.

The more times the neural progenitor cells multiply at this stage, the more neurons there will be later.


As the cells mature and slow their multiplication, they elongate, forming a shape like a stretched ice-cream cone.

Previously, research in mice had shown that their neural progenitor cells mature into a conical shape and slow their multiplication within hours.

Now, brain organoids have allowed researchers to uncover how this development happens in humans, gorillas and chimpanzees.

They found that in gorillas and chimpanzees this transition takes a long time, occurring over approximately five days.

Human progenitors were even more delayed in this transition, taking around seven days. The human progenitor cells maintained their cylinder-like shape for longer than other apes and during this time they split more frequently, producing more cells.This difference in the speed of transition from neural progenitors to neurons means that the human cells have more time to multiply. This could be largely responsible for the approximately three-fold greater number of neurons in human brains compared with gorilla or chimpanzee brains.

Dr Lancaster said: “We have found that a delayed change in the shape of cells in the early brain is enough to change the course of development, helping determine the numbers of neurons that are made.

“It’s remarkable that a relatively simple evolutionary change in cell shape could have major consequences in brain evolution. I feel like we’ve really learnt something fundamental about the questions I’ve been interested in for as long as I can remember — what makes us human.”

To uncover the genetic mechanism driving these differences, the researchers compared gene expression — which genes are turned on and off — in the human brain organoids versus the other apes.

They identified differences in a gene called ‘ZEB2’, which was turned on sooner in gorilla brain organoids than in the human organoids.

To test the effects of the gene in gorilla progenitor cells, they delayed the effects of ZEB2. This slowed the maturation of the progenitor cells, making the gorilla brain organoids develop more similarly to human — slower and larger.

Conversely, turning on the ZEB2 gene sooner in human progenitor cells promoted premature transition in human organoids, so that they developed more like ape organoids.

The researchers note that organoids are a model and, like all models, do not to fully replicate real brains, especially mature brain function. But for fundamental questions about our evolution, these brain tissues in a dish provide an unprecedented view into key stages of brain development that would be impossible to study otherwise.

Dr Lancaster was part of the team that created the first brain organoids in 2013.

This study was funded by the Medical Research Council, European Research Council and Cancer Research UK.

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

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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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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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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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