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Using fMRI, new vision study finds promising model for restoring cone function

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Using fMRI, new vision study finds promising model for restoring cone function


In the retinas of human eyes, the cones are photoreceptor cells responsible for color vision, daylight vision, and the perception of small details. As vision scientists from the Division of Experimental Retinal Therapies at the University of Pennsylvania School of Veterinary Medicine, Gustavo D. Aguirre and William A. Beltran have been working for decades to identify the basis of inherited retinal diseases. They previously showed they could recover missing cone function by reintroducing a copy of the normal gene in photoreceptor cells.

Both humans and dogs are affected by retinal disease, and a new study of daylight vision using a canine model offers a critical insight for evaluating “whether these cell replacements — where we are introducing cones into the retinas of these dogs — is a successful approach for restoring cone vision,” says Beltran, the Corinne R. and Henry Bower Endowed Professor of Ophthalmology.

He and Gustavo Aguirre teamed with researchers including cognitive neuroscientist Geoffrey K. Aguirre, a professor of neurology at the Perelman School of Medicine, bringing together knowledge on the retinal system and brain measurements. In dogs with three different kinds of naturally occurring retinal disease and in dogs with normal vision, the scientists used functional magnetic resonance imaging (fMRI) to assess brain responses to lights that stimulate only the cone cells.

The researchers found that fMRI can detect brain responses to daylight vision for black and white information as well as color information, and it can identify the area of the visual cortex that responds to stimulation of a region in the dog retina that is rich in cones and similar to the human fovea. They also found they can use fMRI to measure the relative degree of loss of daylight vision. Using this technique in animals with a retinal disease caused by a mutation in a gene called NPHP5, they demonstrated that gene augmentation therapy restored the response in the cortex to black and white stimulation. That makes this disease a promising one in which to study photoreceptor cell replacement as a treatment in the future.

Their findings were published in Translational Vision Science & Technology. The other co-authors are Huseyin O. Taskin, a former research specialist at Penn in the GKAguirre Lab and current graduate student at the University of Toronto, and Jacqueline Wivel, a veterinary technician.

“Canine models are useful for studying retinal diseases because they have a variety of different naturally occurring genetic disorders. The ultimate goal is to first demonstrate that these disorders can be treated in canines before translating it to human patients,” says Taskin, the first author. Gustavo Aguirre says, “The hope is that successful therapeutic approaches in people will then become available to veterinarians so that they can benefit man’s four-legged friend.”

Geoffrey Aguirre says, “The purpose of the study was to see, in different versions of these retinal diseases, how much information about daylight vision makes it to the visual system in these dogs.” This knowledge is particularly useful, he says, because figuring out whether a treatment for retinal disease has been effective requires knowing how much vision function was present prior to treatment.

Beltran says this paper shows that gene therapy can recover cone function because it looks at an animal model with no cone function and shows an improvement. He explains that, in the disease caused by the NPHP5 mutation, cones are present but not functional. Animals with this disease are born day-blind but initially have some night vision, though rods — photoreceptors that allow night vision — die over a period of months, making dogs fully blind within a year.

Previous research has measured responses to stimuli through electroretinography and visual behavior tests, which Beltran says can require weeks if not months of dog training. Geoffrey Aguirre says the use of fMRI in this study is significant because it is faster and easier than measuring behavior, and it is not invasive. Taskin also notes that neither visual behavior tests nor electroretinography provide certainty as to what happens in the visual cortex.

A prior study showed that retinal gene therapy in a type of blinding disorder called Leber congential amaurosis is associated with restoration of fMRI responses from the canine visual cortex, but the nature of that study meant that both rod and cone responses could have contributed to cortical activity. The new study furthers knowledge of retinal disease by specifically stimulating the cones.

Huseyin O. Taskin is a former research specialist in the Department of Neurology at the Perelman School of Medicine and a current doctoral candidate in medical sciences at the University of Toronto.

Jacqueline Wivel is a certified veterinary technician in the University of Pennsylvania School of Veterinary Medicine.

Gustavo D. Aguirre is professor of medical genetics and ophthalmology in Penn Vet’s Department of Clinical Sciences & Advanced Medicine.

William A. Beltran is the Corinne R. and Henry Bower Endowed Professor of Ophthalmology and director of the Division of Experimental Retinal Therapies in Penn Vet’s Department of Clinical Sciences & Advanced Medicine.

Geoffrey K. Aguirre is professor of neurology, associate director of the Penn Center for Neuroscience and Society, and associate director of neurology residency in the Department of Neurology at Penn Medicine.

This study was supported by the National Eye Institute (grants R24-EY029890, RO1-EY006855, RO1-EY017549, and P30-EY001583), Fighting Blindness Canada Vision 20/20, The Foundation Fighting Blindness, and the Low Vision Research Award from the Research to Prevent Blindness / Lions Clubs 25 International Foundation.



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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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