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AI tool uncovers genetic blueprint of the brain’s largest communication bridge

For the first time, a research team led by the Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC has mapped the genetic architecture of a crucial part of the human brain known as the corpus callosum—the thick band of nerve fibers that connects the brain’s left and right hemispheres. The findings open new pathways for discoveries about mental illness, neurological disorders and other diseases related to defects in this part of the brain.

The corpus callosum is critical for nearly everything the brain does, from coordinating the movement of our limbs in sync to integrating sights and sounds, to higher-order thinking and decision-making. Abnormalities in its shape and size have long been linked to disorders such as ADHD, bipolar disorder, and Parkinson’s disease. Until now, the genetic underpinnings of this vital structure had remained largely unknown.

In the new study, published in Nature Communications, the team analyzed and from over 50,000 people, ranging from childhood to late adulthood, with the help of a new tool the team created that leverages artificial intelligence.

Hair-thin fiber can control thousands of brain neurons simultaneously

Fiber-optic technology revolutionized the telecommunications industry and may soon do the same for brain research.

A group of researchers from Washington University in St. Louis in both the McKelvey School of Engineering and WashU Medicine have created a new kind of fiber-optic device to manipulate neural activity deep in the brain. The device, called PRIME (Panoramically Reconfigurable IlluMinativE) fiber, delivers multi-site, reconfigurable optical stimulation through a single, hair-thin implant.

“By combining fiber-based techniques with optogenetics, we can achieve deep-brain stimulation at unprecedented scale,” said Song Hu, a professor of biomedical engineering at McKelvey Engineering, who collaborated with the laboratory of Adam Kepecs, a professor of neuroscience and of psychiatry at WashU Medicine.

Optimizing avalanche photodiode design for photodetection in the ultraviolet wavelength

Geiger-mode avalanche photodiodes (GM-APDs) are highly sensitive light detectors, capable of detecting single photons. Photons of certain wavelengths, when absorbed by photodiodes, generate electron-hole pairs in a process called impact ionization which can result in a multiplication of charges when occurring in an electric field.

An avalanche photodiode is biased above its “,” at which point impact ionizations reach a self-sustaining rate, resulting in a distinct electrical pulse that is readily detectable. To detect in the presence of other mechanisms that generate impact ionization, the avalanche diode must simultaneously have a high probability to absorb incident photons of the desired wavelength, known as the unity-gain quantum efficiency (QE). Both being able to support high fields and having good QE at the desired wavelength are critical factors in determining the device’s sensitivity.

Certain GM-APDs based on 4H-silicon carbide (4H-SiC) have high single-photon detection efficiency in the deep-ultraviolet (DUV) wavelengths around 280 nanometers. To reliably detect photons at higher wavelengths where absorption is weaker, SiC GM-APDs need to improve their baseline photon capture efficiency, as indicated by its unity-gain QE. To accomplish this, researchers often employ APDs with much thicker absorber layers. However, this can often lead to design challenges.

Newly developed knitting machine makes solid 3D objects

A new prototype of a knitting machine creates solid, knitted shapes, adding stitches in any direction—forward, backward and diagonal—so users can construct a wide variety of shapes and add stiffness to different parts of the object.

Unlike traditional knitting, which yields a 2D sheet of stitches, this proof-of-concept machine—developed by researchers at Cornell University and Carnegie Mellon University—functions more like a 3D printer, building up solid shapes with horizontal layers of stitches.

“We establish that not only can it be done, but because of the way we attach the stitch, it will give us access to a lot of flexibility about how we control the material,” said François Guimbretière, professor of information science at Cornell. “The expressiveness is very similar to a 3D printer.”

Brain-inspired chips are helping electronic noses better mimic human sense of smell

After years of trying, the electronic nose is finally making major progress in sensing smells, almost as well as its human counterpart. That is the conclusion of a scientific review into the development of neuromorphic olfactory perception chips (NOPCs), published in the journal Nature Reviews Electrical Engineering.

Evolution has perfected the human nose over millions of years. This powerful sense organ, while not the best in the animal kingdom, can still detect around a trillion smells. The quest to develop electronic noses with human nose-like abilities for applications like security, robotics, and medical diagnostics has proved notoriously difficult. So scientists have increasingly been turning to neuromorphic computing, which involves designing software and hardware that mimics the structure and function of the human nose.

In this review, a team of scientists from China highlights some of the key advances in developing olfactory sensing chips. The paper focuses heavily on because they are key components of the system. They must physically detect and convert them into electrical signals.

Startup provides a nontechnical gateway to coding on quantum computers

Quantum computers have the potential to model new molecules and weather patterns better than any computer today. They may also one day accelerate artificial intelligence algorithms at a much lower energy footprint. But anyone interested in using quantum computers faces a steep learning curve that starts with getting access to quantum devices and then figuring out one of the many quantum software programs on the market.

Now qBraid, founded by Kanav Setia and Jason Necaise ‘20, is providing a gateway to quantum computing with a platform that gives users access to the leading and software. Users can log on to qBraid’s cloud-based interface and connect with quantum devices and other computing resources from leading companies like Nvidia, Microsoft, and IBM. In a few clicks, they can start coding or deploy cutting-edge software that works across devices.

“The mission is to take you from not knowing anything about quantum computing to running your first program on these amazing machines in less than 10 minutes,” Setia says. “We’re a one-stop platform that gives access to everything the quantum ecosystem has to offer. Our goal is to enable anyone—whether they’re enterprise customers, academics, or individual users—to build and ultimately deploy applications.”

Infrared sensors gain sensitivity with ultra-thin lens for fire and threat monitoring

Researchers have developed a highly sensitive method for detecting hotspots in the environment, such as bushfires or military threats, by harnessing the focusing power of meta-optical systems.

The key to the approach is innovative lens technology thinner than a , which can collect and process from fires and other heat sources with much improved efficiency. Crucially, it does not need cryogenic cooling, unlike current sensors.

The result is that promises to enhance devices in both the civilian and military spheres, said Dr. Tuomas Haggren, lead researcher on the project.

Physicists achieve high precision in measuring strontium atoms using rubidium neighbor

Having good neighbors can be very valuable—even in the atomic world. A team of Amsterdam physicists was able to determine an important property of strontium atoms, a highly useful element for modern applications in atomic clocks and quantum computers, to unprecedented precision. To achieve this, they made clever use of a nearby cloud of rubidium atoms. The results were published in the journal Physical Review Letters this week.

Strontium. It is perhaps not the most popularly known chemical element, but among a group of physicists it has a much better reputation—and rightfully so.

Strontium is one of six so-called alkaline earth metals, meaning that it shares properties with better-known cousins like magnesium, calcium and radium. Strontium atoms have 38 protons in their nucleus, and a varying number of neutrons—for the variations (or isotopes) of strontium that can be found in nature, either 46, 48, 49 or 50.

Thin-film strontium titanate sets electro-optic performance record at cryogenic temperatures

At 4 degrees Kelvin, most electro-optic materials falter. Nanoelectronics R&D center imec has now successfully engineered thin-film strontium titanate (SrTiO₃) that delivers record electro-optic performance with low optical loss, pointing to shorter, faster building blocks for quantum devices.

Quantum computers and detectors run at temperatures close to absolute zero. In these , even the best room-temperature materials struggle to control light efficiently. This feature is essential to encode, route, and convert information in electro-optic networks, which at room temperature are used in data and telecom applications, but also increasingly for ultra-low temperature quantum links.

In a new paper published today in Science, imec researchers, in collaboration with KU Leuven and Ghent University, report how they re-engineered a common crystal, (SrTiO₃), so it behaves with record performance at .

Scientist Solves 100-Year-Old Physics Puzzle To Track Airborne Killers

Researchers at the University of Warwick have created a straightforward new way to predict how irregularly shaped nanoparticles, a harmful type of airborne pollutant, move through the air.

Each day, people inhale countless microscopic particles such as soot, dust, pollen, microplastics, viruses, and engineered nanoparticles. Many of these particles are so small that they can reach deep into the lungs and even pass into the bloodstream, where they may contribute to serious health problems including heart disease, stroke, and cancer.

While most airborne particles have uneven shapes, existing mathematical models often treat them as perfect spheres because that makes the equations easier to handle. This simplification limits scientists’ ability to accurately describe or track how real, non-spherical particles move, especially those that are more dangerous.

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