It comes down to the behavior of T cells, a type of immune cell. When a person — or a mouse — develops asthma, their T cells become activated. In a new mouse study, researchers discovered that asthma causes the T cells to behave in a way that induces lung inflammation but prevents the growth of brain tumors. What’s bad news for the airways may be good news for the brain.

The findings, available online in Nature Communications, suggest that reprogramming T cells in brain tumor patients to act more like T cells in asthma patients could be a new approach to treating brain tumors.

“Of course, we’re not going to start inducing asthma in anyone; asthma can be a lethal disease,” said senior author David H. Gutmann, MD, PhD, the Donald O. Schnuck Family Professor of Neurology. “But what if we could trick the T cells into thinking they’re asthma T cells when they enter the brain, so they no longer support brain tumor formation and growth? These findings open the door to new kinds of therapies targeting T cells and their interactions with cells in the brain.”

The idea that people with inflammatory diseases, such as asthma or eczema, are less prone to developing brain tumors was first proposed more than 15 years ago, based on epidemiologic observations. But there was no obvious reason why the two very different kinds of diseases would be linked, and some scientists questioned whether the association was real.

Gutmann is an expert on neurofibromatosis (NF), a set of complex genetic disorders that cause tumors to grow on nerves in the brain and throughout the body. Children with NF type 1 (NF1) can develop a kind of brain tumor known as an optic pathway glioma. These tumors grow within the optic nerves, which carries messages between the eyes and the brain. Gutmann, director of the Washington University NF Center, noted an inverse association between asthma and brain tumors among his patients more than five years ago but didn’t know what to make of it. It wasn’t until more recent studies from his lab began to reveal the crucial role that immune cells play in the development of optic pathway gliomas that he began to wonder whether immune cells could account for the association between asthma and brain tumors.

Jit Chatterjee, PhD, a postdoctoral researcher and the paper’s first author, took on the challenge of investigating the association. Working with co-author Michael J. Holtzman, MD, the Selma and Herman Seldin Professor of Medicine and director of the Division of Pulmonary & Critical Care Medicine, Chatterjee studied mice genetically modified to carry a mutation in their NF1 genes and form optic pathway gliomas by 3 months of age.

Chatterjee exposed groups of mice to irritants that induce asthma at age 4 weeks to 6 weeks, and treated a control group with saltwater for comparison. Then, he checked for optic pathway gliomas at 3 months and 6 months of age. The mice with asthma did not form these brain tumors.

Further experiments revealed that inducing asthma in tumor-prone mice changes the behavior of their T cells. After the mice developed asthma, their T cells began secreting a protein called decorin that is well-known to asthma researchers.

In the airways, decorin is a problem. It acts on the tissues that line the airways and exacerbates asthma symptoms. But in the brain, Chatterjee and Gutmann discovered, decorin is beneficial. There, the protein acts on immune cells known as microglia and blocks their activation by interfering with the NFkappaB activation pathway. Activated microglia promote the growth and development of brain tumors.

Treatment with either decorin or caffeic acid phenethyl ester (CAPE), a compound that inhibits the NFkappaB activation pathway, protected mice with NF1 mutations from developing optic pathway gliomas. The findings suggest that blocking microglial activation may be a potentially useful therapeutic approach for brain tumors.

“The most exciting part of this is that it shows that there is a normal communication between T cells in the body and the cells in the brain that support optic pathway glioma formation and growth,” said Gutmann, who is also a professor of genetics, of neurosurgery and of pediatrics. “The next step for us is to see whether this is also true for other kinds of brain tumors. We’re also investigating the role of eczema and early-childhood infections, because they both involve T cells. As we understand this communication between T cells and the cells that promote brain tumors better, we’ll start finding more opportunities to develop clever therapeutics to intervene in the process.”

Machine learning and neural networks have become all the rage in recent years, and quite understandably so, considering their many successes in image recognition, medical diagnosis, e-commerce and many other fields. Still though, this software-based approach to machine intelligence has its drawbacks, not least because it consumes so

Mimicking the human brain

This power issue is one of the reasons that researchers have been trying to develop computers that are much more energy efficient. And to find a solution many are finding inspiration in the human brain, a thinking machine unrivalled in its low power consumption due to how it combines memory and processing.

Neurons in our brain communicate with one another through so-called synapses, which are strengthened each time information flows through them. It is this plasticity that ensures that humans remember and learn.

“In our research, we have taken this model to develop a robot that is able to learn to move through a labyrinth,” explains Imke Krauhausen, PhD student at the department of Mechanical Engineering at TU/e and principal author of the paper.

“Just as a synapse in a mouse brain is strengthened each time it takes the correct turn in a psychologist’s maze, our device is ‘tuned’ by applying a certain amount of electricity. By tuning the resistance in the device, you change the voltage that control the motors. They in turn determine whether the robot turns right or left.”

So how does it work?

The robot that Krauhausen and her colleagues used for their research is a Mindstorms EV³, a robotics kit made by Lego. Equipped with two wheels, traditional guiding software to make sure it can follow a line, and a number of reflectance and touch sensors, it was sent into a 2 m² large maze made up out of black-lined hexagons in a honeycomb-like pattern.

The robot is programmed to turn right by default. Each time it reaches a dead end or diverges from the designated path to the exit (which is indicated by visual cues), it is told to either return or turn left. This corrective stimulus is then remembered in the neuromorphic device for the next effort.

“In the end, it took our robot 16 runs to find the exit successfully,” says Krauhausen. “And, what’s more, once it has learned to navigate this specific route (target path 1), it can navigate any other path that it is given in one go (target path 2). So, the knowledge it has acquired is generalizable.”

Part of the success of the robot’s ability to learn and exit the maze lies in the unique integration of sensors and motors, according to Krauhausen, who cooperated closely with the Max Planck Institute for Polymer Research in Mainz for this research. “This sensorimotor integration, in which sense and movement reinforce one another, is also very much how nature operates, so this is what we tried to emulate in our robot.”

Smart polymers

Another clever thing about the research is the organic material used for the neuromorphic robot. This polymer (known as p(g2T-TT)) is not only stable, but it also is able to ‘retain’ a large part of the specific states in which it has been tuned during the various runs through the labyrinth. This ensures that the learned behaviour ‘sticks’, just like neurons and synapses in a human brain remember events or actions.

The use of polymer instead of silicon in the field of neuromorphic computing was pioneered by Paschalis Gkoupidenis of the Max Planck Institute for Polymer Research in Mainz and Yoeri van de Burgt of TU/e, both co-authors of the paper.

In their research (dating from 2015 and 2017), they proved that the material can be tuned in a much larger range of conduction than inorganic materials, and that it is able to ‘remember’ or store learned states for extended periods. Since then, organic devices have become a hot topic in the field of hardware-based artificial neural networks.

Bionic hands

Polymeric materials also have the added advantage that they can be used in numerous biomedical applications. “Because of their organic nature, these smart devices can in principle be integrated with actual nerve cells. Say you lost your arm during an injury. Then you could potentially use these devices to link your body to a bionic hand,” says Krauhausen.

Another promising application of organic neuromorphic computing lies in small so-called edge computing devices where data from sensors is processed locally outside of the cloud. Van de Burgt: “This is where I see our devices going in the future, our materials will be very useful because they are easy to tune, use much less power, and are cheap to make.”

So will neuromorphic robots one day be able to play a soccer game, just like TU/e’s soccer robots?

Krauhausen: “In principle, that is certainly possible. But there’s a long way to go. Our robots still rely partly on traditional software to move around. And for the neuromorphic robots to execute really complex tasks, we need to build neuromorphic networks in which many devices work together in a grid. That’s something that I will be working on in the next phase of my PhD research.”

A ‘human-like’ brain helps a robot out of a maze: https://www.youtube.com/watch?v=O05YVljxrtg

A catalyst is a substance that increases the rate of a chemical reaction without itself undergoing any permanent chemical change.

The findings are significant because the production of hydrogen is important for “many aspects of our life, such as fuel cells for cars and the manufacture of many useful chemicals such as ammonia,” said the OSU College of Engineering’s Zhenxing Feng, a chemical engineering professor who led the research. “It’s also used in the refining of metals, for producing human-made materials such as plastics and for a range of other purposes.”

Producing hydrogen by splitting water via an electrochemical catalytic process is cleaner and more sustainable than the conventional method of deriving hydrogen from natural gas via a carbon-dioxide-producing process known as methane-steam reforming, Feng said. But the cost of the greener technique has been a barrier in the marketplace.

The new findings, which describe ways to design catalysts that can greatly improve the efficiency of the clean hydrogen production process, were published in Science Advances and JACS Au.

In facilitating reaction processes, catalysts often experience structural changes, Feng said. Sometimes the changes are reversible, other times irreversible, and irreversible restructuring is believed to degrade a catalyst’s stability, leading to a loss of catalytic activity that lowers reaction efficiency.

Feng, OSU Ph.D. student Maoyu Wang and collaborators studied the restructuring of catalysts in reaction and then manipulated their surface structure and composition at the atomic scale to achieve a highly efficient catalytic process for producing hydrogen.

An active phase of a catalyst based on amorphous iridium hydroxide exhibited efficiency 150 times that of its original perovskite structure and close to three orders of magnitude better than the common commercial catalyst, iridium oxide.

“We found at least two groups of materials that undergo irreversible changes that turned out to be significantly better catalysts for hydrogen production,” Feng said. “This can help us produce hydrogen at $2 per kilogram and eventually $1 per kilogram. That’s less expensive than the polluting process in current industries and will help achieve the United States’ goal of zero emissions by 2030.”

Feng notes that the U.S. Department of Energy Hydrogen and Fuel Cell Technologies Office has established benchmarks of technologies that can produce clean hydrogen at $2 per kilogram by 2025 and $1 per kilogram by 2030 as part of the Hydrogen Energy Earthshot target of cutting the cost of clean hydrogen by 80%, from $5 to $1 per kilogram, in one decade.

The water electrolysis technology for clean hydrogen production that Feng’s group is focused on uses electricity from renewable sources to split water to make clean hydrogen. However, the efficiency of water splitting is low, he said, mainly due to the high overpotential — the difference between the actual potential and the theoretical potential of an electrochemical reaction — of one key half-reaction in the process, the oxygen evolution reaction or OER.

“Catalysts are critical to promoting the water-splitting reaction by lowering the overpotential, and thus lowering the total cost for hydrogen production,” Feng said. “Our first study in JACS Au laid the foundation for us, and as demonstrated in our Science Advances article we now can better manipulate atoms on surface to design catalysts with the desired structure and composition.”

The National Science Foundation supported Feng’s research through the Northwest Nanotechnology Infrastructure site at OSU, and the Department of Energy provided funding as well.

Collaborating with Feng and Wang were researchers from Argonne National Laboratory, the University of Texas, Peking University, Pacific Northwest National Laboratory, Northwestern University, South China University of Technology, the University of Cambridge, the University of California, Berkeley, and Singapore’s Nanyang Technological University.

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Materials provided by Oregon State University. Original written by Steve Lundeberg. Note: Content may be edited for style and length.