Aurelia Butler

A new imaging technique developed by engineers at Washington University in St. Louis can give scientists a much closer look at fibril assemblies, stacks of peptides like amyloid beta, most notably associated with Alzheimer’s disease.
These cross-β fibril assemblies are also useful building blocks within designer biomaterials for medical applications, but their resemblance to their amyloid beta cousins, whose tangles are a symptom of neurodegenerative disease, is concerning. Researchers want to learn how different sequences of these peptides are linked to their varying toxicity and function, for both naturally occurring peptides and their synthetic engineered cousins.
Now, scientists can get a close enough look at fibril assemblies to see there are notable differences in how synthetic peptides stack compared with amyloid beta. These results stem from a fruitful collaboration between lead author Matthew Lew, associate professor in the Preston M. Green Department of Electrical & Systems Engineering, and Jai Rudra, associate professor of biomedical engineering, in WashU’s McKelvey School of Engineering.
“We engineer microscopes to enable better nanoscale measurements so that the science can move forward,” Lew said.
In a paper published in ACS Nano, Lew and colleagues outline how they used the Nile red chemical probe to light up cross-β fibrils. Their technique called single-molecule orientation-localization microscopy (SMOLM) uses the flashes of light from Nile red to visualize the fiber structures formed by synthetic peptides and by amyloid beta.
The bottom line: these assemblies are much more complicated and heterogenous than anticipated. But that’s good news, because it means there’s more than one way to safely stack your proteins. With better measurements and images of fibril assemblies, bioengineers can better understand the rules that dictate how protein grammar affects toxicity and biological function, leading to more effective and less toxic therapeutics.
First, scientists need to see the difference between them, something very challenging because of the tiny scale of these assemblies.

“The helical twist of these fibers is impossible to discern using an optical microscope, or even some super-resolution microscopes, because these things are just too small,” Lew said.
With high-dimensional imaging technology developed in Lew’s lab the past couple years, they are able to see the differences.
A typical fluorescence microscope uses florescent molecules as light bulbs to highlight certain aspects of a biological target. In the case of this work, they used one of those probes, Nile red, as a sensor for what was around it. As Nile red randomly explores its environment and collides with the fibrils, it emits flashes of light that they can measure to determine where the fluorescent probe is and its orientation. From that data, they can piece together the full picture of engineered fibrils that stack very differently from the natural ones like amyloid beta.
Their image of these fibril assemblies made the cover of the ACS Nano and was put together by first author Weiyan Zhou, who color-coded the image based on where the Nile reds were pointing. The resulting image is a blueish, red flowing assembly of peptides that looks like a river valley.
They plan to continue to develop techniques like SMOLM to open new avenues of studying biological structures and processes at the nanoscale.
“We are seeing things you can’t see with existing technology,” Lew said. More

A recent United Nations report found that the world generated 137 billion pounds of electronic waste in 2022, an 82% increase from 2010. Yet less than a quarter of 2022’s e-waste was recycled. While many things impede a sustainable afterlife for electronics, one is that we don’t have systems at scale to recycle the printed circuit boards (PCBs) found in nearly all electronic devices.
PCBs — which house and interconnect chips, transistors and other components — typically consist of layers of thin glass fiber sheets coated in hard plastic and laminated together with copper. That plastic can’t easily be separated from the glass, so PCBs often pile up in landfills, where their chemicals can seep into the environment. Or they’re burned to extract their electronics’ valuable metals like gold and copper. This burning, often undertaken in developing nations, is wasteful and can be toxic — especially for those doing the work without proper protections.
A team led by researchers at the University of Washington developed a new PCB that performs on par with traditional materials and can be recycled repeatedly with negligible material loss. Researchers used a solvent that transforms a type of vitrimer — a cutting-edge class of sustainable polymers — to a jelly-like substance without damaging it, allowing the solid components to be plucked out for reuse or recycling.
The vitrimer jelly can then be repeatedly used to make new, high-quality PCBs, unlike conventional plastics that degrade significantly with each recycling. With these “vPCBs” (vitrimer printed circuit boards), researchers recovered 98% of the vitrimer and 100% of the glass fiber, as well as 91% of the solvent used for recycling.
The researchers published their findings April 26 in Nature Sustainability.
“PCBs make up a pretty large fraction of the mass and volume of electronic waste,” said co-senior author Vikram Iyer, a UW assistant professor in the Paul G. Allen School of Computer Science & Engineering. “They’re constructed to be fireproof and chemical-proof, which is great in terms of making them very robust. But that also makes them basically impossible to recycle. Here, we created a new material formulation that has the electrical properties comparable to conventional PCBs as well as a process to recycle them repeatedly.”
Vitrimers are a class of polymers first developed in 2015. When exposed to certain conditions, such as heat above a specific temperature, their molecules can rearrange and form new bonds. This makes them both “healable” (a bent PCB could be straightened, for instance) and highly recyclable.

“On a molecular level, polymers are kind of like spaghetti noodles, which wrap and get compacted,” said co-senior author Aniruddh Vashisth, a UW assistant professor in the mechanical engineering department. “But vitrimers are distinct because the molecules that make up each noodle can unlink and relink. It’s almost like each piece of spaghetti is made of small Legos.”
The team’s process to create the vPCB deviated only slightly from those used for PCBs. Conventionally, semi-cured PCB layers are held in cool, dry conditions where they have a limited shelf life before they’re laminated in a heat press. Because vitrimers can form new bonds, researchers laminated fully cured vPCB layers. The researchers found that to recycle the vPCBs they could immerse the material in an organic solvent that has a relatively low boiling point. This swelled the vPCB’s plastic without damaging the glass sheets and electronic components, letting the researchers extract these for reuse.
This process allows for several paths to more sustainable, circular PCB lifecycles. Damaged circuit boards, such those with cracks or warping, can in some cases be repaired. If they aren’t repaired, they can be separated from their electronic components. Those components can then be recycled or reused, while the vitrimer and glass fibers can get recycled into new vPCBs.
The team tested its vPCB for strength and electrical properties, and found that it performed comparable to the most common PCB material (FR-4). Vashisth and co-author Bichlien H. Nguyen, a principal researcher at Microsoft Research and an affiliate assistant professor in the Allen School, are now using artificial intelligence to explore new vitrimer formulations for different uses.
Producing vPCBs wouldn’t entail major changes to manufacturing processes.
“The nice thing is that a lot of industries — such as aerospace, automotive and even electronics — already have processing set up for the sorts of two-part epoxies that we use here,” said lead author Zhihan Zhang, a UW doctoral student in the Allen School.
The team analyzed the environmental impact and found recycled vPCBs could entail a 48% reduction in global warming potential and an 81% reduction in carcinogenic emissions compared to traditional PCBs. While this work presents a technology solution, the team notes that a significant hurdle to recycling vPCBs at scale would be creating systems and incentives to gather e-waste so it can be recycled.
“For real implementation of these systems, there needs to be cost parity and strong governmental regulations in place,” said Nguyen. “Moving forward, we need to design and optimize materials with sustainability metrics as a first principle.”
Additional co-authors include Agni K. Biswal, a UW postdoctoral scholar in the mechanical engineering department; Ankush Nandi, a UW doctoral student in the mechanical engineering department; Kali Frost, a senior applied scientist at Microsoft Research; Jake A. Smith, a senior researcher at Microsoft Research and an affiliate researcher in the Allen School; and Shwetak Patel, a UW professor in the Allen School and the electrical and computer engineering department. This research is funded by the Microsoft Climate Research Initiative, an Amazon Research Award and the Google Research Scholar Program. Zhang was supported by the UW Clean Energy Institute Graduate Fellowship. More

University of North Carolina at Charlotte Assistant Professor of Bioinformatics Abigail Leavitt LaBella has co-led an ambitious research study — published in the widely influential journal Science — that reports intriguing findings made through innovative artificial intelligence analysis about yeasts, the small fungi that are key contributors to biotechnology, food production and human health. The findings challenge accepted frameworks within which yeast evolution is studied and provide access to an incredibly rich yeast analysis dataset that could have major implications for future evolutionary biology and bioinformatics research.
LaBella, who joined UNC Charlotte’s Department of Bioinformatics in the College of Computing and Informatics as an assistant professor and researcher at the North Carolina Research Campus in 2022, conducted the study with co-lead author Dana A. Opulente of Villanova University. They collaborated with fellow researchers from Vanderbilt University and the University of Wisconsin at Madison, along with colleagues from research institutions across the globe.
This is the flagship study of the Y1000+ Project, a massive inter-institutional yeast genome sequencing and phenotyping endeavor that LaBella joined as a postdoctoral researcher at Vanderbilt University.
“Yeasts are single-celled fungi that play critical roles in our everyday lives. They make bread and beer, are used in the production of medicine, can cause infection, and as close relatives to animals have helped us learn about how cancer works,” said LaBella. “We wanted to know how these small fungi have evolved to have such an incredible range of functions and features. With the characterization of over one thousand yeasts, we found that yeasts do not fit the adage ‘jack of all trades, master of none.'”
This study contributes to basic understanding of how the microbes change over time while generating more than 900 new genome sequences for yeasts — many of which could be leveraged in biofungal fields such as agricultural pest control, drug development and biofuels production.
LaBella and her co-authors — through an artificial intelligence-assisted, machine-learning analysis of the Y1000+ Project’s dataset comprising 1,154 strains of the ancient, single-cell yeast Saccaromycotina — attempted to answer an important question. That is: Why do some yeasts eat (or metabolize) only a few types of carbon for energy while others can eat more than a dozen?
The total number of carbon sources used by a yeast for energy is known in ecological terms as its carbon niche-breadth. Humans also vary in their carbon niche breadth — for example, some people can metabolize lactose while others cannot.

Evolutionary biology research has supported two key overarching paradigms about niche breadth, the phenomenon explaining why some yeast organisms (“specialists”) evolve to be able to metabolize only a small number of carbon forms as fuel while others (“generalists”) evolve to be able to consume and grow on a broad variety of carbon forms. One of these paradigms illustrates that being a generalist comes with certain trade-offs compared to being a specialist. Notably, in the latter case, the ability to process a wide range of carbon forms comes at the expense of the yeast’s capacity to process and grow on each carbon form efficiently. The second is that these yeast specialists and generalists evolve to fit either profile due to the combined effects of different intrinsic traits of their respective genomes and different extrinsic influences based on the varying environments in which yeast organisms exist.
LaBella and her colleagues found ample evidence supporting the idea that there are identifiable, intrinsic genetic differences in yeast specialists versus generalists, specifically that generalists tend to have a larger total number of genes than specialists. For example, they found that generalists are more likely to be able to synthesize carnitine, a molecule that is involved in energy production and often sold as an exercise supplement.
But unexpectedly, their research found very limited evidence for the anticipated evolutionary trade-off of a yeast’s ability to process many forms of carbon coming at the expense of its ability to do so efficiently and grow accordingly, and vice versa.
“We saw that the yeasts that could grow on lots of carbon substrates are actually very good growers,” said LaBella. “That was a very surprising finding to us.”
While the findings of this specific experiment and the innovative machine-learning mechanisms used in its analysis could have major implications for bioinformatics, ecology, metabolics and evolutionary biology, the publishing of this study means that the Y1000+ Project’s massive compendium of yeast data is now available for scholars worldwide to use as a starting point to amplify their own yeast research.
“This dataset will be a huge resource going forward,” said LaBella. More

University of Minnesota Twin Cities researchers have constructed a robot that uses machine learning to fully automate a complicated microinjection process used in genetic research.
In their experiments, the researchers were able to use this automated robot to manipulate the genetics of multicellular organisms, including fruit fly and zebrafish embryos. The technology will save labs time and money while enabling them to more easily conduct new, large-scale genetic experiments that were not possible previously using manual techniques
The research is featured on the cover of the April 2024 issue of GENETICS, a peer-reviewed, open access, scientific journal. The work was co-led by two University of Minnesota mechanical engineering graduate students Andrew Alegria and Amey Joshi. The team is also working to commercialize this technology to make it widely available through the University of Minnesota start-up company, Objective Biotechnology.
Microinjection is a method for introducing cells, genetic material, or other agents directly into embryos, cells, or tissues using a very fine pipette. The researchers have trained the robot to detect embryos that are one-hundredth the size of a grain of rice. After detection, the machine can calculate a path and automate the process of the injections.
“This new process is more robust and reproducible than manual injections,” said Suhasa Kodandaramaiah, a University of Minnesota mechanical engineering associate professor and senior author of the study. “With this model, individual laboratories will be able to think of new experiments that you couldn’t do without this type of technology.”
Typically, this type of research requires highly skilled technicians to perform the microinjection, which many laboratories do not have. This new technology could expand the ability to perform large experiments in labs, while reducing time and costs.
“This is very exciting for the world of genetics. Writing and reading DNA have drastically improved in recent years, but having this technology will increase our ability to perform large-scale genetic experiments in a wide range of organisms,” said Daryl Gohl, a co-author of the study, the group leader of the University of Minnesota Genomics Center’s Innovation Lab and research assistant professor in the Department of Genetics, Cell Biology, and Development.

Not only can this technology be used in genetic experiments, but it can also help to preserve endangered species through cryopreservation, a preservation technique conducted at ultra-low temperatures.
“You can use this robot to inject nanoparticles into cells and tissues that helps in cryopreservation and in the process of rewarming afterwards,” Kodandaramaiah explained.
Other team members highlighted other applications for the technology that could have even more impact.
“We hope that this technology could eventually be used for in vitro fertilization, where you could detect those eggs on the microscale level,” said Andrew Alegria, co-lead author on the paper and University of Minnesota mechanical engineering graduate research assistant in the Biosensing and Biorobotics Lab. More

A collaborative research team from NIMS and Tokyo University of Science has successfully developed a cutting-edge artificial intelligence (AI) device that executes brain-like information processing through few-molecule reservoir computing. This innovation utilizes the molecular vibrations of a select number of organic molecules. By applying this device for the blood glucose level prediction in patients with diabetes, it has significantly outperformed existing AI devices in terms of prediction accuracy.
With the expansion of machine learning applications in various industries, there’s an escalating demand for AI devices that are not only highly computational but also feature low-power consumption and miniaturization. Research has shifted towards physical reservoir computing, leveraging physical phenomena presented by materials and devices for neural information processing. One challenge that remains is the relatively large size of the existing materials and devices.
Our research has pioneered the world’s first implementation of physical reservoir computing that operates on the principle of surface-enhanced Raman scattering, harnessing the molecular vibrations of merely a few organic molecules. The information is inputted through ion-gating, which modulates the adsorption of hydrogen ions onto organic molecules (p-mercaptobenzoic acid, pMBA) by applying voltage. The changes in molecular vibrations of the pMBA molecules, which vary with hydrogen ion adsorption, serve the function of memory and nonlinear waveform transformation for calculation. This process, using a sparse assembly of pMBA molecules, has learned approximately 20 hours of a diabetic patient’s blood glucose level changes and managed to predict subsequent fluctuations over the next 5 minutes with an error reduction of about 50% compared to the highest accuracy achieved by similar devices to date.
The outcome of this study indicates that a minimal quantity of organic molecules can effectively perform computations comparable to a computer. This technological breakthrough of conducting sophisticated information processing with minimal materials and in tiny spaces presents substantial practical benefits. It paves the way for the creation of low-power AI terminal devices that can be integrated with a variety of sensors, opening avenues for broad industrial use. More

Creating robots to safely aid disaster victims is one challenge; executing flexible robot control that takes advantage of the material’s softness is another. The use of pliable soft materials to collaborate with humans and work in disaster areas has drawn much recent attention. However, controlling soft dynamics for practical applications has remained a significant challenge.
In collaboration with the University of Tokyo and Bridgestone Corporation, Kyoto University has now developed a method to control pneumatic artificial muscles, which are soft robotic actuators. Rich dynamics of these drive components can be exploited as a computational resource.
“We’ve demonstrated the actuator’s capability to autonomously generate diverse dynamics, including rhythmic patterns and chaos,” explains Nozomi Akashi of KyotoU’s Graduate School of Informatics.
Traditionally, patterns were generated by externally attaching oscillators to robots, enabling locomotion and repetitive motions. However, these oscillators should be removed from the robot to retain their softness. Akashi’s team addresses this difficult issue to bring out the soft robots’ potential.
“In addition, the pattern-changing bifurcation structures can be embedded into the robotic actuator itself,” says Kohei Nakajima of the University of Tokyo’s Graduate School of Information Science and Technology.
The findings suggest that robots can generate qualitatively different patterns outside the learning data, paving the way for the development of robots capable of more adaptable and flexible movements.
“This could streamline the hardware and software development process, making it more efficient and effective,” concludes Akashi. More

Light-driven molecular motors were first developed nearly 25 years ago at the University of Groningen, the Netherlands. This resulted in a shared Nobel Prize for Chemistry for Professor Ben Feringa in 2016. However, making these motors do actual work proved to be a challenge. A new paper from the Feringa lab, published in Nature Chemistry on 26 April, describes a combination of improvements that brings real-life applications closer.
First author Jinyu Sheng, now a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), adapted a ‘first generation’ light-driven molecular motor during his PhD studies in the Feringa laboratory. His main focus was to increase the efficiency of the motor molecule. ‘It is very fast, but only 2% of the photons that the molecule absorbs drive the rotary movement.’
Increased efficiency
This poor efficiency can get in the way of real-life applications. ‘Besides, increased efficiency would give us better control of the motion,’ adds Sheng. The rotary motion of Feringa’s molecular motor takes place in four steps: two of them are photochemical, while two are temperature-driven. The latter are unidirectional, but the photochemical steps cause an isomerization of the molecule that is usually reversible.
Sheng set out to improve the percentage of absorbed photons that drive rotary motion. ‘It is very difficult to predict how this can be done and, in the end, we accidently discovered a method that worked.’ Sheng added an aldehyde functional group to the motor molecule, as a first step in further transformation. ‘However, I decided to test the motor function of this intermediate version and found it to be very efficient in a way that we had never seen before.’
For this, he cooperated with the Molecular Photonics group at the University of Amsterdam’s Van ‘t Hoff Institute for Molecular Sciences. Using advanced laser spectroscopy and quantum chemical calculations the electronic decay pathways were mapped, providing detailed insight in the working of the molecular motor.
Rotation cycle
Furthermore, it became clear that the adaption indeed gave Sheng better control of the molecule’s rotary movement. As mentioned before, the molecular motor rotates in four discrete steps. Sheng: ‘Previously, if we irradiated a batch of motors with light, we would get a mixture of motors at different stages of the rotation cycle. After the modification, it was possible to synchronize all motors and control them at each stage.’

This opens up all kinds of possibilities. For example, the motors could be used as a chiral dopant in liquid crystals, where the different positions would create different reflection colours. In the Nature Chemistry paper, Sheng and his colleagues present an example of this. Other applications could, for example, be the control of molecular self-assembly.
Applications
The addition of an aldehyde group to the motor molecule also has another interesting effect: it shifts the absorption of light to a longer wavelength. Since longer wavelengths penetrate further into living tissue or bulk material, this means that the motors could work much more efficiently in medical applications and materials science because more light will reach the motor molecule, while this will also use the photons more efficiently.
‘A number of our colleagues are now working with us on this new molecular motor for different applications,’ says Sheng. He expects more papers on this topic in the near future. Meanwhile, there is another challenge for the Feringa lab: ‘The molecular motor is now more efficient but we don’t exactly know why the modification causes this effect. We are currently working on it!’ More

Researchers have developed tiny, flexible devices that can wrap around individual nerve fibres without damaging them.
The researchers, from the University of Cambridge, combined flexible electronics and soft robotics techniques to develop the devices, which could be used for the diagnosis and treatment of a range of disorders, including epilepsy and chronic pain, or the control of prosthetic limbs.
Current tools for interfacing with the peripheral nerves — the 43 pairs of motor and sensory nerves that connect the brain and the spinal cord — are outdated, bulky and carry a high risk of nerve injury. However, the robotic nerve ‘cuffs’ developed by the Cambridge team are sensitive enough to grasp or wrap around delicate nerve fibres without causing any damage.
Tests of the nerve cuffs in rats showed that the devices only require tiny voltages to change shape in a controlled way, forming a self-closing loop around nerves without the need for surgical sutures or glues.
The researchers say the combination of soft electrical actuators with neurotechnology could be an answer to minimally invasive monitoring and treatment for a range of neurological conditions. The results are reported in the journal Nature Materials.
Electric nerve implants can be used to either stimulate or block signals in target nerves. For example, they might help relieve pain by blocking pain signals, or they could be used to restore movement in paralysed limbs by sending electrical signals to the nerves. Nerve monitoring is also standard surgical procedure when operating in areas of the body containing a high concentration of nerve fibres, such as anywhere near the spinal cord.
These implants allow direct access to nerve fibres, but they come with certain risks. “Nerve implants come with a high risk of nerve injury,” said Professor George Malliaras from Cambridge’s Department of Engineering, who led the research. “Nerves are small and highly delicate, so anytime you put something large, like an electrode, in contact with them, it represents a danger to the nerves.”
“Nerve cuffs that wrap around nerves are the least invasive implants currently available, but despite this they are still too bulky, stiff and difficult to implant, requiring significant handling and potential trauma to the nerve,” said co-author Dr Damiano Barone from Cambridge’s Department of Clinical Neurosciences.

The researchers designed a new type of nerve cuff made from conducting polymers, normally used in soft robotics. The ultra-thin cuffs are engineered in two separate layers. Applying tiny amounts of electricity — just a few hundred millivolts — causes the devices to swell or shrink.
The cuffs are small enough that they could be rolled up into a needle and injected near the target nerve. When activated electrically, the cuffs will change their shape to wrap around the nerve, allowing nerve activity to be monitored or altered.
“To ensure the safe use of these devices inside the body, we have managed to reduce the voltage required for actuation to very low values,” said Dr Chaoqun Dong, the paper’s first author. “What’s even more significant is that these cuffs can change shape in both directions and be reprogrammed. This means surgeons can adjust how tightly the device fits around a nerve until they get the best results for recording and stimulating the nerve.”
Tests in rats showed that the cuffs could be successfully placed without surgery, and they formed a self-closing loop around the target nerve. The researchers are planning further testing of the devices in animal models, and are hoping to begin testing in humans within the next few years.
“Using this approach, we can reach nerves that are difficult to reach through open surgery, such as the nerves that control, pain, vision or hearing, but without the need to implant anything inside the brain,” said Barone. “The ability to place these cuffs so they wrap around the nerves makes this a much easier procedure for surgeons, and it’s less risky for patients.”
“The ability to make an implant that can change shape through electrical activation opens up a range of future possibilities for highly targeted treatments,” said Malliaras. “In future, we might be able to have implants that can move through the body, or even into the brain — it makes you dream how we could use technology to benefit patients in future.”
The research was supported in part by the Swiss National Science Foundation, the Cambridge Trust, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). More