Where traditional robots are hard-bodied and stiff, “soft” robots have the opposite problem; they are flexible but weak, and their movements are difficult to control. “Giving robots the ability to switch between liquid and solid states endows them with more functionality,” says Chengfeng Pan, an engineer at The Chinese University of Hong Kong who led the study.

The team created the new phase-shifting material — dubbed a “magnetoactive solid-liquid phase transitional machine” — by embedding magnetic particles in gallium, a metal with a very low melting point (29.8 °C).

“The magnetic particles here have two roles,” says senior author and mechanical engineer Carmel Majidi of Carnegie Mellon University. “One is that they make the material responsive to an alternating magnetic field, so you can, through induction, heat up the material and cause the phase change. But the magnetic particles also give the robots mobility and the ability to move in response to the magnetic field.”

This is in contrast to existing phase-shifting materials that rely on heat guns, electrical currents, or other external heat sources to induce solid-to-liquid transformation. The new material also boasts an extremely fluid liquid phase compared to other phase-changing materials, whose “liquid” phases are considerably more viscous.

Before exploring potential applications, the team tested the material’s mobility and strength in a variety of contexts. With the aid of a magnetic field, the robots jumped over moats, climbed walls, and even split in half to cooperatively move other objects around before coalescing back together. In one video, a robot shaped like a person liquifies to ooze through a grid after which it is extracted and remolded back into its original shape.

“Now, we’re pushing this material system in more practical ways to solve some very specific medical and engineering problems,” says Pan.

On the biomedical side, the team used the robots to remove a foreign object from a model stomach and to deliver drugs on-demand into the same stomach. They also demonstrate how the material could work as smart soldering robots for wireless circuit assembly and repair (by oozing into hard-to-reach circuits and acting as both solder and conductor) and as a universal mechanical “screw” for assembling parts in hard-to-reach spaces (by melting into the threaded screw socket and then solidifying; no actual screwing required.)

“Future work should further explore how these robots could be used within a biomedical context,” says Majidi. “What we’re showing are just one-off demonstrations, proofs of concept, but much more study will be required to delve into how this could actually be used for drug delivery or for removing foreign objects.”

The team found that diabetic mice with low levels of two related amino acids, serine and glycine, are at higher risk for peripheral neuropathy. What’s more, the researchers were able to alleviate neuropathy symptoms in diabetic mice by supplementing their diets with serine.

The study, published January 25, 2023 in Nature, adds to growing evidence that some often-underappreciated, “non-essential” amino acids play important roles in the nervous system. The findings may provide a new way to identify people at high risk for peripheral neuropathy, as well as a potential treatment option.

“We were surprised that dialing up and down a non-essential amino acid had such a profound effect on metabolism and diabetic complications,” says senior author Christian Metallo, a professor in Salk’s Molecular and Cell Biology Laboratory. “It just goes to show that what we think of as dogma can change under different circumstances, such as in disease conditions.” Metallo led the study with first author Michal Handzlik, a postdoctoral researcher in his lab.

Amino acids are the building blocks that make up proteins and specialized fat molecules called sphingolipids, which are abundant in the nervous system. Low levels of the amino acid serine force the body to incorporate a different amino acid in sphingolipids, which changes their structure. These atypical sphingolipids then accumulate, which may contribute to peripheral nerve damage. While the team observed this accumulation in diabetic mice, the same amino acid switch and sphingolipid changes occur in a rare human genetic disease marked by peripheral sensory neuropathy, indicating that the phenomenon is consistent across many species.

To determine whether long-term, chronic serine deficiency drives peripheral neuropathy, Metallo’s team fed mice either control or serine-free diets in combination with either low-fat or high-fat diets for up to 12 months. The researchers were surprised to find that low serine, in combination with a high-fat diet, accelerated the onset of peripheral neuropathy in the mice. In contrast, serine supplementation in diabetic mice slowed the progression of peripheral neuropathy, and the mice fared better.

The researchers also tested the compound myriocin, which inhibits the enzyme that switches out serine for another amino acid as sphingolipids are assembled. Myriocin treatment reduced peripheral neuropathy symptoms in mice fed a high-fat, serine-free diet. These findings underscore the importance of amino acid metabolism and sphingolipid production in the maintenance of a healthy peripheral nervous system.

Serine deficiency has also been associated with various neurodegenerative disorders. For example, Metallo and collaborators previously found a link between altered serine and sphingolipid metabolism in patients with macular telangiectasia type 2, a condition that causes vision loss. In mice, reduced serine led to increased levels of atypical retinal sphingolipids and reduced vision. Serine is currently being tested in clinical trials for its safety and efficacy in treating macular telangiectasia and Alzheimer’s disease.

Peripheral neuropathy is typically managed with dietary changes to reduce blood sugar levels, as well as pain relievers, physical therapy, and mobility aids, such as canes and wheelchairs. Foods naturally rich in serine include soybeans, nuts, eggs, chickpeas, lentils, meat, and fish, and serine supplements are inexpensive and available over the counter.

Yet the researchers say it’s premature to advise people with diabetes to take serine supplements to prevent neuropathy.

“You would likely need to take a lot to make a difference, and not everyone needs extra serine,” Metallo says. “We need more time to understand serine physiology in humans and explore potential downsides to supplementation.”

To this end, Metallo and Handzlik are now developing a serine tolerance test, similar to a glucose tolerance test used to diagnose diabetes.

“We want to identify those at highest risk for peripheral neuropathy so we can treat only those who might benefit most,” says Handzlik.

Other authors included: Jivani M. Gengatharan, Grace H. McGregor, and Courtney R. Green of the Salk Institute and UC San Diego; Katie E. Frizzi, Cameron Martino, Gibraan Rahman, Antonio Gonzalez, Ana M. Moreno, Lucie S. Guernsey, Prashant Mali, Rob Knight, and Nigel A. Calcutt of UC San Diego; Terry Lin, Patrick Tseng, and Satchidananda Panda of the Salk Institute; Yoichiro Ideguchi of Scripps Research; Regis J. Fallon and Marin L. Gantner of the Lowy Medical Research Institute; Amandine Chaix of the University of Utah; and Martina Wallace of University College Dublin in Ireland.

The work was funded by the National Institutes of Health (grants R01CA234245, DK076169, R01AG065993, P30

DK120515), a Camille and Henry Dreyfus Teacher-Scholar Award, the Lowy Medical Research Institute, and the American Heart Association (grant 18CDA34110292).

The goal is to make ultrasound more accessible to a larger population, said Sheng Xu, a professor of nanoengineering at the University of California San Diego, who is leading the project. Currently, echocardiograms- ultrasound examinations for the heart- require highly trained technicians and bulky devices.

“The technology enables anybody to use ultrasound imaging on the go,” Xu said.

Thanks to custom AI algorithms, the device is capable of measuring how much blood the heart is pumping. This is important because the heart not pumping enough blood is at the root of most cardiovascular diseases. And issues with heart function often manifest only when the body is in motion.

The work is described in the Jan. 25 issue of the journal Nature.

Cardiac imaging is an essential clinical tool to assess long-term heart health, detect problems as they arise and care for critically ill patients. This new wearable, non-invasive heart monitor for humans provides real-time, automated insights on the difficult-to-capture pumping activity of the heart, even when a person is exercising.

The wearable heart monitoring system uses ultrasound to continuously capture images of the four chambers of the heart in different angles, and analyze a clinically relevant subset of the images in real time using a custom-built AI technology. The project builds on the team’s previous advances in wearable imaging technologies for deep tissues.

“The increasing risk of heart diseases calls for more advanced and inclusive monitoring procedures,” Xu said. “By providing patients and doctors with more thorough details, continuous and real-time cardiac image monitoring is poised to fundamentally optimize and reshape the paradigm of cardiac diagnoses.”

In comparison, existing non-invasive methods have limited sampling capabilities and provide limited data. The wearable technology developed by Xu’s team enables safe, non-invasive and high-quality cardiac imaging, resulting in images with high spatial resolution, temporal resolution and contrast. “It also minimizes patient discomfort and overcomes some limitations of noninvasive technologies such as CT and PET, which could expose patients to radiation,” said Hao Huang, a PhD student in the Xu group at UC San Diego.

The unique design of the sensor makes it ideal for bodies in motion. “The device can be attached to the chest with minimal constraint to the subjects’ movement, even providing a continuous recording of cardiac activities before, during and after exercise,” said Xiaoxiang Gao, a postdoctoral researcher in the Xu group at UC San Diego.

The importance of cardiac imaging

Cardiac diseases are the leading cause of death among the elderly, and are also becoming more prevalent among the young due to lifestyle factors. The signs of cardiac diseases are transient and unpredictable, making them hard to spot. This has upped demand for more advanced, inclusive, non-invasive and cost-effective monitoring technologies such as long-term cardiac imaging, which this wearable device facilitates.

Cardiac imaging is one of the most powerful tools for screening and diagnosing cardiac issues before they become problems. “The heart undergoes all kinds of different pathologies,” said Hongjie Hu, a postdoctoral researcher in the Xu lab at UC San Diego. “Cardiac imaging will disclose the true story underneath. Whether it be that a strong but normal contraction of heart chambers leads to the fluctuation of volumes, or that a cardiac morphological problem has occurred as an emergency, real-time image monitoring on the heart tells the whole picture in vivid detail and visual effect.”

How it works in detail

The new system gathers information through a wearable patch as soft as human skin, designed for optimal adherence. The patch measures 1.9 cm (L) x 2.2 cm (W) x 0.09 cm (T) , about the size of a postage stamp. It sends and receives the ultrasound waves which are used to generate a constant stream of images of the structure of the heart in real time. This ultrasound patch is soft and stretchable, and it adheres well to human skin, even during exercise.

The system can examine the left ventricle of the heart in separate bi-plane views using ultrasound, generating more clinically useful images than were previously available. As a use case, the team demonstrated imaging of the heart during exercise, which is not possible with the rigid, cumbersome equipment used in clinical settings.

The performance of the heart is characterized by three factors: stroke volume (the volume of blood the heart pumps out each beat), ejection fraction (the percentage of blood pumped out of the left ventricle of the heart every beat) and cardiac output (the volume of blood the heart pumps out every minute).

Xu’s team developed an algorithm to facilitate continuous, AI-assisted automatic processing.

“A deep learning model automatically segments the shape of the left ventricle from the continuous image recording, extracting its volume frame-by-frame and yielding waveforms to measure stroke volume, cardiac output and ejection fraction,” said Mohan Li, a master’s student in the Xu group at UC San Diego.

“Specifically, the AI component involves a deep learning model for image segmentation, an algorithm for heart volume calculation, and a data imputation algorithm,” said Ruixiang Qi, a master’s student in the Xu group at UC San Diego. “We use this machine learning model to calculate the heart volume based on the shape and area of the left ventricle segmentation. The imaging-segmentation deep learning model is the first to be functionalized in wearable ultrasound devices. It enables the device to provide accurate and continuous waveforms of key cardiac indices in different physical states, including static and after exercise, which has never been achieved before.”

Thus, this technology can generate curves of these three indices continuously and noninvasively, as the AI component processes the continuous stream of images to generate numbers and curves.

To create the platform, the team faced some technical challenges that required careful decision-making. To produce the wearable device itself, the researchers used a piezoelectric 1-3 composite bonded with Ag-epoxy backing as the material for transducers in the ultrasound imager, reducing risk and improving efficiency over previous methods. When choosing the transmission configuration of the transducer array, they achieved superior results through wide-beam compounding transmission. They also selected from nine popular models for machine-learning-based image segmentation, landing on FCN-32, which achieved the highest possible accuracy.

In the current iteration, the patch is connected through cables to a computer, which can download the data automatically while the patch is still on. The team has developed a wireless circuit for the patch, which will be covered in a forthcoming publication.

Next steps

Xu plans to commercialize this technology through Softsonics, a company spun off from UC San Diego that he cofounded with engineer Shu Xiang. He also encourages others in his scientific community to follow his lead and work on areas of this research that warrant further exploration.

To follow up on these results, Xu recommends four immediate next steps:

• B-mode imaging, which allows more diagnostic capabilities involving different organs
• The design of the soft imager, which allows researchers to fabricate large transducer probes that cover multiple positions simultaneously
• Miniaturization of the back-end system that powers the soft imager
• Working toward a general machine learning model that fits more subjects

This work was supported by the National Institutes of Health (1R21EB025521-01, 1R21EB027303-01A1, 3R21EB027303-02S1, and 1R01EB033464-01).

Emissions calculators base their estimates on travel class, assuming that someone travelling in a higher class and therefore taking up more space on the plane is responsible for more emissions.

The study, published in Environmental Research Letters, describes how including airfares in calculations shows which passengers contribute the most revenue to the airline operating the aircraft, thereby allowing the plane to fly.

Although in general, premium (business) seats are more expensive than economy, the researchers found when looking at data that many late bookings in economy class, often made for business trips or by high income travellers, cost as much as, or more than, premium seats.

Lead author Dr Stijn van Ewijk (UCL Civil, Environmental & Geomatic Engineering) said: “The paper shows we should follow the money when calculating emissions of individual travellers, as it is revenue that decides whether an airline can operate a plane or not. Someone who has paid twice as much as a fellow traveller contributes twice as much to the revenue of the airline and should be allocated twice the emissions. The seat size of each travel class, which is currently used to allocate emissions, is only a rough approximation of how much passengers pay.”

The researchers say that using airfares to calculate passenger emissions would benefit efforts to address climate change by encouraging people on all budgets to find alternative modes of transport where possible. It would also increase estimates of corporate emissions because it allocates more to expensive late bookings, which are often made for business purposes.

Implementing a tax that is proportionate to the price of the ticket could make the total costs of flying fairer. People buying the most expensive tickets would pay the highest tax, encouraging them to seek alternatives.

Whilst taxes differ between countries, typically the rates are the same across each travel class. Travellers buying expensive tickets, who are more likely to have higher incomes, pay a relatively low tax and are not currently discouraged from flying.

Dr Van Ewijk added: “An equitable approach to reducing airline emissions should not just deter travellers who can only afford the cheapest early bookings but also the big spenders who bankroll the airline. By assigning emissions based on ticket prices, and taxing those emissions, we can make sure everyone pays their fair share, and is equally encouraged to look for alternatives.”

A ticket tax should also take into account the distance flown and the model and age of plane, which can indicate how polluting it is.

The authors used a dataset from the USA to test their fare-based allocation approach. They used the Airline Origin Survey database, which includes ticket fare data, origin and destination, travel class and fare per mile. From this, they calculated the distribution of ticket prices across all passengers on a typical flight.

Based on the price distribution, the authors allocated emissions to passengers, and compared the results with estimates from widely used emissions calculators. Since ticket prices vary strongly by time of booking, the emissions per passenger varied too, far more than on the basis of seat size and travel class.

Using an economic supply-demand model, the researchers estimated how a carbon tax on emissions would affect travellers, depending on whether the emissions the tax applied to were calculated from seat size and travel class, or the airfare. In all scenarios, a tax on emissions calculated from airfares had a more equitable effect because it reduced flying more evenly across income groups.

The researchers hope to effect policy change in calculating and taxing passenger emissions, to ensure travellers on all budgets are encouraged to seek other forms of transport where possible or consider how essential the journey is.