Aurelia Butler

Researchers at North Carolina State University have created a ring-shaped soft robot capable of crawling across surfaces when exposed to elevated temperatures or infrared light. The researchers have demonstrated that these “ringbots” are capable of pulling a small payload across the surface — in ambient air or under water, as well as passing through a gap that is narrower than its ring size.
The ringbots are made of liquid crystal elastomers in the shape of looped ribbon, resembling a bracelet. When you place the ringbot on a surface that is at least 55 degrees Celsius (131 degrees Fahrenheit), which is hotter than the ambient air, the portion of the ribbon touching the surface contracts, while the portion of the ribbon exposed to the air does not. This induces a rolling motion in the ribbon. Video of the ringbots can be found here: https://youtu.be/yL5gVAjh1mQ.
Similarly, when researchers shine infrared light on the ringbot, the portion of the ribbon exposed to the light contracts, while the portion shielded from the light does not. This also induces a rolling motion in the ribbon.
In practical terms, this means that the crawling ringbot moves from the bottom up when placed on a hot surface. But when exposed to infrared light, the movement begins from the top down.
One of the things that drives this continuous motion is the fact that the ringbots are bistable, meaning that there are two shapes when it is at rest. If the ribbon begins to twist, it will either snap back to its original shape, or snap forward into the other bistable state.
Picture a rubber bracelet shaped like a ribbon. If you fold two ends of the bracelet forward a little bit, then let go, it will snap back to its original shape. But if you fold the ends over far enough, it will snap over — essentially folding the bracelet inside out. More

Inspired by living systems, researchers at Aalto University have developed a new material that changes its electrical behaviour based on previous experience, effectively giving it a basic form of adaptive memory. Such adaptive materials could play a vital role in the next generation of medical and environmental sensors, as well as in soft robots or active surfaces.
Responsive materials have become common in a range of applications, from glasses that darken in sunlight to drug delivery systems. But existing materials always react in the same way — their response to a change doesn’t depend on their history, nor do they adapt based on their past.
This is fundamentally different from living systems, which dynamically adapt their behaviour based on previous conditions. ‘One of the next big challenges in material science is to develop truly smart materials inspired by living organisms. We wanted to develop a material that would adjust its behaviour based on its history,’ says Bo Peng, an Academy Research Fellow at Aalto University who was one of the senior authors of this study.
The researchers synthesised micrometre-sized magnetic beads which were then stimulated by a magnetic field. When the magnet was on, the beads stacked up to form pillars. The strength of the magnetic field affects the shape of the pillars, which in turn affects how well they conduct electricity.
‘With this system, we coupled the magnetic field stimulus and the electrical response. Interestingly, we found that the electrical conductivity depends on whether we varied the magnetic field rapidly or slowly. That means that the electrical response depends on the history of the magnetic field. The electrical behaviour was also different if the magnetic field was increasing or decreasing. The response showed bistability, which is an elementary form of memory. The material behaves as though it has a memory of the magnetic field,’ explains Peng.
Basic learning
The system’s memory also allows it to behave in a way that resembles rudimentary learning. Although learning in living organisms is enormously complex, its most basic element in animals is a change in the response of connections between neurons, known as synapses. Depending on how frequently they are stimulated, synapses in a neuron will become harder or easier to activate. This change, known as short-term synaptic plasticity, makes the connection between a pair of neurons stronger or weaker depending on their recent history.
The researchers were able to accomplish something similar with their magnetic beads, even though the mechanism is totally differently. When they exposed the beads to a quickly pulsing magnetic field, the material became better at conducting electricity, whereas slower pulsing made it conduct poorly.
‘This is reminiscent of short term-synaptic plasticity,’ says Aalto’s Distinguished Professor Olli Ikkala. ‘Our material functions a bit like a synapse. What we’ve demonstrated paves the way for the next generation of life-inspired materials, which will draw on biological process of adaptation, memory and learning.’
‘In the future, there could be even more materials that are algorithmically inspired by life-like properties, though they won’t involve the full complexity of biological systems. Such materials will be central to the next generation of soft robots and for medical and environmental monitoring,’ adds Ikkala.
Story Source:
Materials provided by Aalto University. Note: Content may be edited for style and length. More

A study published in Nature Medicine reports the ability of a smartwatch ECG to accurately detect heart failure in nonclinical environments. Researchers at Mayo Clinic applied artificial intelligence (AI) to Apple Watch ECG recordings to identify patients with a weak heart pump. Participants in the study recorded their smartwatch ECGs remotely whenever they wanted, from wherever they were. Periodically, they uploaded the ECGs to their electronic health records automatically and securely via a smartphone app developed by Mayo Clinic’s Center for Digital Health.
“Currently, we diagnose ventricular dysfunction — a weak heart pump — through an echocardiogram, CT scan or an MRI, but these are expensive, time consuming and at times inaccessible. The ability to diagnose a weak heart pump remotely, from an ECG that a person records using a consumer device, such as a smartwatch, allows a timely identification of this potentially life-threatening disease at massive scale,” says Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic in Rochester. Dr. Friedman is the senior author of the study.
People with a weak heart pump might not have symptoms, but this common form of heart disease affects about 2% of the population and 9% of people over 60. When the heart cannot pump enough oxygen-rich blood, symptoms may develop, including shortness of breath, a rapid heart rate and swelling in the legs. Early diagnosis is important because once identified, there are numerous treatments to improve quality of life and decrease the risks of heart failure and death.
Mayo researchers interpreted Apple Watch single-lead ECGs by modifying an earlier algorithm developed for 12-lead ECGs that is proven to detect a weak heart pump. The 12-lead algorithm for low ventricular ejection fraction is licensed to Anumana Inc., an AI-driven health technology company, co-created by nference and Mayo Clinic.
While the data are early, the modified AI algorithm using single-lead ECG data had an area under the curve of 0.88 to detect a weak heart pump. By comparison, this measure of accuracy is as good as or slightly better than a medical treadmill diagnostic test.
“These data are encouraging because they show that digital tools allow convenient, inexpensive, scalable screening for important conditions. Through technology, we can remotely gather useful information about a patient’s heart in an accessible way that can meet the needs of people where they are,” says Zachi Attia, Ph.D., the lead AI scientist in the Department of Cardiovascular Medicine at Mayo Clinic. Dr. Attia is first author of the study.
“Building the capability to ingest data from wearable consumer electronics and provide analytic capabilities to prevent disease or improve health remotely in the manner demonstrated by this study can revolutionize health care. Solutions like this not only enable prediction and prevention of problems, but also will eventually help diminish health disparities and the burden on health systems and clinicians,” says Bradley Leibovich, M.D., the medical director for the Mayo Clinic Center for Digital Health, and co-author on the study.
All 2,454 study participants were Mayo Clinic patients from across the U.S. and 11 countries. They downloaded an app created by the Mayo Clinic Center for Digital Health to securely upload their Apple Watch ECGs to their electronic health records. Participants logged more than 125,000 previous and new Apple Watch ECGs to their electronic health records between August 2021 and February 2022. Clinicians had access to view all the ECG data on an AI dashboard built into the electronic health record, including the day and time it was recorded.
Approximately 420 participants had an echocardiogram — a standard test using sound waves to produce images of the heart — within 30 days of logging an Apple Watch ECG in the app. Of those, 16 patients had low ejection fraction confirmed by the echocardiogram, which provided a comparison for accuracy.
This study was funded by Mayo Clinic with no technical or financial support from Apple. Drs. Attia and Friedman, along with others, are co-inventors of the low ejection fraction algorithm licensed to Anumana and may benefit from its commercialization.
Story Source:
Materials provided by Mayo Clinic. Original written by Terri Malloy. Note: Content may be edited for style and length. More

A new statistical method provides a more efficient way to uncover biologically meaningful changes in genomic data that span multiple conditions — such as cell types or tissues.
Whole genome studies produce enormous amounts of data, ranging from millions of individual DNA sequences to information about where and how many of the thousands of genes are expressed to the location of functional elements across the genome. Because of the amount and complexity of the data, comparing different biological conditions or across studies performed by separate laboratories can be statistically challenging.
“The difficulty when you have multiple conditions is how to analyze the data together in a way that can be both statistically powerful and computationally efficient,” said Qunhua Li, associate professor of statistics at Penn State. “Existing methods are computationally expensive or produce results that are difficult to interpret biologically. We developed a method called CLIMB that improves on existing methods, is computationally efficient, and produces biologically interpretable results. We test the method on three types of genomic data collected from hematopoietic cells — related to blood stem cells — but the method could also be used in analyses of other ‘omic’ data.”
The researchers describe the CLIMB (Composite LIkelihood eMpirical Bayes) method in a paper appearing online Nov. 12 in the journal Nature Communications.
“In experiments where there is so much information but from relatively few individuals, it helps to be able to use information as efficiently as possible,” said Hillary Koch, a graduate student at Penn State at the time of the research and now a senior statistician at Moderna. “There are statistical advantages to be able to look at everything together and even to use information from related experiments. CLIMB allows us to do just that.”
The CLIMB method uses principles from two traditional techniques to analyze data across multiple conditions. One technique uses a series of pairwise comparisons between conditions but becomes increasingly challenging to interpret as additional conditions are added. More

Muscles waste as a result of not being exercised enough, as happens quickly with a broken limb that has been immobilized in a cast, and more slowly in people reaching an advanced age. Muscle atrophy, how clinicians refer to the phenomenon, is also a debilitating symptom in patients suffering from neurological disorders, such as amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), and can be a systemic response to various other diseases, including cancer and diabetes.
Mechanotherapy, a form of therapy given by manual or mechanical means, is thought to have broad potential for tissue repair. The best-known example is massage, which applies compressive stimulation to muscles for their relaxation. However, it has been much less clear whether stretching and contracting muscles by external means can also be a treatment. So far, two major challenges have prevented such studies: limited mechanical systems capable of evenly generating stretching and contraction forces along the length of muscles, and inefficient delivery of these mechanical stimuli to the surface and into the deeper layers of muscle tissue.
Now, bioengineers at the Wyss Institute for Biologically Inspired Engineering at Harvard University and the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a mechanically active adhesive named MAGENTA, which functions as a soft robotic device and solves this two-fold problem. In an animal model, MAGENTA successfully prevented and supported the recovery from muscle atrophy. The team’s findings are published in Nature Materials.
“With MAGENTA, we developed a new integrated multi-component system for the mechanostimulation of muscle that can be directly placed on muscle tissue to trigger key molecular pathways for growth,” said senior author and Wyss Founding Core Faculty member David Mooney, Ph.D. “While the study provides first proof-of-concept that externally provided stretching and contraction movements can prevent atrophy in an animal model, we think that the device’s core design can be broadly adapted to various disease settings where atrophy is a major issue.” Mooney leads the Wyss Institute’s Immuno-Materials Platform, and is also the Robert P. Pinkas Family Professor of Bioengineering at SEAS.
An adhesive that can make muscles move
One of MAGENTA’s major components is an engineered spring made from nitinol, a type of metal known as “shape memory alloy” (SMA) that enables MAGENTA’s rapid actuation when heated to a certain temperature. The researchers actuated the spring by electrically wiring it to a microprocessor unit that allows the frequency and duration of the stretching and contraction cycles to be programmed. The other components of MAGENTA are an elastomer matrix that forms the body of the device and insulates the heated SMA, and a “tough adhesive” that enables the device to be firmly adhered to muscle tissue. In this way, the device is aligned with the natural axis of muscle movement, transmitting the mechanical force generated by SMA deep into the muscle. Mooney’s group is advancing MAGENTA, which stands for “mechanically active gel-elastomer-nitinol tissue adhesive,” as one of several Tough Gel Adhesives with functionalities tailored to various regenerative applications across multiple tissues. More

For more than half a century, researchers around the world have been struggling with an algorithmic problem known as “the single source shortest path problem.” The problem is essentially about how to devise a mathematical recipe that best finds the shortest route between a node and all other nodes in a network, where there may be connections with negative weights.
Sound complicated? Possibly. But in fact, this type of calculation is already used in a wide range of the apps and technologies that we depend upon for finding our ways around — as Google Maps guides us across landscapes and through cities, for example.
Now, researchers from the University of Copenhagen’s Department of Computer Science have succeeded in solving the single source shortest path problem, a riddle that has stumped researchers and experts for decades.
“We discovered an algorithm that solves the problem in virtually linear time, the fastest way possible. It is a fundamental algorithmic problem that has been studied since the 1950s and is taught around the world. This was one of the reasons that prompted us to solve it,” explains Associate Professor Christian Wulff-Nilsen, who clearly finds it tough to leave an unsolved algorithmic problem alone.
Quicker calculations for routing electric cars
Last year, Wulff-Nilsen made another breakthrough in the same area, one that produced a result which addressed how to find the shortest path in a network that changes over time. His solution to the recent riddle builds upon that work. More

Cambridge researchers have highlighted how lack of access to a computer was linked to poorer mental health among young people and adolescents during COVID-19 lockdowns.
The team found that the end of 2020 was the time when young people faced the most difficulties and that the mental health of those young people without access to a computer tended to deteriorate to a greater extent than that of their peers who did have access.
The COVID-19 pandemic had a significant effect on young people’s mental health, with evidence of rising levels of anxiety, depression, and psychological distress. Adolescence is a period when people are particularly vulnerable to developing mental health disorders, which can have long-lasting consequences into adulthood. In the UK, the mental health of children and adolescents was already deteriorating before the pandemic, but the proportion of people in this age group likely to be experiencing a mental health disorder increased from 11% in 2017 to 16% in July 2020.
The pandemic led to the closure of schools and an increase in online schooling, the impacts of which were not felt equally. Those adolescents without access to a computer faced the greatest disruption: in one study 30% of school students from middle-class homes reported taking part in live or recorded school lessons daily, while only 16% of students from working-class homes reported doing so.
In addition to school closures, lockdown often meant that young people could not meet their friends in person. During these periods, online and digital forms of interaction with peers, such as through video games and social media, are likely to have helped reduce the impact of these social disruptions.
Tom Metherell, who at the time of the study was an undergraduate student at Fitzwilliam College, University of Cambridge, said: “Access to computers meant that many young people were still able to ‘attend’ school virtually, carry on with their education to an extent and keep up with friends. But anyone who didn’t have access to a computer would have been at a significant disadvantage, which would only risk increasing their sense of isolation.”
To examine in detail the impact of digital exclusion on the mental health of young people, Metherell and colleagues examined data from 1,387 10-15-year-olds collected as part of Understanding Society, a large UK-wide longitudinal survey. They focused on access to computers rather than smartphones, as schoolwork is largely possible only on a computer while at this age most social interactions occur in person at school. More

New data standards have been created for AI models.
Aspiring bakers are frequently called upon to adapt award-winning recipes based on differing kitchen setups. Someone might use an eggbeater instead of a stand mixer to make prize-winning chocolate chip cookies, for instance.
Being able to reproduce a recipe in different situations and with varying setups is critical for both talented chefs and computational scientists, the latter of whom are faced with a similar problem of adapting and reproducing their own ​”recipes” when trying to validate and work with new AI models. These models have applications in scientific fields ranging from climate analysis to brain research.
“When we talk about data, we have a practical understanding of the digital assets we deal with,” said Eliu Huerta, scientist and lead for Translational AI at the U.S. Department of Energy’s (DOE) Argonne National Laboratory. ​”With an AI model, it’s a little less clear; are we talking about data structured in a smart way, or is it computing, or software, or a mix?”
In a new study, Huerta and his colleagues have articulated a new set of standards for managing AI models. Adapted from recent research on automated data management, these standards are called FAIR, which stands for findable, accessible, interoperable and reusable.
“By making AI models FAIR, we no longer have to build each system from the ground up each time,” said Argonne computational scientist Ben Blaiszik. ​”It becomes easier to reuse concepts from different groups, helping to create cross-pollination across teams.”
According to Huerta, the fact that many AI models are currently not FAIR poses a challenge to scientific discovery. ​”For many studies that have been done to date, it is difficult to gain access to and reproduce the AI models that are referenced in the literature,” he said. ​”By creating and sharing FAIR AI models, we can reduce the amount of duplication of effort and share best practices for how to use these models to enable great science.” More