Aurelia Butler

An interdisciplinary team of University of Minnesota Twin Cities scientists and engineers has developed a first-of-its-kind, plant-inspired extrusion process that enables synthetic material growth. The new approach will allow researchers to build better soft robots that can navigate hard-to-reach places, complicated terrain, and potentially areas within the human body.
The paper is published in the Proceedings of the National Academy of Sciences (PNAS).
“This is the first time these concepts have been fundamentally demonstrated,” said Chris Ellison, a lead author of the paper and professor in the University of Minnesota Twin Cities Department of Chemical Engineering and Materials Science. “Developing new ways of manufacturing are paramount for the competitiveness of our country and for bringing new products to people. On the robotic side, robots are being used more and more in dangerous, remote environments, and these are the kinds of areas where this work could have an impact.”
Soft robotics is an emerging field where robots are made of soft, pliable materials as opposed to rigid ones. Soft growing robots can create new material and “grow” as they move. These machines could be used for operations in remote areas where humans can’t go, such as inspecting or installing tubes underground or navigating inside the human body for biomedical applications.
Current soft growing robots drag a trail of solid material behind them and can use heat and/or pressure to transform that material into a more permanent structure, much like how a 3D printer is fed solid filament to produce its shaped product. However, the trail of solid material gets more difficult to pull around bends and turns, making it hard for the robots to navigate terrain with obstacles or winding paths.
The University of Minnesota team solved this problem by developing a new means of extrusion, a process where material is pushed through an opening to create a specific shape. Using this new process allows the robot to create its synthetic material from a liquid instead of a solid. More

Mathematical models indicate that as few as one in five cases of COVID-19 which occurred during the first 29 months of the pandemic are accounted for in the half billion cases officially reported.
The World Health Organization reported 513,955,910 cases from Jan. 1, 2020 to May 6, 2022 and 6,190,349 deaths, numbers which already moved COVID-19 to a top killer in some countries, including the United States, just behind heart disease and cancer, according to the Centers for Disease Control and Prevention.
Still mathematical models indicate overall underreporting of cases ranging from 1 in 1.2 to 1 in 4.7, investigators report in the journal Current Science. That underreporting translates to global pandemic estimates between 600 million and 2.4 billion cases.
“We all acknowledge a huge impact on us as individuals, a nation and the world, but the true number of cases is very likely much higher than we realize,” says Dr. Arni S.R. Srinivasa Rao, director of the Laboratory for Theory and Mathematical Modeling in the Division of Infectious Diseases at the Medical College of Georgia. “We are trying to understand the extent of underreported cases.”
The wide range of estimated cases generated by their models indicate the problems with accuracy of reported numbers, which include data tampering, the inability to conduct accurate case tracking and the lack of uniformity in how cases are reported, write Rao and his colleagues Dr. Steven G. Krantz, professor of mathematics at Washington University in St. Louis Missouri and Dr. David A. Swanson, Edward A. Dickson Emeritus Professor in the Department of Sociology at the University of California, Riverside.
A dearth of information and inconsistency in reporting cases has been a major problem with getting a true picture of the impact of the pandemic, Rao says.
Mathematical models use whatever information is available as well as relevant factors like global transmission rates and the number of people in the world, including the average population over the 29-month timeframe. That average, referred to as the effective population, better accounts for those who were born and died for any reason and so provides a more realistic number of the people out there who could potentially be infected, Rao says.
“You have to know the true burden on patients and their families, on hospitals and caregivers, on the economy and the government,” Rao says. More accurate numbers also help in assessing indirect implications like the underdiagnosis of potentially long-term neurological and mental disorders that are now known to be directly associated with infection, he says.
The mathematics experts had published similar model-based estimates for eight countries earlier in the pandemic in 2020, to provide more perspective on what they said then was clear underreporting. Their modeling predicted countries like Italy, despite their diligence in reporting, were likely capturing 1 in 4 actual cases while in China, where population numbers are tremendous, they calculated a huge range of potential underreporting, from 1 in 149 to 1 in 1,104 cases.
Other contributors to underreporting include the reality that everyone who has gotten COVID-19 has not been tested. Also, a significant percentage of people, even vaccinated and boosted individuals, are getting infected more than once, and may only go to the doctor for PCR resting the first time and potentially use at home tests or even no test for subsequent illnesses. For example, a recent report in JAMA on reinfection rates in Iceland during the first 74 days of the Omicron variant wave there indicates, based on PCR testing, that reinfection rates were at 10.9% — a high of 15.1% among those 18-29-year-olds — for those who received two or more doses of a vaccine.
The number of fully vaccinated individuals globally reached a reported 5.1 billion by the end of their 29-month study timeframe.
The CDC was reporting downward trends in new cases, hospitalizations and deaths in the United States from August to September. More

A new report from the University of Technology Sydney (UTS) Human Technology Institute outlines a model law for facial recognition technology to protect against harmful use of this technology, but also foster innovation for public benefit.
Australian law was not drafted with widespread use of facial recognition in mind. Led by UTS Industry Professors Edward Santow and Nicholas Davis, the report recommends reform to modernise Australian law, especially to address threats to privacy and other human rights.
Facial recognition and other remote biometric technologies have grown exponentially in recent years, raising concerns about privacy, mass surveillance and unfairness experienced, especially by people of colour and women, when the technology makes mistakes.
In June 2022, an investigation by consumer advocacy group CHOICE revealed that several large Australian retailers were using facial recognition to identify customers entering their stores, leading to considerable community alarm and calls for improved regulation. There have also been widespread calls for reform of facial recognition law — in Australia and internationally.
This new report responds to those calls. It recognises that our faces are special, in the sense that humans rely heavily on each other’s faces to identify and interact. This reliance leaves us particularly vulnerable to human rights restrictions when this technology is misused or overused.
“When facial recognition applications are designed and regulated well, there can be real benefits, helping to identify people efficiently and at scale. The technology is widely used by people who are blind or have a vision impairment, making the world more accessible for those groups,” said Professor Santow, the former Australian Human Rights Commissioner and now Co-Director of the Human Technology Institute. More

Researchers in quantum technology at Chalmers University of Technology have succeeded in developing a technique to control quantum states of light in a three-dimensional cavity. In addition to creating previously known states, the researchers are the first ever to demonstrate the long-sought cubic phase state. The breakthrough is an important step towards efficient error correction in quantum computers.
“We have shown that our technology is on par with the best in the world,” says Simone Gasparinetti, who is head of a research group in experimental quantum physics at Chalmers and one of the study’s senior authors.
Just as a conventional computer is based on bits that can take the value 0 or 1, the most common method of building a quantum computer uses a similar approach. Quantum mechanical systems with two different quantum states, known as quantum bits (qubits), are used as building blocks. One of the quantum states is assigned the value 0 and the other the value 1. However, on account of the quantum mechanical state of superposition, qubits can assume both states 0 and 1 simultaneously, allowing a quantum computer to process huge volumes of data with the possibility of solving problems far beyond the reach of today’s supercomputers.
First time ever for cubic phase state
A major obstacle towards the realisation of a practically useful quantum computer is that the quantum systems used to encode the information are prone to noise and interference, which causes errors. Correcting these errors is a key challenge in the development of quantum computers. A promising approach is to replace qubits with resonators — quantum systems which, instead of having just two defined states, have a very large number of them. These states may be compared to a guitar string, which can vibrate in many different ways. The method is called continuous-variable quantum computing and makes it possible to encode the values 1 and 0 in several quantum mechanical states of a resonator. However, controlling the states of a resonator is a challenge with which quantum researchers all over the world are grappling. And the results from Chalmers provide a way of doing so. The technique developed at Chalmers allows researchers to generate virtually all previously demonstrated quantum states of light, such as for example Schrödinger’s cat or Gottesman-Kitaev-Preskill (GKP)states, and the cubic phase state, a state previously described only in theory.
“The cubic phase state is something that many quantum researchers have been trying to create in practice for twenty years. The fact that we have now managed to do this for the first time is a demonstration of how well our technique works, but the most important advance is that there are so many states of varying complexity and we have found a technique that can create any of them,” says Marina Kudra, a doctoral student at the Department of Microtechnology and Nanoscience and the study’s lead author. More

Smartphone users will be disappointed if they expect their devices and social media to fill their need for purpose and meaning. In fact, it will probably do the opposite, researchers at Baylor and Campbell Universities found in a recently published study.
Christopher M. Pieper, Ph.D., senior lecturer of sociology at Baylor University, and lead author Justin J. Nelson, M.A. ’16, Ph.D. ’19, assistant professor of sociology at Campbell University, partnered to understand the complex relationship between meaning-seeking and technology by analyzing data from the Baylor Religion Survey. Their research — “Maladies of Infinite Aspiration’: Smartphones, Meaning-Seeking, and Anomigenesis” — was published in the journal Sociological Perspectives.
The researchers’ results provide a sociological link to the psychological studies that point to connections between digital devices and media use with feelings of loneliness, depression, unhappiness, suicidal ideation and other poor mental health outcomes.
“Human beings are seekers — we seek meaning in our relationships, our work, our faith, in all areas of social life,” Pieper said. “As researchers, we were interested in the role that smartphones — and the media they give us instant access to — might be playing in meaning-seeking.
“We conclude that smartphone attachment…could be anomigenic, causing a breakdown in social values because of the unstructured and limitless options they provide for seeking meaning and purpose and inadvertently exacerbate feelings of despair while simultaneously promising to resolve them,” Pieper said. “Seeking itself becomes the only meaningful activity, which is the basis of anomie and addiction.”
Nelson and Pieper also found a connection between this search for meaning and feelings of attachment to one’s smartphone — a possible precursor to tech addiction.
“Our research finds that meaning-seeking is associated with increased smartphone attachment — a feeling that you would panic if your phone stopped working,” Nelson said. “Social media use is also correlated with increased feelings of attachment.”
The researchers concentrated on responses to questions used in Wave 5 of the national Baylor Religion Survey that related to information and communication technology (ICT) devices use, as well as questions related to meaning and purpose from the Meaning in Life Questionnaire, to show that while devices promise satisfaction and meaning, they often deliver the opposite.
A key finding of the study is that this feeling of attachment is highest for those who use social media less often. However, the research found that individuals seeking solace or connection through their phones in shorter spurts might exacerbate attachment.
“What is interesting is this association decreases for the heaviest of social media users,” Pieper said. “While we don’t know how this group uses social media, it might be that normalized use at the highest levels erases feelings of attachment for the individual — as we put it, it would be like saying one is attached to their eyes or lungs.”
One positive the researchers found is that identifying a satisfying purpose for life seems to provide a protective effect against this sense of attachment and anomie, though this effect is not as strong as the opposite effect of meaning-seeking. Taken together, it is possible that media use bolstered by purpose, such as through family, work or faith, is less likely to produce alienating effects for the individual, the researchers said. But, not knowing what specific users are doing online, this remains a question for future research.
“What we have uncovered is a social mechanism that draws us into smartphone use, and that might keep us hooked, exacerbating feelings of attachment and anomie, and even disconnection, while they promise the opposite,” Pieper said.
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Materials provided by Baylor University. Note: Content may be edited for style and length. More

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations — all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation.
“We start with this huge object of all these coupled-together differential equations; then we’re using machine learning to turn it into something so small you can count it on your fingers,” says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy.
The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they’re unleashed on more complex quantum systems.
The Hubbard model is deceptively simple, however. For even a modest number of electrons and cutting-edge computational approaches, the problem requires serious computing power. That’s because when electrons interact, their fates can become quantum mechanically entangled: Even once they’re far apart on different lattice sites, the two electrons can’t be treated individually, so physicists must deal with all the electrons at once rather than one at a time. With more electrons, more entanglements crop up, making the computational challenge exponentially harder.
One way of studying a quantum system is by using what’s called a renormalization group. That’s a mathematical apparatus physicists use to look at how the behavior of a system — such as the Hubbard model — changes when scientists modify properties such as temperature or look at the properties on different scales. Unfortunately, a renormalization group that keeps track of all possible couplings between electrons and doesn’t sacrifice anything can contain tens of thousands, hundreds of thousands or even millions of individual equations that need to be solved. On top of that, the equations are tricky: Each represents a pair of electrons interacting.
Di Sante and his colleagues wondered if they could use a machine learning tool known as a neural network to make the renormalization group more manageable. The neural network is like a cross between a frantic switchboard operator and survival-of-the-fittest evolution. First, the machine learning program creates connections within the full-size renormalization group. The neural network then tweaks the strengths of those connections until it finds a small set of equations that generates the same solution as the original, jumbo-size renormalization group. The program’s output captured the Hubbard model’s physics even with just four equations.
“It’s essentially a machine that has the power to discover hidden patterns,” Di Sante says. “When we saw the result, we said, ‘Wow, this is more than what we expected.’ We were really able to capture the relevant physics.”
Training the machine learning program required a lot of computational muscle, and the program ran for entire weeks. The good news, Di Sante says, is that now that they have their program coached, they can adapt it to work on other problems without having to start from scratch. He and his collaborators are also investigating just what the machine learning is actually “learning” about the system, which could provide additional insights that might otherwise be hard for physicists to decipher.
Ultimately, the biggest open question is how well the new approach works on more complex quantum systems such as materials in which electrons interact at long distances. In addition, there are exciting possibilities for using the technique in other fields that deal with renormalization groups, Di Sante says, such as cosmology and neuroscience.
Di Sante co-authored the new study with CCQ guest researcher Matija Medvidović (a graduate student at Columbia University), Alessandro Toschi of TU Wien in Vienna, Giorgio Sangiovanni of the University of Würzburg in Germany, Cesare Franchini of the University of Bologna in Italy, CCQ and Center for Computational Mathematics senior research scientist Anirvan M. Sengupta, and CCQ co-director Andy Millis. Di Sante’s time at the CCQ was supported by a Marie Curie International Fellowship, which encourages transnational scientific collaboration.
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Materials provided by Simons Foundation. Original written by Thomas Sumner. Note: Content may be edited for style and length. More

Placental malaria as a consequence of Plasmodium falciparum infections can lead to severe complications for both mother and child. Each year, placental malaria causes nearly 200,000 newborn deaths, mainly due to low birth weight, as well as 10,000 maternal deaths. Placental malaria results from parasite-infected red blood cells that get stuck within tree-like branch structures that make up the placenta.
Research on human placenta is experimentally challenging due to ethical considerations and inaccessibility of the living organs. The anatomy of the human placenta and architecture of maternal-fetal interface, such as between maternal and fetal blood, are complex and cannot be easily reconstructed in their entirety using modern in vitro models.
Researchers from Florida Atlantic University’s College of Engineering and Computer Science and Schmidt College of Medicine have developed a placenta-on-a-chip model that mimics the nutrient exchange between the fetus and mother under the influence of placental malaria. Combining microbiology with engineering technologies, this novel 3D model uses a single microfluidic chip to study the complicated processes that take place in malaria-infected placenta as well as other placenta-related diseases and pathologies.
Placenta-on-a-chip simulates blood flow and mimics the microenvironment of the malaria-infected placenta in this flow condition. Using this method, researchers closely examine the process that takes place as the infected red blood cells interact with the placental vasculature. This microdevice enables them to measure the glucose diffusion across the modeled placental barrier and the effects of blood infected with a P. falciparum line that can adhere to the surface of placenta using placenta-expressed molecule called CSA.
For the study, trophoblasts or outer layer cells of the placenta and human umbilical vein endothelial cells were cultured on the opposite sides of an extracellular matrix gel in a compartmental microfluidic system, forming a physiological barrier between the co-flow tubular structure to mimic a simplified maternal-fetal interface in placental villi.
Results, published in Scientific Reports,demonstrated that CSA-binding infected erythrocytes added resistance to the simulated placental barrier for glucose perfusion and decreased the glucose transfer across this barrier. The comparison between the glucose transport rate across the placental barrier in conditions when uninfected or P. falciparum infected blood flows on outer layer cells helps to better understand this important aspect of placental malaria pathology and could potentially be used as a model to study ways to treat placental malaria.
“Despite advances in biosensing and live cell imaging, interpreting transport across the placental barrier remains challenging. This is because placental nutrient transport is a complex problem that involves multiple cell types, multi-layer structures, as well as coupling between cell consumption and diffusion across the placental barrier,” said Sarah E. Du, Ph.D., senior author and an associate professor in FAU’s Department of Ocean and Mechanical Engineering. “Our technology supports formation of microengineered placental barriers and mimics blood circulations, which provides alternative approaches for testing and screening.”
Most of the molecular exchange between maternal and fetal blood occurs in the branching tree-like structures called villous trees. Because placental malaria may start only after the beginning of second trimester when intervillous space opens to infected red blood cells and white blood cells, the researchers were interested in the placental model of maternal-fetal interface formed in the second half of pregnancy.
“This study provides vital information on the exchange of nutrients between mother and fetus affected by malaria,” said Stella Batalama, Ph.D., dean, FAU College of Engineering and Computer Science. “Studying the molecular transport between maternal and fetal compartments may help to understand some of the pathophysiological mechanisms in placental malaria. Importantly, this novel microfluidic device developed by our researchers at Florida Atlantic University could serve as a model for other placenta-relevant diseases.”
Study co-authors are Babak Mosavati, Ph.D., a recent graduate in FAU’s College of Engineering and Computer Science; and Andrew Oleinikov, Ph.D., a professor of biomedical science, FAU Schmidt College of Medicine.
The research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Allergy and Infectious Diseases, and the National Science Foundation.
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Materials provided by Florida Atlantic University. Original written by Gisele Galoustian. Note: Content may be edited for style and length. More