Aurelia Butler

In a step toward a future of higher performance memory devices, researchers from National Taiwan Normal University and Kyushu University have developed a new device that needs only a single semiconductor known as perovskite to simultaneously store and visually transmit data.
By integrating a light-emitting electrochemical cell with a resistive random-access memory that are both based on perovskite, the team achieved parallel and synchronous reading of data both electrically and optically in a ‘light-emitting memory.’
At the most fundamental level, digital data is stored as a basic unit of information known as a bit, which is often represented as either a one or a zero. Thus, the pursuit of better data storage comes down to finding more efficient ways to store and read these ones and zeros.
While flash memory has become extremely popular, researchers have been searching for alternatives that could further improve speed and simplify fabrication.
One candidate is nonvolatile resistive random-access memory, or RRAM. Instead of storing charge in transistors like in flash memory, resistive memory uses materials that can switch between states of high and low resistance to represent ones and zeros.
“However, the electrical measurements needed to check the resistance and read zeros and ones from RRAM can limit the overall speed,” explains Chun-Chieh Chang, professor at National Taiwan Normal University and one of the corresponding authors of the study published in Nature Communications. More

The quantum computing market is projected to reach $65 billion by 2030, a hot topic for investors and scientists alike because of its potential to solve incomprehensibly complex problems.
Drug discovery is one example. To understand drug interactions, a pharmaceutical company might want to simulate the interaction of two molecules. The challenge is that each molecule is composed of a few hundred atoms, and scientists must model all the ways in which these atoms might array themselves when their respective molecules are introduced. The number of possible configurations is infinite — more than the number of atoms in the entire universe. Only a quantum computer can represent, much less solve, such an expansive, dynamic data problem.
Mainstream use of quantum computing remains decades away, while research teams in universities and private industry across the globe work on different dimensions of the technology.
A research team led by Xu Yi, assistant professor of electrical and computer engineering at the University of Virginia School of Engineering and Applied Science, has carved a niche in the physics and applications of photonic devices, which detect and shape light for a wide range of uses including communications and computing. His research group has created a scalable quantum computing platform, which drastically reduces the number of devices needed to achieve quantum speed, on a photonic chip the size of a penny.
Olivier Pfister, professor of quantum optics and quantum information at UVA, and Hansuek Lee, assistant professor at the Korean Advanced Institute of Science and Technology, contributed to this success.
Nature Communications recently published the team’s experimental results, A Squeezed Quantum Microcomb on a Chip. Two of Yi’s group members, Zijiao Yang, a Ph.D. student in physics, and Mandana Jahanbozorgi, a Ph.D. student of electrical and computer engineering, are the paper’s co-first authors. A grant from the National Science Foundation’s Engineering Quantum Integrated Platforms for Quantum Communication program supports this research. More

Artificial intelligence (AI) will fundamentally change medicine and healthcare: Diagnostic patient data, e.g. from ECG, EEG or X-ray images, can be analyzed with the help of machine learning, so that diseases can be detected at a very early stage based on subtle changes. However, implanting AI within the human body is still a major technical challenge. TU Dresden scientists at the Chair of Optoelectronics have now succeeded for the first time in developing a bio-compatible implantable AI platform that classifies in real time healthy and pathological patterns in biological signals such as heartbeats. It detects pathological changes even without medical supervision. The research results have now been published in the journal Science Advances.
In this work, the research team led by Prof. Karl Leo, Dr. Hans Kleemann and Matteo Cucchi demonstrates an approach for real-time classification of healthy and diseased bio-signals based on a biocompatible AI chip. They used polymer-based fiber networks that structurally resemble the human brain and enable the neuromorphic AI principle of reservoir computing. The random arrangement of polymer fibers forms a so-called “recurrent network,” which allows it to process data, analogous to the human brain. The nonlinearity of these networks enables to amplify even the smallest signal changes, which — in the case of the heartbeat, for example — are often difficult for doctors to evaluate. However, the nonlinear transformation using the polymer network makes this possible without any problems.
In trials, the AI was able to differentiate between healthy heartbeats from three common arrhythmias with an 88% accuracy rate. In the process, the polymer network consumed less energy than a pacemaker. The potential applications for implantable AI systems are manifold: For example, they could be used to monitor cardiac arrhythmias or complications after surgery and report them to both doctors and patients via smartphone, allowing for swift medical assistance.
“The vision of combining modern electronics with biology has come a long way in recent years with the development of so-called organic mixed conductors,” explains Matteo Cucchi, PhD student and first author of the paper. “So far, however, successes have been limited to simple electronic components such as individual synapses or sensors. Solving complex tasks has not been possible so far. In our research, we have now taken a crucial step toward realizing this vision. By harnessing the power of neuromorphic computing, such as reservoir computing used here, we have succeeded in not only solving complex classification tasks in real time but we will also potentially be able to do this within the human body. This approach will make it possible to develop further intelligent systems in the future that can help save human lives.”
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Materials provided by Technische Universität Dresden. Note: Content may be edited for style and length. More

A new study published in Physical Review Letters describes a way to increase the cargo capacity of microscopic, self-propelled droplets known as “microswimmers.” Researchers from the University of Pennsylvania and the Max Planck Institutefor Dynamics and Self-Organisation found that when a school of microswimmers move in the same direction inside a narrow channel, they can increase the number of particles they can carry by 10-fold. Their findings have implications for applications ranging from drug -elivery systems to materials with active coatings.
Like many scientific endeavors, this one began with a simple observation. While attending a conference dinner at the Georgia Aquarium, physicist Arnold Mathijssen and his colleagues noticed that large schools of swimming fish seemed to be carrying small particles and debris in their wake. This happens because of hydrodynamic entrainment, a process where, as an object moves through liquid, it generates a flow and causes nearby objects to be dragged along with it.
“We were wondering, As the fish in the aquarium are swimming forward, does a particle also get dragged forwards, or is it pushed backwards by their tails?” says Mathijssen. “Our central question was if these guys move things forward or not, and the hypothesis was that, if we can see this happening in the aquarium, maybe this is applicable under a microscope as well.”
To answer the question, Max Planck Institute researchers Chenyu Jin, Yibo Chen, and Corinna Maass ran experiments using synthetic microswimmers, self-propelled droplets of oil and surfactant that are a model system for microscopic robots. Using their microswimmers, the researchers were able to measure the strength of the flows generated by an individual swimmer and the amount of material that an individual could carry with them as they travelled through a two-dimensional channel. Then, once the data were collected, Mathijssen and his group developed a theoretical model to help explain their findings.
One particular challenge for developing the model was devising a way to describe the effects of the walls of the microscopic channel because, unlike at the aquarium, this experiment was conducted in a confined space. “That confinement really affects the flows and, as a result, affects the total volume of stuff you can transport. There is quite a bit of literature in terms of modeling active particles, but it’s difficult to get it right in complex environments,” Mathijssen says.
Using their data and newly-developed model, the researchers found that the transport capacity of an individual microswimmer could be increased by 10-fold when they swam together inside a narrow channel. They also found that the entrainment velocity, or the speed at which particles move forwards, was much larger than initially anticipated. More

Be it nuclear power plants, patient monitoring equipment in hospitals, or self-driving cars — integrations of physical processes with computers and process control, or cyber-physical systems (CPS), are everywhere. However, the widespread application of CPS also makes them prime targets for hackers. A simple change in the value of a sensor can create havoc. Vulnerability to malicious attacks has created the need for systems that can withstand the corruption of sensors and still provide safe and efficient process control.
In a recent study published in IEEE Transactions on Automatic Control, Professor Yongsoon Eun from Daegu Gyeongbuk Institute of Science and Technology, and his colleague from Hyundai Motor Company, Yechan Jeong, have developed a method for resilient state estimation (RSE) for systems that are under attack. State estimation refers to the use of external variables, i.e., sensor readings, to determine the internal state of the system using mathematical models called “observers.” This is a critical step in process control. When the internal state of a system can be determined despite the corruption of sensors, it is called RSE.
“Would you ride an autonomous vehicle or live near a computer-controlled power plant if safety and security were not considered in their design? The importance of resiliency in control systems has been recognized for over a decade.” explains Prof. Eun.
All control systems are subject to variations or “disturbances” in the process, which cause errors in state estimation. However, as the disturbance increases, so does the error, leading to a breakdown in system resiliency. Making use of a kind of observer known as “Unknown Input Observer (UIO),” the new RSE method overcomes this limitation and provides a way for state estimation that can withstand both malicious attacks as well as external disturbances.
In this method, a UIO is designed for each sensor, the estimates from each UIO are combined, and the error is processed to provide the true value of the internal state of the system. The benefit of using a UIO is that its estimation error always converges to zero, regardless of external disturbances to the process. This is unlike other observers, which can only provide a range for estimation error. Another novelty of this method is that it deploys a ‘partial state UIO,’ a technique newly developed by Prof. Eun’s team, by which as much partial information on internal states as possible is extracted from each sensor when full state information extraction is not feasible. This greatly expands the applicability of the new RSE method based on UIO.
“The proposed method gives a system a level of tolerance for faults and attacks and, in cases where it is inevitable, allows graceful degradation of system functionality. This makes it crucial to the design of CPS,” concludes Prof. Eun.
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Materials provided by DGIST (Daegu Gyeongbuk Institute of Science and Technology). Note: Content may be edited for style and length. More

A novel artificial intelligence blood testing technology developed by researchers at the Johns Hopkins Kimmel Cancer Center was found to detect over 90% of lung cancers in samples from nearly 800 individuals with and without cancer.
The test approach, called DELFI (DNA evaluation of fragments for early interception), spots unique patterns in the fragmentation of DNA shed from cancer cells circulating in the bloodstream. Applying this technology to blood samples taken from 796 individuals in Denmark, the Netherlands and the U.S., investigators found that the DELFI approach accurately distinguished between patients with and without lung cancer.
Combining the test with analysis of clinical risk factors, a protein biomarker, and followed by computed tomography imaging, DELFI helped detect 94% of patients with cancer across stages and subtypes. This included 91% of patients with earlier or less invasive stage I/II cancers and 96% of patients with more advanced stage III/IV cancers. These results will be published in the August 20 issue of the journal Nature Communications.
Lung cancer is the most common cause of cancer death, claiming almost 2 million lives worldwide each year. However, fewer than 6% of Americans at risk for lung cancers undergo recommended low-dose computed tomography screening, despite projections that tens of thousands of deaths could be avoided, and even fewer are screened worldwide, explains senior study author Victor E. Velculescu, M.D., Ph.D., professor of oncology and do-director of the Cancer Genetics and Epigenetics Program at the Johns Hopkins Kimmel Cancer Center. This is due to a variety of reasons, including concerns of potential harm from investigation of false positive imaging results, radiation exposure or worries about complications from invasive procedures. “It is clear that there is an urgent, unmet clinical need for development of alternative, noninvasive approaches to improve cancer screening for high-risk individuals and, ultimately, the general population,” says lead author Dimitrios Mathios, a postdoctoral fellow at the Johns Hopkins Kimmel Cancer Center. “We believe that a blood test, or ‘liquid biopsy,’ for lung cancer could be a good way to enhance screening efforts, because it would be easy to do, broadly accessible and cost-effective.”
The DELFI technology uses a blood test to indirectly measure the way DNA is packaged inside the nucleus of a cell by studying the size and amount of cell-free DNA present in the circulation from different regions across the genome. Healthy cells package DNA like a well-organized suitcase, in which different regions of the genome are placed carefully in various compartments. The nuclei of cancer cells, by contrast, are like more disorganized suitcases, with items from across the genome thrown in haphazardly. When cancer cells die, they release DNA in a chaotic manner into the bloodstream. DELFI helps identify the presence of cancer using machine learning, a type of artificial intelligence, to examine millions of cell-free DNA fragments for abnormal patterns, including the size and amount of DNA in different genomic regions. This approach provides a view of cell-free DNA referred to as the “fragmentome.” The DELFI approach only requires low-coverage sequencing of the genome, enabling this technology to be cost-effective in a screening setting, the researchers say.
For the study, investigators from Johns Hopkins, working with researchers in Denmark and the Netherlands, first performed genome sequencing of cell-free DNA in blood samples from 365 individuals participating in a seven-year Danish study called LUCAS. The majority of participants were at high risk for lung cancer and had smoking-related symptoms such as cough or difficulty breathing. The DELFI approach found that patients who were later determined to have cancer had widespread variation in their fragmentome profiles, while patients found not to have cancer had consistent fragmentome profiles. Subsequently, researchers validated the DELFI technology using a different population of 385 individuals without cancer and 46 individuals with cancer. Overall, the approach detected over 90% of patients with lung cancer, including those with early and advanced stages, and with different subtypes. “DNA fragmentation patterns provide a remarkable fingerprint for early detection of cancer that we believe could be the basis of a widely available liquid biopsy test for patients with lung cancer,” says author Rob Scharpf, Ph.D., associate professor of oncology at the Johns Hopkins Kimmel Cancer Center.
A first-of-a-kind national clinical trial called DELFI-L101, sponsored by the Johns Hopkins University spin-out Delfi Diagnostics, is evaluating a test based on the DELFI technology in 1,700 participants in the U.S., including healthy participants, individuals with lung cancers and individuals with other cancers. The group would like to further study DELFI in other types of cancers.
Other scientists who contributed to the work include Stephen Cristiano, Jamie E. Medina, Jillian Phallen, Daniel Bruhm, Noushin Niknafs, Leonardo Ferreira, Vilmos Adleff, Jia Yuee Ciao, Alessandro Leal, Michael Noe, James White, Adith S. Arun, Carolyn Hruban, Akshaya V. Annapragada, Patrick M. Forde, Valsamo Anagnostou and Julie R. Brahmer of Johns Hopkins. Additional authors were from Herlev and Gentofte Hospital and Bispebjerg Hospital in Copenhagen; Aarhus University Hospital in Aarhus, Denmark; Herning Regional Hospital in Herning, Denmark; the Netherlands Cancer Institute in Amsterdam; Delfi Diagnostics; and Hvidovre Hospital in Hvidovre, Denmark.
The work was supported in part by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation; a Stand Up to Cancer /INTIME Lung Cancer Interception Dream Team grant; Stand Up to Cancer-Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415); the Gray Foundation; the Commonwealth Foundation; the Mark Foundation for Cancer Research; the Lundbeck Foundation; an unrestricted grant from Roche Denmark; a research grant from Delfi Diagnostics; and National Institutes of Health grants CA121113, CA006973, CA233259 and 1T32GM136577.
Mathios, Cristiano, Phallen, Leal, Adleff, Scharpf and Velculescu are inventors on patent applications submitted by Johns Hopkins University related to cell-free DNA for cancer detection. Cristiano, Phallen, Leal, Adleff and Scharpf are founders of Delfi Diagnostics, and Adleff and Scharpf are consultants for this organization. Velculescu is a founder of Delfi Diagnostics and of Personal Genome Diagnostics, serves on the board of directors and as a consultant for both organizations, and owns Delfi Diagnostics and Personal Genome Diagnostics stock, which are subject to certain restrictions under university policy. The Johns Hopkins University owns equity in Delfi Diagnostics and Personal Genome Diagnostics. Additionally, Velculescu is an adviser to Bristol-Myers Squibb, Genentech, and Takeda Pharmaceuticals. The terms of these arrangements are managed by The Johns Hopkins University in accordance with its conflict of interest policies. More

One of the biggest marketing trends in the online shopping industry is personalization through curated product recommendations; however, it can change whether people buy a product they had been considering, according to new University of California San Diego research.
The study by Uma R. Karmarkar, assistant professor at the UC San Diego Rady School of Management and School of Global Policy and Strategy, finds that display items that come from the same category as the target product, such as a board game matched with other board games, enhance the chances of a target product’s purchase. In contrast, consumers are less likely to buy the target product if it is mismatched with products from different categories, for example, a board game displayed with kitchen knives.
The study utilized eye-tracking — a sensor technology that makes it possible to know where a person is looking — to examine how different types of displays influenced visual attention. Participants in the study looked at their target product for the same amount of time when it was paired with similar items or with items from different categories; however, shoppers spent more time looking at the mismatched products, even though they were only supposed to be there “for display.”
“What is surprising is that when I asked people how much they liked the target products, their preferences didn’t change between display settings,” Karmarkar said. “The findings show that it is not about how much you like or dislike the item you’re looking at, it’s about your process for buying the item. The surrounding display items don’t seem to change how much attention you give the target product, but they can influence your decision whether to buy it or not.”
Karmarkar, who holds PhDs in consumer behavior and neuroscience, says the findings suggests that seeing similar options on the page reinforces the idea to consumers that they’re making the right kind of decision to purchase an item that fits the category on display.
“When the information is mismatched, it changes the scope of the decision,” she said. “A mismatched display is comparable to shopping in a store with more variety. You may consider a featured board game but if you can see other products to buy, this board game may not be the first kind of purchase you want to make. The mismatched items draw additional attention and compete with the category you were considering.”
The study, to be published in Frontiers in Neuroscience, involved 58 participants, ages 18 to 40, who had to make 36 online shopping decisions for real products with real money. The findings showing differences in purchase rates replicate a set of studies Karmarkar published in 2017. In the new research, she was able to measure what parts of the display were engaging more or less attention. In addition, the upcoming paper shows that matched displays increase purchase rates even when they include more attention-grabbing information, like details about price.
Karmarkar talked with industry experts about product recommendations systems, which shaped her approach to these questions. Recommender algorithms can have different designs to meet a variety of retailers’ respective goals. Products can be shown with “mismatched” displays when retailers are using cross-promoting tactics based on prior customer behavior or inventory they may want to sell more rapidly.
The board game example Karmarkar often uses is based on a real experience she had while shopping online during the month of October.
“I had been browsing games like ‘Bananagrams’ and when I reloaded the product page, a Halloween costumes display popped up,” she said. “Given my search history, the store probably estimated I had a family. So while I’m sure they wanted me to buy the game, they also knew they had an active shopper who might be interested in the Halloween costumes that needed to sell by the end of the month. It looks like a win-win, but our work suggests that creating this mismatched situation could have lowered the chance that I would add the game to my cart.”
While the study is useful for online retailers to know the benefits of showing same-category options on a specific product page, the research is valuable to consumers as well.
“This shows how outside forces shape our decisions in ways we might not recognize,” she said. “If a shopper is looking for something specific, they are likely to focus their attention, regardless of recommender displays. But when people are just ‘browsing stuff online,’ different page designs can create different patterns of attention. Store displays can change what we choose, even when they don’t change what we like.” More

Since the beginning of the pandemic, researchers worldwide have been looking for ways to treat COVID-19. And while the COVID-19 vaccines represent the best measure to prevent the disease, therapies for those who do get infected remain in short supply. A new groundbreaking study from U-M reveals several drug contenders already in use for other purposes — including one dietary supplement — that have been shown to block or reduce SARS-CoV2 infection in cells.
The study, published recently in the Proceedings of the National Academy of Science, uses artificial intelligence-powered image analysis of human cell lines during infection with the novel coronavirus. The cells were treated with more than 1,400 individual FDA-approved drugs and compounds, either before or after viral infection, and screened, resulting in 17 potential hits. Ten of those hits were newly recognized, with seven identified in previous drug repurposing studies, including remdesivir, which is one of the few FDA-approved therapies for COVID-17 in hospitalized patients.
“Traditionally, the drug development process takes a decade — and we just don’t have a decade,” said Jonathan Sexton, Ph.D., Assistant Professor of Internal Medicine at the U-M Medical School and one of the senior authors on the paper. “The therapies we discovered are well positioned for phase 2 clinical trials because their safety has already been established.”
The team validated the 17 candidate compounds in several types of cells, including stem-cell derived human lung cells in an effort to mimic SARS-CoV2 infection of the respiratory tract. Nine showed anti-viral activity at reasonable doses, including lactoferrin, a protein found in human breastmilk that is also available over the counter as a dietary supplement derived from cow’s milk.
“We found lactoferrin had remarkable efficacy for preventing infection, working better than anything else we observed,” Sexton said. He adds that early data suggest this efficacy extends even to newer variants of SARS-CoV2, including the highly transmissible Delta variant.
The team is soon launching clinical trials of the compound to examine its ability to reduce viral loads and inflammation in patients with SARS-CoV2 infection. More