Aurelia Butler

Researchers at The University of Manchester may have cleared a significant hurdle on the path to quantum computing, demonstrating step-change improvements in the spin transport characteristics of nanoscale graphene-based electronic devices.
The team — comprising researchers from the National Graphene Institute (NGI) led by Dr Ivan Vera Marun, alongside collaborators from Japan and including students internationally funded by Ecuador and Mexico — used monolayer graphene encapsulated by another 2D material (hexagonal boron nitride) in a so-called van der Waals heterostructure with one-dimensional contacts. This architecture was observed to deliver an extremely high-quality graphene channel, reducing the interference or electronic ‘doping’ by traditional 2D tunnel contacts.
‘Spintronic’ devices, as they are known, may offer higher energy efficiency and lower dissipation compared to conventional electronics, which rely on charge currents. In principle, phones and tablets operating with spin-based transistors and memories could be greatly improved in speed and storage capacity, exceeding Moore’s Law.
As published in Nano Letters, the Manchester team measured electron mobility up to 130,000cm2/Vs at low temperatures (20K or -253oC). For purposes of comparison, the only previously published efforts to fabricate a device with 1D contacts achieved mobility below 30,000cm2/Vs, and the 130k figure measured at the NGI is higher than recorded for any other previous graphene channel where spin transport was demonstrated.
The researchers also recorded spin diffusion lengths approaching 20μm. Where longer is better, most typical conducting materials (metals and semiconductors) have spin diffusion lengths More

Researchers at the Universidad Carlos III de Madrid (UC3M) have developed a system based on computer vision techniques that allows automatic analysis of biomedical videos captured by microscopy in order to characterise and describe the behaviour of the cells that appear in the images.
These new techniques developed by the UC3M engineering team have been used for measurements on living tissues, in research carried out with scientists from the National Centre for Cardiovascular Research (CNIC in its Spanish acronym). As a result, the team discovered that neutrophils (a type of immune cell) show different behaviours in the blood during inflammatory processes and have identified that one of them, caused by the Fgr molecule, is associated with the development of cardiovascular disease. This work, recently published in the journal Nature, could allow the development of new treatments to minimise the consequences of heart attacks. Researchers from the Vithas Foundation, the University of Castilla-La Mancha, the Singapore Agency for Science, Technology and Research (ASTAR) and Harvard University (USA), among other centres, have participated in the study.
“Our contribution consists of the design and development of a fully automatic system, based on computer vision techniques, which allows us to characterise the cells under study by analysing videos captured by biologists using the intravital microscopy technique,” says one of the authors of this work, Professor Fernando Díaz de María, head of the UC3M Multimedia Processing Group. Automatic measurements of the shape, size, movement and position relative to the blood vessel of a few thousand cells have been made, compared to traditional biological studies that are usually supported by analyses of a few hundred manually characterised cells. In this way, it has been possible to carry out a more advanced biological analysis with greater statistical significance.
This new system has several advantages, according to the researchers, in terms of time and precision. Generally speaking, “it is not feasible to keep an expert biologist segmenting and tracking cells on video for months. On the other hand, to provide an approximate idea (because it depends on the number of cells and 3D volume depth), our system only takes 15 minutes to analyse a 5-minute video,” says another of the researchers, Ivan González Díaz, Associate Professor in the Signal Theory and Communications Department at UC3M.
Deep neural networks, the tools these engineers rely on for cell segmentation and detection, are basically algorithms that learn from examples, so in order to deploy the system in a new context, it is necessary to generate sufficient examples to enable their training. These networks are part of machine learning techniques, which in turn is a discipline within the field of Artificial Intelligence (AI). In addition, the system incorporates other types of statistical techniques and geometric models, all of which are described in another paper, recently published in the Medical Image Analysis journal.
The software that implements the system is versatile and can be adapted to other problems in a few weeks. “In fact, we are already applying it in other different scenarios, studying the immunological behaviour of T cells and dendritic cells in cancerous tissues. And the provisional results are promising,” says another of the researchers from the UC3M team, Miguel Molina Moreno.
In any case, when researching in this field, researchers stress the importance of the work of an interdisciplinary team. “In this context, it is important to recognise the prior communication effort between biologists, mathematicians and engineers, required to understand the basic concepts of other disciplines, before real progress can be made,” concludes Fernando Díaz de María.
YouTube video: https://youtu.be/EiTAvmQkyIo More

Typing on a keyboard, pressing buttons on a remote control or braiding a child’s hair has remained elusive for prosthetic hand users. With current myoelectric prosthetic hands, users can only control one grasp function at a time even though modern artificial hands are mechanically capable of individual control of all five digits.
A first-of-its-kind study using haptic/touch sensation feedback, electromyogram (EMG) control and an innovative wearable soft robotic armband could just be a game changer for users of prosthetic hands who have long awaited advances in dexterity. Findings from the study could catalyze a paradigm shift in the way current and future artificial hands are controlled by limb-absent people.
Researchers from Florida Atlantic University’s College of Engineering and Computer Science in collaboration with FAU’s Charles E. Schmidt College of Science investigated whether people could precisely control the grip forces applied to two different objects grasped simultaneously with a dexterous artificial hand.
For the study, they also explored the role that visual feedback played in this complex multitasking model by systematically blocking visual and haptic feedback in the experimental design. In addition, they studied the potential for time saving in a simultaneous object transportation experiment compared to a one-at-a-time approach. To accomplish these tasks, they designed a novel multichannel wearable soft robotic armband to convey artificial sensations of touch to the robotic hand users.
Results, published in Scientific Reports, showed that multiple channels of haptic feedback enabled subjects to successfully grasp and transport two objects simultaneously with the dexterous artificial hand without breaking or dropping them, even when their vision of both objects was obstructed.
In addition, the simultaneous control approach improved the time required to transport and deliver both objects compared to a one-at-a-time approach commonly used in prior studies. Of note for clinical translation, researchers did not find significant differences between the limb-absent subject and the other subjects for the key performance metrics in the tasks. Importantly, subjects qualitatively rated haptic feedback as considerably more important than visual feedback even when vision was available, because there was often little to no visually perceptible warning before grasped objects were broken or dropped. More

Blood clots form naturally as a way to stop bleeding when someone is injured. But blood clots in patients with medical issues, such as mechanical heart valves or other heart conditions, can lead to a stroke or heart attack. That’s why millions of Americans take blood-thinning medications, such as warfarin, that make it harder for their blood to clot.
Warfarin isn’t perfect, however, and requires patients to be tested frequently to make sure their blood is in the correct range — blood that clots too easily could still lead to a stroke or a heart attack while blood that doesn’t clot can lead to extended bleeding after an injury. To be tested, patients either have to go to a clinic laboratory or use a costly at-home testing system.
Researchers at the University of Washington have developed a new blood-clotting test that uses only a single drop of blood and a smartphone vibration motor and camera. The system includes a plastic attachment that holds a tiny cup beneath the phone’s camera.
A person adds a drop of blood to the cup, which contains a small copper particle and a chemical that starts the blood-clotting process. Then the phone’s vibration motor shakes the cup while the camera monitors the movement of the particle, which slows down and then stops moving as the clot forms. The researchers showed that this method falls within the accuracy range of the standard instruments of the field.
The team published these findings Feb. 11 in Nature Communications.
“Back in the day, doctors used to manually rock tubes of blood back and forth to monitor how long it took a clot to form. This, however, requires a lot of blood, making it infeasible to use in home settings,” said senior author Shyam Gollakota, UW professor in the Paul G. Allen School of Computer Science & Engineering. “The creative leap we make here is that we’re showing that by using the vibration motor on a smartphone, our algorithms can do the same thing, except with a single drop of blood. And we get accuracy similar to the best commercially available techniques.”
Doctors can rank blood-clotting ability using two numbers: the time it takes for the clot to form, what’s known as the “prothrombin time” or PT a ratio calculated from the PT that allows doctors to more easily compare results between different tests or laboratories, called the “international normalized ratio” or INR More

Paying thousands of dollars to advertise on television is a huge proposition — never more so than for the Super Bowl, for which 30-second TV spots this year will cost advertisers as much as $6.5 million. Contrary to Super Bowl advertisements, which are some of the most high-profile commercials, new Cornell University research shows nearly a third of TV ads play to empty rooms.
Advertising pricing relies on measuring how many TVs are tuned in to a specific channel, and not whether people are actually watching the TVs.
“We wanted to quantify whether the current industry standard is doing a good job predicting what advertisers care about,” said lead author Jura Liaukonyte, associate professor in the Cornell SC Johnson College of Business.
For this research, the co-authors worked with TVision Insights, a TV performance metrics company that developed innovative technology to passively monitor who’s in the room and whether they’re actually looking at what’s on the TV screen, while respecting viewer privacy.The research analyzed 4 million ad exposures over the course of a year.
Their findings — including the fact that nearly a third of TV ads play to empty rooms, and that viewers are four times more likely to leave the room than change the channel — are detailed in “How Viewer Tuning, Presence and Attention Respond to Ad Content and Predict Brand Search Lift,” which published Feb. 9 in Marketing Science.
Among other results, the team found that ad viewing behaviors vary depending on channel, time of day, program genre, age and gender. For example, older viewers are more likely to avoid ads by changing channels; younger viewers are more likely to avoid ads by leaving the room or diverting their visual attention — likely due to multitasking with a second screen.
Additionally, ads for recreational products — beer and video games, for example — do the best at retaining viewers, the researchers said. Among the worst at keeping eyes on the screen are prescription drug ads, particularly those for serious conditions.
The Super Bowl, of course, is a different animal from every other show in the TV ad realm, the researchers said.
“It has become sort of like the Oscars for the advertising industry,” Liaukonyte said.
Cornell Chronicle version of story: https://news.cornell.edu/stories/2022/02/nearly-third-tv-ads-play-empty-rooms
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Materials provided by Cornell University. Original written by Tom Fleischman, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length. More

Community structure, including relationships between and within groups, is foundational to our understanding of the world around us. New research by mathematics and statistics professor Kenneth Berenhaut, along with former postdoctoral fellow Katherine Moore and graduate student Ryan Melvin, sheds new light on some fundamental statistical questions.
“When we encounter complex data in areas such as public health, economics or elsewhere, it can be valuable to address questions regarding the presence of discernable groups, and the inherent “cohesion” or glue that holds these groups together. In considering such concepts, socially, the terms “communities,” “networks” and “relationships” may come to mind,” said Berenhaut.
The research leverages abstracted social ideas of conflict, alignment, prominence and support, to tap into the mathematical interplay between distance and cohesiveness — the sort evident when, say, comparing urban and rural settings. This enables adaptations to varied local perspectives.
“For example, we considered psychological survey-based data reflecting differences and similarities in cultural values between regions around the world — in the U.S., China, India and the EU,” Berenhaut said. “We observed distinct cultural groups, with rich internal network structure, despite the analytical challenges caused by the fact that some cohesive groups (such as India and the EU) are far more culturally diverse than others. Mark Twain once referred to India as ‘the country of a hundred nations and a hundred tongues, of a thousand religions and two million gods.’ Regions (such as the Southeast and California in the U.S.) can be perceived as locally distinct, despite their relative similarity in a global context. It is these sorts of characteristics that we are attempting to detect and understand.”
The paper, “A social perspective on perceived distances reveals deep community structure,” published by PNAS (Proceedings of the National Academy of Sciences of the United States) can be found here.
“I am excited by the manner in which a social perspective, along with a probabilistic approach, can illuminate aspects of communities inherent in data from a variety of fields,” said Berenhaut. “The concept of data communities proposed in the paper is derived from and aligns with a shared human social perspective. The work crosses areas with connections to ideas in sociology, psychology, mathematics, physics, statistics and elsewhere.”
Leveraging our experiences and perspectives can lead to valuable mathematical and statistical insights.
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Materials provided by Wake Forest University. Original written by Kim McGrath. Note: Content may be edited for style and length. More

Asymptomatic COVID-19 cases are the bane of computer modelers’ existences — they throw off the modeling data to an unknown degree. You can’t measure what you can’t detect, right? A new approach from Los Alamos National Laboratory’s Theoretical Division, however, explores using historic epidemic data from eight different countries to estimate the transmission rate and fraction of under-reported cases.
“Asymptomatic cases are the ‘dark matter’ of epidemics,” said Nick Hengartner, one of the authors on the report published today in the journal PLOS ONE. “We see only the indirect evidence that more people are sick than reported, and if we don’t account for them, we may wrongly conclude that the epidemic is under control. So we changed the model to focus on the observed counts instead of trying to model the ‘perfect’ world. By looking back through the time series of historical data, we can see from their dynamics what’s missing.”
The importance of capturing the undocumented cases is significant, especially in a disease such as COVID-19, where asymptomatic individuals have accounted for 20-70 percent of all infections.
Co-author Imelda Trejo, a postdoctoral fellow at Los Alamos noted, “This is a new extension of the standard SIR (susceptible-infected-recovered) epidemiological models to study the underreported incidence of infectious disease. The new model reveals that trying to fit an SIR model type directly to raw incidence data will underestimate the true infectious rate. This could actually lead decisionmakers to declare the epidemic under control prematurely.” Instead, the team presented a Bayesian method (a statistical model using probability to represent all uncertainty within the model) to estimate the transmission rate and fraction of underreported cases.
As tested against the data of eight countries (Argentina, Brazil, Chile, Colombia, Mexico, Panama, Peru and the U.S.), the new approach directly describes the dynamics of the observed, under-reported cases. “We use the local dynamics of the observed cases to propose a model that gives us a conditional expectation of new cases, based on the observed history,” Trejo said.
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Materials provided by DOE/Los Alamos National Laboratory. Note: Content may be edited for style and length. More