Computers Math

Researchers around the world are a step closer to a better understanding of the intricacies of COVID-19 thanks to two new web resources developed by investigators at Baylor College of Medicine and the University of California San Diego. The resources are freely available through the Signaling Pathways Project (Baylor) and the Network Data Exchange (UCSD). They put at researchers’ fingertips information about cellular genes whose expression is affected by coronavirus infection and place these data points in the context of the complex network of host molecular signaling pathways. Using this resource has the potential to accelerate the development of novel therapeutic strategies.
The study appears in the journal Scientific Data.
“Our motivation for developing this resource is to contribute to making research about COVID-19 more accessible to the scientific community. When researchers have open access to each other’s work, discoveries move forward more efficiently,” said leading author Dr. Neil McKenna, associate professor of molecular and cellular biology and member of the Dan L Duncan Comprehensive Cancer Center at Baylor.
The Signaling Pathway Project
For years, the scientific community has been generating and archiving molecular datasets documenting how genes are expressed as cells conduct their normal functions, or in association with disease. However, usually this information is not easily accessible.
In 2019, McKenna and his colleagues developed the Signaling Pathways Project, a web-based platform that integrates molecular datasets published in the scientific literature into consensus regulatory signatures, or what they are calling consensomes, that rank genes according to their rates of differential expression.

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In the current study, the researchers generated consensomes for genes affected by infection with three major coronaviruses, Middle East respiratory syndrome coronavirus (MERS) and severe acute respiratory syndrome coronaviruses 1 (SARS1) and 2 (SARS2, which causes COVID-19).
McKenna and his colleagues provide a resource that assists researchers in making the most out of coronavirus’ datasets. The resource identifies the genes whose expression is most consistently affected by the infection and integrates those responses with data about the cells’ molecular signaling pathways, in a sense getting a better picture of what happens inside a cell infected by coronavirus and how the cell responds.
“The collaboration with UCSD makes our analyses available as intuitive Cytoscape-style networks,” says McKenna. “Because using these resources does not require training in meta-analysis, they greatly lower the barriers to usability by bench researchers.”
Providing new insights into COVID-19
The consensus strategy, the researchers explain, can bring to light previously unrecognized links or provide further support for suspected connections between coronavirus infection and human signaling pathways, ultimately simplifying the generation of hypotheses to be tested in the laboratory.

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For example, the connection between pregnancy and susceptibility to COVID-19 has been difficult to evaluate due to lack of clinical data, but McKenna and colleagues’ approach has provided new insights into this puzzle.
“We found evidence that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling mediated by interferon in the airway epithelium. This finding suggests the hypothesis that the suppression of the interferon response to SARS2 infection by elevated circulating progesterone during pregnancy may contribute to the asymptomatic clinical course,” McKenna said.
Consistent with their hypothesis, while this paper was being reviewed, a clinical trial was launched to evaluate progesterone as a treatment for COVID-19 in men.
Scott A. Ochsner at Baylor College of Medicine and Rudolf T. Pillich at the University of California San Diego were also authors of this work.
This study was supported by the National Institute of Diabetes, Digestive and Kidney Diseases NIDDK Information Network (DK097748), the National Cancer Institute (CA125123, CA184427) and by the Brockman Medical Research Foundation. The Signaling Pathways Project website is hosted by the Dan L Duncan Comprehensive Cancer Center. More

The disruptive inventions that make people go “Wow!” tend to come from research in the heart of cities and not in the suburbs, a new study suggests.
Researchers found that, within metro areas, the majority of patents come from innovations created in suburbs — often in the office parks of big tech companies like Microsoft and IBM.
But the unconventional, disruptive innovations — the ones that combine research from different technological fields — are more likely to be produced in cities, said Enrico Berkes, co-author of the study and postdoctoral researcher in economics at The Ohio State University.
These unconventional patents are ones that, for example, may blend research on acoustics with research on information storage — the basis for digital music players like the iPod. Or patents that cite previous work on “vacuum cleaning” and “computing” to produce the Roomba.
“Densely populated cities do not generate more patents than the suburbs, but they tend to generate more unconventional patents,” said Berkes, who did the work as a doctoral student at Northwestern University.
“Our findings suggest that cities provide more opportunities for creative people in different fields to interact informally and exchange ideas, which can lead to more disruptive innovation.”
Berkes conducted the study with Ruben Gaetani, assistant professor of strategic management at the University of Toronto. Their research was published online recently in The Economic Journal.

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Previous research had shown that large metropolitan areas are where patenting activity tends to concentrate, Berkes said, suggesting that population density is an important factor for innovation.
But once Berkes and Gaetani started looking more closely at metro areas, they found that a sizable share of these patents was developed in the suburbs — the least densely populated part. Nearly three-quarters of patents came from places that had density below 3,650 people per square mile in 2000, about the density of Palo Alto, California.
“If new technology is spurred by population density, we wanted to know why so much is happening in the least dense parts of the metro areas,” Berkes said.
So Berkes and Gaetani analyzed more than 1 million U.S. patents granted between January 2002 and August 2014. They used finely geolocated data from the U.S. Patent and Trademark Office that allowed them to see exactly where in metro areas — including city centers and specific suburbs — that patented discoveries were made.
But they were also interested in determining the type of innovations produced — whether they would be considered conventional or unconventional. They did this by analyzing the previous work on which each patent was based.

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The researchers tagged new patents as unconventional if the inventors cited previous work in widely different areas.
For example, a patent from 2000 developed in Pittsburgh is one of the first recorded inventions in wearable technologies and one of the precursors to products such as Fitbit. It was recognized as unconventional because it cites previous patents in both apparel and electrical equipment — two very distant fields.
After analyzing the data, the researchers found that both urban and suburban areas played a prominent role in the innovation process, but in different ways, Berkes said.
Large innovative companies, such as IBM or Microsoft, tend to perform their research in large office parks located outside the main city centers.
“These companies are very successful in taking advantage of formal channels of knowledge diffusion, such as meetings or conferences, where they can capitalize on the expertise of their scientists and have them work together on specialized projects for the company,” Berkes said.
“But it is more difficult for them to tap ideas from other scientific fields because this demands interactions with inventors they’re not communicating with every day or running into in the cafeteria or in the hallway.”
That’s where the urban cores excelled. In cities like San Francisco and Boston, researchers may meet people in entirely different fields at bars, restaurants, museums and cultural events. Any chance encounter could lead to productive partnerships, he said.
“If you want to create something truly new and disruptive, it helps if you have opportunities to casually bump into people from other scientific fields and exchange ideas and experiences and knowledge. That’s what happens in cities,” he said.
“Density plays an important role in the type, rather than the amount, of innovation.”
These findings show the potential value of tech parks that gather technology startup companies in a variety of fields in one place, Berkes said. But they have to be set up properly.
“Our research suggests that informal interactions are important. Tech parks should be structured in a way that people from different startups can easily interact with each other on a regular basis and share ideas,” he said. More

A dose of artificial intelligence can speed the development of 3D-printed bioscaffolds that help injuries heal, according to researchers at Rice University.
A team led by computer scientist Lydia Kavraki of Rice’s Brown School of Engineering used a machine learning approach to predict the quality of scaffold materials, given the printing parameters. The work also found that controlling print speed is critical in making high-quality implants.
Bioscaffolds developed by co-author and Rice bioengineer Antonios Mikos are bonelike structures that serve as placeholders for injured tissue. They are porous to support the growth of cells and blood vessels that turn into new tissue and ultimately replace the implant.
Mikos has been developing bioscaffolds, largely in concert with the Center for Engineering Complex Tissues, to improve techniques to heal craniofacial and musculoskeletal wounds. That work has progressed to include sophisticated 3D printing that can make a biocompatible implant custom-fit to the site of a wound.
That doesn’t mean there isn’t room for improvement. With the help of machine learning techniques, designing materials and developing processes to create implants can be faster and eliminate much trial and error.
“We were able to give feedback on which parameters are most likely to affect the quality of printing, so when they continue their experimentation, they can focus on some parameters and ignore the others,” said Kavraki, an authority on robotics, artificial intelligence and biomedicine and director of Rice’s Ken Kennedy Institute.

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The team reported its results in Tissue Engineering Part A.
The study identified print speed as the most important of five metrics the team measured, the others in descending order of importance being material composition, pressure, layering and spacing.
Mikos and his students had already considered bringing machine learning into the mix. The COVID-19 pandemic created a unique opportunity to pursue the project.
“This was a way to make great progress while many students and faculty were unable to get to the lab,” Mikos said.
Kavraki said the researchers — graduate students Anja Conev and Eleni Litsa in her lab and graduate student Marissa Perez and postdoctoral fellow Mani Diba in the Mikos lab, all co-authors of the paper — took time at the start to establish an approach to a mass of data from a 2016 study on printing scaffolds with biodegradable poly(propylene fumarate), and then to figure out what more was needed to train the computer models.

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“The students had to figure out how to talk to each other, and once they did, it was amazing how quickly they progressed,” Kavraki said.
From start to finish, the COVID-19 window let them assemble data, develop models and get the results published within seven months, record time for a process that can often take years.
The team explored two modeling approaches. One was a classification method that predicted whether a given set of parameters would produce a “low” or “high” quality scaffold. The other was a regression-based approach that approximated the values of print-quality metrics to come to a result. Kavraki said both relied upon a “classical supervised learning technique” called random forest that builds multiple “decision trees” and “merges” them together to get a more accurate and stable prediction.
Ultimately, the collaboration could lead to better ways to quickly print a customized jawbone, kneecap or bit of cartilage on demand.
“A hugely important aspect is the potential to discover new things,” Mikos said. “This line of research gives us not only the ability to optimize a system for which we have a number of variables — which is very important — but also the possibility to discover something totally new and unexpected. In my opinion, that’s the real beauty of this work.
“It’s a great example of convergence,” he said. “We have a lot to learn from advances in computer science and artificial intelligence, and this study is a perfect example of how they will help us become more efficient.”
“In the long run, labs should be able to understand which of their materials can give them different kinds of printed scaffolds, and in the very long run, even predict results for materials they have not tried,” Kavraki said. “We don’t have enough data to do that right now, but at some point we think we should be able to generate such models.”
Kavraki noted The Welch Institute, recently established at Rice to enhance the university’s already stellar reputation for advanced materials science, has great potential to expand such collaborations.
“Artificial intelligence has a role to play in new materials, so what the institute offers should be of interest to people on this campus,” she said. “There are so many problems at the intersection of materials science and computing, and the more people we can get to work on them, the better.” More

Scientists at Oak Ridge National Laboratory used new techniques to create a composite that increases the electrical current capacity of copper wires, providing a new material that can be scaled for use in ultra-efficient, power-dense electric vehicle traction motors.
The research is aimed at reducing barriers to wider electric vehicle adoption, including cutting the cost of ownership and improving the performance and life of components such as electric motors and power electronics. The material can be deployed in any component that uses copper, including more efficient bus bars and smaller connectors for electric vehicle traction inverters, as well as for applications such as wireless and wired charging systems.
To produce a lighter weight conductive material with improved performance, ORNL researchers deposited and aligned carbon nanotubes on flat copper substrates, resulting in a metal-matrix composite material with better current handling capacity and mechanical properties than copper alone.
Incorporating carbon nanotubes, or CNTs, into a copper matrix to improve conductivity and mechanical performance is not a new idea. CNTs are an excellent choice due to their lighter weight, extraordinary strength and conductive properties. But past attempts at composites by other researchers have resulted in very short material lengths, only micrometers or millimeters, along with limited scalability, or in longer lengths that performed poorly.
The ORNL team decided to experiment with depositing single-wall CNTs using electrospinning, a commercially viable method that creates fibers as a jet of liquid speeds through an electric field. The technique provides control over the structure and orientation of deposited materials, explained Kai Li, a postdoctoral researcher in ORNL’s Chemical Sciences Division. In this case, the process allowed scientists to successfully orient the CNTs in one general direction to facilitate enhanced flow of electricity.
The team then used magnetron sputtering, a vacuum coating technique, to add thin layers of copper film on top of the CNT-coated copper tapes. The coated samples were then annealed in a vacuum furnace to produce a highly conductive Cu-CNT network by forming a dense, uniform copper layer and to allow diffusion of copper into the CNT matrix.

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Using this method, ORNL scientists created a copper-carbon nanotube composite 10 centimeters long and 4 centimeters wide, with exceptional properties. The microstructural properties of the material were analyzed using instruments at the Center for Nanophase Materials Sciences at ORNL, a U.S. Department of Energy Office of Science user facility. Researchers found the composite reached 14% greater current capacity, with up to 20% improved mechanical properties compared with pure copper, as detailed in ACS Applied Nano Materials.
Tolga Aytug, lead investigator for the project, said that “by embedding all the great properties of carbon nanotubes into a copper matrix, we are aiming for better mechanical strength, lighter weight and higher current capacity. Then you get a better conductor with less power loss, which in turn increases the efficiency and performance of the device. Improved performance, for instance, means we can reduce volume and increase the power density in advanced motor systems.”
The work builds on a rich history of superconductivity research at ORNL, which has produced superior materials to conduct electricity with low resistance. The lab’s superconductive wire technology was licensed to several industry suppliers, enabling such uses as high-capacity electric transmission with minimal power losses.
While the new composite breakthrough has direct implications for electric motors, it also could improve electrification in applications where efficiency, mass and size are a key metric, Aytug said. The improved performance characteristics, accomplished with commercially viable techniques, means new possibilities for designing advanced conductors for a broad range of electrical systems and industrial applications, he said.
The ORNL team also is exploring the use of double-wall CNTs and other deposition techniques such as ultrasonic spray coating coupled with a roll-to-roll system to produce samples of some 1 meter in length.
“Electric motors are basically a combination of metals — steel laminations and copper windings,” noted Burak Ozpineci, manager of the ORNL Electric Drive Technologies Program and leader of the Power Electronics and Electric Machinery group. “To meet DOE’s Vehicle Technologies Office’s 2025 electric vehicle targets and goals, we need to increase power density of the electric drive and reduce the volume of motors by 8 times, and that means improving material properties.”
Other ORNL scientists on the project were Michael McGuire, Andrew Lupini, Lydia Skolrood, Fred List and Soydan Ozcan. The work was funded by DOE’s Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. More

Tiny magnetic whirls that can occur in materials — so-called skyrmions — hold high promises for novel electronic devices or magnetic memory in which they are used as bits to store information. A fundamental prerequisite for any application is the stability of these magnetic whirls. A research team of the Institute of Theoretical Physics and Astrophysics of Kiel University has now demonstrated that so far neglected magnetic interactions can play a key role for skyrmion stability and can drastically enhance skyrmion lifetime. Their work, which has been published today (September 21, 2020) in Nature Communications, opens also the perspective to stabilize skyrmions in new material systems in which the previously considered mechanisms are not sufficient.
Intensive research on stability at room temperature
Their unique magnetic structure — more precisely their topology — lends stability to skyrmions and protects them from collapse. Therefore, skyrmions are denoted as knots in the magnetization. However, on the atomic lattice of a solid this protection is imperfect and there is only a finite energy barrier. “The situation is comparable to a marble lying in a trough which thus needs a certain impetus, energy, to escape from it. The larger the energy barrier, the higher is the temperature at which the skyrmion is stable,” explains Professor Stefan Heinze from Kiel University. Especially skyrmions with diameters below 10 nanometers, which are needed for future spinelectronic devices, have so far only been detected at very low temperatures. Since applications are typically at room temperature the enhancement of the energy barrier is a key objective in today’s research on skyrmions.
Previously, a standard model of the relevant magnetic interactions contributing to the barrier has been established. A team of theoretical physicists from the research group of Professor Stefan Heinze has now demonstrated that one type of magnetic interactions has so far been overlooked. In the 1920s Werner Heisenberg could explain the occurrence of ferromagnetism by the quantum mechanical exchange interaction which results from the spin dependent “hopping” of electrons between two atoms. “If one considers the electron hopping between more atoms, higher-order exchange interactions occur,” says Dr. Souvik Paul, first author of the study. However, these interactions are much weaker than the pair-wise exchange proposed by Heisenberg and were thus neglected in the research on skyrmions.
Weak higher-order exchange interactions stabilize skyrmions
Based on atomistic simulations and quantum mechanical calculations performed on the super computers of the North-German Supercomputing Alliance (HLRN) the scientists from Kiel have now explained that these weak interactions can still provide a surprisingly large contribution to skyrmion stability. Especially the cyclic hopping over four atomic sites influences the energy of the transition state extraordinarily strongly, where only a few atomic bar magnets are tilted against each other. Even stable antiskyrmions were found in the simulations which are advantageous for some future data storage concepts but typically decay too fast.
Higher-order exchange interactions appear in many magnetic materials used for potential skyrmion applications such as cobalt or iron. They can also stabilize skyrmions in magnetic structures in which the previously considered magnetic interactions cannot occur or are too small. Therefore, the present study opens new promising routes for the research on these fascinating magnetic knots.

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An international research team has developed “electronic skin” sensors capable of mimicking the dynamic process of human motion. This work could help severely injured people, such as soldiers, regain the ability to control their movements, as well as contribute to the development of smart robotics, according to Huanyu “Larry” Cheng, Dorothy Quiggle Early Career Professor in the Penn State Department of Engineering Science and Mechanics.
Cheng and collaborating researchers based in China published their work in a recent issue of Nano Energy.
“The skin of the human hand is amazing — that’s what we tried to imitate,” Cheng said. “How do we capture texture and force? What about the years of evolution that produced the impressive sensitivity of the fingertip? We’re attempting to reproduce this biological and dynamic process to enable objects to behave similarly to the human hand.”
The dual-mode sensor measures both the magnitude and load of movement, such as the effort of swinging a tennis racquet, as well as rate, duration and direction. The trick was to decouple this measurement and understand how the separate parameters influence each other.
For example, bouncing a tennis ball gently on a racquet requires different input than serving a ball to an opponent. Those same variables come into play when a person with a prosthetic arm needs to differentiate between handling an egg or carrying a watermelon.
“We can apply these sensors to help people capture the magnitude for pressing, bending and more,” Cheng said. “We can also use these sensors on soft robotics to manipulate delicate objects, like catching a fish, or even in a disaster when they may need to crawl into irregular spaces and move debris.”
The data is informed by synergy created between the piezoelectric and piezoresistive signals, according to Cheng. Piezoelectric signals measure outside force — such as pressure — to create electrical charge, while piezoresistive signals mitigate the current.?The dual mode sensors are sandwiched together, with two internal layers of pyramid-shaped microstructures facing one another. The microstructures measure magnitude and duration measurements from the piezoresistive layer and the dynamic loading rate and direction from the piezoelectric layer. This synergistic effect allows for a high sensitivity over a broad pressure and frequency range, meaning that researchers can precisely measure the force and flexibility needed to imitate specific movements.
“We combined the best of the best models and sensors to create something new,” Cheng said.

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Materials provided by Penn State. Original written by Ashley J. WennersHerron. Note: Content may be edited for style and length. More

An international team of researchers has now found that computerised training can support preterm children’s academic success. In their randomised controlled study “Fit for School,” the researchers compared two learning apps. The project at the University Hospital Essen and at Ruhr-Universität Bochum was funded by Mercator Research Center Ruhr (Mercur) with approximately 300,000 Euros for four years. Results have been published online as unedited manuscript in the journal Pediatric Research on 12 September 2020.
Every 11. baby is born too early in Germany, over 15 million globally each year. Although survival rates have increased, long-term development has not improved much. At school age, children born preterm often struggle with attention and complex tasks, such as math.
“Preemies need special support,” says neonatologist Dr. Britta Hüning of the Clinic for Pediatrics I, University Hospital Essen. Together with psychologist Dr. Julia Jaekel from the University of Tennessee Knoxville, previously at Ruhr-Universität Bochum, she was part of a multidisciplinary team that led the study with Professor Ursula Felderhoff-Müser, Director of the Clinic for Pediatrics I. Their findings are promising and novel, as few intervention studies have ever shown academic improvements for school-aged preterm children.
Two learning apps tested
The study included 65 first graders, born between five and twelve weeks preterm in the Ruhr Region. They practiced daily for five weeks, using the software app Xtramath or Cogmed. Teachers rated their academic progress in math, attention, reading and writing through first and second grade.
The final results: parents and children liked both apps. “The different trainings supported long-term school success to a similar degree,” says Julia Jaekel. “However, Xtramath received more positive ratings and led to better short-term academic progress.”
In times of increasing remote and online instruction for all children, apps with documented effectiveness are scarce. Parents and teachers may turn to adaptive apps such as Xtramath for learning at home.

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Researchers at the University of Helsinki have developed a technique in which a computer models visual perception by monitoring human brain signals. In a way, it is as if the computer tries to imagine what a human is thinking about. As a result of this imagining, the computer is able to produce entirely new information, such as fictional images that were never before seen.
The technique is based on a novel brain-computer interface. Previously, similar brain-computer interfaces have been able to perform one-way communication from brain to computer, such as spell individual letters or move a cursor.
As far as is known, the new study is the first where both the computer’s presentation of the information and brain signals were modelled simultaneously using artificial intelligence methods. Images that matched the visual characteristics that participants were focusing on were generated through interaction between human brain responses and a generative neural network.
The study was published in the Scientific Reports journal in September. Scientific Reports is an online multidisciplinary, open-access journal from the publishers of Nature.
Neuroadaptive generative modelling
The researchers call this method neuroadaptive generative modelling. A total of 31 volunteers participated in a study that evaluated the effectiveness of the technique. Participants were shown hundreds of AI-generated images of diverse-looking people while their EEG was recorded.

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The subjects were asked to concentrate on certain features, such as faces that looked old or were smiling. While looking at a rapidly presented series of face images, the EEGs of the subjects were fed to a neural network, which inferred whether any image was detected by the brain as matching what the subjects were looking for.
Based on this information, the neural network adapted its estimation as to what kind of faces people were thinking of. Finally, the images generated by the computer were evaluated by the participants and they nearly perfectly matched with the features the participants were thinking of. The accuracy of the experiment was 83 per cent.
“The technique combines natural human responses with the computer’s ability to create new information. In the experiment, the participants were only asked to look at the computer-generated images. The computer, in turn, modelled the images displayed and the human reaction toward the images by using human brain responses. From this, the computer can create an entirely new image that matches the user’s intention,” says Tuukka Ruotsalo, Academy of Finland Research Fellow at the University of Helsinki, Finland and Associate Professor at the University of Copenhagen, Denmark.
Unconscious attitudes may be exposed
Generating images of the human face is only one example of the technique’s potential uses. One practical benefit of the study may be that computers can augment human creativity.
“If you want to draw or illustrate something but are unable to do so, the computer may help you to achieve your goal. It could just observe the focus of attention and predict what you would like to create,” Ruotsalo says. However, the researchers believe that the technique may be used to gain understanding of perception and the underlying processes in our mind.
“The technique does not recognise thoughts but rather responds to the associations we have with mental categories. Thus, while we are not able to find out the identity of a specific ‘old person’ a participant was thinking of, we may gain an understanding of what they associate with old age. We, therefore, believe it may provide a new way of gaining insight into social, cognitive and emotional processes,” says Senior Researcher Michiel Spapé.
According to Spapé, this is also interesting from a psychological perspective.
“One person’s idea of an elderly person may be very different from another’s. We are currently uncovering whether our technique might expose unconscious associations, for example by looking if the computer always renders old people as, say, smiling men.”

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Materials provided by University of Helsinki. Original written by Aino Pekkarinen. Note: Content may be edited for style and length. More