Aurelia Butler

The electronic properties of graphene can be specifically modified by stretching the material evenly, say researchers at the University of Basel. These results open the door to the development of new types of electronic components.
Graphene consists of a single layer of carbon atoms arranged in a hexagonal lattice. The material is very flexible and has excellent electronic properties, making it attractive for numerous applications — electronic components in particular.
Researchers led by Professor Christian Schönenberger at the Swiss Nanoscience Institute and the Department of Physics at the University of Basel have now studied how the material’s electronic properties can be manipulated by mechanical stretching. In order to do this, they developed a kind of rack by which they stretch the atomically thin graphene layer in a controlled manner, while measuring its electronic properties.
Sandwiches on the rack
The scientists first prepared a “sandwich” comprising a layer of graphene between two layers of boron nitride. This stack of layers, furnished with electrical contacts, was placed on a flexible substrate.
The researchers then applied a force to the center of the sandwich from below using a wedge. “This enabled us to bend the stack in a controlled way, and to elongate the entire graphene layer,” explained lead author Dr. Lujun Wang.
“Stretching the graphene allowed us to specifically change the distance between the carbon atoms, and thus their binding energy,” added Dr. Andreas Baumgartner, who supervised the experiment.
Altered electronic states
The researchers first calibrated the stretching of the graphene using optical methods. They then used electrical transport measurements to study how the deformation of the graphene changes the electronic energies. The measurements need to be performed at minus 269°C for the energy changes to become visible.
“The distance between the atomic nuclei directly influences the properties of the electronic states in graphene,” said Baumgartner, summarizing the results. “With uniform stretching, only the electron velocity and energy can change. The energy change is essentially the ‘scalar potential’ predicted by theory, which we have now been able to demonstrate experimentally.”
These results could lead, for example, to the development of new sensors or new types of transistors. In addition, graphene serves as a model system for other two-dimensional materials that have become an important research topic worldwide in recent years.
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Scientists at the University of Sydney and Japan’s National Institute for Material Science (NIMS) have discovered that an artificial network of nanowires can be tuned to respond in a brain-like way when electrically stimulated.
The international team, led by Joel Hochstetter with Professor Zdenka Kuncic and Professor Tomonobu Nakayama, found that by keeping the network of nanowires in a brain-like state “at the edge of chaos,” it performed tasks at an optimal level.
This, they say, suggests the underlying nature of neural intelligence is physical, and their discovery opens an exciting avenue for the development of artificial intelligence.
The study is published today in Nature Communications.
“We used wires 10 micrometres long and no thicker than 500 nanometres arranged randomly on a two-dimensional plane,” said lead author Joel Hochstetter, a doctoral candidate in the University of Sydney Nano Institute and School of Physics.
“Where the wires overlap, they form an electrochemical junction, like the synapses between neurons,” he said. “We found that electrical signals put through this network automatically find the best route for transmitting information. And this architecture allows the network to ‘remember’ previous pathways through the system.”
ON THE EDGE OF CHAOS More

Rice University computer scientists are sending RAMBO to rescue genomic researchers who sometimes wait days or weeks for search results from enormous DNA databases.
DNA sequencing is so popular, genomic datasets are doubling in size every two years, and the tools to search the data haven’t kept pace. Researchers who compare DNA across genomes or study the evolution of organisms like the virus that causes COVID-19 often wait weeks for software to index large, “metagenomic” databases, which get bigger every month and are now measured in petabytes.
RAMBO, which is short for “repeated and merged bloom filter,” is a new method that can cut indexing times for such databases from weeks to hours and search times from hours to seconds. Rice University computer scientists presented RAMBO last week at the Association for Computing Machinery data science conference SIGMOD 2021.
“Querying millions of DNA sequences against a large database with traditional approaches can take several hours on a large compute cluster and can take several weeks on a single server,” said RAMBO co-creator Todd Treangen, a Rice computer scientist whose lab specializes in metagenomics. “Reducing database indexing times, in addition to query times, is crucially important as the size of genomic databases are continuing to grow at an incredible pace.”
To solve the problem, Treangen teamed with Rice computer scientist Anshumali Shrivastava, who specializes in creating algorithms that make big data and machine learning faster and more scalable, and graduate students Gaurav Gupta and Minghao Yan, co-lead authors of the peer-reviewed conference paper on RAMBO.
RAMBO uses a data structure that has a significantly faster query time than state-of-the-art genome indexing methods as well as other advantages like ease of parallelization, a zero false-negative rate and a low false-positive rate. More

A tool developed by the Structural Bioinformatics and Network Biology lab at IRB Barcelona predicts the biological activity of chemical compounds, key information to evaluate their therapeutic potential.
Using artificial neural networks, scientists have inferred experimental data for a million compounds and have developed a package of programs to make estimates for any type of molecule.
The work has been published in the journal Nature Communications.
The Structural Bioinformatics and Network Biology laboratory, led by ICREA Researcher Dr. Patrick Aloy, has completed the bioactivity information for a million molecules using deep machine-learning computational models. It has also disclosed a tool to predict the biological activity of any molecule, even when no experimental data are available.
This new methodology is based on the Chemical Checker, the largest database of bioactivity profiles for pseudo pharmaceuticals to date, developed by the same laboratory and published in 2020. The Chemical Checker collects information from 25 spaces of bioactivity for each molecule. These spaces are linked to the chemical structure of the molecule, the targets with which it interacts or the changes it induces at the clinical or cellular level. However, this highly detailed information about the mechanism of action is incomplete for most molecules, implying that for a particular one there may be information for one or two spaces of bioactivity but not for all 25.
With this new development, researchers integrate all the experimental information available with deep machine learning methods, so that all the activity profiles, from chemistry to clinical level, for all molecules can be completed. More

The examined tissue does not need to be marked for this. The analysis only takes around half an hour. “This is a major step that shows that infrared imaging can be a promising methodology in future diagnostic testing and treatment prediction,” says Professor Klaus Gerwert, director of PRODI. The study is published in the American Journal of Pathology on 1 July 2021.
Treatment decision by means of a genetic mutation analysis
Lung tumours are divided into various types, such as small cell lung cancer, adenocarcinoma and squamous cell carcinoma. Many rare tumour types and sub-types also exist. This diversity hampers reliable rapid diagnostic methods in everyday clinical practice. In addition to histological typing, the tumour samples also need to be comprehensively examined for certain changes at a DNA level. “Detecting one of these mutations is important key information that influences both the prognosis and further therapeutic decisions,” says co-author Professor Reinhard Büttner, head of the Institute of General Pathology and Pathological Anatomy at University Hospital Cologne.
Patients with lung cancer clearly benefit when the driver mutations have previously been characterised: for instance, tumours with activating mutations in the EGFR (epidermal growth factor) gene often respond well to tyrosine kinase inhibitors, whereas non-EGFR-mutated tumours or tumours with other mutations, such as KRAS, do not respond at all to this medication. The differential diagnosis of lung cancer previously took place with immunohistochemical staining of tissue samples and a subsequent extensive genetic analysis to determine the mutation.
Fast and reliable measuring technique
The potential of infrared imaging, IR imaging for short, as a diagnostic tool to classify tissue, called label-free digital pathology, was already shown by the group led by Klaus Gerwert in previous studies. The procedure identifies cancerous tissue without prior staining or other markings and functions automatically with the aid of artificial intelligence (AI). In contrast to the methods used to determine tumour shape and mutations in tumour tissue in everyday clinical practice, which can sometimes take several days, the new procedure only takes around half an hour. In these 30 minutes, it is not only possible to ascertain whether the tissue sample contains tumour cells, but also what type of tumour it is and whether it contains a certain mutation.
Infrared spectroscopy makes genetic mutations visible
The Bochum researchers were able to verify the procedure on samples from over 200 lung cancer patients in their work. When identifying mutations, they concentrated on by far the most common lung tumour, adenocarcinoma, which accounts for over 50 per cent of tumours. Its most common genetic mutations can be determined with a sensitivity and specificity of 95 per cent compared to laborious genetic analysis. “For the first time, we were able to identify spectral markers that allow for a spatially resolved distinction between various molecular conditions in lung tumours,” explains Nina Goertzen from PRODI. A single infrared spectroscopic measurement offers information about the sample which would otherwise require several time-consuming procedures.
A further step towards personalised medicine
The results once again confirm the potential of label-free digital pathology for clinical use. “To further increase reliability and promote a translation of the method as a new diagnostic tool, studies with larger patient numbers adapted to clinical needs and external testing in everyday clinical practice are required,” says Dr. Frederik Großerüschkamp, IR imaging project manager. “In order to translate IR imaging into everyday clinical practice, it is crucial to shorten the measuring time, ensure simple and reliable operation of the measuring instruments, and provide answers to questions that are important and helpful both clinically and for the patients.”
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Scientists at Osaka University, Panasonic Corporation, and Waseda University used scanning electron microscopy (SEM) and X-ray absorption spectroscopy to determine which additives induce crystallization in supercooled aqueous solutions. This work may lead to the development of new energy storage materials based on latent heat.
If you put a bottle of water into the freezer, you will expect to pull out a solid cylinder of ice after a few hours. However, if the water has very few impurities and left undisturbed, it may not be frozen, and instead remain as a supercooled liquid. Be careful, because this state is very unstable, and the water will crystallize quickly if shaken or impurities are added — as many YouTube videos will attest. Supercooling is a phenomenon in which an aqueous solution maintains its liquid state without solidifying, even though its temperature is below the freezing point. Although many studies have been done on additives that trigger the freezing of supercooling liquids, the details of the mechanism are unknown. One potential application might be latent heat storage materials, which rely on freezing and melting to capture and later release heat, like a reusable freezer pack.
Now, a team of researchers led by Osaka University has shown that silver nanoparticles are very effective at inducing crystallization in clathrate hydrates. Clathrate hydrates physically look like ice and are composed of hydrogen-bonded water cages with guest molecules inside. “Using SEM with the freeze-fracture replica method, we captured the moment when a nascent cluster enveloped a silver nanoparticle in the aqueous solution of latent heat storage materials,” corresponding author Professor Takeshi Sugahara explains. This occurs because the nanoparticles serve as a “seed,” or nucleation site, for tiny clusters to form. Once this gets started, the remaining solute and water molecules can quickly form additional clusters and then cluster densification leads to the crystallization. The researchers found that while silver nanoparticles tended to accelerate the formation of these clusters, other metal nanoparticles, such as palladium, gold, and iridium do not promote crystallization. “The supercooling suppression effect obtained in the present study will contribute to achieve the practical use of clathrate hydrates as latent heat storage materials,” Professor Sugahara says. Material design guidelines for enhanced supercooling control, as described in this study, may lead to the application of latent heat storage materials in solar energy and heat recovery technologies with improved efficiency.
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Predicting what someone is about to do next based on their body language comes naturally to humans but not so for computers. When we meet another person, they might greet us with a hello, handshake, or even a fist bump. We may not know which gesture will be used, but we can read the situation and respond appropriately.
In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.
“Our algorithm is a step toward machines being able to make better predictions about human behavior, and thus better coordinate their actions with ours,” said Carl Vondrick, assistant professor of computer science at Columbia, who directed the study, which was presented at the International Conference on Computer Vision and Pattern Recognition on June 24, 2021. “Our results open a number of possibilities for human-robot collaboration, autonomous vehicles, and assistive technology.”
It’s the most accurate method to date for predicting video action events up to several minutes in the future, the researchers say. After analyzing thousands of hours of movies, sports games, and shows like “The Office,” the system learns to predict hundreds of activities, from handshaking to fist bumping. When it can’t predict the specific action, it finds the higher-level concept that links them, in this case, the word “greeting.”
Past attempts in predictive machine learning, including those by the team, have focused on predicting just one action at a time. The algorithms decide whether to classify the action as a hug, high five, handshake, or even a non-action like “ignore.” But when the uncertainty is high, most machine learning models are unable to find commonalities between the possible options.
Columbia Engineering PhD students Didac Suris and Ruoshi Liu decided to look at the longer-range prediction problem from a different angle. “Not everything in the future is predictable,” said Suris, co-lead author of the paper. “When a person cannot foresee exactly what will happen, they play it safe and predict at a higher level of abstraction. Our algorithm is the first to learn this capability to reason abstractly about future events.”
Suris and Liu had to revisit questions in mathematics that date back to the ancient Greeks. In high school, students learn the familiar and intuitive rules of geometry — that straight lines go straight, that parallel lines never cross. Most machine learning systems also obey these rules. But other geometries, however, have bizarre, counter-intuitive properties; straight lines bend and triangles bulge. Suris and Liu used these unusual geometries to build AI models that organize high-level concepts and predict human behavior in the future.
“Prediction is the basis of human intelligence,” said Aude Oliva, senior research scientist at the Massachusetts Institute of Technology and co-director of the MIT-IBM Watson AI Lab, an expert in AI and human cognition who was not involved in the study. “Machines make mistakes that humans never would because they lack our ability to reason abstractly. This work is a pivotal step towards bridging this technological gap.”
The mathematical framework developed by the researchers enables machines to organize events by how predictable they are in the future. For example, we know that swimming and running are both forms of exercising. The new technique learns how to categorize these activities on its own. The system is aware of uncertainty, providing more specific actions when there is certainty, and more generic predictions when there is not.
The technique could move computers closer to being able to size up a situation and make a nuanced decision, instead of a pre-programmed action, the researchers say. It’s a critical step in building trust between humans and computers, said Liu, co-lead author of the paper. “Trust comes from the feeling that the robot really understands people,” he explained. “If machines can understand and anticipate our behaviors, computers will be able to seamlessly assist people in daily activity.”
While the new algorithm makes more accurate predictions on benchmark tasks than previous methods, the next steps are to verify that it works outside the lab, says Vondrick. If the system can work in diverse settings, there are many possibilities to deploy machines and robots that might improve our safety, health, and security, the researchers say. The group plans to continue improving the algorithm’s performance with larger datasets and computers, and other forms of geometry.
“Human behavior is often surprising,” Vondrick commented. “Our algorithms enable machines to better anticipate what they are going to do next.” More