Computers Math

University of Colorado Boulder researchers have discovered that minuscule, self-propelled particles called “nanoswimmers” can escape from mazes as much as 20 times faster than other, passive particles, paving the way for their use in everything from industrial clean-ups to medication delivery.
The findings, published this week in the Proceedings of the National Academy of Sciences, describe how these tiny synthetic nanorobots are incredibly effective at escaping cavities within maze-like environments. These nanoswimmers could one day be used to remediate contaminated soil, improve water filtration or even deliver drugs to targeted areas of the body, like within dense tissues.
“This is the discovery of an entirely new phenomenon that points to a broad potential range of applications,” said Daniel Schwartz, senior author of the paper and Glenn L. Murphy Endowed Professor of chemical and biological engineering.
These nanoswimmers came to the attention of the theoretical physics community about 20 years ago, and people imagined a wealth of real-world applications, according to Schwartz. But unfortunately these tangible applications have not yet been realized, in part because it’s been quite difficult to observe and model their movement in relevant environments — until now.
These nanoswimmers, also called Janus particles (named after a Roman two-headed god), are tiny spherical particles composed of polymer or silica, engineered with different chemical properties on each side of the sphere. One hemisphere promotes chemical reactions to occur, but not the other. This creates a chemical field which allows the particle to take energy from the environment and convert it into directional motion — also known as self-propulsion.
“In biology and living organisms, cell propulsion is the dominant mechanism that causes motion to occur, and yet, in engineered applications, it’s rarely used. Our work suggests that there is a lot we can do with self-propulsion,” said Schwartz. More

A recent proof-of-concept study finds that a low-cost training program can reduce hazardous driving in older adults. Researchers hope the finding will lead to the training becoming more widely available.
“On-road training and simulator training programs have been successful at reducing car accidents involving older drivers — with benefits lasting for years after the training,” says Jing Yuan, first author of the study and a Ph.D. student at North Carolina State University. “However, many older adults are unlikely to have access to these training programs or technologies.”
“We developed a training program, called Drive Aware, that would be accessible to anyone who has a computer,” says Jing Feng, corresponding author of the study and a professor of psychology at NC State. “Specifically, Drive Aware is a cognitive training program for older adults to help them accurately detect road hazards. The goal of our recent study was to determine the extent to which Drive Aware influences driving behaviors when trainees actually get behind the wheel.”
To test Drive Aware, the researchers enlisted 27 adults, ages 65 and older. All of the study participants took a baseline driving test in a driving simulator. Nine of the study participants were then placed in the “active training” group. The active training group received two interactive Drive Aware training sessions, about a week apart. Nine other study participants were placed in a “passive training” group. This group watched video of other people receiving the Drive Aware training sessions. This took place twice, with sessions about a week apart. The remaining nine study participants served as the control group and received no training. All 27 study participants then took a second driving test in the driving simulator.
The researchers found that study participants who were part of the active training group had 25% fewer “unsafe incidents” after the training. Unsafe incidents included accidents with other vehicles, pedestrians, running off the road, etc. There was no statistically significant change in the number of unsafe incidents for study participants in the passive training group or the control group.
“In short, we found that older adults were less likely to have an accident in the driving simulator after receiving the Drive Aware training,” Yuan says.
“This testing was done with a fairly modest number of study participants,” Feng says. “If we can secure the funding, we’d like to scale up our testing to more clearly establish how effective this training is at reducing accidents among older drivers. If the results are as good as they look right now, we’d want to find ways to share the training program as broadly as possible. Not many people can afford one-on-one on-the-road training, or training that involves high-end driving simulators. But we think a lot of people would be able to access Drive Aware, and it has the potential to save a lot of lives.”
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Materials provided by North Carolina State University. Original written by Matt Shipman. Note: Content may be edited for style and length. More

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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Materials provided by Swiss Nanoscience Institute, University of Basel. Note: Content may be edited for style and length. More

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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Materials provided by Ruhr-University Bochum. Note: Content may be edited for style and length. More

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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Materials provided by Osaka University. Note: Content may be edited for style and length. More