Computers Math

With the rise of the digital age, the amount of WiFi sources to transmit information wirelessly between devices has grown exponentially. This results in the widespread use of the 2.4GHz radio frequency that WiFi uses, with excess signals available to be tapped for alternative uses.
To harness this under-utilised source of energy, a research team from the National University of Singapore (NUS) and Japan’s Tohoku University (TU) has developed a technology that uses tiny smart devices known as spin-torque oscillators (STOs) to harvest and convert wireless radio frequencies into energy to power small electronics. In their study, the researchers had successfully harvested energy using WiFi-band signals to power a light-emitting diode (LED) wirelessly, and without using any battery.
“We are surrounded by WiFi signals, but when we are not using them to access the Internet, they are inactive, and this is a huge waste. Our latest result is a step towards turning readily-available 2.4GHz radio waves into a green source of energy, hence reducing the need for batteries to power electronics that we use regularly. In this way, small electric gadgets and sensors can be powered wirelessly by using radio frequency waves as part of the Internet of Things. With the advent of smart homes and cities, our work could give rise to energy-efficient applications in communication, computing, and neuromorphic systems,” said Professor Yang Hyunsoo from the NUS Department of Electrical and Computer Engineering, who spearheaded the project.
The research was carried out in collaboration with the research team of Professor Guo Yong Xin, who is also from the NUS Department of Electrical and Computer Engineering, as well as Professor Shunsuke Fukami and his team from TU. The results were published in Nature Communications on 18 May 2021.
Converting WiFi signals into usable energy
Spin-torque oscillators are a class of emerging devices that generate microwaves, and have applications in wireless communication systems. However, the application of STOs is hindered due to a low output power and broad linewidth. More

A new software tool developed by Earlham Institute researchers will help bioinformaticians improve the quality and accuracy of their biological data, and avoid mis-assemblies. The fast, lightweight, user-friendly tool visualises genome assemblies and gene alignments from the latest next generation sequencing technologies.
Called Alvis, the new visualisation tool examines mappings between DNA sequence data and reference genome databases. This allows bioinformaticians to more easily analyse their data generated from common genomics tasks and formats by producing efficient, ready-made vector images.
First author and post-doctoral scientist at the Earlham Institute Dr Samuel Martin in the Leggett Group, said: “Typically, alignment tools output plain text files containing lists of alignment data. This is great for computer parsing and for being incorporated into a pipeline, but it can be difficult to interpret by humans.
“Visualisation of alignment data can help us to understand the problem at hand. As a new technology, several new alignment formats have been implemented by new tools that are specific to nanopore sequencing technology.
“We found that existing visualisation tools were not able to interpret these formats; Alvis can be used with all common alignment formats, and is easily extensible for future ones.”
A key feature of the new command line tool is its unique ability to automatically highlight chimeric sequences — weak links in the DNA chain. This is where two sequences — from different parts of a genome or different species — are linked together by mistake to make one, affecting the data’s accuracy. More

Classic computers use binary values (0/1) to perform. By contrast, our brain cells can use more values to operate, making them more energy-efficient than computers. This is why scientists are interested in neuromorphic (brain-like) computing. Physicists from the University of Groningen (the Netherlands) have used a complex oxide to create elements comparable to the neurons and synapses in the brain using spins, a magnetic property of electrons. Their results were published on 18 May in the journal Frontiers in Nanotechnology.
Although computers can do straightforward calculations much faster than humans, our brains outperform silicon machines in tasks like object recognition. Furthermore, our brain uses less energy than computers. Part of this can be explained by the way our brain operates: whereas a computer uses a binary system (with values 0 or 1), brain cells can provide more analogue signals with a range of values.
Thin films
The operation of our brains can be simulated in computers, but the basic architecture still relies on a binary system. That is why scientist look for ways to expand this, creating hardware that is more brain-like, but will also interface with normal computers. ‘One idea is to create magnetic bits that can have intermediate states’, says Tamalika Banerjee, Professor of Spintronics of Functional Materials at the Zernike Institute for Advanced Materials, University of Groningen. She works on spintronics, which uses a magnetic property of electrons called ‘spin’ to transport, manipulate and store information.
In this study, her PhD student Anouk Goossens, first author of the paper, created thin films of a ferromagnetic metal (strontium-ruthenate oxide, SRO) grown on a substrate of strontium titanate oxide. The resulting thin film contained magnetic domains that were perpendicular to the plane of the film. ‘These can be switched more efficiently than in-plane magnetic domains’, explains Goossens. By adapting the growth conditions, it is possible to control the crystal orientation in the SRO. Previously, out-of-plane magnetic domains have been made using other techniques, but these typically require complex layer structures.
Magnetic anisotropy
The magnetic domains can be switched using a current through a platinum electrode on top of the SRO. Goossens: ‘When the magnetic domains are oriented perfectly perpendicular to the film, this switching is deterministic: the entire domain will switch.’ However, when the magnetic domains are slightly tilted, the response is probabilistic: not all the domains are the same, and intermediate values occur when only part of the crystals in the domain have switched.
By choosing variants of the substrate on which the SRO is grown, the scientists can control its magnetic anisotropy. This allows them to produce two different spintronic devices. ‘This magnetic anisotropy is exactly what we wanted’, says Goossens. ‘Probabilistic switching compares to how neurons function, while the deterministic switching is more like a synapse.’
The scientists expect that in the future, brain-like computer hardware can be created by combining these different domains in a spintronic device that can be connected to standard silicon-based circuits. Furthermore, probabilistic switching would also allow for stochastic computing, a promising technology which represents continuous values by streams of random bits. Banerjee: ‘We have found a way to control intermediate states, not just for memory but also for computing.’
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Scientists have developed a mathematical model that predicts how the number and effects of bacterial mutations leading to drug resistance will influence the success of antibiotic treatments.
Their model, described today in the journal eLife, provides new insights on the emergence of drug resistance in clinical settings and hints at how to design novel treatment strategies that help avoid this resistance occurring.
Antibiotic resistance is a significant public health challenge, caused by changes in bacterial cells that allow them to survive drugs that are designed to kill them. Resistance often occurs through new mutations in bacteria that arise during the treatment of an infection. Understanding how this resistance emerges and spreads through bacterial populations is important to preventing treatment failure.
“Mathematical models are a crucial tool for exploring the outcome of drug treatment and assessing the risk of the evolution of antibiotic resistance,” explains first author Claudia Igler, Postdoctoral Researcher at ETH Zurich, Switzerland. “These models usually consider a single mutation, which leads to full drug resistance, but multiple mutations that increase antibiotic resistance in bacteria can occur. So there are some mutations that lead to a high level of resistance individually, and some that provide a small level of resistance individually but can accumulate to provide high-level resistance.”
For their study, Igler and her team gathered experimental evidence that drug resistance evolution follows these two patterns: a single mutation and multiple mutations. They then used this information to create an informed modelling framework which predicts the evolution of ‘single-step’ resistance versus ‘multi-step’ resistance in bacteria cells in response to drug type, pharmacokinetics (how the drug decays in the body), and treatment strategies. They investigated how the risk of treatment failure changes when taking into account multiple mutational steps, instead of a single one, and how many different bacterial lineages (bacteria with different mutations) would emerge during the treatment period.
Using their model, the team found that the evolution of drug resistance is limited substantially if more than two mutations are required by the bacteria. Additionally, the extent of this limitation, and therefore the probability of treatment failure, depends strongly on the combination of the drug type and the route of administration, such as orally or via IV infusion.
“Our work provides a crucial step in understanding the emergence of antibiotic resistance in clinically relevant treatment settings,” says senior author Roland Regoes, Group Leader at ETH Zurich. “Together, our findings highlight the importance of measuring the level of antibiotic resistance granted by single mutations to help inform effective antimicrobial treatment strategies.”
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Alzheimer’s disease shares some key similarities with healthy aging, according to a new mathematical model described today in eLife.
The model provides unique insights into the multiscale biological alterations in the elderly and neurodegenerative brain, with important implications for identifying future treatment targets for Alzheimer’s disease.
Researchers developed their mathematical model using a range of biological data — from ‘microscopic’ information using gene activity to ‘macroscopic’ information about the brain’s burden of toxic proteins (tau and amyloid), its neuronal function, cerebrovascular flow, metabolism and tissue structure from molecular PET and MRI scans.
“In both aging and disease research, most studies incorporate brain measurements at either micro or macroscopic scale, failing to detect the direct causal relationships between several biological factors at multiple spatial resolutions,” explains first author Quadri Adewale, a PhD candidate at the Department of Neurology and Neurosurgery, McGill University, Canada. “We wanted to combine whole-brain gene activity measurements with clinical scan data in a comprehensive and personalised model, which we then validated in healthy aging and Alzheimer’s disease.”
The study involved 460 people who had at least four different types of brain scan at four different time points as part of the Alzheimer’s Disease Neuroimaging Initiative cohort. Among the 460 participants, 151 were clinically identified as asymptomatic or healthy control (HC), 161 with early mild cognitive impairment (EMCI), 113 with late mild cognitive impairment (LMCI) and 35 with probable Alzheimer’s disease (AD).
Data from these multimodal scans was combined with data on gene activity from the Allen Human Brain Atlas, which provides detail on whole-brain gene expression for 20,267 genes. The brain was then split into 138 different gray matter regions for the purposes of combining the gene data with the structural and functional data from the scans.
The team then explored causal relationships between the spatial genetic patterns and information from their scans, and cross-referenced this to age-related changes in cognitive function. They found that the ability of the model to predict the extent of decline in cognitive function was highest for Alzheimer’s disease, followed in order by the less pronounced decline in cognition (LCMI, ECMI) and finally the healthy controls. This shows that the model can reproduce the individual multifactorial changes in the brain’s accumulation of toxic proteins, neuronal function and tissue structure seen over time in the clinical scans.
Next, the team used the model to look for genes that cause cognitive decline over time during the normal process of healthy aging, using a subset of healthy control participants who remained clinically stable for nearly eight years. Cognitive changes included memory and executive functions such as flexible thinking. They found eight genes which contributed to the imaging dynamics seen in the scans and corresponded with cognitive changes in healthy individuals. Of note, the genes that changed in healthy aging are also known to affect two important proteins in the development of Alzheimer’s disease, called tau and amyloid beta.
Next, they ran a similar analysis looking for genes that drive the progression of Alzheimer’s disease. Here, they identified 111 genes that were linked with the scan data and with associated cognitive changes in Alzheimer’s disease.
Finally, they studied the functions of the 111 genes identified, and found that they belonged to 65 different biological processes — with most of them commonly linked to neurodegeneration and cognitive decline.
“Our study provides unprecedented insight into the multiscale interactions among aging and Alzheimer’s disease-associated biological factors and the possible mechanistic roles of the identified genes,” concludes senior author Yasser Iturria-Medina, Assistant Professor at the Department of Neurology and Neurosurgery at McGill University. “We’ve shown that Alzheimer’s disease and healthy aging share complex biological mechanisms, even though Alzheimer’s disease is a separate entity with considerably more altered molecular and macroscopic pathways. This personalised model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying targets for future treatments for Alzheimer’s disease progression.”
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A branch of artificial intelligence (AI), called machine learning, can accurately predict the risk of an out of hospital cardiac arrest — when the heart suddenly stops beating — using a combination of timing and weather data, finds research published online in the journal Heart.
Machine learning is the study of computer algorithms, and based on the idea that systems can learn from data and identify patterns to inform decisions with minimal intervention.
The risk of a cardiac arrest was highest on Sundays, Mondays, public holidays and when temperatures dropped sharply within or between days, the findings show.
This information could be used as an early warning system for citizens, to lower their risk and improve their chances of survival, and to improve the preparedness of emergency medical services, suggest the researchers.
Out of hospital cardiac arrest is common around the world, but is generally associated with low rates of survival. Risk is affected by prevailing weather conditions.
But meteorological data are extensive and complex, and machine learning has the potential to pick up associations not identified by conventional one-dimensional statistical approaches, say the Japanese researchers. More

Archaeologists at Northern Arizona University are hoping a new technology they helped pioneer will change the way scientists study the broken pieces left behind by ancient societies.
The team from NAU’s Department of Anthropology have succeeded in teaching computers to perform a complex task many scientists who study ancient societies have long dreamt of: rapidly and consistently sorting thousands of pottery designs into multiple stylistic categories. By using a form of machine learning known as Convolutional Neural Networks (CNNs), the archaeologists created a computerized method that roughly emulates the thought processes of the human mind in analyzing visual information.
“Now, using digital photographs of pottery, computers can accomplish what used to involve hundreds of hours of tedious, painstaking and eye-straining work by archaeologists who physically sorted pieces of broken pottery into groups, in a fraction of the time and with greater consistency,” said Leszek Pawlowicz, adjunct faculty in the Department of Anthropology. He and anthropology professor Chris Downum began researching the feasibility of using a computer to accurately classify broken pieces of pottery, known as sherds, into known pottery types in 2016. Results of their research are reported in the June issue of the peer-reviewed publication Journal of Archaeological Science.
“On many of the thousands of archaeological sites scattered across the American Southwest, archaeologists will often find broken fragments of pottery known as sherds. Many of these sherds will have designs that can be sorted into previously-defined stylistic categories, called ‘types,’ that have been correlated with both the general time period they were manufactured and the locations where they were made” Downum said. “These provide archaeologists with critical information about the time a site was occupied, the cultural group with which it was associated and other groups with whom they interacted.”
The research relied on recent breakthroughs in the use of machine learning to classify images by type, specifically CNNs. CNNs are now a mainstay in computer image recognition, being used for everything from X-ray images for medical conditions and matching images in search engines to self-driving cars. Pawlowicz and Downum reasoned that if CNNs can be used to identify things like breeds of dogs and products a consumer might like, why not apply this approach to the analysis of ancient pottery?
Until now, the process of recognizing diagnostic design features on pottery has been difficult and time-consuming. It could involve months or years of training to master and correctly apply the design categories to tiny pieces of a broken pot. Worse, the process was prone to human error because expert archaeologists often disagree over which type is represented by a sherd, and might find it difficult to express their decision-making process in words. An anonymous peer reviewer of the article called this “the dirty secret in archaeology that no one talks about enough.”
Determined to create a more efficient process, Pawlowicz and Downum gathered thousands of pictures of pottery fragments with a specific set of identifying physical characteristics, known as Tusayan White Ware, common across much of northeast Arizona and nearby states. They then recruited four of the Southwest’s top pottery experts to identify the pottery design type for every sherd and create a ‘training set’ of sherds from which the machine can learn. Finally, they trained the machine to learn pottery types by focusing on the pottery specimens the archaeologists agreed on. More

The University of Kent’s School of Physical Sciences, in collaboration with the Science and Technology Facilities Council (STFC) and the Universities of Cardiff, Durham and Leeds, have developed an algorithm to train computers to analyse signals from subatomic particles embedded in advanced electronic materials.
The particles, called muons, are produced in large particle accelerators and are implanted inside samples of materials in order to investigate their magnetic properties. Muons are uniquely useful as they couple magnetically to individual atoms inside the material and then emit a signal detectable by researchers to obtain information on that magnetism.
This ability to examine magnetism on the atomic scale makes muon-based measurements one of the most powerful probes of magnetism in electronic materials, including “quantum materials” such as superconductors and other exotic forms of matter.
As it is not possible to deduce what is going on in the material by simple examination of the signal, researchers normally compare their data to generic models. In contrast, the present team adapted a data-science technique called Principal Component Analysis (PCA), frequently employed in Face Recognition.
The PCA technique involves a computer being fed many related but distinct images and then running an algorithm identifying a small number “archetypal” images that can be combined to reproduce, with great accuracy, any of the original images. An algorithm trained in this way can then go on to perform tasks such as recognising whether a new image matches a previously-seen one.
Researchers adapted the PCA technique to analyse the signals sent out by muons embedded in complex materials, training the algorithm for a variety of quantum materials using experimental data obtained at the ISIS Neutron and Muon source of the STFC Rutherford Appleton Laboratory.
The results showed the new technique is equally as proficient as the standard method at detecting phase transitions and in some cases could detect transitions beyond the capabilities of standard analyses.
Dr Jorge Quintanilla, Senior Lecturer in Condensed Matter Theory at Kent and leader of the Physics of Quantum Materials research group said: ‘Our research results are exceptional, as this was achieved by an algorithm that knew nothing about the physics of the materials being investigated. This suggests that the new approach might have very broad application and, as such, we have made our algorithms available for use by the worldwide research community.’
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Materials provided by University of Kent. Original written by Sam Wood. Note: Content may be edited for style and length. More