Computers Math

Facial recognition software can be used to spot a face in a crowd; but what if it could also predict where someone else was in the same crowd? While this may sound like science fiction, researchers from Japan have now shown that artificial intelligence can accomplish something very similar on a cellular level.
In a study published in Frontiers in Cell and Developmental Biology, researchers from Nara Institute of Science and Technology (NAIST) have revealed that a machine learning program can accurately predict the location of proteins related to actin, an important part of the cellular skeleton, based on the location of actin itself.
Actin plays a key role in providing shape and structure to cells, and during cell movement helps form lamellipodia, which are fan-shaped structures that cells use to “walk” forwards. Lamellipodia also contain a host of other proteins that bind to actin to help maintain the fan-like structure and keep the cells moving.
“While artificial intelligence has been used previously to predict the direction of cell migration based on a sequence of images, so far it has not been used to predict protein localization,” says lead author of the study, Shiro Suetsugu. This idea came in during the discussion with Yoshinobu Sato at the Data Science Center in NAIST. “We therefore sought to design a machine learning algorithm that can determine where proteins will appear in the cell based on their relationship with other proteins.”
To do this, the researchers trained an artificial intelligence system to predict where actin-associated proteins would be in the cell by showing it pictures of cells in which the proteins were labeled with fluorescent markers to show where they were located. Then, they gave the program pictures in which only actin was labeled and asked it to tell them where the associated proteins were.
“When we compared the predicted images to the actual images, there was a considerable degree of similarity,” states Suetsugu. “Our program accurately predicted the localization of three actin-associated proteins within lamellipodia; and, in the case of one of these proteins, in other structures within the cell.”
On the other hand, when the researchers asked the program to predict where tubulin, which is not directly related to actin, would be in the cell, the program did not perform nearly as well.
“Our findings suggest that machine learning can be used to accurately predict the location of functionally related proteins and describe the physical relationships between them,” says Suetsugu.
Given that lamellipodia are not always easy for non-experts to spot, the program developed in this study could be used to quickly and accurately identify these structures from cell images in the future. In addition, this approach could potentially be used as a sort of artificial cell staining method to avoid the limitations of current cell-staining methods.
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A new Monash review throws the spotlight on recent research in heterostructures of topological insulators and magnetic materials.
In such heterostructures, the interesting interplay of magnetism and topology can give rise to new phenomena such as quantum anomalous Hall insulators, axion insulators and skyrmions. All of these are promising building blocks for future low-power electronics.
Provided suitable candidate materials are found, there is a possibility to realise these exotic states at room temperature and without any magnetic field, hence aiding FLEET’s search for future low-energy, beyond-CMOS electronics.
“Our aim was to investigate promising new methods of achieving the quantum Hall effect,” says the new study’s lead author, Dr Semonti Bhattacharyya at Monash University.
The quantum Hall effect (QHE) is a topological phenomenon that allows high-speed electrons to flow at a material’s edge, which is potentially useful for future low- energy electronics and spintronics.
“However, a severe bottleneck for this technology being useful is the fact that quantum Hall effect always requires high magnetic fields, which are not possible without either high energy use or cryogenic cooling.”
“There’s no point in developing ‘low energy’ electronics that consume more energy to make them work!” says Dr Bhattacharyya, who is a Research Fellow at FLEET, seeking new generation of low-energy electronics. More

Since receiving a $25 million grant in 2019 to become the first National Science Foundation (NSF) Quantum Foundry, UC Santa Barbara researchers affiliated with the foundry have been working to develop materials that can enable quantum information-based technologies for such applications as quantum computing, communications, sensing, and simulation.
They may have done it.
In a new paper, published in the journal Nature Materials, foundry co-director and UCSB materials professor Stephen Wilson, and multiple co-authors, including key collaborators at Princeton University, study a new material developed in the Quantum Foundry as a candidate superconductor — a material in which electrical resistance disappears and magnetic fields are expelled — that could be useful in future quantum computation.
A previous paper published by Wilson’s group in the journal Physical Review Letters and featured in Physics magazine described a new material, cesium vanadium antimonide (CsV3Sb5), that exhibits a surprising mixture of characteristics involving a self-organized patterning of charge intertwined with a superconducting state. The discovery was made by Elings Postdoctoral Fellow Brenden R. Ortiz. As it turns out, Wilson said, those characteristics are shared by a number of related materials, including RbV3Sb5 and KV3Sb5, the latter (a mixture of potassium, vanadium and antimony) being the subject of this most recent paper, titled “Discovery of unconventional chiral charge order in kagome superconductor KV3Sb5.”
Materials in this group of compounds, Wilson noted, “are predicted to host interesting charge density wave physics [that is, their electrons self-organize into a non-uniform pattern across the metal sites in the compound]. The peculiar nature of this self-organized patterning of electrons is the focus of the current work.”
This predicted charge density wave state and other exotic physics stem from the network of vanadium (V) ions inside these materials, which form a corner-sharing network of triangles known as a kagome lattice. KV3Sb5 was discovered to be a rare metal built from kagome lattice planes, one that also superconducts. Some of the material’s other characteristics led researchers to speculate that charges in it may form tiny loops of current that create local magnetic fields. More

A new mathematical model for predicting infectious disease outbreaks incorporates fear — both of disease and of vaccines — to better understand how pandemics can occur in multiple waves of infections, like those we are seeing with COVID-19. The “Triple Contagion” model of disease and fears, developed by researchers at NYU School of Global Public Health, is published in the Journal of The Royal Society Interface.
Human behaviors like social distancing (which suppresses spread) and vaccine refusal (which promotes it) have shaped the dynamics of epidemics for centuries. Yet, traditional epidemic models have overwhelmingly ignored human behavior and the fears that drive it.
“Emotions like fear can override rational behavior and prompt unconstructive behavioral change,” said Joshua Epstein, professor of epidemiology at NYU School of Global Public Health, founding director of the NYU Agent-Based Modeling Laboratory, and the study’s lead author. “Fear of a contagious disease can shift how susceptible individuals behave; they may take action to protect themselves, but abandon those actions prematurely as fear decays.”
For instance, the fear of catching a virus like SARS-CoV-2 can cause healthy people to self-isolate at home or wear masks, suppressing spread. But, because spread is reduced, the fear can evaporate — leading people to stop isolating or wearing masks too early, when there are still many infected people circulating. This pours fuel — in the form of susceptible people — onto the embers, and a new wave explodes.
Likewise, fear of COVID-19 has motivated millions of people to get vaccinated. But as vaccines suppress spread and with it the fear of disease, people may fear the vaccine more than they do the infection and forego vaccination, again producing disease resurgence.
For the first time, the “Triple Contagion” model couples these psychological dynamics to the disease dynamics, uncovering new behavioral mechanisms for pandemic persistence and successive waves of infection. More

The human brain holds the secret to our unique personalities. But did you know that it can also form the basis of highly efficient computing devices? Researchers from Nagoya University, Japan, recently showed how to do this, through graphene-diamond junctions that mimic some of the human brain’s functions.
But, why would scientists try to emulate the human brain? Today, existing computer architectures are subjected to complex data, limiting their processing speed. The human brain, on the other hand, can process highly complex data, such as images, with high efficiency. Scientists have, therefore, tried to build “neuromorphic” architectures that mimic the neural network in the brain.
A phenomenon essential for memory and learning is “synaptic plasticity,” the ability of synapses (neuronal links) to adapt in response to an increased or decreased activity. Scientists have tried to recreate a similar effect using transistors and “memristors” (electronic memory devices whose resistance can be stored). Recently developed light-controlled memristors, or “photomemristors,” can both detect light and provide non-volatile memory, similar to human visual perception and memory. These excellent properties have opened the door to a whole new world of materials that can act as artificial optoelectronic synapses!
This motivated the research team from Nagoya University to design graphene-diamond junctions that can mimic the characteristics of biological synapses and key memory functions, opening doors for next-generation image sensing memory devices. In their recent study published in Carbon, the researchers, led by Dr. Kenji Ueda, demonstrated optoelectronically controlled synaptic functions using junctions between vertically aligned graphene (VG) and diamond. The fabricated junctions mimic biological synaptic functions, such as the production of “excitatory postsynaptic current” (EPSC) — the charge induced by neurotransmitters at the synaptic membrane — when stimulated with optical pulses and exhibit other basic brain functions such as the transition from short-term memory (STM) to long-term memory (LTM).
Dr. Ueda explains, “Our brains are well-equipped to sieve through the information available and store what’s important. We tried something similar with our VG-diamond arrays, which emulate the human brain when exposed to optical stimuli.” He adds, “This study was triggered due to a discovery in 2016, when we found a large optically induced conductivity change in graphene-diamond junctions.” Apart from EPSC, STM, and LTM, the junctions also show a paired pulse facilitation of 300% — an increase in postsynaptic current when closely preceded by a prior synapse.
The VG-diamond arrays underwent redox reactions induced by fluorescent light and blue LEDs under a bias voltage. The researchers attributed this to the presence of differently hybridized carbons of graphene and diamond at the junction interface, which led to the migration of ions in response to the light and in turn allowed the junctions to perform photo-sensing and photo-controllable functions similar to those performed by the brain and retina. In addition, the VG-diamond arrays surpassed the performance of conventional rare-metal-based photosensitive materials in terms of photosensitivity and structural simplicity.
Dr. Ueda says, “Our study provides a better understanding of the working mechanism behind the artificial optoelectronic synaptic behaviors, paving the way for optically controllable brain-mimicking computers better information-processing capabilities than existing computers.” The future of next-generation computing may not be too far away!
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Small electronics, including smartwatches and fitness trackers, aren’t easily dismantled and recycled. So when a new model comes out, most users send the old devices into hazardous waste streams. To simplify small electronics recycling, researchers reporting in ACS Applied Materials & Interfaces have developed a two-metal nanocomposite for circuits that disintegrates when submerged in water. They demonstrated the circuits in a prototype transient device — a functional smartwatch that dissolved within 40 hours.
Planned obsolescence and the fast pace of technology innovations leads to new devices that are continuously replacing old versions, which generates millions of tons of electronic waste per year. Recycling can reduce the volume of e-waste and is mandatory in many places. However, it often isn’t worth the effort to recycle small consumer electronics because their parts must be salvaged by hand, and some processing steps, such as open burning and acid leaching, can cause health issues and environmental pollution. Dissolvable devices that break apart on demand could solve both of those problems. Previously Xian Huang and colleagues developed a zinc-based nanocomposite that dissolved in water for use in temporary circuits, but it wasn’t conductive enough for consumer electronics. So, they wanted to improve their dissolvable nanocomposite’s electrical properties while also creating circuits robust enough to withstand everyday use.
The researchers modified the zinc-based nanocomposite by adding silver nanowires, making it highly conductive. Then, they screen-printed the metallic solution onto pieces of poly(vinyl alcohol) — a polymer that degrades in water — and solidified the circuits by applying small droplets of water that facilitate chemical reactions and then evaporate. With this approach, the team made a smartwatch with multiple nanocomposite-printed circuit boards inside a 3D printed poly(vinyl alcohol) case. The smartwatch had sensors that accurately measured a person’s heart rate, blood oxygen levels and step count, and sent the information to a cellphone app via a Bluetooth connection. The outer package held up to sweat, but once the whole device was fully immersed in water, both the polymer case and circuits dissolved completely within 40 hours. All that was left behind were the watch’s components, such as an organic light-emitting diode (OLED) screen and microcontroller, as well as resistors and capacitors that had been integrated into the circuits. The researchers say the two-metal nanocomposite can be used to produce transient devices with performance matching that of commercial models, which could go a long way toward solving the challenges of small electronics waste.
The authors do not acknowledge a funding source for this study.
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A UNSW mathematician has revealed the origins of applied geometry on a 3700-year-old clay tablet that has been hiding in plain sight in a museum in Istanbul for over a century.
The tablet — known as Si.427 — was discovered in the late 19th century in what is now central Iraq, but its significance was unknown until the UNSW scientist’s detective work was revealed today.
Most excitingly, Si.427 is thought to be the oldest known example of applied geometry — and in the study released today in Foundations of Science, the research also reveals a compelling human story of land surveying.
“Si.427 dates from the Old Babylonian (OB) period — 1900 to 1600 BCE,” says lead researcher Dr Daniel Mansfield from UNSW Science’s School of Mathematics and Statistics.
“It’s the only known example of a cadastral document from the OB period, which is a plan used by surveyors define land boundaries. In this case, it tells us legal and geometric details about a field that’s split after some of it was sold off.”
This is a significant object because the surveyor uses what are now known as “Pythagorean triples” to make accurate right angles. More

A single molecule contains a wealth of information. It includes not only the number of each kind of constituent atom, but also how they’re arranged and how they attach to each other. And during chemical reactions, that information determines the outcome and becomes transformed. Molecules collide, break apart, reassemble, and rebuild in predictable ways.
There’s another way of looking at a chemical reaction, says Santa Fe Institute External Professor Juan-Pérez Mercader, who is a physicist and astrobiologist based at Harvard University. It’s a kind of computation. A computing device is one that takes information as its input, then mechanically transforms that information and produces some output with a functional purpose. The input and output can be almost anything: Numbers, letters, objects, images, symbols, or something else.
Or, says Pérez-Mercader, molecules. When molecules react, they’re following the same steps that describe computation: Input, transformation, output. “It’s a computation that controls when certain events take place,” says Pérez-Mercader, “but at the nanometer scale, or shorter.”
Molecules may be small, but their potential as tools of computation is enormous. “This is a very powerful computing tool that needs to be harnessed,” he says, noting that a single mole of a substance has 10^23 elementary chemical processors capable of computation. For the last few years, Pérez-Mercader has been developing a new field he calls “native chemical computation.” It’s a multifaceted quest: He wants to not only exploit chemical computing but also find challenges for which it’s best-suited.
“If we have such a huge power, what kinds of problems can we tackle?” he asks. They’re not the same as those that might be better solved with a supercomputer, he says. “So what are they good for?”
He has some ideas. Chemical reactions, he says, are very good at building things. So in 2017, his group “programmed” chemical reactions to use a bunch of molecules to assemble a container. The experiment demonstrated that these molecules, in a sense, could recognize information — and transform it in a specific way, analogous to computation.
Pérez-Mercader and his chief collaborator on the project, chemical engineer Marta Dueñas-Díez at Harvard and the Repsol Technology Lab in Madrid, recently published a review of their progress on chemical computation. In it, they describe how chemical reactions can be used, in a lab, to build a wide range of familiar computing systems, from simple logic gates to Turing Machines. Their findings, says Pérez-Mercader, suggest that if chemical reactions can be “programmed” like other types of computing machines, they might be exploited for applications in many areas, including intelligent drug delivery, neural networks, or even artificial cells.
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