Computers Math

Scientists from Queen’s University Belfast have developed a computer-aided data tool that could improve treatment for a range of illnesses.
The computer modelling tool will predict novel sites of binding for potential drugs that are more selective, leading to more effective drug targeting, increasing therapeutic efficacy and reducing side effects.
The data tool or protocol will uncover a novel class of compounds — allosteric drugs in G protein-coupled receptors (GPCRs).
GPCRs are the largest membrane protein family that transduce a signal inside cells from hormones, neurotransmitters, and other endogenous molecules. As a result of their broad influence on human physiology, GPCRs are drug targets in many therapeutic areas such as inflammation, infertility, metabolic and neurological disorders, viral infections and cancer. Currently over a third of drugs act via GPCRs. Despite the substantial therapeutic success, the discovery of GPCR drugs is challenging due to promiscuous binding and subsequent side effects.
Recent studies point to the existence of other binding sites, called allosteric sites that drugs can bind to and provide several therapeutic benefits. However, the discovery of allosteric sites and drugs has been mostly serendipitous. Recent X-ray crystallography, that determines the atomic and molecular structure, and cryo-electron microscopy that offers 3D models of several GPCRs offer opportunities to develop computer-aided methodologies to search for allosteric sites.
The researchers developed a computer-aided protocol to map allosteric sites in GPCRs with a view to start rational search of allosteric drugs, presenting the opportunity for new solutions and therapies for a range of diseases.
Dr Irina Tikhonova from the School of Pharmacy at Queen’s University and senior author, explains: “We have developed a novel, cost-effective and rapid pipeline for the discovery of GPCRs allosteric sites, which overcomes the limitations of current computational protocols such as membrane distortion and non-specific binding.
“Our pipeline can identify allosteric sites in a short time, which makes it suitable for industry settings. As such, our pipeline is a feasible solution to initiate structure-based search of allosteric drugs for any membrane-bound drug targets that have an impact on cancer, inflammation, and CNS diseases.”
This research published in ACS Central Science is a collaboration with Queen’s University Belfast and Queen Mary University of London. It is supported by the European Union ‘s Horizon 2020 research and innovation programme under the Marie-Sklodowska-Curie grants agreement and Biotechnology and Biological Science Research Council.
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When most people think of wearable devices, they think of smart watches, smart glasses, fitness trackers, even smart clothing. These devices, part of a fast-growing market, have two things in common: They all need an external power source, and they all require exacting manufacturing processes. Until now.
Yanliang Zhang, associate professor of aerospace and mechanical engineering at the University of Notre Dame, and doctoral student Yipu Du have created an innovative hybrid printing method — combining multi-material aerosol jet printing and extrusion printing — that integrates both functional and structural materials into a single streamlined printing platform. Their work was recently published in Nano Energy.
Zhang and Du, in collaboration with a team at Purdue University led by professor Wenzhuo Wu, also have developed an all-printed piezoelectric (self-powered) wearable device.
Using their new hybrid printing process, the team demonstrated stretchable piezoelectric sensors, conformable to human skin, with integrated tellurium nanowire piezoelectric materials, silver nanowire electrodes and silicone films. The devices printed by the team were then attached to a human wrist, accurately detecting hand gestures, and to an individual’s neck, detecting the individual’s heartbeat. Neither device used an external power source.
Piezoelectric materials are some of the most promising materials in the manufacture of wearable electronics and sensors because they generate their own electrical charge from applied mechanical stress instead of from a power source.
Yet printing piezoelectric devices is challenging because it often requires high electric fields for poling and high sintering temperatures. This adds to the time and cost of the printing process and can be detrimental to surrounding materials during sensor integration.
“The biggest advantage of our new hybrid printing method is the ability to integrate a wide range of functional and structural materials in one platform,” said Zhang.
“This streamlines the processes, reducing the time and energy needed to fabricate a device, while ensuring the performance of printed devices.”
Vital to the design, said Zhang, are nanostructured materials with piezoelectric properties, which eliminate the need for poling or sintering, and the highly stretchable silver nanowire electrodes, which are important for wearable devices attached to bodies in motion.
“We’re excited to see the wide range of opportunities that will open up for printed electronics and wearable devices because of this very versatile printing process,” said Zhang.
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Materials provided by University of Notre Dame. Original written by Nina Welding. Note: Content may be edited for style and length. More

In physics, “universality” refers to properties of systems that are independent of their details. Establishing the universality of quantum dynamics is one of the key interests of theoretical physicists. Now, researchers from Japan have identified such a universality in disordered quantum systems, characterized by a one-parameter scaling for surface roughness and entanglement entropy (a measure of quantum entanglement).
Many-particle systems in the real world are often imbued with “disorder” or “randomness.” This, in turn, leads to the occurrence of phenomena unique to such systems. For instance, electrons in strongly disordered systems can become localized due to destructive interference, a phenomenon known as “Anderson localization.”
Anderson localization has been studied extensively in terms of one-parameter scaling, where system properties are scaled based on one specific parameter. But while most studies have focused on static properties, disorder can also significantly influence quantum dynamics such as entanglement dynamics and transport phenomena.
In a recent study published in Physical Review Letters, a team of physicists led by Prof. Kazuya Fujimoto from Nagoya University has now demonstrated numerically a dynamical one-parameter scaling called “Family-Vicsek (FV) scaling” for disordered quantum systems.
“While the FV scaling is originally known from classical surface growth, we found the scaling in random quantum systems by introducing a ‘quantum surface height operator’,” explains Prof. Fujimoto.
In their study, the physicists considered a system of non-interacting spinless fermions in a disordered one-dimensional potential for three common models. They found that the surface roughness followed FV scaling characterized with three exponents. Further numerical analysis showed that the surface roughness could be related to the entanglement entropy (EE), thus indicating an FV-type scaling for EE. In addition, they observed anomalous scaling exponents for one of the models and attributed it to the presence of localized states in a delocalized phase, a classic signature of quantum disordered systems.
Importantly, surface roughness can be measured experimentally for cold-atomic systems using microscopy techniques, which makes the experimental estimation of EE viable in non-interacting fermions.
“These findings will deepen our understanding of nonequilibrium physics and provide a novel viewpoint to classify the universal non-equilibrium phenomena emerging in random quantum systems,” says Prof. Fujimoto.
While the findings of the study do not have a direct influence on our daily lives, they certainly pave the way for a better understanding of real-world quantum systems.
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The next time you buy a new couch, you may not ever have to leave your old one to get a feel for the texture of the new material.
Dr. Cynthia Hipwell, Oscar S. Wyatt Jr. ’45 Chair II Professor in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, is leading a team working to better define how the finger interacts with a device with the hope of aiding in the further development of technology that goes beyond sensing and reacting to your touch.
The team’s research was recently published and featured on the cover of the journal Advanced Materials.
The ultimate goal of furthering this human-machine interface is to give touch devices the ability to provide users with a richer touch-based experience by equipping the technology with the ability to mimic the feeling of physical objects. Hipwell shared examples of potential implementations ranging from a more immersive virtual reality platform to tactile display interfaces like those in a motor vehicle dashboard and a virtual shopping experience that would let the user feel the texture of materials before purchasing them.
“This could allow you to actually feel textures, buttons, slides and knobs on the screen,” Hipwell said. “It can be used for interactive touch screen-based displays, but one holy grail would certainly be being able to bring touch into shopping so that you could feel the texture of fabrics and other products while you’re shopping online.”
Hipwell explained that at its essence, the “touch” in current touch screen technology is more for the screen’s benefit than the user. With the emergence and refinement of increasingly sophisticated haptic technology, that relationship between user and device can grow to be more reciprocal.
She added that the addition of touch as a sensory input would ultimately enrich virtual environments and lighten the burden of communication currently carried by audio and visuals.
“When we look at virtual experiences, they’re primarily audio and visual right now and we can get audio and visual overload,” Hipwell said. “Being able to bring touch into the human-machine interface can bring a lot more capability, much more realism, and it can reduce that overload. Haptic effects can be used to draw your attention to make something easier to find or easier to do using a lower cognitive load.”
Hipwell and her team are approaching the research by looking at the multiphysics — the coupled processes or systems involving multiple physical fields occurring at the same time — of the interface between the user’s finger and the device. This interface is incredibly complex and changes with different users and environmental conditions.
“We’re looking at electro-wetting effects (the forces that result from an applied electric field), electrostatic effects, changes in properties of the finger, the material properties and surface geometry of the device, the contact mechanics, the fluid motion, charge transport — really, everything that’s going on in the interface to understand how the device can be designed to be more reliable and higher performing,” Hipwell said. “Ultimately, our goal is to create predictive models than enable a designer to create devices with maximum haptic effect and minimum sensitivity to user and environmental variation.”
As research into and development of the technology continues to progress, Hipwell said she predicts consumers will begin to see early elements implemented into common devices over the next few years, with some early products already in development.
“I think early elements of it will definitely be within the next five years,” Hipwell said. “Then, it will just be a matter of maturing the technology and how advanced, how realistic and how widespread it becomes.”
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Materials provided by Texas A&M University. Original written by Steve Kuhlmann. Note: Content may be edited for style and length. More

Accurate forecasting of epidemic scenarios is critical to implementing effective public health intervention policies. While much progress has been made in predicting the general magnitude and timing of epidemics, there’s still room for improvement in forecasting peak times, as unfortunately evidenced with H1N1 and COVID-19, when peak times occurred later than predicted.
In Chaos, by AIP Publishing, researchers from France and Italy use dynamical stochastic modeling techniques to reveal that infection and recovery rate fluctuations play a critical role in determining peak times for epidemics.
“Some averaged quantities, like infection and recovery rates, are highly sensitive to parameter fluctuations, which means that the latter must be understood, even when average behavior is the only focus of interest,” said co-author Maxence Arutkin. “Our work shows that epidemic peak timing depends on these fluctuations, and neglecting them in epidemiological models can lead to inaccurate epidemic scenarios and unsuitable mitigation policies, not to mention enable viruses to evolve into new variants.”
Using a susceptible-infected-recovered epidemic model that incorporates daily fluctuations on control parameters, the study applies probability theory calculations to infection counts at the beginning of an epidemic wave and at peak times for populations in Italy. While previous works using standard epidemiological models have suggested there is a delay between the epidemic peak date and its prediction (without fluctuations), the researchers suggest the epidemic peak time depends not only on the mean value of the infection and recovery rates but also on their fluctuations.
To predict epidemic trajectory, an important parameter is the basic reproduction number, R0, which describes the average number of infections transmitted from an individual. Infection and recovery rate fluctuations lead to lognormal probability distribution of the number of infected people, similar in its analytical form to price distributions for financial assets.
“In the short term, even when average infections transmitted from a single individual are less than one, we can observe epidemic resurgence due to parameter fluctuations,” said Arutkin. “Also, a dispersion of the epidemic peak time can be quantified showing that, without taking these fluctuations into account, the peak time estimates are biased.”
The study reveals that improved prediction depends on both R0 levels and fluctuations in infection and recovery rates and may provide policymakers with a tool to assess the consequences of parameter fluctuations based on different R0 levels.
“Our findings suggest we must introduce parameter fluctuations in epidemiological models going forward,” said Arutkin.
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Can intelligence be taught to robots? Advances in physical reservoir computing, a technology that makes sense of brain signals, could contribute to creating artificial intelligence machines that think like us.
In Applied Physics Letters, from AIP Publishing, researchers from the University of Tokyo outline how a robot could be taught to navigate through a maze by electrically stimulating a culture of brain nerve cells connected to the machine.
These nerve cells, or neurons, were grown from living cells and acted as the physical reservoir for the computer to construct coherent signals.
The signals are regarded as homeostatic signals, telling the robot the internal environment was being maintained within a certain range and acting as a baseline as it moved freely through the maze.
Whenever the robot veered in the wrong direction or faced the wrong way, the neurons in the cell culture were disturbed by an electric impulse. Throughout trials, the robot was continually fed the homeostatic signals interrupted by the disturbance signals until it had successfully solved the maze task.
These findings suggest goal-directed behavior can be generated without any additional learning by sending disturbance signals to an embodied system. The robot could not see the environment or obtain other sensory information, so it was entirely dependent on the electrical trial-and-error impulses.
“I, myself, was inspired by our experiments to hypothesize that intelligence in a living system emerges from a mechanism extracting a coherent output from a disorganized state, or a chaotic state,” said co-author Hirokazu Takahashi, an associate professor of mechano-informatics.
Using this principle, the researchers show intelligent task-solving abilities can be produced using physical reservoir computers to extract chaotic neuronal signals and deliver homeostatic or disturbance signals. In doing so, the computer creates a reservoir that understands how to solve the task.
“A brain of [an] elementary school kid is unable to solve mathematical problems in a college admission exam, possibly because the dynamics of the brain or their ‘physical reservoir computer’ is not rich enough,” said Takahashi. “Task-solving ability is determined by how rich a repertoire of spatiotemporal patterns the network can generate.”
The team believes using physical reservoir computing in this context will contribute to a better understanding of the brain’s mechanisms and may lead to the novel development of a neuromorphic computer.
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Climate change and its consequences are becoming increasingly obvious, and solar cells that convert the sun’s energy into electricity will play a key role in the world’s future energy supply.
Common semiconductor materials for solar cells, such as silicon, must be grown via an expensive process to avoid defects within their crystal structure that affect functionality. But metal-halide perovskite semiconductors are emerging as a cheaper, alternative material class, with excellent and tunable functionality as well as easy processability.
In APL Materials, from AIP Publishing, researchers present a road map for organic-inorganic hybrid perovskite semiconductors and devices.
Perovskite semiconductors can be processed from solution, and a semiconductor ink can be coated or simply painted over surfaces to form the desired film. This can be incorporated into semiconductor devices, such as solar cells or light-emitting diodes.
“For many years, solution-processed semiconductors were viewed as unable to deliver the same functionality as specially grown crystalline semiconductors,” said Lukas Schmidt-Mende, a co-author from the University of Konstanz in Germany. “The reason behind this thinking was that simple solution processing will inherently lead to a relative high number of defects within the formed crystal structure, which can negatively affect its functionality.”
It turns out organic-inorganic hybrid perovskites are very defect-tolerant. Defects formed after processing do not dramatically influence device functionality, and for the first time, hybrid perovskites are enabling efficient solution-processed devices. More

A team of Brown University researchers has shown a new method of probing the properties of anyons, strange quasiparticles that could be useful in future quantum computers.
In research published in the journal Physical Review Letters, the team describes a means of probing anyons by measuring subtle properties of the way in which they conduct heat. Whereas other methods probe these particles using electrical charge, this new method enables researchers to probe anyons even in non-conducting materials. That’s critical, the researchers say, because non-conducting systems have far less stringent temperature requirements, making them a more practical option for quantum computing.
“We have beautiful ways of probing anyons using charge, but the question has been how do you detect them in the insulating systems that would be useful in what’s known as topological quantum computing,” said Dima Feldman, a physics professor at Brown and study co-author. “We show that it can be done using heat conductance. Essentially, this is a universal test for anyons that works in any state of matter.”
Anyons are of interest because they don’t follow the same rules as particles in the everyday, three-dimensional world. In three dimensions, there are only two broad kinds of particles: bosons and fermions. Bosons follow what’s known as Bose-Einstein statistics, while fermions follow Fermi-Dirac statistics. Generally speaking, those different sets of statistical rules mean that if one boson orbits around another in a quantum system, the particle’s wave function — the equation that fully describes its quantum state — does not change. On the other hand, if a fermion orbits around another fermion, the phase value of its wave function flips from a positive integer to a negative integer. If it orbits again, the wave function returns to its original state.
Anyons, which emerge only in systems that are confined to two dimensions, don’t follow either rule. When one anyon orbits another, its wave function changes by some fraction of an integer. And another orbit does not necessarily restore the original value of the wave function. Instead, it has a new value — almost as if the particle maintains a “memory” of its interactions with the other particle even though it ended up back where it started.
That memory of past interactions can be used to encode information in a robust way, which is why the particles are interesting tools for quantum computing. Quantum computers promise to perform certain types of calculations that are virtually impossible for today’s computers. A quantum computer using anyons — known as a topological quantum computer — has the potential to operate without elaborate error correction, which is a major stumbling block in the quest for usable quantum computers.
But using anyons for computing requires first being able to identify these particles by probing their quantum statistics. Last year, researchers did that for the first time using a technique known as charge interferometry. Essentially, anyons are spun around each other, causing their wave functions to interfere with each other occasionally. The pattern of interference reveals the particles’ quantum statistics. That technique of probing anyons using charge works beautifully in systems that conduct electricity, the researchers say, but it can’t be used to probe anyons in non-conducting systems. And non-conducting systems have the potential to be useful at higher temperatures than conducting systems, which need to be near absolute zero. That makes them a more practical option of topological quantum computing.
For this new research, Feldman, who in 2017 was part of a team that measured the heat conductance of anyons for the first time, collaborated with Brown graduate student Zezhu Wei and Vesna Mitrovic, a Brown physics professor and experimentalist. Wei, Feldman and Mitrovic showed that comparing properties of heat conductance in two-dimensional solids etched in very specific geometries could reveal the statistics of the anyons in those systems.
“Any difference in the heat conductance in the two geometries would be smoking gun evidence of fractional statistics,” Mitrovic said. “What this study does is show exactly how people should set up experiments in their labs to test for these strange statistics.”
Ultimately, the researchers hope the study is a step toward understanding whether the strange behavior of anyons can indeed be harnessed for topological quantum computing.
The research was supported by the National Science Foundation (DMR-1902356, QLCI-1936854, DMR-1905532).
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