Aurelia Butler

Theoretical scientists have used topological mathematics and machine learning to identify a hidden relationship between nano-scale structures and thermal conductivity in amorphous silicon, a glassy form of the material with no repeating crystalline order.
A study describing their technique appeared in the Journal of Chemical Physics on 23 June.
Amorphous solids, such as glass, obsidian, wax, and plastics, have no long-range repeating, or crystalline structure, to the atoms or molecules that they are made out of. This contrasts with crystalline solids, such as salt, most metals and rocks. As they lack long-range order in their structure, the thermal conductivity of amorphous solids can be far lower than a crystalline solid composed of the same material.
However, there can still be some medium-range order on the scale of nanometers. This medium-range order should affect the propagation and diffusion of atomic vibrations, which carry heat. The heat transport in disordered materials is of special interest to physicists due to its importance in industrial applications. The amorphous form of silicon is used in an enormous range of applications in the modern world, from solar cells to image sensors. For this reason, researchers have intensively investigated the structural signature of the medium-range order in amorphous silicon and how it relates to thermal conductivity.
“For better control over applications that make use of amorphous silicon, controlling its thermal properties is high on engineers’ wish list,” said Emi Minamitani, the corresponding author of the study and a theoretical molecular scientist with the Institute for Molecular Science in Okazaki, Japan. “Extracting the nano-scale structural characteristics in amorphous including medium-range order is an important key.”
Unfortunately, researchers have struggled to carry out this task because it is difficult to determine the essential nano-scale features of disordered systems using traditional techniques. More

Communication networks need nodes at which information is processed or rerouted. Physicists at the University of Basel have now developed a network node for quantum communication networks that can store single photons in a vapor cell and pass them on later.
In quantum communication networks, information is transmitted by single particles of light (photons). At the nodes of such a network buffer elements are needed which can temporarily store, and later re-emit, the quantum information contained in the photons.
Researchers at the University of Basel in the group of Prof. Philipp Treutlein have now developed a quantum memory that is based on an atomic gas inside a glass cell. The atoms do not have to be specially cooled, which makes the memory easy to produce and versatile, even for satellite applications. Moreover, the researchers have realized a single photon source which allowed them to test the quality and storage time of the quantum memory. Their results were recently published in the scientific journal PRX Quantum.
Warm atoms in vapor cells
“The suitability of warm atoms in vapor cells for quantum memories has been investigated for the past twenty years,” says Gianni Buser, who worked on the experiment as a PhD student. “Usually, however, attenuated laser beams — and hence classical light — were used.” In classical light, the number of photons hitting the vapor cell in a certain period follows a statistical distribution; on average it is one photon, but sometimes it can be two, three or none.
To test the quantum memory with “quantum light” — that is, always precisely one photon — Treutlein and his co-workers developed a dedicated single photon source that emits exactly one photon at a time. The instant when that happens is heralded by a second photon, which is always sent out simultaneously with the first one. This allows the quantum memory to be activated at the right moment.
The single photon is then directed into the quantum memory where, with the help of a control laser beam, the photon causes more than a billion rubidium atoms to take on a so-called superposition state of two possible energy levels of the atoms. The photon itself vanishes in the process, but the information contained in it is transformed into the superposition state of the atoms. A brief pulse of the control laser can then read out that information after a certain storage time and transform it back into a photon.
Reducing read-out noise
“Up to now, a critical point has been noise — additional light that is produced during the read-out and that can compromise the quality of the photon,” explains Roberto Mottola, another PhD student in Treutlein’s lab. Using a few tricks, the physicists were able to reduce that noise sufficiently so that after storage times of several hundred nanoseconds the single photon quality was still high.
“Those storage times are not very long, and we didn’t actually optimize them for this study,” Treutlein says, “but already now they are more than a hundred times longer than the duration of the stored single photon pulse.” This means that the quantum memory developed by the Basel researchers can already be employed for interesting applications. For instance, it can synchronize randomly produced single photons, which can then be used in various quantum information applications.
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A new deep learning (DL) model that can identify disease-related features from images of eyes has been unveiled by a group of Tohoku University researchers. This ‘lightweight’ DL model can be trained with a small number of images, even ones with a high-degree of noise, and is resource-efficient, meaning it is deployable on mobile devices.
Details were published in the journal Scientific Reports on May 20, 2022.
With many societies aging and limited medical personnel, DL model reliant self-monitory and tele-screening of diseases are becoming more routine. Yet, deep learning algorithms are generally task specific, and identify or detect general objects such as humans, animals, or road signs.
Identifying diseases, on the other hand, demands precise measurement of tumors, tissue volume, or other sorts of abnormalities. To do so requires a model to look at separate images and mark boundaries in a process known as segmentation. But accurate prediction takes greater computational output, rendering them difficult to deploy on mobile devices.
“There is always a trade-off between accuracy, speed and computational resources when it comes to DL models,” says Toru Nakazawa, co-author of the study and professor at Tohoku University’s Department of Ophthalmology. “Our developed model has better segmentation accuracy and enhanced model training reproducibility, even with fewer parameters — making it efficient and more lightweight when compared to other commercial softwares.”
Professor Nakazawa, Associate Professor Parmanand Sharma, Dr Takahiro Ninomiya, and students from the Department of Ophthalmology worked with professor Takayuki Okatani from Tohoku University’s Graduate School of Information Sciences to produce the model.
Using low resource devices, they obtained measurements of the foveal avascular zone, a region with the fovea centralis at the center of the retina, to enhance screening for glaucoma.
“Our model is also capable of detecting/segmenting optic discs and hemorrhages in fundus images with high precision,” added Nakazawa.
In the future, the group is hopeful of deploying the lightweight model to screen for other common eye disorders and other diseases.
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Researchers created a special ultrathin sensor, spun from gold, that can be attached directly to the skin without irritation or discomfort. The sensor can measure different biomarkers or substances to perform on-body chemical analysis. It works using a technique called Raman spectroscopy, where laser light aimed at the sensor is changed slightly depending on whatever chemicals are present on the skin at that point. The sensor can be finely tuned to be extremely sensitive, and is robust enough for practical use.
Wearable technology is nothing new. Perhaps you or someone you know wears a smartwatch. Many of these can monitor certain health matters such as heart rate, but at present they cannot measure chemical signatures which could be useful for medical diagnosis. Smartwatches or more specialized medical monitors are also relatively bulky and often quite costly. Prompted by such shortfalls, a team comprising researchers from the Department of Chemistry at the University of Tokyo sought a new way to sense various health conditions and environmental matters in a noninvasive and cost-effective manner.
“A few years ago, I came across a fascinating method for producing robust stretchable electronic components from another research group at the University of Tokyo,” said Limei Liu, a visiting scholar at the time of the study and currently a lecturer at Yangzhou University in China. “These devices are spun from ultrafine threads coated with gold, so can be attached to the skin without issue as gold does not react with or irritate the skin in any way. As sensors, they were limited to detecting motion however, and we were looking for something that could sense chemical signatures, biomarkers and drugs. So we built upon this idea and created a noninvasive sensor that exceeded our expectations and inspired us to explore ways to improve its functionality even further.”
The main component of the sensor is the fine gold mesh, as gold is unreactive, meaning that when it comes into contact with a substance the team wishes to measure — for example a potential disease biomarker present in sweat — it does not chemically alter that substance. But instead, as the gold mesh is so fine, it can provide a surprisingly large surface for that biomarker to bind to, and this is where the other components of the sensor come in. As a low-power laser is pointed at the gold mesh, some of the laser light is absorbed and some is reflected. Of the light reflected, most has the same energy as the incoming light. However, some incoming light loses energy to the biomarker or other measurable substance, and the discrepancy in energy between reflected and incident light is unique to the substance in question. A sensor called a spectrometer can use this unique energy fingerprint to identify the substance. This method of chemical identification is known as Raman spectroscopy.
“Currently, our sensors need to be finely tuned to detect specific substances, and we wish to push both the sensitivity and specificity even further in future,” said Assistant Professor Tinghui Xiao. “With this, we think applications like glucose monitoring, ideal for sufferers of diabetes, or even virus detection, might be possible.”
“There is also potential for the sensor to work with other methods of chemical analysis besides Raman spectroscopy, such as electrochemical analysis, but all these ideas require a lot more investigation,” said Professor Keisuke Goda. “In any case, I hope this research can lead to a new generation of low-cost biosensors that can revolutionize health monitoring and reduce the financial burden of health care.”
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Researchers from the University of Tsukuba have developed a mathematical model of motor learning that reflects the motor learning process in the human brain. Their findings suggest that motor exploration — that is, increased variability in movements — is important when learning a new task. These results may lead to improved motor rehabilitation in patients after injury or disease.
Even seemingly simple movements are very complex to perform, and the way we learn how to perform new movements remains unclear. Researchers from Japan have recently proposed a new model of motor learning that combines a number of different theories. A study published this month in Neural Networks revealed that their model can simulate motor learning in humans surprisingly well, paving the way for a greater understanding of how our brains work.
For even a relatively simple task, such as to reach out and pick up an object, there are a huge number of potential combinations of angles between your body and the different joints that are involved. The same goes for each of your muscles — there is an almost endless combination of muscles and forces that can be used together to perform an action. With all of these possible combinations of joints and muscles — not to mention the underlying neuronal activity — how do we ever learn to make any movements at all? Researchers at the University of Tsukuba aimed to address this question.
The research team first created a mathematical model to imitate the learning process that occurs for new motor tasks. They designed the model to reflect many of the processes that are thought to occur in the brain when a new skill is learned. The researchers then tested their model by attempting to simulate the results of three recent studies that were conducted in humans, in which individuals were asked to perform completely new motor tasks.
“We were surprised at how well our simulations managed to reproduce many of the results of previous studies in humans,” says Professor Jun Izawa, senior author of the study. “With our model, we were able to bridge the gap between a number of different proposed mechanisms of motor learning, such as motor exploration, redundancy solving, and error-based learning.”
In their model, larger amounts of motor exploration — that is, variability in movements — were found to help with the learning of sensitivity derivatives, which measure how commands from the brain affect motor error. In this way, errors were transformed into motor corrections.
“Our success at simulating real results from human studies was encouraging,” explains first author Lucas Rebelo Dal’Bello. “It suggests that our proposed learning mechanism might accurately reflect what occurs in the brain during motor learning.”
The findings of this study, which indicate the importance of motor exploration in motor learning, provide insights into how motor learning might occur in the human brain. They also suggest that motor exploration should be encouraged when a new motor task is being learned; this may be helpful for motor rehabilitation after injury or disease.
This work was supported by KAKENHI (Scientific Research on Innovative Areas 19H04977 and 19H05729). LD was supported by a Japanese Government (Monbukagakusho: MEXT) Scholarship.
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Techniques used in weather forecasting can be repurposed to provide individuals with a personalized assessment of their risk of exposure to COVID-19 or other viruses, according to new research published by Caltech scientists.
The technique has the potential to be more effective and less intrusive than blanket lockdowns for combatting the spread of disease, says Tapio Schneider, the Theodore Y. Wu Professor of Environmental Science and Engineering; senior research scientist at JPL, which Caltech manages for NASA; and the lead author of a study on the new research that was published by PLOS Computational Biology on June 23.
“For this pandemic, it may be too late,” Schneider says, “but this is not going to be the last epidemic that we will face. This is useful for tracking other infectious diseases, too.”
In principle, the idea is simple: Weather forecasting models ingest a lot of data — for example, measurements of wind speed and direction, temperature, and humidity from local weather stations, in addition to satellite data. They use the data to assess what the current state of the atmosphere is, forecast the weather evolution into the future, and then repeat the cycle by blending the forecast atmospheric state with new data. In the same way, disease risk assessment also harnesses various types of available data to make an assessment about an individual’s risk of exposure to or infection with disease, forecasts the spread of disease across a network of human contacts using an epidemiological model, and then repeats the cycle by blending the forecast with new data. Such assessments might use the results of an institution’s surveillance testing, data from wearable sensors, self-reported symptoms and close contacts as recorded by smartphones, and municipalities’ disease-reporting dashboards.
The research presented in PLOS Computational Biology is proof of concept. However, its end result would be a smart phone app that would provide an individual with a frequently updated numerical assessment (i.e., a percentage) that reflects their likelihood of having been exposed to or infected with a particular infectious disease agent, such as COVID-19.
Such an app would be similar to existing COVID-19 exposure notification apps but more sophisticated and effective in its use of data, Schneider and his colleagues say. Those apps provide a binary exposure assessment (“yes, you have been exposed,” or, in the case of no exposure, radio silence); the new app described in the study would provide a more nuanced understanding of continually changing risks of exposure and infection as individuals come close to others and as data about infections is propagated across a continually evolving contact network. More

The spiral is pervasive throughout the universe — from the smallest DNA molecule to ferns and sunflowers, and from fingerprints to galaxies themselves. In science the ubiquity of this structure is associated with parsimony — that things will organize themselves in the simplest or most economical way.
Researchers from the University of Pittsburgh and Princeton University unexpectedly discovered that this principle also applies to some non-biological systems that convert chemical energy into mechanical action — allowing two-dimensional polymer sheets to rise and rotate in spiral helices without the application of external power.
This self-assembly into coherent three-dimensional structures represents the group’s latest contribution in the field of soft robotics and chemo-mechanical systems.
The research was published this month in Proceedings of the National Academy of Sciences (PNAS) Nexus. Lead author is Raj Kumar Manna with Oleg E. Shklyaev, post-doctoral associates with Anna Balazs, Distinguished Professor of Chemical and Petroleum Engineering and the John A. Swanson Chair of Engineering in Pitt’s Swanson School of Engineering. Contributing author is Howard A. Stone, the Donald R. Dixon ’69 and Elizabeth W. Dixon Professor of Mechanical and Aerospace Engineering at Princeton.
“Through computational modeling, we placed passive, uncoated polymer sheets around a circular, catalytic patch within a fluid-filled chamber. We added hydrogen peroxide to initiate a catalytic reaction, which then generated fluid flow. While one sheet alone did not spin in the solution, multiple sheets autonomously self-assembled into a tower-like structure,” Manna explained. “Then, as the tower experienced an instability, the sheets spontaneously formed an interweaving structure that rotates in the fluid.”
As Balazs pointed out, “The whole thing resembles a thread of twisted yarn being formed by a rotating spindle, which was used to make fibers for weaving. Except, there is no spindle; the system naturally forms the intertwined, rotating structure.”
Flow affects the sheet which affects the flow More