Computers Math

With communities across the nation experiencing a wave of COVID-19 infections, clinicians need effective tools that will enable them to aggressively and accurately treat each patient based on their specific disease presentation, health history, and medical risks.
In research recently published online in Medical Image Analysis, a team of engineers demonstrated how a new algorithm they developed was able to successfully predict whether or not a COVID-19 patient would need ICU intervention. This artificial intelligence-based approach could be a valuable tool in determining a proper course of treatment for individual patients.
The research team, led by Pingkun Yan, an assistant professor of biomedical engineering at Rensselaer Polytechnic Institute, developed this method by combining chest computed tomography (CT) images that assess the severity of a patient’s lung infection with non-imaging data, such as demographic information, vital signs, and laboratory blood test results. By combining these data points, the algorithm is able to predict patient outcomes, specifically whether or not a patient will need ICU intervention.
The algorithm was tested on datasets collected from a total of 295 patients from three different hospitals — one in the United States, one in Iran, and one in Italy. Researchers were able to compare the algorithm’s predictions to what kind of treatment a patient actually ended up needing.
“As a practitioner of AI, I do believe in its power,” said Yan, who is a member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer. “It really enables us to analyze a large quantity of data and also extract the features that may not be that obvious to the human eye.”
This development is the result of research supported by a recent National Institutes of Health grant, which was awarded to provide solutions during this worldwide pandemic. As the team continues its work, Yan said, researchers will integrate their new algorithm with another that Yan had previously developed to assess a patient’s risk of cardiovascular disease using chest CT scans.
“We know that a key factor in COVID mortality is whether a patient has underlying conditions and heart disease is a significant comorbidity,” Yan said. “How much this contributes to their disease progress is, right now, fairly subjective. So, we have to have a quantification of their heart condition and then determine how we factor that into this prediction.”
“This critical work, led by Professor Yan, is offering an actionable solution for clinicians who are in the middle of a worldwide pandemic,” said Deepak Vashishth, the director of CBIS. “This project highlights the capabilities of Rensselaer expertise in bioimaging combined with important partnerships with medical institutions.”
Yan is joined at Rensselaer by Ge Wang, an endowed chair professor of biomedical engineering and member of CBIS, as well as graduate students Hanqing Chao, Xi Fang, and Jiajin Zhang. The Rensselaer team is working in collaboration with Massachusetts General Hospital. When this work is complete, Yan said, the team hopes to translate its algorithm into a method that doctors at Massachusetts General can use to assess their patients.
“We actually are seeing that the impact could go well beyond COVID diseases. For example, patients with other lung diseases,” Yan said. “Assessing their heart disease condition, together with their lung condition, could better predict their mortality risk so that we can help them to manage their condition.”

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Materials provided by Rensselaer Polytechnic Institute. Original written by Torie Wells. Note: Content may be edited for style and length. More

For more than a decade, scientists studying epigenetics have used a powerful method called ChIP-seq to map changes in proteins and other critical regulatory factors across the genome. While ChIP-seq provides invaluable insights into the underpinnings of health and disease, it also faces a frustrating challenge: its results are often viewed as qualitative rather than quantitative, making interpretation difficult.
But, it turns out, ChIP-seq may have been quantitative all along, according to a recent report selected as an Editors’ Pick by and featured on the cover of the Journal of Biological Chemistry.
“ChIP-seq is the backbone of epigenetics research. Our findings challenge the belief that additional steps are required to make it quantitative,” said Brad Dickson, Ph.D., a staff scientist at Van Andel Institute and the study’s corresponding author. “Our new approach provides a way to quantify results, thereby making ChIP-seq more precise, while leaving standard protocols untouched.”
Previous attempts to quantify ChIP-seq results have led to additional steps being added to the protocol, including the use of “spike-ins,” which are additives designed to normalize ChIP-seq results and reveal histone changes that otherwise may be obscured. These extra steps increase the complexity of experiments while also adding variables that could interfere with reproducibility. Importantly, the study also identifies a sensitivity issue in spike-in normalization that has not previously been discussed.
Using a predictive physical model, Dickson and his colleagues developed a novel approach called the sans-spike-in method for Quantitative ChIP-sequencing, or siQ-ChIP. It allows researchers to follow the standard ChIP-seq protocol, eliminating the need for spike-ins, and also outlines a set of common measurements that should be reported for all ChIP-seq experiments to ensure reproducibility as well as quantification.
By leveraging the binding reaction at the immunoprecipitation step, siQ-ChIP defines a physical scale for sequencing results that allows comparison between experiments. The quantitative scale is based on the binding isotherm of the immunoprecipitation products.

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Ludwig-Maximilians-Universitaet (LMU) in Munich physicists have introduced a new method that allows biological pattern-forming systems to be systematically characterized with the aid of mathematical analysis. The trick lies in the use of geometry to characterize the dynamics.
Many vital processes that take place in biological cells depend on the formation of self-organizing molecular patterns. For example, defined spatial distributions of specific proteins regulate cell division, cell migration and cell growth. These patterns result from the concerted interactions of many individual macromolecules. Like the collective motions of bird flocks, these processes do not need a central coordinator. Hitherto, mathematical modelling of protein pattern formation in cells has been carried out largely by means of elaborate computer-based simulations. Now, LMU physicists led by Professor Erwin Frey report the development of a new method which provides for the systematic mathematical analysis of pattern formation processes, and uncovers the their underlying physical principles. The new approach is described and validated in a paper that appears in the journal Physical Review X.
The study focuses on what are called ‘mass-conserving’ systems, in which the interactions affect the states of the particles involved, but do not alter the total number of particles present in the system. This condition is fulfilled in systems in which proteins can switch between different conformational states that allow them to bind to a cell membrane or to form different multicomponent complexes, for example. Owing to the complexity of the nonlinear dynamics in these systems, pattern formation has so far been studied with the aid of time-consuming numerical simulations. “Now we can understand the salient features of pattern formation independently of simulations using simple calculations and geometrical constructions,” explains Fridtjof Brauns, lead author of the new paper. “The theory that we present in this report essentially provides a bridge between the mathematical models and the collective behavior of the system’s components.”
The key insight that led to the theory was the recognition that alterations in the local number density of particles will also shift the positions of local chemical equilibria. These shifts in turn generate concentration gradients that drive the diffusive motions of the particles. The authors capture this dynamic interplay with the aid of geometrical structures that characterize the global dynamics in a multidimensional ‘phase space’. The collective properties of systems can be directly derived from the topological relationships between these geometric constructs, because these objects have concrete physical meanings — as representations of the trajectories of shifting chemical equilibria, for instance. “This is the reason why our geometrical description allows us to understand why the patterns we observe in cells arise. In other words, they reveal the physical mechanisms that determine the interplay between the molecular species involved,” says Frey. “Furthermore, the fundamental elements of our theory can be generalized to deal with a wide range of systems, which in turn paves the way to a comprehensive theoretical framework for self-organizing systems.”

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True random numbers are required in fields as diverse as slot machines and data encryption. These numbers need to be truly random, such that they cannot even be predicted by people with detailed knowledge of the method used to generate them.
As a rule, they are generated using physical methods. For instance, thanks to the tiniest high-frequency electron movements, the electrical resistance of a wire is not constant but instead fluctuates slightly in an unpredictable way. That means measurements of this background noise can be used to generate true random numbers.
Now, for the first time, a research team led by Robert Grass, Professor at the Institute of Chemical and Bioengineering, has described a non-physical method of generating such numbers: one that uses biochemical signals and actually works in practice. In the past, the ideas put forward by other scientists for generating random numbers by chemical means tended to be largely theoretical.
DNA synthesis with random building blocks
For this new approach, the ETH Zurich researchers apply the synthesis of DNA molecules, an established chemical research method frequently employed over many years. It is traditionally used to produce a precisely defined DNA sequence. In this case, however, the research team built DNA molecules with 64 building block positions, in which one of the four DNA bases A, C, G and T was randomly located at each position. The scientists achieved this by using a mixture of the four building blocks, rather than just one, at every step of the synthesis.
As a result, a relatively simple synthesis produced a combination of approximately three quadrillion individual molecules. The scientists subsequently used an effective method to determine the DNA sequence of five million of these molecules. This resulted in 12 megabytes of data, which the researchers stored as zeros and ones on a computer.
Huge quantities of randomness in a small space
However, an analysis showed that the distribution of the four building blocks A, C, G and T was not completely even. Either the intricacies of nature or the synthesis method deployed led to the bases G and T being integrated more frequently in the molecules than A and C. Nonetheless, the scientists were able to correct this bias with a simple algorithm, thereby generating perfect random numbers.
The main aim of ETH Professor Grass and his team was to show that random occurrences in chemical reaction can be exploited to generate perfect random numbers. Translating the finding into a direct application was not a prime concern at first. “Compared with other methods, however, ours has the advantage of being able to generate huge quantities of randomness that can be stored in an extremely small space, a single test tube,” Grass says. “We can read out the information and reinterpret it in digital form at a later date. This is impossible with the previous methods.”

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Materials provided by ETH Zurich. Original written by Fabio Bergamin. Note: Content may be edited for style and length. More

The vision of the future of miniaturisation has produced a series of synthetic molecular motors that are driven by a range of energy sources and can carry out various movements. A research group at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) has now managed to control a catalysis reaction using a light-controlled motor. This takes us one step closer to realising the vision of a nano factory in which combinations of various machines work together, as is the case in biological cells. The results have been published in the Journal of the American Chemical Society.
Laws of mechanics cannot always be applied
Per definition, a motor converts energy into a specific type of kinetic energy. On a molecular level, for example, the protein myosin can produce muscle contractions using chemical energy. Such nanomachines can now be synthetically produced. However, the molecules used are much smaller than proteins and significantly less complex.
‘The laws of mechanical physics cannot simply be applied to the molecular level,’ says Prof. Dr. Henry Dube, Chair of Organic Chemistry I at FAU. Inertia, for example, does not exist at this level, he explains. Triggered by Brownian motion, particles are constantly in motion. ‘Activating a rotating motor is not enough, you need to incorporate a type of ratchet mechanism that prevents it from turning backwards,’ he explains.
In 2015 while at LMU in Munich, Prof. Dube and his team developed a particularly fast molecular motor driven by visible light. In 2018, they developed the first molecular motor that is driven solely by light and functions regardless of the ambient temperature. A year later, they developed a variant capable not only of rotation but also of performing a figure of eight motion. All motors are based on the hemithioindigo molecule, an asymmetric variant of the naturally occurring dye indigo where a sulphur atom takes the place of the nitrogen atom. One part of the molecule rotates in several steps in the opposite direction to the other part of the molecule. The energy-driven steps are triggered by visible light and modify the molecules so that reverse reactions are blocked.
Standard catalysts in use
After coming to FAU, Henry Dube used the rotating motor developed in 2015 to control a separate chemical process for the first time. It moves in four steps around the carbon double bond of the hemithioindigo. Two of the four steps triggered by a photo reaction can be used to control a catalysis reaction. ‘Green light generates a molecular structure that binds a catalyst to the hemithioindigo and blue light releases the catalyst,’ explains the chemist.
A standard catalyst is used that does not have any metal atoms. Using electrostatic forces, the catalyst docks via a hydrogen bond onto an oxygen atom in the ‘motor molecule’. All catalysts that use a hydrogen bond could be used, in principle. ‘The great advantage of hemithioindigo is that its innate structure has a bonding mechanism for catalysts,’ explains Prof. Dube. It would otherwise have to be added using chemical synthesis.
The rotation of the hemithioindigo motor is controlled by visible light. At the same time, the system allows the targeted release and bonding of a catalyst that accelerates or decelerates desired chemical reactions. ‘This project is an important step towards integrating molecular motors in chemical processes simply and in a variety of ways,’ says Prof. Dube. ‘This will let us synthesise complex medication at a high level of precision using molecular machines like a production line in future.’

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Scientists are working on new materials to create neuromorphic computers, with a design based on the human brain. A crucial component is a memristive device, the resistance of which depends on the history of the device — just like the response of our neurons depends on previous input. Materials scientists from the University of Groningen analysed the behaviour of strontium titanium oxide, a platform material for memristor research and used the 2D material graphene to probe it. On 11 November 2020, the results were published in the journal ACS Applied Materials and Interfaces.
Computers are giant calculators, full of switches that have a value of either 0 or 1. Using a great many of these binary systems, computers can perform calculations very rapidly. However, in other respects, computers are not very efficient. Our brain uses less energy for recognizing faces or performing other complex tasks than a standard microprocessor. That is because our brain is made up of neurons that can have many values other than 0 and 1 and because the neurons’ output depends on previous input.
Oxygen vacancies
To create memristors, switches with a memory of past events, strontium titanium oxide (STO) is often used. This material is a perovskite, whose crystal structure depends on temperature, and can become an incipient ferroelectric at low temperatures. The ferroelectric behaviour is lost above 105 Kelvin. The domains and domain walls that accompany these phase transitions are the subject of active research. Yet, it is still not entirely clear why the material behaves the way it does. ‘It is in a league of its own,’ says Tamalika Banerjee, Professor of Spintronics of Functional Materials at the Zernike Institute for Advanced Materials, University of Groningen.
The oxygen atoms in the crystal appear to be key to its behaviour. ‘Oxygen vacancies can move through the crystal and these defects are important,’ says Banerjee. ‘Furthermore, domain walls are present in the material and they move when a voltage is applied to it.’ Numerous studies have sought to find out how this happens, but looking inside this material is complicated. However, Banerjee’s team succeeded in using another material that is in a league of its own: graphene, the two-dimensional carbon sheet.
Conductivity
‘The properties of graphene are defined by its purity,’ says Banerjee, ‘whereas the properties of STO arise from imperfections in the crystal structure. We found that combining them leads to new insights and possibilities.’ Much of this work was carried out by Banerjee’s PhD student Si Chen. She placed graphene strips on top of a flake of STO and measured the conductivity at different temperatures by sweeping a gate voltage between positive and negative values. ‘When there is an excess of either electrons or the positive holes, created by the gate voltage, graphene becomes conductive,’ Chen explains. ‘But at the point where there are very small amounts of electrons and holes, the Dirac point, conductivity is limited.’
In normal circumstances, the minimum conductivity position does not change with the sweeping direction of the gate voltage. However, in the graphene strips on top of STO, there is a large separation between the minimum conductivity positions for the forward sweep and the backward sweep. The effect is very clear at 4 Kelvin, but less pronounced at 105 Kelvin or at 150 Kelvin. Analysis of the results, along with theoretical studies carried out at Uppsala University, shows that oxygen vacancies near the surface of the STO are responsible.
Memory
Banerjee: ‘The phase transitions below 105 Kelvin stretch the crystal structure, creating dipoles. We show that oxygen vacancies accumulate at the domain walls and that these walls offer the channel for the movement of oxygen vacancies. These channels are responsible for memristive behaviour in STO.’ Accumulation of oxygen vacancy channels in the crystal structure of STO explains the shift in the position of the minimum conductivity.
Chen also carried out another experiment: ‘We kept the STO gate voltage at -80 V and measured the resistance in the graphene for almost half an hour. In this period, we observed a change in resistance, indicating a shift from hole to electron conductivity.’ This effect is primarily caused by the accumulation of oxygen vacancies at the STO surface.
All in all, the experiments show that the properties of the combined STO/graphene material change through the movement of both electrons and ions, each at different time scales. Banerjee: ‘By harvesting one or the other, we can use the different response times to create memristive effects, which can be compared to short-term or long-term memory effects.’ The study creates new insights into the behaviour of STO memristors. ‘And the combination with graphene opens up a new path to memristive heterostructures combining ferroelectric materials and 2D materials.’

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Osaka City University scientists and colleagues in Japan have found a way to control an interaction between quantum dots that could greatly improve charge transport, leading to more efficient solar cells. Their findings were published in the journal Nature Communications.
Nanomaterials engineer DaeGwi Kim led a team of scientists at Osaka City University, RIKEN Center for Emergent Matter Science and Kyoto University to investigate ways to control a property called quantum resonance in layered structures of quantum dots called superlattices.
“Our simple method for fine-tuning quantum resonance is an important contribution to both optical materials and nanoscale material processing,” says Kim.
Quantum dots are nanometer-sized semiconductor particles with interesting optical and electronic properties. When light is shone on them, for example, they emit strong light at room temperature, a property called photoluminescence. When quantum dots are close enough to each other, their electronic states are coupled, a phenomenon called quantum resonance. This greatly improves their ability to transport electrons between them. Scientists have been wanting to manufacture devices using this interaction, including solar cells, display technologies, and thermoelectric devices.
However, they have so far found it difficult to control the distances between quantum dots in 1D, 2D and 3D structures. Current fabrication processes use long ligands to hold quantum dots together, which hinders their interactions.
Kim and his colleagues found they could detect and control quantum resonance by using cadmium telluride quantum dots connected with short N-acetyl-L-cysteine ligands. They controlled the distance between quantum dot layers by placing a spacer layer between them made of oppositely charged polyelectrolytes. Quantum resonance is detected between stacked dots when the spacer layer is thinner than two nanometers. The scientists also controlled the distance between quantum dots in a single layer, and thus quantum resonance, by changing the concentration of quantum dots used in the layering process.
The team next plans to study the optical properties, especially photoluminescence, of quantum dot superlattices made using their layer-by-layer approach. “This is extremely important for realizing new optical electronic devices made with quantum dot superlattices,” says Kim.
Kim adds that their fabrication method can be used with other types of water-soluble quantum dots and nanoparticles. “Combining different types of semiconductor quantum dots, or combining semiconductor quantum dots with other nanoparticles, will expand the possibilities of new material design,” says Kim.

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Rochester Institute of Technology students discovered lost text on 15th-century manuscript leaves using an imaging system they developed as freshmen. By using ultraviolet-fluorescence imaging, the students revealed that a manuscript leaf held in RIT’s Cary Graphic Arts Collection was actually a palimpsest, a manuscript on parchment with multiple layers of writing.
At the time the manuscript was written, making parchment was expensive, so leaves were regularly scraped or erased and re-used. While the erased text is invisible to the naked eye, the chemical signature of the initial writing can sometimes be detected using other areas of the light spectrum.
“Using our system, we borrowed several parchments from the Cary Collection here at RIT and when we put one of them under the UV light, it showed this amazing dark French cursive underneath,” said Zoë LaLena, a second-year imaging science student from Fairport, N.Y., who worked on the project. “This was amazing because this document has been in the Cary Collection for about a decade now and no one noticed. And because it’s also from the Ege Collection, in which there’s 30 other known pages from this book, it’s really fascinating that the 29 other pages we know the location of have the potential to also be palimpsests.”
The imaging system was originally built by 19 students enrolled in the Chester F. Carlson Center for Imaging Science’s Innovative Freshman Experience, a yearlong, project-based course that has the imaging science, motion picture science, and photographic sciences programs combine their talents to solve a problem.
When RIT switched to remote instruction in March due to the coronavirus outbreak, the students were unable to finish building it, but thanks to a donation from Jeffrey Harris ’75 (photographic science and instrumentation) and Joyce Pratt, three students received funding to continue to work on the project over the summer. Those three students — LaLena; Lisa Enochs, a second-year student double majoring in motion picture science and imaging science from Mississauga, Ontario; and Malcom Zale, a second-year motion picture science student from Milford, Mass. — finished assembling the system in the fall when classes resumed and began analyzing documents from the Cary Collection.
Steven Galbraith, curator of the Cary Graphic Arts Collection, said he was excited they discovered the manuscript leaf was a palimpsest because similar leaves have been studied extensively by scholars across the country, but never tested with UV light or fully imaged.
Collector, educator, and historian Otto Ege made leaf collections out of medieval manuscripts that were damaged or incomplete and sold them or distributed them to libraries and special collections across North America, including to the Cary Collection. Galbraith said he’s excited because it means that many other cultural and academic institutions with Ege Collection leaves now may have palimpsests in their collection to study.
“The students have supplied incredibly important information about at least two of our manuscript leaves here in the collection and in a sense have discovered two texts that we didn’t know were in the collection,” said Galbraith. “Now we have to figure out what those texts are and that’s the power of spectral imaging in cultural institutions. To fully understand our own collections, we need to know the depth of our collections, and imaging science helps reveal all of that to us.”
The students are interested to see if more manuscript leaves from Ege collections across the country are palimpsests. They imaged another Ege Collection leaf at the Buffalo and Erie County Public Library that turned out to be a palimpsest and are reaching out to other curators across the country. As they begin stitching the lost text back together, paleographers can examine the information they contain.
The students have been selected to share their results at the 2021 International Congress on Medieval Studies and also plan to present the project at next year’s Imagine RIT: Creativity and Innovation Festival.
VIDEO: https://www.youtube.com/watch?v=YEieepHPMA0

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Materials provided by Rochester Institute of Technology. Original written by Luke Auburn. Note: Content may be edited for style and length. More