## Russian researchers simulate the motion of incompressible liquid

A group of researchers from Russia and Italy have conducted a study that produced a more precise scheme of numerical solution of incompressible Navier-Stokes equations for plane motion than that existed before. Details of the research can be found in the Applied Mathematics and Computation journal.

The Navier-Stokes equations are a system of differential equations that can be used to describe the motion of viscous Newtonian fluid. In many cases the dependence of the fluid density from coordinates and time can be neglected, and the density can be considered constant, making the fluid incompressible. With safe approximation, water is one of such fluids.

The Navier-Stokes equations are named after French physicist Claude-Louis Navier and British matematician George Gabriel Stokes. The problem of existence and smoothness of solutions to the Navier-Stokes equations is one of seven problems of the Millennium, the solution to which is rewarded with one million dollars by the Clay Mathematics Institute, USA.

The scheme built allows solving the equations for a flow of incompressible fluid efficiently, using numerical methods. Such fluids are used in different technological processes.

The calculations in the work are given for two-dimensional flow (by axes X and Y plus the time variable). This significantly simplifies the analysis of resulting difference scheme and the work itself, as there is an exact non-stationary solution for that case, which can be compared to the results obtained by approximate methods. Using supercomputing experiments and comparisons with exact solutions, the researchers have verified the obtained numerical solution scheme qualitatively and quantitatively for laminar flow (i.e., one without mixing of the fluid layers and surges), leaving out turbulent flows, in which vortexes are formed and exact solutions are not possible.

"For example, when you turn on a faucet a little, the flow is slow. But when you turn it on fully, under high pressure the flow starts to whirl. This is turbulence, and it is hard to simulate numerically, - one of the authors, prof. Vladimir Gerdt, Doctor of Physical and Mathematical Sciences from Peoples' Friendship University of Russia (RUDN University), commented. - The solutions we study are rather smooth. Turbulent flows, in general, cannot be described with a high precision, and it requires supercomputer technology." To solve the equations with numerical methods, one needs to switch from differential equations to algebraic difference (discrete) equations. "Our goal is to build a good scheme which would with high precision solve a certain problem using numerical methods, as well as meet a number of special properties on top of numerical methods", - the mathematician said.

According to him, in the future the researchers will be able to continue their work and turn to studying three-dimensional solutions, but first they need to make sure their method works well enough.

"When we specify the starting condition that satisfies the point solution, we look at how our numerical solution behaves depending from time, how much it will deviate from the exact solution. Other schemes that are known and studied in our paper have substantially larger errors that grow rather quickly with time, while our error is very small and grows very slowly, it is almost a constant, - the researcher told us. - We compared it to standard numerical methods by other authors and saw that our scheme is better. This is the main result of our work."

## RIT's Lousto maps black hole collisions, gives astronomers hitchhikers guide to help LIGO-Virgo pinpoint mergers

Rochester Institute of Technology researchers helped pinpoint the precise location of a gravitational wave signal—and the black hole merger that produced it—detected by gravitational wave observatories in the United States and in Europe.

For the first time, LIGO and the French-Italian Virgo were used to triangulate the position in the universe where the binary black hole merger occurred 1.8 billion years ago. The black holes are 25 and 31 times the mass of the sun before the collision and 53 times the sun mass after, when a merged black hole formed.

The signal was detected on Aug. 14 by the LIGO detectors in Louisiana and Washington and the Virgo detector near Pisa, Italy. The findings were announced today in a news conference in Turin, Italy, and will appear in Physics Review Letters.

The addition of the third observatory has widened the window on the universe, said RIT professor Carlos Lousto. “We now can pinpoint where those black holes collided in the universe with 10 times higher precision than we had with only two detectors,” Lousto said. “Astronomers can look more accurately toward this direction in sky with conventional telescopes to see if there is an electromagnetic counterpart to such cosmic collisions.”

John Whelan, RIT associate professor and the principal investigator of RIT’s LIGO group, said, “Our Virgo colleagues, who have been collaborating on the analysis since our first joint initial detector runs 10 years ago, have now joined the advanced detector network. We now have, for the first time, three advanced gravitational wave detectors observing together.”

Richard O’Shaughnessy, RIT assistant professor, adds that, “with Virgo, we can now reliably point to where a gravitational wave signal came from. We can tell astronomers when and where to point their telescopes.”

Scientists will gain a deeper understanding of astrophysical phenomena by combining gravitational wave astronomy with traditional methods using the electromagnetic spectrum.

“Precision pointing makes multimessenger astronomy possible,” O’Shaughnessy said.

The current study cites 2005 breakthrough research by Lousto; Manuela Campanelli, RIT professor and director of the Center for Computational Relativity and Gravitation; and Yosef Zlochower, RIT associate professor, which solved Albert Einstein’s strong field equations. The group was one of the first to simulate a black hole on a supercomputer. Their “moving puncture approach” has been adopted by other research groups and helped lay the foundation for gravitational wave astronomy.

“Our supercomputer simulations of black-hole collisions continue to be crucial to determine the astrophysical parameters of those extreme objects and they provide important information for modeling their history, from the death of their progenitor stars to their final merger into a larger black hole,” Lousto said.

The new detection also cites a 2017 paper written by Lousto and James Healy, RIT postdoctoral researcher, and a 2014 paper by Lousto and Zlochower studying extreme black hole spins and mass ratios.

RIT students listed as authors on the LIGO-Virgo paper include Monica Rizzo, an undergraduate physics major; John Bero, an MS student in the astrophysical sciences and technology graduate program; and astrophysical sciences and technology Ph.D. students Jacob Lange, Jared Wofford, Daniel Wysocki and recent Ph.D. recipient Yuanhao Ahang.

Educating the next generation of gravitational wave astronomers is taken seriously at RIT’s Center for Computational Relativity and Gravitation.

“We perform top research integrating faculty, students and postdocs,” Campanelli said. “With RIT and National Science Foundation support, we are upgrading our supercomputer capabilities to solve Einstein equations for binary black holes.”

# Neural networks carry out chemical simulations in record time

Drastic advances in research of artificial intelligence have led to a wide range of fascinating developments in this area over the last decade. Autonomously driven cars, but also everyday applications such as search engines and spam filters illustrate the versatility of methods from the field of artificial intelligence.

Infrared spectroscopy is one of the most valuable experimental methods to gain insight into the world of molecules. Infrared spectra are chemical fingerprints that provide information on the composition and properties of substances and materials. In many cases, these spectra are very complex - a detailed analysis makes computer-aided simulations indispensable. While quantum chemical calculations in principle enable extremely precise prediction of infrared spectra, their applicability in practice is made difficult by the high computational effort associated with them. For this reason, reliable infrared spectra can only be calculated for relatively small chemical systems.

An international group of researchers led by Philipp Marquetand from the Faculty of Chemistry at the University of Vienna has now found a way to accelerate these simulations using artificial intelligence. For this purpose, so-called artificial neural networks are used, mathematical models of the human brain. These are able to learn the complex quantum mechanical relationships that are necessary for the modeling of infrared spectra by using only a few examples. In this way, the scientists can carry out simulations within a few minutes, which would otherwise take thousands of years even with modern supercomputers - without sacrificing reliability. "We can now finally simulate chemical problems that could not be overcome with the simulation techniques used up to now," says Michael Gastegger, the first author of the study.

Based on the results of this study, the researchers are confident that their method of spectra prediction will be widely used in the analysis of experimental infrared spectra in the future.

# Using a supercomputational technique that accounts for how genes interact, scientists revealed genes that may be related to autism spectrum disorder

The identification of genes related to autism spectrum disorder (ASD) could help to better understand the disorder and develop new treatments. While scientists have found many genetic differences in different people with ASD, these often show little overlap and don't appear to be related. Using a new technique that accounts for how genes interact, Italian researchers have identified new networks of related genes that may be involved in ASD - including genes that are related to cancer.

Autism spectrum disorder encompasses a range of neurodevelopmental disorders, and includes conditions like autism and Asperger's syndrome. ASD symptoms vary significantly, but typically start within the first three years of life and include repetitive behaviors, and difficulty communicating and socializing.

Autism spectrum disorder has a significant genetic component, and scientists have found thousands of genetic differences between some people with ASD and those without. Researchers suspect that many of these genetic anomalies can predispose people to the disorder, or directly contribute to it.

However, ASD is complex, and researchers have only begun to unravel its genetic basis. One problem is that many studies have found completely different genetic anomalies in different people with ASD, which show little overlap and don't appear to be related.

This makes it difficult to find common hallmarks that could provide better clues to understanding and treating the disorder. While this genetic variability demonstrates that ASD is incredibly complex, a new technique could help to cast more light on the situation.

When a cell in your body expresses a gene, it produces a specific protein. These proteins interact to form complex signaling pathways that can have wide-reaching effects. Different genes could affect the same pathway, meaning that the different genetic anomalies found in people with ASD could potentially all be affecting similar pathways.

By accounting for how genes interact, rather than just looking at individual genes, scientists might potentially spot biological hallmarks of ASD they might otherwise miss. In a study recently published in Frontiers in Genetics , researchers in Italy used a new supercomputational technique to do just that.

Searching public databases, the team investigated genes that previous studies have associated with ASD. They used another database listing interactions between different proteins, to narrow down the list of genes and account for interactions between them.

Using a supercomputational technique called network diffusion, the team identified networks of genes that are interrelated through their connection to the ASD genes in the databases. They also investigated if the genes were involved in any known signaling pathways.

So, what do these genes do? Some of the ASD genes in the networks are involved in brain function and how neurons develop and transmit information. Others are involved in conditions that tend to occur alongside ASD, such as psychiatric disorders and epilepsy, and interestingly, some of the genes are also involved in cancer.

The team also identified genes that had not been previously linked to ASD, but are heavily involved in numerous protein interactions and signaling pathways. Using their data, the team constructed complex pathway maps that could provide clues about ASD and potential treatments.

For example, many of the genes in the new networks are related to cancer, suggesting that certain cancer treatments that target these genes might also be useful to treat ASD. The team's supercomputational technique can also be used to learn about other conditions.

"The computational method we have proposed can be applied to other data-sets to predict new genes involved in other conditions," says Alessandra Mezzelani, a researcher involved in the study. "We hope that global gene databases will continue to grow, allowing scientist to share and reuse these types of data, and we will update our model as more ASD risk genes are discovered." says Ettore Mosca, who was also involved.

# The chemical and biomolecular engineering assistant professor earned a research grant from the American Chemical Society Petroleum Research Fund.

Srinivas Rangarajan, assistant professor in the department of chemical and biomolecular engineering at Lehigh University, received a research grant from The American Chemical Society Petroleum Research Fund (ACS PRF) in September 2017.

ACS PRF grants support for fundamental research related to petroleum or fossil fuels at nonprofit institutions. Funds are used as seed money, enabling an investigator to initiate a new research direction. In particular, this award is part of ACS's Doctoral New Investigator (DNI) grant to support new faculty. Rangarajan's innovative research involves the computational discovery of new catalysts for olefin production.

Olefins, a class of chemicals that includes ethylene, propylene and butadiene, are derived from petroleum. Although not sold directly to consumers, olefin is a multi-billion-dollar industry--with a cumulative global annual output of over 200 million metric tons--and is the basic component of polymers handled by consumers every day: Fibers, bins, bottles, food packaging films, trash liners, insulation, tile flooring, antifreeze, solvents, detergents, tires, carpet backing and rubber hoses.

Olefins are currently produced through an energy-intensive "steam cracking" process of natural gas liquids and lighter components of crude oil. A more energy-efficient alternative is to produce olefins directly from higher alkanes present in shale gas, an abundant form of natural gas found trapped within shale formations.

Through detailed quantum chemistry calculations, Rangarajan aims to understand, at the atomic scale, where and how ethane interacts with a known catalyst (solid transition metal sulfide) to produce a type of olefin known as ethylene.

The study will then use these calculations to formulate a mechanistic model of ethylene synthesis, laying the foundation for computationally discovering better-performing catalysts for olefin production.

Rangarajan joined Lehigh in January 2017 after his postdoctoral studies at University of Wisconsin, Madison. He received his B.Tech. from the Indian Institute of Technology, Madras in 2007, and his Ph.D. from the University of Minnesota in 2013. His industrial experience includes previous employment at Shell Global Solutions in The Netherlands and India.

# Families of genes encoding proteins involved in communication across synapses define neurons by determining which cells they connect with and how they communicate

In a major step forward in research, scientists at Cold Spring Harbor Laboratory (CSHL) today publish in Cell a discovery about the molecular-genetic basis of neuronal cell types. Neurons are the basic building blocks that wire up brain circuits supporting mental activities and behavior. The study, which involves sophisticated supercomputational analysis of the messages transcribed from genes that are active in a neuron, points to patterns of cell-to-cell communication as the core feature that makes possible rigorous distinctions among neuron types across the mouse brain.

The team, led by CSHL Professor Z. Josh Huang, likens their discovery to the way communication styles and patterns enable us to learn important -- definitive -- things about people. "To figure out who I am," says Anirban Paul, Ph.D., the paper's first author, "you can learn a great deal by studying with whom and how I communicate. When I call my grandma, do I use an app or a phone? How do I speak to her, in terms of tone? How does that compare with when I communicate with my son, or my colleague?"

Using six genetically identifiable types of cortical inhibitory neurons that all release the neurotransmitter GABA, the team sought to discover factors capable of distinguishing their core molecular features. Using a high-resolution RNA sequencing method optimized by Paul and supercomputational tools developed by CSHL Assistant Professor Jesse Gillis and colleague Megan Crow, Ph.D., the team searched for families of genes whose activity exhibited characteristic patterns in each neuron type.

Out of more than 600 gene families, the team found about 40 families whose activity patterns could be used to distinguish the six groups of cells. Surprisingly, Huang says, these cell-defining features fell into just six functional categories of gene families, all of which are vital for cell-to-cell communications. They include proteins that are expressed on either side of the communications interface in neurons: along or near membranes that face one another across the synaptic gap - the narrow space separating a (pre-synaptic) neuron sending a signal and a (post-synaptic) neuron facing it which receives the signal.

The key genes dictate which other cells a neuron connects to and how it communicates with those partners, Huang explains. "Ultimately, the genes code for one striking feature, which is with whom and how these neurons talk to each other."

The communications architecture is effectively hardwired into the molecular makeup of neurons, very likely across the brain, the team says. The ability to generalize the GABA results is based on re-analysis performed by the team of published data from the Allen Brain Atlas. It shows that the six "defining" gene/protein families they identified are also statistically distinguishing in random sets of neurons, including excitatory neurons, throughout the brain.

The findings should help scientists sort out the bewildering array of neurons that are intertwined in the brain. Over the years, as neuroscientists have proposed various approaches for classifying neurons, one question has been whether these classifications reflect cells' biological properties, or are merely useful but arbitrary designations. Hundreds or thousands of types of neurons probably exist in the brain but there has been no agreement among neuroscientists about how to define different types and on what biological basis. In a real sense, says Huang, the CSHL team has discovered the rules of a "core genetic recipe" for constructing diverse types of nerve cells.

## Oxford researcher Steven Reece develops machine learning approach to help hurricane relief efforts

A highly unusual collaboration between information engineers at Oxford, the Zooniverse citizen science platform and international disaster response organization Rescue Global is enabling a rapid and effective response to Hurricane Irma.

The project draws on the power of the Zooniverse, the world's largest and most popular people-powered research platform, to work with volunteers and crowd source the data needed to understand Irma's path of destruction and the damage caused. Combining these insights with detailed artificial intelligence will support rescue relief organisations to understand the scale of the crisis, and deliver aid to those worst affected as soon as possible.

Irma is now judged to be the most powerful Atlantic storm in a decade, breaking previous extreme weather records and causing widespread destruction and death across the Caribbean. Tens of thousands of people have been displaced or made homeless, and well over a million are at risk from loss of critical services such as water and electricity.

The disaster poses huge challenges for crisis response teams, who need to assess as quickly as possible the extent of the destruction on islands spread over thousands of square miles, and ensure that the right aid gets to those in most need in the safest and most efficient way.

In the immediate aftermath of Irma, Oxford researchers have been working round the clock in partnership with Rescue Global, a respected international crisis response charity, to help address this problem. The results have already supported Rescue Global to get aid delivered to some of the areas worst affected by Irma.

On September 12th the Zooniverse, which was founded by Oxford researchers, relaunched its Planetary Response Network. First trialled in the days following the Nepal earthquake of April 2015, the PRN aims to mobilise a 'crowd' to assist in a live disaster that is still unfolding. In recent days thousands of volunteers from around the world have already joined the effort. Their role is to analyse 'before' and 'after' satellite images of the islands hit by Irma and identify features such as damaged buildings, flooding, blocked roads or new temporary settlements which indicate that people are homeless.

Rebekah Yore, Operations Manager at Rescue Global, commented: "By the morning of Friday 15th September, we were told by the Zooniverse team that roughly 300,000 classifications from 7,500 people had taken place through the platform. This extraordinary effort is the equivalent to the output of one person working full-time for just over a year, or that same person working 24/7 without breaks for around 3 months. And the number of volunteers and classifications are increasing daily. This input is already having a direct effect on the ground, helping to provide situational awareness for all deployed teams."

The sheer volume of images would take an individual months to sort through, but can be analysed in a matter of hours by the 'crowd'. Because the images are often of poor quality, human observers are much better placed to perform this part of the task than computers.

For the next step, however, computers are essential, and Oxford engineering researchers have developed a suite of sophisticated artificial intelligence tools which can process the resulting data. Machine learning approaches quickly reconcile inconsistent responses, aggregate the data and integrate information derived from other crowd-sourced mapping materials, such as the Humanitarian Open Street Maps and Tomnod. This approach generates the best information possible to inform relief efforts. This analysis enables the team to build impact 'heat maps' that identify the areas in need of urgent assistance. Oxford has considerable expertise in this area: the tools have been refined over several years and were previously used to assist Rescue Global in its response to the 2015 Nepal and 2016 Ecuador earthquakes.

The 'heat maps' enable Rescue Global to decide where to send its own small reconnaissance planes to conduct detailed aerial assessments, and to share critical information with a multitude of governmental and humanitarian partners. Working closely with Airlink, who are flying in aid to a central location, Rescue Global is using information gathered through the Zooniverse platform and its own needs assessments to coordinate the onward delivery of aid through a network of boats and planes, ensuring that it gets to those who need it most.

This new technology offers an evidence-based, rational approach to disaster management. Through collaboration with crisis responders like Rescue Global, Oxford researchers are making a unique and significant difference to victims of Hurricane Irma.

Dr Steven Reece, Machine Learning Research Fellow and mapping lead at Oxford University, said: 'As always we are extremely grateful to our friends in the satellite industry for providing data and, of course, the crowd for their amazing work interpreting the imagery so quickly. This has been a sustained campaign and we've now produced heat maps for all the Virgin Islands. With Hurricane Maria increasing in strength and bearing down on the same area, we will have a lot more work ahead of us.'

# Pitt researchers awarded \$300k by NSF to identify environmentally sustainable chelating agents

Molecular chelating agents are used in many areas ranging from laundry detergents to paper pulp processing to precious metal refining. However, some chelating agents, especially the most effective ones, do not degrade in nature and may pollute the environment. With support from the National Science Foundation (NSF), researchers at the University of Pittsburgh Swanson School of Engineering are developing machine learning procedures to discover new chelating agents that are both effective and degradable.

Dr. John Keith, a Richard King Mellon Faculty Fellow in Energy and assistant professor of chemical engineering at Pitt, is principal investigator; and Dr. Eric Beckman, Distinguished Service Professor of chemical engineering and co-director of Pitt's Mascaro Center for Sustainable Innovation, is co-PI. Their project titled "SusChEM: Machine learning blueprints for greener chelants" will receive \$299,999 from the NSF.

"Chelating agents are molecules that bind to and isolate metal ions dissolved in water," explains Dr. Keith. "Cleaning detergents normally don't work well in hard water because of metal ions like magnesium and calcium interfering. That's why commercial detergents typically include some chelating agents to hold up those metal ions so the rest of the detergent can focus on cleaning."

While chelating agents are valued for their ability to bind strongly to different metal ions, researchers are also factoring how long it takes them to degrade in the environment and their probabilities of being toxic when searching for more effective chelate structures. "Many of the widely used chelating agents we use end up in water runoffs, where they can be somewhat toxic to wildlife and sometimes to people as well," says Dr. Beckman.

Developing new chelating agents so far has relied on trial and error experimentation. Dr. Beckman continues, "In the past, folks have tried to create better chelating agents by tweaking existing structures, but whenever that produces something less toxic, the chelating agent winds up being much less effective too. We're trying a new approach that uses machine learning to look through much larger and more diverse pools of candidate molecules to find those that would be the most useful."

The Pitt research team will use quantum chemistry calculations to develop machine learning methods that can predict new molecules that would be more effective and greener than existing chelating agents. While computational quantum chemistry can be used to screen through a thousand hypothetical chelating agents in a year, machine learning methods based on quantum chemistry could be used to screen through 100,000s of candidates per week. Once the researchers identify promising candidates, they will synthesize and test them in their labs to validate the efficacy of the machine learning process for designing greener chemicals.

The results of the research will have a significant impact on a range of topics relevant to environmentally-safe engineering and the control of metals in the environment, including supercomputer-aided design of greener chelating agents used in detergents, treatments of heavy metal poisoning, metal extractions for soil treatments, waste remediation, handling normally occurring radioactive materials from hydraulic fracturing sites, and water purification.

"Chelating agents are used in such a wide range of industries, so even a small improvement can have a big impact on sustainability as a whole," says Dr. Keith.

## Solar wind impacts on giant 'space hurricanes' may affect satellite safety

Could the flapping of a butterfly's wings in Costa Rica set off a hurricane in California? The question has been scrutinized by chaos theorists, stock-market analysts and weather forecasters for decades. For most people, this hypothetical scenario may be difficult to imagine on Earth - particularly when a real disaster strikes.

Yet, in space, similarly small fluctuations in the solar wind as it streams toward the Earth's magnetic shield actually can affect the speed and strength of "space hurricanes," researcher Katariina Nykyri of Embry-Riddle Aeronautical University has reported.

The study, published on September 19 in the Journal of Geophysical Research - Space Physics, offers the first detailed description of the mechanism by which solar wind fluctuations can change the properties of so-called space hurricanes, affecting how plasma is transported into the Earth's magnetic shield, or magnetosphere.

Those "hurricanes" are formed by a phenomenon known as Kelvin-Helmholtz (KH) instability. As plasma from the Sun (solar wind) sweeps across the Earth's magnetic boundary, it can produce large vortices (about 10,000-40,000 kilometers in size) along the boundary layer, Nykyri explained.

"The KH wave, or space hurricane, is one of the major ways that solar wind transports energy, mass and momentum into the magnetosphere," said Nykyri, a professor of physics and a researcher with the Center for Space and Atmospheric Research at Embry-Riddle's Daytona Beach, Fla., campus. "Fluctuations in solar wind affect how quickly the KH waves grow and how large they become."

When solar wind speeds are faster, the fluctuations are more powerful, Nykyri reported, and they seed larger space hurricanes that can transport more plasma.

Gaining deeper insights into how solar wind conditions affect space hurricanes may someday provide better space-weather prediction and set the stage for safer satellite navigation through radiation belts, Nykyri said. This is because solar wind can excite ultra-low frequency (ULF) waves by triggering KH instability, which can energize radiation belt particles.

Space hurricanes are universal phenomena, occurring at the boundary layers of Coronal Mass Ejections - giant balls of plasma erupting from the Sun's hot atmosphere - in the magnetospheres of Jupiter, Saturn and other planets, Nykyri noted.

"KH waves can alter the direction and properties of Coronal Mass Ejections, which eventually affect near-Earth space weather," Nykyri explained. "For accurate space weather prediction, it is crucial to understand the detailed mechanisms that affect the growth and properties of space hurricanes."

Furthermore, in addition to playing a role in transporting energy and mass, a recent discovery by Nykyri and her graduate student Thomas W. Moore shows that KH waves also provide an important way of heating plasma by millions of degrees Fahrenheit (Moore et al., Nature Physics, 2016), and therefore may be important for solar coronal heating. It might also be used for transport barrier generation in fusion plasmas.

For the current research, simulations were based on seven years' worth of measurements of the amplitude and velocity of solar wind fluctuations at the edge of the magnetosphere, as captured by NASA's THEMIS (Time History of Events and Macroscale Interactions during Substorms) spacecraft.

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