CAPTION Double row spacing is better for sugarcane plants, soil but sacrifices up to 10% of yield.

As farmers survey their fields this summer, several questions come to mind: How many plants germinated per acre? How does altering row spacing affect my yields? Does it make a difference if I plant my rows north to south or east to west? Now a supercomputer model can answer these questions by comparing billions of virtual fields with different planting densities, row spacings, and orientations.

The University of Illinois and the Partner Institute for Computational Biology in Shanghai developed this computer model to predict the yield of different crop cultivars in a multitude of planting conditions. Published in BioEnergy-Research, the model depicts the growth of 3D plants, incorporating models of the biochemical and biophysical processes that underlie productivity.

Teaming up with the University of Sao Paulo in Brazil, they used the model to address a question for sugarcane producers: How much yield might be sacrificed to take advantage of a possible conservation planting technique?

"Current sugarcane harvesters cut a single row at a time, which is time-consuming and leads to damage of the crop stands," said author Steve Long, Gutgsell Endowed Professor of Plant Biology and Crop Sciences at the Carl R. Woese Institute for Genomic Biology. "This could be solved if the crop was planted in double rows with gaps between the double rows. But plants in double rows will shade each other more, causing a potential loss of profitability."

The model found that double-row spacing costs about 10% of productivity compared to traditional row spacing; however, this loss can be reduced to just 2% by choosing cultivars with more horizontal leaves planted in a north-south orientation.

"This model could be applied to other crops to predict optimal planting designs for specific environments," said Yu Wang, a postdoctoral researcher at Illinois who led the study. "It could also be used in reverse to predict the potential outcome for a field."

The authors predict this model will be especially useful when robotic planting becomes more commonplace, which will allow for many more planting permutations.

CAPTION Dr. Chris Wilmer is assistant professor of chemical and petroleum engineering at the University of Pittsburgh. CREDIT Swanson School of Engineering/Ric Evans

Award recognizes 'outstanding research in computational molecular science and engineering, encompassing both methods and applications'

The American Institute of Chemical Engineers (AIChE) selected Christopher Wilmer, assistant professor of chemical and petroleum engineering at the University of Pittsburgh, as its 2017 recipient of the Young Investigator Award for Modeling and Simulation. The AIChE Computational Molecular Science and Engineering Forum (CoMSEF) presents the award annually to one individual who received his/her highest degree within the past seven years.

"In the three years since Chris came to Pitt, I have watched him pursue research topics with the potential to have a profound impact on energy, the environment, and society as a whole," said Steven Little, the William Kepler Whiteford Professor and Chair of the Department of Chemical and Petroleum Engineering at Pitt. "By reaching so high, he has been able to accomplish so much during the very early stages of what promises to be an extraordinary career. The CoMSEF Young Investigator Award is one of the most prestigious honors in Chemical Engineering simulation and modeling, and truly reflects the breadth and depth of Chris' career over such a short period."

The AIChE CoMSEF Young Investigator Award for Modeling and Simulation accepts applicants throughout academia, industry, or government laboratories. According to AIChE, the award recognizes "outstanding research in computational molecular science and engineering, encompassing both methods and applications."

In addition to the award, Dr. Wilmer will receive a plaque, honorarium, and invitation to give a talk within the CoMSEF Plenary session at the AIChE Annual Meeting in Minneapolis, Minn., this October. Dr. Wilmer is the fifth recipient of this award since its establishment in 2013.

Kaye Innovation Prize winner Prof. Amiram Goldblum at the Hebrew University's Institute for Drug Research

An algorithm developed at the Hebrew University cuts through the immense number of possible solutions to shorten drug discovery times from years to months

Antibiotics for treating particularly resistant diseases, molecules that block immune system overreactions, molecules that inhibit the growth of cancer cells by removing excess iron, molecules that may increase the digestion of fats: all these and more have been discovered in recent years using a unique supercomputerized approach to solving particularly complex problems.

Over the past five years, an Iterative Stochastic Elimination (ISE) algorithm developed in the laboratory of Prof. Amiram Goldblum, at the Hebrew University of Jerusalem's Institute for Drug Research, has been applied to the discovery of potential drugs. The Institute is part of the School of Pharmacy in the Faculty Of Medicine. First tested to solve problems in the structure and function of proteins, the algorithm has since been used to reduce drug discovery times -- from years to months and even to weeks.

Goldblum's solution is different from other algorithms called "heuristics," which are based on deriving solutions using logic and intuition, and suggests better solutions. In this instance, the algorithm produces a model for the activity of small molecules on one or more proteins known to cause the disease. A model is a set of filters of physico-chemical properties that distinguish between active and non-active molecules, or between more and less active ones. Millions of molecules can then be screened by the model, which enables the scoring of each molecule by a number that reflects its ability to pass through the filters based on its own physico-chemical properties.

A model of this type is usually built in a few hours and is capable of screening millions of molecules in less than a day. Therefore, within a few days or more, it is possible to make initial predictions about the candidate molecules for a specific activity to combat a disease. Most of those candidates have never been known before to have any biological activity.

For the development of this algorithm, Prof. Goldblum won an American Chemical Society Prize in 2000. Since then, the algorithm has solved many problems related to understanding various biological systems such as protein flexibility, proteins-small molecules interactions, and more. These and other discoveries stem from collaborations between Goldblum's laboratory, where his students employ the algorithm to solve various problems, and laboratories and pharmaceutical companies in the world that test Goldblum's predictions in Germany, Japan, the United States and of course in Israel.

On the strength of Goldblum's technology, the company Pepticom was founded in 2011 by Yissum, the Technology Transfer arm of the Hebrew University, to revolutionize the discovery of novel peptide drug candidates. Pepticom's key asset is an exceptional artificial intelligence platform aimed at designing peptide ligands based upon solved crystal structures of proteins.

Wide Applications

The algorithm can be applied to other types of problems, in which the number of possibilities is immense and are not solvable even if the world's most powerful computers would work on it together. These include problems in which the number of possible outcomes are 10 to the power of 100 and more, such as problems of land transport, aviation, communications and biological systems.

In the field of transportation, this could involve finding alternative ways to get from one point to another using traffic data on each of the alternative roads leading between the two points. In aviation, an optimal arrangement of landings and takeoffs at busy airports. In telecommunications, finding the least expensive routes within a complex array of communication cables. And in biology, a model that is constructed on the basis of a few dozen or hundreds of molecules serves to screen millions of molecules and to discover new drug candidates. These are then sent to experimental labs to be developed further, and in some cases have been crucial in furthering the development of treatment for Alzheimer's disease and different forms of cancer.

At any given moment, as many as 10 million wild jets of solar material burst from the sun's surface. They erupt as fast as 60 miles per second, and can reach lengths of 6,000 miles before collapsing. These are spicules, and despite their grass-like abundance, scientists didn't understand how they form. Now, for the first time, a supercomputer simulation -- so detailed it took a full year to run -- shows how spicules form, helping scientists understand how spicules can break free of the sun's surface and surge upward so quickly.

This work relied upon high-cadence observations from NASA's Interface Region Imaging Spectrograph, or IRIS, and the Swedish 1-meter Solar Telescope in La Palma, in the Canary Islands. Together, the spacecraft and telescope peer into the lower layers of the sun's atmosphere, known as the interface region, where spicules form. The results of this NASA-funded study were published in Science on June 22, 2017 -- a special time of the year for the IRIS mission, which celebrates its fourth anniversary in space on June 26.

"Numerical models and observations go hand in hand in our research," said Bart De Pontieu, an author of the study and IRIS science lead at Lockheed Martin Solar and Astrophysics Laboratory, in Palo Alto, California. "We compare observations and models to figure out how well our models are performing, and to improve the models when we see major discrepancies."

Observing spicules has been a thorny problem for scientists who want to understand how solar material and energy move through and away from the sun. Spicules are transient, forming and collapsing over the course of just five to 10 minutes. These tenuous structures are also difficult to study from Earth, where the atmosphere often blurs our telescopes' vision.

A team of scientists has been working on this particular model for nearly a decade, trying again and again to create a version that would create spicules. Earlier versions of the model treated the interface region, the lower solar atmosphere, as a hot gas of electrically charged particles -- or more technically, a fully ionized plasma. But the scientists knew something was missing because they never saw spicules in the simulations.

The key, the scientists realized, was neutral particles. They were inspired by Earth's own ionosphere, a region of the upper atmosphere where interactions between neutral and charged particles are responsible for many dynamic processes.

The research team knew that in cooler regions of the sun, such as the interface region, not all gas particles are electrically charged. Some particles are neutral, and neutral particles aren't subject to magnetic fields like charged particles are. Scientists had based previous models on a fully ionized plasma in order to simplify the problem. Indeed, including the necessary neutral particles was very computationally expensive, and the final model took roughly a year to run on the Pleiades supercomputer located at NASA's Ames Research Center in Silicon Valley, and which supports hundreds of science and engineering projects for NASA missions.

The model began with a basic understanding of how plasma moves in the sun's atmosphere. Constant convection, or boiling, of material throughout the sun generates islands of tangled magnetic fields. When boiling carries them up to the surface and farther into the sun's lower atmosphere, magnetic field lines rapidly snap back into place to resolve the tension, expelling plasma and energy. Out of this violence, a spicule is born. But explaining how these complex magnetic knots rise and snap was the tricky part.

"Usually magnetic fields are tightly coupled to charged particles," said Juan Martínez-Sykora, lead author of the study and a solar physicist at Lockheed Martin and the Bay Area Environmental Research Institute in Sonoma, California. "With only charged particles in the model, the magnetic fields were stuck, and couldn't rise beyond the sun's surface. When we added neutrals, the magnetic fields could move more freely."

Neutral particles provide the buoyancy the gnarled knots of magnetic energy need to rise through the sun's boiling plasma and reach the chromosphere. There, they snap into spicules, releasing both plasma and energy. Friction between ions and neutral particles heats the plasma even more, both in and around the spicules.

With the new model, the simulations at last matched observations from IRIS and the Swedish Solar Telescope; spicules occurred naturally and frequently. The 10 years of work that went into developing this numerical model earned scientists Mats Carlsson and Viggo H. Hansteen, both authors of the study from the University of Oslo in Norway, the 2017 Arctowski Medal from the National Academy of Sciences. Martínez-Sykora led the expansion of the model to include the effects of neutral particles.

The scientists' updated model revealed something else about how energy moves in the solar atmosphere. It turns out this whip-like process also naturally generates Alfvén waves, a strong kind of magnetic wave scientists suspect is key to heating the sun's atmosphere and propelling the solar wind, which constantly bathes our solar system and planet with charged particles from the sun.

"This model answers a lot of questions we've had for so many years," De Pontieu said. "We gradually increased the physical complexity of numerical models based on high-resolution observations, and it is really a success story for the approach we've taken with IRIS."

The simulations indicate spicules could play a big role in energizing the sun's atmosphere, by constantly forcing plasma out and generating so many Alfvén waves across the sun's entire surface.

"This is a major advance in our understanding of what processes can energize the solar atmosphere, and lays the foundation for investigations with even more detail to determine how big of a role spicules play," said Adrian Daw, IRIS mission scientist at NASA's Goddard Space Flight Center in Greenbelt, Maryland. "A very nice result on the eve of our launch anniversary."

Every year, severe weather endangers millions of people and causes billions of dollars in damage worldwide. But new research from Penn State's College of Information Sciences and Technology (IST) and AccuWeather has found a way to better predict some of these threats by harnessing the power of big data.

The research team, led by doctoral student Mohammad Mahdi Kamani and including IST professor James Wang, doctoral student Farshid Farhat, and AccuWeather forensic meteorologist Stephen Wistar, has developed a new approach for identifying bow echoes in radar images, a phenomenon associated with fierce and violent winds.

"It was inevitable for meteorology to combine big data, computer vision, and data mining algorithms to seek faster, more robust and accurate results," Kamani said. Their research paper, "Skeleton Matching with Applications in Severe Weather Detection," was published in the journal of Applied Soft Computing and was funded by the National Science Foundation (NSF).

"I think computer-based methods can provide a third eye to the meteorologists, helping them look at things they don't have the time or energy for," Wang said. In the case of bow echoes, this automatic detection would be vital to earlier recognition of severe weather, saving lives and resources.

Wistar, the meteorological authority on the project, explained, "In a line of thunderstorms, a bow echo is one part that moves faster than the other." As the name suggests, once the weather conditions have fully formed, it resembles the shape of a bow. "It can get really exaggerated," he said. "It's important because that's where you are likely to get serious damage, where trees will come down and roofs get blown off."

But currently, when the conditions are just beginning to form, it can be easy for forecasters to overlook. "Once it gets to the blatantly obvious point, (a bow echo) jumps out to a meteorologist," he said. "But on an active weather day? They may not notice it's just starting to bow."

To combat this, the research focused on automating the detection of bow echoes. By drawing on the vast historical data collected by the National Oceanic and Atmosphere Administration (NOAA), bow echoes can be automatically identified the instant they begin to form. Wang said, "That's our project's fundamental goal -- to provide assistance to the meteorologist so they can make decisions quicker and with better accuracy."

By continually monitoring radar imagery from NOAA, the algorithm is able to scan the entire United States and provide alerts whenever and wherever a bow echo is beginning. During times of active severe weather, when resources are likely to be spread thin, it's able to provide instant notifications of the development.

"But this is just the first step," Kamani commented. With the detection algorithm in place, they hope to one day forecast bow echoes before they even form. "The end goal is to have more time to alert people to evacuate or be ready for the straight line winds." With faster, more precise forecasts, the potential impacts can be significant.

"If you can get even a 10, 15 minute jump and get a warning out earlier pinned down to a certain location instead of entire counties, that's a huge benefit," Wistar said. "That could be a real jump for meteorologists if it's possible. It's really exciting to see this progress."

Envisioning the future of meteorology, the researchers see endless potential for the application of big data. "There's so much we can do," Wang said. "If we can predict severe thunderstorms better, we can save lives every year."

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