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ASU researchers evaluate the tech advancement in data viz of ecology

A new study, published in Bioscience, considers the future of ecology, where technological advancement towards a multidimensional science will continue to fundamentally shift the way we view, explore, and conceptualize the natural world.

The study, co-led by Greg Asner, Director of the Arizona State University Center for Global Discovery and Conservation Science, in collaboration with Auburn University, the Oxford Seascape Ecology Lab, and other partners, demonstrates how the integration of remotely sensed 3D information holds great potential to provide new ecological insights on land and in the oceans.

Scientific research into 3D digital applications in ecology has grown in the last decade. Landscape and seascape ecologists can now critically frame 3D ecological questions that have been challenging to answer across broad study areas--until recently. Advances in high-resolution remote sensing systems and data processing are allowing us to model the complex surface of the Earth, both above and below water, with greater detail and accuracy than ever before. 3D seamless land-sea terrain showing lidar-derived ocean floor color (with water removed via models). CREDIT Greg Asner, Center for Global Discovery and Conservation Science, Arizona State University{module INSIDE STORY}

Future research applications in the marine environment should focus on addressing the challenges associated with integrating dynamic oceanographic information into maps capable of capturing 3D variability in the environment over time.

"3D-capable data sources have wide-ranging ecological applications and help in estimating carbon sequestration, quantifying habitat structure, mapping ecosystem services, and measuring and modeling consequences of climate change," said Asner.

As landscape and seascape ecology looks toward the future, the study notes a need for a continued progression toward a 3D science that will shift the way ecological patterns and processes are conceptualized. The paper provides key examples of 3D data application in terrestrial and marine environments to illustrate how state-of-the-art advances in ecology have been achieved through novel data fusion, spatial analysis, and visualization.

"This article highlights the unprecedented opportunity for understanding 3D ecological dynamics and human impacts on land and in the oceans, with a view to better inform management decisions," said Lisa Wedding, co-author and Associate Professor at the University of Oxford.

As a result of this 3D approach, natural resource management may support the development of conservation and management plans and shift the way that policymakers evaluate current and future regulations in a dynamic environment.

RUB researchers discover high-performance multi-element catalysts by computational prediction

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Finding the best material composition among thousands of possibilities is like looking for a needle in a haystack. An international team is combining supercomputer simulations and high-throughput experiments to do this.

Catalysts consisting of at least five chemical elements could be the key to overcoming previous limitations in the production of green hydrogen, fuel cells, batteries, or CO2 reduction. However, finding the optimal composition of these multi-element catalysts is like looking for a needle in a haystack: testing thousands to millions of possible combinations cannot be realized. Therefore, research teams from Ruhr-Universität Bochum (RUB) in Germany and the University of Copenhagen in Denmark have developed an approach that can predict the optimal composition and confirm its accuracy with high-throughput experiments. They report in the journal Angewandte Chemie International Edition of 28. December 2020, DOI: 10.1002/anie.202014374 .

Much less expensive elements than previous catalysts

Many electrochemical reactions go through several steps. Each should be optimized on a catalyst surface if possible, but different requirements apply to each step. “Since previous catalysts usually had only one optimized functionality, one could only make the best compromise possible, and energy losses could not be avoided,” explains Professor Wolfgang Schuhmann from the Center for Electrochemistry at RUB. With complex solid solutions, several functionalities can be realized simultaneously on one catalyst surface, overcoming this limitation. However, this only happens when at least five different elements are combined. There are millions of possibilities in which percentage ratios the respective elements can be combined. The previous challenge of searching for a strategy to find optimal properties seems to be answerable with this class of materials. Now the task is to find out which combination fulfills the goal in the best possible way. “Incidentally, this may also be possible with much more favorable elements than with previous catalysts,” Schuhmann emphasizes. Hundreds of possible material combinations can be tested on such a carrier. © Tobias Löffler{module INSIDE STORY}

Make and check predictions

In their work, the research teams present an approach that offers guidance among the countless possibilities. “We have developed a model that can predict the activity for oxygen reduction as a function of composition, thus enabling calculation of the best composition,“ explains Professor Jan Rossmeisl from the Center for High Entropy Alloy Catalysis at the University of Copenhagen.

The team from Bochum provided the verification of the model. “We can use a combinatorial sputtering system to produce material libraries where each point on the surface of the support has a different composition and there are different but well-defined gradients in each direction,” explains Professor Alfred Ludwig from the Chair of New Materials and Interfaces at RUB. Using a scanning droplet cell, the catalytic properties of 342 compositions on a material library are then automatically measured to identify activity trends.

“We found that the original model did not yet do justice to the complexity and still made imprecise predictions. Therefore, we revised it and had it tested again experimentally,” says Dr. Thomas Batchelor from the Copenhagen team, who was a visiting scientist at RUB as part of the collaboration. This time, prediction and experimental measurement showed excellent agreement, which was confirmed by further material libraries.

This strategy allows the complex mechanisms at the surfaces, which consist of five chemical elements, to be identified, leaving most of the screening effort to the computer. “If the model turns out to be universally applicable to all element combinations and also to other reactions, one of the currently biggest challenges of this catalyst class would be realistically met,” the team said.

Pitt researchers create model to predict the need for surgery in Abdominal Aortic Aneurysms

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An abdominal aortic aneurysm (AAA) can be a ticking time bomb if undiscovered in time. However, researchers at the University of Pittsburgh are developing a new model to better predict at-risk patients. And the tools they are using apply mechanical testing to the human body - which is itself a complex machine.

An AAA occurs when the aorta weakens and begins to irreversibly dilate, like a slowly inflating balloon. If left untreated, the risk of rupture increases and has a 90 percent rate of mortality, making AAA the 15th leading cause of death in the United States with more than 15,000 deaths reported annually.

Once diagnosed, clinicians must determine whether the aorta requires surgery, using the AAA diameter to decide if an aneurysm is clinically relevant. A diameter 5.5 centimeters or larger typically calls for surgical intervention, barring other contraindications, but this one-size-fits-all approach misses nearly 25 percent of patients who experience a rupture at a smaller size. Location and comparison of a normal aorta and abdominal aortic aneurysm. If left untreated the aneurysm will irreversibly grow increasing risk of rupture for a patient. Credit: Shutterstock.{module INSIDE STORY}

Pitt bioengineer David A. Vorp received an award from the National Institutes of Health to track the natural evolution of small AAA and develop a predictive model to improve patient prognosis. His Vascular Bioengineering Lab at the university's Swanson School of Engineering is focused on finding novel diagnoses and treatments for these silent killers.

"It's a ticking time bomb," explained Timothy Chung, a post-doctoral associate in Vorp's lab. "Once you diagnose an abdominal aortic aneurysm, you don't know when or if it's going to rupture.

"Imagine you're blowing up a balloon, and it pops. This event involves the mechanics and forces that are interacting with the wall of the balloon," continued Chung, who will help lead the project. "We're interested in the biomechanics of why elevated pressure or a weakening of the aneurysm wall might lead to rupture or accelerated growth."

The research team hopes that CT scans and other data from a rare, longitudinal clinical trial ("Non-Invasive Treatment of Abdominal Aortic Aneurysm Clinical Trial") will help them identify the risks of elevated growth rate or eventual rupture.

Vorp's lab group will create 3D geometric reconstructions and perform biomechanical simulations on patient datasets at each imaging scan interval (every six months) to learn how small AAA progresses over time. They will then use the scans and unique software tools from their lab to perform shape analyses that will determine which geometries may lead to poor patient outcomes.

"Currently, clinicians are simply applying a one-dimensional shape analysis, using the diameter as a threshold for clinical intervention," said Chung. "The tools developed in the Vascular Bioengineering Lab can help us extract more than one-dimensional measurements. They allow us to create two- and three-dimensional shape indices derived from image-based surface reconstructions, allowing for a more robust analysis."

The team will then feed data from the shape analysis and biomechanical simulations to train a machine-learning algorithm to classify different types of aneurysm outcomes. This will be used to develop a predictive model that can help guide clinicians and determine the need for surgical intervention.

"Early in my career, the advent of finite element analysis - a computational method to predict mechanical wall stress distribution in complex shapes both biological and human-made - provided a game-changing tool to better understand the role of biomechanics in AAA disease," said Vorp, Associate Dean for Research and John A. Swanson Professor of Bioengineering. "Now, machine learning technologies can not only help us better understand the combination of factors that lead toward rupture or clinical intervention but also package that knowledge into a true, personalized health tool for those afflicted with this potentially lethal condition."