Predicting a protein’s location within a cell can help researchers unlock a plethora of biological information that’s critical for developing future scientific discoveries related to drug development and treating diseases like epilepsy. That’s because proteins are the body’s “workhorses,” largely responsible for most cellular functions.

Recently, Dong Xu, Curators Distinguished Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri, and colleagues updated their protein localization prediction model, MULocDeep, with the ability to provide more targeted predictions, including specific models for animals, humans, and plants. The model was created 10 years ago by Xu and fellow MU researcher Jay Thelen, a professor of biochemistry, to originally study proteins in mitochondria.

“Many biological discoveries need to be validated by experiments, but we don’t want researchers to have to spend time and money conducting thousands of experiments to get there,” Xu said. “A more targeted approach saves time. Our tool provides a useful resource for researchers by helping them get to their discoveries faster because we can help them design more targeted experiments from which to advance their research more effectively.”

By harnessing the power of artificial intelligence through a machine learning technique — training computers to make predictions using existing data — the model can help researchers who are studying the mechanisms associated with irregular locations of proteins, known as “mislocalization,” or where a protein goes to a different place than it’s supposed to. This abnormality is often associated with diseases such as metabolic disorders, cancers, and neurological disorders.

“Some diseases are caused by mislocalization, which causes the protein to be unable to perform a function as expected because it either cannot go to a target or goes there inefficiently,” Xu said.

Another application of the team’s predictive model is assisting with drug design by targeting an improperly located protein and moving it to the correct location, Xu said.

This work is currently supported by National Science Foundation. In the future, Xu hopes to receive additional funding to help increase the model’s accuracy and develop more functionalities.

“We want to continue improving the model to determine whether a mutation in a protein could cause mislocalization, whether proteins are distributed in more than one cellular compartment, or how signal peptides can help predict localization more precisely,” Xu said. “While we don’t offer any solutions for drug development or treatments for various diseases per se, our tool may help others with their development of medical solutions. Today’s science is like a big enterprise. Different people play different roles, and by working together we can achieve a lot of good for all.”  

Xu is currently working with colleagues to develop a free, online course for high school and college students based on the biological and bioinformatics concepts used in the model and expects the course will be available later this year. 

A conflict of interest is also noted by Xu and colleagues: While the online version of MULocDeep is available for use by academic users, a standalone version is also available commercially through a licensing fee. 

An interdisciplinary team of researchers, led by the University of Oklahoma, is working to provide decision-makers with better information to improve national security and supply chain resiliency through visual analytics 

A new research effort led by the University of Oklahoma and funded by the Defense Advanced Research Projects Agency, or DARPA, will develop a visual analytics system to help Department of Defense decision-makers understand the different types of risks associated with the global supply chain networks, the various actions that can be taken to protect the interests of national security, and ways to withstand and recover from any supply chain disruptions as quickly as possible. 

Recent events like the COVID-19 pandemic have made apparent how supply chain networks are an essential yet vulnerable necessity for how resources, goods, and services move around the globe.

“Everything that has happened in recent years has emphasized the importance of studying supply chain networks and making those more resilient to a broad range of disruptions, as well as more adaptable to new technologies,” said Andrés D. González, Ph.D., assistant professor in the School of Industrial and Systems Engineering, Gallogly College of Engineering at OU, and the principal investigator of the study.

“For example, COVID-19 caused significant cascading failures, where in diverse circumstances, a delay or a disruption in one of the functions from a single supplier propagated globally throughout the entire supply chain network and had effects in multiple regions and industries,” he added. “Many of these failures were caused by mechanisms that had never been observed before in history, and the depth and complexity of their effects were not adequately foreseen, thus inspiring the type of work we’re doing.”

González, who is also an affiliate faculty in the data science and analytics program in the Gallogly College of Engineering at OU, is leading an interdisciplinary team composed of experts spanning economics, industrial and systems engineering, computer science, and aerospace and mechanical engineering, among others. González is also working with OU’s Data Institute for Societal Challenges and Oklahoma Aerospace and Defense Innovation Institute, whose executive director, retired Lt. Gen. Gene Kirkland, observed that this effort is “yet another example of emerging partnerships between academic colleges and university-wide centers to advance OU’s research in support of national security challenges.”

Throughout the four-year $3.7 million project, the research team plans to create an extensive computational and visual analytic environment using state-of-the-art modeling and predictive techniques, along with visualizations such as spatiotemporal graphs, charts, and maps, to identify vulnerabilities and patterns that can help to better understand and evaluate the interactions and interdependencies between different components in supply-demand systems.

“First, we need to gain adequate supply chain visibility and understand the complex regional and global supply-demand networks, their structures and dynamical properties, using novel data-driven system identification techniques based on multiple data sources such as contracts, partnerships, and flow of commodities and information,” González said. “Once a good understanding of supply chain network structure and dynamics has been achieved, it is critical to developing advanced models for supplier survivability prediction, risk quantification and propagation, and resilience-based mitigation, preparedness, and recovery actions.”

By integrating those components within a visual analytics environment, researchers and practitioners will have a framework that can show not only visual representations of existing supply-demand networks but also provide significant insights and actionable information for stakeholders and decision-makers.

“A strong visual analytics environment can provide valuable information into what-if scenarios associated with a diverse range of disruptions, as well as pre-and post-event policies,” González said. “For example, what if we had another pandemic? What would be the effect of increasing tributary duties in a particular industry? Or, what if there is some political issue that affects some trade deal? The idea is to learn how people make decisions and how that can also give information to mathematical models to improve their predictive power.

“It is also very important to understand the effect that other countries have on the performance of supply chain networks in the U.S., so having an understanding of this will enable us to make better decisions to reduce vulnerabilities, enhance our resilience, and improve cooperation as well,” he added.

Liquid helium-4, which is in a superfluid state at cryogenic temperatures close to absolute zero (-273°C), has a special vortex called a quantized vortex that originates from quantum mechanical effects. When the temperature is relatively high, the normal fluid exists simultaneously in the superfluid helium, and when the quantized vortex is in motion, mutual friction occurs between it and the normal fluid. However, it is difficult to explain precisely how a quantized vortex interacts with a normal fluid in motion. Although several theoretical models have been proposed, it has not been clear which model is correct. 

A research group led by Professor Makoto Tsubota and Specially Appointed Assistant Professor Satoshi Yui, from the Graduate School of Science and the Nambu Yoichiro Institute of Theoretical and Experimental Physics, Osaka Metropolitan University respectively in cooperation with their colleagues from Florida State University and Keio University, investigated numerically the interaction between a quantized vortex and a normal-fluid. Based on the experimental results, researchers decided on the most consistent of several theoretical models. They found that a model that accounts for changes in the normal fluid and incorporates more theoretically accurate mutual friction is the most compatible with the experimental results.

 “The subject of this study, the interaction between a quantized vortex and a normal fluid, has been a great mystery since I began my research in this field 40 years ago,” stated Professor Tsubota. “Computational advances have made it possible to handle this problem, and the brilliant visualization experiment by our collaborators at Florida State University has led to a breakthrough. As is often the case in science, subsequent developments in technology have made it possible to elucidate, and this study is a good example of this.”

Quantum computers might be able to crack currently unsolvable tasks, but it is not easy to expand them to the necessary size. A new technique from a team of Darmstadt physicists could overcome this hurdle.

Darmstadt physicists have developed a technique that could overcome one of the biggest hurdles in building a practically-relevant quantum computer. They make use of an optical effect here discovered by British photo pioneer William Talbot in 1836. The team led by Malte Schlosser and Gerhard Birkl from the Institute of Applied Physics at Technische Universität Darmstadt in Germany presents this success in the journal Physical Review Letters.

Quantum computers can solve certain tasks much quicker even than supercomputers. However, there have so far only been prototypes with a maximum of a few hundred “qubits”. These are the basic units of information in quantum computing, corresponding to “bits” in classical computing. However, unlike bits, qubits can process the two values “0” or “1” simultaneously instead of one after the other, which enables quantum computers to perform a great many calculations in parallel.

Quantum computers with many thousands, if not several millions, of qubits, would be required for practical applications, such as optimizing complex traffic flows. However, adding qubits consumes resources, such as laser output, which has so far hampered the development of quantum computers. The Darmstadt team has now shown how the optical Talbot effect can be used to increase the number of qubits from several hundred to over ten thousand without proportionally requiring additional resources. 

Qubits can be realized in different ways. Tech giants such as Google, for instance, use artificially manufactured superconducting circuit elements. However, individual atoms are also excellent for this purpose. To control these in a targeted manner, single-atom qubits must be held in a regular lattice, similar to a chess board. Physicists usually use an “optical lattice” of regularly arranged points of light for this, which is formed when laser beams cross each other. “If you want to increase the number of qubits by a certain factor, you also have to correspondingly increase the laser output,” explains Birkl.

Optical lattice in an innovative way

His team produces the optical lattice innovatively. They shine a laser onto a glass element the size of a fingernail, on which tiny optical lenses are arranged similar to a chess board. Each microlens bundles a small part of the laser beam, thereby creating a plane of focal points, which can hold atoms.

Now, the Talbot effect is occurring on top, which has so far been considered a nuisance: the layer of focal points is repeated multiple times at equal intervals; what are known as “self-images” are created. Therefore, an optical lattice in 2D becomes one in 3D with many times the points of light. “We get that for free,” says Malte Schlosser, the lead author of the work. He means that no additional laser output is required for this.

The high manufacturing precision of microlenses leads to very regularly arranged self-images, which can be used for qubits. The researchers were able to indeed load the additional layers with individual atoms. With the given laser output, 16 of such free layers were created, potentially allowing for more than 10,000 qubits. According to Schlosser, conventional lasers can be used to quadruple the power in the future. “The microlens field can also be optimized further,” explains Birkl, such as by creating more focal points with smaller lenses. 100,000 qubits and more will therefore be possible in the foreseeable future. The scalability in the number of qubits shown by the team represents an important step towards developing practicable quantum supercomputers.

Schlosser emphasizes that the technology is not limited to quantum computers. “Our platform could also potentially apply to high-precision optical atomic clocks.” The Darmstadt team plans to further develop its new qubit platform and envisages a variety of possible applications in the field of quantum technologies.