Computers Math

Earthquakes do more than buckle streets and topple buildings. Seismic waves generated by earthquakes pass through the Earth, acting like a giant MRI machine and providing clues to what lies inside the planet.
Seismologists have developed methods to take wave signals from the networks of seismometers at the Earth’s surface and reverse engineer features and characteristics of the medium they pass through, a process known as seismic tomography.
For decades, seismic tomography was based on ray theory, and seismic waves were treated like light rays. This served as a pretty good approximation and led to major discoveries about the Earth’s interior. But to improve the resolution of current seismic tomographic models, seismologists need to take into account the full complexity of wave propagation using numerical simulations, known as full-waveform inversion, says Ebru Bozdag, assistant professor in the Geophysics Department at the Colorado School of Mines.
“We are at a stage where we need to avoid approximations and corrections in our imaging techniques to construct these models of the Earth’s interior,” she said.
Bozdag was the lead author of the first full-waveform inversion model, GLAD-M15 in 2016, based on full 3D wave simulations and 3D data sensitivities at the global scale. The model used the open-source 3D global wave propagation solver SPECFEM3D_GLOBE (freely available from Computational Infrastructure for Geodynamics) and was created in collaboration with researchers from Princeton University, University of Marseille, King Abdullah University of Science and Technology (KAUST) and Oak Ridge National Laboratory (ORNL). The work was lauded in the press. Its successor, GLAD-M25 (Lei et al. 2020), came out in 2020 and brought prominent features like subduction zones, mantle plumes, and hotspots into view for further discussions on mantle dynamics.
“We showed the feasibility of using full 3D wave simulations and data sensitivities to seismic parameters at the global scale in our 2016 and 2020 papers. Now, it’s time to use better parameterization to describe the physics of the Earth’s interior in the inverse problem,” she said. More

Because the comparative metabolomics field lacks sophisticated data analysis tools that are available to genomics and proteomics researchers, metabolomics researchers spend a lot of time hunting for candidate compounds that could be useful as leads for the development of new pharmaceuticals or agrochemicals. To solve this problem, scientists have developed Metaboseek, a free, easy-to-use app that integrates multiple data analysis features for the metabolomics community.
As a postdoctoral research associate in the lab of BTI faculty member Frank Schroeder, Max Helf saw his labmates continually struggle when they were analyzing data. So, he decided to do something about it and developed a free, open-source app called Metaboseek, which is now essential to the lab’s work.
The Schroeder lab studies the roundworm Caenorhabditis elegans, one of the most successful model systems for human biology, to discover new metabolites that govern evolutionarily conserved signaling pathways and could be useful as leads for the development of new pharmaceuticals or agrochemicals. The researchers accomplish this task by comparing the metabolites between two different worm populations — a process called comparative metabolomics.
Given that samples routinely have more than 100,000 compounds in them, computational approaches are essential to perform the analysis.
The team had been relying on software packages that did not offer the required level of flexibility to easily customize analysis parameters. That limitation, and the lack of a suitable graphical user interface, meant Helf’s colleagues faced the cumbersome task of visually inspecting mounds of data — for example, to spot possible false positives — and jumping between several other software tools to confirm and filter out those meaningless results.
“It just seemed very inefficient to me, and I couldn’t get over the shortcomings of other software solutions for this problem,” Helf said. “I thought there had to be an easier way, so I started to write code for my own software.”
Helf developed the initial version of his software in 2017, and continued to improve it over the next two years. “Besides addressing the problems my labmates were already facing, I talked to them about what else held them back — what they wanted to do but weren’t even trying — and built those features in the app,” said Helf, who is now a bioinformatics product manager at proteomics company Biognosys AG. “I wanted this new tool to be user-friendly and accessible to anyone who does chemical biology.” More

Even though robots don’t have eyes with retinas, the key to helping them see and interact with the world more naturally and safely may rest in optical coherence tomography (OCT) machines commonly found in the offices of ophthalmologists.
One of the imaging technologies that many robotics companies are integrating into their sensor packages is Light Detection and Ranging, or LiDAR for short. Currently commanding great attention and investment from self-driving car developers, the approach essentially works like radar, but instead of sending out broad radio waves and looking for reflections, it uses short pulses of light from lasers.
Traditional time-of-flight LiDAR, however, has many drawbacks that make it difficult to use in many 3D vision applications. Because it requires detection of very weak reflected light signals, other LiDAR systems or even ambient sunlight can easily overwhelm the detector. It also has limited depth resolution and can take a dangerously long time to densely scan a large area such as a highway or factory floor. To tackle these challenges, researchers are turning to a form of LiDAR called frequency-modulated continuous wave (FMCW) LiDAR.
“FMCW LiDAR shares the same working principle as OCT, which the biomedical engineering field has been developing since the early 1990s,” said Ruobing Qian, a PhD student working in the laboratory of Joseph Izatt, the Michael J. Fitzpatrick Distinguished Professor of Biomedical Engineering at Duke. “But 30 years ago, nobody knew autonomous cars or robots would be a thing, so the technology focused on tissue imaging. Now, to make it useful for these other emerging fields, we need to trade in its extremely high resolution capabilities for more distance and speed.”
In a paper appearing March 29 in the journal Nature Communications, the Duke team demonstrates how a few tricks learned from their OCT research can improve on previous FMCW LiDAR data-throughput by 25 times while still achieving submillimeter depth accuracy.
OCT is the optical analogue of ultrasound, which works by sending sound waves into objects and measuring how long they take to come back. To time the light waves’ return times, OCT devices measure how much their phase has shifted compared to identical light waves that have travelled the same distance but have not interacted with another object. More

Artificial intelligence (AI) is an effective tool for fracture detection that has potential to aid clinicians in busy emergency departments, according to a study in Radiology.
Missed or delayed diagnosis of fractures on X-ray is a common error with potentially serious implications for the patient. Lack of timely access to expert opinion as the growth in imaging volumes continues to outpace radiologist recruitment only makes the problem worse.
AI may help address this problem by acting as an aid to radiologists, helping to speed and improve fracture diagnosis.
To learn more about the technology’s potential in the fracture setting, a team of researchers in England reviewed 42 existing studies comparing the diagnostic performance in fracture detection between AI and clinicians. Of the 42 studies, 37 used X-ray to identify fractures, and five used CT.
The researchers found no statistically significant differences between clinician and AI performance. AI’s sensitivity for detecting fractures was 91-92%.
“We found that AI performed with a high degree of accuracy, comparable to clinician performance,” said study lead author Rachel Kuo, M.B. B.Chir., from the Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences in Oxford, England. “Importantly, we found this to be the case when AI was validated using independent external datasets, suggesting that the results may be generalizable to the wider population.”
The study results point to several promising educational and clinical applications for AI in fracture detection, Dr. Kuo said. It could reduce the rate of early misdiagnosis in challenging circumstances in the emergency setting, including cases where patients may sustain multiple fractures. It has potential as an educational tool for junior clinicians.
“It could also be helpful as a ‘second reader,’ providing clinicians with either reassurance that they have made the correct diagnosis or prompting them to take another look at the imaging before treating patients,” Dr. Kuo said.
Dr. Kuo cautioned that research into fracture detection by AI remains in a very early, pre-clinical stage. Only a minority of the studies that she and her colleagues looked at evaluated the performance of clinicians with AI assistance, and there was only one example where an AI was evaluated in a prospective study in a clinical environment.
“It remains important for clinicians to continue to exercise their own judgment,” Dr. Kuo said. “AI is not infallible and is subject to bias and error.”
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Physicists know about the huge chasm between quantum physics and the theory of gravity. However, in recent decades, theoretical physics has provided some plausible conjecture to bridge this gap and to describe the behaviour of complex quantum many-body systems, for example black holes and wormholes in the universe. Now, a theory group at Freie Universität Berlin and HZB, together with Harvard University, USA, has proven a mathematical conjecture about the behaviour of complexity in such systems, increasing the viability of this bridge. The work is published in Nature Physics.
“We have found a surprisingly simple solution to an important problem in physics,” says Prof. Jens Eisert, a theoretical physicist at Freie Universität Berlin and HZB. “Our results provide a solid basis for understanding the physical properties of chaotic quantum systems, from black holes to complex many-body systems,” Eisert adds.
Using only pen and paper, i.e. purely analytically, the Berlin physicists Jonas Haferkamp, Philippe Faist, Naga Kothakonda and Jens Eisert, together with Nicole Yunger Halpern (Harvard, now Maryland), have succeeded in proving a conjecture that has major implications for complex quantum many-body systems. “This plays a role, for example, when you want to describe the volume of black holes or even wormholes,” explains Jonas Haferkamp, PhD student in the team of Eisert and first author of the paper.
Complex quantum many-body systems can be reconstructed by circuits of so-called quantum bits. The question, however, is: how many elementary operations are needed to prepare the desired state? On the surface, it seems that this minimum number of operations — the complexity of the system — is always growing. Physicists Adam Brown and Leonard Susskind from Stanford University formulated this intuition as a mathematical conjecture: the quantum complexity of a many-particle system should first grow linearly for astronomically long times and then — for even longer — remain in a state of maximum complexity. Their conjecture was motivated by the behaviour of theoretical wormholes, whose volume seems to grow linearly for an eternally long time. In fact, it is further conjectured that complexity and the volume of wormholes are one and the same quantity from two different perspectives. “This redundancy in description is also called the holographic principle and is an important approach to unifying quantum theory and gravity. Brown and Susskind’s conjecture on the growth of complexity can be seen as a plausibility check for ideas around the holographic principle,” explains Haferkamp.
The group has now shown that the quantum complexity of random circuits indeed increases linearly with time until it saturates at a point in time that is exponential to the system size. Such random circuits are a powerful model for the dynamics of many-body systems. The difficulty in proving the conjecture arises from the fact that it can hardly be ruled out that there are “shortcuts,” i.e. random circuits with much lower complexity than expected. “Our proof is a surprising combination of methods from geometry and those from quantum information theory. This new approach makes it possible to solve the conjecture for the vast majority of systems without having to tackle the notoriously difficult problem for individual states,” says Haferkamp.
“The work in Nature Physics is a nice highlight of my PhD,” adds the young physicist, who will take up a position at Harvard University at the end of the year. As a postdoc, he can continue his research there, preferably in the classic way with pen and paper and in exchange with the best minds in theoretical physics.
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Under a project led by the RIKEN Center for Computational Science, researchers have used computer simulations to show that weather phenomena such as sudden downpours could potentially be modified by making small adjustments to certain variables in the weather system. They did this by taking advantage of a system known as a “butterfly attractor” in chaos theory, where a system can have one of two states — like the wings of a butterfly — and that it switches back and forth between the two states depending on small changes in certain conditions.
While weather predictions have reached levels of high accuracy thanks to methods such as supercomputer-based simulations and data assimilation, where observational data is incorporated into simulations, scientists have long hoped to be able to control the weather. Research in this area has intensified due to climate change, which has led to more extreme weather events such as torrential rain and storms.
There are methods at present for weather modification, but they have had limited success. Seeding the atmosphere to induce rain has been demonstrated, but it is only possible when the atmosphere is already in a state where it might rain. Geoengineering projects have been envisioned, but have not been carried out due to concerns about what unpredicted long-term effects they might have.
As a promising approach, researchers from the RIKEN team have looked to chaos theory to create realistic possibilities for mitigating weather events such as torrential rain. Specifically, they have focused on a phenomenon known as a butterfly attractor, proposed by mathematician and meteorologist Edward Lorentz, one of the founders of modern chaos theory. Essentially, this refers to a system that can adopt one of two orbits that look like the wings of a butterfly, but can change the orbits randomly based on small fluctuations in the system.
To perform the work, the RIKEN team ran one weather simulation, to serve as the control of “nature” itself, and then ran other simulations, using small variations in a number of variables describing the convection — how heat moves through the system — and discovered that small changes in several of the variables together could lead to the system being in a certain state once a certain amount of time elapsed.
According to Takemasa Miyoshi of the RIKEN Center for Computational Science, who led the team, “This opens the path to research into the controllability of weather and could lead to weather control technology. If realized, this research could help us prevent and mitigate extreme windstorms, such as torrential rains and typhoons, whose risks are increasing with climate change.”
“We have built a new theory and methodology for studying the controllability of weather,” he continues. “Based on the observing system simulation experiments used in previous predictability studies, we were able to design an experiment to investigate predictability based on the assumption that the true values (nature) cannot be changed, but rather that we can change the idea of what can be changed (the object to be controlled).”
Looking to the future, he says, “In this case we used an ideal low-dimensional model to develop a new theory, and in the future we plan to use actual weather models to study the possible controllability of weather.”
The work, published in Nonlinear Processes of Geophysics, was done as part of the Moonshot R&D Millennia program, contributing to the new Moonshot goal #8.
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A team of scientists has created a powerful new method for generating protein drugs. Using computers, they designed molecules that can target important proteins in the body, such as the insulin receptor, as well as vulnerable proteins on the surface of viruses. This solves a long-standing challenge in drug development and may lead to new treatments for cancer, diabetes, infection, inflammation, and beyond.
The research, appearing March 24 in the journal Nature, was led by scientists in the laboratory of David Baker, professor of biochemistry at the University of Washington School of Medicine and a recipient of the 2021 Breakthrough Prize in Life Sciences.
“The ability to generate new proteins that bind tightly and specifically to any molecular target that you want is a paradigm shift in drug development and molecular biology more broadly,” said Baker.
Antibodies are today’s most common protein-based drugs. They typically function by binding to a specific molecular target, which then becomes either activated or deactivated. Antibodies can treat a wide range of health disorders, including COVID-19 and cancer, but generating new ones is challenging. Antibodies can also be costly to manufacture.
A team led by two postdoctoral scholars in the Baker lab, Longxing Cao and Brian Coventry, combined recent advances in the field of computational protein design to arrive at a strategy for creating new proteins that bind molecular targets in a manner similar to antibodies. They developed software that can scan a target molecule, identify potential binding sites, generate proteins targeting those sites, and then screen from millions of candidate binding proteins to identify those most likely to function.
The team used the new software to generate high-affinity binding proteins against 12 distinct molecular targets. These targets include important cellular receptors such as TrkA, EGFR, Tie2, and the insulin receptor, as well proteins on the surface of the influenza virus and SARS-CoV-2 (the virus that causes COVID-19).
“When it comes to creating new drugs, there are easy targets and there are hard targets,” said Cao, who is now an assistant professor at Westlake University. “In this paper, we show that even very hard targets are amenable to this approach. We were able to make binding proteins to some targets that had no known binding partners or antibodies,”
In total, the team produced over half a million candidate binding proteins for the 12 selected molecular targets. Data collected on this large pool of candidate binding proteins was used to improve the overall method.
“We look forward to seeing how these molecules might be used in a clinical context, and more importantly how this new method of designing protein drugs might lead to even more promising compounds in the future,” said Coventry.
The research team included scientists from the University of Washington School of Medicine, Yale University School of Medicine, Stanford University School of Medicine, Ghent University, The Scripps Research Institute, and the National Cancer Institute, among other institutions.
This work was supported in part by The Audacious Project at the Institute for Protein Design, Open Philanthropy Project, National Institutes of Health (HHSN272201700059C, R01AI140245, R01AI150855, R01AG063845), Defense Advanced Research Project Agency (HR0011835403 contract FA8750-17-C-0219), Defense Threat Reduction Agency (HDTRA1-16-C-0029), Schmidt Futures, Gates Ventures, Donald and Jo Anne Petersen Endowment, and an Azure computing gift for COVID-19 research provided by Microsoft. More

Hitachi, Ltd., University of Utah Health, and Regenstrief Institute, Inc. today announced the development of an AI method to improve care for patients with type 2 diabetes mellitus who need complex treatment. One in 10 adults worldwide have been diagnosed with type 2 diabetes, but a smaller number require multiple medications to control blood glucose levels and avoid serious complications, such as loss of vision and kidney disease.
For this smaller group of patients, physicians may have limited clinical decision-making experience or evidence-based guidance for choosing drug combinations. The solution is to expand the number of patients to support development of general principles to guide decision-making. Combining patient data from multiple healthcare institutions, however, requires deep expertise in artificial intelligence (AI) and wide-ranging experience in developing machine learning models using sensitive and complex healthcare data.
Hitachi, U of U Health, and Regenstrief researchers partnered to develop and test a new AI method that analyzed electronic health record data across Utah and Indiana and learned generalizable treatment patterns of type 2 diabetes patients with similar characteristics. Those patterns can now be used to help determine an optimal drug regimen for a specific patient.
Some of the results of this study are published in the peer-reviewed medical journal, Journal of Biomedical Informatics, in the article, “Predicting pharmacotherapeutic outcomes for type 2 diabetes: An evaluation of three approaches to leveraging electronic health record data from multiple sources.”
Hitachi had been working with U of U Health for several years on development of a pharmacotherapy selection system for diabetes treatment. However, the system was not always able to accurately predict more complex and less prevalent treatment patterns because it did not have enough data. In addition, it was not easy to use data from multiple facilities, as it was necessary to account for differences in patient disease states and therapeutic drugs prescribed among facilities and regions. To address these challenges, the project partnered with Regenstrief to enrich the data it was working with.
The new AI method initially groups patients with similar disease states and then analyzes their treatment patterns and clinical outcomes. It then matches the patient of interest to the disease state groups and predicts the range of potential outcomes for the patient depending on various treatment options. The researchers evaluated how well the method worked in predicting successful outcomes given drug regimens administered to patient with diabetes in Utah and Indiana. The algorithm was able to support medication selection for more than 83 percent of patients, even when two or more medications were used together.
In the future, the research team expects to help patients with diabetes who require complex treatment in checking the efficacy of various drug combinations and then, with their doctors, deciding on a treatment plan that is right for them. This will lead not only to better management of diabetes but increased patient engagement, compliance, and quality of life.
The three parties will continue to evaluate and improve the effectiveness of the new AI method and contribute to future patient care through further research in healthcare informatics.
Hitachi will accelerate efforts, including the practical application of this technology through collaboration between its healthcare and IT business divisions and R&D group. GlobalLogic Inc., a Hitachi Group Company and leader in Digital Engineering, is promoting healthcare-related projects in the U.S., will also deepen the collaboration in this field. Through these efforts, the entire Hitachi group will contribute to the health and safety of people.
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