The OpenDust code operates ten times faster than any existing analog

Understanding the mechanisms of interaction between plasma and microparticles is of critical importance in various fields, including astrophysics, microelectronics, and plasma medicine. A common experimental approach for studying interactions between plasma and microparticles is to place microparticles in a flowing plasma of a gas discharge. In order to achieve a more accurate understanding of the processes occurring in such systems, scientists need fast and efficient tools for calculating forces acting on microparticles in a plasma flow.

Typically, plasma physicists have to independently develop software tailored to a specific task, which is a significant investment of time and resources. Existing open-source programs frequently encounter challenges related to installation, documentation, and sluggish performance. A group of scientists from the Russian National Research University Higher School of Economics, JIHT, the HSE, and, MIPT has developed a novel solution: a fast, open-source code that is easy to install and extensively documented. 

The outcome, OpenDust, performs ten times faster than existing analogs. In order to accelerate calculations, the algorithm uses multiple GPUs simultaneously. 

"OpenDust has a flexible, user-friendly interface written in Python. Users can define the parameters of a simulated system and configure computational resources. For instance, users have the ability to specify the plasma flow rate and the number of GPU accelerators needed for a calculation. The backend, which is the server component of the product responsible for the internal logic, is optimized for high-performance computations and harnesses the power of multiple GPUs. This capability enables substantially increase calculations and processes a larger amount of data", explains Daniil Kolotinskii, study co-author and OpenDust developer, Junior Researcher at the Joint Institute for High Temperatures RAS.

The code, OpenDust, simulates the dynamics of plasma media surrounding a system of microparticles. Scientists can use it to explore intricate physical phenomena within a complex plasma, including self-organization effects and instabilities. Additionally, the code can be applied in various fields of science and industry, such as simulation of plasma purification processes within industrial extreme ultraviolet lithography machines or studying active particle systems.

"Our code is the first-ever open-source program for the multiscale self-consistent simulation of microparticle motion in a plasma flow.  OpenDust can serve as a versatile tool for simulating and studying diverse physical phenomena associated with microparticle motion in a plasma flow. The code has both academic and industrial applications. For example, it can facilitate the development of novel methods for efficiently removing dust from plasma in industrial lithography machines", says Alexey Timofeev, Leading Research Fellow, at HSE International Laboratory for Supercomputer Atomistic Modelling and Multi-scale Analysis.

A groundbreaking machine-learning study has unmasked the best drug combinations to prevent COVID-19 from coming back after initial infection. It turns out these combos are not the same for every patient. 

Using real-world data from a hospital in China, the UC Riverside-led study found that individual characteristics, including age, weight, and additional illness determine which drug combinations most effectively reduce recurrence rates. This finding has been published in the journal Frontiers in Artificial Intelligence. 

That the data came from China is significant for two reasons. First, when patients are treated for COVID-19 in the U.S., it is normally with one or two drugs. Early in the pandemic, doctors in China could prescribe as many as eight different drugs, enabling analysis of more drug combinations. Second, COVID-19 patients in China must quarantine in a government-run hotel after being discharged from the hospital, which allows researchers to learn about reinfection rates more systematically.

“That makes this study unique and interesting. You can’t get this kind of data anywhere else in the world,” said Xinping Cui, UCR statistics professor and study author. 

The study project began in April 2020, about a month into the pandemic. At the time, most studies were focused on death rates. However, doctors in Shenzhen, near Hong Kong, were more concerned about recurrence rates because fewer people there were dying.

“Surprisingly, nearly 30% of patients became positive again within 28 days of being released from the hospital,” said Jiayu Liao, associate professor of bioengineering and study co-author. 

Data for more than 400 COVID patients were included in the study. Their average age was 45, most were infected with moderate cases of the virus, and the group was evenly divided by gender. Most were treated with one of the various combinations of an antiviral, an anti-inflammatory, and an immune-modulating drug, such as interferon or hydroxychloroquine. 

That various demographic groups had better success with different combinations can be traced to the way the virus operates. 

“COVID-19 suppresses interferon, protein cells make to inhibit invading viruses. With defenses lowered, COVID can replicate until the immune system explodes in the body, and destroys tissues,” explained Liao. 

People who had weaker immune systems before COVID infection required an immune-boosting drug to fight the infection effectively. Younger peoples’ immune systems become overactive with infection, which can lead to excessive tissue inflammation and even death. To prevent this, younger people require an immune suppressant as part of their treatment. 

“When we get treatment for diseases, many doctors tend to offer one solution for people 18 and up. We should now reconsider age differences, as well as other disease conditions, such as diabetes and obesity,” Liao said. 

Most of the time, when conducting drug efficacy tests, scientists design a clinical trial in which people having the same disease and baseline characteristics are randomly assigned to either treatment or control groups. But that approach does not consider other medical conditions that may affect how the drug works — or doesn’t work — for specific sub-groups.

Because this study utilized real-world data, the researchers had to adjust for factors that could affect the outcomes they observed. For example, if a certain drug combination was given mostly to older people and proved ineffective, it would not be clear whether the drug is to blame or the person’s age. 

“For this study, we pioneered a technique to attack the challenge of confounding factors by virtually matching people with similar characteristics who were undergoing different treatment combinations,” Cui said. “In this way, we could generalize the efficacy of treatment combinations in different subgroups.”

While COVID-19 is better understood today, and vaccines have greatly reduced death rates, there remains much to be learned about treatments and preventing reinfections. “Now that recurrence is more of a concern, I hope people can use these results,” Cui said.

Machine learning has been used in many areas related to COVID, such as disease diagnosis, vaccine development, and drug design, in addition to this new analysis of multi-drug combinations. Liao believes that technology will have an even bigger role to play going forward.

“In medicine, machine learning and artificial intelligence have not yet had as much impact as I believe they will in the future,” Liao said. “This project is a great example of how we can move toward truly personalized medicine.”

The mystery of a stellar flare is a trillion times more powerful than the largest of Solar flares may have been solved by a team of scientists who believe a massive young planet is burning up in a superheated soup of raw material swirling around it. 

Led by the University of Leicester and funded by the UK Science and Technology Facilities Council (STFC), the scientists have suggested that a planet roughly ten times larger in size than Jupiter is undergoing ‘extreme evaporation’ near the growing star, with the inferno tearing material off the planet and flinging it onto the star. 

Statistics of such flares in developing solar systems suggest that each could witness up to a dozen of similar planet elimination events.

The scientists focused their attention on the protostar FU Ori, located 1,200 light years from our solar system, which significantly increased in brightness 85 years ago and has still not dimmed to the usually expected luminosity. 

While astronomers believe that the increase in FU Ori luminosity is due to more material falling onto the protostar from a cloud of gas and dust called a protoplanetary disc, details of that remained a mystery. 

Lead author Professor Sergei Nayakshin from the University of Leicester School of Physics and Astronomy said: “These discs feed growing stars with more material but also nurture planets. Previous observations provided tantalizing hints of a young massive planet orbiting this star very close. Several ideas were put forward on how the planet may have encouraged such a flare, but the details did not work out. We discovered a new process which you might call a ‘disc inferno’ of young planets.” 

The Leicester-led researchers created a simulation for FU Ori, modeling a gas giant planet formed far out in the disc by gravitational instability in which massive disc fragments make huge clumps more massive than our Jupiter but far less dense. 

The simulation shows how such a planetary seed migrates inward towards its host star very rapidly, drawn by its gravitational pull. As it reaches the equivalent of a tenth of the distance between Earth and our own sun, the material around the star is so hot it effectively ignites the outer layers of the planet’s atmosphere. The planet then becomes a massive source of fresh material feeding the star and causing it to grow and shine brighter.

Study co-author Dr. Vardan Elbakyan, also Leicester-based, adds: “This was the first star that that was observed to undergo this kind of flare. We now have a couple of dozen examples of such flares from other young stars forming in our corner of the Galaxy. While FU Ori events are extreme compared to normal young stars, from the duration and observability of such events, observers concluded that most emerging solar systems flare up like this a dozen or so times while the protoplanetary disc is around.”

Professor Nayakshin adds: “If our model is correct, then it may have profound implications for our understanding of both star and planet formation. Protoplanetary discs are often called nurseries of planets. But we now find that these nurseries are not quiet places that early solar system researchers imagined them to be, they are instead tremendously violent and chaotic places where many – perhaps even most -- young planets get burned and literally eaten by their stars. 

“It is now important to understand whether other flaring stars can indeed be explained with the same scenario.”

Species richness is not a reliable metric for monitoring ecosystems. A new study by Lucie Kuczynski and Helmut Hillebrand shows that systematic biases can mask an imminent decline in biodiversity.

Seemingly healthy ecosystems with a constant or even increasing number of species may already be on the path to the decline and loss of species. Even in long-term datasets, such negative trends may only become apparent with a delay. This is due to systematic distortions in temporal trends for species numbers. 

"Our results are important in order to understand that the species number alone is not a reliable measure of how stable the biological balance in a given ecosystem is at the local level," explains Dr. Lucie Kuczynski, an ecologist at the University of Oldenburg's Institute for Chemistry and Biology of the Marine Environment (ICBM) in Oldenburg, Germany and the lead author of the study, in which she and her colleagues combined observational data for freshwater fish and birds with calculations based on simulations.

The research team, the other members of which were Professor Dr. Helmut Hillebrand from the ICBM and Dr. Vicente Ontiveros from the University of Girona in Spain, was surprised by the results: "We find it very worrying that a constant or even increasing species number does not necessarily mean that all is well in an ecosystem and that the number of species will remain constant in the long term," Hillebrand explains. "Apparently, we have so far underestimated the negative trends for freshwater fish, for example. Species are disappearing faster than expected at the local level," adds Kuczynski.

A dynamic equilibrium

Up to now, biodiversity research had worked on the assumption that the number of species in an ecosystem will remain constant in the long term if the environmental conditions neither deteriorate nor improve. "The hypothesis is that there is a dynamic equilibrium between colonisations and local extinctions," lead author Kuczynski explains. Increasing or decreasing species numbers are interpreted as a response to improving or deteriorating environmental conditions.

To find out whether a constant species richness is a reliable indicator of a stable biological balance, Kuczynski and her colleagues first analyzed several thousand datasets documenting the number of species of freshwater fish and breeding birds in different regions of Europe and North America over many years – 24 years on average for the fish and 37 for the birds – with the aim of identifying trends in individual communities. They then compared the empirical data with various simulation models based on different expectations regarding immigration and extinctions of species.

The team initially observed a general increase in the number of species in both fish and bird populations during the observation periods. However, a comparison with the simulations showed that this increase was smaller than would have been expected. The researchers attributed this discrepancy to an imbalance between colonisations and local extinctions: "According to our simulations organisms such as freshwater fish which have limited potential for dispersal colonise an ecosystem faster than in neutral models, while their extinction occurs later than expected," says Kuczynski.

Doomed to extinction

This means that after an environmental change, species that are in fact doomed to extinction may remain present in an ecosystem for some time, while at the same time new species also move in. This effect disguises the impending loss of biodiversity, she explains. "There are transitional phases in ecosystems in which the number of species is higher than expected. Species extinction occurs only after these transition phases – and then usually faster than expected."

The team anticipates that a reassessment of which methods are best suited for monitoring the state of ecosystems will now be necessary, and that nature conservation targets – which in most cases aim to preserve existing species diversity – may also need to be redefined. The model developed by Kuczynski and her colleagues could serve as a tool to distinguish between the different mechanisms that influence species richness, and also provides information on the extent to which the observational data deviates from expected changes.