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April 05, 2016

In Research

New theory helps explain and predict the activity of sun-like stars


Researchers have developed a new conceptual framework for understanding how stars similar to the sun evolve, helping explain how the rotation of stars, their emission of x-rays, and the intensity of their stellar winds vary with time. According to first author Eric Blackman, professor of physics and astronomy, the work could also “ultimately help to determine the age of stars more precisely than is currently possible.”

In a paper published in Monthly Notices of the Royal Astronomical Society, researchers describe how they have corroborated known, observable data for the activity of sun-like stars with fundamental astrophysics theory. By looking at the physics behind the speeding up or slowing down of a star’s rotation, its x-ray activity, and magnetic field generation, Blackman says the research is a “first attempt to build a comprehensive model for the activity evolution of these stars.”

Using the sun as the calibration point, the model most accurately describes the likely behavior of the sun in the past and how it would be expected to behave in the future. But Blackman adds that there are many stars of similar mass and radius, and so the model is a good starting point for predictions for such stars.

“Our model shows that stars younger than our sun can vary quite significantly in the intensity of their x-ray emission and mass loss,” Blackman says. “But there is a convergence in the activity of the stars after a certain age, so you could say that our sun is very typical for stars of its mass, radius, and its age. They get more predictable as they age.”

He adds: “We’re not yet at the point where we can accurately predict a star’s precise age, because there are simplifying assumptions that go into the model. But in principle, by extending the work to relax some of these assumptions we could predict the age for a wide range of stars based on their x-ray luminosity.”


Immune cells ‘prune’ connections between neurons

A new study in the journal Nature Communications shows that cells normally associated with protecting the brain from infection and injury also play an important role in rewiring the connections between nerve cells. While the discovery sheds new light on the mechanics of neuroplasticity, it could also help explain diseases like autism spectrum disorders, schizophrenia, and dementia, which may arise when the process breaks down and connections between brain cells are not formed or not removed correctly.

“We have long considered the reorganization of the brain’s network of connections as solely the domain of neurons,” says Ania Majewska, associate professor of neuroscience and senior author of the study. “These findings show that a precisely choreographed interaction between multiple cells types is necessary to carry out the formation and destruction of connections that allow proper signaling in the brain.”

The study is another example of a dramatic shift in scientists’ understanding of the role that the immune system, specifically cells called microglia, plays in maintaining brain function. Microglia have been long understood to be the sentinels of the central nervous system, patrolling the brain and spinal cord and springing into action to stamp out infections or gobble up dead cell tissue. However, scientists are now beginning to appreciate that, in addition to serving as the brain’s first line of defense, these cells also have a nurturing side, particularly as it relates to the connections between neurons.

The formation and removal of the physical connections between neurons is a critical part of maintaining a healthy brain and the process of creating new pathways and networks among brain cells enables us to absorb, learn, and memorize new information.

While the constant reorganization of neural networks—called neuroplasticity—has been well understood for some time, the basic mechanisms by which connections between brain cells are made and broken has eluded scientists.

“The brain’s network of connections is like a garden,” says Rebecca Lowery, a graduate student in Majewska’s lab and coauthor of the study. “Not only does it require nourishment and a healthy environment, but every once in a while you need to prune dead branches and pull up weeds in order to allow new flowers to grow.”


Can a computer tell if you’re drinking while tweeting?

The combination of drinking, social media, and sharing has provided Rochester researchers with an innovative test case for analyzing behavior by Twitter users in order to study patterns about drinking in different communities.

In a new paper, PhD student Nabil Hossain reports that he and his collaborators have taught computers to analyze tweets about drinking in an effort to predict where Twitter users are when they report drinking.

Hossain is a student in the computer science group led by Henry Kautz, the Robin and Tim Wentworth Director of the Goergen Institute for Data Science. Hossain posted the paper on the repository after it was accepted for the International AAAI Conference on Web and Social Media to be held in Germany in May.

Until now, predicting a social media user’s home location was done by establishing the place from which a user most frequently tweets or the most common last location of the day from which the user posts.

In the new work, the researchers applied machine-learning techniques to identify in-the-moment user behavior. That allowed them to accurately predict users’ home locations within 100 meters.

Combining the tools, they were able to discover patterns of alcohol use in urban and suburban settings. According to the paper, the goal is that “such methods can help us better understand the occurrence, frequency, and settings of alcohol consumption, a health-risk behavior, and can lead to actionable information in prevention and public health.”

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