Computer model uses virus ‘appearance’ to better predict winter flu strains — ScienceDaily

Combining genetic and experimental data into models about the influenza virus can help predict more accurately which strains will be most common during the next winter, says a study published recently in eLife.

The models could make the design of flu vaccines more accurate, providing fuller protection against a virus that causes around half a million deaths each year globally.

Vaccines are the best protection we have against the flu. But the virus changes its appearance to our immune system every year, requiring researchers to update the vaccine to match. Since a new vaccine takes almost a year to make, flu researchers must predict which flu viruses look the most like the viruses of the future.

The gold-standard ways of studying influenza involve laboratory experiments looking at a key molecule that coats the virus called haemagglutinin. But these methods are labour-intensive and take a long time. Researchers have focused instead on using computers to predict how the flu virus will evolve from the genetic sequence of haemagglutinin alone, but these data only give part of the picture.

“The influenza research community has long recognised the importance of taking into account physical characteristics of the flu virus, such as how haemagglutinin changes over time, as well as genetic information,” explains lead author John Huddleston, a PhD student in the Bedford Lab at Fred Hutchinson Cancer Research Center and Molecular and Cell Biology Program at the University of Washington, Seattle, US. “We wanted to see whether combining genetic sequence-only models of influenza evolution with other high-quality experimental measurements could improve the forecasting of the new strains of flu that will emerge one year down the line.”

Huddleston and the team looked at different components of virus ‘fitness’ — that is, how likely the virus is to thrive and continue to evolve.

Software spots and fixes hang bugs in seconds, rather than weeks — ScienceDaily

Hang bugs — when software gets stuck, but doesn’t crash — can frustrate both users and programmers, taking weeks for companies to identify and fix. Now researchers from North Carolina State University have developed software that can spot and fix the problems in seconds.

“Many of us have experience with hang bugs — think of a time when you were on website and the wheel just kept spinning and spinning,” says Helen Gu, co-author of a paper on the work and a professor of computer science at NC State. “Because these bugs don’t crash the program, they’re hard to detect. But they can frustrate or drive away customers and hurt a company’s bottom line.”

With that in mind, Gu and her collaborators developed an automated program, called HangFix, that can detect hang bugs, diagnose the relevant problem, and apply a patch that corrects the root cause of the error. Video of Gu discussing the program can be found here.

The researchers tested a prototype of HangFix against 42 real-world hang bugs in 10 commonly used cloud server applications. The bugs were drawn from a database of hang bugs that programmers discovered affecting various websites. HangFix fixed 40 of the bugs in seconds.

“The remaining two bugs were identified and partially fixed, but required additional input from programmers who had relevant domain knowledge of the application,” Gu says.

For comparison, it took weeks or months to detect, diagnose and fix those hang bugs when they were first discovered.

“We’re optimistic that this tool will make hang bugs less common — and websites less frustrating for many users,” Gu says. “We are working to integrate Hangfix into InsightFinder.” InsightFinder is the AI-based IT operations and analytics startup founded by Gu.

The paper, “HangFix: Automatically Fixing Software Hang Bugs for Production Cloud Systems,”