An ultrathin section of a Pithovirus particle in an infected Acanthamoeba castellanii cell observed by transmission electron microscopy with enhancement using the artistic filter "plastic packaging" provided by Adobe Photoshop CS5. Credit: Julia Bartoli and Chantal Abergel, IGS and CNRS-AMU.
French scientists said Monday they had revived a giant but harmless virus that had been locked in the Siberian permafrost for more than 30,000 years.
Wakening the long-dormant virus serves as a warning that unknown pathogens entombed in frozen soil may be roused by global warming, they said.
Dubbed Pithovirus sibericum, the virus was found in a 30-metre (98-foot) -deep sample of permanently frozen soil taken from coastal tundra in Chukotka, near the East Siberia Sea, where the average annual temperature is minus 13.4 degrees Celsius (7.8 degrees Fahrenheit).
The team thawed the virus and watched it replicate in a culture in a petri dish, where it infected a simple single-cell organism called an amoeba.
Radiocarbon dating of the soil sample found that vegetation grew there more than 30,000 years ago, a time when mammoths and Neanderthals walked the Earth, according to a paper published in the US journal Proceedings of the National Academy of Sciences (PNAS).
P. sibericum is, on the scale of viruses, a giant—it has 500 genes, whereas the influenza virus has only eight.
It is the first in a new category of viral whoppers, a family known as Megaviridae, for which two other categories already exist.
An ultrathin section of a Pithovirus particle in an infected Acanthamoeba castellanii cell observed by transmission electron microscopy. The length of the particle is ~1.5 µm with a 0.5 µm diameter. Credit: Julia Bartoli and Chantal Abergel, IGS and CNRS-AMU.
The virus gets its name from "pithos," the ancient Greek word for a jar, as it comes in an amphora shape. It is so big (1.5 millionths of a metre) that it can be seen through an optical microscope, rather than the more powerful electron microscope.
Unlike the flu virus, though, P. sibericum is harmless to humans and animals, for it only infects a type of amoeba called Acanthamoeba, the researchers said.
The work shows that viruses can survive being locked up in the permafrost for extremely long periods, France's National Centre for Scientific Research (CNRS) said in a press statement.
"It has important implications for public-health risks in connection with exploiting mineral or energy resources in Arctic Circle regions that are becoming more and more accessible through global warming," it said.
"The revival of viruses that are considered to have been eradicated, such as the smallpox virus, whose replication process is similar to that of Pithovirus, is no longer limited to science fiction.
"The risk that this scenario could happen in real life has to be viewed realistically."
The friends of a Facebook user's friends and the links between them. Two heavily linked clusters are obvious at 12 and 3 o'clock - perhaps the user's workplace and college pals. But notice the somewhat isolated person down around 7 o'clock, who shares links to many smaller clusters of the central user's friends. Computer analysis points to this person as the romantic partner.
From a map of Facebook friends, a computer algorithm developed by Jon Kleinberg, the Tisch University Professor of Computer Science, and Lars Backstrom '04, Ph.D. '09, now at Facebook, will correctly identify a person's spouse, fiancé or other romantic partner about 70 percent of the time.
"We are trying to build up a sort of chemistry kit for finding different elements of a network," Kleinberg said. The team will present their results at the ACM Conference on Computer Supported Cooperative Work and Social Computing, Feb. 15-19 in Baltimore.
As you might guess, the method works best when the couple is married, and works better the longer the relationship has been in force. On the dark side, if the algorithm does not select the person who is the relationship partner, there is a significantly increased chance that in a month or two the couple will break up.
The researchers tested their methods on anonymized data from 1.3 million randomly selected Facebook users aged 20 or older who listed their status as "married," "engaged" or "in a relationship." Along with a list of a Facebook user's friends, the data also show how those friends are linked to one another.
The first guess was that the romantic partner would be "embedded" – that the couple would have many mutual friends. That works, the researchers found, but not very well, finding the partner about 25 percent of the time. So they introduced a concept they call "dispersion," where the couple's mutual friends are not highly connected among themselves, but rather are scattered over many aspects of the central user's life. In real-world terms, your spouse goes where you go, and knows the people in your office, your church, your bridge club and so on, although those people seldom meet one another across group lines.
"You have to ask, 'How did the relationship get that way?'" Kleinberg said. "Your spouse acts as a sort of time traveler in your life, who went back and met all those people."
Combining embededness with dispersion boosted performance. The researchers then factored in the dispersiveness of the dispersed friends – whether the person your romantic partner knows at your office is also connected to some people in your church and your bridge club.
Finally, they added measures of interaction, such as how often people look at each other's profiles, attend the same events or appear together in photos. Ultimately they were able to identify the partner 70.5 percent of the time. Others who might be chosen by the algorithm are most often family members or their partners.
As a spinoff, the researchers were able to determine, 68.3 percent of the time, whether a given user was or was not in a relationship at all, and with 79 percent accuracy if the relationship was a marriage.
There may be other applications for analysis based on dispersion, the researchers said, including grouping people into categories or, for social scientists, finding the person who just doesn't fit a category. Backstrom, who developed Facebook's friend recommender, is looking at ways to evaluate incoming messages to a Facebook user based on the user's relationship with the source.
And identifying people with strong ties to one another may also show where to go to influence a group. "If you're someone who bridges between groups it can be a source of power," Kleinberg explained.
