
Sequences capture the code of the common cold
“We’ve had bits and pieces of these things for a long time,” says Ann Palmenberg, of UW-Madison’s Institute for Molecular Virology and the lead author of the new study. “Now, we have the full genome sequences and we can put them into evolutionary perspective.”
As its name implies, the common cold is an inescapable, highly contagious pathogen. Humans are constantly exposed to cold viruses, and each year adults may endure two to four infections, while schoolchildren can catch as many as 10 colds.
“We know a lot about the common cold virus,” Palmenberg explains, “but we didn’t know how their genomes encoded all that information. Now we do, and all kinds of new things are falling out.”
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The newly sequenced viruses also show, says Palmenberg, why it is unlikely we will ever have an effective, all-purpose cold vaccine: The existing reservoir of viruses worldwide is huge and, according to the new study, they have a tendency to swap genetic sequences when cells are infected by more than one virus, a phenomenon that can lead to new virus strains and clinical manifestations.
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The ability of different cold virus strains to swap genes and make entirely new strains was thought to be impossible, notes Claire M. Fraser-Liggett, a co-author of the new study and director of the Institute for Genome Sciences and professor of medicine and microbiology at the University of Maryland School of Medicine. “There is the possibility that this could lead to the emergence of a new rhinovirus strain with fairly dramatic properties,” says Fraser-Liggett.
Related: Common Cold Alters the Activity of Genes – Learning How Viruses Evade the Immune System – Lethal Secrets of 1918 Flu Virus – images of snowflakes
Correlation is Not Causation: “Fat is Catching” Theory Exposed
Posted on January 5, 2009 Comments (2)
“Fat is catching” theory exposed
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Jason M. Fletcher, Ph.D., assistant professor at the Yale School of Public Health in New Haven, Connecticut, along with Boston economist, Ethan Cohen-Cole, Ph.D., designed an ingenious study. They selected conditions that no one would seriously believe were spread by social networking and online friendships: height, headaches and acne. They then applied the same standard statistical methods used in Christakis and Fowler’s social networking research to “find” that acne, height and headaches have the same “social network effect.”
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As they explained, patterns of association among people can lead to correlations in health conditions between friends that are not caused by direct social network effects at all.
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There is a need for caution when attributing causality to correlations in health outcomes between friends using non-experimental data. Confounding is only one of many empirical challenges to estimating social network effects.
Excellent reminder of the risks of analyzing data for correlations. We continue to, far to often, fail to interpret data properly. Both authors of the study, received PhD’s from the University of Wisconsin-Madison which strengthens my belief that it is teaching students well (just kidding).
Also another example of the scientific inquiry process where scientists challenge the conclusions drawn by other scientists. It is a wonderful system, even if confusing and not the clean idea so many have of how science works.
Related: Correlation is Not Causation – Seeing Patterns Where None Exists – Statistics for Experimenters – 500 Year Floods – Playing Dice and Children’s Numeracy – The Illusion of Understanding – All Models Are Wrong But Some Are Useful – Data Doesn’t Lie But People Can Draw Faulty Conclusions from Data
Categories: Research, Students
Tags: commentary, data, human health, Madison, medical study, quote, scientific inquiry, scientific literacy