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This Week in PNAS: Sep 29, 2015

Editor's Note: Some of the articles described below are not yet available at the PNAS site, but they are scheduled to be posted some time this week.

In the early, online edition of the Proceedings of the National Academy of Sciences, researchers from the King's College London search for genetic associations with ties to face recognition in thousands of twins from the UK. The study participants — 374 identical twin pairs, 549 non-identical twin pairs, and 301 unrelated individuals who were 18-years-old or 19-years-old — were asked to complete tests for face recognition, general object recognition, and overall cognitive abilities. The team then looked at the heritability of face recognition by comparing the genetically identical twins with their non-identical counterparts, demonstrating that roughly 61 percent of face recognition is heritable.

Researchers from the University of Exeter and the University of California, Berkeley explore the consequences of parasite virulence on host sexual selection and host-parasite co-evolution. The team modeled the co-evolution process using the example of a reproductive success-reducing sexually transmitted parasite in a serially monogamous population. From this model, the study's authors conclude that "[c]o-evolution … leads to new predictions for the role of several host and parasite traits on selection for mate choice that will guide future experimental and comparative work."

A team from the University of Texas at Dallas and Rutgers University present a scheme for teasing apart direct and indirect connections in biological networks. After putting together a synthetic regulatory and cascade motif networks, the researchers used small interfering RNAs to upend activity at each node in the network, using so-called non-parametric single-cell data testing, modular response analysis, and other approaches to test the consequences of these perturbations and detect differences in network edge weights depending on the nature of the network tweak. "Incorporating this insight in the analysis of high-throughput experiments may provide a sought-after solution to a longstanding reverse engineering problem," they write. 

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