It's almost Friday, so let's see what's new in PLoS Genetics, PLoS Computational Biology, PLoS Pathogens and PLoS ONE this week. As always, you should rate the articles, post notes and comments and send trackbacks when you blog about the papers. You can now also easily place articles on various social services (CiteULike, Mendeley, Connotea, Stumbleupon, Facebook and Digg) with just one click. Here are my own picks for the week - you go and look for your own favourites:
Predicting the consequences of species' extinction is a crucial problem in ecology. Species are not isolated, but connected to each others in tangled networks of relationships known as food webs. In this work we want to determine which species are critical as they support many other species. The fact that species are not independent, however, makes the problem difficult to solve. Moreover, the number of possible "importance'" rankings for species is too high to allow a solution by enumeration. Here we take a "reverse engineering" approach: we study how we can make biodiversity collapse in the most efficient way in order to investigate which species cause the most damage if removed. We show that adapting the algorithm Google uses for ranking web pages always solves this seemingly intractable problem, finding the most efficient route to collapse. The algorithm works in this sense better than all the others previously proposed and lays the foundation for a complete analysis of extinction risk in ecosystems.
Enamel is the hardest substance in the vertebrate body. One of the key proteins involved in enamel formation is enamelin. Most placental mammals have teeth that are capped with enamel, but there are also lineages without teeth (anteaters, pangolins, baleen whales) or with enamelless teeth (armadillos, sloths, aardvarks, pygmy and dwarf sperm whales). All toothless and enamelless mammals are descended from ancestral forms that possessed teeth with enamel. Given this ancestry, we predicted that mammalian species without teeth or with teeth that lack enamel would have copies of the gene that codes for the enamelin protein, but that the enamelin gene in these species would contain mutations that render it a nonfunctional pseudogene. To test this hypothesis, we sequenced most of the protein-coding region of the enamelin gene in all groups of placental mammals that lack teeth or have enamelless teeth. In every case, we discovered mutations in the enamelin gene that disrupt the proper reading frame that codes for the enamelin protein. Our results link evolutionary change at the molecular level to morphological change in the fossil record and also provide evidence for the enormous predictive power of Charles Darwin's theory of descent with modification.
When we look at a visual scene, neurons in our eyes "fire" short, electrical pulses in a pattern that encodes information about the visual world. This pattern passes through a series of processing stages within the brain, eventually leading to cells whose firing encodes high-level aspects of the scene, such as the identity of a visible object regardless of its position, apparent size or angle. Remarkably, features of these firing patterns, at least at the earlier stages of the pathway, can be predicted by building "efficient" codes for natural images: that is, codes based on models of the statistical properties of the environment. In this study, we have taken a first step towards extending this theoretical success to describe later stages of processing, building a model that extracts a structured representation in much the same way as does the visual system. The model describes discrete, persistent visual elements, whose appearance varies over time--a simplified version of a world built of objects that move and rotate. We show that when fit to natural image sequences, features of the "code" implied by this model match many aspects of processing in the first cortical stage of the visual system, including: the individual firing patterns of types of cells known as "simple" and "complex"; the distribution of coding properties over these cells; and even how these properties depend on the cells' physical proximity. The model thus brings us closer to understanding the functional principles behind the organisation of the visual system.
The stability of visual perception is partly maintained by saccadic suppression: the selective reduction of visual sensitivity that accompanies rapid eye movements. The neural mechanisms responsible for this reduced perisaccadic visibility remain unknown, but the Lateral Geniculate Nucleus (LGN) has been proposed as a likely site. Our data show, however, that the saccadic suppression of a target flashed in the right visual hemifield increased with an increase in background luminance in the left visual hemifield. Because each LGN only receives retinal input from a single hemifield, this hemifield interaction cannot be explained solely on the basis of neural mechanisms operating in the LGN. Instead, this suggests that saccadic suppression must involve processing in higher level cortical areas that have access to a considerable part of the ipsilateral hemifield.