So, let's see what's new in PLoS Genetics, PLoS Computational Biology, PLoS Pathogens, PLoS ONE and PLoS Neglected Tropical Diseases this week. As always, you should rate the articles, post notes and comments and send trackbacks when you blog about the papers. Here are my own picks for the week - you go and look for your own favourites:
The lion Panthera leo, a formidable carnivore with a complex cooperative social system, has fascinated humanity since pre-historical times, inspiring hundreds of religious and cultural allusions. Here, we use a comprehensive sample of 357 individuals from most of the major lion populations in Africa and Asia. We assayed appropriately informative autosomal, Y-chromosome, and mitochondrial genetic markers, and assessed the prevalence and genetic variation of the lion-specific feline immunodeficiency virus (FIVPle), a lentivirus analogous to human immunodeficiency virus (HIV) that causes AIDS-like immunodeficiency disease in domestic cats. We compare the large multigenic dataset from lions with patterns of genetic variation of the FIVPle to characterize the population-genomic legacy of lions. We refute the hypothesis that African lions consist of a single panmictic population, highlighting the importance of preserving populations in decline rather than prioritizing larger-scale conservation efforts. Interestingly, lion and FIVPle variation revealed evidence of unsuspected genetic diversity even in the well-studied lion population of the Serengeti Ecosystem, which consists of recently admixed animals derived from three distinct genetic groups.
One of the striking features of evolution is the appearance of novel structures in organisms. The origin of the ability to generate novelty is one of the main mysteries in evolutionary theory. The molecular mechanisms that enhance the evolution of novelty were recently integrated by Kirschner and Gerhart in their theory of facilitated variation. This theory suggests that organisms have a design that makes it more likely that random genetic changes will result in organisms with novel shapes that can survive. Here we demonstrate how facilitated variation can arise in computer simulations of evolution. We propose a quantitative approach for studying facilitated variation in computational model systems. We find that the evolution of facilitated variation is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals, but in different combinations. Under such varying conditions, the simulated organisms store information about past environments in their genome, and develop a special modular design that can readily generate novel modules.
Functional hierarchy in neural systems, defined as the principle that complex entities may be segmented into simpler elements and that simple elements may be integrated into a complex entity, is a challenging area of study in neuroscience. Such a functional hierarchy may be thought of intuitively in two ways: as hierarchy in space, and as hierarchy in time. An example of hierarchy in space is visual information processing, where elemental information in narrow receptive fields is integrated into complex features of a visual image in a larger space. Hierarchy in time is exemplified by auditory information processing, where syllable-level information within a short time window is integrated into word-level information over a longer time window. Although extensive investigations have illuminated the neural mechanisms of spatial hierarchy, those governing temporal hierarchy are less clear. In the current study, we demonstrate that functional hierarchy can self-organize through multiple timescales in neural activity, without explicit spatial hierarchical structure. Our results suggest that multiple timescales are an essential factor leading to the emergence of functional hierarchy in neural systems. This work could contribute to providing clues regarding the puzzling observation of such hierarchy in the absence of spatial hierarchical structure.
I enjoyed your picks. Thank you.
Personally I thought Probabilistic Models for Continuous Ontogenetic Transition Processes was rather good. The second author is one of the greatest geniuses of this (or any other) era.
Holy cow! I did not notice that! Congratulations!
Thanks. The title is designed to turn people away. :-)
It actually looks like an exciting paper - should have known...