There are 15 new articles in PLoS ONE today. 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:
Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a ÃhiddenÃ population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, MÃ©xico, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics.
A reanalysis of existing data suggests that the established tenet of increasing efficiency of transport with body size in terrestrial locomotion requires re-evaluation. Here, the statistical model that described the data best indicated a dichotomy between the data for small (1 kg). Within and between these two size groups there was no detectable difference in the scaling exponents (slopes) relating metabolic (Emet) and mechanical costs (Emech, CM) of locomotion to body mass (Mb). Therefore, no scaling of efficiency (Emech, CM/Emet) with Mb was evident within each size group. Small animals, however, appeared to be generally less efficient than larger animals (7% and 26% respectively). Consequently, it is possible that the relationship between efficiency and Mb is not continuous, but, rather, involves a step-change. This step-change in the efficiency of locomotion mirrors previous findings suggesting a postural cause for an apparent size dichotomy in the relationship between Emet and Mb. Currently data for Emech, CM is lacking, but the relationship between efficiency in terrestrial locomotion and Mb is likely to be determined by posture and kinematics rather than body size alone. Hence, scaling of efficiency is likely to be more complex than a simple linear relationship across body sizes. A homogenous study of the mechanical cost of terrestrial locomotion across a broad range of species, body sizes, and importantly locomotor postures is a priority for future research.
The peripheral circadian clock in mice is entrained not only by light-dark cycles but also by daily restricted feeding schedules. Behavioral and cell culture experiments suggest an increase in glucose level as a factor in such feeding-induced entrainment. For application of feeding-induced entrainment in humans, nutrient content and dietary variations should be considered. To elucidate the food composition necessary for dietary entrainment, we examined whether complete or partial substitution of dietary nutrients affected phase shifts in liver clocks of mice. Compared with fasting mice or ad libitum fed mice, the liver bioluminescence rhythm advanced by 3-4 h on the middle day in Per2::luciferase knock-in mice that were administered a standard mouse diet, i.e. AIN-93M formula [0.6-0.85 g/10 g mouse BW] (composition: 14% casein, 47% cornstarch, 15% gelatinized cornstarch, 10% sugar, 4% soybean oil, and 10% other [fiber, vitamins, minerals, etc.]), for 2 days. When each nutrient was tested alone (100% nutrient), an insignificant weak phase advance was found to be induced by cornstarch and soybean oil, but almost no phase advance was induced by gelatinized cornstarch, high-amylose cornstarch, glucose, sucrose, or casein. A combination of glucose and casein without oil, vitamin, or fiber caused a significant phase advance. When cornstarch in AIN-93M was substituted with glucose, sucrose, fructose, polydextrose, high-amylose cornstarch, or gelatinized cornstarch, the amplitude of phase advance paralleled the increase in blood glucose concentration. Our results strongly suggest the following: (1) balanced diets containing carbohydrates/sugars and proteins are good for restricted feeding-induced entrainment of the peripheral circadian clock and (2) a balanced diet that increases blood glucose, but not by sugar alone, is suitable for entrainment. These findings may assist in the development of dietary recommendations for on-board meals served to air travelers and shift workers to reduce jet lag-like symptoms.
Genetic structure due to ancestry has been well documented among many divergent human populations. However, the ability to associate ancestry with genetic substructure without using supervised clustering has not been explored in more presumably homogeneous and admixed US populations. The goal of this study was to determine if genetic structure could be detected in a United States population from a single state where the individuals have mixed European ancestry. Using Bayesian clustering with a set of 960 single nucleotide polymorphisms (SNPs) we found evidence of population stratification in 864 individuals from New Hampshire that can be used to differentiate the population into six distinct genetic subgroups. We then correlated self-reported ancestry of the individuals with the Bayesian clustering results. Finnish and Russian/Polish/Lithuanian ancestries were most notably found to be associated with genetic substructure. The ancestral results were further explained and substantiated using New Hampshire census data from 1870 to 1930 when the largest waves of European immigrants came to the area. We also discerned distinct patterns of linkage disequilibrium (LD) between the genetic groups in the growth hormone receptor gene (GHR). To our knowledge, this is the first time such an investigation has uncovered a strong link between genetic structure and ancestry in what would otherwise be considered a homogenous US population.
Feline spongiform encephalopathy (FSE) is considered to be related to bovine spongiform encephalopathy (BSE) and has been reported in domestic cats as well as in captive wild cats including cheetahs, first in the United Kingdom (UK) and then in other European countries. In France, several cases were described in cheetahs either imported from UK or born in France. Here we report details of two other FSE cases in captive cheetah including a 2nd case of FSE in a cheetah born in France, most likely due to maternal transmission. Complete prion protein immunohistochemical study on both brains and peripheral organs showed the close likeness between the two cases. In addition, transmission studies to the TgOvPrP4 mouse line were also performed, for comparison with the transmission of cattle BSE. The TgOvPrP4 mouse brains infected with cattle BSE and cheetah FSE revealed similar vacuolar lesion profiles, PrPd brain mapping with occurrence of typical florid plaques. Collectively, these data indicate that they harbor the same strain of agent as the cattle BSE agent. This new observation may have some impact on our knowledge of vertical transmission of BSE agent-linked TSEs such as in housecat FSE, or vCJD.
Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.
The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns) depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks). Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/â feedback) and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs) epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected) networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1) the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2) the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of multiple perturbations are strongly conditioned by both the regulatory architecture (i.e. pattern of coupled feedback structures) and the dynamic nature of the spatio-temporal expression trajectories displayed by the simulated networks.