Generation of uncorrelated random scale-free networks

Uncorrelated random scale-free networks are useful null models to check the accuracy and the analytical solutions of dynamical processes defined on complex networks. We propose and analyze a model capable of generating random uncorrelated scale-free networks with no multiple and self-connections. The model is based on the classical configuration model, with an additional restriction on the maximum possible degree of the vertices. We check numerically that the proposed model indeed generates scale-free networks with no two- and three-vertex correlations, as measured by the average degree of the nearest neighbors and the clustering coefficient of the vertices of degree k, respectively.

Scale decisions can reverse conclusions on community assembly processes.

AIM: Phylogenetic diversity patterns are increasingly being used to better understand the role of ecological and evolutionary processes in community assembly. Here, we quantify how these patterns are influenced by scale choices in terms of spatial and environmental extent and organismic scales. LOCATION: European Alps. METHODS: We applied 42 sampling strategies differing in their combination of focal scales. For each resulting sub-dataset, we estimated the phylogenetic diversity of the species pools, phylogenetic α-diversities of local communities, and statistics commonly used together with null models in order to infer non-random diversity patterns (i.e. phylogenetic clustering versus over-dispersion). Finally, we studied the effects of scale choices on these measures using regression analyses. RESULTS: Scale choices were decisive for revealing signals in diversity patterns. Notably, changes in focal scales sometimes reversed a pattern of over-dispersion into clustering. Organismic scale had a stronger effect than spatial and environmental extent. However, we did not find general rules for the direction of change from over-dispersion to clustering with changing scales. Importantly, these scale issues had only a weak influence when focusing on regional diversity patterns that change along abiotic gradients. MAIN CONCLUSIONS: Our results call for caution when combining phylogenetic data with distributional data to study how and why communities differ from random expectations of phylogenetic relatedness. These analyses seem to be robust when the focus is on relating community diversity patterns to variation in habitat conditions, such as abiotic gradients. However, if the focus is on identifying relevant assembly rules for local communities, the uncertainty arising from a certain scale choice can be immense. In the latter case, it becomes necessary to test whether emerging patterns are robust to alternative scale choices.

Density-based hierarchical clustering of pyro-sequences on a large scale--the case of fungal ITS1.

MOTIVATION: Analysis of millions of pyro-sequences is currently playing a crucial role in the advance of environmental microbiology. Taxonomy-independent, i.e. unsupervised, clustering of these sequences is essential for the definition of Operational Taxonomic Units. For this application, reproducibility and robustness should be the most sought after qualities, but have thus far largely been overlooked. RESULTS: More than 1 million hyper-variable internal transcribed spacer 1 (ITS1) sequences of fungal origin have been analyzed. The ITS1 sequences were first properly extracted from 454 reads using generalized profiles. Then, otupipe, cd-hit-454, ESPRIT-Tree and DBC454, a new algorithm presented here, were used to analyze the sequences. A numerical assay was developed to measure the reproducibility and robustness of these algorithms. DBC454 was the most robust, closely followed by ESPRIT-Tree. DBC454 features density-based hierarchical clustering, which complements the other methods by providing insights into the structure of the data. AVAILABILITY: An executable is freely available for non-commercial users at ftp://ftp.vital-it.ch/tools/dbc454. It is designed to run under MPI on a cluster of 64-bit Linux machines running Red Hat 4.x, or on a multi-core OSX system. CONTACT: dbc454@vital-it.ch or nicolas.guex@isb-sib.ch.

Glasgow Coma Score für den Patienten mit Schädel-Hirn-Trauma [Glasgow Coma Scale in traumatic brain injury].

Even 30 years after its first publication the Glasgow Coma Scale (GCS) is still used worldwide to describe and assess coma. The GCS consists of three components, the ocular, motor and verbal response to standardized stimulation, and is used as a severity of illness indicator for coma of various origins. The GCS facilitates information transfer and monitoring changes in coma. In addition, it is used as a triage tool in patients with traumatic brain injury. Its prognostic value regarding the outcome after a traumatic brain injury still lacks evidence. One of the main problems is the evaluation of the GCS in sedated, paralysed and/or intubated patients. A multitude of pseudoscores exists but a universal definition has yet to be defined.

Large scale purification of presynaptic plasma membranes from Torpedo marmorata electric organ

The presynaptic plasma membrane (PSPM) of cholinergic nerve terminals was purified from Torpedo electric organ using a large-scale procedure. Up to 500 g of frozen electric organ were fractioned in a single run, leading to the isolation of greater than 100 mg of PSPM proteins. The purity of the fraction is similar to that of the synaptosomal plasma membrane obtained after subfractionation of Torpedo synaptosomes as judged by its membrane-bound acetylcholinesterase activity, the number of Glycera convoluta neurotoxin binding sites, and the binding of two monoclonal antibodies directed against PSPM. The specificity of these antibodies for the PSPM is demonstrated by immunofluorescence microscopy.

Network-based scoring system for genome-scale metabolic reconstructions

Background: Network reconstructions at the cell level are a major development in Systems Biology. However, we are far from fully exploiting its potentialities. Often, the incremental complexity of the pursued systems overrides experimental capabilities, or increasingly sophisticated protocols are underutilized to merely refine confidence levels of already established interactions. For metabolic networks, the currently employed confidence scoring system rates reactions discretely according to nested categories of experimental evidence or model-based likelihood. Results: Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets, and enables further confirmation of modeling results. Conclusions: We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions given the natural bipartivity of many biological networks.

Nanometer-scale oxidation of Si(100) surfaces by tapping mode atomic force microscopy

The nanometer¿scale oxidation of Si(100) surfaces in air is performed with an atomic force microscope working in tapping mode. Applying a positive voltage to the sample with respect to the tip, two kinds of modifications are induced on the sample: grown silicon oxide mounds less than 5 nm high and mounds higher than 10 nm (which are assumed to be gold depositions). The threshold voltage necessary to produce the modification is studied as a function of the average tip¿to¿sample distance.

An evolutionary perspective on self-organized division of labor in social insects

Division of labor is a complex phenomenon observed throughout nature. Theoretical studies have focused either on its emergence through self-organization mechanisms or on its adaptive consequences. We suggest that the interaction of self-organization, which undoubtedly characterizes division of labor in social insects, and evolution should be further explored. We review the factors empirically shown to influence task choice. In light of these factors, we review the most important self-organization and evolutionary models for division of labor and outline their advantages and limitations. We describe ways to unify evolution and self-organization in the theoretical study of division of labor and recent results in this area. Finally, we discuss some benchmarks and primary challenges of this approach.

Assessment of debris-flow susceptibility at medium-scale in the Barcelonnette Basin, France

Debris flows are among the most dangerous processes in mountainous areas due to their rapid rate of movement and long runout zone. Sudden and rather unexpected impacts produce not only damages to buildings and infrastructure but also threaten human lives. Medium- to regional-scale susceptibility analyses allow the identification of the most endangered areas and suggest where further detailed studies have to be carried out. Since data availability for larger regions is mostly the key limiting factor, empirical models with low data requirements are suitable for first overviews. In this study a susceptibility analysis was carried out for the Barcelonnette Basin, situated in the southern French Alps. By means of a methodology based on empirical rules for source identification and the empirical angle of reach concept for the 2-D runout computation, a worst-case scenario was first modelled. In a second step, scenarios for high, medium and low frequency events were developed. A comparison with the footprints of a few mapped events indicates reasonable results but suggests a high dependency on the quality of the digital elevation model. This fact emphasises the need for a careful interpretation of the results while remaining conscious of the inherent assumptions of the model used and quality of the input data.

Debris flow modeling for susceptibility mapping at regional to national scale in Norway

Debris flows and related landslide processes occur in many regions all over Norway and pose a significant hazard to inhabited areas. Within the framework of the development of a national debris flows susceptibility map, we are working on a modeling approach suitable for Norway with a nationwide coverage. The discrimination of source areas is based on an index approach, which includes topographic parameters and hydrological settings. For the runout modeling, we use the Flow-R model (IGAR, University of Lausanne), which is based on combined probabilistic and energetic algorithms for the assessment of the spreading of the flow and maximum runout distances. First results for different test areas have shown that runout distances can be modeled reliably. For the selection of source areas, however, additional factors have to be considered, such as the lithological and quaternary geological setting, in order to accommodate the strong variation in debris flow activity in the different geological, geomorphological and climate regions of Norway.