Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Robust Detection of Dynamic Community Structure in Networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (`optimization variance') and over randomizations of network structure (`randomization variance'). Because the modularity quality function typically has a large number of nearly-degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.Comment: 18 pages, 11 figure

Outer Trench Slope Flexure and Faulting at Pacific Basin Subduction Zones

Flexure and fracturing of the seafloor on the outer trench wall of subduction zones reflects bending of the lithosphere beyond its elastic limit. To investigate these inelastic processes, we have developed a full non-linear inversion approach for estimating the bending moment, curvature, and outer trench wall fracturing using shipboard bathymetry and satellite altimetry derived gravity data as constraints. Bending moments and downward forces are imposed along curved trench axes and an iterative method is used to calculate the non-linear response for 26 sites in the circum-Pacific region having seafloor age ranging from 15 to 148 Ma. We use standard thermal and yield strength envelope models to develop the non-linear moment versus curvature relationship. Two coefficients of friction of 0.6 and 0.3 are considered and we find the lower value provides a better overall fit to the data. The main result is that the lithosphere is nearly moment saturated at the trench axis. The effective elastic thickness of the plate on the outer trench slope is at least three times smaller than the elastic thickness of the plate before bending at the outer rise, in agreement with previous studies. The average seafloor depth of the unbent plate in these 26 sites matches the Parsons & Sclater (1977) depth versus age model beyond 120 Ma. We also use the model to predict the offsets of normal faults on the outer trench walls and compare this with the horst and graben structures observed by multibeam surveys. The model with the lower coefficient of friction fits the fault offset data close to the trench axis. However, the model predicts significant fracturing of the lithosphere between 75 and 150 kilometres away from the trench axis where no fracturing is observed. To reconcile these observations, we impose a thermoelastic pre-stress in the lithosphere (Wessel 1992) prior to subduction. This pre-stress delays the onset of fracturing in better agreement with the data

The Relationship between the UniProt Knowledgebase (UniProtKB) and the IntAct Molecular Interaction Databases

IntAct provides a freely available, open source database system and analysis tools for protein interaction data. All interactions are derived from literature curation or direct user submission and all experimental information relating to binary protein-protein
interactions is entered into the IntAct database by curators, via a web-based editor. Interaction information is added to the SUBUNIT comment and the RP line of the relevant publication within the UniProtKB entry. There may be a single INTERACTION comment present within a UniProtKB entry, which conveys information relevant to binary protein-protein interactions. This is automatically derived from the IntAct database and is updated on a triweekly basis. Interactions can be derived by any appropriate experimental method but must be confirmed by a second interaction if resulting from a single yeast2hybrid experiment. For large-scale experiments, interactions are considered if a high confidence score is assigned by the authors. The INTERACTION line contains a direct link to IntAct that provides detailed information for the experimental support. These lines are not changed manually and any discrepancy is reported to IntAct for updates. There is also a database crossreference line within the UniProtKB entry i.e.: DR IntAct _UniProtKB AC, which directs the user to additional interaction data for that molecule. 
UniProt is supported by grants from the National Institutes of Health, European Commission, Swiss Federal Government and PATRIC BRC.
IntAct is funded by the European Commission under FELICS, contract number 021902 (RII3) within the Research Infrastructure Action of the FP6 "Structuring the European Research Area" Programme