Dynamical mechanism of atrial fibrillation: a topological approach

While spiral wave breakup has been implicated in the emergence of atrial fibrillation, its role in maintaining this complex type of cardiac arrhythmia is less clear. We used the Karma model of cardiac excitation to investigate the dynamical mechanisms that sustain atrial fibrillation once it has been established. The results of our numerical study show that spatiotemporally chaotic dynamics in this regime can be described as a dynamical equilibrium between topologically distinct types of transitions that increase or decrease the number of wavelets, in general agreement with the multiple wavelets hypothesis. Surprisingly, we found that the process of continuous excitation waves breaking up into discontinuous pieces plays no role whatsoever in maintaining spatiotemporal complexity. Instead this complexity is maintained as a dynamical balance between wave coalescence -- a unique, previously unidentified, topological process that increases the number of wavelets -- and wave collapse -- a different topological process that decreases their number.Comment: 15 pages, 14 figure

NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms

Prediction of nuclear proteins is one of the major challenges in genome annotation. A method, NcPred is described, for predicting nuclear proteins with higher accuracy exploiting n-mer statistics with different classification algorithms namely Alternating Decision (AD) Tree, Best First (BF) Tree, Random Tree and Adaptive (Ada) Boost. On BaCello dataset [1], NcPred improves about 20% accuracy with Random Tree and about 10% sensitivity with Ada Boost for Animal proteins compared to existing techniques. It also increases the accuracy of Fungal protein prediction by 20% and recall by 4% with AD Tree. In case of Human protein, the accuracy is improved by about 25% and sensitivity about 10% with BF Tree. Performance analysis of NcPred clearly demonstrates its suitability over the contemporary in-silico nuclear protein classification research

Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

FIRE Arctic Clouds Experiment

An overview is given of the First ISCCP Regional Experiment (FIRE) Arctic Clouds Experiment that was conducted in the Arctic during April through July, 1998. The principal goal of the field experiment was to gather the data needed to examine the impact of arctic clouds on the radiation exchange between the surface, atmosphere, and space, and to study how the surface influences the evolution of boundary layer clouds. The observations will be used to evaluate and improve climate model parameterizations of cloud and radiation processes, satellite remote sensing of cloud and surface characteristics, and understanding of cloud-radiation feedbacks in the Arctic. The experiment utilized four research aircraft that flew over surface-based observational sites in the Arctic Ocean and Barrow, Alaska. In this paper we describe the programmatic and science objectives of the project, the experimental design (including research platforms and instrumentation), conditions that were encountered during the field experiment, and some highlights of preliminary observations, modelling, and satellite remote sensing studies

Hyperpaths in network based on transit schedules

The concept of a hyperpath was introduced for handling passenger strategies in route choice behavior for public transit, especially in a frequency-based transit service environment. This model for handling route choice behavior has been widely used for planning transit services, and hyperpaths are now applied in areas beyond public transit. A hyperpath representing more specific passenger behaviors on a network based on transit schedules is proposed. A link-based time-expanded (LBTE) network for transit schedules is introduced; in the network each link represents a scheduled vehicle trip (or trip segment) with departure time and travel time (or arrival time) between two consecutive stops. The proposed LBTE network reduces the effort to build a network based on transit schedules because the network is expanded with scheduled links. A link-based representation of a hypergraph with existing hyperpath model properties that is directly integrated with the LBTE network is also proposed. Transit passenger behavior was incorporated for transfers in the link-based hyperpath. The efficiency of the proposed hyperpath model was demonstrated. The proposed models were applied on a test network and a real transit network represented by the general specification of Google's transit feed

New variants of Perfect Non-crossing Matchings

Given a set of points in the plane, we are interested in matching them with straight line segments. We focus on perfect (all points are matched) non-crossing (no two edges intersect) matchings. Apart from the well known MinMax variation, where the length of the longest edge is minimized, we extend work by looking into different optimization variants such as MaxMin, MinMin, and MaxMax. We consider both the monochromatic and bichromatic versions of these problems and by employing diverse techniques we provide efficient algorithms for various input point configurations

Surface-Enhanced Nitrate Photolysis on Ice

Heterogeneous nitrates photolysis is the trigger for many chemical processes occurring in the polar boundary layer and is widely believed to occur in a quasi-liquid layer (QLL) at the surface of ice. The dipole forbidden character of the electronic transition relevant to boundary layer atmospheric chemistry and the small photolysis/photoproducts quantum yields in ice (and in water) may confer a significant enhancement and interfacial specificity to this important photochemical reaction at the surface of ice. Using amorphous solid water films at cryogenic temperatures as models for the disordered interstitial air/ice interface within the snowpack suppresses the diffusive uptake kinetics thereby prolonging the residence time of nitrate anions at the surface of ice. This approach allows their slow heterogeneous photolysis kinetics to be studied providing the first direct evidence that nitrates adsorbed onto the first molecular layer at the surface of ice are photolyzed more effectively than those dissolved within the bulk. Vibrational spectroscopy allows the ~3-fold enhancement in photolysis rates to be correlated with the nitrates’ distorted intramolecular geometry thereby hinting at the role played by the greater chemical heterogeneity in their solvation environment at the surface of ice than in the bulk. A simple 1D kinetic model suggests 1-that a 3(6)-fold enhancement in photolysis rate for nitrates adsorbed onto the ice surface could increase the photochemical NO[subscript 2] emissions from a 5(8) nm thick photochemically active interfacial layer by 30%(60)%, and 2-that 25%(40%) of the NO[subscript 2] photochemical emissions to the snowpack interstitial air are released from the top-most molecularly thin surface layer on ice. These findings may provide a new paradigm for heterogeneous (photo)chemistry at temperatures below those required for a QLL to form at the ice surface

The CanOE Strategy: Integrating Genomic and Metabolic Contexts across Multiple Prokaryote Genomes to Find Candidate Genes for Orphan Enzymes

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates “genomic metabolons”, i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12