807 research outputs found
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
Lidar System Model for Use With Path Obscurants and Experimental Validation
When lidar pulses travel through a short path that includes a relatively high concentration of aerosols, scattering phenomena can alter the power and temporal properties of the pulses significantly, causing undesirable effects in the received pulse. In many applications the design of the lidar transmitter and receiver must consider adverse environmental aerosol conditions to ensure the desired performance. We present an analytical model of lidar system operation when the optical path includes aerosols for use in support of instrument design, simulations, and system evaluation. The model considers an optical path terminated with a solid object, although it can also be applied, with minor modifications, to cases where the expected backscatter occurs from nonsolid objects. The optical path aerosols are characterized by their attenuation and backscatter coefficients derived by the Mie theory from the concentration and particle size distribution of the aerosol. Other inputs include the lidar system parameters and instrument response function, and the model output is the time-resolved received pulse. The model is demonstrated and experimentally validated with military fog oil smoke for short ranges (several meters). The results are obtained with a lidar system operating at a wavelength of 0.905 μm within and outside the aerosol. The model goodness of fit is evaluated using the statistical coefficient of determination whose value ranged from 0.88 to 0.99 in this study
Improving the Price of Anarchy for Selfish Routing via Coordination Mechanisms
We reconsider the well-studied Selfish Routing game with affine latency
functions. The Price of Anarchy for this class of games takes maximum value
4/3; this maximum is attained already for a simple network of two parallel
links, known as Pigou's network. We improve upon the value 4/3 by means of
Coordination Mechanisms.
We increase the latency functions of the edges in the network, i.e., if
is the latency function of an edge , we replace it by
with for all . Then an
adversary fixes a demand rate as input. The engineered Price of Anarchy of the
mechanism is defined as the worst-case ratio of the Nash social cost in the
modified network over the optimal social cost in the original network.
Formally, if \CM(r) denotes the cost of the worst Nash flow in the modified
network for rate and \Copt(r) denotes the cost of the optimal flow in the
original network for the same rate then [\ePoA = \max_{r \ge 0}
\frac{\CM(r)}{\Copt(r)}.]
We first exhibit a simple coordination mechanism that achieves for any
network of parallel links an engineered Price of Anarchy strictly less than
4/3. For the case of two parallel links our basic mechanism gives 5/4 = 1.25.
Then, for the case of two parallel links, we describe an optimal mechanism; its
engineered Price of Anarchy lies between 1.191 and 1.192.Comment: 17 pages, 2 figures, preliminary version appeared at ESA 201
Scheduling periodic tasks in a hard real-time environment
We consider a real-time scheduling problem that occurs in the design
of software-based aircraft control. The goal is to distribute tasks
on a minimum number of identical machines and to
compute offsets for the tasks such that no collision occurs. A
task releases a job of running time at each time and a collision occurs if two jobs are
simultaneously active on the same machine.
We shed some light on the complexity and approximability landscape of this problem.
Although the problem cannot be approximated
within a factor of for any , an interesting restriction
is much more tractable: If the periods are dividing (for each one has or ), the problem allows for a better structured representation of solutions, which leads
to a 2-approximation. This result is tight, even asymptotically
EcID. A database for the inference of functional interactions in E. coli
The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es
EcID. A database for the inference of functional interactions in E. coli
The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es
An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae
Background: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard Fellowship (EMM). These agencies were not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.Cellular and Molecular Biolog
CODA: Accurate Detection of Functional Associations between Proteins in Eukaryotic Genomes Using Domain Fusion
Background: In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Gene fusion prediction is one approach to detection of such functional relationships. Its use is however known to be problematic in higher eukaryotic genomes due to the presence of large homologous domain families. Here we introduce CODA (Co-Occurrence of Domains Analysis), a method to predict functional associations based on the gene fusion idiom.Methodology/Principal Findings: We apply a novel scoring scheme which takes account of the genome-specific size of homologous domain families involved in fusion to improve accuracy in predicting functional associations. We show that CODA is able to accurately predict functional similarities in human with comparison to state-of-the-art methods and show that different methods can be complementary. CODA is used to produce evidence that a currently uncharacterised human protein may be involved in pathways related to depression and that another is involved in DNA replication.Conclusions/Significance: The relative performance of different gene fusion methodologies has not previously been explored. We find that they are largely complementary, with different methods being more or less appropriate in different genomes. Our method is the only one currently available for download and can be run on an arbitrary dataset by the user. The CODA software and datasets are freely available from ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/v6.1.0/CODA/. Predictions are also available via web services from http://funcnet.eu/
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
Characterisation of local ICRF heat loads on the JET ILW
When using Ion Cyclotron Range of Frequency (ICRF) heating, enhanced
heat-fluxes are commonly observed on some plasma facing components close to the
antennas. Experiments have recently been carried out on JET with the new
ITER-Like-Wall (ILW) to characterize the heat flux to the JET ICRF antennas.
Using Infra-Red thermography and thermal models of the tiles, heat-fluxes were
evaluated from the surface temperature increase during the RF phase of L-mode
plasmas. The maximum observed heat-flux intensity was ~ 4.5 MW/m2 when
operating with -{\pi}/2 current drive strap phasing at power level of 2MW per
antenna and with a 4 cm distance between the plasma and the outer limiters.
Heat-fluxes are reduced when using dipole strap phasing. The fraction of ICRF
power deposited on the antenna limiters or septa was in the range 2-10% for
dipole phasing and 10-20% with +/-{\pi}/2 phasing.Comment: 22 pages, 6 figure
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