676 research outputs found
Toxicity of Pyrolysis Gases from Elastomers
The toxicity of the pyrolysis gases from six elastomers was investigated. The elastomers were polyisoprene (natural rubber), styrene-butadiene rubber (SBR), ethylene propylene diene terpolymer (EPDM), acrylonitrile rubber, chlorosulfonated polyethylene rubber, and polychloroprene. The rising temperature and fixed temperature programs produced exactly the same rank order of materials based on time to death. Acryltonitrile rubber exhibited the greatest toxicity under these test conditions; carbon monoxide was not found in sufficient concentrations to be the primary cause of death
Evolutionary Entropy: A Predictor of Body Size, Metabolic Rate and Maximal Life Span
Body size of organisms spans 24 orders of magnitude, and metabolic rate and life span present comparable differences across species. This article shows that this variation can be explained in terms of evolutionary entropy, a statistical parameter which characterizes the robustness of a population, and describes the uncertainty in the age of the mother of a randomly chosen newborn. We show that entropy also has a macroscopic description: It is linearly related to the logarithm of the variables body size, metabolic rate, and life span. Furthermore, entropy characterizes Darwinian fitness, the efficiency with which a population acquires and converts resources into viable offspring. Accordingly, entropy predicts the outcome of natural selection in populations subject to different classes of ecological constraints. This predictive property, when integrated with the macroscopic representation of entropy, is the basis for enormous differences in morphometric and life-history parameters across species
Characterizing Distances of Networks on the Tensor Manifold
At the core of understanding dynamical systems is the ability to maintain and
control the systems behavior that includes notions of robustness,
heterogeneity, or regime-shift detection. Recently, to explore such functional
properties, a convenient representation has been to model such dynamical
systems as a weighted graph consisting of a finite, but very large number of
interacting agents. This said, there exists very limited relevant statistical
theory that is able cope with real-life data, i.e., how does perform analysis
and/or statistics over a family of networks as opposed to a specific network or
network-to-network variation. Here, we are interested in the analysis of
network families whereby each network represents a point on an underlying
statistical manifold. To do so, we explore the Riemannian structure of the
tensor manifold developed by Pennec previously applied to Diffusion Tensor
Imaging (DTI) towards the problem of network analysis. In particular, while
this note focuses on Pennec definition of geodesics amongst a family of
networks, we show how it lays the foundation for future work for developing
measures of network robustness for regime-shift detection. We conclude with
experiments highlighting the proposed distance on synthetic networks and an
application towards biological (stem-cell) systems.Comment: This paper is accepted at 8th International Conference on Complex
Networks 201
Maximal entropy random walk in community finding
The aim of this paper is to check feasibility of using the maximal-entropy
random walk in algorithms finding communities in complex networks. A number of
such algorithms exploit an ordinary or a biased random walk for this purpose.
Their key part is a (dis)similarity matrix, according to which nodes are
grouped. This study encompasses the use of the stochastic matrix of a random
walk, its mean first-passage time matrix, and a matrix of weighted paths count.
We briefly indicate the connection between those quantities and propose
substituting the maximal-entropy random walk for the previously chosen models.
This unique random walk maximises the entropy of ensembles of paths of given
length and endpoints, which results in equiprobability of those paths. We
compare performance of the selected algorithms on LFR benchmark graphs. The
results show that the change in performance depends very strongly on the
particular algorithm, and can lead to slight improvements as well as
significant deterioration.Comment: 7 pages, 4 figures, submitted to European Physical Journal Special
Topics following the 4-th Conference on Statistical Physics: Modern Trends
and Applications, July 3-6, 2012 Lviv, Ukrain
Molecular Evolution in Time Dependent Environments
The quasispecies theory is studied for dynamic replication landscapes. A
meaningful asymptotic quasispecies is defined for periodic time dependencies.
The quasispecies' composition is constantly changing over the oscillation
period. The error threshold moves towards the position of the time averaged
landscape for high oscillation frequencies and follows the landscape closely
for low oscillation frequencies.Comment: 5 pages, 3 figures, Latex, uses Springer documentclass llncs.cl
Durable Advanced Flexible Reusable Surface Insulation
An improved flexible blanket includes a nickel-based alloy foil layer brazed to a nickel-based alloy fabric layer. The fabric layer is stitched to an underlying ceramic insulation layer
On dynamic network entropy in cancer
The cellular phenotype is described by a complex network of molecular
interactions. Elucidating network properties that distinguish disease from the
healthy cellular state is therefore of critical importance for gaining
systems-level insights into disease mechanisms and ultimately for developing
improved therapies. By integrating gene expression data with a protein
interaction network to induce a stochastic dynamics on the network, we here
demonstrate that cancer cells are characterised by an increase in the dynamic
network entropy, compared to cells of normal physiology. Using a fundamental
relation between the macroscopic resilience of a dynamical system and the
uncertainty (entropy) in the underlying microscopic processes, we argue that
cancer cells will be more robust to random gene perturbations. In addition, we
formally demonstrate that gene expression differences between normal and cancer
tissue are anticorrelated with local dynamic entropy changes, thus providing a
systemic link between gene expression changes at the nodes and their local
network dynamics. In particular, we also find that genes which drive
cell-proliferation in cancer cells and which often encode oncogenes are
associated with reductions in the dynamic network entropy. In summary, our
results support the view that the observed increased robustness of cancer cells
to perturbation and therapy may be due to an increase in the dynamic network
entropy that allows cells to adapt to the new cellular stresses. Conversely,
genes that exhibit local flux entropy decreases in cancer may render cancer
cells more susceptible to targeted intervention and may therefore represent
promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte
Virus Replication as a Phenotypic Version of Polynucleotide Evolution
In this paper we revisit and adapt to viral evolution an approach based on
the theory of branching process advanced by Demetrius, Schuster and Sigmund
("Polynucleotide evolution and branching processes", Bull. Math. Biol. 46
(1985) 239-262), in their study of polynucleotide evolution. By taking into
account beneficial effects we obtain a non-trivial multivariate generalization
of their single-type branching process model. Perturbative techniques allows us
to obtain analytical asymptotic expressions for the main global parameters of
the model which lead to the following rigorous results: (i) a new criterion for
"no sure extinction", (ii) a generalization and proof, for this particular
class of models, of the lethal mutagenesis criterion proposed by Bull,
Sanju\'an and Wilke ("Theory of lethal mutagenesis for viruses", J. Virology 18
(2007) 2930-2939), (iii) a new proposal for the notion of relaxation time with
a quantitative prescription for its evaluation, (iv) the quantitative
description of the evolution of the expected values in in four distinct
"stages": extinction threshold, lethal mutagenesis, stationary "equilibrium"
and transient. Finally, based on these quantitative results we are able to draw
some qualitative conclusions.Comment: 23 pages, 1 figure, 2 tables. arXiv admin note: substantial text
overlap with arXiv:1110.336
Improved Imputation of Common and Uncommon Single Nucleotide Polymorphisms (SNPs) with a New Reference Set
Statistical imputation of genotype data is an important technique for analysis of genome-wide association studies (GWAS). We have built a reference dataset to improve imputation accuracy for studies of individuals of primarily European descent using genotype data from the Hap1, Omni1, and Omni2.5 human SNP arrays (Illumina). Our dataset contains 2.5-3.1 million variants for 930 European, 157 Asian, and 162 African/African-American individuals. Imputation accuracy of European data from Hap660 or OmniExpress array content, measured by the proportion of variants imputed with R^2^>0.8, improved by 34%, 23% and 12% for variants with MAF of 3%, 5% and 10%, respectively, compared to imputation using publicly available data from 1,000 Genomes and International HapMap projects. The improved accuracy with the use of the new dataset could increase the power for GWAS by as much as 8% relative to genotyping all variants. This reference dataset is available to the scientific community through the NCBI dbGaP portal. Future versions will include additional genotype data as well as non-European populations
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