25 research outputs found

    Several examples of network topologies, illustrating emergence of “creative elements”.

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    <p>Here creative elements are defined as proteins whose PageRank or CheiRank can not be simply explained by the number of incoming or outgoing connections correspondingly. A and B) The size of the node is proportional to its PageRank value. C) The size of the protein is proportional to its CheiRank value. The color of the proteins in all three panels reflects the ratio between the number of incoming and outgoing edges. If the color is greenish then the protein is “importer” receiving more incoming regulations than having out-going edges. The reddish proteins are “exporters” regulating more proteins downstream than the number of incoming regulations.</p

    ST2 in HFpEF and HFrEF, similarities and differences

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    International audienceno abstrac

    Predictors of mortality from pump failure and sudden cardiac death in patients with systolic heart failure and left ventricular dyssynchrony: results of the CARE-HF trial.

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    BACKGROUND: Determining a specific death cause may facilitate individualized therapy in patients with heart failure (HF). Cardiac resynchronization therapy (CRT) decreased mortality in the Cardiac Resynchronization in Heart Failure trial by reducing pump failure and sudden cardiac death (SCD). This study analyzes predictors of specific causes of death. METHODS AND RESULTS: Univariate and multivariate analyses used 8 baseline and 3-month post-randomization variables to predict pump failure and SCD (categorized as "definite," "probable," and "possible"). Of 255 deaths, 197 were cardiovascular. There were 71 SCDs with a risk reduction by CRT of 0.47 (95% confidence interval 0.29-0.76; P = .002) with similar reductions in SCD classified as definite, probable, and possible. Univariate SCD predictors were 3-month HF status (mitral regurgitation [MR] severity, plasma brain natriuretic peptide [BNP], end-diastolic volume, and systolic blood pressure), whereas randomization to CRT decreased risk. Multivariate SCD predictors were randomization to CRT 0.56 (0.53-0.96, P = .035) and 3-month MR severity 1.82 (1.77-2.60, P = .0012). Univariate pump failure death predictors related to baseline HF state (quality of life score, interventricular mechanical delay, end-diastolic volume, plasma BNP, MR severity, and systolic pressure), whereas randomization to CRT and nonischemic cardiomyopathy decreased risk; multivariate predictors of pump failure death were baseline plasma BNP and systolic pressure and randomization to CRT. CONCLUSION: CRT decreased SCD in patients with systolic HF and ventricular dyssynchrony. SCD risk was increased with increased severity of MR (including the 3-month value for MR as a time-dependent covariate) and reduced by randomization to CRT. HF death was increased related to the level of systolic blood pressure, log BNP, and randomization to CRT. These results emphasize the importance and interdependence of HF severity to mortality from pump failure and SCD

    Solubility of carbon dioxide and ethane in lemon oil at elevated pressures

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    The solubility of ethane and carbon dioxide in lemon oil at elevated pressures has been measured using a variable volume cell, over temperature ranges of 298 - 308 K and 303 - 313 K, respectively, at pressures from 0.44 to 8.75 MPa. Partial liquid miscibility was not observed at any concentration measured. Molar densities of the saturated liquid mixtures are also reported. The Soave<FONT FACE="Symbol">-</FONT> Redlich<FONT FACE="Symbol">-</FONT> Kwong equation of state was used to fit experimental vapor<FONT FACE="Symbol">-</FONT> liquid equilibria of near critical light component<FONT FACE="Symbol">-</FONT> lemon oil mixtures. An appropriate representation of the experimental data was obtained by using a binary interaction parameter in the combinatorial rule for the cross energy parameter a ij. The application of a quadratic mixing rule for the co<FONT FACE="Symbol">-</FONT> volume b with a binary interaction coefficient l ij did not improve greatly the performance of the model
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