61,604 research outputs found

    Intrarenal Resistance Index as a Prognostic Parameter in Patients with Liver Cirrhosis Compared with Other Hepatic Scoring Systems

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    Background and Aims: Patients with advanced liver cirrhosis who develop renal dysfunction have a poor prognosis. Elevated intrarenal resistance indices (RIs) due to renal vascular constriction have been described before in cirrhotic patients. In the current study, we prospectively investigated the course of intrarenal RIs and compared their prognostic impact with those of the Model for End-Stage Liver Disease (MELD) and the Child-Pugh scores. Methods: Sixty-three patients with liver cirrhosis underwent a baseline visit which included a sonographic examination and laboratory tests. Forty-four patients were prospectively monitored. The end points were death or survival at the day of the follow-up visit. Results: In 28 patients, a follow-up visit was performed after 22 8 months (group 1). Sixteen patients died during follow-up after 12 8 months (group 2). Group 2 patients showed a significantly higher baseline RI (0.76 +/- 0.05) than group 1 patients (RI = 0.72 +/- 0.06; p < 0.05). As shown by receiver operating characteristic analysis, the RI and the MELD score achieved similar sensitivity and specificity {[}area under the curve (AUC): 0.722; 95% confidence interval (95% CI): 0.575-0.873 vs. AUC: 0.724; 95% CI: 0.575-0.873, z = 0.029, n.s.] in predicting survival and were superior to the Child-Pugh score (AUC: 0.677; 96% Cl: 0.518-0.837). Conclusion: The RI is not inferior in sensitivity and specificity to the MELD score. Cirrhotic patients with elevated RIs have impaired short- and long-term survival. The RI may help identify high-risk patients that require special therapeutic care. Copyright (C) 2012 S. Karger AG, Base

    Realization of Pan Jiazheng′s extremum principle with optimization methods

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    2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Semi-Persistent Resource Allocation Based on Traffic Prediction for Vehicular Communications

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    Modifying the thermal conductivity of small molecule organic semiconductor thin films with metal nanoparticles

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    Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

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    Š 2018 Elsevier Inc. This paper aims at solving the sparse reconstruction (SR) problem via a multiobjective evolutionary algorithm. Existing multiobjective evolutionary algorithms for the SR problem have high computational complexity, especially in high-dimensional reconstruction scenarios. Furthermore, these algorithms focus on estimating the whole Pareto front rather than the knee region, thus leading to limited diversity of solutions in knee region and waste of computational effort. To tackle these issues, this paper proposes an adaptive decomposition-based evolutionary approach (ADEA) for the SR problem. Firstly, we employ the decomposition-based evolutionary paradigm to guarantee a high computational efficiency and diversity of solutions in the whole objective space. Then, we propose a two-stage iterative soft-thresholding (IST)-based local search operator to improve the convergence. Finally, we develop an adaptive decomposition-based environmental selection strategy, by which the decomposition in the knee region can be adjusted dynamically. This strategy enables to focus the selection effort on the knee region and achieves low computational complexity. Experimental results on simulated signals, benchmark signals and images demonstrate the superiority of ADEA in terms of reconstruction accuracy and computational efficiency, compared to five state-of-the-art algorithms

    Interacting Turing-Hopf Instabilities Drive Symmetry-Breaking Transitions in a Mean-Field Model of the Cortex: A Mechanism for the Slow Oscillation

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    Electrical recordings of brain activity during the transition from wake to anesthetic coma show temporal and spectral alterations that are correlated with gross changes in the underlying brain state. Entry into anesthetic unconsciousness is signposted by the emergence of large, slow oscillations of electrical activity (≲1  Hz) similar to the slow waves observed in natural sleep. Here we present a two-dimensional mean-field model of the cortex in which slow spatiotemporal oscillations arise spontaneously through a Turing (spatial) symmetry-breaking bifurcation that is modulated by a Hopf (temporal) instability. In our model, populations of neurons are densely interlinked by chemical synapses, and by interneuronal gap junctions represented as an inhibitory diffusive coupling. To demonstrate cortical behavior over a wide range of distinct brain states, we explore model dynamics in the vicinity of a general-anesthetic-induced transition from “wake” to “coma.” In this region, the system is poised at a codimension-2 point where competing Turing and Hopf instabilities coexist. We model anesthesia as a moderate reduction in inhibitory diffusion, paired with an increase in inhibitory postsynaptic response, producing a coma state that is characterized by emergent low-frequency oscillations whose dynamics is chaotic in time and space. The effect of long-range axonal white-matter connectivity is probed with the inclusion of a single idealized point-to-point connection. We find that the additional excitation from the long-range connection can provoke seizurelike bursts of cortical activity when inhibitory diffusion is weak, but has little impact on an active cortex. Our proposed dynamic mechanism for the origin of anesthetic slow waves complements—and contrasts with—conventional explanations that require cyclic modulation of ion-channel conductances. We postulate that a similar bifurcation mechanism might underpin the slow waves of natural sleep and comment on the possible consequences of chaotic dynamics for memory processing and learning

    Consensus of multi-agent systems with faults and mismatches under switched topologies using a delta operator method

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    Š 2018 Elsevier B.V. This paper studies the consensus of multi-agent systems with faults and mismatches under switched topologies using a delta operator method. Since faults and mismatches can result in failure of the consensus even for a fixed topology with a spanning tree, how to reach a consensus is a complicated and challenging problem under such circumstances especially when part topologies have no spanning tree. Although some works studied the influence of faults and mismatches on the consensus, there is little work on reaching a consensus for the multi-agent systems with faults and mismatches. In this paper, we introduce the delta operator to unify the consensus analysis for continuous, discrete, or sampled systems under one framework. We develop the theories on the delta operator systems first and then apply theories of the delta operator systems to the consensus problems. By converting the consensus problems into stability problems, we investigate and prove consensus and the associated conditions for systems 1) without any fault, 2) with a known fault, and 3) with unknown faults, under switching topologies with matching or mismatching coefficients. Numerical examples are provided and validate the effectiveness of the theoretical results
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