The Association between Intrauterine Inflammation and Spontaneous Vaginal Delivery at Term: A Cross-Sectional Study

BACKGROUND:Different factors contribute to the onset of labor at term. In animal models onset of labor is characterized by an inflammatory response. The role of intrauterine inflammation, although implicated in preterm birth, is not yet established in human term labor. We hypothesized that intrauterine inflammation at term is associated with spontaneous onset of labor. METHODS/RESULTS:In two large urban hospitals in the Netherlands, a cross-sectional study of spontaneous onset term vaginal deliveries and elective caesarean sections (CS), without signs of labor, was carried out. Placentas and amniotic fluid samples were collected during labor and/or at delivery. Histological signs of placenta inflammation were determined. Amniotic fluid proinflammatory cytokine concentrations were measured using ELISA. A total of 375 women were included. In term vaginal deliveries, more signs of intrauterine inflammation were found than in elective CS: the prevalence of chorioamnionitis was higher (18 vs 4%, p = 0.02) and amniotic fluid concentration of IL-6 was higher (3.1 vs 0.37 ng/mL, p<0.001). Similar results were obtained for IL-8 (10.93 vs 0.96 ng/mL, p<0.001) and percentage of detectable TNF-alpha (50 vs 4%, p<0.001). CONCLUSIONS:This large cross-sectional study shows that spontaneous term delivery is characterized by histopathological signs of placenta inflammation and increased amniotic fluid proinflammatory cytokines

Evolutionarily Conserved Protein Sequences of Influenza A Viruses, Avian and Human, as Vaccine Targets

10.1371/journal.pone.0001190PLoS ONE21

Eulerian simulations of bubbling behaviour in gas-solid fluidised beds

In literature little attempt has been made to verify experimentally Eulerian-Eulerian gas-solid model simulations of bubbling fluidised beds with existing correlations for bubble size or bubble velocity. In the present study, a CFD model for a free bubbling fluidised bed was implemented in the commercial code CFX of AEA Technology. This CFD model is based on a two fluid model including the kinetic theory of granular flow. Simulations of the bubble behaviour in fluidised beds at different superficial gas velocities and at different column diameters are compared to the Darton et al. (1977) equation for the bubble diameter versus the height in the column and to the Hilligardt and Werther (1986) equation, corrected for the two dimensional geometry using the bubble rise velocity correlation of Pyle and Harrison (1967). It is shown that the predicted bubble sizes are in agreement with the Darton et al. (1977) bubble size equation. Comparison of the predicted bubble velocity with the Hilligardt and Werther (1986) equation shows a deviation for the velocity of smaller bubbles. To explain this, the predicted bubbles are divided into two bubble classes : bubbles that have either coalesced, broken-up or have touched the wall, and bubbles without these occurrences. The bubbles of this second class are in agreement with the Hilligardt and Werther (1986) equation. Fit parameters of Hilligardt and Werther (1986) are compared to the fit parameters obtained in this work. It is shown that coalescence, break-up, and direct wall interactions are very important effects, often dominating the dynamic bubble behaviour, but these effects are not accounted for by the Hilligardt and Werther (1986) equation. © 1998 Elsevier Science Ltd. All rights reserved

Chaotic attractor learning and how to deal with nonlinear singularities

© 2000 IEEE. In linear regression it is common practice to use principal component analysis (PCA) to find and remove directions in the input space that are not covered by the observed data. PCA fails to identify these 'singular directions' if the data lie on a lower dimensional nonlinear subspace. Typically, this is the case for data observed from deterministic chaotic systems. In this paper we present a viable nonlinear counterpart for principal component regression, and show why this algorithm can learn stable models for chaotic dynamics where other approaches often fail. The algorithm is applied to an experimental chaotic bubble column, with data highly contaminated with system noise and measurement errors

Characterization of fluidization regimes by time-series analysis of pressure fluctuations

This work compares time, frequency and state-space analyses of pressure measurements from fluidized beds. The experiments were carried out in a circulating fluidized bed, operated under ambient conditions and under different fluidization regimes. Interpretation of results in time domain, such as standard deviation of the pressure fluctuations, may lead to erroneous conclusions about the flow regime. The results from the frequency domain (power spectra) and state-space analyses (correlation dimension, DML, and Kolmogorov entropy, KML, together with a non-linearity test) of the pressure fluctuations are generally in agreement and can be used complementary to each other. The power spectra can be divided into three regions, a region corresponding to the macro-structure (due to the bubble flow) and, at higher frequencies, two regions representing finer structures that are not predominantly governed by the macro structure of the flow. In all fluidization regimes, the measured pressure fluctuations exhibited an intermittent structure, which is not revealed by power spectral analysis of the original signals. Fluctuations with pronounced peaks in the power spectrum and in the auto-correlation function, corresponding to passage of single bubbles through the bed, are non-linear with a low dimension (DML5.5 both KML (bits/cycle) and DML are insensitive to changes in the distribution of energy in power spectra. Thus, the state-space analysis reflects that non-linearity is mostly found in the macro-structure of the flow. Fluidized bed time series treated in this work are available at http://www.entek.chalmers.se/fij

Similarity between chaos analysis and frequency analysis of pressure fluctuations in fluidized beds

In literature the dynamic behavior of fluidized beds is frequently characterized by spectral anal. and chaos anal. of the pressure fluctuations that are caused by the gas-solids flow. In case of spectral anal., most often the power spectral d. (PSD) function is quantified, for example, by the frequency at the largest power and/or by the power-law fall-off at the higher frequencies. In case of chaos anal., most often the correlation entropy (or Kolmogorov entropy) is used to characterize the fluidized bed dynamics. It is shown theor. that a relation exists between the correlation entropy and the PSD function. This relation is exptl. verified by a large set of exptl. pressure fluctuation data from slugging, bubbling, and circulating fluidized beds. It is shown that the correlation entropy is linearly proportional to the av. frequency that is obtained from the PSD function. As the av. frequency can be directly interpreted in terms of the phys. phenomena underlying the pressure fluctuations, the av. frequency is suggested as a first, simple, characteristic of fluidized bed dynamic

CFD modeling of gas-fluidized beds with a bimodal particle mixture

A computational fluid dynamics model was developed for gas-solid fluidized beds containing a mixture of two particle species. To calculate stresses of the solid phase, the kinetic theory of granular flow was extended to consider a binary mixture of smooth, nearly elastic, spheres. The developed model was simulated to demonstrate key features of binary mixture fluidization. Bed expansion with a binary mixture of different size particles, but with identical densities, was much higher than that of a system consisting of mono-sized particles of the same mean size as the bimodal mixture. Minimum fluidization velocity for the binary particle system was significantly lowered. The mixing behavior of the binary mixture of particles, characterized by the mixing index, increased with increasing superficial gas velocity. For a binary mixture of particles of larger size with lower density and smaller size with higher density, larger, lighter particles segregated to the top of the fluid bed, while smaller, heavier particles segregated to the bottom. With increasing fluidization velocity, this segregation pattern reversed and inversion occurred. The drag and gravity force difference between small, heavy particles and large, light particles was dominant at low gas velocities. With an increase in gas velocity, however, the gradients in granular temperature and pressure became dominant terms in the equations for the relative force and thus velocity between two different particle species

Similarity between chaos analysis and frequency analysis of pressure fluctuations in fluidized beds

In literature the dynamic behavior of fluidized beds is frequently characterized by spectral analysis and chaos analysis of the pressure fluctuations that are caused by the gas-solids flow. In case of spectral analysis, most often the power spectral density (PSD) function is quantified, for example, by the frequency at the largest power and/or by the power-law fall-off at the higher frequencies. In case of chaos analysis, most often the correlation entropy (or Kolmogorov entropy) is used to characterize the fluidized bed dynamics. In this paper, it is shown theoretically that a relationship exists between the correlation entropy and the PSD function. This relationship is experimentally verified by a large set of experimental pressure fluctuation data from slugging, bubbling, and circulating fluidized beds. It is shown that the correlation entropy is linearly proportional to the average frequency that is obtained from the PSD function. As the average frequency can be directly interpreted in terms of the physical phenomena underlying the pressure fluctuations, the average frequency is suggested as a first, simple, characteristic of fluidized bed dynamics. (C) 2004 Elsevier Ltd. All rights reserved