Systemic Inflammation and the Breakdown of Intestinal Homeostasis Are Key Events in Chronic Spinal Cord Injury Patients

Our aim was to investigate the subset distribution and function of circulating monocytes, proinflammatory cytokine levels, gut barrier damage, and bacterial translocation in chronic spinal cord injury (SCI) patients. Thus, 56 SCI patients and 28 healthy donors were studied. The levels of circulating CD14^+highCD16⁻, CD14^+highCD16⁺, and CD14^+lowCD16⁺ monocytes, membrane TLR2, TLR4, and TLR9, phagocytic activity, ROS generation, and intracytoplasmic TNF-α, IL-1, IL-6, and IL-10 after lipopolysaccharide (LPS) stimulation were analyzed by polychromatic flow cytometry. Serum TNF-α, IL-1, IL-6 and IL-10 levels were measured by Luminex and LPS-binding protein (LBP), intestinal fatty acid-binding protein (I-FABP) and zonulin by ELISA. SCI patients had normal monocyte counts and subset distribution. CD14^+highCD16⁻ and CD14^+highCD16⁺ monocytes exhibited decreased TLR4, normal TLR2 and increased TLR9 expression. CD14^+highCD16⁻ monocytes had increased LPS-induced TNF-α but normal IL-1, IL-6, and IL-10 production. Monocytes exhibited defective phagocytosis but normal ROS production. Patients had enhanced serum TNF-α and IL-6 levels, normal IL-1 and IL-10 levels, and increased circulating LBP, I-FABP, and zonulin levels. Chronic SCI patients displayed impaired circulating monocyte function. These patients exhibited a systemic proinflammatory state characterized by enhanced serum TNF-α and IL-6 levels. These patients also had increased bacterial translocation and gut barrier damage.     

Impact of the anode operating conditions on the liquid water distribution in the cathode gas diffusion layer

Previous experimental results indicate that the humidification conditions at the anode have an impact on the liquid water distribution in the cathode gas diffusion layer. Numerical simulations are developed to reproduce and analyze this effect. Results consistent with the experimental results are first obtained by playing with the partition coefficients of an advanced pore network model computing the liquid water formation and transfer in the cathode gas diffusion layer (GDL) for a large range of operating conditions. Then, a model for the full anode – cathode assembly is developed by combining the pore network model of the cathode GDL and a 1D model describing the heat and water transfer in the various components of the anode-cathode assembly. This enables one to generalize the dry – wet regime diagram introduced in a previous work by incorporating the effect of the humidity condition at the anode.     

Mining statistically significant associations for exploratory analysis of human sleep data.

We introduce a specialized association rule mining technique that can extract patterns from complex sleep data comprising polysomnographic recordings, clinical summaries, and sleep questionnaire responses. The rules mined can describe associations among temporally annotated events and questionnaire or summary data; e.g., the likelihood that an occurrence of a rapid eye movement (REM) sleep stage during the second 100 sleep epochs of the night is associated with moderate caffeine intake. We use chi2 analysis to ensure statistical significance of the mined rules at the level P < 0.05. Our results, obtained by mining sleep-related data from 242 human subjects, reveal clinically interesting associations among the polysomnographic and summary variables. Our experience suggests that association mining may also be useful for selection of variables prior to using logistic regression.     

The first five responses in the method of constant stimuli.

Examined whether the method of constant stimuli with the standard presented first produces a constant error that depends on adaptation level. 120 university students were shown 5 series of 5 pairs of standard and variable lines. They had to decide whether the standard or the variable was longer or shorter than the preceding variable, or standard, respectively. In order to establish when the constant error occurred, the point of subjective equality was determined for each response. It was found that the 1st response was nearly random. The constant error was present from the 2nd response, and its magnitude did not change in succeeding series, contrary to expectations from adaptation-level theory. (French abstract) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dynamical modelling of an activated sludge system of a petrochemical plant operating at high temperatures.

The Mexican petrochemical industry, Morelos S.A. de C.V., is one of the biggest and more important petroleum industries in Mexico and Latin America. It has an activated sludge system to treat its wastewater flow, which is approximately 7,000 m3/d. The wastewater contains volatile organic carbon substances classified as toxics. The old surface aeration system was changed for fine bubble diffusers; however, one major drawback of the new aeration system is that the temperature in the bioreactor has increased due to the compression of the air, which at the compressor exit reaches 85 degrees C. This effect results in the temperature in the bioreactor attaining 32 degrees C during the fall, whereas in the spring and summer, the bioreactor temperature reaches higher values than 40 degrees C. The high temperatures reduce the microorganism activity and cause a higher volatilisation rate of volatile compounds, among other effects, which affect the performance of the biological treatment. This work was performed to obtain a better modelling of the wastewater treatment from the petrochemical industry. The model describes the effect of the temperature on the performance of the biological treatment. The model was obtained from tests that were carried out in laboratory reactors with 14 L capacity, which were operated at different temperatures (from 30 to 45 degrees C), with the same wastewater and conditions as the actual system.     

Neural network systems to reproduce crash behavior of structural components

The use of neural networks as global approximation tool in crashworthiness problems is here investigated. Neural networks are not only asked to return some meaningful indices of the structural behavior but also to reproduce load-time curves during crash phenomena. To contain the number of examples required for the training process, parallel subsystems of small neural networks are designed. Design points for the training process are obtained by explicit finite element analyses performed by PAMCRASH. The settlement of the points in the design domain is defined using a maximum distance concept. The procedure is applied to different typical absorption structures made of aluminum alloy: riveted tubes, honeycomb structures, longitudinal keel beam and intersection elements of helicopter subfloors.     

All‐hexahedral element meshing: automatic elimination of self‐intersecting dual lines

There has been some degree of success in all‐hexahedral meshing. Standard methods start with the object geometry defined by means of an all‐quadrilateral mesh, followed by the use of the combinatorial dual to the mesh in order to define the internal connectivities among elements. For all of the known methods using the dual concept, it is necessary to first prevent or eliminate self‐intersecting (SI) dual lines of the given quadrilateral mesh. The relevant features of SI lines are studied, giving a method to remove them, which avoids deforming the original geometry. Some examples of resulting meshes are shown where the current meshing method has been successfully applied. Copyright © 2002 John Wiley & Sons, Ltd.