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.     

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.     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

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)     

A shell for cooperating expert systems

Abstract: This paper describes a shell for cooperating expert systems that has been developed at the University of Porto. The main goal of this shell is two-fold: to generate a community of cooperative knowledge-based systems and to develop several special reasoning techniques which can be used under a distributed and cooperative paradigm. UPShell is able to convert a set of generated intelligent systems (ISs) into a community of cooperative ISs. In this first version it is already possible to generate different intelligent systems which are able to run 'simultaneously' as separate Unix processes and, using a message-passing mechanism, to communicate among themselves. They can be set to pursue an overall goal in a cooperative way. Moreover, several tasks can be given to each IS to be solved simultaneously, and the IS can switch from task to task according to dynamic priorities reflecting the urgency attached to the specific sub-tasks that emerge. The shell described here may also be used to test, within a distributed environment, some time-bounded reasoning techniques that are presently being developed. The paper has three main parts: a general overview of the UPShell (Section 1); a tutorial explaining, by means of examples, how to use the package (Section 2); and, finally, some considerations on the reasoning techniques used and future improvements (Sections 3–5).     

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.     

Organic matter release in low temperature thermal treatment of biological sludge for reduction of excess sludge production.

Thermal treatment applied in association with a biological system allows for a significant reduction in excess sludge production (approximately 50%). In general, heat treatment is described as a sludge disintegration technique. This paper offers a thorough study on the impact of heat treatment, at temperatures below 100 degrees C, on the solubilisation of the sludge COD and its biodegradability. Discontinuous heating experiments were performed on activated and digested sludge. At all temperatures tested the released COD for digested sludge was systematically higher than that for activated sludge (15 and 40%, respectively, at 95 degrees C for 40 min of contact time). For the first 30 min, a 1st order kinetic, with respect to the residual COD, was systematically found. In the range of 40-95 degrees C, digested sludge had a lower activation energy than activated sludge (26 kcal/mol compared to 70-160 kcal/mol). COD solubilisation is thus more positively influenced by temperature in the case of activated sludge. This may be due to the significant difference in the ratio of protein/carbohydrate in digested and activated sludge (1-5 and 0.2-0.7, respectively). The increase in the COD/TKN ratio in the solubilised fraction after thermal treatment of activated sludge suggests a preferential solubilisation of proteins over carbohydrates. Respirometric tests performed on the solubilised COD showed that whatever the sludge origin, only 40-50% of released COD is biodegradable at a conventional hydraulic retention time (i.e., 24 h). Hence, heat treatment would act more through organic matter solubilisation rather than by a biodegradability increase.     

Moral value transfer from regulatory fit: What feels right is right and what feels wrong is wrong.

People experience regulatory fit (E. T. Higgins, 2000) when the strategic manner of their goal pursuit suits their regulatory orientation, and this regulatory fit feels right. Fit violation feels wrong. Four studies tested the proposal that experiences of fit can transfer to moral evaluations. The authors examined transfer of feeling wrong from fit violation by having participants in a promotion or prevention focus recall transgressions of commission or omission (Studies 1 and 2). Both studies found that when the type of transgression was a fit violation, participants expressed more guilt. Studies 3 and 4 examined transfer of feeling right from regulatory fit. Participants evaluated conflict resolutions (Study 3) and public policies (Study 4) as more right when the means pursued had fit. (PsycINFO Database Record (c) 2010 APA, all rights reserved)