Blood glucose level on postoperative day 1 is predictive of adverse outcomes after cardiovascular surgery.

AIM: Hyperglycaemia is now a recognized predictive factor of morbidity and mortality after coronary artery bypass grafting (CABG). For this reason, we aimed to evaluate the postoperative management of glucose control in patients undergoing cardiovascular surgery, and to assess the impact of glucose levels on in-hospital mortality and morbidity. METHODS: This was a retrospective study investigating the association between postoperative blood glucose and outcomes, including death, post-surgical complications, and length of stay in the intensive care unit (ICU) and in hospital. RESULTS: A total of 642 consecutive patients were enrolled into the study after cardiovascular surgery (CABG, carotid endarterectomy and bypass in the lower limbs). Patients' mean age was 68+/-10 years, and 74% were male. In-hospital mortality was 5% in diabetic patients vs 2% in non-diabetic patients (OR: 1.66, P=0.076). Having blood glucose levels in the upper quartile range (> or =8.8 mmol/L) on postoperative day 1 was independently associated with death (OR: 10.16, P=0.0002), infectious complications (OR: 1.76, P=0.04) and prolonged ICU stay (OR: 3.10, P<0.0001). Patients presenting with three or more hypoglycaemic episodes (<4.1 mmol/L) had increased rates of mortality (OR: 9.08, P<0.0001) and complications (OR: 8.57, P<0.0001). CONCLUSION: Glucose levels greater than 8.8 mmol/L on postoperative day 1 and having three or more hypoglycaemic episodes in the postoperative period were predictive of mortality and morbidity among patients undergoing cardiovascular surgery. This suggests that a multidisciplinary approach may be able to achieve better postoperative blood glucose control.

Will climate change drive alien invasive plants into areas of high protection value? An improved model-based regional assessment to prioritise the management of invasions.

Species distribution models (SDMs) studies suggest that, without control measures, the distribution of many alien invasive plant species (AIS) will increase under climate and land-use changes. Due to limited resources and large areas colonised by invaders, management and monitoring resources must be prioritised. Choices depend on the conservation value of the invaded areas and can be guided by SDM predictions. Here, we use a hierarchical SDM framework, complemented by connectivity analysis of AIS distributions, to evaluate current and future conflicts between AIS and high conservation value areas. We illustrate the framework with three Australian wattle (Acacia) species and patterns of conservation value in Northern Portugal. Results show that protected areas will likely suffer higher pressure from all three Acacia species under future climatic conditions. Due to this higher predicted conflict in protected areas, management might be prioritised for Acacia dealbata and Acacia melanoxylon. Connectivity of AIS suitable areas inside protected areas is currently lower than across the full study area, but this would change under future environmental conditions. Coupled SDM and connectivity analysis can support resource prioritisation for anticipation and monitoring of AIS impacts. However, further tests of this framework over a wide range of regions and organisms are still required before wide application.

Regional consumption of antibiotics : a demand system approach

Through this paper. we have attempted to model the demand for different classes of antibiotics used for respiratory infections in outpatient care in Switzerland using a spatial version of the linear approximate Almost Ideal Demand System (AIDS) model. This model takes spatial dependency into account by means of spatial lags of antibiotic budget shares. We control for the health status of patients and the potential harmful effects of antibiotic use in terms of bacterial resistance. Elasticities to socioeconomic determinants of consumption and own- and cross-price elasticities between different groups of antibiotic have also been computed in this paper. Significant cross-price elasticities are found between newer or more expensive generations and older or less expensive generations of antibiotics. (C) 2009 Elsevier B.V. All rights reserved.

Structural and mean level analyses of the Five-Factor Model and Locus of Control: Further evidence from Africa

The present study examines the Five-Factor Model (FFM) of personality and locus of control in French-speaking samples in Burkina Faso (N = 470) and Switzerland (Ns = 1,090, 361), using the Revised NEO Personality Inventory (NEO-PI-R) and Levenson's Internality, Powerful others, and Chance (IPC) scales. Alpha reliabilities were consistently lower in Burkina Faso, but the factor structure of the NEO-PI-R was replicated in both cultures. The intended three-factor structure of the IPC could not be replicated, although a two-factor solution was replicable across the two samples. Although scalar equivalence has not been demonstrated, mean level comparisons showed the hypothesized effects for most of the five factors and locus of control; Burkinabè scored higher in Neuroticism than anticipated. Findings from this African sample generally replicate earlier results from Asian and Western cultures, and are consistent with a biologically-based theory of personality.

Delimitation of kala-azar risk areas in the district of Vaishali in Bihar (India) using a geo-environmental approach

Remote sensing and geographical information technologies were used to discriminate areas of high and low risk for contracting kala-azar or visceral leishmaniasis. Satellite data were digitally processed to generate maps of land cover and spectral indices, such as the normalised difference vegetation index and wetness index. To map estimated vector abundance and indoor climate data, local polynomial interpolations were used based on the weightage values. Attribute layers were prepared based on illiteracy and the unemployed proportion of the population and associated with village boundaries. Pearson's correlation coefficient was used to estimate the relationship between environmental variables and disease incidence across the study area. The cell values for each input raster in the analysis were assigned values from the evaluation scale. Simple weighting/ratings based on the degree of favourable conditions for kala-azar transmission were used for all the variables, leading to geo-environmental risk model. Variables such as, land use/land cover, vegetation conditions, surface dampness, the indoor climate, illiteracy rates and the size of the unemployed population were considered for inclusion in the geo-environmental kala-azar risk model. The risk model was stratified into areas of "risk"and "non-risk"for the disease, based on calculation of risk indices. The described approach constitutes a promising tool for microlevel kala-azar surveillance and aids in directing control efforts.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA) or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors) was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have limited fit.

Evaluation of the antitumoral effect sunitinib eluting beads in VX2 liver tumour in a rabbit model.

Background: We demonstrated that DC Bead (Biocompatibles UK, Ltd) could be loaded with sunitinib and injected intra-arterially in the rabbit without unexpected toxicity. The purpose of this study is to evaluate the antitumoral effect of sunitinib eluting beads in the VX2 tumor model of liver cancer. Methods: VX2 tumors were implanted in the left liver lobe of New-Zealand white rabbits. Animals were assigned to 3 groups: Group 1 (n=6) received 1.5mg of sunitinib loaded in 0.05ml of 100-300um DC Bead, group 2 (n=5) received 0.05ml of 100-300um DC Bead, group 3 (n=5) received 0.05ml NaCl 0.9% in the left hepatic artery. One animal in each group was sacrificed at 24 hours and the others were followed for survival until day 15. Liver enzymes were measured daily. In group 1, plasmatic sunitinib concentration were measured daily by LC MS/MS tandem mass spectroscopy. At day 15 all living animals were sacrificed. After sacrifice, the livers were harvested for determination of the VEGF receptor tyrosine kinase activity by western blot and histopathological examination. Results: In group 1, no animals died during follow-up. In group 2, 2 animals died during follow-up on day x. In control group 3, 3 animals died during follow up on day x. In group 1 plasmatic sunitinib levels remained under therapeutic concentration throughout the experiment. Very high concentrations of sunitinib were measured in the liver tissue 24 and 15 days after embolization. Inhibition of the phosphorylation of the RTK was demonstrated at 24h and 15 days in groups 1. Sunitinib eluting beads seemed to penetrate in the tumor more effectively and there was more necrosis around the beads than their bland counterparts. Conclusions: Administration of sunitinib eluting beads in VX2 carrying rabbits resulted invery high drug concentrations at the site of embolization with minimal systemic passage. Despite the very high tissular sunitinib concentration we did not observe any additional toxicity with loaded beads. Sunitinib eluting beads inhibit the activation of RTK's triggered by ischemia and seem to prolong survival of the treated animals. Therefore we consider that local treatment with sunitinib may provide a promising approach for the treatment of liver cancer.

The appropriateness of renal angioplasty: the ANPARIA software: a multidisciplinary expert panel approach

Percutaneous transluminal renal angioplasty (PTRA) is an invasive technique that is costly and involves the risk of complications and renal failure. The ability of PTRA to reduce the administration of antihypertensive drugs has been demonstrated. A potentially greater benefit, which nevertheless remains to be proven, is the deferral of the need for chronic dialysis. The aim of the study (ANPARIA) was to assess the appropriateness of PTRA to impact on the evolution of renal function. A standardized expert panel method was used to assess the appropriateness of medical treatment alone or medical treatment with revascularization in various clinical situations. The choice of revascularization by either PTRA or surgery was examined for each clinical situation. Analysis was based on a detailed literature review and on systematically elicited expert opinion, which were obtained during a two-round modified Delphi process. The study provides detailed responses on the appropriateness of PTRA for 1848 distinct clinical scenarios. Depending on the major clinical presentation, appropriateness of revascularization varied from 32% to 75% for individual scenarios (overal 48%). Uncertainty as to revascularization was 41% overall. When revascularization was appropriate, PTRA was favored over surgery in 94% of the scenarios, except in certain cases of aortic atheroma where sugery was the preferred choice. Kidney size [7 cm, absence of coexisting disease, acute renal failure, a high degree of stenosis (C70%), and absence of multiple arteries were identified as predictive variables of favorable appropriateness ratings. Situations such as cardiac failure with pulmonary edema or acute thrombosis of the renal artery were defined as indications for PTRA. This study identified clinical situations in which PTRA or surgery are appropriate for renal artery disease. We built a decision tree which can be used via Internet: the ANPARIA software (http://www.chu-clermontferrand.fr/anparia/). In numerous clinical situations uncertainty remains as to whether PTRA prevents deterioration of renal function.

The Impact of Personality and Culture on the Job Demands-Control Model of Job Stress

Among the various work stress models, one of the most popular has been the job demands-control (JDC) model developed by Karasek (1979), which postulates that work-related strain is highest under work conditions characterized by high demands and low autonomy. The absence of social support at work further increases negative outcomes. This model, however, does not apply equally to all individuals and to all cultures. This review demonstrates how various individual characteristics, especially some personality dimensions, influence the JDC model and could thus be considered buffering or moderator factors. Moreover, we review how the cultural context impacts this model as suggested by results obtained in European, American, and Asian contexts. Yet there are almost no data from Africa or South America. More crosscultural studies including populations from these continents would be valuable for a better understanding of the impact of the cultural context on the JDC model.

Implementation of zonal control algorithms in adaptive optical systems with sparse control matrices

Miralls deformables més i més grans, amb cada cop més actuadors estan sent utilitzats actualment en aplicacions d'òptica adaptativa. El control dels miralls amb centenars d'actuadors és un tema de gran interès, ja que les tècniques de control clàssiques basades en la seudoinversa de la matriu de control del sistema es tornen massa lentes quan es tracta de matrius de dimensions tan grans. En aquesta tesi doctoral es proposa un mètode per l'acceleració i la paral.lelitzacó dels algoritmes de control d'aquests miralls, a través de l'aplicació d'una tècnica de control basada en la reducció a zero del components més petits de la matriu de control (sparsification), seguida de l'optimització de l'ordenació dels accionadors de comandament atenent d'acord a la forma de la matriu, i finalment de la seva posterior divisió en petits blocs tridiagonals. Aquests blocs són molt més petits i més fàcils de fer servir en els càlculs, el que permet velocitats de càlcul molt superiors per l'eliminació dels components nuls en la matriu de control. A més, aquest enfocament permet la paral.lelització del càlcul, donant una com0onent de velocitat addicional al sistema. Fins i tot sense paral. lelització, s'ha obtingut un augment de gairebé un 40% de la velocitat de convergència dels miralls amb només 37 actuadors, mitjançant la tècnica proposada. Per validar això, s'ha implementat un muntatge experimental nou complet , que inclou un modulador de fase programable per a la generació de turbulència mitjançant pantalles de fase, i s'ha desenvolupat un model complert del bucle de control per investigar el rendiment de l'algorisme proposat. Els resultats, tant en la simulació com experimentalment, mostren l'equivalència total en els valors de desviació després de la compensació dels diferents tipus d'aberracions per als diferents algoritmes utilitzats, encara que el mètode proposat aquí permet una càrrega computacional molt menor. El procediment s'espera que sigui molt exitós quan s'aplica a miralls molt grans.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

A hierarchical approach to model parameter optimization for developmental systems.

In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.

A fractionally integrated approach to monetary policy and inflation dynamics

This paper relaxes the standard I(0) and I(1) assumptions typically stated in the monetary VAR literature by considering a richer framework that encompasses the previous two processes as well as other fractionally integrated possibilities. First, a timevarying multivariate spectrum is estimated for post WWII US data. Then, a structural fractionally integrated VAR (VARFIMA) is fitted to each of the resulting time dependent spectra. In this way, both the coefficients of the VAR and the innovation variances are allowed to evolve freely. The model is employed to analyze inflation persistence and to evaluate the stance of US monetary policy. Our findings indicate a strong decline in the innovation variances during the great disinflation, consistent with the view that the good performance of the economy during the 80’s and 90’s is in part a tale of good luck. However, we also find evidence of a decline in inflation persistence together with a stronger monetary response to inflation during the same period. This last result suggests that the Fed may still play a role in accounting for the observed differences in the US inflation history. Finally, we conclude that previous evidence against drifting coefficients could be an artifact of parameter restriction towards the stationary region. Keywords: monetary policy, inflation persistence, fractional integration, timevarying coefficients, VARFIMA. JEL Classification: E52, C32

Reduction des risques au cabinet du généraliste chez les consommateurs de cocaine: une perspective motivationnelle [Risk reduction among cocaine users: a motivational approach in the GP's office]

Today, cocaine use is a public health issue. Cocaine is a powerfully addictive stimulant drug which use is increasing among some part of the population. After a brief description of the physical and psychological effects of cocaine use, the article presents a motivational way for general practitioners to deal with risk-reduction issues. Based on the Transtheoretical Model of human behavior change and providing clinical examples, the article focuses particularly on the two earliest stages of change: "pre-contemplation" and "contemplation".