A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk

Background We present a method (The CHD Prevention Model) for modelling the incidence of fatal and nonfatal coronary heart disease (CHD) within various CHD risk percentiles of an adult population. The model provides a relatively simple tool for lifetime risk prediction for subgroups within a population. It allows an estimation of the absolute primary CHD risk in different populations and will help identify subgroups of the adult population where primary CHD prevention is most appropriate and cost-effective. Methods The CHD risk distribution within the Australian population was modelled, based on the prevalence of CHD risk, individual estimates of integrated CHD risk, and current CHD mortality rates. Predicted incidence of first fatal and nonfatal myocardial infarction within CHD risk strata of the Australian population was determined. Results Approximately 25% of CHD deaths were predicted to occur amongst those in the top 10 percentiles of integrated CHD risk, regardless of age group or gender. It was found that while all causes survival did not differ markedly between percentiles of CHD risk before the ages of around 50-60, event-free survival began visibly to differ about 5 years earlier. Conclusions The CHD Prevention Model provides a means of predicting future CHD incidence amongst various strata of integrated CHD risk within an adult population. It has significant application both in individual risk counselling and in the identification of subgroups of the population where drug therapy to reduce CHD risk is most cost-effective. J Cardiovasc Risk 8:31-37 (C) 2001 Lippincott Williams & Wilkins.

Os fatores de risco da rendibilidade das ações: análise e evidência empírica em países da Zona Euro

Tese de Doutoramento em Ciências Empresariais.

Country effects in ISSP-1993 environmental data: Comparison of SEM approaches

Structural equation models (SEM) are commonly used to analyze the relationship between variables some of which may be latent, such as individual ``attitude'' to and ``behavior'' concerning specific issues. A number of difficulties arise when we want to compare a large number of groups, each with large sample size, and the manifest variables are distinctly non-normally distributed. Using an specific data set, we evaluate the appropriateness of the following alternative SEM approaches: multiple group versus MIMIC models, continuous versus ordinal variables estimation methods, and normal theory versus non-normal estimation methods. The approaches are applied to the ISSP-1993 Environmental data set, with the purpose of exploring variation in the mean level of variables of ``attitude'' to and ``behavior''concerning environmental issues and their mutual relationship across countries. Issues of both theoretical and practical relevance arise in the course of this application.

Biological monitoring of chemical exposure : toxicokinetic differences due to age and sex

Human biomonitoring is a widely used method in the assessment of occupational exposure to chemical substances and recommended biological limits are published periodically for interpretation and decision-making. However, it is increasingly recognized that a large variability is associated with biological monitoring, making interpretation less efficient than assumed. In order to improve the applicability of biological monitoring, specific factors responsible for this variability should be identified and their contribution quantified. Among these factors, age and sex are easily identifiable, and present knowledge about pharmaceutical chemicals suggests that they play an important role on the toxicokinetics of occupational chemical agents, and therefore on the biological monitoring results.The aim of the present research project was to assess the influence of age and sex on biological indicators corresponding to organic solvents. This has been done experimentally and by toxicokinetic computer simulation. Another purpose was to explore the effect of selected CYP2E1 polymorphisms on the toxicokinetic profile.Age differences were identified by numerical simulations using a general toxicokinetic model from a previous study which was applied to 14 chemicals, representing 21 specific biological entities, with, among others, toluene, phenol, lead and mercury. These models were runn with the modified parameters, indicating in some cases important differences due to age. The expected changes are mostly of the order of 10-20 %, but differences up to 50 % were observed in some cases. These differences appear to depend on the chemical and on the biological entity considered.Sex differences were quantified by controlled human exposures, which were carried out in a 12 m3 exposure chamber for three organic solvents separately: methyl ethyl ketone, 1-methoxy-2-propanol and 1,1,1-trichloroethane. The human volunteer groups were composed 12 of ten young men and fifteen young women, the latter subdivided into those with and without hormonal contraceptive. They were exposed during six hours at rest and at half of the threshold limit value. The kinetics of the parent compounds (organic volatiles) and their metabolite(s) were followed in blood, urine and expired air over time. Analyses of the solvent and their metabolites were performed by using headspace gas chromatography, CYP2E1 genotypes by using PCR-based RFLP methods. Experimental data were used to calibrate the toxicokinetic models developed for the three solvents. The results obtained for the different biomarkers of exposure mainly showed an effect on the urinary levels of several biomarkers among women due to the use of hormonal contraceptive, with an increase of about 50 % in the metabolism rate. The results also showed a difference due to the genotype CYP2E1*6, when exposed to methyl ethyl ketone, with a tendency to increase CYP2E1 activity when volunteers were carriers of the mutant allele. Simulations showed that it is possible to use simple toxicokinetic tools in order to predict internal exposure when exposed to organic solvents. Our study suggests that not only physiological differences but also exogenous sex hormones could influence CYP2E1 enzyme activity. The variability among the urinary biological indicators levels gives evidence of an interindividual susceptibility, an aspect that should have its place in the approaches for setting limits of occupational exposure.

Inequality measurement for ordered response health data

Because self-reported health status [SRHS] is an ordered response variable, inequality measurement for SRHS data requires a numerical scale for converting individual responses into a summary statistic. The choice of scale is however problematic, since small variations in the numerical scale may reverse the ordering of a given pair of distributions of SRHS data in relation to conventional inequality indices such as the variance. This paper introduces a parametric family of inequality indices, founded on an inequality ordering proposed by Allison and Foster [Allison, R.A., Foster, J., 2004. Measuring health inequalities using qualitative data. Journal of Health Economics 23, 505-524], which satisfy a suitable invariance property with respect to the choice of numerical scale. Several key members of the parametric family are also derived, and an empirical application using data from the Swiss Health Survey illustrates the proposed methodology. [Authors]

Posição de caixa e o retorno das ações no mercado acionário brasileiro, 1994-2009

Este trabalho busca, através dos princípios de Finanças Corporativas e de Apreçamento de Ativos, mensurar o impacto do nível de liquidez das companhias na expectativa de retorno das ações no mercado acionário brasileiro. O pressuposto básico dessa relação é que a posição de caixa representa um tipo de risco não capturado por outras variáveis. Para mensurar esse risco, será utilizada a modelagem de fatores para apreçamento de ativos. O modelo básico utilizado será o de três fatores de Fama e French, adaptado para a inclusão da variável caixa. A partir da base de dados, se tentará estimar a sensibilidade do retorno esperado das ações brasileiras ao fator caixa.

Learning reward timing in cortex through reward dependent expression of synaptic plasticity.

The ability to represent time is an essential component of cognition but its neural basis is unknown. Although extensively studied both behaviorally and electrophysiologically, a general theoretical framework describing the elementary neural mechanisms used by the brain to learn temporal representations is lacking. It is commonly believed that the underlying cellular mechanisms reside in high order cortical regions but recent studies show sustained neural activity in primary sensory cortices that can represent the timing of expected reward. Here, we show that local cortical networks can learn temporal representations through a simple framework predicated on reward dependent expression of synaptic plasticity. We assert that temporal representations are stored in the lateral synaptic connections between neurons and demonstrate that reward-modulated plasticity is sufficient to learn these representations. We implement our model numerically to explain reward-time learning in the primary visual cortex (V1), demonstrate experimental support, and suggest additional experimentally verifiable predictions.

Insights into transcription enhancer factor 1 (TEF-1) activity from the solution structure of the TEA domain.

Transcription enhancer factor 1 is essential for cardiac, skeletal, and smooth muscle development and uses its N-terminal TEA domain (TEAD) to bind M-CAT elements. Here, we present the first structure of TEAD and show that it is a three-helix bundle with a homeodomain fold. Structural data reveal how TEAD binds DNA. Using structure-function correlations, we find that the L1 loop is essential for cooperative loading of TEAD molecules on to tandemly duplicated M-CAT sites. Furthermore, using a microarray chip-based assay, we establish that known binding sites of the full-length protein are only a subset of DNA elements recognized by TEAD. Our results provide a model for understanding the regulation of genome-wide gene expression during development by TEA/ATTS family of transcription factors.

Altered transmission of HOX and apoptotic SNPs identify a potential common pathway for clubfoot.

Clubfoot is a common birth defect that affects 135,000 newborns each year worldwide. It is characterized by equinus deformity of one or both feet and hypoplastic calf muscles. Despite numerous study approaches, the cause(s) remains poorly understood although a multifactorial etiology is generally accepted. We considered the HOXA and HOXD gene clusters and insulin-like growth factor binding protein 3 (IGFBP3) as candidate genes because of their important roles in limb and muscle morphogenesis. Twenty SNPs from the HOXA and HOXD gene clusters and 12 SNPs in IGFBP3 were genotyped in a sample composed of non-Hispanic white and Hispanic multiplex and simplex families (discovery samples) and a second sample of non-Hispanic white simplex trios (validation sample). Four SNPs (rs6668, rs2428431, rs3801776, and rs3779456) in the HOXA cluster demonstrated altered transmission in the discovery sample, but only rs3801776, located in the HOXA basal promoter region, showed altered transmission in both the discovery and validation samples (P = 0.004 and 0.028). Interestingly, HOXA9 is expressed in muscle during development. An SNP in IGFBP3, rs13223993, also showed altered transmission (P = 0.003) in the discovery sample. Gene-gene interactions were identified between variants in HOXA, HOXD, and IGFBP3 and with previously associated SNPs in mitochondrial-mediated apoptotic genes. The most significant interactions were found between CASP3 SNPS and variants in HOXA, HOXD, and IGFBP3. These results suggest a biologic model for clubfoot in which perturbation of HOX and apoptotic genes together affect muscle and limb development, which may cause the downstream failure of limb rotation into a plantar grade position.

Cognitive change processes in a group cognitive behavior therapy of depression

The present study attempted to examine the causal relationships among changes in automatic thoughts, dysfunctional attitudes, and depressive symptoms in a 12-week group cognitive behavior therapy (GCBT) program for depression. In all, 35 depressed patients attending the GCBT program were monitored with the Automatic Thoughts Questionnaire, Dysfunctional Attitudes Scale, and Beck Depression Inventory at the pre-treatment, 4th and 8th sessions, and post-treatment. The results were as follows: (1) GCBT reduces negative cognitions; (2) changes in automatic thoughts and dysfunctional attitudes lead to change in depressive symptoms; and (3) automatic thoughts play a mediating role between dysfunctional attitudes and depression. The findings taken as a whole support the Causal Cognition Model of depression. (C) 2003 Elsevier Science Ltd. All rights reserved.

A review of two journals found that articles using multivariable logistic regression frequently did not report commonly recommended assumptions

Background and Objective: To examine if commonly recommended assumptions for multivariable logistic regression are addressed in two major epidemiological journals. Methods: Ninety-nine articles from the Journal of Clinical Epidemiology and the American Journal of Epidemiology were surveyed for 10 criteria: six dealing with computation and four with reporting multivariable logistic regression results. Results: Three of the 10 criteria were addressed in 50% or more of the articles. Statistical significance testing or confidence intervals were reported in all articles. Methods for selecting independent variables were described in 82%, and specific procedures used to generate the models were discussed in 65%. Fewer than 50% of the articles indicated if interactions were tested or met the recommended events per independent variable ratio of 10: 1. Fewer than 20% of the articles described conformity to a linear gradient, examined collinearity, reported information on validation procedures, goodness-of-fit, discrimination statistics, or provided complete information on variable coding. There was no significant difference (P >.05) in the proportion of articles meeting the criteria across the two journals. Conclusion: Articles reviewed frequently did not report commonly recommended assumptions for using multivariable logistic regression. (C) 2004 Elsevier Inc. All rights reserved.

Algebraic Bethe ansatz method for the exact calculation of energy spectra and form factors: applications to models of Bose-Einstein condensates and metallic nanograins

In this review we demonstrate how the algebraic Bethe ansatz is used for the calculation of the-energy spectra and form factors (operator matrix elements in the basis of Hamiltonian eigenstates) in exactly solvable quantum systems. As examples we apply the theory to several models of current interest in the study of Bose-Einstein condensates, which have been successfully created using ultracold dilute atomic gases. The first model we introduce describes Josephson tunnelling between two coupled Bose-Einstein condensates. It can be used not only for the study of tunnelling between condensates of atomic gases, but for solid state Josephson junctions and coupled Cooper pair boxes. The theory is also applicable to models of atomic-molecular Bose-Einstein condensates, with two examples given and analysed. Additionally, these same two models are relevant to studies in quantum optics; Finally, we discuss the model of Bardeen, Cooper and Schrieffer in this framework, which is appropriate for systems of ultracold fermionic atomic gases, as well as being applicable for the description of superconducting correlations in metallic grains with nanoscale dimensions.; In applying all the above models to. physical situations, the need for an exact analysis of small-scale systems is established due to large quantum fluctuations which render mean-field approaches inaccurate.

Integration of geometric consistency contributory factors in three-leg junctions collision prediction models of Portuguese two-lane national highways

This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.

Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants

Protective and Aggravating Effects of Nlrp3 Inflammasome Activation in IBD Models: Influence of Genetic and Environmental Factors.

Background: Inflammatory bowel disease (IBD) is characterized by chronic intestinal inflammation due to dysregulation of the mucosal immune system. The cytokines IL-1β and IL-18 appear early in intestinal inflammation and their pro-forms are processed via the caspase-1-activating multiprotein complex, the Nlrp3 inflammasome. Previously, we reported that the uptake of dextran sodium sulfate (DSS) by macrophages activates the Nlrp3 inflammasome and that Nlrp3(-/-) mice are protected in the acute DSS colitis model. Of note, other groups have reported opposing effects in regards to DSS susceptibility in Nlrp3(-/-) mice. Recently, mice lacking inflammasomes were found to develop a distinct intestinal microflora. Methods: To reconcile the contradicting observations, we investigated the role of Nlrp3 deficiency in two different IBD models: acute DSS colitis and TNBS (2,4,6-trinitrobenzene sulfonic acid)-induced colitis. In addition, we investigated the impact of the intestinal flora on disease severity by performing cohousing experiments of wild-type and Nlrp3(-/-) mice, as well as by antibiotic treatment. Results: Nlrp3(-/-) mice treated with either DSS or TNBS exhibited attenuated colitis and lower mortality. This protective effect correlated with an increased frequency of CD103+ lamina propria dendritic cells expressing a tolerogenic phenotype in Nlrp3(-/-) mice in steady state conditions. Interestingly, after cohousing, Nlrp3(-/-) mice were as susceptible as wild-type mice, indicating that transmission of endogenous bacterial flora between the two mouse strains might increase susceptibility of Nlrp3(-/-) mice towards DSS-induced colitis. Accordingly, treatment with antibiotics almost completely prevented colitis in the DSS model. Conclusions: The composition of the intestinal microflora significantly influences disease severity in IBD models comparing wild-type and Nlrp3(-/-) mice. This observation may - at least in part - explain contradictory results concerning the role of the inflammasome in different labs. Further studies are required to define the role of the Nlrp3 inflammasome in noninflamed mucosa under steady state conditions and in IBD.