O efeito da deposição salina nas características das escorrências rodoviárias em zonas costeiras

Tese de Doutoramento em Engenharia Civil.

Global validation of the WSES Sepsis Severity Score for patients with complicated intra-abdominal infections: a prospective multicentre study (WISS Study).

BACKGROUND To validate a new practical Sepsis Severity Score for patients with complicated intra-abdominal infections (cIAIs) including the clinical conditions at the admission (severe sepsis/septic shock), the origin of the cIAIs, the delay in source control, the setting of acquisition and any risk factors such as age and immunosuppression. METHODS The WISS study (WSES cIAIs Score Study) is a multicenter observational study underwent in 132 medical institutions worldwide during a four-month study period (October 2014-February 2015). Four thousand five hundred thirty-three patients with a mean age of 51.2 years (range 18-99) were enrolled in the WISS study. RESULTS Univariate analysis has shown that all factors that were previously included in the WSES Sepsis Severity Score were highly statistically significant between those who died and those who survived (p < 0.0001). The multivariate logistic regression model was highly significant (p < 0.0001, R2 = 0.54) and showed that all these factors were independent in predicting mortality of sepsis. Receiver Operator Curve has shown that the WSES Severity Sepsis Score had an excellent prediction for mortality. A score above 5.5 was the best predictor of mortality having a sensitivity of 89.2 %, a specificity of 83.5 % and a positive likelihood ratio of 5.4. CONCLUSIONS WSES Sepsis Severity Score for patients with complicated Intra-abdominal infections can be used on global level. It has shown high sensitivity, specificity, and likelihood ratio that may help us in making clinical decisions.

Validation of the Portuguese Version of EHP-30 (The Endometriosis Health Profile-30)

Introduction: Endometriosis Health Profile Questionnaire-30 is currently the most used questionnaire for quality of life measurement in women with endometriosis. The aim of this study is to evaluate the psychometric properties and to validate the Portuguese Endometriosis Health Profile Questionnaire-30 version. MATERIAL AND METHODS A sequential sample of 152 patients with endometriosis, followed in a Portugal reference center, were asked to complete a questionnaire on social and demographic features, the Portuguese version of the Endometriosis Health Profile Questionnaire-30 and of the Short Form Health Survey 36 Item â version 2. Appropriate statistical analysis was performed using descriptive statistics, factor analysis, internal consistency, item-total correlation and convergent validity. RESULTS Factorial analysis confirmed the validity of the five-dimension structure of the Endometriosis Health Profile Questionnaire-30 core questionnaire, which explained 83.2% of the total variance. All item-total correlations presented acceptable results and high internal consistency, with Cronbach's alpha ranging between 0.876 and 0.981 for the core questionnaire and between 0.863 and 0.951 for the modular questionnaire. Significant negative associations between similar scales of Endometriosis Health Profile Questionnaire-30 and Short Form Health Survey 36 Item â version 2 were demonstrated. Data completeness achieved was high for all dimensions. The emotional well-being scale in the core questionnaire and the infertility scale in the modular section had the highest median scores, and therefore the most negative impact on the quality of life of participating women. DISCUSSION The test-retest reliability and responsiveness of the questionnaire should be evaluated in future studies. CONCLUSION The present study demonstrates that the Portuguese version of the Endometriosis Health Profile Questionnaire-30 is a valid, reliable and acceptable tool for evaluating the health-related quality of life of Portuguese women with endometriosis.

Previsão de cheias em Barcelos

Dissertação de mestrado integrado em Engenharia Civil

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.

Introduction [to Contact force models for multibody dynamics]

"Series: Solid mechanics and its applications, vol. 226"

Pure elastic contact force models

"Series: Solid mechanics and its applications, vol. 226"

Dissipative contact force models

"Series: Solid mechanics and its applications, vol. 226"

Contact force models for multibody dynamics

"Series: Solid mechanics and its applications, vol. 226"

An evolutionary approach to the use of Petri net based models: from parallel controllers to HW/SW co-design

"A workshop within the 19th International Conference on Applications and Theory of Petri Nets - ICATPN’1998"

Medida da aliança parental: validação portuguesa e construção de uma versão reduzida

Objetivo: A Medida de Aliança Parental (PAM) avalia a qualidade da relação interparental na prestação de cuidados da criança. O presente artigo apresenta a validação de uma versão portuguesa da medida, bem como examina as qualidades psicométricas de uma versão reduzida com 6 itens do instrumento (PAM-R). Método: A amostra foi constituída por 182 pais (63% mães), dos quais 72 preencheram um instrumento de avaliação dos problemas de ajustamento psicológico das crianças. Resultados: As análises fatoriais confirmatórias não corroboraram a estrutura dos dois modelos testados. No entanto, excelentes valores foram encontrados nos índices de adequação do modelo da PAM-R. Não foram encontrados erros de especificação no modelo unidimensional testado, o que suporta a validade fatorial da versão reduzida da PAM. A PAM-R apresentou excelentes valores de consistência interna e uma correlação negativa e significativa com a medida de problemas de ajustamento das crianças. Conclusões: PAM-R emerge como uma medida que possibilita a avaliação do impacto das dimensões familiares no funcionamento e desenvolvimento psicológico das crianças, em contextos de prestação de cuidados de saúde primários.

Study of validation of the Portuguese version of the inventory «Experiences in Close Relationships»

The Experiences in Close Relationships Inventory permits to evaluate attachment in close relationships during adulthood based on two dimensions able to be present in this kind of relationships: the avoidance of proximity and the anxiety related with to abandonment. It is a self-report 7- points likert scale composed by 36 items. The Portuguese version was administered to a sample of 551 university students (60% female), the majority with ages between 19 and 24 years old (88%) in a dating relationship (86%). The principal components analysis with oblimin rotation was performed. The total scale has good internal consistency (α=.86), as also has the 2 sub-scales: anxiety (α=.86) and avoidance (α=.88). The two dimensions evaluated are significantly correlated with socio-demographics, relational characteristics (jealousy, relationship distress, and compromise), wishes (enmeshment versus differentiation) and fears (abandonment versus control) related to attitudes in significant relationships, which testify the construct validity of the instrument. The results obtained are coherent with the original version and other ECR‘s adaptations. Practitioners and researchers in the context of clinical psychology and related areas have now at their disposal the Portuguese version of the ECR inventory, which has shown its very high usefulness in the study of close relationships, and specifically attachment in adulthood.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.