Carotid Intima-Media Thickness and Carotid Plaques Improves Prediction of Obstructive Angiographic Coronary Artery Disease in Women

Does carotid intima-media thickness (cIMT), a surrogate marker of cardiovascular events, have predictive incremental value over established risk factors for stable coronary artery disease (CAD)? Prospective study of 300 patients, with suspected stable CAD, admitted for an elective coronary angiography and carotid ultrasound. The CAD patients had a higher cIMT, which showed a modest predictive accuracy for CAD (area under the receiver-operating characteristic curve 0.638, 95% confidence interval 0.576-0.701, P < .001). The cIMT was an independent predictor of CAD, together with age, gender, and diabetes. C-statistic for CAD prediction by traditional risk factors was not significantly different from a model that included cIMT, carotid plaque presence, or both. However, in women, it was significantly increased by the addition of cIMT or carotid plaque presence. Although cIMT cannot be used as a sole indicator of CAD, it should be considered in the panel of investigations that is requested, particularly in women who are candidates for coronary angiography.

Estudo da resistência e mecanismos de dano em juntas adesivas entre materiais compósitos com defeitos de adesão

A necessidade de utilizar métodos de ligação entre componentes de forma mais rápida, eficaz e com melhores resultados tem causado a crescente utilização das juntas adesivas, em detrimento dos métodos tradicionais de ligação. A utilização das juntas adesivas tem vindo a aumentar em diversas aplicações industriais por estas apresentarem vantagens, das quais se destacam a redução de peso, redução de concentrações de tensões e facilidade de fabrico. No entanto, uma das limitações das juntas adesivas é a dificuldade em prever a resistência da junta após fabrico e durante a sua vida útil devido à presença de defeitos no adesivo. Os defeitos são normalmente gerados pela preparação inadequada das juntas ou degradação do adesivo devido ao ambiente (por exemplo, humidade), reduzindo a qualidade da ligação e influenciando a resistência da junta. Neste trabalho é apresentado um estudo experimental e numérico de juntas de sobreposição simples (JSS) com a inclusão de defeitos centrados na camada de adesivo para comprimentos de sobreposição (LO) diferentes. Os adesivos utilizados foram o Araldite® AV138, apresentado como sendo frágil, e o adesivo Sikaforce® 7752, intitulado como adesivo dúctil. A parte experimental consistiu no ensaio à tração das diferentes JSS permitindo a obtenção das curvas força-deslocamento (P-δ). A análise numérica por modelos de dano coesivo (MDC) foi realizada para analisar as tensões de arrancamento ((σy) e as tensões de corte (τxy) na camada adesiva, para estudar a variável de dano do MDC durante o processo de rotura e para avaliar a capacidade dos MDC na previsão da resistência da junta. Constatou-se um efeito significativo dos defeitos de diferentes dimensões na resistência das juntas, que também depende do tipo de adesivo utilizado e do valor de LO. Os modelos numéricos permitiram a descrição detalhada do comportamento das juntas e previsão de resistência, embora para o adesivo dúctil a utilização de uma lei coesiva triangular tenha provocado alguma discrepância relativamente aos resultados experimentais.

High or low initial setting price?: Initial setting price as prediction of intention to bid in on line auctions

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Correction of the anisotropy in resistivity: application to pore pressure prediction

Dissertação para obtenção do Grau de Mestre em Engenharia Geológica (Georrecursos)

Creation of a web application for smart park system and parking prediction study for system integration

In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

Numerical prediction of diffusion and electric field-induced iron nanoparticle transport

Zero valent iron nanoparticles (nZVI) are considered very promising for the remediation of contaminated soils and groundwaters. However, an important issue related to their limited mobility remains unsolved. Direct current can be used to enhance the nanoparticles transport, based on the same principles of electrokinetic remediation. In this work, a generalized physicochemical model was developed and solved numerically to describe the nZVI transport through porous media under electric field, and with different electrolytes (with different ionic strengths). The model consists of the Nernst–Planck coupled system of equations, which accounts for the mass balance of ionic species in a fluid medium, when both the diffusion and electromigration of the ions are considered. The diffusion and electrophoretic transport of the negatively charged nZVI particles were also considered in the system. The contribution of electroosmotic flow to the overall mass transport was included in the model for all cases. The nZVI effective mobility values in the porous medium are very low (10−7–10−4 cm2 V−1 s−1), due to the counterbalance between the positive electroosmotic flow and the electrophoretic transport of the negatively charged nanoparticles. The higher the nZVI concentration is in the matrix, the higher the aggregation; therefore, low concentration of nZVI suspensions must be used for successful field application.

Development of 3D in vitro models for prediction of hepatic metabolism and toxicity

The development of human cell models that recapitulate hepatic functionality allows the study of metabolic pathways involved in toxicity and disease. The increased biological relevance, cost-effectiveness and high-throughput of cell models can contribute to increase the efficiency of drug development in the pharmaceutical industry. Recapitulation of liver functionality in vitro requires the development of advanced culture strategies to mimic in vivo complexity, such as 3D culture, co-cultures or biomaterials. However, complex 3D models are typically associated with poor robustness, limited scalability and compatibility with screening methods. In this work, several strategies were used to develop highly functional and reproducible spheroid-based in vitro models of human hepatocytes and HepaRG cells using stirred culture systems. In chapter 2, the isolation of human hepatocytes from resected liver tissue was implemented and a liver tissue perfusion method was optimized towards the improvement of hepatocyte isolation and aggregation efficiency, resulting in an isolation protocol compatible with 3D culture. In chapter 3, human hepatocytes were co-cultivated with mesenchymal stem cells (MSC) and the phenotype of both cell types was characterized, showing that MSC acquire a supportive stromal function and hepatocytes retain differentiated hepatic functions, stability of drug metabolism enzymes and higher viability in co-cultures. In chapter 4, a 3D alginate microencapsulation strategy for the differentiation of HepaRG cells was evaluated and compared with the standard 2D DMSO-dependent differentiation, yielding higher differentiation efficiency, comparable levels of drug metabolism activity and significantly improved biosynthetic activity. The work developed in this thesis provides novel strategies for 3D culture of human hepatic cell models, which are reproducible, scalable and compatible with screening platforms. The phenotypic and functional characterization of the in vitro systems performed contributes to the state of the art of human hepatic cell models and can be applied to the improvement of pre-clinical drug development efficiency of the process, model disease and ultimately, development of cell-based therapeutic strategies for liver failure.

Credit default swap (CDS) prediction model & trading strategy

This project focuses on the study of different explanatory models for the behavior of CDS security, such as Fixed-Effect Model, GLS Random-Effect Model, Pooled OLS and Quantile Regression Model. After determining the best fitness model, trading strategies with long and short positions in CDS have been developed. Due to some specifications of CDS, I conclude that the quantile regression is the most efficient model to estimate the data. The P&L and Sharpe Ratio of the strategy are analyzed using a backtesting analogy, where I conclude that, mainly for non-financial companies, the model allows traders to take advantage of and profit from arbitrages.

The cellular basis of a congenital heart defect Drosophila

RESUMO: Mutações em genes envolvidos na formação do coração e anomalias em qualquer etapa deste processo causam frequentemente malformações cardíacas, que representam o tipo mais comum de defeitos em neonatais, afetando cerca de 1% dos nascimentos por ano. Assim, estima-se que 20 milhões de pessoas sejam portadoras de um defeito cardíaco congénito. O coração da Drosophila melanogaster (mosca-da-fruta), denominado vaso dorsal, é um órgão relativamente simples que actua como uma bomba muscular, contraindo automaticamente para permitir a circulação da hemolinfa através do corpo. A formação do vaso dorsal na mosca é muito semelhante ao desenvolvimento do coração em vertebrados, representando por isso, um poderoso modelo para estudar a rede de genes e os padrões regulatórios relacionados com o desenvolvimento deste órgão. Anteriormente, nós identificámos um gene em Drosophila, darhgef10, fortemente expresso no coração em desenvolvimento e cuja deleção induz anormalidades cardíacas subtis mas prevalentes. Os mutantes para darhgef10 são viáveis e férteis no ambiente controlado de laboratório. Este trabalho teve como objectivos caracterizar fenotipicamente os mutantes nulos para darhgef10, determinar a localização subcelular da proteína dArhgef10 e investigar a base celular subjacente ao defeito no alinhamento dos cardioblastos observado nos mutantes. Os nossos resultados revelaram que a deleção de darhgef10 provoca uma severa redução da viabilidade, sem no entanto comprometer o tempo de desenvolvimento e a longevidade. Por outro lado, o aumento da expressão de darhgef10 em músculos, glândulas salivares e no disco imaginal do olho afeta drasticamente a integridade destes tecidos. A expressão ectópica de darhgef10 in vitro e in vivo revelou que a proteína está localiza no citoplasma com enriquecimento junto à membrana celular, com associação à actina F. Live imaging de embriões mutantes para darhgef10 revelou que os defeitos observados no coração podem estar associados a um defeito na adesão dos músculos alary e/ou das células pericardiais ao vaso dorsal. O homólogo humano de darhgef10, ARHGEF10, também é expresso no coração e está associação a uma maior susceptibilidade para a ocorrência de acidentes vasculares cerebrais aterotrombóticos, sugerindo que o que aprendemos sobre darhgef10 em Drosophila pode ter implicações do ponto de vista clínico para a saúde humana. ----------------------------- ABSTRACT: Mutations in genes controlling heart development and abnormalities in any of its steps frequently cause cardiac malformations, the most common type of birth defects in humans, affecting nearly 1% of births per year. Hence around 20 million adults are expected to live with a congenital heart defect. The Drosophila melanogaster heart, called dorsal vessel, is a relatively simple organ that acts as a muscular pump contracting automatically to allow the circulation of hemolymph. Drosophila heart formation shares many similarities with heart development in vertebrates providing a powerful system to study gene networks and regulatory pathways involved in heart development. We have previously identified a Drosophila gene, darhgef10, which is strongly expressed in the developing heart and when deleted, leads to flies with highly prevalent yet subtle heart abnormalities, compatible with unchallenged life in the laboratory. Our aims were to phenotypically characterize homozygous null darhgef10 mutants, characterize the subcellular localization of dArhgef10 and to study the cellular basis of the misaligned cardioblasts defect. We found that about half of darhgef10 mutants die during development. However, the survivors surprisingly have a nearly normal developmental time, adult locomotor behavior and total lifespan. Detection of transgene-derived dArhgef10 protein in vitro and in vivo using custom antibodies revealed a cytosolic protein slightly enriched in the cellular membranes and associated with F-actin. Tissue-specific darhgef10 expression disrupts the normal morphology of developing muscles, salivary glands and the eye. Live imaging of darhgef10 mutant embryos revealed that heart defect could be caused by a reduced capacity of attachment of pericardial cells and/or alary muscle to dorsal vessel. The human homolog of darhgef10 is also expressed in the heart and is a susceptibility gene for atherothrombotic stroke, suggesting that what we learn about the function of this gene and its phenotypes in Drosophila could have implications to human health.

Prediction of hyperglycemia in preterm newborn infants

Many conditions are associated with hyperglycemia in preterm neonates because they are very susceptible to changes in carbohydrate homeostasis. The purpose of this study was to evaluate the occurrence of hyperglycemia in preterm infants undergoing glucose infusion during the first week of life, and to enumerate the main variables predictive of hyperglycemia. This prospective study (during 1994) included 40 preterm neonates (gestational age <37 weeks); 511 determinations of glycemic status were made in these infants (average 12.8/infant), classified by gestational age, birth weight, glucose infusion rate and clinical status at the time of determination (based on clinical and laboratory parameters). The clinical status was classified as stable or unstable, as an indication of the stability or instability of the mechanisms governing glucose homeostasis at the time of determination of blood glucose; 59 episodes of hyperglycemia (11.5%) were identified. A case-control study was used (case = hyperglycemia; control = normoglycemia) to derive a model for predicting glycemia. The risk factors considered were gestational age (<=31 vs. >31 weeks), birth weight (<=1500 vs. >1500 g), glucose infusion rate (<=6 vs. >6 mg/kg/min) and clinical status (stable vs. unstable). Multivariate analysis by logistic regression gave the following mathematical model for predicting the probability of hyperglycemia: 1/exp{-3.1437 + 0.5819(GA) + 0.9234(GIR) + 1.0978(Clinical status)} The main predictive variables in our study, in increasing order of importance, were gestational age, glucose infusion rate and, the clinical status (stable or unstable) of the preterm newborn infant. The probability of hyperglycemia ranged from 4.1% to 36.9%.

Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

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.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.

Impact performance prediction of injection-molded talc-filled polypropylene through thermomechanical environment assessment

Due to the fact that different injection molding conditions tailor the mechanical response of the thermoplastic material, such effect must be considered earlier in the product development process. The existing approaches implemented in different commercial software solutions are very limited in their capabilities to estimate the influence of processing conditions on the mechanical properties. Thus, the accuracy of predictive simulations could be improved. In this study, we demonstrate how to establish straightforward processing-impact property relationships of talc-filled injection-molded polypropylene disc-shaped parts by assessing the thermomechanical environment (TME). To investigate the relationship between impact properties and the key operative variables (flow rate, melt and mold temperature, and holding pressure), the design of experiments approach was applied to systematically vary the TME of molded samples. The TME is characterized on computer flow simulation outputsanddefined bytwo thermomechanical indices (TMI): the cooling index (CI; associated to the core features) and the thermo-stress index (TSI; related to the skin features). The TMI methodology coupled to an integrated simulation program has been developed as a tool to predict the impact response. The dynamic impact properties (peak force, peak energy, and puncture energy) were evaluated using instrumented falling weight impact tests and were all found to be similarly affected by the imposed TME. The most important molding parameters affecting the impact properties were found to be the processing temperatures (melt andmold). CI revealed greater importance for the impact response than TSI. The developed integrative tool provided truthful predictions for the envisaged impact properties.