Generalized collisional radiative model for light elements: C: Data for the B isonuclear sequence

A first stage collision database is assembled which contains electron-impact excitation, ionization,\r and recombination rate coefficients for B, B + , B 2+ , B 3+ , and B 4+ . The first stage database\r is constructed using the R-matrix with pseudostates, time-dependent close-coupling, and perturbative\r distorted-wave methods. A second stage collision database is then assembled which contains\r generalized collisional-radiative ionization, recombination, and power loss rate coefficients as a\r function of both temperature and density. The second stage database is constructed by solution of\r the collisional-radiative equations in the quasi-static equilibrium approximation using the first\r stage database. Both collision database stages reside in electronic form at the IAEA Labeled Atomic\r Data Interface (ALADDIN) database and the Atomic Data Analysis Structure (ADAS) open database.

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.

Risk indicators associated with root decay in independently living older adults

Objective: To determine the risk indicators associated with root caries experience in a cohort of independently living older adults in Ireland.
Methods: The data reported in the present study were obtained from a prospective longitudinal study conducted on the risk factors associated with root caries incidence in a cohort of independently living older adults (n=334). Each subject underwent an oral examination, performed by a single calibrated examiner, to determine the root caries index and other clinical variables. Questionnaires were used to collect data on oral hygiene habits, diet, smoking and alcohol habits and education level. A regression analysis with the outcome variable of root caries experience (no/yes) was conducted.
Results: A total of 334 older adults with a mean age of 69.1 years were examined. 53.3% had at least one filled or decayed root surface. The median root caries index was 3.13 (IQR 0.00, 13.92). The results from the multivariate regression analysis indicated that individuals with poor plaque control (OR 9.59, 95%CI 3.84-24.00), xerostomia (OR 18.49, 95%CI 2.00-172.80), two or more teeth with coronal decay (OR 4.50, 95% CI 2.02-10.02) and 37 or more exposed root surfaces (OR 5.48, 95% CI 2.49-12.01) were more likely to have been affected by root caries.
Conclusions: The prevalence of root caries was high in this cohort. This study suggests a correlation between root caries and the variables poor plaque control, xerostomia, coronal decay (≥2 teeth affected) and exposed root surfaces (≥37). The significance of these risk indicators and the resulting prediction model should be further evaluated in a prospective study of root caries incidence.
Clinical Significance: Identification of risk indicators for root caries in independently living older adults would facilitate dental practitioners to identify those who would benefit most from interventions aimed at prevention.

Methodologies for a sustainable development of estuarine ecosystems

As zonas costeiras, estuarinas e lagunares são consideradas áreas muito produtivas e dotadas de grande biodiversidade sendo, por isso, consideradas de elevado valor ecológico e económico. No entanto, nas últimas décadas tem vindo a verificar-se um aumento da contaminação destes ecossistemas como resultado de diversas actividades antrópicas. As abordagens actualmente disponíveis para avaliação do impacto da poluição em ecossistemas estuarinos e lagunares apresentam diversos tipos de lacunas, pelo que é importante desenvolver metodologias mais eficazes com organismos autóctones. Neste contexto, o objectivo central desta dissertação consistiu em desenvolver e validar métodos ecologicamente relevantes para avaliação da contaminação estuarina e dos seus efeitos, utilizando o góbio-comum (Pomatoschistus microps), quer como organismo-teste quer como espécie sentinela, devido à importante função que desempenha nas cadeias tróficas de diversos estuários da costa Portuguesa. A Ria de Aveiro foi seleccionada como área de estudo principalmente pelo facto de possuir zonas com diferentes tipos de contaminação predominante e de haver conhecimento científico de base abundante e de elevada qualidade sobre este ecosistema. Na primeira fase do estudo, foram investigados os efeitos agudos de dois hidrocarbonetos aromáticos policíclicos (HAPs) (benzo[a]pireno e antraceno), de um fuel-óleo e de dois metais (cobre e mercúrio) em P. microps, utilizando ensaios laboratoriais baseados em biomarcadores e em parâmetros comportamentais, os quais foram avaliados utilizando um dispositivo expressamente desenvolvido para o efeito, designado por speed performance device (SPEDE). Como biomarcadores foram utilizados parâmetros envolvidos em funções fisiológicas determinantes para a sobrevivência e desempenho dos animais (neurotransmissão, obtenção de energia, destoxificação e defesas anti-oxidantes), nomeadamente a actividade das enzimas acetilcolinesterase, lactato desidrogenase, CYP1A1, glutationa S-transferases, glutationa reductase, glutationa peroxidase, superóxido dismutase, catalase, tendo ainda sido determinados os níveis de peroxidação lipídica como indicador de danos oxidativos. De forma global, os resultados indicaram que os agentes e a mistura testados têm a capacidade de interferir com a função neurológica, de alterar as vias utilizadas para obtenção de energia celular, induzir as defesas antioxidantes e, no caso do cobre e do mercúrio, de causarem peroxidação lipídica. Foram ainda obtidas relações concentração-resposta a nível dos parâmetros comportamentais testados, nomeadamente a capacidade de nadar contra a corrente e a distância percorrida a nadar contra o fluxo de água, sugerindo que os agentes testados podem, por exemplo, diminuir a capacidade de fuga aos predadores, as probabilidades de captura de presas e o sucesso reprodutivo. Na segunda fase, tendo sido já adaptadas técnicas para determinação de vários biomarcadores em P. microps e estudada a sua resposta a dois grupos de poluentes particularmente relevantes em ecossistemas estuarinos e lagunares (metais e HAPs), foi efectuado um estudo de monitorização utilizando P. microps como bioindicador e que incluiu diversos parâmetros ecológicos e ecotoxicológicos, nomedamente: 20 parâmetros indicativos da qualidade da água e do sedimento, concentração de 9 metais em sedimentos e no corpo de P. microps, 8 biomarcadores e 2 índices de condição na espécie seleccionada. A amostragem foi efectuada em quatro locais da Ria de Aveiro, um considerado como referência (Barra) e três com diferentes tipos predominantes de contaminação (Vagueira, Porto de Aveiro e Cais do Bico), sazonalmente, durante um ano. Os resultados obtidos permitiram uma caracterização ecotoxicológica dos locais, incluindo informação sobre a qualidade da água, concentrações de contaminantes ambientais prioritários nos sedimentos e nos tecidos de P. microps, capacidade desta espécie para bioacumular metais, efeitos exercidos pelas complexas misturas de poluentes presentes em cada uma das zonas de amostragem nesta espécie e possíveis consequências para a população. A análise multivariada permitiu analisar de forma integrada todos os resultados, proporcionando informação que não poderia ser obtida analisando os dados de forma compartimentalizada. Em conclusão, os resultados obtidos no âmbito desta dissertação indicam que P. microps possui características adequadas para ser utilizado como organismoteste em ensaios laboratoriais (e.g. abundância, fácil manutenção, permite a determinação de diferentes tipos de critérios de efeito utilizando um número relativamente reduzido de animais, entre outras) e como organismo sentinela em estudos de monitorização da poluição e da qualidade ambiental, estando portanto de acordo com estudos de menor dimensão previamente efectuados. O trabalho desenvolvido permitiu ainda adaptar a P. microps diversas técnicas bioquímicas vulgarmente utilizadas como biomarcadores em Ecotoxicologia e validá-las quer no laboratório quer em cenários reais; desenvolver um novo bioensaio, utilizando um dispositivo de teste especialmente concebido para peixes epibentónicos baseado na performance natatória de uma espécie autóctone e em biomarcadores; relacionar os efeitos a nível bioquímico com parâmetros comportamentais que ao serem afectados podem reduzir de forma drástica e diversificada (e.g. aumento da mortalidade, diminuição do sucesso reprodutivo, redução do crescimento) a contribuição individual para a população. Finalmente, foi validada uma abordagem multidisciplinar, combinando metodologias ecológicas, ecotoxicológicas e químicas que, quando considerada de forma integrada utilizando análises de estatística multivariada, fornece informação científica da maior relevância susceptível de ser utilizada como suporte a medidas de conservação e gestão em estuários e sistemas lagunares.

Pregnancy and newborns disorders followed by urine metabolomics

Chapter 1 introduces the scope of the work by identifying the clinically relevant prenatal disorders and presently available diagnostic methods. The methodology followed in this work is presented, along with a brief account of the principles of the analytical and statistical tools employed. A thorough description of the state of the art of metabolomics in prenatal research concludes the chapter, highlighting the merit of this novel strategy to identify robust disease biomarkers. The scarce use of maternal and newborn urine in previous reports enlightens the relevance of this work. Chapter 2 presents a description of all the experimental details involved in the work performed, comprising sampling, sample collection and preparation issues, data acquisition protocols and data analysis procedures. The proton Nuclear Magnetic Resonance (NMR) characterization of maternal urine composition in healthy pregnancies is presented in Chapter 3. The urinary metabolic profile characteristic of each pregnancy trimester was defined and a 21-metabolite signature found descriptive of the metabolic adaptations occurring throughout pregnancy. 8 metabolites were found, for the first time to our knowledge, to vary in connection to pregnancy, while known metabolic effects were confirmed. This chapter includes a study of the effects of non-fasting (used in this work) as a possible confounder. Chapter 4 describes the metabolomic study of 2nd trimester maternal urine for the diagnosis of fetal disorders and prediction of later-developing complications. This was achieved by applying a novel variable selection method developed in the context of this work. It was found that fetal malformations (FM) (and, specifically those of the central nervous system, CNS) and chromosomal disorders (CD) (and, specifically, trisomy 21, T21) are accompanied by changes in energy, amino acids, lipids and nucleotides metabolic pathways, with CD causing a further deregulation in sugars metabolism, urea cycle and/or creatinine biosynthesis. Multivariate analysis models´ validation revealed classification rates (CR) of 84% for FM (87%, CNS) and 85% for CD (94%, T21). For later-diagnosed preterm delivery (PTD), preeclampsia (PE) and intrauterine growth restriction (IUGR), it is found that urinary NMR profiles have early predictive value, with CRs ranging from 84% for PTD (11-20 gestational weeks, g.w., prior to diagnosis), 94% for PE (18-24 g.w. pre-diagnosis) and 94% for IUGR (2-22 g.w. pre-diagnosis). This chapter includes results obtained for an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) study of pre-PTD samples and correlation with NMR data. One possible marker was detected, although its identification was not possible. Chapter 5 relates to the NMR metabolomic study of gestational diabetes mellitus (GDM), establishing a potentially predictive urinary metabolic profile for GDM, 2-21 g.w. prior to diagnosis (CR 83%). Furthermore, the NMR spectrum was shown to carry information on individual phenotypes, able to predict future insulin treatment requirement (CR 94%). Chapter 6 describes results that demonstrate the impact of delivery mode (CR 88%) and gender (CR 76%) on newborn urinary profile. It was also found that newborn prematurity, respiratory depression, large for gestational age growth and malformations induce relevant metabolic perturbations (CR 82-92%), as well as maternal conditions, namely GDM (CR 82%) and maternal psychiatric disorders (CR 91%). Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the value of maternal or newborn urine metabolomics for pregnancy monitoring and disease prediction, towards the development of new early and non-invasive diagnostic methods.

Integer-valued APARCH processes in the analysis of time series of counts

The Asymmetric Power Arch representation for the volatility was introduced by Ding et al.(1993) in order to account for asymmetric responses in the volatility in the analysis of continuous-valued financial time series like, for instance, the log-return series of foreign exchange rates, stock indices or share prices. As reported by Brannas and Quoreshi (2010), asymmetric responses in volatility are also observed in time series of counts such as the number of intra-day transactions in stocks. In this work, an asymmetric power autoregressive conditional Poisson model is introduced for the analysis of time series of counts exhibiting asymmetric overdispersion. Basic probabilistic and statistical properties are summarized and parameter estimation is discussed. A simulation study is presented to illustrate the proposed model. Finally, an empirical application to a set of data concerning the daily number of stock transactions is also presented to attest for its practical applicability in data analysis.

Assessment of reserve effect in a marine protected area: the case study of the Professor Luiz Saldanha Marine Park (Portugal)

Dissertação de mest., Biologia Marinha (Ecologia e Conservação Marinhas), Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2011

Chromatographic analysis and comparison of chemical composition of the samples between olive oils in Europe and some chinese oils

Dissertação de mest., Qualidade em Análises, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2013

Catchment influences on the hydrological flows to lake terra alta (Linhares, ES, Brazil) and ecohydrology perspectives

Dissertação de mestrado, Ecohidrologia, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Knowledge representation for data integration and exploration in translational medicine

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014

Design and Qualitative/Quantitative Analysis of Multi-Agent Spatial Simulation Library

Thesis (Master's)--University of Washington, 2012

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Power law and entropy analysis of catastrophic phenomena

Catastrophic events, such as wars and terrorist attacks, tornadoes and hurricanes, earthquakes, tsunamis, floods and landslides, are always accompanied by a large number of casualties. The size distribution of these casualties has separately been shown to follow approximate power law (PL) distributions. In this paper, we analyze the statistical distributions of the number of victims of catastrophic phenomena, in particular, terrorism, and find double PL behavior. This means that the data sets are better approximated by two PLs instead of a single one. We plot the PL parameters, corresponding to several events, and observe an interesting pattern in the charts, where the lines that connect each pair of points defining the double PLs are almost parallel to each other. A complementary data analysis is performed by means of the computation of the entropy. The results reveal relationships hidden in the data that may trigger a future comprehensive explanation of this type of phenomena.

Impact of prescribed fire on the chemical properties of soil

Prescribed fire is a common forest management tool used in Portugal to reduce the fuel load availability and minimize the occurrence of wildfires. In addition, the use of this technique also causes an impact to ecosystems. In this presentation we propose to illustrate some results of our project in two forest sites, both located in Northwest Portugal, where the effect of prescribed fire on soil properties were recorded during a period of 6 months. Changes in soil moisture, organic matter, soil pH and iron, were examined by Principal Component Analysis multivariate statistics technique in order to determine impact of prescribed fire on these soil properties in these two different types of soils and determine the period of time that these forest soils need to recover to their pre-fire conditions, if they can indeed recover. Although the time allocated to this study does not allow for a widespread conclusion, the data analysis clearly indicates that the pH values are positively correlated with iron values at both sites. In addition, geomorphologic differences between both sampling sites, Gramelas and Anjos, are relevant as the soils’ properties considered have shown different performances in time. The use of prescribed fire produced a lower impact in soils originated from more amended bedrock and therefore with a ticker humus covering (Gramelas) than in more rocky soils with less litter covering (Anjos) after six months after the prescribed fire occurrence.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.