Experiência do silêncio no auto-retrato : o silêncio como sensação

Dissertação de Mestrado em Filosofia – Estética

Sub-notificação de reacções adversas medicamentosas : um problema com solução? : contribuição para o conhecimento da sub-notificação de RAMs em Portugal e implementação de medidas minimizadoras do impacte

RESUMO - Introdução: As Reacções Adversas Medicamentosas (RAMs) constituem um grave problema de Saúde Pública em termos da mortalidade e morbilidade provocadas, tendo também um impacto económico considerável nos Sistemas de Saúde. Os Sistemas de Notificacão Espontânea de RAMs são considerados como o método de vigilância de medicamentos mais eficaz, sendo a sub-notificação de RAMs uma das suas maiores limitações. Em termos globais, foi estimado que apenas 6% de todas as reacções adversas são notificadas. Portugal apresenta uma taxa de notificação de RAMs relativamente baixa quando comparada com os países mais notificadores da Europa. São objectivos deste estudo: 1) caracterizar as atitudes e os comportamentos dos médicos, dos farmacêuticos e dos enfermeiros em Portugal Continental relativamente à notificação de RAMs; e 2) caracterizar a efectividade de intervenções educacionais destinadas a reduzir a sub-notificação de RAMs. Métodos: Numa primeira fase será efectuado um estudo de caso-controlo em médicos, farmacêuticos e enfermeiros de Portugal Continental, a exercer actividade no Servico Nacional de Saúde (SNS), de modo a caracterizar as suas atitudes e comportamentos relativamente à notificação de RAMs. Como casos serão considerados os Profissionais de Saúde que notificaram pelo menos uma RAM num determinado período e os controlos os Profissionais de Saúde que não notificaram qualquer RAM nesse mesmo período, sendo estes útimos seleccionados aleatoriamente. O estudo será conduzido através de um questionário de auto-resposta, em que as questões relativas às atitudes e comportamentos são baseadas nos “sete pecados mortais” de Inman. Será utilizada uma Escala Visual Analógica para registar as respostas, podendo estas ir de zero (totalmente em desacordo) até 10 (totalmente de acordo). Será utilizada uma análise de regressão logística para determinar o odds ratio ajustado (ORadj) da notificação de RAMs para uma mudança de exposição correspondente ao range interquartil para cada atitude. Numa segunda fase, será efectuado ensaio aleatorizado controlado de cluster, para caracterizar a efectividade das intervenções educacionais realizadas sobre as causas identificadas na primeira parte do trabalho, com o intuito de reduzir a sub-notificação de RAMs. Com base em informacão de 2007 foram identificados 43 clusters dispersos pelas várias Regiões de Saúde. As intervenções educacionais são compostas por uma apresentação com uma hora de duração complementada por um folheto recordatório. Serão ainda realizados dois sub-estudos, em que o V1.0, Final 28Set09 viii Sub-notificação de RAMs em Portugal – Um problema com solução ? primeiro tentará caracterizar o efeito de contaminação entre Profissionais de Saúde e o segundo pretende caracterizar a duração do efeito das intervenções educacionais. Resultados a atingir: Pretende-se, com a implementação deste projecto, aumentar o número de notificações de RAMs pelos médicos, farmacêuticos e enfermeiros em cerca de 110%, de modo a atingir-se uma taxa de notificação de aproximadamente 300 notificações por milhão de habitantes por ano (i.e., multiplicar por 2,1 o número notificações existentes). -------------------ABSTRACT - Introduction: The Adverse Drug Reactions (ADRs) are a serious Public Health problem in terms of mortality and morbidity caused, being also an economic burden for the health systems. The Spontaneous Adverse Event Reporting Systems are considered as the most effective drug surveillance methods, in which the ADR under-reporting represents one of its biggest limitations. It was estimated that only 6% of all adverse reactions are notified globally. When comparing with high ADR reporting rate countries Portugal shows a low ADR reporting rate. This study aims to: 1) characterize the physicians, pharmacists and nurses attitudes and behaviours related to ADR under-reporting; 2) characterize the educational interventions effectiveness to decrease the ADRs under-reporting. Methods: During a first phase a case-control study will be conducted in physicians, pharmacists and nurses in Continental Portugal working in the National Health System (NHS) in order to characterize their attitudes and behaviours related to ADR reporting. The Healthcare Professionals that have reported at least one ADR during a determined period will be considered as the cases and those that have not reported any ADR during the same period will be considered as the controls. The controls will be randomly selected. The study will be conducted through a self-administered questionnaire in which the questions related to the attitudes and behaviours are based in the Inmans’s “seven mortal sins”. A Visual Analogue Scale will be used to record the responses. The responses can range from 0 (totally disagree) to 10 (totally agree). Logistic regression will be used to determine the ADR reporting adjusted odds ratio (ORadj) for a change in the exposure corresponding to the interquartile range for each attitude. In the second phase of the study a cluster-randomized controlled trial will be conducted to characterize the educational interventions effectiveness focused on the first phase identified causes with the aim to decrease the ADRs under-reporting. Based in 2007’s information 43 clusters have been identified throughout the several Health Regions. The educational interventions are composed of one hour presentation complemented by an informational leaflet. Two sub-studies will be also conducted in which the first one will try to characterize the contamination effect between the Healthcare Professionals and the second to characterize the educational interventions effect duration. V1.0, Final 28Set09 x Sub-notificação de RAMs em Portugal – Um problema com solução ? Outcome: With the project implementation an increase of the ADR notifications performed by the physicians, pharmacists and nurses by 110% is aimed in order to obtain approximately 300 notifications per million habitants per year (i.e., multiply by 2,1 the existent notifications).

Embryology of Triatoma infestans (KLUG), (Hemiptera, Reduviidae), a Chagas' disease vector

This study reports the embryogenesis of T. infestans (Hemiptera, Reduviidae). Morphological parameters of growth sequences from oviposition until hatching (12-14 d 28ºC) were established. Five periods, as percent of time of development (TD), were characterized from oviposition until hatching. The most important morphological features were: 1) formation of blastoderm within 7% of TD; 2) germ band and gastrulation within 30% of TD; 3) nerve cord, limb budding, thoracic and abdominal segmentation and formation of body cavity within 50% of TD; 4) nervous system and blastokinesis end, and development of embryonic cuticle within 65% of TD; 5) differentiation of the mouth parts, fat body, and Malphigian tubules during final stage and completion of embryo at day 12 to day 14 around hatching. These signals were chosen as appropriate morphological parameters which should enable the evaluation of embryologic modifications due to the action/s of different insecticides

Lethal effect of a bait for Rhodnius prolixus (Hemiptera: Reduviidae), the vector of Chagas' disease, containing hexachlorocyclohexane (HCH), under laboratory conditions

The lethal effect of a bait containing an aqueous hexachlorocyclohexane (HCH) suspension at the concentration of 1g/l and maintained at room temperature was studied in the laboratory over a period of 12 weeks. The suspension was placed in a latex bag hanging inside a 1000-ml beaker tightly covered with nylon netting, and left there with no changes for 85 days. Sixteen groups of R. prolixas bugs, consisting on average of 30 specimens each, were successively exposed to the bait and observed at different intervals for one week each. The mortality rate was 100% for all groups, except for the 16th, whose mortality rate was 96.7%. As the groups succeeded one another, mortality started to occur more rapidly and was more marked at the 6- and 24-h intervals. Later tests respectively started at 6:00 a.m. and 6:00 p.m. showed that diurnal and nocturnal periodicity in the offer of food had no effect on mortality. First- and 2nd- instar nymphs and adults male were more sensitive and 5th- instar nymphs were more resistant to the active principle of the bait.

Cinema na primeira pessoa: auto-representação e auto reflexividade no documentário contemporâneo: trabalho de projecto

Trabalho de Projeto submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento do Projeto Cinematográfico - especialização em Dramaturgia e Realização.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Distribucion geografica de Lutzomyia verrucarum (Townsend, 1913) (Diptera, Psychodidae, Phlebotominae), vector de la batonellosis humana en el Peru

Lutzomyia verrucarum (Townsend, 1913) (Diptera: Psychodidae), vector natural de la verruga peruana o enfermedad de Carrión es una especie propia del Perú. Su distribución geográfica esta entre los paralelos 5º y 13º25' de latitud Sur, se encuentra en los valles Occidentales e Interandinos de los Andes. La distribución altitudinal de Lu. verrucarum en los diversos valles es variable; asi: Occidentales, desde 1100 hasta 2980 msnm e Interandinos, de 1200 a 3200 msnm. En ciertas áreas verrucógenas no hay correlación entre la presencia de Lu. verrucarum y la enfermedad de Carrión lo que suguiere la existencia de vectores secundarios.

Predicting Xerostomia induced by IMRT treatments: A logistic regression approach

Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated Radiation Therapy (IMRT) is one technique of radiation treatment that allows the achievement of a high degree of conformity between the area to be treated and the dose absorbed by healthy tissues. Nevertheless, it is still not possible to eliminate completely the potential treatments’ side-effects. In this retrospective study we use the clinical data from patients with head-and-neck cancer treated at the Portuguese Institute of Oncology of Coimbra and explore the possibility of classifying new and untreated patients according to the probability of xerostomia 12 months after the beginning of IMRT treatments by using a logistic regression approach. The results obtained show that the classifier presents a high discriminative ability in predicting the binary response “at risk for xerostomia at 12 months”

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Planear e inovar : contributo para o planeamento de recursos com base nas necessidades auto-avaliadas pela população com 65 e mais anos

RESUMO - Enquadramento: O envelhecimento da população ocorre em todas as sociedades desenvolvidas, resultando num aumento da prevalência da dependência funcional, associado recorrentemente à presença de doenças crónicas. Estes novos padrões demográficos, epidemiológicos, implicando populações vulneráveis com necessidades específicas, resultam em desafios incontestáveis. Como resposta a este novo paradigma, em 2006, Portugal implementa a Rede Nacional de Cuidados Continuados Integrados (RNCCI). Finalidade/objectivos: Caracterização da população com base no perfil das necessidades auto-referidas pelas pessoas com ≥65 anos, com algum nível de independência/dependência nas actividades de vida diária e/ou com pelo menos uma doença crónica. Pretende-se, ainda, desenvolver uma metodologia que permita simular cenários que contribuam para o planeamento do número de camas para internamento de carácter permanente em Unidades de Longa Duração e Manutenção (ULDM) da RNCCI. Metodologia: Construção de dois indicadores: índice de independência/dependência e existência ou não de doenças crónicas. Análise estatística e caracterização, individual e conjunta, das variáveis sociodemográficas, socioeconómicas, auto-avaliação do estado de saúde, nível de independência/dependência e/ou existência de pelo menos uma doença crónica. Simulação de cenários com base nas metas definidas pela RNCCI para 2013. Resultados e Conclusões: Da aplicação do índice de independência/dependência, resulta que 78,8% são independentes na realização das actividades de vida diária e 21,2% apresentam algum nível de dependência. À excepção do Centro, todas as regiões apresentam padrões similares. Globalmente, os resultados obtidos vão de encontro aos enunciados na literatura internacional, realçando-se apenas alguns mais pertinentes: Observa-se uma predominância de mulheres idosas. Destaca-se também uma relação directa entre a idade e os níveis de dependência. As variáveis socioeconómicas indicam que a existência de algum nível de dependência tende a ser mais frequente entre os que têm menor escolaridade e rendimento. Em média o estado de saúde é auto-avaliado como mau, piorando com o aumento da idade e níveis de dependência mais acentuados e melhorando com o aumento da escolaridade. Da simulação de cenários destaca-se que, face às 4 camas previstas nas metas de 2013, seria de alocar em média 1,7 camas ou 1 cama ao internamento permanente em ULDM. Trabalhar em rede implica canais de comunicação. A incorporação da distribuição espacial das necessidades e serviços com recurso aos sistemas de informação geográfica torna-se numa mais-valia. Possibilita avaliar hipóteses, análises sustentadas e disseminação de informação e resultados, contribuindo para um planeamento, monitorização e avaliação mais eficaz e eficiente das actividades do sector da saúde. ---------------------------------- ABSTRACT - Background: Population aging occurs in all developed societies resulting in an increased prevalence of functional dependence, frequently associated with the presence of chronic diseases. These new demographic and epidemiological patterns, which include dependency ad vulnerability situations, with specific needs, result in undeniable challenges. In response to this new paradigm, in 2006, Portugal implements the National Network for Integrated Care (RNCCI). Aim/Objectives: Characterize the population based on the self-reported needs of ≥65 year’s people, with some level of independence/dependency in activities of daily living and/or with at least one chronic disease. Also intends to develop a methodological approach that allows scenarios simulation which contributes to the planning of the number of permanent inpatient beds in Long Term Care Units (ULDM) of RNCCI. Methods: Construction of two indicators: independence/dependence index and existence of chronic diseases. Statistical analysis and characterization, individually and jointly, of sociodemographics, socioeconomics, selfassessment of health status, level of independence/dependence and/or existence of at least one chronic disease variables. Scenarios simulation based on RNCCI targets set for 2013. Results and Conclusions: According with independence/dependence index, 78.8% are independent in carrying out the activities of daily living and 21.2% have some level of dependency. With the exception of the Centroregion, all regions have similar patterns. Generally, the results are concordant with international literature, highlighting here only some of the most relevant results: A predominance of older women is observed. A direct relationship between age and levels of dependence is emphasized. Socio-economic variables indicate that the existence of some level of dependency tends to be more frequent among those with lower income and education levels. On average, health status is self-assessed as poor, being even more critical with aging and higher dependency level. On the other hand, high education levels are related with better health status. Scenarios simulations highlights that, based on 4 beds considered in the 2013 planned goals, an average of 1.7 or 1 beds in ULDM should be allocated to permanent inpatient beds. Networking involves communication channels. The incorporation of spatial distribution of needs and services using geographical information systems becomes an added value. It enables hypothesis, evaluation, sustainable analysis and information and results dissemination, contributing to a more effective and efficient planning, monitoring and assessment of the health sector activities.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

A liderança pedagógica e auto-eficácia dos professores do ensino superior

Dissertação apresenta para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ciências da Educação, na área da Análise e Intervenção em Educação