Políticas para el aseguramiento de la calidad en la enseñanza obligatoria de España

La presente comunicación se enmarca en el proyecto de investigación “Evaluaciones nacionales e internacionales como política de mejora de la educación en España en el contexto Europeo” (referencia CEMU-2013-20); financiado en el marco de la segunda convocatoria competitiva de Proyectos de Investigación Multidisciplinares de la Universidad Autónoma de Madrid, aprobada por el Consejo de Gobierno del 8 de febrero de 2013. En este caso, el objetivo principal de la presente comunicación es presentar los sistemas de aseguramiento de la calidad que han implementado en tres Comunidades Autónomas (CCAA) de España: País Vasco, Andalucía y Madrid. En el marco del proyecto mencionado, denominamos políticas de aseguramiento de la calidad a aquellas prácticas, relativamente recientes, orientadas a generar conocimiento sobre el sistema educativo, en este caso, vinculado a la enseñanza obligatoria y acciones dirigidas a su mejora. Con mucha frecuencia, estas prácticas han terminado estando reducidas a evaluaciones externas centradas en el rendimiento de los estudiantes, sin ninguna o con escasas acciones posteriores. Por otra parte, estos sistemas de aseguramiento de la calidad han recibido diversas críticas, en gran parterelacionadas con su lógica original más próxima al mundo empresarial que al educativo. En este sentido, junto con dar a conocer, desde un punto de vista fundamentalmente descriptivo los sistemas de aseguramiento de la calidad de las cuatro CCAA mencionadas, se aborda la discusión centrada en los siguientes aspectos: ¿es posible hablar de sistemas de aseguramiento de la calidad? ¿qué concepciones de calidad subyacente predominan? ¿qué consecuencias tienen para las prácticas educativas?

Assessment of genotoxic effects in nurses handling cytostatic drugs

Several antineoplastic drugs have been classified as carcinogens by the International Agency for Research on Cancer (IARC) on the basis of epidemiological findings, animal carcinogenicity data, and outcomes of in vitro genotoxicity studies. 5-Fluorouracil (5-FU), which is easily absorbed through the skin, is the most frequently used antineoplastic agent in Portuguese hospitals and therefore may be used as an indicator of surface contamination. The aims of the present investigation were to (1) examine surface contamination by 5-FU and (2) assess the genotoxic risk using cytokinesis-block micronucleus assay in nurses from two Portuguese hospitals. The study consisted of 2 groups: 27 nurses occupationally exposed to cytostatic agents (cases) and 111 unexposed individuals (controls). Peripheral blood lymphocytes (PBL) were collected in order to measure micronuclei (MN) in both groups. Hospital B showed a higher numerical level of contamination but not significantly different from Hospital A. However; Hospital A presented the highest value of contamination and also a higher proportion of contaminated samples. The mean frequency of MN was significantly higher in exposed workers compared with controls. No significant differences were found among MN levels between the two hospitals. The analysis of confounding factors showed that age is a significant variable in MN frequency occurrence. Data suggest that there is a potential genotoxic damage related to occupational exposure to cytostatic drugs in oncology nurses.

Antineoplastic drugs contamination of workplace surfaces in two Portuguese hospitals

Despite the classification as known or suspected human carcinogens, by the International Agency for Research on Cancer, the antineoplastic drugs are extensively used in cancer treatment due to their specificity and efficacy. As human carcinogens, these drugs represent a serious threat to the healthcare workers involved in their preparation and administration. This work aims to contribute to better characterize the occupational exposure of healthcare professionals to antineoplastic drugs, by assessing workplace surfaces contamination of pharmacy and administration units of two Portuguese hospitals. Surface contamination was assessed by the determination of cyclophosphamide, 5-fluorouracil, and paclitaxel. These three drugs were used as surrogate markers for surfaces contamination by cytotoxic drugs. Wipe samples were taken and analyzed by HPLCDAD. From the total of 327 analyzed samples, in 121 (37%) was possible to detect and quantify at least one drug. Additionally, 28 samples (8.6 %) indicate contamination by more than one antineoplastic drug, mainly in the administration unit, in both hospitals. Considering the findings in both hospitals, specific measures should be taken, particularly those related with the promotion of good practices and safety procedures and also routine monitoring of surfaces contamination in order to guarantee the appliance of safety measures.

Surfaces contamination with 5-Fluorouracil in two Portuguese hospitals

Background - The use of antineoplastic drugs in cancer therapy is increasing due to their action in cancer cells. Carcinogenic, mutagenic and teratogenic effects. Some studies demonstrated that nurses and pharmacy personnel involved in preparation or administration are exposed to antineoplastic drugs. Aim: assess 5-Fluorouracil (5-FU) contamination on the surfaces of two Portuguese Hospitals (preparation and administration units). 5-FU is one of the most frequently antineoplastic agent used in Portuguese Hospitals and can be easily absorbed through the skin. This drug can be used as an marker of surfaces contamination and exposure and have been extensively discussed in other studies.

Coloração de May-Grünwald Giemsa em amostras de secreções brônquicas processadas em ThinPrep®: comparação de três métodos de pós-processamento

O processamento de amostras citológicas em meio líquido e a coloração de May-Grünwald Giemsa (MGG) fazem parte da rotina em anatomia patológica. Na origem desta investigação esteve a possibilidade do uso desta coloração em amostras processadas em ThinPrep (TP). Estudou-se a fase compreendida entre o processamento de amostras em TP e a coloração com MGG – pós-processamento. O objetivo do estudo consistiu em avaliar diferentes métodos de pós-processamento em amostras de secreções brônquicas processadas pela metodologia TP e coradas com MGG. Utilizaram-se 32 amostras de secreções brônquicas, processadas em TP. De cada amostra obtiveram-se três lâminas, nas quais se aplicaram três métodos de pós-processamento: secagem ao ar; imersão em solução salina de tampão Tris; imersão em etanol a 96%. Realizou-se a coloração de MGG e as lâminas foram avaliadas por três avaliadores independentes, relativamente à constituição da amostra e qualidade da coloração. Este último parâmetro resultou da soma da pontuação obtida para os detalhes nuclear e citoplasmático (escala de 0 a 4 valores). Aplicaram-se os testes estatísticos One-Way ANOVA (p=0,05) e de Tukey. Para a qualidade de coloração, os métodos imersão em solução tampão, imersão em etanol a 96% e secagem ao ar obtiveram a pontuação média de 2,39 (s=1,309), 2,15 (s=1,248) e 1,22 (s=1,250), respetivamente. Verificou-se que existia diferença estatisticamente significativa entre o método secagem ao ar e os métodos imersão em solução tampão e em etanol a 96% (p=,000). O pós-processamento por secagem ao ar demonstrou qualidade da coloração não aceitável, ou seja pontuação média inferior a 2 valores. Pelo contrário, os pós-processamentos por imersão em solução tampão e em etanol a 96% apresentaram qualidade de coloração aceitável, podendo ser utilizados na rotina laboratorial para coloração com MGG de amostras processadas em TP.

Assessment of data-driven modeling strategies for water delivery canals

The aim of this paper is to develop models for experimental open-channel water delivery systems and assess the use of three data-driven modeling tools toward that end. Water delivery canals are nonlinear dynamical systems and thus should be modeled to meet given operational requirements while capturing all relevant dynamics, including transport delays. Typically, the derivation of first principle models for open-channel systems is based on the use of Saint-Venant equations for shallow water, which is a time-consuming task and demands for specific expertise. The present paper proposes and assesses the use of three data-driven modeling tools: artificial neural networks, composite local linear models and fuzzy systems. The canal from Hydraulics and Canal Control Nucleus (A parts per thousand vora University, Portugal) will be used as a benchmark: The models are identified using data collected from the experimental facility, and then their performances are assessed based on suitable validation criterion. The performance of all models is compared among each other and against the experimental data to show the effectiveness of such tools to capture all significant dynamics within the canal system and, therefore, provide accurate nonlinear models that can be used for simulation or control. The models are available upon request to the authors.

Epidemiologic transition in northern Angola: discussion of cumulative results from CISA (Health Research Centre of Angola)

The 41 years of armed conflict (1961 to 2002) resulted in a poor development of the health care and education infrastructures, and forced the relocation of people to safer places, namely major urban cities like Luanda. This phase was characterized by typical demographic, nutritional and epidemiological profiles. With the end of this period Angola has been repeatedly ranked as one of the three fastest growing economies in the world, and along with the social stabilization and globalization, the country is facing the introduction of new medical technologies, improvement of health sys-tems and services, better access to them, and overall better quality of life. These changes could also be translating into socio-cultural, demographic and nutritional changes which in turn may leading to changes in the epidemiological profile of the country. Thus, the emergence of non-communicable diseases are likely to become an increasingly im-portant public health problem in Angola. Also, considering that several infectious diseases persist, our weakened health system will have to face a double burden. Thus, disease surveillance data on non-communicable diseases to determine their prevalence and impact, along with the major behavioural risk factors like consumption of tobacco, alcohol, diets and physical inactivity are urgently needed.

Exposição ocupacional a mercúrio: associação com a atividade da paraoxonase humana e vitaminas A e E

Mestrado em Segurança e Higiene no Trabalho

Estratégias pedagógicas para jovens com a síndrome de Asperger na aprendizagem do trompete: relatório de estágio

Resumo I - O Relatório de estágio foi idealizado no âmbito da Unidade Curricular de Estágio do Ensino Especializado, Mestrado em Ensino da Música pela Escola Superior de Música de Lisboa. O documento incide sobre a prática pedagógica desenvolvida no Conservatório Regional de Palmela no ano letivo 2013/2014, abrangendo três alunos dos diferentes graus de ensino. No conteúdo do relatório, para além da caracterização do estabelecimento de ensino onde o estágio foi desenvolvido, foram caracterizados os diferentes alunos realçando as linhas pedagógicas seguidas com cada um dos intervenientes, salientando os aspetos de competências motora, auditiva e expressiva desenvolvidas durante o processo. Os objetivos do trabalho desenvolvido com os alunos é descrito, tendo como base a organização dos materiais a utilizar de acordo com as características de cada aluno, e consequentemente de acordo com a análise crítica da atividade docente. O professor tem a responsabilidade de conduzir o ensino dos alunos para que estes saibam apreciar e compreender as execuções musicais, independentemente da aptidão musical que já possuem.

O regime da arbitragem tributária

Mestrado em Fiscalidade

Loas a María: religiosidad popular en Portugal

El trabajo de la profesora Lucília José Justino supone a todas luces una contribución en un género textual poco estudiado desde una perspectiva textual, como son la Loas a Nuestra Senora de Nazaré. in Prefácio, Eloy Martos Núnez

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

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.

Determining the number of clusters in categorical data

Cluster analysis for categorical data has been an active area of research. A well-known problem in this area is the determination of the number of clusters, which is unknown and must be inferred from the data. In order to estimate the number of clusters, one often resorts to information criteria, such as BIC (Bayesian information criterion), MML (minimum message length, proposed by Wallace and Boulton, 1968), and ICL (integrated classification likelihood). In this work, we adopt the approach developed by Figueiredo and Jain (2002) for clustering continuous data. They use an MML criterion to select the number of clusters and a variant of the EM algorithm to estimate the model parameters. This EM variant seamlessly integrates model estimation and selection in a single algorithm. For clustering categorical data, we assume a finite mixture of multinomial distributions and implement a new EM algorithm, following a previous version (Silvestre et al., 2008). Results obtained with synthetic datasets are encouraging. The main advantage of the proposed approach, when compared to the above referred criteria, is the speed of execution, which is especially relevant when dealing with large data sets.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.