Análise do padrão de recrutamento abdominal em crianças com dor espinal, entre os 7 e os 10 anos, durante a flexão rápida do ombro, com recurso a electromiografia de superfície

Estudos epidemiológicos dão conta de um aumento exponencial de crianças que reportam dor espinal nalgum momento da vida, tendo-se vindo a atribuir a esta um interesse crescente. Nesta sequência têm vindo a ser estudados factores de risco para a dor espinal, cujo leque tem aumentado devido ao contexto social em que nos inserimos. Um dos aspectos sobre o qual recai a nossa investigação relaciona-se com a activação muscular nas crianças com dor espinal, aspecto ainda não estudado nesta população em particular. A literatura indica que, na população adulta sem dor espinal existe pré-activação muscular abdominal aquando da flexão rápida do ombro e a maioria dos estudos revistos apontam para a inexistência da mesma nos indivíduos com dor espinal. Apesar disso, não existem evidências que o demonstrem em crianças pelo que o nosso estudo pretende descrever o padrão de recrutamento abdominal utilizado pelas crianças com dor espinal, aquando do movimento rápido do membro superior bem como analisar os principais factores de risco. Para recolha dos dados utilizou-se o Questionário de Dor Adaptado, para rastrear a amostra com dor espinal e descrever a sua história ocupacional, e Electromiografia de Superfície, com utilização do acelerómetro, que nos deu conta do início do movimento. Os dados obtidos neste estudo indicam que existe activação muscular abdominal, no momento imediatamente prévio ao início do movimento de flexão do ombro, em quase toda a musculatura abdominal, em crianças com dor espinal excepto em dois participantes que revelam um atraso na activação do músculo oblíquo interno direito e num outro que revela um atraso na activação do recto abdominal. Um dos participantes apresentou pré-activação em todos os músculos estudados. Isto provavelmente encontra-se relacionado com o processo de maturação e indica que possivelmente esta é uma boa altura para prevenir a evolução da dor e possíveis futuros problemas ocupacionais daí advindos, como faltar ao trabalho e ter uma baixa participação social. Estudos futuros devem debruçar-se sobre esta temática e sobre a delineação de novos programas, desta feita de prevenção, de modo a evitar problemas ocupacionais na idade adulta, já que crianças com dor são mais susceptíveis de se tornarem adultos com dor crónica.

Cardiorespiratory fitness is negatively associated with metabolic risk factors independently of the adherence to a healthy dietary pattern

Background and aim: Cardiorespiratory fitness (CRF) and diet have been involved as significant factors towards the prevention of cardio-metabolic diseases. This study aimed to assess the impact of the combined associations of CRF and adherence to the Southern European Atlantic Diet (SEADiet) on the clustering of metabolic risk factors in adolescents. Methods and Results: A cross-sectional school-based study was conducted on 468 adolescents aged 15-18, from the Azorean Islands, Portugal. We measured fasting glucose, insulin, total cholesterol (TC), HDL-cholesterol, triglycerides, systolic blood pressure, waits circumference and height. HOMA, TC/HDL-C ratio and waist-to-height ratio were calculated. For each of these variables, a Z-score was computed by age and sex. A metabolic risk score (MRS) was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had 1SD of this score. CRF was measured with the 20 m-Shuttle-Run- Test. Adherence to SEADiet was assessed with a semi-quantitative food frequency questionnaire. Logistic regression showed that, after adjusting for potential confounders, unfit adolescents with low adherence to SEADiet had the highest odds of having MRS (OR Z 9.4; 95%CI:2.6e33.3) followed by the unfit ones with high adherence to the SEADiet (OR Z 6.6; 95% CI: 1.9e22.5) when compared to those who were fit and had higher adherence to SEADiet.

sMapReduce: a programming pattern for wireless sensor networks

Wireless Sensor Networks (WSNs) are increasingly used in various application domains like home-automation, agriculture, industries and infrastructure monitoring. As applications tend to leverage larger geographical deployments of sensor networks, the availability of an intuitive and user friendly programming abstraction becomes a crucial factor in enabling faster and more efficient development, and reprogramming of applications. We propose a programming pattern named sMapReduce, inspired by the Google MapReduce framework, for mapping application behaviors on to a sensor network and enabling complex data aggregation. The proposed pattern requires a user to create a network-level application in two functions: sMap and Reduce, in order to abstract away from the low-level details without sacrificing the control to develop complex logic. Such a two-fold division of programming logic is a natural-fit to typical sensor networking operation which makes sensing and topological modalities accessible to the user.

Fractional central pattern generators for bipedal locomotion

Locomotion has been a major research issue in the last few years. Many models for the locomotion rhythms of quadrupeds, hexapods, bipeds and other animals have been proposed. This study has also been extended to the control of rhythmic movements of adaptive legged robots. In this paper, we consider a fractional version of a central pattern generator (CPG) model for locomotion in bipeds. A fractional derivative D α f(x), with α non-integer, is a generalization of the concept of an integer derivative, where α=1. The integer CPG model has been proposed by Golubitsky, Stewart, Buono and Collins, and studied later by Pinto and Golubitsky. It is a network of four coupled identical oscillators which has dihedral symmetry. We study parameter regions where periodic solutions, identified with legs’ rhythms in bipeds, occur, for 0<α≤1. We find that the amplitude and the period of the periodic solutions, identified with biped rhythms, increase as α varies from near 0 to values close to unity.

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.