Evaluation of the World Health Organization 2009 classification of dengue severity in autopsied individuals, during the epidemics of 2011 and 2012 in Brazil

Abstract: INTRODUCTION: The dengue classification proposed by the World Health Organization (WHO) in 2009 is considered more sensitive than the classification proposed by the WHO in 1997. However, no study has assessed the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue. In the present study, we evaluated the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue in Northeast Brazil, where the disease is endemic. METHODS: This retrospective study included 121 autopsied individuals suspected of having dengue in Northeast Brazil during the epidemics of 2011 and 2012. All the autopsied individuals included in this study were confirmed to have dengue based on the findings of laboratory examinations. RESULTS: The median age of the autopsied individuals was 34 years (range, 1 month to 93 years), and 54.5% of the individuals were males. According to the WHO 1997 classification, 9.1% (11/121) of the cases were classified as dengue hemorrhagic fever (DHF) and 3.3% (4/121) as dengue shock syndrome. The remaining 87.6% (106/121) of the cases were classified as dengue with complications. According to the 2009 classification, 100% (121/121) of the cases were classified as severe dengue. The absence of plasma leakage (58.5%) and platelet counts <100,000/mm3 (47.2%) were the most frequent reasons for the inability to classify cases as DHF. CONCLUSIONS: The WHO 2009 classification is more sensitive than the WHO 1997 classification for identifying dengue deaths among autopsied individuals suspected of having dengue.

Analysis of classification algorithms for crop detection using LANDSAT 8 images

Remote sensing - the acquisition of information about an object or phenomenon without making physical contact with the object - is applied in a multitude of different areas, ranging from agriculture, forestry, cartography, hydrology, geology, meteorology, aerial traffic control, among many others. Regarding agriculture, an example of application of this information is regarding crop detection, to monitor existing crops easily and help in the region’s strategic planning. In any of these areas, there is always an ongoing search for better methods that allow us to obtain better results. For over forty years, the Landsat program has utilized satellites to collect spectral information from Earth’s surface, creating a historical archive unmatched in quality, detail, coverage, and length. The most recent one was launched on February 11, 2013, having a number of improvements regarding its predecessors. This project aims to compare classification methods in Portugal’s Ribatejo region, specifically regarding crop detection. The state of the art algorithms will be used in this region and their performance will be analyzed.

Distúrbios osteomioarticulares : lombalgia e escoliose : prevalência e fatores associados : um estudo analítico e transversal em adolescentes da região do Algarve

RESUMO - Introdução: Os distúrbios osteomioarticulares envolvem diversas condições donde se destacam a lombalgia e a escoliose, a primeira considerando o fato que a sua prevalência tem vindo a aumentar em adolescentes consistindo num problema crescente de saúde pública que envolve custos indiretos e a escoliose pela ausência de estudos nacionais. Diversos fatores físicos, genéticos, mecânicos, comportamentais e ambientais podem estar envolvidos na patogénese das lombalgias e escolioses. O ambiente escolar, incluindo as posturas adotadas pelos alunos e o transporte das mochilas escolares, e alguns hábitos de estilos de vida constituem fatores que podem contribuir para o desenvolvimento destes distúrbios osteomioarticulares. Este estudo também aborda o estado ponderal, nomeadamente o excesso de peso e a obesidade, pois este é referido frequentemente como um potencial fator de risco destes distúrbios osteomioarticulares (apesar de ainda apresentar controvérsia na literatura), além de ser, por si só, atualmente considerado como um dos mais graves problemas de saúde pública a nível mundial. Objetivos do estudo: (1) determinar a prevalência pontual, anual e ao longo da vida de lombalgia, assim como a prevalência de escoliose em adolescentes da região do Algarve; (2) identificar os fatores associados ao desenvolvimento destes distúrbios osteomioarticulares; (3) determinar a prevalência de excesso de peso e de obesidade e explorar a sua eventual associação com a prevalência de lombalgia e escoliose em adolescentes; (4) comparar os resultados obtidos nos diferentes métodos antropométricos (Índice de massa corporal - IMC, medição das pregas cutâneas e circunferência abdominal) e verificar a sua concordância. Material e métodos: O desenho deste estudo foi de natureza observacional, analítico e transversal. O estudo foi aprovado pela Comissão de Ética da Administração Regional de Saúde do Algarve, pela Direção Regional de Educação do Algarve, pela Direção-Geral de Inovação e de Desenvolvimento Curricular, Ministério da Educação e Ciência, e pelas Direções dos Agrupamentos de Escolas que participaram do projeto. A amostra incluiu 966 adolescentes da região do Algarve, sul de Portugal, com idades compreendidas entre os 10 e 16 anos (12,24±1,53 anos), sendo 437 (45,2%) do sexo masculino e 529 (54,8%) do feminino. O método de amostragem foi aleatório estratificado, com base nos concelhos da região do Algarve, assumindo que poderia existir heterogeneidades geográficas. Os instrumentos de medida foram aplicados num único momento (2011/2012) e incluíram o Questionário de Lombalgia e Hábitos Posturais para caracterizar a presença de lombalgia e os hábitos posturais adotados pelos alunos em casa e na escola, o escoliómetro para avaliar a presença de escoliose, a balança, o estadiómetro (sendo posteriormente calculado o IMC), o adipómetro e a fita métrica. A análise dos dados incluiu técnicas de estatística descritiva, gráficas e analíticas aplicadas à todas as variáveis em estudo. Para determinar a associação entre as variáveis do estudo foi utilizada a estatística inferencial, nomeadamente o teste de independência do Qui-quadrado. Para analisar as correlações entre as medidas obtidas com os métodos antropométricos (na sua forma quantitativa), foi utilizado o coeficiente de Spearman. A influência das diversas variáveis na presença de lombalgia foi aferida através de regressões logísticas binárias, sendo os resultados apresentados como odds ratios brutos e ajustados e respetivos intervalos de confiança. Resultados: O presente estudo revelou uma elevada prevalência de lombalgia (anual: 47,2%; pontual: 15,7%; ao longo da vida: 62,1%). As raparigas apresentaram 2,05 de probabilidade de apresentar lombalgia comparativamente aos rapazes (IC 95%: 1,58-2,65; p<0,001), assim como os alunos com idades mais avançadas (13-16 anos) comparativamente aos mais novos (10-12 anos) que tiveram 1,54 de chances (IC 95%: 1,19-1,99; p=0,001). Os alunos que indicaram adotar uma postura de sentado com a coluna vertebral posicionada incorretamente apresentaram 2,49 de probabilidade de revelar lombalgia (IC 95%: 1,91-3,24; p<0,001), os alunos que afirmaram se posicionar de forma inadequada para assistir televisão ou jogar videojogos tiveram a probabilidade de 2,01 (IC 95%: 1,55- 2,61; p<0,001) e aqueles que adotaram a postura de pé incorretamente tiveram 3,39 de chance de apresentar lombalgia (IC 95%: 2,19-5,23; p<0,001). A escoliose esteve presente em 41 (4,2%) alunos. As raparigas apresentaram a maior prevalência (4,5% versus 3,9%) do que os rapazes e o mesmo foi observado nas raparigas que apresentaram a menarca tardia (8,6% versus 3,3%) e os que foram classificados como magros (7,1%), não sendo no entanto estas diferenças estatisticamente significativas. Relativamente à prevalência de excesso de peso e obesidade, os valores variaram de 31,6%, 61,4% e 41,1% de acordo com a medição do IMC, pregas cutâneas e circunferência abdominal, respetivamente. Os valores obtidos com a avaliação dos três métodos antropométricos apresentaram um elevado alto grau de correlação entre o IMC e as pregas cutâneas (p<0,001; r=0,712), entre o IMC e circunferência abdominal (p<0,001; r=0,884) e entre a circunferência abdominal e as pregas cutâneas (p<0,001; r=0,701). Conclusões: O presente estudo revelou valores de prevalência de lombalgia semelhante a estudos anteriores sendo que os alunos com idade mais avançada, ou do sexo feminino ou aqueles que adotavam a postura sentada e de pé de forma inadequada ou os que transportavam indevidamente a mochila escolar apresentaram a maior prevalência. Quanto à presença de escoliose, observou-se uma baixa prevalência não sendo verificada nenhuma associação significativa com os fatores analisados. Relativamente ao estado ponderal, verificou-se uma elevada prevalência de excesso de peso e obesidade, com a utilização dos três métodos antropométricos: IMC, medição das pregas cutâneas e circunferência abdominal, tendo sido verificado um elevado grau de correlação entre estes três métodos antropométricos. Este estudo contribuiu para determinar a magnitude destes distúrbios osteomiarticulares nesta população específica, assim como seus possíveis fatores associados. De acordo os resultados obtidos no presente estudo, torna-se necessário ações de intervenção nas escolas, envolvendo não somente os alunos, mas toda a comunidade escolar, com o objetivo de prevenção destes distúrbios osteomioarticulares através da promoção de hábitos de vida saudável.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

The impact of work clothing design on workers’ comfort

Physical and physiological comfort, at work and during leisure time, is important to human health and motivation. A growing number of jobs require workers to sit. Most clothes, except those intended for wheelchair users, were designed for walking or the standing position. Clothing designs should be user-oriented and meet users’ needs. Garment design should conform to body position and posture, not just shape and size. In this paper we present the ergometric impact of a new type of trousers designed to adapt to changes in position. Concentrations of compression forces, temperature and pressure were documented in an exploratory pilot study and contrasted to traditional designs. The new trousers showed significant decreases in compression force concentration, especially in and around the knees and waist. Most participants identified comfort as an important factor when purchasing a pair of trousers and that, for working purposes, they would prefer these special trousers rather than traditional designs.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Supercritical fluid and pharmaceutical applications. Part I: Process classification

The supercritical fluid technology has been target of many pharmaceuticals investigations in particles production for almost 35 years. This is due to the great advantages it offers over others technologies currently used for the same purpose. A brief history is presented, as well the classification of supercritical technology based on the role that the supercritical fluid (carbon dioxide) performs in the process.

Ontological representation of tumor-node-metastasis classification and an ontology-driven classifier: a study on colorectal cancer

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)