Using the International Classification of Functioning, Disability and Health as a tool for analysis of the effect of physical therapy on spasticity in HAM/TSP patients

INTRODUCTION: This study aimed to evaluate spasticity in human T-lymphotropic virus type 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) patients before and after physical therapy using the International Classification of Functioning, Disability and Health (ICF). METHODS: Nine subjects underwent physical therapy. Spasticity was evaluated using the Modified Ashworth Scale. The obtained scores were converted into ICF body functions scores. RESULTS: The majority of subjects had a high degree of spasticity in the quadriceps muscles. According to the ICF codes, the spasticity decreased after 20 sessions of physical therapy. CONCLUSIONS: The ICF was effective in evaluating spasticity in HAM/TSP patients.

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.

Vegetation near Santa Teresinha, NE Mato Grosso.

The Santa Tenesinha region in northeaster Mato Grosso has a varied vegetation which is principally hammock pantanal. The flat clayey alluvial ground between the hummocks is coveted with a continuous non-cerrado ground cover dominated by grasses but which harbors sedges and a lange herb flora. No woody plants grow in it. The tops of the 10-20m wide, slightly elliptical hummocks, 1.5-2 m high, 10-40 per hectare, are covered with cerrado plants: herbs, semlshrubs, thin- and thick-stemmed shrubs and low trees. For 4-5 months during the latter part of the rainy season, the regional water table rises to the surface and the ground between the hummocks becomes saturated or floods up to 1.5-2 m deep. The tops of the hummocks almost always remain above high water level. In the dry season the surface soil dries out completely. This alternation of saturation or shallow flooding and dryness, prevents woody plant, growth between the hummocks, and except for a few tolerant species, also prevents woody plant. growth on the lower part of the hummochs. The gallery forests in the pantanal are seasonally flooded more deeply but their soil does not dry out so thonoughly in the dry season so woody plant growth is not prevented.

TRANSFERTS DE QUANTITE DE MOUVEMENT AU-DESSUS ET A L'INTERIEUR DE LA VEGETATION

The aborptlon of momentum by the vegetation Is studied In this work through an analytical approach which also provides the appropriate formulations to describe wind velocity and drffusivities profiles above and Inside the space occupied by the foliage elements. A first comparison between the observed and calculated profiles of wind volocity for Amazonian forest (Réserva Pucke, Manaus - AM) is presented to test the realism of the model.

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.

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation.

Mangrove vegetation in Amazonia: a review of studies from the coast of Pará and Maranhão States, north Brazil

The present study is a compilation of the literature about vegetation of mangrove forest of the north coast of Brazil. It synthesizes the knowledge about this important ecosystem and lists the currently available literature. The study focuses on the coast of Pará and Maranhão states, which are covered by a continuous belt of mangroves. The mangrove flora comprises six mangrove tree species and several associated species. Mangrove tree height and stem diameter vary as a function of abiotic local stand parameters. Seasonal variation in rainfall and salinity affect the species' phenology and litter fall. Local population use products derived from mangrove plants for different purposes (e.g. fuel; medicinal; rural construction). The increase in the coastal population has given rise to conflicts, which impact on mangrove forest.

Contribution to Early Holocene vegetation and climate history of Eastern Orinoco Llanos, Venezuela, from a paleoecological record of a Mauritia L.f. swamp

A palynological analysis of an organic paleosol found at 150-125 cm depth in a Mauritia swamp from the Eastern Orinoco Llanos is presented. The 25 cm pollen record summarizes the vegetation history during the Early Holocene, from 10,225 to 7,800 calendar yr BP. The vegetation was characterized by a Poaceae marsh, where Asteraceae, Melastomataceae, Schefflera-type and Phyllanthus were the most abundant shrubs and trees. Pollen-types richness was lower than that recorded today in similar environments, and Mauritia pollen was absent. Results suggest that climate was as humid as present during the beginning of the Holocene, with a decreasing trend in humidity from around 8,000-7,000 yr BP, in coincidence with the beginning of the "Early-Mid-Holocene Dryness" that affected deeply the Amazon Basin and neighboring areas. Dry climatic conditions could have existed in the study site until the Mid-Late Holocene when a Mauritia swamp developed, and humid conditions similar to present established. Main climate phases inferred in our study site fit well with regional trends recorded in other places located north Amazon Basin. However, conclusions are still limited by the lack of additional Quaternary records in the Orinoco Llanos area, avoiding regional correlations.

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.

Litterfall production in intact and selectively logged forests in southern of Amazonia as a function of basal area of vegetation and plant density

Nutrient recycling in the forest is linked to the production and decomposition of litter, which are essential processes for forest maintenance, especially in regions of nutritionally poor soils. Human interventions in forest such as selecttive logging may have strong impacts on these processes. The objectives of this study were to estimate litterfall production and evaluate the influence of environmental factors (basal area of vegetation, plant density, canopy cover, and soil physicochemical properties) and anthropogenic factors (post-management age and exploited basal area) on this production, in areas of intact and exploited forest in southern Amazonia, located in the northern parts of Mato Grosso state. This study was conducted at five locations and the average annual production of litterfall was 10.6 Mg ha-1 year-1, higher than the values for the Amazon rainforest. There were differences in litterfall productions between study locations. Effects of historical logging intensity on litterfall production were not significant. Effects of basal area of vegetation and tree density on litterfall production were observed, highlighting the importance of local vegetation characteristics in litterfall production. This study demonstrated areas of transition between the Amazonia-Cerrado tend to have a higher litterfall production than Cerrado and Amazonia regions, and this information is important for a better understanding of the dynamics of nutrient and carbon cycling in these transition regions.

Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables were: elevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.

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.

Deteção remota na avaliação das alterações da cobertura do solo no Parque Nacional da Peneda-Gerês

Dissertação de mestrado integrado em Engenharia Civil