Retrospective histopathological classification of 1,108 skin biopsies from patients clinically suspected of having leprosy from Bahia, Northeast Brazil

We report a retrospective histopathological classification carried out under laboratory conditions by the method of Ridley & Jopling of 1,108 skin biopsies from patients clinically suspected of having leprosy from Bahia, Northeast Brazil.

Mental health care assessed based on structure, process and outcome : a retrospective cohort study

RESUMO: As doenças mentais são comuns, universais e associadas a uma significativa sobrecarga pessoal, familiar, social e económica. Os Serviços de Saúde Mental devem abordar de forma adequada as necessidades dos pacientes e familiares tanto ao nível clínico como também ao nível social. O presente estudo foi realizado num período de grande transformação nos sistemas de saúde primário e de saúde mental em Portugal, num Departamento de Psiquiatria desenvolvido com base nos princípios da OMS. Os objectivos incluem a caracterização: 1) das Unidades Funcionais do Departamento; 2) dos pacientes internados pela primeira vez no internamento de agudos; 3) da utilização dos serviços nas equipas comunitárias após a alta; e 4) da avaliação de alguns dos indicadores de qualidade do departamento, com recurso ao modelo de Donabedian sobre a articulação entre a Estrutura-Processo-Resultados. Metodologia: Foi escolhido um estudo de coorte retrospectivo. Todos os pacientes internados pela primeira vez entre 2008 e 2010 foram incluídos no estudo. Os seus processos clínicos e a base de dados do hospital onde são registados todos os contactos que estes tiveram com os profissionais de saúde mental foram revistos de forma a obter dados sociodemográficos e clínicos, durante o período do estudo e após a alta. Os instrumentos utilizados foram o WHO-ICMHC (Classificação Internacional de Cuidados de Saúde Mental), para caracterizar o Departamento, o AIESMP (Avaliação Inicial de Enfermagem em Saúde Mental e Psiquiatria) para recolha dos dados sociodemográficos, e o VSSS (Escala de Satisfação com os Serviços de Verona) de forma a avaliar a satisfação dos pacientes em relação aos cuidados recebidos. A análise estatística incluiu a análise descritiva, quantitativa e qualitativa dos dados. Resultados: As Unidades Funcionais do Departamento revelaram níveis elevados de articulação e consistência com as necessidades de cuidados psiquiátricos e reabilitação psicossocial dos pacientes. Os 543 pacientes admitidos pela primeira vez eram maioritariamente (56.9%) mulheres, caucasianas (81.2%), com diagnóstico de perturbações do humor (66.3%), internadas voluntariamente (59.7%), e uma idade média de 45.1 anos. Estas eram significativamente mais velhas, mais frequentemente empregadas, casadas/coabitar e tinham uma prevalência mais elevada de perturbações do humor, comparativamente aos homens. O internamento compulsivo era mais significativo nos homens (54.7%). A taxa de abandono no pós-alta (4.2%) e a taxa de reinternamentos (2.9%) na quinzena após a alta revelaram-se inferiores aos padrões na literatura internacional. De forma global, a satisfação dos pacientes com os cuidados de saúde mental foi positiva. Conclusões: Os cuidados prestados mostraram-se eficazes, adaptados e baseados nas necessidades e problemas específicos dos pacientes. A continuidade e a abrangência de cuidados foram difundidos e mantidos ao longo do processo de cuidados. Este Departamento pode ser considerado um exemplo de como proporcionar tratamento digno e eficiente, e uma referência para futuros serviços de psiquiatria.-------------- ABSTRACT: Mental health disorders are common, universal, and associated with heavy personal, family, social and economic burden. Mental health services should be aimed at adequately addressing patients’ and families’ needs at clinical and social level. The current study was carried out at a time of great transformation in the health and mental health systems in Portugal, in a Psychiatric Department developed taking in consideration the WHO principles. The objectives included characterizing: 1) the Psychiatric Department’s different units; 2) the patients admitted for the first time to the inpatient unit; 3) their use of community mental health services after discharge; and 4) assessing some of the department’s quality indicators, with resource to Donabedian’s Structure-Process-Outcome model. Methodology: A retrospective cohort design was chosen. All the firstly admitted patients in the period between 2008 and 2010 were included in the study. Their clinical records and the hospital’s database which registers all of the contacts the patients had with the mental health professionals during the study period, were reviewed to retrieve sociodemographic and clinical data and information on follow-up. The instruments used were the WHO International Classification of Mental Health Care (ICMHC) to characterize the department, the Initial Nurses’ Assessment in Mental Health and Psychiatry (AIESMP) for patients’ sociodemographic data, and the Verona Service Satisfaction Scale (VSSS) to assess patients’ satisfaction with care received. Statistical analysis included descriptive, quantitative and qualitative analysis of the data. Results: The Department’s Functional units revealed high levels of articulation, and were consistent with patients’ needs for psychiatric care and psychosocial rehabilitation. The 543 patients firstly admitted were mainly (56.9%) female, Caucasian (81.2%), diagnosed with mood disorders (66.3%), voluntarily admitted (59.7%), and with a mean age of 45.1 years. Female patients were significantly older, more frequently employed, married/cohabiting and had a higher prevalence of mood disorders when compared to males. Involuntary admission was more significant in males (54.7%). Dropout rates during follow-up (4.2%) and readmission rates (2.9%) in the fortnight following discharge were lower than standards in international literature. Overall patients’ satisfaction with mental health care was positive. Conclusions: The care delivered was effective, adapted and based on the patients’ specific needs and problems. Continuity and comprehensiveness of care was endorsed and maintained throughout the care process. This department may be considered an example of both humane and effective treatment, and a reference for future psychiatric care.

Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic.

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles.

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.

Universal banks and corporate control: Evidence from the global syndicated loan market

We investigate the effects of bank control over borrower firms whether by representation on boards of directors or by the holding of shares through bank asset management divisions. Using a large sample of syndicated loans, we find that banks are more likely to act as lead arrangers in loans when they exert some control over the borrower firm. Bank-firm governance links are associated with higher loan spreads during the 2003-2006 credit boom, but lower spreads during the 2007-2008 financial crisis. Additionally, these links mitigate credit rationing effects during the crisis. The results are robust to several methods to correct for the endogeneity of the bank- firm governance link. Our evidence, consistent with intertemporal smoothing of loan rates, suggests there are costs and benefits from banks’ involvement in firm governance.

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.

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.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Triagem universal de alunos em risco de apresentarem dificuldades de aprendizagem específicas na leitura: um estudo quantitativo no 3º ano do 1º Ciclo do Ensino Básico

Doutoramento em Estudos da Criança (área de especialização em Educação Especial).

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.