Monitoramento de indicador de qualidade de ar por processamento de imagens de satélite

The scarcity in the number of meteorological centers for sampling in Brazil makes data on air quality scarce. The lack of frequent monitoring of air quality indicators is only noticed when it reaches levels that are harmful to health. There are several studies on air quality indicators, mainly focused on the PM2.5 indicator. Contamination by PM2.5, which is 2.5μm particulate matter, can cause heart problems, asthma in children and many respiratory problems. These risk factors demonstrate the importance of studying this indicator, since the type of particle it represents can affect the entire population of an area, impairing the quality of life and health of the inhabitants. Remote sensing is a tool that uses satellite images to calculate the conditions of various indicators, such as temperature, soil quality and air quality. In order to have a good idea of air quality, this study proposes to deepen the knowledge about remote sensing and satellite image processing, applied to the problem of estimating the amount of PM2.5 in the air. A systematic review of the approaches and methods being used to address this problem was carried out. After analyzing this review, the objective of this work was to study the functioning of systems and concepts, focusing on measurements, for evaluation and estimation. Finally, a system to predict PM2.5 was developed, and used to predict PM2.5 in São Paulo, obtaining '2 = 0.112 and RMSE = 17.15. Then, this work raises the possibility of applying the methods studied in Brazil, and a conclusion of the most current situation in Brazil, arriving at the need for the most recent meteorological studies for Brazil.A escassez no número de centros meteorológicos para amostragem no Brasil torna os dados sobre qualidade do ar escassos. A falta de acompanhamento frequente dos indicadores de qualidade do ar somente é notada quando chega a níveis nocivos à saúde. Existem diversos estudos sobre indicadores de qualidade do ar, focados principalmente no indicador de PM2,5. A contaminação por PM2,5, que é o material particulado de 2,5μm, pode causar problemas cardíacos, asma em crianças e diversos problemas respiratórios. Esses fatores de risco demonstram a importância do estudo desse indicador, visto que o tipo de partícula que ele representa pode afetar toda a população de uma área, prejudicando a qualidade de vida e saúde dos habitantes. O sensoriamento remoto é uma ferramenta que utiliza imagens de satélite para calcular as condições de diversos indicadores, como temperatura, qualidade do solo e qualidade do ar. Para termos uma boa noção da qualidade do ar, este estudo tem como proposta aprofundar o conhecimento sobre sensoriamento remoto e processamento de imagem de satélite, aplicados ao problema de estimar quantidade de PM2,5 no ar. Foi feita uma revisão sistemática das abordagens e métodos que estão sendo utilizados para esse problema. Após análise dessa revisão, o objetivo deste trabalho foi estudar o funcionamento dos sistemas existentes e conceitos, focando em métricas para avaliação e de estimativas. Por fim, um sistema para predizer PM2,5 foi desenvolvido, e utilizado na predição de PM2,5 em São Paulo, obtendo '2 = 0.112 e RMSE = 17.15. Então, este trabalho levanta a possibilidade de aplicar os métodos estudados no Brasil, e faz uma análise da situação atual, chegando a conclusão que é necessário mais estações meteorológicas para amostragem no Brasil.São Cristóvão, S

Mosquitoes infected with dengue viruses in Brazil

Dengue epidemics have been reported in Brazil since 1985. The scenery has worsened in the last decade because several serotypes are circulating and producing a hyper-endemic situation, with an increase of DHF/DSS cases as well as the number of fatalities. Herein, we report dengue virus surveillance in mosquitoes using a Flavivirus genus-specific RT-Hemi-Nested-PCR assay. The mosquitoes (Culicidae, n = 1700) collected in the Northeast, Southeast and South of Brazil, between 1999 and 2005, were grouped into 154 pools. Putative genomes of DENV-1, -2 and -3 were detected in 6 mosquito pools (3.8%). One amplicon of putative DENV-1 was detected in a pool of Haemagogus leucocelaenus suggesting that this virus could be involved in a sylvatic cycle. DENV-3 was found infecting 3 pools of larvae of Aedes albopictus and the nucleotide sequence of one of these viruses was identified as DENV-3 of genotype III, phylogenetically related to other DENV-3 isolated in Brazil. This is the first report of a nucleotide sequence of DENV-3 from larvae of Aedes albopictus

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

A inclusão escolar para pacientes com deficiência intelectual ou atraso cognitivo: School inclusion for patients with intellectual disability or cognitive delay

A educação inclusiva é fundamental para que crianças e adolescentes vivenciem ideias e experiências de ensino aprendizagem significativa, desenvolvam a autonomia e conquistem direitos de cidadania. No entanto, existem obstáculos que precisam ser compreendidos e superados e estratégias que podem ser adotadas para promover a inclusão de crianças com deficiência intelectual ou atraso cognitivo. Diante disso, este estudo tem como objetivo compreender o processo de inclusão escolar de alunos com deficiência intelectual ou atraso cognitivo. Para isso, trata-se de uma revisão sistemática de literatura, desenvolvida a partir da seleção de estudos nas bases de dados Scielo, Pubmed e BVS/Medline a partir do uso de descritores DeCS/MeSH e aplicação de critérios de inclusão e exclusão. Após a análise e interpretação dos dados, concluiu-se que, no processo de inclusão de alunos com deficiência intelectual ou atraso cognitivo no ambiente escolar, a educação inclusiva interfere positivamente na qualidade de vida desses. Para isso, destacam-se uma série de estratégias relevantes, tais como: envolvimento de escola como um todo, dos professores e da família; compreender a deficiência; valorizar os interesses e habilidades dos alunos com deficiência; estimular a autodeterminação desses e a convivência entre pessoas deficientes e não deficientes; promover a socialização por meio de jogos; utilizar atividades adaptadas; e cuidar da formação inicial e continuada dos professores, contemplando ideias sobre educação inclusiva

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Oral primary care: an analysis of its impact on the incidence and mortality rates of oral cancer

Abstract Background Oral cancer is a potentially fatal disease, especially when diagnosed in advanced stages. In Brazil, the primary health care (PHC) system is responsible for promoting oral health in order to prevent oral diseases. However, there is insufficient evidence to assess whether actions of the PHC system have some effect on the morbidity and mortality from oral cancer. The purpose of this study was to analyze the effect of PHC structure and work processes on the incidence and mortality rates of oral cancer after adjusting for contextual variables. Methods An ecological, longitudinal and analytical study was carried out. Data were obtained from different secondary data sources, including three surveys that were nationally representative of Brazilian PHC and carried out over the course of 10 years (2002–2012). Data were aggregated at the state level at different times. Oral cancer incidence and mortality rates, standardized by age and gender, served as the dependent variables. Covariables (sociodemographic, structure of basic health units, and work process in oral health) were entered in the regression models using a hierarchical approach based on a theoretical model. Analysis of mixed effects with random intercept model was also conducted (alpha = 5%). Results The oral cancer incidence rate was positively association with the proportion of of adults over 60 years (β = 0.59; p = 0.010) and adult smokers (β = 0.29; p = 0.010). The oral cancer related mortality rate was positively associated with the proportion of of adults over 60 years (β = 0.24; p < 0.001) and the performance of preventative and diagnostic actions for oral cancer (β = 0.02; p = 0.002). Mortality was inversely associated with the coverage of primary care teams (β = −0.01; p < 0.006) and PHC financing (β = −0.52−9; p = 0.014). Conclusions In Brazil, the PHC structure and work processes have been shown to help reduce the mortality rate of oral cancer, but not the incidence rate of the disease. We recommend expanding investments in PHC in order to prevent oral cancer related deaths