AUTOMATIC METHOD FOR GLAUCOMA CLASSIFICATION USING TEXTURE ANALYSIS, XGBOOST AND GRID SEARCH

Glaucoma is an irreversible pathology, generated by increased intraocular pressure. Early detection is critical and can pre- vent total vision loss. Clinical examinations are commonly used to detect the disease. Still, the time and cost of identi- fication is quite high. This paper presents a computational methodology that aims to assist specialists in the discov- ery of glaucoma through Computer Vision techniques. The proposed methodology consists in the application of several texture descriptors combined with a parameter optimiza- tion done through Grid search with the XGBoost classifier. A result was obtained with accuracy of 82.37% and ROC of 82.08%

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

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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Associação entre o perfil clínico e sociodemográficos das gestantes com pré-eclâmpsia

Submitted by JÉSSICA SANTOS ([email protected]) on 2018-07-17T15:29:33Z No. of bitstreams: 1 ve_João_Neto_et_al_2018.pdf: 243235 bytes, checksum: d647ae2778a588603763a6a252ea7303 (MD5)Approved for entry into archive by JÉSSICA SANTOS ([email protected]) on 2018-07-17T15:47:47Z (GMT) No. of bitstreams: 1 ve_João_Neto_et_al_2018.pdf: 243235 bytes, checksum: d647ae2778a588603763a6a252ea7303 (MD5)Made available in DSpace on 2018-07-17T15:47:47Z (GMT). No. of bitstreams: 1 ve_João_Neto_et_al_2018.pdf: 243235 bytes, checksum: d647ae2778a588603763a6a252ea7303 (MD5) Previous issue date: 2018Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil / Fundação Oswaldo Cruz. Escritório Técnico Regional do Piauí. Teresina, PI, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil / Universidade de Pernambuco. Recife, PE, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Faculdade de Ciências e Tecnologia do Maranhão. Caxias, MA, Brasil.Objetivo: O estudo propôs delinear e correlacionar o perfil clínico e sociodemográficos de gestantes com pré-eclâmpsia em uma maternidade e investigar as complicações associadas a pré-eclâmpsia. Metódos: Tratou-se de uma pesquisa de campo, do tipo documental, exploratório, descritiva com abordagem quantitativa. A população foi composta por 97 prontuários/ficha clínica de pacientes com pré-eclâmpsia em uma maternidade pública, no período de março a novembro de 2016. Resultados: Os resultados revelaram que a maioria das mulheres eram solteiras, com recorte etário entre 21 a 35 anos, parda, 2º grau completo, multíparas, hipertensas e com presença de proteinuria. Quanto ao diagnóstico admissional 62,9% apresentaram história de hipertensão arterial, sendo que em 70,1% estavam com hipertensão moderada. Na análise dos sinais e sintomas, predominou a dor epigástrica, distúrbio visual e cefaleia, em 42,3%. Em relação ao período gestacional, 75,3% se encontravam com idade gestacional entre 37 a 41 semanas com predomínio de partos pre-maturos (11,3%) Quanto aos exames de proteinuria grande parte das gestante apresentaram proteína na urina (91,8%). Conclusão: As gestantes com pré-eclâmpsia apresentou um quadro de hipertensão arterial, proteinúria, com distúrbio visual, com dor epigástrica configurando um importante quadro de saúde com potenciais de complicações para seus portadores