Proposta de aplicação web para análise de dados abertos usando um banco de dados orientado a grafos

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.O presente trabalho apresenta o desenvolvimento de uma aplicação Web cliente, a Ana Lisa, para inserir e visualizar dados em um formato de grafo. Seu principal objetivo é trazer um modo de interpretar os dados de forma mais clara, a fim de criar relações entre as entidades executando um processo de investigação para o melhoramento do entendimento dos dados. Esse software permite, a partir de um arquivo CSV (como uma forma de estruturar os dados de entrada), inserir as entidades em um banco de dados orientado a grafos e criar os relacionamentos entre elas, utilizando a ontologia como uma maneira de se estabelecer a semântica do que se está sendo visualizado. Para saber qual seria o banco de dados orientado a grafo escolhido no trabalho, foi feita uma comparação entre o Neo4j e o OrientDB, onde se estabeleceu suas vantagens e suas características para guiar a opção de qual SGBD usar. No seu desenvolvimento, utilizou-se bibliotecas baseadas em JavaScript e CSS de visualização, de construção de interface e de transformação de dados. Por fim, a Usabilidade foi empregada na implantação do software para garantir uma experiência do usuário adequada aos objetivos da Ana Lisa.The present work shows the development of a client-side Web application to upload and visualize data in a graph format. The name of the software is Ana Lisa. Its main purpose is to provide a way of interpreting data more clearly to create relationships between the input entities performing a process of research to improve the data understanding. This software allows, from a CSV file (as a way of structuring the input data), to insert entities in a graph database and create the relationships between them, using ontology concepts to establish the Semantics of what is being visualized. To choose a graph database that best fits this job, a comparison was made between Neo4j and OrientDB, to determine their advantages and their characteristics. In its development, this work used libraries based on JavaScript and CSS for visualization, interface construction and data transformation. Finally, Usability played an important role in the software development process to ensure a user experience appropriate to Ana Lisa's goals

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

<i>Cryptosporidium</i> spp. and <i>Giardia</i> spp. in feces and water and the associated exposure factors on dairy farms

<div><p>The aims of this study were to verify the prevalence of <i>Cryptosporidium</i> spp. and <i>Giardia</i> spp. in animal feces and drinking water on dairy farms and to identify a possible relation between the exposure factors and the presence of these parasites. Fecal samples from cattle and humans and water samples were collected on dairy farms in Paraná, Brazil. Analysis of (oo)cysts in the feces was performed by the modified Ziehl-Neelsen staining and centrifugal flotation in zinc sulfate. Test-positive samples were subjected to nested PCR amplification of <i>the 18SSU</i> ribosomal RNA gene for identification of <i>Cryptosporidium</i> and <i>Giardia</i> and of the <i>gp60</i> gene for subtyping of <i>Cryptosporidium</i>. Microbiological analysis of water was carried out by the multiple-tube method and by means of a chromogenic substrate, and parasitological analysis was performed on 31 samples by direct immunofluorescence and nested PCR of the genes mentioned above. Identification of the species of <i>Cryptosporidium</i> was performed by sequencing and PCR with analysis of restriction fragment length polymorphisms. The prevalence of <i>Giardia</i> and <i>Cryptosporidium</i> was higher in calves than in adults. Among the samples of cattle feces, <i>Cryptosporidium parvum</i> was identified in 41 (64%), <i>C</i>. <i>ryanae</i> in eight (12.5%), <i>C</i>. <i>bovis</i> in four (6.3%), <i>C</i>. <i>andersoni</i> in five (7.8%), and a mixed infection in 20 samples (31.3%). These parasites were not identified in the samples of human feces. Thermotolerant coliform bacteria were identified in 25 samples of water (45.5%). <i>Giardia duodenalis</i> and <i>C</i>. <i>parvum</i> were identified in three water samples. The <i>gp60</i> gene analysis of <i>C</i>. <i>parvum</i> isolates revealed the presence of two strains (IIaA20G1R1 and IIaA17G2R2) in the fecal samples and one (IIaA17G2R1) in the water samples. The presence of coliforms was associated with the water source, structure and degradation of springs, rain, and turbidity. The prevalence of protozoa was higher in calves up to six months of age. <i>C</i>. <i>parvum</i> and <i>G</i>. <i>duodenalis</i> were identified in the water of dairy farms, as were thermotolerant coliforms; these findings point to the need for guidance on handling of animals, preservation of water sources, and water treatment.</p></div

Variables with a statistically significant association with the presence of cysts of <i>Giardia</i> spp. and/or oocysts of <i>Cryptosporidium</i> spp. in fecal samples from 937 heads of dairy cattle in Paraná, Brazil, from 2012 to 2014.

<p>Variables with a statistically significant association with the presence of cysts of <i>Giardia</i> spp. and/or oocysts of <i>Cryptosporidium</i> spp. in fecal samples from 937 heads of dairy cattle in Paraná, Brazil, from 2012 to 2014.</p

Springhead, turbidity, bacteriological parameters of water samples positive for <i>Cryptosporidium</i> or <i>Giardia</i> species identified by genetic sequencing, and rainfall data 24 and 48 h prior to collection on four dairy farms in Paraná, Brazil, in 2014.

<p>Springhead, turbidity, bacteriological parameters of water samples positive for <i>Cryptosporidium</i> or <i>Giardia</i> species identified by genetic sequencing, and rainfall data 24 and 48 h prior to collection on four dairy farms in Paraná, Brazil, in 2014.</p

Variables with a statistically significant association with the presence of thermotolerant coliforms in 124 water samples from 55 dairy farms in Paraná, Brazil, from 2012 to 2014.

<p>Variables with a statistically significant association with the presence of thermotolerant coliforms in 124 water samples from 55 dairy farms in Paraná, Brazil, from 2012 to 2014.</p

Prevalence of <i>Giardia</i> spp and <i>Cryptosporidium</i> spp in feces of cattle from 55 dairy farms in Paraná, Brazil, from 2012 to 2014.

<p>Prevalence of <i>Giardia</i> spp and <i>Cryptosporidium</i> spp in feces of cattle from 55 dairy farms in Paraná, Brazil, from 2012 to 2014.</p