Construção de modelos físicos dos orbitais atômicos e moleculares para o ensino de quí

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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

O uso das TICs como recurso didático no processo de ensino-aprendizado de língua portuguesa: implicações dessa interface em sala de aula

A estratégia pedagógica mais utilizada atualmente para despertar o interesse dos alunos pelas aulas é o uso de recursos tecnológicos. O ensino de Língua Portuguesa também tem se adaptado a essa realidade. Por esse motivo, esta pesquisa, de natureza qualitativa, tem como objetivo verificar se a inserção da Tecnologia da Informação e Comunicação como recurso didático na disciplina de Língua Portuguesa influencia a aprendizagem dos alunos. Encontros foram realizados, em ambiente pedagógico, com alunos de duas turmas de ensino médio do IFPB, Campus Campina Grande, no ano letivo de 2019. Atividades pedagógicas foram realizadas, com e sem o uso de recursos tecnológicos, contemplando os conteúdos de leitura e interpretação de crônicas e a leitura, interpretação e produção de microcontos. O principal resultado obtido foi o de que o uso de recursos tecnológicos pode, sim, influenciar o processo pedagógico assim como o desempenho acadêmico dos estudantes, de forma positiva. Além disso, os alunos ficaram mais atentos e participativos nas aulas. Houve, inclusive, uma melhoria na relação aluno/aluno em função das atividades propostas

High prevalence of atypical virulotype and genetically diverse background among Pseudomonas aeruginosa isolates from a referral hospital in the Brazilian Amazon

This research was supported by funding from Fundac¸ão de Amparo à Pesquisa do Pará / Universidade do Estado do Pará (FAPESPA/UEPA) [Cooperation grant N˚004/2019], Programa Institucional de Bolsas de Iniciação Científica/ Instituto Evandro Chagas - Conselho Nacional de Desenvolvimento Científico e Tecnológico (PIBIC/ IEC-CNPq) and Instituto Evandro Chagas/ Ministério da Saúde/Secretaria de Vigilância em Saúde (IEC/MS/SVS).Universidade do Estado do Pará. Centro de Ciências Biológicas e da Saúde. Programa de Pós-graduação em Biologia Parasitária na Amazônia. Belém, PA, Brazil.Centro Universitário do Pará. Programa de Pós-graduação em Educação em Saúde. Belém, PA, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Biologia Molecular. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Biologia Molecular. Ananindeua, PA, Brasil.Universidade do Estado do Pará. Centro de Ciências Biológicas e da Saúde. Departamento de Patologia. Belém, PA, Brazil.Universidade do Estado do Pará. Centro de Ciências Biológicas e da Saúde. Programa de Pós-graduação em Biologia Parasitária na Amazônia. Belém, PA, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Biologia Molecular. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Biologia Molecular. Ananindeua, PA, Brasil.Universidade do Estado do Pará. Centro de Ciências Biológicas e da Saúde. Programa de Pós-graduação em Biologia Parasitária na Amazônia. Belém, PA, Brazil.Universidade do Estado do Pará. Centro de Ciências Biológicas e da Saúde. Programa de Pós-graduação em Biologia Parasitária na Amazônia. Belém, PA, Brazil / Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Biologia Molecular. Ananindeua, PA, Brasil.Universidade do Estado do Pará. Centro de Ciências Biológicas e da Saúde. Programa de Pós-graduação em Biologia Parasitária na Amazônia. Belém, PA, Brazil / Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Biologia Molecular. Ananindeua, PA, Brasil.Pseudomonas aeruginosa is an opportunistic pathogen causing different types of infections, particularly in intensive care unit patients. Characteristics that favor its persistence artificial environments are related to its high adaptability, wide arsenal of virulence factors and resistance to several antimicrobial classes. Among the several virulence determinants, T3SS stands as the most important due to the clinical impact of exoS and exoU genes in patient’s outcome. The molecular characterization of P. aeruginosa isolates helps in the comprehension of transmission dynamics and enhance knowledge of virulence and resistance roles in infection process. In the present study, we investigated virulence and resistance properties and the genetic background of P. aeruginosa isolated from ICUs patients at a referral hospital in Brazilian Amazon. A total of 54 P. aeruginosa isolates were characterized by detecting 19 virulence-related genes, antimicrobial susceptibility testing, molecular detection of β-lactamase-encoding genes and genotyping by MLST and rep-PCR. Our findings showed high prevalence of virulence-related markers, where 53.7% of the isolates presented at least 17 genes among the 19 investigated (P = 0.01). The rare exoS+/exoU+ cytotoxic virulotype was detected in 55.6% of isolates. Antimicrobial susceptibility testing revealed percentages of antibiotic resistance above 50% to carbapenems, cephalosporins and fluoroquinolones associated to MDR/XDR isolates. Isolates harboring both blaSPM-1 and blaOXA genes were also detected. Genotyping methods demonstrated a wide genetic diversity of strains spread among the different intensive care units, circulation of international MDR/XDR high-risk clones (ST111, ST235, ST244 and ST277) and emergence of seven novel MLST lineages. Finally, our findings highlight the circulation of strains with high virulence potential and resistance to antimicrobials and may be useful on comprehension of pathogenicity process, treatment guidance and establishment of strategies to control the spread of epidemic P. aeruginosa strains