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

Genome of Herbaspirillum seropedicae Strain SmR1, a Specialized Diazotrophic Endophyte of Tropical Grasses

The molecular mechanisms of plant recognition, colonization, and nutrient exchange between diazotrophic endophytes and plants are scarcely known. Herbaspirillum seropedicae is an endophytic bacterium capable of colonizing intercellular spaces of grasses such as rice and sugar cane. The genome of H. seropedicae strain SmR1 was sequenced and annotated by The Paraná State Genome Programme—GENOPAR. The genome is composed of a circular chromosome of 5,513,887 bp and contains a total of 4,804 genes. The genome sequence revealed that H. seropedicae is a highly versatile microorganism with capacity to metabolize a wide range of carbon and nitrogen sources and with possession of four distinct terminal oxidases. The genome contains a multitude of protein secretion systems, including type I, type II, type III, type V, and type VI secretion systems, and type IV pili, suggesting a high potential to interact with host plants. H. seropedicae is able to synthesize indole acetic acid as reflected by the four IAA biosynthetic pathways present. A gene coding for ACC deaminase, which may be involved in modulating the associated plant ethylene-signaling pathway, is also present. Genes for hemagglutinins/hemolysins/adhesins were found and may play a role in plant cell surface adhesion. These features may endow H. seropedicae with the ability to establish an endophytic life-style in a large number of plant species

Educomunicação, Transformação Social e Desenvolvimento Sustentável

Esta publicação apresenta os principais trabalhos dos GTs do II Congresso Internacional de Comunicação e Educação nos temas Transformação social, com os artigos que abordam principalmente Educomunicação e/ou Mídia-Educação, no contexto de políticas de diversidade, inclusão e equidade; e, em Desenvolvimento Sustentável os artigos que abordam os avanços da relação comunicação/educação no contexto da educação ambiental e desenvolvimento sustentável

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

Healthcare and unhealthy eating among children aged under two years: data from the National Health Survey, Brazil, 2013

Abstract Objectives: to examine indicators relating to access to child health care and markers of unhealthy patterns of eating in Brazilian children aged under two years and to set these in the context of the National Health System 's current child healthcare agenda. Methods: a descriptive cross-sectional study using data from the 2013 National Health Survey. Prevalences and confidence intervals of 95% (CI95%) were estimated for the total population, Brazilian macroregions and urban or rural location of household. Results: a first medical consult before the seventh day of life was reported in only 28.7% of children. Supervision of growth and child development was carried out primarily at basic health units (57.2%; CI95%: 54.8-59.6). Theneonatal screening, newborn hearing screening and red reflex tests were conducted with a frequency of 70.8% (CI95%: 69.0-72.7), 56.0% (CI95%: 53.8-58.3) and 51.1% (CI95%: 48.9-53.3), respectively. Disparities were found in preventive health care, with lower access among children living in rural households or in the North and Northeast regions. Soda consumption was reported for 32.3% and consumption of biscuits or cake for 60.8% of children,indicating premature introduction of unhealthy foods into the child's diet. Conclusions: the findingspoint to disparities in access to child healthcare and a high prevalence of unhealthy eating habits in infancy

Application Of A Model Based On Fuzzy Logic For Evaluating Nursing Diagnostic Accuracy Of Students.

To describe a model for assessing nursing diagnostic accuracy and its application to undergraduate students, comparing students' performance according to the course year. This model, based on the theory of fuzzy sets, guides a student through three steps: (a) the student must parameterize the model by establishing relationship values between defining characteristic/risk factors and nursing diagnoses; (b) presentation of a clinical case; (c) the student must define the presence of each defining characteristic/risk factors for the clinical case. Subsequently, the model computes the most plausible diagnoses by taking into account the values indicated by the student. This gives the student a performance score in comparison with parameters and diagnoses that were previously provided by nursing experts. These nursing experts collaborated with the construction of the model indicating the strength of the relationship between the concepts, meaning, they parameterized the model to compare the student's choice with the expert's choice (gold standard), thus generating performance scores for the student. The model was tested using three clinical cases presented to 38 students in their third and fourth years of the undergraduate nursing course. Third year students showed superior performance in identifying the presence of defining characteristic/risk factors, while fourth year students showed superior performance in the diagnoses by the model. The Model for Evaluation of Diagnostic Accuracy Based on Fuzzy Logic applied in this study is feasible and can be used to evaluate students' performance. In this regard, it will open a broad variety of applications for learning and nursing research. Despite the ease in filling the printed questionnaires out, the number of steps and fields to fill in may explain the considerable number of questionnaires with incorrect or missing data. This was solved in the digital version of the questionnaire. In addition, in more complex cases, it is possible that an expert opinion can lead to a wrong decision due to the subjectivity of the diagnostic process.82875-8

Prevalência de diabetes autorreferido no Brasil: resultados da Pesquisa Nacional de Saúde 2013

OBJETIVO:estimar a prevalência de diabetes autorreferido no Brasil e descrevê-la segundo características sociodemográficas.MÉTODOS:estudo descritivo da prevalência de diagnóstico médico de diabetes em adultos (≥18 anos) sobre dados da Pesquisa Nacional de Saúde (PNS), inquérito domiciliar realizado no Brasil em 2013.Resultados:foram entrevistados 60.202 moradores; a prevalência da doença reportada foi de 6,2% (IC95% 5,9-6,6), maior nas mulheres (7,0%; IC95%6,5-7,5) do que nos homens (5,4%; IC95% 4,8-5,9), e entre os moradores da área urbana (6,5%; IC95% 6,1-6,9) do que da área rural (4,6%; IC95% 4,0-5,2); estimou-se um total de aproximadamente 9 milhões de pessoas com diabetes no país, cerca de 3,5 milhões delas com 65 anos ou mais de idade.Conclusão:os resultados da PNS foram consistentes com outras pesquisas realizadas e mostram um contingente populacional elevado de pessoas com o diagnóstico da doença no país, especialmente nas áreas urbanas