Diagnósticos de enfermagem identificados em pacientes transplantados renais de um hospital de ensino

O transplante de órgãos constitui uma última esperança de sucesso na recuperação de doenças em estágio terminal. A Enfermagem participa de forma atuante desde a captação do órgão até as consultas no ambulatório após o transplante, exigindo pessoal capacitado. Esse trabalho tem por objetivos identificar os principais diagnósticos de enfermagem em pacientes transplantados renais, em uma unidade de internação de um hospital de ensino no município de Fortaleza-Ce, e propor intervenções baseadas nas reais necessidades desses pacientes. Estudo descritivo-exploratório, de natureza qualitativa. A população foi constituída por pacientes transplantados em pós-operatório mediato e com complicações pós-transplante, sendo a amostra de doze pacientes. A coleta dos dados ocorreu através da aplicação de um histórico de enfermagem e para a análise foi abordada a seqüência das etapas do PE, utilizando-se a taxonomia II da NANDA. Foram identificados dezessete diagnósticos de enfermagem, o que permitiu a elaboração de intervenções baseadas nas reais necessidades desses pacientes. Acreditamos que, as intervenções sugeridas fundamentadas com referencial teórico, serão de suma importância para a prática dos enfermeiros que trabalham nessa área, e principalmente no hospital em questã

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,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

QTL mapping for protein content in soybean cultivated in two tropical environments

The objectives of this study were to detect quantitative trait loci (QTL) for protein content in soybean grown in two distinct tropical environments and to build a genetic map for protein content. One hundred eighteen soybean recombinant inbred lines (RIL), obtained from a cross between cultivars BARC 8 and Garimpo, were used. The RIL were cultivated in two distinct Brazilian tropical environments: Cascavel county, in Paraná, and Viçosa county, in Minas Gerais (24º57'S, 53º27'W and 20º45'S, 42º52'W, respectively). Sixty-six SSR primer pairs and 65 RAPD primers were polymorphic and segregated at a 1:1 proportion. Thirty poorly saturated linkage groups were obtained, with 90 markers and 41 nonlinked markers. For the lines cultivated in Cascavel, three QTL were mapped in C2, E and N linkage groups, which explained 14.37, 10.31 and 7.34% of the phenotypic variation of protein content, respectively. For the lines cultivated in Viçosa, two QTL were mapped in linkage groups G and #1, which explained 9.51 and 7.34% of the phenotypic variation of protein content. Based on the mean of the two environments, two QTL were identified: one in the linkage group E (9.90%) and other in the group L (7.11%). In order for future studies to consistently detect QTL effects of different environments, genotypes with greater stability should be used

Mapeamento de QTL quanto ao conteúdo de proteína em soja cultivada em dois ambientes tropicais

The objectives of this study were to detect quantitative trait loci (QTL) for protein content in soybean grown in two distinct tropical environments and to build a genetic map for protein content. One hundred eighteen soybean recombinant inbred lines (RIL), obtained from a cross between cultivars BARC 8 and Garimpo, were used. The RIL were cultivated in two distinct Brazilian tropical environments: Cascavel county, in Paraná, and Viçosa county, in Minas Gerais (24º57'S, 53º27'W and 20º45'S, 42º52'W, respectively). Sixty-six SSR primer pairs and 65 RAPD primers were polymorphic and segregated at a 1:1 proportion. Thirty poorly saturated linkage groups were obtained, with 90 markers and 41 nonlinked markers. For the lines cultivated in Cascavel, three QTL were mapped in C2, E and N linkage groups, which explained 14.37, 10.31 and 7.34% of the phenotypic variation of protein content, respectively. For the lines cultivated in Viçosa, two QTL were mapped in linkage groups G and #1, which explained 9.51 and 7.34% of the phenotypic variation of protein content. Based on the mean of the two environments, two QTL were identified: one in the linkage group E (9.90%) and other in the group L (7.11%). In order for future studies to consistently detect QTL effects of different environments, genotypes with greater stability should be used.Os objetivos deste trabalho foram detectar QTL relativos ao conteúdo de proteína, em soja cultivada em dois ambientes tropicais divergentes, e construir um mapa genético para o conteúdo de proteína em genótipos adaptados a condições tropicais. Foram usadas 118 linhagens recombinantes endogâmicas de soja, obtidas do cruzamento entre as cultivares BARC 8 e Garimpo. A população de linhagens recombinantes endogâmicas foi cultivada em dois ambientes contrastantes: Cascavel, PR, e Viçosa, MG (24º57'S, 53º27'W; e 20º45'S, 42º52'W, respectivamente). Sessenta e seis pares de iniciadores SSR e 65 iniciadores RAPD apresentaram fragmentos polimórficos que segregaram à proporção de 1:1. Foram obtidos 30 grupos de ligação pouco saturados, com 90 marcadores, além de 41 marcas não ligadas. Para as famílias cultivadas em Cascavel, três QTL foram mapeados nos grupos de ligação C2, E, e N, que explicaram 14,37, 10,31 e 7,34% da variação fenotípica do conteúdo de proteína, respectivamente. Para as famílias cultivadas em Viçosa, dois QTL foram mapeados nos grupos de ligação G e #1, que explicaram 9,51 e 7,34% da variação fenotípica do conteúdo de proteína. Com base na média dos dois ambientes, dois QTL foram identificados: um no grupo de ligação E (9,90%) e outro no grupo L (7,11%). Genótipos com maior estabilidade devem ser uados em trabalhos futuros, para a detecção de QTL com efeitos consistentes, em diferentes ambientes

Distribuição de nutrientes e sintomas visuais de deficiência de boro em raízes de coqueiro-anão verde

As raízes, geralmente, são os primeiros órgãos das plantas a apresentarem sintomas de deficiência de B. Plantas de grande porte são difíceis de serem manipuladas experimentalmente a fim de diagnosticar os efeitos das deficiências minerais em seus sistemas radiculares. Neste experimento, objetivou-se elucidar o efeito da deficiência de B na formação de raízes de diferentes diâmetros e na acumulação de nutrientes, bem como caracterizar sintomas visuais da deficiência de B em raízes de coqueiro-anão verde. Foram aplicados os seguintes tratamentos: solução nutritiva completa (+B) e solução nutritiva sem B (-B), distribuídos em delineamento inteiramente casualizado, com seis repetições. A unidade experimental constou de uma planta em um vaso plástico com 90 L de areia de praia purificada. Até o 60º dia após o transplante (4/3/2006), todas as plantas receberam solução nutritiva completa. No 61º dia depois do transplante, aplicaram-se os tratamentos citados. Decorridos 513 dias da indução da deficiência, coletaram-se as raízes (1/6/2007); após o processo de lavagem, as raízes foram então separadas em três diâmetros: finas (< 1 mm), médias (1 a 5 mm) e grossas (> 5 mm). Depois da separação, imergiram-se as raízes em água várias vezes para retirada da areia e, finalmente, elas foram lavadas em água desionizada. Após secagem em estufa, determinou-se a massa seca de cada tipo de raiz e, posteriormente, os teores de N, P, K, Ca, Mg, S, B, Cu, Fe, Mn e Zn. Os resultados indicaram que os maiores teores de N, P, Ca, Mg, S, B, Cu, Fe, Mn e Zn foram encontrados nas raízes finas do coqueiro em ambos os tratamentos, porém os de K foram maiores nas raízes grossas. A deficiência de B aumentou os teores de N, P e K em todas as raízes, os teores de Mg, S, Cu e Zn nas raízes finas, mas não afetou os teores de Ca, Fe e Mn. A deficiência de B reduziu a produção de raízes totais e finas em 29,7 e 48,3 %, respectivamente, e promoveu o engrossamento e escurecimento das raízes com ramificações curtas; as pontas das raízes necrosaram, causando superbrotamento radicular