Sources of medicines for hypertension and diabetes in Brazil: telephone survey results from brazilian state capitals and the Federal District, 2011

Analisar as diferenças entre diabéticos e hipertensos em relação ao tratamento medicamentoso e suas fontes de obtenção. Trata-se de estudo transversal com dados do VIGITEL, realizado em 2011 nas capitais brasileiras. Cerca de 72% dos 15.027 hipertensos e 78,2% dos 4.083 diabéticos estavam em tratamento medicamentoso; 45,8% dos hipertensos obtiveram medicamento nas unidades de saúde públicas, 15,9% no Farmácia Popular e 38,3% em drogarias/farmácias e outras fontes. Entre os diabéticos, encontrou-se 54,4%; 16,2%; e 29,4%, respectivamente. Nas unidades de saúde os percentuais foram mais elevados entre os menos escolarizados, cor de pele preta ou parda e sem plano privado de saúde, e as prevalências de obtenção na Farmácia Popular, drogarias/farmácias e outras fontes foram mais elevadas entre os mais escolarizados, cor de pele branca e com plano privado. O acesso às diferentes fontes de medicamentos apresentou disparidades entre as regiões e capitais brasileiras e entre os segmentos sociais da população322113This study aimed to analyze differences between patients with diabetes and hypertension in drug treatment and their sources for obtaining medication. This was a cross-sectional study with data from the VIGITEL telephone survey in 2011 in Brazil’s state capitals and Federal District. Some 72% of the 15,027 hypertensive patients and 78.2% of the 4,083 diabetics were on medication; 45.8% of the hypertensive patients obtained their medications from public health units, 15.9% from the Popular Pharmacy program, and 38.3% from drugstores, pharmacies, and other sources. The rates among diabetics were 54.4%, 16.2%, and 29.4%, respectively. In the public health units the percentages were highest among individuals with less schooling, black or brown skin, and without private health plans, while the percentages in the Popular Pharmacy program and drugstores/pharmacies and other sources were higher among individuals with more schooling, white skin, and private health plans. Access to different sources of medicines showed disparities between Brazil’s regions and state capitals and between social segments of the populatio

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

Relationship between heterosis and genetic divergence for phosphorus use efficiency and its components in tropical maize Relação entre heterose e divergência genética para a eficiência no uso do fósforo e seus componentes em milho tropical

The objective of this study was to determine the relationship between heterosis and genetic divergence for phosphorus use efficiency (PUE) in tropical maize. It was used two groups of genitors, each consisting of seven lines, contrasting with each other in the nitrogen and phosphorus use efficiency. It was obtained 41 hybrid combinations between these groups, which were evaluated in low phosphorus. Randomized complete block design with two replications was used. For obtaining the components of variance and the breeding values were used REML/BLUP method. In the genotyping of the parental lines were used 80 microsatellite markers. Through the correlation between genetic distance obtained by the markers and specific combining ability it was not possible to determine with accuracy by molecular markers, the crosses that produced hybrids with the highest heterosis for PUE. Thus, is possible to conclude that there is no relationship between genetic divergence and heterosis for phosphorus use efficiency and its components in tropical maize.<br>O objetivo deste estudo foi determinar a relação entre divergência genética e heterose para a eficiência no uso de fósforo (EUP) em milho tropical. Utilizaram-se dois grupos de genitores, compostos de sete linhagens cada, contrastantes entre si para as eficiências no uso de nitrogênio e fósforo. Foram obtidas 41 combinações híbridas entre esses grupos, as quais foram avaliadas em baixo fósforo. Usou-se o delineamento em blocos ao acaso com duas repetições. A obtenção dos componentes de variância e valores genéticos foi realizada via REML/BLUP e, para genotipagem das linhagens genitoras, foram utilizados 80 marcadores microssatélites. Através da correlação entre a distância genética obtida pelos marcadores e a capacidade específica de combinação, observou-se não ser possível a determinação com acurácia, via marcadores moleculares, dos cruzamentos que produziram os híbridos com as maiores heteroses para EUP. Com isso, é possível concluir que não há relação entre divergência genética e heterose para eficiência no uso de fósforo e seus componentes em milho tropical