Ability of body mass index to predict abnormal waist circumference : receiving operating characteristics analysis.

Body mass index (BMI) and waist circumference (WC) are the most used anthropometric measures to identify obesity. While BMI is considered to be a simple and accurate estimate of general adiposity, WC is an alternative surrogate measure of visceral obesity. However, WC is subject to significant inter-examiner variation. The aim of the present study was to correlate BMI and WC measures in a group of Brazilian adults to determine the most accurate BMI values for predicting abnormal WC

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

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

Produção de melão rendilhado em sistema hidropônico com rejeito da dessalinização de água em solução nutritiva Melon yield in a hydroponic system with wastewater from desalination plant added in the nutrient solution

Desenvolveu-se esta pesquisa visando o aproveitamento do rejeito da dessalinização da água no cultivo hidropônico do meloeiro. O experimento foi conduzido no Departamento de Ciências Ambientais da UFERSA, localizado no município de Mossoró, RN, em vasos com substrato de fibra de coco sob condições protegidas. O delineamento experimental utilizado foi blocos ao acaso, com 3 repetições cujos tratamentos corresponderam a cinco níveis de salinidade da solução nutritiva obtidos com ou sem a necessidade de diluição do rejeito da dessalinização da água (2,1; 3,6; 4,9; 6,0 e 7,0 dS m-1). Avaliaram-se: altura de plantas, diâmetro de colo, área foliar, rendimento total e comercial de frutos por planta e, ainda, peso médio de frutos comerciais e totais. Em geral, as variáveis estudadas decresceram linearmente com o incremento da salinidade da solução nutritiva demonstrando que a salinidade da solução com água de rejeito reduz a disponibilidade de água para as plantas, devido ao efeito osmótico.<br>To use of waster water from desalting in the hydroponic cultivation, an experiment was carried out at the Department of Environmental Science of the Universidade Federal Rural do Semi-Árido-UFERSA, in the municipal district of Mossoró-RN, in pots with substrates of coconut fiber under greenhouse conditions. The experimental design was in randomized blocks, with three repetitions. Treatments were composed of five levels of salinity of the nutrient solution obtained with, and without, dilution of the waster water from desalting (2.1, 3.6, 4.9, 6.0 and 7.0 dS m-1). The variables plant height, stem diameter, leaf area, mean weight of fruit and total and marketable yield were detemined. In general, the variables decreased linearly with the increase of the salinity of the nutrient solution, showing that the salinity with waster water from desalting reduces the absorption of water by the plants due to the osmotic effect