Antioxidant activity and acute toxicity of Neoglaziovia variegata (Bromeliaceae)

Antioxidant activities of Neoglaziovia variegata were evaluated by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging and β-carotene-linoleic acid bleaching and was compared with ascorbic acid, butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT). The total phenolics content of the extracts was determined by the Folin-Ciocalteu method. Total flavonoid was also determined. The most significant total phenolic content was of 543.50 ± 9.38 mg of gallic acid equivalent/g for ethyl acetate extract (AcOEt), which presented the best antioxidant activity (IC50 5.08 ± 0.20 μg/ml) for DPPH scavenging. The acute toxicity of Nv-EtOH was performed 2.0 g/kg intraperitoneally and 5.0 g/kg orally in mice. No mortality and no toxicity signs were observed, indicating low toxicity of the extract. Blood was removed after 14 days for laboratory analysis of hematological and biochemical parameters. Alterations of aspartate aminotransferase (AST) and creatinine were observed. The data obtained showed that the doses induced microscopic alterations in the liver and kidney. In conclusion, the Nv-EtOH can be considered of low toxicity.Keywords: Antioxidant activity, acute toxicity, Neoglaziovia variegata, Bromeliacea

“Só a Ipanema Tem!”: uma análise dos aspectos visuais da campanha publicitária Sandálias Ipanema

O presente artigo tem por objetivo analisar o processo persuasivo imagético presente em uma campanha publicitária da marca das sandálias Ipanema, veiculada somente em mídia impressa. A metodologia de análise guiou-se pelos estudos teóricos de Martine Joly (1996), sendo a abordagem do assunto complementada por revisão bibliográfica. Todo o referencial teórico trazido neste texto intencionou guiar nosso olhar na busca da compreensão das peças publicitárias escolhidas para análise, como também para compreensão de todo o processo de construção das mensagens publicitárias

An Overview of the Environmental Applicability of Vermicompost: From Wastewater Treatment to the Development of Sensitive Analytical Methods

The use of vermicompost (humified material) for treating wastewaters, remediating polluted soils, improving agricultural productivity, protecting crop production, and developing sensitive analytical methods is reviewed here, covering the past 17 years. The main advantages of vermicompost, considering all applications covered in this paper, comprise (i) easy acquisition, (ii) low costs, (iii) structural, chemical, and biological characteristics responsible for exceptional adsorptive capacities as well as pollutant degradation, and (iv) the promotion of biocontrol. Specifically, for wastewater decontamination, a considerable number of works have verified the adsorption of toxic metals, but the application of vermicompost is still scarce for the retention of organic compounds. Problems related to the final disposal of enriched vermicompost (after treatment steps) are often found, in spite of some successful destinations such as organic fertilizer. For decontaminating soils, the use of vermicompost is quite scarce, mainly for inorganic pollutants. In agricultural productivity and biocontrol, vermicompost imparts remarkable benefits regarding soil aggregation, plant nutrition, and the development of beneficial microorganisms against phytopathogens. Finally, the use of vermicompost in sensitive analytical methods for quantifying toxic metals is the newest application of this adsorbent

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