Full three-dimensional isotropic carpet cloak designed by quasi-conformal transformation optics

A fully three-dimensional carpet cloak presenting invisibility in all viewing angles is theoretically demonstrated. The design is developed using transformation optics and threedimensional quasi-conformal mapping. Parametrization strategy and numerical optimization of the coordinate transformation deploying a quasi-Newton method is applied. A discussion about the minimum achievable anisotropy in the 3D transformation optics is presented. The method allows to reduce the anisotropy in the cloak and an isotropic medium could be considered. Numerical simulations confirm the strategy employed enabling the design of an isotropic reflectionless broadband carpet cloak independently of the incident light direction and polarization25192351723522CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIGnão temnão temnão te

Identificação do nível de conhecimento básico de alunos da 5º serie do ensino fundamental acerca do uso consciente de água.

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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

Biotransformation of the Diterpene Ent

The diterpene ent-18,19-dihydroxytrachylobane was biotransformed for the first time by Rhizopus stolonifer, and yielded the new ent-11β,18,19-trihydroxytrachylobane derivative besides the new ent-kaur-11-ene diterpenes ent-16α,18,19-trihydroxykaur-11-ene and ent-18,19-dihydroxy-16α-methoxykaur-11-ene. Their structures were determined by spectrometric methods

The Alpha-Lipoic Acid Improves Survival and Prevents Irinotecan-Induced Inflammation and Intestinal Dysmotility in Mice

Irinotecan, an anticancer drug, induces diarrhea and intestinal inflammation, resulting in an increase in the cost of care and in treatment delays. In this study, we investigated whether alpha-lipoic acid (α-LA) could improve irinotecan-mediated intestinal inflammation, diarrhea and dysmotility. Intestinal mucositis was induced by irinotecan injection (75 mg/kg, i.p., for 4 days) in Swiss mice. α-LA (50, 100 or 200 mg/kg, gavage) was administered daily 1 h before the injection of irinotecan. Duodenum tissues were obtained for inflammation and proliferation analysis. The outcomes: diarrhea, intestinal dysmotility, weight body loss and survival were evaluated. Compared with the control condition, irinotecan diminished (p < 0.05) intestinal villus height, caused a loss of crypt integrity and intense inflammatory cell infiltration, increased myeloperoxidase (MPO), IL-6 and IL-1β levels and decreased reduced glutathione (GSH) levels in duodenum segments and increased gastric retention and decreased liquid retention in the medial intestinal segment, resulting in increased intestinal transit, severe diarrhea and reduced survival (approximately 72%). Furthermore, α-LA (200 mg/kg) pretreatment ameliorated (p < 0.05) these irinotecan-induced effects. Our findings show that α-LA reduced irinotecan-induced inflammation, intestinal dysmotility and diarrhea, resulting in improved survival. α-LA may be a useful therapeutic agent for the treatment of gut dysmotility in patients with intestinal mucositis associated with irinotecan treatment