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

Aspectos anatomopatológicos das neoplasias malignas renais: Anatomopathological aspects of malignant renal neoplasms

As neoplasias renais correspondem ao crescimento exacerbado de células tumorais no interior dos rins, classificadas como benignas ou malignas. Neste estudo será abordado sobre as neoplasias malignas renais, a qual correspondem a maior prevalência e são representadas pelo carcinoma de células renais e o tumor de Wilms, com a finalidade de descrever a respeito dos aspectos anatomopatológicos, disseminando informações para o diagnóstico e manejo precoce. O carcinoma de células renais é mais prevalente no sexo masculino, indivíduos mais velhos, geralmente assintomático, contribuindo para o diagnóstico tardio junto a existência de metástases e terapêutica irresponsiva. Não se trata de uma doença genética, sendo o caráter esporádico o predominante, neste contexto os fatores de risco, sobretudo o tabagismo em seguida de obesidade hemodiálise e doenças genéticas são potenciais desencadeantes da enfermidade. Os exames complementares associado a clínica, junto ao acompanhamento eleva a possibilidade de identificação antes de avanços metastáticos. O tumor de Wilms é típico de crianças, acometendo um ou ambos os rins, normalmente com alguma anomalia genética, sendo os sinais inespecíficos, mas sempre manifestando massa palpável e dor abdominal, a qual os métodos de imagem confirmam o diagnóstico e estimam o prognóstico deste. Neste contexto, elucida-se a transcendência que os aspectos anatomopatológicos das neoplasias malignas renais oferecem para a diagnose precoce, devido a escassez e inespecificidafe das manifestações clínicas. Logo, a junção do perfil de cada neoplasia abordado conduz ao manejo adequado e reduz a incidência de tratamentos agressivos e irresponsivos

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

DEGRADAÇÃO FOTOCATALÍTICA DE ATRAZINA NA PRESENÇA DE CATALISADORES NANOPARTICULADOS

Atrazine is a persistent herbicide and several studies have detected their presence in drinking water sources. In this work the atrazine photocatalytic degradation was investigated in aqueous solution using TiO2, ZnO and TiO2/ZnO catalysts. The catalysts were synthesized by Pechini's method and characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The process was monitored using absorption spectroscopy in ultraviolet and visible (UV-vis) region and high performance liquid chromatography (HPLC). The prepared catalysts were used in ultrafine powder form and its characterization showed nanometer order particle sizes. The atrazine absorbance decrease was observed after samples irradiation in catalysts presence and HPLC analysis showed a significant herbicide reduction and other compounds formation as degradation products. The ZnO catalyst use was more effective in atrazine molecule degradation compared to the other nanoparticles