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

Neurobiologia do transtorno de humor bipolar e tomada de decisão na abordagem psicofarmacológica

O Transtorno do Humor Bipolar (THB) caracteriza-se por oscilações do humor que causam prejuízos significativos no âmbito biopsicossocial. O interesse da comunidade científica por este transtorno vem aumentando nos últimos cinco anos em função de sua crescente prevalência associada ao refinamento diagnóstico, à ampliação do arsenal terapêutico e ao conhecimento dos avanços nas pesquisas da neurobiologia do transtorno. A presente revisão aborda questões diagnosticas e terapêuticas aplicadas à neurobiologia dos THB, relacionando-as diretamente à terapêutica dos quadros de mania, hipomania, estados mistos, depressão bipolar e ciclagem rápida, da infância à idade adulta. São revisados criticamente importantes estudos realizados com diferentes fármacos potencialmente eficazes como estabilizadores do humor, nos diversos subdiagnósticos do THB. São analisados fármacos, tais como o lítio, anticonvulsivantes, antipsicóticos, benzodiazepínicos, bloqueadores dos canais de cálcio e hormônio tireoideo, bem como as possíveis bases biológicas para seus efeitos terapêuticos. Em síntese, este trabalho aborda os avanços da psicofarmacologia cuja eficácia é comprovada nos subtipos do THB, procurando relacioná-los com a neurobiologia deste transtorno.Bipolar Disorder (BD) is characterized by mood swings that cause significant impairment in social, occupational, or other areas of functioning. During the last years, new insights have been provided in the diagnosis, etiology, neurobiological basis and treatment of bipolar disorder. This paper emphasizes recent studies related to some diagnostic and therapeutic aspects during manic episode, hypomanic, mixed episode, bipolar depression and rapid cycling, in children, adolescents and adults. Studies using proposed mood stabilizers, which present adequate metodological basis, including double–blind, controlled studies and which presented a significant number of patients were included and critically evaluated in this revision. Drugs such as the lithium, anticonvulsants, antipsychotics, benzodiazepines, calcium channels blockers and thyroid augmentation are proposed to be effective in certain diagnostic profiles. The possible biological bases for these drugs therapeutic effects are also revised. In summary, this article focuses on recent and important psychopharmacological progresses on the treatment of BD subtypes. Furthermore, the revision presents possible biological basis to explain the therapeutic profile of these drugs