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

Laboratory tests in the detection of extended spectrum beta-lactamase production: National Committee for Clinical Laboratory Standards (NCCLS) screening test, the E-test, the double disk confirmatory test, and cefoxitin susceptibility testing

Extended spectrum beta-lactamase (ESBL) production by Klebsiella sp. and E. coli is an emerging problem. In this study, 107 clinical isolates (53 E. coli, 47 K. pneumoniae and 7 K. oxytoca) screened as ESBL producers by the NCCLS disk diffusion procedure were submitted to a double disk confirmatory test (DDT) and to the E-test double strip for confirmation of ESBL production by demonstration of clavulanic acid inhibition effect (CAIE). Only 72/107 (67%) of the isolates were confirmed as ESBL producers by DDT, with diverse results among species. By the E-test, 58/107 (54%) isolates were confirmed as ESBL producers, and 18/107 (17%) were not determinable. Susceptibility to cefoxitin was found in 57/68 (83%) of strains that did not show CAIE. ESBL detection remains a controversial issue and clinical laboratories are in need of a simple and effective way to recognize strains with this kind of resistance

Megalocitose de células luteínicas grandes de vacas prenhes com seneciose crônica

Diversas espécies de Senecio estão amplamente difundidas nas pastagens de propriedades rurais do Sul do Brasil. Criadores dessa região relatam quedas nos índices reprodutivos dos rebanhos bovinos, muitas vezes de causas não determinadas. Várias plantas tóxicas são capazes de causar alterações reprodutivas diretas e indiretas em bovinos em diversos países, incluindo o Brasil, no entanto seus mecanismos patogenéticos ainda são pouco compreendidos. O objetivo desse trabalho é descrever lesões ovarianas em vacas com seneciose crônica proveniente de propriedades rurais da mesorregião Sudoeste Rio-grandense. Foram estudados 21 casos positivos de seneciose crônica diagnosticados entre 2011 e 2014. O estudo revelou que a seneciose crônica é a principal causa de morte de bovinos adultos na região. Quatro vacas prenhes apresentaram lesões hepáticas clássicas da intoxicação por Senecio spp. Essas vacas tiveram seus ovários avaliados histologicamente e células luteínicas grandes (CLG) desses ovários apresentavam megalocitose e pseudoinclusões nucleares. Algumas CLG apresentaram núcleos com até 23,69μm de diâmetro e o aumento no tamanho desses núcleos foi significativamente maior que os de vacas controle. Conclui-se que a intoxicação por Senecio spp. causa alterações ovarianas em vacas e é possível que a intoxicação cause perdas reprodutivas nos rebanhos bovinas da região