Influence of abiotic factors on the chemical composition of copaiba oil (Copaifera multijuga Hayne): soil composition, seasonality and diameter at breast height

Copaiba oils are important medicinal products used primarily for their healing and anti-inflammatory activities. Consisting of sesquiterpenes and diterpenes, these oils have variable composition which, according to the literature, may originate from several factors. In order to analyze the relationship between chemical composition and abiotic factors such as seasonality, diameter at breast height (DBH) and soil composition, sixteen of oilresin samples of Copaifera multijuga Hayne, from the Ducke Forest Reserve (Manaus City, Amazon State, Brazil), were analyzed by gas chromatography with flame ionization detection (GC-FID) and coupled with mass spectrometry (GC-MS). Thirty-five compounds were identified and the results evaluated by multivariate analysis (hierarchical cluster analysis (HCA) and principal component analysis (PCA)), allowing differentiation of the samples into two groups with different compositions. One of them presented β-caryophyllene as the major constituent, while the other presented caryophyllene oxide. This variation in composition appears to depend on soil type. Other factors previously described as essential for defining the chemical composition of copaiba oils, such as seasonality and DBH, showed no significant influence on the chemical composition of oils of this species

DETECÇÃO MOLECULAR DE HEMOPLASMAS EM BOVINOS E OVINOS EM SISTEMA DE CRIAÇÃO CONSORCIADA DO NORDESTE DO BRASIL – DADOS PRELIMINARES

Micoplasmas hemotrópicos (hemoplasmas) são microrganismos gram-negativos e que ficam aderidos aos eritrócitos de diversas espécies de mamíferos. Em pequenos ruminantes, Mycoplasma ovis, e em bovinos, Mycoplasma wenyonii e ‘Candidatus Mycoplasma haemobos’ são as espécies já descritas. Nessas espécies animais a transmissão dos hemoplasmas pode estar relacionada à infestação por carrapatos ou picadas de moscas hematófagas. A infecção por hemoplasmas pode causar anemia hemolítica aguda, porém os sinais clínicos diferem de acordo com a espécie de hemoplasma envolvido, do animal parasitado, idade e sistema de produção em que é criado. Embora a hemoplasmose tenha sido relatada causando perdas econômicas significativas na criação de ruminantes em todo o mundo, dados de hemoplasmas em sistema de criação consorciada são inexistentes. Assim, o objetivo deste estudo é determinar a prevalência de hemoplasmas em bovinos e pequenos ruminantes provenientes de um sistema de criação consorciada no nordeste do Brasil. Até o momento, um total de 15 amostras (10 ovinos e cinco bovinos) foram triadas utilizando um protocolo de PCR para o gene 16S rRNA de hemoplasmas. As amostras positivas foram submetidas a uma PCR para o gene 23S rRNA de hemoplasmas. Todas as amostras foram positivas para o gene endógeno gliceraldeído 3-fosfato desidrogenase (gapdh). Todos as amostras de ovinos foram negativas para hemoplasmas. Três de cinco (60%) bovinos foram positivos para Mycoplasma spp. O estudo envolverá a triagem das amostras por PCR em tempo real

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

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