The choice of the spectral region in the use of spectroscopic and chemometric methods

A method is presented for the choice of spectral regions when absorption measurements are coupled to chemometric tools to perform quantitative analyses. The method is based on the spectral distribution of the relative standard deviation of concentration (s c/c). It has been applied to the development of PLS-FTNIR calibration models for the determination of density and MON of gasoline, and ethanol content and density of ethanol fuel. The new method was also compared with the correlation (R²) method and has proved to generate PLS calibration models that present better accuracy and precision than those based on R²

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

Assessment of risk scores to predict mortality of COVID-19 patients admitted to the intensive care unit

ObjectivesTo assess the ABC2-SPH score in predicting COVID-19 in-hospital mortality, during intensive care unit (ICU) admission, and to compare its performance with other scores (SOFA, SAPS-3, NEWS2, 4C Mortality Score, SOARS, CURB-65, modified CHA2DS2-VASc, and a novel severity score).Materials and methodsConsecutive patients (≥ 18 years) with laboratory-confirmed COVID-19 admitted to ICUs of 25 hospitals, located in 17 Brazilian cities, from October 2020 to March 2022, were included. Overall performance of the scores was evaluated using the Brier score. ABC2-SPH was used as the reference score, and comparisons between ABC2-SPH and the other scores were performed by using the Bonferroni method of correction. The primary outcome was in-hospital mortality.ResultsABC2-SPH had an area under the curve of 0.716 (95% CI 0.693–0.738), significantly higher than CURB-65, SOFA, NEWS2, SOARS, and modified CHA2DS2-VASc scores. There was no statistically significant difference between ABC2-SPH and SAPS-3, 4C Mortality Score, and the novel severity score.ConclusionABC2-SPH was superior to other risk scores, but it still did not demonstrate an excellent predictive ability for mortality in critically ill COVID-19 patients. Our results indicate the need to develop a new score, for this subset of patients

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

Modelos de calibração multivariada associados à espectroscopia vibracional para análise de misturas diesel-óleos vegetais

Dissertação (mestrado)—Universidade de Brasília, Instituto de Química, 2006.Recentemente, o governo brasileiro autorizou o uso comercial do biodiesel através da lei no 11.097, de 13/01/2005 que dispõe sobre a introdução do biodiesel na matriz energética brasileira. Inicialmente, o novo combustível deverá ser adicionado ao diesel de petróleo, formando uma mistura com 2% de biodiesel (B2), para o uso em veículos equipados com motores do ciclo diesel. A meta estabelecida pela lei é que em 2013 a mistura contenha 5% de biodiesel (B5). A produção de biodiesel conta com uma série de isenções fiscais, que podem levar a alguns tipos de adulteração nas misturas Diesel/Biodiesel, considerando os antecedentes históricos e atuais de adulteração de combustíveis no Brasil. Neste sentido, desenvolvemos neste trabalho uma metodologia para identificar adulterações nas misturas B2 e B5. A metodologia utilizada consiste basicamente na elaboração de modelos de calibração multivariada (Regressão por mínimos quadrados parciais, Regressão por mínimos quadrados parciais) e Redes neurais artificiais combinados à espectroscopia vibraconal (FT-Raman, FT-NIR e FT-IR). Para os modelos foram preparadas 225 amostras de misturas diesel e óleos (soja, mamona e dendê) vegetais ou álcoois (etanol e metanol), cobrindo uma faixa de concentração de 0 a 5% (m/m). Das três técnicas espectroscópicas investigadas, a que se mostrou mais apropriada para as finalidades propostas foi o modelo PLS/FT-IR, uma vez que apresentou melhor exatidão (menor valor de RMSEP, 0,135) para um coeficiente de variação de 4,67%. Outro estudo realizado foi a verificação da influência da resolução espectral nos erros de predição de modelos PLS/FT-IR e PLS/FT-Raman. Para um número fixo de varreduras e resoluções variando de 4 a 64 cm-1, os resultados mostraram que um aumento na resolução espectral provoca uma diminuição exponencial nos valores de RMSECV se a região espectral selecionada apresenta bandas vibracionais suficientemente distantes umas das outras. _________________________________________________________________________________________ ABSTRACTRecently, the Brazilian Government has allowed the commercial use of biodiesel blends according to the law no 11.097, from 01/13/2005 and introducing the biodiesel in the Brazilian energetic matrix. At first, the biodiesel will be added to the diesel, to make a 2% of biodiesel blend (B2) that is going to be used in vehicles equipped with diesel engines. The goals set up by the law are that B2 blends will be mandatory in 2008 and the B5 (5% of biodiesel) in 2013. Considering the previous and actual problems with fuel adulterations, the tax benefits already valid for biodiesel being commercialized would encourage some types of adulterations of the Diesel/Biodiesel blends. For this reason, we have developed a methodology to identify adulterations in the B2 and B5 blends. This methodology consists basically in the design of calibration models based on multivariate analysis (Partial least square regression, Principal component regression) and Artificial neural network combined to vibrational spectroscopy (FT-Raman, FT-NIR e FT-IR). A set of 225 samples have been prepared to train the calibration models. These samples were blends of diesel, vegetable (soybean, castor, and palm tree) oils, and alcohols (ethanol and methanol), at concentrations ranging from 0 to 5% (w/w). The results have shown that among the three spectroscopic techniques investigated. PLS/FT-IR shows the best performance for the goals we purposed is the, once it showed the best accuracy (smallest RMSEP value, 0.135) for a coefficient of variation of 4.67%. Further investigations have been conducted in order to evaluate the influence of the spectral resolution in the prediction errors of the PLS/FT/IR and PLS/FT-Raman calibration models. For a fixed number of scans and resolutions varying from 4 to 64 cm-1, the results have shown that an increase in the resolutions causes an exponential decrease in the RMSECV values, if in the selected spectral regions the vibrational bands are sufficiently apart from each other

O uso de líquidos iônicos na obtenção de materiais magnéticos nanoestruturados

Tese (doutorado)—Universidade de Brasília, Instituto de Química, 2010.As propriedades dos materiais magnéticos são fortemente influenciadas pelo diâmetro, morfologia e distribuição de tamanho das partículas. Neste sentido, a busca por novos materiais magnéticos e por métodos alternativos de síntese com tamanho e forma controlados, através do uso de moduladores e reagentes menos tóxicos, continua sendo um desafio. Esse tipo de material encontra aplicações especiais em controle de poluição ambiental, biomedicina e catálise. Neste trabalho reporta-se a obtenção de uma nova classe de materiais magnéticos baseados em dispersões estáveis de nanopartículas de -Fe2O3 ou CoFe2O4 com a superfície modificada no líquido iônico BMI.BF4. As amostras foram caracterizadas por raios X, microscopia eletrônica de transmissão, medidas de magnetização e espectroscopia Raman. A estabilidade das nanopartículas foi explicada pela formação de uma camada protetora semi-organizada composta de agregados supramoleculares do tipo [(BMI)2(BF4)3]-. Também é apresentado um processo de síntese e modificação de nanopartículas magnéticas, em uma só etapa, através da decomposição térmica do Fe(acac)3 em BMI.NTf2, como solvente, e oleilamina. Esse processo pode ser utilizado para a síntese de ferritas do tipo MFe2O4 ( com M= Co, Ni, Mn) pela adição do precursor de acetilacetonato do metal desejado. A temperatura e o tempo de reação utilizado são inferiores aos reportados na literatura. O tamanho e morfologia das nanopartículas podem ser modulados pela temperatura ou tempo de reação. _________________________________________________________________________________ ABSTRACTThe properties of these magnetic materials are strongly influenced by the diameter, shape and size distribution of the nanoparticles. Therefore, the search for new magnetic materials and for alternative methods that allow the synthesis of these magnetic nanoparticles with controlled size and shape using modulate fluids and less toxic reactants is still a challenge. These kinds of materials find special applications in environmental pollution control, biomedicine and catalysis. Here we reported a disclosure of new class of magnetic material based on a stables dispersions of surface modified -Fe2O3 or CoFe2O4 nanoparticles in the BMI.BF4 ionic liquid. The magnetic NPs were characterized by X-ray powder diffraction, transmission electron microscopy, Raman spectroscopy and magnetic measurements. The stability of the nanoparticles in BMI.BF4 was explained by the formation of a semi-organized protective layer composed of supramolecular aggregates in the form of [(BMI)2(BF4)3]-. We also report an easy synthesis of magnetic nanoparticles by thermal decomposition of Fe(acac)3 as a precursor, BMI.NTf2 as solvent and oleylamine. These process can be extended to the synthesis of MFe2O4 nanoparticles ( with M= Co, Ni, Mn) by simple adding a different metal acetylacetonate precursor to the mixture. The reaction temperature is lower than those used in the decomposition processes reported above. The size and shape of these nanoparticles can be modulated by the reaction temperature or time

Catálogo da exposição Corpos que Resistem

"APRESENTAÇÃO Esta é uma exposição concatenada pela turma de Museologia e ComunicaçãoIV, do curso de Museologia, da Universidade de Brasília do segundo semestredo ano de 2022, sob a orientação da professora Dra. Marijara Queiroz , cujo tema orbita à resistência de corpos na necropolítica (conceito emprestado de Achille Mbembe (2022 [2018]), teórico que definiu o desenvolvimento desse processo curatorial e inspirará as linhas que se seguem).

Giants of the Amazon: how does environmental variation drive the diversity patterns of large trees?

For more than three decades, major efforts in sampling and analyzing tree diversity in South America have focused almost exclusively on trees with stems of at least 10 and 2.5 cm diameter, showing highest species diversity in the wetter western and northern Amazon forests. By contrast, little attention has been paid to patterns and drivers of diversity in the largest canopy and emergent trees, which is surprising given these have dominant ecological functions. Here, we use a machine learning approach to quantify the importance of environmental factors and apply it to generate spatial predictions of the species diversity of all trees (dbh ≥ 10 cm) and for very large trees (dbh ≥ 70 cm) using data from 243 forest plots (108,450 trees and 2832 species) distributed across different forest types and biogeographic regions of the Brazilian Amazon. The diversity of large trees and of all trees was significantly associated with three environmental factors, but in contrasting ways across regions and forest types. Environmental variables associated with disturbances, for example, the lightning flash rate and wind speed, as well as the fraction of photosynthetically active radiation, tend to govern the diversity of large trees. Upland rainforests in the Guiana Shield and Roraima regions had a high diversity of large trees. By contrast, variables associated with resources tend to govern tree diversity in general. Places such as the province of Imeri and the northern portion of the province of Madeira stand out for their high diversity of species in general. Climatic and topographic stability and functional adaptation mechanisms promote ideal conditions for species diversity. Finally, we mapped general patterns of tree species diversity in the Brazilian Amazon, which differ substantially depending on size class