Efeitos das mudanças do uso da terra na biogeoquímica dos corpos d'água da bacia do rio Ji-Paraná, Rondônia

Este trabalho discute os efeitos das mudanças do uso do solo na biogequímica dos rios da bacia de drenagem do rio Ji-Paraná (Rondônia). Nesta região, a distribuição espacial do desmatamento e das propriedades do solo resultam em sinais diferentes, possibilitando a divisão dos sistemas fluviais em três grupos: rios com águas pobres em íons e baixo impacto; rios com conteúdo iônico intermediário e impacto médio e rios com elevados conteúdo iônico e impacto antropogênico. As características biogeoquímicas dos rios têm relação significativa com a área de pasto, melhor parâmetro para prever a condutividade elétrica (r² = 0,87) e as concentrações de sódio (r² = 0,75), cloreto (r² = 0,69), potássio (r² = 0,63), fosfato (r² = 0.78), nitrogênio inorgânico (r² = 0.52), carbono inorgânico (r² = 0.81) e carbono orgânico (rain ² = 0.51) dissolvidos. Cálcio e magnésio tiveram sua variância explicada pelas características do solo e pastagem. Nossos resultados indicam que as mudanças observadas na micro-escala constituem "sinais biogeoquímicos" gerados pelo processamento do material nas margens dos rios. A medida em que os rios evoluem para ordens superiores, os sinais persistentes nos canais fluviais estão mais associdados às características da bacia de drenagem (solos e uso da terra). Apesar dos efeitos das mudanças observadas no uso do solo não serem ainda detectáveis na macro-escala (bacia amazônica), a disrupção da estrutura e funcionamento dos ecossistemas é detectável nas micro e meso escalas, com alterações significativas na ciclagem de nutrientes nos ecossistemas fluviais.

Técnicas avançadas de sensoriamento remoto aplicadas ao estudo de mudanças climáticas e ao funcionamento dos ecossistemas amazônicos

Este artigo se propõe a apresentar exemplos de questões científicas que puderam ser respondidas no contexto do Projeto LBA (Large Sale Biosphere-Atmosphere Experiment in Amazonia) graças à contribuição de informações derivadas de sensoriamento remoto. Os métodos de sensoriamento remoto permitem integrar informações sobre os vários processos físicos e biológicos em diferentes escalas de tempo e espaço. Nesse artigo, são enfatizados aqueles avanços de conhecimento que jamais seriam alcançados sem a concorrência da informação derivada de sensoriamento.

Práticas centradas na família: identificação de comportamentos para uma prática de qualidade nos distritos de Braga, Viana do Castelo e Vila Real

Dissertação de mestrado em Educação Especial (área de especialização em Intervenção Precoce)

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in -carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis--carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Biological markers in noninvasive brain stimulation trials in major depressive disorder: a systematic review

Objectives: The therapeutic effects of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation in patients with major depression have shown promising results; however, there is a lack of mechanistic studies using biological markers (BMs) as an outcome. Therefore, our aim was to review noninvasive brain stimulation trials in depression using BMs. Methods: The following databases were used for our systematic review: MEDLINE, Web of Science, Cochrane, and SCIELO. We examined articles published before November 2012 that used TMS and transcranial direct current stimulation as an intervention for depression and had BM as an outcome measure. The search was limited to human studies written in English. Results: Of 1234 potential articles, 52 articles were included. Only studies using TMS were found. Biological markers included immune and endocrine serum markers, neuroimaging techniques, and electrophysiological outcomes. In 12 articles (21.4%), end point BM measurements were not significantly associated with clinical outcomes. All studies reached significant results in the main clinical rating scales. Biological marker outcomes were used as predictors of response, to understand mechanisms of TMS, and as a surrogate of safety. Conclusions: Functional magnetic resonance imaging, single-photon emission computed tomography, positron emission tomography, magnetic resonance spectroscopy, cortical excitability, and brain-derived neurotrophic factor consistently showed positive results. Brain-derived neurotrophic factor was the best predictor of patients’ likeliness to respond. These initial results are promising; however, all studies investigating BMs are small, used heterogeneous samples, and did not take into account confounders such as age, sex, or family history. Based on our findings, we recommend further studies to validate BMs in noninvasive brain stimulation trials in MDD.

Florestas estacionais e áreas de ecótono no estado do Tocantins, Brasil: parâmetros estruturais, classificação das fitofisionomias florestais e subsídios para conservação

O objetivo deste estudo foi descrever a riqueza, estrutura e diversidade de espécies arbóreas em áreas de Floresta Estacional e ecótono (Floresta Estacional/Floresta Ombrófila) no estado do Tocantins, buscando subsídios para a conservação, manejo florestal, compensação de reserva legal e recuperação ambiental, além de discutir as identidades fitogeográficas em comparação com outras florestas do Brasil. Em 18 bacias hidrográficas, conduziu-se amostragem da vegetação arbórea (DAP > 5 cm) de 22 áreas (amostras) por meio do inventário de 477 parcelas de 400 m². Foram elaboradas análises de classificação pelo método TWINSPAN, em duas escalas distintas. A primeira avaliou a diversidade beta entre as parcelas amostradas no estado do Tocantins e a segunda buscou analisar a similaridade das florestas do Tocantins em relação a outras florestas do bioma Cerrado e suas áreas de tensão ecológica. As florestas amostradas apresentaram ampla variação em termos de riqueza (33 a 243 espécies), densidade (486 a 1.179 ind.ha-1), área basal (14,04 e 37,49 m².ha-1), índices de diversidade (H´ = 2,75 a 4,59) e de equabilidade (J´= 0,72 a 0,86). As análises de classificação convergiram para resultados comuns, identificando quatro ambientes dissimilares em termos florísticos e estruturais no estado do Tocantins: Floresta Estacional Decidual, Floresta Estacional Semidecidual, ecótono Floresta Estacional Semidecidual/Floresta Ombrófila e ecótono Floresta Estacional Decidual/Floresta Ombrófila. A fim de manter a diversidade de plantas e de ambientes na região de transição Floresta Amazônica e Cerrado, sugere-se que o processo de criação de unidades de conservação no estado do Tocantins deva ser intensificado e tenha como base para seleção das áreas critérios biogeográficos.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Analysis of physicochemical interaction between the components of asphalt mixtures with rubber

Tese de Doutoramento em Engenharia Civil

Psychiatric morbidity and quality of life of primary care attenders in two cities in Brazil

Objective: To identify the associations among quality of life (QoL), social determinants and psychological distress in primary care in two cities in Brazil. Methods: A cross-sectional study with 1,466 patients from 2009 to 2010. The statistical analysis used the t-test to compare the variables of interest to the study. Results: The prevalence of Common Mental Disorders (CMD3), severe forms of Common Mental Disorders (CMD5), anxiety and depression were 20.5%, 32%, 37% and 25.1% respectively. Thes presence of psychological distress is associated with worse QoL among the patients studied, especially those older than 40 years of age. In cases of CMD3, those with higher income and educational levels presented higher QoL in the psychical and psychological domains. For the cases of probable anxiety, those with higher educational levels presented lower scores on the physical and social relationship scores. Conclusion: Psychological distress can be associated with a worse QoL among those studied and can be influenced by socioeconomic conditions. Therefore, it is important to structure patient-centered help, which should also include patients’ social contexts.

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Examen classis dioeciae.

v. 1 [series pt. 5]

Examen classis dioeciae.

v. 2 [series pt. 5]