Zone trapping/merging zones in flow analysis: A novel approach for rapid assays involving relatively slow chemical reactions

AbstractA novel strategy for accomplishing zone trapping in flow analysis is proposed. The sample and the reagent solutions are simultaneously inserted into convergent carrier streams and the established zones merge together before reaching the detector, where the most concentrated portion of the entire sample zone is trapped. The main characteristics, potentialities and limitations of the strategy were critically evaluated in relation to an analogous flow system with zone stopping. When applied to the spectrophotometric determination of nitrite in river waters, the main figures of merit were maintained, exception made for the sampling frequency which was calculated as 189h−1, about 32% higher relatively to the analogous system with zone stopping. The sample inserted volume can be increased up to 1.0mL without affecting sampling frequency and no problems with pump heating or malfunctions were noted after 8-h operation of the system. In contrast to zone stopping, only a small portion of the sample zone is halted with zone trapping, leading to these beneficial effects

Partitioning of environmental and taxonomic controls on Brazilian foliar content of carbon and nitrogen and stable isotopes

The Neotropics harbor some of the most diversified woody species in the world, and to understand the nutrient dynamics in these ecosystems, it is crucial to understand the role of plant taxonomy. In addition, biological nitrogen (N) fixation (BNF) in the tropics is one of the key processes affecting the global N cycle. Our objective was to (i) investigate the role of taxonomy and sampling site as predictors of foliar carbon (C) and N concentration and its stable isotopes (i.e., δ13C and δ15N); (ii) assess differences in foliar N, C:N ratio, and δ15N among three functional groups: species of N2-fixers and non-fixers of the Fabaceae family, as well as non-Fabaceae species; and (iii) examine the effect of wood density on tree foliar properties. We hypothesized that Fabaceae specimens in symbiosis with N2-fixers would possess a higher foliar N than non-fixing plants, including those of the Fabaceae family, as well as high-density trees would have higher foliar C and C:N ratio relative to low-density trees, where the latter invest in nutrients instead of structural C. We used a data set composed of 3,668 specimens sampled in three main biomes of Brazil: Amazon, Atlantic Forest, and Cerrado. The partitioning of variance had a higher influence of taxonomy on leaf C, N, and C:N ratio. Conversely, foliar δ13C and δ15N were environmentally constrained. While family was the most important taxonomy level for C, N, and C:N ratio, species played a major role for δ13C and δ15N. Foliar N followed the pattern fixers > non-fixers > non-Fabaceae, while C:N ratio had an opposite trend. In addition, foliar C was correlated with wood density, where high-density > medium-density and low-density woods. The large variability of δ15N was observed among Fabaceae species, demonstrates the complexity of using δ15N as an indicator of BNF. The higher foliar N of Fabaceae non-fixers than non-Fabaceae specimens support the hypothesis that an N-demanding lifestyle is an inherent pattern in this family. Lastly, although observed in some studies, the prediction of foliar properties using wood density is challenging, and future research on this topic is needed

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

The shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiver sity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxo nomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world’s known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world’s most biodiverse countries. We further identify collection gaps and summarize future goals that extend be yond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still un equally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the coun try. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora.Fil: Gomes da Silva, Janaina. Jardim Botânico do Rio de Janeiro: Rio de Janeiro, BrasilFil: Filardi, Fabiana L.R. Jardim Botânico do Rio de Janeiro; BrasilFil: Barbosa, María Regina de V. Universidade Federal da Paraíba: Joao Pessoa; BrasilFil: Baumgratz, José Fernando Andrade. Jardim Botânico do Rio de Janeiro; BrasilFil: de Mattos Bicudo, Carlos Eduardo. Instituto de Botânica. Núcleo de Pesquisa em Ecologia; BrasilFil: Cavalcanti, Taciana. Empresa Brasileira de Pesquisa Agropecuária Recursos Genéticos e Biotecnologia; BrasilFil: Coelho, Marcus. Prefeitura Municipal de Campinas; BrasilFil: Ferreira da Costa, Andrea. Federal University of Rio de Janeiro. Museu Nacional. Department of Botany; BrasilFil: Costa, Denise. Instituto de Pesquisas Jardim Botanico do Rio de Janeiro; BrasilFil: Dalcin, Eduardo C. Rio de Janeiro Botanical Garden Research Institute; BrasilFil: Labiak, Paulo. Universidade Federal do Parana; BrasilFil: Cavalcante de Lima, Haroldo. Jardim Botânico do Rio de Janeiro; BrasilFil: Lohmann, Lucia. Universidade de São Paulo; BrasilFil: Maia, Leonor. Universidade Federal de Pernambuco; BrasilFil: Mansano, Vidal de Freitas. Instituto de Pesquisas Jardim Botânico do Rio de Janeiro; Brasil. Jardim Botânico do Rio de Janeiro; BrasilFil: Menezes, Mariângela. Federal University of Rio de Janeiro. Museu Nacional. Department of Botany; BrasilFil: Morim, Marli. Instituto de Pesquisas Jardim Botânico do Rio de Janeiro; BrasilFil: Moura, Carlos Wallace do Nascimento. Universidade Estadual de Feira de Santana. Department of Biological Science; BrasilFil: Lughadha, Eimear NIck. Royal Botanic Gardens; Reino UnidoFil: Peralta, Denilson. Instituto de Pesquisas Ambientais; BrazilFil: Prado, Jefferson. Instituto de Pesquisas Ambientais; BrasilFil: Roque, Nádia. Universidade Federal da Bahia; BrasilFil: Stehmann, Joao. Universidade Federal de Minas Gerais; BrasilFil: da Silva Sylvestre, Lana. Universidade Federal do Rio de Janeiro; BrasilFil: Trierveiler-Pereira, Larissa. Universidade Estadual de Maringá. Departamento de Análises Clínicas e Biomedicina; BrasilFil: Walter, Bruno Machado Teles. EMBRAPA Cenargen Brasília; BrasilFil: Zimbrão, Geraldo. Universidade Federal do Rio de Janeiro; BrasilFil: Forzza, Rafaela C. Jardim Botânico do Rio de Janeiro; BrasilFil: Morales, Matías. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Morón. Facultad de Agronomía y Ciencias Agroalimentarias; Argentin

Zone trapping/merging zones in flow analysis: A novel approach for rapid assays involving relatively slow chemical reactions

A novel strategy for accomplishing zone trapping in flow analysis is proposed. The sample and the reagent solutions are simultaneously inserted into convergent carrier streams and the established zones merge together before reaching the detector, where the most concentrated portion of the entire sample zone is trapped. The main characteristics, potentialities and limitations of the strategy were critically evaluated in relation to an analogous flow system with zone stopping. When applied to the spectrophotometric determination of nitrite in river waters, the main figures of merit were maintained, exception made for the sampling frequency which was calculated as 189h(-1), about 32% higher relatively to the analogous system with zone stopping. The sample inserted volume can be increased up to 1.0 mL without affecting sampling frequency and no problems with pump heating or malfunctions were noted after 8-h operation of the system. In contrast to zone stopping, only a small portion of the sample zone is halted with zone trapping, leading to these beneficial effects. (C) 2011 Elsevier B.V. All rights reserved.FAPESP[2006/07309-3]FAPESP[2010/00972-4]CNPq[134056/2009-4

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.CNPqCAPESFAPESPFACEPEFCT [PTDC/EIA/81178 /2006