Fluazuron-induced morphological changes in Rhipicephalus sanguineus Latreille, 1806 (Acari: Ixodidae) nymphs: An ultra-structural evaluation of the cuticle formation and digestive processes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A cytochemical approach to describe oocyte development in the freshwater ostariophysan, Serrasalmus maculatus (Characiformes)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development and characterization of microsatellite markers for Brazilian four-eyed frogs (genus Pleurodema) endemic to the Caatinga biome

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Cover crops affecting levels of ammonium and nitrate in the soil and upland rice development

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

Combining ALOS/PALSAR derived vegetation structure and inundation patterns to characterize major vegetation types in the Mamiraua Sustainable Development Reserve, Central Amazon floodplain, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of a simple method for determination of NO2 in air using digital scanner images

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Biology Teachers'Conceptions of the Diversity of Life and the Historical Development of Evolutionary Concepts

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ac2-26 mimetic peptide of annexin A1 inhibits local and systemic inflammatory processes induced by bothrops moojeni venom and the lys-49 phospholipase A2 in a rat model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Biossorção & biodegradação de azo corante: desenvolvimento de processos utilizando resíduos como fonte de microorganismos = Azo dye biosorption & biodegradation : process development with residues as source of microorganisms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Using landscape metrics to analyze micro-scale soil erosion processes

Methods of recording soil erosion using photographs exist but they are not commonly considered in scientific studies. Digital images may hold an expressive amount of information that can be extracted quickly in different manners. The investigation of several metrics that were initially developed for landscape ecology analysis constitutes one method. In this study we applied a method of landscape metrics to quantify the spatial configuration of surface micro-topography and erosion-related features, in order to generate a possible complementary tool for environmental management. In a 3.7 m wide and 9.7 m long soil box used during a rainfall simulation study, digital images were systematically acquired in four instances: (a) when the soil was dry; (b) after a short duration rain for initial wetting; (c) after the first erosive rain; and (d) after the 2nd erosive rain. Thirteen locations were established in the box and digital photos were taken at these locations with the camera positioned at the same orthogonal distance from the soil surface under the same ambient light intensity. Digital photos were converted into bimodal images and seven landscape metrics were analyzed: percentage of land, number of patches, density of patches, largest patch index, edge density, shape index, and fractal dimension. Digital images were an appropriate tool because they can generate data very quickly. The landscape metrics were sensitive to changes in soil surface micro-morphology especially after the 1st erosive rain event, indicating significant erosional feature development between the initial wetting and first erosive rainfall. The method is considered suitable for spatial patterns of soil micro-topography evolution from rainfall events that bear similarity to landscape scale pattern evolution from eco-hydrological processes. Although much more study is needed for calibrating the landscape metrics at the micro-scale, this study is a step forward in demonstrating the advantages of the method.