8 resultados para Deperimetrização. Segurança de redes. Cartões-inteligentes. SAML. ICP-Brasil

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Mapping of elements in biological tissue by laser induced mass spectrometry is a fast growing analytical methodology in life sciences. This method provides a multitude of useful information of metal, nonmetal, metalloid and isotopic distribution at major, minor and trace concentration ranges, usually with a lateral resolution of 12-160 µm. Selected applications in medical research require an improved lateral resolution of laser induced mass spectrometric technique at the low micrometre scale and below. The present work demonstrates the applicability of a recently developed analytical methodology - laser microdissection associated to inductively coupled plasma mass spectrometry (LMD ICP-MS) - to obtain elemental images of different solid biological samples at high lateral resolution. LMD ICP-MS images of mouse brain tissue samples stained with uranium and native are shown, and a direct comparison of LMD and laser ablation (LA) ICP-MS imaging methodologies, in terms of elemental quantification, is performed.

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Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

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Animal welfare has been an important research topic in animal production mainly in its ways of assessment. Vocalization is found to be an interesting tool for evaluating welfare as it provides data in a non-invasive way as well as it allows easy automation of process. The present research had as objective the implementation of an algorithm based on artificial neural network that had the potential of identifying vocalization related to welfare pattern indicatives. The research was done in two parts, the first was the development of the algorithm, and the second its validation with data from the field. Previous records allowed the development of the algorithm from behaviors observed in sows housed in farrowing cages. Matlab® software was used for implementing the network. It was selected a retropropagation gradient algorithm for training the network with the following stop criteria: maximum of 5,000 interactions or error quadratic addition smaller than 0.1. Validation was done with sows and piglets housed in commercial farm. Among the usual behaviors the ones that deserved enhancement were: the feed dispute at farrowing and the eventual risk of involuntary aggression between the piglets or between those and the sow. The algorithm was able to identify through the noise intensity the inherent risk situation of piglets welfare reduction.

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física