945 resultados para Experimental techniques
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The phase transition from the non-polar α-phase to the polar β-phase of poly(vinylidene fluoride) (PVDF) has been investigated using micro-Raman spectroscopy, which is advantageous for being a non-destructive technique. Films of α-PVDF were subjected to stretching under controlled rates and at 80°C, the transition to β-PVDF being monitored by the decrease in the Raman band at 794 cm-1 characteristic of the α-phase, with the concomitant increase in the 839 cm-1 band characteristic of the β-phase. Poling with negative corona discharge was found to affect the a-PVDF morphology improving the Raman bands related to this crystalline phase. This effect is minimized for films stretched to higher ratios. Significantly, corona-induced effects could not be observed with the other experimental techniques, viz. X-ray diffraction and infrared spectroscopy.
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Migration of components from plastic packaging into foodstuffs or into medicines is a very important issue, concerning public health. Using experimental techniques, like gas chromatography-mass spectrometry, these essays measure total migration and specific migration of components from plastic packaging. This work presents an explanation and applications of a numerical technique tool for this measurement, allowing the comprehension of the diffusion process and the estimate of component migration in difficult or impractical measurements. As an application example, the non-uniform influence of initial concentration profile on the migration is presented, demonstrating the necessity of this profile determination for high quality considerations on involved metrology.
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Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.
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Pós-graduação em Física - IGCE
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Estudo das propriedades ópticas, linear e não linear, e elétrica dos vidros da família Li2O-TeO2-WO3
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Ciências Biológicas (Genética) - IBB
Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Ciência e Tecnologia de Materiais - FC