21 resultados para Simple interest


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Dissertação de mestrado em Economia Social

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A highly robust hydrogel device made from a single biopolymer formulation is reported. Owing to the presence of covalent and non-covalent crosslinks, these engineered systems were able to (i) sustain a compressive strength of ca. 20 MPa, (ii) quickly recover upon unloading, and (iii) encapsulate cells with high viability rates.

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Load-bearing soft tissues such as cartilage, blood vessels and muscles are able to withstand a remarkable compressive stress of several MPa without fracturing. Interestingly, most of these structural tissues are mainly composed of water and in this regard, hydrogels, as highly hydrated 3D-crosslinked polymeric networks, constitute a promising class of materials to repair lesions on these tissues. Although several approaches can be employed to shape the mechanical properties of artificial hydrogels to mimic the ones found on biotissues, critical issues regarding, for instance, their biocompatibility and recoverability after loading are often neglected. Therefore, an innovative hydrogel device made only of chitosan (CHI) was developed for the repair of robust biological tissues. These systems were fabricated through a dual-crosslinking process, comprising a photo- and an ionic-crosslinking step. The obtained CHIbased hydrogels exhibited an outstanding compressive strength of ca. 20 MPa at 95% of strain, which is several orders of magnitude higher than those of the individual components and close to the ones found in native soft tissues. Additionally, both crosslinking processes occur rapidly and under physiological conditions, enabling cellsâ encapsulation as confirmed by high cell survival rates (ca. 80%). Furthermore, in contrast with conventional hydrogels, these networks quickly recover upon unloading and are able to keep their mechanical properties under physiological conditions as result of their non-swell nature.

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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.

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[Excerpt] Introduction: There has been a considerable amount of controversy about the use of manometric methods to measure catalase activity. As Maehly and Chance point out in their excellent review] the advantages of these methods is "... that they can be used for any kind of biological material, and purification of the enzyme is not required. The assay is independent of small amounts of peroxidase activity. It is fairly simple to perform, it is rapid and it can be adapted to continuous reading of the reaction". A variety of drawbacks are also listed by the same authors, viz, the inactivation of the enzyme under the experimental conditions and the time lag before a constant rate of oxygen evolution is reached. [...]

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