980 resultados para REDOX PROCESSES
Resumo:
Mixing and transport processes in surface waters strongly influence the structure of aquatic ecosystems. The impact of mixing on algal growth is species-dependent, affecting the competition among species and acting as a selective factor for the composition of the biocoenose. Were it not for the ever-changing ”aquatic weather”, the composition of pelagic ecosystems would be relatively simple. Probably just a few optimally adapted algal species would survive in a given water-body. In contrast to terrestrial ecosystems, in which the spatial heterogeneity is primarily responsible for the abundance of niches, in aquatic systems (especially in the pelagic zone) the niches are provided by the temporal structure of physical processes. The latter are discussed in terms of the relative sizes of physical versus biological time-scales. The relevant time-scales of mixing and transport cover the range between seconds and years. Correspondingly, their influence on growth of algae is based on different mechanisms: rapid changes are relevant for the fast biological processes such as nutrient uptake and photosynthesis, and the slower changes are relevant for the less dynamic processes such as growth, respiration, mineralization, and settling of algal cells. Mixing time-scales are combined with a dynamic model of photosynthesis to demonstrate their influence on algal growth.
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Chronic diseases of the central nervous system are poorly treated due to the inability of most therapeutics to cross the blood-brain barrier. The blood-brain barrier is an anatomical and physiological barrier that severely restricts solute influx, including most drugs, from the blood to the brain. One promising method to overcome this obstacle is to use endogenous solute influx systems at the blood-brain barrier to transport drugs. Therapeutics designed to enter the brain through transcytosis by binding the transferrin receptor, however, are restricted within endothelial cells. The focus of this work was to develop a method to increase uptake of transferrin-containing nanoparticles into the brain by overcoming these restrictive processes.
To accomplish this goal, nanoparticles were prepared with surface transferrin molecules bound through various liable chemical bonds. These nanoparticles were designed to shed the targeting molecule during transcytosis to allow increased accumulation of nanoparticles within the brain.
Transferrin was added to the surface of nanoparticles through either redox or pH sensitive chemistry. First, nanoparticles with transferrin bound through disulfide bonds were prepared. These nanoparticles showed decreased avidity for the transferrin receptor after exposure to reducing agents and increased ability to enter the brain in vivo compared to those lacking the disulfide link.
Next, transferrin was attached through a chemical bond that cleaves at mildly acidic pH. Nanoparticles containing a cleavable link between transferrin and gold nanoparticle cores were found to both cross an in vitro model of the blood-brain barrier and accumulate within the brain in significantly higher numbers than similar nanoparticles lacking the cleavable bond. Also, this increased accumulation was not seen when using this same strategy with an antibody to transferrin receptor, indicating that behavior of nanoparticles at the blood-brain barrier varies depending on what type of targeting ligand is used.
Finally, polymeric nanoparticles loaded with dopamine and utilizing a superior acid-cleavable targeting chemistry were investigated as a potential treatment for Parkinson’s disease. These nanoparticles were capable of increasing dopamine quantities in the brains of healthy mice, highlighting the therapeutic potential of this design. Overall, this work describes a novel method to increase targeted nanoparticle accumulation in the brain.
Resumo:
IDOKI SCF Technologies S.L. is a technology-based company, set up on September 2006 in Derio (Biscay) with the main scope of developing extraction and purification processes based on the use of supercritical fluid extraction technology (SFE) in food processing, extraction of natural products and the production of personal care products. IDOKI¿s researchers have been working on many different R&D projects so far, most of them using this technology. However, the optimization of a SFE method for the different matrices cannot be performed unless we have an analytical method for the characterisation of the extracts obtained in each experiment. The analytical methods are also essential for the quality control of the raw materials that are going to be used and also for the final product. This PhD thesis was born to tackle this problem and therefore, it is based on the development of different analytical methods for the characterisation of the extracts and products. The projects that we could include in this thesis were the following: the extraction propolis, the recovery of agroindustrial residues (soy and wine) and the dealcoholisation of wine.On the one hand, for the extraction of propolis, several UV-Vis spectroscopic methods were used in order to measure the antioxidant capacity and the total polyphenol and flavonoid content of the extracts. A SFC method was also developed in order to measure more specific phenolic compounds. On the other hand, for the recovery of agroindustrial residues UV-Vis spectroscopy was used to determine the total polyphenol content and two SFC methods were developed to analyse different phenolic compounds. Extraction methods such as MAE, FUSE and rotary agitation were also evaluated for the characterisation of the raw materials.Finally, for the dealcoholisation of wine, the development of a SBSE-TD-GC-MS and DHS-TD-GC-MS methods for the analysis of aromas and a NIR spectroscopic method for the determination of ethanol content with the help of chemometrics was necessary. Most of these methods are typically used in IDOKI¿s lab as routine analyses apart from others not included in this PhD thesis.
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This article investigates the convergence properties of iterative processes involving sequences of self-mappings of metric or Banach spaces. Such sequences are built from a set of primary self-mappings which are either expansive or non-expansive self-mappings and some of the non-expansive ones can be contractive including the case of strict contractions. The sequences are built subject to switching laws which select each active self-mapping on a certain activation interval in such a way that essential properties of boundedness and convergence of distances and iterated sequences are guaranteed. Applications to the important problem of stability of dynamic switched systems are also given.
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O desenvolvimento de fibrose pulmonar (FP) induzida por bleomicina tem sido associado com as características genéticas e estresse oxidativo. Nosso objetivo foi investigar a influência do equilíbrio redox sobre a resistência a fibrose pulmonar induzida por bleomicina em diferentes linhagens de camundongos. Uma única dose de bleomicina (0,1 U/camundongo) ou salina (50 mL) foi administrada por via intratraqueal (i.t.) em camundongos C57BL/6, DBA/2 e camundongos BALB/c. Vinte e um dias após a administração de bleomicina, a taxa de mortalidade foi acima de 50% em camundongos C57BL/6 e 20% em DBA/2, enquanto não foi observada em BALB/c. Houve um aumento na elastância (p<0.001), ΔP2 (p<0.05), ΔPtot (p<0.01) e DE,l (p<0.05) em camundongos C57BL/6. O volume dos septos aumentaram em camundongos C57BL/6 (p<0.05) e DBA/2 (p<0.001). Os níveis de INF-γ foram reduzidas em camundongos C57BL/6 (p<0.01). Níveis OH-prolina foram aumentados em camundongos C57BL/6 e DBA/2 (p<0.05). Atividade e expressão de SOD foram reduzidas em camundongos C57BL/6 e DBA/2 (p<0.001 e p<0.001, respectivamente), enquanto que a atividade de CAT reduziu em todas as linhagens (C57BL/6: p<0.05; DBA/2: p<0.01, BALB/c: p<0.01). A atividade da GPx e expressão GPx 1/2 diminuiram em camundongos C57BL/6 (p<0.001). Nós concluímos que a resistência da FP pode também estar relacionada com a atividade e expressão de SOD em camundongos BALB/c
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110 p.
Resumo:
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.