16 resultados para Human Model
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Dissertação apresentada para a obtenção do Grau de Mestre em Genética Molecular e Biomedicina, pela Universidade N ova de Lisboa, Faculdade de Ciências e Tecnologia
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There is a family of models with Physical, Human capital and R&D for which convergence properties have been discussed (Arnold, 2000a; Gómez, 2005). However, spillovers in R&D have been ignored in this context. We introduce spillovers in this model and derive its steady-state and stability properties. This new feature implies that the model is characterized by a system of four differential equations. A unique Balanced Growth Path along with a two dimensional stable manifold are obtained under simple and reasonable conditions. Transition is oscillatory toward the steady-state for plausible values of parameters.
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Until now, in models of endogenous growth with physical capital, human capital and R&D such as in Arnold [Journal of Macroeconomics 20 (1998)] and followers, steady-state growth is independent of innovation activities. We introduce absorption in human capital accumulation and describe the steady-state and transition of the model. We show that this new feature provides an effect of R&D in growth, consumption and welfare. We compare the quantitative effects of R&D productivity with the quantitative effects of Human Capital productivity in wealth and welfare.
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In this thesis we implement estimating procedures in order to estimate threshold parameters for the continuous time threshold models driven by stochastic di®erential equations. The ¯rst procedure is based on the EM (expectation-maximization) algorithm applied to the threshold model built from the Brownian motion with drift process. The second procedure mimics one of the fundamental ideas in the estimation of the thresholds in time series context, that is, conditional least squares estimation. We implement this procedure not only for the threshold model built from the Brownian motion with drift process but also for more generic models as the ones built from the geometric Brownian motion or the Ornstein-Uhlenbeck process. Both procedures are implemented for simu- lated data and the least squares estimation procedure is also implemented for real data of daily prices from a set of international funds. The ¯rst fund is the PF-European Sus- tainable Equities-R fund from the Pictet Funds company and the second is the Parvest Europe Dynamic Growth fund from the BNP Paribas company. The data for both funds are daily prices from the year 2004. The last fund to be considered is the Converging Europe Bond fund from the Schroder company and the data are daily prices from the year 2005.
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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Thesis submitted to Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, in partial fulfillment of the requirements for the degree of Master in Computer Science
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Dissertation to obtain master degree in Genética Molecular e Biomedicina
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Dissertation presented to obtain the Ph.D degree in Biology
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J Biol Inorg Chem (2007) 12:777–787 DOI 10.1007/s00775-007-0229-7
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Dissertação para obtenção do Grau de Mestre em Biotecnologia
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The convergence features of an Endogenous Growth model with Physical capital, Human Capital and R&D have been studied. We add an erosion effect (supported by empirical evidence) to this model, and fully characterize its convergence properties. The dynamics is described by a fourth-order system of differential equations. We show that the model converges along a one-dimensional stable manifold and that its equilibrium is saddle-path stable. We also argue that one of the implications of considering this “erosion effect” is the increase in the adherence of the model to data.
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Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the pro-gressive loss of motoneurons (MN). Increasing evidence points glial cells as key players for ALS onset and progression. Indeed, MN-glia signalling pathways involving either neuroprotection or inflammation are likely to be altered in ALS. We aimed to study the molecules related with glial function and/or reactivity by evaluating glial markers and hemichannels, mainly present in astrocytes. We also studied molecules involved in mi-croglia-MN dialogue (CXCR3/CCL21; CX3CR1/CX3CL1; MFG-E8), as well as proliferation (Ki-67) and inflammatory-related molecules (TLR2/4, NLRP3; IL-18) and alarming/calming signals (HMGB1/autotaxin). We used lumbar spinal cord (SC) homogenates from mice expressing a mutant human-SOD1 protein (mSOD1) at presymptomatic and late-symptomatic ALS stages. SJL (WT) mice at same ages were used as controls. We observed decreased expression of genes associated with astrocytic (GFAP and S100B) and microglial (CD11b) markers in mSOD1 at the presymptomatic phase, as well as diminished levels of gap junction components pannexin1 and connexin43 and expression of Ki-67 and decreased autotax-in. In addition, microglial-MN communication was negatively affected in mSOD1 mice as well as in-flammatory response. Interestingly, we observed astrocytic (S100B) and microglial (CD11b) reactivity, increased proliferation (Ki-67) and increased autotaxin expression in symptomatic mSOD1 mice. In-creased MN-microglial dialogue (CXCR3/CCL21; CX3CR1/CX3CL1; MFG-E8) and hemichannel activ-ity, namely connexin43 and pannexin1, were also observed in mSOD1 at the symptomatic phase, along with an elevated inflammatory response as indicated by increased levels of HMGB1 and NLRP3. Our results suggest that decreased autotaxin expression is a feature of the presymptomatic stage, and precede the network of pro-inflammatory-related symptomatic determinants, including HMGB1, CCL21, CX3CL1, and NLRP3. The identification of the molecules and signaling pathways that are dif-ferentially activated along ALS progression will contribute for a better design of therapeutic strategies for disease onset and progression.
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The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.
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Tenofovir (TFV) is one of the most used antiretroviral drugs. However, it is associated with tubular damage with mitochondria as a possible target. Tubulopathy precedes glomerular dysfunction, thus classic markers of renal function like the glomerular filtration rate (GFR) do not detect early TFV damage. Prediction and management of drug induced renal injury (DIRI) rely on the mechanisms of the drug insult and in optimal animal models to explore it. Zebrafish (Danio rerio) offers unique advantages for assessing DIRI, since the pronephros is structurally very similar to its human counterpart and is fully developed at 3.5 days postfertilization. The main aim of the present work was to evaluate the effects of TFV, as well as its pro-drug, tenofovir disoproxil fumarate (TDF), on the GFR and in mitochondria morphology in tubular cells of zebrafish larvae. Lethality curves were performed to understand the relationship between drug concentration and lethality. LC10 was selected to explore the renal function using the FITC-inulin assay and to analyze the mitochondrial toxicity by electron microscopy on larvae exposed to TDF, TFV, paracetamol and gentamicin (positive controls) or water (negative control). Lethality curves showed that gentamicin was the most lethal drug, followed by TDF, TFV and paracetamol. Gentamicin and paracetamol decreased the GFR, but no differences were found for either TDF or TFV, when compared to controls (%FITC Control = 33±8; %FITC TDF = 35±10; %FITC TFV = 30±10; %FITC Gentamicin = 46±17; %FITC Paracetamol = 83±14). Tubular mitochondria from treated larvae were notably different from non-treated larvae, showing swelling, irregular shapes, decreased mitochondria network, cristae disruption and loss of matrix granules. These results are in agreement with the effects of these drugs in humans and thus, demonstrate that zebrafish larvae can be a good model to assess the functional and structural damage associated with DIRI.
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This work aimed to contribute to drug discovery and development (DDD) for tauopathies, while expanding our knowledge on this group of neurodegenerative disorders, including Alzheimer’s disease (AD). Using yeast, a recognized model for neurodegeneration studies, useful models were produced for the study of tau interaction with beta-amyloid (Aβ), both AD hallmark proteins. The characterization of these models suggests that these proteins co-localize and that Aβ1-42, which is toxic to yeast, is involved in tau40 phosphorylation (Ser396/404) via the GSK-3β yeast orthologue, whereas tau seems to facilitate Aβ1-42 oligomerization. The mapping of tau’s interactome in yeast, achieved with a tau toxicity enhancer screen using the yeast deletion collection, provided a novel framework, composed of 31 genes, to identify new mechanisms associated with tau pathology, as well as to identify new drug targets or biomarkers. This genomic screen also allowed to select the yeast strain mir1Δ-tau40 for development of a new GPSD2TM drug discovery screening system. A library of unique 138 marine bacteria extracts, obtained from the Mid-Atlantic Ridge hydrothermal vents, was screened with mir1Δ-tau40. Three extracts were identified as suppressors of tau toxicity and constitute good starting points for DDD programs. mir1Δ strain was sensitive to tau toxicity, relating tau pathology with mitochondrial function. SLC25A3, the human homologue of MIR1, codes for the mitochondrial phosphate carrier protein (PiC). Resorting to iRNA, SLC25A3 expression was silenced in human neuroglioma cells, as a first step towards the engineering of a neural model for replicating the results obtained in yeast. This model is essential to understand the mechanisms of tau toxicity at the mitochondrial level and to validate PiC as a relevant drug target. The set of DDD tools here presented will foster the development of innovative and efficacious therapies, urgently needed to cope with tau-related disorders of high human and social-economic impact.