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Orientador: Renato de Mello Prado / Abstract: Global warming is predicted to increase the intensity and duration of extreme weather events, such as droughts, heat waves, and floods, especially in tropical regions. Climate change affect growth of forage species. However, information regarding the effects of global climate change on the nutritional performance of tropical pastures is lacking, especially under field conditions. We, thus, conducted two field experiment with Panicum maximum and Stylosanthes capitata using a temperature free-air controlled enhancement system and evaluated the effects of two temperature conditions, ambient temperature and moderate warming (2°C above ambient canopy temperature), and two levels of water availability, irrigated and non-irrigated, on nutrients accumulation, nutrient use efficiency (NUE), the stoichiometric patterns of C:N:P and leaf biomass production. Both experiments was conducted using a randomized complete block design in a factorial arrangement. Our findings revealed in plants of P. maximum (C4- grass) that the N and P leaf concentration greatly decreased under water-stressed, which increased the C:N and C:P ratios, while warming increased the N:P ratio. Leaf biomass production was impaired by up to 16% under water stress and ambient temperature conditions, but the biomass production was improved by 20% under warming and irrigated conditions. Our results also showed that homeostatic instability under rainfed conditions resulted in decreased leaf biomass production, and it was ... (Complete abstract click electronic access below) / Resumo: Prevê-se que o aquecimento global aumente a intensidade e a duração dos eventos climáticos extremos, como secas, ondas de calor e inundações, especialmente nas regiões tropicais. Mudanças climáticas afetam o crescimento de espécies forrageiras. No entanto, faltam informações sobre os efeitos das mudanças climáticas globais no desempenho nutricional de pastagens tropicais, especialmente em condições de campo. Nós, assim, conduzimos dois experimento em campos com as forrageiras Panicum maximum e Stylosanthes capitata utilizando um sistema de temperatura controlada do aquecimento do ar (T-Face) e avaliou-se os efeitos de duas condições de temperatura, (temperatura ambiente) e aquecimento moderado (2°C acima da temperatura ambiente) e dois níveis de disponibilidade hídrica, (irrigada e não irrigados), no acúmulo de nutrientes, eficiência de uso de nutrientes (NUE), nos padrões estequiométricos de C:N:P e na produção de biomassa foliar. Ambos experimentos foram conduzidos utilizando um delineamento de blocos completos casualizados em arranjo fatorial. Nossos resultados revelaram que em plantas de P. maximum (pastagem C4) a concentração foliar de N e P diminuiu sob estresse hídrico, o que aumentou as relações C:N e C:P, enquanto o aquecimento aumentou a relação N:P. A produção de biomassa foliar foi prejudicada em até 16% sob condições de estresse hídrico e temperatura ambiente, mas a produção de biomassa foi melhorada em 20% sob condições de aquecimento e irrigação. Nossos resulta... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

Abstract Silicon carbide is a versatile material amenable to variety of applications from electrical insulation to surface passivation, diffusion-barrier in optoelectronic and high-frequency devices. This research presents a fundamental study of a-SiC:H films with variable stoichiometries deposited using novel technique, DC saddle-field plasma-enhanced chemical-vapour deposition, a departure from conventional RF PECVD commonly used in industry. DCSF PECVD is an alternative technique for low temperature large area deposition. Stoichiometric a-SiC:H obtained by fine-tuning precursor gas mixture. Annealing up to 800oC showed no significant change in elemental composition; particularly indicating thermal stability at stoichiometry. Ellipsometry showed wide range of optical gaps whose maximum surpasses values reported in literature. Refractive index measured and change in values studied as function of increasing carbon content in the films. Also attainment of very smooth surface morphology for stoichiometric a-SiC:H films reported. Surface roughness of 1 nm rms demonstrated for films grown at temperature as low as 225oC.

Abstract Silicon carbide is a versatile material amenable to variety of applications from electrical insulation to surface passivation, diffusion-barrier in optoelectronic and high-frequency devices. This research presents a fundamental study of a-SiC:H films with variable stoichiometries deposited using novel technique, DC saddle-field plasma-enhanced chemical-vapour deposition, a departure from conventional RF PECVD commonly used in industry. DCSF PECVD is an alternative technique for low temperature large area deposition. Stoichiometric a-SiC:H obtained by fine-tuning precursor gas mixture. Annealing up to 800oC showed no significant change in elemental composition; particularly indicating thermal stability at stoichiometry. Ellipsometry showed wide range of optical gaps whose maximum surpasses values reported in literature. Refractive index measured and change in values studied as function of increasing carbon content in the films. Also attainment of very smooth surface morphology for stoichiometric a-SiC:H films reported. Surface roughness of 1 nm rms demonstrated for films grown at temperature as low as 225oC.

Recombinant protein production by mammalian cells is a core component of today’s multi-billion dollar biopharmaceutical industry. Transcriptome and proteome technologies have been used to probe for cellular components that correlate with higher cell-specific productivity, but have yet to yield results that can be translated into practical metabolic engineering strategies. The recognition of cellular complexity has led to an increasing adoption of systems biology, a holistic investigation approach that aims to bring together different omics technologies and to analyze the resulting datasets under a unifying context. Fluxomics is chosen as the platform context to investigate cell metabolism because it captures the integrated effects of gene expression, enzyme activity, metabolite availability and regulation, thereby providing a global picture of the cell’s metabolic phenotype. At present, the routine quantification of cell metabolism revolves around very basic cellular parameters: growth, substrate utilization and product formation. For a systems approach, however, just measuring gross metabolic features is insufficient; we are compelled to perform high-resolution, large-scale fluxomics in order to match the scale of other omics datasets. The challenges of performing large-scale fluxomics come from two opposing fronts. Metabolic flux analysis (MFA) is the estimation of intracellular fluxes from experimental data using a stoichiometric model, a process very much susceptible to modelling biases. The in silico challenge is to construct the most comprehensive model to represent the metabolism of a specific cell, while the in vivo challenge is to resolve as many fluxes as possible using experimental measurements or constraints. A compromise needs to be established between maximizing the resolution of the MFA model and working within technical limitations of the flux experiment. Conventional MFA models assembled from textbook pathways have been available for animal cell culture for the past 15 years. A state-of-the-art model was developed and used to analyse continuous hybridoma culture and batch CHO cell culture data (Chapter 3). Reasonable metabolic assumptions combined with constraint based analysis exploiting irreversibility constraints enabled the resolution of most fluxes in central carbon metabolism. However, while the results appear consistent, there is insufficient information in conventional measurement of uptake, secretion and growth data to assess the completeness of the model and validity of all assumptions. 13C metabolic flux analysis (13C MFA) can potentially resolve fluxes in the central carbon metabolism using flux constraints generated from 13C enrichment patterns of metabolites, but the multitude of substrate uptakes (glucose and amino acids) seen in mammalian cells, in addition to the lack of 13C enrichment data from proteinogenic amino acids, makes it very difficult to anticipate how a labelling experiment should be carried out. The challenges above have led to the development of a systematic workflow to perform large-scale MFA for mammalian cells. A genome-scale model (GeMs), an accurate compilation of gene-protein-reaction-metabolite associations, is the starting basis to perform whole-cell fluxomics. A semi-automated method was developed in order to rapidly extract a prototype of GeM from KEGG and UniProtKB databases (Chapter 4). Core metabolic pathways in the mouse GeM are mostly complete, suggesting that these databases are comprehensive and sufficient. The rapid prototyping system takes advantage of this, making long term maintenance of an accurate and up-to-date GeM by an individual possible. A large number of under-determined pathways in the mouse GeM cannot be resolved by 13C MFA because they do not produce any distinctive 13C enrichment patterns among the carbon metabolites. This has led to the development of SLIPs (short linearly independent pathways) for visualizing these under-determined metabolic pathways contained in large-scale GeMs (Chapter 5). Certain SLIPs are subsequently removed based on careful consideration of their pathway functions and the implications of their removal. A majority of SLIPs have a cyclic configuration, sharing similar redox or energy co-metabolites; very few represent true conversion of substrates to products. Of the 266 under-determined SLIPs generated from the mouse GeM, only 27 SLIPs were incorporated into the final working model under the criterion that they are significant pathways and are potentially resolvable by tracer experiments. Most of these SLIPs are degradation pathways of essential amino acids and inter-conversion of non-essential amino acids (Chapter 8). In parallel, OpenFLUX was developed to perform large-scale isotopic 13C MFA (Chapter 6). This software was built to accept multiple labelled substrates, and no restriction has been placed on the model type or enrichment data. These are necessary features to support large-scale flux analysis for mammalian cells. This was followed by the development of a design strategy that uses analytical gradients of isotopomer measurements to predict resolvability of free fluxes, from which the effectiveness of various 13C experimental scenarios using different combinations of input substrates and isotopomer measurements can be evaluated (Chapter 7). Hypothetical and experimental results have confirmed the predictions that, when glucose and glutamate/glutamine are simultaneously consumed, two separate experiments using [U-13C]- and [1-13C]-glucose, respectively, should be performed. If there is a restriction to a single experiment, then the 80:20 mixture of [U-13C]- and [1-13C]-glucose can provide a better resolution than other labelled glucose mixtures (Chapter 7 and Chapter 8). The tools and framework developed in this thesis brings us within reach of performing large-scale, high-resolution fluxomics for animal cells and hence realising systems-level investigation of mammalian metabolism. Moreover, with the establishment of a more rigorous, systematic modelling approach and higher functioning computational tools, we are now at a position to validate mammalian cell culture flux experiments performed 15 years ago.

Recombinant protein production by mammalian cells is a core component of today’s multi-billion dollar biopharmaceutical industry. Transcriptome and proteome technologies have been used to probe for cellular components that correlate with higher cell-specific productivity, but have yet to yield results that can be translated into practical metabolic engineering strategies. The recognition of cellular complexity has led to an increasing adoption of systems biology, a holistic investigation approach that aims to bring together different omics technologies and to analyze the resulting datasets under a unifying context. Fluxomics is chosen as the platform context to investigate cell metabolism because it captures the integrated effects of gene expression, enzyme activity, metabolite availability and regulation, thereby providing a global picture of the cell’s metabolic phenotype. At present, the routine quantification of cell metabolism revolves around very basic cellular parameters: growth, substrate utilization and product formation. For a systems approach, however, just measuring gross metabolic features is insufficient; we are compelled to perform high-resolution, large-scale fluxomics in order to match the scale of other omics datasets. The challenges of performing large-scale fluxomics come from two opposing fronts. Metabolic flux analysis (MFA) is the estimation of intracellular fluxes from experimental data using a stoichiometric model, a process very much susceptible to modelling biases. The in silico challenge is to construct the most comprehensive model to represent the metabolism of a specific cell, while the in vivo challenge is to resolve as many fluxes as possible using experimental measurements or constraints. A compromise needs to be established between maximizing the resolution of the MFA model and working within technical limitations of the flux experiment. Conventional MFA models assembled from textbook pathways have been available for animal cell culture for the past 15 years. A state-of-the-art model was developed and used to analyse continuous hybridoma culture and batch CHO cell culture data (Chapter 3). Reasonable metabolic assumptions combined with constraint based analysis exploiting irreversibility constraints enabled the resolution of most fluxes in central carbon metabolism. However, while the results appear consistent, there is insufficient information in conventional measurement of uptake, secretion and growth data to assess the completeness of the model and validity of all assumptions. 13C metabolic flux analysis (13C MFA) can potentially resolve fluxes in the central carbon metabolism using flux constraints generated from 13C enrichment patterns of metabolites, but the multitude of substrate uptakes (glucose and amino acids) seen in mammalian cells, in addition to the lack of 13C enrichment data from proteinogenic amino acids, makes it very difficult to anticipate how a labelling experiment should be carried out. The challenges above have led to the development of a systematic workflow to perform large-scale MFA for mammalian cells. A genome-scale model (GeMs), an accurate compilation of gene-protein-reaction-metabolite associations, is the starting basis to perform whole-cell fluxomics. A semi-automated method was developed in order to rapidly extract a prototype of GeM from KEGG and UniProtKB databases (Chapter 4). Core metabolic pathways in the mouse GeM are mostly complete, suggesting that these databases are comprehensive and sufficient. The rapid prototyping system takes advantage of this, making long term maintenance of an accurate and up-to-date GeM by an individual possible. A large number of under-determined pathways in the mouse GeM cannot be resolved by 13C MFA because they do not produce any distinctive 13C enrichment patterns among the carbon metabolites. This has led to the development of SLIPs (short linearly independent pathways) for visualizing these under-determined metabolic pathways contained in large-scale GeMs (Chapter 5). Certain SLIPs are subsequently removed based on careful consideration of their pathway functions and the implications of their removal. A majority of SLIPs have a cyclic configuration, sharing similar redox or energy co-metabolites; very few represent true conversion of substrates to products. Of the 266 under-determined SLIPs generated from the mouse GeM, only 27 SLIPs were incorporated into the final working model under the criterion that they are significant pathways and are potentially resolvable by tracer experiments. Most of these SLIPs are degradation pathways of essential amino acids and inter-conversion of non-essential amino acids (Chapter 8). In parallel, OpenFLUX was developed to perform large-scale isotopic 13C MFA (Chapter 6). This software was built to accept multiple labelled substrates, and no restriction has been placed on the model type or enrichment data. These are necessary features to support large-scale flux analysis for mammalian cells. This was followed by the development of a design strategy that uses analytical gradients of isotopomer measurements to predict resolvability of free fluxes, from which the effectiveness of various 13C experimental scenarios using different combinations of input substrates and isotopomer measurements can be evaluated (Chapter 7). Hypothetical and experimental results have confirmed the predictions that, when glucose and glutamate/glutamine are simultaneously consumed, two separate experiments using [U-13C]- and [1-13C]-glucose, respectively, should be performed. If there is a restriction to a single experiment, then the 80:20 mixture of [U-13C]- and [1-13C]-glucose can provide a better resolution than other labelled glucose mixtures (Chapter 7 and Chapter 8). The tools and framework developed in this thesis brings us within reach of performing large-scale, high-resolution fluxomics for animal cells and hence realising systems-level investigation of mammalian metabolism. Moreover, with the establishment of a more rigorous, systematic modelling approach and higher functioning computational tools, we are now at a position to validate mammalian cell culture flux experiments performed 15 years ago.

In biochemical networks, complex dynamical features such as superlinear growth and oscillations are classically considered a consequence of autocatalysis. For the large class of parameter-rich kinetic models, which includes Generalized Mass Ac- tion kinetics and Michaelis-Menten kinetics, we show that certain submatrices of the stoichiometric matrix, so-called unstable cores, are sufficient for a reaction network to admit instability and potentially give rise to such complex dynami- cal behavior. The determinant of the submatrix distinguishes unstable-positive feedbacks, with a single real-positive eigenvalue, and unstable-negative feedbacks without real-positive eigenvalues. Autocatalytic cores turn out to be exactly the unstable-positive feedbacks that are Metzler matrices. Thus there are sources of dynamical instability in chemical networks that are unrelated to autocatalysis. We use such intuition to design non-autocatalytic biochemical networks with su- perlinear growth and oscillations.

Nonlinear optical frequency conversion is one of the most important key techniques in order to obtain lasers with wavelengths targeted for specific applications. In order to realize efficient and tailored lasers, the quasi-phase-matching (QPM) approach using periodically-poled ferroelectric crystals is getting increasingly important. Also understanding of damage mechanisms in nonlinear materials is necessary to be able to design reliable and well working lasers. This is especially true for high power application lasers, which is a rapidly growing field, where the damage problem normally is the ultimate limiting factor. In this thesis work, several promising novel ferroelectric materials have been investigated for nonlinear optical applications and the emphasis has been put on QPM devices consisting of periodically-poled structures. The materials were selected from three different types of ferroelectric materials: 1) MgO-doped stoichiometric LiNbO3 (MgO:SLN) and LiTaO3 (MgO:SLT), and non-doped stoichiometric LiTaO3 (SLT), 2) KTiOPO4 (KTP) and its isomorphs RbTiOPO4 (RTP), and 3) KNbO3 (KN). The focus in our investigations have been put on the spontaneous polarization switching phenomena, optimization of the periodic poling conditions, and the photochromic optical damage properties which were characterized by the help of blue light-induced infrared absorption (BLIIRA) measurements. With electrical studies of the spontaneous polarization switching, we were able to determine quantitatively, and compare, the coercive field values of different materials by applying triangularly shaped electric fields. We found that the values of the coercive fields depended on the increase rate of the applied electric field. The coercive field of KN was the lowest (less than 0.5 kV/mm) followed by the ones of KTP, SLT, and MgO:SLT (1.5 to 2.5 kV/mm). MgO:SLN, and RTP had relatively high coercive fields, approximately 5.0 to 6.0 kV/mm, respectively. Based on the domain switching characteristics we found, we successfully fabricated periodically-poled devices in all of the investigated materials with 30 μm periodicities and sample thickness of 1 mm. Blue light-induced infrared absorption (BLIIRA) has been characterized for unpoled bulk and periodically-poled samples using a high-sensitivity, thermal-lens spectroscopy technique. SLT showed a large photorefraction effect and the BLIIRA signal could not be properly measured because of the large distortion of the probe beam. The rise and relaxation time of BLIIRA, after switching the blue light on and off was in a time span of 10 to 30 sec except for KTP and its isomorphs, which needed minutes to hours in order to saturate at a fixed value. KN and MgO:SLN showed the lowest susceptibility to the induced absorption. Periodic poling slightly increased the susceptibility of KTP, MgO:SLT, and KN. Relatively high thresholds were observed in MgO:SLT and KN. By increasing the peak-power intensity of the blue light, the induced absorption for MgO:SLN, KTP and KN saturated at a constant value while that of MgO:SLT increase in a constant fashion. This trend is critical issue for the device reliability at high-power applications. / QC 20100830

Large chemical reaction networks often exhibit distinctive features that can be interpreted as higher-level structures. Prime examples are metabolic pathways in a biochemical context. We review mathematical approaches that exploit the stoichiometric structure, which can be seen as a particular directed hypergraph, to derive an algebraic picture of chemical organizations. We then give an alternative interpretation in terms of set-valued set functions that encapsulate the production rules of the individual reactions. From the mathematical point of view, these functions define generalized topological spaces on the set of chemical species. We show that organization-theoretic concepts also appear in a natural way in the topological language. This abstract representation in turn suggests the exploration of the chemical meaning of well-established topological concepts. As an example, we consider connectedness in some detail.

This thesis describes the synthesis, characterisation and reactivity of diamide-amine and bis(cyclopentadienyl) supported Group 4 hydrazido(2-) compounds towards unsaturated molecules. The mechanisms of these transformations are probed using a range of structural, kinetic and computational methods.