992 resultados para Particle Protein


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Background: Although excessive ethanol consumption is known to lead to a variety of adverse effects in the heart, the molecular mechanisms of such effects have remained poorly defined. We hypothesized that posttranslational covalent binding of reactive molecular species to proteins occurs in the heart in response to acute ethanol exposure. Methods: The generation of protein adducts with several aldehydic species was examined by using monospecific antibodies against adducts with malondialdehyde (MDA), acetaldehyde (AA), MDA-AA hybrids, and hydroxyethyl radicals. Specimens of heart tissue were obtained from rats after intraperitoneal injections with alcohol (75 mmol/kg body weight) with or without pretreatment with cyanamide (0.05 mmol/kg body weight), an aldehyde dehydrogenase inhibitor. Results: The amounts of MDA and unreduced AA adducts were found to be significantly increased in the heart of the rats treated with ethanol, cyanamide, or both, whereas no other adducts were detected in statistically significant quantities. Immunohistochemical studies for characterization of adduct distribution revealed sarcolemmal adducts of both MDA and AA in the rats treated with ethanol and cyanamide in addition to intracellular adducts, which were also present in the group treated with ethanol alone. Conclusions: These findings support the role of enhanced lipid peroxidation and the generation of protein-aldehyde condensates in vivo as a result of excessive ethanol intake. These findings may have implications in the molecular mechanisms of cardiac dysfunction in alcoholics.

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Xanthine dehydrogenase (XDH) from the bacterium Rhodobacter capsulatus catalyzes the hydroxylation of xanthine to uric acid with NAD(+) as the electron acceptor. R. capsulatus XDH forms an (alphabeta)(2) heterotetramer and is highly homologous to homodimeric eukaryotic XDHs. The crystal structures of bovine XDH and R. capsulatus XDH showed that the two proteins have highly similar folds; however, R. capsulatus XDH is at least 5 times more active than bovine XDH and, unlike mammalian XDH, does not undergo the conversion to the oxidase form. Here we demonstrate electrocatalytic activity of the recombinant enzyme, expressed in Escherichia coli, while immobilized on an edge plane pyrolytic graphite working electrode. Furthermore, we have determined all redox potentials of the four cofactors (Mo-VI/V, Mo-V/IV, FAD/FADH, FADH/FADH(2) and two distinct [2Fe-2S](2+/+) clusters) using a combination of potentiometric and voltammetric methods. A novel feature identified in catalytic voltammetry of XDH concerns the potential for the onset of catalysis (ca. 400 mV), which is at least 600 mV more positive than that of the highest potential cofactor. This unusual observation is explained on the basis of a pterin-associated oxidative switch during voltammetry that precedes catalysis.

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Experimental antoimmune encephalomyelitis (EAE) is an organ-specific autoimmune disease characterised by inflammation and demyelination of the central nervous system and is the best available animal model of multiple sclerosis (MS). Since previous studies have shown that EAE is less severe or is delayed in onset during pregnancy and that administration of the pregnancy hormone early pregnancy factor (EPF) down-regulates EAE, experiments in the present study were designed to explore further the role of EPF in EAE. By using the rosette inhibition test, the standard bioassay for EPF and, by semi-quantitative RT-PCR techniques, we have now shown that inflammatory cells from the spinal cord of rats with EAE can produce and secrete EPF, with production being greatest during recovery from disease. Administration of EPF to rats with EAE resulted in a significant increase in the expression of IL-4 and IL-10 mRNA and a significant decrease in IFN-gamma mRNA expression in spinal cord inflammatory cells. Encephalitogenic MBP-specific T cell lines were prepared from popliteal lymph nodes of rats with EAE. Proliferation assays using these cells demonstrated the ability of exogenous EPF to down-regulate the responses of T lymphocytes to MBP. (C) 2003 Elsevier B.V. All rights reserved.

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A fruta-pão é um fruto rico em vitaminas e minerais do qual pode ser extraído o amido ou processado à forma de farinha aumentando sua vida de prateleira. Essa farinha pode substituir parcialmente a farinha de trigo em produtos panificáveis. O objetivo do presente estudo foi extrair e analisar algumas características físicas, químicas e tecnológicas do amido nativo de fruta-pão, além de realizar a caracterização físico-química da farinha obtida desse fruto, de suas misturas com a farinha de trigo e verificar a viabilidade destas na elaboração de pão de forma. O amido de fruta-pão foi submetido às análises de umidade, capacidade de ligação de água a frio (CLAF), poder de inchamento (PI), índice de solubilidade (IS), sinérese, microscopia eletrônica de varredura (MEV) e difração de raio X. O amido de milho foi analisado para efeito de comparação. O amido de fruta-pão apresentou maior umidade (18,32%), CLAF (80,33%), PI (198,71 g/g a 90 0C), IS (7,22% a 90 0C) e sinérese (2,69 mL) quando comparado com o amido de milho. Pela análise de MEV, verificou-se que o amido de fruta-pão apresentou grânulos menores que os do amido de milho, mas ambos com formato poliédrico. A difração de raio X permitiu classificar o amido de milho como tipo A e o de fruta-pão como tipo B. As amostras de farinha analisadas continham um percentual de farinha de fruta-pão de 0% (controle), 5%, 10%, 15%, 20% e 100% e foram submetidas às análises de umidade, cinzas, extrato etéreo, carboidratos, proteína, energia, fibra total, solúvel e insolúvel, pH, cor e granulometria. Os mesmos percentuais de substituição foram utilizados para a elaboração dos pães, analisados em relação a umidade, peso, volume, volume específico, altura, perfil de textura, aceitação sensorial e intenção de compra. A substituição da farinha de trigo pela farinha de fruta-pão influenciou na granulometria das misturas, reduziu o teor de proteína de 10,8% para 3,99%, elevou o percentual de carboidratos, cinzas e pH de 73,84%, 0,52% e 5,94 para 83,10%, 2,19% e 6,34 , respectivamente. No entanto, para a umidade, teor de lipídeos e diferença global de cor das amostras não foi verificada influência significativa do percentual de substituição. A farinha de fruta-pão apresentou um maior percentual de fibras totais (21,17%), solúveis (10,39%) e insolúveis (10,77%). Para os pães de forma, o aumento da substituição não influenciou na umidade e no peso dos pães, exceto para a amostra com 100% de farinha de fruta-pão, que apresentou 41,28% de umidade e peso de 588,65 g. Para as demais análises, os maiores percentuais de substituição influenciaram negativamente as características do produto. Em relação a caracterização físico-química das amostras de farinha, o aumento do percentual de substituição influenciou na maioria dos parâmetros avaliados. Para os pães, as formulações com maiores notas hedônicas e com melhor intenção de compra foram as com substituição de 5% e de 10%.

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Protein aggregation became a widely accepted marker of many polyQ disorders, including Machado-Joseph disease (MJD), and is often used as readout for disease progression and development of therapeutic strategies. The lack of good platforms to rapidly quantify protein aggregates in a wide range of disease animal models prompted us to generate a novel image processing application that automatically identifies and quantifies the aggregates in a standardized and operator-independent manner. We propose here a novel image processing tool to quantify the protein aggregates in a Caenorhabditis elegans (C. elegans) model of MJD. Confocal mi-croscopy images were obtained from animals of different genetic conditions. The image processing application was developed using MeVisLab as a platform to pro-cess, analyse and visualize the images obtained from those animals. All segmenta-tion algorithms were based on intensity pixel levels.The quantification of area or numbers of aggregates per total body area, as well as the number of aggregates per animal were shown to be reliable and reproducible measures of protein aggrega-tion in C. elegans. The results obtained were consistent with the levels of aggrega-tion observed in the images. In conclusion, this novel imaging processing applica-tion allows the non-biased, reliable and high throughput quantification of protein aggregates in a C. elegans model of MJD, which may contribute to a significant improvement on the prognosis of treatment effectiveness for this group of disor-ders

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In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention

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Information on the spatial distribution of particle size fractions is essential for use planning and management of soils. The aim of this work to was to study the spatial variability of particle size fractions of a Typic Hapludox cultivated with conilon coffee. The soil samples were collected at depths of 0-0.20 and 0.20-0.40 m in the coffee canopy projection, totaling 109 georeferentiated points. At the depth of 0.2-0.4 m the clay fraction showed average value significantly higher, while the sand fraction showed was higher in the depth of 0-0.20 m. The silt showed no significant difference between the two depths. The particle size fractions showed medium and high spatial variability. The levels of total sand and clay have positive and negative correlation, respectively, with the altitude of the sampling points, indicating the influence of landscape configuration.

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The efficiency of sources used for soil acidity correction depends on reactivity rate (RR) and neutralization power (NP), indicated by effective calcium carbonate (ECC). Few studies establish relative efficiency of reactivity (RER) for silicate particle-size fractions, therefore, the RER applied for lime are used. This study aimed to evaluate the reactivity of silicate materials affected by particle size throughout incubation periods in comparison to lime, and to calculate the RER for silicate particle-size fractions. Six correction sources were evaluated: three slags from distinct origins, dolomitic and calcitic lime separated into four particle-size fractions (2, 0.84, 0.30 and <0.30-mm sieves), and wollastonite, as an additional treatment. The treatments were applied to three soils with different texture classes. The dose of neutralizing material (calcium and magnesium oxides) was applied at equal quantities, and the only variation was the particle-size material. After a 90-day incubation period, the RER was calculated for each particle-size fraction, as well as the RR and ECC of each source. The neutralization of soil acidity of the same particle-size fraction for different sources showed distinct solubility and a distinct reaction between silicates and lime. The RER for slag were higher than the limits established by Brazilian legislation, indicating that the method used for limes should not be used for the slags studied here.

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LHC has found hints for a Higgs particle of 125 GeV. We investigate the possibility that such a particle is a mixture of scalar and pseudoscalar states. For definiteness, we concentrate on a two-Higgs doublet model with explicit CP violation and soft Z(2) violation. Including all Higgs production mechanisms, we determine the current constraints obtained by comparing h -> yy with h -> VV*, and comment on the information which can be gained by measurements of h -> b (b) over bar. We find bounds vertical bar s(2)vertical bar less than or similar to 0.83 at one sigma, where vertical bar s(2)vertical bar = 0 (vertical bar s(2)vertical bar = 1) corresponds to a pure scalar (pure pseudoscalar) state.

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We study a model consisting of particles with dissimilar bonding sites ("patches"), which exhibits self-assembly into chains connected by Y-junctions, and investigate its phase behaviour by both simulations and theory. We show that, as the energy cost epsilon(j) of forming Y-junctions increases, the extent of the liquid-vapour coexistence region at lower temperatures and densities is reduced. The phase diagram thus acquires a characteristic "pinched" shape in which the liquid branch density decreases as the temperature is lowered. To our knowledge, this is the first model in which the predicted topological phase transition between a fluid composed of short chains and a fluid rich in Y-junctions is actually observed. Above a certain threshold for epsilon(j), condensation ceases to exist because the entropy gain of forming Y-junctions can no longer offset their energy cost. We also show that the properties of these phase diagrams can be understood in terms of a temperature-dependent effective valence of the patchy particles. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3605703]

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This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

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This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

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Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

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Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

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The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.