904 resultados para Single-process Models


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Darwin observed that multiple, lowly organized, rudimentary, or exaggerated structures show increased relative variability. However, the cellular basis for these laws has never been investigated. Some animals, such as the nematode Caenorhabditis elegans, are famous for having organs that possess the same number of cells in all individuals, a property known as eutely. But for most multicellular creatures, the extent of cell number variability is unknown. Here we estimate variability in organ cell number for a variety of animals, plants, slime moulds, and volvocine algae. We find that the mean and variance in cell number obey a power law with an exponent of 2, comparable to Taylor's law in ecological processes. Relative cell number variability, as measured by the coefficient of variation, differs widely across taxa and tissues, but is generally independent of mean cell number among homologous tissues of closely related species. We show that the power law for cell number variability can be explained by stochastic branching process models based on the properties of cell lineages. We also identify taxa in which the precision of developmental control appears to have evolved. We propose that the scale independence of relative cell number variability is maintained by natural selection.

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We review research on the neural bases of verbal working memory, focusing on human neuroimaging studies. We first consider experiments that indicate that verbal working memory is composed of multiple components. One component involves the subvocal rehearsal of phonological information and is neurally implemented by left-hemisphere speech areas, including Broca’s area, the premotor area, and the supplementary motor area. Other components of verbal working memory may be devoted to pure storage and to executive processing of the contents of memory. These studies rest on a subtraction logic, in which two tasks are imaged, differing only in that one task presumably has an extra process, and the difference image is taken to reflect that process. We then review studies that show that the previous results can be obtained with experimental methods other than subtraction. We focus on the method of parametric variation, in which a parameter that presumably reflects a single process is varied. In the last section, we consider the distinction between working memory tasks that require only storage of information vs. those that require that the stored items be processed in some way. These experiments provide some support for the hypothesis that, when a task requires processing the contents of working memory, the dorsolateral prefrontal cortex is disproportionately activated.

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Multiscale asymptotic methods developed previously to study macromechanical wave propagation in cochlear models are generalized here to include active control of a cochlear partition having three subpartitions, the basilar membrane, the reticular lamina, and the tectorial membrane. Activation of outer hair cells by stereocilia displacement and/or by lateral wall stretching result in a frequency-dependent force acting between the reticular lamina and basilar membrane. Wavelength-dependent fluid loads are estimated by using the unsteady Stokes' equations, except in the narrow gap between the tectorial membrane and reticular lamina, where lubrication theory is appropriate. The local wavenumber and subpartition amplitude ratios are determined from the zeroth order equations of motion. A solvability relation for the first order equations of motion determines the subpartition amplitudes. The main findings are as follows: The reticular lamina and tectorial membrane move in unison with essentially no squeezing of the gap; an active force level consistent with measurements on isolated outer hair cells can provide a 35-dB amplification and sharpening of subpartition waveforms by delaying dissipation and allowing a greater structural resonance to occur before the wave is cut off; however, previously postulated activity mechanisms for single partition models cannot achieve sharp enough tuning in subpartitioned models.

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The organizational structure of the companies in the biomass energy sector, regarding the supply chain management services, can be greatly improved through the use of software decision support tools. These tools should be able to provide real-time alternative scenarios when deviations from the initial production plans are observed. To make this possible it is necessary to have representative production chain process models where several scenarios and solutions can be evaluated accurately. Due to its nature, this type of process is more adequately represented by means of event-based models. In particular, this work presents the modelling of a typical biomass production chain using the computing platform SIMEVENTS. Throughout the article details about the conceptual model, as well as simulation results, are provided

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Dynamic spatial analysis addresses computational aspects of space–time processing. This paper describes the development of a spatial analysis tool and modelling framework that together offer a solution for simulating landscape processes. A better approach to integrating landscape spatial analysis with Geographical Information Systems is advocated in this paper. Enhancements include special spatial operators and map algebra language constructs to handle dispersal and advective flows over landscape surfaces. These functional components to landscape modelling are developed in a modular way and are linked together in a modelling framework that performs dynamic simulation. The concepts and modelling framework are demonstrated using a hydrological modelling example. The approach provides a modelling environment for scientists and land resource managers to write and to visualize spatial process models with ease.

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This paper investigates the tidal effects on aeration conditions for plant root respiration in a tidal marsh. We extend the work of Ursino et al. ( 2004) by using a two-phase model for air and water flows in the marsh. Simulations have been conducted to examine directly the link between the airflow dynamics and the aeration condition in the marsh soil. The results show that the effects of entrapped air on water movement in the vadose zone are significant in certain circumstances. Single-phase models based on Richards' equation, which neglect such effects, may not be adequate for quantifying the aeration condition in tidal marsh. The optimal aeration condition, represented by the maximum of the integral magnitude of tidally advected air mass ( TAAM) flux, is found to occur near the tidal creek for the four soil textures simulated. This may explain the observation that some salt marsh plant species grow better near tidal creeks than in the inner marsh areas. Our analyses, based on the two-phase model and predicted TAAM flux magnitude, provide further insight into the positive feedback'' mechanism proposed by Ursino et al. ( 2004). That is, pioneer plants may grow successfully near the creek where the root aeration condition is optimal. The roots of the pioneer plants can soften and loosen the rhizosphere soil, which increases the evapotranspiration rate, the soil porosity, and absolute permeability and weakens the capillary effects. These, in turn, improve further the root aeration conditions and may lead to colonization by plants less resistant to anaerobic conditions.

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Essential hypertension is one of the most common diseases in the Western world, affecting about 26.4% of the adult population, and it is increasing (1). Its causes are heterogeneous and include genetic and environmental factors (2), but several observations point to an important role of the kidney in its genesis (3). In addition to variations in tubular transport mechanisms that could, for example, affect salt handling, structural characteristics of the kidney might also contribute to hypertension. The burden of chronic kidney disease is also increasing worldwide, due to population growth, increasing longevity, and changing risk factors. Although single-cause models of disease are still widely promoted, multideterminant or multihit models that can accommodate multiple risk factors in an individual or in a population are probably more applicable (4,5). In such a framework, nephron endowment is one potential determinant of disease susceptibility. Some time ago, Brenner and colleagues (6,7) proposed that lower nephron numbers predispose both to essential hypertension and to renal disease. They also proposed that hypertension and progressive renal insufficiency might be initiated and accelerated by glomerular hypertrophy and intraglomerular hypertension that develops as nephron number is reduced (8). In this review, we summarize data from recent studies that shed more light on these hypotheses. The data supply a new twist to possible mechanisms of the Barker hypothesis, which proposes that intrauterine growth retardation predisposes to chronic disease in later life (9). The review describes how nephron number is estimated and its range and some determinants and morphologic correlates. It then considers possible causes of low nephron numbers. Finally, associations of hypertension and renal disease with reduced nephron numbers are considered, and some potential clinical implications are discussed.

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In this paper, we present a top down approach for integrated process modelling and distributed process execution. The integrated process model can be utilized for global monitoring and visualization and distributed process models for local execution. Our main focus in this paper is the presentation of the approach to support automatic generation and linking of distributed process models from an integrated process definition.

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A Bayesian procedure for the retrieval of wind vectors over the ocean using satellite borne scatterometers requires realistic prior near-surface wind field models over the oceans. We have implemented carefully chosen vector Gaussian Process models; however in some cases these models are too smooth to reproduce real atmospheric features, such as fronts. At the scale of the scatterometer observations, fronts appear as discontinuities in wind direction. Due to the nature of the retrieval problem a simple discontinuity model is not feasible, and hence we have developed a constrained discontinuity vector Gaussian Process model which ensures realistic fronts. We describe the generative model and show how to compute the data likelihood given the model. We show the results of inference using the model with Markov Chain Monte Carlo methods on both synthetic and real data.

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Combined Heat and Power (CHP) is the simultaneous generation of usable heat and power in a single process. Despite its obvious advantages in terms of increased efficiency when compared to a single heat or power generation unit, there are a number of technical and economic reasons that have limited their selection. Biomass resources can be, and actually are used as fuel in CHP installations; however several hurdles have to be sorted beforehand, among the most important is the fact that biomass energy sources are not as energy intense as conventional CHP fuels. The ultimate outcome is a limited number of CHP units making use of biomass as fuel. Even fewer CHP units use bioliquids (e.g.: fast pyrolysis biomass liquids, biodiesel and vegetable oil). The Bioliquid-CHP project is carried out by a consortium of seven European and Russian complementary partners, funded by the EU and by the Federal Agency for Science and Innovation of the Russian Federation. The project aim is to develop microturbine and internal combustion engine adaptations in order to adjust these prime movers to bioliquids for CHP applications. This paper will show a summary of the current biomass CHP installations in the UK and the Netherlands, making reference to number of units, capacity, fuel used, the conversion technology involved and the preferred prime movers. The information will give an insight of the current market, with probable future trends and areas where growth could be expected. A similar paper describing the biomass CHP situation in Italy and Russia will be prepared in the near future.

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This paper describes the implementation of a sensitive, on-chip immunoassay for the analysis of intracellular proteins, developed using microdroplet technology. The system offers a number of analytical functionalities, enabling the lysis of low cell numbers, as well as protein detection and quantification, integrated within a single process flow. Cells were introduced into the device in suspension and were electrically lysed in situ. The cell lysate was subsequently encapsulated together with antibody-functionalized beads into stable, water-in-oil droplets, which were stored on-chip. The binding of intracellular proteins to the beads was monitored fluorescently. By analyzing many individual droplets and quantifying the data obtained against standard additions, we measured the level of two intracellular proteins, namely, HRas-mCitrine, expressed within HEK-293 cells, and actin-EGFP, expressed within MCF-7 cells. We determined the concentrations of these proteins over 5 orders of magnitude, from ~50 pM to 1 µM. The results from this semiautomated method were compared to those for determinations made using Western blots, and were found not only to be faster, but required a smaller number of cells. © 2011 American Chemical Society.

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Cystic fibrosis (CF) is the most common lethal inherited disease among Caucasians and arises due to mutations in a chloride channel, called cystic fibrosis transmembrane conductance regulator. A hallmark of this disease is the chronic bacterial infection of the airways, which is usually, associated with pathogens such as Pseudomonas aeruginosa, S. aureus and recently becoming more prominent, B. cepacia. The excessive inflammatory response, which leads to irreversible lung damage, will in the long term lead to mortality of the patient at around the age of 40 years. Understanding the pathogenesis of CF currently relies on animal models, such as those employing genetically-modified mice, and on single cell culture models, which are grown either as polarised or non-polarised epithelium in vitro. Whilst these approaches partially enable the study of disease progression in CF, both types of models have inherent limitations. The overall aim of this thesis was to establish a multicellular co-culture model of normal and CF human airways in vitro, which helps to partially overcome these limitations and permits analysis of cell-to-cell communication in the airways. These models could then be used to examine the co-ordinated response of the airways to infection with relevant pathogens in order to validate this approach over animals/single cell models. Therefore epithelial cell lines of non-CF and CF background were employed in a co-culture model together with human pulmonary fibroblasts. Co-cultures were grown on collagen-coated permeable supports at air-liquid interface to promote epithelial cell differentiation. The models were characterised and essential features for investigating CF infections and inflammatory responses were investigated and analysed. A pseudostratified like epithelial cell layer was established at air liquid interface (ALI) of mono-and co-cultures and cell layer integrity was verified by tight junction (TJ) staining and transepithelial resistance measurements (TER). Mono- and co-cultures were also found to secrete the airway mucin MUC5AC. Influence of bacterial infections was found to be most challenging when intact S. aureus, B. cepacia and P. aeruginosa were used. CF mono- and co-cultures were found to mimic the hyperinflammatory state found in CF, which was confirmed by analysing IL-8 secretions of these models. These co-culture models will help to elucidate the role fibroblasts play in the inflammatory response to bacteria and will provide a useful testing platform to further investigate the dysregulated airway responses seen in CF.

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Besides their well-described use as delivery systems for water-soluble drugs, liposomes have the ability to act as a solubilizing agent for drugs with low aqueous solubility. However, a key limitation in exploiting liposome technology is the availability of scalable, low-cost production methods for the preparation of liposomes. Here we describe a new method, using microfluidics, to prepare liposomal solubilising systems which can incorporate low solubility drugs (in this case propofol). The setup, based on a chaotic advection micromixer, showed high drug loading (41 mol%) of propofol as well as the ability to manufacture vesicles with at prescribed sizes (between 50 and 450 nm) in a high-throughput setting. Our results demonstrate the ability of merging liposome manufacturing and drug encapsulation in a single process step, leading to an overall reduced process time. These studies emphasise the flexibility and ease of applying lab-on-a-chip microfluidics for the solubilisation of poorly water-soluble drugs.

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On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically. A method of taking into account explicitly a priori probabilities of alternative hypotheses on the structure of information initiating memory trace retrieval and modified ROCs (mROCs, a posteriori probabilities of correct recall vs. false alarm probability) are introduced. The comparison of empirical and calculated ROCs (or mROCs) demonstrates that they coincide quantitatively and in this way intensities of cues used in appropriate experiments may be estimated. It has been found that basic ROC properties which are one of experimental findings underpinning dual-process models of recognition memory can be explained within our one-factor NNAMM.