993 resultados para coefficients


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Self- and cross-velocity correlation functions and related transport coefficients of molten salts are studied by molecular-dynamics simulation. Six representative systems are considered, i.e., NaCl and KCl alkali halides, CuCl and CuBr noble-metal halides, and SrCl2 and ZnCl2 divalent metal-ion halides. Computer simulation results are compared with experimental self-diffusion coefficients and electrical conductivities. Special attention is paid to dynamic cross correlations and their dependence on the Coulomb interactions as well as on the size and mass differences between anions and cations.

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Serum uric acid (SUA) concentration is independently associated with blood pressure (BP) in adults. We examined this association in young adults at an age where anti-hypertension treatment, other potential confounding factors and co-morbidity are unlikely to occur. We assessed BP, anthropometric variables including weight, height, waist circumference (WC), body fat percent (using bioimpedance), lifestyle behaviors, SUA and blood lipids in 549 participants aged 19-20 years from a population-based cohort study (Seychelles Child Development Study). Mean (s.d.) SUA was higher in males than females, 0.33 (0.08) and 0.24 (0.07) mmol l(-1), respectively. Body mass index (BMI) was higher in females than males but BP was markedly higher in males than in females. SUA was associated with both systolic and diastolic BP. However, the magnitude of the linear regression coefficients relating BP and SUA decreased by up to 50% upon adjustment for BMI, WC or body fat percent. The association between SUA and BP was not altered upon further adjustment for alcohol intake, smoking, triglycerides or renal function. In fully adjusted models, SUA remained associated with BP (P<0.05) in females. In conclusion, adiposity substantially decreased the association between SUA and BP in young adults, and BP was independently associated with SUA in females. These findings suggest a role of adiposity in the link between hyperuricemia and hypertension.

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ABSTRACT: INTRODUCTION: Lipoprotein-associated phospholipase A2 (Lp-PLA2) is a circulating enzyme with pro-inflammatory and oxidative activities associated with cardiovascular disease and ischemic stroke. While high plasma Lp-PLA2 activity was reported as a risk factor for dementia in the Rotterdam study, no association between Lp-PLA2 mass and dementia or Alzheimer's disease (AD) was detected in the Framingham study. The objectives of the current study were to explore the relationship of plasma Lp-PLA2 activity with cognitive diagnoses (AD, amnestic mild cognitive impairment (aMCI), and cognitively healthy subjects), cardiovascular markers, cerebrospinal fluid (CSF) markers of AD, and apolipoprotein E (APOE) genotype. METHODS: Subjects with mild AD (n = 78) and aMCI (n = 59) were recruited from the Memory Clinic, University Hospital, Basel, Switzerland; cognitively healthy subjects (n = 66) were recruited from the community. Subjects underwent standardised medical, neurological, neuropsychological, imaging, genetic, blood and CSF evaluation. Differences in Lp-PLA2 activity between the cognitive diagnosis groups were tested with ANOVA and in multiple linear regression models with adjustment for covariates. Associations between Lp-PLA2 and markers of cardiovascular disease and AD were explored with Spearman's correlation coefficients. RESULTS: There was no significant difference in plasma Lp-PLA2 activity between AD (197.1 (standard deviation, SD 38.4) nmol/min/ml) and controls (195.4 (SD 41.9)). Gender, statin use and low-density lipoprotein cholesterol (LDL) were independently associated with Lp-PLA2 activity in multiple regression models. Lp-PLA2 activity was correlated with LDL and inversely correlated with high-density lipoprotein (HDL). AD subjects with APOE-ε4 had higher Lp-PLA2 activity (207.9 (SD 41.2)) than AD subjects lacking APOE-ε4 (181.6 (SD 26.0), P = 0.003) although this was attenuated by adjustment for LDL (P = 0.09). No strong correlations were detected for Lp-PLA2 activity and CSF markers of AD. CONCLUSION: Plasma Lp-PLA2 was not associated with a diagnosis of AD or aMCI in this cross-sectional study. The main clinical correlates of Lp-PLA2 activity in AD, aMCI and cognitively healthy subjects were variables associated with lipid metabolism.

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New economic geography models show that there may be a strong relationship between economic integration and the geographical concentration of industries. Nevertheless, this relationship is neither unique nor stable, and may follow a ?-shaped pattern in the long term. The aim of the present paper is to analyze the evolution of the geographical concentration of manufacturing across Spanish regions during the period 1856-1995. We construct several geographical concentration indices for different points in time over these 140 years. The analysis is carried out at two levels of aggregation, in regions corresponding to the NUTS-II and NUTS-III classifications. We confirm that the process of economic integration stimulated the geographical concentration of industrial activity. Nevertheless, the localization coefficients only started to fall after the beginning of the integration of the Spanish Economy into the international markets in the mid-70s, and this new path was not interrupted by Spain¿s entry in the European Union some years later

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Beta coefficients are not stable if we modify the observation periods of the returns. The market portfolio composition also varies, whereas changes in the betas are the same, whether they are calculated as regression coefficients or as a ratio of the risk premiums. The instantaneous beta, obtained when the capitalization frequency approaches infinity, may be a useful tool in portfolio selection.

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Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.

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We consider diffusion of a passive substance C in a phase-separating nonmiscible binary alloy under turbulent mixing. The substance is assumed to have different diffusion coefficients in the pure phases A and B, leading to a spatially and temporarily dependent diffusion ¿coefficient¿ in the diffusion equation plus convective term. In this paper we consider especially the effects of a turbulent flow field coupled to both the Cahn-Hilliard type evolution equation of the medium and the diffusion equation (both, therefore, supplemented by a convective term). It is shown that the formerly observed prolonged anomalous diffusion [H. Lehr, F. Sagués, and J.M. Sancho, Phys. Rev. E 54, 5028 (1996)] is no longer seen if a flow of sufficient intensity is supplied.

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In this paper we consider diffusion of a passive substance C in a temporarily and spatially inhomogeneous two-dimensional medium. As a realization for the latter we choose a phase-separating medium consisting of two substances A and B, whose dynamics is determined by the Cahn-Hilliard equation. Assuming different diffusion coefficients of C in A and B, we find that the variance of the distribution function of the said substance grows less than linearly in time. We derive a simple identity for the variance using a probabilistic ansatz and are then able to identify the interface between A and B as the main cause for this nonlinear dependence. We argue that, finally, for very large times the here temporarily dependent diffusion "constant" goes like t-1/3 to a constant asymptotic value D¿. The latter is calculated approximately by employing the effective-medium approximation and by fitting the simulation data to the said time dependence.

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We consider the distribution of cross sections of clusters and the density-density correlation functions for the A+B¿0 reaction. We solve the reaction-diffusion equations numerically for random initial distributions of reactants. When both reactant species have the same diffusion coefficients the distribution of cross sections and the correlation functions scale with the diffusion length and obey superuniversal laws (independent of dimension). For different diffusion coefficients the correlation functions still scale, but the scaling functions depend on the dimension and on the diffusion coefficients. Furthermore, we display explicitly the peculiarities of the cluster-size distribution in one dimension.

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BACKGROUND: While the assessment of analytical precision within medical laboratories has received much attention in scientific enquiry, the degree of as well as the sources causing variation between them remains incompletely understood. In this study, we quantified the variance components when performing coagulation tests with identical analytical platforms in different laboratories and computed intraclass correlations coefficients (ICC) for each coagulation test. METHODS: Data from eight laboratories measuring fibrinogen twice in twenty healthy subjects with one out of 3 different platforms and single measurements of prothrombin time (PT), and coagulation factors II, V, VII, VIII, IX, X, XI and XIII were analysed. By platform, the variance components of (i) the subjects, (ii) the laboratory and the technician and (iii) the total variance were obtained for fibrinogen as well as (i) and (iii) for the remaining factors using ANOVA. RESULTS: The variability for fibrinogen measurements within a laboratory ranged from 0.02 to 0.04, the variability between laboratories ranged from 0.006 to 0.097. The ICC for fibrinogen ranged from 0.37 to 0.66 and from 0.19 to 0.80 for PT between the platforms. For the remaining factors the ICC's ranged from 0.04 (FII) to 0.93 (FVIII). CONCLUSIONS: Variance components that could be attributed to technicians or laboratory procedures were substantial, led to disappointingly low intraclass correlation coefficients for several factors and were pronounced for some of the platforms. Our findings call for sustained efforts to raise the level of standardization of structures and procedures involved in the quantification of coagulation factors.

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New economic geography models show that there may be a strong relationship between economic integration and the geographical concentration of industries. Nevertheless, this relationship is neither unique nor stable, and may follow a ?-shaped pattern in the long term. The aim of the present paper is to analyze the evolution of the geographical concentration of manufacturing across Spanish regions during the period 1856-1995. We construct several geographical concentration indices for different points in time over these 140 years. The analysis is carried out at two levels of aggregation, in regions corresponding to the NUTS-II and NUTS-III classifications. We confirm that the process of economic integration stimulated the geographical concentration of industrial activity. Nevertheless, the localization coefficients only started to fall after the beginning of the integration of the Spanish Economy into the international markets in the mid-70s, and this new path was not interrupted by Spain¿s entry in the European Union some years later

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Studies of hybrid zones can inform our understanding of reproductive isolation and speciation. Two species of brown lemur (Eulemur rufifrons and E. cinereiceps) form an apparently stable hybrid zone in the Andringitra region of south-eastern Madagascar. The aim of this study was to identify factors that contribute to this stability. We sampled animals at 11 sites along a 90-km transect through the hybrid zone and examined variation in 26 microsatellites, the D-loop region of mitochondrial DNA, six pelage and nine morphological traits; we also included samples collected in more distant allopatric sites. Clines in these traits were noncoincident, and there was no increase in either inbreeding coefficients or linkage disequilibrium at the centre of the zone. These results could suggest that the hybrid zone is maintained by weak selection against hybrids, conforming to either the tension zone or geographical selection-gradient model. However, a closer examination of clines in pelage and microsatellites indicates that these clines are not sigmoid or stepped in shape but instead plateau at their centre. Sites within the hybrid zone also occur in a distinct habitat, characterized by greater seasonality in precipitation and lower seasonality in temperature. Together, these findings suggest that the hybrid zone may follow the bounded superiority model, with exogenous selection favouring hybrids within the transitional zone. These findings are noteworthy, as examples supporting the bounded superiority model are rare and may indicate a process of ecologically driven speciation without geographical isolation.

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Genetically constructed microbial biosensors for measuring organic pollutants are mostly applied in aqueous samples. Unfortunately, the detection limit of most biosensors is insufficient to detect pollutants at low but environmentally relevant concentrations. However, organic pollutants with low levels of water solubility often have significant gas-water partitioning coefficients, which in principle makes it possible to measure such compounds in the gas rather than the aqueous phase. Here we describe the first use of a microbial biosensor for measuring organic pollutants directly in the gas phase. For this purpose, we reconstructed a bioluminescent Pseudomonas putida naphthalene biosensor strain to carry the NAH7 plasmid and a chromosomally inserted gene fusion between the sal promoter and the luxAB genes. Specific calibration studies were performed with suspended and filter-immobilized biosensor cells, in aqueous solution and in the gas phase. Gas phase measurements with filter-immobilized biosensor cells in closed flasks, with a naphthalene-contaminated aqueous phase, showed that the biosensor cells can measure naphthalene effectively. The biosensor cells on the filter responded with increasing light output proportional to the naphthalene concentration added to the water phase, even though only a small proportion of the naphthalene was present in the gas phase. In fact, the biosensor cells could concentrate a larger proportion of naphthalene through the gas phase than in the aqueous suspension, probably due to faster transport of naphthalene to the cells in the gas phase. This led to a 10-fold lower detectable aqueous naphthalene concentration (50 nM instead of 0.5 micro M). Thus, the use of bacterial biosensors for measuring organic pollutants in the gas phase is a valid method for increasing the sensitivity of these valuable biological devices.

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PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

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This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 representative pavement sites across Iowa were selected. The selected pavement sites represent flexible, rigid, and composite pavement systems throughout Iowa. The required MEPDG inputs and the historical performance data for the selected sites were extracted from a variety of sources. The accuracy of the nationally-calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified to improve the accuracy of model predictions. The identified local calibration coefficients are presented with other significant findings and recommendations for use in MEPDG/DARWin-ME for Iowa pavement systems.