977 resultados para Mobile Spatial Interaction


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Using the framework of Desmet and Rossi-Hansberg (forthcoming), we present a model of spatial takeoff that is calibrated using spatially-disaggregated occupational data for England in c.1710. The model predicts changes in the spatial distribution of agricultural and manufacturing employment which match data for c.1817 and 1861. The model also matches a number of aggregate changes that characterise the first industrial revolution. Using counterfactual geographical distributions, we show that the initial concentration of productivity can matter for whether and when an industrial takeoff occurs. Subsidies to innovation in either sector can bring forward the date of takeoff while subsidies to the use of land by manufacturing firms can significantly delay a takeoff because it decreases spatial concentration of activity.

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B cells are the primary targets of infection for mouse mammary tumor virus (MMTV). However, for productive retroviral infection, T cell stimulation through the virally-encoded superantigen (SAG) is necessary. It activates B cells and leads to cell division and differentiation. To characterize the role of B cell differentiation for the MMTV life cycle, we studied the course of infection in transgenic mice deficient for CD28/CTLA4-B7 interactions (mCTLA4-H gamma 1 transgenic mice). B cell infection occurred in CTLA4-H gamma 1 transgenic mice as integrated proviral DNA could be detected in draining lymph node cells early after infection by polymerase chain reaction analysis. In mice expressing I-E, B cells were able to present the viral SAG efficiently to V beta 6+ T cells. These cells expanded specifically and were triggered to express the activation marker CD69. Further stages of progression of infection appeared to be defective. Kinetics experiments indicated that T and B cell stimulation stopped more rapidly than in control mice. B cells acquired an activated CD69+ phenotype, were induced to produce IgM but only partially switched to IgG secretion. Finally, the dissemination of infected cells to other lymph nodes and spleen was reduced and the peripheral deletion of V beta 6+ T cells was minimal. In contrast, in mice lacking I-E, T cell stimulation was also impaired and B cell activation undetectable. These data implicate B7-dependent cellular interactions for superantigenic T cell stimulation by low-affinity TCR ligands and suggest a role of B cell differentiation in viral dissemination and peripheral T cell deletion.

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Using the framework of Desmet and Rossi-Hansberg (forthcoming), we present a model of spatial takeoff that is calibrated using spatially-disaggregated occupational data for England in c.1710. The model predicts changes in the spatial distribution of agricultural and manufacturing employment which match data for c.1817 and 1861. The model also matches a number of aggregate changes that characterise the first industrial revolution. Using counterfactual geographical distributions, we show that the initial concentration of productivity can matter for whether and when an industrial takeoff occurs. Subsidies to innovation in either sector can bring forward the date of takeoff while subsidies to the use of land by manufacturing firms can significantly delay a takeoff because it decreases spatial concentration of activity.

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Two published case reports showed that addition of risperidone (1 and 2 mg/d) to a clozapine treatment resulted in a strong increase of clozapine plasma levels. As clozapine is metabolized by cytochrome P450 isozymes, a study was initiated to assess the in vivo interaction potential of risperidone on various cytochrome P450 isozymes. Eight patients were phenotyped with dextromethorphan (CYP2D6), mephenytoin (CYP2C19), and caffeine (CYP1A2) before and after the introduction of risperidone. Before risperidone, all eight patients were phenotyped as being extensive metabolizers of CYP2D6 and CYP2C19. Risperidone at dosages between 2 and 6 mg/d does not appear to significantly inhibit CYP1A2 and CYP2C19 in vivo (median plasma paraxanthine/caffeine ratios before and after risperidone: 0.65, 0.69; p = 0.89; median urinary (S)/(R) mephenytoin ratios before and after risperidone:0.11, 0.12; p = 0.75). Although dextromethorphan metabolic ratio is significantly increased by risperidone (median urinary dextromethorphan/dextrorphan ratios before and after risperidone: 0.010, 0.018; p = 0.042), risperidone can be considered a weak in vivo CYP2D6 inhibitor, as this increase is modest and none of the eight patients was changed from an extensive to a poor metabolizer. The reported increase of clozapine concentrations by risperidone can therefore not be explained by an inhibition of CYP1A2, CYP2D6, CYP2C19 or by any combination of the three.

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OBJECTIVE: To assess the relationships and possible interactions between polymorphisms related to HDL levels and alcohol consumption. METHODS: Cross-sectional population-based study including 2863 women and 2546 men aged 35-75 years (CoLaus study). Alcohol intake was assessed by the reported alcohol consumption of the last 7 days. Nineteen candidate genes known to influence HDL levels were studied. RESULTS: Alcohol consumption increased HDL cholesterol levels in both genders. After multivariate adjustment for gender, age, body mass index, smoking, hypolipidaemic drug treatment, physical activity and alcohol consumption, APOA5, CETP, LIPC and LPL gene polymorphisms were significantly (10(-5) threshold) related with HDL cholesterol levels, while no genexalcohol intake interaction was found for all SNPs studied. ABCA1 polymorphisms were related to HDL cholesterol levels on bivariate analysis but the relationship was no longer significant after multivariate analysis. CONCLUSION: Our data confirm the association of alcohol consumption and of APOA5, CETP, LIPC and LPL gene polymorphisms with HDL cholesterol levels. Conversely, no genexalcohol consumption interactions were found, suggesting that the effect of alcohol consumption on HDL cholesterol levels is not mediated via a modulation of HDL related genes.

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Colistin is a last resort's antibacterial treatment in critically ill patients with multi-drug resistant Gram-negative infections. As appropriate colistin exposure is the key for maximizing efficacy while minimizing toxicity, individualized dosing optimization guided by therapeutic drug monitoring is a top clinical priority. Objective of the present work was to develop a rapid and robust HPLC-MS/MS assay for quantification of colistin plasma concentrations. This novel methodology validated according to international standards simultaneously quantifies the microbiologically active compounds colistin A and B, plus the pro-drug colistin methanesulfonate (colistimethate, CMS). 96-well micro-Elution SPE on Oasis Hydrophilic-Lipophilic-Balanced (HLB) followed by direct analysis by Hydrophilic Interaction Liquid Chromatography (HILIC) with Ethylene Bridged Hybrid - BEH - Amide phase column coupled to tandem mass spectrometry allows a high-throughput with no significant matrix effect. The technique is highly sensitive (limit of quantification 0.014 and 0.006μg/mL for colistin A and B), precise (intra-/inter-assay CV 0.6-8.4%) and accurate (intra-/inter-assay deviation from nominal concentrations -4.4 to +6.3%) over the clinically relevant analytical range 0.05-20μg/mL. Colistin A and B in plasma and whole blood samples are reliably quantified over 48h at room temperature and at +4°C (<6% deviation from nominal values) and after three freeze-thaw cycles. Colistimethate acidic hydrolysis (1M H2SO4) to colistin A and B in plasma was completed in vitro after 15min of sonication while the pro-drug hydrolyzed spontaneously in plasma ex vivo after 4h at room temperature: this information is of utmost importance for interpretation of analytical results. Quantification is precise and accurate when using serum, citrated or EDTA plasma as biological matrix, while use of heparin plasma is not appropriate. This new analytical technique providing optimized quantification in real-life conditions of the microbiologically active compounds colistin A and B offers a highly efficient tool for routine therapeutic drug monitoring aimed at individualizing drug dosing against life-threatening infections.

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The proprotein convertases (PCs) are a family of nine mammalian enzymes that play key roles in the maintenance of cell homeostasis by activating or inactivating proteins via limited proteolysis under temporal and spatial control. A wide range of pathogens, including major human pathogenic viruses can hijack cellular PCs for their own purposes. In particular, productive infection with many enveloped viruses critically depends on the processing of their fusion-active viral envelope glycoproteins by cellular PCs. Based on their crucial role in virus-host interaction, PCs can be important determinants for viral pathogenesis and represent promising targets of therapeutic antiviral intervention. In the present review we will cover basic aspects and recent developments of PC-mediated maturation of viral envelope glycoproteins of selected medically important viruses. The molecular mechanisms underlying the recognition of PCs by viral glycoproteins will be described, including recent findings demonstrating differential PC-recognition of viral and cellular substrates. We will further discuss a possible scenario how viruses during co-evolution with their hosts adapted their glycoproteins to modulate the activity of cellular PCs for their own benefit and discuss the consequences for virus-host interaction and pathogenesis. Particular attention will be given to past and current efforts to evaluate cellular PCs as targets for antiviral therapeutic intervention, with emphasis on emerging highly pathogenic viruses for which no efficacious drugs or vaccines are currently available.

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The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

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In this paper, we study how access pricing affects network competition when subscription demand is elastic and each network uses non-linear prices and can apply termination-based price discrimination. In the case of a fixed per minute termination charge, we find that a reduction of the termination charge below cost has two opposing effects: it softens competition but helps to internalize network externalities. The former reduces mobile penetration while the latter boosts it. We find that firms always prefer termination charge below cost for either motive while the regulator prefers termination below cost only when this boosts penetration. Next, we consider the retail benchmarking approach (Jeon and Hurkens, 2008) that determines termination charges as a function of retail prices and show that this approach allows the regulator to increase penetration without distorting call volumes.

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Empirical studies on the determinants of industrial location typically use variables measured at the available administrative level (municipalities, counties, etc.). However, this amounts to assuming that the effects these determinants may have on the location process do not extent beyond the geographical limits of the selected site. We address the validity of this assumption by comparing results from standard count data models with those obtained by calculating the geographical scope of the spatially varying explanatory variables using a wide range of distances and alternative spatial autocorrelation measures. Our results reject the usual practice of using administrative records as covariates without making some kind of spatial correction. Keywords: industrial location, count data models, spatial statistics JEL classification: C25, C52, R11, R30