Using stochastic volatility models to analyse weekly ozone averages in Mexico City

In this paper we make use of some stochastic volatility models to analyse the behaviour of a weekly ozone average measurements series. The models considered here have been used previously in problems related to financial time series. Two models are considered and their parameters are estimated using a Bayesian approach based on Markov chain Monte Carlo (MCMC) methods. Both models are applied to the data provided by the monitoring network of the Metropolitan Area of Mexico City. The selection of the best model for that specific data set is performed using the Deviance Information Criterion and the Conditional Predictive Ordinate method.

Hedging options in a garch environment: testing the term structure of stochastic volatility models

This paper develops a methodology for testing the term structure of volatility forecasts derived from stochastic volatility models, and implements it to analyze models of S&P500 index volatility. U sing measurements of the ability of volatility models to hedge and value term structure dependent option positions, we fmd that hedging tests support the Black-Scholes delta and gamma hedges, but not the simple vega hedge when there is no model of the term structure of volatility. With various models, it is difficult to improve on a simple gamma hedge assuming constant volatility. Ofthe volatility models, the GARCH components estimate of term structure is preferred. Valuation tests indicate that all the models contain term structure information not incorporated in market prices.

Simulation-based smoothing and filtering in factor stochastic volatility models : two econometric applications

In this article we use factor models to describe a certain class of covariance structure for financiaI time series models. More specifical1y, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. We build on previous work by allowing the factor loadings, in the factor mo deI structure, to have a time-varying structure and to capture changes in asset weights over time motivated by applications with multi pIe time series of daily exchange rates. We explore and discuss potential extensions to the models exposed here in the prediction area. This discussion leads to open issues on real time implementation and natural model comparisons.

Simulation -based smoothing and filtering in factor stochastic volatility models : two econometric applications (July-2001)

The past decade has wítenessed a series of (well accepted and defined) financial crises periods in the world economy. Most of these events aI,"e country specific and eventually spreaded out across neighbor countries, with the concept of vicinity extrapolating the geographic maps and entering the contagion maps. Unfortunately, what contagion represents and how to measure it are still unanswered questions. In this article we measure the transmission of shocks by cross-market correlation\ coefficients following Forbes and Rigobon's (2000) notion of shift-contagion,. Our main contribution relies upon the use of traditional factor model techniques combined with stochastic volatility mo deIs to study the dependence among Latin American stock price indexes and the North American indexo More specifically, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. From a theoretical perspective, we improve currently available methodology by allowing the factor loadings, in the factor model structure, to have a time-varying structure and to capture changes in the series' weights over time. By doing this, we believe that changes and interventions experienced by those five countries are well accommodated by our models which learns and adapts reasonably fast to those economic and idiosyncratic shocks. We empirically show that the time varying covariance structure can be modeled by one or two common factors and that some sort of contagion is present in most of the series' covariances during periods of economical instability, or crisis. Open issues on real time implementation and natural model comparisons are thoroughly discussed.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Reduction of insulin signalling pathway IRS-1/IRS-2/AKT/mTOR and decrease of epithelial cell proliferation in the prostate of glucocorticoid-treated rats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Subinhibitory Concentrations of Triclosan Promote Streptococcus mutans Biofilm Formation and Adherence to Oral Epithelial Cells

Triclosan is a general membrane-active agent with a broad-spectrum antimicrobial activity that is commonly used in oral care products. In this study, we investigated the effect of sub-minimum inhibitory concentrations (MICs) of triclosan on the capacity of the cariogenic bacterium Streptococcus mutans to form biofilm and adhere to oral epithelial cells. As quantified by crystal violet staining, biofilm formation by two reference strains of S. mutans was dose-dependently promoted, in the range of 2.2- to 6.2-fold, by 1/2 and 1/4 MIC of triclosan. Observations by scanning electron microscopy revealed the presence of a dense biofilm attached to the polystyrene surface. Growth of S. mutans in the presence of triclosan at subMICs also increased its capacity to adhere to a monolayer of gingival epithelial cells. The expression of several genes involved in adherence and biofilm formation in S. mutans was investigated by quantitative RT-PCR. It was found that subMICs of triclosan significantly increased the expression of comD, gtfC, and luxS, and to a lesser extent of gtfB and atlA genes. These findings stress the importance of maintaining effective bactericidal concentrations of therapeutic triclosan since subMICs may promote colonization of the oral cavity by S. mutans.

Influence of the Paracoccidioides brasiliensis 14-3-3 and gp43 proteins on the induction of apoptosis in A549 epithelial cells

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Influence of environmental factors in the adherence of an atypical enteropathogenic Escherichia coli strain to epithelial cells

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Competitive interactions between C. albicans, C. glabrata and C. krusei during biofilm formation and development of experimental Candidiasis

In this study, we evaluated the interactions between Candida albicans, Candida krusei and Candida glabrata in mixed infections. Initially, these interactions were studied in biofilms formed in vitro. CFU/mL values of C. albicans were lower in mixed biofilms when compared to the single biofilms, verifying 77% and 89% of C. albicans reduction when this species was associated with C. glabrata and C. krusei, respectively. After that, we expanded this study for in vivo host models of experimental candidiasis. G. mellonella larvae were inoculated with monotypic and heterotypic Candida suspensions for analysis of survival rate and quantification of fungal cells in the haemolymph. In the groups with single infections, 100% of the larvae died within 18 h after infection with C. albicans. However, interaction groups achieved 100% mortality after 72 h of infection by C. albicans-C. glabrata and 96 h of infection by C. albicans-C. krusei. C. albicans CFU/mL values from larvae hemolymph were lower in the interacting groups compared with the monoespecies group after 12 h of infection. In addition, immunosuppressed mice were also inoculated with monotypic and heterotypic microbial suspensions to induce oral candidiasis. C. albicans CFU/mL values recovered from oral cavity of mice were higher in the group with single infection by C. albicans than the groups with mixed infections by C. albicans-C. glabrata and C. albicans-C. krusei. Moreover, the group with single infection by C. albicans had a higher degree of hyphae and epithelial changes in the tongue dorsum than the groups with mixed infections. We concluded that single infections by C. albicans were more harmful for animal models than mixed infections with non-albicans species, suggesting that C. albicans establish competitive interactions with C. krusei and C. glabrata during biofilm formation and development of experimental candidiasis.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

Magnetic fabric of Araguainha complex impact structure (Central Brazil): Implications for deformation mechanisms and central uplift formation

The weakening mechanisms involved in the collapse of complex impact craters are controversial. The Araguainha impact crater, in Brazil, exposes a complex structure of 40 km in diameter, and is an excellent object to address this issue. Its core is dominated by granite. In addition to microstructural observations, magnetic studies reveal its internal fabric acquired during the collapse phase. All granite samples exhibit impact-related planar deformation features (PDFs) and planar fractures (PFs), which were overprinted by cataclasis. Cataclastic deformation has evolved from incipient brittle fracturing to the development of discrete shear bands in the center of the structure. Fracture planes are systematically decorated by tiny grains (<10 mu m) of magnetite and hematite, and the orientation of magnetic lineation and magnetic foliation obtained by the anisotropies of magnetic susceptibility (AMS) and anhysteretic remanence (AAR) are perfectly coaxial in all studied sites. Therefore, we could track the orientation of deformation features which are decorated by iron oxides using the AMS and AAR. The magnetic fabrics show a regular pattern at the borders of the central peak, with orientations consistent with the fabric of sediments at the crater's inner collar and complex in the center of the structure. Both the cataclastic flow revealed from microstructural observations and the structural pattern of the magnetic anisotropy match the predictions from numerical models of complex impact structures. The widespread occurrence of cataclasis in the central peak, and its orientations revealed by magnetic studies indicate that acoustic fluidization likely operates at all scales, including the mineral scales. The cataclastic flow made possible by acoustic fluidization results in an apparent plastic deformation at the macroscopic scale in the core. (C) 2012 Elsevier B.V. All rights reserved.

RGMa and RGMb expression pattern during chicken development suggest unexpected roles for these repulsive guidance molecules in notochord formation, somitogenesis, and myogenesis

Background: Repulsive guidance molecules (RGM) are high-affinity ligands for the Netrin receptor Neogenin, and they are crucial for nervous system development including neural tube closure; neuronal and neural crest cell differentiation and axon guidance. Recent studies implicated RGM molecules in bone morphogenetic protein signaling, which regulates a variety of developmental processes. Moreover, a role for RGMc in iron metabolism has been established. This suggests that RGM molecules may play important roles in non-neural tissues. Results: To explore which tissues and processed may be regulated by RGM molecules, we systematically investigated the expression of RGMa and RGMb, the only RGM molecules currently known for avians, in the chicken embryo. Conclusions: Our study suggests so far unknown roles of RGM molecules in notochord, somite and skeletal muscle development. Developmental Dynamics, 2012. (C) 2012 Wiley Periodicals, Inc.