Genotyping, physiological features and proteolytic activities of a potentially pathogenic Acanthamoeba sp isolated from tap water in Brazil

Acanthamoeba spp., known to cause keratitis and granulomatous encephalitis in humans, are frequently isolated from a variety of water sources. Here we report for the first time the characterization of an Acanthamoeba sp. (ACC01) isolated from tap water in Brazil. This organism is currently being maintained in an axenic growth medium. Phylogenetic analysis based on SSU rRNA gene sequences positioned the new isolate in genotype T4, closest to the keratitis-causing isolate, A. polyphaga ATCC 30461 (similar to 99% similarity). Acanthamoeba ACC01 and A. polyphaga 30461 both grew at 37 degrees C and were osmotically resistant, multiplying in hyperosmolar medium. Both isolates secreted comparable amounts of proteolytic enzymes, including serine peptidases that were optimally active at a near neutral/alkaline pH and resolved identically in gelatin gels. Incubation of gels at pH 4.0 with 2 mM DTT also indicated the secretion of similar cysteine peptidases. Altogether, the results point to the pathogenic potential of Acanthamoeba ACC01. (C) 2009 Elsevier Inc. All rights reserved.

A new genotype of Trypanosoma cruzi associated with bats evidenced by phylogenetic analyses using SSU rDNA, cytochrome b and Histone H2B genes and genotyping based on ITS1 rDNA

We characterized 15 Trypanosoma cruzi isolates from bats captured in the Amazon, Central and Southeast Brazilian regions. Phylogenetic relationships among T. cruzi lineages using SSU rDNA, cytochrome b, and Histone H2B genes positioned all Amazonian isolates into T. cruzi I (TCI). However, bat isolates from the other regions, which had been genotyped as T. cruzi II (TC II) by the traditional genotyping method based on mini-exon gene employed in this study, Were not nested within any of the previously defined TCII sublineages, constituting a new genotype designated as TCbat. Phylogenetic analyses demonstrated that TCbat indeed belongs to T. cruzi and not to other closely related bat trypanosomes of the subgenus Schizotrypanum, and that although separated by large genetic distances TO-tat is closest to lineage TCI. A genotyping method targeting ITS1 rDNA distinguished TCbat from established T. cruzi lineages, and from other Schizotrypanum species. In experimentally infected mice, TCbat lacked virulence and yielded loss parasitaemias. Isolates of TCbat presented distinctive morphological features and behaviour in triatomines. To date, TCbat genotype wall found only in bats from anthropic environments of Central and Southeast Brazil. Our findings indicate that the complexity of T. cruzi is larger than currently known, and confirmed bats as important reservoirs and potential source of T. cruzi infections to humans.

Trypanosoma rangeli isolates of bats from Central Brazil: Genotyping and phylogenetic analysis enable description of a new lineage using spliced-leader gene sequences

Trypanosoma rangeli infects several mammalian orders but has never confidently been described in Chiroptera, which are commonly parasitized by many trypanosome species. Here, we described trypanosomes from bats captured in Central Brazil identified as T rangeli,.T. dionisii, T cruzimarinkellei and T cruzi. Two isolates, Tra643 from Platyrrhinus lineatus and Tra1719 from Artibeus plamirostris were identified as T rangeli by morphological, biological and molecular methods, and confirmed by phylogenetic analyses. Analysis using SSU rDNA sequences clustered these bat trypanosomes together with T rangeli from other hosts, and separated them from other trypanosomes from bats. Genotyping based on length and sequence polymorphism of PCR-amplified intergenic spliced-leader gene sequences assigned Tra1719 to the lineage A whereas Tra643 was shown to be a new genotype and was assigned to the new lineage E. To our knowledge, these two isolates are the earliest T rangeli from bats and the first isolates from Central Brazil molecularly characterized. Rhodnius stali captured for this study was found infected by T rangeli and T cruzi. (c) 2008 Elsevier B.V. All rights reserved.

Quantum States Allowing Minimum Uncertainty Product of phi and L(z)

We provide necessary and sufficient conditions for states to have an arbitrarily small uncertainty product of the azimuthal angle phi and its canonical moment L(z). We illustrate our results with analytical examples.

Dispersion and uncertainty in multislit matter wave diffraction

We show that single and multislit experiments involving matter waves may be constructed to assess dispersively generated correlations between the position and momentum of a single free particle. These correlations give rise to position dependent phases which develop dynamically as a result of dispersion and may play an important role in the interference patterns. To the extent that initial transverse coherence is preserved throughout the proposed diffraction setup, such interference patterns are noticeably different from those of a classical dispersion free wave. (c) 2007 Published by Elsevier B.V.

Recovery of DNA for the detection and genotyping of human papillomavirus from clinical cervical specimens stored for up to 2 years in a universal collection medium with denaturing reagent

The recovery and stability of DNA for the detection and genotyping of HPV in UCM-containing specimens, after exposure to denaturing reagents and stored for up to 2 years were evaluated. Samples were collected from 60 women who had cervical cytology specimens harboring cervical intraepithelial neoplasia (CIN) 2 or 3. All samples were stored in UCM and had been frozen at -20 degrees C following the addition of the denaturing reagent (sodium hydroxide) and the removal of the aliquot required for Hybrid Capture 2 testing for the identification of HPV DNA. The samples had been stored for 6, 12 and 24 months (20 samples for each storage time). HPV DNA extraction was performed according to a protocol designed specifically and the presence and quality of DNA was confirmed by human P-globin detection using the consensus primers G73 and G74. HPV DNA was amplified using the consensus primers PGMY09 and PGMY11, and reverse line-blot hybridization was used to detect type-specific amplicons for 37 HPV types. The DNA extracted from the denatured specimen was recovered in 57/60 (95%) of the samples. HPV DNA was detected in 56/57 (98%) of the recovered samples. Twenty-six of the 56 samples recovered (48%) were genotyped successfully. (c) 2007 Elsevier B.V. All rights reserved.

Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Uncertainty Characterization Of A Conceptual Rainfall-Runoff Model By Using Grey Numbers

A procedure for characterizing global uncertainty of a rainfall-runoff simulation model based on using grey numbers is presented. By using the grey numbers technique the uncertainty is characterized by an interval; once the parameters of the rainfall-runoff model have been properly defined as grey numbers, by using the grey mathematics and functions it is possible to obtain simulated discharges in the form of grey numbers whose envelope defines a band which represents the vagueness/uncertainty associated with the simulated variable. The grey numbers representing the model parameters are estimated in such a way that the band obtained from the envelope of simulated grey discharges includes an assigned percentage of observed discharge values and is at the same time as narrow as possible. The approach is applied to a real case study highlighting that a rigorous application of the procedure for direct simulation through the rainfall-runoff model with grey parameters involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the grey numbers representing the simulated discharges. Relying on this simplified procedure, the conceptual rainfall-runoff grey model is thus calibrated and the uncertainty bands obtained both downstream of the calibration process and downstream of the validation process are compared with those obtained by using a well-established approach, like the GLUE approach, for characterizing uncertainty. The results of the comparison show that the proposed approach may represent a valid tool for characterizing the global uncertainty associable with the output of a rainfall-runoff simulation model.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

The welfare cost of macroeconomic uncertainty in the post-war period

Lucas (1987) has shown the surprising result that the welfare cost of business cycles is quite small. Using standard assumptions on preferences and a fully-áedged econometric model we computed the welfare costs of macroeconomic uncertainty for the post-WWII era using the multivariate Beveridge-Nelson decomposition for trends and cycles, which considers not only business-cycle uncertainty but also uncertainty from the stochastic trend in consumption. The post-WWII period is relatively quiet, with the welfare costs of uncertainty being about 0:9% of per-capita consumption. Although changing the decomposition method changed substantially initial results, the welfare cost of uncertainty is qualitatively small in the post-WWII era - about $175.00 a year per-capita in the U.S. We also computed the marginal welfare cost of macroeconomic uncertainty using this same technique. It is about twice as large as the welfare cost ñ$350.00 a year per-capita.

Normality under uncertainty

Consider the demand for a good whose consumption be chosen prior to the resolution of uncertainty regarding income. How do changes in the distribution of income affect the demand for this good? In this paper we show that normality, is sufficient to guarantee that consumption increases of the Radon-Nikodym derivative of the new distribution with respect to the old is non-decreasing in the whole domain. However, if only first order stochastic dominance is assumed more structure must be imposed on preferences to guanantee the validity of the result. Finally a converse of the first result also obtains. If the change in measure is characterized by non-decreasing Radon-Nicodyn derivative, consumption of such a good will always increase if and only if the good is normal.

Uncertainty aversion and the optmal choice of portfolio

In this paper we apply the theory of declsion making with expected utility and non-additive priors to the choice of optimal portfolio. This theory describes the behavior of a rational agent who i5 averse to pure 'uncertainty' (as well as, possibly, to 'risk'). We study the agent's optimal allocation of wealth between a safe and an uncertain asset. We show that there is a range of prices at which the agent neither buys not sells short the uncertain asset. In contrast the standard theory of expected utility predicts that there is exactly one such price. We also provide a definition of an increase in uncertainty aversion and show that it causes the range of prices to increase.