CspC and CspD are essential for Caulobacter crescentus stationary phase survival

The cold shock response in bacteria involves the expression of low-molecular weight cold shock proteins (CSPs) containing a nucleic acid-binding cold shock domain (CSD), which are known to destabilize secondary structures on mRNAs, facilitating translation at low temperatures. Caulobacter crescentus cspA and cspB are induced upon cold shock, while cspC and cspD are induced during stationary phase. In this work, we determined a new coding sequence for the cspC gene, revealing that it encodes a protein containing two CSDs. The phenotypes of C. crescentus csp mutants were analyzed, and we found that cspC is important for cells to maintain viability during extended periods in stationary phase. Also, cspC and cspCD strains presented altered morphology, with frequent non-viable filamentous cells, and cspCD also showed a pronounced cell death at late stationary phase. In contrast, the cspAB mutant presented increased viability in this phase, which is accompanied by an altered expression of both cspC and cspD, but the triple cspABD mutant loses this characteristic. Taken together, our results suggest that there is a hierarchy of importance among the csp genes regarding stationary phase viability, which is probably achieved by a fine tune balance of the levels of these proteins.

Growth, encystment and survival of Acanthamoeba castellanii grazing on different bacteria

Free-living amoebae of the genus Acanthamoeba are widely distributed in soil and water collections, where trophozoites (vegetative, multiplicative stages) feed mainly by phagocytosis and thus control bacterial populations in the environment. Here, we examined the growth, encystment and survival of Acanthamoeba castellanii receiving different bacteria (Escherichia coli, Pseudomonas aeruginosa, Enterobacter cloacae, Bacillus subtilis, Bacillus megaterium, Micrococcus luteus, and Staphylococcus aureus) in nonnutrient saline. All bacteria assayed induced a dose-dependent proliferative response, in most cases maximized with a bacterial dose of 1 x 10(9) mL(-1); except for M. luteus, trophozoites grew better with viable than with heat-killed bacteria. In addition, Acanthamoeba growth was improved by adding bacteria on alternate days. Single-dose experiments indicated a temporal association between the growth of trophozoite and bacterial consumption, and higher consumption of M. luteus, E. coli and P. aeruginosa, bacterial species that allowed the highest trophozoite yields. Long-term Acanthamoeba-bacteria incubation revealed that encystment was significantly delayed by almost all the bacteria assayed (including S. aureus, which elicited a poor growth response) and that the presence of bacteria markedly increased cyst yield; final cyst recovery clearly depended on both the dose and the type of the bacterium given, being much higher with E. coli, M. luteus and P. aeruginosa.

Corneal graft survival after therapeutic keratoplasty for Acanthamoeba keratitis

Purpose: To describe corneal graft survival and visual outcome after therapeutic penetrating keratoplasty in patients with Acanthamoeba keratitis (AK) that is unresponsive to clinical treatment. Methods: Retrospective study. Thirty-two patients with AK who underwent therapeutic penetrating keratoplasty (tPK) from August 1996 to August 2005 were included. Data relating to clinical features, visual acuity, surgical technique, graft survival and complications were collected. Graft survival was evaluated by the Kaplan-Meier method and comparisons were performed using the Log-rank test. Results: Most patients (62.5%) were female. Mean age [+/- standard deviation (SD)] was 35 (+/- 13) years (range 15-68 years). All patients were contact lens wearers. Eighteen patients (56%) presented paralytic mydriasis and glaucoma during the treatment. Thirteen patients (40%) developed glaucoma after surgery; eight of them (61%) required a second PK because of graft failure. Of the 32 keratoplasty eyes, 56.2% presented graft failure at any follow-up point. Forty-five per cent of graft failures occurred before the 12 month follow-up, so 55% remained clear in the first year after surgery. Twelve patients underwent a second PK; seven of them failed and 45% were clear at 1 year. Two patients presented graft recurrence of amoebic infection. There was no significant difference in graft survival when eyes with or without mydriasis were compared (P = 0.40). Eyes with glaucoma presented a significantly shorter graft survival (P = 0.01). Conclusion: Penetrating keratoplasty is a treatment option for eyes that are unresponsive to clinical treatment infections. However, graft survival is poor; postoperative glaucoma is frequent and is associated with shorter graft survival.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

A flexible model for survival data with a cure rate: a Bayesian approach

In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real dataset.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A hands-on approach for fitting long-term survival models under the GAMLSS framework

In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. in this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

COM-Poisson cure rate survival models and an application to a cutaneous melanoma data

In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discussed in the literature. Next, we discuss the maximum likelihood estimation of the parameters of this cure rate survival model. Finally, we illustrate the usefulness of this model by applying it to a real cutaneous melanoma data. (C) 2009 Elsevier B.V. All rights reserved.

A Bayesian Long-term Survival Model Parametrized in the Cured Fraction

The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.

On the unification of long-term survival models

In this paper we extend the long-term survival model proposed by Chen et al. [Chen, M.-H., Ibrahim, J.G., Sinha, D., 1999. A new Bayesian model for survival data with a surviving fraction. journal of the American Statistical Association 94, 909-919] via the generating function of a real sequence introduced by Feller [Feller, W., 1968. An Introduction to Probability Theory and its Applications, third ed., vol. 1, Wiley, New York]. A direct consequence of this new formulation is the unification of the long-term survival models proposed by Berkson and Gage [Berkson, J., Gage, R.P., 1952. Survival cure for cancer patients following treatment. journal of the American Statistical Association 47, 501-515] and Chen et al. (see citation above). Also, we show that the long-term survival function formulated in this paper satisfies the proportional hazards property if, and only if, the number of competing causes related to the occurrence of an event of interest follows a Poisson distribution. Furthermore, a more flexible model than the one proposed by Yin and Ibrahim [Yin, G., Ibrahim, J.G., 2005. Cure rate models: A unified approach. The Canadian journal of Statistics 33, 559-570] is introduced and, motivated by Feller`s results, a very useful competing index is defined. (c) 2008 Elsevier B.V. All rights reserved.

An extinction-survival-type phase transition in the probabilistic cellular automaton p182-q200

We investigate the critical behaviour of a probabilistic mixture of cellular automata (CA) rules 182 and 200 (in Wolfram`s enumeration scheme) by mean-field analysis and Monte Carlo simulations. We found that as we switch off one CA and switch on the other by the variation of the single parameter of the model, the probabilistic CA (PCA) goes through an extinction-survival-type phase transition, and the numerical data indicate that it belongs to the directed percolation universality class of critical behaviour. The PCA displays a characteristic stationary density profile and a slow, diffusive dynamics close to the pure CA 200 point that we discuss briefly. Remarks on an interesting related stochastic lattice gas are addressed in the conclusions.

A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered. A simulation study is conducted to evaluate the performance of the proposed estimator and the jackknife resampling method is used to estimate the variance of such estimator. An application to a real data set is also included.

Regression analysis of mean quality-adjusted survival time based on pseudo-observations

Regression models for the mean quality-adjusted survival time are specified from hazard functions of transitions between two states and the mean quality-adjusted survival time may be a complex function of covariates. We discuss a regression model for the mean quality-adjusted survival (QAS) time based on pseudo-observations, which has the advantage of directly modeling the effect of covariates in the QAS time. Both Monte Carlo Simulations and a real data set are studied. Copyright (C) 2009 John Wiley & Sons, Ltd.

A new class of survival regression models with heavy-tailed errors: robustness and diagnostics

Birnbaum-Saunders models have largely been applied in material fatigue studies and reliability analyses to relate the total time until failure with some type of cumulative damage. In many problems related to the medical field, such as chronic cardiac diseases and different types of cancer, a cumulative damage caused by several risk factors might cause some degradation that leads to a fatigue process. In these cases, BS models can be suitable for describing the propagation lifetime. However, since the cumulative damage is assumed to be normally distributed in the BS distribution, the parameter estimates from this model can be sensitive to outlying observations. In order to attenuate this influence, we present in this paper BS models, in which a Student-t distribution is assumed to explain the cumulative damage. In particular, we show that the maximum likelihood estimates of the Student-t log-BS models attribute smaller weights to outlying observations, which produce robust parameter estimates. Also, some inferential results are presented. In addition, based on local influence and deviance component and martingale-type residuals, a diagnostics analysis is derived. Finally, a motivating example from the medical field is analyzed using log-BS regression models. Since the parameter estimates appear to be very sensitive to outlying and influential observations, the Student-t log-BS regression model should attenuate such influences. The model checking methodologies developed in this paper are used to compare the fitted models.

Estimation of the mean quality-adjusted survival using a multistate model for the sojourn times

In clinical trials, it may be of interest taking into account physical and emotional well-being in addition to survival when comparing treatments. Quality-adjusted survival time has the advantage of incorporating information about both survival time and quality-of-life. In this paper, we discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models for the sojourn times in health states. Semiparametric and parametric (with exponential distribution) approaches are considered. A simulation study is presented to evaluate the performance of the proposed estimator and the jackknife resampling method is used to compute bias and variance of the estimator. (C) 2007 Elsevier B.V. All rights reserved.