Estudio de los mecanismos moleculares responsables de la adaptación del desarrollo de los bacteriófagos [Fi]29 y Nf al estado fisiológico de la célula de "Bacillus subtilis" infectada

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de consulta: 15-02-200

Differential Spo0A-mediated effects on transcription and replication of the related Bacillus subtilis phages Nf and ϕ29 explain their different behaviours in vivo

Members of groups 1 (e.g. ϕ29) and 2 (e.g. Nf) of the ϕ29 family of phages infect the spore forming bacterium Bacillus subtilis. Although classified as lytic phages, the lytic cycle of ϕ29 can be suppressed and its genome can become entrapped into the B. subtilis spore. This constitutes an alternative infection strategy that depends on the presence of binding sites for the host-encoded protein Spo0A in the ϕ29 genome. Binding of Spo0A to these sites represses ϕ29 transcription and prevents initiation of DNA replication. Although the Nf genome can also become trapped into B. subtilis spores, in vivo studies showed that its lytic cycle is less susceptible to spo0A-mediated suppression than that of ϕ29. Here we have analysed the molecular mechanism underlying this difference showing that Spo0A differently affects transcription and replication initiation of the genomes of these phages. Thus, whereas Spo0A represses all three main early promoters of ϕ29, it only represses one out of the three equivalent early promoters of Nf. In addition, contrary to ϕ29, Spo0A does not prevent the in vitro initiation of Nf DNA replication. Altogether, the differences in Spo0A-mediated regulation of transcription and replication between ϕ29 and Nf explain their different behaviours in vivo

Estimation and Testing on Independent Not Identically Distributed Observations Based on Rényi’s Pseudodistances

In real life we often deal with independent but not identically distributed observations (i.n.i.d.o), for which the most well-known statistical model is the multiple linear regression model (MLRM) with non-random covariates. While the classical methods are based on the maximum likelihood estimator (MLE), it is well known its lack of robustness to small deviations from the assumed conditions. In this paper, and based on the Rényi’s pseudodistance (RP), we introduce a new family of estimators in case our information about the unknown parameter is given for i.n.i.d.o.. This family of estimators, let us say minimum RP estimators (as they are obtained by minimizing the RP between the assumed distribution and the empirical distribution of the data), contains the MLE as a particular case and can be applied, among others, to the MLRM with non-random covariates. Based on these estimators, we introduce Wald-type tests for testing simple and composite null hypotheses, as an extension of the classical MLE-based Wald test. Influence functions for the estimators and Wald-type tests are also obtained and analysed. Finally, a simulation study is developed in order to asses the performance of the proposed methods and some real-life data are analysed for illustrative purpose

Divergence-based robust inference under proportional hazards model for one-shot device life-test

In this paper, we develop robust estimators and tests for one-shot device testing under proportional hazards assumption based on divergence measures. Through a detailed Monte Carlo simulation study and a numerical example, the developed inferential procedures are shown to be more robust than the classical procedures, based on maximum likelihood estimators

Robust inference for non-destructive one-shot device testing under step-stress model with exponential lifetimes

One-shot devices analysis involves an extreme case of interval censoring, wherein one can only know whether the failure time is either before or after the test time. Some kind of one-shot devices do not get destroyed when tested, and so can continue within the experiment, providing extra information for inference, if they did not fail before an inspection time. In addition, their reliability can be rapidly estimated via accelerated life tests (ALTs) by running the tests at varying and higher stress levels than working conditions. In particular, step-stress tests allow the experimenter to increase the stress levels at pre-fixed times gradually during the life-testing experiment. The cumulative exposure model is commonly assumed for step-stress models, relating the lifetime distribution of units at one stress level to the lifetime distributions at preceding stress levels. In this paper, we develop robust estimators and Z-type test statistics based on the density power divergence (DPD) for testing linear null hypothesis for non-destructive one-shot devices under the step-stress ALTs with exponential lifetime distribution. We study asymptotic and robustness properties of the estimators and test statistics, yielding point estimation and conffidence intervals for different lifetime characteristic such as reliability, distribution quantiles and mean lifetime of the devices. A simulation study is carried out to assess the performance of the methods of inference developed here and some real-life data sets are analyzed ffinally for illustrative purpose

Power divergence approach for one-shot device testing under competing risks

Most work on one-shot devices assume that there is only one possible cause of device failure. However, in practice, it is often the case that the products under study can experience any one of various possible causes of failure. Robust estimators and Wald-type tests are developed here for the case of one-shot devices under competing risks. An extensive simulation study illustrates the robustness of these divergence-based estimators and test procedures based on them. A data-driven procedure is proposed for choosing the optimal estimator for any given data set which is then applied to an example in the context of survival analysis

Model Selection in a Composite Likelihood Framework Based on Density Power Divergence

This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α. After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion

Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators

Since the two seminal papers by Fisher (1915, 1921) were published, the test under a fixed value correlation coefficient null hypothesis for the bivariate normal distribution constitutes an important statistical problem. In the framework of asymptotic robust statistics, it remains being a topic of great interest to be investigated. For this and other tests, focused on paired correlated normal random samples, Rényi's pseudodistance estimators are proposed, their asymptotic distribution is established and an iterative algorithm is provided for their computation. From them the Wald-type test statistics are constructed for different problems of interest and their influence function is theoretically studied. For testing null correlation in different contexts, an extensive simulation study and two real data based examples support the robust properties of our proposal

Tutorial interactivo de ejemplos básicos y ejercicios de inferencia estadística no-paramétrica mediante software libre: implementación mediante R, R-studio y Swirl

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Tutoriales guiados de prácticas en “Estadística: Análisis de Datos e Inferencia” mediante el software libre SAS University Edition

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