In this study, we investigated the reverse transcriptase subunit of telomerase in the dimorphic fungus Ustilago maydis. This protein (Trt1) contains 1371 amino acids and all of the characteristic TERT motifs. Mutants created by disrupting trt1 had senescent traits, such as delayed growth, low replicative potential, and reduced survival, that were reminiscent of the traits observed in est2 budding yeast mutants. Telomerase activity was observed in wild-type fungus sporidia but not those of the disruption mutant. The introduction of a self-replicating plasmid expressing Trt1 into the mutant strain restored growth proficiency and replicative potential. Analyses of trt1 crosses in planta suggested that Trt1 is necessary for teliospore formation in homozygous disrupted diploids and that telomerase is haploinsufficient in heterozygous diploids. Additionally, terminal restriction fragment analysis in the progeny hinted at alternative survival mechanisms similar to those of budding yeast.

In this work, some non-homogeneous Poisson models are considered to study the behaviour of ozone in the city of Puebla, Mexico. Several functions are used as the rate function for the non-homogeneous Poisson process. In addition to their dependence on time, these rate functions also depend on some parameters that need to be estimated. In order to estimate them, a Bayesian approach will be taken. The expressions for the distributions of the parameters involved in the models are very complex. Therefore, Markov chain Monte Carlo algorithms are used to estimate them. The methodology is applied to the ozone data from the city of Puebla, Mexico.

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Confidence intervals are valuable tools in statistical practice for estimating binomial proportions, with the most well-known being the Wald and Clopper-Pearson intervals. However, it is known that these intervals perform poorly in terms of coverage probability and expected mean length, leading to the proposal of alternative intervals in the literature, although these may also have deficiencies. In this work, we investigate the performance of several of these confidence intervals using the parametric family p ^ c = X + c n + 2 c {\widehat{p}}_{c}=\frac{X+c}{n+2c} with c ≥ 0 c\ge 0 to estimate the parameter p p . Rather than using the confidence intervals approach, this analysis is done from the hypothesis tests approach. Our primary goal with this work is to identify values of c c that result in better-performing tests and to establish an optimal procedure.

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