Survival analysis concepts to be used in sensory shelf life studies were introduced, together with the equations necessary for calculations. The survival function was defined as the probability of consumers accepting a product beyond a certain storage time. Censoring phenomena, a key concept in survival analysis, was defined and has been shown to occur in sensory shelf-life data. Concepts and calculations were applied to a data set obtained from 50 consumers who each tasted seven yogurt samples with different storage times, answering "yes" or "no" to whether they would consume the samples. From this censored data set, nonparametric and parametric models were obtained that allowed shelf-life estimations.

BackgroundThe efficacy of the CTL component of a future HIV-1 vaccine will depend on the induction of responses with the most potent antiviral activity and broad HLA class I restriction. However, current HIV vaccine designs are largely based on viral sequence alignments only, not incorporating experimental data on T cell function and specificity.MethodsHere, 950 untreated HIV-1 clade B or -C infected individuals were tested for responses to sets of 410 overlapping peptides (OLP) spanning the entire HIV-1 proteome. For each OLP, a "protective ratio" (PR) was calculated as the ratio of median viral loads (VL) between OLP non-responders and responders.ResultsFor both clades, there was a negative relationship between the PR and the entropy of the OLP sequence. There was also a significant additive effect of multiple responses to beneficial OLP. Responses to beneficial OLP were of significantly higher functional avidity than responses to non-beneficial OLP. They also had superior in-vitro antiviral activities and, importantly, were at least as predictive of individuals' viral loads than their HLA class I genotypes.ConclusionsThe data thus identify immunogen sequence candidates for HIV and provide an approach for T cell immunogen design applicable to other viral infections.

Background. The standard approach for survival analysis of the elderly population is to define the survival time as the elapsed time from entry into the study until death, and to adjust by age using stratification and regression procedures. However, the interest is in the study of the aging process and the risk factors related to it, not in the use of timeon-study as the'time scale. Here, we present methods to use age as the time scale and compare inferences and interpretations with those obtained using the standard approach.

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