How people's feelings change across time can be represented as trajectories in a core affect space defined by the dimensions of valence and activation. In this article, the authors analyzed individual differences in within-person affective variability defined as characteristics of core affect trajectories, introducing new ways to conceptualize affective variability. In 2 studies, participants provided multiple reports across time describing how they were feeling in terms of core affect. From these data, characteristics of participants' core affect trajectories were derived. Across both studies, core affect variability was negatively related to average valence, self-esteem, and agreeableness, and it was positively related to neuroticism and depression. Moreover, spin, a measure of how much people experienced qualitatively different feelings within the core affect space, was related more consistently to trait measures of adjustment and personality than other measures of within-person variability, including widely used measures of within-person single-dimension standard deviations.

The nature of the association between anger and 5 appraisal-action tendency components-goal obstacle, other accountability, unfairness, control, and antagonism-was examined in terms of specificity, necessity, and sufficiency. In 2 studies, participants described recently experienced unpleasant situations in which 1 of the appraisal-action tendency components was present or absent and indicated which emotions they had experienced. The results showed that (a) other accountability and arrogant entitlement, as an instance of unfairness, are specific appraisals for anger; and most important, (b) none of the components is necessary or sufficient for anger. The findings suggest that the relation between emotions and appraisalaction tendency components should be conceptualized instead as a contingent association, meaning that they usually co-occur.

We report on a hitherto poorly characterized class of genes that are expressed in all tissues, except in one. Often, these genes have been classified as housekeeping genes, based on their nearly ubiquitous expression. However, the specific repression in one tissue defines a special class of “disallowed genes.” In this paper, we used the intersection-union test to screen for such genes in a multi-tissue panel of genome-wide mRNA expression data. We propose that disallowed genes need to be repressed in the specific target tissue to ensure correct tissue function. We provide mechanistic data of repression with two metabolic examples, exercise-induced inappropriate insulin release and interference with ketogenesis in liver. Developmentally, this repression is established during tissue maturation in the early postnatal period involving epigenetic changes in histone methylation. In addition, tissue-specific expression of microRNAs can further diminish these repressed mRNAs. Together, we provide a systematic analysis of tissue-specific repression of housekeeping genes, a phenomenon that has not been studied so far on a genome-wide basis and, when perturbed, can lead to human disease.

BackgroundHousekeeping genes are needed in every tissue as their expression is required for survival, integrity or duplication of every cell. Housekeeping genes commonly have been used as reference genes to normalize gene expression data, the underlying assumption being that they are expressed in every cell type at approximately the same level. Often, the terms “reference genes” and “housekeeping genes” are used interchangeably. In this paper, we would like to distinguish between these terms. Consensus is growing that housekeeping genes which have traditionally been used to normalize gene expression data are not good reference genes. Recently, ribosomal protein genes have been suggested as reference genes based on a meta-analysis of publicly available microarray data.Methodology/Principal FindingsWe have applied several statistical tools on a dataset of 70 microarrays representing 22 different tissues, to assess and visualize expression stability of ribosomal protein genes. We confirmed the housekeeping status of these genes, but further estimated expression stability across tissues in order to assess their potential as reference genes. One- and two-way ANOVA revealed that all ribosomal protein genes have significant expression variation across tissues and exhibit tissue-dependent expression behavior as a group. Via multidimensional unfolding analysis, we visualized this tissue-dependency. In addition, we explored mechanisms that may cause tissue dependent effects of individual ribosomal protein genes.Conclusions/SignificanceHere we provide statistical and biological evidence that ribosomal protein genes exhibit important tissue-dependent variation in mRNA expression. Though these genes are most stably expressed of all investigated genes in a meta-analysis they cannot be considered true reference genes.

The authors present 2 studies to explain the variability in the duration of emotional experience. Participants were asked to report the duration of their fear, anger, joy, gratitude, and sadness episodes on a daily basis. Information was further collected with regard to potential predictor variables at 3 levels: trait predictors, episode predictors, and moment predictors. Discrete-time survival analyses revealed that, for all 5 emotions under study, the higher the importance of the emotion-eliciting situation and the higher the intensity of the emotion at onset, the longer the emotional experience lasts. Moreover, a reappearance, either physically or merely mentally, of the eliciting stimulus during the emotional episode extended the duration of the emotional experience as well. These findings display interesting links with predictions within N. H. Frijda's theory of emotion, with the phenomenon of reinstatement (as studied within the domain of learning psychology), and with the literature on rumination.

When two alternative treatments (A and B) are available, some subgroup of patients may display a better outcome with treatment A than with B, whereas for another subgroup, the reverse may be true. If this is the case, a qualitative (i.e., disordinal) treatment-subgroup interaction is present. Such interactions imply that some subgroups of patients should be treated differently and are therefore most relevant for personalized medicine. In case of data from randomized clinical trials with many patient characteristics that could interact with treatment in a complex way, a suitable statistical approach to detect qualitative treatment-subgroup interactions is not yet available. As a way out, in the present paper, we propose a new method for this purpose, called QUalitative INteraction Trees (QUINT). QUINT results in a binary tree that subdivides the patients into terminal nodes on the basis of patient characteristics; these nodes are further assigned to one of three classes: a first for which A is better than B, a second for which B is better than A, and an optional third for which type of treatment makes no difference. Results of QUINT on simulated data showed satisfactory performance, with regard to optimization and recovery. Results of an application to real data suggested that, compared with other approaches, QUINT provided a more pronounced picture of the qualitative interactions that are present in the data.

Summary. In problems with missing or latent data, a standard approach is to first impute the unobserved data, then perform all statistical analyses on the completed dataset-corresponding to the observed data and imputed unobserved data-using standard procedures for complete-data inference. Here, we extend this approach to model checking by demonstrating the advantages of the use of completed-data model diagnostics on imputed completed datasets. The approach is set in the theoretical framework of Bayesian posterior predictive checks (but, as with missing-data imputation, our methods of missing-data model checking can also be interpreted as "predictive inference" in a non-Bayesian context). We consider the graphical diagnostics within this framework. Advantages of the completed-data approach include: (1) One can often check model fit in terms of quantities that are of key substantive interest in a natural way, which is not always possible using observed data alone. (2) In problems with missing data, checks may be devised that do not require to model the missingness or inclusion mechanism; the latter is useful for the analysis of ignorable but unknown data collection mechanisms, such as are often assumed in the analysis of sample surveys and observational studies. (3) In many problems with latent data, it is possible to check qualitative features of the model (for example, independence of two variables) that can be naturally formalized with the help of the latent data. We illustrate with several applied examples.

An emotional experience can last for only a couple of seconds up to several hours or even longer. In the present study, we examine to which extent covert intrapersonal actions (cognitions both related and unrelated to the emotion-eliciting stimulus) as well as overt interpersonal actions (social sharing) account for this variability in emotion duration. Participants were asked to report the duration of their anger, sadness, joy, and gratitude episodes on a daily basis during five days. Furthermore, information was collected with regard to their cognitions during the episodes and their social sharing behavior. Discrete-time survival analyses revealed that for three of the four emotions under study, stimulus-related cognitions with the same valence as the emotion lead to a prolongation of the episode; in contrast, both stimulus-related and stimulus-unrelated cognitions with a valence opposite to the emotion lead to a shortening. Finally, for the four emotions under study, social sharing was associated with a prolongation. The findings are discussed in terms of a possible process basis underlying the time dynamics of negative as well as positive emotions.