Somatic cells can be reprogrammed into induced pluripotent stem (iPS) cells by overexpressing combinations of factors such as Oct4, Sox2, Klf4, and c-Myc. Reprogramming is slow and stochastic, suggesting the existence of barriers limiting its efficiency. Here we identify senescence as one such barrier. Expression of the four reprogramming factors triggers senescence by up-regulating p53, p16INK4a , and p21 CIP1. Induction of DNA damage response and chromatin remodeling of the INK4a/ARF locus are two of the mechanisms behind senescence induction. Crucially, ablation of different senescence effectors improves the efficiency of reprogramming, suggesting novel strategies for maximizing the generation of iPS cells.Supplemental material is available at http://www.genesdev.org.

We investigate the evolution of online ratings over time and sequence. We first establish that there exist two distinct dynamic processes, one as a function of the amount of time a book has been available for review and another as a function of the sequence of reviews themselves. We find that, once we control for calendar date, the residual average temporal pattern is increasing. This is counter to existing findings that suggest that without this calendar-date control, the pattern is decreasing. With respect to sequential dynamics, we find that ratings decrease: the nth rating is, on average, lower than the n-1th when controlling for time, reviewer effects, and book effects. We test and find some support for existing theories for this decline based on motivation. We then offer two additional explanations for this “order effect.” We find support for the idea that one's ability to assess the diagnosticity of previous reviews decreases: when previous reviewers are very different, more reviews may thus lead to more purchase errors and lower ratings.

Social scientists often rely on economic experiments such as ultimatum and dictator games to understand human cooperation. Systematic deviations from economic predictions have inspired broader conceptions of self-interest that incorporate concerns for fairness. Yet no framework can describe all of the major results. We take a different approach by asking players directly about their self-interest--defined as what they want to do (pleasure-maximizing options). We also ask players directly about their sense of fairness--defined as what they think they ought to do (fairness-maximizing options). Player-defined measures of self-interest and fairness predict (a) the majority of ultimatum-game and dictator-game offers, (b) ultimatum-game rejections, (c) exiting behavior (i.e., escaping social expectations to cooperate) in the dictator game, and (d) who cooperates more after a positive mood induction. Adopting the players' perspectives of self-interest and fairness permits better predictions about who cooperates, why they cooperate, and when they punish noncooperators.

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