In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

Input: Consider Input: TP53 is the most frequently mutated gene in human cancer. Functionally, p53 is activated by a host of stress stimuli and, in turn, governs an exquisitely complex anti-proliferative transcriptional program that touches upon a bewildering array of biological responses. Despite the many unveiled facets of the p53 network, a clear appreciation of how and in what contexts p53 exerts its diverse effects remains unclear. How can we interpret p53's disparate activities and the consequences of its dysfunction to understand how cell type, mutation profile, and epigenetic cell state dictate outcomes, and how might we restore its tumor-suppressive activities in cancer?

Output: Putting p53 in Context

Input: Consider Input: In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al. (2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under more general settings where the first and third quartiles are also available for the trials. Through simulation studies, we demonstrate that the proposed methods greatly improve the existing methods and enrich the literature. We conclude our work with a summary table that serves as a comprehensive guidance for performing meta-analysis in different situations.

Output: Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

Input: Consider Input: Haematopoietic stem cells (HSCs) and their subsequent progenitors produce blood cells, but the precise nature and kinetics of this production is a contentious issue. In one model, lymphoid and myeloid production branch after the lymphoid-primed multipotent progenitor (LMPP), with both branches subsequently producing dendritic cells. However, this model is based mainly on in vitro clonal assays and population-based tracking in vivo, which could miss in vivo single-cell complexity. Here we avoid these issues by using a new quantitative version of 'cellular barcoding' to trace the in vivo fate of hundreds of LMPPs and HSCs at the single-cell level. These data demonstrate that LMPPs are highly heterogeneous in the cell types that they produce, separating into combinations of lymphoid-, myeloid- and dendritic-cell-biased producers. Conversely, although we observe a known lineage bias of some HSCs, most cellular output is derived from a small number of HSCs that each generates all cell types. Crucially, in vivo analysis of the output of sibling cells derived from single LMPPs shows that they often share a similar fate, suggesting that the fate of these progenitors was imprinted. Furthermore, as this imprinting is also observed for dendritic-cell-biased LMPPs, dendritic cells may be considered a distinct lineage on the basis of separate ancestry. These data suggest a 'graded commitment' model of haematopoiesis, in which heritable and diverse lineage imprinting occurs earlier than previously thought.
Output: Diverse and heritable lineage imprinting of early haematopoietic progenitors