IER5, a dna damage response gene, is required for notch-mediated induction of squamous cell differentiation

Notch signaling regulates squamous cell proliferation and differentiation and is frequently disrupted in squamous cell carcinomas, in which Notch is tumor suppressive. Here, we show that conditional activation of Notch in squamous cells activates a context-specific gene expression program through lineage-specific regulatory elements. Among direct Notch target genes are multiple DNA damage response genes, including IER5, which we show is required for Notch-induced differentiation of squamous carcinoma cells and TERT-immortalized keratinocytes. IER5 is epistatic to PPP2R2A, a gene that encodes the PP2A B55α subunit, which we show interacts with IER5 in cells and in purified systems. Thus, Notch and DNA-damage response pathways converge in squamous cells on common genes that promote differentiation, which may serve to eliminate damaged cells from the proliferative pool. We further propose that crosstalk involving Notch and PP2A enables tuning and integration of Notch signaling with other pathways that regulate squamous differentiation

Segmentation in Markov chain consumer behaviour models

Contents * A: Historical development of credit and behavioural scoring * R W Johnson: Legal, social and economic issues in implementing scoring in the US * R Eisenbeis: Problems in applying discriminant analysis in credit scoring models * M A Hopper and E M Lewis: Behaviour scoring and adaptive control systems * B: Objectives and measures in credit scoring * A D Wilkie: Measures for comparing scoring systems * G Wilkinson and J Tingay: The use of affordability data - does it add real value? * R L Keeney and R M Oliver: Improving lender offers using consumer preferences * C: Practical implementation of scoring systems * A Lucas: Updating scorecards: Removing the mystique * R M Oliver and E Wells: Efficient frontier cut-off policies in credit portfolios * D: Features of scoring * D J Hand and W E Henley: Can reject inference ever work? * G A Overstreet Jr, E L Bradley, and R S Kemp Jr: The flat-maximum effect and generic linear scoring models: a test * J N Crook, L C Thomas, and R Hamilton: The degradation of the scorecard over the business cycle * G Bennett, G Platts, and J Crossley: Inferring the inferred * E: Other applications of scoring in credit risk * K J Leonard: Detecting credit card fraud using expert systems * G Platts and I Howe: A single European scorecard * A Lucas and J Powell: Small sample scoring * F: Alternative approaches to scoring systems * B Narain: Survival analysis and the credit granting decision * P Sewart and J Whittaker: Graphical models in credit scoring * M B Yobas, J N Crook, and P Ross: Credit scoring using neural and evolutionary techniques * J Ho, L C Thomas, T A Pomrey, and W T Scherer: Segmenting in Markov chain consumer credit behaviour model