Genome-wide analysis of p63 binding sites identifies AP-2 factors as co-regulators of epidermal differentiation

The p63 transcription factor (TP63) is critical in development, growth and differentiation of stratifying epithelia. This is highlighted by the severity of congenital abnormalities caused by TP63 mutations in humans, the dramatic phenotypes in knockout mice and de-regulation of TP63 expression in neoplasia altering the tumour suppressive roles of the TP53 family. In order to define the normal role played by TP63 and provide the basis for better understanding how this network is perturbed in disease, we used chromatin immunoprecipitation combined with massively parallel sequencing (ChIP-seq) to identify >7500 high-confidence TP63-binding regions across the entire genome, in primary human neonatal foreskin keratinocytes (HFKs). Using integrative strategies, we demonstrate that only a subset of these sites are bound by TP53 in response to DNA damage. We identify a role for TP63 in transcriptional regulation of multiple genes genetically linked to cleft palate and identify AP-2alpha (TFAP2A) as a co-regulator of a subset of these genes. We further demonstrate that AP-2gamma (TFAP2C) can bind a subset of these regions and that acute depletion of either TFAP2A or TFAP2C alone is sufficient to reduce terminal differentiation of organotypic epidermal skin equivalents, indicating overlapping physiological functions with TP63.

Statistical-mechanical description of classical test-particle dynamics in the presence of an external force field: modelling noise and damping from first principles

Aiming to establish a rigorous link between macroscopic random motion (described e.g. by Langevin-type theories) and microscopic dynamics, we have undertaken a kinetic-theoretical study of the dynamics of a classical test-particle weakly coupled to a large heat-bath in thermal equilibrium. Both subsystems are subject to an external force field. From the (time-non-local) generalized master equation a Fokker-Planck-type equation follows as a "quasi-Markovian" approximation. The kinetic operator thus defined is shown to be ill-defined; in specific, it does not preserve the positivity of the test-particle distribution function f(x, v; t). Adopting an alternative approach, previously introduced for quantum open systems, is proposed to lead to a correct kinetic operator, which yields all the expected properties. A set of explicit expressions for the diffusion and drift coefficients are obtained, allowing for modelling macroscopic diffusion and dynamical friction phenomena, in terms of an external field and intrinsic physical parameters.

Max separation clustering for feature extraction from Optical Emission Spectroscopy Data

This paper proposes max separation clustering (MSC), a new non-hierarchical clustering method used for feature extraction from optical emission spectroscopy (OES) data for plasma etch process control applications. OES data is high dimensional and inherently highly redundant with the result that it is difficult if not impossible to recognize useful features and key variables by direct visualization. MSC is developed for clustering variables with distinctive patterns and providing effective pattern representation by a small number of representative variables. The relationship between signal-to-noise ratio (SNR) and clustering performance is highlighted, leading to a requirement that low SNR signals be removed before applying MSC. Experimental results on industrial OES data show that MSC with low SNR signal removal produces effective summarization of the dominant patterns in the data.