No abstract

Over the past decade, rapid advances in epigenomics research have extensively characterized critical roles for chromatin regulatory events during normal periods of eukaryotic cell development and plasticity, as well as part of aberrant processes implicated in human disease. Application of such approaches to studies of the central nervous system (CNS), however, is more recent. Here, we provide a comprehensive overview of currently available tools to analyze neuroepigenomics data, as well as a discussion of pending challenges specific to the field of neuroscience. Integration of numerous unbiased genome-wide and proteomic approaches will be necessary to fully understand the neuroepigenome and the extraordinarily complex nature of the human brain. This will be critical to the development of future diagnostic and therapeutic strategies aimed at alleviating the vast array of heterogeneous and genetically distinct disorders of the CNS.

a b s t r a c tIn this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a 'real-life' problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single-objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of 'right-firsttime production' of metals.

a b s t r a c tIn this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance particle swarm optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using 'real' industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions.

† Electronic supplementary information (ESI) available: XRD, ICP and N 2 adsorption characterization results of samples synthesized with different SiO 2 /Al 2 O 3 ratios. See