The expression of the galanin gene (GAL) in the paraventricular nucleus (PVN) and in the amygdala of higher vertebrates suggests the requirement for highly conserved, but unidentified, regulatory sequences that are critical to allow the galanin gene to control alcohol and fat intake and modulate mood. We used comparative genomics to identify a highly conserved sequence that lay 42 kb 5' of the human GAL transcriptional start site that we called GAL5.1. GAL5.1 activated promoter activity in neurones of the PVN, arcuate nucleus and amygdala that also expressed the galanin peptide. Analysis in neuroblastoma cells demonstrated that GAL5.1 acted as an enhancer of promoter activity after PKC activation. GAL5.1 contained two polymorphisms; rs2513280(C/G) and rs2513281(A/G), that occurred in two allelic combinations (GG or CA) where the dominant GG alelle occurred in 70-83 % of the human population. Intriguingly, both SNPs were found to be in LD (R(2) of 0.687) with another SNP (rs2156464) previously associated with major depressive disorder (MDD). Recreation of these alleles in reporter constructs and subsequent magnetofection into primary rat hypothalamic neurones showed that the CA allele was 40 % less active than the GG allele. This is consistent with the hypothesis that the weaker allele may affect food and alcohol preference. The linkage of the SNPs analysed in this study with a SNP previously associated with MDD together with the functioning of GAL5.1 as a PVN and amygdala specific enhancer represent a significant advance in our ability to understand alcoholism, obesity and major depressive disorder.

Background: Human genetic variation produces the wide range of phenotypic differences that make us individual. However, little is known about the distribution of variation in the most conserved functional regions of the human genome. We examined whether different subsets of the conserved human genome have been subjected to similar levels of selective constraint within the human population. We used set theory and high performance computing to carry out an analysis of the density of Single Nucleotide Polymorphisms (SNPs) within the evolutionary conserved human genome, at three different selective stringencies, intersected with exonic, intronic and intergenic coordinates.

a b s t r a c tIn industries which employ large numbers of mobile field engineers (resources), there is a need to optimize the task allocation process. This particularly applies to utility companies such as electricity, gas and water suppliers as well as telecommunications. The process of allocating tasks to engineers involves finding the optimum area for each engineer to operate within where the locations available to the engineers depends on the work area she/he is assigned to. This particular process is termed as work area optimization and it is a sub-domain of workforce optimization. The optimization of resource scheduling, specifically the work area in this instance, in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction.In previous attempts to tackle workforce optimization in real world scenarios, single objective optimization algorithms employing crisp logic were employed. The problem is that there are usually many objectives that need to be satisfied and hence multi-objective based optimization methods will be more suitable. Type-2 fuzzy logic systems could also be employed as they are able to handle the high level of uncertainties associated with the dynamic and changing real world workforce optimization and scheduling problems. This paper presents a novel multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization, which was employed in real world scheduling problems. This system had to overcome challenges, like how working areas were constructed, how teams were generated for each new area and how to realistically evaluate the newly suggested working areas. These problems were overcome by a novel neighborhood based clustering algorithm, sorting team members by skill, location and effect, and by creating an evaluation simulation that could accurately assess working areas by simulating one day's worth of work, for each engineer in the working area, while taking into account uncertainties.The results show strong improvements when the proposed system was applied to the work area optimization problem, compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in better utilization of the mobile field workforce in utilities and telecommunications companies.

The effective modelling of high-dimensional data with hundreds to thousands of features remains a challenging task in the field of machine learning. This process is a manually intensive task and requires skilled data scientists to apply exploratory data analysis techniques and statistical methods in pre-processing datasets for meaningful analysis with machine learning methods. However, the massive growth of data has brought about the need for fully automated data analysis methods. One of the key challenges is the accurate selection of a set of relevant features, which can be buried in high-dimensional data along with irrelevant noisy features, by choosing a subset of the complete set of input features that predicts the output with higher accuracy comparable to the performance of the complete input set. Kohonen’s self-organising neural network map has been utilised in various ways for this task, such as with the weighted self-organising map (WSOM) approach and this method is reviewed for its efficacy. The study demonstrates that the WSOM approach can result in different results on different runs on a given dataset due to the inappropriate use of the steepest descent optimisation method to minimise the weighted SOM’s cost function. An alternative feature weighting approach based on analysis of the SOM after training is presented; the proposed approach allows the SOM to converge before analysing the input relevance, unlike the WSOM that aims to apply weighting to the inputs during the training which distorts the SOM’s cost function, resulting in multiple local minimums meaning the SOM does not consistently converge to the same state. We demonstrate the superiority of the proposed method over the WSOM and a standard SOM in feature selection with improved clustering analysis.

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