A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

Fuzzy Coding of Genetic Algorithms

A well-cited paper suggesting fuzzy coding as an alternative to the conventional binary, grey and floating-point representations used in genetic algorithms.

Alternative Factor Models and Heritability of the Short Leyton Obsessional Inventory-Children's Version

An alternative models framework was used to test three confirmatory factor analytic models for the Short Leyton Obsessional Inventory-Children's Version (Short LOI-CV) in a general population sample of 517 young adolescent twins (11-16 years). A one-factor model as implicit in current classification systems of Obsessive-Compulsive Disorder (OCD), a two-factor obsessions and compulsions model, and a multidimensional model corresponding to the three proposed subscales of the Short LOI-CV (labelled Obsessions/Incompleteness, Numbers/Luck and Cleanliness) were considered. The three-factor model was the only model to provide an adequate explanation of the data. Twin analyses suggested significant quantitative sex differences in heritability for both the Obsessions/Incompleteness and Numbers/Luck dimensions with these being significantly heritable in males only (heritability of 60% and 65% respectively). The correlation between the additive genetic effects for these two dimensions in males was 0.95 suggesting they largely share the same genetic risk factors.

Unique genetic variation at a species' rear edge is under threat from global climate change

Global climate change is having a significant effect on the distributions of a wide variety of species, causing both range shifts and population extinctions. To date, however, no consensus has emerged on how these processes will affect the range-wide genetic diversity of impacted species. It has been suggested that species that recolonized from low-latitude refugia might harbour high levels of genetic variation in rear-edge populations, and that loss of these populations could cause a disproportionately large reduction in overall genetic diversity in such taxa. In the present study, we have examined the distribution of genetic diversity across the range of the seaweed Chondrus crispus, a species that has exhibited a northward shift in its southern limit in Europe over the last 40 years. Analysis of 19 populations from both sides of the North Atlantic using mitochondrial single nucleotide polymorphisms (SNPs), sequence data from two singlecopy nuclear regions and allelic variation at eight microsatellite loci revealed unique genetic variation for all marker classes in the rear-edge populations in Iberia, but not in the rear-edge populations in North America. Palaeodistribution modelling and statistical testing of alternative phylogeographic scenarios indicate that the unique genetic diversity in Iberian populations is a result not only of persistence in the region during the last glacial maximum, but also because this refugium did not contribute substantially to the recolonization of Europe after the retreat of the ice. Consequently, loss of these rear-edge populations as a result of ongoing climate change will have a major effect on the overall genetic diversity of the species, particularly in Europe, and this could compromise the adaptive potential of the species as a whole in the face of future global warming.

Alzheimer's disease and age-related macular degeneration have different genetic models for complement gene variation

Alzheimer's disease (AD) and age-related macular degeneration (AMD) are both neurodegenerative disorders which share common pathological and biochemical features of the complement pathway. The aim of this study was to investigate whether there is an association between well replicated AMD genetic risk factors and AD. A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD. Our study provides further support to the hypothesis that while activation of the alternative complement pathway is central to AMD pathogenesis, it is less involved in AD.

Functional and genetic analysis of haplotypic sequence variation at the nicastrin genomic locus

Nicastrin (NCSTN) is a component of the ?-secretase complex and therefore potentially a candidate risk gene for Alzheimer's disease. Here, we have developed a novel functional genomics methodology to express common locus haplotypes to assess functional differences. DNA recombination was used to engineer 5 bacterial artificial chromosomes (BACs) to each express a different haplotype of the NCSTN locus. Each NCSTN-BAC was delivered to knockout nicastrin (Ncstn(-/-)) cells and clonal NCSTN-BAC(+)/Ncstn(-/-) cell lines were created for functional analyses. We showed that all NCSTN-BAC haplotypes expressed nicastrin protein and rescued ?-secretase activity and amyloid beta (Aß) production in NCSTN-BAC(+)/Ncstn(-/-) lines. We then showed that genetic variation at the NCSTN locus affected alternative splicing in human postmortem brain tissue. However, there was no robust functional difference between clonal cell lines rescued by each of the 5 different haplotypes. Finally, there was no statistically significant association of NCSTN with disease risk in the 4 cohorts. We therefore conclude that it is unlikely that common variation at the NCSTN locus is a risk factor for Alzheimer's disease.

EEG decoding of semantic category reveals distributed representations for single concepts

Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100. ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon. © 2010 Elsevier Inc.

Genetic structure of the lumpfish Cyclopterus lumpus across the North Atlantic

Lumpfish, or lumpsucker, Cyclopterus lumpus (Linnaeus, 1758) is widely distributed in the North Atlantic Ocean. It has a considerable economic value and substantial fisheries occur in several North Atlantic regions owing to the use of its fully ripe internal egg masses in the ovaries as an alternative to sturgeon caviar. Despite being intensively fished in several locations, biological knowledge is limited and no genetic structure information is available. In this study, the stock structure of C. lumpus was investigated across the North Atlantic using ten microsatellite loci. Out of ten loci, two exhibited higher level of differentiation but their inclusion/ exclusion from the analyses did not drastically change the observed genetic pattern. A total of three distinct genetic groups were detected: Maine-Canada-Greenland, Iceland-Norway and Baltic Sea. These results, discussed in terms of origin of differentiation, gene flow, and selection, showed that gene flow was rather limited among the detected groups, and also between Greenland and Maine-Canada.