The narrative potential of the British Birth Cohort Studies

This paper draws attention to the narrative potential of longitudinal studies such as the British Birth Cohort Studies (BBCS), and explores the possibility of creating narrative case histories and conducting narrative analysis based on information available from the studies. The BBCS have historically adopted a quantitative research design and used structured interviews and questionnaires to collect data from large samples of individuals born in specific years. However, the longitudinal nature of these studies means that they follow the same sample of individuals from birth through childhood into adult life, and this leads to the creation of data that can be understood as a quantitative auto/biography

Robust Fuzzy Clustering via Trimming and Constraints

Producción CientíficaA methodology for robust fuzzy clustering is proposed. This methodology can be widely applied in very different statistical problems given that it is based on probability likelihoods. Robustness is achieved by trimming a fixed proportion of “most outlying” observations which are indeed self-determined by the data set at hand. Constraints on the clusters’ scatters are also needed to get mathematically well-defined problems and to avoid the detection of non-interesting spurious clusters. The main lines for computationally feasible algorithms are provided and some simple guidelines about how to choose tuning parameters are briefly outlined. The proposed methodology is illustrated through two applications. The first one is aimed at heterogeneously clustering under multivariate normal assumptions and the second one migh be useful in fuzzy clusterwise linear regression problems.Ministerio de Economía, Industria y Competitividad (MTM2014-56235-C2-1-P)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA212U13

Formalized Conceptual Spaces with a Geometric Representation of Correlations

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define various operations for our formalization, both for creating new concepts from old ones and for measuring relations between concepts. We present an illustrative toy-example and sketch a research project on concept formation that is based on both our formalization and its implementation.Comment: Published in the edited volume "Conceptual Spaces: Elaborations and Applications". arXiv admin note: text overlap with arXiv:1706.06366, arXiv:1707.02292, arXiv:1707.0516

Baseline factors predictive of serious suicidality at follow-up: findings focussing on age and gender from a community-based study

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-244X/10/41Background: Although often providing more reliable and informative findings relative to other study designs, longitudinal investigations of prevalence and predictors of suicidal behaviour remain uncommon. This paper compares 12-month prevalence rates for suicidal ideation and suicide attempt at baseline and follow-up; identifies new cases and remissions; and assesses the capacity of baseline data to predict serious suicidality at follow-up, focusing on age and gender differences. Methods: 6,666 participants aged 20-29, 40-49 and 60-69 years were drawn from the first (1999-2001) and second (2003-2006) waves of a general population survey. Analyses involved multivariate logistic regression. Results: At follow-up, prevalence of suicidal ideation and suicide attempt had decreased (8.2%-6.1%, and 0.8%-0.5%, respectively). However, over one quarter of those reporting serious suicidality at baseline still experienced it four years later. Females aged 20-29 never married or diagnosed with a physical illness at follow-up were at greater risk of serious suicidality (OR = 4.17, 95% CI = 3.11-5.23; OR = 3.18, 95% CI = 2.09-4.26, respectively). Males aged 40-49 not in the labour force had increased odds of serious suicidality (OR = 4.08, 95% CI = 1.6-6.48) compared to their equivalently-aged and employed counterparts. Depressed/anxious females aged 60-69 were nearly 30% more likely to be seriously suicidal. Conclusions: There are age and gender differentials in the risk factors for suicidality. Life-circumstances contribute substantially to the onset of serious suicidality, in addition to symptoms of depression and anxiety. These findings are particularly pertinent to the development of effective population-based suicide prevention strategies.A Kate Fairweather-Schmidt, Kaarin J Anstey, Agus Salim and Bryan Rodger

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

A Granular Multi-Sensor Data Fusion Method for Situation Observability in Life Support Systems

©2012 by the American Institute of Aeronautics and Astronautics, Inc.Presented at the 2012 Global Space Exploration Conference, 22-24 May 2012, Washington DC, USA.DOI: 10.2514/6.2012-3434Slow-changing characteristics of controlled environmental systems and the increasing availability of data from sensors and measurements offer opportunities for the development of computational methods to enhance situation observability, decrease human workload, and support real-time decision making. Multi-sensor data fusion, which combines observations and measurements from di_erent sources to provide a complete description of a system and its environment, can be used in user-centered interfaces in support situation awareness and observability. Situation observability enables humans to perceive and comprehend the state of the system at a given instant, and helps human operators to decide what actions to take at any given time that may affect the projection of such state into the near future. This paper presents a multi-sensor data fusion method that collects discrete human-inputs and measurements to generate a granular perception function that supports situation observability. These human-inputs are situation-rich, meaning they combine measurements defining the operational condition of the system with a subjective assessment of its situation. As a result, the perception function produces situation-rich signals that may be employed in user-interfaces or in adaptive automation. The perception function is a fuzzy associative memory (FAM) composed of a number of granules equal to the number of situations that may be detected by human-experts; its development is based on their interaction with the system. The human-input data sets are transformed into a granular structure by an adaptive method based on particle swarms. The paper proposed describes the multi-sensor data fusion method and its application to a ground-based aquatic habitat working as a small-scale environmental system