Congenital disorder of glycosylation type Ia presenting as early-onset cerebellar ataxia in an adult

Congenital disorders of glycosylation (CDG) are a recently described, underrecognized group of syndromes characterized biochemically by abnormal glycosylation of serum and cellular glycoproteins. We report a previously undiagnosed adult male who presented with early-onset cerebellar ataxia in the context of mental impairment, peripheral neuropathy, retinopathy, body dysmorphism, cardiomyopathy, and hypogonadism. Newly available screening and genetic testing confirmed the diagnosis as CDG type Ia. This case emphasizes that CDG should be considered as a differential diagnosis for adults with early-onset cerebellar ataxia, particularly in those persons with the aforementioned features, and that undiagnosed cases of childhood ataxia may require reassessment now that testing is available. © 2006 Movement Disorder Society

Linkage analyses of event-related potential slow wave phenotypes recorded in a working memory task

Working memory is an essential component of wide-ranging cognitive functions. It is a complex genetic trait probably influenced by numerous genes that individually have only a small influence. These genes may have an amplified influence on phenotypes closer to the gene action. In this study, event-related potential (ERP) phenotypes recorded during a working-memory task were collected from 656 adolescents from 299 families for whom genotypes were available. Univariate linkage analyses using the MERLIN variance-components method were conducted on slow wave phenotypes recorded at multiple sites while participants were required to remember the location of a target. Suggestive linkage (LOD > 2.2) was found on chromosomes 4, 5, 6, 10, 17, and 20. After correcting for multiple testing, suggestive linkage remained on chromosome 10. Empirical thresholds were computed for the most promising phenotypes. Those on chromosome 10 remained suggestive. A number of genes reported to regulate neural differentiation and function (i.e. NRP1, ANK3, and CHAT) were found under these linkage peaks and may influence the levels of neural activity occurring in individuals participating in a spatial working-memory task.

IJDWM Special Issue: Advances in Data Mining Applications

This special issue is a collection of the selected papers published on the proceedings of the First International Conference on Advanced Data Mining and Applications (ADMA) held in Wuhan, China in 2005. The articles focus on the innovative applications of data mining approaches to the problems that involve large data sets, incomplete and noise data, or demand optimal solutions.

Investigating ontogenetic space with developmental cell lineages

Development plays a significant role in biological evolution, and is likely to prove an effective route to overcoming the limitations of direct genotype-phenotype mappings in artificial evolution. Nonetheless, the relationship between development and evolution is complex and still poorly understood. One question of current interest concerns the possible role that developmental processes may play in orienting evolution. A first step towards exploring this issue from a theoretical perspective is understanding the structure of ontogenetic space: the space of possible genotype-phenotype mappings. Using a quantitative model of development that enables ontogenetic space to be characterised in terms of complexity, we show that ontogenetic landscapes have a characteristic structure that varies with genotypic properties.

Dissecting query performance in logical data models: Parsimony vs. greater ontological clarity

Even when data repositories exhibit near perfect data quality, users may formulate queries that do not correspond to the information requested. Users’ poor information retrieval performance may arise from either problems understanding of the data models that represent the real world systems, or their query skills. This research focuses on users’ understanding of the data structures, i.e., their ability to map the information request and the data model. The Bunge-Wand-Weber ontology was used to formulate three sets of hypotheses. Two laboratory experiments (one using a small data model and one using a larger data model) tested the effect of ontological clarity on users’ performance when undertaking component, record, and aggregate level tasks. The results indicate for the hypotheses associated with different representations but equivalent semantics that parsimonious data model participants performed better for component level tasks but that ontologically clearer data model participants performed better for record and aggregate level tasks.

A framework of issues in large process modeling projects

As process management projects have increased in size due to globalised and company-wide initiatives, a corresponding growth in the size of process modeling projects can be observed. Despite advances in languages, tools and methodologies, several aspects of these projects have been largely ignored by the academic community. This paper makes a first contribution to a potential research agenda in this field by defining the characteristics of large-scale process modeling projects and proposing a framework of related issues. These issues are derived from a semi -structured interview and six focus groups conducted in Australia, Germany and the USA with enterprise and modeling software vendors and customers. The focus groups confirm the existence of unresolved problems in business process modeling projects. The outcomes provide a research agenda which directs researchers into further studies in global process management, process model decomposition and the overall governance of process modeling projects. It is expected that this research agenda will provide guidance to researchers and practitioners by focusing on areas of high theoretical and practical relevance.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Virtual training environments to improve train driver's crisis decision making

This paper reports on a current research project in which virtual reality simulators are being investigated as a means of simulating hazardous Rail work conditions in order to allow train drivers to practice decision-making under stress. When working under high stress conditions train drivers need to move beyond procedural responses into a response activated through their own problem-solving and decision-making skills. This study focuses on the use of stress inoculation training which aims to build driver’s confidence in the use of new decision-making skills by being repeatedly required to respond to hazardous driving conditions. In particular, the study makes use of a train cab driving simulator to reproduce potentially stress inducing real-world scenarios. Initial pilot research has been undertaken in which drivers have experienced the training simulation and subsequently completed surveys on the level of immersion experienced. Concurrently drivers have also participated in a velocity perception experiment designed to objectively measure the fidelity of the virtual training environment. Baseline data, against which decision-making skills post training will be measured, is being gathered via cognitive task analysis designed to identify primary decision requirements for specific rail events. While considerable efforts have been invested in improving Virtual Reality technology, little is known about how to best use this technology for training personnel to respond to workplace conditions in the Rail Industry. To enable the best use of simulators for training in the Rail context the project aims to identify those factors within virtual reality that support required learning outcomes and use this information to design training simulations that reliably and safely train staff in required workplace accident response skills.