Distinguishing phenotypes of childhood wheeze and cough using latent class analysis

Airway disease in childhood comprises a heterogeneous group of disorders. Attempts to distinguish different phenotypes have generally considered few disease dimensions. The present study examines phenotypes of childhood wheeze and chronic cough, by fitting a statistical model to data representing multiple disease dimensions. From a population-based, longitudinal cohort study of 1,650 preschool children, 319 with parent-reported wheeze or chronic cough were included. Phenotypes were identified by latent class analysis using data on symptoms, skin-prick tests, lung function and airway responsiveness from two preschool surveys. These phenotypes were then compared with respect to outcome at school age. The model distinguished three phenotypes of wheeze and two phenotypes of chronic cough. Subsequent wheeze, chronic cough and inhaler use at school age differed clearly between the five phenotypes. The wheeze phenotypes shared features with previously described entities and partly reconciled discrepancies between existing sets of phenotype labels. This novel, multidimensional approach has the potential to identify clinically relevant phenotypes, not only in paediatric disorders but also in adult obstructive airway diseases, where phenotype definition is an equally important issue.

Computational simulations of latent heat thermal energy storage systems - with innovative and first-principles based simulation for the underlying unsteady melting (and solidification) processes

This thesis develops an effective modeling and simulation procedure for a specific thermal energy storage system commonly used and recommended for various applications (such as an auxiliary energy storage system for solar heating based Rankine cycle power plant). This thermal energy storage system transfers heat from a hot fluid (termed as heat transfer fluid - HTF) flowing in a tube to the surrounding phase change material (PCM). Through unsteady melting or freezing process, the PCM absorbs or releases thermal energy in the form of latent heat. Both scientific and engineering information is obtained by the proposed first-principle based modeling and simulation procedure. On the scientific side, the approach accurately tracks the moving melt-front (modeled as a sharp liquid-solid interface) and provides all necessary information about the time-varying heat-flow rates, temperature profiles, stored thermal energy, etc. On the engineering side, the proposed approach is unique in its ability to accurately solve – both individually and collectively – all the conjugate unsteady heat transfer problems for each of the components of the thermal storage system. This yields critical system level information on the various time-varying effectiveness and efficiency parameters for the thermal storage system.

Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.

Novel screening tools for latent tuberculosis: time to leave an old friend?

PURPOSE OF REVIEW: Therapeutic inhibition of tumour necrosis factor-alpha strongly increases the risk of reactivation in latent tuberculosis infection. Recent blood tests based on antigen-specific T cell response and measuring production of interferon-gamma, so called interferon-gamma release assays (IGRAs), are promising novel tools to identify infected patients. The performance of diagnostic testing for latent tuberculosis infection in patients with rheumatic diseases will be discussed. RECENT FINDINGS: In patients with rheumatoid arthritis, IGRAs are more sensitive and more specific than traditional tuberculin skin testing. They are unaffected by Bacillus-Calmette-Guérin vaccination and most nontuberculous mycobacteria. Most comparative studies show a better performance of the IGRAs than tuberculin skin testing in terms of a higher specificity. The rate of indeterminate results may be affected by glucocorticoids and the underlying disease but appears independent of disease-modifying antirheumatic drugs. Despite using identical Mycobacterium tuberculosis antigens, the two commercially available tests show differences in clinical performance. SUMMARY: The current information about the performance of the tuberculin skin testing and the IGRAs in the detection of latent tuberculosis infection in patients with rheumatic diseases strongly suggest a clinically relevant advantage of the IGRAs. Their use will help to reduce overuse and underuse of preventive treatment in tumour necrosis factor inhibition.

Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

Integrated choice and latent variable (ICLV) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

Semantic Methods for Intelligent Distributed Design Environments

Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.

Enterprise Integration of Smart Objects using Semantic Service Descriptions

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.

Prometheus Framework for Fuzzy Information Retrieval in Semantic Spaces

This paper introduces a novel vision for further enhanced Internet of Things services. Based on a variety of data (such as location data, ontology-backed search queries, in- and outdoor conditions) the Prometheus framework is intended to support users with helpful recommendations and information preceding a search for context-aware data. Adapted from artificial intelligence concepts, Prometheus proposes user-readjusted answers on umpteen conditions. A number of potential Prometheus framework applications are illustrated. Added value and possible future studies are discussed in the conclusion.