The risk of non-union per fracture:current myths and revised figures from a population of over 4 million adults

<p>Background and purpose — Fracture non-union remains a major clinical problem, yet there are no data available regarding the overall risk of fractures progressing to non-union in a large population. We investigated the rate of non-union per fracture in a large adult population.</p> <p>Methods — National data collected prospectively over a 5-year period and involving just under 5,000 non-unions were analyzed and compared to the incidence of fracture in the same period.</p> <p>Results and interpretation — The overall risk of non-union per fracture was 1.9%, which is considerably less than previously believed. However, for certain fractures in specific age groups the risk of non-union rose to 9%. As expected, these higher rates of non-union were observed with tibial and clavicular fractures, but—less expectedly—it was in the young and middle-aged adults rather than in the older and elderly population. This study is the first to examine fracture non-union rates in a large population according to age and site, and provides more robust (and lower) estimates of non-union risk than those that are frequently quoted.</p

The NCBO OBOF to OWL Mapping

Two of the most significant formats for biomedical ontologies are the Open Biomedical Ontologies Format (OBOF) and the Web Ontology Language (OWL). To make it possible to translate ontologies between these two representation formats, the National Center for Biomedical Ontology (NCBO) has developed a mapping between the OBOF and OWL formats as well as inter-conversion software. The goal was to allow the sharing of tools, ontologies, and associated data between the OBOF and Semantic Web communities.&#xd;&#xa;&#xd;&#xa;OBOF does not have a formal grammar, so the NCBO had to capture its intended semantics to map it to OWL.&#xd;&#xa;&#xd;&#xa;This official NCBO mapping was used to make all OBO Foundry ontologies available in OWL. &#xd;&#xa;&#xd;&#xa;Availability: This mapping functionality can be embedded into OBO-Edit and Prote&#x301;ge&#x301;-OWL ontology editors. This software is available at: http://bioontology.org/wiki/index.php/OboInOwl:Main_Pag

IMPROVE-DD: Integrating Multiple Phenotype Resources Optimises Variant Evaluation in genetically determined Developmental Disorders

Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs

Intelligent Agents for Coalition Search and Rescue Task Support

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.The Coalition Search and Rescue Task Support demonstration shows cooperative agents supporting a highly dynamic mission in which AI task planning, inter-agent collaboration, workflow enactment, policy-managed communications, semantic web queries, semantic web services matchmaking and knowledge-based notifications are employed

Policy and Contract Management for Semantic Web Services

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.This paper summarizes our efforts to develop capabilities for policy and contract management for Semantic Web Services applications. KAoS services and tools allow for the specification, management, analyzes, disclosure and enforcement of policies represented in OWL. We discuss three current Semantic Web Services applications as examples of the kinds of roles that a policy management framework can play: as an authorization service in grid computing environments, as a distributed policy specification and enforcement capability for a semantic matchmaker, and as a verification tool for services composition and contract management

KAoS Policy Management for Semantic Web Services

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.Despite rapid advances in Web Services, the user community as demanding requirements continue to outstrip available technology solutions. To help close this gap, Semantic Web Services advocates are defining and implementing many new and significant capabilities (www.swsi.org). These new capabilities should more fully harness Web Services' power through explicit representations of Web resources' underlying semantics and the development of an intelligent Web infrastructure that can fully exploit them. Semantic Web languages, such as OWL, extend RDF to let users specify ontologies comprising taxonomies of classes and inference rules. Both people and software agents can effectively use Semantic Web Services. Agents will increasingly use the combination of semantic markup languages and Semantic Web Services to understand and autonomously manipulate Web content in significant ways. Agents will discover, communicate, and cooperate with other agents and services and - as we'll describe - will rely on policy-based management and control mechanisms to ensure respect for human-imposed constraints on agent interaction. Policy-based controls of Semantic Web Services can also help govern interaction with traditional (nonagent) clients. In the mid 1990s, we began to define the initial version of KAoS, a set of platform-independent services that let people define policies ensuring adequate predictability and controllability of both agents and traditional distributed systems. With various research partners, we' re also developing and evaluating a generic model of human-agent teamwork that includes policies to assure natural and effective interaction in mixed teams of people and agents - both software and robotic. We're exploiting the power of Semantic Web representations to address some of the challenges currently limiting Semantic Web Services' widespread deployment

WT1 expression in breast cancer disrupts the epithelial/mesenchymal balance of tumour cells and correlates with the metabolic response to docetaxel

WT1 is a transcription factor which regulates the epithelial-mesenchymal balance during embryonic development and, if mutated, can lead to the formation of Wilms' tumour, the most common paediatric kidney cancer. Its expression has also been reported in several adult tumour types, including breast cancer, and usually correlates with poor outcome. However, published data is inconsistent and the role of WT1 in this malignancy remains unclear. Here we provide a complete study of WT1 expression across different breast cancer subtypes as well as isoform specific expression analysis. Using in vitro cell lines, clinical samples and publicly available gene expression datasets, we demonstrate that WT1 plays a role in regulating the epithelial-mesenchymal balance of breast cancer cells and that WT1-expressing tumours are mainly associated with a mesenchymal phenotype. WT1 gene expression also correlates with CYP3A4 levels and is associated with poorer response to taxane treatment. Our work is the first to demonstrate that the known association between WT1 expression in breast cancer and poor prognosis is potentially due to cancer-related epithelial-to-mesenchymal transition (EMT) and poor chemotherapy response

The SOFG Anatomy Entry List (SAEL):an annotation tool for functional genomics data

A great deal of data in functional genomics studies needs to be annotated with low-resolution anatomical terms. For example, gene expression assays based on manually dissected samples (microarray, SAGE, etc.) need high-level anatomical terms to describe sample origin. First-pass annotation in high-throughput assays (e.g. large-scale in situ gene expression screens or phenotype screens) and bibliographic applications, such as selection of keywords, would also benefit from a minimum set of standard anatomical terms. Although only simple terms are required, the researcher faces serious practical problems of inconsistency and confusion, given the different aims and the range of complexity of existing anatomy ontologies. A Standards and Ontologies for Functional Genomics (SOFG) group therefore initiated discussions between several of the major anatomical ontologies for higher vertebrates. As we report here, one result of these discussions is a simple, accessible, controlled vocabulary of gross anatomical terms, the SOFG Anatomy Entry List (SAEL). The SAEL is available from http://www.sofg.org and is intended as a resource for biologists, curators, bioinformaticians and developers of software supporting functional genomics. It can be used directly for annotation in the contexts described above. Importantly, each term is linked to the corresponding term in each of the major anatomy ontologies. Where the simple list does not provide enough detail or sophistication, therefore, the researcher can use the SAEL to choose the appropriate ontology and move directly to the relevant term as an entry point. The SAEL links will also be used to support computational access to the respective ontologies