63 research outputs found

    Xenopus

    Get PDF
    This book focuses on the amphibian, Xenopus, one of the most commonly used model animals in the biological sciences. Over the past 50 years, the use of Xenopus has made possible many fundamental contributions to our knowledge in cell biology, developmental biology, molecular biology, and neurobiology. In recent years, with the completion of the genome sequence of the main two species and the application of genome editing techniques, Xenopus has emerged as a powerful system to study fundamental disease mechanisms and test treatment possibilities. Xenopus has proven an essential vertebrate model system for understanding fundamental cell and developmental biological mechanisms, for applying fundamental knowledge to pathological processes, for deciphering the function of human disease genes, and for understanding genome evolution. Key Features Provides historical context of the contributions of the model system Includes contributions from an international team of leading scholars Presents topics spanning cell biology, developmental biology, genomics, and disease model Describes recent experimental advances Incorporates richly illustrated diagrams and color images Related Titles Green, S. L. The Laboratory Xenopus sp. (ISBN 978-1-4200-9109-0) Faber, J. & P. D. Nieuwkoop. Normal Table of Xenopus laevis (Daudin): A Systematical & Chronological Survey of the Development from the Fertilized Egg till the End of Metamorphosis (ISBN 978-0-8153-1896-5) Jarret, R. L. & K. McCluskey. The Biological Resources of Model Organisms (ISBN 978-1-0320-9095-5

    Developing an ontology of mechanisms of action in behaviour change interventions

    Get PDF
    Background: Behaviour change interventions can influence behaviours central to health and sustainability. To design better interventions, a strong evidence base about ‘why’ interventions work is needed, i.e., their mechanisms of action (MoAs). MoAs are often labelled and defined inconsistently across intervention reports, creating challenges for understanding interventions and synthesising evidence. An ontology can address this problem by providing a classification system that labels and defines classes for MoAs and their relationships. Aims: To develop an ontology of MoAs in behaviour change interventions, and to explore challenges in understanding MoAs and their links to behaviour change techniques (BCTs) Methods: Behavioural scientists’ challenges to understanding MoAs and BCT-MoA links were investigated using a thematic analysis (Study 1 [S1]). To help better understand MoAs, Studies 2-7 developed the MoA Ontology: (S2) Identifying and grouping MoAs from 83 behavioural theories; (S3) Converting the groupings into an ontology by drawing on relevant ontologies; (S4) Restructuring the ontology to be more usable and ontologically correct; (S5) Applying and refining the ontology to code MoAs in 135 intervention reports; (S6) Nine behavioural scientists reviewing the ontology’s comprehensiveness and clarity, informing revisions; (S7) Investigating the inter-rater reliability of researchers double-coding MoAs in reports using the ontology, informing changes to the ontology. Results: Study 1 suggested challenges to understanding broad and underspecified MoAs. To form the basis of a detailed ontology, Study 2 identified 1062 MoAs and formed 104 MoA groups. Building on these groups, Studies 3-7 created the MoA Ontology, which had 261 classes (e.g., ‘belief’) on seven hierarchical levels. Inter-rater reliability was ‘acceptable’ for researchers familiar with the ontology but lower for researchers unfamiliar with the ontology (Study 7). Conclusions: The developed ontology captured MoAs with greater detail than previous classification systems. With its clear class labels and definitions, the ontology provides a controlled vocabulary for MoAs

    Exploiting general-purpose background knowledge for automated schema matching

    Full text link
    The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

    Results of the Ontology Alignment Evaluation Initiative 2021

    Get PDF
    The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaig

    Understanding Addiction

    Get PDF
    The addiction literature is fraught with conceptual confusions, stalled debates, and an unfortunate lack of clear and careful attempts to delineate the phenomenon of addiction in a way that might lead to consensus. My dissertation has two overarching aims, one metaphysical and one practical. The first aim is to defend an account of addiction as the systematic disposition to fail to control one’s desires to engage in certain types of behaviors. I defend the inclusion of desires and impaired control in the definition, and I flesh out the notion of systematicity central to my dispositionalist framework. I engage the so-called ‘disease vs. choice’ debate, criticizing its presupposition that we are dealing here with a dichotomy and arguing that the movement towards a middle ground is the right track to take. I explain how the dispositionalist account can capture this middle ground and how it serves to expand upon existing views, in particular by filling in the metaphysical details. The second aim is to show how the account I defend can help to unify the extant views and disciplinary perspectives in the literature. Both the dispositionalist aspect of my framework and the methodology adopted (applied ontology and systematic metaphysics) can move the literature towards both substantive and methodological unification. This will help to clear up conceptual confusions, resolve (or sometimes dissolve) apparently intractable disputes, situate different research perspectives with respect to each other, facilitate interdisciplinary dialogue, and help to frame important questions about addiction. Finally, I offer the beginnings of an ontology of addiction, which will provide a terminologically well-structured guide to the addiction literature in a way that will facilitate more effective and efficient communication and data management across disciplines

    Preface

    Get PDF
    corecore