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  • 1
    Publication Date: 2024-04-07
    Description: There is a broad consensus across European states and the EU that social and economic inequality is a problem that needs to be addressed. Yet inequality policy is notoriously complex and contested. This book approaches the issue from two linked perspectives. First, a focus on functional requirements highlights what policymakers think they need to deliver policy successfully, and the gap between their requirements and reality. We identify this gap in relation to the theory and practice of policy learning, and to multiple sectors, to show how it manifests in health, education, and gender equity policies. Second, a focus on territorial politics highlights how the problem is interpreted at different scales, subject to competing demands to take responsibility. This contestation and spread of responsibilities contributes to different policy approaches across spatial scales. We conclude that governments promote many separate equity initiatives, across territories and sectors, without knowing if they are complementary or contradictory. This outcome could reflect the fact that ambiguous policy problems and complex policymaking processes are beyond the full knowledge or control of governments. It could also be part of a strategy to make a rhetorically radical case while knowing that they will translate into safer policies. It allows them to replace debates on values, regarding whose definition of equity matters and which inequalities to tolerate, with more technical discussions of policy processes. Governments may be offering new perspectives on spatial justice or new ways to reduce political attention to inequalities.
    Keywords: inequalities, education equity, gender equality, spatial justice, territorial cohesion, multi-level policymaking, multi-sectoral policymaking, health in all policies, rescaling ; thema EDItEUR::J Society and Social Sciences::JP Politics and government::JPA Political science and theory ; thema EDItEUR::J Society and Social Sciences::JP Politics and government::JPP Public administration
    Language: English
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  • 2
    Publication Date: 2019-06-02
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2019-01-24
    Description: Motivation Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, we present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both. Results We generated simulated SNP array and sequencing data and applied CloneSeeker along with two other methods. We demonstrate that CloneSeeker is more accurate than existing algorithms at determining the number of clones, distribution of cancer cells among clones, and mutation and/or copy numbers belonging to each clone. Next, we applied CloneSeeker to SNP array data from samples of 258 previously untreated CLL patients to gain a better understanding of the characteristics of CLL tumors and to elucidate the relationship between clonal heterogeneity and clinical outcome. We found that a significant majority of CLL patients appear to have multiple clones distinguished by copy number alterations alone. We also found that the presence of multiple clones corresponded with significantly worse survival among CLL patients. These findings may prove useful for improving the accuracy of prognosis and design of treatment strategies. Availability and implementation Code available on R-Forge: https://r-forge.r-project.org/projects/CloneSeeker/ Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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