Ageing and health : the politics of better policies / / Scott L. Greer, University of Michigan, Ann Arbor, [and six others] [[electronic resource]]

• Record Nr.: UNINA9910585960703321
• Autore: Greer Scott L.
• Pubbl/distr/stampa: Cambridge University Press, 2021; Cambridge : , : Cambridge University Press, , 2021
• ISBN: 1-108-96829-5; 1-108-96807-4; 1-108-97323-X
• Descrizione fisica: 1 online resource (xvii, 167 pages) : digital, PDF file(s)
• Collana: European observatory on health systems and policies
• Classificazione: POL024000POL024000
• Disciplina: 362.1084/6
• Soggetti: Older people - Medical care - Europe; Older people - Medical care - Europe - Economic aspects; Older people - Medical care - Europe - Political aspects
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Title from publisher's bibliographic system (viewed on 22 Jul 2021).
• Nota di contenuto: Cover -- Half-title page -- Series page -- Title page -- Copyright page -- Contents -- List of Figures -- List of Tables -- List of Boxes -- Acknowledgements -- 1 Introduction -- 1.1 Two Very Different Narratives Depicting Ageing Societies -- 1.2 What Are the Consequences of Seeing Population Ageing in a Negative Light? -- 1.3 Are Policy Concerns about Population Ageing Evidence-Based? -- 1.3.1 Population Ageing Will Not Become a Major Driver of Health Expenditure Growth -- 1.3.2 Population Ageing Will Lead to Changes in Paid and Unpaid Work, but These Can Be Managed -- 1.4 The Coronavirus Pandemic: Intergenerational Conflict or Revealing Consequences of Longstanding Inequalities? -- 1.5 Win-Win Policy and Politics: the Life-Course Approach -- 1.6 The Book in Brief -- 1.7 Conclusion -- 2 Older People in Europe -- 2.1 Diversity and Inequality -- 2.1.1 Income Insecurity Varies across the European Region, but It Is Better to Be on the Margins in Northern &amp -- Western Europe Than in Eastern Europe -- 2.1.2 Most Older People Are Not in Paid Work but the Odds of Not Working Are Higher in Eastern Europe Than in ; Northern and Western Europe -- 2.1.3 Older People in Eastern Europe Are Most Likely to Live in Multigenerational Households -- 2.1.4 The Health of Older People Varies across Regions -- 2.2 What Do Commonly Used Data Say about Population Ageing and Its Effects on Society? -- 3 Ageing Equally: Politics, Health and Solidarity -- 3.1 The 'Greedy Geezer' Narrative -- 3.2 The Demand-Side Explanation for Win-Lose Policies: Partially, but Only Partially, Correct -- 3.2.1 Older People Do Make Up a Large Share of Voters -- 3.2.2 Sometimes Older Adults Prefer Win-Lose Policies, and Act Politically to Try to Get Them -- 3.2.3 Social Policy Preferences of Older and Younger People Are Often Not As Different As We Expect.; 3.3 Older Voters Do Not Vote As a Bloc -- 3.4 The Supply-Side Explanation for Win-Lose Policies Is Also Partly, but Only Partly, Right -- 3.4.1 There Is Some Evidence of Politicians Responding to Demands from Older Voters When Making Social Policy Choices -- 3.4.2 Policy Is Mainly a Response to Factors Other Than Pressure from Older People -- 3.5 Weighing the Evidence -- 3.5.1 Are Older People 'Greedy', Rationally Demanding or Deserving? -- 3.5.2 Social Policies Generally Result Mainly from Considerations Unrelated to Demand from Voters -- 3.6 Conclusion -- 3.7 Appendix -- 4 The Coalitional Politics of Win-Wins -- 4.1 Introduction -- 4.1.1 Intra- and Intergenerational Solidarity Across Europe -- 4.2 Intra- and Intergenerational Solidarity in an Era of Austerity -- 4.3 The Politics of Healthy Ageing -- 4.3.1 Why is the Win-Win So Difficult to Achieve? -- 4.4 Coalitions and Healthy Ageing -- 4.5 New Challenges -- 4.6 Conclusion -- 5 Unequal Ageing: the Politics of Ageing As the Politics of Health Inequalities -- 5.1 Introduction -- 5.2 Unequal Ageing: Who Gets to Be Old? -- 5.2.1 Gender Inequalities in Health -- 5.2.2 Ethnic Inequalities in Health -- 5.2.3 Socioeconomic Inequalities in Health -- 5.2.4 Geographical Inequalities in Health -- 5.2.5 Intersectional Inequalities -- 5.2.6 Trends in Health Inequalities -- 5.2.7 COVID-19 Pandemic and Health Inequalities -- 5.3 What Causes Health Inequalities? -- 5.3.1 Material Resources: the Social Determinants of Health -- 5.3.2 Explaining Geographic Inequalities in Health -- 5.4 Beyond the Social Position and Place: the Political Economy Approach -- 5.5 Conclusion -- 6 The Implications of Win-Win and Win-Lose Policies for the 'Ageing Crisis' -- 6.1 Introduction -- 6.2 Win-Win Policies and Healthy Ageing -- 6.2.1 The English Health Inequalities Strategy as a Win-Win Strategy.; 6.2.2 German Reunification: Drawing Lessons from an Unusual Win-Win -- 6.3 Win-Lose Policies and the Implications for Healthy Ageing -- 6.3.1 Austerity Politics and Ageing in the UK -- 6.3.2 Health Inequalities and the "Americanization" of European Political Economy -- 6.4 Conclusion -- 7 Conclusion -- 7.1 Tearing Down Straw Men -- 7.2 Equity, Intergenerational and Other -- 7.3 After the Straw Men: Understanding the Politics of Ageing and Health -- 7.4 Getting to a Win-Win -- Bibliography -- Index.
• Sommario/riassunto: One of the most important political and economic challenges facing Europe and elsewhere is the ageing of societies. Must ageing populations create conflict between generations and crisis for health systems? Our answer is no. The problem is not so much demographic change as the political and policy challenge of creating fair, sustainable and effective policies for people of all ages. This book, based on a large European Observatory study, uses new evidence to challenge some of the myths surrounding ageing and its effects on economies and health systems. Cataclysmic views of population ageing are often based on stereotypes and anecdotes unsupported by evidence. How we address ageing societies is a choice. Societies can choose policies that benefit people of all ages, promoting equity both within and between ; generations, and political coalitions can be built to support such policies. This title is available as Open Access on Cambridge Core.

Biomolecular networks [[electronic resource] ] : methods and applications in systems biology / / Luonan Chen, Rui-Sheng Wang, Xiang-Sun Zhang

• Record Nr.: UNINA9910143084903321
• Autore: Chen Luonan <1962->
• Pubbl/distr/stampa: Hoboken, NJ, : Wiley, c2009
• ISBN: 1-282-18602-7; 9786612186028; 0-470-48806-9; 0-470-48805-0
• Descrizione fisica: 1 online resource (420 p.)
• Collana: Wiley series on bioinformatics
• Altri autori (Persone): WangRui-Sheng; ZhangXiang-Sun <1943->
• Disciplina: 572.80285
• Soggetti: Molecular biology - Data processing; Computational biology; Bioinformatics; Biological systems - Research - Data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: BIOMOLECULAR NETWORKS; CONTENTS; PREFACE; ACKNOWLEDGMENTS; LIST OF ILLUSTRATIONS; ACRONYMS; 1 Introduction; 1.1 Basic Concepts in Molecular Biology; 1.1.1 Genomes, Genes, and DNA Replication Process; 1.1.2 Transcription Process for RNA Synthesis; 1.1.3 Translation Process for Protein Synthesis; 1.2 Biomolecular Networks in Cells; 1.3 Network Systems Biology; 1.4 About This Book; I GENE NETWORKS; 2 Transcription Regulation: Networks and Models; 2.1 Transcription Regulation and Gene Expression; 2.1.1 Transcription and Gene Regulation; 2.1.2 Microarray Experiments and Databases; 2.1.3 ChIP-Chip Technology and Transcription Factor Databases2.2 ; Networks in Transcription Regulation; 2.3 Nonlinear Models Based on Biochemical Reactions; 2.4 Integrated Models for Regulatory Networks; 2.5 Summary; 3 Reconstruction of Gene Regulatory Networks; 3.1 Mathematical Models of Gene Regulatory Network; 3.1.1 Boolean Networks; 3.1.2 Bayesian Networks; 3.1.3 Markov Networks; 3.1.4 Differential Equations; 3.2 Reconstructing Gene Regulatory Networks; 3.2.1 Singular Value Decomposition; 3.2.2 Model-Based Optimization; 3.3 Inferring Gene Networks from Multiple Datasets; 3.3.1 General Solutions and a Particular Solution of Network Structures for Multiple Datasets3.3.2 Decomposition Algorithm; 3.3.3 Numerical Validation; 3.4 Gene Network-Based Drug Target Identification; 3.4.1 Network Identification Methods; 3.4.2 Linear Programming Framework; 3.5 Summary; 4 Inference of Transcriptional Regulatory Networks; 4.1 Predicting TF Binding Sites and Promoters; 4.2 Inference of Transcriptional Interactions; 4.2.1 Differential Equation Methods; 4.2.2 Bayesian Approaches; 4.2.3 Data Mining and Other Methods; 4.3 Identifying Combinatorial Regulations of TFs; 4.4 Inferring Cooperative Regulatory Networks4.4.1 Mathematical Models; 4.4.2 Estimating TF Activity; 4.4.3 Linear Programming Models; 4.4.4 Numerical Validation; 4.5 Prediction of Transcription Factor Activity; 4.5.1 Matrix Factorization; 4.5.2 Nonlinear Models; 4.6 Summary; II PROTEIN INTERACTION NETWORKS; 5 Prediction of Protein-Protein Interactions; 5.1 Experimental Protein-Protein Interactions; 5.2 Prediction of Protein-Protein Interactions; 5.2.1 Association Methods; 5.2.2 Maximum-Likelihood Estimation; 5.2.3 Deterministic Optimization Approaches; 5.3 Protein Interaction Prediction Based on Multidomain Pairs5.3.1 Cooperative Domains, Strongly Cooperative Domains, Superdomains; 5.3.2 Inference of Multidomain Interactions; 5.3.3 Numerical Validation; 5.3.4 Reconstructing Complexes by Multidomain Interactions; 5.4 Domain Interaction Prediction Methods; 5.4.1 Statistical Method; 5.4.2 Domain Pair Exclusion Analysis; 5.4.3 Parsimony Explanation Approaches; 5.4.4 Integrative Approaches; 5.5 Summary; 6 Topological Structure of Biomolecular Networks; 6.1 Statistical Properties of Biomolecular Networks; 6.2 Evolution of Protein Interaction Networks
• Sommario/riassunto: Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the impor