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Umfang: 1 online resource (667 pages)

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ISBN: 9783319471433

Anmerkung: Intro -- Preface -- Maternal and Child Health, Life Course Health Development, and the Life Course Research Network -- References -- Acknowledgments -- Contents -- Contributors -- Introduction to the Handbook of Life Course Health Development -- 1 Introduction -- 2 Rationale -- 2.1 The Emergence of a New Field -- 2.2 The Maturation of the LCHD Field -- 3 The Purpose, Structure, and Content of This Volume -- 3.1 Section I: Emerging Frameworks -- 3.2 Section II: Life Stages -- 3.3 Section III: The Life Course Origins and Consequences of Specific Diseases and Health Conditions -- 3.4 Section IV: Cross-Cutting Topics in Life Course Health Development -- 3.5 Section V: Methodological Approaches -- 3.6 Section VI: Conclusions -- References -- Part I: Emerging Frameworks -- The Emerging Theoretical Framework of Life Course Health Development -- 1 Part 1: Context and Background -- 2 Part 2: Emergence of the Life Course Health Development Framework -- 3 Part 3: Principles of the Life Course Health Development Framework -- 4 Principle 1: Health Development -- 4.1 What We Mean by "Health Development" -- 4.2 Theories and Frameworks Relevant to Health Development -- 4.3 Implications of the Health Development Principle -- 5 Principle 2: Unfolding -- 5.1 What We Mean by "Unfolding" -- 5.2 Theories and Frameworks Relevant to Unfolding -- 5.3 Implications of the Unfolding Principle -- 6 Principle 3: Complexity -- 6.1 What We Mean by "Complexity" -- 6.2 Theories and Frameworks Relevant to Complexity -- 6.3 Implications of the Complexity Principle -- 7 Principle 4: Timing -- 7.1 What We Mean by "Timing" -- 7.2 Theories and Frameworks Relevant to Timing -- 7.3 Implications of the Timing Principle -- 8 Principle 5: Plasticity -- 8.1 What We Mean by "Plasticity" -- 8.2 Theories and Frameworks Relevant to Plasticity. , 8.3 Implications of the Plasticity Principle -- 9 Principle 6: Thriving -- 9.1 What We Mean by "Thriving" -- 9.2 Theories and Frameworks Relevant to Thriving -- 9.3 Implications of the Thriving Principle -- 10 Principle 7: Harmony -- 10.1 What We Mean by "Harmony" -- 10.2 Theories and Frameworks Relevant to Harmony -- 10.3 Implication of the Harmony Principle -- 11 Summary -- References -- Part II: Life Stages -- Preconception and Prenatal Factors and Metabolic Risk -- 1 Introduction -- 2 The Effects of Maternal Preconceptional Obesity and/or Diabetes and Micronutrient Status on Offspring Metabolic Outcomes -- 2.1 Maternal Prepregnancy Obesity and Gestational Weight Gain -- 2.2 Preexistent and Gestational Diabetes -- 2.3 Maternal Micronutrient Status -- 2.4 Adverse Birth Outcomes -- 3 Mechanisms/Pathways Underlying Early Life Origins of Metabolic Risk -- 3.1 Genetics -- 3.2 Intrauterine Environment -- 3.3 Gene and Environment Interaction and Epigenetics -- 4 Preconception and Prenatal Care -- 5 Recommendations for Future Study and Perspectives -- 5.1 Major Themes and Findings -- 5.2 Research Priorities -- 5.2.1 Epidemiologic research -- 5.2.2 Mechanism research -- 5.2.3 Translational research -- 5.3 Data and Methods Development Priorities -- 6 Conclusions -- 7 Source of Funding -- References -- Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities -- 1 Introduction: What's So Special About the Early Years? -- 2 Susceptibility and Continuity: The Echoes of Childhood -- 3 Constitution, Context, and the Nonrandom Distribution of Morbidity -- 4 Six Domains of Early Developmental Research -- 4.1 Social Stratification, Poverty, and Subordination -- 4.2 Vulnerability, Resilience, and Neurobiological Susceptibility -- 4.3 The Topography of Vulnerability. , 4.4 Marginalization and Scapegoating -- 4.5 History and Affliction -- 4.6 Critical Periodicity -- 5 Interactions Between Context and Constitution: The Biological Embedding of Early Adversity -- 5.1 Chromatin Modification and the Molecular Biology of Epigenesis -- 5.2 The Epigenetics of Social Adversity -- 5.3 Gene-Environment Interplay in Brain Development -- 6 Summary: What We Know and What We Don't -- 7 An Agenda for Future Research -- References -- Middle Childhood: An Evolutionary-Developmental Synthesis -- 1 What Is Middle Childhood? -- 1.1 Adrenarche -- 2 The Transition to Middle Childhood as a Developmental Switch Point -- 2.1 A Switch Point in Life History Development -- 3 Three Insights in the Nature of Middle Childhood -- 3.1 Insight 1: Social Integration and Social Competition Are Complementary Functions of Middle Childhood -- 3.2 Insight 2: Sexual Selection Contributes to the Emergence and Intensification of Sex Differences in Middle Childhood -- 3.3 Insight 3: In Middle Childhood, Heightened Sensitivity to the Environment Goes Hand in Hand with the Expression of New Genetic Factors -- 4 Implications for Health Development -- 5 Conclusions -- References -- Adolescent Health Development: A Relational Developmental Systems Perspective -- 1 Viewing Adolescent Development Through the Lens of the Relational Developmental Systems Metatheory -- 2 Three Moments of Analysis in an RDS Approach to Adolescent Health Development -- 3 Developmental Regulations, Adaptive Developmental Regulations, and Human Agency in RDS Metatheory -- 4 Implications for Research About Adolescent Health Development -- 5 Conclusions -- References -- Emerging Adulthood as a Critical Stage in the Life Course -- 1 Introduction to Emerging Adulthood -- 2 Conceptual Framework -- 3 Macro-level Influences on the Trajectory of Emerging Adulthood. , 4 Meso-level Influences of Life Trajectories During Emerging Adulthood -- 4.1 Earlier Parent-Child Relationships -- 4.2 Childhood Socioeconomic Status -- 5 Microlevel Influences on the Trajectories During Emerging Adulthood -- 5.1 Cognitive Development -- 5.2 Identity Formation -- 5.3 Resilience in Emerging Adulthood -- 6 Trajectories During Emerging Adulthood for Emerging Adults with Chronic Health Condition -- 6.1 Autism Spectrum Disorders (ASDs) -- 6.2 Type 1 Diabetes Mellitus (DM) -- 6.3 Chronic Kidney Disease -- 6.4 Mental Health and Substance Use -- 7 Protective and Risk Factors That Impact Emerging Adulthood -- 8 Services and Supports -- 9 Recommendations for Research Priorities -- References -- Pregnancy Characteristics and Women's Cardiovascular Health -- 1 Introduction -- 2 Associations of Parity and Pregnancy Complications with CVD Risk in Mothers -- 2.1 Parity and CVD -- 2.2 Common Pregnancy Complications and CVD in Mothers -- 2.3 Recurrent Pregnancy Complications, Last Pregnancy Complications, and Maternal CVD Risk -- 3 Physiological Mechanisms Linking Pregnancy Complications to Maternal CVD Risk -- 3.1 Cardiovascular Risk Factors Preceding Pregnancy Complications -- 3.2 Cardiovascular Risk Factors During Pregnancy -- 3.2.1 Cardiovascular Adaptation in Normal Pregnancy -- 3.2.2 Cardiovascular Risk Factors During Pregnancy Complications -- 3.3 Cardiovascular Risk Factors After Pregnancy -- 3.3.1 Enduring Cardiovascular Impact of Normal Pregnancy -- 3.3.2 Cardiovascular Risk After Pregnancy Complications -- 4 Recommendations for Future Research -- 4.1 Major Themes and Findings -- 4.2 Research Priorities -- 4.3 Data and Methods Development Priorities -- 4.4 Translational Priorities -- 5 Conclusions -- References. , Part III: The Life Course Origins and Consequences of Select Major Health Conditions and Issues -- Early in the Life Course: Time for Obesity Prevention -- 1 Introduction -- 2 Conceptual Framework -- 3 Measurement of Overweight and Obesity -- 4 Prenatal Period -- 4.1 Birth Weight -- 4.2 Maternal Prepregnancy Obesity -- 4.3 Gestational Diabetes Mellitus (GDM) -- 4.4 Gestational Weight Gain (GWG) -- 4.5 Maternal Smoking During Pregnancy -- 4.6 Hormonal Influences: Leptin -- 5 Infancy -- 5.1 Rapid Weight Gain -- 5.2 Breastfeeding -- 5.3 Disparities in Obesity Partially Explained by Early-Life Factors -- 6 Early to Mid-childhood -- 6.1 Family -- 6.2 Diet, Physical Activity, and Inactivity -- 6.3 Food Insecurity -- 6.4 Child Care and School -- 7 Adolescence -- 7.1 Social Influences -- 8 Macro-level Factors -- 8.1 Environment -- 8.2 Local and State -- 8.3 National -- 9 Recommendations -- 9.1 Major Themes -- 9.2 Research Priorities -- 9.3 Data/Methods Development Priorities -- 9.4 Translational Priorities -- References -- Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development Framework -- 1 Introduction -- 2 The Backdrop -- 2.1 Genetics -- 2.2 Prevalence and Incidence of T2DM Related to Age, Race, and Ethnicity -- 2.3 Economic Status -- 2.4 Social (Psychological) Stressors -- 2.5 Summary -- 3 Preconception and Intrauterine Life -- 3.1 Maternal Nutrition: Prior to and During Pregnancy -- 3.1.1 Maternal Under Nutrition -- 3.1.2 Maternal Overnutrition -- 3.2 Maternal Stress -- 3.3 Maternal Hypoxia/Placental Insufficiency -- 3.4 Environmental Exposures -- 3.5 Microbiome -- 3.6 Gestational Diabetes (GDM) -- 3.7 Summary -- 4 Infancy -- 4.1 Feeding -- 4.2 Infant Growth Patterns -- 4.3 Sleep Duration -- 4.4 Parenting and Postpartum Depression -- 4.5 Summary -- 5 Childhood. , 5.1 Childhood Overweight and Obesity.

Weitere Ausg.: Print version: Halfon, Neal Handbook of Life Course Health Development Cham : Springer International Publishing AG,c2017 ISBN 9783319471419

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Mehr zum Autor: Reussner, Ralf

Mehr zum Autor: Hasselbring, Wilhelm 1964-

Mehr zum Autor: Goedicke, Michael

Mehr zum Autor: Märtin, Lukas

Mehr zum Autor: Vogel-Heuser, Birgit 1961-

UID:

Umfang: 1 online resource (471 pages)

ISBN: 981-327-982-6 , 981-327-981-8

Inhalt: The Pacific Symposium on Biocomputing (PSB) 2019 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2019 will be held on January 3 – 7, 2019 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference. PSB 2019 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's "hot topics." In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

Anmerkung: Intro -- Preface -- PATTERN RECOGNITION IN BIOMEDICAL DATA: CHALLENGES IN PUTTING BIG DATA TO WORK -- Session introduction -- Introduction -- References -- Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes -- 1. Introduction -- 2. Methods -- 2.1. Source Code -- 2.2. Data Source -- 2.3. Data Selection and Preprocessing -- 2.3.1. Reference ICD9 Example -- 2.3.2. Real Member Analyses -- 2.4. Poincaré Embeddings -- 2.5. Processing and Evaluating Embeddings -- 3. Results -- 3.1. ICD9 Hierarchy Evaluation -- 3.2. Poincaré Embeddings on 10 Million Members -- 3.3. Comparison with Euclidean Embeddings -- 3.4. Cohort Specific Embeddings -- 4. Discussion and Conclusion -- 5. Acknowledgments -- References -- The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data -- 1. Introduction -- 2. Background -- 2.1. Multitask nets -- 3. Methods -- 3.1. Dataset Construction and Design -- 3.2. Experimental Design -- 4. Experiments and Results -- 4.1. When Does Multitask Learning Improve Performance? -- 4.2. Relationship Between Performance and Number of Tasks -- 4.3. Comparison with Logistic Regression Baseline -- 4.4. Interaction between Phenotype Prevalence and Complexity -- 5. Limitations -- 6. Conclusion -- Acknowledgments -- References -- ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites -- 1. Introduction -- 1.1. Integrate evidence from multiple clinical sites -- 1.2. Distributed Computing -- 2. Material and Method -- 2.1. Clinical Cohort and Motivating Problem -- 2.2. Algorithm -- 2.3. Simulation Design -- 3. Results -- 3.1. Simulation Results -- 3.2. Fetal Loss Prediction via ODAL -- 4. Discussion -- References. , PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier -- 1. Introduction -- 2. Methods -- 2.1. Data Set and Implementation -- 2.2. Proposed PVC Detection Method -- 2.2.1. Feature Extraction -- 2.2.2. Classification -- 3. Results -- 3.1. Full Database Evaluation -- 3.2. Timing Disturbance Evaluation -- 3.3. Cross-Patient Training Evaluation -- 3.4. Estimated Parameters and Convergence -- 4. Discussion -- References -- Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications -- 1. Introduction -- 2. Related Work -- 3. Confounder Filtering (CF) Method -- 3.1. Overview -- 3.2. Method -- 3.3. Availability -- 4. Experiments -- 4.1. lung adenocarcinoma prediction -- 4.1.1. Data -- 4.1.2. Results -- 4.2. Segmentation on right ventricle(RV) of Heart -- 4.2.1. Data -- 4.2.2. Results -- 4.3. Students' confusion status prediction -- 4.3.1. Data -- 4.3.2. Results -- 4.4. Brain tumor prediction -- 4.4.1. Data -- 4.4.2. Results -- 4.5. Analyses of the method behaviors -- 5. Conclusion -- 6. Acknowledgement -- References -- DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM -- 1. Introduction -- 2. METHODS -- 2.1 Data Set Preparation -- 2.2 Input Encoding -- 2.3 Model Architecture -- 2.4 Evaluation criteria -- 3. RESULTS AND DISCUSSION -- 3.1 Parameter configuration experiments on test data -- 3.2 Comparison with Other Domain Boundary Predictors -- 3.2.1 Free modeling targets from CASP 9 -- 3.2.2 Multi-domain targets from CASP 9 -- 3.2.3 Discontinuous domain target from CASP 8 -- 4. CONCLUSION -- 5. ACKNOWLEDGEMENTS -- REFERENCES -- Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions -- 1. Introduction -- 2. Background theory. , 3. Dataset for training and testing -- 3.1. Source databases -- 3.2. Dataset generation -- 4. Methods -- 4.1. ResNet architecture in our model -- 4.2. Tandem inputs of forward- and reverse-strand sequences -- 4.3. Biallelic high-level network structure -- 4.4. Incorporating HaploReg SNP annotation features -- 4.5. Training of models -- 5. Results -- 6. Conclusions and discussion -- Acknowledgements -- References -- DNA Steganalysis Using Deep Recurrent Neural Networks -- 1. Introduction -- 2. Background -- 2.1. Notations -- 2.2. Hiding Messages -- 2.3. Determination of Message-Hiding Regions -- 3. Methods -- 3.1. Proposed DNA Steganalysis Principle -- 3.2. Proposed Steganalysis RNN Model -- 4. Results -- 4.1. Dataset -- 4.2. Input Representation -- 4.3. Model Training -- 4.4. Evaluation Procedure -- 4.5. Performance Comparison -- 5. Discussion -- Acknowledgments -- References -- Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature -- 1. Introduction -- 2. Related Work -- 3. Methods -- 3.1. Toponym Detection -- 3.1.1. Recurrent Neural Networks -- 3.1.2. LSTM -- 3.1.3. Other Gated RNN Architectures -- 3.1.4. Hyperparameter search and optimization -- 3.2. Toponym Disambiguation -- 3.2.1. Building Geonames Index -- 3.2.2. Searching Geonames Index -- 4. Results and Discussion -- 4.1. Toponym Disambiguation -- 4.2. Toponym Resolution -- 5. Limitations and Future Work -- 6. Conclusion -- Acknowledgments -- Funding -- References -- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning -- 1. Introduction -- 2. Related Work -- 3. Method -- 3.1. Model Framework -- 3.2. Deep Reinforcement Learning for Organizing Actions -- 3.3. Preprocessing and Name Entity Recognition with UMLS -- 3.4. Bidirectional LSTM for Relation Classification. , 3.5. Algorithm -- 3.6. Implementation Specification -- 4. Experiments -- 4.1. Data -- 4.2. Evaluation -- 4.3. Results -- 4.3.1. Improved Reliability -- 4.3.2. Robustness in Real-world Situations -- 4.3.3. Number of Articles Read -- 5. Conclusions and Future Work -- 6. Acknowledgement -- References -- Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies -- 1. Introduction -- 2. Methods -- 2.1. Performance measures: definitions and estimation -- 2.2. Positive-unlabeled setting -- 2.3. Performance measure correction -- 3. Experiments and Results -- 3.1. A case study -- 3.2. Data sets -- 3.3. Experimental protocols -- 3.4. Results -- 4. Conclusions -- Acknowledgements -- References -- PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction -- 1. Introduction -- 2. System and methods -- 2.1. Data -- 2.2. Single views and co-training -- 2.3. Maximizing agreement across views through label assignment -- 3. Results -- 3.1. Preliminary experiments to optimize PLATYPUS performance -- 3.2. Predicting drug sensitivity in cell lines -- 3.3. Key features from PLATYPUS models -- 4. Conclusions -- Acknowledgments -- References -- Computational KIR copy number discovery reveals interaction between inhibitory receptor burden and survival -- 1. Introduction -- 2. Materials and Methods -- 2.1 Data collection -- 2.2 K-mer selection -- 2.3 NGS pipeline and k-mer extraction -- 2.4 Data cleaning -- 2.5 Normalization of k-mer frequencies -- 2.6 Copy number segregation and cutoff selection -- 2.7 Validation of copy number -- 2.8 Survival analysis -- 2.9 Additional immune analysis -- 3. Results and Discussions -- 3.1 Establishing unique k-mers -- 3.2 Varying coverage of KIR region by exome capture kit -- 3.3 Inference of KIR copy number -- 3.4 Population variation of the KIR region. , 3.5 KIR inhibitory gene burden correlates with survival in cervical and uterine cancer -- 5. Conclusions -- 6. Acknowledgements -- 7. Supplementary Material -- References -- Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier -- 1. Introduction -- 2. Data -- 2.1. Preprocessing -- 3. Deep Cancer Classifier -- 3.1. Training & -- testing -- 3.2. Parameter tuning -- 3.3. Feature importance -- 4. Results and Discussion -- 4.1. Model selection -- 4.2. Classifier performance -- 4.3. Comparison with other methods -- 4.4. Feature importance -- 5. Conclusion -- References -- Implementing and Evaluating A Gaussian Mixture Framework for Identifying Gene Function from TnSeq Data -- 1. Introduction -- 1.1. TnSeq Motivation and Background -- 1.2. Motivation and New Methods -- 2. Methods -- 2.1. TnSeq Experimental Data -- 2.2. Mixture framework -- 2.3. Classification methods -- 2.3.1. Novel method - EM -- 2.3.2. Current method - t-statistic -- 2.3.3. Bayesian hierarchical model -- 2.3.4. Data partitioning for the Bayesian model -- 2.4. Simulation -- 2.5. Real data -- 3. Results -- 3.1.1. Classification rate -- 3.1.2. False positive rate -- 3.1.3. Positive classification rate -- 3.1.4. Cross entropy -- 3.2. Simulation Results -- 3.3. Comparisons on real data -- 3.4. Software -- 4. Discussion -- References -- SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs -- 1. Introduction -- 2. Results -- 2.1. SNPs2ChIP analysis framework overview -- 2.2. Batch normalization of heterogeneous epigenetic features -- 2.3. Latent factor discovery and their biological characterization -- 2.4. SNPs2ChIP identifies relevant functions of the non-coding genome -- 2.4.1. Genome-wide SNPs coverage of the reference datasets -- 2.4.2. Non-coding GWAS SNPs of systemic lupus erythematosus -- 2.4.3. ChIP-seq peaks for vitamin D receptors. , 2.5. Robustness Analysis in the latent factor identification. , English

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ISBN: 1-78374-752-8

Inhalt: "Conservation Biology in Sub-Saharan Africa comprehensively explores the challenges and potential solutions to key conservation issues in Sub-Saharan Africa. Easy to read, this lucid and accessible textbook includes fifteen chapters that cover a full range of conservation topics, including threats to biodiversity, environmental laws, and protected areas management, as well as related topics such as sustainability, poverty, and human-wildlife conflict. This rich resource also includes a background discussion of what conservation biology is, a wide range of theoretical approaches to the subject, and concrete examples of conservation practice in specific African contexts. Strategies are outlined to protect biodiversity whilst promoting economic development in the region. Boxes covering specific themes written by scientists who live and work throughout the region are included in each chapter, together with recommended readings and suggested discussion topics. Each chapter also includes an extensive bibliography. Conservation Biology in Sub-Saharan Africa provides the most up-to-date study in the field. It is an essential resource, available on-line without charge, for undergraduate and graduate students, as well as a handy guide for professionals working to stop the rapid loss of biodiversity in Sub-Saharan Africa and elsewhere. Visit the 'Additional Resources' section to find out more about how to download individual chapters and images, upload material to the teaching platform that will be launched in the forthcoming months or join the textbook's discussion forum."--Publisher's website

Anmerkung: Includes index. , What is Conservation Biology? Introduction to Sub-Saharan Africa What is Biodiversity? Why Should We Protect Biodiversity? The Scramble for Space Our Warming World Pollution, Overharvesting, Invasive Species, and Disease Extinction is Forever Applied Population Biology Conserving Ecosystems Preventing Extinctions Biodiversity and the Law The Importance of Protected Areas Conservation on Unprotected Lands An Agenda for the Future Appendix A. Selected Sources of Information Appendix B. Selected Environmental Organisations Appendix C. Obtaining Conservation Funding Appendix D. Environmental Calendar

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Sprache: Englisch

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Inhalt: The Pacific Symposium on Biocomputing (PSB) 2017 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2017 will be held on January 4 – 8, 2017 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference. PSB 2017 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's "hot topics." In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

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ISBN: 978-3-88053-201-4

Serie: Beiträge aus der Staatsbibliothek zu Berlin - Preußischer Kulturbesitz 48

Anmerkung: Beitr. überw. dt., teilw. engl.

Sprache: Deutsch

Fachgebiete: Allgemeines

Schlagwort(e): 1924-2016 Rabenau, Konrad von ; Bucheinband ; Aufsatzsammlung ; Bibliografie ; Festschrift ; Bibliografie ; Aufsatzsammlung ; Festschrift ; Festschrift

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Mehr zum Autor: Nickel, Holger 1941-

Mehr zum Autor: Rabenau, Konrad von 1924-2016

Mehr zum Autor: Schneider-Kempf, Barbara 1954-

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Umfang: 1 Online-Ressource (301 p.)

ISBN: 9789048534623

Serie: Atlantis Advances in Nanotechnology, Material Science and Energy Technologies

Inhalt: This book explores nanotechnology, a rapidly evolving and growing field with applications in a large number of areas. The concepts and physics are highlighted through topics such as nanoscience, quantum effects, nanostructures, and new forms of carbon. Applications and potential health and safety implications of nanomaterials are discussed for healthcare, food production, electronics, defence and more. Accessible and timely, this introduction to nanotechnology will interest students, teachers, politicians, and everyone else eager to learn more about this dynamic field

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Fachgebiete: Germanistik

Schlagwort(e): 1897-1945 Goebbels, Joseph ; Fiktionale Darstellung ; Fiktionale Darstellung ; Fiktionale Darstellung

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Mehr zum Autor: Schroeder, Bernd 1944-2023

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Umfang: 1 online resource (282 pages)

ISBN: 9783662489598

Serie: Earth Systems Data and Models Ser. ; v.2

Anmerkung: Intro -- Acknowledgments -- Contents -- About the Authors -- Introduction -- Part I Basic Principles and the Problem of Climate Forecasts -- 1 Key Concepts in Climate Modeling -- 1.1 What Is Climate? -- 1.2 What Is a Model? -- 1.3 Uncertainty -- 1.3.1 Model Uncertainty -- 1.3.2 Scenario Uncertainty -- 1.3.3 Initial Condition Uncertainty -- 1.3.4 Total Uncertainty -- 1.4 Summary -- 2 Components of the Climate System -- 2.1 Components of the Earth System -- 2.1.1 The Atmosphere -- 2.1.2 The Ocean and Sea Ice -- 2.1.3 Terrestrial Systems -- 2.2 Timescales and Interactions -- 2.3 Summary -- 3 Climate Change and Global Warming -- 3.1 Coupling of the Pieces -- 3.2 Forcing the Climate System -- 3.3 Climate History -- 3.4 Understanding Where the Energy Goes -- 3.5 Summary -- 4 Essence of a Climate Model -- 4.1 Scientific Principles in Climate Models -- 4.2 Basic Formulation and Constraints -- 4.2.1 Finite Pieces -- 4.2.2 Processes -- 4.2.3 Marching Forward in Time -- 4.2.4 Examples of Finite Element Models -- 4.3 Coupled Models -- 4.4 A Brief History of Climate Models -- 4.5 Computational Aspects of Climate Modeling -- 4.5.1 The Computer Program -- 4.5.2 Running a Model -- 4.6 Summary -- Part II Model Mechanics -- 5 Simulating the Atmosphere -- 5.1 Role of the Atmosphere in Climate -- 5.2 Types of Atmospheric Models -- 5.3 General Circulation -- 5.4 Parts of an Atmosphere Model -- 5.4.1 Clouds -- 5.4.2 Radiative Energy -- 5.4.3 Chemistry -- 5.5 Weather Models Versus Climate Models -- 5.6 Challenges for Atmospheric Models -- 5.6.1 Uncertain and Unknown Processes -- 5.6.2 Scales -- 5.6.3 Feedbacks -- 5.6.4 Cloud Feedback -- 5.7 Applications: Impacts of Tropical Cyclones -- 5.8 Summary -- 6 Simulating the Ocean and Sea Ice -- 6.1 Understanding the Ocean -- 6.1.1 Structure of the Ocean -- 6.1.2 Forcing of the Ocean -- 6.2 "Limited" Ocean Models. , 6.3 Ocean General Circulation Models -- 6.3.1 Topography and Grids -- 6.3.2 Deep Ocean -- 6.3.3 Eddies in the Ocean -- 6.3.4 Surface Ocean -- 6.3.5 Structure of an Ocean Model -- 6.3.6 Ocean Versus Atmosphere Models -- 6.4 Sea-Ice Modeling -- 6.5 The Ocean Carbon Cycle -- 6.6 Challenges -- 6.6.1 Challenges in Ocean Modeling -- 6.6.2 Challenges in Sea Ice Modeling -- 6.7 Applications: Sea-Level Rise, Norfolk, Virginia -- 6.8 Summary -- 7 Simulating Terrestrial Systems -- 7.1 Role of the Land Surface in Climate -- 7.1.1 Precipitation and the Water Cycle -- 7.1.2 Vegetation -- 7.1.3 Ice and Snow -- 7.1.4 Human Impacts -- 7.2 Building a Land Surface Simulation -- 7.2.1 Evolution of a Terrestrial System Model -- 7.2.2 Biogeophysics: Surface Fluxes and Heat -- 7.2.3 Biogeophysics: Hydrology -- 7.2.4 Ecosystem Dynamics (Vegetation and Land Cover/Use Change) -- 7.2.5 Summary: Structure of a Land Model -- 7.3 Biogeochemistry: Carbon and Other Nutrient Cycles -- 7.4 Land-Atmosphere Interactions -- 7.5 Land Ice -- 7.6 Humans -- 7.7 Integrated Assessment Models -- 7.8 Challenges in Terrestrial System Modeling -- 7.8.1 Ice Sheet Modeling -- 7.8.2 Surface Albedo Feedback -- 7.8.3 Carbon Feedback -- 7.9 Applications: Wolf and Moose Ecosystem, Isle Royale National Park -- 7.10 Summary -- 8 Bringing the System Together: Coupling and Complexity -- 8.1 Types of Coupled Models -- 8.1.1 Regional Models -- 8.1.2 Statistical Models and Downscaling -- 8.1.3 Integrated Assessment Models -- 8.2 Coupling Models Together: Common Threads -- 8.3 Key Interactions in Climate Models -- 8.3.1 Intermixing of the Feedback Loops -- 8.3.2 Water Feedbacks -- 8.3.3 Albedo Feedbacks -- 8.3.4 Ocean Feedbacks -- 8.3.5 Sea-Level Change -- 8.4 Coupled Modes of Climate Variability -- 8.4.1 Tropical Cyclones -- 8.4.2 Monsoons -- 8.4.3 El Niño -- 8.4.4 Precipitation and the Land Surface. , 8.4.5 Carbon Cycle and Climate -- 8.5 Challenges -- 8.6 Applications: Integrated Assessment of Water Resources -- 8.7 Summary -- Part III Using Models -- 9 Model Evaluation -- 9.1 Evaluation Versus Validation -- 9.1.1 Evaluation and Missing Information -- 9.1.2 Observations -- 9.1.3 Model Improvement -- 9.2 Climate Model Evaluation -- 9.2.1 Types of Comparisons -- 9.2.2 Model Simulations -- 9.2.3 Using Model Evaluation to Guide Further Observations -- 9.3 Predicting the Future: Forecasts Versus Projections -- 9.3.1 Forecasts -- 9.3.2 Projections -- 9.4 Applications of Climate Model Evaluation: Ozone Assessment -- 9.5 Summary -- 10 Predictability -- 10.1 Knowledge and Key Uncertainties -- 10.1.1 Physics of the System -- 10.1.2 Variability -- 10.1.3 Sensitivity to Changes -- 10.2 Types of Uncertainty and Timescales -- 10.2.1 Predicting the Near Term: Initial Condition Uncertainty -- 10.2.2 Predicting the Next 30-50 Years: Scenario Uncertainty -- 10.2.3 Predicting the Long Term: Model Uncertainty Versus Scenario Uncertainty -- 10.3 Ensembles: Multiple Models and Simulations -- 10.4 Applications: Developing and Using Scenarios -- 10.5 Summary -- 11 Results of Current Models -- 11.1 Organization of Climate Model Results -- 11.2 Prediction and Uncertainty -- 11.2.1 Goals of Prediction -- 11.2.2 Uncertainty -- 11.2.3 Why Models? -- 11.3 What Is the Confidence in Predictions? -- 11.3.1 Confident Predictions -- 11.3.1.1 Temperature -- 11.3.1.2 Precipitation -- 11.3.2 Uncertain Predictions: Where to Be Cautious -- 11.3.3 Bad Predictions -- 11.3.4 How Do We Predict Extreme Events? -- 11.4 Climate Impacts and Extremes -- 11.4.1 Tropical Cyclones -- 11.4.2 Stream Flow and Extreme Events -- 11.4.3 Electricity Demand and Extreme Events -- 11.5 Application: Climate Model Impacts in Colorado -- 11.6 Summary. , 12 Usability of Climate Model Projections by Practitioners -- 12.1 Knowledge Systems -- 12.2 Interpretation and Translation -- 12.2.1 Barriers to the Use of Climate Model Projections -- 12.2.2 Downscaled Datasets -- 12.2.3 Climate Assessments -- 12.2.4 Expert Analysis -- 12.3 Uncertainty -- 12.3.1 Ensembles -- 12.3.2 Uncertainty in Assessment Reports -- 12.4 Framing Uncertainty -- 12.5 Summary -- 13 Summary and Final Thoughts -- 13.1 What Is Climate? -- 13.2 Key Features of a Climate Model -- 13.3 Components of the Climate System -- 13.3.1 The Atmosphere -- 13.3.2 The Ocean -- 13.3.3 Terrestrial Systems -- 13.3.4 Coupled Components -- 13.4 Evaluation and Uncertainty -- 13.4.1 Evaluation -- 13.4.2 Uncertainty -- 13.5 What We Know (and Do not Know) -- 13.6 The Future of Climate Modeling -- 13.6.1 Increasing Resolution -- 13.6.2 New and Improved Processes -- 13.6.3 Challenges -- 13.7 Final Thoughts -- Climate Modeling Text Glossary -- Index.

Weitere Ausg.: Print version: Gettelman, Andrew Demystifying Climate Models Berlin, Heidelberg : Springer Berlin / Heidelberg,c2016 ISBN 9783662489574

Sprache: Englisch

Fachgebiete: Geographie

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Mehr zum Autor: Emunds, Bernhard 1962-