Part of the "What Do I Do Now?" series, Peripheral Nerve and Muscle Disease uses a case-based approach to cover common and important topics in the diagnosis and treatment of neuromuscular disorders. Each chapter provides an overview of the approach to the problem in question followed by a discussion of the diagnosis, key points to remember, and selected references for further reading. In this edition, new cases include: Lambert-Eaton Syndrome, Botulism, Facioscapulohumeral Muscular Dystrophy, and Small Fiber Neuropathy Associated with Fibromyalgia. Peripheral Nerve and Muscle Disease is an engaging collection of thought-provoking cases which clinicians can utilize when they encounter difficult patients on the ward or in the clinic. The volume is also a self-assessment tool that tests the reader's ability to answer the question, "What do I do now?
Part of the "What Do I Do Now?" series, Peripheral Nerve and Muscle Disease uses a case-based approach to cover common and important topics in the diagnosis and treatment of neuromuscular disorders. Each chapter provides an overview of the approach to the problem in question followed by a discussion of the diagnosis, key points to remember, and selected references for further reading. In this edition, new cases include: Lambert-Eaton Syndrome, Botulism, Facioscapulohumeral Muscular Dystrophy, and Small Fiber Neuropathy Associated with Fibromyalgia. Peripheral Nerve and Muscle Disease is an engaging collection of thought-provoking cases which clinicians can utilize when they encounter difficult patients on the ward or in the clinic. The volume is also a self-assessment tool that tests the reader's ability to answer the question, "What do I do now?
Future Sustainable Ecosystems: Complexity, Risk, Uncertainty provides an interdisciplinary, integrative overview of environmental problem-solving using statistics. It shows how statistics can be used to solve diverse environmental and socio-economic problems involving food, water, energy scarcity, and climate change risks. It synthesizes interdisciplinary theory, concepts, definitions, models and findings involved in complex global sustainability problem-solving, making it an essential guide and reference. It includes real-world examples and applications making the book accessible to a broader interdisciplinary readership. Discussions include a broad, integrated perspective on sustainability, integrated risk, multi-scale changes and impacts taking place within ecosystems worldwide. State-of-the-art statistical techniques, including Bayesian hierarchical, spatio-temporal, agent-based and game-theoretic approaches are explored. The author then focuses on the real-world integration of observational and experimental data and its use within statistical models.
This chapter reviews issues of current research in reinforcement learning theories and their neural substrates. We consider how the formal constructs of states, actions, and rewards that these theories describe can be understood to map onto counterparts experienced by biological organisms learning in the real world. In each case, this correspondence involves significant difficulties. However, elaborated theoretical accounts from computer science clarify, in each case, how to extend these theories to more realistic circumstances while still preserving the core prediction error-driven learning mechanism that has been prominent in neuroeconomic accounts.
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