Zhong Wang – författare

First edition received a prestigious 2010 IBPA Benjamin Franklin Silver Award for excellence in publishing An anatomical, patient-oriented approach to neurologic diagnosis from renowned neurosurgeons Despite strides in advanced neuroimaging techniques, what remains constant in the practice of neurology and neurosurgery is the enduring value of observing and interpreting patient signs and symptoms at bedside. Authored by renowned neurosurgeons Cary D. Alberstone, Edward C. Benzel, Michael P. Steinmetz and distinguished colleagues, Anatomic Basis of Neurologic Diagnosis, Second Edition details neuroanatomy in an organized, thorough manner, tipping its hat to the art and science of neurologic diagnosis. Like the widely acclaimed first edition, the updated volume emphasizes a logical, patient-oriented approach to neurologic diagnosis, conspicuously absent from most standard neuroanatomy texts. New to this second edition are a portfolio of carefully curated MRI images and sections on neuroplasticity, peripheral nerve architecture, peripheral nerve injury and recovery, electrodiagnostic diagnosis of radiculopathy, tremor, deep brain stimulation targets (and side effects), autonomic disorders, altered states of consciousness, and spontaneous intracerebral hemorrhage. Two sophisticated new chapters address our current understanding of Memory and Neural Networks. Key Features: Clinically pertinent anatomy is described succinctly and in plain language The cardinal manifestations of regional nervous system disorders facilitate localization of lesions Logical approaches to system-based complaints aid in differential diagnosis Learning objectives begin each chapter, graced by a variety of easy-to-grasp conceptual drawings MRI images, new to this edition, enrich and amplify the text The authors present a practical, reader-friendly, and patient-centric approach to diagnosing neurological disorders. Residents, fellows, and practitioners in neurology, neurosurgery, and neuroradiology, and those studying for board examinations, will enjoy reading this indispensable resource.

This book explores the "triple helix" of data, cloud computing, and artificial intelligence, exploring how this powerful convergence is reshaping life sciences. It serves as a comprehensive guide to the technologies driving this revolution, from the foundational principles of scalable cloud infrastructure to the architecture of the generative AI and Large Language Models that are learning to speak the language of biology. The book examines the practical application of these tools across several domains, including pandemic mitigation, the future of personalized health, and the development of a sustainable bioeconomy, offering readers a clear understanding of both the technological underpinnings and their real-world impact.Moving beyond theory, the book provides a pragmatic roadmap for implementation, tackling the significant challenges of R&D productivity, data quality, and operational efficiency. It founds a blueprint for building trustworthy AI by detailing the essential pillars of security, privacy, and compliance. Finally, this work presents a compelling vision for an "Intelligent Discovery Environment for Science," where human-AI partnerships dissolve the barriers between idea and execution. It is a call to action for researchers, technologists, and leaders to collaboratively build a more efficient, equitable, and human-centric future for science.

This book explores the "e;triple helix"e; of data, cloud computing, and artificial intelligence, exploring how this powerful convergence is reshaping life sciences. It serves as a comprehensive guide to the technologies driving this revolution, from the foundational principles of scalable cloud infrastructure to the architecture of the generative AI and Large Language Models that are learning to speak the language of biology. The book examines the practical application of these tools across several domains, including pandemic mitigation, the future of personalized health, and the development of a sustainable bioeconomy, offering readers a clear understanding of both the technological underpinnings and their real-world impact.Moving beyond theory, the book provides a pragmatic roadmap for implementation, tackling the significant challenges of R&D productivity, data quality, and operational efficiency. It founds a blueprint for building trustworthy AI by detailing the essential pillars of security, privacy, and compliance. Finally, this work presents a compelling vision for an "e;Intelligent Discovery Environment for Science,"e; where human-AI partnerships dissolve the barriers between idea and execution. It is a call to action for researchers, technologists, and leaders to collaboratively build a more efficient, equitable, and human-centric future for science.