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This awe-inspiring sixth installment of the profound consciousness series by Dr. David R. Hawkins reveals the true essence of Enlightenment, from world-renowned author, psychiatrist, clinician, and spiritual teacher David R. Hawkins, M.D., Ph.D. A true instruction manual for the serious spiritual devotee, this masterpiece from Dr. David R. Hawkins reveals information only known by those who have transcended the ego to reach Divine Realization. Chapters Include: Devotional Nonduality The Inner Path Spiritual Practices The "Experiencer" The Razor's Edge Allness Versus Nothingness Spirituality and the World Teachers and Teachings The Devotee Transcending Identification with the Ego/Self Enlightenment: The Presence of Self Progressive States of Consciousness This spiritual book is the inner route from the self to the Self and an invitation into the profound depths of higher consciousness and enlightenment. It walks you through the path to divine consciousness through the fusion of psychology, philosophy, metaphysics, and spirituality. Immerse yourself in a devotional exploration of non-duality, a profound philosophy that bridges the gap between existential questions and spiritual answers. This transformative work will help you evolve spiritually by connecting to divine love. Dr. David Hawkins explains complex concepts with clarity, making them accessible and relatable for everyone, from spiritual seekers to business professionals seeking personal growth. His spiritual awakening guidance offers meditation techniques for inner peace and provides tools to transcend the confines of the mundane, illuminating the path to spiritual growth. Drawing on his profound understanding of spiritual liberation, Dr. David Hawkins' words guide us toward our spiritual evolution and higher consciousness. Through this journey, you will discover an empowering understanding of your divine consciousness, leading to a sense of inner peace and a heightened state of spiritual awareness.
Major depressive disorder (MDD) is a prevalent, chronic, and recurring mental disorder. This disorder is a leading source of disability worldwide, and is associated with excess mortality rates. Currently approved antidepressants primarily enhance, or otherwise modulate monoaminergic neurotransmission, without curing the disease. Evidence indicates that only one third of patients with MDD achieve remission after treatment with a first-line antidepressant agent. Research in the past two decades has provided valuable insights into the pathophysiological understanding of MDD. However, there is an acknowledged ‘translational gap’ in the field, and few genuinely novel antidepressants have been approved for the treatment of MDD. The Search for Anti Depressants provides readers an in-depth picture of the main pathophysiological mechanisms responsible for the development of MDD in patients. Chapters in the volume focus on possible strategies to spur the discovery of novel antidepressants. This book is an indispensable reference for mental health care providers, students at both under-graduate and graduate levels, and neuroscientists interested in the neurobiology of MDD and recent advances towards the discovery of next generation antidepressants.
Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.
An essential outline of the main facets of polypharmacology in drug discovery research Extending drug discovery opportunities beyond the "one drug, one target" philosophy, a polypharmacological approach to the treatment of complex diseases is emerging as a hot topic in both industry and academic research. Polypharmacology in Drug Discovery presents an overview of the various facets of polypharmacology and how it can be applied as an innovative concept for developing medicines for treating bacterial infections, epilepsy, cancer, psychiatric disorders, and more. Filled with a collection of instructive case studies that reinforce the material and illuminate the subject, this practical guide: Covers the two-sided nature of polypharmacology—its contribution to adverse drug reactions and its benefit in certain therapeutic drug classes Addresses the important topic of polypharmacology in drug discovery, a subject that has not been thoroughly covered outside of scattered journal articles Overviews state-of-the-art approaches and developments to help readers understand concepts and issues related to polypharmacology Fosters interdisciplinary drug discovery research by embracing computational, synthetic, in vitro and in vivo pharmacological and clinical aspects of polypharmacology A clear road map for helping readers successfully navigate around the problems involved with promiscuous ligands and targets, Polypharmacology in Drug Discovery provides real examples, in-depth explanations and discussions, and detailed reviews and opinions to spark inspiration for new drug discovery projects.
Since year 2000, scientists on artificial and natural intelligences started to study chance discovery - methods for discovering events/situations that significantly affect decision making. Partially because the editors Ohsawa and Abe are teaching at schools of Engineering and of Literature with sharing the interest in chance discovery, this book reflects interdisciplinary aspects of progress: First, as an interdisciplinary melting pot of cognitive science, computational intelligence, data mining/visualization, collective intelligence, ... etc, chance discovery came to reach new application domains e.g. health care, aircraft control, energy plant, management of technologies, product designs, innovations, marketing, finance etc. Second, basic technologies and sciences including sensor technologies, medical sciences, communication technologies etc. joined this field and interacted with cognitive/computational scientists in workshops on chance discovery, to obtain breakthroughs by stimulating each other. Third, “time” came to be introduced explicitly as a significant variable ruling causalities - background situations causing chances and chances causing impacts on events and actions of humans in the future. Readers may urge us to list the fourth, fifth, sixth, ... but let us stop here and open this book.
Born out of a project of the IUPAC's committee on Medicinal Chemistry and Drug Development, this reference addresses past and current strategies for successful drug analog development, extending the previously published volume by nine new analog classes and eight case studies. Like its precursor, this volume also contains a general section discussing universally applicable strategies for analog discovery and development. Spanning a wide range of therapeutic fields and chemical classes, the two volumes together constitute the first systematic approach to drug analog development. Of interest to virtually every researcher working in drug discovery and pharmaceutical chemistry.