Hallucinogens: mechanisms and medical complications

Thesis (M.A.)--Boston UniversityHallucinogens are drugs that alter consciousness by distorting primarily auditory and visual perception but they can affect any sensory system. Hallucinogens also affect judgment, orientation, memory, or emotion. Despite the profound alteration in perception, adverse effects are minimal and hallucinogens are not addictive. Hallucinogen use has its roots in shamanic practices of indigenous cultures and is even incorporated in today’s religions like the Native American Church. By putting a person in an altered state of consciousness, many religions believed that the user was able to see beyond the boundaries of reality and reach out to mythical beings. Hallucinogen use in scientific research was not popular until the 1950’s when Albert Hoffman discovered lysergic acid diethylamide (LSD). The discovery of drug encouraged further research into understanding its mechanisms and its relationship with mental diseases like schizophrenia. Unfortunately, the Comprehensive Drug Abuse Prevention and Control Act of 1970 significantly limited hallucinogenic research and human research for the last 42 years. However, animal research in the last 20 years has determined the importance of serotonergic mechanisms and more specifically the 5-HT2A receptors in mediating LSD’s hallucinogenic effects. Researchers continue to identify mechanisms of LSD action. In addition to serotonergic actions, LSD is active with dopaminergic and metabotropic glutamate receptors. PET scans and fMRI’s have also revealed the importance of the prefrontal cortical region and its interaction with other areas during a hallucinogenic state. The relationship between LSD and acute psychosis is also being explored via animal models. Although human clinical research is limited, recent research sees a much deeper relationship by linking LSD brain activity and neurotransmitter levels to psychotic behaviors. This further understanding of hallucinogens on a physiological and psychological level has led to possible psychotherapeutic areas of research in anxiety and substance abuse. This thesis describes a brief history of hallucinogenic research, the pharmacology and neuroanatomy of serotonergic hallucinogens, the acute and chronic adverse effects of serotonergic hallucinogens, the possible treatments for complications of hallucinogens, the epidemiology, the relationship between hallucinogens and schizophrenia, and possible therapeutic uses of serotonergic hallucinogens. With its minimal adverse effects in humans and its powerful influence on the human psyche, serotonergic hallucinogens are invaluable tools for understanding the human mind

Combined Nucleosynthetic Yields of Multiple First Stars

Modern numerical simulations of the formation of the first stars predict that the first stars formed in multiples. In those cases, the chemical yields of multiple supernova explosions may have contributed to the formation of a next generation star. We match the chemical abundances of the oldest observed stars in the universe to a database of theoretical supernova models, to show that it is likely that the first stars formed from the ashes of two or more progenitors.Comment: 3 pages, 2 figures, NIC 2016 Conference Proceeding

One-dimensional wave equations defined by fractal Laplacians

We study one-dimensional wave equations defined by a class of fractal Laplacians. These Laplacians are defined by fractal measures generated by iterated function systems with overlaps, such as the well-known infinite Bernoulli convolution associated with the golden ratio and the 3-fold convolution of the Cantor measure. The iterated function systems defining these measures do not satisfy the post-critically finite condition or the open set condition. By using second-order self-similar identities introduced by Strichartz et al., we discretize the equations and use the finite element and central difference methods to obtain numerical approximations to the weak solutions. We prove that the numerical solutions converge to the weak solution, and obtain estimates for the rate of convergence

Computer Vision to Analyze Protests in Social Media

Images are central to understanding protests and mass activism today for its impact in shaping public opinion. Previously, analyzing protest images required human annotation, which is laborious and expensive. In the modern era of social media, an automated and systematic method is required to analyze the vast amounts of social media images. In this thesis, I introduce a deep-learning computational framework to analyze protest images. This system comprises of (1) processing and parsing social media images from Twitter, (2) a model to identify common protest image characteristics, such as violence, fire, and police, models to (3) detect and (4) classify faces of protesters to understand demographics, and (5) an deduplication algorithm to identify the most shared images

A Novel Framework for Building Organizational Capabilities

The pace of change in the business environment is constantly being accelerated by rapidly emerging technologies and evolving customer demands. It is no longer sufficient for organizations to keep their strategic focus on elevating their existing capabilities to achieve excellence in their product and service offerings. It is critical to be able to develop new organizational capabilities which are aligned with new strategic insights. Existing frameworks have their limitations in terms of helping to identify new organizational capabilities and ensuring that they are closely aligned with the overall business strategy. In response to these challenges, a novel framework for designing and building new organizational capabilities is proposed and elaborated in this paper. Instead of focusing on People, Processes and Technologies (as most conventional approaches do), the proposed framework has its emphasis on Relationship, Thinking and Action. By integrating key theoretical perspectives and practical insights, this framework offers a comprehensive and flexible roadmap for organizations seeking to enhance their competitive advantage. Practitioners can apply the framework throughout the three key phases in building new organizational capabilities: diagnosis, design, and implementation. The framework emphasizes the importance of fostering a learning culture and leveraging technology as enablers for capability development. Exploration will also be made in how ChatGPT can be deployed in the application of the framework. By presenting this novel framework, this paper contributes to the existing body of knowledge on organizational capabilities and provides practical guidance for organizations seeking to enhance their competitiveness in a rapidly changing business landscape