Putative changes in dopaminergic neurotransmission following nicotine induced behavioural sensitisation

Behavioural sensitisation is a progressive enhancement of stereotypic or locomotor behaviour following repeated intermittent administration of a psychostimulant or stress. It is a phenomenon thought to underlie many neuropsychiatric disorders (e.g. schizophrenia, addiction, depressive disorders, dyskinesia, and psychosis) although its own mechanism remains contentious. In this thesis a multidisciplinary approach was used to investigate the role of dopamine in behavioural sensitisation. Different in vivo and ex vivo techniques were used to assess and elucidate putative changes in dopaminergic neurotransmission of behaviourally sensitised animals. If so, this improved understanding of behavioural sensitisation could provide a better understanding of the pathophysiologies of neuropsychiatric disorders and provide more insight into why existing pharmacotherapies for these disorders are able to confer only modest benefit. Moreover, this improved understanding can lead to development of new medication and more effective therapies to treat neuropsychiatric disorders and therapies that address the specific problems associated with them. Previously, an oversimplified view of neurotransmitter release was used for the development of current available drugs, i.e. stabilising either the attenuated or increased release of neurotransmitters without considering the involvement of synaptic plasticity. Therapies being used with modest effectiveness regulate dopamine transmission levels, suggesting a putative role for dopamine. The present study used chronic intermittent nicotine administration in rodents to induce behavioural sensitisation which was monitored behaviourally by measuring locomotor activity. Further studies were performed ex vivo assessing receptor binding, intracellular cAMP accumulation and electrically stimulated dopamine release. Prior to the pharmacological assessments, a novel LC-MS/MS method to measure (cyclic-) nucleotides was developed and a fast cyclic voltammetry (FCV) technique was established to measure real-time neurotransmitter release. Specific pharmacological tools were used to identify the role of dopaminergic neurotransmission in behavioural sensitisation. Finally, the ex vivo findings using tissue from sensitised and non-sensitised animals were compared to those findings obtained in vivo

Putative changes in dopaminergic neurotransmission following nicotine induced behavioural sensitisation

Behavioural sensitisation is a progressive enhancement of stereotypic or locomotor behaviour following repeated intermittent administration of a psychostimulant or stress. It is a phenomenon thought to underlie many neuropsychiatric disorders (e.g. schizophrenia, addiction, depressive disorders, dyskinesia, and psychosis) although its own mechanism remains contentious. In this thesis a multidisciplinary approach was used to investigate the role of dopamine in behavioural sensitisation. Different in vivo and ex vivo techniques were used to assess and elucidate putative changes in dopaminergic neurotransmission of behaviourally sensitised animals. If so, this improved understanding of behavioural sensitisation could provide a better understanding of the pathophysiologies of neuropsychiatric disorders and provide more insight into why existing pharmacotherapies for these disorders are able to confer only modest benefit. Moreover, this improved understanding can lead to development of new medication and more effective therapies to treat neuropsychiatric disorders and therapies that address the specific problems associated with them. Previously, an oversimplified view of neurotransmitter release was used for the development of current available drugs, i.e. stabilising either the attenuated or increased release of neurotransmitters without considering the involvement of synaptic plasticity. Therapies being used with modest effectiveness regulate dopamine transmission levels, suggesting a putative role for dopamine. The present study used chronic intermittent nicotine administration in rodents to induce behavioural sensitisation which was monitored behaviourally by measuring locomotor activity. Further studies were performed ex vivo assessing receptor binding, intracellular cAMP accumulation and electrically stimulated dopamine release. Prior to the pharmacological assessments, a novel LC-MS/MS method to measure (cyclic-) nucleotides was developed and a fast cyclic voltammetry (FCV) technique was established to measure real-time neurotransmitter release. Specific pharmacological tools were used to identify the role of dopaminergic neurotransmission in behavioural sensitisation. Finally, the ex vivo findings using tissue from sensitised and non-sensitised animals were compared to those findings obtained in vivo

The first colour blind American president?

This paper explores the link between a mindset of respect for differences and the benefits thereof for global organisations and persons. The profound influence of a person's mindset or worldview towards differences has on the performance of teams and people in multicultural and diverse settings is demonstrated with relatable anecdotes. The influence of the (subconscious) mindsets in these anecdotes is used to explain a model that plots the mental stages of development of intercultural sensitivity. It demonstrates the required mindset of global organizations, executives, leaders and individuals for dealing with the increasing awareness of differences stemming from globalisation that continuously "shrinks" the world of today. It is shown that a mindset towards respect for differences is not only essential to benefit from the hidden potential in multicultural and diverse settings, but also to prevent that same diversity from negatively influencing organisation and team performance. A meta-level approach for effectively dealing with the complexities and uncertainty attributed to multiculturalism and diversity in organisations and teams is briefly introduced

The Threatening Effect of Invoked Help from Highly Competent Intelligent Agents

Empowered with artificial intelligence, intelligent agents (IAs) increasingly offer help not only in response to user prompts (i.e., user-invoked help) but also without user prompts (i.e., IA-invoked help). Additionally, IAs are becoming more competent and even surpassing users in performing many computational and repetitive tasks. Drawing on self-affirmation theory, we investigate users’ acceptance of IA- versus user-invoked help for identity-defining tasks from IAs with different levels of relative competence. We conducted an experiment with 199 software developers and found that IA-invoked (vs. user-invoked) help increases self-threat and thus reduces users’ willingness to accept help from IAs. Moreover, relative competence moderates this effect, in that only IAs having relatively higher (vs. lower or equal) competence cause self-threat. Our study contributes to a better understanding of the self-threatening effects of IA-invoked (vs. user-invoked) help from IAs and the related role of relative competence that crucially shapes effective user-IA collaborations

Automatically Quantifying Customer Need Tweets: Towards a Supervised Machine Learning Approach

The elicitation of customer needs is an important task for businesses in order to design customer-centric products and services. While there are different approaches available, most lack automation, scalability and monitoring capabilities. In this work, we demonstrate the feasibility to automatically identify and quantify customer needs by training and evaluating on previously-labeled Twitter data. To achieve that, we utilize a supervised machine learning approach. Our results show that the classification performances are statistically superior-”but can be further improved in the future

Artificial intelligence and machine learning

Within the last decade, the application of "artificial intelligence" and "machine learning" has become popular across multiple disciplines, especially in information systems. The two terms are still used inconsistently in academia and industry—sometimes as synonyms, sometimes with different meanings. With this work, we try to clarify the relationship between these concepts. We review the relevant literature and develop a conceptual framework to specify the role of machine learning in building (artificial) intelligent agents. Additionally, we propose a consistent typology for AI-based information systems. We contribute to a deeper understanding of the nature of both concepts and to more terminological clarity and guidance—as a starting point for interdisciplinary discussions and future research