The selected examples of successful dosaging ranges are provided, while emphasizing the necessity of empirically determined dose-response relationships based on the precise parameters and conditions inherent to a specific hypothesis. This review provides a new, experimentally based compilation of species-specific dose selection for studies on the in vivo effects of nicotine.

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

From many perspectives, the election of Donald Trump was seen as a departure from long-standing political norms. An analysis of Trump’s word use in the presidential debates and speeches indicated that he was exceptionally informal but at the same time, spoke with a sense of certainty. Indeed, he is lower in analytic thinking and higher in confidence than almost any previous American president. Closer analyses of linguistic trends of presidential language indicate that Trump’s language is consistent with long-term linear trends, demonstrating that he is not as much an outlier as he initially seems. Across multiple corpora from the American presidents, non-US leaders, and legislative bodies spanning decades, there has been a general decline in analytic thinking and a rise in confidence in most political contexts, with the largest and most consistent changes found in the American presidency. The results suggest that certain aspects of the language style of Donald Trump and other recent leaders reflect long-evolving political trends. Implications of the changing nature of popular elections and the role of media are discussed.

Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

Acute and chronic effects of nicotine on the immune system are usually opposite; acute treatment stimulates while chronic nicotine suppresses immune and inflammatory responses. Nicotine acutely raises intracellular calcium ([Ca2+]i) in T cells, but the mechanism of this response is unclear. Nicotinic acetylcholine receptors (nAChRs) are present on neuronal and non-neuronal cells, but while in neurons, nAChRs are cation channels that participate in neurotransmission; their structure and function in nonexcitable cells are not well-defined. In this communication, we present evidence that T cells express α7-nAChRs that are critical in increasing [Ca2+]i in response to nicotine. Cloning and sequencing of the receptor from human T cells showed a full-length transcript essentially identical to the neuronal α7-nAChR subunit (>99.6% homology). These receptors are up-regulated and tyrosine phosphorylated by treatment with nicotine, anti-TCR Abs, or Con A. Furthermore, knockdown of the α7-nAChR subunit mRNA by RNA interference reduced the nicotine-induced Ca2+ response, but unlike the neuronal receptor, α-bungarotoxin and methyllycaconitine not only failed to block, but also actually raised [Ca2+]i in T cells. The nicotine-induced release of Ca2+ from intracellular stores in T cells did not require extracellular Ca2+, but, similar to the TCR-mediated Ca2+ response, required activation of protein tyrosine kinases, a functional TCR/CD3 complex, and leukocyte-specific tyrosine kinase. Moreover, CD3ζ and α7-nAChR coimmunoprecipitated with anti-CD3ζ or anti-α7-nAChR Abs. These results suggest that in T cells, α7-nAChR, despite its close sequence homology with neuronal α7-nAChR, fails to form a ligand-gated Ca2+ channel, and that the nicotine-induced rise in [Ca2+]i in T cells requires functional TCR/CD3 and leukocyte-specific tyrosine kinase.

Allosteric modulation of neuronal nicotinic acetylcholine receptors (nAChRs) is considered to be one of the most promising approaches for therapeutics. We have previously reported on the pharmacological activity of several compounds that act as negative allosteric modulators (NAMs) of nAChRs. In the following studies, the effects of 30 NAMs from our small chemical library on both human ␣4␤2 (H␣4␤2) and human ␣3␤4 (H␣3␤4) nAChRs expressed in human embryonic kidney ts201 cells were investigated. During calcium accumulation assays, these NAMs inhibited nAChR activation with IC 50 values ranging from 2.4 M to more than 100 M. Several NAMs showed relative selectivity for H␣4␤2 nAChRs with IC 50 values in the low micromolar range. A lead molecule, KAB-18, was identified that shows relative selectivity for H␣4␤2 nAChRs. This molecule contains three phenyl rings, one piperidine ring, and one ester bond linkage. Structure-activity relationship (SAR) analyses of our data revealed three regions of KAB-18 that contribute to its relative selectivity. Predictive three-dimensional quantitative SAR (comparative molecular field analysis and comparative molecular similarity indices analysis) models were generated from these data, and a pharmacophore model was constructed to determine the chemical features that are important for biological activity. Using docking approaches and molecular dynamics on a H␣4␤2 nAChR homology model, a binding mode for KAB-18 at the ␣/␤ subunit interface that corresponds to the predicted pharmacophore is described. This binding mode was supported by mutagenesis studies. In summary, these studies highlight the importance of SAR, computational, and molecular biology approaches for the design and synthesis of potent and selective antagonists targeting specific nAChR subtypes.

Personality is typically defined as the consistent set of traits, attitudes, emotions, and behaviors that people have. For several decades, a majority of researchers have tacitly agreed that the gold standard for measuring personality was with self-report questionnaires. Surveys are fast, inexpensive, and display beautiful psychometric properties. A considerable problem with this method, however, is that self-reports reflect only one aspect of personality-people's explicit theories of what they think they are like. We propose a complementary model that draws on a big data solution: the analysis of the words people use. Language use is relatively reliable over time, internally consistent, and differs considerably between people. Language-based measures of personality can be useful for capturing/modeling lower-level personality processes that are more closely associated with important objective behavioral outcomes than traditional personality measures. Additionally, the increasing availability of language data and advances in both statistical methods and technological power are rapidly creating new opportunities for the study of personality at "big data" scale. Such opportunities allow researchers to not only better understand the fundamental nature of personality, but at a scale never before imagined in psychological research.

Scholars across disciplines have long debated the existence of a common structure that underlies narratives. Using computer-based language analysis methods, several structural and psychological categories of language were measured across ~40,000 traditional narratives (e.g., novels and movie scripts) and ~20,000 nontraditional narratives (science reporting in newspaper articles, TED talks, and Supreme Court opinions). Across traditional narratives, a consistent underlying story structure emerged that revealed three primary processes: staging, plot progression, and cognitive tension. No evidence emerged to indicate that adherence to normative story structures was related to the popularity of the story. Last, analysis of fact-driven texts revealed structures that differed from story-based narratives.