52 research outputs found

    Examination of Dental Distress and Anxiety-Related Vulnerability Factors

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    Apprehension towards dental services has been documented for decades, and despite modern scientific advances, dental anxiety and phobia continue to adversely affect individuals’ oral and mental health. Furthermore, the prevalence of dental distress is estimated to range from 8% to nearly 30% among community samples. Given the prevalence and impairment of dental distress, understanding the underlying mechanisms is pertinent to finding more effective treatments. In particular, transdiagnostic processes that may be implicated in dental distress include anxiety sensitivity (AS), pain sensitivity (PS), and distress tolerance (DT). As such, the aims of the current study were 1.) to characterize dental distress and 2.) to examine transdiagnostic vulnerabilities in this population. The results indicated that female students and individuals with a prior traumatic dental experience reported significantly higher dental distress. In addition, dental distress was significantly associated with higher levels of AS, greater PS, and less DT. Furthermore, data showed that both AS and DT were unique predictors of dental distress after accounting for sex and history of a traumatic dental experience. The results suggest that treatments that target reductions in anxiety sensitivity and increases in the ability to tolerate psychological and physiological distress may be beneficial in easing dental distress

    AI is a viable alternative to high throughput screening: a 318-target study

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    : High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

    Mahatma Gandhi and the Prisoner’s Dilemma: Strategic Civil Disobedience and Great Britain’s Great Loss of Empire in India

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    This paper examines the relationship between statutory monopoly and collective action as a multi-person assurance game culminating in an end to British Empire in India. In a simple theoretical model, it is demonstrated whether or not a collective good enjoys (or is perceived to enjoy) pure jointness of production and why the evolutionary stable strategy of non-violence was supposed to work on the principle that the coordinated reaction of a ethnically differentiated religious crowd to a conflict between two parties (of colonizer and colonized) over confiscatory salt taxation would significantly affect its course. Following Mancur Olson (1965) and Dennis Chong (1991), a model of strategic civil disobedience is created which is used to demonstrate how collective action can be used to produce an all-or-nothing public good to achieve economic and political independence
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