Is Persistent Motor or Vocal Tic Disorder a Milder Form of Tourette Syndrome?

BACKGROUND: Persistent motor or vocal tic disorder (PMVT) has been hypothesized to be a forme fruste of Tourette syndrome (TS). Although the primary diagnostic criterion for PMVT (presence of motor or vocal tics, but not both) is clear, less is known about its clinical presentation. OBJECTIVE: The goals of this study were to compare the prevalence and number of comorbid psychiatric disorders, tic severity, age at tic onset, and family history for TS and PMVT. METHODS: We analyzed data from two independent cohorts using generalized linear equations and confirmed our findings using meta‐analyses, incorporating data from previously published literature. RESULTS: Rates of obsessive–compulsive disorder (OCD) and attention deficit hyperactivity disorder (ADHD) were lower in PMVT than in TS in all analyses. Other psychiatric comorbidities occurred with similar frequencies in PMVT and TS in both cohorts, although meta‐analyses suggested lower rates of most psychiatric disorders in PMVT compared with TS. ADHD and OCD increased the odds of comorbid mood, anxiety, substance use, and disruptive behaviors, and accounted for observed differences between PMVT and TS. Age of tic onset was approximately 2 years later, and tic severity was lower in PMVT than in TS. First‐degree relatives had elevated rates of TS, PMVT, OCD, and ADHD compared with population prevalences, with rates of TS equal to or greater than PMVT rates. CONCLUSIONS: Our findings support the hypothesis that PMVT and TS occur along a clinical spectrum in which TS is a more severe and PMVT a less severe manifestation of a continuous neurodevelopmental tic spectrum disorder. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Societ

Modelling interactions of acid–base balance and respiratory status in the toxicity of metal mixtures in the American oyster Crassostrea virginica

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology 155 (2010): 341-349, doi:10.1016/j.cbpa.2009.11.019.Heavy metals, such as copper, zinc and cadmium, represent some of the most common and serious pollutants in coastal estuaries. In the present study, we used a combination of linear and artificial neural network (ANN) modelling to detect and explore interactions among low-dose mixtures of these heavy metals and their impacts on fundamental physiological processes in tissues of the Eastern oyster, Crassostrea virginica. Animals were exposed to Cd (0.001 – 0.400 μM), Zn (0.001 – 3.059 μM) or Cu (0.002 – 0.787 μM), either alone or in combination for 1 to 27 days. We measured indicators of acid-base balance (hemolymph pH and total CO2), gas exchange (Po2), immunocompetence (total hemocyte counts, numbers of invasive bacteria), antioxidant status (glutathione, GSH), oxidative damage (lipid peroxidation; LPx), and metal accumulation in the gill and the hepatopancreas. Linear analysis showed that oxidative membrane damage from tissue accumulation of environmental metals was correlated with impaired acid-base balance in oysters. ANN analysis revealed interactions of metals with hemolymph acid-base chemistry in predicting oxidative damage that were not evident from linear analyses. These results highlight the usefulness of machine learning approaches, such as ANNs, for improving our ability to recognize and understand the effects of sub-acute exposure to contaminant mixtures.This study was supported by NOAA’s Center of Excellence in Oceans and Human Health at HML and the National Science Foundation

Addressing climate change with behavioral science: a global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors