Urinary titin in myotonic dystrophy type 1.

INTRODUCTION/AIMS: Urinary titin, an easy-to-obtain marker, has been investigated in muscular dystrophies, but not in myotonic dystrophy type 1 (DM1). We investigated the role of titin as a biomarker of muscle injury in DM1. METHODS: We compared the urinary titin N-fragment/creatinine ratio in 29 patients with DM1 vs. 30 healthy controls. We also recorded clinical data such as muscle strength, serum creatine kinase, DM1-related outcome measures, and the 20-item DM1-activ questionnaire. The severity of the disease was graded using the Muscular Impairment Rating Scale (MIRS). RESULTS: The titin/creatinine ratio was significantly higher in the urine samples of DM1 patients than of healthy controls (median ± mean absolute deviation [MAD]: 39.313 ± 26.546 vs. 6.768 ± 5.245 pmol/mg creatinine; P < .001), and was related to muscle impairment graded by MIRS (τ = 0.503, P = .038). DISCUSSION: Urinary titin may be a biomarker for DM1. Long-term follow-up of DM1 patients is needed to investigate the potential role of titin as a biomarker for disease activity and progression.

The emergence of action-effect-related motor adaptation amidst outcome unpredictability.

Previous research indicates that quick, repetitive actions (pinches, taps, button presses) are executed with smaller force when followed by predictable and salient action effects (tones, light flashes). It has been suggested that successive actions become gradually softer until an optimum is reached, which presumably reflects a balance between the ability to maintain a high probability of action success, and the reduction of exerted force to conserve energy. In the present experiments, we investigated whether this action-effect-related motor adaptation appeared when the arrival of the action effect was unpredictable. Young adult participants produced evenly spaced pinches (Experiment 1) or taps (Experiment 2), which resulted in a tone in 50% of the trials. The presence of the tone effect varied randomly from trial to trial, leading to action sequences with various tone-elicitation patterns. We have found that pinches and taps preceded by sequences of tone-eliciting actions were softer than actions preceded by sequences of tone-absent trials. In the case of pinches, actions were also modulated on the fly, with the current action being softer and briefer when a tone was elicited. Our results demonstrate that action effects can modulate subsequent and ongoing actions even when the arrival of these effects is unpredictable. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A machine learning analysis of the relationship of demographics and social gathering attendance from 41 countries during pandemic.

Knowing who to target with certain messages is the prerequisite of efficient public health campaigns during pandemics. Using the COVID-19 pandemic situation, we explored which facets of the society-defined by age, gender, income, and education levels-are the most likely to visit social gatherings and aggravate the spread of a disease. Analyzing the reported behavior of 87,169 individuals from 41 countries, we found that in the majority of the countries, the proportion of social gathering-goers was higher in male than female, younger than older, lower-educated than higher educated, and low-income than high-income subgroups of the populations. However, the data showed noteworthy heterogeneity between the countries warranting against generalizing from one country to another. The analysis also revealed that relative to other demographic factors, income was the strongest predictor of avoidance of social gatherings followed by age, education, and gender. Although the observed strength of these associations was relatively small, we argue that incorporating demographic-based segmentation into public health campaigns can increase the efficiency of campaigns with an important caveat: the exploration of these associations needs to be done on a country level before using the information to target populations in behavior change interventions.

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.

A multi-lab test of the facial feedback hypothesis by the Many Smiles Collaboration.

Following theories of emotional embodiment, the facial feedback hypothesis suggests that individuals' subjective experiences of emotion are influenced by their facial expressions. However, evidence for this hypothesis has been mixed. We thus formed a global adversarial collaboration and carried out a preregistered, multicentre study designed to specify and test the conditions that should most reliably produce facial feedback effects. Data from n = 3,878 participants spanning 19 countries indicated that a facial mimicry and voluntary facial action task could both amplify and initiate feelings of happiness. However, evidence of facial feedback effects was less conclusive when facial feedback was manipulated unobtrusively via a pen-in-mouth task.

To which world regions does the valence-dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .