Improving Oral Hygiene Skills by Computer-Based Training: A Randomized Controlled Comparison of the Modified Bass and the Fones Techniques

Background: Gingivitis and other plaque-associated diseases have a high prevalence in western communities even though the majority of adults report daily oral hygiene. This indicates a lack of oral hygiene skills. Currently, there is no clear evidence as to which brushing technique would bring about the best oral hygiene skills. While the modified Bass technique is often recommended by dentists and in textbooks, the Fones technique is often recommended in patient brochures. Still, standardized comparisons of the effectiveness of teaching these techniques are lacking. Methodology/Principal Findings: In a final sample of n=56 students, this multidisciplinary, randomized, examiner-blinded, controlled study compared the effects of parallel and standardized interactive computer presentations teaching either the Fones or the modified Bass technique. A control group was taught the basics of tooth brushing alone. Oral hygiene skills (remaining plaque after thorough oral hygiene) and gingivitis were assessed at baseline and 6, 12, and 28 weeks after the intervention. We found a significant group×time interaction for gingivitis (F(4/102)=3.267; p=0.016; e=0.957; ?2=0.114) and a significant main effect of group for oral hygiene skills (F(2/51)=7.088; p=0.002; ?2=0.218). Fones was superior to Bass; Bass did not differ from the control group. Group differences were most prominent after 6 and 12 weeks. Conclusions/Significance: The present trial indicates an advantage of teaching the Fones as compared to the modified Bass technique with respect to oral hygiene skills and gingivitis. Future studies are needed to analyze whether the disadvantage of teaching the Bass technique observed here is restricted to the teaching method employed. Trial Registration: German Clinical Trials Register http://www.drks.de/DRKS0000348

Interaction Between Convection and Pulsation

This article reviews our current understanding of modelling convection dynamics in stars. Several semi-analytical time-dependent convection models have been proposed for pulsating one-dimensional stellar structures with different formulations for how the convective turbulent velocity field couples with the global stellar oscillations. In this review we put emphasis on two, widely used, time-dependent convection formulations for estimating pulsation properties in one-dimensional stellar models. Applications to pulsating stars are presented with results for oscillation properties, such as the effects of convection dynamics on the oscillation frequencies, or the stability of pulsation modes, in classical pulsators and in stars supporting solar-type oscillations.Comment: Invited review article for Living Reviews in Solar Physics. 88 pages, 14 figure

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. BárbaraAvelar-Pereira 9 , EthanRoy2 , Valerie J.Sydnor3,4,5, JasonD.Yeatman1,2, The Fibr Community Science Consortium*, TheodoreD.Satterthwaite3,4,5,88 & Ariel Roke