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DNA methylation is a heritable epigenetic modification that plays an essential role in mammalian development. Genomic methylation patterns are dynamically maintained, with DNA methyltransferases mediating inheritance of methyl marks onto nascent DNA over cycles of replication. A recently developed experimental technique employing immunoprecipitation of bromodeoxyuridine labeled nascent DNA followed by bisulfite sequencing (Repli-BS) measures post-replication temporal evolution of cytosine methylation, thus enabling genome-wide monitoring of methylation maintenance. In this work, we combine statistical analysis and stochastic mathematical modeling to analyze Repli-BS data from human embryonic stem cells. We estimate site-specific kinetic rate constants for the restoration of methyl marks on >10 million uniquely mapped cytosines within the CpG (cytosine-phosphate-guanine) dinucleotide context across the genome using Maximum Likelihood Estimation. We find that post-replication remethylation rate constants span approximately two orders of magnitude, with half-lives of per-site recovery of steady-state methylation levels ranging from shorter than ten minutes to five hours and longer. Furthermore, we find that kinetic constants of maintenance methylation are correlated among neighboring CpG sites. Stochastic mathematical modeling provides insight to the biological mechanisms underlying the inference results, suggesting that enzyme processivity and/or collaboration can produce the observed kinetic correlations. Our combined statistical/mathematical modeling approach expands the utility of genomic datasets and disentangles heterogeneity in methylation patterns arising from replication-associated temporal dynamics versus stable cell-to-cell differences., Competing Interests: The authors have declared that no competing interests exist.

Objective: The objective of this study was to describe participant engagement and examine predictors of weight loss and points earned through the point-based incentive system of the Social Pounds Off Digitally (POD) app., Materials and Methods: Overweight and obese adults with Android smartphones/tablets (body mass index 25-49.9 kg/m 2 ; N  = 24) were recruited for a 3-month weight loss intervention. Participants completed a survey assessing demographics and personality and had their weight measured. Participants received the content of the intervention via podcasts and used the Social POD app to self-monitor diet, physical activity, and weight. The Social POD app contained: tracking features; in-app notifications to track; pre-set goals for tracking; newsfeed for updates on others' goal attainment; ability to earn and track points for usage (exchanged for study-provided prizes); and a message screen. Analyses examined relationships between percent weight loss, personality characteristics, and total points earned., Results: A total of 4843 points were earned (mean = 202 ± 105 points/participant). Most participants earned all three prizes (62.5%), followed by two prizes (21%), no prizes (12.5%), and one prize (4%). Total points earned significantly predicted percent weight loss ( B =  -0.02, p  = .01), and higher conscientiousness significantly predicted greater total points earned ( B =  10.27, p =  .01), but other personality characteristics assessed did not., Conclusion: A mobile app yielded moderately high participant engagement, as demonstrated by points earned. Earning points was significantly associated with percent weight loss, and conscientiousness was significantly associated with total points earned. Future research should examine whether point systems impact health behavior and weight loss when rewards are discontinued., Clinical Trial Registration Number: NCT02344836., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: To test the efficacy of a weight loss mobile app based on recommender systems and developed by experts in health promotion and computer science to target social support and self-monitoring of diet, physical activity (PA), and weight (Social POD app), compared to a commercially available diet and PA tracking app (standard)., Materials and Methods: Overweight adults [N=51] were recruited and randomly assigned to either the experimental group [n=26; theory-based podcasts (TBP)+Social POD app] or the comparison group (n=25; TBP+standard app). The Social POD app issued notifications to encourage users to self-monitor and send theory-based messages to support users who had not self-monitored in the previous 48h. Independent samples t-test were used to examine group differences in kilograms lost and change in BMI. Analysis of covariance was used to analyze secondary outcomes while controlling for baseline values., Results: Participant attrition was 12% (n=3 experimental and n=3 comparison). Experimental group participants lost significantly more weight (-5.3kg, CI: -7.5, -3.0) than comparison group (-2.23kg, CI: -3.6, -1.0; d=0.8, r=0.4, p=0.02) and had a greater reduction in BMI (p=0.02). While there were significant differences in positive outcome expectations between groups (p=0.04) other secondary outcomes (e.g., caloric intake and social support) were not significant., Discussion: Use of the Social POD app resulted in significantly greater weight loss than use of a commercially available tracking app. This mobile health intervention has the potential to be widely disseminated to reduce the risk of chronic disease associated with overweight and obesity., (Published by Elsevier Ireland Ltd.)

Background: Mobile health (mHealth) has shown promise as a way to deliver weight loss interventions, including connecting users for social support., Objective: To develop, refine, and pilot test the Social Pounds Off Digitally (POD) Android app for personalized health monitoring and interaction., Methods: Adults who were overweight and obese with Android smartphones (BMI 25-49.9 kg/m(2); N=9) were recruited for a 2-month weight loss pilot intervention and iterative usability testing of the Social POD app. The app prompted participants via notification to track daily weight, diet, and physical activity behaviors. Participants received the content of the behavioral weight loss intervention via podcast. In order to re-engage infrequent users (did not use the app within the previous 48 hours), the app prompted frequent users to select 1 of 3 messages to send to infrequent users targeting the behavioral theory constructs social support, self-efficacy, or negative outcome expectations. Body weight, dietary intake (2 24-hr recalls), and reported calories expended during physical activity were assessed at baseline and 2 months. All participants attended 1 of 2 focus groups to provide feedback on use of the app., Results: Participants lost a mean of 0.94 kg (SD 2.22, P=.24) and consumed significantly fewer kcals postintervention (1570 kcal/day, SD 508) as compared to baseline (2384 kcal/day, SD 993, P=.01). Participants reported expending a mean of 171 kcal/day (SD 153) during intentional physical activity following the intervention as compared to 138 kcal/day (SD 139) at baseline, yet this was not a statistically significant difference (P=.57). There was not a statistically significant correlation found between total app entries and percent weight loss over the course of the intervention (r=.49, P=.19). Mean number of app entries was 77.2 (SD 73.8) per person with a range of 0 to 219. Messages targeting social support were selected most often (32/68, 47%), followed by self-efficacy (28/68, 41%), and negative outcome expectations (8/68, 12%). Themes from the focus groups included functionality issues, revisions to the messaging system, and the addition of a point system with rewards for achieving goals., Conclusions: The Social POD app provides an innovative way to re-engage infrequent users by encouraging frequent users to provide social support. Although more time is needed for development, this mHealth intervention can be disseminated broadly for many years and to many individuals without the need for additional intensive in-person hours.

More than 90% of protein structures submitted to the PDB each year are homologous to some previously characterized protein structure. The extensive resources that are required for structural characterization of proteins can be justified for the 10% of the novel structures, but not for the remaining 90%. This report presents the 2D-PDPA method, which utilizes unassigned residual dipolar coupling in order to address the economics of structure determination of routine proteins by reducing the data acquisition and processing time. 2D-PDPA has been demonstrated to successfully identify the correct structure of an array of proteins that range from 46 to 445 residues in size from a library of 619 decoy structures by using unassigned simulated RDC data. When using experimental data, 2D-PDPA successfully identified the correct NMR structures from the same library of decoy structures. In addition, the most homologous X-ray structure was also identified as the second best structural candidate. Finally, success of 2D-PDPA in identifying and evaluating the most appropriate structure from a set of computationally predicted structures in the case of a previously uncharacterized protein Pf2048.1 has been demonstrated. This protein exhibits less than 20% sequence identity to any protein with known structure and therefore presents a compelling and practical application of our proposed work.