Climacteric fruit ripening: Ethylene-dependent and independent regulation of ripening pathways in melon fruit

Cantaloupe melons have a typical climacteric behaviour with ethylene playing a major role in the regulation of the ripening process and affecting the ripening rate. Crossing of Cantaloupe Charentais melon with a non-climacteric melon indicated that the climacteric character is genetically dominant and conferred by two duplicated loci only. However, other experiments made by crossing two non-climacteric melons have generated climacteric fruit, indicating that different and complex genetic regulation exists for the climacteric character. Suppression of ethylene production by antisense ACC oxidase RNA in Charentais melon has shown that, while many ripening pathways were regulated by ethylene (synthesis of aroma volatiles, respiratory climacteric and degreening of the rind), some were ethylene-independent (initiation of climacteric, sugar accumulation, loss of acidity and coloration of the pulp). Softening of the flesh comprised both ethylene-dependent and independent components that were correlated with differential regulation of cell wall degrading genes. These results indicate that climacteric (ethylene-dependent) and non-climacteric (ethylene-independent) regulation coexist during climacteric fruit ripening. In addition, ethylenesuppressed melons allowed demonstrating that the various ethylene-dependent events exhibited differential sensitivity to ethylene and that ethylene was promoting sensitivity to chilling injury. Throughout this review, the data generated with melon are compared with those obtained with tomato and other fruit

Effects of mindful physical activity on perceived exercise exertion and other physiological and psychological responses: results from a within-subjects, counter-balanced study

BackgroundMost adults are insufficiently active. Mindfulness training may increase moderate to vigorous physical activity (MVPA) adoption and adherence. However, physiological and psychological factors underlying these effects are not well understood. This study examined the effects of an acute bout of MVPA, mindfulness training, and combined MVPA and mindfulness training on physiological and psychological outcomes.MethodsHealthy adults (N = 29, Mage = 28.6) completed 20-min counterbalanced conditions: (a) mindfulness training (MIND); (b) moderate intensity walking (PA), and (c) moderate intensity walking while listening to MVPA-specific guided mindfulness training (PAMIND). Heart rate (HR), Rating of Perceived Exertion (RPE), Feeling Scale (FS) and Blood Pressure (BP) were measured at rest, at regular intervals during each condition, and post-condition. Mindfulness, state anxiety, and self-efficacy were assessed pre- and post-condition.ResultsAverage and peak HR, systolic BP (SBP), and RPE were significantly higher, and average and peak FS were significantly lower during the PA and PAMIND conditions compared to MIND (p < 0.001). Average RPE was significantly higher for PA compared to PAMIND (p < 0.001). Heart rate, feeling scale, body and mental events mindfulness, and self-efficacy for walking increased from pre to post (all p’s < 0.001) for all conditions. Time by condition interactions were significant for change in heart rate, mental events mindfulness, and state anxiety from pre- to post-condition.ConclusionThe physiological response to MVPA and PAMIND were similar. However, RPE was rated lower in the PAMIND condition, which could have implications for MVPA adoption and maintenance. Future work should further explore RPE combining MVPA and mindfulness training

Ergatis: a web interface and scalable software system for bioinformatics workflows

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.https://doi.org/10.1186/1471-2105-12-35

Well-being in the time of COVID-19: Do metaphors and mindsets matter?

Communications about the Coronavirus Disease 2019 (COVID-19) often employ metaphors, which can help people understand complex issues. For example, public health messages may focus on “fighting” the disease, attempting to rouse people to action by instilling a sense of urgency. In contrast, change-focused metaphors may foster growth mindsets and self-efficacy—cornerstones of well-being and action. We randomly assigned participants to read one of two articles—either an article about coronavirus that focused on fighting the war or an article that highlighted the possibility of change. In Study 1 (N = 426), participants who read the war, relative to the change, message reported lower growth mindsets and self-efficacy and these in turn, predicted lower well-being and weaker intentions to engage in health behaviours. In Study 2, (N = 702), we sought to replicate findings and included a no treatment control. We failed to replicate the effects of message condition, although both messages predicted greater self-efficacy compared to the control. Similar to Study 1, growth mindsets predicted intentions to engage in recommended health behaviours and self-efficacy predicted both well-being and action. We discuss theoretical reasons for discrepancies as well as practical applications for developing public health communications

Relationships Between Blood Pressure Reduction, Weight Loss, and Engagement in a Digital App–Based Hypertension Care Program: Observational Study

BackgroundHome blood pressure (BP) monitoring is recommended for people with hypertension; however, meta-analyses have demonstrated that BP improvements are related to additional coaching support in combination with self-monitoring, with little or no effect of self-monitoring alone. High-contact coaching requires substantial resources and may be difficult to deliver via human coaching models. ObjectiveThis observational study assessed changes in BP and body weight following participation in a fully digital program called Lark Hypertension Care with coaching powered by artificial intelligence (AI). MethodsParticipants (N=864) had a baseline systolic BP (SBP) ≥120 mm Hg, provided their baseline body weight, and had reached at least their third month in the program. The primary outcome was the change in SBP at 3 and 6 months, with secondary outcomes of change in body weight and associations of changes in SBP and body weight with participant demographics, characteristics, and program engagement. ResultsBy month 3, there was a significant drop of –5.4 mm Hg (95% CI –6.5 to –4.3; P<.001) in mean SBP from baseline. BP did not change significantly (ie, the SBP drop maintained) from 3 to 6 months for participants who provided readings at both time points (P=.49). Half of the participants achieved a clinically meaningful drop of ≥5 mm Hg by month 3 (178/349, 51.0%) and month 6 (98/199, 49.2%). The magnitude of the drop depended on starting SBP. Participants classified as hypertension stage 2 had the largest mean drop in SBP of –12.4 mm Hg (SE 1.2 mm Hg) by month 3 and –13.0 mm Hg (SE 1.6 mm Hg) by month 6; participants classified as hypertension stage 1 lowered by –5.2 mm Hg (SE 0.8) mm Hg by month 3 and –7.3 mm Hg (SE 1.3 mm Hg) by month 6; participants classified as elevated lowered by –1.1 mm Hg (SE 0.7 mm Hg) by month 3 but did not drop by month 6. Starting SBP (β=.11; P<.001), percent weight change (β=–.36; P=.02), and initial BMI (β=–.56; P<.001) were significantly associated with the likelihood of lowering SBP ≥5 mm Hg by month 3. Percent weight change acted as a mediator of the relationship between program engagement and drop in SBP. The bootstrapped unstandardized indirect effect was –0.0024 (95% CI –0.0052 to 0; P=.002). ConclusionsA hypertension care program with coaching powered by AI was associated with a clinically meaningful reduction in SBP following 3 and 6 months of program participation. Percent weight change was significantly associated with the likelihood of achieving a ≥5 mm Hg drop in SBP. An AI-powered solution may offer a scalable approach to helping individuals with hypertension achieve clinically meaningful reductions in their BP and associated risk of cardiovascular disease and other serious adverse outcomes via healthy lifestyle changes such as weight loss

Discovering Engagement Personas in a Digital Diabetes Prevention Program

Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions