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BackgroundA dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users’ behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement.ObjectiveMyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions.MethodsMyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior’s personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions.ResultsIn qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior’s personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001).ConclusionsMyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information...

Summary N6‐adenosine methylation (m6A) of mRNA is an essential process in most eukaryotes, but its role and the status of factors accompanying this modification are still poorly understood.Using combined methods of genetics, proteomics and RNA biochemistry, we identified a core set of mRNA m6A writer proteins in Arabidopsis thaliana.The components required for m6A in Arabidopsis included MTA, MTB, FIP37, VIRILIZER and the E3 ubiquitin ligase HAKAI. Downregulation of these proteins led to reduced relative m6A levels and shared pleiotropic phenotypes, which included aberrant vascular formation in the root, indicating that correct m6A methylation plays a role in developmental decisions during pattern formation.The conservation of these proteins amongst eukaryotes and the demonstration of a role in writing m6A for the E3 ubiquitin ligase HAKAI is likely to be of considerable relevance beyond the plant sciences.

Solar energy‐driven conversion of CO2 into fuels with H2O as a sacrificial agent is a challenging research field in photosynthesis. Herein, a series of crystalline porphyrin‐tetrathiafulvalene covalent organic frameworks (COFs) are synthesized and used as photocatalysts for reducing CO2 with H2O, in the absence of additional photosensitizer, sacrificial agents, and noble metal co‐catalysts. The effective photogenerated electrons transfer from tetrathiafulvalene to porphyrin by covalent bonding, resulting in the separated electrons and holes, respectively, for CO2 reduction and H2O oxidation. By adjusting the band structures of TTCOFs, TTCOF‐Zn achieved the highest photocatalytic CO production of 12.33 μmol with circa 100 % selectivity, along with H2O oxidation to O2. Furthermore, DFT calculations combined with a crystal structure model confirmed the structure–function relationship. Our work provides a new sight for designing more efficient artificial crystalline photocatalysts.

A strategy to covalently connect crystalline covalent organic frameworks (COFs) with semiconductors to create stable organic–inorganic Z‐scheme heterojunctions for artificial photosynthesis is presented. A series of COF–semiconductor Z‐scheme photocatalysts combining water‐oxidation semiconductors (TiO2, Bi2WO6, and α‐Fe2O3) with CO2 reduction COFs (COF‐316/318) was synthesized and exhibited high photocatalytic CO2‐to‐CO conversion efficiencies (up to 69.67 μmol g−1 h−1), with H2O as the electron donor in the gas–solid CO2 reduction, without additional photosensitizers and sacrificial agents. This is the first report of covalently bonded COF/inorganic‐semiconductor systems utilizing the Z‐scheme applied for artificial photosynthesis. Experiments and calculations confirmed efficient semiconductor‐to‐COF electron transfer by covalent coupling, resulting in electron accumulation in the cyano/pyridine moieties of the COF for CO2 reduction and holes in the semiconductor for H2O oxidation, thus mimicking natural photosynthesis.