Nutrition Literacy Measurement Tool With Multiple Features for Chinese Adults

Background: Nutrition literacy is an emerging term which is increasingly used in policy and research. Progression is limited by the lack of an accepted method to measure nutrition literacy in Chinese adult, even research in this area is growing. Objective: The objective of this study is to develop a valid instrument to assess nutrition literacy in Chinese adults. Methods: The process involved 2 steps: constructed nutrition literacy conceptual framework, and developed potential items of scale based on literature review; and conducted 2 rounds of Delphi consultation to select items of the preliminary questionnaire. Results: In Delphi survey, the content validity index for each domain, level, and dimension of nutrition literacy was 1.0, coefficient of variation was less than 0.10, and Kendall’s coefficient of concordance was greater than 0.83. All of the 2 domains, 3 levels, and 6 dimensions initially formulated by our research team were reserved in the conceptual framework of nutrition literacy. Furthermore, a 43-item nutrition literacy measurement scale was established. Each item kept in the final scale reaches a high degree of concentration and a high degree of coordination, with the mean of importance ranging from 4.38 to 5.00. Conclusions: A nutrition literacy measurement scale with multiple features was established for Chinese adults, providing an operationalized tool to assess comprehensively nutrition literacy for research and practice in the field of nutrition, diet, and health.

Enhancing neural markers of attention in children with ADHD using a digital therapeutic

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition characterized by diminished attentional control. Critically, these difficulties are related to negative consequences in real-life functioning both during development and into adulthood. There is now growing evidence that modulating the underlying neural circuits related to attention can improve behavior and brain function in children with ADHD. We have previously shown that game-based digital therapeutics targeting a key neural marker of attention–midline frontal theta (MFT)–yield positive effects on attentional control in several populations. However, the effects of such digital therapeutics in children with ADHD and no other comorbidities has not been yet examined. To address this gap, we assessed a sample of 25 children with ADHD (8–12 years old) on neural, behavioral, and clinical metrics of attention before and after a 4-week at-home intervention on an iPad targeting MFT circuitry. We found that children showed enhancements on a neural measure of attention (MFT power), as well as on objective behavioral measures of attention and parent reports of clinical ADHD symptoms. Importantly, we observed relationships between the neural and behavioral cognitive improvements, demonstrating that those children who showed the largest intervention-related neural gains were also those that improved the most on the behavioral tasks indexing attention. These findings provide support for using targeted, digital therapeutics to enhance multiple features of attentional control in children with ADHD. Study registration: ClinicalTrials.gov registry (NCT03844269) https://clinicaltrials.gov/ct2/show/NCT03844269.

Disentangling Socioeconomic Status and Race in Infant Outcomes: A Neural Network Analysis

Race is commonly used as a proxy for multiple features including socioeconomic status. It is critical to dissociate these factors, identify mechanisms that impact infant outcomes, such as birthweight, and direct appropriate interventions and shape public policy. Demographic, socioeconomic, and clinical variables were used to model infant birthweight. Non-linear neural networks better model infant birthweight than linear models (R^2=0.172 vs. R^2=0.145, p-value=0.005). In contrast to linear models, non-linear models ranked income, neighborhood disadvantage, and experiences of discrimination higher in importance while modeling birthweight than race. Consistent with extant social science literature, findings suggest race is a linear proxy for non-linear factors. The ability to disentangle and identify the source of effects for socioeconomic status and other social factors that often correlate with race is critical for the ability to appropriately target interventions and public policies designed to improve infant outcomes as well as point out the disparities in these outcomes.

TapChain: A Rule Chain Recognition Model Based on Multiple Features

Trigger-action programming (TAP) is an intelligent tool, which makes it easy for users to make intelligent rules for IoT devices and applications. Unfortunately, with the popularization of TAP and more and more rules, the rule chain from multiple rules appears gradually and brings more and more threats. Previous work pays more attention to the construction of the security model, but few people focus on how to accurately identify the rule chain from multiple rules. Inaccurate identification of rule chains will lead to the omission of rule chains with threats. This paper proposes a rule chain recognition model based on multiple features, TapChain, which can more accurately identify the rule chain without source code. We design a correction algorithm for TapChain to help us get the correct NLP analysis results. We extract 12 features from 5 aspects of the rules to make the recognition of the rule chain more accurate. According to the evaluation, compared with the previous work, the accuracy rate of TapChain is increased by 3.1%, the recall rate is increased by 1.4%, and the precision rate can reach 88.2%. More accurate identification of the rule chain can help to better implement the security policies and better balance security and availability. What’s more, according to the rule chain that TapChain can recognize, there is a new kind of rule chain with threats. We give the relevant case studies in the evaluation.

A multi-state dynamical process underpins mechano-adaptation in the bacterial flagellar motor

Adaptation is a defining feature of living systems. The bacterial flagellar motor adapts to changes in external mechanical environment by adding or removing torque-generating stator units. However, the molecular mechanism for mechanosensitive motor remodeling remains unclear. Here, we induced stator disassembly using electrorotation, followed by the time-dependent assembly of the individual stator units into the motor. From these experiments, we extracted detailed statistics of the dwell times underlying the stochastic dynamics of stator unit binding and unbinding. The dwell time distribution contains multiple timescales, indicating the existence of multiple stator unit states. Based on these results, we propose a minimal model with four stator unit states – two bound states with different unbinding rates, a diffusive unbound state, and a recently described transiently detached state. Our minimal model quantitatively explains multiple features of the experimental data and allows us to determine the transition rates between all four states. Our experiments and modeling point towards an emergent picture for mechano-adaptive remodeling of the bacterial flagellar motor in which torque generated by bound stator units controls their effective unbinding rate by modulating the transition between the two bound states. Furthermore, the binding rate of stator units with the motor has a non-monotonic dependence on the number of bound units, likely due to two counter-acting effects of motor’s rotation on the binding process.