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•Intestinal oxidative stress depends on the balance of ROS.•The antioxidant defense system protects intestine from oxidative stress damage.•Phytochemicals with health effects could be ...developed to nutritional supplements.
Oxidative stress occurs when there exists an imbalance between the generation and elimination of reactive oxygen species (ROS). As inevitable exposure to foreign substances and microbial pathogens, intestine is a key resource of ROS. Disproportionate generation and long-term exposure to ROS lead to various intestinal diseases, such as inflammatory bowel diseases (IBD), enteric infections, ischemic intestinal injury and colorectal cancers. Natural nutrients including vitamins, proteins, fats, minerals and phytochemicals provide numerous evidences that they can protect the health of intestine and alleviate the damage caused by oxidative stress, which can be developed as novel functional foods. This review summarized the recent research progress on the insights of the causes, mechanisms of intestinal oxidative stress and the health intervention effects of nutrients. This review has also given the prospects that the new discovered nutrients with health benefits might be developed as novel functional foods or possible nutraceutical agents.
The purpose of this work was to meta-analyze empirical evidence about the effectiveness of digital-based interventions for students with mathematical learning difficulties. Furthermore, we ...investigated whether the school level of the participants and the software instructional approach were decisive modulated factors. A systematic search of randomized controlled studies published between 2003 and 2019 was conducted. A total of 15 studies with 1073 participants met the study selection criterion. A random effects meta-analysis indicated that digital-based interventions generally improved mathematical performance (mean ES = 0.55), though there was a significant heterogeneity across studies. There was no evidence that videogames offer additional advantages with respect to digital-based drilling and tutoring approaches. Moreover, effect size was not moderated when interventions were delivered in primary school or in preschool.
•This meta-analysis investigated the effects of digital tools for students with mathematical learning difficulties.•Technological tools positively impact mathematics achievement of students with mathematical learning difficulties.•Interventions show similar effects for preschool and primary-school children.•Videogames do not offer additional advantages with respect to digital-based drilling and tutoring approaches.
This study aims to verify the effectiveness of M-O-A telenursing intervention model in improving the health status and quality of life of the empty-nest older adult individuals with chronic diseases ...by a randomized comparative trial.AimThis study aims to verify the effectiveness of M-O-A telenursing intervention model in improving the health status and quality of life of the empty-nest older adult individuals with chronic diseases by a randomized comparative trial.M-O-A telenursing intervention model was constructed based on the needs of the participants. The control group (N = 39) received routine nursing, the experimental group (N = 39) received M-O-A telenursing intervention in addition to routine nursing. After 12 weeks of intervention, the intervention effects of being a participant in the two groups were evaluated. SPSS 26.0 was used for data analysis.MethodsM-O-A telenursing intervention model was constructed based on the needs of the participants. The control group (N = 39) received routine nursing, the experimental group (N = 39) received M-O-A telenursing intervention in addition to routine nursing. After 12 weeks of intervention, the intervention effects of being a participant in the two groups were evaluated. SPSS 26.0 was used for data analysis.After 12 weeks of intervention, for the experimental group, each dimension of quality of life based on EQ-5D-3L became better, especially for "pain/discomfort," "anxiety/depression," "HRQoL" and "EQ-VAS" (all p < 0.05) and each dimension of quality of life based on SF-36 became better too, especially for "GH," "BP," "RE," "MH," "VT," "SF," "PCS," "MCS," "SF-36" (all p < 0.05). In addition, there was a statistical downward trend in blood pressure, blood glucose, weight, BMI, fat rate, nap duration, number of nocturnal awakenings, light sleep rate and a statistical upward trend in water rate, basal metabolic rate, nighttime sleep duration, deep sleep rate, rapid eye movement sleep rate, especially at the end of intervention (all p < 0.05). While for the control group, there was no statistical improvement in all these aspects.ResultsAfter 12 weeks of intervention, for the experimental group, each dimension of quality of life based on EQ-5D-3L became better, especially for "pain/discomfort," "anxiety/depression," "HRQoL" and "EQ-VAS" (all p < 0.05) and each dimension of quality of life based on SF-36 became better too, especially for "GH," "BP," "RE," "MH," "VT," "SF," "PCS," "MCS," "SF-36" (all p < 0.05). In addition, there was a statistical downward trend in blood pressure, blood glucose, weight, BMI, fat rate, nap duration, number of nocturnal awakenings, light sleep rate and a statistical upward trend in water rate, basal metabolic rate, nighttime sleep duration, deep sleep rate, rapid eye movement sleep rate, especially at the end of intervention (all p < 0.05). While for the control group, there was no statistical improvement in all these aspects.The M-O-A telenursing model could effectively regulate quality of life and health condition of the empty-nest older adult individuals with chronic diseases, making it worthy of further promotion and application.ConclusionThe M-O-A telenursing model could effectively regulate quality of life and health condition of the empty-nest older adult individuals with chronic diseases, making it worthy of further promotion and application.
•Examining long-term effects of child maltreatment interventions is challenging.•This study examined collateral intervention effects on child maltreatment proxies.•The usefulness of administrative ...data in effectiveness research was also explored.•No evidence was found for collateral effects of three parent programs.•We conclude that administrative data can be very useful in effectiveness research.
Collecting child maltreatment data from participants is expensive and time-consuming, and often suffers from substantial attrition rates. Administrative population data may prove fruitful to overcome these barriers. The aim of this study was twofold: (1) to illustrate how administrative data may be used in evaluating long-term intervention effects; and (2) to examine collateral effects of three preventive early childhood interventions offered to families in the Netherlands (Supportive Parenting, VoorZorg, and Incredible Years). Using population data, four proxies of child maltreatment were assessed to examine collateral intervention effects: incidences of child protection orders, placements of children in residential care, crime victimization of children or their parents, and parental registrations as a crime suspect. The results revealed no significant differences between experimental and control conditions on any of these proxies, with very small effect sizes (ranging from Cramer’s V = 0.01 to Cramer’s V = 0.10). We conclude that the results do not provide support for collateral effects, but that studying other outcomes may provide this support. We further discuss that small sample sizes and low prevalences challenge studies using administrative data. Notwithstanding these limitations, we conclude that administrative data can strengthen the evidence base for collateral and direct intervention effects.
A few studies on relative-clause processing report an unexpected facilitatory effect on the matrix verb that follows an Object Relative (ORC) clause (e.g. Staub, Dillon and Clifton jr. 2017). In ...this study we present the results of a novel eye-tracking experiment that replicated this effect on Italian. The advantage of ORCs is discussed under the hypothesis that subject-verb agreement in the matrix benefits from a general trace-reactivation mechanisms, subsumed from activation-based retrieval models (Lewis and Vasishth 2005).
Abstract
Background
Improved sanitation has been associated with improved child growth in observational studies, but multiple randomized trials that delivered improved sanitation found no effect on ...child growth. We assessed to what extent differences in the effect estimated in the two study designs (the effect of treatment in observational studies and the effect of treatment assignment in trials) could explain the contradictory results.
Methods
We used parametric g-computation in five prospective studies (n = 21 524) and 59 cross-sectional Demographic and Health Surveys (DHS; n = 158 439). We compared the average treatment effect (ATE) for improved sanitation on mean length-for-age z-score (LAZ) among children aged <2 years to population intervention effects (PIEs), which are the observational analogue of the effect estimated in trials in which some participants are already exposed.
Results
The ATE was >0.15 z-scores, a clinically meaningful difference, in most prospective studies but in <20% of DHS surveys. The PIE was always smaller than the ATE, and the magnitude of difference depended on the baseline prevalence of the improved sanitation. Interventions with suboptimal coverage and interventions delivered in populations with higher mean LAZ had a smaller effect on population-level LAZ.
Conclusions
Estimates of PIEs corresponding to anticipated trial results were often smaller than clinically meaningful effects. Incongruence between observational associations and null trial results may in part be explained by expected differences between the effects estimated. Using observational ATEs to set expectations for trials may overestimate the impact that sanitation interventions can achieve. PIEs predict realistic effects and should be more routinely estimated.
Recent calls have been made to evaluate the range, rather than the frequency of use, of strategies within adolescents' emotion regulation repertoire. It is unknown whether an emotion regulation ...intervention may increase adolescents' emotion regulation repertoire. To examine the direct effect of an emotion regulation intervention on adolescents' perceived emotion regulation repertoire from baseline to immediately postintervention, when controlling for baseline problems with emotional awareness and participant sex. Seventh-grade students (N = 420) participated in a 6-week emotion regulation and sexual health promotion randomized control trial. Adolescent-report measures of emotion regulation and problems with emotional awareness were collected. On average, adolescents used one additional strategy after completing the intervention; they endorsed using four (out of eight) strategies at baseline and five strategies immediately after the intervention. Emotion regulation interventions may expand adolescents' repertoire. Future research should explore whether such expansion may guide downstream effects on psychosocial functioning and prevent health risk behaviors.
The aim of this study is to emphasize the importance of artificial intelligence (AI) and causality modelling of food quality and analysis with 'big data'. AI with structural causal modelling (SCM), ...based on Bayesian networks and deep learning, enables the integration of theoretical field knowledge in food technology with process production, physicochemical analytics and consumer organoleptic assessments. Food products have complex nature and data are highly dimensional, with intricate interrelations (correlations) that are difficult to relate to consumer sensory perception of food quality. Standard regression modelling techniques such as multiple ordinary least squares (OLS) and partial least squares (PLS) are effectively applied for the prediction by linear interpolations of observed data under cross-sectional stationary conditions. Upgrading linear regression models by machine learning (ML) accounts for nonlinear relations and reveals functional patterns, but is prone to confounding and failed predictions under unobserved nonstationary conditions. Confounding of data variables is the main obstacle to applications of the regression models in food innovations under previously untrained conditions. Hence, this manuscript focuses on applying causal graphical models with Bayesian networks to infer causal relationships and intervention effects between process variables and consumer sensory assessment of food quality.
This study is based on the data available in the literature on the process of wheat bread baking quality, consumer sensory quality assessments of fermented milk products, and professional wine tasting data. The data for wheat baking quality were regularized by the least absolute shrinkage and selection operator (LASSO elastic net). Bayesian statistics was applied for the evaluation of the model joint probability function for inferring the network structure and parameters. The obtained SCMs are presented as directed acyclic graphs (DAG). D-separation criteria were applied to block confounding effects in estimating direct and total causal effects of process variables and consumer perception on food quality. Probability distributions of causal effects of the intervention of individual process variables on quality are presented as partial dependency plots determined by Bayesian neural networks. In the case of wine quality causality, the total causal effects determined by SCMs are positively validated by the double machine learning (DML) algorithm.
The data set of 45 continuous variables corresponding to different chemical, physical and biochemical variables of wheat properties from seven Croatian cultivars during two years of controlled cultivation were analysed. LASSO regularization of the data set yielded the ten key predictors, accounting for 98 % variance of the baking quality data. Based on the key variables, the quality predictive random forest model with 75 % cross-validation accuracy was derived. Causal analysis between the quality and key predictors was based on the Bayesian model shown as a DAG graph. Protein content shows the most important direct causal effect with the corresponding path coefficient of 0.71, and THMM (total high-molecular-mass glutenin subunits) content was an indirect cause with a path coefficient of 0.42, and protein total average causal effect (ACE) was 0.65. The large data set of the quality of fermented milk products included binary consumer sensory data (taste, odour, turbidity), continuous physical variables (temperature, fat, pH, colour) and three grade classes of products by consumer quality assessment. A random forest model was derived for the prediction of the quality classification with an out-of-bag (OOB) error of 0.28 %. The Bayesian network model predicts that the direct causes of the taste classification are temperature, colour and fat content, while the direct causes of the quality classification are temperature, turbidity, odour and fat content. The key quality grade ACE of temperature -0.04 grade/°C and 0.3 quality grade/fat content were estimated. The temperature ACE dependency shows a nonlinear type as negative saturation with the 'breaking' point at 60 °C, while for fat ACE had a positive linear trend. Causal quality analysis of red and white wine was based on the large data set of eleven continuous variables of physical and chemical properties and quality assessments classified in ten classes, from 1 to 10. Each classification was obtained in triplicate by a panel of professional wine tasters. A non-structural double machine learning (DML) algorithm was applied for total ACE quality assessment. The alcohol content of red and white wine had the key positive ACE relative factor of 0.35 quality/alcohol, while volatile acidity had the key negative ACE of -0.2 quality/acidity. The obtained ACE predictions by the unstructured DML algorithm are in close agreement with the ACE obtained by the structural SCM.
Novel methodologies and results for the application of causal artificial intelligence models in the analysis of consumer assessment of the quality of food products are presented. The application of Bayesian network structural causal models (SCM) enables the d-separation of pronounced effects of confounding between parameters in noncausal regression models. Based on the SCM, inference of ACE provides substantiated and validated research hypotheses for new products and support for decisions of potential interventions for improvement in product design, new process introduction, process control, management and marketing.
Calls continue for randomized interventions in organizational settings. In many cases, however, practical constraints require researchers to use 2-wave randomized pretest-posttest control group ...designs. We discuss the importance of randomized trials for theory development with a focus on analytic options for 2-wave designs. Our discussion has implications for both designing studies and interpreting results. We review 23 published work and organizational health psychology intervention studies and find that a majority of studies featured a statistical model known to have low statistical power relative to other options. Furthermore, a majority of studies invoked terminology implying the direction of change without providing explicit statistical tests. To improve research practice, we detail statistical power differences in 3 commonly used statistical models and emphasize the distinction between (a) intervention effects and (b) the size and direction of change over time. We encourage researchers to provide inferential evidence for both types of information and show that only 1 of the 3 reviewed models provides information on the direction of change over time, but at a potential expense for statistical power to detect intervention effects. A reanalysis of data from a published work-family workplace intervention illustrates these nuances and supports recommendations for research practice. We conclude by providing recommendations.
This paper presents several new empirical observations regarding some interpretive effects and structural restrictions of modals that occur in sentence-initial positions in Chinese. It provides a new ...analysis of sentence-initial modal sentences in terms of the overt head-movement of a modal to the sentence periphery to value strong focus features and to focus-mark either the proposition or the subject of a sentence. This new proposal helps explain the markedness exhibited by such sentences, correctly predicts the structural and semantic restrictions of modal sentences, and directly explains the scopal interactions observed between modals and various types of focus constructions. It also shows that changes in word order in Chinese are not ascribable simply to an optional or free derivation in syntax but are related to an understudied mechanism in that language, i.e. T-to-C movement, and that the roles of information structure are represented as formal features in syntax. The results shed new light on how Chinese – though profoundly different from Germanic and Romance languages typologically – exemplifies a similarly fine structure in the sentence-internal domain, parallel associations of scope-bearing units with sentences’ left peripheries, and a neat interaction of syntax with discourse configurations.
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