Single-component versus multicomponent dietary goals for the metabolic syndrome: a randomized trial.

BACKGROUND: Few studies have compared diets to determine whether a program focused on 1 dietary change results in collateral effects on other untargeted healthy diet components. OBJECTIVE: To evaluate a diet focused on increased fiber consumption versus the multicomponent American Heart Association (AHA) dietary guidelines. DESIGN: Randomized, controlled trial from June 2009 to January 2014. (ClinicalTrials.gov: NCT00911885). SETTING: Worcester, Massachusetts. PARTICIPANTS: 240 adults with the metabolic syndrome. INTERVENTION: Participants engaged in individual and group sessions. MEASUREMENTS: Primary outcome was weight change at 12 months. RESULTS: At 12 months, mean change in weight was -2.1 kg (95% CI, -2.9 to -1.3 kg) in the high-fiber diet group versus -2.7 kg (CI, -3.5 to -2.0 kg) in the AHA diet group. The mean between-group difference was 0.6 kg (CI, -0.5 to 1.7 kg). During the trial, 12 (9.9%) and 15 (12.6%) participants dropped out of the high-fiber and AHA diet groups, respectively (P = 0.55). Eight participants developed diabetes (hemoglobin A1c level ≥6.5%) during the trial: 7 in the high-fiber diet group and 1 in the AHA diet group (P = 0.066). LIMITATIONS: Generalizability is unknown. Maintenance of weight loss after cessation of group sessions at 12 months was not assessed. Definitive conclusions cannot be made about dietary equivalence because the study was powered for superiority. CONCLUSION: The more complex AHA diet may result in up to 1.7 kg more weight loss; however, a simplified approach to weight reduction emphasizing only increased fiber intake may be a reasonable alternative for persons with difficulty adhering to more complicated diet regimens. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.

Link Prediction on Complex Networks: An Experimental Survey.

Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.

On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development.

Machine learning (ML) model interpretability has attracted much attention recently given the promising performance of ML methods in crash frequency studies. Extracting accurate relationship between risk factors and crash frequency is important for understanding the causal effects of risk factors and developing safety countermeasures. However, there is no study that comprehensively summarizes ML model interpretation methods and provides guidance for safety researchers and practitioners. This research aims to fill this gap. Model-based and post-hoc ML interpretation methods are critically evaluated and compared to study their suitability in crash frequency modeling. These methods include classification and regression tree (CART), multivariate adaptive regression splines (MARS), Local Interpretable Model-agnostic Explanations (LIME), Local Sensitivity Analysis (LSA), Partial Dependence Plots (PDP), Global Sensitivity Analysis (GSA), and SHapley Additive exPlanations (SHAP). Model-based interpretation methods cannot reveal the detailed interaction relationships among risk factors. LIME can only be used to analyze the effects of a risk factor at the prediction level. LSA and PDP assume that different risk factors are independently distributed. Both GSA and SHAP can account for the potential correlation among risk factors. However, only SHAP can visualize the detailed relationships between crash outcomes and risk factors. This study also demonstrates the potential and benefits of using ML and SHAP to derive Crash Modification Factors (CMF). Finally, it is emphasized that statistical and ML models may not directly differentiate causation from correlation. Understanding the differences between them is critical for developing reliable safety countermeasures.