Child mental health and income gradient from early childhood to adolescence: Evidence from the UK.

Background: Children from low income families are likely to have poorer mental health than their more affluent peers. However, it is unclear how this association varies at different developmental stages and what the potential underpinning mechanisms are. This study investigates the relationship between family income and mental health problems from early childhood to adolescence in the UK, and examines the potential mediating role of family-related factors over time. Methods: Data were drawn from the UK Millennium Cohort Study at ages 3, 5, 7, 11, 14 and 17 years. Child mental health was measured by the Strengths and Difficulties Questionnaire Total Difficulties Score, and the Internalising and Externalising subscales. Family income was operationalised as permanent income. Cross-sectional analyses were conducted at each age to examine the association between income and mental health problems, and to examine potential mechanisms based on the Parental Stress and Parental Investment theories. Results: The samples included 8096 children aged up to 14 years, of which 5667 remained in the study at age 17. Results indicated a statistically significant association between lower family income and poorer mental health in all age groups after adjusting for confounding factors. The strength of the association was reduced after adjustment for Parental Stress and Parental Investment factors, with the larger attenuation driven by Parental Stress factors in most cases. Fully adjusted models suggested an increased independent association between maternal psychological distress and children's mental health as children grew older. Conclusions: While lower family income is associated with a child's poorer mental health, much of this association is explained by other factors such as maternal psychological distress, and therefore the direct association is relatively small. This suggests that policies targeting income redistribution may reduce child mental health problems, and also benefit the wider family, reducing the prevalence of other associated risk factors.

A Weighting Method for Feature Dimension by Semisupervised Learning With Entropy.

In this article, a semisupervised weighting method for feature dimension based on entropy is proposed for classification, dimension reduction, and correlation analysis. For real-world data, different feature dimensions usually show different importance. Generally, data in the same class are supposed to be similar, so their entropy should be small; and those in different classes are supposed to be dissimilar, so their entropy should be large. According to this, we propose a way to construct the weights of feature dimensions with the whole entropy and the innerclass entropies. The weights indicate the contribution of their corresponding feature dimensions in classification. They can be used to improve the performance of classification by giving a weighted distance metric and can be applied to dimension reduction and correlation analysis as well. Some numerical experiments are given to test the proposed method by comparing it with some other representative methods. They demonstrate that the proposed method is feasible and efficient in classification, dimension reduction, and correlation analysis.

Simple Fabrication of Polyvinylidene Fluoride/Graphene Composite Membrane with Good Lipophilicity for Oil Treatment.

Graphene (GE) is an emerging type of two-dimensional functional nanoparticle with a tunable passageway for oil molecules. Herein, polyvinylidene fluoride (PVDF)/GE composite membranes with controllable pore structure were fabricated with a simple non-solvent-induced phase separation method. The change of crystallinity and crystal structure (α, ß, γ, etc.) generated is due to the addition of GE, which benefits the design of a suitable pore structure for oil channels. Meanwhile, the hydrophobicity and thermal stability of the composite membrane were obviously enhanced. With 3 wt % GE, the contact angle was 124.6°, which was increased greatly compared to that of the GE-0 sample. Moreover, the rate of the phase transition process was affected by the concentration of casting solution, temperature, and composition of the coagulation bath. For example, the composite membrane showed better oil-water separation properties when the coagulation bath was dioctyl phthalate. In particular, the oil flux and separation efficiencies were up to 2484.08 L/m2·h and 99.24%, respectively. Consequently, PVDF/GE composite membranes with excellent lipophilicity may have good prospects for oily wastewater treatment.

Eliciting risk preferences that predict risky health behavior: A comparison of two approaches.

Information on attitudes to risk could increase understanding of and explain risky health behaviors. We investigate two approaches to eliciting risk preferences in the health domain, a novel "indirect" lottery elicitation approach with health states as outcomes and a "direct" approach where respondents are asked directly about their willingness to take risks. We compare the ability of the two approaches to predict health-related risky behaviors in a general adult population. We also investigate a potential framing effect in the indirect lottery elicitation approach. We find that risk preferences elicited using the direct approach can better predict health-related risky behavior than those elicited using the indirect approach. Moreover, a seemingly innocuous change to the framing of the lottery question results in significantly different risk preference estimates, and conflicting conclusions about the ability of the indicators to predict risky health behaviors.

A Novel Neural Model With Lateral Interaction for Learning Tasks.

We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.