Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK

Background Adolescents are among the highest consumers of social media while research has shown that their well-being decreases with age. The temporal relationship between social media interaction and well-being is not well established. The aim of this study was to examine whether the changes in social media interaction and two well-being measures are related across ages using parallel growth models. Methods Data come from five waves of the youth questionnaire, 10-15 years, of the Understanding Society, the UK Household Longitudinal Study (pooled n =9859). Social media interaction was assessed through daily frequency of chatting on social websites. Well-being was measured by happiness with six domains of life and the Strengths and Difficulties Questionnaire. Results Findings suggest gender differences in the relationship between interacting on social media and well-being. There were significant correlations between interacting on social media and well-being intercepts and between social media interaction and well-being slopes among females. Additionally higher social media interaction at age 10 was associated with declines in well-being thereafter for females, but not for males. Results were similar for both measures of well-being. Conclusions High levels of social media interaction in early adolescence have implications for well-being in later adolescence, particularly for females. The lack of an association among males suggests other factors might be associated with their reduction in well-being with age. These findings contribute to the debate on causality and may inform future policy and interventions

High throughput production of recombinant human proteins for crystallography.

This chapter presents in detail the process used in high throughput bacterial production of recombinant human proteins for crystal structure determination. The core principles are: (1) Generating at least 10 truncated constructs from each target gene. (2) Ligation-independent cloning (LIC) into a bacterial expression vector. All proteins are expressed with an N-terminal, TEV protease cleavable fusion peptide. (3) Small-scale test expression to identify constructs producing soluble protein. (4) Liter-scale production in shaker flasks. (5) Purification by Ni-affinity chromatography and gel filtration. (6) Protein characterization and preparation for crystallography. The chapter also briefly presents alternative procedures, to be applied based on specific knowledge of protein families or when the core protocol is unsatisfactory. This scheme has been applied to more than 550 human proteins (>10,000 constructs) and has resulted in the deposition of 112 unique structures. The methods presented do not depend on specialized equipment or robotics; hence, they provide an effective approach for handling individual proteins in a regular research lab

Effect of emotional feedback in a decision-making system for an autonomous agent

The point of view of Isaac Asimov is unlikely in a close future, but machines that develop tasks in a sensible manner are already a fact. In light of this remark, recent research tries to understand the requirements and design options that imply providing an autonomous agent with means for detecting emotions. If we think about of exporting this model to machines, it is possible that they become capable to evolve emotionally according to such models and would take part in the society more or less cooperatively, according to the perceived emotional state. The main purpose of this research is the implementation of a decision model affected by emotional feedback in a cognitive robotic assistant that can capture information about the world around it. The robot will use multi-modal communication to assist the societal participation of persons deprived of conventional modes of communication. The aim is a machine that can predict what the user will do next and be ready to give the best possible assistance, taking in account the emotional factor. The results indicate the benefits and importance of emotional feedback in the closed loop human-robot interaction framework. Cognitive agents are shown to be capable of adapting to emotional information from humans