Association between serotonin 5-HT-2C receptor gene (HTR2C) polymorphisms and obesity- and mental health-related phenotypes in a large population-based cohort

OBJECTIVE: Studies have shown that common single-nucleotide polymorphisms (SNPs) in the serotonin 5-HT-2C receptor (HTR2C) are associated with antipsychotic agent-induced weight gain and the development of behavioural and psychological symptoms. We aimed to analyse whether variation in the HTR2C is associated with obesity- and mental health-related phenotypes in a large population-based cohort. METHOD: Six tagSNPs, which capture all common genetic variation in the HTR2C gene, were genotyped in 4978 men and women from the European Prospective Investigation into Cancer (EPIC)-Norfolk study, an ongoing prospective population-based cohort study in the United Kingdom. To confirm borderline significant associations, the -759C/T SNP (rs3813929) was genotyped in the remaining 16 003 individuals from the EPIC-Norfolk study. We assessed social and psychological circumstances using the Health and Life Experiences Questionnaire. Genmod models were used to test associations between the SNPs and the outcomes. Logistic regression was performed to test for association of SNPs with obesity- and mental health- related phenotypes. RESULTS: Of the six HTR2C SNPs, only the T allele of the -759C/T SNP showed borderline significant associations with higher body mass index (BMI) (0.23 kg m(-2); (95% confidence interval (CI): 0.01-0.44); P=0.051) and increased risk of lifetime major depressive disorder (MDD) (Odds ratio (OR): 1.13 (95% CI: 1.01-1.22), P=0.02). The associations between the -759C/T and BMI and lifetime MDD were independent. As associations only achieved borderline significance, we aimed to validate our findings on the -759C/T SNP in the full EPIC-Norfolk cohort (n=20 981). Although the association with BMI remained borderline significant (beta=0.20 kg m(-2); 95% CI: 0.04-0.44, P=0.09), that with lifetime MDD (OR: 1.01; 95% CI: 0.94-1.09, P=0.73) was not replicated. CONCLUSIONS: Our findings suggest that common HTR2C gene variants are unlikely to have a major role in obesity- and mental health-related traits in the general population.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.