To unravel the evolutionarily conserved genetic network underlying energy homeostasis, we performed a systematic in vivo gene knockdown screen in Drosophila. We used a transgenic RNAi library enriched for fly orthologs of human genes to functionally impair about half of all Drosophila genes specifically in adult fat storage tissue. This approach identified 77 genes, which affect the body fat content of the fly, including 58 previously unknown obesity-associated genes. These genes function in diverse biological processes such as lipid metabolism, vesicle-mediated trafficking, and the universal store-operated calcium entry (SOCE). Impairment of the SOCE core component Stromal interaction molecule (Stim), as well as other components of the pathway, causes adiposity in flies. Acute Stim dysfunction in the fat storage tissue triggers hyperphagia via remote control of the orexigenic short neuropeptide F in the brain, which in turn affects the coordinated lipogenic and lipolytic gene regulation, resulting in adipose tissue hypertrophy.

Mobile health (mHealth) apps are an ideal tool for monitoring and tracking long-term health conditions; they are becoming incredibly popular despite posing risks to personal data privacy and security. In this paper, we propose a testing method for Android mHealth apps which is designed using a threat analysis, considering possible attack scenarios and vulnerabilities specific to the domain. To demonstrate the method, we have applied it to apps for managing hypertension and diabetes, discovering a number of serious vulnerabilities in the most popular applications. Here we summarise the results of that case study, and discuss the experience of using a testing method dedicated to the domain, rather than out-of-the-box Android security testing methods. We hope that details presented here will help design further, more automated, mHealth security testing tools and methods.

Abstract. Mobile health (mHealth) apps are an ideal tool for monitoring and tracking long-term health conditions. In this paper, we examine whether mHealth apps succeed in ensuring the privacy, security, and safety of the health data entrusted to them. We investigate 154 apps from Android app stores using both automatic code and metadata analysis and a manual analysis of functionality and data leakage. Our study focuses on hypertension and diabetes, two common health conditions that require careful tracking of personal health data. We find that many apps do not provide privacy policies or safe communications, are implemented in an insecure fashion, fail basic input validation tests and often have overall low code quality which suggests additional security and safety risks. We conclude with recommendations for App Stores, App developers, and end users.

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