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Inhibitors of enzymes that inactivate amine neurotransmitters (dopamine, serotonin), such as catechol-O-methyltransferase (COMT) and monoamine oxidase (MAO), are thought to increase neurotransmitter levels and are widely used to treat Parkinson's disease and psychiatric disorders, yet the role of these enzymes in regulating behavior remains unclear. Here, we investigated the genetic loss of a similar enzyme in the model organismDrosophila melanogaster. Because the enzyme Ebony modifies and inactivates amine neurotransmitters, its loss is assumed to increase neurotransmitter levels, increasing behaviors such as aggression and courtship and decreasing sleep. Indeed,ebonymutants have been described since 1960 as aggressive mutants, though this behavior has not been quantified. Using automated machine learning-based analyses, we quantitatively confirmed thatebonymutants exhibited increased aggressive behaviors such as boxing but also decreased courtship behaviors and increased sleep. Through tissue-specific knockdown, we found thatebony’s role in these behaviors was specific to glia. Unexpectedly, direct measurement of amine neurotransmitters inebonybrains revealed that their levels were not increased but reduced. Thus, increased aggression is the anomalous behavior for this neurotransmitter profile. We further found thatebonymutants exhibited increased aggression only when fighting each other, not when fighting wild-type controls. Moreover, fights betweenebonymutants were less likely to end with a clear winner than fights between controls or fights betweenebonymutants and controls. Inebonyvs. control fights,ebonymutants were more likely to win. Together, these results suggest thatebonymutants exhibit prolonged aggressive behavior only in a specific context, with an equally dominant opponent.

 

In December, 2020, Apple began requiring developers to disclose their data collection and use practices to generate a “privacy label” for their application. The use of mobile application Software Development Kits (SDKs) and third-party libraries, coupled with a typical lack of expertise in privacy, makes it challenging for developers to accurately report their data collection and use practices. In this work we discuss the design and evaluation of a tool to help iOS developers generate privacy labels. The tool combines static code analysis to identify likely data collection and use practices with interactive functionality designed to prompt developers to elucidate analysis results and carefully reflect on their applications’ data practices. We conducted semi-structured interviews with iOS developers as they used an initial version of the tool. We discuss how these results motivated us to develop an enhanced software tool, Privacy Label Wiz, that more closely resembles interactions developers reported to be most useful in our semi-structured interviews. We present findings from our interviews and the enhanced tool motivated by our study. We also outline future directions for software tools to better assist developers communicating their mobile app’s data practices to different audiences.