With chronic exposure, morphine acutely enhances 50-kHz calling and differentially promotes trill calls, mainly at the expense of flat calls. These effects appear consistent with a positive affect interpretation of 50-kHz vocalizations.

The 50-kHz call subtype shift seen during antagonist-precipitated morphine withdrawal was opposite in direction to that previously associated with rewards, and hence may reveal negative affect.

No abstract

Mobile devices, with an extensive array of capabilities and flexibility, are sometimes said to be an extension of the human body. Enhancing device capabilities and incorporating them into everyday life have always been a huge focus of the mobile industry. In the area of mobile data collection, existing works collect various types of user behavior data via mobile device usage, and use the data to aid in further understanding of human behavior. Typical data collection utilizes application or background service installed on the mobile device with user permission to collect data such as accelerometer, call logs, location, wifi transmission, etc. In this process, sensitive user information is tracked through a data tainting process. Contrary to the existing works, this research aims at collecting application behavior instead of user behavior. The goal is to provide a means to analyze how background services access mobile resources, and potentially identify suspicious applications that access sensitive user information. This investigation proposes an approach to track the access of mobile resources in a real time and sequential way. Specifically, the approach integrates the concept of taint tracking. Each identified user privacy sensitive resource is tagged and marked for tracking. The approach is composed of three different components: collection mechanism, collection client, and collection server. The collection mechanism resides in the Android OS to detect any incoming activity to privacy sensitive mobile resources. Whenever detection occurs, the collection client processes the formatted information. The collection client then communicates with an external server to store the gathered data. From these data, responsible applications, affected v resources, and transmitted data were identified along with sequences of activity resulting from specific user actions. The result is a dynamic, real-time resource for monitoring the process flow of applications. Statistical analysis of sample data collected will be presented to demonstrate some interesting application behaviors and the potential usage of the application behavior data collection process.