The balance between accumulation of stress-induced polyamines and reactive oxygen species (ROS) is arguably a critical factor in plant tolerance to salt stress. Polyamines are compounds, which accumulate in plants under salt stress and help maintain cellular ROS homeostasis. In this review we first outline the role of polyamines in mediating salt stress responses through their modulation of redox homeostasis. The two proposed roles of polyamines in regulating ROS-as antioxidative molecules and source of ROS synthesis-are discussed and exemplified with recent studies. Second, the proposed function of polyamines as modulators of ion transport is discussed in the context of plant salt stress. Finally, we highlight the apparent connection between polyamine accumulation and programmed cell death induction during stress. Thus, polyamines have a complex functional role in regulating cellular signaling and metabolism during stress. By focusing future efforts on how polyamine accumulation and turnover is regulated, research in this area may provide novel targets for developing stress tolerance.

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Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand).The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved.