Temple University

About: Temple University is a education organization based out in Philadelphia, Pennsylvania, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32154 authors who have published 64375 publications receiving 2219828 citations.

Changes in Arabidopsis thaliana gene expression in response to silver nanoparticles and silver ions.

Abstract: The release of silver nanoparticles (AgNPs) in the environment has raised concerns about their effects on living organisms, including plants. In this study, changes in gene expression in Arabidopsis thaliana exposed to polyvinylpyrrolidone-coated AgNPs and silver ions (Ag+) were analyzed using Affymetrix expression microarrays. Exposure to 5 mg/L AgNPs (20 nm) for 10 days resulted in upregulation of 286 genes and downregulation of 81 genes by reference to nonexposed plants. Exposure to 5 mg/L Ag+ for 10 days resulted in upregulation of 84 genes and downregulation of 53 genes by reference to nonexposed plants. Many genes differentially expressed by AgNPs and Ag+ were found to be involved in the response of plants to various stresses: upregulated genes were primarily associated with the response to metals and oxidative stress (e.g., vacuolar cation/proton exchanger, superoxide dismutase, cytochrome P450-dependent oxidase, and peroxidase), while downregulated genes were more associated with response to patho...

“Having it All” on Social Media: Entrepreneurial Femininity and Self-Branding Among Fashion Bloggers

Abstract: Against the backdrop of the widespread individualization of the creative workforce, various genres of social media production have emerged from the traditionally feminine domains of fashion, beauty...

Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring

Abstract: Radar is considered an important technology for health monitoring and fall detection in elderly assisted living due to a number of attributes not shared by other sensing modalities. In this article, we describe the signal processing algorithms and techniques involved in elderly fall detection using radar. A human?s radar signal returns differ in their Doppler characteristics, depending on the nature of the human gross motor activities. These signals are nonstationary in nature, inviting time-frequency analysis in both its linear and bilinear aspects, to play a fundamental role in motion identification, including fall features determination and classification. This article employs real fall data to demonstrate the success of existing detection algorithms as well as to report on some of the challenges facing technology developments for fall detection.