A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures

MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA–pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA–pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA–pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes (“hubs”), most nodes in the miRNA–pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA–pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available

Navigating Ethnic Hierarchies in Community-Academic Partnerships: A Case Study on Koreatown Community Politics

Based on the experiences of a Koreatown scholar, the executive director of a Koreatown nonprofit, and a longtime resident student, the article advocates for greater attention to the complex and dynamic power structures of ethnic enclaves in community-academic partnerships. We discuss the changing landscapes of Koreatown as the global nexus of the Pacific Rim economy, the city of Los Angeles’s urban redevelopment plans, and growing diversity and inequality. Programs that aim to engage effectively with ethnic communities must reassess how knowledge is produced and conveyed, how we structure partnerships within stratified communities, and how to grow from issue-based partnerships to broader communities of interest

SHADOW DETECTION FOR VEHICLES BY LOCATING THE OBJECT-SHADOW BOUNDARY

We introduce in this paper a shadow detection method for vehicles in traffic video sequences. Our method approximates the boundary between vehicles and their associated shadows by one or more straight lines. These lines are located in the image by exploiting both local information (e.g. statistics in intensity differences) and global information (e.g. principal edge directions). The proposed method does not assume a particular lighting condition, and requires no human interaction nor parameter training. Experiments on practical real-world traffic video sequences demonstrate that our method is simple, robust and efficient under traffic scenes with different lighting conditions. Accurate positioning of target vehicles is thus achieved even in the presence of cast shadows. KEY WORDS Shadow detection, shadow identification, object detection, video segmentation