Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Diagnostic microRNA markers for screening sporadic human colon cancer and active ulcerative colitis in stool and tissue.

Abstract: By routinely and systematically being able to perform quantitative stem-loop reverse transcriptase followed by TaqMan PCR expression analysis on stool and tissue samples using fifteen human (Homo sapiens, hsa) micro(mi)RNA genes selected by careful analysis of the peer-reviewed literature, we were able to monitor changes at various stages of CRC, allowing for reliable diagnostic screening of colon cancer particularly at the early, pre-malignant stages, and for difficult-to-treat active ulcerative colitis (UC). Although the expression of some of the miRNA genes tested in tissue showed less variability in CRC or UC patients than in stool, the stool by itself appears well-suited to screening. A miRNA approach using stool samples promises to offer more sensitivity and specificity than currently used screening genomic, methylomic or proteomic methods for colon cancer. Larger prospective clinical studies utilizing stool derived from many control, colon cancer or UC patients, to allow for a statistically valid analysis, are now urgently required to standardize test performance and determine the true sensitivity and specificity of the miRNA screening approach, and to provide a numerical underpinning for these diseases as a function of total RNA. Moreover, when a miRNA screening test is combined with analysis of a messenger(m)RNA expression test, which has also been considered in earlier studies to be a highly sensitive and a very specific and reliable transcriptomic approach, as outlined in this article, bioinformatics can be used to correlate microRNA seed data with mRNA target data in order to gain a mechanistic understanding of how miRNAs regulate gene expression, enabling understanding of why these miRNA genes should be informative in a screening test.

Method and system for generating affinity based playlists

Abstract: A system and method of generating a playlist of affinity related media files using affinity relationship data. In one aspect the relationship affinity data is based upon a user rating score obtained from a population of user's ratings associated with media file attributes. In one aspect, a media file attribute which can include an artist, album, title, and genre information associated with a media file is received from a user, indicating the user's desire to create an affinity playlist of media files having an affinity relationship based upon the selected media file attribute.

Humanitarian Space as Arena: A Perspective on the Everyday Politics of Aid

Abstract: 'Humanitarian space' denotes the physical or symbolic space which humanitarian agents need to deliver their services according to the principles they uphold. This concept, which separates humanitarian action from its politicized environment, is widely used in policy documents and academic texts, even though empirical evidence abounds that this space is in fact highly politicized. To some extent the uncritical use of the concept of humanitarian space is understandable because of its aspirational character. This article explores a different angle: how different actors use the concept and the language of humanitarian space and principles in the everyday politics of aid delivery. It proposes an empirical perspective that approaches humanitarian space from the perspective of everyday practices of policy and implementation. It maintains that the humanitarian space is an arena where a multitude of actors, including humanitarians and the disaster-affected recipients of aid, shape the everyday realities of humanitarian action. The paper develops this perspective for two humanitarian operations: a protracted refugee camp in Kakuma, Kenya, and the tsunami response in Sri Lanka.

Access to trusted user-generated content using social networks

Abstract: A method to access trusted user generated content (UGC) is provided. User registration information containing one or more identities is obtained. Each identity corresponds to an internet social network that is facilitated by one of a plurality of social network sites. The social relationships are collected using the provided user identities at the different social network sites and user extended social networks are created for each user by joining the social relationships collected. Then, UGC is collected from the plurality of social network sites and the collected UGC is correlated with the extended social networks. The correlated UGC is filtered according to the user configuration of a user making a request, and then the results are presented to the requesting user. A search function is provided to obtain information on demand, or alternatively, a user receives feeds of information according to configured information regarding the user's extended social network.

Randomized Dimensionality Reduction for $k$ -Means Clustering

Abstract: We study the topic of dimensionality reduction for $k$ -means clustering. Dimensionality reduction encompasses the union of two approaches: 1) feature selection and 2) feature extraction. A feature selection-based algorithm for $k$ -means clustering selects a small subset of the input features and then applies $k$ -means clustering on the selected features. A feature extraction-based algorithm for $k$ -means clustering constructs a small set of new artificial features and then applies $k$ -means clustering on the constructed features. Despite the significance of $k$ -means clustering as well as the wealth of heuristic methods addressing it, provably accurate feature selection methods for $k$ -means clustering are not known. On the other hand, two provably accurate feature extraction methods for $k$ -means clustering are known in the literature; one is based on random projections and the other is based on the singular value decomposition (SVD). This paper makes further progress toward a better understanding of dimensionality reduction for $k$ -means clustering. Namely, we present the first provably accurate feature selection method for $k$ -means clustering and, in addition, we present two feature extraction methods. The first feature extraction method is based on random projections and it improves upon the existing results in terms of time complexity and number of features needed to be extracted. The second feature extraction method is based on fast approximate SVD factorizations and it also improves upon the existing results in terms of time complexity. The proposed algorithms are randomized and provide constant-factor approximation guarantees with respect to the optimal $k$ -means objective value.