University of Minnesota

About: University of Minnesota is a education organization based out in Minneapolis, Minnesota, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 117432 authors who have published 257986 publications receiving 11944239 citations. The organization is also known as: University of Minnesota, Twin Cities & University of Minnesota-Twin Cities.

The global spectrum of plant form and function

Abstract: The authors found that the key elements of plant form and function, analysed at global scale, are largely concentrated into a two-dimensional plane indexed by the size of whole plants and organs on the one hand, and the construction costs for photosynthetic leaf area, on the other.

Improving recommendation lists through topic diversification

Abstract: In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.

Non-local sparse models for image restoration

Abstract: We propose in this paper to unify two different approaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has proven to be very effective in image reconstruction and classification tasks. On the other hand, explicitly exploiting the self-similarities of natural images has led to the successful non-local means approach to image restoration. We propose simultaneous sparse coding as a framework for combining these two approaches in a natural manner. This is achieved by jointly decomposing groups of similar signals on subsets of the learned dictionary. Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.

ULK-Atg13-FIP200 Complexes Mediate mTOR Signaling to the Autophagy Machinery

Abstract: Autophagy, the starvation-induced degradation of bulky cytosolic components, is up-regulated in mammalian cells when nutrient supplies are limited. Although mammalian target of rapamycin (mTOR) is known as the key regulator of autophagy induction, the mechanism by which mTOR regulates autophagy has remained elusive. Here, we identify that mTOR phosphorylates a mammalian homologue of Atg13 and the mammalian Atg1 homologues ULK1 and ULK2. The mammalian Atg13 binds both ULK1 and ULK2 and mediates the interaction of the ULK proteins with FIP200. The binding of Atg13 stabilizes and activates ULK and facilitates the phosphorylation of FIP200 by ULK, whereas knockdown of Atg13 inhibits autophagosome formation. Inhibition of mTOR by rapamycin or leucine deprivation, the conditions that induce autophagy, leads to dephosphorylation of ULK1, ULK2, and Atg13 and activates ULK to phosphorylate FIP200. These findings demonstrate that the ULK-Atg13-FIP200 complexes are direct targets of mTOR and important regulators of autophagy in response to mTOR signaling.