Cross-Species Network Analysis Uncovers Conserved Nitrogen-Regulated Network Modules in Rice

In this study, we used a cross-species network approach to uncover nitrogen-regulated network modules conserved across a model and a crop species. By translating gene “network knowledge” from the data-rich model Arabidopsis (Arabidopsis thaliana) to a crop (Oryza sativa), we identified evolutionarily conserved N-regulatory modules as targets for translational studies to improve N-use efficiency in transgenic plants. To uncover such conserved N-regulatory network modules, we first generated a N-regulatory network based solely on rice (O. sativa) transcriptome and gene interaction data. Next, we enhanced the “network knowledge” in the rice N-regulatory network using transcriptome and gene interaction data from Arabidopsis and new data from Arabidopsis and rice plants exposed to the same N-treatment conditions. This cross-species network analysis uncovered a set of N-regulated transcription factors (TFs) predicted to target the same genes and network modules in both species. Supernode analysis of the TFs and their targets in these conserved network modules uncovered genes directly related to nitrogen use (e.g. N-assimilation) and to other shared biological processes indirectly related to nitrogen. This cross-species network approach was validated with members of two TF families in the supernode network, bZIP-TGA and HRS1/HHO family, have recently been experimentally validated to mediate the N-response in Arabidopsis.Fil: Obertello, Mariana. University of New York; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular ; ArgentinaFil: Shrivastava, Stuti. University of New York; Estados UnidosFil: Katari, Manpreet S.. University of New York; Estados UnidosFil: Coruzzi, Gloria M.. University of New York; Estados Unido

New Approach of Inter-Cross: An Efficient Multilevel Cache Management Policy

Cache performance has been critical for large scale systems. Until now, many multilevel cache management policies LRU-K, PROMOTE, DEMOTE have been developed but still there is performance issue. Many approaches have been proposed to reduce the gap between different levels such as hint-based multilevel cache. Some approaches like demote or promote are based on the latest cache history information, which is inadequate for applications where there are regular demote and promote operations occur. The major drawback of these policies is selecting a victim. In this paper, the new multilevel cache replacement policy called Inter-cross is implemented to improve the cache performance of a system. The decision of promotion and demotion is based on the block\u27s previous N-step promotion or demotion history and the size and resident time of the block in the cache. Comparative study between inter-cross and existing multilevel policies shows that, existing keeps track on last K references of the block within a last cache level, while inter-cross keeps track of the information of the last K movements of blocks among all the cache levels. Inter-cross algorithms are designed that can efficiently describe the activeness of any blocks in any cache level. Experimental results show that inter-cross achieves better performance compared to existing multilevel cache replacement policies such as LRU-K, PROMOTE, and DEMOTE under different workloads

End-to-End Localization and Ranking for Relative Attributes

We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute's features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end-to-end framework also significantly speeds up processing and is much faster than previous methods. We show state-of-the-art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network's ability to learn meaningful image patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201