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Articles by Annalisa Polverari in JoVE
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葡萄全基因组鉴定与表达的综合工作流 E3 泛素连接基因家族的 Meta 分析
Pietro Ariani*1, Elodie Vandelle*1, Darren Wong2, Alejandro Giorgetti1, Andrea Porceddu3, Salvatore Camiolo3, Annalisa Polverari1
1Dipartimento di Biotecnologie, Università degli Studi di Verona, 2Ecology and Evolution, Research School of Biology, The Australian National University, 3Dipartimento di Agraria, SACEG, Università degli Studi di Sassari
本文介绍了一个基因家族的鉴定和描述的程序, 适用于在 Levadura 的家庭的葡萄Tóxicos 的拟南芥(ATL) E3 泛素酶。
Other articles by Annalisa Polverari on PubMed
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The Functions of Nitric Oxide-mediated Signaling and Changes in Gene Expression During the Hypersensitive Response
Antioxidants & Redox Signaling.
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Pubmed ID: 12626115 Nitric oxide (NO) is a highly reactive molecule that rapidly diffuses and permeates cell membranes. In animals, NO is implicated in a number of diverse physiological processes, such as neurotransmission, vascular smooth muscle relaxation, and platelet inhibition. It may have beneficial effects, e.g., as a messenger in immune responses, but it is also potentially toxic when the antioxidant system is overwhelmed and reactive oxygen intermediates (ROI) accumulate. During the last few years, NO has been detected in several plant species, and an increasing number of reports on its function have implicated NO as an important effector in plant growth, development, and defense. The broad chemistry of NO involves an array of interrelated redox forms with different chemical reactivities and numerous potential biological targets in plants. NO signaling functions depend on its reactivity. ROI are key modulators of NO in triggering cell death, but the nature of the mechanisms by which this occurs in plants is different from those commonly observed in animals. This review focuses on the signaling functions of NO, when channeled through the cell death pathway by ROI.
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Nitric Oxide-mediated Transcriptional Changes in Arabidopsis Thaliana
Molecular Plant-microbe Interactions : MPMI.
Dec, 2003 |
Pubmed ID: 14651343 Nitric oxide (NO) is an essential regulatory molecule in several developmental processes and in the stress response in both animal and plant systems. Furthermore, key features of plant resistance to pathogens have been shown to depend on NO production, e.g., defense gene expression and the activation of a hypersensitive reaction (HR) in synergy with reactive oxygen species (ROS). Due to the many possible mechanisms of NO action, a clear picture of its involvement in plant resistance to pathogens is far from being achieved. Transcriptional changes related to NO action are likely to play a significant role in resistance and cell death. We investigated the changes in the expression profiles of Arabidopsis thaliana following infiltration with the NO donor sodium nitroprusside, by cDNA-amplification fragment length polymorphism (AFLP) transcript profiling. Altered expression patterns were detected for 120 of the approximately 2,500 cDNAs examined. Sequence analysis revealed homologies with genes involved in signal transduction, disease resistance and stress response, photosynthesis, cellular transport, and basic metabolism or with sequences coding for unknown proteins. Comparison of the expression profiles with data from public microarray sources revealed that many of the identified genes modulated by NO were previously reported to be modulated in disease-related experiments.
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Investigating Topic Models' Capabilities in Expression Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics / IEEE, ACM.
Nov-Dec, 2012 |
Pubmed ID: 23221091 In recent years a particular class of probabilistic graphical models-called topic models-has proven to represent an useful and interpretable tool for understanding and mining microarray data. In this context, such models have been almost only applied in the clustering scenario, whereas the classification task has been disregarded by researchers. In this paper, we thoroughly investigate the use of topic models for classification of microarray data, starting from ideas proposed in other fields (e.g., computer vision). A classification scheme is proposed, based on highly interpretable features extracted from topic models, resulting in a hybrid generative-discriminative approach; an extensive experimental evaluation, involving 10 different literature benchmarks, confirms the suitability of the topic models for classifying expression microarray data.
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