We have developed a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry. This workflow has decoded as a computational strategy called as pGlyco+, a dedicat...
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We have developed a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry. This workflow has decoded as a computational strategy called as pGlyco+, a dedicated search engine for glycopeptide analysis. Our high-throughput method has enable us to interpret a total of 5,564 distinct site-specific N-glycoforms, on 1,478 glycosylation sites of relative 809 glycoproteins from five mouse tissues, and in 20 hours of mass-spectrometry analysis. Strict quality control(1% false discovery rate) was performed not only for the peptide matches but novel for glycan ones as well. We have also compared our search engine with a routinely used glycoproteomic software and believed that our method is a rather robust tool for site-specific glycosylation study likely. To demonstrate the importance of comprehensive quality control for both the glycan and peptide matches, we have conducted a fair performance comparison between pGlyco+ and Byonic, a routinely used glycoproteomic software: exactly same MS/MS data, and parameters such as tolerance, databases of protein and glycan were used. Under 1% FDR, pGlyco+ identified 23,086 glycopeptide spectra, while Byonic only identified 8,866 glycopeptide spectra. The corresponding number of identified distinct glycopeptides were 5,430 for pGlyco+ and 2,206 for Byoinc. To reach the same sensitivity of pGlyco+(23,086 identified spectra under 1% FDR), Byonic would have unacceptably 30% FDR using the same criteria.
Understanding the dynamics of information spreading over social networks has been of great interest for a long time. It has been convincingly demonstrated that the topology of a network can significantly affect inform...
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Understanding the dynamics of information spreading over social networks has been of great interest for a long time. It has been convincingly demonstrated that the topology of a network can significantly affect information spreading over it. Information spreading is often compared on small-world networks and random networks. In this paper, we find that the heterogeneity of spreading abilities of network ties can lead to completely different spreading results over small-world and random networks. In more detail, the more heterogeneous the spreading abilities of network ties, the lower the efficiency of information spreading over small-world networks. Moreover, such heterogeneity not only slows down the spreading pace, but also lengthens the life span of the information spread over small-world networks. On the contrary, if the spreading abilities of network ties are similar or equal, small-world networks are much better than random networks for information spreading.
The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social f...
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Spelling correction has been studied for many decades, which can be classified into two categories: (1) regular text spelling correction, (2) query spelling correction. Although the two tasks share many common techniq...
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Although significant success has been achieved in fine-grained visual categorization, most of existing methods require bounding boxes or part annotations for training and test, resulting in limited usability and flexi...
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Although significant success has been achieved in fine-grained visual categorization, most of existing methods require bounding boxes or part annotations for training and test, resulting in limited usability and flexibility. To conquer these limitations, we aim to automatically detect the bounding box and parts for fine-grained object classification. The bounding boxes are acquired by a transferring strategy which infers the locations of objects from a set of annotated training images. Based on the generated bounding box, we propose a multiple-layer Orientational Spatial Part (OSP) model to generate a refined description for the object. Finally, we employ the output of deep Convolutional Neural network (dCNN) as the feature and train a linear SVM as object classifier. Extensive experiments on public benchmark datasets manifest the impressive performance of our method, i.e., Classification accuracy achieves 63.9% on CUB-200-2011 and 75.6% on Aircraft, which are actually higher than many existing methods using manual annotations.
Sentiment analysis is a hard problem, while multilingual sentiment analysis is even harder due to the different expression styles in different languages. Although many methods for multilingual sentiment analysis have ...
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ISBN:
(纸本)9781479941421
Sentiment analysis is a hard problem, while multilingual sentiment analysis is even harder due to the different expression styles in different languages. Although many methods for multilingual sentiment analysis have been developed in the open literature, most of them suffer from two major problems. The first is their excessive dependence on external tools or resources (e.g., Machine translation systems or bilingual dictionaries), which may not be readily obtained, especially for minority languages, The second is conflictive sentiments, i.e., The sentiment polarity of some parts of a text is inconsistent with its overall sentiment polarity. It is observed that in a product or service review there usually exist a few sentences which play a more important role in determining its sentiment polarity, as compared to others. Therefore, differentiating key sentences from trivial ones may be helpful to improve sentiment analysis. Inspired by this observation in this paper we propose a novel framework to estimate the sentiment polarity of reviews by virtue of opinion lexica and key sentences automatically extracted from unlabelled data. This framework cannot only overcome the problem of excessive dependence on external resources, but also is able to capture the overall sentiment polarity of reviews. Experimental results on realistic review datasets demonstrate that the proposed framework is effective and competitive with the representative baselines.
Clustering is one of the effective ways to solve energy hole problem for Wireless Sensor network. So far, the approaches of heterogeneous cluster size mainly have concentrated on the design of unequal cluster protocol...
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The network resource competition of today' data enters is extremely intense between long-lived elephant flows and latency-sensitive mice flows. Achieving both goals of high throughput and low latency respectively ...
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In this paper, we introduce a framework of social evolutionary games (SEG) for investigating the evolution of social networks. In a SEG, a coevolutionary mechanism is adopted by agents who aim to improve his short-ter...
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In this paper, we introduce a framework of social evolutionary games (SEG) for investigating the evolution of social networks. In a SEG, a coevolutionary mechanism is adopted by agents who aim to improve his short-term utility and long-term reputation. Two examples are presented to demonstrate SEG, prisoner's dilemma game for pairwise interaction and public goods game for group interaction. Numerical simulations are performed on the two examples with different parameter settings, and the results indicates that SEG can be used as a metaphor for investigating the evolution of social networks in some scenarios.
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