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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2010.04.069 Byline: Shuigeng Zhou (a)(b), Ming Xu (a)(b), Jihong Guan (c) Keywords: Lecture notes; Metasearch engines; Peer-to-peer networks; Content sharing; Query routing Abstract: In this paper, we present a system LESSON for lecture notes searching and sharing, which is dedicated to both instructors and students for effectively supporting their Web-based teaching and learning activities. The LESSON system employs a metasearch engine for lecture notes searching from Web and a peer-to-peer (P2P) overlay network for lecture notes sharing among the users. A metasearch engine provides an unified access to multiple existing component search engines and has better performance than general-purpose search engines. With the help of a P2P overlay network, all computers used by instructors and students can be connected into a virtual society over the Internet and communicate directly with each other for lecture notes sharing, without any centralized server and manipulation. In order to merge results from multiple component search engines into a single ranked list, we design the RSF strategy that takes rank, similarity and features of lecture notes into account. To implement query routing decision for effectively supporting lecture notes sharing, we propose a novel query routing mechanism. Experimental results indicate that the LESSON system has better performance in lecture notes searching from Web than some popular general-purpose search engines and some existing metasearch schemes; while processing queries within the system, it outperforms some typical routing methods. Concretely, it can achieve relatively high query hit rate with low bandwidth consumption in different types of network topologies. Author Affiliation: (a) School of Computer Science, Fudan University, Shanghai 200433, China (b) Shanghai Key Lab of Intelligent Information Processing, Shanghai 200433, China (c) Department of Computer Science & Technology, Tongji University, Shanghai 201804, China Article History: Received 17 June 2009; Revised 28 April 2010; Accepted 30 April 2010

Nucleosomes and modifications on the histones constitute the fundamental genomic structure of eukaryotes. It was reported that particular combinations of histone modifications occurring on the genome indicate specific chromatin states, which are related to a series of cell states. Although the histone code principle has been found for over a decade, the mechanism of the code is not yet known clearly. In this paper, we first conducted an extensive analysis on 38 histone modifications and 1 histone variant in human CD4+ T cells to reveal possible connections among these modifications. Then, we analyzed the different roles that histone modifications play in highly differentiated cells and undifferentiated cells, and found that the number of histone modifications on genes of differentiated cells is correlated to the expression levels of genes, while there is no direct effect of histone modification on gene expression level in undifferentiated cells. Finally, we compared the distributions of histone modifications of CD4+ T cell line and K562 cell line, and observed that they have similar distribution patterns. So we guess that there may exist certain conservative regions of histone modifications. [ABSTRACT FROM AUTHOR]

Abstract: Recently, histograms have been considered as an effective way to produce quick approximate answers to decision support queries. They are also taken as a basic tool for data visualization and analysis. In this paper, we propose a new approach to constructing histograms for selectivity estimation in query processing optimization. Our approach uses a new criterion, i.e., aggregate error minimization, to direct the construction of the target histogram. We develop the algorithm of aggregate error minimization based histogram construction, and demonstrate the effectiveness and efficiency of the proposed approach by experiments over both real-world and synthetic datasets. [Copyright &y& Elsevier]

Abstract: The frequent closed itemsets determine exactly the complete set of frequent itemsets and are usually much smaller than the later. However, mining frequent closed itemsets from a landmark window over data streams is a challenging problem. To solve the problem, this paper presents a novel algorithm (called FP-CDS) that can capture all frequent closed itemsets and a new storage structure (called FP-CDS tree) that can be dynamically adjusted to reflect the evolution of itemsets’ frequencies over time. A landmark window is divided into several basic windows and these basic windows are used as updating units. Potential frequent closed itemsets in each basic window are mined and stored in FP-CDS tree based on some proposed strategies. Extensive experiments are conducted to validate the proposed method. [Copyright &y& Elsevier]

The significant improvement of KE07 series in catalytic activities shows the great success of computational design approaches combined with directed evolution in protein design. Understanding the protein dynamics in the evolutionary optimization process of computationally designed enzyme will provide profound implication to study enzyme function and guide protein design. Here, side chain squared generalized order parameters and entropy of each protein are calculated using 50 ns molecular dynamics simulation data in both apo and bound states. Our results show a correlation between the increase of side chain motion amplitude and catalytic efficiency. By analyzing the relationship between these two values, we find side chain squared generalized order parameter is linearly related to side chain entropy, which indicates the computationally designed KE07 series have similar dynamics property with natural enzymes. [ABSTRACT FROM AUTHOR]

Nucleosome organization dictates eukaryotic DNA compaction and access, and plays a critical regulatory role in various eukaryotic genomic functions. It is well known that DNA sequence dependent histone octamer binding is important for nucleosome formation. However, many aspects of the phenomenon remain elusive. Here, we systematically analyze diverse sequence-dependent properties, consisting of multi-scale sequence compositional properties and twelve structural properties on the entire Saccharomyces cerevisiae genome. Based on comparative correlation analysis, the features that are highly correlated with nucleosome formation are identified and are integrated into a multilayer perceptron model for genome-wide nucleosome occupancy prediction. Furthermore, we predict nucleosome positions along genome based on the integrative profile of these properties. The results indicate the substantial effect on nucleosome formation and positioning along the entire genome. [ABSTRACT FROM AUTHOR]

Most existing algorithms for learning Bayesian networks (BNs) can be categorized as constraint-based or score-based methods. Constraint-based algorithms use conditional independence (CI) tests to judge the presence or absence of an edge. Though effective and applicable to (high-dimensional data) large-scale networks, CI tests require a large number of samples to determine the independencies. Thus they can be unreliable especially when the sample size is small. On the other hand, score-based methods employ a score metric to evaluate each candidate network structure, but they are inefficient in learning large-scale networks due to the extremely large search space. In this paper, we propose a new hybrid Bayesian network learning method, SAR (the abbreviation of S eparation A nd R eunion), which maintains the merits of both types of learning techniques while avoiding their drawbacks. Extensive experiments show that our method generally achieves better performance than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) plays an important role in various oceanic applications, including climate prediction, ocean environment monitoring and marine disaster warning. Although many approaches have been developed for predicting SST, most of them conduct the prediction only based on historical SST data. However, SST is essentially affected by many external factors, e.g. , the short-wave radiation from the sun and the long-wave radiation from the atmosphere and ground, which are ignored by existing approaches. In this work, we proposed a Multi-source Spatio-Temporal data fusion model (MUST) to fuse multi-source data, including SST data and external factors, to improve the accuracy of short-term SST prediction. Concretely, MUST first introduces Bicubic Convolutional Interpolation (BCI) to address the issue of inconsistent spatial resolutions of multi-source data, then employs the Spatio-Temporal Dilated ConvLSTM (ST-DC) to learn the spatio-temporal features of SST and external factors, and finally fuses the learned features from multi-source data to predict SST with a Cross Data Fusion (CDF) component. As validated on two real datasets, MUST achieves much better performance than existing SST prediction approaches. • Multiple sources of data improve the prediction of short-term SST. • A MUST model was proposed to predict the SST for future seven days. • A case study was conducted in the regions of Nino3.4 and East China Sea. • The proposed MUST model outperforms CNN, LeNet and ConvLSTM. [ABSTRACT FROM AUTHOR]

Privacy preserving is a paramount concern in publishing datasets that contain sensitive information. Preventing privacy disclosure and providing useful information to legitimate users for data analyzing/mining are conflicting goals. Randomized response is a class of techniques that perturbs each sensitive value in a certain way, so that personal privacy is protected while the large-trend of the entire dataset is still recoverable. However, existing randomized response techniques do not allow to flexibly configure the level of privacy protection, support only a few types of aggregate queries, and cannot achieve the best answer accuracy from perturbed data. These drawbacks impair the effectiveness of those techniques. This paper proposes a general framework based on randomized response techniques, which has good flexibility and extensibility, and can improve the effectiveness of randomized response methods. Our approach is validated by extensive experiments and comparison with existing randomized response and generalization methods. • A general framework for data publication is proposed based on randomized response techniques, which, by utilizing matrix decomposition method and properties of Kronecker product, can reduce the computational complexity of reconstructing unbiased estimate answers from exponential correlation to linear correlation. • A general approach for constructing a recovery matrix from arbitrary perturbation matrix is proposed, which can minimize the variance of unbiased estimate answers. • Perturbation and reconstruction algorithms for boolean and categorical attributes are developed, which can be extended to numerical attributes. • Both theoretical analysis and experimental results are given, which validate the proposed approach. [ABSTRACT FROM AUTHOR]