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Search Results: 1 - 10 of 38088 matches for " Jie Liang "
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New Trends in Multimedia Standards: MPEG4 and JPEG2000
Jie Liang
Informing Science The International Journal of an Emerging Transdiscipline , 1999,
Abstract: The dramatic increase in both computational power, brought on by the introduction of increasingly powerful chips, and the communications bandwidth, unleashed by the introduction of cable modem and ADSL, lays a solid foundation for the take-off of multimedia applications. Standards always play an important role in multimedia applications due to the need for wide distribution of multimedia contents. Standards have long played pivotal roles in the development of multimedia equipment and contents. MPEG4 and JPEG2000 are two recent multimedia standards under development under the auspice of the International Standards Organization (ISO). These new standards introduce new technology and new features that will become enabling technology for many emerging applications. In this paper, we describe the new trends and new developments that shape these new standards, and illustrate the potential impact of these new standards on multimedia applications.
Computation of protein geometry and its applications: Packing and function prediction
Jie Liang
Quantitative Biology , 2006, DOI: 10.1007/978-0-387-68372-0_6
Abstract: This chapter discusses geometric models of biomolecules and geometric constructs, including the union of ball model, the weigthed Voronoi diagram, the weighted Delaunay triangulation, and the alpha shapes. These geometric constructs enable fast and analytical computaton of shapes of biomoleculres (including features such as voids and pockets) and metric properties (such as area and volume). The algorithms of Delaunay triangulation, computation of voids and pockets, as well volume/area computation are also described. In addition, applications in packing analysis of protein structures and protein function prediction are also discussed.
Prediction of transmembrane helix orientation in polytopic membrane proteins
Larisa Adamian, Jie Liang
BMC Structural Biology , 2006, DOI: 10.1186/1472-6807-6-13
Abstract: We present a method for prediction of the TM helix orientation, which is an essential step in ab initio modeling of membrane proteins. Our method is based on a canonical model of the heptad repeat originally developed for coiled coils. We identify the helical surface patches that interface with lipid molecules at an accuracy of about 88% from the sequence information alone, using an empirical scoring function LIPS (LIPid-facing Surface), which combines lipophilicity and conservation of residues in the helix. We test and discuss results of prediction of helix-lipid interfaces on 162 transmembrane helices from 18 polytopic membrane proteins and present predicted orientations of TM helices in TRPV1 channel. We also apply our method to two structures of homologous cytochrome b6f complexes and find discrepancy in the assignment of TM helices from subunits PetG, PetN and PetL. The results of LIPS calculations and analysis of packing and H-bonding interactions support the helix assignment found in the cytochrome b6f structure from green alga but not the assignment of TM helices in the cyanobacterium b6f structure.LIPS calculations can be used for the prediction of helix orientation in ab initio modeling of polytopic membrane proteins. We also show with the example of two cytochrome b6f structures that our method can identify questionable helix assignments in membrane proteins. The LIPS server is available online at http://gila.bioengr.uic.edu/lab/larisa/lips.html webcite.A significant increase in the number of structures of alpha helical membrane proteins in recent years revealed a remarkable complexity of interacting transmembrane (TM) helices. A great variation in length, shape, and tilt angles relative to the membrane plane is found in helical membrane proteins. For example, a structure of protein transporter (1RH5) contains a helix that is only about one half the length of the TM region, the structure of ClC chloride channel (1KPL) contains discontinuous helices, while
Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks
Jinghang Liang, Jie Han
BMC Systems Biology , 2012, DOI: 10.1186/1752-0509-6-113
Abstract: This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN). An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n), where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a network inferred from a T cell immune response dataset. An SBN can also implement the function of an asynchronous PBN and is potentially useful in a hybrid approach in combination with a continuous or single-molecule level stochastic model.Stochastic Boolean networks (SBNs) are proposed as an efficient approach to modelling gene regulatory networks (GRNs). The SBN approach is able to recover biologically-proven regulatory behaviours, such as the oscillatory dynamics of the p53-Mdm2 network and the dynamic attractors in a T cell immune response network. The proposed approach can further predict the network dynamics when the genes are under perturbation, thus providing biologically meaningful insights for a better understanding of the dynamics of GRNs. The algorithms and methods described in this paper have been i
Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability
Youfang Cao, Jie Liang
BMC Systems Biology , 2008, DOI: 10.1186/1752-0509-2-30
Abstract: We have developed an algorithm that can exhaustively enumerate the microstates of a molecular network of small copy numbers under the condition that the net gain in newly synthesized molecules is smaller than a predefined limit. We also describe a simple method for computing the exact mean or steady state landscape probability distribution over microstates. We show how the full landscape probability for the gene networks of the self-regulating gene and the toggle-switch in the steady state can be fully characterized. We also give an example using the MAPK cascade network. Data and server will be available at URL: http://scsb.sjtu.edu.cn/statespace webcite.Our algorithm works for networks of small copy numbers buffered with a finite copy number of net molecules that can be synthesized, regardless of the reaction stoichiometry, and is optimal in both storage and time complexity. The algorithm can also be used to calculate the rates of all transitions between microstates from given reactions and reaction rates. The buffer size is limited by the available memory or disk storage. Our algorithm is applicable to a class of biological networks when the copy numbers of molecules are small and the network is closed, or the network is open but the net gain in newly synthesized molecules does not exceed a predefined buffer capacity. For these networks, our method allows full stochastic characterization of the mean landscape probability distribution, and the steady state when it exists.Networks of interacting biomolecules are at the heart of the regulation of cellular processes, and stochasticity plays important roles in many networks, including those responsible for gene regulation, protein synthesis, and signal transduction [1-5]. The stochasticity originates intrinsically from the small copy numbers of the molecular species in a cell, which frequently occur when molecular concentrations are in the range of 0.1 μM to 1nM (typically from about 100 to 10 copies in a cell) [2,6].
A Corpus-based Study of Developmental Stages of Demonstratives in Chinese English Majors’ Writing
Jie Sun,Xia Liang
Asian Social Science , 2009, DOI: 10.5539/ass.v5n11p117
Abstract: On the basis of comparisons between the corpus of English Majors’ Composition of Ludong University (EMC corpus) and other corpora as well as among different levels in EMC corpus, this thesis studies the feature of using frequency and stages of development of demonstratives used by Chinese English Majors. The result reveals that English Majors of lower and higher grades tend to overuse and underuse demonstratives respectively; with their better and better command of English, the using frequency of demonstratives tends to be closer to that of native speakers’ except for the proportion of the using frequency of singular to plural demonstratives as demonstrative pronouns as well as the using frequency of plural demonstratives.
Multi-Resolution Compressed Sensing via Approximate Message Passing
Xing Wang,Jie Liang
Mathematics , 2015,
Abstract: In this paper, we consider the problem of multi-resolution compressed sensing (MR-CS) reconstruction, which has received little attention in the literature. Instead of always reconstructing the signal at the original high resolution (HR), we enable the reconstruction of a better-quality low-resolution (LR) signal when the number of available CS samples is too low. We propose an approximate message passing (AMP)-based solution dubbed MR-AMP, and derive its state evolution and phase transition, which show that in addition to reduced complexity, our method can recover a LR signal with bounded mean squared error (MSE) even when the MSE of the conventional HR reconstruction is unbounded. We then apply the MR-AMP to image reconstruction using either soft-thresholding or total variation denoiser, and develop the corresponding up-/down-sampling operators in transform or spatial domain. The performance of the proposed scheme is demonstrated by both 1-D and 2-D data.
Approximate Message Passing-based Compressed Sensing Reconstruction with Generalized Elastic Net Prior
Xing Wang,Jie Liang
Computer Science , 2013,
Abstract: In this paper, we study the compressed sensing reconstruction problem with generalized elastic net prior (GENP), where a sparse signal is sampled via a noisy underdetermined linear observation system, and an additional initial estimation of the signal (the GENP) is available during the reconstruction. We first incorporate the GENP into the LASSO and the approximate message passing (AMP) frameworks, denoted by GENP-LASSO and GENP-AMP respectively. We then investigate the parameter selection, state evolution, and noise-sensitivity analysis of GENP-AMP. We show that, thanks to the GENP, there is no phase transition boundary in the proposed frameworks, i.e., the reconstruction error is bounded in the entire plane. The error is also smaller than those of the standard AMP and scalar denoising. A practical parameterless version of the GENP-AMP is also developed, which does not need to know the sparsity of the unknown signal and the variance of the GENP. Simulation results are presented to verify the efficiency of the proposed schemes.
Knowledge-based energy functions for computational studies of proteins
Xiang Li,Jie Liang
Quantitative Biology , 2006, DOI: 10.1007/978-0-387-68372-0_3
Abstract: This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.
Unified Control Strategy of PV Inverter Mixed Power Quality Control  [PDF]
Jie Zhang, Kai Yao, Liang Li, Wei Zhao
Energy and Power Engineering (EPE) , 2013, DOI: 10.4236/epe.2013.54B044
Abstract: Due to the restriction of light illumination condition, the effective utilization of PV grid-connected systems is very low. In view of this question, this paper presents a unified control strategy based on PV grid-connected and active power filter. The distributed small-capacity grid-connected inverter is chose to be the research object. It is preferential to eliminate the deviation between power qualities of the national standards according to the control of decision tables, and adjust the proportion of the compensation currents dynamically. Finally, the simulation results demonstrate the effectiveness of this unified control strategy.
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