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Search Results: 1 - 10 of 127059 matches for " Ziliang Li "
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The Formation of Lee Reversal Flow and Moisture Distribution Effect on the Characteristics of Precipitation  [PDF]
Jinqing Liu, Ziliang Li
Atmospheric and Climate Sciences (ACS) , 2013, DOI: 10.4236/acs.2013.32023

Moist flow over a bell-shaped mountain is investigated using the advanced regional prediction system (ARPS). Three closely related issues are addressed: the upslope precipitation mechanism, periodic evolution of precipitation associated with mountain waves, and lee precipitation induced by reversal flow. The results show that precipitation is strongly the moist distribution and terrain scale dependent. Beginning with the case of uniformly stratified flow over mountain, upslope precipitation and lee wave precipitation pattern are obtained. Most importantly, lee precipitation induced by reversal flow can be caused by layered flow over mountain, wherein lee reversal flow exerts a significant influence on the orographic precipitation.

Analyses and Numerical Modeling of Gravity Waves Generated by Flow over Nanling Mountains  [PDF]
Ziliang Li, Jin Zhou
Atmospheric and Climate Sciences (ACS) , 2014, DOI: 10.4236/acs.2014.42032
Abstract: Although there have been many observational and modeling studies of gravity waves excited by topograpghy, the detailed structure and its changes in real world are still poorly understood. The interaction of topography and background flow are described in details for a better understanding of the gravity waves observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery over Nanling Mountains. The evolutionary process and spatial structure of gravity waves were investigated by using almost all available observational data, including MODIS satellite imagery, the Final Analyses (FNL) data issued by National Centers for Environmental Prediction (NCEP), the aerosol backscattering signal data from Lidar, the surface observational data and the sounding data of Nanling mountain regions. In order to study its development mechanism, choosing the initial sounding of Jiangxi Gaizhou station located in the upstream of Nanling regions, and using the Advanced Regional Prediction System (ARPS), the numerical simulation was performed. It is shown that the ARPS model reproduced the main features of gravity waves reasonably well, where the gravity waves and turbulent mixed layer are consistent with the satellite image and the aerosol backscattering signal from Lidar observation. It is well-known that gravity wave-induced turbulence and thus turbulent mixing could affect the local composition of chemical species, which plays a significant role in the formation of low visibility and precipitation associated with local orography.
The Cyclic Behavior of Mountain Gravity Waves Generated by Flow over Topography  [PDF]
Ziliang Li, Changji Chen, Jinqing Liu
International Journal of Geosciences (IJG) , 2013, DOI: 10.4236/ijg.2013.43051

The cyclic behavior of lee wave systems, generated by stratified flow over mountains is investigated by the Advanced Regional Prediction System (ARPS) model. The results show that, surface friction has a direct impact upon the number and timing of mountain gravity waves cycle generation. Cyclic generation of mountain lee waves and down-slope winds was found to be extremely sensitive to the magnitude of the surface drag coefficient, where mountain waves amplitude and intensity varies with the magnitude of the drag coefficient, and the interaction of mountain waves and boundary layer process determinates the wave characteristics. For the typical drag Cd = 10–3, surface friction promotes the formation of the stationary mountain lee waves and hydraulic jump, especially, promotes boundary layer separation, the generation of low-level turbulent zones and rotor circulation or reversal flow within boundary layer. When drag coefficient becomes Cd = 10–4, lee waves remain steady states and the first evolution cycle maintains much longer than that of Cd = 10–3. In the case of the highest drag coefficient Cd = 10

Local Government Debt and Economic Growth in China—An Empirical Study Based on Granger Causality Test  [PDF]
Ziliang Wang
Modern Economy (ME) , 2019, DOI: 10.4236/me.2019.102024

In both the theoretical and empirical levels, both at home and abroad, the research of the relativity between government debt and economic growth is still inconclusive. Combined with the practical situation of China, this article takes 31 provinces of our country from 1995 to 2014 and 18 (2005, 2013, rejecting) local government debt and economic growth data as samples. First, we conduct unit root test on two variables DEBT and GDP to ensure that data is stationary, and then, on this basis of unit root test, we conduct cointegration test to determine whether there is a long-term cointegration relationship between DEBT and GDP. Finally, on the basis of the first two steps, we conduct Granger causality test on DEBT and GDP. The results show that the local government debt and economic growth in China is the second order sequences

Predicting DNA methylation status using word composition  [PDF]
Lingyi Lu, Kao Lin, Ziliang Qian, Haipeng Li, Yudong Cai, Yixue Li
Journal of Biomedical Science and Engineering (JBiSE) , 2010, DOI: 10.4236/jbise.2010.37091
Abstract: Background: DNA methylation will influence the gene expression pattern and cause the changes of the genetic functions. Computational analysis of the methylation status for nucleotides can help to explore the underlying reasons for developing methylations. Results: We present a DNA sequence based method to analyze the methylation status of CpG dinucleotides using 5bp (5-mer) DNA fragments – named as the word composition encoding method. The prediction accuracy is 75.16% when all 5bp word compositions are used (totally 45 = 1024). Furthermore, 5-bp DNA fragments/words having the most impact on the methylation status are identified by mRMR (Maximum-Relevant-Minimum-Redundancy) feature selection method. As a result, 58 words are selected, and they are used to build a compact predictor, which achieves 77.45% prediction accuracy. When the word composition encoding method and the feature selection strategy are coupled together, the meaning of these words can be analyzed through their contribution towards the prediction. The biological evidence in the literature supports that the surrounding DNA sequence of the CpG dinucleotides will affect the methylation of the CpG dinucleotides. Conclusions: The main contribution of this paper is to find out and analyze the key DNA words taken from the neighbor-hood of the CpG dinucleotides that are inducing the DNA methylation.
Analysis of Terrain Height Effects on the Asymmetric Precipitation Patterns during the Landfall of Typhoon Meranti (2010)  [PDF]
Jinqing Liu, Ziliang Li, Mengxiang Xu
Atmospheric and Climate Sciences (ACS) , 2019, DOI: 10.4236/acs.2019.93024
Abstract: The predictions of heavy rainfall in an accurate and timely fashion are some of the most important challenges in disastrous weather forecast when a typhoon passes over land. Numerical simulations using the advanced weather research and forecasting (WRF) model are performed to study the effect of terrain height and land surface processes on the rainfall of landfall typhoon Meranti (2010). The experimental results indicate that terrain height could enhance convection and precipitation. The heavy rainfall is concentrated on the west side of typhoon track, which is mainly associated with the distribution of deep convection. The terrain height exacerbated the asymmetric distribution of heavy rainfall. The most striking feature is that enhanced rainfall is mainly caused by secondary circulation, which is induced by terrain height and can be explained by a highly simplified theoretical model. Finally, it is worth pointing out that perturbation potential temperature or buoyancy processes forced by terrain height could be taken as an indicator for accurate prediction of heavy rainfall during the landfall of a tropical cyclone.
An efficient method for statistical significance calculation of transcription factor binding sites
Ziliang Qian,Lingyi Lu,Liu Qi,Yixue Li
Bioinformation , 2007,
Abstract: Various statistical models have been developed to describe the DNA binding preference of transcription factors, by which putative transcription factor binding sites (TFBS) can be identified according to scores assigned. Statistical significance of these scores, usually known as the p-value, play a critical role in identification. We developed an efficient algorithm to provide precise calculation of the statistical significance, remarkably enhancing the calculation efficiency by reducing the time complexity from an exponent scale to a linear scale, and successfully extended the application of this algorithm to a wide range of models, from the commonly used position weight matrix models to the complicated Bayesian Network models. Further, we calculated p-values of all transcription factor DNA binding sites recorded in the database, JASPAR, and based on these, we investigated some unseen properties of p-values as a whole, such as the p-value distribution of different models and the p-value variance according to changed scoring schemes. We hope that our algorithm and the result of computational experiments would offer an improved solution to the statistical significance of transcription factor binding sites. The software to implement our method can be downloaded from http://pcal.biosino.org/pCal.html.
Gene-Centric Characteristics of Genome-Wide Association Studies
Changzheng Dong, Ziliang Qian, Peilin Jia, Ying Wang, Wei Huang, Yixue Li
PLOS ONE , 2007, DOI: 10.1371/journal.pone.0001262
Abstract: Background The high-throughput genotyping chips have contributed greatly to genome-wide association (GWA) studies to identify novel disease susceptibility single nucleotide polymorphisms (SNPs). The high-density chips are designed using two different SNP selection approaches, the direct gene-centric approach, and the indirect quasi-random SNPs or linkage disequilibrium (LD)-based tagSNPs approaches. Although all these approaches can provide high genome coverage and ascertain variants in genes, it is not clear to which extent these approaches could capture the common genic variants. It is also important to characterize and compare the differences between these approaches. Methodology/Principal Findings In our study, by using both the Phase II HapMap data and the disease variants extracted from OMIM, a gene-centric evaluation was first performed to evaluate the ability of the approaches in capturing the disease variants in Caucasian population. Then the distribution patterns of SNPs were also characterized in genic regions, evolutionarily conserved introns and nongenic regions, ontologies and pathways. The results show that, no mater which SNP selection approach is used, the current high-density SNP chips provide very high coverage in genic regions and can capture most of known common disease variants under HapMap frame. The results also show that the differences between the direct and the indirect approaches are relatively small. Both have similar SNP distribution patterns in these gene-centric characteristics. Conclusions/Significance This study suggests that the indirect approaches not only have the advantage of high coverage but also are useful for studies focusing on various functional SNPs either in genes or in the conserved regions that the direct approach supports. The study and the annotation of characteristics will be helpful for designing and analyzing GWA studies that aim to identify genetic risk factors involved in common diseases, especially variants in genes and conserved regions.
The combination approach of SVM and ECOC for powerful identification and classification of transcription factor
Guangyong Zheng, Ziliang Qian, Qing Yang, Chaochun Wei, Lu Xie, Yangyong Zhu, Yixue Li
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-282
Abstract: The support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL).The SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.Transcription factors (TFs) are special DNA-binding proteins, which are commonly recognized by RNA polymerases for transcription initiation. Under certain physiologic conditions, TFs regulate expression levels of downstream genes effectively by binding to specific DNA fragments in the promoter regions. Such a process is closely related to important biological processes such as activation of cell cycle, regulation of differentiation, and maintenance of immunologic tolerance etc [1-3]. Generally, according to their structure and function, TFs can be grouped into four classes: (1) TFs with basic domains (basic-TFs), (2) TFs with zinc-coordinating DNA binding domains (zinc-TFs), (3) TFs with Helix-turn-helix (helix-TFs), and (4) TFs with Beta-Scaffold factors (beta-TFs). It is well known that interaction mechanisms of TFs and motifs differ for d
Using GeneReg to construct time delay gene regulatory networks
Tao Huang, Lei Liu, Ziliang Qian, Kang Tu, Yixue Li, Lu Xie
BMC Research Notes , 2010, DOI: 10.1186/1756-0500-3-142
Abstract: The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases.GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.With the rapid development of microarray technology, more and more short time course gene expression data have been generated; with such abundant high-throughput screening data available, researchers have tried to infer, or reverse-engineer, gene networks. In general, the existing models for network inference can be grouped into three categories: logical models, continuous models and single-molecule level models [1]. Logical models such as Boolean networks and Petri nets could represent the network structure but are unable to describe dynamic processes. While single-molecule level models such as stochastic simulation algorithm could provide high resolution modeling and analysis, but only on limited molecules with well-known reactions among them. Single-molecule level models are not suitable for large scale regulatory network reconstruction. There were several widely-used general algorithms for network
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