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Search Results: 1 - 10 of 44105 matches for " Baolin Wu "
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PSMIX: an R package for population structure inference via maximum likelihood method
Baolin Wu, Nianjun Liu, Hongyu Zhao
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-317
Abstract: We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture.Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy, but more efficient computations.PSMIX and its supplemental documents are freely available at http://bioinformatics.med.yale.edu/PSMIX webcite.Information about population structure, namely population stratification and admixture, is useful in a variety of situations, such as association studies of genes underlying complex traits, subspecies classification, genetic barrier detection, and evolutionary study [1-10]. For example, it is very important to identify genetic ancestry and admixture in admixture mapping [7,8]. The presence of population stratification or admixture may pose a practical nuisance as well. In association studies, case-control design is often used to identify genetic variants underlying complex traits by comparing allele frequencies between unrelated individuals that are affected and those unaffected. However, the presence of population stratification or admixture in the sample can lead to spurious associations between a candidate marker and a phenotype [5,10,11]. In forensic studies, the identification of reference groups is central but becomes difficult when there exists population stratification [12,13]. In the estimation of the magnitude of inbreeding, it is useful to distinguish between the causes for the excess homozygosity which might be consanguineous mating or population substructure, or an artifact due to factors like null alleles [14]. In all these situations, identifying population stratification or admixture has been an important component.Population structure can be identified based on visible characters such as language, culture, physical appearance, and geographic region. But this can be subjective and may bear no relevance to genetics. Evanno et al. [15] gav
Alternative Sigma Factor σH Modulates Prophage Integration and Excision in Staphylococcus aureus
Liang Tao equal contributor,Xiaoqian Wu equal contributor,Baolin Sun
PLOS Pathogens , 2010, DOI: 10.1371/journal.ppat.1000888
Abstract: The prophage is one of the most important components of variable regions in bacterial genomes. Some prophages carry additional genes that may enhance the toxicity and survival ability of their host bacteria. This phenomenon is predominant in Staphylococcus aureus, a very common human pathogen. Bioinformatics analysis of several staphylococcal prophages revealed a highly conserved 40-bp untranslated region upstream of the int gene. A small transcript encoding phage integrase was identified to be initiated from the region, demonstrating that the untranslated region contained a promoter for int. No typical recognition sequence for either σA or σB was identified in the 40-bp region. Experiments both in vitro and in vivo demonstrated that σH recognized the promoter and directed transcription. Genetic deletion of sigH altered the int expression, and subsequently, the excision proportion of prophage DNAs. Phage assays further showed that sigH affected the ability of spontaneous lysis and lysogenization in S. aureus, suggesting that sigH plays a role in stabilizing the lysogenic state. These findings revealed a novel mechanism of prophage integration specifically regulated by a host-source alternative sigma factor. This mechanism suggests a co-evolution strategy of staphylococcal prophages and their host bacteria.
Information assessment on predicting protein-protein interactions
Nan Lin, Baolin Wu, Ronald Jansen, Mark Gerstein, Hongyu Zhao
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-154
Abstract: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.Proteins transmit regulatory signals throughout the cell, catalyze large numbers of chemical reactions, and are crucial for the stability of numerous cellular structures. Interactions among proteins are key for cell functioning and identifying such interactions is crucial for deciphering the fundamental molecular mechanisms of the cell. As relevant genomic information is exponentially increasing both in quantity and complexity, in silico predictions of protein-protein interactions have been possible but also challenging. A number of techniques have been developed that exploit combinations of protein features in training data and can predict protein-protein interactions when applied to novel proteins. Our study is motivated by a study by Jansen et al. [1], who proposed a Bayesian method to use the MIPS [2] compl
Bayesian Mass Spectra Peak Alignment from Mass Charge Ratios
Junfeng Liu,Weichuan Yu,Baolin Wu,Hongyu Zhao
Cancer Informatics , 2008,
Abstract: Proteomics studies based on mass spectrometry (MS) are gaining popular applications in biomedical research for protein identification/quantification and biomarker discovery, especially for potential early diagnosis and prognosis of severe disease before the occurrence of symptoms. However, MS data collected using current technologies are very noisy and appropriate data preprocessing is critical for successful applications of MS-based approaches. Among various data preprocessing steps, peak alignment from multiple spectra based on detected peak sample locations presents special statistical challenges when effective experimental calibration is not feasible due to relatively large peak location variation. To avoid intensive tuning parameter optimization, we propose a simple novel Bayesian algorithm “random grafting-pruning Markov chain Monte Carlo (RGPMCMC)” that can be applied to global MS peak alignment and to follow certain modelbased sample classification criterion for using aligned peaks to classify spectrum samples. The usefulness of our approach is demonstrated through simulation study by making extensive comparison with other algorithms in the literature. Its application to an ovarian cancer MALDI-MS data set achieves a smaller 10-fold cross validation error rate than other current large scale methodologies.
Ovarian Cancer Classification based on Mass Spectrometry Analysis of Sera
Baolin Wu,Tom Abbott,David Fishman,Walter McMurray
Cancer Informatics , 2006,
Abstract: In our previous study [1], we have compared the performance of a number of widely used discrimination methods for classifying ovarian cancer using Matrix Assisted Laser Desorption Ionization (MALDI) mass spectrometry data on serum samples obtained from Reflectron mode. Our results demonstrate good performance with a random forest classifier. In this follow-up study, to improve the molecular classification power of the MALDI platform for ovarian cancer disease, we expanded the mass range of the MS data by adding data acquired in Linear mode and evaluated the resultant decrease in classification error. A general statistical framework is proposed to obtain unbiased classification error estimates and to analyze the effects of sample size and number of selected m/z features on classification errors. We also emphasize the importance of combining biological knowledge and statistical analysis to obtain both biologically and statistically sound results. Our study shows improvement in classification accuracy upon expanding the mass range of the analysis. In order to obtain the best classification accuracies possible, we found that a relatively large training sample size is needed to obviate the sample variations. For the ovarian MS dataset that is the focus of the current study, our results show that approximately 20-40 m/z features are needed to achieve the best classification accuracy from MALDI-MS analysis of sera. Supplementary information can be found at http://bioinformatics.med.yale.edu/proteomics/BioSupp2.html.
Mechanics and Tunable Bandgap by Straining in Single-Layer Hexagonal Boron-Nitride
Jiangtao Wu,Baolin Wang,Yujie Wei,Ronggui Yang,Mildred Dresselhaus
Physics , 2013, DOI: 10.1080/21663831.2013.824516
Abstract: Current interest in two-dimensional materials extends from graphene to others systems like single-layer hexagonal boron-nitride (h-BN), for the possibility of making heterogeneous structures to achieve exceptional properties that cannot be realized in graphene.The electrically insulating h-BN and semi-metal graphene may open good opportunities to realize a semiconductor by manipulating the morphology and composition of such heterogeneous structures.Here we report the mechanical properties of h-BN and its band structures tuned by mechanical straining by using the density functional theory calculations.The elastic properties, both the Young's modulus and bending rigidity for h-BN, are isotropic.We reveal that there is a bi-linear dependence of band gap on the applied tensile strains in h-BN. Mechanical strain can tune single-layer h-BN from an insulator to a semiconductor, with a band gap in the 4.7eV to 1.5eV range.
Comparative Study of the Shielding Properties of Slotted Enclosure with Inner or Outer Excitations Using FEM  [PDF]
Baolin Nie, Pingan Du
Open Journal of Applied Sciences (OJAppS) , 2013, DOI: 10.4236/ojapps.2013.32B009
Abstract: Finite element method (FEM) is employed in this paper to conduct the comparative study of the shielding properties of enclosure with outer and inner excitations. Plane wave is adopted for the outer excitation case while coaxial cable is utilized to model the inner excitation source. Moreover, the resonance phenomena of slotted enclosure under different excitation are studied in detail. Finally, some conclusions with regard to the relationships and distinctions between the inner and outer excitations for the same enclosure are proposed.
A Physical Explanation on Solar Microwave Zebra Pattern with the Current-carrying Plasma Loop Model
Baolin Tan
Physics , 2009, DOI: 10.1007/s10509-009-0193-5
Abstract: Microwave zebra pattern structure is an intriguing fine structure on the dynamic spectra of solar type IV radio burst. Up to now, there isn't a perfect physical model for the origin of the solar microwave zebra pattern. Recently, Ledenev, Yan and Fu (2006) put forward an interference mechanism to explain the features of microwave zebra patterns in solar continuum events. This model needs a structure with a multitude of discrete narrow-band sources of small size. Based on the model of current-carrying plasma loop and the theory of tearing mode instability, we proposed that the above structure does exist and may provide the main conditions for the interference mechanism. With this model, we may explain the frequency upper limit, the formation of the parallel and equidistant stripes, the superfine structure and intermediate frequency drift rate of the zebra stripes. If this explanation is valid, the zebra pattern structures can reveal some information of the motion and the inner structures of the coronal plasma loops.
Coronal Heating Driven by A Magnetic-gradient Pumping Mechanism in Solar Plasmas
Baolin Tan
Physics , 2014, DOI: 10.1088/0004-637X/795/2/140
Abstract: The solar coronal heating is a longstanding mystery in astrophysics. Considering that the solar magnetic field is spatially inhomogeneous with considerable magnetic gradient from solar surface to the corona, this work proposes a magnetic gradient pumping (MGP) mechanism and try to explain the formation of hot plasma upflows, such as the hot type II spicules and hot plasma ejections, etc. In MGP mechanism, the magnetic gradients drive the energetic particles to move upwards from the underlying solar atmosphere and form hot upflows. These upflow energetic particles deposit in corona and make it becoming very hot. Roughly estimations indicate that the solar corona can be heated to above 1 million degrees, and the upflow velocity is about 40 km/s in chromosphere and about 130 km/s in the corona. The solar magnetic flux tubes act as pumpers to extract energetic particles from the underlying thermal photosphere, convey them and deposit in the corona. The deposition of energetic particles will make the corona become hot, and their escaping from the photosphere will make the underlying photosphere a bit cold. This mechanism present a natural explanation to the mystery of solar coronal heating.
A Peculiar Microwave Quasi-periodic Pulsation with Zigzag Pattern in a CME-related Flare on 2005-01-15
Baolin Tan
Physics , 2013, DOI: 10.1017/S1743921313003281
Abstract: A peculiar microwave quasi-periodic pulsation with zigzag pattern (Z-QPP) is observed first by the Chinese Solar Broadband Spectrometer in Huairou (SBRS/Huairou) at 1.10-1.34 GHz in a solar flare on 2005-01-15. The Z-QPP occurred just in the early rising phase of the flare with weakly right-handed circular polarization. Its period is only several decades millisecond. Particularly, before and after the pulsation, there are many spectral fine structures, such as zebra patterns, fibers, and millisecond spikes. The microwave Z-QPP can provide some kinematic information of the source region in the early rising phase of the flare, and the source width changes from about 1000 km to 3300 km, even if we have no imaging observations. The abundant spectral fine structures possibly reflect the dynamic features of non-thermal particles.
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