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Search Results: 1 - 10 of 7888 matches for " Hongyue Dai "
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The Extremely High Energy Cosmic Rays
Shigeru Yoshida,Hongyue Dai
Physics , 1998, DOI: 10.1088/0954-3899/24/5/002
Abstract: Experimental results from Haverah Park, Yakutsk, AGASA and Fly's Eye are reviewed. All these experiments work in the energy range above 0.1 EeV. The 'dip' structure around 3 EeV in the energy spectrum is well established by all the experiments, though the exact position differs slightly. Fly's Eye and Yakutsk results on the chemical composition indicate that the cosmic rays are getting lighter over the energy range from 0.1 EeV to 10 EeV, but the exact fraction is hadronic interaction model dependent, as indicated by the AGASA analysis. The arrival directions of cosmic rays are largely isotropic, but interesting features may be starting to emerge. Most of the experimental results can best be explained with the scenario that an extragalactic component gradually takes over a galactic population as energy increases and cosmic rays at the highest energies are dominated by particles coming from extragalactic space. However, identification of the extragalactic sources has not yet been successful because of limited statistics and the resolution of the data.
Extremely High Energy Neutrinos and their Detection
Shigeru Yoshida,Hongyue Dai,Charles C. H. Jui,Paul Sommers
Physics , 1996, DOI: 10.1086/303923
Abstract: We discuss in some detail the production of extremely high energy (EHE) neutrinos with energies above 10^18 eV. The most certain process for producing such neutrinos results from photopion production by EHE cosmic rays in the cosmic background photon field. However, using assumptions for the EHE cosmic ray source evolution which are consistent with results from the deep QSO survey in the radio and X-ray range, the resultant flux of neutrinos from this process is not strong enough for plausible detection. A measurable flux of EHE neutrinos may be present, however, if the highest energy cosmic rays which have recently been detected well beyond 10^20 eV are the result of the annihilation of topological defects which formed in the early universe. Neutrinos resulting from such decays reach energies of the grand unification (GUT) scale, and collisions of superhigh energy neutrinos with the cosmic background neutrinos initiate neutrino cascading which enhances the EHE neutrino flux at Earth. We have calculated the neutrino flux including this cascading effect for either massless or massive neutrinos and we find that these fluxes are conceivably detectable by air fluorescence detectors now in development. The neutrino-induced showers would be recognized by their starting deep in the atmosphere. We evaluate the feasibility of detecting EHE neutrinos this way using air fluorescence air shower detectors and derive the expected event rate. Other processes for producing deeply penetrating air showers constitute a negligible background.
Cosmic Ray Induced EM Showers in the NO$ν$A Detectors
Hongyue Duyang
Physics , 2015,
Abstract: The NO$\nu$A experiment is an electron neutrino appearance neutrino oscillation experiment at Fermilab. Electron neutrino events are identified by the electromagnetic (EM) showers induced by electrons in the final state of neutrino interactions. EM showers induced by cosmic muons or rock muons, are abundant in NO$\nu$A detectors. We use a Muon-Removal Technique to get pure EM shower samples from cosmic and rock muon data. Those samples can be used to characterize the EM signature and provide valuable checks of the MC simulation, reconstruction, PID algorithms, and calibration across the NO$\nu$A detectors.
Inferring causal genomic alterations in breast cancer using gene expression data
Linh M Tran, Bin Zhang, Zhan Zhang, Chunsheng Zhang, Tao Xie, John R Lamb, Hongyue Dai, Eric E Schadt, Jun Zhu
BMC Systems Biology , 2011, DOI: 10.1186/1752-0509-5-121
Abstract: We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments.To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.Tumors arise from the activation of oncogenes along with the inactivation of tumor suppressor genes via somatic gene mutations or copy number variation (CNV). Identification of the genetic/genomic changes that drive biological processes associated with cancer onset or progression assists in the development of therapeutics targeting the affected proteins or their downstream consequences[1-4]. Although extensive gene expression studies have been conducted for identifying tumor signature genes associated with poor outcome[5,6], the reproducibility of these signatures is low[7,8], posing a major challenge for identifying the causal genetic/genomic variations.Genome-wide DNA copy
Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters
Ke Hao, John M Luk, Nikki PY Lee, Mao Mao, Chunsheng Zhang, Mark D Ferguson, John Lamb, Hongyue Dai, Irene O Ng, Pak C Sham, Ronnie TP Poon
BMC Cancer , 2009, DOI: 10.1186/1471-2407-9-389
Abstract: We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation setOur findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7).This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment.Hepatocellular carcinoma (HCC) is the fifth most common malignancies in the world, accounting for approximately one million deaths with an increasing trend of new incidences annually [1-3] Surgery is regarded as the one of the standard curative treatments of HCC if the tumor is resectable [4,5]. However, prognosis fol
Development of a microarray platform for FFPET profiling: application to the classification of human tumors
Sven Duenwald, Mingjie Zhou, Yanqun Wang, Serguei Lejnine, Amit Kulkarni, Jaime Graves, Ryan Smith, John Castle, George Tokiwa, Bernard Fine, Hongyue Dai, Thomas Fare, Matthew Marton
Journal of Translational Medicine , 2009, DOI: 10.1186/1479-5876-7-65
Abstract: We developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al [1] and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples.When a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples (leave-one-out cross validation) with an error rate of ~14% (p-value = 3.7 × 10-7). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling.We have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for cl
A Comparison of Two Test Statistics for Poisson Overdispersion/Underdispersion  [PDF]
Hongyue Wang, Changyong Feng, Xinming Tu, Jeanne Kowalski
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.37118
Abstract: Within the family of zero-inflated Poisson distributions, the data has Poisson distribution if any only if the mean equals the variance. In this paper we compare two closely related test statistics constructed based on this idea. Our results show that although these two tests are asymptotically equivalent under the null hypothesis and are equally efficient, one test is always more efficient than the other one for small and medium sample sizes.
A Note on Generalized Inverses of Distribution Function and Quantile Transformation  [PDF]
Changyong Feng, Hongyue Wang, Xin M. Tu, Jeanne Kowalski
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.312A289
Abstract:

In this paper we study the relations of four possible generalized inverses of a general distribution functions and their right-continuity properties. We correct a right-continuity result of the generalized inverse used in statistical literature. We also prove the validity of a new generalized inverse which is always right-continuous.

PPARα siRNA–Treated Expression Profiles Uncover the Causal Sufficiency Network for Compound-Induced Liver Hypertrophy
Xudong Dai ,Angus T. De Souza,Hongyue Dai,David L Lewis,Chang-kyu Lee,Andy G Spencer,Hans Herweijer,Jim E Hagstrom,Peter S Linsley,Douglas E Bassett,Roger G Ulrich,Yudong D He
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030030
Abstract: Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor α (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARα-induced liver hypertrophy is supported by their ability to predict non-PPARα–induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.
Gene Signatures Derived from a c-MET-Driven Liver Cancer Mouse Model Predict Survival of Patients with Hepatocellular Carcinoma
Irena Ivanovska, Chunsheng Zhang, Angela M. Liu, Kwong F. Wong, Nikki P. Lee, Patrick Lewis, Ulrike Philippar, Dimple Bansal, Carolyn Buser, Martin Scott, Mao Mao, Ronnie T. P. Poon, Sheung Tat Fan, Michele A. Cleary, John M. Luk, Hongyue Dai
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0024582
Abstract: Biomarkers derived from gene expression profiling data may have a high false-positive rate and must be rigorously validated using independent clinical data sets, which are not always available. Although animal model systems could provide alternative data sets to formulate hypotheses and limit the number of signatures to be tested in clinical samples, the predictive power of such an approach is not yet proven. The present study aims to analyze the molecular signatures of liver cancer in a c-MET-transgenic mouse model and investigate its prognostic relevance to human hepatocellular carcinoma (HCC). Tissue samples were obtained from tumor (TU), adjacent non-tumor (AN) and distant normal (DN) liver in Tet-operator regulated (TRE) human c-MET transgenic mice (n = 21) as well as from a Chinese cohort of 272 HBV- and 9 HCV-associated HCC patients. Whole genome microarray expression profiling was conducted in Affymetrix gene expression chips, and prognostic significances of gene expression signatures were evaluated across the two species. Our data revealed parallels between mouse and human liver tumors, including down-regulation of metabolic pathways and up-regulation of cell cycle processes. The mouse tumors were most similar to a subset of patient samples characterized by activation of the Wnt pathway, but distinctive in the p53 pathway signals. Of potential clinical utility, we identified a set of genes that were down regulated in both mouse tumors and human HCC having significant predictive power on overall and disease-free survival, which were highly enriched for metabolic functions. In conclusions, this study provides evidence that a disease model can serve as a possible platform for generating hypotheses to be tested in human tissues and highlights an efficient method for generating biomarker signatures before extensive clinical trials have been initiated.
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