scholarly journals The use of dispersion modeling to determine the feasibility of vegetative environmental buffers (VEBS) at controlling odor dispersion

2012 ◽  
Author(s):  
Eric E. Weber
Keyword(s):  
Dispersion Modeling ◽  
Odor Dispersion

Livestock Odor Dispersion Modeling: A Review

2010 ◽  
Vol 53 (4) ◽  
pp. 1231-1244 ◽  
Author(s):  
Z. Yu ◽  
H. Guo ◽  
C. Laguë
Keyword(s):  
Dispersion Modeling ◽  
Livestock Odor ◽  
Odor Dispersion

AIR DISPERSION MODELING OF VOCs EMITTED FROM MSW LANDFILLS

2006 ◽  
Vol 5 (4) ◽  
pp. 731-741
Author(s):  
Fatih Taspinar ◽  
Ertan Durmusoglu ◽  
Aykan Karademir
Keyword(s):  
Dispersion Modeling ◽  
Air Dispersion Modeling ◽  
Air Dispersion

scholarly journals An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

2021 ◽  
Vol 20 (1) ◽  
pp. 1-15
Author(s):  
Qi Zhang ◽  
Zheng Xu ◽  
Yutong Lai
Keyword(s):  
Long Range ◽  
Latent Variables ◽  
Empirical Bayes ◽  
Genome Structure ◽  
Peak Detection ◽  
Data Matrix ◽  
Dispersion Modeling ◽  
Empirical Bayes Approach ◽  
Over Dispersion ◽  
Bayes Approach

Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

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