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Full-Text Articles in Physical Sciences and Mathematics
Utilizing Vegetation Indices As A Proxy To Characterize The Stability Of A Railway Embankment In A Permafrost Region, Priscilla Addison, Pasi T. Lautala, Thomas Oommen
Utilizing Vegetation Indices As A Proxy To Characterize The Stability Of A Railway Embankment In A Permafrost Region, Priscilla Addison, Pasi T. Lautala, Thomas Oommen
Michigan Tech Publications
Degrading permafrost conditions around the world are posing stability issues for infrastructure constructed on them. Railway lines have exceptionally low tolerances for differential settlements associated with permafrost degradation due to the potential for train derailments. Railway owners with tracks in permafrost regions therefore make it a priority to identify potential settlement locations so that proper maintenance or embankment stabilization measures can be applied to ensure smooth and safe operations. The extensive discontinuous permafrost zone along the Hudson Bay Railway (HBR) in Northern Manitoba, Canada, has been experiencing accelerated deterioration, resulting in differential settlements that necessitate continuous annual maintenance to avoid …
Bottom-Up Ggm Algorithm For Constructing Multilayered Hierarchical Gene Regulatory Networks That Govern Biological Pathways Or Processes, Sapna Kupari, Wenping Deng, Chathura J. Gunasekara, Vincent Chiang, Huann-Sheng Chen, Hairong Wei, Et. Al.
Bottom-Up Ggm Algorithm For Constructing Multilayered Hierarchical Gene Regulatory Networks That Govern Biological Pathways Or Processes, Sapna Kupari, Wenping Deng, Chathura J. Gunasekara, Vincent Chiang, Huann-Sheng Chen, Hairong Wei, Et. Al.
Michigan Tech Publications
Background: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.
Results: A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct an ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory …