other:
Article Title: A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.
Article Snippet: We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA).. To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction.. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices.
Article Title: Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter
Article Snippet: [12] D. Lesage, E. D. Angelini, I. Bloch, and G. Funka-Lea, “Bayesian maximal paths for coronary artery segmentation from 3D CT angiograms,” in Proc.
Quantitative Proteomics:Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. The Majority of Transcriptional Changes Are Initiated by 6 hpi To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
Microarray:Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. The Majority of Transcriptional Changes Are Initiated by 6 hpi To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
Expressing:Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. The Majority of Transcriptional Changes Are Initiated by 6 hpi To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
Gene Expression:Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. The Majority of Transcriptional Changes Are Initiated by 6 hpi To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
Article Title: Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000
Article Snippet: .. To take full advantage of the temporal nature of our data sets and to ensure we captured the breadth of the transcriptional response, DEGs were found using three techniques specifically adapted to time series data ( Supplemental Figure 2 and Supplemental Data Set 2 ): a locally adapted Gaussian Process two-sample test modeling time series ( GP2S ; Stegle et al., 2010 ) that calculates differential expression based on a Bayes factor calculated between two models, one that assumes that the microarray time series in both conditions are samples drawn from an identical shared distribution and an alternative model that describes the time series in both conditions as samples from two independent distributions; Bayesian Analysis of Time Series data ( Angelini et al., 2008 ), which uses the ratios of the expression in the different time series treatments to calculate the Bayes factor, indicating whether a gene is differentially expressed and fits a function to this expression-ratio time series; and Microarray Analysis of Variance ( MAANOVA ; Wu et al., 2003 ), which can be applied to the statistical analysis of gene expression data from two-color cDNA microarrays with sophisticated experimental design. ..
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