Identification accuracies of the five methods on simulation data with different samples.

<p>Identification accuracies of the five methods on simulation data with different samples.</p

The PRFE model of gene expression data used for gene identification.

<p>The PRFE model of gene expression data used for gene identification.</p

The detailed information of the 30 genes identified by PRFE.

<p>The detailed information of the 30 genes identified by PRFE.</p

AUC statistics for simulation data.

<p>AUC statistics for simulation data.</p

The detailed information of the 5 'unique' genes identified by PRFE.

<p>The detailed information of the 5 'unique' genes identified by PRFE.</p

The sample number of each stress type in the raw data.

<p>The sample number of each stress type in the raw data.</p

Identification accuracies of the five methods on simulation data with different parameters, where <i>p</i>_<i>S</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>L</i> = 1 to test the performance of different <i>p</i>_<i>S</i> values; <i>p</i>_<i>L</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>S</i> = 1 to test the performance of different <i>p</i>_<i>L</i> values; <i>α</i>₁, <i>α</i>₂ and <i>γ</i> are the control-sparsity parameters of PMD, CIPMD and SPCA, respectively.

<p>Identification accuracies of the five methods on simulation data with different parameters, where <i>p</i>_<i>S</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>L</i> = 1 to test the performance of different <i>p</i>_<i>S</i> values; <i>p</i>_<i>L</i> is taken as the parameter in the case of PRFE <i>p</i>_<i>S</i> = 1 to test the performance of different <i>p</i>_<i>L</i> values; <i>α</i>₁, <i>α</i>₂ and <i>γ</i> are the control-sparsity parameters of PMD, CIPMD and SPCA, respectively.</p

Venn diagram of five methods on leukemia data.

<p>Venn diagram of five methods on leukemia data.</p

ROC curve for simulation data.

<p>ROC curve for simulation data.</p

Different Proteomic Processes Related to the Cultivar-Dependent Cadmium Accumulation of <i>Amaranthus gangeticus</i>

To deal with the Cd contaminant of agricultural soil, pollution-safe cultivar (PSC) is developed to minimize the Cd accumulation risk in crops. The present study aimed to investigate the different proteomic responses related to Cd accumulation in different tissues between two <i>Amaranthus gangeticus</i> cultivars, Pen and Nan. A significantly higher Cd accumulation in Pen than in Nan was unraveled, especially in shoot. The proportions of soluble Cd in root and stem of Nan were significantly lower than those of Pen, implying lower Cd transportation from root to shoot in Nan. Higher contents of NaCl-extracted Cd in Pen than in Nan were probably attributed to the enhancement of GSH related metabolism in Pen, which activated the transportation of Cd from root to shoot. Alteration of other proteins involved in Cd detoxification and energy production also demonstrated that Pen had exhibited a stronger tolerance than Nan in dealing with Cd stress. Thus, differences in the proteomic processes associated with biochemical differences between the two typical cultivars suggested a cultivar-dependent capacity of Cd tolerance and accumulation in amaranth for the first time