Activated carbon was prepared from sugarcane bagasse with phosphoric acid activation by a mechanochemical process. The effects of milling time on adsorption properties and pore structure of activated carbon were evaluated. The results showed that phosphoric acid activation was assisted by the mechanochemical process, which can reduce the processing time and improve the adsorption performance of the prepared activated carbon. The iodine number, the methylene blue adsorption value, and the specific surface area of the prepared activated carbons were improved from 647.94 mg/g, 150 mg/g and 1075.21 m 2 /g to 889.37 mg/g, 177 mg/g and 1254.52 m 2 /g, respectively. Compared with conventional phosphoric acid activation, the activated carbon produced by the mechanochemical process achieved the advantages of shorter processing time, greater adsorption capacity, and higher adsorbed amounts of iodine, methylene blue, and nitrogen.
BackgroundAnalysis of gene expression data for tumor classification is an important application of bioinformatics methods. But it is hard to analyse gene expression data from DNA microarray experiments by commonly used classifiers, because there are only a few observations but with thousands of measured genes in the data set. Dimension reduction is often used to handle such a high dimensional problem, but it is obscured by the existence of amounts of redundant features in the microarray data set.ResultsDimension reduction is performed by combing feature extraction with redundant gene elimination for tumor classification. A novel metric of redundancy based on DIScriminative Contribution (DISC) is proposed which estimates the feature similarity by explicitly building a linear classifier on each gene. Compared with the standard linear correlation metric, DISC takes the label information into account and directly estimates the redundancy of the discriminative ability of two given features. Based on the DISC metric, a novel algorithm named REDISC (Redundancy Elimination based on Discriminative Contribution) is proposed, which eliminates redundant genes before feature extraction and promotes performance of dimension reduction. Experimental results on two microarray data sets show that the REDISC algorithm is effective and reliable to improve generalization performance of dimension reduction and hence the used classifier.ConclusionDimension reduction by performing redundant gene elimination before feature extraction is better than that with only feature extraction for tumor classification, and redundant gene elimination in a supervised way is superior to the commonly used unsupervised method like linear correlation coefficients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.