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Since tumor is seriously harmful to human health, effective diagnosis measures are in urgent need for tumor therapy. Early detection of tumor is particularly important for better treatment of patients. A notable issue is how to effectively discriminate tumor samples from normal ones. Many classification methods, such as Support Vector Machines (SVMs), have been proposed for tumor classification. Recently, deep learning has achieved satisfactory performance in the classification task of many areas. However, the application of deep learning is rare in tumor classification due to insufficient training samples of gene expression data. In this paper, a Sample Expansion method is proposed to address the problem. Inspired by the idea of Denoising Autoencoder (DAE), a large number of samples are obtained by randomly cleaning partially corrupted input many times. The expanded samples can not only maintain the merits of corrupted data in DAE but also deal with the problem of insufficient training samples of gene expression data to a certain extent. Since Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) models show excellent performance in classification task, the applicability of SAE and 1-dimensional CNN (1DCNN) on gene expression data is analyzed. Finally, two deep learning models, Sample Expansion-Based SAE (SESAE) and Sample Expansion-Based 1DCNN (SE1DCNN), are designed to carry out tumor gene expression data classification by using the expanded samples. Experimental studies indicate that SESAE and SE1DCNN are very effective in tumor classification.

In this work, an organic/inorganic hybrid polymer containing siloxyl functional groups was synthesized and applied to encapsulate phase change materials (PCMs). Owing to the mild conditions of the hypercrosslinking reaction, which only requires the addition of ac atalytic amount of aqueous alkaline solution, both organic and inorganic PCMs are tolerated. It is noteworthy that the initial homogeneous state of the reaction mixture allowed the ultimate encapsulation rate of the PCMs and the uniform blending of the thirdn anoadditives with the aim of thermal conductivity enhancement. Further study reveals that the presence of this hybrid hydrophobic polymer in aphase change composite endows the latter with au nique self-cleaning property.T his novel PCM encapsulation protocol is suitable for nanoparticles including carbon-based nanomaterials,m etal oxide nanoparticles,a nd inorganic oxide nanoparticles.Athermal conductivity enhancement of 600 %w as achieved along with 93.7 %l ight-tothermal conversion efficiency with al atent heat of 180 Jg À1 without leakage.

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Identification of characteristic genes associated with specific biological processes of different cancers could provide insights into the underlying cancer genetics and cancer prognostic assessment. It is of critical importance to select such characteristic genes effectively. In this paper, a novel unsupervised characteristic gene selection method based on sample learning and sparse filtering, Sample Learning based on Deep Sparse Filtering (SLDSF), is proposed. With sample learning, the proposed SLDSF can better represent the gene expression level by the transformed sample space. Most unsupervised characteristic gene selection methods did not consider deep structures, while a multilayer structure may learn more meaningful representations than a single layer, therefore deep sparse filtering is investigated here to implement sample learning in the proposed SLDSF. Experimental studies on several microarray and RNA-Seq datasets demonstrate that the proposed SLDSF is more effective than several representative characteristic gene selection methods (e.g., RGNMF, GNMF, RPCA and PMD) for selecting cancer characteristic genes.