Oxidation and phase transfer of individual Cr-doped dendritic FeOx particles visualized by full-field nano-XAFS spectroimaging.

Iron oxides with various compositions and polymorphs have been widely used as compounds that require reversible redox properties, such as catalysts. However, partial decomposition during phase transitions often causes irreversible degradation of the redox properties of iron oxides. Cr doping into the crystalline framework of iron oxide dendrites improves the stability of the structural transformation of iron oxides. We spatially visualized the FeOx-dendrite phase distribution during oxidation in crystalline dendritic FeOx and Cr-FeOx particles by full-field nano-X-ray absorption fine structure spectroimaging. The spectroimaging visualized propagation in the phase transitions in the individual FeOx particles and changes in the phase transition behaviors of the Cr-FeOx particles. The statistical analysis of the spectroimaging data revealed the phase transition trends in parts of the FeOx and Cr-FeOx particles in three Fe density zones (particle thicknesses) and the probability densities of the phase proportions in the dendrites.

Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer.

The selection of genes that are important for obtaining gene expression data is challenging. Here, we developed a deep learning-based feature selection method suitable for gene selection. Our novel deep learning model includes an additional feature-selection layer. After model training, the units in this layer with high weights correspond to the genes that worked effectively in the processing of the networks. Cancer tissue samples and adjacent normal pancreatic tissue samples were collected from 13 patients with pancreatic ductal adenocarcinoma during surgery and subsequently frozen. After processing, gene expression data were extracted from the specimens using RNA sequencing. Task 1 for the model training was to discriminate between cancerous and normal pancreatic tissue in six patients. Task 2 was to discriminate between patients with pancreatic cancer (n = 13) who survived for more than one year after surgery. The most frequently selected genes were ACACB, ADAMTS6, NCAM1, and CADPS in Task 1, and CD1D, PLA2G16, DACH1, and SOWAHA in Task 2. According to The Cancer Genome Atlas dataset, these genes are all prognostic factors for pancreatic cancer. Thus, the feasibility of using our deep learning-based method for the selection of genes associated with pancreatic cancer development and prognosis was confirmed.