We propose a method for imaging through a turbid medium by using a single-shot decoherence polarization gate (DPG). The DPG is made up of a polarizer, an analyzer, and a weakly scattering medium. Contrary to intuition, we discover that the preferential utilization of sparsely scattered photons by introducing weakly scattering mediums can lead to better image quality. The experimental results show that the visibilities of the images acquired from the DPG imaging method are obviously improved. The contrast of the bar can be increased by 50% by the DPG imaging technique. Furthermore, we study the effect of the volume concentration of the weakly scattering medium on the speckle suppression and the enhancement of the visibilities of the images. The variances of the contrasts of the image show that there exists an optimum optical depth (∼0.8) of the weakly scattering medium for DPG imaging through a specific turbid medium.
Background Acute myeloid leukemia (AML), which is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues, and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers are still incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of AML. Methods Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression and AML-related genes in public databases, were employed for investigating potential biomarkers for prognosis of AML. Results This study screened a total of 6,383 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expr essed genes are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. Conclusions With the utilization of WGCNA mining, seven gene co-expression modules were identified from TCGA database and there are unreported genes that may as potential driven genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drag targets.
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