ABSTRACT Objective: MCM3AP-AS1 has been characterized as an oncogenic long non-coding RNA (lncRNA) in several cancers including papillary thyroid cancer (PTC), but its role in PTC has not been fully elucidated. Considering the critical role of lncRNAs in cancer biology, further functional analysis of MCM3AP-AS1 in PTC may provide novel insights into PTC management. Subjects and methods: Paired tumor and non-tumor tissues were collected from 63 papillary thyroid carcinoma (PTC) patients. Expression levels of MCM3AP-AS1 , miR-218 and GLUT1 in tissue samples were analyzed by qRT-PCR. Cell transfection was performed to explore the interactions among MCM3AP-AS1 , miR-218 and GLUT1 . Cell proliferation assay was performed to evaluate the effects of MCM3AP-AS1 and miR-218 on cell proliferation. Results: MCM3AP-AS1 accumulated to high levels in PTC tissues and was affected by clinical stage. MCM3AP-AS1 showed a positive correlation with GLUT1 across PTC tissues. RNA interaction prediction showed that MCM3AP-AS1 could bind to miR-218 , which can directly target GLUT1 . MCM3AP-AS1 and miR-218 showed no regulatory role regulating the expression of each other, but overexpression of MCM3AP-AS1 upregulated GLUT1 and enhanced cell proliferation. In contrast, overexpression of miR-218 downregulated GLUT1 and attenuated cell proliferation. In addition, miR-218 suppressed the role of MCM3AP-AS1 in regulating the expression of GLUT1 and cell proliferation. Conclusions: MCM3AP-AS1 may serve as a competing endogenous RNA of miR-218 to upregulate GLUT1 in PTC, thereby promoting cell proliferation. The MCM3AP-AS1/miR-218/GLUT1 pathway characterized in the present study might serve as a potential target to treat PTC.

Abstract The function and mechanism of long intergenic non-protein coding RNA 974 (LINC00974) are rarely reported in ovarian cancer (OC). The study aimed to investigate how LINC00974 affects the progression of OC. The expression levels of LINC00974, microRNA-33a (miR-33a), and high mobility group box 2 (HMGB2) mRNA were detected by qRT-PCR. The LINC00974/miR-33a/HMGB2 axis was confirmed by dual-luciferase reporter, RNA-binding protein immunoprecipitation (RIP), and biotinylated RNA pull-down assays. A series of in vitro experiments were employed to assess the effects of LINC00974/miR-33a/HMGB2 axis on the proliferation, invasion and epithelial mesenchymal transition (EMT) of OC cells. Results showed that LINC00974 and HMGB2 mRNA expression were upregulated in OC cells, while miR-33a expression was downregulated. HMGB2 was a direct target gene of miR-33a. LINC00974 act as a competing endogenous RNA (ceRNA) to directly bind with miR-33a, thereby upregulated HMGB2 expression. Notably, silencing of LINC00974 suppressed cell proliferation, invasion and EMT of OC cells, whereas miR-33a knockdown partially reversed the phenotypes of LINC00974 on OC cells. Overall, our study demonstrated that LINC00974 sponges miR-33a to promote cell proliferation, invasion, and EMT of OC through HMGB2 upregulation. LINC00974/miR-33a/HMGB2 axis may be an important signaling pathway in the progression of OC.

RESUMEN La clasificación de imágenes de detección remota de suelos es la más difícil en el trabajo del Censo Nacional de Suelos en China. Los métodos vigentes de clasificación de imágenes de teledetección del suelo basados en el aprendizaje profundo y la estimación de máxima probabilidad no satisfacen las necesidades actuales. Por lo tanto, este documento combina el aprendizaje profundo con la estimación de máxima verosimilitud y propone un método de clasificación para estas imágenes de teledetección. En primer lugar, se lleva a cabo el preprocesamiento de la imagen de teledetección del suelo, lo que incluye tres procesos: imagen gris, eliminación de ruido y corrección de imagen; en segundo lugar, el objetivo de la imagen del suelo se detecta mediante un algoritmo de aprendizaje profundo; tercero, el algoritmo de máxima verosimilitud se usa para clasificar la imagen de detección remota del suelo; y, finalmente, el rendimiento de la clasificación se prueba con un ejemplo. Los resultados muestran que este método puede segmentar efectivamente la imagen de detección remota del suelo, y la precisión de la segmentación es alta, lo que demuestra la efectividad y superioridad del método.

ABSTRACT Soil remote sensing image classification is the most difficult in the National Soil Census work. Current soil remote sensing image classification methods based on deep learning and maximum likelihood estimation are challenging to meet the actual needs. Therefore, this paper combines deep learning with maximum likelihood estimation and proposes a maximum likelihood classification method for soil remote sensing images based on deep learning. The method is divided into four parts. Firstly, the pretreatment of soil remote sensing image is carried out, including three processes: image gray, image denoising, and image correction; secondly, the target of soil remote sensing image is detected by deep learning algorithm; thirdly, the maximum likelihood algorithm is used to classify soil remote sensing image; finally, the classification performance is tested by an example. The results show that this method can effectively segment the remote sensing image of soil, and the segmentation accuracy is high, which proves the effectiveness and superiority of the method.

A series of HPW/Zn-Ti layered double hydroxides (LDHs) were prepared by immobilizing phosphotungstic acid (HPW) on the Zn-Ti LDHs. Systematically characterizations were performed to elucidate their structure. These HPW/Zn-Ti LDHs hybrids were used as heterogeneous catalysts in the epoxidation of fatty acid methyl ester (FAMEs) with H2O2. The HPW/Zn-Ti LDHs showed higher activity than bulk HPW and Zn-Ti LDHs. The conversion of FAMEs and the selectivity to epoxy fatty acid methyl ester (EFAME) were up to 90.6 and 86.0%, respectively. (PO4[WO2(O2)]4)3- was presumed to be the active site for the epoxidation in this work. The formed oxygen-donating intermediate Ti-OOH transferred oxygen free radicals to (PO4[WO2(O2)]4)3- in the catalytic process, accelerating the transfer of oxygen free radicals. Therefore, high catalytic activity of HPW/Zn-Ti LDHs is related to the potential synergistic interactions of Ti-OOH and PW anions.