Evidence for an oncogenic role of HOXC6 in human non-small cell lung cancer

Background Identification of specific biomarkers is important for the diagnosis and treatment of non-small cell lung cancer (NSCLC). HOXC6 is a homeodomain-containing transcription factor that is highly expressed in several human cancers; however, its role in NSCLC remains unknown. Methods The expression and protein levels of HOXC6 were assessed in NSCLC tissue samples by Quantitative real-time PCR (qRT-PCR) and immunohistochemistry, respectively. HOXC6 was transfected into the NSCLC cell lines A549 and PC9, and used to investigate its effect on proliferation, migration, and invasion using CFSE, wound healing, and Matrigel invasion assays. Next-generation sequencing was also used to identify downstream targets of HOXC6 and to gain insights into the molecular mechanisms underlying its biological function. Results HOXC6 expression was significantly increased in 66.6% (20/30) of NSCLC tumor samples in comparison to normal controls. HOXC6 promoted proliferation, migration, and invasion of NSCLC cells in vitro. RNA-seq analysis demonstrated the upregulation of 310 and 112 genes in A549-HOXC6 and PC9-HOXC6 cells, respectively, and the downregulation of 665 and 385 genes in A549-HOXC6 and PC9-HOXC6 cells, respectively. HOXC6 was also found to regulate the expression of genes such as CEACAM6, SPARC, WNT6, CST1, MMP2, and KRT13, which have documented pro-tumorigenic functions. Discussion HOXC6 is highly expressed in NSCLC, and it may enhance lung cancer progression by regulating the expression of pro-tumorigenic genes involved in proliferation, migration, and invasion. Our study highlighted the oncogenic potential of HOXC6, and suggests that it may be a novel biomarker for the diagnosis and treatment of NSCLC

Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer.Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential.Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups.Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer

Fungi and cercozoa regulate methane-associated prokaryotes in wetland methane emissions

Wetlands are natural sources of methane (CH4) emissions, providing the largest contribution to the atmospheric CH4 pool. Changes in the ecohydrological environment of coastal salt marshes, especially the surface inundation level, cause instability in the CH4 emission levels of coastal ecosystems. Although soil methane-associated microorganisms play key roles in both CH4 generation and metabolism, how other microorganisms regulate methane emission and their responses to inundation has not been investigated. Here, we studied the responses of prokaryotic, fungal and cercozoan communities following 5 years of inundation treatments in a wetland experimental site, and molecular ecological networks analysis (MENs) was constructed to characterize the interdomain relationship. The result showed that the degree of inundation significantly altered the CH4 emissions, and the abundance of the pmoA gene for methanotrophs shifted more significantly than the mcrA gene for methanogens, and they both showed significant positive correlations to methane flux. Additionally, we found inundation significantly altered the diversity of the prokaryotic and fungal communities, as well as the composition of key species in interactions within prokaryotic, fungal, and cercozoan communities. Mantel tests indicated that the structure of the three communities showed significant correlations to methane emissions (p < 0.05), suggesting that all three microbial communities directly or indirectly contributed to the methane emissions of this ecosystem. Correspondingly, the interdomain networks among microbial communities revealed that methane-associated prokaryotic and cercozoan OTUs were all keystone taxa. Methane-associated OTUs were more likely to interact in pairs and correlated negatively with the fungal and cercozoan communities. In addition, the modules significantly positively correlated with methane flux were affected by environmental stress (i.e., pH) and soil nutrients (i.e., total nitrogen, total phosphorus and organic matter), suggesting that these factors tend to positively regulate methane flux by regulating microbial relationships under inundation. Our findings demonstrated that the inundation altered microbial communities in coastal wetlands, and the fungal and cercozoan communities played vital roles in regulating methane emission through microbial interactions with the methane-associated community

Optimization and Mechanical Properties of Fabricated 2D Wood Pyramid Lattice Sandwich Structure

In order to obtain a lightweight, high strength, and large design space wooden sandwich structure to meet the needs of modern wooden buildings, the mechanical properties of a fabricated 2D wooden pyramid lattice sandwich structure were studied. In this paper, the mechanical and compressive properties of the specimens with different arrangement of Lattice Sandwich unit cells are studied. The upper and lower panels and core materials are made into a single unit cell by inserting glue, and the prefabricated 2D wooden pyramid lattice truss core sandwich structure is prepared by the mortise tenon splicing method. The results show that the arrangement of the unit cells in the specimen has a significant effect on the bearing capacity, energy absorption, and failure mode of the specimen, and the flat compression performance of the panel-reinforced specimen is better than that of the specimen with unreinforced veneer. The results of finite element analysis are consistent with the test results. The main failure modes are core fracture and panel cracking. These results provide a theoretical basis for the system design of wood-based lattice sandwich structure in the future

Is Urbanisation Rate a Feasible Supplemental Parameter in Forecasting Electricity Consumption in China?

Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh

A Real-Time FPGA Implementation of Infrared and Visible Image Fusion Using Guided Filter and Saliency Detection

Taking advantage of the functional complementarity between infrared and visible light sensors imaging, pixel-level real-time image fusion based on infrared and visible light images of different resolutions is a promising strategy for visual enhancement, which has demonstrated tremendous potential for autonomous driving, military reconnaissance, video surveillance, etc. Great progress has been made in this field in recent years, but the fusion speed and quality of visual enhancement are still not satisfactory. Herein, we propose a multi-scale FPGA-based image fusion technology with substantially enhanced visual enhancement capability and fusion speed. Specifically, the source images are first decomposed into three distinct layers using guided filter and saliency detection, which are the detail layer, saliency layer and background layer. Fusion weight map of the saliency layer is subsequently constructed using attention mechanism. Afterwards weight fusion strategy is used for saliency layer fusion and detail layer fusion, while weight average fusion strategy is used for the background layer fusion, followed by the incorporation of image enhancement technology to improve the fused image contrast. Finally, high-level synthesis tool is used to design the hardware circuit. The method in the present study is thoroughly tested on XCZU15EG board, which could not only effectively improve the image enhancement capability in glare and smoke environments, but also achieve fast real-time image fusion with 55FPS for infrared and visible images with a resolution of 640 × 470

Identification of inflammatory factor-related genes associated with the prognosis and immune cell infiltration in colorectal cancer patients

This study aims to identify the inflammatory factor-related genes which help to predict the prognosis of patients with colorectal cancer. GSEA (Gene Set Enrichment Analysis) was used to acquire inflammation-related genes and the corresponding expression information was collected from TCGA database to determine the DEGs (differentially-expressed genes) in CRC patients. We conducted enrichment analysis and PPI (protein–protein interaction) of these DEGs. Besides, key genes that are both differentially-expressed and prognosis-related were screened out, which were used to establish the prognostic model. We obtained 79 DEGs and 19 prognostic genes, 10 prognostic-related differential genes were eventually screened. These genes were used to construct the prognostic model. We also identified that the immune infiltration score of macrophages between different risk groups was significantly different and similar distinction was witnessed in immune function score of APC (antigen-presenting cell) co-stimulation and type I IFN (interferon) response