Colorectal cancer (CRC) is one of the most common malignancies and morbidity and mortality are increasing rapidly. Increasing evidence showed the close correlation between aberrant expression of certain RNAs and the occurrence and development of CRC. However, comprehensive analyses of differentially expressed profiles of linRNA in CRC based on large sample size have been lacking. In the present study, based on RNA-seq data obtained from the TCGA (The Cancer Genome Atlas) database, we identified 1176 lncRNAs, 245 miRNAs and 2083 mRNAs whichaberrantly expressed in the colorectal cancer tissues compared with the adjacent non-tumorous tissues. A Kaplan-Meier curve analysis was used to study the overall survival rate of the three RNA-related CRC patients. After constructing the ceRNA network, we performed the KEGG enrichment pathway analysis on ceRNA-related differentially expressed mRNAs and found that these mRNAs were remarkably enriched in the pathways associated with CRC. Combining the differentially expressed lncRNAs with clinical pathological variables of CRC patients, we also found that LINC00400 and LINC00355 not only contribute to the regulation of ceRNA network, but also show significantchanges in its expression in multiple CRC pathological stages, indicating that LINC00400 and LINC00355 can be considered as promising therapeutic targets for CRC.

The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

Background: Similar to humans, the zebrafish brain plays a central role in regulating sexual reproduction, maturation and sexual behavior. However, systematic studies of the dimorphic patterns of gene expression in the brain of male and female zebrafish are lacking. Results: In this study, the mRNA and lncRNA expression profiles were obtained from the brain tissue samples of the three male and three female zebrafish by high-throughput transcriptome sequencing. We identified a total of 108 mRNAs and 50 lncRNAs with sex-based differential expression. We randomly selected four differentially expressed genes for RT-qPCR verification and the results certified that the expression pattern showed a similar trend between RNA-seq and RT-qPCR results. Protein-protein interaction network analysis, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to obtain the biological significance of differentially expressed mRNA in the brain dimorphism of zebrafish. Finally, a Pearson correlation analysis was performed to construct the co-expression network of the mRNAs and lncRNAs. Conclusions: We found that 12 new lncRNAs not only have significant gender specificity in the brain of zebrafish, and this finding may provide a clue to further study of the functional difference between male and female zebrafish brain.

The mechanism was proposed of heterogeneous catalytic CO ₂ absorptions with primary/secondary amines involving ‘catalytic carbamate formation’. Compared with the non-catalytic ‘Zwitterion mechanism’, this Eley–Rideal model was proposed for CO ₂ + RR′NH with MCO ₃ (M = Ca, Mg, and Ba) with four elementary reaction steps: (B1) amine adsorption, (B2) Zwitterion formation, (B3) carbamate formation, and (B4) carbamate desorption. The rate law if determining step of each elementary step was generated based on ‘steady-state approximation’. Furthermore, the solid chemicals were characterized by SEM and BET, and this rate model was verified with 39 sets of experimental datasets of catalytic CO ₂ –MEA absorptions with the existence of 0–25 g CaCO ₃ , MgCO ₃ and BaCO ₃ . The results indicated that the rate-determining step was B1 as amine adsorption onto solid surface, which was pseudo-first-order for MEA. This was the first time that the Eley–Rideal model had been adopted onto the reactions of CO ₂ + primary/secondary amines over alkaline earth metal carbonate (MCO ₃ ).

In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-supervised semantic segmentation. Most methods use manual labeling. In this paper, super-pixels with similar features are combined using the relationship between each pixel after super-pixel segmentation to form a plurality of super-pixel blocks. Rough predictions are generated by the fully convolutional networks (FCN) so that certain super-pixel blocks will be labeled. We perceive and find other positive areas in an iterative way through the marked areas. This reduces the feature extraction vector and reduces the data dimension due to super-pixels. The algorithm not only uses superpixel merging to narrow down the target’s range but also compensates for the lack of weakly-supervised semantic segmentation at the pixel level. In the training of the network, we use the method of region merging to improve the accuracy of contour recognition. Our extensive experiments demonstrated the effectiveness of the proposed method with the PASCAL VOC 2012 dataset. In particular, evaluation results show that the mean intersection over union (mIoU) score of our method reaches as high as 44.6%. Because the cavity convolution is in the pooled downsampling operation, it does not degrade the network’s receptive field, thereby ensuring the accuracy of image semantic segmentation. The findings of this work thus open the door to leveraging the dilated convolution to improve the recognition accuracy of small objects.