Identification of Epithelial Mesenchymal Transition-Related lncRNAs Associated with Prognosis and Tumor Immune Microenvironment of Hepatocellular Carcinoma

Purpose. The long noncoding RNAs (lncRNAs) play the important role in tumor occurrence and progression, and the epithelial to mesenchymal transition (EMT) is the critical process for tumor migration. However, the role of EMT-related lncRNA in hepatocellular carcinoma (HCC) has not been elucidated. Methods. In this study, we selected the EMT-related lncRNAs in HCC by using data from The Cancer Genome Atlas database (TCGA). Two prognostic models of the overall survival (OS) and relapse-free survival (RFS) were constructed and validated through Cox regression model, Kaplan-Meier analysis, and the receiver-operating characteristic (ROC) curves. The unsupervised clustering analysis was utilized to investigate the association between EMT-lncRNAs with tumor immune microenvironment. ESTIMATE algorithm and gene set enrichment analysis (GSEA) were used to estimate tumor microenvironment and associated KEGG pathways. Results. Two EMT-related lncRNA prognostic models of OS and RFS were constructed. Kaplan-Meier curves showed the dismal prognosis of OS and RFS in the group with high-risk score. The ROC curves and AUC values in two prognostic models indicated the discriminative value in the training set and validation set. Patients with HCC were clustered into two subgroups according the unsupervised clustering analysis. Lnc-CCNY-1 was selected as the key lncRNA. GSVA analysis showed that lnc-CCNY-1 was negatively associated with peroxisome proliferator-activated receptor (PPAR) signaling pathway and positively correlated with CELL cycle pathway. Conclusion. Two EMT-related lncRNA prognostic models of OS and RFS were constructed to discriminate patients and predict prognosis of HCC. EMT-related lncRNAs may play a role on prognosis of HCC by influencing the immune microenvironment. Lnc-CCNY-1 was selected as the key EMT-related lncRNA for further exploration.

Comparative transcriptome investigation of Nosema ceranae infecting eastern honeybee workers

Apis cerana is the original host for Nosema ceranae, a widespread fungal parasite resulting in bee nosemosis, which leads to severe losses for apiculture industry throughout the world. However, knowledge of N. ceranae infecting eastern honeybees is extremely limited. Currently, the mechanism underlying N. ceranae infection is still largely unknown. Based on our previously gained high-quality transcriptome datasets, comparative transcriptomic investigation was conducted in this work, with a focus on virulence factor-associated differentially expressed genes (DEGs). Microscopic observation showed that A. c. cerana workers midguts were effectively infected after inoculation with clean spores of N. ceranae. Totally, 1411, 604, and 38 DEGs were identified from NcCK vs. NcT1, NcCK vs. NcT2 and NcT1 vs. NcT2 comparison groups. Venn analysis showed that ten up-regulated genes and nine down-regulated ones were shared by aforementioned comparison groups. GO category indicated these DEGs were involved in a series of functional terms relevant to biological process, cellular component, and molecular function, such as metabolic process, cell part, and catalytic activity. Additionally, KEGG pathway analysis suggested that the DEGs were engaged in an array of pathways of great importance, such as metabolic pathway, glycolysis, and biosynthesis of secondary metabolites. Further, expression clustering analysis demonstrated that majority of genes encoding virulence factors such as ricin B lectins and polar tube proteins displayed apparent up-regulation, whereas a few virulence factor-associated genes such as hexokinase gene and 6-phosphofructokinase gene presented down-regulation during the fungal infection. Finally, the expression trend of 14 DEGs was confirmed by RT-qPCR, validating the reliability of our transcriptome datasets. These results together demonstrated that an overall alteration of the transcriptome of N. ceranae occurred during the infection of A. c. ceranae workers, and most of virulence factor-related genes were induced to activation to promote the fungal invasion. Our findings not only lay a foundation for clarifying the molecular mechanism underlying N. ceranae infection of eastern honeybee workers, but also shed light on developing novel targets for microsporidiosis control.

Fuzzy Cluster Analysis and Prediction of Psychiatric Health Data Based on BPNN

PMH (psychiatry/mental health) is affected by many factors, among which there are numerous connections, so the prediction of PMH is a nonlinear problem. In this paper, BPNN (Back Propagation Neural Network) is applied to fuzzy clustering analysis and prediction of PMH data, and the rules and characteristics of PMH and behavioral characteristics of people with mental disorders are analyzed, and various internal relations among psychological test data are mined, thus providing scientific basis for establishing and perfecting early prevention and intervention of mental disorders in colleges and universities. Artificial neural network is a mathematical model of information processing, which is composed of synapses similar to the structure of brain neurons. The fuzzy clustering analysis and data prediction ability of optimized PMH data are obviously improved. Applying BPNN to the fuzzy clustering analysis and prediction of PMH data, analyzing the rules and characteristics of PMH and the behavioral characteristics of patients with mental disorders, can explore various internal relations among psychological test data, and provide scientific basis for establishing early prevention and intervention of mental disorders.

Component-based Access Design Method for Dynamic Spatial Database

In order to realize the optimal access of dynamic spatial database, a component-based optimal access method of dynamic spatial database is proposed. The statistical information distribution model for storing the characteristic data of association rules is constructed in the dynamic spatial database. The fuzzy information features are extracted by using the dynamic component fusion clustering analysis method. Combined with the distributed association feature quantity, the fusion scheduling is carried out to control the dynamic information clustering. Combined with fuzzy c-means clustering analysis method, dynamic attribute classification analysis is carried out. The dynamic component block matching model is used for update iterative optimization, and the optimal access to the dynamic spatial database is realized in the cluster center. Simulation results show that this method has strong adaptability to the optimal access of dynamic spatial database, and has high accuracy and good convergence for data information extraction in dynamic spatial database.

The CAR mRNA-Interaction Surface is a Zipper Extension of the Ribosome A Site

The ribosome CAR interaction surface behaves like an extension of the decoding center A site and has H-bond interactions with the +1 codon that is next in line to enter the A site. Through molecular dynamics simulations, we investigated the codon sequence specificity of this CAR-mRNA interaction and discovered a strong preference for GCN codons, suggesting that there may be a sequence-dependent layer of translational regulation dependent on the CAR interaction surface. Dissection of the CAR-mRNA interaction through nucleotide substitution experiments showed that the first nucleotide of the +1 codon dominates over the second nucleotide position, consistent with an energetically favorable zipper-like activity that emanates from the A site through the CAR-mRNA interface. The +1 codon/CAR interaction is also affected by the identity of nucleotide 3 of +1 GCN codons which influences the stacking of G and C. Clustering analysis suggests that the A site decoding center adopts different neighborhood substates that depend on the identity of the +1 codon.

COPRAS ve MAIRCA Yöntemleriyle Çorum İlinde Kurulabilecek Kompost Tesislerinin Optimallik Sıralaması

The compost product, which is a biologically active substance, emerges as a result of microbial decomposition of organic materials under controlled conditions. This product, which is used for the improvement of soil structure and the development of agricultural products, also offers opportunities in terms of minimizing the damage caused by organic wastes to the environment. It is important to encourage efforts for compost production, especially in terms of both disposal and economic evaluation of wastes generated in animal production farms. Determining the most suitable location of a facility for the utilization of animal wastes as compost, which will be obtained from livestock enterprises scattered in different geographical areas, will be an essential study in terms of minimizing operating costs. For such a facility, it would be an appropriate approach to use multi-criteria decision making methods to choose among predetermined facility location alternatives. In this study, a total of 17 facility location alternatives with 83,163 cattle potential in Çorum province were ranked according to the criteria determined and weighted by means of SWARA method. The optimal ranking of 17 alternatives determined by K-Means clustering analysis was carried out by COPRAS and MAIRCA methods. According to the ranking results obtained from both methods, it was determined that cluster number 6 was in the first rank, cluster number 4 was in the second rank, and cluster number 3 was in the third rank.