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IntroductionAs a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass, impairment of bone microstructure, and a high global morbidity rate. There is increasing evidence that microRNAs (miRNAs) are associated with the pathogenesis of OP. Weighted gene co-expression network analysis (WGCNA) is a systematic method for identifying clinically relevant genes involved in disease pathogenesis. However, the study of the miRNA–messenger RNA (mRNA) regulatory network in combination with WGCNA in OP is still lacking.MethodsThe GSE93883 and GSE7158 microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs) and differentially expressed genes (DEGs) were analyzed with the limma package. OP-related miRNAs from the most clinically relevant module were identified by the WGCNA method. The overlap of DE-miRNAs and OP-related miRNAs was identified as OP-related DE-miRNAs. Both upstream transcription factors and downstream targets of OP-related DE-miRNAs were predicted by FunRich. An intersection of predicted target genes and DEGs was confirmed as downstream target genes of OP-related DE-miRNAs. With the use of clusterProfiler in R, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed on target genes. Finally, both the protein–protein interaction (PPI) network and miRNA–mRNA network were constructed and analyzed.ResultsA total of 79 OP-related DE-miRNAs were obtained, most of which were predicted to be regulated by specificity protein 1 (SP1). Subsequently, 197 downstream target genes were screened out. The target genes were enriched in multiple pathways, including signaling pathways closely related to the onset of OP, such as Ras, PI3K-Akt, and ErbB signaling pathways. Through the construction of the OP-related miRNA–mRNA regulatory network, a hub network that may play a prominent role in the formation of OP was documented.ConclusionBy using WGCNA, we constructed a potential OP-related miRNA–mRNA regulatory network, offering a novel perspective on miRNA regulatory mechanisms in OP.

Pattern recognition, especially fuzzy pattern recognition, has been a study hotspot and important application of the artificial intelligence and machine learning. To solve the shortages of the existing fuzzy pattern recognition, this paper puts forward a new method, which is based on extension theory. It firstly takes advantage of the basal concepts, such as matter-element, dependent function in extension theory, to set up an extension-based fuzzy pattern recognition model, and then uses an emulational instance-the voice recognition to testify it. It is proved that this new method can not only conquer the shortages of the existing fuzzy pattern recognition methods, but also have more efficiency and applications.

Load balancing is a classical problem and a research hotspot of Web intelligence. DNS load balancing is the pioneer load balancing technology. But the existing DNS dynamic load balancing strategies have shortages. This paper puts forward a new load balancing method, which is to connect extension theory with load balancing. Extension engineering method is initially proposed by Prof. Cai Wen. It has been successfully used in various applications. In this paper, we use the operation of matter-element theory, extension set, and dependent function in extension theory as well as the membership degree of fuzzy math to set up an extension-based dynamic load balancing model of heterogeneous server cluster. It is proved by experiment that the load balancing strategy is more effective, dynamic, steady-going and in real time by using this new model.