No abstract

As a number of software evolution process models increased, as modeled by EPMM (software evolution process meta model), verifying the correctness of these models becomes the important. This paper extends EPMM with ACP (algebra of communicating processes) and proposes EPMM-A (software evolution meta modelalgebra). In order to discuss behavior verification in the unified framework of EPMM-A, a process term is used to define an algebraic semantics of a software evolution process model. Based on equational reasoning of EPMM-A, behavior verification of a software evolution process model emphasizes algebraic reasoning as opposed to modelbased reasoning. This method combines the advantages of both Petri nets and ACP, which can effectively support software evolution process formal verification.

Software process play an important role in improving the quality of software products. Formal software process model is conducive to the accurate description of software processes. A software process meta-model is a formal tool used to define software process models. In this paper, a software process metamodel SPMM is designed. Firstly, five important properties in software processes are discussed. Furthermore, based on CSP and Hoare Logic, the definition of SPMM and the rules to build software process models are proposed. In SPMM, not only are the important components, e.g. tasks, activities and software processes in software processes, formally defined, but also the five important properties of software process are embodied. Therefore, SPMM can model software processes at different abstract levels. Thus, the basis to simulate, control, analyze, measure and improve software processes is established.

Background: The presence of breast cancer in the brain, also known as brain metastasis (BMS), is the primary reason for a bad prognosis in cases of breast cancer. Breast cancer is the most prevalent malignant tumor seen in women in developing nations. At present, there is no effective method to inhibit brain metastasis of breast cancer. Therefore, it is necessary to conduct a systematic study on BMS of breast cancer, which will not provide ideas and sites for follow-up studies on the treatment and inhibition of BMS.Methods: In this study, data set GSE43837 was screened from gene expression omnibus database, and then R language tool was used for differential analysis of its expression spectrum, The gene ontology functional enrichment and Kyoto encyclopedia of genes and genomes signal pathway enrichment analyses, as well as the interactive gene retrieval tool for hub-gene analysis, were performed.Results: According to the findings, the primary genes linked to breast cancer brain metastases are those that involve interactions between cytokines and their respective receptors and between neuroactive ligands and their respective receptors. The majority of the gene ontology enrichment took place in the extracellular structural tissues, the extracellular matrix tissues, and the second message-mediated signaling. We were able to identify 8 genes that are linked to breast cancer spreading to the brain. The gene score for matrix metallopeptidase1 (MMP-1) was the highest among them, and the genes MMP10, tumor necrosis factor alphainducible protein 8, collagen type I alpha 2 chain, vascular cell adhesion molecule 1, and TNF superfamily member 11 were all connected to 1 another in an interaction way.Conclusions: There is a possibility that the 8 key genes that were identified in this research are connected to the progression of BMS in breast cancer. Among them, MMP1 is 1 that has the potential to have a role in the diagnosis and treatment of BMS in breast cancer.Abbreviations: BBB = blood-brain barrier, BMS = brain metastasis, CC = cellular component, COL1A2 = collagen type I alpha 2 chain, GEO = gene expression omnibus, GO = gene ontology, KEGG = Kyoto encyclopedia of genes and genomes, MMP = matrix metallopeptidase, PPI = protein-protein interaction, PLCG2 = phospholipase C gamma 2, STRING = interactive gene retrieval tool, VCAM1 = vascular cell adhesion molecule 1.

No abstract