Statistical methods for identification of golden ratio

Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure

Background

Analyzing disease–disease relationships plays an important role for understanding etiology, disease classification, and drug repositioning. However, as cardiovascular diseases with causative links, the molecular relationship among stable angina pectoris (SAP), ischemic cardiomyopathy (ICM) and chronic heart failure (CHF) is not clear.

Methods

In this study, by integrating the multi-database data, we constructed paired disease progression modules (PDPMs) to identified relationship among SAP, ICM and CHF based on module reconstruction pairs (MRPs) of K-value calculation (a Euclidean distance optimization by integrating module topology parameters and their weights) methods. Finally, enrichment analysis, literature validation and structural variation (SV) were performed to verify the relationship between the three diseases in PDPMs.

Results

Total 16 PDPMs were found with K > 0.3777 among SAP, ICM and CHF, in which 6 pairs in SAP–ICM, 5 pairs for both ICM–CHF and SAP–CHF. SAP–ICM was the most closely related by having the smallest average K-value (K = 0.3899) while the maximum is SAP–CHF (K = 0.4006). According to the function of the validation gene, inflammatory response were through each stage of SAP–ICM–CHF, while SAP–ICM was uniquely involved in fibrosis, and genes were related in affecting the upstream of PI3K–Akt signaling pathway. 4 of the 11 genes (FLT1, KDR, ANGPT2 and PGF) in SAP–ICM–CHF related to angiogenesis in HIF-1 signaling pathway. Furthermore, we identified 62.96% SVs were protein deletion in SAP–ICM–CHF, and 53.85% SVs were defined as protein replication in SAP–ICM, while ICM–CHF genes were mainly affected by protein deletion.

Conclusion

The PDPMs analysis approach combined with genomic structural variation provides a new avenue for determining target associations contributing to disease progression and reveals that inflammation and angiogenesis may be important links among SAP, ICM and CHF progression.

An Analysis of Characteristics of Children's Growth through Practical Art

Children's paintings reflect their growth environments and psychological conditions, and these growth environments represent children's family and educational environments in primary schools. The characteristics of these paintings change as children grow, and children's expressiveness in the paintings also improves. Children's paintings are a representation of their perceptions of things; children transform their perceptions into images that can be understood and observed by people. This research studies the growth characteristics of children based on professional painting techniques. A digital image analysis method was used to analyze the painting techniques of children aged between 7 and 13. The growth characteristics of the different age groups were combined to analyze the representative characteristics of children's paintings at different ages. Lastly, the results of part of a questionnaire survey were used to assist in studying these characteristics. Analysis of these paintings shows that children have a poor ability to control the scale of the objects. Furthermore, the details of the objects are ignored, and children have a poor imitation ability. Young children have lower spatial cognitive abilities than older children, and girls prefer to participate in painting more than boys.