A Study on Orbital Volume of Korean People in Their 20s or 40s

Aims: To measure the orbital volume of normal Korean people in two different age groups (subjects were in their 20s or 40s), and analyze the differences of orbital volume with respect to age and gender. In addition, to analyze correlation between body parameters (height and weight) and the orbital volume. Methods: Magnetic resonance imaging (MRI) data were acquired for a total of 143 subjects, consisting of 71 subjects in their 20s (32 males and 39 females) and 72 subjects in their 40s (30 males and 42 females). Two-way ANOVA was used to analyze how orbital volume changes with respect to gender and age. A multiple regression analysis was performed to investigate the correlation between body parameters and the orbital volume. Results: The orbital volume of subjects in their 20s was larger than that of subjects in their 405, and the volume was larger in men than in women. As age increased, the decrease in the orbital volume of women was greater than that of men. While weight and height showed positive correlations with orbital volume in male and female subjects in their 20s, respectively, weight showed a positive correlation with orbital volume in male and female subjects in their 40s. Conclusions: These results provide basic information about the effect of age, gender, and body parameters on orbital volume of Korean people in their 20s or 40s.open

Genealogies: Pedigrees and Phylogenies are Reticulating Networks Not Just Divergent Trees

Pedigrees illustrate the genealogical relationships among individuals, and phylogenies do the same for groups of organisms (such as species, genera, etc.). Here, I provide a brief survey of current concepts and methods for calculating and displaying genealogical relationships. These relationships have long been recognized to be reticulating, rather than strictly divergent, and so both pedigrees and phylogenies are correctly treated as networks rather than trees. However, currently most pedigrees are instead presented as “family trees”, and most phylogenies are presented as phylogenetic trees. Nevertheless, the historical development of concepts shows that networks pre-dated trees in most fields of biology, including the study of pedigrees, biology theory, and biology practice, as well as in historical linguistics in the social sciences. Trees were actually introduced in order to provide a simpler conceptual model for historical relationships, since trees are a specific type of simple network. Computationally, trees and networks are a part of graph theory, consisting of nodes connected by edges. In this mathematical context they differ solely in the absence or presence of reticulation nodes, respectively. There are two types of graphs that can be called phylogenetic networks: (1) rooted evolutionary networks, and (2) unrooted affinity networks. There are quite a few computational methods for unrooted networks, which have two main roles in phylogenetics: (a) they act as a generic form of multivariate data display; and (b) they are used specifically to represent haplotype networks. Evolutionary networks are more difficult to infer and analyse, as there is no mathematical algorithm for reconstructing unique historical events. There is thus currently no coherent analytical framework for computing such networks.</p