In this paper we characterize the convex dominating sets in the composition and Cartesian product of two connected graphs. The concepts of clique dominating set and clique domination number of a graph are defined. It is shown that the convex domination number of a composition $G[H]$ of two non-complete connected graphs $G$ and $H$ is equal to the clique domination number of $G$. The convex domination number of the Cartesian product of two connected graphs is related to the convex domination numbers of the graphs involved.
Along with derivation, composition represents the second most important word-formative process in Czech, primarily with certain names (such as professional terms). The paper deals with two specific word-formative types of deverbative names of persons, traditionally referred to as nouns of agents (nomina agentis) -compounds with suffixes -tel and -č. These compound names, excerpted from the Czech National Corpus (SYN2010) and confronted with Czech dictionaries (including neologisms), are compared with parallel derived-names, namely in terms of onomasiological and semantic functions of their constituent parts. Their systemic and empirical (textual) productivity (based on corpora) is further considered. Presented analysis is a part of larger research of Czech compounds conducted currently by the author.
Several systems supporting development and application of graphical
Markov inodels are widely used; perliaps the most famous are HUGIN and NETICA, which are supporting Bayesian networks. The goal of this paper is to introduce system MUDIM, which is intended to support non-graphical multidimensional models, namely cornpositional models. The basic idea of these inodels resembles jig-saw puzzle, where a picture must be assembled from a great number of pieces, each bearing a small part of a picture. Analogously, compositional models of a multidimensional distribution are assenililed (composed) of a great number of low-dirnensional distributions.
One of the advantages of this approach is that the same apparatus that is based on operators of composition, can be applied for description of both probabilistic and possibilistic models. This is also the goal for future MUDIM development, to extend it in the way that it will be able to process both probabilistic and possibilistic models.
Free fatty acids (FFAs) are natural ligands of the PPARγ2 receptor. FFA plasma concentration and composition may represent one of the factors accounting for high heterogeneity of conclusions concerning the effect of the Pro12Ala on BMI, insulin sensitivity or diabetes type 2 (DM2) susceptibility. Our objective was to investigate the relation and possible interactions between the Pro12Ala polymorphism and FFA status, metabolic markers, and body composition in 324 lean nondiabetic subjects (M/F: 99/225; age 32±11 years; BMI 23.9±4.0 kg/m2) with and without family history of DM2. Family history of DM2 was associated with lower % PUFA and slightly higher % MUFA. The presence of Pro12Ala polymorphism was not associated with fasting plasma FFA concentration or composition, anthropometric or metabolic markers of glucose and lipid metabolism in tested population. However, the interaction of carriership status with FFA levels influenced the basal glucose levels, insulin sensitivity and disposition indices, triglycerides, HDL-cholesterol and leptin levels, especially in women. The metabolic effects of 12Ala carriership were influenced by FFA levels – the beneficial role of 12Ala was seen only in the presence of low concentration of plasma FFA. Surprisingly, a high PUFA/SFA ratio was associated with lower insulin sensitivity, the protective effect of 12Ala allele was apparent in subjects with family history of DM2. On the basis of our findings and published data we recommend the genotyping of diabetic patients for Pro12Ala polymorphism of the PPARγ2 gene before treatment with thiazolidinediones and education of subjects regarding diet and physical activity, which modulate metabolic outcomes., B. Bendlová, D. Vejražková, J. Včelák, P. Lukášová, D. Burkoňová, M. Kunešová, J. Vrbíková, K. Dvořáková, K. Vondra, M. Vaňková., and Obsahuje bibliografii a bibliografické odkazy