The French Conseil D\u27 Etat: A Case Study in Boundary Maintenance

Very little is known about the role that courts play in the total political system of a nation. In two recent works Professors Walter Murphy and Joseph Tanenhaus have centered attention on this question and have isolated some of the major functions of courts and developed several working hypotheses concerning these functions. They suggest that one of the major functions of constitutional courts consists of defining the rules of the political game and determining the boundaries of authority between competing public officials as well as the boundaries between governmental authority and individual liberty. In approving or disapproving the acts of governmental bodies, the court performs the additional function of legitimizer

Genes, age, and alcoholism: analysis of GAW14 data

A genetic analysis of age of onset of alcoholism was performed on the Collaborative Study on the Genetics of Alcoholism data released for Genetic Analysis Workshop 14. Our study illustrates an application of the log-normal age of onset model in our software Genetic Epidemiology Models (GEMs). The phenotype ALDX1 of alcoholism was studied. The analysis strategy was to first find the markers of the Affymetrix SNP dataset with significant association with age of onset, and then to perform linkage analysis on them. ALDX1 revealed strong evidence of linkage for marker tsc0041591 on chromosome 2 and suggestive linkage for marker tsc0894042 on chromosome 3. The largest separation in mean ages of onset of ALDX1 was 19.76 and 24.41 between male smokers who are carriers of the risk allele of tsc0041591 and the non-carriers, respectively. Hence, male smokers who are carriers of marker tsc0041591 on chromosome 2 have an average onset of ALDX1 almost 5 years earlier than non-carriers

A study of genetic association with electrophysiological measures related to alcoholism: GAW14 data

Recently, alcohol-related traits have been shown to have a genetic component. Here, we study the association of specific genetic measures in one of the three sets of electrophysiological measures in families with alcoholism distributed as part of the Genetic Analysis Workshop 14 data, the NTTH (non-target case of Visual Oddball experiment for 4 electrode placements) phenotypes: ntth1, ntth2, ntth3, and ntth4. We focused on the analysis of the 786 Affymetrix markers on chromosome 4. Our desire was to find at least a partial answer to the question of whether ntth1, ntth2, ntth3, and ntth4 are separately or jointly genetically controlled, so we studied the principal components that explain most of the covariation of the four quantitative traits. The first principal component, which explains 70% of the covariation, showed association but not genetic linkage to two markers: tsc0272102 and tsc0560854. On the other hand, ntth1 appeared to be the trait driving the variation in the second principal component, which showed association and genetic linkage at markers in four regions: tsc0045058, tsc1213381, tsc0055068, and tsc0051777 at map distances 53.26, 85.42, 89.31, and 172.86, respectively. These results show that the partial answer to our starting question for this brief analysis is that the NTTH phenotypes are not jointly genetically controlled. The component ntth1 displays marked genetic linkage

Interrelationships Among Changes in Leptin, Insulin, Cortisol and Growth Hormone and Weight Status in Youth

Objective: While acute alterations in leptin, insulin, cortisol and growth hormone (GH) levels have been reported in children following weight change interventions, little is known about natural hormonal changes as children grow and how these changes are affected b

The Relationship between Changes in Weight Status and Insulin Resistance in Youth

This study examined changes in insulin resistance (IR) in 120 youth over two years. IR was quantified via homeostatic model (HOMA-IR), and weight status changes were quantified via body mass index (BMI). When all participants were considered, the mean HOMA-IR and BMI increased 13.4% and 1.65 units, respectively. Change in BMI z-score and percent change in HOMA-IR were moderately associated (r = 0.39). Follow-up analyses were performed for the following weight groups: NN (normal at baseline and two years later), NO (normal to overweight), ON (overweight to normal), and OO (overweight at both points). The NO group had a greater change in HOMA-IR (+50%) compared to other groups: ON (−8%), NN (+2%), and OO (−0.1%) (P < .05). The association between changes in BMI z-score and HOMA-IR was r = 0.49 when only the NO and ON groups were included. These results reinforce the importance of preventing youth from becoming overweight to control IR