A novel SNP in COMT is associated with alcohol dependence but not opiate or nicotine dependence: a case control study

Background It is well established that COMT is a strong candidate gene for substance use disorder and schizophrenia. Recently we identified two SNPs in COMT (rs4680 and rs165774) that are associated with schizophrenia in an Australian cohort. Individuals with schizophrenia were more than twice as likely to carry the GG genotype compared to the AA genotype for both the rs165774 and rs4680 SNPs. Association of both rs4680 and rs165774 with substance dependence, a common comorbidity of schizophrenia has not been investigated. Methods To determine whether COMT is important in substance dependence, rs165774 and rs4680 were genotyped and haplotyped in patients with nicotine, alcohol and opiate dependence. Results The rs165774 SNP was associated with alcohol dependence. However, it was not associated with nicotine or opiate dependence. Individuals with alcohol dependence were more than twice as likely to carry the GG or AG genotypes compared to the AA genotype, indicating a dominant mode of inheritance. The rs4680 SNP showed a weak association with alcohol dependence at the allele level that did not reach significance at the genotype level but it was not associated with nicotine or opiate dependence. Analysis of rs165774/rs4680 haplotypes also revealed association with alcohol dependence with the G/G haplotype being almost 1.5 times more common in alcohol-dependent cases. Conclusions Our study provides further support for the importance of the COMT in alcohol dependence in addition to schizophrenia. It is possible that the rs165774 SNP, in combination with rs4680, results in a common molecular variant of COMT that contributes to schizophrenia and alcohol dependence susceptibility. This is potentially important for future studies of comorbidity. As our participant numbers are limited our observations should be viewed with caution until they are independently replicated.

Risk of metabolic syndrome among children living in metropolitan Kuala Lumpur: A case control study

Background With the increasing prevalence of childhood obesity, the metabolic syndrome has been studied among children in many countries but not in Malaysia. Hence, this study aimed to compare metabolic risk factors between overweight/obese and normal weight children and to determine the influence of gender and ethnicity on the metabolic syndrome among school children aged 9-12 years in Kuala Lumpur and its metropolitan suburbs. Methods A case control study was conducted among 402 children, comprising 193 normal-weight and 209 overweight/obese. Weight, height, waist circumference (WC) and body composition were measured, and WHO (2007) growth reference was used to categorise children into the two weight groups. Blood pressure (BP) was taken, and blood was drawn after an overnight fast to determine fasting blood glucose (FBG) and full lipid profile, including triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC). International Diabetes Federation (2007) criteria for children were used to identify metabolic syndrome. Results Participants comprised 60.9% (n = 245) Malay, 30.9% (n = 124) Chinese and 8.2% (n = 33) Indian. Overweight/obese children showed significantly poorer biochemical profile, higher body fat percentage and anthropometric characteristics compared to the normal-weight group. Among the metabolic risk factors, WC ≥90th percentile was found to have the highest odds (OR = 189.0; 95%CI 70.8, 504.8), followed by HDL-C≤1.03 mmol/L (OR = 5.0; 95%CI 2.4, 11.1) and high BP (OR = 4.2; 95%CI 1.3, 18.7). Metabolic syndrome was found in 5.3% of the overweight/obese children but none of the normal-weight children (p < 0.01). Overweight/obese children had higher odds (OR = 16.3; 95%CI 2.2, 461.1) of developing the metabolic syndrome compared to normal-weight children. Binary logistic regression showed no significant association between age, gender and family history of communicable diseases with the metabolic syndrome. However, for ethnicity, Indians were found to have higher odds (OR = 5.5; 95%CI 1.5, 20.5) compared to Malays, with Chinese children (OR = 0.3; 95%CI 0.0, 2.7) having the lowest odds. Conclusions We conclude that being overweight or obese poses a greater risk of developing the metabolic syndrome among children. Indian ethnicity is at higher risk compared to their counterparts of the same age. Hence, primary intervention strategies are required to prevent this problem from escalating.

Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Genetic algorithms to schedule container transfers at multimodal terminals

Optimising the container transfer schedule at the multimodal terminals is known to be NP-hard, which implies that the best solution becomes computationally infeasible as problem sizes increase. Genetic Algorithm (GA) techniques are used to reduce container handling/transfer times and ships' time at the port by speeding up handling operations. The GA is chosen due to the relatively good results that have been reported even with the simplest GA implementations to obtain near-optimal solutions in reasonable time. Also discussed, is the application of the model to assess the consequences of increased scheduled throughput time as well as different strategies such as the alternative plant layouts, storage policies and number of yard machines. A real data set used for the solution and subsequent sensitivity analysis is applied to the alternative plant layouts, storage policies and number of yard machines.