Abstract
The aim of this study was to evaluate the effect of a lifestyle intervention programme (nutrition and exercise counselling) on metabolic syndrome (MS) components, adipokines (leptin, adiponectin) and ghrelin levels in overweight children. A total of 61 overweight children aged 7-9 years (>= 85th body mass index (BMI) percentile; 27 boys/34 girls) were randomly assigned and completed a 1-year individual (IT) or group-based treatment (GT). Anthropometric and biochemical parameters were assessed at baseline, at 6 months and at 1 year. Twenty-two normal weight children (< 85th BMI percentile; 7-9 years old; 13 boys/nine girls) were also evaluated at baseline. Insulin resistance (IR) was determined by the homeostasis model assessment of IR (HOMA-IR). Overweight children presented significantly higher blood pressure, triglycerides, apolipoprotein B, insulin, HOMA-IR, leptin, C-reactive protein and homocysteine levels, while apolipoprotein A-I was significantly lower. At baseline, MS was present in ten overweight children, of which only five maintained it at 1 year. Leptin and ghrelin levels were associated with IR and MS components. MS was predicted by apolipoprotein A-I, insulin and pre-puberty. The lifestyle intervention led to a significant improvement in standard deviation score of BMI, waist circumference/height ratio and lipid profile. Changes in insulin, HOMA-IR, leptin and adiponectin were not significant. Ghrelin behaved differently between IT and GT. The GT intervention seems to be more successful, with a decrease in BMI Z-score and an improvement of metabolic parameters. In conclusion, overweight children have multiple risk factors associated with MS. A lifestyle intervention programme seems to be an effective mean for reducing obesity and MS components and improving adipokines concentrations.

Abstract
Excess weight may be related to the development of adverse cardiometabolic risk factors in children. The aim of this study was to evaluate the effect of a lifestyle intervention program (nutrition and exercise counseling) on anthropometric parameters and metabolic syndrome (MS) components in Portuguese overweight/obese children. A total of 83 overweight/obese children aged 7-9 years were assigned to a 1-year individual or group-based treatment (GT); 61 children (z-score BMI (zBMI): 1.93 +/- A 0.28; 27 boys and 34 girls) completed the program. Anthropometric and biochemical parameters were assessed at baseline, at 6 months and at 1 year. The overweight/obese children, compared to normal-weight ones, presented significantly higher blood pressure, total-cholesterol, total-cholesterol/high density lipoprotein cholesterol (HDL) ratio, triglycerides, Apolipoprotein B and C-reactive protein levels, while HDL and Apolipoprotein A-I were significantly lower. At baseline, the prevalence of MS was 16.4% in overweight/obese and 0% in normal-weight children. The number of components of MS was significantly higher in children with higher zBMI. Lifestyle intervention led to a significant improvement in zBMI, waist circumference/height ratio, HDL, triglycerides, Apolipoprotein A-I, and Apolipoprotein B levels. The prevalence of MS decreased to 14.8%. The GT intervention seems to be more successful, with a significant decrease in zBMI and an increase in HDL and a lower drop-out rate. Overweight/obese children have multiple risk factors associated with the MS. Lifestyle intervention, both individual and group-based treatment, led to an improvement in the degree of overweight/obesity and in MS components.

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In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose a genetic approach based on a random key alphabet and present several algorithms based on this approach. These versions differ on the generation of both the initial population and the individuals added in the migration step, as well as on the use of local search. The proposed procedures are compared with the best existing heuristics, as well as with optimal solutions for the smaller instance sizes. The computational results show that the proposed algorithms clearly outperform the existing procedures and are quite close to the optimum. The improvement over the existing heuristics increases with both the difficulty and the size of the instances. The performance of the proposed genetic approach is improved by the initialization of the initial population, the generation of greedy randomized solutions, and the addition of the local search procedure. Indeed, the more sophisticated versions can obtain similar or better solutions and are much faster. The genetic version that incorporates all the considered features is the new heuristic of choice for small and medium size instances.