Hypoadiponectinemia is associated with increased insulin resistance, dyslipidemia and presence of type 2 diabetes in non obese central Indian population

Background: Accumulating evidence suggests that adiponectin, a major adipocyte secretory protein, has insulin-sensitizing and anti-atherogenic properties and protects against later development of type 2 diabetes. We investigated the association of adiponectin with insulin resistance, blood lipids and type 2 diabetes in non obese central Indian population.Methods: Anthropometric and biochemical parameters were measured in 149 (81 male and 68 female) newly diagnosed non obese type 2 diabetic patients and 157 (85 male and 72 female) age and body mass index (BMI) matched controls.Results: Adiponectin level (p<0.0001) was significantly lower in the diabetic group than in non diabetic control. In an age, gender and BMI adjusted model, adiponectin level was significantly negatively correlated with waist circumference, waist to hip ratio, systolic blood pressure, fasting insulin, homeostasis model assessment-insulin resistance (HOMA-IR) (p= 0.0034), HbA1C, total cholesterol, LDL-cholesterol, and triglycerides (p<0.0001) and positively correlated with HDL-cholesterol (p =0.0014) in non obese type 2 diabetic group. However, there was no significant correlation between adiponectin and glucose in this study. In stepwise linear regression analysis, adjusted for potential confounder, significant inverse association was observed between serum adiponectin level and HOMA-IR (p = 0.0001). In multivariate logistic regression model, adjusted for age, gender, BMI, waist circumference, and waist-hip ratio, lower adiponectin was independently associated with the presence of type 2 diabetes (p<0.0001).Conclusions: Lower adiponectin levels in non obese type 2 diabetic patients were significantly related to the increased insulin resistance, dyslipidemia, and presence of type 2 diabetes, independently of overall and abdominal adiposity, thereby suggesting a direct link between adiponectin and carbohydrate and lipid metabolism in human

DYNAMIC IMPACT TESTING OF W152x13.4 (W6x9) STEEL POSTS ON A 2:1 SLOPE

Dynamic impact testing of W152x13.4 (W6x9) steel posts at various embedment depths has been detailed, and the results stated. A total of 17 bogie tests have been performed on a 2:1 slope, with post lengths varying from 1,829 mm (6 ft) through 2,743 mm (9 ft). A total of four bogie tests were performed on level terrain using 1,829-mm (6-ft) long steel posts at two different impact speeds. For each bogie test, raw acceleration data, obtained from accelerometers, was filtered and then force-displacement and energy-displacement graphs were plotted. From the energy-displacement graphs, the average post-soil forces were calculated for a 381-mm (15-in.) displacement at the center rail height. Post-soil forces were then compared to the required post capacity of 28.5 kN (6.4 kips), including energy dissipation characteristics, for the MGS placed on level terrain. From these comparisons, a recommended post length was selected for standard post spacing. A 2,743-mm (9-ft) long post with a 1,930-mm (76-in.) embedment depth was found to best meet the post requirements, while providing an average force of 28.42 kN (6.39 kips). As such, this post configuration was recommended for evaluation using computer simulation modeling

Surface reconstruction by layer peeling

10.1007/s00371-006-0048-9Visual Computer229-11593-603VICO

Streaming surface sampling using Gaussian ε-nets

We propose a robust, feature preserving and user-steerable mesh sampling algorithm, based on the one-to-many mapping of a regular sampling of the Gaussian sphere onto a given manifold surface. Most of the operations are local, and no global information is maintained. For this reason, our algorithm is amenable to a parallel or streaming implementation and is most suitable in situations when it is not possible to hold all the input data in memory at the same time. Using ε-nets, we analyze the sampling method and propose solutions to avoid shortcomings inherent to all localized sampling methods. Further, as a byproduct of our sampling algorithm, a shape approximation is produced. Finally, we demonstrate a streaming implementation that handles large meshes with a small memory footprint