An affected pedigree member analysis of linkage between the dopamine D2 receptor gene Taql polymorphism and obesity and hypertension

BACKGROUND: Dopamine modulates a variety of physiological functions including natriuresis and satiety. We have previously reported that the TaqI polymorphism of the dopamine D2 receptor (DD2R) gene is associated with both blood pressure and obesity indices in a normoglycaemic Hong Kong Chinese population. In this study, we present evidence confirming the linkage between this gene polymorphism, obesity and hypertension. METHODS: Two hundred and seventy-four siblings from 96 normoglycaemic hypertensive families were recruited, including 133 who were hypertensive. Central obesity was defined as a waist-to-hip ratio of > or = 0.9 and > or = 0.85 in males and females, respectively, and was identified in 99 of the siblings. The DD2R gene TaqI polymorphism was identified with a polymerase chain reaction based restriction fragment length polymorphism protocol. The affected pedigree member (APM) linkage analysis (sib-pair program, version 0.99.9, by D.L. Duffy) was used to assess for linkage between this gene polymorphism, obesity and hypertension in 73 families with siblings discordant for hypertension. RESULTS: The A1 allele frequencies were similar in the 133 hypertensive, and 141 normotensive siblings, including the 99 centrally obese siblings at 0.431, 0.421 and 0.418, respectively. APM linkage analysis suggested that the DD2R gene TaqI polymorphism had evidence of linkage with blood pressure (T = -1.86, P = 0.013), as well as with obesity (T = -1.58, P = 0.007). CONCLUSION: Our data in normoglycaemic Hong Kong Chinese supports that the DD2R gene TaqI polymorphism is a marker associated with the pathogenesis of obesity and hypertension.postprin

Energy balance simulation of a wheat canopy using the RZ-SHAW (RZWQM-SHAW) model

RZ-SHAW is a new hybrid model coupling the Root Zone Water Quality Model (RZWQM) and the Simultaneous Heat and Water (SHAW) model to extend RZWQM applications to conditions of frozen soil and crop residue cover. RZ-SHAW offers the comprehensive land management options of RZWQM with the additional capability to simulate diurnal changes in energy balance needed for simulating the near-surface microclimate and leaf temperature. The objective of this study was to evaluate RZ-SHAW for simulations of radiation balance and sensible and latent heat fluxes over plant canopies. Canopy energy balance data were collected at various growing stages of winter wheat in the North China Plain (36° 57'N, 116° 6'E, 28 m above sea level). RZ-SHAW and SHAW simulations using hourly meteorological data were compared with measured net radiation, latent heat flux, sensible heat flux, and soil heat flux. RZ-SHAW provided similar goodness-of-prediction statistics as the original SHAW model for all the energy balance components when using observed plant growth input data. The root mean square error (RMSE) for simulated net radiation, latent heat, sensible heat, and soil heat fluxes was 29.7, 30.7, 29.9, and 25.9 W m -2 for SHAW and 30.6, 32.9, 34.2, and 30.6 W m -2 for RZ-SHAW, respectively. Nash-Sutcliffe R 2 ranged from 0.67 for sensible heat flux to 0.98 for net radiation. Subsequently, an analysis was performed using the plant growth component of RZ-SHAW instead of inputting LAI and plant height. The model simulation results agreed with measured plant height, yield, and LAI very well. As a result, RMSE for the energy balance components were very similar to the original RZ-SHAW simulation, and latent, sensible, and soil heat fluxes were actually simulated slightly better. RMSE for simulated net radiation, latent heat, sensible heat, and soil heat fluxes was 31.5, 30.4, 30.2, and 27.6 W m -2, respectively. Overall, the results demonstrated a successful coupling of RZWQM and SHAW in terms of canopy energy balance simulation, which has important implications for prediction of crop growth, crop water stress, and irrigation scheduling

Smoking cessation and carotid atherosclerosis: The guangzhou biobank cohort studydCVD

Introduction Smoking has been shown to be associated with carotid atherosclerosis in cross-sectional and prospective studies in Western populations. However, few studies have examined the reversal of risk resulting from quitting smoking, and the results are conflicting. Methods 959 men aged 50e85 years were randomly selected from phase III (2006e2007) of the Guangzhou Biobank Cohort Study into this cross-sectional study. Common carotid artery intima-media thickness (CCAIMT) was measured by B-mode ultrasonography, and carotid artery plaques were identified. Major cardiovascular risk factors, including fasting triglyceride, low-density and high-density lipoprotein (LDL and HDL) cholesterol and glucose, and systolic and diastolic blood pressure, were assessed. Results CCA-IMT and the number of carotid plaque increased from never to former to current smokers (both p≤0.001). Among former smokers compared to current smokers, after adjustment for cigarette pack-years and other potential confounders, the adjusted ORs (95% CI) for quitting for 1-9, 10-19 and 20+ years were 0.77 (0.47 to 1.26), 0.45 (0.26 to 0.79) and 0.37 (0.17 to 0.77) for the presence of CCA atherosclerosis, and 0.69 (0.43 to 1.12), 0.47 (0.27 to 0.82) and 0.45 (0.23 to 0.96) for the presence of carotid plaques, respectively. Longer duration of quitting smoking was also significantly associated with decreasing risk of the severity of CCA atherosclerosis and carotid plaques (all p≤0.001). Conclusion Smoking cessation was beneficial in attenuating the risk of carotid atherosclerosis associated with cigarette smoking. The short duration of cessation in earlier studies is a likely explanation for the inconsistent results.published_or_final_versio

The association of pulmonary function with carotid atherosclerosis in older Chinese: Guangzhou Biobank Cohort Study-CVD Subcohort

postprin

One step creation of multifunctional 3D architectured hydrogels inducing bone regeneration

Structured hydrogels showing form stability and elastic properties individually tailorable on different length scales are accessible in a one-step process. They support cell adhesion and differentiation and display growing pore size during degradation. In vivo experiments demonstrate their efficacy in biomaterial-induced bone regeneration, not requiring addition of cells or growth factors

Indisulam targets RNA splicing and metabolism to serve as a therapeutic strategy for high-risk neuroblastoma

Neuroblastoma is the most common paediatric solid tumour and prognosis remains poor for high-risk cases despite the use of multimodal treatment. Analysis of public drug sensitivity data showed neuroblastoma lines to be sensitive to indisulam, a molecular glue that selectively targets RNA splicing factor RBM39 for proteosomal degradation via DCAF15-E3-ubiquitin ligase. In neuroblastoma models, indisulam induces rapid loss of RBM39, accumulation of splicing errors and growth inhibition in a DCAF15-dependent manner. Integrative analysis of RNAseq and proteomics data highlight a distinct disruption to cell cycle and metabolism. Metabolic profiling demonstrates metabolome perturbations and mitochondrial dysfunction resulting from indisulam. Complete tumour regression without relapse was observed in both xenograft and the Th-MYCN transgenic model of neuroblastoma after indisulam treatment, with RBM39 loss, RNA splicing and metabolic changes confirmed in vivo. Our data show that dual-targeting of metabolism and RNA splicing with anticancer indisulam is a promising therapeutic approach for high-risk neuroblastoma

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196