Pressure Driven Flow of Polymer Solutions in Nanoscale Slit Pores

Polymer solutions subject to pressure driven flow and in nanoscale slit pores are systematically investigated using the dissipative particle dynamics approach. We investigated the effect of molecular weight, polymer concentration and flow rate on the profiles across the channel of the fluid and polymer velocities, polymers density, and the three components of the polymers radius of gyration. We found that the mean streaming fluid velocity decreases as the polymer molecular weight or/and polymer concentration is increased, and that the deviation of the velocity profile from the parabolic profile is accentuated with increase in polymer molecular weight or concentration. We also found that the distribution of polymers conformation is highly anisotropic and non-uniform across the channel. The polymer density profile is also found to be non-uniform, exhibiting a local minimum in the center-plane followed by two symmetric peaks. We found a migration of the polymer chains either from or towards the walls. For relatively long chains, as compared to the thickness of the slit, a migration towards the walls is observed. However, for relatively short chains, a migration away from the walls is observed.Comment: 11 pages, 13 figure

Metabolic Syndrome Derived from Principal Component Analysis and Incident Cardiovascular Events: The Multi Ethnic Study of Atherosclerosis (MESA) and Health, Aging, and Body Composition (Health ABC).

Background. The NCEP metabolic syndrome (MetS) is a combination of dichotomized interrelated risk factors from predominantly Caucasian populations. We propose a continuous MetS score based on principal component analysis (PCA) of the same risk factors in a multiethnic cohort and compare prediction of incident CVD events with NCEP MetS definition. Additionally, we replicated these analyses in the Health, Aging, and Body composition (Health ABC) study cohort. Methods and Results. We performed PCA of the MetS elements (waist circumference, HDL, TG, fasting blood glucose, SBP, and DBP) in 2610 Caucasian Americans, 801 Chinese Americans, 1875 African Americans, and 1494 Hispanic Americans in the multiethnic study of atherosclerosis (MESA) cohort. We selected the first principal component as a continuous MetS score (MetS-PC). Cox proportional hazards models were used to examine the association between MetS-PC and 5.5 years of CVD events (n = 377) adjusting for age, gender, race, smoking and LDL-C, overall and by ethnicity. To facilitate comparison of MetS-PC with the binary NCEP definition, a MetS-PC cut point was chosen to yield the same 37% prevalence of MetS as the NCEP definition (37%) in the MESA cohort. Hazard ratio (HR) for CVD events were estimated using the NCEP and Mets-PC-derived binary definitions. In Cox proportional models, the HR (95% CI) for CVD events for 1-SD (standard deviation) of MetS-PC was 1.71 (1.54-1.90) (P < 0.0001) overall after adjusting for potential confounders, and for each ethnicity, HRs were: Caucasian, 1.64 (1.39-1.94), Chinese, 1.39 (1.06-1.83), African, 1.67 (1.37-2.02), and Hispanic, 2.10 (1.66-2.65). Finally, when binary definitions were compared, HR for CVD events was 2.34 (1.91-2.87) for MetS-PC versus 1.79 (1.46-2.20) for NCEP MetS. In the Health ABC cohort, in a fully adjusted model, MetS-PC per 1-SD (Health ABC) remained associated with CVD events (HR = 1.21, 95%CI 1.12-1.32) overall, and for each ethnicity, Caucasian (HR = 1.24, 95%CI 1.12-1.39) and African Americans (HR = 1.16, 95%CI 1.01-1.32). Finally, when using a binary definition of MetS-PC (cut point 0.505) designed to match the NCEP definition in terms of prevalence in the Health ABC cohort (35%), the fully adjusted HR for CVD events was 1.39, 95%CI 1.17-1.64 compared with 1.46, 95%CI 1.23-1.72 using the NCEP definition. Conclusion. MetS-PC is a continuous measure of metabolic syndrome and was a better predictor of CVD events overall and in individual ethnicities. Additionally, a binary MetS-PC definition was better than the NCEP MetS definition in predicting incident CVD events in the MESA cohort, but this superiority was not evident in the Health ABC cohort

Green supply chain management – food for thought?

This paper investigates the impact of green supply chain management (GSCM) practices on the performance of UK food retail small and medium-sized enterprises (SMEs). A quantitative approach using a non-probability sampling of 84 participants was employed. Based on the literature review, five hypotheses were developed and tested using the partial least square-structural equation modeling (SEM-Smart PLS 2.03) approach. The reviewed literature revealed that key internal drivers (ID) and external pressures (EP) stimulate organizations to initiate GSCM practices in UK food retail SMEs. Though empirical findings strongly supported the statement that ID influence GSCM practices, they did not show a significant relationship between EP and GSCM practices. Literature also suggests that practicing GSCM can help improve the efficiency, brand image (BI) and profitability, and thus improve the overall firm performance which is also empirically proved. This study helps enrich existing theories on SCM and organizational performance. As to practical impact, this study should facilitate SMEs in GSCM practices and thus help green the economy. While the findings of this study have limited generalisability as the data were collected from UK SMEs only and the sample size was comparatively small, this research establishes a foundation for further study in this domain

3-\u3ci\u3eO\u3c/i\u3e sulfation of heparin leads to hepatotropism and longer circulatory half-life

Introduction: Heparins are common blood anticoagulants that are critical for many surgical and biomedical procedures used in modern medicine. In contrast to natural heparin derived from porcine gut mucosa, synthetic heparins are homogenous by mass, polymer length, and chemistry. Materials & methods: Stable cell lines expressing the human and mouse Stabilin receptors were used to evaluate endocytosis of natural and synthetic heparin. We chemoenzymatically produced synthetic heparin consisting of 12 sugars (dodecamers) containing 14 sulfate groups resulting in a non-3-O sulfated structure (n12mer). Half of the n12mer was modified with a 3-O sulfate on a single GlcNS sugar producing the 3-O sulfated heparin (12mer). Wildtype (WT), Stabilin-1 knock-out (KO), and Stabilin-2 KO C57BL/6 mice were developed and used for metabolic studies and provided as a source for primary liver sinusoidal endothelial cells. Results & conclusions: Human and mouse Stabilin-2 receptors had very similar endocytosis rates of both the 12mer and n12mer, suggesting that they are functionally similar in primary cells. Subcutaneous injections of the n12mer and 12mer revealed that the 12mer had a much longer half-life in circulation and a higher accumulation in liver. The n12mer never accumulated in circulation and was readily excreted by the kidneys before liver accumulation could occur. Liver sinusoidal endothelial cells from the Stabilin-2 KO mice had lower uptake rates for both dodecamers, whereas, the Stabilin-1 KO mice had lower endocytosis rates for the 12mer than the n12mer. 3-O sulfation of heparin is correlated to both a longer circulatory half-life and hepatotropism which is largely performed by the Stabilin receptors

Genetic Architecture of Gene Expression Traits Across Diverse Populations

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 \u3e 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop

Transcriptome Prediction Performance Across Machine Learning Models and Diverse Ancestries

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits

Application of an integrated physical and functional screening approach to identify inhibitors of the Wnt pathway

Large-scale proteomic approaches have been used to study signaling pathways. However, identification of biologically relevant hits from a single screen remains challenging due to limitations inherent in each individual approach. To overcome these limitations, we implemented an integrated, multi-dimensional approach and used it to identify Wnt pathway modulators. The LUMIER protein–protein interaction mapping method was used in conjunction with two functional screens that examined the effect of overexpression and siRNA-mediated gene knockdown on Wnt signaling. Meta-analysis of the three data sets yielded a combined pathway score (CPS) for each tested component, a value reflecting the likelihood that an individual protein is a Wnt pathway regulator. We characterized the role of two proteins with high CPSs, Ube2m and Nkd1. We show that Ube2m interacts with and modulates β-catenin stability, and that the antagonistic effect of Nkd1 on Wnt signaling requires interaction with Axin, itself a negative pathway regulator. Thus, integrated physical and functional mapping in mammalian cells can identify signaling components with high confidence and provides unanticipated insights into pathway regulators

Human Lipoxygenase Pathway Gene Variation and Association with Markers of Subclinical Atherosclerosis in the Diabetes Heart Study

Aims. Genes of the 5-lipoxygenase pathway are compelling candidates for atherosclerosis. We hypothesize that polymorphisms in ALOX12, ALOX15, ALOX5, and ALOX5AP genes are associated with subclinical atherosclerosis in multiple vascular beds. Methods. Families with two or more siblings with type 2 diabetes and their nondiabetic siblings were studied as part of the Diabetes Heart Study (DHS). European American diabetic (n = 828) and nondiabetic (n = 170) siblings were genotyped for SNPs in the ALOX12, ALOX15, ALOX5, and ALOX5AP genes. Subclinical measures of atherosclerosis (IMT, coronary (CorCP), carotid (CarCP) and aortic (AorCP) calcified plaque) were obtained. Results. Associations were observed between ALOX12 with CorCP, ALOX5 with CorCP, AorCP, and IMT, and ALOX5AP with CorCP and CarCP, independent of known epidemiologic risk factors. Further, lipoxygenase pathway SNPs that were associated with measures of atherosclerosis were associated with markers of inflammation (CRP, ICAM-1) and calcification (MGP). Conclusions. Polymorphisms within ALOX12, ALOX5, and ALOX5AP are genetically associated with subclinical atherosclerosis and with biomarkers of disease in families with type 2 diabetes. These results suggest that variants in lipoxygenase pathway genes may have pleiotropic effects on multiple components that determine risk of cardiovascular disease