Ruiyan Luo

Quantitative proteomics of caveolin-1-regulated proteins: characterization of polymerase i and transcript release factor/CAVIN-1 IN endothelial cells.

Caveolae are organelles abundant in the plasma membrane of many specialized cells including endothelial cells (ECs), epithelial cells, and adipocytes, and in these cells, caveolin-1 (Cav-1) is the major coat protein essential for the formation of caveolae. To identify proteins that require Cav-1 for stable incorporation into membrane raft domains, a quantitative proteomics analysis using isobaric tagging for relative and absolute quantification was performed on rafts isolated from wild-type and Cav-1-deficient mice. In three independent experiments, 117 proteins were consistently identified in membrane rafts with the largest differences in the levels of Cav-2 and in the caveola regulatory proteins Cavin-1 and Cavin-2. Because the lung is highly enriched in ECs, we validated and characterized the role of the newly described protein Cavin-1 in several cardiovascular tissues and in ECs. Cavin-1 was highly expressed in ECs lining blood vessels and in cultured ECs. Knockdown of Cavin-1 reduced the levels of Cav-1 and -2 and weakly influenced the formation of high molecular weight oligomers containing Cav-1 and -2. Cavin-1 silencing enhanced basal nitric oxide release from ECs but blocked proangiogenic phenotypes such as EC proliferation, migration, and morphogenesis in vitro. Thus, these data support an important role of Cavin-1 as a regulator of caveola function in ECs.

Sparse principal component analysis by choice of norm

Recent years have seen the developments of several methods for sparse principal component analysis due to its importance in the analysis of high dimensional data. Despite the demonstration of their usefulness in practical applications, they are limited in terms of lack of orthogonality in the loadings (coefficients) of different principal components, the existence of correlation in the principal components, the expensive computation needed, and the lack of theoretical results such as consistency in high-dimensional situations. In this paper, we propose a new sparse principal component analysis method by introducing a new norm to replace the usual norm in traditional eigenvalue problems, and propose an efficient iterative algorithm to solve the optimization problems. With this method, we can efficiently obtain uncorrelated principal components or orthogonal loadings, and achieve the goal of explaining a high percentage of variations with sparse linear combinations. Due to the strict convexity of the new norm, we can prove the convergence of the iterative method and provide the detailed characterization of the limits. We also prove that the obtained principal component is consistent for a single component model in high dimensional situations. As illustration, we apply this method to real gene expression data with competitive results.

Blood transcriptomics and metabolomics for personalized medicine

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine.

The effectiveness of a short food frequency questionnaire in determining vitamin D intake in children

Previous studies have found a high prevalence of vitamin D deficiency in children, yet few validated dietary vitamin D assessment tools are available for use in children. Our objective was to determine whether a short food frequency questionnaire (SFFQ) can effectively assess vitamin D intake in children. Vitamin D intake ascertained by a SFFQ was compared with assessments by a previously validated long food frequency questionnaire (LFFQ) in a population of 296 healthy 6- to 14-y-old children (54% male, 60% African American) from Pittsburgh, PA. The questionnaires were completed at two points 6 mo apart. Median reported daily vitamin D intake from the SFFQ (baseline: 380 IU, follow-up: 363 IU) was higher than the LFFQ (255 IU and 254 IU, respectively). Reported median dairy intake, including milk, cheese, and yogurt, was 3.7 cups/day, which meets the USDA recommendation for children. Vitamin D intake reported by the 2 questionnaires was modestly correlated at baseline and follow-up (r = 0.35 and r = 0.37, respectively; p < 0.001). These associations were stronger in Caucasians (r = 0.48 and r = 0.49, p < 0.001) than in African Americans (r = 0.27 and r = 0.31; p = 0.001). The sensitivity of the SFFQ for predicting daily vitamin D intake, defined as intake of ≥ 400 IU on both the SFFQ and LFFQ, was 65%. Specificity, defined as intake of < 400 IU on both questionnaires, was 42%. Vitamin D requirements may not be met despite adequate consumption of dairy products. The SFFQ was found to be a modestly valid and sensitive tool for dietary assessment of vitamin D intake in children.

Mortality rates and the causes of death related to diabetes mellitus in Shanghai Songjiang District: an 11-year retrospective analysis of death certificates

BackgroundChina is one of the countries with the highest prevalence of diabetes in the world. We analysed all the death certificates mentioning diabetes from 2002 to 2012 in Songjiang District of Shanghai to estimate morality rates and examine cause of death patterns.MethodsMortality data of 2654 diabetics were collected from the database of local CDC. The data set comprises all causes of death, contributing causes and the underlying cause, thereby the mortality rates of diabetes and its specified complications were analysed.ResultsThe leading underlying causes of death were various cardiovascular diseases (CVD), which collectively accounted for about 30 % of the collected death certificates. Diabetes was determined as the underlying cause of death on 28.7 %. The trends in mortality showed that the diabetes related death rate increased about 1.78 fold in the total population during the 11-year period, and the death rate of diabetes and CVD comorbidity increased 2.66 fold. In all the diabetes related deaths, the proportion of people dying of ischaemic heart disease or cerebrovascular disease increased from 18.0 % in 2002 to 30.5 % in 2012. But the proportions attributed directly to diabetes showed a downtrend, from 46.7–22.0 %.ConclusionsThe increasing diabetes related mortality could be chiefly due to the expanding prevalence of CVD, but has nothing to do with diabetes as the underlying cause. Policy makers should pay more attention to primary prevention of diabetes and on the prevention of cardiovascular complications to reduce the burden of diabetes on survival.

Escalation with overdose control using all toxicities and time to event toxicity data in cancer Phase I clinical trials.

The primary purposes of Phase I cancer clinical trials are to determine the maximum tolerated dose (MTD) and the treatment schedule of a new drug. Phase I trials usually involve a small number of patients so that fully utilizing all toxicity information including time to event toxicity data is key to improving the trial efficiency and the accuracy of MTD estimation. Chen et al. proposed a novel normalized equivalent toxicity score (NETS) system to fully utilize multiple toxicities per patient instead of a binary indicator of dose limiting toxicity (DLT). Cheung and Chappell developed the time to toxicity event (TITE) approach to incorporate time to toxicity event data. Escalation with overdose control (EWOC) is an adaptive Bayesian Phase I design which can allow rapid dose escalation while controlling the probability of overdosing patients. In this manuscript, we use EWOC as a framework and integrate it with the NETS system and the TITE approach to develop an advanced Phase I design entitled EWOC-NETS-TITE. We have conducted simulation studies to compare its operating characteristics using selected derived versions of EWOC because EWOC itself has already been extensively compared with common Phase I designs [3]. Simulation results demonstrate that EWOC-NETS-TITE can substantially improve the trial efficiency and accuracy of MTD determination as well as allow patients to be entered in a staggered fashion to significantly shorten trial duration. Moreover, user-friendly software for EWOC-NETS-TITE is under development.

Sparse Regression by Projection and Sparse Discriminant Analysis

Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Function-on-Function Linear Regression by Signal Compression

ABSTRACT We consider functional linear regression models with a functional response and multiple functional predictors, with the goal of finding the best finite-dimensional approximation to the signal part of the response function. Defining the integrated squared correlation coefficient between a random variable and a random function, we propose to solve a penalized generalized functional eigenvalue problem, whose solutions satisfy that projections on the original predictors generate new scalar uncorrelated variables and these variables have the largest integrated squared correlation coefficient with the signal function. With these new variables, we transform the original function-on-function regression model to a function-on-scalar regression model whose predictors are uncorrelated, and estimate the model by penalized least-square method. This method is also extended to models with both multiple functional and scalar predictors. We provide the asymptotic consistency and the corresponding convergence rates for our estimates. Simulation studies in various settings and for both one and multiple functional predictors demonstrate that our approach has good predictive performance and is very computational efficient. Supplementary materials for this article are available online.

Changing Places and Partners: Associations of Neighborhood Conditions With Sexual Network Turnover Among African American Adults Relocated From Public Housing

Neighborhood conditions and sexual network turnover have been associated with the acquisition of HIV and other sexually transmitted infections (STIs). However, few studies investigate the influence of neighborhood conditions on sexual network turnover. This longitudinal study used data collected across 7 visits from a predominantly substance-misusing cohort of 172 African American adults relocated from public housing in Atlanta, Georgia, to determine whether post-relocation changes in exposure to neighborhood conditions influence sexual network stability, the number of new partners joining sexual networks, and the number of partners leaving sexual networks over time. At each visit, participant and sexual network characteristics were captured via survey, and administrative data were analyzed to describe the census tracts where participants lived. Multilevel models were used to longitudinally assess the relationships of tract-level characteristics to sexual network dynamics over time. On average, participants relocated to neighborhoods that were less economically deprived and violent, and had lower alcohol outlet densities. Post-relocation reductions in exposure to alcohol outlet density were associated with fewer new partners joining sexual networks. Reduced perceived community violence was associated with more sexual partners leaving sexual networks. These associations were marginally significant. No post-relocation changes in place characteristics were significantly associated with overall sexual network stability. Neighborhood social context may influence sexual network turnover. To increase understanding of the social–ecological determinants of HIV/STIs, a new line of research should investigate the combined influence of neighborhood conditions and sexual network dynamics on HIV/STI transmission over time.

Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data

Recent technological advances have made it possible to simultaneously measure multiple protein activities at the single cell level. With such data collected under different stimulatory or inhibitory conditions, it is possible to infer the causal relationships among proteins from single cell interventional data. In this article we propose a Bayesian hierarchical modeling framework to infer the signaling pathway based on the posterior distributions of parameters in the model. Under this framework, we consider network sparsity and model the existence of an association between two proteins both at the overall level across all experiments and at each individual experimental level. This allows us to infer the pairs of proteins that are associated with each other and their causal relationships. We also explicitly consider both intrinsic noise and measurement error. Markov chain Monte Carlo is implemented for statistical inference. We demonstrate that this hierarchical modeling can effectively pool information from different interventional experiments through simulation studies and real data analysis.