Urinary microbiome dysbiosis is associated with an inflammatory environment and perturbed fatty acids metabolism in the pathogenesis of bladder cancer.

BACKGROUND: Bladder cancer is a common malignancy with high recurrence rate. Early diagnosis and recurrence surveillance are pivotal to patients' outcomes, which require novel minimal-invasive diagnostic tools. The urinary microbiome is associated with bladder cancer and can be used as biomarkers, but the underlying mechanism is to be fully illustrated and diagnostic performance to be improved. METHODS: A total of 23 treatment-naïve bladder cancer patients and 9 non-cancerous subjects were enrolled into the Before group and Control group. After surgery, 10 patients from the Before group were further assigned into After group. Void mid-stream urine samples were collected and sent for 16S rDNA sequencing, targeted metabolomic profiling, and flow cytometry. Next, correlations were analyzed between microbiota, metabolites, and cytokines. Finally, receiver operating characteristic (ROC) curves of the urinary biomarkers were plotted and compared. RESULTS: Comparing to the Control group, levels of IL-6 (p < 0.01), IL-8 (p < 0.05), and IL-10 (p < 0.05) were remarkably elevated in the Before group. The α diversity of urine microbiome was also significantly higher, with the feature microbiota positively correlated to the level of IL-6 (r = 0.58, p < 0.01). Significant differences in metabolic composition were also observed between the Before and Control groups, with fatty acids and fatty acylcarnitines enriched in the Before group. After tumor resection, cytokine levels and the overall microbiome structure in the After group remained similar to that of the Before group, but fatty acylcarnitines were significantly reduced (p < 0.05). Pathway enrichment analysis revealed beta-oxidation of fatty acids was significantly involved (p < 0.001). ROC curves showed that the biomarker panel of Actinomycetaceae + arachidonic acid + IL-6 had superior diagnostic performance, with sensitivity of 0.94 and specificity of 1.00. CONCLUSIONS: Microbiome dysbiosis, proinflammatory environment and altered fatty acids metabolism are involved in the pathogenesis of bladder cancer, which may throw light on novel noninvasive diagnostic tool development.

Sparse group variable selection for gene-environment interactions in the longitudinal study.

Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G × $\times $ E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G × $\times $ E study by using high-dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

Formation of monodomain polymer-stabilized blue phase liquid crystals using surface acoustic waves.

This work uses surface acoustic waves (SAWs) that are generated by a piezoelectric substrate containing an interdigital transducer (IDT) to which a low voltage of 2 mV was applied at a frequency of 1 kHz to fabricate a polymer-stabilized blue phase liquid crystal (PS-BPLC) layer. The PS-BPLC layer has a more uniform optical microscope (OM) image at a voltage of 2 mV than at zero voltage, and its reflective spectrum exhibits a smaller full width at half maximum (FWHM) at the former than at the latter. The uniform OM image and small FWHM reveal that the lattices in the PS-BPLC layer have monodomain structure. The monodomain PS-BPLC layer is formed because the SAWs cause longitudinal and transverse vibrations of the PS-BPLC lattices in the vertical plane along their traveling direction. The proposed method for fabricating the monodomain PS-BPLC layer using the SAWs has potential for the development of reflective optical devices that consume low power during their fabrication.

Robust Bayesian variable selection for gene-environment interactions.

Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

The Bayesian Regularized Quantile Varying Coefficient Model.

The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable, and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. Although extensive studies have been conducted to examine variable selection for the high-dimensional quantile varying coefficient models, the Bayesian analysis has been rarely developed. The Bayesian regularized quantile varying coefficient model has been proposed to incorporate robustness against data heterogeneity while accommodating the non-linear interactions between the effect modifier and predictors. Selecting important varying coefficients can be achieved through Bayesian variable selection. Incorporating the multivariate spike-and-slab priors further improves performance by inducing exact sparsity. The Gibbs sampler has been derived to conduct efficient posterior inference of the sparse Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit of the proposed model in selection and estimation accuracy over the alternatives has been systematically investigated in simulation under specific quantile levels and multiple heavy-tailed model errors. In the case study, the proposed model leads to identification of biologically sensible markers in a non-linear gene-environment interaction study using the NHS data.

A weighted empirical Bayes risk prediction model using multiple traits.

With rapid advances in high-throughput sequencing technology, millions of single-nucleotide variants (SNVs) can be simultaneously genotyped in a sequencing study. These SNVs residing in functional genomic regions such as exons may play a crucial role in biological process of the body. In particular, non-synonymous SNVs are closely related to the protein sequence and its function, which are important in understanding the biological mechanism of sequence evolution. Although statistically challenging, models incorporating such SNV annotation information can improve the estimation of genetic effects, and multiple responses may further strengthen the signals of these variants on the assessment of disease risk. In this work, we develop a new weighted empirical Bayes method to integrate SNV annotation information in a multi-trait design. The performance of this proposed model is evaluated in simulation as well as a real sequencing data; thus, the proposed method shows improved prediction accuracy compared to other approaches.

Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis.

In cancer genomic studies, an important objective is to identify prognostic markers associated with patients' survival. Network-based regularization has achieved success in variable selections for high-dimensional cancer genomic data, because of its ability to incorporate the correlations among genomic features. However, as survival time data usually follow skewed distributions, and are contaminated by outliers, network-constrained regularization that does not take the robustness into account leads to false identifications of network structure and biased estimation of patients' survival. In this study, we develop a novel robust network-based variable selection method under the accelerated failure time model. Extensive simulation studies show the advantage of the proposed method over the alternative methods. Two case studies of lung cancer datasets with high-dimensional gene expression measurements demonstrate that the proposed approach has identified markers with important implications.

Posttransplant Outcome of Lean Compared With Obese Nonalcoholic Steatohepatitis in the United States: The Obesity Paradox.

Morbid obesity is considered a relative contraindication for liver transplantation (LT). We investigated if body mass index (BMI; lean versus obese) is a risk factor for post-LT graft and overall survival in nonalcoholic steatohepatitis (NASH) and non-NASH patients. Using the United Network for Organ Sharing (UNOS) database, LT recipients from January 2002 to June 2013 (age ≥18 years) with follow-up until 2017 were included. The association of BMI categories calculated at LT with graft and overall survival after LT were examined. After adjusting for confounders, all obesity cohorts (overweight and class 1, class 2, and class 3 obesity) among LT recipients for NASH had significantly reduced risk of graft and patient loss at 10 years of follow-up compared with the lean BMI cohort. In contrast, the non-NASH group of LT recipients had no increased risk for graft and patient loss for overweight, class 1, and class 2 obesity groups but had significantly increased risk for graft (P < 0.001) and patient loss (P = 0.005) in the class 3 obesity group. In this retrospective analysis of the UNOS database, adult recipients selected for first LT and NASH patients with the lowest BMI have the worse longterm graft and patient survival as opposed to non-NASH patients where the survival was worse with higher BMI.

Semiparametric Bayesian variable selection for gene-environment interactions.

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Study of gene-environment (G×E) interactions is important for elucidating the disease etiology. Existing Bayesian methods for G×E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting G×E interactions in "large p, small n" settings. However, Bayesian variable selection, which can provide fresh insight into G×E study, has not been widely examined. We propose a novel and powerful semiparametric Bayesian variable selection model that can investigate linear and nonlinear G×E interactions simultaneously. Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework. Spike-and-slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. The proposed method conducts Bayesian variable selection more efficiently than existing methods. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. The proposed Bayesian method leads to the identification of main and interaction effects with important implications in a high-throughput profiling study with high-dimensional SNP data.

Optical amplification assisted by two-photon processes in a 3-level transmon artificial atom.

We experimentally study interactions between two microwave fields mediated by 3-level transmon artificial atom with two-photon processes. The transmon has good selection rule, preventing one-photon transition, but allowing two-photon transition from ground state(0) to 2nd excited state(2). By pumping a control tone in resonance to the transition between 1st(1) and 2nd excited state(2), we control the one-photon transparency for 0 to 1 transition and two-photon transparency for 0 to 2 transition. The results are explained by the Autler-Townes splitting induced by the control microwave. In addition, two possible microwave amplification processes involving two-photon processes are also studied. The 4-wave mixing scheme increases the transmission by 3% while 2-photon optical pumping produces a 11% narrowband increment. All these phenomena can be operated with control and probe tones in a narrow band.

Additive varying-coefficient model for nonlinear gene-environment interactions.

Gene-environment (G×E) interaction plays a pivotal role in understanding the genetic basis of complex disease. When environmental factors are measured continuously, one can assess the genetic sensitivity over different environmental conditions on a disease trait. Motivated by the increasing awareness of gene set based association analysis over single variant based approaches, we proposed an additive varying-coefficient model to jointly model variants in a genetic system. The model allows us to examine how variants in a gene set are moderated by an environment factor to affect a disease phenotype. We approached the problem from a variable selection perspective. In particular, we select variants with varying, constant and zero coefficients, which correspond to cases of G×E interaction, no G×E interaction and no genetic effect, respectively. The procedure was implemented through a two-stage iterative estimation algorithm via the smoothly clipped absolute deviation penalty function. Under certain regularity conditions, we established the consistency property in variable selection as well as effect separation of the two stage iterative estimators, and showed the optimal convergence rates of the estimates for varying effects. In addition, we showed that the estimate of non-zero constant coefficients enjoy the oracle property. The utility of our procedure was demonstrated through simulation studies and real data analysis.

Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.

Identification of gene-environment (G × E) interactions associated with disease phenotypes has posed a great challenge in high-throughput cancer studies. The existing marginal identification methods have suffered from not being able to accommodate the joint effects of a large number of genetic variants, while some of the joint-effect methods have been limited by failing to respect the "main effects, interactions" hierarchy, by ignoring data contamination, and by using inefficient selection techniques under complex structural sparsity. In this article, we develop an effective penalization approach to identify important G × E interactions and main effects, which can account for the hierarchical structures of the 2 types of effects. Possible data contamination is accommodated by adopting the least absolute deviation loss function. The advantage of the proposed approach over the alternatives is convincingly demonstrated in both simulation and a case study on lung cancer prognosis with gene expression measurements and clinical covariates under the accelerated failure time model.

An alternative route to cyclic terpenes by reductive cyclization in iridoid biosynthesis.

The iridoids comprise a large family of distinctive bicyclic monoterpenes that possess a wide range of pharmacological activities, including anticancer, anti-inflammatory, antifungal and antibacterial activities. Additionally, certain iridoids are used as sex pheromones in agriculturally important species of aphids, a fact that has underpinned innovative and integrated pest management strategies. To harness the biotechnological potential of this natural product class, the enzymes involved in the biosynthetic pathway must be elucidated. Here we report the discovery of iridoid synthase, a plant-derived enzyme that generates the iridoid ring scaffold, as evidenced by biochemical assays, gene silencing, co-expression analysis and localization studies. In contrast to all known monoterpene cyclases, which use geranyl diphosphate as substrate and invoke a cationic intermediate, iridoid synthase uses the linear monoterpene 10-oxogeranial as substrate and probably couples an initial NAD(P)H-dependent reduction step with a subsequent cyclization step via a Diels-Alder cycloaddition or a Michael addition. Our results illustrate how a short-chain reductase was recruited as cyclase for the production of iridoids in medicinal plants. Furthermore, we highlight the prospects of using unrelated reductases to generate artificial cyclic scaffolds. Beyond the recognition of an alternative biochemical mechanism for the biosynthesis of cyclic terpenes, we anticipate that our work will enable the large-scale heterologous production of iridoids in plants and microorganisms for agricultural and pharmaceutical applications.

Vascularization of pancreatic decellularized scaffold with endothelial progenitor cells.

Vascularization remains a large obstacle for creating a functional pancreas-tissue equivalent for transplantation. In this study, a pre-vascularized pancreatic decellularized scaffold was prepared through endothelializing with endothelial progenitor cells (EPCs) in a bioreactor, and the ability to regenerate new blood vessels was detected in vivo. Initially, pancreases of Sprague-Dawley (SD) rats were perfused with 1% Triton X-100 and 0.1% ammonium hydroxide to remove the cellular components while the intact vascular network was preserved. Then, the decellularized scaffold was reseed with EPCs, which were primarily characterized by dual staining for dil-labeled acetylated low-density lipoprotein (Dil-acLDL) and fluorescein isothiocyanate labeled ulex europaeus agglutinin 1 (FITC-UEA-1), to reconstruct the vascular network. Thus, a scaffold covered with EPCs in the vessel structure was created. After that, the scaffold was transplanted into the rat in vivo to observe the anastomosis with the host vascular network. The results showed that EPCs can be located around the blood vessel wall, and re-endothelialized scaffold connected with the host through new blood vessel formation earlier than the control group (p < 0.05). These findings all indicated that the pancreatic decellularized scaffold endothelialized with EPCs may be further applied to solve the problem of blood supply and support the function of insulin-secreting cells after in vivo transplantation.

A selective review of robust variable selection with applications in bioinformatics.

A drastic amount of data have been and are being generated in bioinformatics studies. In the analysis of such data, the standard modeling approaches can be challenged by the heavy-tailed errors and outliers in response variables, the contamination in predictors (which may be caused by, for instance, technical problems in microarray gene expression studies), model mis-specification and others. Robust methods are needed to tackle these challenges. When there are a large number of predictors, variable selection can be as important as estimation. As a generic variable selection and regularization tool, penalization has been extensively adopted. In this article, we provide a selective review of robust penalized variable selection approaches especially designed for high-dimensional data from bioinformatics and biomedical studies. We discuss the robust loss functions, penalty functions and computational algorithms. The theoretical properties and implementation are also briefly examined. Application examples of the robust penalization approaches in representative bioinformatics and biomedical studies are also illustrated.

Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes.

BACKGROUND: Over the past decades, the prevalence of type 2 diabetes mellitus (T2D) has been steadily increasing around the world. Despite large efforts devoted to better understand the genetic basis of the disease, the identified susceptibility loci can only account for a small portion of the T2D heritability. Some of the existing approaches proposed for the high dimensional genetic data from the T2D case-control study are limited by analyzing a few number of SNPs at a time from a large pool of SNPs, by ignoring the correlations among SNPs and by adopting inefficient selection techniques. METHODS: We propose a network constrained regularization method to select important SNPs by taking the linkage disequilibrium into account. To accomodate the case control study, an iteratively reweighted least square algorithm has been developed within the coordinate descent framework where optimization of the regularized logistic loss function is performed with respect to one parameter at a time and iteratively cycle through all the parameters until convergence. RESULTS: In this article, a novel approach is developed to identify important SNPs more effectively through incorporating the interconnections among them in the regularized selection. A coordinate descent based iteratively reweighed least squares (IRLS) algorithm has been proposed. CONCLUSIONS: Both the simulation study and the analysis of the Nurses's Health Study, a case-control study of type 2 diabetes data with high dimensional SNP measurements, demonstrate the advantage of the network based approach over the competing alternatives.

Boosting signals in gene-based association studies via efficient SNP selection.

Set-based association studies based on genes or pathways have shown great promise in interpreting association signals associated with complex diseases. These approaches are particularly useful when variants in a set have moderate effects and are difficult to be detected with single marker analysis, especially when variants function jointly in a complicated manner. The set-based analyses use a summary statistic such as the maximum or average of individual signal (e.g. a chi-square statistic) over all variants in a set, or consider their joint distribution to assess the significance of the set. The signal obtained with this treatment, however, could be potentially diluted when noisy variants are not taken good care of, leading to either inflated false negatives or false positives. Thus, the selection of disease informative single-nucleotide polymorphism (diSNPs) plays a crucial role in improving the power of the set-based association study. In this work, we propose an efficient diSNP selection method based on the information theory. We select diSNP variants by considering their relative information contribution to a disease status, which is different from the usual tag SNP selection. The relative merit of pre-selecting diSNPs in a set-based association analysis is demonstrated through extensive simulation studies and real data analysis.

A penalized robust semiparametric approach for gene-environment interactions.

In genetic and genomic studies, gene-environment (G×E) interactions have important implications. Some of the existing G×E interaction methods are limited by analyzing a small number of G factors at a time, by assuming linear effects of E factors, by assuming no data contamination, and by adopting ineffective selection techniques. In this study, we propose a new approach for identifying important G×E interactions. It jointly models the effects of all E and G factors and their interactions. A partially linear varying coefficient model is adopted to accommodate possible nonlinear effects of E factors. A rank-based loss function is used to accommodate possible data contamination. Penalization, which has been extensively used with high-dimensional data, is adopted for selection. The proposed penalized estimation approach can automatically determine if a G factor has an interaction with an E factor, main effect but not interaction, or no effect at all. The proposed approach can be effectively realized using a coordinate descent algorithm. Simulation shows that it has satisfactory performance and outperforms several competing alternatives. The proposed approach is used to analyze a lung cancer study with gene expression measurements and clinical variables. Copyright © 2015 John Wiley & Sons, Ltd.

Luminescence and X-ray absorption studies on 0.5% Ce(3+) doped BaCa2MgSi2O8 phosphor.

0.5% Ce(3+) doped BaCa2MgSi2O8 phosphor was prepared by a conventional solid state reaction method. Luminescence spectra as well as fluorescence decay were monitored in the VUV-UV range. Ce(3+) emissions are assigned to cerium ions on a Ba(2+) site, and the five 4f-5d excitation bands of Ce(3+) were determined at low temperature. The light yield is estimated to be around 10,600 ph MeV(-1) under X-ray excitation. X-ray absorption near-edge structure (XANES) was explored to study the energy transfer efficiency to optical centers from each element in the phosphor; the results show that the contributions to luminescence are not identical for each element.

Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model.

Consider the integrative analysis of genetic data with multiple correlated response variables. The goal is to identify important gene-environment (G × E) interactions along with main gene and environment effects that are associated with the responses. The homogeneity and heterogeneity models can be adopted to describe the genetic basis of multiple responses. To accommodate possible nonlinear effects of some environment effects, a multi-response partially linear varying coefficient model is assumed. Penalization is adopted for marker selection. The proposed penalization method can select genetic variants with G × E interactions, no G × E interactions, and no main effects simultaneously. It adopts different penalties to accommodate the homogeneity and heterogeneity models. The proposed method can be effectively computed using a coordinate descent algorithm. Simulation study and the analysis of Health Professionals Follow-up Study, which has two correlated continuous traits, SNP measurements and multiple environment effects, show superior performance of the proposed method over its competitors.