Search

Preeclampsia is a pregnancy‐associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time‐to‐event setting of “semi‐competing risks” in which a non‐terminal event of interest is subject to the occurrence of a terminal event of interest. The semi‐competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non‐terminal event, which we call “clinically immediate” and “non‐immediate” terminal events. Though such phenomena arise in many clinical contexts, to‐date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty‐based illness‐death model with a binary submodel to distinguish risk of immediate terminal event following the non‐terminal event. The model admits direct dependence of the terminal event on the non‐terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions. [ABSTRACT FROM AUTHOR]

BACKGROUND:Microbially mediated oral diseases can signal underlying HIV/AIDS progression in HIV-infected adults. The role of the oral microbiota in HIV-infected youth is not known. The Adolescent Master Protocol of the Pediatric HIV/AIDS Cohort Study is a longitudinal study of perinatally HIV-infected (PHIV) and HIV-exposed, uninfected (PHEU) youth. We compared oral microbiome levels and associations with caries or periodontitis in 154 PHIV and 100 PHEU youth. RESULTS:Species richness and alpha diversity differed little between PHIV and PHEU youth. Group differences in average counts met the significance threshold for six taxa; two Corynebacterium species were lower in PHIV and met thresholds for noteworthiness. Several known periodontitis-associated organisms (Prevotella nigrescens, Tannerella forsythia, Aggregatibacter actinomycetemcomitans, and Filifactor alocis) exhibited expected associations with periodontitis in PHEU youth, associations not observed in PHIV youth. In both groups, odds of caries increased with counts of taxa in four genera, Streptococcus, Scardovia, Bifidobacterium, and Lactobacillus. CONCLUSIONS:The microbiomes of PHIV and PHEU youth were similar, although PHIV youth seemed to have fewer "health"-associated taxa such as Corynebacterium species. These results are consistent with the hypothesis that HIV infection, or its treatment, may contribute to oral dysbiosis.

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia. [ABSTRACT FROM AUTHOR]

Two new cyclopropanoic fatty acid glycosides, named parisveroside A (1) and parisveroside B (2), were isolated from a MeOH extract of the roots of Paris verticillata (Liliaceae) together with three other known compounds, salicin (3), 3-(β-D-glucopyranosyloxymethyl)-2-(4-hydroxy-3-methoxyphenyl)-5- (3-hydroxypropyl)-7-methoxy-(2R,3S)-dihydrobenzofuran (4), and allantoin (5). Their structures were elucidated on the basis of spectroscopic data, including 1D and 2D NMR, HR-FAB-MS, and chemical evidence. To investigate the antineuroinflammatory effects of the isolated compounds (1-5), nitric oxide (NO) production was evaluated in the lipopolysaccharide-activated microglia cell line, BV-2. Compounds 2 and 4 significantly inhibited NO production with [IC.sub.50] values of 74.8 and 60.5 µmol/L, respectively. Key words: Paris verticillata, Liliaceae, fatty acid glycosides, parisveroside A, parisveroside B, neuroinflammation. Utilisant comme produit de depart le melange obtenu par extraction des racines de Paris verticillata (Liliaceee) a l'aide de methanol, on a isole; deux nouveaux glycosides deriveis de l'acide gras cyclopropanoique, soit le parisveroside A (1) et le parisveiroside B (2), ainsi que trois autres produits connus, la salicine (3), le (β-D-glucopyranosyloxymeithyl)-2(4-hydroxy-3-methoxypheenyl)-5- (3-hydroxypropyl)-7- meithoxy-(2R,3S)-dihydrobenzofurane (4) et l'allantoine (5). On a elucide les structures en se basant sur des preuves chimiques et des donnees spectroscopiques, dont celles des RMN 1D et 2D et de la spectrometrie de masse a haute resolution avec bombardement avec des atomes rapides (SM-HR-BAR). Pour etudier les effets anti-neuroinflammatoires des composes isoles, on a evaluee la production d'oxyde d'azote (NO) dans des cellules BV-2 de la lignee cellulaire de microglie activeie par un lipopolysaccharide. Les composes 2 et 4 inhibent d'une facon significative la production de NO avec des valeurs de [IC.sub.50] de 74,8 et 60,5 µmol/L respectivement. Mots-cles : Paris verticillata, Liliaceie, glycosides d'acides gras, parisveroside A, parisveroside B, neuroinflammation., Introduction The genus Paris consists of less than two dozen herbaceous plants that have 4- to 11-merous flowers. (1) Some Paris species are used in traditional Chinese medicine for their [...]

Summary: In this work, sulfonated diblock copolymers (SDBCs) were prepared by polycondensation of sulfonated poly(ether‐ether‐ketone) (SPEEK) and hydrophobic oligomer, which were combined with sintered anatase titanium oxide (S‐An‐TiO2) to create a hybrid membrane for apply in proton exchange membrane fuel cells (PEMFCs) operating with low relative humidity (RH). Then, a series of composite membranes (SDBC/S‐An‐TiO2) were prepared by varying the wt% of S‐An‐TiO2 blended with SDBC. The results showed that appropriate quantity (ie, 15 wt%) of S‐An‐TiO2 can significantly improve the proton conductivity and physiochemical properties of prepared composite membrane, as well as the PEMFC performance and durability under 20% RH. The 1.5 wt% of SDBC/S‐An‐TiO2 offers high current output, power output, and durability values at 60°C under 20% RH, which are 0.207 A cm−2, 0.074 W cm−2 and over 90 hours, respectively. These results can be attributed to the good interfacial compatibility between S‐An‐TiO2 and SDBC. [ABSTRACT FROM AUTHOR]

In Bayesian semi-parametric analyses of time-to-event data, non-parametric process priors are adopted for the baseline hazard function or the cumulative baseline hazard function for a given finite partition of the time axis. However, it would be controversial to suggest a general guideline to construct an optimal time partition. While a great deal of research has been done to relax the assumption of the fixed split times for other non-parametric processes, to our knowledge, no methods have been developed for a gamma process prior, which is one of the most widely used in Bayesian survival analysis. In this paper, we propose a new Bayesian framework for proportional hazards models where the cumulative baseline hazard function is modelled a priori by a gamma process. A key feature of the proposed framework is that the number and position of interval cutpoints are treated as random and estimated based on their posterior distributions. [ABSTRACT FROM AUTHOR]

Microorganisms play critical roles in human health and disease. They live in diverse communities in which they interact synergistically or antagonistically. Thus for estimating microbial associations with clinical covariates, such as treatment effects, joint (multivariate) statistical models are preferred. Multivariate models allow one to estimate and exploit complex interdependencies among multiple taxa, yielding more powerful tests of exposure or treatment effects than application of taxon-specific univariate analyses. Analysis of microbial count data also requires special attention because data commonly exhibit zero inflation, i.e., more zeros than expected from a standard count distribution. To meet these needs, we developed a Bayesian variable selection model for multivariate count data with excess zeros that incorporates information on the covariance structure of the outcomes (counts for multiple taxa), while estimating associations with the mean levels of these outcomes. Though there has been much work on zero-inflated models for longitudinal data, little attention has been given to high-dimensional multivariate zero-inflated data modeled via a general correlation structure. Through simulation, we compared performance of the proposed method to that of existing univariate approaches, for both the binary ("excess zero") and count parts of the model. When outcomes were correlated the proposed variable selection method maintained type I error while boosting the ability to identify true associations in the binary component of the model. For the count part of the model, in some scenarios the univariate method had higher power than the multivariate approach. This higher power was at a cost of a highly inflated false discovery rate not observed with the proposed multivariate method. We applied the approach to oral microbiome data from the Pediatric HIV/AIDS Cohort Oral Health Study and identified five (of 44) species associated with HIV infection. [ABSTRACT FROM AUTHOR]

Summary: Organic‐inorganic composite membranes were prepared by introducing silicon dioxide (SiO2) or functionalized SiO2 (ƒ‐SiO2) with various particle sizes into sulfonated poly (arylene ether ketone) (SPAEK) containing pendant groups, and the membrane was manufactured via directly casting which is a cost‐competitive method. The structure and morphology of the composite membranes were confirmed by 1H NMR, FT‐IR, XRD, and FE‐SEM analysis which demonstrated that inorganic nanofillers were successfully introduced. The FE‐SEM surface images showed that SiO2 and ƒ‐SiO2 particles were very well dispersed within the membrane sheets. The water uptake and swelling ratio of the composite membranes including SiO2 or ƒ‐SiO2 almost did not change when compared with the pristine SPAEK membrane. All fabricated membranes demonstrated good thermal/dimensional stabilities, robust mechanical behavior, and excellent proton conductivity. In particular, the SPAEK/ƒ‐SiO2 composite membranes exhibited improved ionic conductivity compared with the pristine membrane at 70% relative humidity (RH) due to hydrogen bonding between ─SO3H groups of functionalized inorganic filler and polymer backbone. Furthermore, the maximum power density of SPAEK/ƒ‐SiO2 reached as high as 273.11 mW cm−2 at 60°C under 70% RH. Therefore, the composite membranes with ƒ‐SiO2 testify to great potential as polymer electrolyte membrane. [ABSTRACT FROM AUTHOR]

Tooth enamel forms in an ephemeral protein matrix where changes in protein abundance, composition and posttranslational modifications are critical to achieve healthy enamel properties. Amelogenin (AMELX) with its splice variants is the most abundant enamel matrix protein, with only one known phosphorylation site at serine 16 shown in vitro to be critical for regulating mineralization. The phosphorylated form of AMELX stabilizes amorphous calcium phosphate, while crystalline hydroxyapatite forms in the presence of the unphosphorylated protein. While AMELX regulates mineral transitions over space and time, it is unknown whether and when un-phosphorylated amelogenin occurs during enamel mineralization. This study aims to reveal the spatiotemporal distribution of the cleavage products of the most abundant AMLEX splice variants including the full length P173, the shorter leucine-rich amelogenin protein (LRAP), and the exon 4-containing P190 in forming enamel, all within the context of the changing enamel matrix proteome during mineralization. We microsampled permanent pig molars, capturing known stages of enamel formation from both crown surface and inner enamel. Nano-LC-MS/MS proteomic analyses after tryptic digestion rendered more than 500 unique protein identifications in enamel, dentin, and bone. We mapped collagens, keratins, and proteolytic enzymes (CTSL, MMP2, MMP10) and determined distributions of P173, LRAP, and P190 products, the enamel proteins enamelin (ENAM) and ameloblastin (AMBN), and matrix-metalloprotease-20 (MMP20) and kallikrein-4 (KLK4). All enamel proteins and KLK4 were near-exclusive to enamel and in excellent agreement with published abundance levels. Phosphorylated P173 and LRAP products decreased in abundance from recently deposited matrix toward older enamel, mirrored by increasing abundances of testicular acid phosphatase (ACPT). Our results showed that hierarchical clustering analysis of secretory enamel links closely matching distributions of unphosphorylated P173 and LRAP products with ACPT and non-traditional amelogenesis proteins, many associated with enamel defects. We report higher protein diversity than previously published and Gene Ontology (GO)-defined protein functions related to the regulation of mineral formation in secretory enamel (e.g., casein α-S1, CSN1S1), immune response in erupted enamel (e.g., peptidoglycan recognition protein, PGRP), and phosphorylation. This study presents a novel approach to characterize and study functional relationships through spatiotemporal mapping of the ephemeral extracellular matrix proteome. [ABSTRACT FROM AUTHOR]

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools. [ABSTRACT FROM AUTHOR]

Abstract A series of composite membranes for application as anion exchange membranes were prepared by introducing various amounts (0.1–5.0 wt%) of reduced graphene oxide (rGO) into chloromethylated poly(arylene ether ketone) (CM-PAEK), followed by quarternization and anionization. CM-PAEK/rGO composite membranes with controlled weight ratios of rGO were fabricated by cost-competitive solution casting method. The field emission scanning electron microscopy (FE-SEM) images of the composite membranes showed that the distribution of rGO was uniform, and the prepared membranes have similar thicknesses confirmed by the cross-sectional image. The composite membranes exhibited enhanced the mechanical strength due to π-π interaction between the aromatic chain of rGO and the polymer backbone, and after the quaternization, hydroxide conduction was improved by hydrogen bonding between rGO and functional ammonium groups. Among the QN-PAEK/rGO membranes with various amounts of rGO, QN-PAEK/rGO 1.0 wt% demonstrated the highest ionic conductivity of 115 mS cm−1, and other composite membranes, which were blended more than that (3 wt% and 5 wt%), showed a decrease in ionic conductivity due to aggregation of inorganic nano fillers. Furthermore, alkaline resistance, mechanical strength, and dimensional stability of composite membranes have been greatly improved due to the introduction of rGO, which demonstrate great potential application as anion exchange membrane for fuel cells. Graphical abstract Image 1 Highlights • QN-PAEK/rGO composite membranes were prepared by introducing various amount of rGO. • The QN-PAEK/rGO composite membranes demonstrated superior OH− conductivity. • The prepared membranes showed improved mechanical and dimensional stabilities. • The membranes exhibited remarkable alkaline stability in a 2 M NaOH solution for 600 h. [ABSTRACT FROM AUTHOR]

Background: The aim of this systematic review and meta-analysis was to compare the clinical efficacy of the early dental implant placement protocol with immediate and delayed dental implant placement protocols.Methods: An electronic and manual search of literature was made to identify clinical studies comparing early implant placement with immediate or delayed placement. Data from the included studies were pooled and quantitative analyses were performed for the implant outcomes reported as the number of failed implants (primary outcome variable) and for changes in peri-implant marginal bone level, peri-implant probing depth, and peri-implant soft tissue level (secondary outcome variables).Results: Twelve studies met the inclusion criteria. Significant difference in risk of implant failure was found neither between the early and immediate placement protocols (risk difference = -0.018; 95% confidence interval [CI] = -0.06, 0.025; P = 0.416) nor between early and delayed placement protocols (risk difference = -0.008; 95% CI = -0.044, 0.028; P = 0.670). Pooled data of changes in peri-implant marginal bone level demonstrated significantly less marginal bone loss for implants placed using the early placement protocol compared with those placed in fresh extraction sockets (P = 0.001; weighted mean difference = -0.14 mm; 95% CI = -0.22, -0.05). No significant differences were found between the protocols for the other variables.Conclusions: The available evidence supports the clinical efficacy of the early implant placement protocol. Present findings indicate that the early implant placement protocol results in implant outcomes similar to immediate and delayed placement protocols and a superior stability of peri-implant hard tissue compared with immediate implant placement. [ABSTRACT FROM AUTHOR]

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City. [ABSTRACT FROM AUTHOR]

Background The birth prevalence of cleft lip with or without cleft palate (CL/P) in Brazil increased between the years from 1975 to 1994 but has not been evaluated for temporal trend since then. Methods We used data from the Brazilian National Health Information System for the years 2000 through 2013. We calculated the reported CL/P birth prevalence each year per 10,000 live births and estimated the average increase in reported prevalence per year (and 95% confidence interval [CI]) by fitting a negative binomial regression model. We also estimated the temporal trend in each of the five Brazilian regions for this time period. Results The overall reported birth prevalence was 4.85 (95% CI, 4.78-4.91) per 10,000 live births. The reported birth prevalence of CL/P increased over this time period, from 3.94 (95% CI, 3.73-4.17) per 10,000 in 2000 to 5.46 (95% CI, 5.20-5.74) per 10,000 in 2013. The temporal trend differed for different Brazilian geographic regions, being confined primarily to the Northeast (4.7% per year; 95% CI, 4.0%-5.5%), North (3.3% per year; 95% CI, 1.8%-4.7%), and Central (2.9% per year; 95% CI, 0.9%-4.9%) regions. Conclusion In recent years, there appears to be an upward trend in the reported prevalence of CL/P in Brazil, confined to the less developed regions of the country. The increase likely reflects improved surveillance; whether it also reflects etiologic differences is unknown. Birth Defects Research (Part A) 106:789-792, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Hospital readmission is a key marker of quality of health care. Notwithstanding its widespread use, however, it remains controversial in part because statistical methods used to analyze readmission, primarily logistic regression and related models, may not appropriately account for patients who die before experiencing a readmission event within the time frame of interest. Toward resolving this, we describe and illustrate the semi-competing risks framework, which refers to the general setting where scientific interest lies with some nonterminal event (eg, readmission), the occurrence of which is subject to a terminal event (eg, death). Although several statistical analysis methods have been proposed for semi-competing risks data, we describe in detail the use of illness-death models primarily because of their relation to well-known methods for survival analysis and the availability of software. We also describe and consider in detail several existing approaches that could, in principle, be used to analyze semi-competing risks data, including composite end point and competing risks analyses. Throughout we illustrate the ideas and methods using data on N=49 763 Medicare beneficiaries hospitalized between 2011 and 2013 with a principle discharge diagnosis of heart failure. [ABSTRACT FROM AUTHOR]

Background: Although the prognosis for HIV-infected individuals has improved after antiretroviral therapy (ART) scale-up, limited data exist on the incidence of AIDS-defining opportunistic infections (ADIs) and mortality during ART in resource-limited settings. Methods: HIV-infected adults in two large hospitals in urban Hanoi were enrolled to the prospective cohort, from October 2007 through December 2013. Those who started ART less than one year before enrollment were assigned to the survival analysis. Data on ART history and ADIs were collected retrospectively at enrollment and followed-up prospectively until April 2014. Results: Of 2,070 cohort participants, 1,197 were eligible for analysis and provided 3,446 person-years (PYs) of being on ART. Overall, 161 ADIs episodes were noted at a median of 3.20 months after ART initiation (range 0.03–75.8) with an incidence 46.7/1,000 PYs (95% confidence interval [CI] 39.8–54.5). The most common ADI was tuberculosis with an incidence of 29.9/1,000 PYs. Mortality after ART initiation was 8.68/1,000 PYs and 45% (19/45) died of AIDS-related illnesses. Age over 50 years at ART initiation was significantly associated with shorter survival after controlling for baseline CD4 count, but neither having injection drug use (IDU) history nor previous ADIs were associated with poor survival. Semi-competing risks analysis in 951 patients without ADIs history prior to ART showed those who developed ADIs after starting ART were at higher risk of death in the first six months than after six months. Conclusion: ADIs were not rare in spite of being on effective ART. Age over 50 years, but not IDU history, was associated with shorter survival in the cohort. This study provides in-depth data on the prognosis of patients on ART in Vietnam during the first decade of ART scale-up. [ABSTRACT FROM AUTHOR]

The presented work is motivated by the need of reliably estimating and predicting the survival rates for individuals diagnosed with cancer, when gene expression profiles are available for identifying molecular risks factors for cancer. The regression analysis of such data is challenged by three characteristics of data: (i) time-to-event outcome, (ii) high-dimensional covariate space, and (iii) a group structure of genes. One strategy to simultaneously deal with all three of the aforementioned challenges is to build a penalized regression model using special penalty functions such as elastic net, fused lasso, and group lasso. To our knowledge, existing methods are sparse or non-existent, however, when a Bayesian estimation/inference is the goal for the penalized regression models. In this article, we propose a Bayesian semi-parametric framework for the regression analysis of gene expression data with survival outcomes. Our proposed Bayesian methods permit researchers to take advantage of numerous benefits including the ability to incorporate substantive prior information, the straightforward and automated quantification of uncertainty in prediction, and the prescriptive nature of computation. The performance of our proposed models for variable selection and prediction is thoroughly investigated through simulation studies, where we consider four scenarios based on different underlying group structures of covariates and covariate effects. The results generally show the satisfactory variable selection capability and predictability of our methods. Finally, we apply our proposed framework to three different gene expression data sets. We developed an efficient Markov chain Monte Carlo algorithm for the implementation of our proposed framework and provided an easy-to-use R package. [ABSTRACT FROM AUTHOR]

The hydrothermal synthesis behavior of ZnO nanowire (NW) on carbon nanotubes (CNTs) with respect to molar concentrations of zinc nitrate hexahydrate and hexamethylenetetramine and temperature was investigated. The ZnO NWs could be synthesized with a single-crystalline wurtzite structure and a diameter in the range of tens of nanometers. Their size does not strongly depend on the temperature, while it increases with increasing molar concentration. The NWs tend to have pyramidal shape at a growth front with increasing temperature and molar concentration probably due to enriched Zn2+ ion concentration. This demonstrates the morphological dependence of ZnO NWs on the relative rates between the decomposition of Zn precursor and subsequent oxidation of Zn to ZnO in hydrothermal synthesis. [ABSTRACT FROM AUTHOR]

Variable selection for high dimensional data has recently received a great deal of attention. However, due to the complex structure of the likelihood, only limited developments have been made for time-to-event data where censoring is present. In this paper, we propose a Bayesian variable selection scheme for a Bayesian semiparametric survival model for right censored survival data sets. A special shrinkage prior on the coefficients corresponding to the predictor variables is used to handle cases when the explanatory variables are of very high-dimension. The shrinkage prior is obtained through a scale mixture representation of Normal and Gamma distributions. Our proposed variable selection prior corresponds to the well known lasso penalty. The likelihood function is based on the Cox proportional hazards model framework, where the cumulative baseline hazard function is modeled a priori by a gamma process. We assign a prior on the tuning parameter of the shrinkage prior and adaptively control the sparsity of our model. The primary use of the proposed model is to identify the important covariates relating to the survival curves. To implement our methodology, we have developed a fast Markov chain Monte Carlo algorithm with an adaptive jumping rule. We have successfully applied our method on simulated data sets under two different settings and real microarray data sets which contain right censored survival time. The performance of our Bayesian variable selection model compared with other competing methods is also provided to demonstrate the superiority of our method. A short description of the biological relevance of the selected genes in the real data sets is provided, further strengthening our claims. [ABSTRACT FROM AUTHOR]