VILIP-1 Downregulation in Non-Small Cell Lung Carcinomas: Mechanisms and Prediction of Survival

VILIP-1, a member of the neuronal Ca++ sensor protein family, acts as a tumor suppressor gene in an experimental animal model by inhibiting cell proliferation, adhesion and invasiveness of squamous cell carcinoma cells. Western Blot analysis of human tumor cells showed that VILIP-1 expression was undetectable in several types of human tumor cells, including 11 out of 12 non-small cell lung carcinoma (NSCLC) cell lines. The down-regulation of VILIP-1 was due to loss of VILIP-1 mRNA transcripts. Rearrangements, large gene deletions or mutations were not found. Hypermethylation of the VILIP-1 promoter played an important role in gene silencing. In most VILIP-1-silent cells the VILIP-1 promoter was methylated. In vitro methylation of the VILIP-1 promoter reduced its activity in a promoter-reporter assay. Transcriptional activity of endogenous VILIP-1 promoter was recovered by treatment with 5′-aza-2′-deoxycytidine (5′-Aza-dC). Trichostatin A (TSA), a histone deacetylase inhibitor, potently induced VILIP-1 expression, indicating that histone deacetylation is an additional mechanism of VILIP-1 silencing. TSA increased histone H3 and H4 acetylation in the region of the VILIP-1 promoter. Furthermore, statistical analysis of expression and promoter methylation (n = 150 primary NSCLC samples) showed a significant relationship between promoter methylation and protein expression downregulation as well as between survival and decreased or absent VILIP-1 expression in lung cancer tissues (p<0.0001). VILIP-1 expression is silenced by promoter hypermethylation and histone deacetylation in aggressive NSCLC cell lines and primary tumors and its clinical evaluation could have a role as a predictor of short-term survival in lung cancer patients

A Primer on Regression Methods for Decoding cis-Regulatory Logic

The rapidly emerging field of systems biology is helping us to understand the molecular determinants of phenotype on a genomic scale [1]. Cis-regulatory elements are major sequence-based determinants of biological processes in cells and tissues [2]. For instance, during transcriptional regulation, transcription factors (TFs) bind to very specific regions on the promoter DNA [2,3] and recruit the basal transcriptional machinery, which ultimately initiates mRNA transcription (Figure 1A). Learning cis-Regulatory Elements from Omics Data A vast amount of work over the past decade has shown that omics data can be used to learn cis-regulatory logic on a genome-wide scale [4-6]--in particular, by integrating sequence data with mRNA expression profiles. The most popular approach has been to identify over-represented motifs in promoters of genes that are coexpressed [4,7,8]. Though widely used, such an approach can be limiting for a variety of reasons. First, the combinatorial nature of gene regulation is difficult to explicitly model in this framework. Moreover, in many applications of this approach, expression data from multiple conditions are necessary to obtain reliable predictions. This can potentially limit the use of this method to only large data sets [9]. Although these methods can be adapted to analyze mRNA expression data from a pair of biological conditions, such comparisons are often confounded by the fact that primary and secondary response genes are clustered together--whereas only the primary response genes are expected to contain the functional motifs [10]. A set of approaches based on regression has been developed to overcome the above limitations [11-32]. These approaches have their foundations in certain biophysical aspects of gene regulation [26,33-35]. That is, the models are motivated by the expected transcriptional response of genes due to the binding of TFs to their promoters. While such methods have gathered popularity in the computational domain, they remain largely obscure to the broader biology community. The purpose of this tutorial is to bridge this gap. We will focus on transcriptional regulation to introduce the concepts. However, these techniques may be applied to other regulatory processes. We will consider only eukaryotes in this tutorial

Ultraviolet photoconductive devices with an n-GaN nanorodgraphene hybrid structure synthesized by metal-organic chemical vapor deposition

The superior photoconductive behavior of a simple, cost-effective n-GaN nanorod (NR)-graphene hybrid device structure is demonstrated for the first time. The proposed hybrid structure was synthesized on a Si (111) substrate using the high-quality graphene transfer method and the relatively low-temperature metal-organic chemical vapor deposition (MOCVD) process with a high V/III ratio to protect the graphene layer from thermal damage during the growth of n-GaN nanorods. Defect-free n-GaN NRs were grown on a highly ordered graphene monolayer on Si without forming any metal-catalyst or droplet seeds. The prominent existence of the undamaged monolayer graphene even after the growth of highly dense n-GaN NRs, as determined using Raman spectroscopy and high-resolution transmission electron microscopy (HR-TEM), facilitated the excellent transport of the generated charge carriers through the photoconductive channel. The highly matched n-GaN NR-graphene hybrid structure exhibited enhancement in the photocurrent along with increased sensitivity and photoresponsivity, which were attributed to the extremely low carrier trap density in the photoconductive channelclose00

Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment

The paper proposes a first-order macroscopic stochastic dynamic traffic model, namely the stochastic cell transmission model (SCTM), to model traffic flow density on freeway segments with stochastic demand and supply. The SCTM consists of five operational modes corresponding to different congestion levels of the freeway segment. Each mode is formulated as a discrete time bilinear stochastic system. A set of probabilistic conditions is proposed to characterize the probability of occurrence of each mode. The overall effect of the five modes is estimated by the joint traffic density which is derived from the theory of finite mixture distribution. The SCTM captures not only the mean and standard deviation (SD) of density of the traffic flow, but also the propagation of SD over time and space. The SCTM is tested with a hypothetical freeway corridor simulation and an empirical study. The simulation results are compared against the means and SDs of traffic densities obtained from the Monte Carlo Simulation (MCS) of the modified cell transmission model (MCTM). An approximately two-miles freeway segment of Interstate 210 West (I-210W) in Los Ageles, Southern California, is chosen for the empirical study. Traffic data is obtained from the Performance Measurement System (PeMS). The stochastic parameters of the SCTM are calibrated against the flow-density empirical data of I-210W. Both the SCTM and the MCS of the MCTM are tested. A discussion of the computational efficiency and the accuracy issues of the two methods is provided based on the empirical results. Both the numerical simulation results and the empirical results confirm that the SCTM is capable of accurately estimating the means and SDs of the freeway densities as compared to the MCS. © 2010 Elsevier Ltd.link_to_subscribed_fulltex

Modeling the impacts of mandatory and discretionary lane-changing maneuvers

202211 bckwAccepted ManuscriptRGCOthersNational Natural Science Foundation of ChinaPublishe

Prediction of travel time on urban road links with and without point detectors

202307 bcchVersion of RecordRGCOthersTransport Department of the Government of the Hong Kong Special Administrative Region; Hong Kong Polytechnic UniversityPublishe