HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments

<p>Abstract</p> <p>Background</p> <p>Three-color microarray experiments can be performed to assess drug effects on the genomic scale. The methodology may be useful in shortening the cycle, reducing the cost, and improving the efficiency in drug discovery and development compared with the commonly used dual-color technology. A visualization tool, the hexaMplot, is able to show the interrelations of gene expressions in normal-disease-drug samples in three-color microarray data. However, it is not enough to assess the complicated drug therapeutic effects based on the plot alone. It is important to explore more effective tools so that a deeper insight into gene expression patterns can be gained with three-color microarrays.</p> <p>Results</p> <p>Based on the celebrated Hough transform, a novel algorithm, HoughFeature, is proposed to extract line features in the hexaMplot corresponding to different drug effects. Drug therapy results can then be divided into a number of levels in relation to different groups of genes. We apply the framework to experimental microarray data to assess the complex effects of Rg1 (an extract of Chinese medicine) on Hcy-related HUVECs in details. Differentially expressed genes are classified into 15 functional groups corresponding to different levels of drug effects.</p> <p>Conclusion</p> <p>Our study shows that the HoughFeature algorithm can reveal natural cluster patterns in gene expression data of normal-disease-drug samples. It provides both qualitative and quantitative information about up- or down-regulated genes. The methodology can be employed to predict disease susceptibility in gene therapy and assess drug effects on the disease based on three-color microarray data.</p

Wind Effects on Dome Structures and Evaluation of CFD Simulations through Wind Tunnel Testing

In the Study, a Series of Wind Tunnel Tests Were Conducted to Investigate Wind Effects Acting on Dome Structures (1/60 Scale) Induced by Straight-Line Winds at a Reynolds Number in the Order of 106. Computational Fluid Dynamics (CFD) Simulations Were Performed as Well, Including a Large Eddy Simulation (LES) and Reynolds-Averaged Navier–Stokes (RANS) Simulation, and their Performances Were Validated by a Comparison with the Wind Tunnel Testing Data. It is Concluded that Wind Loads Generally Increase with Upstream Wind Velocities, and They Are Reduced over Suburban Terrain Due to Ground Friction. the Maximum Positive Pressure Normally Occurs Near the Base of the Dome on the Windward Side Caused by the Stagnation Area and Divergence of Streamlines. the Minimum Suction Pressure Occurs at the Apex of the Dome Because of the Blockage of the Dome and Convergence of Streamlines. Suction Force is the Most Significant among All Wind Loads, and Special Attention Should Be Paid to the Roof Design for Proper Wind Resistance. Numerical Simulations Also Indicate that LES Results Match Better with the Wind Tunnel Testing in Terms of the Distribution Pattern of the Mean Pressure Coefficient on the Dome Surface and Total Suction Force. the Mean and Root-Mean-Square Errors of the Meridian Pressure Coefficient Associated with the LES Are About 60% Less Than Those Associated with RANS Results, and the Error of Suction Force is About 40–70% Less. Moreover, the LES is More Accurate in Predicting the Location of Boundary Layer Separation and Reproducing the Complex Flow Field Behind the Dome, and is Superior in Simulating Vortex Structures Around the Dome to Further Understand the Unsteadiness and Dynamics in the Flow Field

Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression

Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining clinical features and gene co-expression data to improve the accuracy of the prediction of prostate cancer progression. The top-scoring pair (TSP) method was used to select genes for the model. The proposed models not only preserved the basic properties of the TSP algorithm but also incorporated the clinical features into the prognostic models. Based on the statistical inference with the iterative cross validation, we demonstrated that prediction LR models that included genes selected by the TSP method provided better predictions of prostate cancer progression than those using clinical variables only and/or those that included genes selected by the one-gene-at-a-time approach. Thus, we conclude that TSP selection is a useful tool for feature (and/or gene) selection to use in prognostic models and our model also provides an alternative for predicting prostate cancer progression

Multi-channel convolutional neural network for targeted sentiment classification

In recent years, targeted sentiment analysis has received great attention as a fine-grained sentiment analysis. Determining the sentiment polarity of a specific target in a sentence is the main task. This paper proposes a multi-channel convolutional neural network (MCL-CNN) for targeted sentiment classification. Our approach can not only parallelize over the words of a sentence, but also extract local features effectively. Contexts and targets can be more comprehensively utilized by using part-of-speech information, semantic information and interactive information, so that diverse features can be obtained. Finally, experimental results on the SemEval 2014 dataset demonstrate the effectiveness of this method

Discovery of a high-altitude ecotype and ancient lineage of Arabidopsis thaliana from Tibet

Arabidopsis thaliana (A. thaliana) has long been a model species for dicotyledon study, and was the first flowering plant to get its genome completed sequenced [1]. Although most wild A. thaliana are collected in Europe, several studies have found a rapid A. thaliana west-east expansion from Central Asia [2]. The Qinghai-Tibet Plateau (QTP) is close to Central Asia and known for its high altitude, unique environments and biodiversity [3]. However, no wild-type A. thaliana had been either discovered or sequenced from QTP. Studies on the A. thaliana populations collected under 2000 m asl have shown that the adaptive variations associated with climate and altitudinal gradients [4]. Hence a high-altitude A. thaliana provides a precious natural material to investigate the evolution and adaptation process. Here, we present the genome of a new ecotype of A. thaliana collected in the Gongga County, Tibet (4200 m asl) (Fig. 1a), to demonstrate its evolutionary history and adaptation to highaltitude regions

Prioritizing genes associated with prostate cancer development

<p>Abstract</p> <p>Background</p> <p>The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.</p> <p>Methods</p> <p>A <it>Z </it>score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.</p> <p>Results</p> <p>Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--<it>CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4</it>, and <it>AURKA</it>--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.</p> <p>Conclusions</p> <p>By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.</p

Agegraphic dark energy as a quintessence

Recently, a dark energy model characterized by the age of the universe, dubbed ``agegraphic dark energy'', was proposed by Cai. In this paper, a connection between the quintessence scalar-field and the agegraphic dark energy is established, and accordingly, the potential of the agegraphic quintessence field is constructed.Comment: 9 pages, 3 figures; accepted by Eur. Phys. J.

Gene expression profiling of 1200 pancreatic ductal adenocarcinoma reveals novel subtypes

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in the world with a five-year survival rate of less than 5%. Not all PDAC are the same, because there exist intra-tumoral heterogeneity between PDAC, which poses a great challenge to personalized treatments for PDAC. Methods To dissect the molecular heterogeneity of PDAC, we performed a retrospective meta-analysis on whole transcriptome data from more than 1200 PDAC patients. Subtypes were identified based on non-negative matrix factorization (NMF) biclustering method. We used the gene set enrichment analysis (GSEA) and survival analysis to conduct the molecular and clinical characterization of the identified subtypes, respectively. Results Six molecular and clinical distinct subtypes of PDAC: L1-L6, are identified and grouped into tumor-specific (L1, L2 and L6) and stroma-specific subtypes (L3, L4 and L5). For tumor-specific subtypes, L1 (~ 22%) has enriched carbohydrate metabolism-related gene sets and has intermediate survival. L2 (~ 22%) has the worst clinical outcomes, and is enriched for cell proliferation-related gene sets. About 23% patients can be classified into L6, which leads to intermediate survival and is enriched for lipid and protein metabolism-related gene sets. Stroma-specific subtypes may contain high non-epithelial contents such as collagen, immune and islet cells, respectively. For instance, L3 (~ 12%) has poor survival and is enriched for collagen-associated gene sets. L4 (~ 14%) is enriched for various immune-related gene sets and has relatively good survival. And L5 (~ 7%) has good clinical outcomes and is enriched for neurotransmitter and insulin secretion related gene sets. In the meantime, we identified 160 subtype-specific markers and built a deep learning-based classifier for PDAC. We also applied our classification system on validation datasets and observed much similar molecular and clinical characteristics between subtypes. Conclusions Our study is the largest cohort of PDAC gene expression profiles investigated so far, which greatly increased the statistical power and provided more robust results. We identified six molecular and clinical distinct subtypes to describe a more complete picture of the PDAC heterogeneity. The 160 subtype-specific markers and a deep learning based classification system may be used to better stratify PDAC patients for personalized treatments