Using Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lung and Brain Cancers

Tumor shape is a key factor that affects tumor growth and metastasis. This paper proposes a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examines its effect on the time-to-event data. The proposed topological features are invariant to scale-preserving transformation and can summarize various tumor shape patterns. The topological features are represented in functional space and used as functional predictors in a functional Cox proportional hazards model. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using consecutive 143 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have significantly (at the level of 0.01) worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression

The 3D finite element analysis of cold wave impact effect

AbstractIn April, 1999, Guangdong Changsha arch dam application new technique of MgO-Admixed concrete arch dam without transverse joints, Changsha arch dam is only 90 d complete high 55.5 m, 31000 m3 concrete dam body, the reservoir filling and power generation, is initiative at home and abroad in the history of arch dam. In January 2000, downstream dam surface appear fine cracks, crack width only 0.2–0.3 mm, depth 1.3–4.2 m, all did not throughout the dam upstream face. With the hyperbolic model based on MgO-admixed concrete and APDL language, 3 d finite element in simulation analysis, cleared that design conditions of the stress of MgO-admixed concrete compensation accord with the stress state of conventional arch dam structure. Downstream dam surface crack and using the MgO-mixed concrete dams of new technology without necessarily linked. Before to the stationary temperature field in the dam, the role of heat preservation measures template made of polystyrene foamed plastic board for stress improvement is significant. Cold wave impact cracks appear to great effect, worsened the dam stress state greatly, and put the dam in a high stress state. The original design dam stress state is not ideal, crack resistance caused by concrete quality of dam project is reduced, especially heat preservation measures is invalid during the cold wave impact, caused cracks of ChangSha dam. 1–2 mm tiny cracks of Changsha arch dam used under no harmful effects. Concrete crack resistance of Changsha arch dam is improved greatly after dam grouting, the main performance indexes is also improved obviously, and dam is used has been more than 12 years under safety operation. The analysis of cold wave impact provides important basic material to promote the new technology application, and reference value to similar projects

Operon information improves gene expression estimation for cDNA microarrays

BACKGROUND: In prokaryotic genomes, genes are organized in operons, and the genes within an operon tend to have similar levels of expression. Because of co-transcription of genes within an operon, borrowing information from other genes within the same operon can improve the estimation of relative transcript levels; the estimation of relative levels of transcript abundances is one of the most challenging tasks in experimental genomics due to the high noise level in microarray data. Therefore, techniques that can improve such estimations, and moreover are based on sound biological premises, are expected to benefit the field of microarray data analysis RESULTS: In this paper, we propose a hierarchical Bayesian model, which relies on borrowing information from other genes within the same operon, to improve the estimation of gene expression levels and, hence, the detection of differentially expressed genes. The simulation studies and the analysis of experiential data demonstrated that the proposed method outperformed other techniques that are routinely used to estimate transcript levels and detect differentially expressed genes, including the sample mean and SAM t statistics. The improvement became more significant as the noise level in microarray data increases. CONCLUSION: By borrowing information about transcriptional activity of genes within classified operons, we improved the estimation of gene expression levels and the detection of differentially expressed genes

Comparing Statistical Methods for Constructing Large Scale Gene Networks

The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity