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Collapse is one of the most significant geological hazards in mountain tunnel construction, and it is crucial to accurately predict the collapse probability. By introducing the reliability theory, this paper proposes a calculation method for the collapse probability in mountain tunnel construction based on numerical simulation, support vector regression (SVR), and the Monte Carlo (MC) method. Taking the Jinzhupa Tunnel Project in Fujian Province as a case study, three-dimensional models were constructed, and the safety factors of the surrounding rock were determined using the strength reduction method. By defining the shear strength parameters of the surrounding rock as random variables, the problem was formulated as a reliability model, and the safety factor was chosen as the reliability index. To increase computational efficiency, the SVR model was trained to replace numerical simulations, and the MC method was adopted to calculate the probability of collapse. The results showed that the cause of the collapse was the change in the excavation method and the very late installation of supports. The feasibility and reliability of the proposed method have been verified, indicating that the method can be used to predict the probability of collapse in a practical risk assessment of mountain tunnel construction.

Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675–83. ©2016 AACR.

We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independently of the background. Treating these initial segments as pseudo-labels, we learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We use this ensemble to detect new instances of the "moving" type, even if they are not moving, and add these as new pseudo-labels. Our method is an expectation-maximization algorithm, where in the expectation step we fire all modules and look for agreement among them, and in the maximization step we re-train the modules to improve this agreement. The constraint of ensemble agreement helps combat contamination of the generated pseudo-labels (during the E step), and data augmentation helps the modules generalize to yet-unlabelled data (during the M step). We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI, and show strong improvements over the state-of-the-art.

Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis. Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve. Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP). Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis. Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675–83. ©2016 AACR.

Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which fully leverages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems., 10 pages, 6 figures

Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction based on a gene expression dataset and patients' survival records. We evaluated the performance of the regression model on an independent dataset. Compared with the biomarker candidates selected by DE and DN analyses, those selected through INDEED led to more accurate survival time prediction.

With recent advancement of omics technologies, fueled by decreased cost and increased number of available datasets, computational methods for differential expression analysis are sought to identify disease-associated biomolecules. Conventional differential expression analysis methods (e.g. student's t-test, ANOVA) focus on assessing mean and variance of biomolecules in each biological group. On the other hand, network-based approaches take into account the interactions between biomolecules in choosing differentially expressed ones. These interactions are typically evaluated by correlation methods that tend to generate over-complicated networks due to many seemingly indirect associations. In this paper, we introduce a new R/Bioconductor package INDEED that allows users to construct a sparse network based on partial correlation, and to identify biomolecules that have significant changes both at individual expression and pairwise interaction levels. We applied INDEED for analysis of two omic datasets acquired in a cancer biomarker discovery study to help rank disease-associated biomolecules. We believe biomolecules selected by INDEED lead to improved sensitivity and specificity in detecting disease status compared to those selected by conventional statistical methods. Also, INDEED's framework is amenable to further expansion to integrate networks from multi-omic studies, thereby allowing selection of reliable disease-associated biomolecules or disease biomarkers.

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

In this paper, we introduce a novel computational method for constructing protein networks based on reverse phase protein array (RPPA) data to identify complex patterns in protein signaling. The method is applied to phosphoproteomic profiles of basal expression and activation/phosphorylation of 76 key signaling proteins in three breast cancer cell lines (MCF7, LCC1, and LCC9). Temporal RPPA data are acquired at 48h, 96h, and 144h after knocking down four genes in separate experiments. These genes are selected from a previous study as important determinants for breast cancer survival. Interaction networks are constructed by analyzing the expression levels of protein pairs using a multivariate analysis of variance model. A new scoring criterion is introduced to determine relevant protein pairs. Through a network topology based analysis, we search for wiring patterns to identify key proteins that are associated with significant changes in expression levels across various experimental conditions.

Biological network inference is a major challenge in systems biology. Traditional correlation-based network analysis results in too many spurious edges since correlation cannot distinguish between direct and indirect associations. To address this issue, Gaussian graphical models (GGM) were proposed and have been widely used. Though they can significantly reduce the number of spurious edges, GGM are insufficient to uncover a network structure faithfully due to the fact that they only consider the full order partial correlation. Moreover, when the number of samples is smaller than the number of variables, further technique based on sparse regularization needs to be incorporated into GGM to solve the singular covariance inversion problem. In this paper, we propose an efficient and mathematically solid algorithm that infers biological networks by computing low order partial correlation (LOPC) up to the second order. The bias introduced by the low order constraint is minimal compared to the more reliable approximation of the network structure achieved. In addition, the algorithm is suitable for a dataset with small sample size but large number of variables. Simulation results show that LOPC yields far less spurious edges and works well under various conditions commonly seen in practice. The application to a real metabolomics dataset further validates the performance of LOPC and suggests its potential power in detecting novel biomarkers for complex disease.

Background— Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. Methods and Results— In this article, we present a Bayesian network classifier that is customized to (1) increase the ability to integrate diverse information from different sources, (2) effectively predict protein–protein interactions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model in discovering protein biomarkers of hepatocellular carcinoma leads to the identification of functional modules that provide insights into the mechanism of the development and progression of hepatocellular carcinoma. These functional modules include cell cycle deregulation, increased angiogenesis (eg, vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. Conclusions— The discoveries and conclusions derived from our customized Bayesian network classifier are consistent with previously published results. The proposed approach for determining Bayesian network structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.

Background Conventional differential gene expression analysis by methods such as student’s t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups. Results Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method. Conclusions The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1515-1) contains supplementary material, which is available to authorized users.

Multi-omic approaches offer the opportunity to characterize complex diseases such as cancer at various molecular levels. In this paper, we present transcriptomic, proteomic/glycoproteomic, glycomic, and metabolomic (TPGM) data we acquired by analysis of liver tissues from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. We evaluated changes in the levels of transcripts, proteins, glycans, and metabolites between tumor and cirrhotic tissues by statistical methods. We demonstrated the potential of multi-omic approaches and network analysis to investigate the interactions among these biomolecules in the progression of liver cirrhosis to HCC. Also, we showed the significance of multi-omic approaches to identify pathways altered in HCC.