Impact of statistical reconstruction and compressed sensing algorithms on projection data elimination during X-ray CT image reconstruction.

OBJECTIVES: To examine the effect of incomplete, or total elimination of, projection data on computed tomography (CT) images subjected to statistical reconstruction and/or compressed sensing algorithms. METHODS: Multidetector row CT images were used. The algebraic reconstruction technique (ART) and the maximum likelihood-expectation maximization (ML-EM) method were compared with filtered back-projection (FBP). Effects on reconstructed images were studied when the projection data of 360° (360 projections) were decreased to 180 or 90 projections by reducing the collection angle or thinning the image data. The total variation (TV) regularization method using compressed sensing was applied to images processed by the ART. Image noise was subjectively evaluated using the root-mean-square error and signal-to-noise ratio. RESULTS: When projection data were reduced by one-half or three-quarters, ART and ML-EM produced better image quality than FBP. Both ART and ML-EM resulted in high quality at a spread of 90 projections over 180° rotation. Computational loading was high for statistical reconstruction, but not for ML-EM, compared with the ART. TV regularization made it possible to use only 36 projections while still achieving acceptable image quality. CONCLUSIONS: Incomplete projection data-accomplished by reducing the angle to collect image data or thinning the projection data without reducing the angle of rotation over which it is collected-made it possible to reduce the radiation dose while retaining image quality with statistical reconstruction algorithms and/or compressed sensing. Despite heavier computational calculation loading, these methods should be considered for reducing radiation doses.

TGF-ß1/Smad3 Signaling Pathway Mediates T-2 Toxin-Induced Decrease of Type II Collagen in Cultured Rat Chondrocytes.

T-2 toxin can cause damage to the articular cartilage, but the molecular mechanism remains unclear. By employing the culture of rat chondrocytes, we investigated the effect of the TGF-ß1/Smad3 signaling pathway on the damage to chondrocytes induced by T-2 toxin. It was found that T-2 toxin could reduce cell viability and increased the number of apoptotic cells when compared with the control group. After the addition of the T-2 toxin, the production of type II collagen was reduced at mRNA and protein levels, while the levels of TGF-ß1, Smad3, ALK5, and MMP13 were upregulated. The production of the P-Smad3 protein was also increased. Inhibitors of TGF-ß1 and Smad3 were able to reverse the effect of the T-2 toxin on the protein level of above-mentioned signaling molecules. The T-2 toxin could promote the level of MMP13 via the stimulation of TGF-ß1 signaling in chondrocytes, resulting in the downregulation of type II collagen and chondrocyte damage. Smad3 may be involved in the degradation of type II collagen, but the Smad3 has no connection with the regulation of MMP13 level. This study provides a new clue to elucidate the mechanism of T-2 toxin-induced chondrocyte damage.

Inferring microRNA-mRNA causal regulatory relationships from expression data.

MOTIVATION: microRNAs (miRNAs) are known to play an essential role in the post-transcriptional gene regulation in plants and animals. Currently, several computational approaches have been developed with a shared aim to elucidate miRNA-mRNA regulatory relationships. Although these existing computational methods discover the statistical relationships, such as correlations and associations between miRNAs and mRNAs at data level, such statistical relationships are not necessarily the real causal regulatory relationships that would ultimately provide useful insights into the causes of gene regulations. The standard method for determining causal relationships is randomized controlled perturbation experiments. In practice, however, such experiments are expensive and time consuming. Our motivation for this study is to discover the miRNA-mRNA causal regulatory relationships from observational data. RESULTS: We present a causality discovery-based method to uncover the causal regulatory relationship between miRNAs and mRNAs, using expression profiles of miRNAs and mRNAs without taking into consideration the previous target information. We apply this method to the epithelial-to-mesenchymal transition (EMT) datasets and validate the computational discoveries by a controlled biological experiment for the miR-200 family. A significant portion of the regulatory relationships discovered in data is consistent with those identified by experiments. In addition, the top genes that are causally regulated by miRNAs are highly relevant to the biological conditions of the datasets. The results indicate that the causal discovery method effectively discovers miRNA regulatory relationships in data. Although computational predictions may not completely replace intervention experiments, the accurate and reliable discoveries in data are cost effective for the design of miRNA experiments and the understanding of miRNA-mRNA regulatory relationships.

Eight-signature classifier for prediction of nasopharyngeal [corrected] carcinoma survival.

PURPOSE: Currently, nasopharyngeal carcinoma (NPC) prognosis evaluation is based primarily on the TNM staging system. This study aims to identify prognostic markers for NPC. PATIENTS AND METHODS: We detected expression of 18 biomarkers by immunohistochemistry in NPC tumors from 209 patients and evaluated the association between gene expression level and disease-specific survival (DSS). We used support vector machine (SVM)--based methods to develop a prognostic classifier for NPC (NPC-SVM classifier). Further validation of the NPC-SVM classifier was performed in an independent cohort of 1,059 patients. RESULTS: The NPC-SVM classifier integrated patient sex and the protein expression level of seven genes, including Epstein-Barr virus latency membrane protein 1, CD147, caveolin-1, phospho-P70S6 kinase, matrix metalloproteinase 11, survivin, and secreted protein acidic and rich in cysteine. The NPC-SVM classifier distinguished patients with NPC into low- and high-risk groups with significant differences in 5-year DSS in the evaluated patients (87% v 37.7%; P < .001) in the validation cohort. In multivariate analysis adjusted for age, TNM stage, and histologic subtype, the NPC-SVM classifier was an independent predictor of 5-year DSS in the evaluated patients (hazard ratio, 4.9; 95% CI, 3.0 to 7.9) in the validation cohort. CONCLUSION: As a powerful predictor of 5-year DSS among patients with NPC, the newly developed NPC-SVM classifier based on tumor-associated biomarkers will facilitate patient counseling and individualize management of patients with NPC.

Combined feature selection and cancer prognosis using support vector machine regression.

Prognostic prediction is important in medical domain, because it can be used to select an appropriate treatment for a patient by predicting the patient's clinical outcomes. For high-dimensional data, a normal prognostic method undergoes two steps: feature selection and prognosis analysis. Recently, the L1-L2-norm Support Vector Machine (L1-L2 SVM) has been developed as an effective classification technique and shown good classification performance with automatic feature selection. In this paper, we extend L1-L2 SVM for regression analysis with automatic feature selection. We further improve the L1-L2 SVM for prognostic prediction by utilizing the information of censored data as constraints. We design an efficient solution to the new optimization problem. The proposed method is compared with other seven prognostic prediction methods on three realworld data sets. The experimental results show that the proposed method performs consistently better than the medium performance. It is more efficient than other algorithms with the similar performance.

Feature fusion using locally linear embedding for classification.

In most complex classification problems, many types of features have been captured or extracted. Feature fusion is used to combine features for better classification and to reduce data dimensionality. Kernel-based feature fusion methods are very effective for classification, but they do not reduce data dimensionality. In this brief, we propose an effective feature fusion method using locally linear embedding (LLE). The proposed method overcomes the limitations of LLE, which could not handle different types of features and is inefficient for classification. We propose an efficient algorithm to solve the optimization problem in obtaining weights of different features, and design an efficient method for LLE-based classification. In comparison to other kernel-based feature fusion methods, the proposed method fuses features to a significantly lower dimensional feature space with the same discriminant power. We have conducted experiments to demonstrate the effectiveness of the proposed feature fusion method.

Three immunomarker support vector machines-based prognostic classifiers for stage IB non-small-cell lung cancer.

PURPOSE: Approximately 30% of patients with stage IB non-small-cell lung cancer (NSCLC) die within 5 years after surgery. Current staging methods are inadequate for predicting the prognosis of this particular subgroup. This study identifies prognostic markers for NSCLC. PATIENTS AND METHODS: We used computer-generated random numbers to study 148 paraffin-embedded specimens for immunohistochemical analysis. We studied gene expression in paraffin-embedded specimens of lung cancer tissue from 73 randomly selected patients with stage IB NSCLC who had undergone radical surgical resection and evaluated the association between the level of expression and survival. We used support vector machines (SVM)-based methods to develop three immunomarker-SVM-based prognostic classifiers for stage IB NSCLC. For validation, we used randomly assigned specimens from 75 other patients. RESULTS: We devised three immunomarker-SVM-based prognostic classifiers, including SVM1, SVM2, and SVM3, to refine prognosis of stage IB NSCLC successfully. The SVM1 model integrates age, cancer cell type, and five markers, including CD34MVD, EMA, p21ras, p21WAF1, and tissue inhibitors of metalloproteinases (TIMP) -2. The SVM2 model integrates age, cancer cell type, and 19 markers, including BCL2, caspase-9, CD34MVD, low-molecular-weight cytokeratin, high-molecular-weight cytokeratin, cyclo-oxygenase-2, EMA, HER2, matrix metalloproteinases (MMP) -2, MMP-9, p16, p21ras, p21WAF1, p27kip1, p53, TIMP-1, TIMP-2, vascular endothelial growth factor (VEGF), and beta-catenin. The SVM3 model consists of SVM1 and SVM2. The three models were independent predictors of overall survival. We validated the classifiers with data from an independent cohort of 75 patients with stage IB NSCLC. CONCLUSION: The three immunomarker-SVM-based prognostic characteristics are closely associated with overall survival among patients with stage IB NSCLC.