Predicting recurrence and progression in patients with non-muscle-invasive bladder cancer: utility of the EORTC and CUETO scoring models

The goal of our study was to distinguish putative important predictive factors and to evaluate the utility of EORTC and CUETO existing models in patients with non-muscle-invasive bladder cancer (NMIBC). A retrospective single center study was performed including treated patients with NMIBC between January 2003 and December 2011 at our department. The following clinical and pathologic data were analyzed: gender, age, prior recurrence rate, number of tumors, tumor size, location of tumors, tumor stage, tumor grade, presence of CIS, second TURB, second TUR pathology, intravesical treatment, recurrence and progression of bladder tumor. Patients were stratified into three risk categories according to the EAU guidelines. Of the 611 patients, 197 (32%), 251 (41%) and 163 (27%) were assigned to the low, intermediate, and high risk category, respectively. Of these patients 535 (87.6%) underwent a second TUR. Overall, 528 patients were included ultimately in our follow-up study. The median follow-up was 60 months (range: 1-143 months). The overall recurrence rates was 18.6%, 33.7%, and 43.9% after the 1st, 2nd and 5th year, respectively. The corresponding progression rates were 0.9%, 2.6%, and 6.6%. Overall, prior recurrence rate and second TUR pathology are independent predictors of disease recurrence, whereas age, prior recurrence rate, tumor stage, tumor grade, second TUR, and second TUR pathology are prognostic factors for disease progression. The CUETO recurrence risk table severely underestimates the risk of disease recurrence in our cohort. However, the EORTC and CUETO risk tables are suitable tools to estimate disease progression in our cohort. Second TUR is of paramount importance and should be applied to all NMIBC patients. The EORTC and CUETO risk models are suitable to estimate progression risk. However, both risk calculators do not accurately predict risk of recurrence. The latter may be due to the routine use of second TUR in our cohort

Protein Function Prediction by Integrating Multiple Kernels ∗

Determining protein function constitutes an exercise in integrating information derived from several heterogeneous high-throughput experiments. To utilize the information spread across multiple sources in a combined fashion, these data sources are transformed into kernels. Several protein function prediction methods follow a two-phased approach: they first optimize the weights on individual kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these methods result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some methods optimize the loss of binary classifiers, and learn weights for the different kernels iteratively. A protein has multiple functions, and each function can be viewed as a label. These methods solve the problem of optimizing weights on the input kernels for each of the labels. This is computationally expensive and ignores inter-label correlations. In this paper, we propose a method called Protein Function Prediction by Integrating Multiple Kernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reducing the empirical loss of a multi-label classifier for each of the labels simultaneously, using a combined objective function. ProMK can assign larger weights to smooth kernels and downgrade the weights on noisy kernels. We evaluate the ability of ProMK to predict the function of proteins using several standard benchmarks. We show that our approach performs better than previously proposed protein function prediction approaches that integrate data from multiple networks, and multi-label multiple kernel learning methods.

Study Of Conceptual Design Of The Extension Method For Mechanical Products,

ABSTRACT On the foundation summing up existing intelligent conceptual design method, this paper puts forward the research content, characters, path, and method of the conceptual design of extension for mechanical products. This paper rounds the core technology of intelligent conceptual design to research the modeling method of extension design in function-principlelayout-configuration. It includes the function expression, function decomposition and synthesis, function illation and decision. The computers are utilized to simulate the human dialectic thought when resolve problems in this method. The given example shows that the extension method has been applied in the field of conceptual design for mechanical products. This method has important significance to resolve the bottleneck problem of theory studying and engineering realizing of intelligent CAD

Multi-View Multiple Clusterings using Deep Matrix Factorization

Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and interesting from different perspectives. However, how to find multiple, meaningful, and diverse clustering results from multi-view data is still a rarely studied and challenging topic in multi-view clustering and multiple clusterings. In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. DMClusts gradually factorizes multi-view data matrices into representational subspaces layer-by-layer and generates one clustering in each layer. To enforce the diversity between generated clusterings, it minimizes a new redundancy quantification term derived from the proximity between samples in these subspaces. We further introduce an iterative optimization procedure to simultaneously seek multiple clusterings with quality and diversity. Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions